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Li S, Sun Y. Phytochemicals targeting epidermal growth factor receptor (EGFR) for the prevention and treatment of HNSCC: A review. Medicine (Baltimore) 2023; 102:e34439. [PMID: 37800790 PMCID: PMC10553117 DOI: 10.1097/md.0000000000034439] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/24/2023] [Accepted: 06/30/2023] [Indexed: 10/07/2023] Open
Abstract
Head and neck squamous cell carcinoma (HNSCC) develops from the mucosal epithelium of the oral cavity, pharynx, and larynx, and is the most common malignancy of the head and neck, the incidence of which continues to rise. The epidermal growth factor receptor is thought to play a key role in the pathogenesis of HNSCC. Inhibition of epidermal growth factor receptor has been identified as an effective target for the treatment of HNSCC. Many phytochemicals have emerged as potential new drugs for the treatment of HNSCC. A systematic search was conducted for research articles published in PubMed, and Medline on relevant aspects. This review provides an overview of the available literature and reports highlighting the in vitro effects of phytochemicals on epidermal growth factor in various HNSCC cell models and in vivo in animal models and emphasizes the importance of epidermal growth factor as a current therapeutic target for HNSCC. Based on our review, we conclude that phytochemicals targeting the epidermal growth factor receptor are potentially effective candidates for the development of new drugs for the treatment of HNSCC. It provides an idea for further development and application of herbal medicines for cancer treatment.
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Affiliation(s)
- Shaling Li
- The Affiliated Hospital of Traditional Chinese Medicine of Southwest Medical University, Longmatan District, Luzhou City, Sichuan Province, China
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Shao Y, Fan X, Yang X, Li S, Huang L, Zhou X, Zhang S, Zheng M, Sun J. Impact of Cuproptosis-related markers on clinical status, tumor immune microenvironment and immunotherapy in colorectal cancer: A multi-omic analysis. Comput Struct Biotechnol J 2023; 21:3383-3403. [PMID: 37389187 PMCID: PMC10300104 DOI: 10.1016/j.csbj.2023.06.011] [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: 02/07/2023] [Revised: 06/07/2023] [Accepted: 06/10/2023] [Indexed: 07/01/2023] Open
Abstract
Background Cuproptosis, a novel identified cell death form induced by copper, is characterized by aggregation of lipoylated mitochondrial enzymes and the destabilization of Fe-S cluster proteins. However, the function and potential clinical value of cuproptosis and cuproptosis-related biomarkers in colorectal cancer (CRC) remain largely unknown. Methods A comprehensive multi-omics (transcriptomics, genomics, and single-cell transcriptome) analysis was performed for identifying the influence of 16 cuproptosis-related markers on clinical status, molecular functions and tumor microenvironment (TME) in CRC. A novel cuproptosis-related scoring system (CuproScore) based on cuproptosis-related markers was also constructed to predict the prognosis of CRC individuals, TME and the response to immunotherapy. In addition, our transcriptome cohort of 15 paired CRC tissue, tissue-array, and various assays in 4 kinds of CRC cell lines in vitro were applied for verification. Results Cuproptosis-related markers were closely associated with both clinical prognosis and molecular functions. And the cuproptosis-related molecular phenotypes and scoring system (CuproScore) could distinguish and predict the prognosis of CRC patients, TME, and the response to immunotherapy in both public and our transcriptome cohorts. Besides, the expression, function and clinical significance of these markers were also checked and analyzed in CRC cell lines and CRC tissues in our own cohorts. Conclusions In conclusion, we indicated that cuproptosis and CPRMs played a significant role in CRC progression and in modeling the TME. Inducing cuproptosis may be a useful tool for tumor therapy in the future.
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Affiliation(s)
- Yanfei Shao
- Department of General Surgery, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Shanghai Minimally Invasive Surgery Center, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Shanghai Institute of Digestive Surgery, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Xiaodong Fan
- Department of General Surgery, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Shanghai Minimally Invasive Surgery Center, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Shanghai Institute of Digestive Surgery, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Xiao Yang
- Department of General Surgery, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Shanghai Minimally Invasive Surgery Center, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Shuchun Li
- Department of General Surgery, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Shanghai Minimally Invasive Surgery Center, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Ling Huang
- Department of General Surgery, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Shanghai Minimally Invasive Surgery Center, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Shanghai Institute of Digestive Surgery, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Xueliang Zhou
- Department of General Surgery, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Shanghai Minimally Invasive Surgery Center, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Shanghai Institute of Digestive Surgery, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Sen Zhang
- Department of General Surgery, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Shanghai Minimally Invasive Surgery Center, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Minhua Zheng
- Department of General Surgery, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Shanghai Minimally Invasive Surgery Center, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Jing Sun
- Department of General Surgery, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Shanghai Minimally Invasive Surgery Center, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
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3
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Fröhlich E. The Variety of 3D Breast Cancer Models for the Study of Tumor Physiology and Drug Screening. Int J Mol Sci 2023; 24:ijms24087116. [PMID: 37108283 PMCID: PMC10139112 DOI: 10.3390/ijms24087116] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Revised: 04/01/2023] [Accepted: 04/07/2023] [Indexed: 04/29/2023] Open
Abstract
Breast cancer is the most common cancer in women and responsible for multiple deaths worldwide. 3D cancer models enable a better representation of tumor physiology than the conventional 2D cultures. This review summarizes the important components of physiologically relevant 3D models and describes the spectrum of 3D breast cancer models, e.g., spheroids, organoids, breast cancer on a chip and bioprinted tissues. The generation of spheroids is relatively standardized and easy to perform. Microfluidic systems allow control over the environment and the inclusion of sensors and can be combined with spheroids or bioprinted models. The strength of bioprinting relies on the spatial control of the cells and the modulation of the extracellular matrix. Except for the predominant use of breast cancer cell lines, the models differ in stromal cell composition, matrices and fluid flow. Organoids are most appropriate for personalized treatment, but all technologies can mimic most aspects of breast cancer physiology. Fetal bovine serum as a culture supplement and Matrigel as a scaffold limit the reproducibility and standardization of the listed 3D models. The integration of adipocytes is needed because they possess an important role in breast cancer.
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Affiliation(s)
- Eleonore Fröhlich
- Center for Medical Research, Medical University of Graz, 8010 Graz, Austria
- Research Center Pharmaceutical Engineering GmbH, 8010 Graz, Austria
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4
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Onkar SS, Carleton NM, Lucas PC, Bruno TC, Lee AV, Vignali DAA, Oesterreich S. The Great Immune Escape: Understanding the Divergent Immune Response in Breast Cancer Subtypes. Cancer Discov 2023; 13:23-40. [PMID: 36620880 PMCID: PMC9833841 DOI: 10.1158/2159-8290.cd-22-0475] [Citation(s) in RCA: 43] [Impact Index Per Article: 43.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2022] [Revised: 08/30/2022] [Accepted: 09/26/2022] [Indexed: 12/12/2022]
Abstract
Breast cancer, the most common type of cancer affecting women, encompasses a collection of histologic (mainly ductal and lobular) and molecular subtypes exhibiting diverse clinical presentation, disease trajectories, treatment options, and outcomes. Immunotherapy has revolutionized treatment for some solid tumors but has shown limited promise for breast cancers. In this review, we summarize recent advances in our understanding of the complex interactions between tumor and immune cells in subtypes of breast cancer at the cellular and microenvironmental levels. We aim to provide a perspective on opportunities for future immunotherapy agents tailored to specific features of each subtype of breast cancer. SIGNIFICANCE Although there are currently over 200 ongoing clinical trials testing immunotherapeutics, such as immune-checkpoint blockade agents, these are largely restricted to the triple-negative and HER2+ subtypes and primarily focus on T cells. With the rapid expansion of new in vitro, in vivo, and clinical data, it is critical to identify and highlight the challenges and opportunities unique for each breast cancer subtype to drive the next generation of treatments that harness the immune system.
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Affiliation(s)
- Sayali S. Onkar
- Department of Immunology, University of Pittsburgh School of Medicine, Pittsburgh, PA 15213, USA
- Tumor Microenvironment Center, UPMC Hillman Cancer Center, Pittsburgh, PA 15213, USA
- Graduate Program of Microbiology and Immunology, University of Pittsburgh School of Medicine, Pittsburgh, PA 15213, USA
| | - Neil M. Carleton
- Women’s Cancer Research Center, Magee-Women’s Research Institute, University of Pittsburgh, Pittsburgh, PA 15213, USA
| | - Peter C Lucas
- Women’s Cancer Research Center, Magee-Women’s Research Institute, University of Pittsburgh, Pittsburgh, PA 15213, USA
- Cancer Biology Program, UPMC Hillman Cancer Center, Pittsburgh, PA 15213, USA
- Department of Pathology, University of Pittsburgh School of Medicine, Pittsburgh, PA 15213, USA
| | - Tullia C Bruno
- Department of Immunology, University of Pittsburgh School of Medicine, Pittsburgh, PA 15213, USA
- Tumor Microenvironment Center, UPMC Hillman Cancer Center, Pittsburgh, PA 15213, USA
- Cancer Immunology and Immunotherapy Program, UPMC Hillman Cancer Center, Pittsburgh, PA 15213, USA
| | - Adrian V Lee
- Women’s Cancer Research Center, Magee-Women’s Research Institute, University of Pittsburgh, Pittsburgh, PA 15213, USA
- Cancer Biology Program, UPMC Hillman Cancer Center, Pittsburgh, PA 15213, USA
- Department of Pharmacology and Chemical Biology, University of Pittsburgh School of Medicine, Pittsburgh, PA 15213, USA
| | - Dario AA Vignali
- Department of Immunology, University of Pittsburgh School of Medicine, Pittsburgh, PA 15213, USA
- Tumor Microenvironment Center, UPMC Hillman Cancer Center, Pittsburgh, PA 15213, USA
- Cancer Immunology and Immunotherapy Program, UPMC Hillman Cancer Center, Pittsburgh, PA 15213, USA
| | - Steffi Oesterreich
- Women’s Cancer Research Center, Magee-Women’s Research Institute, University of Pittsburgh, Pittsburgh, PA 15213, USA
- Cancer Biology Program, UPMC Hillman Cancer Center, Pittsburgh, PA 15213, USA
- Department of Pharmacology and Chemical Biology, University of Pittsburgh School of Medicine, Pittsburgh, PA 15213, USA
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Abstract
Cancer is a complex disease and a significant cause of mortality worldwide. Over the course of nearly all cancer types, collagen within the tumor microenvironment influences emergence, progression, and metastasis. This review discusses collagen regulation within the tumor microenvironment, pathological involvement of collagen, and predictive values of collagen and related extracellular matrix components in main cancer types. A survey of predictive tests leveraging collagen assays using clinical cohorts is presented. A conclusion is that collagen has high predictive value in monitoring cancer processes and stratifying by outcomes. New approaches should be considered that continue to define molecular facets of collagen related to cancer.
