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Raj-Kumar PK, Lin X, Liu T, Sturtz LA, Gritsenko MA, Petyuk VA, Sagendorf TJ, Deyarmin B, Liu J, Praveen-Kumar A, Wang G, McDermott JE, Shukla AK, Moore RJ, Monroe ME, Webb-Robertson BJM, Hooke JA, Fantacone-Campbell L, Mostoller B, Kvecher L, Kane J, Melley J, Somiari S, Soon-Shiong P, Smith RD, Mural RJ, Rodland KD, Shriver CD, Kovatich AJ, Hu H. Proteogenomic characterization of difficult-to-treat breast cancer with tumor cells enriched through laser microdissection. Breast Cancer Res 2024; 26:76. [PMID: 38745208 PMCID: PMC11094977 DOI: 10.1186/s13058-024-01835-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2024] [Accepted: 05/05/2024] [Indexed: 05/16/2024] Open
Abstract
BACKGROUND Breast cancer (BC) is the most commonly diagnosed cancer and the leading cause of cancer death among women globally. Despite advances, there is considerable variation in clinical outcomes for patients with non-luminal A tumors, classified as difficult-to-treat breast cancers (DTBC). This study aims to delineate the proteogenomic landscape of DTBC tumors compared to luminal A (LumA) tumors. METHODS We retrospectively collected a total of 117 untreated primary breast tumor specimens, focusing on DTBC subtypes. Breast tumors were processed by laser microdissection (LMD) to enrich tumor cells. DNA, RNA, and protein were simultaneously extracted from each tumor preparation, followed by whole genome sequencing, paired-end RNA sequencing, global proteomics and phosphoproteomics. Differential feature analysis, pathway analysis and survival analysis were performed to better understand DTBC and investigate biomarkers. RESULTS We observed distinct variations in gene mutations, structural variations, and chromosomal alterations between DTBC and LumA breast tumors. DTBC tumors predominantly had more mutations in TP53, PLXNB3, Zinc finger genes, and fewer mutations in SDC2, CDH1, PIK3CA, SVIL, and PTEN. Notably, Cytoband 1q21, which contains numerous cell proliferation-related genes, was significantly amplified in the DTBC tumors. LMD successfully minimized stromal components and increased RNA-protein concordance, as evidenced by stromal score comparisons and proteomic analysis. Distinct DTBC and LumA-enriched clusters were observed by proteomic and phosphoproteomic clustering analysis, some with survival differences. Phosphoproteomics identified two distinct phosphoproteomic profiles for high relapse-risk and low relapse-risk basal-like tumors, involving several genes known to be associated with breast cancer oncogenesis and progression, including KIAA1522, DCK, FOXO3, MYO9B, ARID1A, EPRS, ZC3HAV1, and RBM14. Lastly, an integrated pathway analysis of multi-omics data highlighted a robust enrichment of proliferation pathways in DTBC tumors. CONCLUSIONS This study provides an integrated proteogenomic characterization of DTBC vs LumA with tumor cells enriched through laser microdissection. We identified many common features of DTBC tumors and the phosphopeptides that could serve as potential biomarkers for high/low relapse-risk basal-like BC and possibly guide treatment selections.
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Affiliation(s)
- Praveen-Kumar Raj-Kumar
- Chan Soon-Shiong Institute of Molecular Medicine at Windber, Windber, PA, USA
- Murtha Cancer Center Research Program, Department of Surgery, Uniformed Services University of the Health Sciences, Bethesda, MD, USA
| | - Xiaoying Lin
- Chan Soon-Shiong Institute of Molecular Medicine at Windber, Windber, PA, USA
- Murtha Cancer Center Research Program, Department of Surgery, Uniformed Services University of the Health Sciences, Bethesda, MD, USA
| | - Tao Liu
- Pacific Northwest National Laboratory, Richland, WA, USA
| | - Lori A Sturtz
- Chan Soon-Shiong Institute of Molecular Medicine at Windber, Windber, PA, USA
- Murtha Cancer Center Research Program, Department of Surgery, Uniformed Services University of the Health Sciences, Bethesda, MD, USA
| | | | | | | | - Brenda Deyarmin
- Chan Soon-Shiong Institute of Molecular Medicine at Windber, Windber, PA, USA
| | - Jianfang Liu
- Chan Soon-Shiong Institute of Molecular Medicine at Windber, Windber, PA, USA
| | | | - Guisong Wang
- Murtha Cancer Center Research Program, Department of Surgery, Uniformed Services University of the Health Sciences, Bethesda, MD, USA
- The Henry M. Jackson Foundation for the Advancement of Military Medicine, Inc, Bethesda, MD, USA
| | | | - Anil K Shukla
- Pacific Northwest National Laboratory, Richland, WA, USA
| | - Ronald J Moore
- Pacific Northwest National Laboratory, Richland, WA, USA
| | | | | | - Jeffrey A Hooke
- Murtha Cancer Center Research Program, Department of Surgery, Uniformed Services University of the Health Sciences, Bethesda, MD, USA
- The Henry M. Jackson Foundation for the Advancement of Military Medicine, Inc, Bethesda, MD, USA
| | - Leigh Fantacone-Campbell
- Murtha Cancer Center Research Program, Department of Surgery, Uniformed Services University of the Health Sciences, Bethesda, MD, USA
- The Henry M. Jackson Foundation for the Advancement of Military Medicine, Inc, Bethesda, MD, USA
| | - Brad Mostoller
- Chan Soon-Shiong Institute of Molecular Medicine at Windber, Windber, PA, USA
| | - Leonid Kvecher
- Chan Soon-Shiong Institute of Molecular Medicine at Windber, Windber, PA, USA
- Murtha Cancer Center Research Program, Department of Surgery, Uniformed Services University of the Health Sciences, Bethesda, MD, USA
| | - Jennifer Kane
- Chan Soon-Shiong Institute of Molecular Medicine at Windber, Windber, PA, USA
| | - Jennifer Melley
- Chan Soon-Shiong Institute of Molecular Medicine at Windber, Windber, PA, USA
| | - Stella Somiari
- Chan Soon-Shiong Institute of Molecular Medicine at Windber, Windber, PA, USA
| | | | | | - Richard J Mural
- Chan Soon-Shiong Institute of Molecular Medicine at Windber, Windber, PA, USA
| | | | - Craig D Shriver
- Murtha Cancer Center Research Program, Department of Surgery, Uniformed Services University of the Health Sciences, Bethesda, MD, USA.
- Department of Surgery, Walter Reed National Military Medical Center, Bethesda, MD, USA.
| | - Albert J Kovatich
- Murtha Cancer Center Research Program, Department of Surgery, Uniformed Services University of the Health Sciences, Bethesda, MD, USA
- The Henry M. Jackson Foundation for the Advancement of Military Medicine, Inc, Bethesda, MD, USA
| | - Hai Hu
- Chan Soon-Shiong Institute of Molecular Medicine at Windber, Windber, PA, USA.
- Murtha Cancer Center Research Program, Department of Surgery, Uniformed Services University of the Health Sciences, Bethesda, MD, USA.
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2
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Castresana-Aguirre M, Johansson A, Matikas A, Foukakis T, Lindström LS, Tobin NP. Clinically relevant gene signatures provide independent prognostic information in older breast cancer patients. Breast Cancer Res 2024; 26:38. [PMID: 38454481 PMCID: PMC10921680 DOI: 10.1186/s13058-024-01797-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2023] [Accepted: 02/27/2024] [Indexed: 03/09/2024] Open
Abstract
BACKGROUND The clinical utility of gene signatures in older breast cancer patients remains unclear. We aimed to determine signature prognostic capacity in this patient subgroup. METHODS Research versions of the genomic grade index (GGI), 70-gene, recurrence score (RS), cell cycle score (CCS), PAM50 risk-of-recurrence proliferation (ROR-P), and PAM50 signatures were applied to 39 breast cancer datasets (N = 9583). After filtering on age ≥ 70 years, and the presence of estrogen receptor (ER) and survival data, 871 patients remained. Signature prognostic capacity was tested in all (n = 871), ER-positive/lymph node-positive (ER + /LN + , n = 335) and ER-positive/lymph node-negative (ER + /LN-, n = 374) patients using Kaplan-Meier and multivariable Cox-proportional hazard (PH) modelling. RESULTS All signatures were statistically significant in Kaplan-Meier analysis of all patients (Log-rank P < 0.001). This significance remained in multivariable analysis (Cox-PH, P ≤ 0.05). In ER + /LN + patients all signatures except PAM50 were significant in Kaplan-Meier analysis (Log-rank P ≤ 0.05) and remained so in multivariable analysis (Cox-PH, P ≤ 0.05). In ER + /LN- patients all except RS were significant in Kaplan-Meier analysis (Log-rank P ≤ 0.05) but only the 70-gene, CCS, ROR-P, and PAM50 signatures remained so in multivariable analysis (Cox-PH, P ≤ 0.05). CONCLUSIONS We found that gene signatures provide prognostic information in survival analyses of all, ER + /LN + and ER + /LN- older (≥ 70 years) breast cancer patients, suggesting a potential role in aiding treatment decisions in older patients.
