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Lee S, Sun M, Hu Y, Wang Y, Islam MN, Goerlitz D, Lucas PC, Lee AV, Swain SM, Tang G, Wang XS. iGenSig-Rx: an integral genomic signature based white-box tool for modeling cancer therapeutic responses using multi-omics data. BMC Bioinformatics 2024; 25:220. [PMID: 38898383 PMCID: PMC11186173 DOI: 10.1186/s12859-024-05835-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2024] [Accepted: 06/10/2024] [Indexed: 06/21/2024] Open
Abstract
Multi-omics sequencing is poised to revolutionize clinical care in the coming decade. However, there is a lack of effective and interpretable genome-wide modeling methods for the rational selection of patients for personalized interventions. To address this, we present iGenSig-Rx, an integral genomic signature-based approach, as a transparent tool for modeling therapeutic response using clinical trial datasets. This method adeptly addresses challenges related to cross-dataset modeling by capitalizing on high-dimensional redundant genomic features, analogous to reinforcing building pillars with redundant steel rods. Moreover, it integrates adaptive penalization of feature redundancy on a per-sample basis to prevent score flattening and mitigate overfitting. We then developed a purpose-built R package to implement this method for modeling clinical trial datasets. When applied to genomic datasets for HER2 targeted therapies, iGenSig-Rx model demonstrates consistent and reliable predictive power across four independent clinical trials. More importantly, the iGenSig-Rx model offers the level of transparency much needed for clinical application, allowing for clear explanations as to how the predictions are produced, how the features contribute to the prediction, and what are the key underlying pathways. We anticipate that iGenSig-Rx, as an interpretable class of multi-omics modeling methods, will find broad applications in big-data based precision oncology. The R package is available: https://github.com/wangxlab/iGenSig-Rx .
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Affiliation(s)
- Sanghoon Lee
- UPMC Hillman Cancer Center, University of Pittsburgh, Pittsburgh, PA, 15213, USA
- Department of Pathology, School of Medicine, University of Pittsburgh, Pittsburgh, PA, 15213, USA
- Department of Biomedical Informatics, School of Medicine, University of Pittsburgh, Pittsburgh, PA, 15206, USA
| | - Min Sun
- UPMC Hillman Cancer Center, University of Pittsburgh, Pittsburgh, PA, 15213, USA
- Department of Medicine, School of Medicine, University of Pittsburgh, Pittsburgh, PA, 15261, USA
| | - Yiheng Hu
- UPMC Hillman Cancer Center, University of Pittsburgh, Pittsburgh, PA, 15213, USA
- Department of Pathology, School of Medicine, University of Pittsburgh, Pittsburgh, PA, 15213, USA
| | - Yue Wang
- UPMC Hillman Cancer Center, University of Pittsburgh, Pittsburgh, PA, 15213, USA
- Department of Pathology, School of Medicine, University of Pittsburgh, Pittsburgh, PA, 15213, USA
| | - Md N Islam
- Genomics and Epigenomics Shared Resource (GESR), Georgetown University Medical Center, Washington, DC, 20057, USA
| | - David Goerlitz
- Lombardi Comprehensive Cancer Center, Georgetown University Medical Center, Washington, DC, 20057, USA
| | - Peter C Lucas
- UPMC Hillman Cancer Center, University of Pittsburgh, Pittsburgh, PA, 15213, USA
- Department of Pathology, School of Medicine, University of Pittsburgh, Pittsburgh, PA, 15213, USA
- National Surgical Adjuvant Breast and Bowel Project (NSABP), Pittsburgh, PA, 15213, USA
| | - Adrian V Lee
- UPMC Hillman Cancer Center, University of Pittsburgh, Pittsburgh, PA, 15213, USA
- Department of Pharmacology and Chemical Biology, University of Pittsburgh, Pittsburgh, PA, 15213, USA
| | - Sandra M Swain
- National Surgical Adjuvant Breast and Bowel Project (NSABP), Pittsburgh, PA, 15213, USA
| | - Gong Tang
- Department of Biostatistics, School of Public Health, University of Pittsburgh, Pittsburgh, PA, 15261, USA
- National Surgical Adjuvant Breast and Bowel Project (NSABP), Pittsburgh, PA, 15213, USA
| | - Xiao-Song Wang
- UPMC Hillman Cancer Center, University of Pittsburgh, Pittsburgh, PA, 15213, USA.
- Department of Pathology, School of Medicine, University of Pittsburgh, Pittsburgh, PA, 15213, USA.
- Department of Biomedical Informatics, School of Medicine, University of Pittsburgh, Pittsburgh, PA, 15206, USA.
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2
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Sinha S, Vegesna R, Mukherjee S, Kammula AV, Dhruba SR, Wu W, Kerr DL, Nair NU, Jones MG, Yosef N, Stroganov OV, Grishagin I, Aldape KD, Blakely CM, Jiang P, Thomas CJ, Benes CH, Bivona TG, Schäffer AA, Ruppin E. PERCEPTION predicts patient response and resistance to treatment using single-cell transcriptomics of their tumors. NATURE CANCER 2024; 5:938-952. [PMID: 38637658 DOI: 10.1038/s43018-024-00756-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/20/2023] [Accepted: 03/08/2024] [Indexed: 04/20/2024]
Abstract
Tailoring optimal treatment for individual cancer patients remains a significant challenge. To address this issue, we developed PERCEPTION (PERsonalized Single-Cell Expression-Based Planning for Treatments In ONcology), a precision oncology computational pipeline. Our approach uses publicly available matched bulk and single-cell (sc) expression profiles from large-scale cell-line drug screens. These profiles help build treatment response models based on patients' sc-tumor transcriptomics. PERCEPTION demonstrates success in predicting responses to targeted therapies in cultured and patient-tumor-derived primary cells, as well as in two clinical trials for multiple myeloma and breast cancer. It also captures the resistance development in patients with lung cancer treated with tyrosine kinase inhibitors. PERCEPTION outperforms published state-of-the-art sc-based and bulk-based predictors in all clinical cohorts. PERCEPTION is accessible at https://github.com/ruppinlab/PERCEPTION . Our work, showcasing patient stratification using sc-expression profiles of their tumors, will encourage the adoption of sc-omics profiling in clinical settings, enhancing precision oncology tools based on sc-omics.
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Affiliation(s)
- Sanju Sinha
- Cancer Data Science Laboratory, National Cancer Institute, Bethesda, MD, USA.
- NCI-Designated Cancer Center, Sanford Burnham Prebys Medical Discovery Institute, San Diego, CA, USA.
| | - Rahulsimham Vegesna
- Cancer Data Science Laboratory, National Cancer Institute, Bethesda, MD, USA
| | - Sumit Mukherjee
- Cancer Data Science Laboratory, National Cancer Institute, Bethesda, MD, USA
| | - Ashwin V Kammula
- Cancer Data Science Laboratory, National Cancer Institute, Bethesda, MD, USA
- University of Maryland, College Park, MD, USA
| | | | - Wei Wu
- Department of Medicine, University of California, San Francisco, San Francisco, CA, USA
| | - D Lucas Kerr
- Department of Medicine, University of California, San Francisco, San Francisco, CA, USA
| | - Nishanth Ulhas Nair
- Cancer Data Science Laboratory, National Cancer Institute, Bethesda, MD, USA
| | - Matthew G Jones
- Center for Computational Biology, University of California, Berkeley, Berkeley, CA, USA
- Department of Electrical Engineering and Computer Science, University of California, Berkeley, Berkeley, CA, USA
- Integrative Program in Quantitative Biology, University of California, San Francisco, San Francisco, CA, USA
- Whitehead Institute, Cambridge, MA, USA
| | - Nir Yosef
- Center for Computational Biology, University of California, Berkeley, Berkeley, CA, USA
- Department of Electrical Engineering and Computer Science, University of California, Berkeley, Berkeley, CA, USA
| | | | - Ivan Grishagin
- Rancho BioSciences, San Diego, CA, USA
- Division of Preclinical Innovation, National Center for Advancing Translational Sciences, National Institutes of Health, Rockville, MD, USA
| | - Kenneth D Aldape
- Laboratory of Pathology, Center for Cancer Research, National Cancer Institute, Bethesda, MD, USA
| | - Collin M Blakely
- Department of Medicine, University of California, San Francisco, San Francisco, CA, USA
- Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, CA, USA
| | - Peng Jiang
- Cancer Data Science Laboratory, National Cancer Institute, Bethesda, MD, USA
| | - Craig J Thomas
- Division of Preclinical Innovation, National Center for Advancing Translational Sciences, National Institutes of Health, Rockville, MD, USA
- Lymphoid Malignancies Branch, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Cyril H Benes
- Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Trever G Bivona
- Department of Medicine, University of California, San Francisco, San Francisco, CA, USA
- Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, CA, USA
- Department of Cellular and Molecular Pharmacology, University of California, San Francisco, San Francisco, CA, USA
- Chan Zuckerberg Biohub Investigator, San Francisco, CA, USA
| | | | - Eytan Ruppin
- Cancer Data Science Laboratory, National Cancer Institute, Bethesda, MD, USA.
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3
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Swarbrick A, Fernandez-Martinez A, Perou CM. Gene-Expression Profiling to Decipher Breast Cancer Inter- and Intratumor Heterogeneity. Cold Spring Harb Perspect Med 2024; 14:a041320. [PMID: 37137498 PMCID: PMC10759991 DOI: 10.1101/cshperspect.a041320] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/05/2023]
Abstract
Breast cancer is heterogeneous and differs substantially across different tumors (intertumor heterogeneity) and even within an individual tumor (intratumor heterogeneity). Gene-expression profiling has considerably impacted our understanding of breast cancer biology. Four main "intrinsic subtypes" of breast cancer (i.e., luminal A, luminal B, HER2-enriched, and basal-like) have been consistently identified by gene expression, showing significant prognostic and predictive value in multiple clinical scenarios. Thanks to the molecular profiling of breast tumors, breast cancer is a paradigm of treatment personalization. Several standardized prognostic gene-expression assays are presently being used in the clinic to guide treatment decisions. Moreover, the development of single-cell-level resolution molecular profiling has allowed us to appreciate that breast cancer is also heterogeneous within a single tumor. There is an evident functional heterogeneity within the neoplastic and tumor microenvironment cells. Finally, emerging insights from these studies suggest a substantial cellular organization of neoplastic and tumor microenvironment cells, thus defining breast cancer ecosystems and highlighting the importance of spatial localizations.
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Affiliation(s)
- Alexander Swarbrick
- Cancer Ecosystems Program, Garvan Institute of Medical Research, Darlinghurst, New South Wales 2010, Australia
- St Vincent's Clinical School, Faculty of Medicine, University of New South Wales, Sydney, New South Wales 2052, Australia
| | - Aranzazu Fernandez-Martinez
- Lineberger Comprehensive Center, University of North Carolina, Chapel Hill, North Carolina 27599, USA
- Department of Genetics, University of North Carolina, Chapel Hill, North Carolina 27514, USA
| | - Charles M Perou
- Lineberger Comprehensive Center, University of North Carolina, Chapel Hill, North Carolina 27599, USA
- Department of Genetics, University of North Carolina, Chapel Hill, North Carolina 27514, USA
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Zagami P, Fernandez-Martinez A, Rashid NU, Hoadley KA, Spears PA, Curigliano G, Perou CM, Carey LA. Association of PIK3CA Mutation With Pathologic Complete Response and Outcome by Hormone Receptor Status and Intrinsic Subtype in Early-Stage ERBB2/HER2-Positive Breast Cancer. JAMA Netw Open 2023; 6:e2348814. [PMID: 38117494 PMCID: PMC10733807 DOI: 10.1001/jamanetworkopen.2023.48814] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/15/2023] [Accepted: 11/06/2023] [Indexed: 12/21/2023] Open
Abstract
Importance PIK3CA mutations may be associated with outcomes of patients with ERBB2/HER2-positive early breast cancer (EBC). Objectives To assess if PIK3CA mutations among patients with ERBB2/HER2-positive EBC are associated with treatment response and outcome, and if these associations vary by hormone receptor (HR) status or intrinsic molecular subtype (IMS). Design, Setting, and Participants This cohort study derived data on 184 patients from the phase 3 neoadjuvant Cancer and Leukemia Group B (CALGB) 40601 trial that enrolled patients with ERBB2/HER2-positive EBC in North America between January 1, 2008, and December 31, 2012. Participants received neoadjuvant paclitaxel with trastuzumab, lapatinib, or both. Statistical analysis was performed from March 23, 2022, to March 9, 2023. Exposures Gene expression profiling by RNA sequencing with Prediction Analysis of Microarray 50-determined IMS and PIK3CA mutations from whole-exome sequencing were obtained from pretreatment biopsies from 184 of 305 trial participants. Main Outcomes and Measures The primary end point was pathologic complete response (pCR) and the secondary end point of event-free survival (EFS). The association of PIK3CA mutations with pCR and EFS by HR status and IMS was estimated using logistic and Cox proportional hazards regression models. Results All 184 participants were women, with a median age of 49 years (range 24-75 years). A total of 121 participants (66%) had clinical stage II tumors; 32 (17%) had PIK3CA mutations, most frequently H1047R (38% [12 of 32]) and E545K (22% [7 of 32]). PIK3CA mutations were present in 20 of 102 cases of HR-positive EBC (20%) and 12 of 82 cases HR-negative EBC (15%) and varied by IMS (luminal B, 9 of 25 [36%]; luminal A, 2 of 21 [10%]; and ERBB2/HER2-enriched tumors, 19 of 102 [19%]). Pathologic complete response rates were lower in PIK3CA mutated than PIK3CA wild type in the overall population (34% [11 of 32] vs 49% [74 of 152]; P = .14) and were significantly different among those receiving trastuzumab (30% [7 of 23] vs 54% [63 of 117]; P = .045). At a median follow-up of 9 years, PIK3CA mutations were significantly associated with worse EFS in the overall cohort (hazard ratio, 2.58 [95% CI, 1.24-5.35]; P = .01), which persisted in a multivariable model including pCR, HR status, stage, and IMS (hazard ratio, 2.52 [95% CI, 1.16-5.47]; P = .02). The negative association of PIK3CA mutation was significant in HR-positive (hazard ratio, 3.60 [95% CI, 1.45-8.96]; P = .006) and luminal subtypes (hazard ratio, 4.84 [95% CI, 1.08-21.70]; P = .04), but not in nonluminal and HR-negative tumors. Conclusions and Relevance In ERBB2/HER2-positive EBC, PIK3CA mutations were associated with lower pCR rates and independently associated with worse long-term EFS. These findings appear to be associated with PIK3CA mutations in HR-positive and luminal EBC.
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Affiliation(s)
- Paola Zagami
- Lineberger Comprehensive Cancer Center, University of North Carolina, Chapel Hill
- Division of Medical Oncology, University of Milan, Milan, Italy
| | - Aranzazu Fernandez-Martinez
- Lineberger Comprehensive Cancer Center, University of North Carolina, Chapel Hill
- Department of Genetics, University of North Carolina, Chapel Hill
| | - Naim U. Rashid
- Department of Biostatistics, University of North Carolina, Chapel Hill
| | - Katherine A. Hoadley
- Lineberger Comprehensive Cancer Center, University of North Carolina, Chapel Hill
- Department of Genetics, University of North Carolina, Chapel Hill
| | - Patricia A. Spears
- Lineberger Comprehensive Cancer Center, University of North Carolina, Chapel Hill
| | - Giuseppe Curigliano
- Division of Medical Oncology, University of Milan, Milan, Italy
- Division of New Drugs and Early Drug Development, European Institute of Oncology IRCCS, Milan, Italy
| | - Charles M. Perou
- Lineberger Comprehensive Cancer Center, University of North Carolina, Chapel Hill
- Department of Genetics, University of North Carolina, Chapel Hill
| | - Lisa A. Carey
- Lineberger Comprehensive Cancer Center, University of North Carolina, Chapel Hill
- Division of Medical Oncology, Department of Medicine, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill
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5
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Lee S, Sun M, Hu Y, Wang Y, Islam MN, Goerlitz D, Lucas PC, Lee AV, Swain SM, Tang G, Wang XS. iGenSig-Rx: an integral genomic signature based white-box tool for modeling cancer therapeutic responses using multi-omics data. RESEARCH SQUARE 2023:rs.3.rs-3649238. [PMID: 38077030 PMCID: PMC10705599 DOI: 10.21203/rs.3.rs-3649238/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/21/2023]
Abstract
Multi-omics sequencing is expected to become clinically routine within the next decade and transform clinical care. However, there is a paucity of viable and interpretable genome-wide modeling methods that can facilitate rational selection of patients for tailored intervention. Here we develop an integral genomic signature-based method called iGenSig-Rx as a white-box tool for modeling therapeutic response based on clinical trial datasets with improved cross-dataset applicability and tolerance to sequencing bias. This method leverages high-dimensional redundant genomic features to address the challenges of cross-dataset modeling, a concept similar to the use of redundant steel rods to reinforce the pillars of a building. Using genomic datasets for HER2 targeted therapies, the iGenSig-Rx model demonstrates stable predictive power across four independent clinical trials. More importantly, the iGenSig-Rx model offers the level of transparency much needed for clinical application, allowing for clear explanations as to how the predictions are produced, how the features contribute to the prediction, and what are the key underlying pathways. We expect that iGenSig-Rx as a class of biologically interpretable multi-omics modeling methods will have broad applications in big-data based precision oncology. The R package is available: https://github.com/wangxlab/iGenSig-Rx. NOTE: the Github website will be released upon publication and the R package is available for review through google drive: https://drive.google.com/drive/folders/1KgecmUoon9-h2Dg1rPCyEGFPOp28Ols3?usp=sharing.
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Affiliation(s)
| | | | | | | | | | | | | | | | - Sandra M Swain
- National Surgical Adjuvant Breast and Bowel Project (NSABP)
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6
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Yoshino R, Yoshida N, Ujiie N, Nakatsubo M, Tanino M, Kitada M. The Add-On Effect of Fluorouracil, Epirubicin, and Cyclophosphamide Regimens for Neoadjuvant Chemotherapy in Human Epidermal Receptor 2 (HER2)-Positive Breast Cancer: A Single-Center Retrospective Study. Cureus 2023; 15:e48255. [PMID: 38054134 PMCID: PMC10694781 DOI: 10.7759/cureus.48255] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/04/2023] [Indexed: 12/07/2023] Open
Abstract
BACKGROUND The addition of pertuzumab to trastuzumab in neoadjuvant chemotherapy (NAC) for anti-human epidermal receptor 2 (HER2) positive breast cancer has shown a significant improvement in the pathologic complete response (pCR) rate. However, the add-on effect of an anthracycline-based regimen (standard-of-care regimen) remains unclear. In this retrospective, observational study, participants received pertuzumab combination therapy as NAC for HER2-positive primary breast cancer. METHODS This study was conducted from January 1, 2020, to December 31, 2022. Patients who had not received at least three courses of pertuzumab owing to adverse events or those who had received preoperative radiotherapy were excluded. RESULTS The pCR rate was 35.3% (12/34 patients). The pCR group had a significantly higher percentage of histopathologic grade III (1/11 patients, p=0.030) and a significantly higher percentage of hormone receptor-negative patients (7/12 patients, p=0.015) than the non-pCR group. The non-pCR group had a significantly higher incidence of vascular invasion than the pCR group (7/22 patients, p=0.036). Menopausal status, stage, and ki-67 values were not significantly different between the two groups. CONCLUSIONS This study suggests an unlikely add-on effect of an anthracycline-based regimen for NAC in HER2-positive breast cancer. Moreover, our results support that the pCR rate is high in patients with hormone receptor-negative, HER2-positive breast cancer.
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Affiliation(s)
- Ryusei Yoshino
- Thoracic Surgery and Breast Surgery, Asahikawa Medical University Hospital, Asahikawa, JPN
| | - Nana Yoshida
- Thoracic Surgery and Breast Surgery, Asahikawa Medical University Hospital, Asahikawa, JPN
| | - Nanami Ujiie
- Thoracic Surgery and Breast Surgery, Asahikawa Medical University Hospital, Asahikawa, JPN
| | - Masaki Nakatsubo
- Thoracic Surgery and Breast Surgery, Asahikawa Medical University Hospital, Asahikawa, JPN
| | - Mishie Tanino
- Diagnostic Pathology, Asahikawa Medical University Hospital, Asahikawa, JPN
| | - Masahiro Kitada
- Thoracic Surgery and Breast Surgery, Asahikawa Medical University Hospital, Asahikawa, JPN
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7
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Debets DO, Stecker KE, Piskopou A, Liefaard MC, Wesseling J, Sonke GS, Lips EH, Altelaar M. Deep (phospho)proteomics profiling of pre- treatment needle biopsies identifies signatures of treatment resistance in HER2 + breast cancer. Cell Rep Med 2023; 4:101203. [PMID: 37794585 PMCID: PMC10591042 DOI: 10.1016/j.xcrm.2023.101203] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2022] [Revised: 07/06/2023] [Accepted: 08/31/2023] [Indexed: 10/06/2023]
Abstract
Patients with early-stage HER2-overexpressing breast cancer struggle with treatment resistance in 20%-40% of cases. More information is needed to predict HER2 therapy response and resistance in vivo. In this study, we perform (phospho)proteomics analysis of pre-treatment HER2+ needle biopsies of early-stage invasive breast cancer to identify molecular signatures predictive of treatment response to trastuzumab, pertuzumab, and chemotherapy. Our data show that accurate quantification of the estrogen receptor (ER) and HER2 biomarkers, combined with the assessment of associated biological features, has the potential to enable better treatment outcome prediction. In addition, we identify cellular mechanisms that potentially precondition tumors to resist therapy. We find proteins with expression changes that correlate with resistance and constitute to a strong predictive signature for treatment success in our patient cohort. Our results highlight the multifactorial nature of drug resistance in vivo and demonstrate the necessity of deep tumor profiling.