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Asleh K, Negri GL, Spencer Miko SE, Colborne S, Hughes CS, Wang XQ, Gao D, Gilks CB, Chia SKL, Nielsen TO, Morin GB. Proteomic analysis of archival breast cancer clinical specimens identifies biological subtypes with distinct survival outcomes. Nat Commun 2022; 13:896. [PMID: 35173148 PMCID: PMC8850446 DOI: 10.1038/s41467-022-28524-0] [Citation(s) in RCA: 47] [Impact Index Per Article: 23.5] [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: 01/24/2022] [Indexed: 12/14/2022] Open
Abstract
Despite advances in genomic classification of breast cancer, current clinical tests and treatment decisions are commonly based on protein level information. Formalin-fixed paraffin-embedded (FFPE) tissue specimens with extended clinical outcomes are widely available. Here, we perform comprehensive proteomic profiling of 300 FFPE breast cancer surgical specimens, 75 of each PAM50 subtype, from patients diagnosed in 2008-2013 (n = 178) and 1986-1992 (n = 122) with linked clinical outcomes. These two cohorts are analyzed separately, and we quantify 4214 proteins across all 300 samples. Within the aggressive PAM50-classified basal-like cases, proteomic profiling reveals two groups with one having characteristic immune hot expression features and highly favorable survival. Her2-Enriched cases separate into heterogeneous groups differing by extracellular matrix, lipid metabolism, and immune-response features. Within 88 triple-negative breast cancers, four proteomic clusters display features of basal-immune hot, basal-immune cold, mesenchymal, and luminal with disparate survival outcomes. Our proteomic analysis characterizes the heterogeneity of breast cancer in a clinically-applicable manner, identifies potential biomarkers and therapeutic targets, and provides a resource for clinical breast cancer classification.
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Affiliation(s)
- Karama Asleh
- Genetic Pathology Evaluation Centre, Department of Pathology and Laboratory Medicine, University of British Columbia, Vancouver, BC, Canada
- Interdisciplinary Oncology Program, Faculty of Medicine, University of British Columbia, Vancouver, BC, Canada
| | - Gian Luca Negri
- Canada's Michael Smith Genome Sciences Centre, BC Cancer Research Institute, University of British Columbia, Vancouver, BC, Canada
| | - Sandra E Spencer Miko
- Canada's Michael Smith Genome Sciences Centre, BC Cancer Research Institute, University of British Columbia, Vancouver, BC, Canada
| | - Shane Colborne
- Canada's Michael Smith Genome Sciences Centre, BC Cancer Research Institute, University of British Columbia, Vancouver, BC, Canada
| | - Christopher S Hughes
- Department of Molecular Oncology, BC Cancer Research Institute, University of British Columbia, Vancouver, BC, Canada
| | - Xiu Q Wang
- Genetic Pathology Evaluation Centre, Department of Pathology and Laboratory Medicine, University of British Columbia, Vancouver, BC, Canada
| | - Dongxia Gao
- Genetic Pathology Evaluation Centre, Department of Pathology and Laboratory Medicine, University of British Columbia, Vancouver, BC, Canada
| | - C Blake Gilks
- Division of Anatomical Pathology, Vancouver General Hospital, University of British Columbia, Vancouver, Canada
- Canadian Immunohistochemistry Quality Control, University of British Columbia, Vancouver, BC, Canada
| | - Stephen K L Chia
- Division of Medical Oncology, British Columbia Cancer Centre, University of British Columbia, Vancouver, BC, Canada
| | - Torsten O Nielsen
- Genetic Pathology Evaluation Centre, Department of Pathology and Laboratory Medicine, University of British Columbia, Vancouver, BC, Canada
- Division of Anatomical Pathology, Vancouver General Hospital, University of British Columbia, Vancouver, Canada
| | - Gregg B Morin
- Canada's Michael Smith Genome Sciences Centre, BC Cancer Research Institute, University of British Columbia, Vancouver, BC, Canada.
- Department of Medical Genetics, University of British Columbia, Vancouver, BC, Canada.
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The Prognostic Role of Intratumoral Stromal Content in Lobular Breast Cancer. Cancers (Basel) 2022; 14:cancers14040941. [PMID: 35205688 PMCID: PMC8870094 DOI: 10.3390/cancers14040941] [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: 12/13/2021] [Revised: 01/27/2022] [Accepted: 02/12/2022] [Indexed: 11/17/2022] Open
Abstract
Simple Summary High intratumoral stromal content is related to worse outcomes in several types of cancer. However, its prognostic role in breast cancer seems to differ between different subtypes. High intratumoral stromal content is a negative prognostic marker in triple-negative breast cancer, while the opposite is the case for estrogen-receptor-positive breast cancer, in which higher stromal content is indicative of a better prognosis. Most lobular breast cancers are estrogen-receptor-positive, and the tumor tissue has a clearly defined histological appearance, often with a high intratumoral stromal content. To date, the prognostic role of intratumoral stromal content in lobular breast cancer remains unclear. In this study, we aimed to investigate the prognostic importance of intratumoral stromal content in estrogen-receptor-positive lobular breast cancer. Our results show that high intratumoral stromal content is an easily assessed and clinically useful indicator of a good prognosis in lobular breast cancer. Abstract Previous studies have shown that high intratumoral stromal content is associated with a worse prognosis in breast cancer, especially in the triple-negative subtype. However, contradictory results have been reported for estrogen-receptor-positive (ER+) breast cancer, indicating that the prognostic role of intratumoral stromal content may be subtype-dependent. In this study, we investigated the importance of intratumoral stromal content for breast cancer-specific mortality (BCM) in a well-defined subgroup (n = 182) of ER+/human-epidermal growth-factor-receptor-2 negative (HER2−) invasive lobular breast cancer (ILC). The intratumoral stromal content was assessed on hematoxylin–eosin-stained whole sections and graded into high stroma (>50%) or low stroma (≤50%). A total of 82 (45%) patients had high-stroma tumors, and 100 (55%) had low-stroma tumors. High-stroma tumors were associated with a lower Nottingham histological grade, low Ki67, and a luminal A-like subtype. After a 10-year follow-up, the patients with high-stroma tumors had a lower BCM (HR: 0.43, 95% CI: 0.21–0.89, p = 0.023) in univariable analysis. Essentially the same effect was found in both the multivariable analysis (10-year follow-up) and univariable analysis (25-year follow-up), but these findings were not strictly significant. In ER+/HER2− ILC, high intratumoral stromal content is an easily assessable histological indicator of a good prognosis.
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Roles of the Immune/Methylation/Autophagy Landscape on Single-Cell Genotypes and Stroke Risk in Breast Cancer Microenvironment. OXIDATIVE MEDICINE AND CELLULAR LONGEVITY 2021; 2021:5633514. [PMID: 34457116 PMCID: PMC8397558 DOI: 10.1155/2021/5633514] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/05/2021] [Revised: 06/29/2021] [Accepted: 07/14/2021] [Indexed: 12/16/2022]
Abstract
This study sought to perform integrative analysis of the immune/methylation/autophagy landscape on breast cancer prognosis and single-cell genotypes. Breast Cancer Recurrence Risk Score (BCRRS) and Breast Cancer Prognostic Risk Score (BCPRS) were determined based on 6 prognostic IMAAGs obtained from the TCGA-BRCA cohort. BCRRS and BCPRS, respectively, were used to construct a risk prediction model of overall survival and progression-free survival. Predictive capacity of the model was evaluated using clinical data. Analysis showed that BCRRS is associated with a high risk of stroke. In addition, PPI and drug-ceRNA networks based on differences in BCPRS were constructed. Single cells were genotyped through integrated scRNA-seq of the TNBC samples based on clustering results of BCPRS-related genes. The findings of this study show the potential regulatory effects of IMAAGs on breast cancer tumor microenvironment. High AUCs of 0.856 and 0.842 were obtained for the OS and PFS prognostic models, respectively. scRNA-seq analysis showed high expression levels of adipocytes and adipose tissue macrophages (ATMs) in high BCPRS clusters. Moreover, analysis of ligand-receptor interactions and potential regulatory mechanisms were performed. The LINC00276&MALAT1/miR-206/FZD4-Wnt7b pathway was also identified which may be useful in future research on targets against breast cancer metastasis and recurrence. Neural network-based deep learning models using BCPRS-related genes showed that these genes can be used to map the tumor microenvironment. In summary, analysis of IMAAGs, BCPRS, and BCRRS provides information on the breast cancer microenvironment at both the macro- and microlevels and provides a basis for development of personalized treatment therapy.
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Strell C, Stenmark Tullberg A, Jetne Edelmann R, Akslen LA, Malmström P, Fernö M, Holmberg E, Östman A, Karlsson P. Prognostic and predictive impact of stroma cells defined by PDGFRb expression in early breast cancer: results from the randomized SweBCG91RT trial. Breast Cancer Res Treat 2021; 187:45-55. [PMID: 33661437 PMCID: PMC8062362 DOI: 10.1007/s10549-021-06136-4] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2020] [Accepted: 02/05/2021] [Indexed: 11/26/2022]
Abstract
Purpose Predictive biomarkers are needed to aid the individualization of radiotherapy (RT) in breast cancer. Cancer-associated fibroblasts have been implicated in tumor radioresistance and can be identified by platelet-derived growth factor receptor-beta (PDGFRb). This study aims to analyze how PDGFRb expression affects RT benefit in a large randomized RT trial. Methods PDGFRb was assessed by immunohistochemistry on tissue microarrays from 989 tumors of the SweBCG91RT trial, which enrolled lymph node-negative, stage I/IIA breast cancer patients randomized to RT after breast-conserving surgery. Outcomes were analyzed at 10 years for ipsilateral breast tumor recurrence (IBTR) and any recurrence and 15 years for breast cancer specific death (BCSD). Results PDGFRb expression correlated with estrogen receptor negativity and younger age. An increased risk for any recurrence was noted in univariable analysis for the medium (HR 1.58, CI 95% 1.11–2.23, p = 0.011) or PDGFRb high group (1.49, 1.06–2.10, p = 0.021) compared to the low group. No differences in IBTR or BCSD risk were detected. RT benefit regarding IBTR risk was significant in the PDGFRb low (0.29, 0.12–0.67, p = 0.004) and medium (0.31, 0.16–0.59, p < 0.001) groups but not the PDGFRb high group (0.64, 0.36–1.11, p = 0.110) in multivariable analysis. Likewise, risk reduction for any recurrence was less pronounced in the PDGFRb high group. No significant interaction between RT and PDGFRb-score could be detected. Conclusion A higher PDGFRb-score conferred an increased risk of any recurrence, which partly can be explained by its association with estrogen receptor negativity and young age. Reduced RT benefit was noted among patients with high PDGFRb, however without significant interaction.
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Affiliation(s)
- Carina Strell
- Department of Oncology-Pathology, Karolinska Institutet, Karolinska University Hospital, Stockholm, Sweden
| | - Axel Stenmark Tullberg
- Department of Oncology, Sahlgrenska University Hospital, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - Reidunn Jetne Edelmann
- Department of Clinical Medicine, Centre for Cancer Biomarkers CCBIO, University of Bergen, Bergen, Norway
| | - Lars Andreas Akslen
- Department of Clinical Medicine, Centre for Cancer Biomarkers CCBIO, University of Bergen, Bergen, Norway
| | - Per Malmström
- Division of Oncology and Pathology, Department of Clinical Sciences, Lund University, Lund, Sweden
- Department of Haematology, Oncology and Radiation Physics, Skåne University Hospital, Lund, Sweden
| | - Mårten Fernö
- Division of Oncology and Pathology, Department of Clinical Sciences, Lund University, Lund, Sweden
| | - Erik Holmberg
- Department of Oncology, Sahlgrenska University Hospital, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - Arne Östman
- Department of Oncology-Pathology, Karolinska Institutet, Karolinska University Hospital, Stockholm, Sweden
| | - Per Karlsson
- Department of Oncology, Sahlgrenska University Hospital, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden.