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Affiliation(s)
- Miguel Castresana-Aguirre
- Department of Oncology and Pathology, BioClinicum, Karolinska Institutet and University Hospital, Visionsgatan 4, 171 64, Stockholm, Sweden
- Breast Center, Karolinska Comprehensive Cancer Center, Karolinska University Hospital, Stockholm, Sweden
| | - Annelie Johansson
- Department of Oncology and Pathology, BioClinicum, Karolinska Institutet and University Hospital, Visionsgatan 4, 171 64, Stockholm, Sweden
- Breast Cancer Now Research Unit, School of Cancer and Pharmaceutical Sciences, Guy's Cancer Center, King's College London, London, UK
| | - Alexios Matikas
- Department of Oncology and Pathology, BioClinicum, Karolinska Institutet and University Hospital, Visionsgatan 4, 171 64, Stockholm, Sweden
- Breast Center, Karolinska Comprehensive Cancer Center, Karolinska University Hospital, Stockholm, Sweden
| | - Theodoros Foukakis
- Department of Oncology and Pathology, BioClinicum, Karolinska Institutet and University Hospital, Visionsgatan 4, 171 64, Stockholm, Sweden
- Breast Center, Karolinska Comprehensive Cancer Center, Karolinska University Hospital, Stockholm, Sweden
| | - Linda S Lindström
- Department of Oncology and Pathology, BioClinicum, Karolinska Institutet and University Hospital, Visionsgatan 4, 171 64, Stockholm, Sweden
- Breast Center, Karolinska Comprehensive Cancer Center, Karolinska University Hospital, Stockholm, Sweden
| | - Nicholas P Tobin
- Department of Oncology and Pathology, BioClinicum, Karolinska Institutet and University Hospital, Visionsgatan 4, 171 64, Stockholm, Sweden.
- Breast Center, Karolinska Comprehensive Cancer Center, Karolinska University Hospital, Stockholm, Sweden.
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3
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Trujillo-Ortíz R, Espinal-Enríquez J, Hernández-Lemus E. The Role of Transcription Factors in the Loss of Inter-Chromosomal Co-Expression for Breast Cancer Subtypes. Int J Mol Sci 2023; 24:17564. [PMID: 38139393 PMCID: PMC10743684 DOI: 10.3390/ijms242417564] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2023] [Revised: 12/05/2023] [Accepted: 12/06/2023] [Indexed: 12/24/2023] Open
Abstract
Breast cancer encompasses a diverse array of subtypes, each exhibiting distinct clinical characteristics and treatment responses. Unraveling the underlying regulatory mechanisms that govern gene expression patterns in these subtypes is essential for advancing our understanding of breast cancer biology. Gene co-expression networks (GCNs) help us identify groups of genes that work in coordination. Previous research has revealed a marked reduction in the interaction of genes located on different chromosomes within GCNs for breast cancer, as well as for lung, kidney, and hematopoietic cancers. However, the reasons behind why genes on the same chromosome often co-express remain unclear. In this study, we investigate the role of transcription factors in shaping gene co-expression networks within the four main breast cancer subtypes: Luminal A, Luminal B, HER2+, and Basal, along with normal breast tissue. We identify communities within each GCN and calculate the transcription factors that may regulate these communities, comparing the results across different phenotypes. Our findings indicate that, in general, regulatory behavior is to a large extent similar among breast cancer molecular subtypes and even in healthy networks. This suggests that transcription factor motif usage does not fully determine long-range co-expression patterns. Specific transcription factor motifs, such as CCGGAAG, appear frequently across all phenotypes, even involving multiple highly connected transcription factors. Additionally, certain transcription factors exhibit unique actions in specific subtypes but with limited influence. Our research demonstrates that the loss of inter-chromosomal co-expression is not solely attributable to transcription factor regulation. Although the exact mechanism responsible for this phenomenon remains elusive, this work contributes to a better understanding of gene expression regulatory programs in breast cancer.
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Affiliation(s)
- Rodrigo Trujillo-Ortíz
- Computational Genomics Division, Instituto Nacional de Medicina Genómica, Mexico City 14610, Mexico;
| | - Jesús Espinal-Enríquez
- Computational Genomics Division, Instituto Nacional de Medicina Genómica, Mexico City 14610, Mexico;
- Center for Complexity Sciences, Universidad Nacional Autónoma de México, Mexico City 01010, Mexico
| | - Enrique Hernández-Lemus
- Computational Genomics Division, Instituto Nacional de Medicina Genómica, Mexico City 14610, Mexico;
- Center for Complexity Sciences, Universidad Nacional Autónoma de México, Mexico City 01010, Mexico
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Paul ED, Huraiová B, Valková N, Birknerova N, Gábrišová D, Gubova S, Ignačáková H, Ondris T, Bendíková S, Bíla J, Buranovská K, Drobná D, Krchnakova Z, Kryvokhyzha M, Lovíšek D, Mamoilyk V, Mančíková V, Vojtaššáková N, Ristová M, Comino-Méndez I, Andrašina I, Morozov P, Tuschl T, Pareja F, Čekan P. Multiplexed RNA-FISH-guided Laser Capture Microdissection RNA Sequencing Improves Breast Cancer Molecular Subtyping, Prognostic Classification, and Predicts Response to Antibody Drug Conjugates. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.12.05.23299341. [PMID: 38105959 PMCID: PMC10723508 DOI: 10.1101/2023.12.05.23299341] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/19/2023]
Abstract
On a retrospective cohort of 1,082 FFPE breast tumors, we demonstrated the analytical validity of a test using multiplexed RNA-FISH-guided laser capture microdissection (LCM) coupled with RNA-sequencing (mFISHseq), which showed 93% accuracy compared to immunohistochemistry. The combination of these technologies makes strides in i) precisely assessing tumor heterogeneity, ii) obtaining pure tumor samples using LCM to ensure accurate biomarker expression and multigene testing, and iii) providing thorough and granular data from whole transcriptome profiling. We also constructed a 293-gene intrinsic subtype classifier that performed equivalent to the research based PAM50 and AIMS classifiers. By combining three molecular classifiers for consensus subtyping, mFISHseq alleviated single sample discordance, provided near perfect concordance with other classifiers (κ > 0.85), and reclassified 30% of samples into different subtypes with prognostic implications. We also use a consensus approach to combine information from 4 multigene prognostic classifiers and clinical risk to characterize high, low, and ultra-low risk patients that relapse early (< 5 years), late (> 10 years), and rarely, respectively. Lastly, to identify potential patient subpopulations that may be responsive to treatments like antibody drug-conjugates (ADC), we curated a list of 92 genes and 110 gene signatures to interrogate their association with molecular subtype and overall survival. Many genes and gene signatures related to ADC processing (e.g., antigen/payload targets, endocytosis, and lysosome activity) were independent predictors of overall survival in multivariate Cox regression models, thus highlighting potential ADC treatment-responsive subgroups. To test this hypothesis, we constructed a unique 19-feature classifier using multivariate logistic regression with elastic net that predicted response to trastuzumab emtansine (T-DM1; AUC = 0.96) better than either ERBB2 mRNA or Her2 IHC alone in the T-DM1 arm of the I-SPY2 trial. This test was deployed in a research-use only format on 26 patients and revealed clinical insights into patient selection for novel therapies like ADCs and immunotherapies and de-escalation of adjuvant chemotherapy.