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Affiliation(s)
- Donna O Debets
- Biomolecular Mass Spectrometry and Proteomics, Bijvoet Center for Biomolecular Research and Utrecht Institute for Pharmaceutical Sciences, University of Utrecht, 3584 Utrecht, the Netherlands
| | - Kelly E Stecker
- Biomolecular Mass Spectrometry and Proteomics, Bijvoet Center for Biomolecular Research and Utrecht Institute for Pharmaceutical Sciences, University of Utrecht, 3584 Utrecht, the Netherlands
| | - Anastasia Piskopou
- Biomolecular Mass Spectrometry and Proteomics, Bijvoet Center for Biomolecular Research and Utrecht Institute for Pharmaceutical Sciences, University of Utrecht, 3584 Utrecht, the Netherlands
| | - Marte C Liefaard
- Department of Molecular Pathology, The Netherlands Cancer Institute, Amsterdam, the Netherlands
| | - Jelle Wesseling
- Department of Molecular Pathology, The Netherlands Cancer Institute, Amsterdam, the Netherlands; Department of Pathology, Leiden University Medical Center, Leiden, the Netherlands
| | - Gabe S Sonke
- Department of Medical Oncology, The Netherlands Cancer Institute, Amsterdam, the Netherlands; Department of Medical Oncology, University of Amsterdam, Amsterdam, the Netherlands
| | - Esther H Lips
- Department of Molecular Pathology, The Netherlands Cancer Institute, Amsterdam, the Netherlands
| | - Maarten Altelaar
- Biomolecular Mass Spectrometry and Proteomics, Bijvoet Center for Biomolecular Research and Utrecht Institute for Pharmaceutical Sciences, University of Utrecht, 3584 Utrecht, the Netherlands.
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Gaynor N, Blanco A, Madden SF, Moran B, Fletcher JM, Kaukonen D, Ramírez JS, Eustace AJ, McDermott MSJ, Canonici A, Toomey S, Teiserskiene A, Hennessy BT, O'Donovan N, Crown J, Collins DM. Alterations in immune cell phenotype and cytotoxic capacity in HER2+ breast cancer patients receiving HER2-targeted neo-adjuvant therapy. Br J Cancer 2023; 129:1022-1031. [PMID: 37507543 PMCID: PMC10491671 DOI: 10.1038/s41416-023-02375-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2023] [Revised: 06/13/2023] [Accepted: 07/18/2023] [Indexed: 07/30/2023] Open
Abstract
BACKGROUND The phase II neo-adjuvant clinical trial ICORG10-05 (NCT01485926) compared chemotherapy in combination with trastuzumab, lapatinib or both in patients with HER2+ breast cancer. We studied circulating immune cells looking for alterations in phenotype, genotype and cytotoxic capacity (direct and antibody-dependent cell-mediated cytotoxicity (ADCC)) in the context of treatment response. METHODS Peripheral blood mononuclear cells (PBMCs) were isolated from pre- (n = 41) and post- (n = 25) neo-adjuvant treatment blood samples. Direct/trastuzumab-ADCC cytotoxicity of patient-derived PBMCs against K562/SKBR3 cell lines was determined ex vivo. Pembrolizumab was interrogated in 21 pre-treatment PBMC ADCC assays. Thirty-nine pre-treatment and 21 post-treatment PBMC samples were immunophenotyped. Fc receptor genotype, tumour infiltrating lymphocyte (TIL) levels and oestrogen receptor (ER) status were quantified. RESULTS Treatment attenuated the cytotoxicity/ADCC of PBMCs. CD3+/CD4+/CD8+ T cells increased following therapy, while CD56+ NK cells/CD14+ monocytes/CD19+ B cells decreased with significant post-treatment immune cell changes confined to patients with residual disease. Pembrolizumab-augmented ex vivo PBMC ADCC activity was associated with residual disease, but not pathological complete response. Pembrolizumab-responsive PBMCs were associated with lower baseline TIL levels and ER+ tumours. CONCLUSIONS PBMCs display altered phenotype and function following completion of neo-adjuvant treatment. Anti-PD-1-responsive PBMCs in ex vivo ADCC assays may be a biomarker of treatment response.
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Affiliation(s)
- Nicola Gaynor
- Cancer Biotherapeutics Research Group, National Institute for Cellular Biotechnology, Dublin City University, Dublin, Ireland
| | - Alfonso Blanco
- Flow Cytometry Core Technology, UCD Conway Institute, University College Dublin, Dublin, Ireland
| | - Stephen F Madden
- Data Science Centre, School of Population Heath Sciences, RCSI University of Medicine and Health Sciences, Dublin, Ireland
| | - Barry Moran
- School of Biochemistry and Immunology, Trinity Biomedical Sciences Institute, Trinity College Dublin, Dublin, Ireland
| | - Jean M Fletcher
- School of Biochemistry and Immunology, Trinity Biomedical Sciences Institute, Trinity College Dublin, Dublin, Ireland
- School of Medicine, Trinity Biomedical Sciences Institute, Trinity College Dublin, Dublin, Ireland
| | - Damien Kaukonen
- Data Science Centre, School of Population Heath Sciences, RCSI University of Medicine and Health Sciences, Dublin, Ireland
| | - Javier Sánchez Ramírez
- Cancer Biotherapeutics Research Group, National Institute for Cellular Biotechnology, Dublin City University, Dublin, Ireland
| | - Alex J Eustace
- School of Biotechnology, Dublin City University, Dublin, Ireland
| | - Martina S J McDermott
- National Institute for Cellular Biotechnology, Dublin City University, Dublin, Ireland
| | - Alexandra Canonici
- National Institute for Cellular Biotechnology, Dublin City University, Dublin, Ireland
| | - Sinead Toomey
- Medical Oncology Group, Department of Molecular Medicine, Royal College of Surgeons in Ireland, Dublin, Ireland
| | - Ausra Teiserskiene
- Cancer Trials Ireland, RCSI House, 121 St. Stephen's Green, Dublin, Ireland
| | - Bryan T Hennessy
- Medical Oncology Group, Department of Molecular Medicine, Royal College of Surgeons in Ireland, Dublin, Ireland
- Cancer Trials Ireland, RCSI House, 121 St. Stephen's Green, Dublin, Ireland
| | - Norma O'Donovan
- National Institute for Cellular Biotechnology, Dublin City University, Dublin, Ireland
| | - John Crown
- Cancer Biotherapeutics Research Group, National Institute for Cellular Biotechnology, Dublin City University, Dublin, Ireland
- Department of Medical Oncology, St Vincent's University Hospital, Dublin, Ireland
| | - Denis M Collins
- Cancer Biotherapeutics Research Group, National Institute for Cellular Biotechnology, Dublin City University, Dublin, Ireland.
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9
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Nolan E, Lindeman GJ, Visvader JE. Deciphering breast cancer: from biology to the clinic. Cell 2023; 186:1708-1728. [PMID: 36931265 DOI: 10.1016/j.cell.2023.01.040] [Citation(s) in RCA: 96] [Impact Index Per Article: 96.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2022] [Revised: 01/14/2023] [Accepted: 01/30/2023] [Indexed: 03/17/2023]
Abstract
Breast cancer remains a leading cause of cancer-related mortality in women, reflecting profound disease heterogeneity, metastasis, and therapeutic resistance. Over the last decade, genomic and transcriptomic data have been integrated on an unprecedented scale and revealed distinct cancer subtypes, critical molecular drivers, clonal evolutionary trajectories, and prognostic signatures. Furthermore, multi-dimensional integration of high-resolution single-cell and spatial technologies has highlighted the importance of the entire breast cancer ecosystem and the presence of distinct cellular "neighborhoods." Clinically, a plethora of new targeted therapies has emerged, now being rapidly incorporated into routine care. Resistance to therapy, however, remains a crucial challenge for the field.
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Affiliation(s)
- Emma Nolan
- Auckland Cancer Society Research Centre, University of Auckland, Auckland 1023, New Zealand
| | - Geoffrey J Lindeman
- ACRF Cancer Biology and Stem Cells Division, The Walter and Eliza Hall Institute of Medical Research, Parkville, VIC 3052, Australia; Department of Medicine, Royal Melbourne Hospital, University of Melbourne, Parkville, VIC 3050, Australia; Department of Medical Oncology, Peter MacCallum Cancer Centre, Melbourne, VIC 3000, Australia
| | - Jane E Visvader
- ACRF Cancer Biology and Stem Cells Division, The Walter and Eliza Hall Institute of Medical Research, Parkville, VIC 3052, Australia; Department of Medical Biology, University of Melbourne, Parkville, VIC 3010, Australia.
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10
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Shin J, Ham B, Seo JH, Lee SB, Park IA, Gong G, Kim SB, Lee HJ. Immune repertoire and responses to neoadjuvant TCHP therapy in HER2-positive breast cancer. Ther Adv Med Oncol 2023; 15:17588359231157654. [PMID: 36865681 PMCID: PMC9972050 DOI: 10.1177/17588359231157654] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2022] [Accepted: 01/30/2023] [Indexed: 03/02/2023] Open
Abstract
Background Despite the introduction of trastuzumab, pathologic complete response (pCR) is not attained in approximately 30-40% of Human epithelial growth factor receptor-2-positive breast cancer. Tumor-infiltrating lymphocytes (TIL) have been suggested as a predictive marker of treatment response, albeit not always effective. We investigated the relationship between trastuzumab, docetaxel, carboplatin, and pertuzumab (TCHP) treatment and immune repertoire as a treatment response predictor. Design In all, 35 cases were divided into two experimental groups: 10 and 25 cases in the preliminary and main experiments, respectively. In the preliminary experiment, the biopsy tissues before TCHP treatment and the surgical tissues after TCHP treatment were compared. In the main experiment, the biopsy tissues before TCHP treatment were compared according to the TCHP treatment response. Methods The T-cell repertoire for TRA, TRB, TRG, and TRD, and B-cell repertoire for immunoglobulin heavy, immunoglobulin kappa, and immunoglobulin lambda were evaluated. Whole transcriptome sequencing was also performed. Results In the preliminary experiment, the density and richness of the T-cell receptor (TCR) and B-cell receptor (BCR) repertoires decreased after treatment, regardless of TCHP response. In the main experiment, the Shannon's entropy index, density, and length of CDR3 of the TCR and BCR repertoires did not differ significantly in patients who did and did not achieve pCR. The pCR and non-pCR subgroups according to the level of TILs revealed that the non-pCR/lowTIL group had a higher proportion of low-frequency clones than the pCR/lowTIL group in TRA (non-pCR/lowTIL versus pCR/lowTIL, 0.01-0.1%, 63% versus 45.3%; <0.01%, 32.9% versus 51.8%, p < 0.001) and TRB (non-pCR/lowTIL versus pCR/lowTIL, 0.01-0.1%, 26.5% versus 14.7%; <0.01%, 72.0% versus 84.1%, p < 0.001). Conclusions The role of the diversity, richness, and density of the TCR and BCR repertoires as predictive markers for TCHP response was not identified. Compositions of low-frequency clones could be candidates for predictive factors of TCHP response; however, validation studies and further research are necessary.
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Affiliation(s)
- Junyoung Shin
- Department of Pathology, Asan Medical Center,
University of Ulsan College of Medicine, Seoul, Korea
| | | | | | - Sae Byul Lee
- Department of Breast Surgery, Asan Medical
Center, University of Ulsan College of Medicine, Seoul, Korea
| | - In Ah Park
- Department of Pathology, Kangbuk Samsung
Hospital, Seoul, Republic of Korea
| | - Gyungyub Gong
- Department of Pathology, Asan Medical Center,
University of Ulsan College of Medicine, Seoul, Korea
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11
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Graeser M, Gluz O, Biehl C, Ulbrich-Gebauer D, Christgen M, Palatty J, Kuemmel S, Grischke EM, Augustin D, Braun M, Potenberg J, Wuerstlein R, Krauss K, Schumacher C, Forstbauer H, Reimer T, Stefek A, Fischer HH, Pelz E, zu Eulenburg C, Kates R, Ni H, Kolberg-Liedtke C, Feuerhake F, Kreipe HH, Nitz U, Harbeck N. Impact of RNA Signatures on pCR and Survival after 12-Week Neoadjuvant Pertuzumab plus Trastuzumab with or without Paclitaxel in the WSG-ADAPT HER2+/HR- Trial. Clin Cancer Res 2023; 29:805-814. [PMID: 36441798 PMCID: PMC9932580 DOI: 10.1158/1078-0432.ccr-22-1587] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2022] [Revised: 08/26/2022] [Accepted: 11/21/2022] [Indexed: 11/30/2022]
Abstract
PURPOSE To identify associations of biological signatures and stromal tumor-infiltrating lymphocytes (sTIL) with pathological complete response (pCR; ypT0 ypN0) and survival in the Phase II WSG-ADAPT HER2+/HR- trial (NCT01817452). EXPERIMENTAL DESIGN Patients with cT1-cT4c, cN0-3 HER2+/HR- early breast cancer (EBC) were randomized to pertuzumab+trastuzumab (P+T, n = 92) or P+T+paclitaxel (n = 42). Gene expression signatures were analyzed in baseline biopsies using NanoString Breast Cancer 360 panel (n = 117); baseline and on-treatment (week 3) sTIL levels were available in 119 and 76 patients, respectively. Impacts of standardized gene expression signatures on pCR and invasive disease-free survival (iDFS) were estimated by logistic and Cox regression. RESULTS In all patients, ERBB2 [OR, 1.70; 95% confidence interval (CI), 1.08-2.67] and estrogen receptor (ER) signaling (OR, 1.72; 95% CI, 1.13-2.61) were favorable, whereas PTEN (OR, 0.57; 95% CI, 0.38-0.87) was unfavorable for pCR. After 60 months median follow-up, 13 invasive events occurred (P+T: n = 11, P+T+paclitaxel: n = 2), none following pCR. Gene signatures related to immune response (IR) and ER signaling were favorable for iDFS, all with similar HR about 0.43-0.55. These patterns were even more prominent in the neoadjuvant chemotherapy-free group, where additionally BRCAness signature was unfavorable (HR, 2.00; 95% CI, 1.04-3.84). IR signatures were strongly intercorrelated. sTILs (baseline/week 3/change) were not associated with pCR or iDFS, though baseline sTILs correlated positively with IR signatures. CONCLUSIONS Distinct gene signatures were associated with pCR versus iDFS in HER2+/HR- EBC. The potential role of IR in preventing recurrence suggests that patients with upregulated IR signatures could be candidates for de-escalation concepts in HER2+ EBC.
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Affiliation(s)
- Monika Graeser
- West German Study Group, Moenchengladbach, Germany.,Ev. Hospital Bethesda, Breast Center Niederrhein, Moenchengladbach, Germany.,Department of Gynecology, University Medical Center Hamburg, Hamburg, Germany.,Corresponding Author: Monika Graeser, Bethesda Hospital, West German Study Group, University Medical Center Hamburg—Eppendorf, Moenchengladbach and Hamburg 41061, Germany. Phone: 49-216-1981-2330; Fax: 49-216-1566-2319; E-mail:
| | - Oleg Gluz
- West German Study Group, Moenchengladbach, Germany.,Ev. Hospital Bethesda, Breast Center Niederrhein, Moenchengladbach, Germany.,University Clinics Cologne, Women's Clinic and Breast Center, Cologne, Germany
| | - Claudia Biehl
- Westphalian Brest Center Dortmund, Dortmund, Germany
| | | | | | | | - Sherko Kuemmel
- West German Study Group, Moenchengladbach, Germany.,Breast Unit, Kliniken Essen-Mitte, Essen, Germany.,Department of Gynecology with Breast Center, University Hospital Charité, Humboldt University, Berlin, Germany
| | | | | | - Michael Braun
- Department of Gynecology, Breast Center, Red Cross Hospital Munich, Munich, Germany
| | | | - Rachel Wuerstlein
- West German Study Group, Moenchengladbach, Germany.,Department of Gynecology and Obstetrics and CCCLMU, Breast Center, LMU University Hospital, Munich, Germany
| | - Katja Krauss
- University Hospital Aachen, Breast Center, Aachen, Germany
| | | | | | - Toralf Reimer
- University Hospital Gynecology and Policlinic Rostock, Rostock, Germany
| | - Andrea Stefek
- Johanniter Women's Clinic Stendal, Breast Center, Stendal, Germany
| | | | | | - Christine zu Eulenburg
- West German Study Group, Moenchengladbach, Germany.,Department of Medical Biometry and Epidemiology, University Medical Center Hamburg, Hamburg, Germany
| | - Ronald Kates
- West German Study Group, Moenchengladbach, Germany
| | - Hua Ni
- Department of Gynecology and Obstetrics and CCCLMU, Breast Center, LMU University Hospital, Munich, Germany
| | - Cornelia Kolberg-Liedtke
- Department of Gynecology with Breast Center, University Hospital Charité, Humboldt University, Berlin, Germany.,University Clinics Essen, Women's Clinic, Essen, Germany
| | - Friedrich Feuerhake
- Medical School Hannover, Institute of Pathology, Hannover, Germany.,Institute of Neuropathology, University Clinic Freiburg, Freiburg, Germany
| | | | - Ulrike Nitz
- West German Study Group, Moenchengladbach, Germany.,Ev. Hospital Bethesda, Breast Center Niederrhein, Moenchengladbach, Germany
| | - Nadia Harbeck
- West German Study Group, Moenchengladbach, Germany.,Department of Gynecology and Obstetrics and CCCLMU, Breast Center, LMU University Hospital, Munich, Germany
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12
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Price BA, Marron JS, Mose LE, Perou CM, Parker JS. Translating transcriptomic findings from cancer model systems to humans through joint dimension reduction. Commun Biol 2023; 6:179. [PMID: 36797360 PMCID: PMC9935626 DOI: 10.1038/s42003-023-04529-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2022] [Accepted: 01/25/2023] [Indexed: 02/18/2023] Open
Abstract
Model systems are an essential resource in cancer research. They simulate effects that we can infer into humans, but come at a risk of inaccurately representing human biology. This inaccuracy can lead to inconclusive experiments or misleading results, urging the need for an improved process for translating model system findings into human-relevant data. We present a process for applying joint dimension reduction (jDR) to horizontally integrate gene expression data across model systems and human tumor cohorts. We then use this approach to combine human TCGA gene expression data with data from human cancer cell lines and mouse model tumors. By identifying the aspects of genomic variation joint-acting across cohorts, we demonstrate how predictive modeling and clinical biomarkers from model systems can be improved.
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Affiliation(s)
- Brandon A. Price
- grid.10698.360000000122483208Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC USA ,grid.10698.360000000122483208Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC USA
| | - J. S. Marron
- grid.10698.360000000122483208Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC USA ,grid.10698.360000000122483208Department of Statistics and Operations Research, University of North Carolina at Chapel Hill, Chapel Hill, NC USA
| | - Lisle E. Mose
- grid.10698.360000000122483208Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC USA
| | - Charles M. Perou
- grid.10698.360000000122483208Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC USA ,grid.10698.360000000122483208Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC USA
| | - Joel S. Parker
- grid.10698.360000000122483208Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC USA ,grid.10698.360000000122483208Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC USA
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13
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Chen F, Wang Y, Zhang X, Fang J. Five hub genes contributing to the oncogenesis and trastuzumab-resistance in gastric cancer. Gene 2023; 851:146942. [DOI: 10.1016/j.gene.2022.146942] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2022] [Revised: 09/26/2022] [Accepted: 09/28/2022] [Indexed: 11/06/2022]
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14
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Sorokin M, Zolotovskaia M, Nikitin D, Suntsova M, Poddubskaya E, Glusker A, Garazha A, Moisseev A, Li X, Sekacheva M, Naskhletashvili D, Seryakov A, Wang Y, Buzdin A. Personalized targeted therapy prescription in colorectal cancer using algorithmic analysis of RNA sequencing data. BMC Cancer 2022; 22:1113. [PMCID: PMC9623986 DOI: 10.1186/s12885-022-10177-3] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2021] [Accepted: 09/26/2022] [Indexed: 12/24/2022] Open
Abstract
Background: Overall survival of advanced colorectal cancer (CRC) patients remains poor, and gene expression analysis could potentially complement detection of clinically relevant mutations to personalize CRC treatments. Methods: We performed RNA sequencing of formalin-fixed, paraffin-embedded (FFPE) cancer tissue samples of 23 CRC patients and interpreted the data obtained using bioinformatic method Oncobox for expression-based rating of targeted therapeutics. Oncobox ranks cancer drugs according to the efficiency score calculated using target genes expression and molecular pathway activation data. The patients had primary and metastatic CRC with metastases in liver, peritoneum, brain, adrenal gland, lymph nodes and ovary. Two patients had mutations in NRAS, seven others had mutated KRAS gene. Patients were treated by aflibercept, bevacizumab, bortezomib, cabozantinib, cetuximab, crizotinib, denosumab, panitumumab and regorafenib as monotherapy or in combination with chemotherapy, and information on the success of totally 39 lines of therapy was collected. Results: Oncobox drug efficiency score was effective biomarker that could predict treatment outcomes in the experimental cohort (AUC 0.77 for all lines of therapy and 0.91 for the first line after tumor sampling). Separately for bevacizumab, it was effective in the experimental cohort (AUC 0.87) and in 3 independent literature CRC datasets, n = 107 (AUC 0.84–0.94). It also predicted progression-free survival in univariate (Hazard ratio 0.14) and multivariate (Hazard ratio 0.066) analyses. Difference in AUC scores evidences importance of using recent biosamples for the prediction quality. Conclusion: Our results suggest that RNA sequencing analysis of tumor FFPE materials may be helpful for personalizing prescriptions of targeted therapeutics in CRC. Supplementary Information The online version contains supplementary material available at 10.1186/s12885-022-10177-3.