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Profiling of conditionally reprogrammed cell lines for in vitro chemotherapy response prediction of pancreatic cancer. EBioMedicine 2021; 65:103218. [PMID: 33639403 PMCID: PMC7921470 DOI: 10.1016/j.ebiom.2021.103218] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2020] [Revised: 01/05/2021] [Accepted: 01/07/2021] [Indexed: 12/15/2022] Open
Abstract
BACKGROUND The establishment of patient-derived models for pancreatic ductal adenocarcinoma (PDAC) using conventional methods has been fraught with low success rate, mainly because of the small number of tumour cells and dense fibrotic stroma. Here, we sought to establish patient-derived model of PDAC and perform genetic analysis with responses to anticancer drug by using the conditionally reprogrammed cell (CRC) methodology. METHODS We performed in vitro and in vivo tumourigenicity assays and analysed histological characteristics by immunostaining. We investigated genetic profiles including mutation patterns and copy number variations using targeted deep sequencing and copy-number analyses. We assessed the responses of cultured CRCs to the available clinical anticancer drugs based on patient responsiveness. FINDINGS We established a total of 28 CRCs from patients. Of the 28 samples, 27 showed KRAS mutations in codon 12/13 or codon 61. We found that somatic mutations were shared in the primary-CRC pairs and shared mutations included key oncogenic mutations such as KRAS (9 pairs), TP53 (8 pairs), and SMAD4 (3 pairs). Overall, CRCs preserved the genetic characteristics of primary tumours with high concordance, with additional confirmation of low-AF NPM1 mutation in CRC (35 shared mutations out of 36 total, concordance rate=97.2%). CRCs of the responder group were more sensitive to anticancer agents than those of the non-responder group (P < 0.001). INTERPRETATION These results show that a pancreatic cancer cell line model can be efficiently established using the CRC methodology, to better support a personalized therapeutic approach for pancreatic cancer patients. FUNDING 2014R1A1A1006272, HI19C0642-060019, 2019R1A2C2008050, 2020R1A2C209958611, and 2020M3E5E204028211.
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Tumour Stroma Ratio Assessment Using Digital Image Analysis Predicts Survival in Triple Negative and Luminal Breast Cancer. Cancers (Basel) 2020; 12:cancers12123749. [PMID: 33322174 PMCID: PMC7764351 DOI: 10.3390/cancers12123749] [Citation(s) in RCA: 38] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2020] [Revised: 12/09/2020] [Accepted: 12/11/2020] [Indexed: 12/16/2022] Open
Abstract
We aimed to determine the clinical significance of tumour stroma ratio (TSR) in luminal and triple negative breast cancer (TNBC) using digital image analysis and machine learning algorithms. Automated image analysis using QuPath software was applied to a cohort of 647 breast cancer patients (403 luminal and 244 TNBC) using digital H&E images of tissue microarrays (TMAs). Kaplan-Meier and Cox proportional hazards were used to ascertain relationships with overall survival (OS) and breast cancer specific survival (BCSS). For TNBC, low TSR (high stroma) was associated with poor prognosis for both OS (HR 1.9, CI 1.1-3.3, p = 0.021) and BCSS (HR 2.6, HR 1.3-5.4, p = 0.007) in multivariate models, independent of age, size, grade, sTILs, lymph nodal status and chemotherapy. However, for luminal tumours, low TSR (high stroma) was associated with a favourable prognosis in MVA for OS (HR 0.6, CI 0.4-0.8, p = 0.001) but not for BCSS. TSR is a prognostic factor of most significance in TNBC, but also in luminal breast cancer, and can be reliably assessed using quantitative image analysis of TMAs. Further investigation into the contribution of tumour subtype stromal phenotype may further refine these findings.
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12
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Heesch A, Maurer J, Stickeler E, Beheshti M, Mottaghy FM, Morgenroth A. Development of Radiotracers for Breast Cancer-The Tumor Microenvironment as an Emerging Target. Cells 2020; 9:cells9102334. [PMID: 33096754 PMCID: PMC7590199 DOI: 10.3390/cells9102334] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2020] [Revised: 10/19/2020] [Accepted: 10/19/2020] [Indexed: 12/17/2022] Open
Abstract
Molecular imaging plays an increasingly important role in the diagnosis and treatment of different malignancies. Radiolabeled probes enable the visualization of the primary tumor as well as the metastases and have been also employed in targeted therapy and theranostic approaches. With breast cancer being the most common malignancy in women worldwide it is of special interest to develop novel targeted treatments. However, tumor microenvironment and escape mechanisms often limit their therapeutic potential. Addressing tumor stroma associated targets provides a promising option to inhibit tumor growth and angiogenesis and to disrupt tumor tissue architecture. This review describes recent developments on radiolabeled probes used in diagnosis and treatment of breast cancer especially in triple negative type with the focus on potential targets offered by the tumor microenvironment, like tumor associated macrophages, cancer associated fibroblasts, and endothelial cells.
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Affiliation(s)
- Amelie Heesch
- Department of Nuclear Medicine, University Hospital Aachen, RWTH Aachen University, 52074 Aachen, Germany; (A.H.); (M.B.); (F.M.M.)
| | - Jochen Maurer
- Department of Obstetrics and Gynecology, University Hospital Aachen (UKA), 52074 Aachen, Germany; (J.M.); (E.S.)
| | - Elmar Stickeler
- Department of Obstetrics and Gynecology, University Hospital Aachen (UKA), 52074 Aachen, Germany; (J.M.); (E.S.)
| | - Mohsen Beheshti
- Department of Nuclear Medicine, University Hospital Aachen, RWTH Aachen University, 52074 Aachen, Germany; (A.H.); (M.B.); (F.M.M.)
- Department of Nuclear Medicine, University Hospital Salzburg, Paracelsus Medical University, 5020 Salzburg, Austria
| | - Felix M. Mottaghy
- Department of Nuclear Medicine, University Hospital Aachen, RWTH Aachen University, 52074 Aachen, Germany; (A.H.); (M.B.); (F.M.M.)
- Department of Radiology and Nuclear Medicine, Maastricht University Medical Center (MUMC+), 6202 Maastricht, The Netherlands
| | - Agnieszka Morgenroth
- Department of Nuclear Medicine, University Hospital Aachen, RWTH Aachen University, 52074 Aachen, Germany; (A.H.); (M.B.); (F.M.M.)
- Correspondence:
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13
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Becker LM, O'Connell JT, Vo AP, Cain MP, Tampe D, Bizarro L, Sugimoto H, McGow AK, Asara JM, Lovisa S, McAndrews KM, Zielinski R, Lorenzi PL, Zeisberg M, Raza S, LeBleu VS, Kalluri R. Epigenetic Reprogramming of Cancer-Associated Fibroblasts Deregulates Glucose Metabolism and Facilitates Progression of Breast Cancer. Cell Rep 2020; 31:107701. [PMID: 32492417 PMCID: PMC7339325 DOI: 10.1016/j.celrep.2020.107701] [Citation(s) in RCA: 147] [Impact Index Per Article: 36.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2019] [Revised: 12/03/2019] [Accepted: 05/06/2020] [Indexed: 01/09/2023] Open
Abstract
The mechanistic contributions of cancer-associated fibroblasts (CAFs) in breast cancer progression remain to be fully understood. While altered glucose metabolism in CAFs could fuel cancer cells, how such metabolic reprogramming emerges and is sustained needs further investigation. Studying fibroblasts isolated from patients with benign breast tissues and breast cancer, in conjunction with multiple animal models, we demonstrate that CAFs exhibit a metabolic shift toward lactate and pyruvate production and fuel biosynthetic pathways of cancer cells. The depletion or suppression of the lactate production of CAFs alter the tumor metabolic profile and impede tumor growth. The glycolytic phenotype of the CAFs is in part sustained through epigenetic reprogramming of HIF-1α and glycolytic enzymes. Hypoxia induces epigenetic reprogramming of normal fibroblasts, resulting in a pro-glycolytic, CAF-like transcriptome. Our findings suggest that the glucose metabolism of CAFs evolves during tumor progression, and their breast cancer-promoting phenotype is partly mediated by oxygen-dependent epigenetic modifications.
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Affiliation(s)
- Lisa M Becker
- Department of Cancer Biology, Metastasis Research Center, University of Texas MD Anderson Cancer Center, Houston, TX 77054, USA
| | - Joyce T O'Connell
- Division of Matrix Biology, Department of Medicine, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA 02215, USA
| | - Annie P Vo
- Division of Matrix Biology, Department of Medicine, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA 02215, USA
| | - Margo P Cain
- Department of Cancer Biology, Metastasis Research Center, University of Texas MD Anderson Cancer Center, Houston, TX 77054, USA
| | - Desiree Tampe
- Department of Nephrology and Rheumatology, Göttingen University Medical Center, Georg August University, Göttingen 37075, Germany
| | - Lauren Bizarro
- Department of Cancer Biology, Metastasis Research Center, University of Texas MD Anderson Cancer Center, Houston, TX 77054, USA; Division of Matrix Biology, Department of Medicine, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA 02215, USA
| | - Hikaru Sugimoto
- Department of Cancer Biology, Metastasis Research Center, University of Texas MD Anderson Cancer Center, Houston, TX 77054, USA; Division of Matrix Biology, Department of Medicine, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA 02215, USA
| | - Anna K McGow
- Department of Radiology, Brigham and Women's Hospital, Boston, MA 02215, USA
| | - John M Asara
- Division of Signal Transduction, Beth Israel Deaconess Medical Center, Boston, MA 02215, USA; Department of Medicine, Harvard Medical School, Boston, MA 02215, USA
| | - Sara Lovisa
- Department of Cancer Biology, Metastasis Research Center, University of Texas MD Anderson Cancer Center, Houston, TX 77054, USA
| | - Kathleen M McAndrews
- Department of Cancer Biology, Metastasis Research Center, University of Texas MD Anderson Cancer Center, Houston, TX 77054, USA
| | - Rafal Zielinski
- Department of Experimental Therapeutics, University of Texas MD Anderson Cancer Center, Houston, TX 77054, USA
| | - Philip L Lorenzi
- Metabolomics Core Facility, Department of Bioinformatics & Computational Biology, University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Michael Zeisberg
- Department of Nephrology and Rheumatology, Göttingen University Medical Center, Georg August University, Göttingen 37075, Germany
| | - Sughra Raza
- Department of Radiology, Brigham and Women's Hospital, Boston, MA 02215, USA
| | - Valerie S LeBleu
- Department of Cancer Biology, Metastasis Research Center, University of Texas MD Anderson Cancer Center, Houston, TX 77054, USA; Division of Matrix Biology, Department of Medicine, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA 02215, USA; Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA.
| | - Raghu Kalluri
- Department of Cancer Biology, Metastasis Research Center, University of Texas MD Anderson Cancer Center, Houston, TX 77054, USA; Division of Matrix Biology, Department of Medicine, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA 02215, USA; Department of Bioengineering, Rice University, Houston, TX 77030, USA; Department of Molecular and Cellular Biology, Baylor College of Medicine, Houston, TX 77030, USA.
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14
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Li B, Geng R, Wu Q, Yang Q, Sun S, Zhu S, Xu Z, Sun S. Alterations in Immune-Related Genes as Potential Marker of Prognosis in Breast Cancer. Front Oncol 2020; 10:333. [PMID: 32226776 PMCID: PMC7080956 DOI: 10.3389/fonc.2020.00333] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2019] [Accepted: 02/25/2020] [Indexed: 01/07/2023] Open
Abstract
The tumor microenvironment (TME) is a heterogeneous system that contributes to breast cancer progression. The Cancer Genome Atlas (TCGA) database provides global gene expression profiling data for further analysis of various malignancies, including breast cancer. Based on the ESTIMATE algorithm, immune and stromal scores were calculated according to immune or stromal components in the TME. We divided breast cancer cases into high- and low-score groups and identified differentially expressed genes (DEGs) that were significantly associated with overall survival. We performed enrichment analysis and constructed a protein-protein interaction network and found that the DEGs were mainly involved in primary immunodeficiency, T cell receptor signaling pathway and cytokine-cytokine receptor reaction. Furthermore, we explored the effect of aging on immune and stromal scores, which was validated by lower immune/stromal scores, lower infiltration of T cells and lower expression of immune checkpoints in the elder group. In conclusion, certain differentially expressed immune-related genes contribute to longer overall survival, and aging influences the immune microenvironment and immunotherapy efficacy by changing the tumor-infiltrating lymphocyte (TIL) abundance and checkpoint expression in breast cancer.