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Affiliation(s)
- Evan D. Paul
- MultiplexDX, s.r.o., Comenius University Science Park, Bratislava, Slovakia
- MultiplexDX, Inc., Rockville, MD, USA
| | - Barbora Huraiová
- MultiplexDX, s.r.o., Comenius University Science Park, Bratislava, Slovakia
- MultiplexDX, Inc., Rockville, MD, USA
| | - Natália Valková
- MultiplexDX, s.r.o., Comenius University Science Park, Bratislava, Slovakia
- MultiplexDX, Inc., Rockville, MD, USA
- Institute of Clinical Biochemistry and Diagnostics, University Hospital, Faculty of Medicine in Hradec Kralove, Charles University, Hradec Kralove, Czech Republic
| | - Natalia Birknerova
- MultiplexDX, s.r.o., Comenius University Science Park, Bratislava, Slovakia
- MultiplexDX, Inc., Rockville, MD, USA
| | - Daniela Gábrišová
- MultiplexDX, s.r.o., Comenius University Science Park, Bratislava, Slovakia
- MultiplexDX, Inc., Rockville, MD, USA
| | - Sona Gubova
- MultiplexDX, s.r.o., Comenius University Science Park, Bratislava, Slovakia
- MultiplexDX, Inc., Rockville, MD, USA
| | - Helena Ignačáková
- MultiplexDX, s.r.o., Comenius University Science Park, Bratislava, Slovakia
- MultiplexDX, Inc., Rockville, MD, USA
| | - Tomáš Ondris
- MultiplexDX, s.r.o., Comenius University Science Park, Bratislava, Slovakia
- MultiplexDX, Inc., Rockville, MD, USA
| | - Silvia Bendíková
- MultiplexDX, s.r.o., Comenius University Science Park, Bratislava, Slovakia
- MultiplexDX, Inc., Rockville, MD, USA
| | - Jarmila Bíla
- MultiplexDX, s.r.o., Comenius University Science Park, Bratislava, Slovakia
- MultiplexDX, Inc., Rockville, MD, USA
| | - Katarína Buranovská
- MultiplexDX, s.r.o., Comenius University Science Park, Bratislava, Slovakia
- MultiplexDX, Inc., Rockville, MD, USA
| | - Diana Drobná
- MultiplexDX, s.r.o., Comenius University Science Park, Bratislava, Slovakia
- MultiplexDX, Inc., Rockville, MD, USA
| | - Zuzana Krchnakova
- MultiplexDX, s.r.o., Comenius University Science Park, Bratislava, Slovakia
- MultiplexDX, Inc., Rockville, MD, USA
| | - Maryna Kryvokhyzha
- MultiplexDX, s.r.o., Comenius University Science Park, Bratislava, Slovakia
- MultiplexDX, Inc., Rockville, MD, USA
| | - Daniel Lovíšek
- MultiplexDX, s.r.o., Comenius University Science Park, Bratislava, Slovakia
- MultiplexDX, Inc., Rockville, MD, USA
| | - Viktoriia Mamoilyk
- MultiplexDX, s.r.o., Comenius University Science Park, Bratislava, Slovakia
- MultiplexDX, Inc., Rockville, MD, USA
| | - Veronika Mančíková
- MultiplexDX, s.r.o., Comenius University Science Park, Bratislava, Slovakia
- MultiplexDX, Inc., Rockville, MD, USA
| | - Nina Vojtaššáková
- MultiplexDX, s.r.o., Comenius University Science Park, Bratislava, Slovakia
- MultiplexDX, Inc., Rockville, MD, USA
| | - Michaela Ristová
- MultiplexDX, s.r.o., Comenius University Science Park, Bratislava, Slovakia
- MultiplexDX, Inc., Rockville, MD, USA
- Wellcome Centre for Cell Biology, School of Biological Sciences, University of Edinburgh, Edinburgh, Scotland, UK
| | - Iñaki Comino-Méndez
- Unidad de Gestión Clínica Intercentros de Oncología Medica, Hospitales Universitarios Regional y Virgen de la Victoria. The Biomedical Research Institute of Málaga (IBIMA-CIMES-UMA), Málaga, Spain
| | - Igor Andrašina
- Department of Radiotherapy and Oncology, East Slovakia Institute of Oncology, Košice, Slovakia
| | - Pavel Morozov
- Laboratory for RNA Molecular Biology, The Rockefeller University, New York NY, USA
| | - Thomas Tuschl
- Laboratory for RNA Molecular Biology, The Rockefeller University, New York NY, USA
| | - Fresia Pareja
- Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Pavol Čekan
- MultiplexDX, s.r.o., Comenius University Science Park, Bratislava, Slovakia
- MultiplexDX, Inc., Rockville, MD, USA
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Murillo OD, Petrosyan V, LaPlante EL, Dobrolecki LE, Lewis MT, Milosavljevic A. Deconvolution of cancer cell states by the XDec-SM method. PLoS Comput Biol 2023; 19:e1011365. [PMID: 37578979 PMCID: PMC10449115 DOI: 10.1371/journal.pcbi.1011365] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2023] [Revised: 08/24/2023] [Accepted: 07/17/2023] [Indexed: 08/16/2023] Open
Abstract
Proper characterization of cancer cell states within the tumor microenvironment is a key to accurately identifying matching experimental models and the development of precision therapies. To reconstruct this information from bulk RNA-seq profiles, we developed the XDec Simplex Mapping (XDec-SM) reference-optional deconvolution method that maps tumors and the states of constituent cells onto a biologically interpretable low-dimensional space. The method identifies gene sets informative for deconvolution from relevant single-cell profiling data when such profiles are available. When applied to breast tumors in The Cancer Genome Atlas (TCGA), XDec-SM infers the identity of constituent cell types and their proportions. XDec-SM also infers cancer cells states within individual tumors that associate with DNA methylation patterns, driver somatic mutations, pathway activation and metabolic coupling between stromal and breast cancer cells. By projecting tumors, cancer cell lines, and PDX models onto the same map, we identify in vitro and in vivo models with matching cancer cell states. Map position is also predictive of therapy response, thus opening the prospects for precision therapy informed by experiments in model systems matched to tumors in vivo by cancer cell state.
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Affiliation(s)
- Oscar D. Murillo
- Translational Science and Therapeutics Division, Fred Hutchinson Cancer Center, Seattle, Washington, United States of America
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas, United States of America
| | - Varduhi Petrosyan
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas, United States of America
| | - Emily L. LaPlante
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas, United States of America
| | - Lacey E. Dobrolecki
- Lester and Sue Smith Breast Center, Baylor College of Medicine, Houston, Texas, United States of America
| | - Michael T. Lewis
- Lester and Sue Smith Breast Center, Baylor College of Medicine, Houston, Texas, United States of America
- Dan L Duncan Comprehensive Cancer Center, Baylor College of Medicine, Houston, Texas, United States of America
- Departments of Molecular and Cellular Biology and Radiology, Baylor College of Medicine, Houston, Texas, United States of America
| | - Aleksandar Milosavljevic
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas, United States of America
- Dan L Duncan Comprehensive Cancer Center, Baylor College of Medicine, Houston, Texas, United States of America
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6
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Xu L, Han F, Zhu L, Ding W, Zhang K, Kan C, Hou N, Li Q, Sun X. Advances in understanding the role and mechanisms of tumor stem cells in HER2-positive breast cancer treatment resistance (Review). Int J Oncol 2023; 62:48. [PMID: 36866766 PMCID: PMC9990588 DOI: 10.3892/ijo.2023.5496] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2022] [Accepted: 02/15/2023] [Indexed: 03/04/2023] Open
Abstract
Approximately 15-20% of breast carcinomas exhibit human epidermal growth factor receptor (HER2) protein overexpression. HER2-positive breast cancer (BC) is a heterogeneous and aggressive subtype with poor prognosis and high relapse risk. Although several anti-HER2 drugs have achieved substantial efficacy, certain patients with HER2-positive BC relapse due to drug resistance after a treatment period. There is increasing evidence that BC stem cells (BCSCs) drive therapeutic resistance and a high rate of BC recurrence. BCSCs may regulate cellular self-renewal and differentiation, as well as invasive metastasis and treatment resistance. Efforts to target BCSCs may yield new methods to improve patient outcomes. In the present review, the roles of BCSCs in the occurrence, development and management of BC treatment resistance were summarized; BCSC-targeted strategies for the treatment of HER2-positive BC were also discussed.