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Affiliation(s)
- Maxim Sorokin
- grid.448878.f0000 0001 2288 8774I.M. Sechenov First Moscow State Medical University, 119991 Moscow, Russia ,grid.18763.3b0000000092721542Moscow Institute of Physics and Technology, 141701 Moscow Region, Russia ,OmicsWay Corp, 91789 Walnut, CA USA
| | - Marianna Zolotovskaia
- grid.18763.3b0000000092721542Moscow Institute of Physics and Technology, 141701 Moscow Region, Russia
| | - Daniil Nikitin
- OmicsWay Corp, 91789 Walnut, CA USA ,grid.418853.30000 0004 0440 1573Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, 117997 Moscow, Russia
| | - Maria Suntsova
- grid.448878.f0000 0001 2288 8774World-Class Research Center “Digital biodesign and personalized healthcare”, Sechenov First Moscow State Medical University, 119991 Moscow, Russia
| | - Elena Poddubskaya
- grid.448878.f0000 0001 2288 8774I.M. Sechenov First Moscow State Medical University, 119991 Moscow, Russia ,Clinical Center Vitamed, 121309 Moscow, Russia
| | - Alexander Glusker
- grid.448878.f0000 0001 2288 8774I.M. Sechenov First Moscow State Medical University, 119991 Moscow, Russia
| | | | - Alexey Moisseev
- grid.448878.f0000 0001 2288 8774I.M. Sechenov First Moscow State Medical University, 119991 Moscow, Russia
| | - Xinmin Li
- grid.19006.3e0000 0000 9632 6718Department of Pathology and Laboratory Medicine, University of California, 90095 Los Angeles, CA USA
| | - Marina Sekacheva
- grid.448878.f0000 0001 2288 8774World-Class Research Center “Digital biodesign and personalized healthcare”, Sechenov First Moscow State Medical University, 119991 Moscow, Russia
| | - David Naskhletashvili
- grid.466904.90000 0000 9092 133XN.N. Blokhin Russian Cancer Research Center, 115478 Moscow, Russia
| | | | - Ye Wang
- grid.410645.20000 0001 0455 0905Core Laboratory, The Affiliated Qingdao Central Hospital of Qingdao University, Qingdao, China
| | - Anton Buzdin
- grid.18763.3b0000000092721542Moscow Institute of Physics and Technology, 141701 Moscow Region, Russia ,grid.418853.30000 0004 0440 1573Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, 117997 Moscow, Russia ,grid.448878.f0000 0001 2288 8774World-Class Research Center “Digital biodesign and personalized healthcare”, Sechenov First Moscow State Medical University, 119991 Moscow, Russia
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15
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Wang XS, Lee S, Zhang H, Tang G, Wang Y. An integral genomic signature approach for tailored cancer therapy using genome-wide sequencing data. Nat Commun 2022; 13:2936. [PMID: 35618721 PMCID: PMC9135729 DOI: 10.1038/s41467-022-30449-7] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2021] [Accepted: 04/29/2022] [Indexed: 11/19/2022] Open
Abstract
Low-cost multi-omics sequencing is expected to become clinical routine and transform precision oncology. Viable computational methods that can facilitate tailored intervention while tolerating sequencing biases are in high demand. Here we propose a class of transparent and interpretable computational methods called integral genomic signature (iGenSig) analyses, that address the challenges of cross-dataset modeling through leveraging information redundancies within high-dimensional genomic features, averaging feature weights to prevent overweighing, and extracting unbiased genomic information from large tumor cohorts. Using genomic dataset of chemical perturbations, we develop a battery of iGenSig models for predicting cancer drug responses, and validate the models using independent cell-line and clinical datasets. The iGenSig models for five drugs demonstrate predictive values in six clinical studies, among which the Erlotinib and 5-FU models significantly predict therapeutic responses in three studies, offering clinically relevant insights into their inverse predictive signature pathways. Together, iGenSig provides a computational framework to facilitate tailored cancer therapy based on multi-omics data.
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Affiliation(s)
- Xiao-Song Wang
- UPMC Hillman Cancer Center, University of Pittsburgh, Pittsburgh, PA, 15213, USA.
- Department of Pathology, University of Pittsburgh, Pittsburgh, PA, 15213, USA.
| | - Sanghoon Lee
- UPMC Hillman Cancer Center, University of Pittsburgh, Pittsburgh, PA, 15213, USA
- Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA, 15206, USA
| | - Han Zhang
- UPMC Hillman Cancer Center, University of Pittsburgh, Pittsburgh, PA, 15213, USA
- Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA, 15206, USA
| | - Gong Tang
- Department of Biostatistics, University of Pittsburgh, Pittsburgh, PA, 15261, USA
| | - Yue Wang
- UPMC Hillman Cancer Center, University of Pittsburgh, Pittsburgh, PA, 15213, USA
- Department of Pathology, University of Pittsburgh, Pittsburgh, PA, 15213, USA
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16
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Zou Y, Zheng S, Xie X, Ye F, Hu X, Tian Z, Yan SM, Yang L, Kong Y, Tang Y, Tian W, Xie J, Deng X, Zeng Y, Chen ZS, Tang H, Xie X. N6-methyladenosine regulated FGFR4 attenuates ferroptotic cell death in recalcitrant HER2-positive breast cancer. Nat Commun 2022; 13:2672. [PMID: 35562334 PMCID: PMC9106694 DOI: 10.1038/s41467-022-30217-7] [Citation(s) in RCA: 79] [Impact Index Per Article: 39.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2021] [Accepted: 04/21/2022] [Indexed: 12/26/2022] Open
Abstract
Intrinsic and acquired anti-HER2 resistance remains a major hurdle for treating HER2-positive breast cancer. Using genome-wide CRISPR/Cas9 screening in vitro and in vivo, we identify FGFR4 as an essential gene following anti-HER2 treatment. FGFR4 inhibition enhances susceptibility to anti-HER2 therapy in resistant breast cancer. Mechanistically, m6A-hypomethylation regulated FGFR4 phosphorylates GSK-3β and activates β-catenin/TCF4 signaling to drive anti-HER2 resistance. Notably, suppression of FGFR4 dramatically diminishes glutathione synthesis and Fe2+ efflux efficiency via the β-catenin/TCF4-SLC7A11/FPN1 axis, resulting in excessive ROS production and labile iron pool accumulation. Ferroptosis, a unique iron-dependent form of oxidative cell death, is triggered after FGFR4 inhibition. Experiments involving patient-derived xenografts and organoids reveals a synergistic effect of anti-FGFR4 with anti-HER2 therapy in breast cancer with either intrinsic or acquired resistance. Together, these results pinpoint a mechanism of anti-HER2 resistance and provide a strategy for overcoming resistance via FGFR4 inhibition in recalcitrant HER2-positive breast cancer. Anti-HER2 resistance causes treatment failure in HER2-positive breast cancers. Here the authors identify FGFR4 as one of the vulnerabilities of anti-HER2 resistant breast cancer and show that FGRR4 inhibition enhances sensitivity to anti-HER2 treatment in these resistant cells by triggering ferroptosis.
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Affiliation(s)
- Yutian Zou
- Sun Yat-sen University Cancer Center; State Key Laboratory of Oncology in South China; Collaborative Innovation Center for Cancer Medicine, Guangzhou, China
| | - Shaoquan Zheng
- Sun Yat-sen University Cancer Center; State Key Laboratory of Oncology in South China; Collaborative Innovation Center for Cancer Medicine, Guangzhou, China
| | - Xinhua Xie
- Sun Yat-sen University Cancer Center; State Key Laboratory of Oncology in South China; Collaborative Innovation Center for Cancer Medicine, Guangzhou, China
| | - Feng Ye
- Sun Yat-sen University Cancer Center; State Key Laboratory of Oncology in South China; Collaborative Innovation Center for Cancer Medicine, Guangzhou, China
| | - Xiaoqian Hu
- School of Biomedical Sciences, Faculty of Medicine, The University of Hong Kong, Hong Kong, China
| | - Zhi Tian
- College of Pharmacy, University of South Florida, Tampa, FL, USA
| | - Shu-Mei Yan
- Sun Yat-sen University Cancer Center; State Key Laboratory of Oncology in South China; Collaborative Innovation Center for Cancer Medicine, Guangzhou, China
| | - Lu Yang
- Sun Yat-sen University Cancer Center; State Key Laboratory of Oncology in South China; Collaborative Innovation Center for Cancer Medicine, Guangzhou, China
| | - Yanan Kong
- Sun Yat-sen University Cancer Center; State Key Laboratory of Oncology in South China; Collaborative Innovation Center for Cancer Medicine, Guangzhou, China
| | - Yuhui Tang
- Sun Yat-sen University Cancer Center; State Key Laboratory of Oncology in South China; Collaborative Innovation Center for Cancer Medicine, Guangzhou, China
| | - Wenwen Tian
- Sun Yat-sen University Cancer Center; State Key Laboratory of Oncology in South China; Collaborative Innovation Center for Cancer Medicine, Guangzhou, China
| | - Jindong Xie
- Sun Yat-sen University Cancer Center; State Key Laboratory of Oncology in South China; Collaborative Innovation Center for Cancer Medicine, Guangzhou, China
| | - Xinpei Deng
- Sun Yat-sen University Cancer Center; State Key Laboratory of Oncology in South China; Collaborative Innovation Center for Cancer Medicine, Guangzhou, China
| | - Yan Zeng
- Sun Yat-sen University Cancer Center; State Key Laboratory of Oncology in South China; Collaborative Innovation Center for Cancer Medicine, Guangzhou, China
| | - Zhe-Sheng Chen
- College of Pharmacy and Health Sciences, St. John's University, Queens, NY, USA.
| | - Hailin Tang
- Sun Yat-sen University Cancer Center; State Key Laboratory of Oncology in South China; Collaborative Innovation Center for Cancer Medicine, Guangzhou, China.
| | - Xiaoming Xie
- Sun Yat-sen University Cancer Center; State Key Laboratory of Oncology in South China; Collaborative Innovation Center for Cancer Medicine, Guangzhou, China.
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17
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Gudkov A, Shirokorad V, Kashintsev K, Sokov D, Nikitin D, Anisenko A, Borisov N, Sekacheva M, Gaifullin N, Garazha A, Suntsova M, Koroleva E, Buzdin A, Sorokin M. Gene Expression-Based Signature Can Predict Sorafenib Response in Kidney Cancer. Front Mol Biosci 2022; 9:753318. [PMID: 35359606 PMCID: PMC8963850 DOI: 10.3389/fmolb.2022.753318] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2021] [Accepted: 01/28/2022] [Indexed: 01/07/2023] Open
Abstract
Sorafenib is a tyrosine kinase inhibitory drug with multiple molecular specificities that is approved for clinical use in second-line treatments of metastatic and advanced renal cell carcinomas (RCCs). However, only 10–40% of RCC patients respond on sorafenib-containing therapies, and personalization of its prescription may help in finding an adequate balance of clinical efficiency, cost-effectiveness, and side effects. We investigated whether expression levels of known molecular targets of sorafenib in RCC can serve as prognostic biomarker of treatment response. We used Illumina microarrays to profile RNA expression in pre-treatment formalin-fixed paraffin-embedded (FFPE) samples of 22 metastatic or advanced RCC cases with known responses on next-line sorafenib monotherapy. Among them, nine patients showed partial response (PR), three patients—stable disease (SD), and 10 patients—progressive disease (PD) according to Response Evaluation Criteria In Solid Tumors (RECIST) criteria. We then classified PR + SD patients as “responders” and PD patients as “poor responders”. We found that gene signature including eight sorafenib target genes was congruent with the drug response characteristics and enabled high-quality separation of the responders and poor responders [area under a receiver operating characteristic curve (AUC) 0.89]. We validated these findings on another set of 13 experimental annotated FFPE RCC samples (for 2 PR, 1 SD, and 10 PD patients) that were profiled by RNA sequencing and observed AUC 0.97 for 8-gene signature as the response classifier. We further validated these results in a series of qRT-PCR experiments on the third experimental set of 12 annotated RCC biosamples (for 4 PR, 3 SD, and 5 PD patients), where 8-gene signature showed AUC 0.83.
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Affiliation(s)
- Alexander Gudkov
- I. M. Sechenov First Moscow State Medical University, Moscow, Russia
| | | | | | - Dmitriy Sokov
- Moscow City Clinical Oncological Dispensary №. 1, Moscow, Russia
| | | | | | | | - Marina Sekacheva
- World-Class Research Center “Digital Biodesign and Personalized Healthcare”, Sechenov First Moscow State Medical University, Moscow, Russia
| | - Nurshat Gaifullin
- Department of Pathology, Faculty of Medicine, Lomonosov Moscow State University, Moscow, Russia
| | | | - Maria Suntsova
- World-Class Research Center “Digital Biodesign and Personalized Healthcare”, Sechenov First Moscow State Medical University, Moscow, Russia
| | - Elena Koroleva
- Moscow Institute of Physics and Technology, Moscow, Russia
| | - Anton Buzdin
- Moscow Institute of Physics and Technology, Moscow, Russia
- World-Class Research Center “Digital Biodesign and Personalized Healthcare”, Sechenov First Moscow State Medical University, Moscow, Russia
- OmicsWay Corp, Walnut, CA, United States
- Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Moscow, Russia
| | - Maksim Sorokin
- I. M. Sechenov First Moscow State Medical University, Moscow, Russia
- Moscow Institute of Physics and Technology, Moscow, Russia
- OmicsWay Corp, Walnut, CA, United States
- European Organization for Research and Treatment of Cancer (EORTC), Biostatistics and Bioinformatics Subgroup, Brussels, Belgium
- *Correspondence: Maksim Sorokin,
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18
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Chantada-Vázquez MDP, Conde-Amboage M, Graña-López L, Vázquez-Estévez S, Bravo SB, Núñez C. Circulating Proteins Associated with Response and Resistance to Neoadjuvant Chemotherapy in HER2-Positive Breast Cancer. Cancers (Basel) 2022; 14:cancers14041087. [PMID: 35205837 PMCID: PMC8870308 DOI: 10.3390/cancers14041087] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2021] [Revised: 01/24/2022] [Accepted: 02/16/2022] [Indexed: 12/13/2022] Open
Abstract
Simple Summary The goal of this study was to find circulating proteins that can be easily sampled and incorporated into a clinical setting to improve predictive treatment response in HER2-positive breast cancer patients receiving neoadjuvant chemotherapy. We looked for potential biomarkers in serum, which we identified using two proteomics techniques: qualitative LC-MS/MS and a quantitative assay that assessed protein expression between responders and non-responders HER2-positive breast cancer patients to neoadjuvant chemotherapy. Abstract Despite the increasing use of neoadjuvant chemotherapy (NAC) in HER2-positive breast cancer (BC) patients, the clinical problem of predicting individual treatment response remains unanswered. Furthermore, the use of ineffective chemotherapeutic regimens should be avoided. Serum biomarker levels are being studied more and more for their ability to predict therapy response and aid in the development of personalized treatment regimens. This study aims to identify effective protein networks and biomarkers to predict response to NAC in HER2-positive BC patients through an exhaustive large-scale LC-MS/MS-based qualitative and quantitative proteomic profiling of serum samples from responders and non-responders. Serum samples from HER2-positive BC patients were collected before NAC and were processed by three methods (with and without nanoparticles). The qualitative analysis revealed differences in the proteomic profiles between responders and non-responders, mainly in proteins implicated in the complement and coagulation cascades and apolipoproteins. Qualitative analysis confirmed that three proteins (AFM, SERPINA1, APOD) were correlated with NAC resistance. In this study, we show that serum biomarker profiles can predict treatment response and outcome in the neoadjuvant setting. If these findings are further developed, they will be of significant clinical utility in the design of treatment regimens for individual BC patients.
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Affiliation(s)
- María del Pilar Chantada-Vázquez
- Research Unit, Lucus Augusti University Hospital (HULA), Servizo Galego de Saúde (SERGAS), 27002 Lugo, Spain;
- Proteomic Unit, Health Research Institute of Santiago de Compostela (IDIS), University Clinical Hospital of Santiago de Compostela (CHUS), 15706 Santiago de Compostela, Spain
| | - Mercedes Conde-Amboage
- Models of Optimization Decision, Statistics and Applications Research Group (MODESTYA), Department of Statistics, Mathematical Analysis and Optimization, Universidade de Santiago de Compostela, 15782 Santiago de Compostela, Spain;
- CITMAga, 15782 Santiago de Compostela, Spain
| | - Lucía Graña-López
- Breast Pathology Group, Lucus Augusti University Hospital (HULA)-IDIS, Servizo Galego de Saúde (SERGAS), 27002 Lugo, Spain;
- Radiology Department, Lucus Augusti University Hospital (HULA), Servizo Galego de Saúde (SERGAS), 27002 Lugo, Spain
| | - Sergio Vázquez-Estévez
- Oncology Division, Lucus Augusti University Hospital (HULA), Servizo Galego de Saúde (SERGAS), 27002 Lugo, Spain;
| | - Susana B. Bravo
- Proteomic Unit, Health Research Institute of Santiago de Compostela (IDIS), University Clinical Hospital of Santiago de Compostela (CHUS), 15706 Santiago de Compostela, Spain
- Correspondence: (S.B.B.); (C.N.)
| | - Cristina Núñez
- Research Unit, Lucus Augusti University Hospital (HULA), Servizo Galego de Saúde (SERGAS), 27002 Lugo, Spain;
- Correspondence: (S.B.B.); (C.N.)
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19
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Sorokin M, Rabushko E, Efimov V, Poddubskaya E, Sekacheva M, Simonov A, Nikitin D, Drobyshev A, Suntsova M, Buzdin A. Experimental and Meta-Analytic Validation of RNA Sequencing Signatures for Predicting Status of Microsatellite Instability. Front Mol Biosci 2021; 8:737821. [PMID: 34888350 PMCID: PMC8650122 DOI: 10.3389/fmolb.2021.737821] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2021] [Accepted: 10/19/2021] [Indexed: 01/16/2023] Open
Abstract
Microsatellite instability (MSI) is an important diagnostic and prognostic cancer biomarker. In colorectal, cervical, ovarian, and gastric cancers, it can guide the prescription of chemotherapy and immunotherapy. In laboratory diagnostics of susceptible tumors, MSI is routinely detected by the size of marker polymerase chain reaction products encompassing frequent microsatellite expansion regions. Alternatively, MSI status is screened indirectly by immunohistochemical interrogation of microsatellite binding proteins. RNA sequencing (RNAseq) profiling is an emerging source of data for a wide spectrum of cancer biomarkers. Recently, three RNAseq-based gene signatures were deduced for establishing MSI status in tumor samples. They had 25, 15, and 14 gene products with only one common gene. However, they were developed and tested on the incomplete literature of The Cancer Genome Atlas (TCGA) sampling and never validated experimentally on independent RNAseq samples. In this study, we, for the first time, systematically validated these three RNAseq MSI signatures on the literature colorectal cancer (CRC) (n = 619), endometrial carcinoma (n = 533), gastric cancer (n = 380), uterine carcinosarcoma (n = 55), and esophageal cancer (n = 83) samples and on the set of experimental CRC RNAseq samples (n = 23) for tumors with known MSI status. We found that all three signatures performed well with area under the curve (AUC) ranges of 0.94–1 for the experimental CRCs and 0.94–1 for the TCGA CRC, esophageal cancer, and uterine carcinosarcoma samples. However, for the TCGA endometrial carcinoma and gastric cancer samples, only two signatures were effective with AUC 0.91–0.97, whereas the third signature showed a significantly lower AUC of 0.69–0.88. Software for calculating these MSI signatures using RNAseq data is included.