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Affiliation(s)
- Bei Li
- Department of Breast and Thyroid Surgery, Renmin Hospital of Wuhan University, Wuhan, China
| | - Rongxin Geng
- Department of Neurosurgery, Renmin Hospital of Wuhan University, Wuhan, China.,Brain Tumor Clinical Center of Wuhan, Wuhan, China
| | - Qi Wu
- Department of Breast and Thyroid Surgery, Renmin Hospital of Wuhan University, Wuhan, China.,Faculty of Medicine, University of Paris Sud-Saclay, Le Kremlin-Bicêtre, France
| | - Qian Yang
- Department of Breast and Thyroid Surgery, Renmin Hospital of Wuhan University, Wuhan, China
| | - Si Sun
- Department of Clinical Laboratory, Renmin Hospital of Wuhan University, Wuhan, China
| | - Shan Zhu
- Department of Breast and Thyroid Surgery, Renmin Hospital of Wuhan University, Wuhan, China
| | - Zhiliang Xu
- Department of Breast and Thyroid Surgery, Renmin Hospital of Wuhan University, Wuhan, China
| | - Shengrong Sun
- Department of Breast and Thyroid Surgery, Renmin Hospital of Wuhan University, Wuhan, China
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15
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Xu M, Li Y, Li W, Zhao Q, Zhang Q, Le K, Huang Z, Yi P. Immune and Stroma Related Genes in Breast Cancer: A Comprehensive Analysis of Tumor Microenvironment Based on the Cancer Genome Atlas (TCGA) Database. Front Med (Lausanne) 2020; 7:64. [PMID: 32195260 PMCID: PMC7066229 DOI: 10.3389/fmed.2020.00064] [Citation(s) in RCA: 42] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2019] [Accepted: 02/12/2020] [Indexed: 01/02/2023] Open
Abstract
Background: Tumor microenvironment is essential for breast cancer progression and metastasis. Our study sets out to examine the genes affecting stromal and immune infiltration in breast cancer progression and prognosis. Materials and Methods: This work provides an approach for quantifying stromal and immune scores by using ESTIMATE algorithm based on gene expression matrix of breast cancer patients in TCGA database. We found differentially expressed genes (DEGs) through limma R package. Functional enrichments were accessed through Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis. Besides, we constructed a protein-protein network, identified several hub genes in Cytoscape, and discovered functionally similar genes in GeneMANIA. Hub genes were validated with prognostic data by Kaplan-Meier analysis both in The Cancer Genome Atlas (TCGA) database and Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) database and a meta-analysis of hub genes prognosis data was utilized in multiple databases. Furthermore, their relationship with infiltrating immune cells was evaluated by Tumor IMmune Estimation Resource (TIMER) web tool. Cox regression was utilized for overall survival (OS) and recurrence-free survival (RFS) in TCGA database and OS in METABRIC database in order to evaluate the impact of stromal and immune scores on patients prognosis. Results: One thousand and eighty-five breast cancer patients were investigated and 480 differentiated expressed genes (DEGs) were found based on the analysis of mRNA expression profiles. Functional analysis of DEGs revealed their potential functions in immune response and extracellular interaction. Protein-protein interaction network gave evidence of 10 hub genes. Some of the hub genes could be used as predictive markers for patients prognosis. In this study, we found that tumor purity and specific immune cells infiltration varied in response to hub genes expression. The multivariate cox regression highlighted the fact that immune score played a detrimental role in overall survival (HR = 0.45, 95% CI: 0.27–0.74, p = 0.002) and recurrence-free survival (HR = 0.41, 95% CI: 0.22–0.77, p = 0.006) in TCGA database. These result was confirmed in METABRIC database that immune score was a protector of OS (HR = 0.88, 95% CI: 0.77–0.99, p = 0.039). Conclusions: Our findings promote a better understanding of the potential genes behind the regulation of tumor microenvironment and cells infiltration. Immune score should be considered as a prognostic factor for patients' survival.
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Affiliation(s)
- Ming Xu
- Department of Breast and Thyroid Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Yu Li
- Department of Breast and Thyroid Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Wenhui Li
- Department of Breast and Thyroid Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Qiuyang Zhao
- Department of Breast and Thyroid Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Qiulei Zhang
- Department of Breast and Thyroid Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Kehao Le
- Department of Breast and Thyroid Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Ziwei Huang
- Department of Breast and Thyroid Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Pengfei Yi
- Department of Breast and Thyroid Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
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16
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Sonnenblick A, Salmon-Divon M, Salgado R, Dvash E, Pondé N, Zahavi T, Salmon A, Loibl S, Denkert C, Joensuu H, Ameye L, Van den Eynden G, Kellokumpu-Lehtinen PL, Azaria A, Loi S, Michiels S, Richard F, Sotiriou C. Reactive stroma and trastuzumab resistance in HER2-positive early breast cancer. Int J Cancer 2020; 147:266-276. [PMID: 31904863 DOI: 10.1002/ijc.32859] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2019] [Revised: 11/25/2019] [Accepted: 11/26/2019] [Indexed: 12/21/2022]
Abstract
We investigated the value of reactive stroma as a predictor for trastuzumab resistance in patients with early HER2-positive breast cancer receiving adjuvant therapy. The pathological reactive stroma and the mRNA gene signatures that reflect reactive stroma in 209 HER2-positive breast cancer samples from the FinHer adjuvant trial were evaluated. Levels of stromal gene signatures were determined as a continuous parameter, and pathological reactive stromal findings were defined as stromal predominant breast cancer (SPBC; ≥50% stromal) and correlated with distant disease-free survival. Gene signatures associated with reactive stroma in HER2-positive early breast cancer (N = 209) were significantly associated with trastuzumab resistance in estrogen receptor (ER)-negative tumors (hazard ratio [HR] = 1.27 p interaction = 0.014 [DCN], HR = 1.58, p interaction = 0.027 [PLAU], HR = 1.71, p interaction = 0.019 [HER2STROMA, novel HER2 stromal signature]), but not in ER-positive tumors (HR = 0.73 p interaction = 0.47 [DCN], HR = 0.71, p interaction = 0.73 [PLAU], HR = 0.84; p interaction = 0.36 [HER2STROMA]). Pathological evaluation of HER2-positive/ER-negative tumors suggested an association between SPBC and trastuzumab resistance. Reactive stroma did not correlate with tumor-infiltrating lymphocytes (TILs), and the expected benefit from trastuzumab in patients with high levels of TILs was pronounced only in tumors with low stromal reactivity (SPBC <50%). In conclusion, reactive stroma in HER2-positive/ER-negative early breast cancer tumors may predict resistance to adjuvant trastuzumab therapy.
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Affiliation(s)
- Amir Sonnenblick
- Institute of Oncology, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel.,Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Mali Salmon-Divon
- Department of Molecular Biology, Adelson School of Medicine, Ariel University, Ariel, Israel
| | - Roberto Salgado
- Department of Pathology, GZA-ZNA, Antwerp, Belgium.,Division of Research, Peter Mac Callum Cancer Center, Melbourne, Australia
| | - Efrat Dvash
- Institute of Oncology, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel.,Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Noam Pondé
- Institut Jules Bordet, Université Libre de Bruxelles, Brussels, Belgium.,Medical Oncology Department, AC Camargo Cancer Center, São Paulo, Brazil
| | - Tamar Zahavi
- Sharett Institute of Oncology, Hadassah Hebrew University Medical Center, Jerusalem, Israel
| | - Asher Salmon
- Sharett Institute of Oncology, Hadassah Hebrew University Medical Center, Jerusalem, Israel
| | - Sibylle Loibl
- German Breast Group, Neu-Isenburg and Goethe University Frankfurt and Centre for Haematology and Oncology, Bethanien, Frankfurt, Germany
| | - Carsten Denkert
- Institute of Pathology, Philipps-University Marburg and UKGM Marburg, Marburg, Germany
| | - Heikki Joensuu
- Department of Oncology, Helsinki University Hospital and University of Helsinki, Helsinki, Finland
| | - Lieveke Ameye
- Data Management Unit, Institut Jules Bordet, Université Libre de Bruxelles, Belgium
| | - Gert Van den Eynden
- Molecular Immunology Lab, Institut Jules Bordet, Université Libre de Bruxelles, Brussels, Belgium
| | | | - Amos Azaria
- Department of Computer Science, Ariel University, Ariel, Israel
| | - Sherene Loi
- Peter MacCallum Cancer Centre, University of Melbourne, Parkville, Victoria, Australia
| | - Stefan Michiels
- Service de Biostatistique et d'Epidémiologie, Gustave Roussy, CESP U108, University Paris-Sud, University Paris-Saclay, Villejuif, France
| | - François Richard
- Laboratory for Translational Breast Cancer Research, Department of Oncology, KU Leuven, Leuven, Belgium
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17
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Venanzi FM, Gabai V, Mariotti F, Magi GE, Vullo C, Sufianov AA, Kolesnikov SI, Shneider A. p62-DNA-encoding plasmid reverts tumor grade, changes tumor stroma, and enhances anticancer immunity. Aging (Albany NY) 2019; 11:10711-10722. [PMID: 31754084 PMCID: PMC6914433 DOI: 10.18632/aging.102486] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2019] [Accepted: 11/08/2019] [Indexed: 12/31/2022]
Abstract
Previously, we reported that the administration of a p62/SQSTM1-encoding plasmid demonstrates high safety and signs of clinical benefits for human cancer patients. The treatment also suppressed tumor growth and metastasis in dogs and mouse models. Here we investigated some mechanistic aspects of these effects. In mammary tumors bearing-dogs, i.m. injections of p62 plasmid reduced tumor sizes and their aggressive potential in 5 out of 6 animals, with one carcinoma switching to adenoma. The treatment increased levels of smooth muscle actin in stroma cells and type III collagen in the extracellular matrix, which correlate with a good clinical prognosis. The p62 treatment also increased the abundance of intratumoral T-cells. Because of the role of adaptive immunity cannot be tested in dogs, we compared the protective effects of the p62 plasmid against B16 melanoma in wild type C57BL/6J mice versus their SCID counterpart lacking lymphocytes. The plasmid was only protective in the wild type strain. Also, p62 plasmid amplified the anti-tumor effect of T-cell transfer from tumor-bearing animals to animals challenged with the same tumors. We conclude that the plasmid acts via re-modeling of the tumor microenvironment, making it more favorable for increased anti-cancer immunity. Thus, the p62-encoding plasmid might be a new adjuvant for cancer treatments.