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Affiliation(s)
- Linfei Xu
- Department of Endocrinology and Metabolism, Affiliated Hospital of Weifang Medical University, Weifang, Shandong 261031, P.R. China
| | - Fang Han
- Department of Endocrinology and Metabolism, Affiliated Hospital of Weifang Medical University, Weifang, Shandong 261031, P.R. China
| | - Liang Zhu
- Department of Endocrinology and Metabolism, Affiliated Hospital of Weifang Medical University, Weifang, Shandong 261031, P.R. China
| | - Wenli Ding
- Department of Pathology, Affiliated Hospital of Weifang Medical University, Weifang, Shandong 261031, P.R. China
| | - Kexin Zhang
- Department of Endocrinology and Metabolism, Affiliated Hospital of Weifang Medical University, Weifang, Shandong 261031, P.R. China
| | - Chengxia Kan
- Department of Endocrinology and Metabolism, Affiliated Hospital of Weifang Medical University, Weifang, Shandong 261031, P.R. China
| | - Ningning Hou
- Department of Endocrinology and Metabolism, Affiliated Hospital of Weifang Medical University, Weifang, Shandong 261031, P.R. China
| | - Qinying Li
- Department of Endocrinology and Metabolism, Affiliated Hospital of Weifang Medical University, Weifang, Shandong 261031, P.R. China
| | - Xiaodong Sun
- Department of Endocrinology and Metabolism, Affiliated Hospital of Weifang Medical University, Weifang, Shandong 261031, P.R. China
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7
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Thacker G, Henry S, Nandi A, Debnath R, Singh S, Nayak A, Susnik B, Boone MM, Zhang Q, Kesmodel SB, Gumber S, Das GM, Kambayashi T, Dos Santos CO, Chakrabarti R. Immature natural killer cells promote progression of triple-negative breast cancer. Sci Transl Med 2023; 15:eabl4414. [PMID: 36888695 PMCID: PMC10875969 DOI: 10.1126/scitranslmed.abl4414] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2021] [Accepted: 01/26/2023] [Indexed: 03/10/2023]
Abstract
Natural killer (NK) cells are cytotoxic lymphocytes that accumulate within the tumor microenvironment and are generally considered to be antitumorigenic. Using single-cell RNA sequencing and functional analysis of multiple triple-negative breast cancer (TNBC) and basal tumor samples, we observed a unique subcluster of Socs3highCD11b-CD27- immature NK cells that were present only in TNBC samples. These tumor-infiltrating NK cells expressed a reduced cytotoxic granzyme signature and, in mice, were responsible for activating cancer stem cells through Wnt signaling. NK cell-mediated activation of these cancer stem cells subsequently enhanced tumor progression in mice, whereas depletion of NK cells or Wnt ligand secretion from NK cells by LGK-974 decreased tumor progression. In addition, NK cell depletion or inhibition of their function improved anti-programmed cell death ligand 1 (PD-L1) antibody or chemotherapy response in mice with TNBC. Furthermore, tumor samples from patients with TNBC and non-TNBC revealed that increased numbers of CD56bright NK cells were present in TNBC tumors and were correlated to poor overall survival in patients with TNBC. Together, our findings identify a population of protumorigenic NK cells that may be exploited for both diagnostic and therapeutic strategies to improve outcomes for patients with TNBC.
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Affiliation(s)
- Gatha Thacker
- Department of Biomedical Sciences, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Surgery, Miller School of Medicine, University of Miami, Miami, FL 33136, USA
| | - Samantha Henry
- Cold Spring Harbor Laboratory, 1 Bungtown Road, Cold Spring Harbor, NY 11724, USA
| | - Ajeya Nandi
- Department of Biomedical Sciences, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Rahul Debnath
- Department of Biomedical Sciences, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Snahlata Singh
- Department of Biomedical Sciences, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Anupma Nayak
- Department of Pathology and Laboratory Medicine at the Hospital of the University of Pennsylvania, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA
| | - Barbara Susnik
- Department of Pathology, Sylvester Comprehensive Cancer Center, University of Miami Miller School of Medicine, Miami, FL 33136, USA
| | - Melinda M Boone
- Sylvester Comprehensive Cancer Center, University of Miami, Miller School of Medicine, Miami, FL 33136, USA
| | - Qing Zhang
- Department of Pathology, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA
| | - Susan B Kesmodel
- DeWitt Daughtry Family Department of Surgery, Division of Surgical Oncology, Miller School of Medicine, University of Miami, Miami, FL 33136, USA
| | - Sanjeev Gumber
- Department of Pathology, Emory University School of Medicine, Atlanta, GA 30322, USA
| | - Gokul M Das
- Department of Pharmacology and Therapeutics, Roswell Park Cancer Institute, Buffalo, NY 14263, USA
| | - Taku Kambayashi
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Camila O. Dos Santos
- Cold Spring Harbor Laboratory, 1 Bungtown Road, Cold Spring Harbor, NY 11724, USA
| | - Rumela Chakrabarti
- Department of Biomedical Sciences, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Surgery, Miller School of Medicine, University of Miami, Miami, FL 33136, USA
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8
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Chang X, Obianwuna UE, Wang J, Zhang H, Qi G, Qiu K, Wu S. Glycosylated proteins with abnormal glycosylation changes are potential biomarkers for early diagnosis of breast cancer. Int J Biol Macromol 2023; 236:123855. [PMID: 36868337 DOI: 10.1016/j.ijbiomac.2023.123855] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2022] [Revised: 02/22/2023] [Accepted: 02/23/2023] [Indexed: 03/05/2023]
Abstract
Conventional cancer management relies on tumor type and stage for diagnosis and treatment, which leads to recurrence and metastasis and death in young women. Early detection of proteins in the serum aids diagnosis, progression, and clinical outcomes, possibly improving survival rate of breast cancer patients. In this review, we provided an insight into the influence of aberrant glycosylation on breast cancer development and progression. Examined literatures revealed that mechanisms underlying glycosylation moieties alteration could enhance early detection, monitoring, and therapeutic efficacy in breast cancer patients. This would serve as a guide for the development of new serum biomarkers with higher sensitivity and specificity, providing possible serological biomarkers for breast cancer diagnosis, progression, and treatment.
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Affiliation(s)
- Xinyu Chang
- National Engineering Research Center of Biological Feed, Institute of Feed Research, Chinese Academy of Agricultural Sciences, Beijing 100081, China
| | - Uchechukwu Edna Obianwuna
- National Engineering Research Center of Biological Feed, Institute of Feed Research, Chinese Academy of Agricultural Sciences, Beijing 100081, China
| | - Jing Wang
- National Engineering Research Center of Biological Feed, Institute of Feed Research, Chinese Academy of Agricultural Sciences, Beijing 100081, China
| | - Haijun Zhang
- National Engineering Research Center of Biological Feed, Institute of Feed Research, Chinese Academy of Agricultural Sciences, Beijing 100081, China
| | - Guanghai Qi
- National Engineering Research Center of Biological Feed, Institute of Feed Research, Chinese Academy of Agricultural Sciences, Beijing 100081, China
| | - Kai Qiu
- National Engineering Research Center of Biological Feed, Institute of Feed Research, Chinese Academy of Agricultural Sciences, Beijing 100081, China.
| | - Shugeng Wu
- National Engineering Research Center of Biological Feed, Institute of Feed Research, Chinese Academy of Agricultural Sciences, Beijing 100081, China
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9
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Hamaneh M, Yu YK. A Simple Method for Robust and Accurate Intrinsic Subtyping of Breast Cancer. Cancer Inform 2023; 22:11769351231159893. [PMID: 37008073 PMCID: PMC10052604 DOI: 10.1177/11769351231159893] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2022] [Accepted: 02/07/2023] [Indexed: 04/04/2023] Open
Abstract
Motivation The PAM50 signature/method is widely used for intrinsic subtyping of breast cancer samples. However, depending on the number and composition of the samples included in a cohort, the method may assign different subtypes to the same sample. This lack of robustness is mainly due to the fact that PAM50 subtracts a reference profile, which is computed using all samples in the cohort, from each sample before classification. In this paper we propose modifications to PAM50 to develop a simple and robust single-sample classifier, called MPAM50, for intrinsic subtyping of breast cancer. Like PAM50, the modified method uses a nearest centroid approach for classification, but the centroids are computed differently, and the distances to the centroids are determined using an alternative method. Additionally, MPAM50 uses unnormalized expression values for classification and does not subtract a reference profile from the samples. In other words, MPAM50 classifies each sample independently, and so avoids the previously mentioned robustness issue. Results A training set was employed to find the new MPAM50 centroids. MPAM50 was then tested on 19 independent datasets (obtained using various expression profiling technologies) containing 9637 samples. Overall good agreement was observed between the PAM50- and MPAM50-assigned subtypes with a median accuracy of 0.792, which (we show) is comparable with the median concordance between various implementations of PAM50. Additionally, MPAM50- and PAM50-assigned intrinsic subtypes were found to agree comparably with the reported clinical subtypes. Also, survival analyses indicated that MPAM50 preserves the prognostic value of the intrinsic subtypes. These observations demonstrate that MPAM50 can replace PAM50 without loss of performance. On the other hand, MPAM50 was compared with 2 previously published single-sample classifiers, and with 3 alternative modified PAM50 approaches. The results indicated a superior performance by MPAM50. Conclusions MPAM50 is a robust, simple, and accurate single-sample classifier of intrinsic subtypes of breast cancer.