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Affiliation(s)
- Maksim Sorokin
- Laboratory For Clinical and Genomic Bioinformatics, I.M. Sechenov First Moscow State Medical University, Moscow, Russia.,Moscow Institute of Physics and Technology, Dolgoprudny, Russia.,OmicsWay Corp., Walnut, CA, United States
| | - Elizaveta Rabushko
- Laboratory For Clinical and Genomic Bioinformatics, I.M. Sechenov First Moscow State Medical University, Moscow, Russia.,Faculty of Biology, Lomonosov Moscow State University, Moscow, Russia
| | - Victor Efimov
- Moscow Institute of Physics and Technology, Dolgoprudny, Russia.,World-Class Research Center "Digital Biodesign and Personalized Healthcare", Sechenov First Moscow State Medical University, Moscow, Russia.,Oncobox Ltd., Moscow, Russia
| | - Elena Poddubskaya
- World-Class Research Center "Digital Biodesign and Personalized Healthcare", Sechenov First Moscow State Medical University, Moscow, Russia
| | - Marina Sekacheva
- World-Class Research Center "Digital Biodesign and Personalized Healthcare", Sechenov First Moscow State Medical University, Moscow, Russia
| | - Alexander Simonov
- World-Class Research Center "Digital Biodesign and Personalized Healthcare", Sechenov First Moscow State Medical University, Moscow, Russia.,Oncobox Ltd., Moscow, Russia
| | - Daniil Nikitin
- Oncobox Ltd., Moscow, Russia.,Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Moscow, Russia
| | - Aleksey Drobyshev
- Laboratory For Clinical and Genomic Bioinformatics, I.M. Sechenov First Moscow State Medical University, Moscow, Russia
| | - Maria Suntsova
- Moscow Institute of Physics and Technology, Dolgoprudny, Russia.,World-Class Research Center "Digital Biodesign and Personalized Healthcare", Sechenov First Moscow State Medical University, Moscow, Russia
| | - Anton Buzdin
- Moscow Institute of Physics and Technology, Dolgoprudny, Russia.,OmicsWay Corp., Walnut, CA, United States.,World-Class Research Center "Digital Biodesign and Personalized Healthcare", Sechenov First Moscow State Medical University, Moscow, Russia.,Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Moscow, Russia
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20
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Sorokin M, Gorelyshev A, Efimov V, Zotova E, Zolotovskaia M, Rabushko E, Kuzmin D, Seryakov A, Kamashev D, Li X, Poddubskaya E, Suntsova M, Buzdin A. RNA Sequencing Data for FFPE Tumor Blocks Can Be Used for Robust Estimation of Tumor Mutation Burden in Individual Biosamples. Front Oncol 2021; 11:732644. [PMID: 34650919 PMCID: PMC8506044 DOI: 10.3389/fonc.2021.732644] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2021] [Accepted: 09/06/2021] [Indexed: 01/16/2023] Open
Abstract
Tumor mutation burden (TMB) is a well-known efficacy predictor for checkpoint inhibitor immunotherapies. Currently, TMB assessment relies on DNA sequencing data. Gene expression profiling by RNA sequencing (RNAseq) is another type of analysis that can inform clinical decision-making and including TMB estimation may strongly benefit this approach, especially for the formalin-fixed, paraffin-embedded (FFPE) tissue samples. Here, we for the first time compared TMB levels deduced from whole exome sequencing (WES) and RNAseq profiles of the same FFPE biosamples in single-sample mode. We took TCGA project data with mean sequencing depth 23 million gene-mapped reads (MGMRs) and found 0.46 (Pearson)–0.59 (Spearman) correlation with standard mutation calling pipelines. This was converted into low (<10) and high (>10) TMB per megabase classifier with area under the curve (AUC) 0.757, and application of machine learning increased AUC till 0.854. We then compared 73 experimental pairs of WES and RNAseq profiles with lower (mean 11 MGMRs) and higher (mean 68 MGMRs) RNA sequencing depths. For higher depth, we observed ~1 AUC for the high/low TMB classifier and 0.85 (Pearson)–0.95 (Spearman) correlation with standard mutation calling pipelines. For the lower depth, the AUC was below the high-quality threshold of 0.7. Thus, we conclude that using RNA sequencing of tumor materials from FFPE blocks with enough coverage can afford for high-quality discrimination of tumors with high and low TMB levels in a single-sample mode.
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Affiliation(s)
- Maxim Sorokin
- Biostatistics and Bioinformatics Subgroup, European Organization for Research and Treatment of Cancer (EORTC), Brussels, Belgium.,The Laboratory of Clinical and Genomic Bioinformatics, I.M. Sechenov First Moscow State Medical University, Moscow, Russia.,Laboratory for Translational Genomic Bioinformatics, Moscow Institute of Physics and Technology, Dolgoprudny, Russia.,OmicsWay Corp., Walnut, CA, United States
| | - Alexander Gorelyshev
- Laboratory for Translational Genomic Bioinformatics, Moscow Institute of Physics and Technology, Dolgoprudny, Russia.,OmicsWay Corp., Walnut, CA, United States
| | - Victor Efimov
- Laboratory for Translational Genomic Bioinformatics, Moscow Institute of Physics and Technology, Dolgoprudny, Russia
| | - Evgenia Zotova
- The Laboratory of Clinical and Genomic Bioinformatics, I.M. Sechenov First Moscow State Medical University, Moscow, Russia
| | - Marianna Zolotovskaia
- Laboratory for Translational Genomic Bioinformatics, Moscow Institute of Physics and Technology, Dolgoprudny, Russia
| | - Elizaveta Rabushko
- The Laboratory of Clinical and Genomic Bioinformatics, I.M. Sechenov First Moscow State Medical University, Moscow, Russia
| | - Denis Kuzmin
- Laboratory for Translational Genomic Bioinformatics, Moscow Institute of Physics and Technology, Dolgoprudny, Russia
| | | | - Dmitry Kamashev
- Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Russian Academy of Sciences, Moscow, Russia
| | - Xinmin Li
- Department of Pathology & Laboratory Medicine, University of California Los Angeles (UCLA) Technology Center for Genomics & Bioinformatics, Los Angeles, CA, United States
| | - Elena Poddubskaya
- World-Class Research Center "Digital Biodesign and Personalized Healthcare", Sechenov First Moscow State Medical University, Moscow, Russia
| | - Maria Suntsova
- World-Class Research Center "Digital Biodesign and Personalized Healthcare", Sechenov First Moscow State Medical University, Moscow, Russia
| | - Anton Buzdin
- Biostatistics and Bioinformatics Subgroup, European Organization for Research and Treatment of Cancer (EORTC), Brussels, Belgium.,Laboratory for Translational Genomic Bioinformatics, Moscow Institute of Physics and Technology, Dolgoprudny, Russia.,OmicsWay Corp., Walnut, CA, United States.,Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Russian Academy of Sciences, Moscow, Russia.,World-Class Research Center "Digital Biodesign and Personalized Healthcare", Sechenov First Moscow State Medical University, Moscow, Russia
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21
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Wang CC. Metabolic Stress Adaptations Underlie Mammary Gland Morphogenesis and Breast Cancer Progression. Cells 2021; 10:2641. [PMID: 34685621 PMCID: PMC8534177 DOI: 10.3390/cells10102641] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2021] [Revised: 09/26/2021] [Accepted: 09/28/2021] [Indexed: 11/23/2022] Open
Abstract
Breast cancers display dynamic reprogrammed metabolic activities as cancers develop from premalignant lesions to primary tumors, and then metastasize. Numerous advances focus on how tumors develop pro-proliferative metabolic signaling that differs them from adjacent, non-transformed epithelial tissues. This leads to targetable oncogene-driven liabilities among breast cancer subtypes. Other advances demonstrate how microenvironments trigger stress-response at single-cell resolution. Microenvironmental heterogeneities give rise to cell regulatory states in cancer cell spheroids in three-dimensional cultures and at stratified terminal end buds during mammary gland morphogenesis, where stress and survival signaling juxtapose. The cell-state specificity in stress signaling networks recapture metabolic evolution during cancer progression. Understanding lineage-specific metabolic phenotypes in experimental models is useful for gaining a deeper understanding of subtype-selective breast cancer metabolism.
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Affiliation(s)
- Chun-Chao Wang
- Institute of Molecular Medicine, National Tsing Hua University, Hsinchu 30013, Taiwan; ; Tel.: +886-3-516-2589
- Department of Medical Science, National Tsing Hua University, Hsinchu 30013, Taiwan
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22
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Liu S, Thennavan A, Garay JP, Marron JS, Perou CM. MultiK: an automated tool to determine optimal cluster numbers in single-cell RNA sequencing data. Genome Biol 2021; 22:232. [PMID: 34412669 PMCID: PMC8375188 DOI: 10.1186/s13059-021-02445-5] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2021] [Accepted: 07/29/2021] [Indexed: 01/02/2023] Open
Abstract
Single-cell RNA sequencing (scRNA-seq) provides new opportunities to characterize cell populations, typically accomplished through some type of clustering analysis. Estimation of the optimal cluster number (K) is a crucial step but often ignored. Our approach improves most current scRNA-seq cluster methods by providing an objective estimation of the number of groups using a multi-resolution perspective. MultiK is a tool for objective selection of insightful Ks and achieves high robustness through a consensus clustering approach. We demonstrate that MultiK identifies reproducible groups in scRNA-seq data, thus providing an objective means to estimating the number of possible groups or cell-type populations present.
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Affiliation(s)
- Siyao Liu
- Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Marsico Hall, 5th floor, CB#7599, 125 Mason Farm Road, Chapel Hill, NC, 27599, USA
- Department of Genetics, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
- Curriculum in Bioinformatics and Computational Biology, University of North Carolina, Chapel Hill, NC, 27599, USA
| | - Aatish Thennavan
- Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Marsico Hall, 5th floor, CB#7599, 125 Mason Farm Road, Chapel Hill, NC, 27599, USA
- Oral and Craniofacial Biomedicine Program, School of Dentistry, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
| | - Joseph P Garay
- Department of Surgery, Oregon Health & Science University, Portland, OR, 97239, USA
| | - J S Marron
- Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Marsico Hall, 5th floor, CB#7599, 125 Mason Farm Road, Chapel Hill, NC, 27599, USA.
- Department of Statistics and Operation Research, University of North Carolina at Chapel Hill, 352 Hanes Hall CB#3260, Chapel Hill, NC, 27599, USA.
| | - Charles M Perou
- Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Marsico Hall, 5th floor, CB#7599, 125 Mason Farm Road, Chapel Hill, NC, 27599, USA.
- Department of Genetics, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA.
- Department of Pathology and Laboratory Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA.
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23
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Zolotovskaia M, Tkachev V, Sorokin M, Garazha A, Kim E, Kantelhardt SR, Bikar SE, Zottel A, Šamec N, Kuzmin D, Sprang B, Moisseev A, Giese A, Efimov V, Jovčevska I, Buzdin A. Algorithmically Deduced FREM2 Molecular Pathway Is a Potent Grade and Survival Biomarker of Human Gliomas. Cancers (Basel) 2021; 13:4117. [PMID: 34439271 PMCID: PMC8394245 DOI: 10.3390/cancers13164117] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2021] [Revised: 08/11/2021] [Accepted: 08/13/2021] [Indexed: 01/17/2023] Open
Abstract
Gliomas are the most common malignant brain tumors with high mortality rates. Recently we showed that the FREM2 gene has a role in glioblastoma progression. Here we reconstructed the FREM2 molecular pathway using the human interactome model. We assessed the biomarker capacity of FREM2 expression and its pathway as the overall survival (OS) and progression-free survival (PFS) biomarkers. To this end, we used three literature and one experimental RNA sequencing datasets collectively covering 566 glioblastomas (GBM) and 1097 low-grade gliomas (LGG). The activation level of deduced FREM2 pathway showed strong biomarker characteristics and significantly outperformed the FREM2 expression level itself. For all relevant datasets, it could robustly discriminate GBM and LGG (p < 1.63 × 10-13, AUC > 0.74). High FREM2 pathway activation level was associated with poor OS in LGG (p < 0.001), and low PFS in LGG (p < 0.001) and GBM (p < 0.05). FREM2 pathway activation level was poor prognosis biomarker for OS (p < 0.05) and PFS (p < 0.05) in LGG with IDH mutation, for PFS in LGG with wild type IDH (p < 0.001) and mutant IDH with 1p/19q codeletion(p < 0.05), in GBM with unmethylated MGMT (p < 0.05), and in GBM with wild type IDH (p < 0.05). Thus, we conclude that the activation level of the FREM2 pathway is a potent new-generation diagnostic and prognostic biomarker for multiple molecular subtypes of GBM and LGG.
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Affiliation(s)
- Marianna Zolotovskaia
- Omicsway Corp., Walnut, CA 91789, USA; (M.S.); (A.G.); (A.M.)
- Moscow Institute of Physics and Technology, Dolgoprudny 141701, Russia; (V.T.); (D.K.); (V.E.); (A.B.)
- Department of Oncology, Hematology and Radiotherapy, Pirogov Russian National Research Medical University, Moscow 117997, Russia
| | - Victor Tkachev
- Moscow Institute of Physics and Technology, Dolgoprudny 141701, Russia; (V.T.); (D.K.); (V.E.); (A.B.)
| | - Maxim Sorokin
- Omicsway Corp., Walnut, CA 91789, USA; (M.S.); (A.G.); (A.M.)
- Moscow Institute of Physics and Technology, Dolgoprudny 141701, Russia; (V.T.); (D.K.); (V.E.); (A.B.)
- Laboratory of Clinical Genomic Bioinformatics, I.M. Sechenov First Moscow State Medical University, Moscow 119991, Russia
| | - Andrew Garazha
- Omicsway Corp., Walnut, CA 91789, USA; (M.S.); (A.G.); (A.M.)
| | - Ella Kim
- Clinic for Neurosurgery, Laboratory of Experimental Neurooncology, Johannes Gutenberg University Medical Centre, Langenbeckstrasse 1, 55124 Mainz, Germany; (E.K.); (S.R.K.); (B.S.)
| | - Sven Rainer Kantelhardt
- Clinic for Neurosurgery, Laboratory of Experimental Neurooncology, Johannes Gutenberg University Medical Centre, Langenbeckstrasse 1, 55124 Mainz, Germany; (E.K.); (S.R.K.); (B.S.)
| | - Sven-Ernö Bikar
- StarSEQ GmbH, Joh.-Joachim-Becher-Weg 30a, 55128 Mainz, Germany;
| | - Alja Zottel
- Medical Center for Molecular Biology, Institute of Biochemistry and Molecular Genetics, Faculty of Medicine, University of Ljubljana, Vrazov Trg 2, 1000 Ljubljana, Slovenia; (A.Z.); (N.Š.); (I.J.)
| | - Neja Šamec
- Medical Center for Molecular Biology, Institute of Biochemistry and Molecular Genetics, Faculty of Medicine, University of Ljubljana, Vrazov Trg 2, 1000 Ljubljana, Slovenia; (A.Z.); (N.Š.); (I.J.)
| | - Denis Kuzmin
- Moscow Institute of Physics and Technology, Dolgoprudny 141701, Russia; (V.T.); (D.K.); (V.E.); (A.B.)
| | - Bettina Sprang
- Clinic for Neurosurgery, Laboratory of Experimental Neurooncology, Johannes Gutenberg University Medical Centre, Langenbeckstrasse 1, 55124 Mainz, Germany; (E.K.); (S.R.K.); (B.S.)
| | - Alexey Moisseev
- Omicsway Corp., Walnut, CA 91789, USA; (M.S.); (A.G.); (A.M.)
| | - Alf Giese
- Orthocentrum Hamburg, Hansastrasse 1, 20149 Hamburg, Germany;
| | - Victor Efimov
- Moscow Institute of Physics and Technology, Dolgoprudny 141701, Russia; (V.T.); (D.K.); (V.E.); (A.B.)
| | - Ivana Jovčevska
- Medical Center for Molecular Biology, Institute of Biochemistry and Molecular Genetics, Faculty of Medicine, University of Ljubljana, Vrazov Trg 2, 1000 Ljubljana, Slovenia; (A.Z.); (N.Š.); (I.J.)
| | - Anton Buzdin
- Moscow Institute of Physics and Technology, Dolgoprudny 141701, Russia; (V.T.); (D.K.); (V.E.); (A.B.)
- Laboratory of Clinical Genomic Bioinformatics, I.M. Sechenov First Moscow State Medical University, Moscow 119991, Russia
- Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Moscow 117997, Russia
- European Organization for Research and Treatment of Cancer (EORTC), Biostatistics and Bioinformatics Subgroup, 1200 Brussels, Belgium
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24
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Transcriptome Analysis Identifies GATA3-AS1 as a Long Noncoding RNA Associated with Resistance to Neoadjuvant Chemotherapy in Locally Advanced Breast Cancer Patients. J Mol Diagn 2021; 23:1306-1323. [PMID: 34358678 DOI: 10.1016/j.jmoldx.2021.07.014] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2020] [Revised: 06/21/2021] [Accepted: 07/07/2021] [Indexed: 12/30/2022] Open
Abstract
Breast cancer is one of the leading causes of mortality in women worldwide, and neoadjuvant chemotherapy has emerged as an option for the management of locally advanced breast cancer. Extensive efforts have been made to identify new molecular markers to predict the response to neoadjuvant chemotherapy. Transcripts that do not encode proteins, termed long noncoding RNAs (lncRNAs), have been shown to display abnormal expression profiles in different types of cancer, but their role as biomarkers in response to neoadjuvant chemotherapy has not been extensively studied. Herein, lncRNA expression was profiled using RNA sequencing in biopsies from patients who subsequently showed either response or no response to treatment. The GATA3-AS1 transcript was overexpressed in the nonresponder group and was the most stable feature when performing selection in multiple random forest models. GATA3-AS1 was experimentally validated by RT-qPCR in an extended group of 68 patients. Expression analysis confirmed that GATA3-AS1 is overexpressed primarily in patients who were nonresponsive to neoadjuvant chemotherapy, with a sensitivity of 92.9%, a specificity of 75.0%, and an area under the curve of approximately 0.90, as measured by receiver operating characteristic curve analysis. The statistical model was based on luminal B-like patients and adjusted by menopausal status and phenotype (odds ratio, 37.49; 95% CI, 6.74-208.42; P = 0.001); GATA3-AS1 was established as an independent predictor of response. Thus, lncRNA GATA3-AS1 is proposed as a potential predictive biomarker of nonresponse to neoadjuvant chemotherapy.
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25
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Garay JP, Smith R, Devlin K, Hollern DP, Liby T, Liu M, Boddapati S, Watson SS, Esch A, Zheng T, Thompson W, Babcock D, Kwon S, Chin K, Heiser L, Gray JW, Korkola JE. Sensitivity to targeted therapy differs between HER2-amplified breast cancer cells harboring kinase and helical domain mutations in PIK3CA. Breast Cancer Res 2021; 23:81. [PMID: 34344439 PMCID: PMC8336338 DOI: 10.1186/s13058-021-01457-0] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2021] [Accepted: 07/18/2021] [Indexed: 12/01/2022] Open
Abstract
BACKGROUND HER2-amplified breast cancer is a clinically defined subtype of breast cancer for which there are multiple viable targeted therapies. Resistance to these targeted therapies is a common problem, but the mechanisms by which resistance occurs remain incompletely defined. One mechanism that has been proposed is through mutation of genes in the PI3-kinase pathway. Intracellular signaling from the HER2 pathway can occur through PI3-kinase, and mutations of the encoding gene PIK3CA are known to be oncogenic. Mutations in PIK3CA co-occur with HER2-amplification in ~ 20% of cases within the HER2-amplified subtype. METHODS We generated isogenic knockin mutants of each PIK3CA hotspot mutation in HER2-amplified breast cancer cells using adeno-associated virus-mediated gene targeting. Isogenic clones were analyzed using a combinatorial drug screen to determine differential responses to HER2-targeted therapy. Western blot analysis and immunofluorescence uncovered unique intracellular signaling dynamics in cells resistant to HER2-targeted therapy. Subsequent combinatorial drug screens were used to explore neuregulin-1-mediated resistance to HER2-targeted therapy. Finally, results from in vitro experiments were extrapolated to publicly available datasets. RESULTS Treatment with HER2-targeted therapy reveals that mutations in the kinase domain (H1047R) but not the helical domain (E545K) increase resistance to lapatinib. Mechanistically, sustained AKT signaling drives lapatinib resistance in cells with the kinase domain mutation, as demonstrated by staining for the intracellular product of PI3-kinase, PIP3. This resistance can be overcome by co-treatment with an inhibitor to the downstream kinase AKT. Additionally, knockout of the PIP3 phosphatase, PTEN, phenocopies this result. We also show that neuregulin-1, a ligand for HER-family receptors, confers resistance to cells harboring either hotspot mutation and modulates response to combinatorial therapy. Finally, we show clinical evidence that the hotspot mutations have distinct expression profiles related to therapeutic resistance through analysis of TCGA and METABRIC data cohorts. CONCLUSION Our results demonstrate unique intracellular signaling differences depending on which mutation in PIK3CA the cell harbors. Only mutations in the kinase domain fully activate the PI3-kinase signaling pathway and maintain downstream signaling in the presence of HER2 inhibition. Moreover, we show there is potentially clinical importance in understanding both the PIK3CA mutational status and levels of neuregulin-1 expression in patients with HER2-amplified breast cancer treated with targeted therapy and that these problems warrant further pre-clinical and clinical testing.