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Affiliation(s)
- Franco M. Venanzi
- Sechenov First Moscow State Medical University, Moscow, Russia
- CureLab Oncology, Inc, Deadham, MA 02026, USA
| | - Vladimir Gabai
- CureLab Oncology, Inc, Deadham, MA 02026, USA
- Department of Biochemistry, Boston University School of Medicine, Boston, MA 02118, USA
| | - Francesca Mariotti
- School of Biosciences and Veterinary Medicine, University of Camerino, Camerino, Italy
| | - Gian Enrico Magi
- School of Biosciences and Veterinary Medicine, University of Camerino, Camerino, Italy
| | - Cecilia Vullo
- School of Biosciences and Veterinary Medicine, University of Camerino, Camerino, Italy
| | - Albert A. Sufianov
- Sechenov First Moscow State Medical University, Moscow, Russia
- Federal Center of Neurosurgery, Tyumen, Russia
| | - Sergey I. Kolesnikov
- Russian Academy of Sciences, Moscow, Russia
- Lomonosov Moscow State University, Moscow, Russia
- Research Center of Family Health and Reproduction Problems, Irkutsk, Russia
| | - Alexander Shneider
- Sechenov First Moscow State Medical University, Moscow, Russia
- CureLab Oncology, Inc, Deadham, MA 02026, USA
- Department of Molecular Biology, Ariel University, Ariel, Israel
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18
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Breast cancer quantitative proteome and proteogenomic landscape. Nat Commun 2019; 10:1600. [PMID: 30962452 PMCID: PMC6453966 DOI: 10.1038/s41467-019-09018-y] [Citation(s) in RCA: 136] [Impact Index Per Article: 27.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2018] [Accepted: 02/14/2019] [Indexed: 02/07/2023] Open
Abstract
In the preceding decades, molecular characterization has revolutionized breast cancer (BC) research and therapeutic approaches. Presented herein, an unbiased analysis of breast tumor proteomes, inclusive of 9995 proteins quantified across all tumors, for the first time recapitulates BC subtypes. Additionally, poor-prognosis basal-like and luminal B tumors are further subdivided by immune component infiltration, suggesting the current classification is incomplete. Proteome-based networks distinguish functional protein modules for breast tumor groups, with co-expression of EGFR and MET marking ductal carcinoma in situ regions of normal-like tumors and lending to a more accurate classification of this poorly defined subtype. Genes included within prognostic mRNA panels have significantly higher than average mRNA-protein correlations, and gene copy number alterations are dampened at the protein-level; underscoring the value of proteome quantification for prognostication and phenotypic classification. Furthermore, protein products mapping to non-coding genomic regions are identified; highlighting a potential new class of tumor-specific immunotherapeutic targets. Gene expression profiles can classify breast cancer into five clinically relevant subtypes. Here, the authors perform an in-depth quantitative profiling of the proteome of 45 breast tumors, and show they can recapitulate the transcriptome-based classifications and identify many potentially antigenic tumour-specific peptides.
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19
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Role of protein phosphatases in the cancer microenvironment. BIOCHIMICA ET BIOPHYSICA ACTA-MOLECULAR CELL RESEARCH 2019; 1866:144-152. [DOI: 10.1016/j.bbamcr.2018.07.006] [Citation(s) in RCA: 29] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/22/2018] [Revised: 06/29/2018] [Accepted: 07/11/2018] [Indexed: 12/15/2022]
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20
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Wang Y, Lu S, Xiong J, Singh K, Hui Y, Zhao C, Brodsky AS, Yang D, Jolly G, Ouseph M, Schorl C, DeLellis RA, Resnick MB. ColXα1 is a stromal component that colocalizes with elastin in the breast tumor extracellular matrix. JOURNAL OF PATHOLOGY CLINICAL RESEARCH 2018; 5:40-52. [PMID: 30207088 PMCID: PMC6317058 DOI: 10.1002/cjp2.115] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/12/2018] [Revised: 09/05/2018] [Accepted: 09/07/2018] [Indexed: 12/21/2022]
Abstract
The tumor microenvironment regulates tissue development and homeostasis, and its dysregulation contributes to neoplastic progression. Increased expression of type X collagen α‐1 (ColXα1) in tumor‐associated stroma correlates with poor pathologic response to neoadjuvant chemotherapy in estrogen receptor (ER) and human epidermal growth factor receptor 2 (HER2)‐positive breast cancers. Evaluation of ColXα1 expression patterns suggests a potential connection with elastin fibers. To investigate the possible interaction between ColXα1 and elastin, we evaluated the expression of ColXα1 in relation to elastin fibers in normal breast tissue, ductal carcinoma in situ, and invasive breast carcinomas at cellular and subcellular levels. Our findings demonstrate that ColXα1 colocalizes with elastin in invasive breast cancer‐associated stroma by immunohistochemistry, immunofluorescence, and electron microscopy. In 212 invasive breast carcinomas, this complex was aberrantly and selectively expressed in tumor extracellular matrix in 79% of ER+/HER2−, 80% of ER+/HER2+, 76% of ER−/HER2+, and 58% of triple negative breast cancers. In contrast, ColXα1 was generally absent, while elastin was present perivascularly in normal breast tissue. ColXα1 and elastin were coexpressed in 58% of ductal carcinoma in situ (DCIS) in periductal areas. In mass‐forming DCIS with desmoplastic stroma, the complex was intensely expressed in periductal areas as well as within the tumor‐associated stroma in all cases. Our data suggest that the breast carcinoma neoplastic process may involve aberrant expression of ColXα1 and elastin in the tumor microenvironment emerging early at the DCIS stage. Enrichment of these complexes in tumor‐associated stroma may represent a stromal signature indicative of intrinsic differences between breast cancers. These findings shed light on investigation into the role of aberrant collagen complex expression in tumorigenesis and tumor progression which may be leveraged in therapeutic and theranostic applications.
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Affiliation(s)
- Yihong Wang
- Department of Pathology and Laboratory Medicine, Rhode Island Hospital and Lifespan Medical Center, Warren Alpert Medical School of Brown University, Providence, RI, USA
| | - Shaolei Lu
- Department of Pathology and Laboratory Medicine, Rhode Island Hospital and Lifespan Medical Center, Warren Alpert Medical School of Brown University, Providence, RI, USA
| | - Jinjun Xiong
- Department of Pathology, Women and Infants Hospital, Warren Alpert Medical School of Brown University, Providence, RI, USA
| | - Kamaljeet Singh
- Department of Pathology, Women and Infants Hospital, Warren Alpert Medical School of Brown University, Providence, RI, USA
| | - Yiang Hui
- Department of Pathology and Laboratory Medicine, Rhode Island Hospital and Lifespan Medical Center, Warren Alpert Medical School of Brown University, Providence, RI, USA
| | - Chaohui Zhao
- Department of Pathology and Laboratory Medicine, Rhode Island Hospital and Lifespan Medical Center, Warren Alpert Medical School of Brown University, Providence, RI, USA
| | - Alexander S Brodsky
- Department of Pathology and Laboratory Medicine, Rhode Island Hospital and Lifespan Medical Center, Warren Alpert Medical School of Brown University, Providence, RI, USA
| | - Dongfang Yang
- Department of Pathology and Laboratory Medicine, Rhode Island Hospital and Lifespan Medical Center, Warren Alpert Medical School of Brown University, Providence, RI, USA
| | - Grant Jolly
- Department of Pathology and Laboratory Medicine, Rhode Island Hospital and Lifespan Medical Center, Warren Alpert Medical School of Brown University, Providence, RI, USA
| | - Madhu Ouseph
- Department of Pathology and Laboratory Medicine, Rhode Island Hospital and Lifespan Medical Center, Warren Alpert Medical School of Brown University, Providence, RI, USA
| | - Christoph Schorl
- Molecular Biology, Cell Biology, and Biochemistry, Brown University, Providence, RI, USA
| | - Ronald A DeLellis
- Department of Pathology and Laboratory Medicine, Rhode Island Hospital and Lifespan Medical Center, Warren Alpert Medical School of Brown University, Providence, RI, USA
| | - Murray B Resnick
- Department of Pathology and Laboratory Medicine, Rhode Island Hospital and Lifespan Medical Center, Warren Alpert Medical School of Brown University, Providence, RI, USA
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21
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Fleming JM, Yeyeodu ST, McLaughlin A, Schuman D, Taylor DK. In Situ Drug Delivery to Breast Cancer-Associated Extracellular Matrix. ACS Chem Biol 2018; 13:2825-2840. [PMID: 30183254 DOI: 10.1021/acschembio.8b00396] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
The extracellular matrix (ECM) contributes to tumor progression through changes induced by tumor and stromal cell signals that promote increased ECM density and stiffness. The increase in ECM stiffness is known to promote tumor cell invasion into surrounding tissues and metastasis. In addition, this scar-like ECM creates a protective barrier around the tumor that reduces the effectiveness of innate and synthetic antitumor agents. Herein, clinically approved breast cancer therapies as well as novel experimental approaches that target the ECM are discussed, including in situ hydrogel drug delivery systems, an emerging technology the delivers toxic chemotherapeutics, gene-silencing microRNAs, and tumor suppressing immune cells directly inside the tumor. Intratumor delivery of therapeutic agents has the potential to drastically reduce systemic side effects experienced by the patient and increase the efficacy of these agents. This review also describes the opposing effects of ECM degradation on tumor progression, where some studies report improved drug delivery and delayed cancer progression and others report enhanced metastasis and decreased patient survival. Given the recent increase in ECM-targeting drugs entering preclinical and clinical trials, understanding and addressing the factors that impact the effect of the ECM on tumor progression is imperative for the sake of patient safety and survival outcome.
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Affiliation(s)
- Jodie M. Fleming
- Department of Biological and Biomedical Sciences, North Carolina Central University, Durham, North Carolina, United States
| | - Susan T. Yeyeodu
- Charles River Discovery Services, Morrisville, North Carolina, United States
| | - Ashley McLaughlin
- Department of Biological and Biomedical Sciences, North Carolina Central University, Durham, North Carolina, United States
| | - Darren Schuman
- Department of Chemistry and Biochemistry, North Carolina Central University, Durham, North Carolina, United States
| | - Darlene K. Taylor
- Department of Chemistry and Biochemistry, North Carolina Central University, Durham, North Carolina, United States
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22
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An Y, Adams JR, Hollern DP, Zhao A, Chang SG, Gams MS, Chung PED, He X, Jangra R, Shah JS, Yang J, Beck LA, Raghuram N, Kozma KJ, Loch AJ, Wang W, Fan C, Done SJ, Zacksenhaus E, Guidos CJ, Perou CM, Egan SE. Cdh1 and Pik3ca Mutations Cooperate to Induce Immune-Related Invasive Lobular Carcinoma of the Breast. Cell Rep 2018; 25:702-714.e6. [PMID: 30332649 PMCID: PMC6276789 DOI: 10.1016/j.celrep.2018.09.056] [Citation(s) in RCA: 36] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2018] [Revised: 06/15/2018] [Accepted: 09/17/2018] [Indexed: 11/20/2022] Open
Abstract
CDH1 and PIK3CA are the two most frequently mutated genes in invasive lobular carcinoma (ILC) of the breast. Transcription profiling has identified molecular subtypes for ILC, one of which, immune-related (IR), is associated with gene expression linked to lymphocyte and macrophage infiltration. Here, we report that deletion of Cdh1, together with activation of Pik3ca in mammary epithelium of genetically modified mice, leads to formation of IR-ILC-like tumors with immune cell infiltration, as well as gene expression linked to T-regulatory (Treg) cell signaling and activation of targetable immune checkpoint pathways. Interestingly, these tumors show enhanced Rac1- and Yap-dependent transcription and signaling, as well as sensitivity to PI3K, Rac1, and Yap inhibitors in culture. Finally, high-dimensional immunophenotyping in control mouse mammary gland and IR-ILC tumors by mass cytometry shows dramatic alterations in myeloid and lymphoid populations associated with immune suppression and exhaustion, highlighting the potential for therapeutic intervention via immune checkpoint regulators.