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Affiliation(s)
| | - Yi-Kuo Yu
- Yi-Kuo Yu, National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, 8600 Rockville Pike, Bethesda, MD 20894, USA.
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10
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The Transcriptomic Landscape of Pediatric Astrocytoma. Int J Mol Sci 2022; 23:ijms232012696. [PMID: 36293551 PMCID: PMC9604090 DOI: 10.3390/ijms232012696] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2022] [Revised: 09/29/2022] [Accepted: 10/11/2022] [Indexed: 11/17/2022] Open
Abstract
Central nervous system tumors are the most common solid neoplasia during childhood and represent one of the leading causes of cancer-related mortality. Tumors arising from astrocytic cells (astrocytomas) are the most frequently diagnosed, and according to their histological and pathological characteristics, they are classified into four categories. However, an additional layer of molecular classification considering the DNA sequence of the tumorigenesis-associated genes IDH1/2 and H3F3A has recently been incorporated into the classification guidelines. Although mutations in H3F3A are found exclusively in a subtype of grade IV pediatric astrocytoma, mutations in IDH1/2 genes are very rare in children under 14 years of age. The transcriptomic profiles of astrocytoma in adults and children have been extensively studied. However, there is scarce information on these profiles in pediatric populations considering the status of tumorigenesis-associated genes. Therefore, here we report the transcriptomic landscape of the four grades of pediatric astrocytoma by RNA sequencing. We found several well-documented biological functions associated with the misregulated genes in the four grades of astrocytoma, as well as additional biological pathways. Among the four grades of astrocytoma, we found shared misregulated genes that could have implications in tumorigenesis. Finally, we identified a transcriptional signature for almost all grades of astrocytoma that could be used as a transcription-based identification method.
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11
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Murgić J, Fröbe A, Challapalli A, Bahl A. ROLE OF ANDROGEN RECEPTOR-TARGETED AGENTS IN LOCALIZED PROSTATE CANCER. Acta Clin Croat 2022; 61:51-56. [PMID: 36938555 PMCID: PMC10022413 DOI: 10.20471/acc.2022.61.s3.7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/21/2023] Open
Abstract
Anti-androgen therapy continues to be a basic pilar of treatment for both localized and metastatic prostate cancer. The advent of new generation of androgen receptor targeted agents (ARTA) transformed the care of patients with advanced disease. After such a success, the steps were taken to incorporate a new generation of ARTAs into the treatment landscape of localized prostate cancer. High-risk prostate cancer represents the most aggressive form of localized disease with significant metastatic potential and poor outcome. Here, the impact of novel therapies will likely be profound and transforming. This clinical space has already been a showcase for multidisciplinary treatment where the combination of local therapies with systemic treatment gradually improved patient outcomes and the chances of cure. The most recent step in redefining the treatment of localized disease is the adoption of novel ARTAs moving forward the multidisciplinary platform. In this narrative review, we discuss current clinical evidence supporting the use of novel ARTAs in patients with localized high-risk prostate cancer and cover recent developments in biomarker-driven strategies for treatment individualization in this clinical context.
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Affiliation(s)
- Jure Murgić
- Department of Oncology and Nuclear Medicine, Sestre Milosrdnice University Hospital Center, Zagreb, Croatia
| | - Ana Fröbe
- Department of Oncology and Nuclear Medicine, Sestre Milosrdnice University Hospital Center, Zagreb, Croatia
- School of Dental Medicine, University of Zagreb, Zagreb, Croatia
| | - Amarnath Challapalli
- University Hospitals Bristol NHS Foundation Trust, Marlborough Street, Bristol BS13NU, UK
| | - Amit Bahl
- University Hospitals Bristol NHS Foundation Trust, Marlborough Street, Bristol BS13NU, UK
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12
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Selem NA, Youness RA, Gad MZ. What is beyond LncRNAs in breast cancer: A special focus on colon cancer-associated Transcript-1 (CCAT-1). Noncoding RNA Res 2021; 6:174-186. [PMID: 34938928 PMCID: PMC8666458 DOI: 10.1016/j.ncrna.2021.11.001] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2021] [Revised: 11/28/2021] [Accepted: 11/29/2021] [Indexed: 02/07/2023] Open
Abstract
Long non-coding RNAs (LncRNAs) play a vital role in the process of malignant transformation. In breast cancer (BC), lncRNAs field is currently under intensive investigations. Yet, the role of lncRNAs as promising diagnostic and/or prognostic biomarkers and as therapeutic target/tool among BC patients still needs a special focus from the biomedical scientists. In BC, triple negative breast cancer patients (TNBC) are the unlucky group as they are always represented with the worst prognosis and the highest mortality rates. For that reason, a special focus on TNBC and associated lncRNAs was addressed in this review. Colon cancer-associated transcript 1 (CCAT-1) is a newly discovered oncogenic lncRNA that has been emerged as a vital biomarker for diagnosis, prognosis and therapeutic interventions in multiple malignancies and showed differential expression among TNBC patients. In this review, the authors shed the light onto the general role of lncRNAs in BC and the specific functional activities, molecular mechanisms, competing endogenous ncRNA role of CCAT-1 in TNBC.
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Affiliation(s)
- Noha A. Selem
- Biochemistry Department, Faculty of Pharmacy and Biotechnology, German University in Cairo, Egypt
| | - Rana A. Youness
- Molecular Genetics Research Team (MGRT), Pharmaceutical Biology Department, Faculty of Pharmacy and Biotechnology, German University in Cairo, Egypt
- School of Life and Medical Sciences, University of Hertfordshire Hosted By Global Academic Foundation, New Administrative Capital, 11586, Cairo, Egypt
| | - Mohamed Z. Gad
- Biochemistry Department, Faculty of Pharmacy and Biotechnology, German University in Cairo, Egypt
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13
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Reed ER, Monti S. Multi-resolution characterization of molecular taxonomies in bulk and single-cell transcriptomics data. Nucleic Acids Res 2021; 49:e98. [PMID: 34226941 PMCID: PMC8464061 DOI: 10.1093/nar/gkab552] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2020] [Revised: 06/07/2021] [Accepted: 06/18/2021] [Indexed: 12/21/2022] Open
Abstract
As high-throughput genomics assays become more efficient and cost effective, their utilization has become standard in large-scale biomedical projects. These studies are often explorative, in that relationships between samples are not explicitly defined a priori, but rather emerge from data-driven discovery and annotation of molecular subtypes, thereby informing hypotheses and independent evaluation. Here, we present K2Taxonomer, a novel unsupervised recursive partitioning algorithm and associated R package that utilize ensemble learning to identify robust subgroups in a 'taxonomy-like' structure. K2Taxonomer was devised to accommodate different data paradigms, and is suitable for the analysis of both bulk and single-cell transcriptomics, and other '-omics', data. For each of these data types, we demonstrate the power of K2Taxonomer to discover known relationships in both simulated and human tissue data. We conclude with a practical application on breast cancer tumor infiltrating lymphocyte (TIL) single-cell profiles, in which we identified co-expression of translational machinery genes as a dominant transcriptional program shared by T cells subtypes, associated with better prognosis in breast cancer tissue bulk expression data.