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Affiliation(s)
- Joseph P Garay
- Department of Biomedical Engineering, Oregon Health & Science University, Portland, OR, USA
| | - Rebecca Smith
- Department of Biomedical Engineering, Oregon Health & Science University, Portland, OR, USA
| | - Kaylyn Devlin
- Department of Biomedical Engineering, Oregon Health & Science University, Portland, OR, USA
| | - Daniel P Hollern
- Department of Genetics, University of North Carolina, Chapel Hill, Chapel Hill, NC, USA
| | - Tiera Liby
- Department of Biomedical Engineering, Oregon Health & Science University, Portland, OR, USA
| | - Moqing Liu
- Department of Biomedical Engineering, Oregon Health & Science University, Portland, OR, USA
| | - Shanta Boddapati
- Department of Biomedical Engineering, Oregon Health & Science University, Portland, OR, USA
| | - Spencer S Watson
- Department of Biomedical Engineering, Oregon Health & Science University, Portland, OR, USA
| | - Amanda Esch
- Department of Biomedical Engineering, Oregon Health & Science University, Portland, OR, USA
| | - Ting Zheng
- Department of Biomedical Engineering, Oregon Health & Science University, Portland, OR, USA
| | - Wallace Thompson
- Department of Biomedical Engineering, Oregon Health & Science University, Portland, OR, USA
| | - Darcie Babcock
- Department of Biomedical Engineering, Oregon Health & Science University, Portland, OR, USA
| | - Sunjong Kwon
- Department of Biomedical Engineering, Oregon Health & Science University, Portland, OR, USA
| | - Koei Chin
- Department of Biomedical Engineering, Oregon Health & Science University, Portland, OR, USA
| | - Laura Heiser
- Department of Biomedical Engineering, Oregon Health & Science University, Portland, OR, USA
| | - Joe W Gray
- Department of Biomedical Engineering, Oregon Health & Science University, Portland, OR, USA.
| | - James E Korkola
- Department of Biomedical Engineering, Oregon Health & Science University, Portland, OR, USA.
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26
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Venet D, Rediti M, Maetens M, Fumagalli D, Brown DN, Majjaj S, Salgado R, Pusztai L, Harbeck N, El-Abed S, Wang Y, Saura C, Gomez H, Semiglazov VF, de Azambuja E, Huober J, Nuciforo P, Di Cosimo S, Piccart M, Loi S, Rothé F, Sotiriou C. Copy Number Aberration Analysis to Predict Response to Neoadjuvant Anti-HER2 Therapy: Results from the NeoALTTO Phase III Clinical Trial. Clin Cancer Res 2021; 27:5607-5618. [PMID: 34321278 DOI: 10.1158/1078-0432.ccr-21-1317] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2021] [Revised: 06/29/2021] [Accepted: 07/23/2021] [Indexed: 11/16/2022]
Abstract
PURPOSE The heterogeneity of response to anti-HER2 agents represents a major challenge in patients with HER2-positive breast cancer. To better understand the sensitivity and resistance to trastuzumab and lapatinib, we investigated the role of copy number aberrations (CNA) in predicting pathologic complete response (pCR) and survival outcomes in the NeoALTTO trial. EXPERIMENTAL DESIGN The neoadjuvant phase III NeoALTTO trial enrolled 455 patients with HER2-positive early-stage breast cancer. DNA samples from 269 patients were assessed for genome-wide copy number profiling. Recurrent CNAs were found with GISTIC2.0. RESULTS CNA estimates were obtained for 184 patients included in NeoALTTO. Among those, matched transcriptome and whole-exome data were available for 154 and 181 patients, respectively. A significant association between gene copy number and pCR was demonstrated for ERBB2 amplification. Nevertheless, ERBB2 amplification ceased to be predictive once ERBB2 expression level was considered. GISTIC2.0 analysis revealed 159 recurrent CNA regions. Lower copy number levels of the 6q23-24 locus predicted absence of pCR in the whole cohort and in the estrogen receptor-positive subgroup. 6q23-24 deletion was significantly more frequent in TP53 wild-type (WT) compared with TP53-mutated, resulting in copy number levels significantly associated with lack of pCR only in the TP53 WT subgroup. Interestingly, a gene-ontology analysis highlighted several immune processes correlated to 6q23-24 copy number. CONCLUSIONS Our analysis identified ERBB2 copy number as well as 6q23-24 CNAs as predictors of response to anti-HER2-based treatment. ERBB2 expression outperformed ERBB2 amplification. The complexity of the 6q23-24 region warrants further investigation.
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Affiliation(s)
- David Venet
- Breast Cancer Translational Research Laboratory J.-C. Heuson, Institut Jules Bordet, Université Libre de Bruxelles (U.L.B.), Brussels, Belgium
| | - Mattia Rediti
- Breast Cancer Translational Research Laboratory J.-C. Heuson, Institut Jules Bordet, Université Libre de Bruxelles (U.L.B.), Brussels, Belgium
| | - Marion Maetens
- Breast Cancer Translational Research Laboratory J.-C. Heuson, Institut Jules Bordet, Université Libre de Bruxelles (U.L.B.), Brussels, Belgium.,Department of Oncology, Laboratory for Translational Breast Cancer Research, KU Leuven, Leuven, Belgium
| | | | - David N Brown
- Breast Cancer Translational Research Laboratory J.-C. Heuson, Institut Jules Bordet, Université Libre de Bruxelles (U.L.B.), Brussels, Belgium.,Center for Molecular Oncology, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Samira Majjaj
- Breast Cancer Translational Research Laboratory J.-C. Heuson, Institut Jules Bordet, Université Libre de Bruxelles (U.L.B.), Brussels, Belgium
| | - Roberto Salgado
- Department of Pathology, GZA-ZNA Ziekenhuizen, Antwerp, Belgium.,Division of Research, Peter MacCallum Cancer Centre, Melbourne, Australia
| | - Lajos Pusztai
- Breast Medical Oncology, Yale Cancer Center, Yale School of Medicine, New Haven, Connecticut
| | - Nadia Harbeck
- Breast Center, Dept OB&GYN and CCC Munich, LMU University Hospital, Munich, Germany
| | | | - Yingbo Wang
- Novartis Pharmaceuticals AG, Basel, Switzerland
| | | | - Henry Gomez
- Department of Medical Oncology, Instituto Nacional de Enfermedades Neoplasicas, Lima, Peru
| | | | - Evandro de Azambuja
- Institut Jules Bordet, Université Libre de Bruxelles (U.L.B.), Brussels, Belgium
| | - Jens Huober
- Department of Obstetrics and Gynecology, University of Ulm, Ulm, Germany.,Breast Center, Cantonal Hospital St Gallen, St Gallen, Switzerland
| | - Paolo Nuciforo
- Molecular Oncology Group, Vall d'Hebron Institute of Oncology, Barcelona, Spain
| | - Serena Di Cosimo
- Department of Applied Research and Technological Development, Fondazione IRCCS Istituto Nazionale dei Tumori, Milano, Italy
| | - Martine Piccart
- Department of Medical Oncology, Institut Jules Bordet, Université Libre de Bruxelles (U.L.B.), Brussels, Belgium
| | - Sherene Loi
- Division of Research, Peter MacCallum Cancer Centre, Melbourne, Australia
| | - Françoise Rothé
- Breast Cancer Translational Research Laboratory J.-C. Heuson, Institut Jules Bordet, Université Libre de Bruxelles (U.L.B.), Brussels, Belgium
| | - Christos Sotiriou
- Breast Cancer Translational Research Laboratory J.-C. Heuson, Institut Jules Bordet, Université Libre de Bruxelles (U.L.B.), Brussels, Belgium.
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27
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Li L, Chen M, Zheng S, Li H, Chi W, Xiu B, Zhang Q, Hou J, Wang J, Wu J. Clinical and Genetic Predictive Models for the Prediction of Pathological Complete Response to Optimize the Effectiveness for Trastuzumab Based Chemotherapy. Front Oncol 2021; 11:592393. [PMID: 34336634 PMCID: PMC8319743 DOI: 10.3389/fonc.2021.592393] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2020] [Accepted: 06/24/2021] [Indexed: 12/09/2022] Open
Abstract
Background Trastuzumab shows excellent benefits for HER2+ breast cancer patients, although 20% treated remain unresponsive. We conducted a retrospective cohort study to optimize neoadjuvant chemotherapy and trastuzumab treatment in HER2+ breast cancer patients. Methods Six hundred patients were analyzed to identify clinical characteristics of those not achieving a pathological complete response (pCR) to develop a clinical predictive model. Available RNA sequence data was also reviewed to develop a genetic model for pCR. Results The pCR rate was 39.8% and pCR was associated with superior disease free survival and overall survival. ER negativity and PR negativity, higher HER2 IHC scores, higher Ki-67, and trastuzumab use were associated with improved pCR. Weekly paclitaxel and carboplatin had the highest pCR rate (46.70%) and the anthracycline+taxanes regimen had the lowest rate (11.11%). Four published GEO datasets were analyzed and a 10-gene model and immune signature for pCR were developed. Non-pCR patients were ER+PR+ and had a lower immune signature and gene model score. Hormone receptor status and immune signatures were independent predictive factors of pCR. Conclusion Hormone receptor status and a 10-gene model could predict pCR independently and may be applied for patient selection and drug effectiveness optimization.
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Affiliation(s)
- Lun Li
- Department of Breast Surgery, Shanghai Cancer Center, Fudan University, Shanghai, China and Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Min Chen
- Department of Breast Surgery, Shanghai Cancer Center, Fudan University, Shanghai, China and Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Shuyue Zheng
- Department of Breast Surgery, Shanghai Cancer Center, Fudan University, Shanghai, China and Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Hanlu Li
- Department of Breast Surgery, Shanghai Cancer Center, Fudan University, Shanghai, China and Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Weiru Chi
- Department of Breast Surgery, Shanghai Cancer Center, Fudan University, Shanghai, China and Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Bingqiu Xiu
- Department of Breast Surgery, Shanghai Cancer Center, Fudan University, Shanghai, China and Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Qi Zhang
- Department of Breast Surgery, Shanghai Cancer Center, Fudan University, Shanghai, China and Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Jianjing Hou
- Department of Breast Surgery, Shanghai Cancer Center, Fudan University, Shanghai, China and Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Jia Wang
- Department of Breast Surgery, Shanghai Cancer Center, Fudan University, Shanghai, China and Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Jiong Wu
- Department of Breast Surgery, Shanghai Cancer Center, Fudan University, Shanghai, China and Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China.,Collaborative Innovation Center for Cancer Medicine, Shanghai, China
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28
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Ahn SG, Kim SK, Shepherd JH, Cha YJ, Bae SJ, Kim C, Jeong J, Perou CM. Clinical and genomic assessment of PD-L1 SP142 expression in triple-negative breast cancer. Breast Cancer Res Treat 2021; 188:165-178. [PMID: 33770313 PMCID: PMC8233296 DOI: 10.1007/s10549-021-06193-9] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2021] [Accepted: 03/10/2021] [Indexed: 01/12/2023]
Abstract
PURPOSE The SP142 PD-L1 assay is a companion diagnostic for atezolizumab in metastatic triple-negative breast cancer (TNBC). We strove to understand the biological, genomic, and clinical characteristics associated with SP142 PD-L1 positivity in TNBC patients. METHODS Using 149 TNBC formalin-fixed paraffin-embedded tumor samples, tissue microarray (TMA) and gene expression microarrays were performed in parallel. The VENTANA SP142 assay was used to identify PD-L1 expression from TMA slides. We next generated a gene signature reflective of SP142 status and evaluated signature distribution according to TNBCtype and PAM50 subtypes. A SP142 gene expression signature was identified and was biologically and clinically evaluated on the TNBCs of TCGA, other cohorts, and on other malignancies treated with immune checkpoint inhibitors (ICI). RESULTS Using SP142, 28.9% of samples were PD-L1 protein positive. The SP142 PD-L1-positive TNBC had higher CD8+ T cell percentage, stromal tumor-infiltrating lymphocyte levels, and higher rate of the immunomodulatory TNBCtype compared to PD-L1-negative samples. The recurrence-free survival was prolonged in PD-L1-positive TNBC. The SP142-guided gene expression signature consisted of 94 immune-related genes. The SP142 signature was associated with a higher pathologic complete response rate and better survival in multiple TNBC cohorts. In the TNBC of TCGA, this signature was correlated with lymphocyte-infiltrating signature scores, but not with tumor mutational burden or total neoantigen count. In other malignancies treated with ICIs, the SP142 genomic signature was associated with improved response and survival. CONCLUSIONS We provide multi-faceted evidence that SP142 PDL1-positive TNBC have immuno-genomic features characterized as highly lymphocyte-infiltrated and a relatively favorable survival.
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Affiliation(s)
- Sung Gwe Ahn
- Department of Surgery, Gangnam Severance Hospital, Yonsei University College of Medicine, 712 Eon-juro, Gangnam-gu, Seoul, Republic of Korea
- Lineberger Comprehensive Cancer Center, University of North Carolina, Chapel Hill, NC, 27599, USA
| | - Seon-Kyu Kim
- Personalized Genomic Medicine Research Center, Korea Research Institute of Bioscience and Biotechnology (KRIBB),, Daejeon, Korea
| | - Jonathan H Shepherd
- Lineberger Comprehensive Cancer Center, University of North Carolina, Chapel Hill, NC, 27599, USA
| | - Yoon Jin Cha
- Department of Pathology, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Soong June Bae
- Department of Surgery, Gangnam Severance Hospital, Yonsei University College of Medicine, 712 Eon-juro, Gangnam-gu, Seoul, Republic of Korea
| | - Chungyeul Kim
- Department of Pathology, Guro Hospital, Korea University College of Medicine, Seoul, Korea
| | - Joon Jeong
- Department of Surgery, Gangnam Severance Hospital, Yonsei University College of Medicine, 712 Eon-juro, Gangnam-gu, Seoul, Republic of Korea.
| | - Charles M Perou
- Lineberger Comprehensive Cancer Center, University of North Carolina, Chapel Hill, NC, 27599, USA.
- Department of Genetics, University of North Carolina, Chapel Hill, NC, USA.
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29
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Gan FJ, Li Y, Xu MX, Zhou T, Wu S, Hu K, Li Y, Sun SH, Luo Q. LncRNA BCAR4 expression and predicts the clinical response to neoadjuvant chemotherapy in patients with locally advanced breast cancer. Cancer Biomark 2021; 32:339-351. [PMID: 34151842 PMCID: PMC8673519 DOI: 10.3233/cbm-210048] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
BACKGROUND Neoadjuvant chemotherapy (NAC) is an important treatment for locally advanced breast cancer (LABC). However, there are no effective biomarkers to predict the efficacy. Therefore, there is an urgent need for new biomarkers to predict the response of LABC to NAC. LncRNA BCAR4 has been detected in a variety of malignant tumor tissues and used as a new biomarker for diagnosis and prognosis. However, LncRNA BCAR4 predicts the response of LABC to NAC is unclear. OBJECTIVE Explore the predictive effect of LncRNA BCAR4 on the efficacy of NAC for LABC in three different evaluation systems. METHODS First, the TCGA database was used to analyze the expression of LncRNA BCAR4 in 33 kinds of malignant tumors, and further explore its expression in breast cancer and its impact on the survival and prognosis of breast cancer. Furthermore, quantitative methods were used to measure the expression level of LncRNA BCAR4 in cancer tissues of 48 LABC patients, and the correlation between LncRNA BCAR4 and clinicopathological status and response to NAC under the evaluation system of 3, RECIST1.1, Miller-Payne (MP) score and whether it reaches pCR,was analyzed. RESULTS TCGA data analysis found that LncRNA is highly expressed in a variety of malignant tumor tissues, including breast cancer. And relatively low expression, the shorter the overall survival time of high expression patients. The high expression of LncRNA BCAR4 is related to the size of the tumor, and there are differences in expression between stage I and other stages, but there is no obvious correlation with the positive lymph node and hormone receptor status. Among the three evaluation systems, only in the RECIST 1.1 evaluation system LncRNA BCAR4 has a predictive effect on NAC for LABC. The expression of LncRNA BCAR4 has no significant correlation with clinical stage, Ki-67% and hormone receptor status, and has no significant correlation with whether patients with locally advanced breast cancer obtain pCR during neoadjuvant chemotherapy. CONCLUSION LncRNA BCAR4 is highly expressed in LABC tissues and may be an effective marker for predicting the efficacy of NAC for LABC.
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Affiliation(s)
- Feng-Jiao Gan
- Department of Thyroid and Breast Surgery, Affiliated Hospital of Zunyi Medical University, Zunyi, Guizhou, China
| | - Yi Li
- Department of Oncology, Affiliated Hospital of Zunyi Medical University, Zunyi, Guizhou, China
| | - Meng-Xi Xu
- Department of Thyroid and Breast Surgery, Affiliated Hospital of Zunyi Medical University, Zunyi, Guizhou, China
| | - Tie Zhou
- Department of Thyroid and Breast Surgery, Affiliated Hospital of Zunyi Medical University, Zunyi, Guizhou, China
| | - Shun Wu
- Department of Thyroid and Breast Surgery, Affiliated Hospital of Zunyi Medical University, Zunyi, Guizhou, China
| | - Kang Hu
- Department of Thyroid and Breast Surgery, Affiliated Hospital of Zunyi Medical University, Zunyi, Guizhou, China
| | - Yan Li
- Department of Thyroid and Breast Surgery, Affiliated Hospital of Zunyi Medical University, Zunyi, Guizhou, China
| | - Su-Hong Sun
- Department of Thyroid and Breast Surgery, Affiliated Hospital of Zunyi Medical University, Zunyi, Guizhou, China.,Department of Thyroid and Breast Surgery, Affiliated Hospital of Zunyi Medical University, Zunyi, Guizhou, China
| | - Qing Luo
- Department of Oncology, Affiliated Hospital of Zunyi Medical University, Zunyi, Guizhou, China.,Department of Thyroid and Breast Surgery, Affiliated Hospital of Zunyi Medical University, Zunyi, Guizhou, China
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30
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Xiang Y, Zou X, Shi H, Xu X, Wu C, Zhong W, Wang J, Zhou W, Zeng X, He M, Wang Y, Huang L, Wang X. Elastic Net Models Based on DNA Copy Number Variations Predicts Clinical Features, Expression Signatures, and Mutations in Lung Adenocarcinoma. Front Genet 2021; 12:668040. [PMID: 34135942 PMCID: PMC8202527 DOI: 10.3389/fgene.2021.668040] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2021] [Accepted: 04/26/2021] [Indexed: 11/30/2022] Open
Abstract
In the precision medicine of lung adenocarcinoma, the identification and prediction of tumor phenotypes for specific biomolecular events are still not studied in depth. Various earlier researches sheds light on the close correlation between genetic expression signatures and DNA copy number variations (CNVs), for which analysis of CNVs provides valuable information about molecular and phenotypic changes in tumorigenesis. In this study, we propose a comprehensive analysis combining genome-wide association analysis and an Elastic Net Regression predictive model, focus on predicting the levels of many gene expression signatures in lung adenocarcinoma, based upon DNA copy number features alone. Additionally, we predicted many other key phenotypes, including clinical features (pathological stage), gene mutations, and protein expressions. These Elastic Net prediction methods can also be applied to other gene sets, thereby facilitating their use as biomarkers in monitoring therapy.
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Affiliation(s)
- Yi Xiang
- Department of Oncology, The First Affiliated Hospital, Gannan Medical University, Ganzhou, China
| | - Xiaohuan Zou
- Department of Critical Care Medicine, The First Affiliated Hospital, Gannan Medical University, Ganzhou, China
| | - Huaqiu Shi
- Department of Oncology, The First Affiliated Hospital, Gannan Medical University, Ganzhou, China
| | - Xueming Xu
- Department of Oncology, The First Affiliated Hospital, Gannan Medical University, Ganzhou, China
| | - Caixia Wu
- First Clinical Medical College, Gannan Medical University, Ganzhou, China
| | - Wenjuan Zhong
- Department of Oncology, The First Affiliated Hospital, Gannan Medical University, Ganzhou, China
| | - Jinfeng Wang
- Department of Oncology, The First Affiliated Hospital, Gannan Medical University, Ganzhou, China
| | - Wenting Zhou
- Department of Oncology, The First Affiliated Hospital, Gannan Medical University, Ganzhou, China
| | - Xiaoli Zeng
- Department of Oncology, The First Affiliated Hospital, Gannan Medical University, Ganzhou, China
| | - Miao He
- Department of Oncology, The First Affiliated Hospital, Gannan Medical University, Ganzhou, China
| | - Ying Wang
- First Clinical Medical College, Gannan Medical University, Ganzhou, China
| | - Li Huang
- Department of Oncology, The First Affiliated Hospital, Gannan Medical University, Ganzhou, China
| | - Xiangcai Wang
- Department of Oncology, The First Affiliated Hospital, Gannan Medical University, Ganzhou, China
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31
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He L, Yang H, Huang J. The tumor immune microenvironment and immune-related signature predict the chemotherapy response in patients with osteosarcoma. BMC Cancer 2021; 21:581. [PMID: 34016089 PMCID: PMC8138974 DOI: 10.1186/s12885-021-08328-z] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2021] [Accepted: 05/07/2021] [Indexed: 02/08/2023] Open
Abstract
BACKGROUND Genome-wide expression profiles have been shown to predict the response to chemotherapy. The purpose of this study was to develop a novel predictive signature for chemotherapy in patients with osteosarcoma. METHODS We analysed the relevance of immune cell infiltration and gene expression profiles of the tumor samples of good responders with those of poor responders from the TARGET and GEO databases. Immune cell infiltration was evaluated using a single-sample gene set enrichment analysis (ssGSEA) and the CIBERSORT algorithm between good and poor chemotherapy responders. Differentially expressed genes were identified based on the chemotherapy response. LASSO regression and binary logistic regression analyses were applied to select the differentially expressed immune-related genes (IRGs) and developed a predictive signature in the training cohort. A receiver operating characteristic (ROC) curve analysis was employed to assess and validate the predictive accuracy of the predictive signature in the validation cohort. RESULTS The analysis of immune infiltration showed a positive relationship between high-level immune infiltration and good responders, and T follicular helper cells and CD8 T cells were significantly more abundant in good responders with osteosarcoma. Two hundred eighteen differentially expressed genes were detected between good and poor responders, and a five IRGs panel comprising TNFRSF9, CD70, EGFR, PDGFD and S100A6 was determined to show predictive power for the chemotherapy response. A chemotherapy-associated predictive signature was developed based on these five IRGs. The accuracy of the predictive signature was 0.832 for the training cohort and 0.720 for the validation cohort according to ROC analysis. CONCLUSIONS The novel predictive signature constructed with five IRGs can be effectively utilized to predict chemotherapy responsiveness and help improve the efficacy of chemotherapy in patients with osteosarcoma.