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Affiliation(s)
- Yeji An
- Program in Cell Biology, The Peter Gilgan Center for Research and Learning, The Hospital for Sick Children, Toronto, ON M5G-0A4, Canada; Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada
| | - Jessica R Adams
- Program in Cell Biology, The Peter Gilgan Center for Research and Learning, The Hospital for Sick Children, Toronto, ON M5G-0A4, Canada
| | - Daniel P Hollern
- Lineberger Comprehensive Cancer Center, Departments of Genetics and Pathology, University of North Carolina, Chapel Hill, NC 27599, USA
| | - Anthony Zhao
- Program in Developmental and Stem Cell Biology, The Peter Gilgan Center for Research and Learning, The Hospital for Sick Children, Toronto, ON M5G-0A4, Canada; Department of Immunology, University of Toronto, Toronto, ON, Canada
| | - Stephen G Chang
- Program in Developmental and Stem Cell Biology, The Peter Gilgan Center for Research and Learning, The Hospital for Sick Children, Toronto, ON M5G-0A4, Canada; Department of Immunology, University of Toronto, Toronto, ON, Canada
| | - Miki S Gams
- Program in Developmental and Stem Cell Biology, The Peter Gilgan Center for Research and Learning, The Hospital for Sick Children, Toronto, ON M5G-0A4, Canada; Department of Immunology, University of Toronto, Toronto, ON, Canada
| | - Philip E D Chung
- Division of Cell and Molecular Biology, Toronto General Research Institute, University Health Network, and Department of Medicine, University of Toronto, Toronto, ON, Canada; Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, ON, Canada
| | - Xiaping He
- Lineberger Comprehensive Cancer Center, Departments of Genetics and Pathology, University of North Carolina, Chapel Hill, NC 27599, USA
| | - Rhea Jangra
- Program in Cell Biology, The Peter Gilgan Center for Research and Learning, The Hospital for Sick Children, Toronto, ON M5G-0A4, Canada
| | - Juhi S Shah
- Program in Cell Biology, The Peter Gilgan Center for Research and Learning, The Hospital for Sick Children, Toronto, ON M5G-0A4, Canada
| | - Joanna Yang
- Program in Cell Biology, The Peter Gilgan Center for Research and Learning, The Hospital for Sick Children, Toronto, ON M5G-0A4, Canada
| | - Lauren A Beck
- Program in Cell Biology, The Peter Gilgan Center for Research and Learning, The Hospital for Sick Children, Toronto, ON M5G-0A4, Canada
| | - Nandini Raghuram
- Program in Cell Biology, The Peter Gilgan Center for Research and Learning, The Hospital for Sick Children, Toronto, ON M5G-0A4, Canada; Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada
| | - Katelyn J Kozma
- Program in Cell Biology, The Peter Gilgan Center for Research and Learning, The Hospital for Sick Children, Toronto, ON M5G-0A4, Canada; Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada
| | - Amanda J Loch
- Program in Cell Biology, The Peter Gilgan Center for Research and Learning, The Hospital for Sick Children, Toronto, ON M5G-0A4, Canada
| | - Wei Wang
- Program in Cell Biology, The Peter Gilgan Center for Research and Learning, The Hospital for Sick Children, Toronto, ON M5G-0A4, Canada
| | - Cheng Fan
- Lineberger Comprehensive Cancer Center, Departments of Genetics and Pathology, University of North Carolina, Chapel Hill, NC 27599, USA
| | - Susan J Done
- Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, ON, Canada; Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada; The Campbell Family Institute for Breast Cancer Research at the Princess Margaret Cancer Centre and The Laboratory Medicine Program, University Health Network, Toronto, ON, Canada
| | - Eldad Zacksenhaus
- Division of Cell and Molecular Biology, Toronto General Research Institute, University Health Network, and Department of Medicine, University of Toronto, Toronto, ON, Canada; Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, ON, Canada
| | - Cynthia J Guidos
- Program in Developmental and Stem Cell Biology, The Peter Gilgan Center for Research and Learning, The Hospital for Sick Children, Toronto, ON M5G-0A4, Canada; Department of Immunology, University of Toronto, Toronto, ON, Canada
| | - Charles M Perou
- Lineberger Comprehensive Cancer Center, Departments of Genetics and Pathology, University of North Carolina, Chapel Hill, NC 27599, USA.
| | - Sean E Egan
- Program in Cell Biology, The Peter Gilgan Center for Research and Learning, The Hospital for Sick Children, Toronto, ON M5G-0A4, Canada; Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada.
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23
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Li J, Wang W, Xia P, Wan L, Zhang L, Yu L, Wang L, Chen X, Xiao Y, Xu C. Identification of a five-lncRNA signature for predicting the risk of tumor recurrence in patients with breast cancer. Int J Cancer 2018; 143:2150-2160. [PMID: 29707762 DOI: 10.1002/ijc.31573] [Citation(s) in RCA: 57] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2017] [Revised: 04/20/2018] [Accepted: 04/23/2018] [Indexed: 01/03/2023]
Abstract
Long non-coding RNAs (lncRNAs) are a major class of non-coding RNAs, and the functional deregulations of lncRNAs have been shown to be associated with the development and progression of BC. In this work, we conduct an integrative analysis on five re-annotated lncRNA expression datasets from the Gene Expression Omnibus (GEO) which included a total of 891 BC samples. We identified a five-lncRNA signature that was significantly associated with DFS in the training cohort of 327 patients. We found the five-lncRNA signature could effectively stratify patients in the training dataset into high- and low-risk groups with significantly different DFS (p = 3.29 × 10-5 , log-rank test). The five-lncRNA signature was effectively validated in four independent cohorts, and prognostic analysis results showed that the five-lncRNA signature was independent of clinical prognostic factors, such as BC subtypes and adjuvant treatments. Furthermore, GSEA suggested that the five-lncRNA signature was involved in BC metastasis-related pathways. Our findings indicate that these five lncRNAs may be implicated in BC pathogenesis, and further, these lncRNAs may potentially serve as novel candidate biomarkers for the identification of BC patients at high risk for tumor recurrence.
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Affiliation(s)
- Jie Li
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang, China
| | - Weida Wang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang, China
| | - Peng Xia
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang, China
| | - Linyun Wan
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang, China
| | - Li Zhang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang, China
| | - Lei Yu
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang, China
| | - Lily Wang
- Division of Biostatistics, Department of Public Health Sciences, University of Miami Miller School of Medicine, Miami, FL.,University of Miami Miller School of Medicine, Sylvester Comprehensive Cancer Center, Miami, FL
| | - Xi Chen
- Division of Biostatistics, Department of Public Health Sciences, University of Miami Miller School of Medicine, Miami, FL.,University of Miami Miller School of Medicine, Sylvester Comprehensive Cancer Center, Miami, FL
| | - Yun Xiao
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang, China.,Key Laboratory of Cardiovascular Medicine Research, Harbin Medical University, Ministry of Education, China
| | - Chaohan Xu
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang, China
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Abstract
Motivation In many applications, inter-sample heterogeneity is crucial to understanding the complex biological processes under study. For example, in genomic analysis of cancers, each patient in a cohort may have a different driver mutation, making it difficult or impossible to identify causal mutations from an averaged view of the entire cohort. Unfortunately, many traditional methods for genomic analysis seek to estimate a single model which is shared by all samples in a population, ignoring this inter-sample heterogeneity entirely. In order to better understand patient heterogeneity, it is necessary to develop practical, personalized statistical models. Results To uncover this inter-sample heterogeneity, we propose a novel regularizer for achieving patient-specific personalized estimation. This regularizer operates by learning two latent distance metrics-one between personalized parameters and one between clinical covariates-and attempting to match the induced distances as closely as possible. Crucially, we do not assume these distance metrics are already known. Instead, we allow the data to dictate the structure of these latent distance metrics. Finally, we apply our method to learn patient-specific, interpretable models for a pan-cancer gene expression dataset containing samples from more than 30 distinct cancer types and find strong evidence of personalization effects between cancer types as well as between individuals. Our analysis uncovers sample-specific aberrations that are overlooked by population-level methods, suggesting a promising new path for precision analysis of complex diseases such as cancer. Availability and implementation Software for personalized linear and personalized logistic regression, along with code to reproduce experimental results, is freely available at github.com/blengerich/personalized_regression.
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Affiliation(s)
| | - Bryon Aragam
- Machine Learning Department, Carnegie Mellon University, Pittsburgh, PA, USA
| | - Eric P Xing
- Computer Science Department, Carnegie Mellon University, Pittsburgh, PA, USA
- Machine Learning Department, Carnegie Mellon University, Pittsburgh, PA, USA
- Petuum, Inc., Pittsburgh, PA, USA
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25
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Clarke CN, Lee MS, Wei W, Manyam G, Jiang ZQ, Lu Y, Morris J, Broom B, Menter D, Vilar-Sanchez E, Raghav K, Eng C, Chang GJ, Simon I, Bernards R, Overman M, Mills GB, Maru D, Kopetz S. Proteomic Features of Colorectal Cancer Identify Tumor Subtypes Independent of Oncogenic Mutations and Independently Predict Relapse-Free Survival. Ann Surg Oncol 2017; 24:4051-4058. [PMID: 28936799 PMCID: PMC6063735 DOI: 10.1245/s10434-017-6054-5] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2017] [Indexed: 01/07/2023]
Abstract
BACKGROUND The directed study of the functional proteome in colorectal cancer (CRC) has identified critical protein markers and signaling pathways; however, the prognostic relevance of many of these proteins remains unclear. METHODS We determined the prognostic implications of the functional proteome in 263 CRC tumor samples from patients treated at MD Anderson Cancer Center (MDACC) and 462 patients from The Cancer Genome Atlas (TCGA) to identify patterns of protein expression that drive tumorigenesis. A total of 163 validated proteins were analyzed by reverse phase protein array (RPPA). Unsupervised hierarchical clustering of the tumor proteins from the MDACC cohort was performed, and clustering was validated using RPPA data from TCGA CRC. Cox regression was used to identify predictors of tumor recurrence. RESULTS Clustering revealed dichotomization, with subtype A notable for a high epithelial-mesenchymal transition (EMT) protein signature, while subtype B was notable for high Akt/TSC/mTOR pathway components. Survival data were only available for the MDACC cohort and were used to evaluate prognostic relevance of these protein signatures. Group B demonstrated worse relapse-free survival (hazard ratio 2.11, 95% confidence interval 1.04-4.27, p = 0.039), although there was no difference in known genomic drivers between the two proteomic groups. Proteomic grouping and stage were significant predictors of recurrence on multivariate analysis. Eight proteins were found to be significant predictors of tumor recurrence on multivariate analysis: Collagen VI, FOXO3a, INPP4B, LcK, phospho-PEA15, phospho-PRAS40, Rad51, phospho-S6. CONCLUSION CRC can be classified into distinct subtypes by proteomic features independent of common oncogenic driver mutations. Proteomic analysis has identified key biomarkers with prognostic importance, however these findings require further validation in an independent cohort.
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Affiliation(s)
- Callisia N Clarke
- Division of Surgical Oncology, Department of Surgery, Medical College of Wisconsin, Milwaukee, WI, USA.
| | - Michael S Lee
- Division of Hematology and Oncology, University of North Carolina, Chapel Hill, NC, USA
| | - Wei Wei
- Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Ganiraju Manyam
- Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Zhi-Qin Jiang
- Department of GI Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Yiling Lu
- Department of Systems Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Jeffrey Morris
- Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Bradley Broom
- Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - David Menter
- Department of GI Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Eduardo Vilar-Sanchez
- Department of Clinical Cancer Prevention, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Kanwal Raghav
- Department of GI Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Cathy Eng
- Department of GI Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - George J Chang
- Department of Surgical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | | | - Rene Bernards
- Division of Molecular Carcinogenesis, Cancer Systems Biology Centre and Cancer Genomics Centre, The Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Michael Overman
- Department of GI Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Gordon B Mills
- Department of Systems Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Dipen Maru
- Department of Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Scott Kopetz
- Department of GI Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
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26
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Robertson AG, Kim J, Al-Ahmadie H, Bellmunt J, Guo G, Cherniack AD, Hinoue T, Laird PW, Hoadley KA, Akbani R, Castro MAA, Gibb EA, Kanchi RS, Gordenin DA, Shukla SA, Sanchez-Vega F, Hansel DE, Czerniak BA, Reuter VE, Su X, de Sa Carvalho B, Chagas VS, Mungall KL, Sadeghi S, Pedamallu CS, Lu Y, Klimczak LJ, Zhang J, Choo C, Ojesina AI, Bullman S, Leraas KM, Lichtenberg TM, Wu CJ, Schultz N, Getz G, Meyerson M, Mills GB, McConkey DJ, Weinstein JN, Kwiatkowski DJ, Lerner SP. Comprehensive Molecular Characterization of Muscle-Invasive Bladder Cancer. Cell 2017; 171:540-556.e25. [PMID: 28988769 PMCID: PMC5687509 DOI: 10.1016/j.cell.2017.09.007] [Citation(s) in RCA: 1420] [Impact Index Per Article: 202.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2017] [Revised: 06/30/2017] [Accepted: 09/06/2017] [Indexed: 12/20/2022]
Abstract
We report a comprehensive analysis of 412 muscle-invasive bladder cancers characterized by multiple TCGA analytical platforms. Fifty-eight genes were significantly mutated, and the overall mutational load was associated with APOBEC-signature mutagenesis. Clustering by mutation signature identified a high-mutation subset with 75% 5-year survival. mRNA expression clustering refined prior clustering analyses and identified a poor-survival "neuronal" subtype in which the majority of tumors lacked small cell or neuroendocrine histology. Clustering by mRNA, long non-coding RNA (lncRNA), and miRNA expression converged to identify subsets with differential epithelial-mesenchymal transition status, carcinoma in situ scores, histologic features, and survival. Our analyses identified 5 expression subtypes that may stratify response to different treatments.