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Affiliation(s)
- Eric R Reed
- Section of Computational Biomedicine, Boston University School of Medicine, Boston, MA 02118, USA
- Bioinformatics Program, College of Engineering, Boston University, Boston, MA 02118, USA
| | - Stefano Monti
- Section of Computational Biomedicine, Boston University School of Medicine, Boston, MA 02118, USA
- Bioinformatics Program, College of Engineering, Boston University, Boston, MA 02118, USA
- Department of Biostatistics, Boston University School of Public Health, Boston, MA 02118, USA
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14
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Bolf EL, Gillis NE, Davidson CD, Cozzens LM, Kogut S, Tomczak JA, Frietze S, Carr FE. Common tumor-suppressive signaling of thyroid hormone receptor beta in breast and thyroid cancer cells. Mol Carcinog 2021; 60:874-885. [PMID: 34534367 DOI: 10.1002/mc.23352] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2021] [Revised: 09/01/2021] [Accepted: 09/02/2021] [Indexed: 01/16/2023]
Abstract
The thyroid hormone receptor beta (TRβ) is a tumor suppressor in multiple types of solid tumors, most prominently in breast and thyroid cancer. An increased understanding of the molecular mechanisms by which TRβ abrogates tumorigenesis will aid in understanding the core tumor-suppressive functions of TRβ. Here, we restored TRβ expression in the MDA-MB-468 basal-like breast cancer cell line and perform RNA-sequencing to determine the TRβ-mediated changes in gene expression and associated signaling pathways. The TRβ expressing MDA-MB-468 cells exhibit a more epithelial character as determined by principle component analysis-based iterative PAM50 subtyping score and through reduced expression of mesenchymal cytokeratins. The epithelial to mesenchymal transition pathway is also significantly reduced. The MDA-MB-468 data set was further compared with RNA sequencing results from TRβ expressing thyroid cancer cell line SW1736 to determine which genes are TRβ correspondingly regulated across both cell types. Several pathways including lipid metabolism and chromatin remodeling processes were observed to be altered in the shared gene set. These data provide novel insights into the molecular mechanisms by which TRβ suppresses breast tumorigenesis.
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Affiliation(s)
- Eric L Bolf
- Department of Pharmacology, Larner College of Medicine, University of Vermont, Burlington, Vermont, USA.,University of Vermont Cancer Center, University of Vermont, Burlington, Vermont, USA
| | - Noelle E Gillis
- Department of Pharmacology, Larner College of Medicine, University of Vermont, Burlington, Vermont, USA.,University of Vermont Cancer Center, University of Vermont, Burlington, Vermont, USA
| | - Cole D Davidson
- Department of Pharmacology, Larner College of Medicine, University of Vermont, Burlington, Vermont, USA.,University of Vermont Cancer Center, University of Vermont, Burlington, Vermont, USA
| | - Lauren M Cozzens
- Department of Pharmacology, Larner College of Medicine, University of Vermont, Burlington, Vermont, USA
| | - Sophie Kogut
- Department of Biomedical and Health Sciences, College of Nursing and Health Sciences, University of Vermont, Burlington, Vermont, USA
| | - Jennifer A Tomczak
- Department of Pharmacology, Larner College of Medicine, University of Vermont, Burlington, Vermont, USA
| | - Seth Frietze
- University of Vermont Cancer Center, University of Vermont, Burlington, Vermont, USA.,Department of Biomedical and Health Sciences, College of Nursing and Health Sciences, University of Vermont, Burlington, Vermont, USA
| | - Frances E Carr
- Department of Pharmacology, Larner College of Medicine, University of Vermont, Burlington, Vermont, USA.,University of Vermont Cancer Center, University of Vermont, Burlington, Vermont, USA
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15
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Nwosu IO, Piccolo SR. A systematic review of datasets that can help elucidate relationships among gene expression, race, and immunohistochemistry-defined subtypes in breast cancer. Cancer Biol Ther 2021; 22:417-429. [PMID: 34412551 DOI: 10.1080/15384047.2021.1953902] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022] Open
Abstract
Scholarly requirements have led to a massive increase of transcriptomic data in the public domain, with millions of samples available for secondary research. We identified gene-expression datasets representing 10,214 breast-cancer patients in public databases. We focused on datasets that included patient metadata on race and/or immunohistochemistry (IHC) profiling of the ER, PR, and HER-2 proteins. This review provides a summary of these datasets and describes findings from 32 research articles associated with the datasets. These studies have helped to elucidate relationships between IHC, race, and/or treatment options, as well as relationships between IHC status and the breast-cancer intrinsic subtypes. We have also identified broad themes across the analysis methodologies used in these studies, including breast cancer subtyping, deriving predictive biomarkers, identifying differentially expressed genes, and optimizing data processing. Finally, we discuss limitations of prior work and recommend future directions for reusing these datasets in secondary analyses.
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Affiliation(s)
| | - Stephen R Piccolo
- Department of Biology, Brigham Young University, Provo, Utah, United States
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16
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Łukasiewicz S, Czeczelewski M, Forma A, Baj J, Sitarz R, Stanisławek A. Breast Cancer-Epidemiology, Risk Factors, Classification, Prognostic Markers, and Current Treatment Strategies-An Updated Review. Cancers (Basel) 2021; 13:cancers13174287. [PMID: 34503097 PMCID: PMC8428369 DOI: 10.3390/cancers13174287] [Citation(s) in RCA: 454] [Impact Index Per Article: 151.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2021] [Revised: 08/19/2021] [Accepted: 08/23/2021] [Indexed: 02/07/2023] Open
Abstract
Simple Summary Breast cancer is the most common cancer among women. It is estimated that 2.3 million new cases of BC are diagnosed globally each year. Based on mRNA gene expression levels, BC can be divided into molecular subtypes that provide insights into new treatment strategies and patient stratifications that impact the management of BC patients. This review addresses the overview on the BC epidemiology, risk factors, classification with an emphasis on molecular types, prognostic biomarkers, as well as possible treatment modalities. Abstract Breast cancer (BC) is the most frequently diagnosed cancer in women worldwide with more than 2 million new cases in 2020. Its incidence and death rates have increased over the last three decades due to the change in risk factor profiles, better cancer registration, and cancer detection. The number of risk factors of BC is significant and includes both the modifiable factors and non-modifiable factors. Currently, about 80% of patients with BC are individuals aged >50. Survival depends on both stage and molecular subtype. Invasive BCs comprise wide spectrum tumors that show a variation concerning their clinical presentation, behavior, and morphology. Based on mRNA gene expression levels, BC can be divided into molecular subtypes (Luminal A, Luminal B, HER2-enriched, and basal-like). The molecular subtypes provide insights into new treatment strategies and patient stratifications that impact the management of BC patients. The eighth edition of TNM classification outlines a new staging system for BC that, in addition to anatomical features, acknowledges biological factors. Treatment of breast cancer is complex and involves a combination of different modalities including surgery, radiotherapy, chemotherapy, hormonal therapy, or biological therapies delivered in diverse sequences.
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Affiliation(s)
- Sergiusz Łukasiewicz
- Department of Surgical Oncology, Center of Oncology of the Lublin Region St. Jana z Dukli, 20-091 Lublin, Poland; (S.Ł.); (A.S.)
| | - Marcin Czeczelewski
- Department of Forensic Medicine, Medical University of Lublin, 20-090 Lublin, Poland; (M.C.); (A.F.)
| | - Alicja Forma
- Department of Forensic Medicine, Medical University of Lublin, 20-090 Lublin, Poland; (M.C.); (A.F.)
| | - Jacek Baj
- Department of Human Anatomy, Medical University of Lublin, 20-090 Lublin, Poland;
| | - Robert Sitarz
- Department of Surgical Oncology, Center of Oncology of the Lublin Region St. Jana z Dukli, 20-091 Lublin, Poland; (S.Ł.); (A.S.)
- Department of Human Anatomy, Medical University of Lublin, 20-090 Lublin, Poland;
- Correspondence:
| | - Andrzej Stanisławek
- Department of Surgical Oncology, Center of Oncology of the Lublin Region St. Jana z Dukli, 20-091 Lublin, Poland; (S.Ł.); (A.S.)
- Department of Oncology, Chair of Oncology and Environmental Health, Medical University of Lublin, 20-081 Lublin, Poland
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17
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Qureshi R, Zhu M, Yan H. Visualization of Protein-Drug Interactions for the Analysis of Drug Resistance in Lung Cancer. IEEE J Biomed Health Inform 2021; 25:1839-1848. [PMID: 32991295 DOI: 10.1109/jbhi.2020.3027511] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
Non-small cell lung cancer (NSCLC) caused by mutation of the epidermal growth factor receptor (EGFR) is a major cause of death worldwide. Tyrosine kinase inhibitors (TKIs) of EGFR have been developed and show promising results at the initial stage of therapy. However, in most cases, their efficacy becomes limited due to the emergence of secondary mutations causing drug resistance after about a year. In this work, we investigated the mechanism of drug resistance due to these mutations. We performed molecular dynamics (MD) simulations of EGFR-drug interactions to obtain Euclidean distance and binding free energy values to analyse drug resistance and visualize drug-protein interactions. A PCA-based method is proposed to find normal, rigid, flexible, and critical residues. We have established a systematic method for the visualization of protein-drug interactions, which provides an effective framework for the analysis of drug resistance in lung cancer at the atomic level.