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Affiliation(s)
- Lijiang He
- Department of Orthopaedics, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, Fujian, China
| | - Hainan Yang
- Department of Ultrasound, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, Fujian, China
| | - Jingshan Huang
- Department of General Surgery, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, 362000, Fujian, China.
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32
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Borisov N, Sergeeva A, Suntsova M, Raevskiy M, Gaifullin N, Mendeleeva L, Gudkov A, Nareiko M, Garazha A, Tkachev V, Li X, Sorokin M, Surin V, Buzdin A. Machine Learning Applicability for Classification of PAD/VCD Chemotherapy Response Using 53 Multiple Myeloma RNA Sequencing Profiles. Front Oncol 2021; 11:652063. [PMID: 33937058 PMCID: PMC8083158 DOI: 10.3389/fonc.2021.652063] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2021] [Accepted: 03/19/2021] [Indexed: 12/17/2022] Open
Abstract
Multiple myeloma (MM) affects ~500,000 people and results in ~100,000 deaths annually, being currently considered treatable but incurable. There are several MM chemotherapy treatment regimens, among which eleven include bortezomib, a proteasome-targeted drug. MM patients respond differently to bortezomib, and new prognostic biomarkers are needed to personalize treatments. However, there is a shortage of clinically annotated MM molecular data that could be used to establish novel molecular diagnostics. We report new RNA sequencing profiles for 53 MM patients annotated with responses on two similar chemotherapy regimens: bortezomib, doxorubicin, dexamethasone (PAD), and bortezomib, cyclophosphamide, dexamethasone (VCD), or with responses to their combinations. Fourteen patients received both PAD and VCD; six received only PAD, and 33 received only VCD. We compared profiles for the good and poor responders and found five genes commonly regulated here and in the previous datasets for other bortezomib regimens (all upregulated in the good responders): FGFR3, MAF, IGHA2, IGHV1-69, and GRB14. Four of these genes are linked with known immunoglobulin locus rearrangements. We then used five machine learning (ML) methods to build a classifier distinguishing good and poor responders for two cohorts: PAD + VCD (53 patients), and separately VCD (47 patients). We showed that the application of FloWPS dynamic data trimming was beneficial for all ML methods tested in both cohorts, and also in the previous MM bortezomib datasets. However, the ML models build for the different datasets did not allow cross-transferring, which can be due to different treatment regimens, experimental profiling methods, and MM heterogeneity.
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Affiliation(s)
- Nicolas Borisov
- Moscow Institute of Physics and Technology, Laboratory for Translational Genomic Bioinformatics, Dolgoprudny, Russia
| | - Anna Sergeeva
- National Research Center for Hematology, Ministry of Health of the Russian Federation, Moscow, Russia
| | - Maria Suntsova
- I.M. Sechenov First Moscow State Medical University, Institute of Personalized Medicine, Moscow, Russia
- Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Group for Genomic Analysis of Cell Signaling Systems, Moscow, Russia
| | - Mikhail Raevskiy
- Moscow Institute of Physics and Technology, Laboratory for Translational Genomic Bioinformatics, Dolgoprudny, Russia
| | - Nurshat Gaifullin
- Department of Pathology, Faculty of Medicine, Lomonosov Moscow State University, Moscow, Russia
| | - Larisa Mendeleeva
- National Research Center for Hematology, Ministry of Health of the Russian Federation, Moscow, Russia
| | - Alexander Gudkov
- I.M. Sechenov First Moscow State Medical University, Institute of Personalized Medicine, Moscow, Russia
| | - Maria Nareiko
- National Research Center for Hematology, Ministry of Health of the Russian Federation, Moscow, Russia
| | - Andrew Garazha
- Omicsway Corp., Research Department, Walnut, CA, United States
- Oncobox Ltd., Research Department, Moscow, Russia
| | - Victor Tkachev
- Omicsway Corp., Research Department, Walnut, CA, United States
- Oncobox Ltd., Research Department, Moscow, Russia
| | - Xinmin Li
- Department of Pathology and Laboratory Medicine, University of California Los Angeles, Los Angeles, CA, United States
| | - Maxim Sorokin
- I.M. Sechenov First Moscow State Medical University, Institute of Personalized Medicine, Moscow, Russia
- Omicsway Corp., Research Department, Walnut, CA, United States
- Oncobox Ltd., Research Department, Moscow, Russia
| | - Vadim Surin
- National Research Center for Hematology, Ministry of Health of the Russian Federation, Moscow, Russia
| | - Anton Buzdin
- I.M. Sechenov First Moscow State Medical University, Institute of Personalized Medicine, Moscow, Russia
- Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Group for Genomic Analysis of Cell Signaling Systems, Moscow, Russia
- Omicsway Corp., Research Department, Walnut, CA, United States
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33
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Synthetic lethality-mediated precision oncology via the tumor transcriptome. Cell 2021; 184:2487-2502.e13. [PMID: 33857424 DOI: 10.1016/j.cell.2021.03.030] [Citation(s) in RCA: 50] [Impact Index Per Article: 16.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2020] [Revised: 10/29/2020] [Accepted: 03/12/2021] [Indexed: 01/27/2023]
Abstract
Precision oncology has made significant advances, mainly by targeting actionable mutations in cancer driver genes. Aiming to expand treatment opportunities, recent studies have begun to explore the utility of tumor transcriptome to guide patient treatment. Here, we introduce SELECT (synthetic lethality and rescue-mediated precision oncology via the transcriptome), a precision oncology framework harnessing genetic interactions to predict patient response to cancer therapy from the tumor transcriptome. SELECT is tested on a broad collection of 35 published targeted and immunotherapy clinical trials from 10 different cancer types. It is predictive of patients' response in 80% of these clinical trials and in the recent multi-arm WINTHER trial. The predictive signatures and the code are made publicly available for academic use, laying a basis for future prospective clinical studies.
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34
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McNamara KL, Caswell-Jin JL, Joshi R, Ma Z, Kotler E, Bean GR, Kriner M, Zhou Z, Hoang M, Beechem J, Zoeller J, Press MF, Slamon DJ, Hurvitz SA, Curtis C. Spatial proteomic characterization of HER2-positive breast tumors through neoadjuvant therapy predicts response. NATURE CANCER 2021; 2:400-413. [PMID: 34966897 PMCID: PMC8713949 DOI: 10.1038/s43018-021-00190-z] [Citation(s) in RCA: 40] [Impact Index Per Article: 13.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Abstract
The addition of HER2-targeted agents to neoadjuvant chemotherapy has dramatically improved pathological complete response (pCR) rates in early-stage, HER2-positive breast cancer. Nonetheless, up to 50% of patients have residual disease after treatment, while others are likely overtreated. Here, we performed multiplex spatial proteomic characterization of 122 samples from 57 HER2-positive breast tumors from the neoadjuvant TRIO-US B07 clinical trial sampled pre-treatment, after 14-21 d of HER2-targeted therapy and at surgery. We demonstrated that proteomic changes after a single cycle of HER2-targeted therapy aids the identification of tumors that ultimately undergo pCR, outperforming pre-treatment measures or transcriptomic changes. We further developed and validated a classifier that robustly predicted pCR using a single marker, CD45, measured on treatment, and showed that CD45-positive cell counts measured via conventional immunohistochemistry perform comparably. These results demonstrate robust biomarkers that can be used to enable the stratification of sensitive tumors early during neoadjuvant HER2-targeted therapy, with implications for tailoring subsequent therapy.
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Affiliation(s)
- Katherine L. McNamara
- Department of Medicine, Division of Oncology, Stanford University School of Medicine, Stanford, CA, USA.,Stanford Cancer Institute, Stanford University School of Medicine, Stanford, CA, USA.,Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA
| | - Jennifer L. Caswell-Jin
- Department of Medicine, Division of Oncology, Stanford University School of Medicine, Stanford, CA, USA
| | - Rohan Joshi
- Department of Pathology, Stanford University School of Medicine, Stanford, CA, USA
| | - Zhicheng Ma
- Stanford Cancer Institute, Stanford University School of Medicine, Stanford, CA, USA
| | - Eran Kotler
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA
| | - Gregory R. Bean
- Department of Pathology, Stanford University School of Medicine, Stanford, CA, USA
| | | | - Zoey Zhou
- NanoString Technologies, Seattle, WA, USA
| | | | | | - Jason Zoeller
- Department of Cell Biology, Harvard Medical School, Boston, MA, USA
| | - Michael F. Press
- Department of Pathology Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Dennis J. Slamon
- David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Sara A. Hurvitz
- David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Christina Curtis
- Department of Medicine, Division of Oncology, Stanford University School of Medicine, Stanford, CA, USA.,Stanford Cancer Institute, Stanford University School of Medicine, Stanford, CA, USA.,Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA.,Correspondence and requests for materials should be addressed to C.C.
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Liefaard MC, Lips EH, Wesseling J, Hylton NM, Lou B, Mansi T, Pusztai L. The Way of the Future: Personalizing Treatment Plans Through Technology. Am Soc Clin Oncol Educ Book 2021; 41:1-12. [PMID: 33793316 DOI: 10.1200/edbk_320593] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
Advances in tissue analysis methods, image analysis, high-throughput molecular profiling, and computational tools increasingly allow us to capture and quantify patient-to patient variations that impact cancer risk, prognosis, and treatment response. Statistical models that integrate patient-specific information from multiple sources (e.g., family history, demographics, germline variants, imaging features) can provide individualized cancer risk predictions that can guide screening and prevention strategies. The precision, quality, and standardization of diagnostic imaging are improving through computer-aided solutions, and multigene prognostic and predictive tests improved predictions of prognosis and treatment response in various cancer types. A common theme across many of these advances is that individually moderately informative variables are combined into more accurate multivariable prediction models. Advances in machine learning and the availability of large data sets fuel rapid progress in this field. Molecular dissection of the cancer genome has become a reality in the clinic, and molecular target profiling is now routinely used to select patients for various targeted therapies. These technology-driven increasingly more precise and quantitative estimates of benefit versus risk from a given intervention empower patients and physicians to tailor treatment strategies that match patient values and expectations.
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Affiliation(s)
- Marte C Liefaard
- Division of Molecular Pathology, The Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Esther H Lips
- Division of Molecular Pathology, The Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Jelle Wesseling
- Division of Molecular Pathology, The Netherlands Cancer Institute, Amsterdam, The Netherlands
- Department of Pathology, Leiden University Medical Center, Leiden, The Netherlands
| | - Nola M Hylton
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, CA
| | - Bin Lou
- Digital Technology and Innovation, Siemens Healthineers, Princeton, NJ
| | - Tommaso Mansi
- Digital Technology and Innovation, Siemens Healthineers, Princeton, NJ
| | - Lajos Pusztai
- Yale Cancer Center, Yale School of Medicine, New Haven, CT
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Hurvitz SA, Caswell-Jin JL, McNamara KL, Zoeller JJ, Bean GR, Dichmann R, Perez A, Patel R, Zehngebot L, Allen H, Bosserman L, DiCarlo B, Kennedy A, Giuliano A, Calfa C, Molthrop D, Mani A, Chen HW, Dering J, Adams B, Kotler E, Press MF, Brugge JS, Curtis C, Slamon DJ. Pathologic and molecular responses to neoadjuvant trastuzumab and/or lapatinib from a phase II randomized trial in HER2-positive breast cancer (TRIO-US B07). Nat Commun 2020; 11:5824. [PMID: 33203854 PMCID: PMC7673127 DOI: 10.1038/s41467-020-19494-2] [Citation(s) in RCA: 39] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2020] [Accepted: 10/15/2020] [Indexed: 02/07/2023] Open
Abstract
In this multicenter, open-label, randomized phase II investigator-sponsored neoadjuvant trial with funding provided by Sanofi and GlaxoSmithKline (TRIO-US B07, Clinical Trials NCT00769470), participants with early-stage HER2-positive breast cancer (N = 128) were recruited from 13 United States oncology centers throughout the Translational Research in Oncology network. Participants were randomized to receive trastuzumab (T; N = 34), lapatinib (L; N = 36), or both (TL; N = 58) as HER2-targeted therapy, with each participant given one cycle of this designated anti-HER2 therapy alone followed by six cycles of standard combination chemotherapy with the same anti-HER2 therapy. The primary objective was to estimate the rate of pathologic complete response (pCR) at the time of surgery in each of the three arms. In the intent-to-treat population, we observed similar pCR rates between T (47%, 95% confidence interval [CI] 30-65%) and TL (52%, 95% CI 38-65%), and a lower pCR rate with L (25%, 95% CI 13-43%). In the T arm, 100% of participants completed all protocol-specified treatment prior to surgery, as compared to 69% in the L arm and 74% in the TL arm. Tumor or tumor bed tissue was collected whenever possible pre-treatment (N = 110), after one cycle of HER2-targeted therapy alone (N = 89), and at time of surgery (N = 59). Higher-level amplification of HER2 and hormone receptor (HR)-negative status were associated with a higher pCR rate. Large shifts in the tumor, immune, and stromal gene expression occurred after one cycle of HER2-targeted therapy. In contrast to pCR rates, the L-containing arms exhibited greater proliferation reduction than T at this timepoint. Immune expression signatures increased in all arms after one cycle of HER2-targeted therapy, decreasing again by the time of surgery. Our results inform approaches to early assessment of sensitivity to anti-HER2 therapy and shed light on the role of the immune microenvironment in response to HER2-targeted agents.
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Affiliation(s)
- Sara A Hurvitz
- David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA.
| | - Jennifer L Caswell-Jin
- Department of Medicine, Division of Oncology, Stanford University School of Medicine, Stanford, CA, USA
- Stanford Cancer Institute, Stanford University School of Medicine, Stanford, CA, USA
| | - Katherine L McNamara
- Department of Medicine, Division of Oncology, Stanford University School of Medicine, Stanford, CA, USA
- Stanford Cancer Institute, Stanford University School of Medicine, Stanford, CA, USA
- Department of Genetics, Stanford University School of Medicine, Stanford, California, USA
| | - Jason J Zoeller
- Department of Cell Biology, Harvard Medical School, Boston, MA, USA
| | - Gregory R Bean
- Department of Pathology, Stanford University School of Medicine, Stanford, CA, USA
| | | | - Alejandra Perez
- Department of Medicine, University of Miami Miller School of Medicine, Miami, FL, USA
| | | | - Lee Zehngebot
- Florida Cancer Specialists & Research Institute, Orlando, FL, USA
| | - Heather Allen
- Comprehensive Cancer Centers of Nevada, Las Vegas, NV, USA
| | | | - Brian DiCarlo
- David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | | | | | - Carmen Calfa
- Department of Medicine, University of Miami Miller School of Medicine, Miami, FL, USA
| | - David Molthrop
- Florida Cancer Specialists & Research Institute, Orlando, FL, USA
| | | | - Hsiao-Wang Chen
- David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Judy Dering
- David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Brad Adams
- David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Eran Kotler
- Department of Medicine, Division of Oncology, Stanford University School of Medicine, Stanford, CA, USA
- Stanford Cancer Institute, Stanford University School of Medicine, Stanford, CA, USA
- Department of Genetics, Stanford University School of Medicine, Stanford, California, USA
| | - Michael F Press
- Department of Pathology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Joan S Brugge
- Department of Cell Biology, Harvard Medical School, Boston, MA, USA
| | - Christina Curtis
- Department of Medicine, Division of Oncology, Stanford University School of Medicine, Stanford, CA, USA.
- Stanford Cancer Institute, Stanford University School of Medicine, Stanford, CA, USA.
- Department of Genetics, Stanford University School of Medicine, Stanford, California, USA.
| | - Dennis J Slamon
- David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
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Thanopoulou E, Khader L, Caira M, Wardley A, Ettl J, Miglietta F, Neven P, Guarneri V. Therapeutic Strategies for the Management of Hormone Receptor-Positive, Human Epidermal Growth Factor Receptor 2-Positive (HR+/HER2+) Breast Cancer: A Review of the Current Literature. Cancers (Basel) 2020; 12:E3317. [PMID: 33182657 PMCID: PMC7696181 DOI: 10.3390/cancers12113317] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2020] [Revised: 10/22/2020] [Accepted: 11/05/2020] [Indexed: 02/07/2023] Open
Abstract
Enormous advances have been made in the understanding and treatment of human epidermal growth factor receptor 2-positive breast cancer (HER2+ BC) in the last 30 years that have resulted in survival gains for affected patients. A growing body of evidence suggests that hormone receptor-positive (HR+)/HER2+ BC and HR-negative (HR-)/HER2+ BC are biologically different, with complex molecular bidirectional crosstalk between the estrogen receptor and HER2 pathway potentially affecting sensitivity to both HER2-targeted and endocrine therapy in patients with HR+/HER2+ BC. Subgroup analyses from trials enrolling patients with HER2+ BC and the results of clinical trials specifically designed to evaluate therapy in patients with HR+/HER2+ BC are helping to guide treatment decisions. In this context, encouraging results with strategies aimed at delaying or reversing drug resistance, including extended adjuvant therapy and the addition of drugs targeting alternative pathways, such as cyclin-dependent kinase (CDK) 4 and 6 inhibitors, have recently emerged. We have reached the point where tailoring the treatment according to risk and biology has become the paradigm in early BC. However, further clinical trials are needed that integrate translational research principles and identify and consider specific patient subgroups and biomarkers.
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Affiliation(s)
- Eirini Thanopoulou
- Eli Lilly and Company Limited, Erl Wood Manor, Windlesham, Surrey GU20 6PH, UK;
| | - Leila Khader
- Eli Lilly Italia S.p.A., 50019 Comune di Sesto Fiorentino, Florence, Italy; (L.K.); (M.C.)
| | - Morena Caira
- Eli Lilly Italia S.p.A., 50019 Comune di Sesto Fiorentino, Florence, Italy; (L.K.); (M.C.)
| | - Andrew Wardley
- The NIHR Manchester Clinical Research Facility at The Christie NHS Foundation Trust, School of Medical Sciences, Faculty of Biology Medicine & Health, University of Manchester, Manchester M204BX, UK;
| | - Johannes Ettl
- Department of Obstetrics and Gynecology, Klinikum rechts der Isar, Technical University of Munich, 81675 Munich, Germany;
| | - Federica Miglietta
- Medical Oncology 2, Istituto Oncologico Veneto IOV IRCCS, 35128 Padua, Italy;
- Department of Surgery, Oncology and Gastroenterology, University of Padova, 35128 Padua, Italy
| | - Patrick Neven
- Multidisciplinary Breast Center and Department of Gynecology and Obstetrics, UZ Leuven, 3000 Leuven, Belgium;
- Department of Oncology, KU Leuven, 3000 Leuven, Belgium
| | - Valentina Guarneri
- Medical Oncology 2, Istituto Oncologico Veneto IOV IRCCS, 35128 Padua, Italy;
- Department of Surgery, Oncology and Gastroenterology, University of Padova, 35128 Padua, Italy
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Fernandez-Martinez A, Krop IE, Hillman DW, Polley MY, Parker JS, Huebner L, Hoadley KA, Shepherd J, Tolaney S, Henry NL, Dang C, Harris L, Berry D, Hahn O, Hudis C, Winer E, Partridge A, Perou CM, Carey LA. Survival, Pathologic Response, and Genomics in CALGB 40601 (Alliance), a Neoadjuvant Phase III Trial of Paclitaxel-Trastuzumab With or Without Lapatinib in HER2-Positive Breast Cancer. J Clin Oncol 2020; 38:4184-4193. [PMID: 33095682 DOI: 10.1200/jco.20.01276] [Citation(s) in RCA: 63] [Impact Index Per Article: 15.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022] Open
Abstract
PURPOSE CALGB 40601 assessed whether dual versus single human epidermal growth factor receptor 2 (HER2) -targeting drugs added to neoadjuvant chemotherapy increased pathologic complete response (pCR). Here, we report relapse-free survival (RFS), overall survival (OS), and gene expression signatures that predict pCR and survival. PATIENTS AND METHODS Three hundred five women with untreated stage II and III HER2-positive breast cancer were randomly assigned to receive weekly paclitaxel combined with trastuzumab plus lapatinib (THL), trastuzumab (TH), or lapatinib (TL). The primary end point was pCR, and secondary end points included RFS, OS, and gene expression analyses. mRNA sequencing was performed on 264 pretreatment samples. RESULTS One hundred eighteen patients were randomly allocated to THL, 120 to TH, and 67 to TL. At more than 7 years of follow-up, THL had significantly better RFS and OS than did TH (RFS hazard ratio, 0.32; 95% CI, 0.14 to 0.71; P = .005; OS hazard ratio, 0.34; 95% CI, 0.12 to 0.94; P = .037), with no difference between TH and TL. Of 688 previously described gene expression signatures, significant associations were found in 215 with pCR, 45 with RFS, and only 22 with both pCR and RFS (3.2%). Specifically, eight immune signatures were significantly correlated with a higher pCR rate and better RFS. Among patients with residual disease, the immunoglobulin G signature was an independent, good prognostic factor, whereas the HER2-enriched signature, which was associated with a higher pCR rate, showed a significantly shorter RFS. CONCLUSION In CALGB 40601, dual HER2-targeting resulted in significant RFS and OS benefits. Integration of intrinsic subtype and immune signatures allowed for the prediction of pCR and RFS, both overall and within the residual disease group. These approaches may provide means for rational escalation and de-escalation treatment strategies in HER2-positive breast cancer.