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Affiliation(s)
- A Gordon Robertson
- Canada's Michael Smith Genome Sciences Center, BC Cancer Agency, Vancouver, BC V5Z 4S6, Canada
| | - Jaegil Kim
- Cancer Program, The Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Hikmat Al-Ahmadie
- Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Joaquim Bellmunt
- PSMAR-IMIM Lab, Bladder Cancer Center, Department of Medicine, Dana-Farber Cancer Institute and Harvard University, Boston, MA 02215, USA
| | - Guangwu Guo
- Department of Medical Oncology, Dana-Farber Cancer Institute and Harvard University, Boston, MA 02115, USA
| | - Andrew D Cherniack
- Cancer Program, The Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Toshinori Hinoue
- Center for Epigenetics, Van Andel Research Institute, Grand Rapids, MI 49503, USA
| | - Peter W Laird
- Center for Epigenetics, Van Andel Research Institute, Grand Rapids, MI 49503, USA
| | - Katherine A Hoadley
- Department of Genetics, Lineberger Comprehensive Cancer Center, The University of North Carolina at Chapel Hill, Chapel Hill, NC 27514, USA
| | - Rehan Akbani
- Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Mauro A A Castro
- Bioinformatics and Systems Biology Laboratory, Federal University of Paraná Polytechnic Center, Curitiba, PR CEP 80.060-000, Brazil
| | - Ewan A Gibb
- Canada's Michael Smith Genome Sciences Center, BC Cancer Agency, Vancouver, BC V5Z 4S6, Canada
| | - Rupa S Kanchi
- Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Dmitry A Gordenin
- Genome Integrity and Structural Biology Laboratory, National Institute of Environmental Health Sciences, NIH, Research Triangle Park, NC 27709, USA
| | - Sachet A Shukla
- Department of Medical Oncology, Dana-Farber Cancer Institute and Harvard University, Boston, MA 02115, USA
| | - Francisco Sanchez-Vega
- Center for Molecular Oncology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Donna E Hansel
- Department of Pathology, School of Medicine, University of California, San Diego, La Jolla, CA 92093, USA
| | - Bogdan A Czerniak
- Department of Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Victor E Reuter
- Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Xiaoping Su
- Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Benilton de Sa Carvalho
- Biostatistics and Computational Biology Laboratory, Department of Statistics, University of Campinas, São Paulo, 13.083-859, Brazil
| | - Vinicius S Chagas
- Bioinformatics and Systems Biology Laboratory, Federal University of Paraná Polytechnic Center, Curitiba, PR CEP 80.060-000, Brazil
| | - Karen L Mungall
- Canada's Michael Smith Genome Sciences Center, BC Cancer Agency, Vancouver, BC V5Z 4S6, Canada
| | - Sara Sadeghi
- Canada's Michael Smith Genome Sciences Center, BC Cancer Agency, Vancouver, BC V5Z 4S6, Canada
| | | | - Yiling Lu
- Department of Systems Biology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Leszek J Klimczak
- Integrative Bioinformatics Support Group, National Institute of Environmental Health Sciences, NIH, Research Triangle Park, NC 27709, USA
| | - Jiexin Zhang
- Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Caleb Choo
- Canada's Michael Smith Genome Sciences Center, BC Cancer Agency, Vancouver, BC V5Z 4S6, Canada
| | - Akinyemi I Ojesina
- Department of Epidemiology, University of Alabama at Birmingham, Birmingham, AL 35294, USA
| | - Susan Bullman
- Cancer Program, The Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Kristen M Leraas
- Biospecimen Core Resource, The Research Institute at Nationwide Children's Hospital, Columbus, OH 43205, USA
| | - Tara M Lichtenberg
- Biospecimen Core Resource, The Research Institute at Nationwide Children's Hospital, Columbus, OH 43205, USA
| | - Catherine J Wu
- Department of Medical Oncology, Dana-Farber Cancer Institute, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Nicholaus Schultz
- Center for Molecular Oncology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Gad Getz
- Cancer Program, The Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Matthew Meyerson
- Pathology and Medical Oncology, Dana-Farber Cancer Institute and Harvard University, Boston, MA 02115, USA
| | - Gordon B Mills
- Department of Systems Biology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - David J McConkey
- Greenberg Bladder Cancer Institute, Johns Hopkins University, Baltimore, MD 21218, USA
| | - John N Weinstein
- Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA; Department of Systems Biology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030 USA.
| | - David J Kwiatkowski
- Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA.
| | - Seth P Lerner
- Scott Department of Urology, Dan L. Duncan Cancer Center, Baylor College of Medicine, Houston, TX 77030, USA.
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Wang Y, Brodsky AS, Xiong J, Lopresti ML, Yang D, Resnick MB. Stromal Clusterin Expression Predicts Therapeutic Response to Neoadjuvant Chemotherapy in Triple Negative Breast Cancer. Clin Breast Cancer 2017; 18:e373-e379. [PMID: 28890185 DOI: 10.1016/j.clbc.2017.08.007] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2017] [Revised: 07/13/2017] [Accepted: 08/13/2017] [Indexed: 11/17/2022]
Abstract
BACKGROUND Expression of clusterin correlates with tumor progression and therapeutic response in several human malignancies, including breast cancer. However, its predictive value in the neoadjuvant setting in breast cancer remains unexplored. The objective of this explorative study was to determine whether clusterin expression in breast cancer correlated with clinical pathologic characteristics and whether its expression was predictive of response to neoadjuvant chemotherapy (NAC). MATERIALS AND METHODS We determined the clusterin expression pattern in 72 triple negative breast cancers (TNBC) treated with NAC before surgery. Clusterin expression was evaluated by immunohistochemistry and was correlated with pathologic characteristics and response to NAC using residual cancer burden score. RESULTS Immunohistochemistry analysis revealed a differential pattern of expression between tumor and stroma. Clusterin expression in the tumor associated stroma as opposed to expression by the neoplastic epithelium was significantly associated with neoadjuvant-treated TNBC. Low stromal clusterin, low stromal content, and high tumor-infiltrating lymphocytes were associated with a significantly greater likelihood of achieving a good pathologic response as reflected by lower residual cancer burden scores (P = .002, P = .003, and P = .001, respectively). Tumor and/or stromal clusterin expression were not associated with patient age, tumor histologic grade, stage, and lymph node status. CONCULSION This study suggests a potential role for the assessment of stromal clusterin as a predictive biomarker for response of TNBC to neoadjuvant therapy. Further validation of this biomarker in a large study is needed.
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Affiliation(s)
- Yihong Wang
- Department of Pathology and Laboratory Medicine, Rhode Island Hospital and Lifespan Medical Center, Warren Alpert Medical School of Brown University, Providence, RI.
| | - Alexander S Brodsky
- Department of Pathology and Laboratory Medicine, Rhode Island Hospital and Lifespan Medical Center, Warren Alpert Medical School of Brown University, Providence, RI
| | - Jinjun Xiong
- Department of Pathology, Women and Infants Hospital, Warren Alpert Medical School of Brown University, Providence, RI
| | - Mary L Lopresti
- Department of Medicine and Oncology, Rhode Island Hospital and Lifespan Medical Center, Warren Alpert Medical School of Brown University, Providence, RI
| | - Dongfang Yang
- Department of Pathology and Laboratory Medicine, Rhode Island Hospital and Lifespan Medical Center, Warren Alpert Medical School of Brown University, Providence, RI
| | - Murray B Resnick
- Department of Pathology and Laboratory Medicine, Rhode Island Hospital and Lifespan Medical Center, Warren Alpert Medical School of Brown University, Providence, RI
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28
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Merlino G, Miodini P, Callari M, D'Aiuto F, Cappelletti V, Daidone MG. Prognostic and functional role of subtype-specific tumor-stroma interaction in breast cancer. Mol Oncol 2017; 11:1399-1412. [PMID: 28672102 PMCID: PMC5623822 DOI: 10.1002/1878-0261.12107] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2017] [Revised: 06/16/2017] [Accepted: 06/20/2017] [Indexed: 12/22/2022] Open
Abstract
None of the clinically relevant gene expression signatures available for breast cancer were specifically developed to capture the influence of the microenvironment on tumor cells. Here, we attempted to build subtype‐specific signatures derived from an in vitro model reproducing tumor cell modifications after interaction with activated or normal stromal cells. Gene expression signatures derived from HER2+, luminal, and basal breast cancer cell lines (treated by normal fibroblasts or cancer‐associated fibroblasts conditioned media) were evaluated in clinical tumors by in silico analysis on published gene expression profiles (GEPs). Patients were classified as microenvironment‐positive (μENV+ve), that is, with tumors showing molecular profiles suggesting activation by the stroma, or microenvironment‐negative (μENV−ve) based on correlation of their tumors' GEP with the respective subtype‐specific signature. Patients with estrogen receptor alpha (ER)+/HER2−/μENV+ve tumors were characterized by 2.5‐fold higher risk of developing distant metastases (HR = 2.546; 95% CI: 1.751–3.701, P = 9.84E‐07), while μENV status did not affect, or only suggested the risk of distant metastases, in women with HER2+ (HR = 1.541; 95% CI: 0.788–3.012, P = 0.206) or ER‐/HER2− tumors (HR = 1.894; 95% CI: 0.938–3.824; P = 0.0747), respectively. In ER+/HER2− tumors, the μENV status remained significantly associated with metastatic progression (HR = 2.098; CI: 1.214–3.624; P = 0.00791) in multivariable analysis including size, age, and Genomic Grade Index. Validity of our in vitro model was also supported by in vitro biological endpoints such as cell growth (MTT assay) and migration/invasion (Transwell assay). In vitro‐derived gene signatures tracing the bidirectional interaction with cancer activated fibroblasts are subtype‐specific and add independent prognostic information to classical prognostic variables in women with ER+/HER2− tumors.