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18
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Liu M, Xi L, Tan T, Jin L, Wang Z, He N. A novel aptamer-based histochemistry assay for specific diagnosis of clinical breast cancer tissues. CHINESE CHEM LETT 2021. [DOI: 10.1016/j.cclet.2020.11.072] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
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19
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Del Giudice M, Peirone S, Perrone S, Priante F, Varese F, Tirtei E, Fagioli F, Cereda M. Artificial Intelligence in Bulk and Single-Cell RNA-Sequencing Data to Foster Precision Oncology. Int J Mol Sci 2021; 22:ijms22094563. [PMID: 33925407 PMCID: PMC8123853 DOI: 10.3390/ijms22094563] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2021] [Revised: 04/21/2021] [Accepted: 04/23/2021] [Indexed: 02/01/2023] Open
Abstract
Artificial intelligence, or the discipline of developing computational algorithms able to perform tasks that requires human intelligence, offers the opportunity to improve our idea and delivery of precision medicine. Here, we provide an overview of artificial intelligence approaches for the analysis of large-scale RNA-sequencing datasets in cancer. We present the major solutions to disentangle inter- and intra-tumor heterogeneity of transcriptome profiles for an effective improvement of patient management. We outline the contributions of learning algorithms to the needs of cancer genomics, from identifying rare cancer subtypes to personalizing therapeutic treatments.
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Affiliation(s)
- Marco Del Giudice
- Cancer Genomics and Bioinformatics Unit, IIGM—Italian Institute for Genomic Medicine, c/o IRCCS, Str. Prov.le 142, km 3.95, 10060 Candiolo, TO, Italy; (M.D.G.); (S.P.); (S.P.); (F.P.); (F.V.)
- Candiolo Cancer Institute, FPO—IRCCS, Str. Prov.le 142, km 3.95, 10060 Candiolo, TO, Italy
| | - Serena Peirone
- Cancer Genomics and Bioinformatics Unit, IIGM—Italian Institute for Genomic Medicine, c/o IRCCS, Str. Prov.le 142, km 3.95, 10060 Candiolo, TO, Italy; (M.D.G.); (S.P.); (S.P.); (F.P.); (F.V.)
- Department of Physics and INFN, Università degli Studi di Torino, via P.Giuria 1, 10125 Turin, Italy
| | - Sarah Perrone
- Cancer Genomics and Bioinformatics Unit, IIGM—Italian Institute for Genomic Medicine, c/o IRCCS, Str. Prov.le 142, km 3.95, 10060 Candiolo, TO, Italy; (M.D.G.); (S.P.); (S.P.); (F.P.); (F.V.)
- Department of Physics, Università degli Studi di Torino, via P.Giuria 1, 10125 Turin, Italy
| | - Francesca Priante
- Cancer Genomics and Bioinformatics Unit, IIGM—Italian Institute for Genomic Medicine, c/o IRCCS, Str. Prov.le 142, km 3.95, 10060 Candiolo, TO, Italy; (M.D.G.); (S.P.); (S.P.); (F.P.); (F.V.)
- Department of Physics, Università degli Studi di Torino, via P.Giuria 1, 10125 Turin, Italy
| | - Fabiola Varese
- Cancer Genomics and Bioinformatics Unit, IIGM—Italian Institute for Genomic Medicine, c/o IRCCS, Str. Prov.le 142, km 3.95, 10060 Candiolo, TO, Italy; (M.D.G.); (S.P.); (S.P.); (F.P.); (F.V.)
- Department of Life Science and System Biology, Università degli Studi di Torino, via Accademia Albertina 13, 10123 Turin, Italy
| | - Elisa Tirtei
- Paediatric Onco-Haematology Division, Regina Margherita Children’s Hospital, City of Health and Science of Turin, 10126 Turin, Italy; (E.T.); (F.F.)
| | - Franca Fagioli
- Paediatric Onco-Haematology Division, Regina Margherita Children’s Hospital, City of Health and Science of Turin, 10126 Turin, Italy; (E.T.); (F.F.)
- Department of Public Health and Paediatric Sciences, University of Torino, 10124 Turin, Italy
| | - Matteo Cereda
- Cancer Genomics and Bioinformatics Unit, IIGM—Italian Institute for Genomic Medicine, c/o IRCCS, Str. Prov.le 142, km 3.95, 10060 Candiolo, TO, Italy; (M.D.G.); (S.P.); (S.P.); (F.P.); (F.V.)
- Candiolo Cancer Institute, FPO—IRCCS, Str. Prov.le 142, km 3.95, 10060 Candiolo, TO, Italy
- Correspondence: ; Tel.: +39-011-993-3969
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20
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Lim DK, Rashid NU, Ibrahim JG. MODEL-BASED FEATURE SELECTION AND CLUSTERING OF RNA-SEQ DATA FOR UNSUPERVISED SUBTYPE DISCOVERY. Ann Appl Stat 2021; 15:481-508. [PMID: 34457104 PMCID: PMC8386505 DOI: 10.1214/20-aoas1407] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Clustering is a form of unsupervised learning that aims to uncover latent groups within data based on similarity across a set of features. A common application of this in biomedical research is in delineating novel cancer subtypes from patient gene expression data, given a set of informative genes. However, it is typically unknown a priori what genes may be informative in discriminating between clusters, and what the optimal number of clusters are. Few methods exist for performing unsupervised clustering of RNA-seq samples, and none currently adjust for between-sample global normalization factors, select cluster-discriminatory genes, or account for potential confounding variables during clustering. To address these issues, we propose the Feature Selection and Clustering of RNA-seq (FSCseq): a model-based clustering algorithm that utilizes a finite mixture of regression (FMR) model and the quadratic penalty method with a Smoothly-Clipped Absolute Deviation (SCAD) penalty. The maximization is done by a penalized Classification EM algorithm, allowing us to include normalization factors and confounders in our modeling framework. Given the fitted model, our framework allows for subtype prediction in new patients via posterior probabilities of cluster membership, even in the presence of batch effects. Based on simulations and real data analysis, we show the advantages of our method relative to competing approaches.
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Affiliation(s)
- David K Lim
- University of North Carolina at Chapel Hill, NC, USA
| | - Naim U Rashid
- University of North Carolina at Chapel Hill, NC, USA
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21
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Rojas K, Baliu-Piqué M, Manzano A, Saiz-Ladera C, García-Barberán V, Cimas FJ, Pérez-Segura P, Pandiella A, Győrffy B, Ocana A. In silico transcriptomic mapping of integrins and immune activation in Basal-like and HER2+ breast cancer. Cell Oncol (Dordr) 2021; 44:569-580. [PMID: 33469836 DOI: 10.1007/s13402-020-00583-9] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2020] [Revised: 12/09/2020] [Accepted: 12/11/2020] [Indexed: 12/11/2022] Open
Abstract
PURPOSE Integrins, transmembrane receptors that mediate cell-extracellular matrix and cell-cell interactions, have been linked to several cancer-associated features. A less explored function of integrins in cancer is their role in leukocyte homing and activation. Understanding their relationship with immune cell infiltrates and immune checkpoints is an area of interest in cancer research. METHODS The expression of 33 different integrins was evaluated in relation with breast cancer patient outcome using transcriptomic data (Affymetrix dataset, exploratory cohort) and the METABRIC study (validation cohort). The TIMER online tool was used to assess the association of the identified integrin genes with immune cell infiltration, and the TCGA and METABRIC studies to assess correlations between integrin gene expression and genomic signatures of immune activation. RESULTS We identified 7 genes coding for integrin α and β subunits, i.e., ITGA4, ITGB2, ITGAX, ITGB7, ITGAM, ITGAL and ITGA8, which predict a favorable prognosis in Basal-like and HER2+ breast cancers. Their expression positively correlated with the presence of immune cell infiltrates within the tumor (dendritic cells, CD4+ T-cells, neutrophils, CD8+ T-cells and B-cells), with markers of T-cell activation and antigen presentation, and with gene signatures of immune surveillance (cytotoxic T lymphocyte activation and IFN gamma signature). By contrast, we found that genes coding for integrins that predicted a detrimental outcome (IBSP, ITGB3BP, ITGB6, ITGB1 and ITGAV) were not associated with any of these parameters. CONCLUSIONS We identified an integrin signature composed of 7 genes with potential to recognize immune infiltrated and activated Basal-like and HER2+ breast cancers with a favorable prognosis.