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Affiliation(s)
- Aranzazu Fernandez-Martinez
- Lineberger Comprehensive Center, University of North Carolina, Chapel Hill, NC.,Department of Genetics, University of North Carolina, Chapel Hill, NC
| | - Ian E Krop
- Department of Medical Oncology, Dana-Farber/Partners CancerCare, Boston, MA
| | - David W Hillman
- Alliance Statistics and Data Center, Mayo Clinic, Rochester, MN
| | - Mei-Yin Polley
- Alliance Statistics and Data Center, Mayo Clinic, Rochester, MN
| | - Joel S Parker
- Lineberger Comprehensive Center, University of North Carolina, Chapel Hill, NC.,Department of Genetics, University of North Carolina, Chapel Hill, NC
| | - Lucas Huebner
- Alliance Statistics and Data Center, Mayo Clinic, Rochester, MN
| | - Katherine A Hoadley
- Lineberger Comprehensive Center, University of North Carolina, Chapel Hill, NC.,Department of Genetics, University of North Carolina, Chapel Hill, NC
| | - Jonathan Shepherd
- Lineberger Comprehensive Center, University of North Carolina, Chapel Hill, NC.,Department of Genetics, University of North Carolina, Chapel Hill, NC
| | - Sara Tolaney
- Department of Medical Oncology, Dana-Farber/Partners CancerCare, Boston, MA
| | - N Lynn Henry
- University of Michigan Rogel Cancer Center, Ann Arbor, MI
| | - Chau Dang
- Memorial Sloan Kettering Cancer Center, New York, NY
| | - Lyndsay Harris
- National Cancer Institute, Cancer Diagnostics Program, Bethesda, MD
| | - Donald Berry
- Division of Biostatistics, MD Anderson Cancer Center, Houston, TX
| | - Olwen Hahn
- Alliance Protocol Operations Office, University of Chicago, Chicago, IL
| | | | - Eric Winer
- Department of Medical Oncology, Dana-Farber/Partners CancerCare, Boston, MA
| | - Ann Partridge
- Department of Medical Oncology, Dana-Farber/Partners CancerCare, Boston, MA
| | - Charles M Perou
- Lineberger Comprehensive Center, University of North Carolina, Chapel Hill, NC.,Department of Genetics, University of North Carolina, Chapel Hill, NC
| | - Lisa A Carey
- Lineberger Comprehensive Center, University of North Carolina, Chapel Hill, NC.,Division of Hematology-Oncology, University of North Carolina, Chapel Hill, NC
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39
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Cancer gene expression profiles associated with clinical outcomes to chemotherapy treatments. BMC Med Genomics 2020; 13:111. [PMID: 32948183 PMCID: PMC7499993 DOI: 10.1186/s12920-020-00759-0] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2020] [Accepted: 07/27/2020] [Indexed: 12/18/2022] Open
Abstract
Background Machine learning (ML) methods still have limited applicability in personalized oncology due to low numbers of available clinically annotated molecular profiles. This doesn’t allow sufficient training of ML classifiers that could be used for improving molecular diagnostics. Methods We reviewed published datasets of high throughput gene expression profiles corresponding to cancer patients with known responses on chemotherapy treatments. We browsed Gene Expression Omnibus (GEO), The Cancer Genome Atlas (TCGA) and Tumor Alterations Relevant for GEnomics-driven Therapy (TARGET) repositories. Results We identified data collections suitable to build ML models for predicting responses on certain chemotherapeutic schemes. We identified 26 datasets, ranging from 41 till 508 cases per dataset. All the datasets identified were checked for ML applicability and robustness with leave-one-out cross validation. Twenty-three datasets were found suitable for using ML that had balanced numbers of treatment responder and non-responder cases. Conclusions We collected a database of gene expression profiles associated with clinical responses on chemotherapy for 2786 individual cancer cases. Among them seven datasets included RNA sequencing data (for 645 cases) and the others – microarray expression profiles. The cases represented breast cancer, lung cancer, low-grade glioma, endothelial carcinoma, multiple myeloma, adult leukemia, pediatric leukemia and kidney tumors. Chemotherapeutics included taxanes, bortezomib, vincristine, trastuzumab, letrozole, tipifarnib, temozolomide, busulfan and cyclophosphamide.
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40
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Swain SM, Tang G, Brauer HA, Goerlitz DS, Lucas PC, Robidoux A, Harris BT, Bandos H, Ren Y, Geyer CE, Rastogi P, Mamounas EP, Wolmark N. NSABP B-41, a Randomized Neoadjuvant Trial: Genes and Signatures Associated with Pathologic Complete Response. Clin Cancer Res 2020; 26:4233-4241. [PMID: 32371537 PMCID: PMC7724952 DOI: 10.1158/1078-0432.ccr-20-0152] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2020] [Revised: 03/23/2020] [Accepted: 04/30/2020] [Indexed: 12/13/2022]
Abstract
PURPOSE In NSABP B-41, pathologic complete response (pCR) was associated with prolonged survival among women with HER2-positive operable breast cancer treated with neoadjuvant chemotherapy and lapatinib, trastuzumab, or the combination. We used a large human breast cancer gene expression panel to select candidate prognostic biomarkers for pCR among women treated with trastuzumab in NSABP B-41. PATIENTS AND METHODS Eligible patients had a baseline preadjuvant treatment core biopsy sample, known pCR status, and no withdrawal of consent. We analyzed extracted RNA using the human nCounter Breast Cancer 360 gene expression panel. Gene counts were normalized to housekeeping genes and transformed into logarithmic scale with base 2. To screen for candidate genes and metagene signatures prognostic of pCR, we used univariate logistic regression. Variable selection was done by multivariable logistic regression with lasso regularization. RESULTS Analyses of data from 130 patients revealed that a composite of gene expression from 19 genes and one gene signature appeared to predict pCR in women with HER2-positive early-stage breast cancer undergoing neoadjuvant chemotherapy with trastuzumab-containing regimens. The identified genes are involved in important pathways such as epithelial-mesenchymal transition, adhesion and migration, estrogen receptor signaling, DNA damage and repair, apoptosis, and proliferation. The AUC from a 10-fold cross-validation on predicting pCR, with these 20 genomic markers in a logistic regression model, was 0.73. CONCLUSIONS The expression level of ERBB2, ESR1, and a few other genomic markers was highly predictive of pCR after trastuzumab-containing regimens. These findings need to be validated and calibrated in future studies.
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Affiliation(s)
- Sandra M Swain
- Lombardi Comprehensive Cancer Center, Georgetown University Medical Center, MedStar Health, Washington, DC.
- NSABP Foundation, Pittsburgh, Pennsylvania
| | - Gong Tang
- NSABP Foundation, Pittsburgh, Pennsylvania
- University of Pittsburgh, Pittsburgh, Pennsylvania
| | | | - David S Goerlitz
- Lombardi Comprehensive Cancer Center, Georgetown University Medical Center, MedStar Health, Washington, DC
| | - Peter C Lucas
- NSABP Foundation, Pittsburgh, Pennsylvania
- University of Pittsburgh, Pittsburgh, Pennsylvania
| | - André Robidoux
- NSABP Foundation, Pittsburgh, Pennsylvania
- Centre hospitalier de l'Université de Montréal, Montréal, Quebec, Canada
| | - Brent T Harris
- Lombardi Comprehensive Cancer Center, Georgetown University Medical Center, MedStar Health, Washington, DC
| | - Hanna Bandos
- NSABP Foundation, Pittsburgh, Pennsylvania
- University of Pittsburgh, Pittsburgh, Pennsylvania
| | - Yuqi Ren
- NanoString Technologies, Inc., Seattle, Washington
| | - Charles E Geyer
- NSABP Foundation, Pittsburgh, Pennsylvania
- Houston Methodist Cancer Center, Houston, Texas
| | - Priya Rastogi
- NSABP Foundation, Pittsburgh, Pennsylvania
- University of Pittsburgh, Pittsburgh, Pennsylvania
- Magee-Womens Hospital, Pittsburgh, Pennsylvania
| | - Eleftherios P Mamounas
- NSABP Foundation, Pittsburgh, Pennsylvania
- Orlando Health UF Health Cancer Center, Orlando, Florida
| | - Norman Wolmark
- NSABP Foundation, Pittsburgh, Pennsylvania
- University of Pittsburgh, Pittsburgh, Pennsylvania
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Sorokin M, Ignatev K, Barbara V, Vladimirova U, Muraveva A, Suntsova M, Gaifullin N, Vorotnikov I, Kamashev D, Bondarenko A, Baranova M, Poddubskaya E, Buzdin A. Molecular Pathway Activation Markers Are Associated with Efficacy of Trastuzumab Therapy in Metastatic HER2-Positive Breast Cancer Better than Individual Gene Expression Levels. BIOCHEMISTRY (MOSCOW) 2020; 85:758-772. [DOI: 10.1134/s0006297920070044] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
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Hollern DP, Xu N, Thennavan A, Glodowski C, Garcia-Recio S, Mott KR, He X, Garay JP, Carey-Ewend K, Marron D, Ford J, Liu S, Vick SC, Martin M, Parker JS, Vincent BG, Serody JS, Perou CM. B Cells and T Follicular Helper Cells Mediate Response to Checkpoint Inhibitors in High Mutation Burden Mouse Models of Breast Cancer. Cell 2020; 179:1191-1206.e21. [PMID: 31730857 DOI: 10.1016/j.cell.2019.10.028] [Citation(s) in RCA: 276] [Impact Index Per Article: 69.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2019] [Revised: 09/12/2019] [Accepted: 10/23/2019] [Indexed: 12/28/2022]
Abstract
This study identifies mechanisms mediating responses to immune checkpoint inhibitors using mouse models of triple-negative breast cancer. By creating new mammary tumor models, we find that tumor mutation burden and specific immune cells are associated with response. Further, we developed a rich resource of single-cell RNA-seq and bulk mRNA-seq data of immunotherapy-treated and non-treated tumors from sensitive and resistant murine models. Using this, we uncover that immune checkpoint therapy induces T follicular helper cell activation of B cells to facilitate the anti-tumor response in these models. We also show that B cell activation of T cells and the generation of antibody are key to immunotherapy response and propose a new biomarker for immune checkpoint therapy. In total, this work presents resources of new preclinical models of breast cancer with large mRNA-seq and single-cell RNA-seq datasets annotated for sensitivity to therapy and uncovers new components of response to immune checkpoint inhibitors.
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Affiliation(s)
- Daniel P Hollern
- Lineberger Comprehensive Cancer Center, University of North Carolina, Chapel Hill, NC 27599, USA; Department of Genetics, University of North Carolina, Chapel Hill, NC 27599, USA
| | - Nuo Xu
- Department of Microbiology and Immunology, University of North Carolina, Chapel Hill, NC 27599, USA
| | - Aatish Thennavan
- Lineberger Comprehensive Cancer Center, University of North Carolina, Chapel Hill, NC 27599, USA; Department of Genetics, University of North Carolina, Chapel Hill, NC 27599, USA; Oral and Craniofacial Biomedicine Program, School of Dentistry, University of North Carolina, Chapel Hill, NC, USA
| | - Cherise Glodowski
- Lineberger Comprehensive Cancer Center, University of North Carolina, Chapel Hill, NC 27599, USA; Department of Pathology and Laboratory Medicine, University of North Carolina, Chapel Hill, NC 27599, USA
| | - Susana Garcia-Recio
- Lineberger Comprehensive Cancer Center, University of North Carolina, Chapel Hill, NC 27599, USA; Department of Genetics, University of North Carolina, Chapel Hill, NC 27599, USA
| | - Kevin R Mott
- Lineberger Comprehensive Cancer Center, University of North Carolina, Chapel Hill, NC 27599, USA; Department of Genetics, University of North Carolina, Chapel Hill, NC 27599, USA
| | - Xiaping He
- Lineberger Comprehensive Cancer Center, University of North Carolina, Chapel Hill, NC 27599, USA; Department of Genetics, University of North Carolina, Chapel Hill, NC 27599, USA
| | - Joseph P Garay
- Lineberger Comprehensive Cancer Center, University of North Carolina, Chapel Hill, NC 27599, USA; Department of Genetics, University of North Carolina, Chapel Hill, NC 27599, USA
| | - Kelly Carey-Ewend
- Lineberger Comprehensive Cancer Center, University of North Carolina, Chapel Hill, NC 27599, USA; Department of Genetics, University of North Carolina, Chapel Hill, NC 27599, USA
| | - David Marron
- Lineberger Comprehensive Cancer Center, University of North Carolina, Chapel Hill, NC 27599, USA; Department of Genetics, University of North Carolina, Chapel Hill, NC 27599, USA
| | - John Ford
- Lineberger Comprehensive Cancer Center, University of North Carolina, Chapel Hill, NC 27599, USA; Department of Genetics, University of North Carolina, Chapel Hill, NC 27599, USA
| | - Siyao Liu
- Lineberger Comprehensive Cancer Center, University of North Carolina, Chapel Hill, NC 27599, USA; Department of Genetics, University of North Carolina, Chapel Hill, NC 27599, USA
| | - Sarah C Vick
- Department of Microbiology and Immunology, University of North Carolina, Chapel Hill, NC 27599, USA
| | - Miguel Martin
- Instituto de Investigación Sanitaria Gregorio Marañon, CIBERONC, Universidad Complutense, Madrid, Spain
| | - Joel S Parker
- Lineberger Comprehensive Cancer Center, University of North Carolina, Chapel Hill, NC 27599, USA; Department of Genetics, University of North Carolina, Chapel Hill, NC 27599, USA
| | - Benjamin G Vincent
- Lineberger Comprehensive Cancer Center, University of North Carolina, Chapel Hill, NC 27599, USA; Department of Microbiology and Immunology, University of North Carolina, Chapel Hill, NC 27599, USA; Department of Hematology/Oncology, Department of Medicine, University of North Carolina, Chapel Hill, NC 27599, USA; Curriculum in Bioinformatics and Computational Biology, University of North Carolina, Chapel Hill, NC 27599, USA
| | - Jonathan S Serody
- Lineberger Comprehensive Cancer Center, University of North Carolina, Chapel Hill, NC 27599, USA; Department of Microbiology and Immunology, University of North Carolina, Chapel Hill, NC 27599, USA; Department of Hematology/Oncology, Department of Medicine, University of North Carolina, Chapel Hill, NC 27599, USA
| | - Charles M Perou
- Lineberger Comprehensive Cancer Center, University of North Carolina, Chapel Hill, NC 27599, USA; Department of Genetics, University of North Carolina, Chapel Hill, NC 27599, USA.
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Zhang S, Wang Y, Wang Y, Peng J, Yuan C, Zhou L, Xu S, Lin Y, Du Y, Yang F, Zhang J, Dai H, Yin W, Lu J. Serum miR-222-3p as a Double-Edged Sword in Predicting Efficacy and Trastuzumab-Induced Cardiotoxicity for HER2-Positive Breast Cancer Patients Receiving Neoadjuvant Target Therapy. Front Oncol 2020; 10:631. [PMID: 32426280 PMCID: PMC7212359 DOI: 10.3389/fonc.2020.00631] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2019] [Accepted: 04/06/2020] [Indexed: 12/12/2022] Open
Abstract
Background: We aimed to explore whether the expression of serum miR-222-3p might contribute to early prediction of therapeutic response, clinical outcomes, and adverse events for HER2-positive breast cancer patients receiving neoadjuvant therapy (NAT). Methods: A total of 65 HER2-positive breast cancer patients receiving NAT were analyzed. The concentration of serum miR-222-3p was detected by quantitative real-time PCR. Logistic regression analysis was used to identify the association of serum miR-222-3p with pathological complete response (pCR). The relationship of serum miR-222-3p with disease-free survival (DFS) and overall survival (OS) was examined via log-rank test and Cox proportional hazards analysis. The ordered logistic regression was applied to evaluate the association between serum miR-222-3p and adverse events. Results: The miR-222-3p low group was more likely to achieve pCR [odds ratio (OR) = 0.258, P = 0.043]. The interaction between miR-222-3p and presenting Ki67 level was also detected for pCR (OR = 49.230, Pinteraction = 0.025). The miR-222-3p low group was correlated with superior DFS (P = 0.029) and OS (P = 0.0037). The expression of serum miR-222-3p was the independent protective factor for trastuzumab-induced cardiotoxicity (P < 0.05) and anemia (P = 0.013). Conclusions: Serum miR-222-3p is the potential factor to predict pCR, survival benefit and trastuzumab-induced cardiotoxicity for HER2-positive breast cancer patients receiving NAT.
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Affiliation(s)
- Shan Zhang
- Department of Breast Surgery, Renji Hospital, School of Medicine, Shanghai Jiaotong University, Shanghai, China
| | - Yaohui Wang
- Department of Breast Surgery, Renji Hospital, School of Medicine, Shanghai Jiaotong University, Shanghai, China
| | - Yan Wang
- Department of Breast Surgery, Renji Hospital, School of Medicine, Shanghai Jiaotong University, Shanghai, China
| | - Jing Peng
- Department of Breast Surgery, Renji Hospital, School of Medicine, Shanghai Jiaotong University, Shanghai, China
| | - Chenwei Yuan
- Department of Breast Surgery, Renji Hospital, School of Medicine, Shanghai Jiaotong University, Shanghai, China
| | - Liheng Zhou
- Department of Breast Surgery, Renji Hospital, School of Medicine, Shanghai Jiaotong University, Shanghai, China
| | - Shuguang Xu
- Department of Breast Surgery, Renji Hospital, School of Medicine, Shanghai Jiaotong University, Shanghai, China
| | - Yanping Lin
- Department of Breast Surgery, Renji Hospital, School of Medicine, Shanghai Jiaotong University, Shanghai, China
| | - Yueyao Du
- Department of Breast Surgery, Renji Hospital, School of Medicine, Shanghai Jiaotong University, Shanghai, China
| | - Fan Yang
- Department of Breast Surgery, Renji Hospital, School of Medicine, Shanghai Jiaotong University, Shanghai, China
| | - Jie Zhang
- Department of Breast Surgery, Renji Hospital, School of Medicine, Shanghai Jiaotong University, Shanghai, China
| | - Huijuan Dai
- Department of Breast Surgery, Renji Hospital, School of Medicine, Shanghai Jiaotong University, Shanghai, China
| | - Wenjin Yin
- Department of Breast Surgery, Renji Hospital, School of Medicine, Shanghai Jiaotong University, Shanghai, China
| | - Jinsong Lu
- Department of Breast Surgery, Renji Hospital, School of Medicine, Shanghai Jiaotong University, Shanghai, China
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Sorokin M, Ignatev K, Poddubskaya E, Vladimirova U, Gaifullin N, Lantsov D, Garazha A, Allina D, Suntsova M, Barbara V, Buzdin A. RNA Sequencing in Comparison to Immunohistochemistry for Measuring Cancer Biomarkers in Breast Cancer and Lung Cancer Specimens. Biomedicines 2020; 8:E114. [PMID: 32397474 PMCID: PMC7277916 DOI: 10.3390/biomedicines8050114] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2020] [Revised: 05/02/2020] [Accepted: 05/07/2020] [Indexed: 12/11/2022] Open
Abstract
RNA sequencing is considered the gold standard for high-throughput profiling of gene expression at the transcriptional level. Its increasing importance in cancer research and molecular diagnostics is reflected in the growing number of its mentions in scientific literature and clinical trial reports. However, the use of different reagents and protocols for RNA sequencing often produces incompatible results. Recently, we published the Oncobox Atlas of RNA sequencing profiles for normal human tissues obtained from healthy donors killed in road accidents. This is a database of molecular profiles obtained using uniform protocol and reagents settings that can be broadly used in biomedicine for data normalization in pathology, including cancer. Here, we publish new original 39 breast cancer (BC) and 19 lung cancer (LC) RNA sequencing profiles obtained for formalin-fixed paraffin-embedded (FFPE) tissue samples, fully compatible with the Oncobox Atlas. We performed the first correlation study of RNA sequencing and immunohistochemistry-measured expression profiles for the clinically actionable biomarker genes in FFPE cancer tissue samples. We demonstrated high (Spearman's rho 0.65-0.798) and statistically significant (p < 0.00004) correlations between the RNA sequencing (Oncobox protocol) and immunohistochemical measurements for HER2/ERBB2, ER/ESR1 and PGR genes in BC, and for PDL1 gene in LC; AUC: 0.963 for HER2, 0.921 for ESR1, 0.912 for PGR, and 0.922 for PDL1. To our knowledge, this is the first validation that total RNA sequencing of archived FFPE materials provides a reliable estimation of marker protein levels. These results show that in the future, RNA sequencing can complement immunohistochemistry for reliable measurements of the expression biomarkers in FFPE cancer samples.