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Affiliation(s)
- Giuseppe Merlino
- Biomarker Unit, Department of Experimental Oncology and Molecular Medicine, Fondazione IRCCS Istituto Nazionale dei Tumori, Milano, Italy
| | - Patrizia Miodini
- Biomarker Unit, Department of Experimental Oncology and Molecular Medicine, Fondazione IRCCS Istituto Nazionale dei Tumori, Milano, Italy
| | - Maurizio Callari
- Biomarker Unit, Department of Experimental Oncology and Molecular Medicine, Fondazione IRCCS Istituto Nazionale dei Tumori, Milano, Italy
| | - Francesca D'Aiuto
- Biomarker Unit, Department of Experimental Oncology and Molecular Medicine, Fondazione IRCCS Istituto Nazionale dei Tumori, Milano, Italy
| | - Vera Cappelletti
- Biomarker Unit, Department of Experimental Oncology and Molecular Medicine, Fondazione IRCCS Istituto Nazionale dei Tumori, Milano, Italy
| | - Maria Grazia Daidone
- Biomarker Unit, Department of Experimental Oncology and Molecular Medicine, Fondazione IRCCS Istituto Nazionale dei Tumori, Milano, Italy
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Aure MR, Vitelli V, Jernström S, Kumar S, Krohn M, Due EU, Haukaas TH, Leivonen SK, Vollan HKM, Lüders T, Rødland E, Vaske CJ, Zhao W, Møller EK, Nord S, Giskeødegård GF, Bathen TF, Caldas C, Tramm T, Alsner J, Overgaard J, Geisler J, Bukholm IRK, Naume B, Schlichting E, Sauer T, Mills GB, Kåresen R, Mælandsmo GM, Lingjærde OC, Frigessi A, Kristensen VN, Børresen-Dale AL, Sahlberg KK. Integrative clustering reveals a novel split in the luminal A subtype of breast cancer with impact on outcome. Breast Cancer Res 2017; 19:44. [PMID: 28356166 PMCID: PMC5372339 DOI: 10.1186/s13058-017-0812-y] [Citation(s) in RCA: 63] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2016] [Accepted: 02/05/2017] [Indexed: 01/19/2023] Open
Abstract
BACKGROUND Breast cancer is a heterogeneous disease at the clinical and molecular level. In this study we integrate classifications extracted from five different molecular levels in order to identify integrated subtypes. METHODS Tumor tissue from 425 patients with primary breast cancer from the Oslo2 study was cut and blended, and divided into fractions for DNA, RNA and protein isolation and metabolomics, allowing the acquisition of representative and comparable molecular data. Patients were stratified into groups based on their tumor characteristics from five different molecular levels, using various clustering methods. Finally, all previously identified and newly determined subgroups were combined in a multilevel classification using a "cluster-of-clusters" approach with consensus clustering. RESULTS Based on DNA copy number data, tumors were categorized into three groups according to the complex arm aberration index. mRNA expression profiles divided tumors into five molecular subgroups according to PAM50 subtyping, and clustering based on microRNA expression revealed four subgroups. Reverse-phase protein array data divided tumors into five subgroups. Hierarchical clustering of tumor metabolic profiles revealed three clusters. Combining DNA copy number and mRNA expression classified tumors into seven clusters based on pathway activity levels, and tumors were classified into ten subtypes using integrative clustering. The final consensus clustering that incorporated all aforementioned subtypes revealed six major groups. Five corresponded well with the mRNA subtypes, while a sixth group resulted from a split of the luminal A subtype; these tumors belonged to distinct microRNA clusters. Gain-of-function studies using MCF-7 cells showed that microRNAs differentially expressed between the luminal A clusters were important for cancer cell survival. These microRNAs were used to validate the split in luminal A tumors in four independent breast cancer cohorts. In two cohorts the microRNAs divided tumors into subgroups with significantly different outcomes, and in another a trend was observed. CONCLUSIONS The six integrated subtypes identified confirm the heterogeneity of breast cancer and show that finer subdivisions of subtypes are evident. Increasing knowledge of the heterogeneity of the luminal A subtype may add pivotal information to guide therapeutic choices, evidently bringing us closer to improved treatment for this largest subgroup of breast cancer.
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Affiliation(s)
- Miriam Ragle Aure
- Department of Cancer Genetics, Institute for Cancer Research, Oslo University Hospital, The Norwegian Radium Hospital, Oslo, Norway
- K.G. Jebsen Centre for Breast Cancer Research, Institute for Clinical Medicine, University of Oslo, Oslo, Norway
| | - Valeria Vitelli
- Oslo Center for Biostatistics and Epidemiology, Institute of Basic Medical Science, University of Oslo, Oslo, Norway
| | - Sandra Jernström
- Department of Cancer Genetics, Institute for Cancer Research, Oslo University Hospital, The Norwegian Radium Hospital, Oslo, Norway
- K.G. Jebsen Centre for Breast Cancer Research, Institute for Clinical Medicine, University of Oslo, Oslo, Norway
| | - Surendra Kumar
- Department of Cancer Genetics, Institute for Cancer Research, Oslo University Hospital, The Norwegian Radium Hospital, Oslo, Norway
- K.G. Jebsen Centre for Breast Cancer Research, Institute for Clinical Medicine, University of Oslo, Oslo, Norway
- Department of Clinical Molecular Biology (EpiGen), Division of Medicine, Akershus University Hospital, Lørenskog, Norway
| | - Marit Krohn
- Department of Cancer Genetics, Institute for Cancer Research, Oslo University Hospital, The Norwegian Radium Hospital, Oslo, Norway
- K.G. Jebsen Centre for Breast Cancer Research, Institute for Clinical Medicine, University of Oslo, Oslo, Norway
| | - Eldri U. Due
- Department of Cancer Genetics, Institute for Cancer Research, Oslo University Hospital, The Norwegian Radium Hospital, Oslo, Norway
- K.G. Jebsen Centre for Breast Cancer Research, Institute for Clinical Medicine, University of Oslo, Oslo, Norway
| | - Tonje Husby Haukaas
- K.G. Jebsen Centre for Breast Cancer Research, Institute for Clinical Medicine, University of Oslo, Oslo, Norway
- Department of Circulation and Medical Imaging, Norwegian University of Science and Technology (NTNU), Trondheim, Norway
| | - Suvi-Katri Leivonen
- Genome-Scale Biology Research Program, University of Helsinki, Helsinki, Finland
| | - Hans Kristian Moen Vollan
- Department of Cancer Genetics, Institute for Cancer Research, Oslo University Hospital, The Norwegian Radium Hospital, Oslo, Norway
- K.G. Jebsen Centre for Breast Cancer Research, Institute for Clinical Medicine, University of Oslo, Oslo, Norway
| | - Torben Lüders
- K.G. Jebsen Centre for Breast Cancer Research, Institute for Clinical Medicine, University of Oslo, Oslo, Norway
- Department of Clinical Molecular Biology (EpiGen), Division of Medicine, Akershus University Hospital, Lørenskog, Norway
| | - Einar Rødland
- Department of Tumor Biology, Institute for Cancer Research, Oslo University Hospital, The Norwegian Radium Hospital, Oslo, Norway
| | | | - Wei Zhao
- Department of Systems Biology, University of Texas M.D. Anderson Cancer Center, Houston, TX USA
| | - Elen K. Møller
- Department of Cancer Genetics, Institute for Cancer Research, Oslo University Hospital, The Norwegian Radium Hospital, Oslo, Norway
- K.G. Jebsen Centre for Breast Cancer Research, Institute for Clinical Medicine, University of Oslo, Oslo, Norway
| | - Silje Nord
- Department of Cancer Genetics, Institute for Cancer Research, Oslo University Hospital, The Norwegian Radium Hospital, Oslo, Norway
- K.G. Jebsen Centre for Breast Cancer Research, Institute for Clinical Medicine, University of Oslo, Oslo, Norway
| | - Guro F. Giskeødegård
- Department of Circulation and Medical Imaging, Norwegian University of Science and Technology (NTNU), Trondheim, Norway
| | - Tone Frost Bathen
- K.G. Jebsen Centre for Breast Cancer Research, Institute for Clinical Medicine, University of Oslo, Oslo, Norway
- Department of Circulation and Medical Imaging, Norwegian University of Science and Technology (NTNU), Trondheim, Norway
| | - Carlos Caldas
- Cambridge University Hospitals Trust, Addenbrookes Hospital, Cambridge, UK
- Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, UK
| | - Trine Tramm
- Department of Experimental Clinical Oncology, Aarhus University Hospital, Aarhus, Denmark
| | - Jan Alsner
- Department of Experimental Clinical Oncology, Aarhus University Hospital, Aarhus, Denmark
| | - Jens Overgaard
- Department of Experimental Clinical Oncology, Aarhus University Hospital, Aarhus, Denmark
| | - Jürgen Geisler
- Department of Oncology, Akershus University Hospital, Lørenskog, Norway
- Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Oslo, Norway
| | - Ida R. K. Bukholm
- Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Oslo, Norway
- Department of Surgery, Akershus University Hospital, Lørenskog, Norway
| | - Bjørn Naume
- K.G. Jebsen Centre for Breast Cancer Research, Institute for Clinical Medicine, University of Oslo, Oslo, Norway
- Department of Oncology, Division of Cancer Medicine, Oslo University Hospital, Oslo, Norway
| | - Ellen Schlichting
- K.G. Jebsen Centre for Breast Cancer Research, Institute for Clinical Medicine, University of Oslo, Oslo, Norway
- Department of Breast and Endocrine Surgery, Oslo University Hospital, Oslo, Norway
| | - Torill Sauer
- K.G. Jebsen Centre for Breast Cancer Research, Institute for Clinical Medicine, University of Oslo, Oslo, Norway
- Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Oslo, Norway
- Department of Pathology, Akershus University Hospital, Lørenskog, Norway
| | - Gordon B. Mills
- Department of Systems Biology, University of Texas M.D. Anderson Cancer Center, Houston, TX USA
| | - Rolf Kåresen
- K.G. Jebsen Centre for Breast Cancer Research, Institute for Clinical Medicine, University of Oslo, Oslo, Norway
- Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Oslo, Norway
- Department of Breast and Endocrine Surgery, Oslo University Hospital, Oslo, Norway
| | - Gunhild M. Mælandsmo
- K.G. Jebsen Centre for Breast Cancer Research, Institute for Clinical Medicine, University of Oslo, Oslo, Norway
- Department of Tumor Biology, Institute for Cancer Research, Oslo University Hospital, The Norwegian Radium Hospital, Oslo, Norway
| | - Ole Christian Lingjærde
- K.G. Jebsen Centre for Breast Cancer Research, Institute for Clinical Medicine, University of Oslo, Oslo, Norway
- Centre for Cancer Biomedicine, University of Oslo, Oslo, Norway
- Department of Computer Science, University of Oslo, Oslo, Norway
| | - Arnoldo Frigessi
- Oslo Center for Biostatistics and Epidemiology, Institute of Basic Medical Science, University of Oslo, Oslo, Norway
- Oslo Center for Biostatistics and Epidemiology, Oslo University Hospital, Oslo, Norway
| | - Vessela N. Kristensen
- Department of Cancer Genetics, Institute for Cancer Research, Oslo University Hospital, The Norwegian Radium Hospital, Oslo, Norway
- K.G. Jebsen Centre for Breast Cancer Research, Institute for Clinical Medicine, University of Oslo, Oslo, Norway
- Department of Clinical Molecular Biology (EpiGen), Division of Medicine, Akershus University Hospital, Lørenskog, Norway
| | - Anne-Lise Børresen-Dale
- Department of Cancer Genetics, Institute for Cancer Research, Oslo University Hospital, The Norwegian Radium Hospital, Oslo, Norway
- K.G. Jebsen Centre for Breast Cancer Research, Institute for Clinical Medicine, University of Oslo, Oslo, Norway
| | - Kristine K. Sahlberg
- Department of Cancer Genetics, Institute for Cancer Research, Oslo University Hospital, The Norwegian Radium Hospital, Oslo, Norway
- K.G. Jebsen Centre for Breast Cancer Research, Institute for Clinical Medicine, University of Oslo, Oslo, Norway
- Department of Research, Vestre Viken Hospital Trust, Drammen, Norway
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