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Affiliation(s)
- Maxim Sorokin
- Institute of Personalized Medicine, I.M. Sechenov First Moscow State Medical University, 119048 Moscow, Russia; (M.S.); (E.P.); (D.A.); (M.S.)
- Omicsway Corp., Walnut, CA 91789, USA;
- Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, 117997 Moscow, Russia;
| | - Kirill Ignatev
- Karelia Republic Oncological Hospital, 185000 Petrozavodsk, Russia;
| | - Elena Poddubskaya
- Institute of Personalized Medicine, I.M. Sechenov First Moscow State Medical University, 119048 Moscow, Russia; (M.S.); (E.P.); (D.A.); (M.S.)
- Vitamed Oncological Clinical Center, 121309 Moscow, Russia
| | - Uliana Vladimirova
- Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, 117997 Moscow, Russia;
| | - Nurshat Gaifullin
- Faculty of Fundamental Medicine, Lomonosov Moscow State University, 119991 Moscow, Russia;
| | - Dmitriy Lantsov
- Kaluga Regional Oncological Hospital, 248007 Kaluga, Russia;
| | | | - Daria Allina
- Institute of Personalized Medicine, I.M. Sechenov First Moscow State Medical University, 119048 Moscow, Russia; (M.S.); (E.P.); (D.A.); (M.S.)
| | - Maria Suntsova
- Institute of Personalized Medicine, I.M. Sechenov First Moscow State Medical University, 119048 Moscow, Russia; (M.S.); (E.P.); (D.A.); (M.S.)
| | - Victoria Barbara
- Oncological Dispensary of the Republic of Karelia, 185002 Petrozavodsk, Russia;
| | - Anton Buzdin
- Institute of Personalized Medicine, I.M. Sechenov First Moscow State Medical University, 119048 Moscow, Russia; (M.S.); (E.P.); (D.A.); (M.S.)
- Omicsway Corp., Walnut, CA 91789, USA;
- Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, 117997 Moscow, Russia;
- Moscow Institute of Physics and Technology, 141701 Moscow, Russia
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45
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Pereira EJ, Burns JS, Lee CY, Marohl T, Calderon D, Wang L, Atkins KA, Wang CC, Janes KA. Sporadic activation of an oxidative stress-dependent NRF2-p53 signaling network in breast epithelial spheroids and premalignancies. Sci Signal 2020; 13:13/627/eaba4200. [PMID: 32291314 DOI: 10.1126/scisignal.aba4200] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
Breast and mammary epithelial cells experience different local environments during tissue development and tumorigenesis. Microenvironmental heterogeneity gives rise to distinct cell regulatory states whose identity and importance are just beginning to be appreciated. Cellular states diversify when clonal three-dimensional (3D) spheroids are cultured in basement membrane, and one such state is associated with stress tolerance and poor response to anticancer therapeutics. Here, we found that this state was jointly coordinated by the NRF2 and p53 pathways, which were costabilized by spontaneous oxidative stress within 3D cultures. Inhibition of NRF2 or p53 individually disrupted some of the transcripts defining the regulatory state but did not yield a notable phenotype in nontransformed breast epithelial cells. In contrast, combined perturbation prevented 3D growth in an oxidative stress-dependent manner. By integrating systems models of NRF2 and p53 signaling in a single oxidative stress network, we recapitulated these observations and made predictions about oxidative stress profiles during 3D growth. NRF2 and p53 signaling were similarly coordinated in normal breast epithelial tissue and hormone-negative ductal carcinoma in situ lesions but were uncoupled in triple-negative breast cancer (TNBC), a subtype in which p53 is usually mutated. Using the integrated model, we correlated the extent of this uncoupling in TNBC cell lines with the importance of NRF2 in the 3D growth of these cell lines and their predicted handling of oxidative stress. Our results point to an oxidative stress tolerance network that is important for single cells during glandular development and the early stages of breast cancer.
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Affiliation(s)
- Elizabeth J Pereira
- Department of Biomedical Engineering, University of Virginia, Charlottesville, VA 22908, USA
| | - Joseph S Burns
- Department of Biomedical Engineering, University of Virginia, Charlottesville, VA 22908, USA
| | - Christina Y Lee
- Department of Biomedical Engineering, University of Virginia, Charlottesville, VA 22908, USA
| | - Taylor Marohl
- Department of Biomedical Engineering, University of Virginia, Charlottesville, VA 22908, USA
| | - Delia Calderon
- Biology and Chemistry Programs, California State University Channel Islands, Camarillo, CA 93012, USA
| | - Lixin Wang
- Department of Biomedical Engineering, University of Virginia, Charlottesville, VA 22908, USA
| | - Kristen A Atkins
- Department of Pathology, University of Virginia, Charlottesville, VA 22908, USA
| | - Chun-Chao Wang
- Institute of Molecular Medicine and Department of Medical Science, National Tsing Hua University, Hsinchu 30013, Taiwan
| | - Kevin A Janes
- Department of Biomedical Engineering, University of Virginia, Charlottesville, VA 22908, USA. .,Department of Biochemistry and Molecular Genetics, University of Virginia, Charlottesville, VA 22908, USA
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46
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Sorokin M, Poddubskaya E, Baranova M, Glusker A, Kogoniya L, Markarova E, Allina D, Suntsova M, Tkachev V, Garazha A, Sekacheva M, Buzdin A. RNA sequencing profiles and diagnostic signatures linked with response to ramucirumab in gastric cancer. Cold Spring Harb Mol Case Stud 2020; 6:a004945. [PMID: 32060041 PMCID: PMC7133748 DOI: 10.1101/mcs.a004945] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2019] [Accepted: 02/03/2020] [Indexed: 02/06/2023] Open
Abstract
Gastric cancer (GC) is the fifth-ranked cancer type by associated mortality. The proportion of early diagnosis is low, and most patients are diagnosed at the advanced stages. First-line therapy standardly includes fluoropyrimidines and platinum compounds with trastuzumab for HER2-positive cases. For recurrent disease, there are several alternative options including ramucirumab, a monoclonal therapeutic antibody that inhibits VEGF-mediated tumor angiogenesis by binding with VEGFR2, alone or in combination with other cancer drugs. However, overall response rate following ramucirumab or its combinations is 30%-80% of the patients, suggesting that personalization of drug prescription is needed to increase efficacy of treatment. We report here original tumor RNA sequencing profiles for 15 advanced GC patients linked with data on clinical response to ramucirumab or its combinations. Three genes showed differential expression in the tumors for responders versus nonresponders: CHRM3, LRFN1, and TEX15 Of them, CHRM3 was up-regulated in the responders. Using the bioinformatic platform Oncobox we simulated ramucirumab efficiency and compared output model results with actual tumor response data. An agreement was observed between predicted and real clinical outcomes (AUC ≥ 0.7). These results suggest that RNA sequencing may be used to personalize the prescription of ramucirumab for GC and indicate potential molecular mechanisms underlying ramucirumab resistance. The RNA sequencing profiles obtained here are fully compatible with the previously published Oncobox Atlas of Normal Tissue Expression (ANTE) data.
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Affiliation(s)
- Maxim Sorokin
- I.M. Sechenov First Moscow State Medical University, Moscow, 119991, Russia
- Omicsway Corp., Walnut, California 91789, USA
- Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Moscow, 117997, Russia
| | - Elena Poddubskaya
- I.M. Sechenov First Moscow State Medical University, Moscow, 119991, Russia
| | - Madina Baranova
- N.N. Blokhin Russian Cancer Research Center, Moscow, 115478, Russia
- Clinical Center Vitamed, Moscow, 121309, Russia
| | - Alex Glusker
- I.M. Sechenov First Moscow State Medical University, Moscow, 119991, Russia
| | - Lali Kogoniya
- M.F. Vladimirsky Moscow Regional Research Clinical Institute, Moscow, 129110, Russia
| | - Ekaterina Markarova
- M.F. Vladimirsky Moscow Regional Research Clinical Institute, Moscow, 129110, Russia
| | - Daria Allina
- I.M. Sechenov First Moscow State Medical University, Moscow, 119991, Russia
| | - Maria Suntsova
- Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Moscow, 117997, Russia
| | | | | | - Marina Sekacheva
- I.M. Sechenov First Moscow State Medical University, Moscow, 119991, Russia
| | - Anton Buzdin
- I.M. Sechenov First Moscow State Medical University, Moscow, 119991, Russia
- Omicsway Corp., Walnut, California 91789, USA
- Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Moscow, 117997, Russia
- Moscow Institute of Physics and Technology, Moscow Region, 141701, Russia
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47
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Goutsouliak K, Veeraraghavan J, Sethunath V, De Angelis C, Osborne CK, Rimawi MF, Schiff R. Towards personalized treatment for early stage HER2-positive breast cancer. Nat Rev Clin Oncol 2020; 17:233-250. [PMID: 31836877 PMCID: PMC8023395 DOI: 10.1038/s41571-019-0299-9] [Citation(s) in RCA: 157] [Impact Index Per Article: 39.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/28/2019] [Indexed: 12/13/2022]
Abstract
Advances in HER2-targeted therapies have improved the survival of patients with HER2-positive breast cancer. The standard-of-care treatment for localized disease has been chemotherapy and 1 year of adjuvant HER2-targeted therapy, typically with the anti-HER2 antibody trastuzumab. Despite the effectiveness of this treatment, disease relapse occurs in a subset of patients; thus, focus has been placed on escalating treatment by either combining different HER2-targeted agents or extending the duration of HER2-targeted therapy. Indeed, dual HER2-targeted therapies and extended-duration anti-HER2 therapy, as well as adjuvant therapy with the anti-HER2 antibody-drug conjugate T-DM1, have all been approved for clinical use. Emerging evidence suggests, however, that some patients do not derive sufficient benefit from these additional therapies to offset the associated toxicities and/or costs. Similarly, the universal use of chemotherapy might not benefit all patients, and treatment de-escalation through omission of chemotherapy has shown promise in clinical trials and is currently being explored further. The future of precision medicine should therefore involve tailoring of therapy based on the genetics and biology of each tumour and the clinical characteristics of each patient. Predictive biomarkers that enable the identification of patients who will benefit from either escalated or de-escalated treatment will be crucial to this approach. In this Review, we summarize the available HER2-targeted agents and associated mechanisms of resistance, and describe the current therapeutic landscape of early stage HER2-positive breast cancer, focusing on strategies for treatment escalation or de-escalation.
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Affiliation(s)
- Kristina Goutsouliak
- Lester and Sue Smith Breast Center, Baylor College of Medicine, Houston, TX, USA
- Dan L. Duncan Comprehensive Cancer Center, Baylor College of Medicine, Houston, TX, USA
- Department of Medicine, Baylor College of Medicine, Houston, TX, USA
| | - Jamunarani Veeraraghavan
- Lester and Sue Smith Breast Center, Baylor College of Medicine, Houston, TX, USA
- Dan L. Duncan Comprehensive Cancer Center, Baylor College of Medicine, Houston, TX, USA
- Department of Medicine, Baylor College of Medicine, Houston, TX, USA
| | - Vidyalakshmi Sethunath
- Lester and Sue Smith Breast Center, Baylor College of Medicine, Houston, TX, USA
- Dan L. Duncan Comprehensive Cancer Center, Baylor College of Medicine, Houston, TX, USA
- Verna and Marrs McLean Department of Biochemistry and Molecular Biology, Baylor College of Medicine, Houston, TX, USA
| | - Carmine De Angelis
- Lester and Sue Smith Breast Center, Baylor College of Medicine, Houston, TX, USA
- Dan L. Duncan Comprehensive Cancer Center, Baylor College of Medicine, Houston, TX, USA
- Department of Clinical Medicine and Surgery, University of Naples Federico II, Naples, Italy
| | - C Kent Osborne
- Lester and Sue Smith Breast Center, Baylor College of Medicine, Houston, TX, USA
- Dan L. Duncan Comprehensive Cancer Center, Baylor College of Medicine, Houston, TX, USA
- Department of Medicine, Baylor College of Medicine, Houston, TX, USA
- Department of Molecular and Cellular Biology, Baylor College of Medicine, Houston, TX, USA
| | - Mothaffar F Rimawi
- Lester and Sue Smith Breast Center, Baylor College of Medicine, Houston, TX, USA.
- Dan L. Duncan Comprehensive Cancer Center, Baylor College of Medicine, Houston, TX, USA.
- Department of Medicine, Baylor College of Medicine, Houston, TX, USA.
| | - Rachel Schiff
- Lester and Sue Smith Breast Center, Baylor College of Medicine, Houston, TX, USA.
- Dan L. Duncan Comprehensive Cancer Center, Baylor College of Medicine, Houston, TX, USA.
- Department of Medicine, Baylor College of Medicine, Houston, TX, USA.
- Department of Molecular and Cellular Biology, Baylor College of Medicine, Houston, TX, USA.
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48
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Testa U, Castelli G, Pelosi E. Breast Cancer: A Molecularly Heterogenous Disease Needing Subtype-Specific Treatments. Med Sci (Basel) 2020; 8:E18. [PMID: 32210163 PMCID: PMC7151639 DOI: 10.3390/medsci8010018] [Citation(s) in RCA: 56] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2019] [Revised: 02/23/2020] [Accepted: 03/11/2020] [Indexed: 12/12/2022] Open
Abstract
Breast cancer is the most commonly occurring cancer in women. There were over two-million new cases in world in 2018. It is the second leading cause of death from cancer in western countries. At the molecular level, breast cancer is a heterogeneous disease, which is characterized by high genomic instability evidenced by somatic gene mutations, copy number alterations, and chromosome structural rearrangements. The genomic instability is caused by defects in DNA damage repair, transcription, DNA replication, telomere maintenance and mitotic chromosome segregation. According to molecular features, breast cancers are subdivided in subtypes, according to activation of hormone receptors (estrogen receptor and progesterone receptor), of human epidermal growth factors receptor 2 (HER2), and or BRCA mutations. In-depth analyses of the molecular features of primary and metastatic breast cancer have shown the great heterogeneity of genetic alterations and their clonal evolution during disease development. These studies have contributed to identify a repertoire of numerous disease-causing genes that are altered through different mutational processes. While early-stage breast cancer is a curable disease in about 70% of patients, advanced breast cancer is largely incurable. However, molecular studies have contributed to develop new therapeutic approaches targeting HER2, CDK4/6, PI3K, or involving poly(ADP-ribose) polymerase inhibitors for BRCA mutation carriers and immunotherapy.
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Affiliation(s)
- Ugo Testa
- Department of Oncology, Istituto Superiore di Sanità, Regina Elena 299, 00161 Rome, Italy; (G.C.); (E.P.)
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49
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Genetic determinants of the molecular portraits of epithelial cancers. Nat Commun 2019; 10:5666. [PMID: 31827079 PMCID: PMC6906458 DOI: 10.1038/s41467-019-13588-2] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2019] [Accepted: 11/11/2019] [Indexed: 12/21/2022] Open
Abstract
The ability to characterize and predict tumor phenotypes is crucial to precision medicine. In this study, we present an integrative computational approach using a genome-wide association analysis and an Elastic Net prediction method to analyze the relationship between DNA copy number alterations and an archive of gene expression signatures. Across breast cancers, we are able to quantitatively predict many gene signatures levels within individual tumors with high accuracy based upon DNA copy number features alone, including proliferation status and Estrogen-signaling pathway activity. We can also predict many other key phenotypes, including intrinsic molecular subtypes, estrogen receptor status, and TP53 mutation. This approach is also applied to TCGA Pan-Cancer, which identify repeatedly predictable signatures across tumor types including immune features in lung squamous and basal-like breast cancers. These Elastic Net DNA predictors could also be called from DNA-based gene panels, thus facilitating their use as biomarkers to guide therapeutic decision making. Effective precision medicine strategies rely on the ability to predict tumour behaviour based on molecular characteristics. Here, the authors build models to predict multiple distinct gene expression patterns using DNA copy number alterations
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50
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Stover DG, Reinbolt RE, Adams EJ, Asad S, Tolliver K, Abdel-Rasoul M, Timmers CD, Gillespie S, Chen JL, Ali SM, Collier KA, Cherian MA, Noonan AM, Sardesai S, VanDeusen J, Wesolowski R, Williams N, Lee CN, Shapiro CL, Macrae ER, Ramaswamy B, Lustberg MB. Prospective Decision Analysis Study of Clinical Genomic Testing in Metastatic Breast Cancer: Impact on Outcomes and Patient Perceptions. JCO Precis Oncol 2019; 3:1900090. [PMID: 32923860 DOI: 10.1200/po.19.00090] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/26/2019] [Indexed: 01/28/2023] Open
Abstract
PURPOSE To evaluate the impact of targeted DNA sequencing on selection of cancer therapy for patients with metastatic breast cancer (MBC). PATIENTS AND METHODS In this prospective, single-center, single-arm trial, patients with MBC were enrolled within 10 weeks of starting a new therapy. At enrollment, tumor samples underwent next-generation sequencing for any of 315 cancer-related genes to high depth (> 500×) using FoundationOne CDx. Sequencing results were released to providers at the time of disease progression, and physician treatment recommendations were assessed via questionnaire. We evaluated three prespecified questions to assess patients' perceptions of genomic testing. RESULTS In all, 100 patients underwent genomic testing, with a median of five mutations (range, 0 to 13 mutations) detected per patient. Genomic testing revealed one or more potential therapies in 98% of patients (98 of 100), and 60% of patients (60 of 100) had one or more recommended treatments with level I/II evidence for actionability. Among the 94 genomic text reports that were released, there was physician questionnaire data for 87 patients (response rate, 92.6%) and 31.0% of patients (27 of 87) had treatment change recommended by their physician. Of these, 37.0% (10 of 27) received the treatment supported by genomic testing. We did not detect a statistically significant difference in time-to-treatment failure (log-rank P = .87) or overall survival (P = .71) among patients who had treatment change supported by genomic testing versus those who had no treatment change. For patients who completed surveys before and after genomic testing, there was a significant decrease in confidence of treatment success, specifically among patients who did not have treatment change supported by genomic testing (McNemar's test of agreement P = .001). CONCLUSION In this prospective study, genomic profiling of tumors in patients with MBC frequently identified potential treatments and resulted in treatment change in a minority of patients. Patients whose therapy was not changed on the basis of genomic testing seemed to have a decrease in confidence of treatment success.
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Affiliation(s)
- Daniel G Stover
- The Ohio State University College of Medicine, Columbus, OH.,Stefanie Spielman Comprehensive Breast Center, Columbus, OH
| | - Raquel E Reinbolt
- The Ohio State University College of Medicine, Columbus, OH.,Stefanie Spielman Comprehensive Breast Center, Columbus, OH
| | | | - Sarah Asad
- The Ohio State University Comprehensive Cancer Center, Columbus, OH
| | - Katlyn Tolliver
- The Ohio State University Comprehensive Cancer Center, Columbus, OH.,Stefanie Spielman Comprehensive Breast Center, Columbus, OH
| | | | - Cynthia D Timmers
- The Ohio State University College of Medicine, Columbus, OH.,The Ohio State University Comprehensive Cancer Center, Columbus, OH
| | - Susan Gillespie
- The Ohio State University Comprehensive Cancer Center, Columbus, OH.,Stefanie Spielman Comprehensive Breast Center, Columbus, OH
| | - James L Chen
- The Ohio State University College of Medicine, Columbus, OH.,The Ohio State University Comprehensive Cancer Center, Columbus, OH
| | | | - Katharine A Collier
- The Ohio State University College of Medicine, Columbus, OH.,The Ohio State University Comprehensive Cancer Center, Columbus, OH
| | - Mathew A Cherian
- The Ohio State University College of Medicine, Columbus, OH.,Stefanie Spielman Comprehensive Breast Center, Columbus, OH
| | - Anne M Noonan
- The Ohio State University College of Medicine, Columbus, OH.,Stefanie Spielman Comprehensive Breast Center, Columbus, OH
| | - Sagar Sardesai
- The Ohio State University College of Medicine, Columbus, OH.,Stefanie Spielman Comprehensive Breast Center, Columbus, OH
| | - Jeffrey VanDeusen
- The Ohio State University College of Medicine, Columbus, OH.,Stefanie Spielman Comprehensive Breast Center, Columbus, OH
| | - Robert Wesolowski
- The Ohio State University College of Medicine, Columbus, OH.,Stefanie Spielman Comprehensive Breast Center, Columbus, OH
| | - Nicole Williams
- The Ohio State University College of Medicine, Columbus, OH.,Stefanie Spielman Comprehensive Breast Center, Columbus, OH
| | - Clara N Lee
- The Ohio State University Comprehensive Cancer Center, Columbus, OH.,The Ohio State University College of Public Health, Columbus, OH
| | | | | | - Bhuvaneswari Ramaswamy
- The Ohio State University College of Medicine, Columbus, OH.,Stefanie Spielman Comprehensive Breast Center, Columbus, OH
| | - Maryam B Lustberg
- The Ohio State University College of Medicine, Columbus, OH.,Stefanie Spielman Comprehensive Breast Center, Columbus, OH
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