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Affiliation(s)
- Katerin Rojas
- Experimental Therapeutics Unit, Medical Oncology Department, Hospital Clínico San Carlos (HCSC), Instituto de Investigación Sanitaria (IdISSC) and CIBERONC, Calle Del Prof Martín Lagos, s/n, 28040, Madrid, Spain
| | - Mariona Baliu-Piqué
- Experimental Therapeutics Unit, Medical Oncology Department, Hospital Clínico San Carlos (HCSC), Instituto de Investigación Sanitaria (IdISSC) and CIBERONC, Calle Del Prof Martín Lagos, s/n, 28040, Madrid, Spain
| | - Aránzazu Manzano
- Experimental Therapeutics Unit, Medical Oncology Department, Hospital Clínico San Carlos (HCSC), Instituto de Investigación Sanitaria (IdISSC) and CIBERONC, Calle Del Prof Martín Lagos, s/n, 28040, Madrid, Spain
| | - Cristina Saiz-Ladera
- Experimental Therapeutics Unit, Medical Oncology Department, Hospital Clínico San Carlos (HCSC), Instituto de Investigación Sanitaria (IdISSC) and CIBERONC, Calle Del Prof Martín Lagos, s/n, 28040, Madrid, Spain
| | - Vanesa García-Barberán
- Experimental Therapeutics Unit, Medical Oncology Department, Hospital Clínico San Carlos (HCSC), Instituto de Investigación Sanitaria (IdISSC) and CIBERONC, Calle Del Prof Martín Lagos, s/n, 28040, Madrid, Spain
| | - Francisco J Cimas
- Translational Research Unit, Translational Oncology Laboratory, Albacete University Hospital, Albacete, Spain
- Centro Regional de Investigaciones Biomedicas, Castilla-La Mancha University (CRIB-UCLM), Albacete, Spain
| | - Pedro Pérez-Segura
- Experimental Therapeutics Unit, Medical Oncology Department, Hospital Clínico San Carlos (HCSC), Instituto de Investigación Sanitaria (IdISSC) and CIBERONC, Calle Del Prof Martín Lagos, s/n, 28040, Madrid, Spain
| | - Atanasio Pandiella
- Instituto de Biología Molecular y Celular del Cáncer and CIBERONC, CSIC , Salamanca, Spain
| | - Balázs Győrffy
- Department of Bioinformatics, Semmelweis University, Budapest, Hungary
- 2nd Department of Paediatrics, Semmelweis University, Budapest, Hungary
- TTK Lendület Cancer Biomarker Research Group, Institute of Enzymology, Budapest, Hungary
| | - Alberto Ocana
- Experimental Therapeutics Unit, Medical Oncology Department, Hospital Clínico San Carlos (HCSC), Instituto de Investigación Sanitaria (IdISSC) and CIBERONC, Calle Del Prof Martín Lagos, s/n, 28040, Madrid, Spain.
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22
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Szymiczek A, Lone A, Akbari MR. Molecular intrinsic versus clinical subtyping in breast cancer: A comprehensive review. Clin Genet 2020; 99:613-637. [PMID: 33340095 DOI: 10.1111/cge.13900] [Citation(s) in RCA: 41] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2020] [Revised: 12/12/2020] [Accepted: 12/14/2020] [Indexed: 12/15/2022]
Abstract
Breast cancer is a heterogeneous disease manifesting diversity at the molecular, histological and clinical level. The development of breast cancer classification was centered on informing clinical decisions. The current approach to the classification of breast cancer, which categorizes this disease into clinical subtypes based on the detection of estrogen receptor, progesterone receptor, human epidermal growth factor receptor 2, and proliferation marker Ki67, is not ideal. This is manifested as a heterogeneity of therapeutic responses and outcomes within the clinical subtypes. The newer classification model, based on gene expression profiling (intrinsic subtyping) informs about transcriptional responses downstream from IHC single markers, revealing deeper appreciation for the disease heterogeneity and capturing tumor biology in a more comprehensive way than an expression of a single protein or gene alone. While accumulating evidences suggest that intrinsic subtypes provide clinically relevant information beyond clinical surrogates, it is imperative to establish whether the current conventional immunohistochemistry-based clinical subtyping approach could be improved by gene expression profiling and if this approach has a potential to translate into clinical practice.
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Affiliation(s)
- Agata Szymiczek
- Women's College Research Institute, University of Toronto, Toronto, Ontario, Canada
| | - Amna Lone
- Women's College Research Institute, University of Toronto, Toronto, Ontario, Canada
| | - Mohammad R Akbari
- Women's College Research Institute, University of Toronto, Toronto, Ontario, Canada.,Institute of Medical Science, Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada.,Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada
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23
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Amirkhani Namagerdi A, d'Angelo D, Ciani F, Iannuzzi CA, Napolitano F, Avallone L, De Laurentiis M, Giordano A. Triple-Negative Breast Cancer Comparison With Canine Mammary Tumors From Light Microscopy to Molecular Pathology. Front Oncol 2020; 10:563779. [PMID: 33282730 PMCID: PMC7689249 DOI: 10.3389/fonc.2020.563779] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2020] [Accepted: 10/19/2020] [Indexed: 12/12/2022] Open
Abstract
Many similar characteristics in human and dog cancers including, spontaneous development, clinical presentation, tumor heterogeneity, disease progression, and response to standard therapies have promoted the approval of this comparative model as an alternative to mice. Breast cancer represents the second most frequent neoplasm in humans after lung cancer. Triple-negative breast cancers (TNBC) constitute around 15% of all cases of breast cancer and do not express estrogen receptor (ER), progesterone receptor (PR), and do not overexpress human epidermal growth factor receptor 2 (HER2). As a result, they do not benefit from hormonal or trastuzumab-based therapy. Patients with TNBC have worse overall survival than patients with non-TNBC. Lehmann and collaborators described six different molecular subtypes of TNBC which further demonstrated its transcriptional heterogeneity. This six TNBC subtype classification has therapeutic implications. Breast cancer is the second most frequent neoplasm in sexually intact female dogs after skin cancer. Canine mammary tumors are a naturally occurring heterogeneous group of cancers that have several features in common with human breast cancer (HBC). These similarities include etiology, signaling pathway activation, and histological classification. Molecularly CMTs are more like TNBCs, and therefore dogs are powerful spontaneous models of cancer to test new therapeutic approaches, particularly for human TNBCs. More malignant tumors of the breast are more often ER and PR negative in both humans and dogs. Promising breast cancer biomarkers in both humans and canines are cancer-associated stroma (CAS), circulating tumor cells and tumor DNA (ctDNA), exosomes and miRNAs, and metabolites.
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Affiliation(s)
| | - Danila d'Angelo
- Department of Veterinary Medicine and Animal Production, University of Naples Federico II, Naples, Italy
| | - Francesca Ciani
- Department of Veterinary Medicine and Animal Production, University of Naples Federico II, Naples, Italy
| | | | - Francesco Napolitano
- Department of Veterinary Medicine and Animal Production, University of Naples Federico II, Naples, Italy.,CCEINGE, Biotecnologie Avanzate, Naples, Italy
| | - Luigi Avallone
- Department of Veterinary Medicine and Animal Production, University of Naples Federico II, Naples, Italy
| | - Michelino De Laurentiis
- Breast Oncology Division, Istituto Nazionale Tumori IRCCS Fondazione G. Pascale, Naples, Italy
| | - Antonio Giordano
- Center for Biotechnology, College of Science and Technology, Sbarro Institute for Cancer Research and Molecular Medicine, Temple University, Philadelphia, PA, United States.,Department of Medical Biotechnologies, University of Siena, Siena, Italy
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24
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Santolla MF, Maggiolini M. The FGF/FGFR System in Breast Cancer: Oncogenic Features and Therapeutic Perspectives. Cancers (Basel) 2020; 12:E3029. [PMID: 33081025 PMCID: PMC7603197 DOI: 10.3390/cancers12103029] [Citation(s) in RCA: 50] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2020] [Revised: 10/12/2020] [Accepted: 10/16/2020] [Indexed: 12/13/2022] Open
Abstract
One of the major challenges in the treatment of breast cancer is the heterogeneous nature of the disease. With multiple subtypes of breast cancer identified, there is an unmet clinical need for the development of therapies particularly for the less tractable subtypes. Several transduction mechanisms are involved in the progression of breast cancer, therefore making the assessment of the molecular landscape that characterizes each patient intricate. Over the last decade, numerous studies have focused on the development of tyrosine kinase inhibitors (TKIs) to target the main pathways dysregulated in breast cancer, however their effectiveness is often limited either by resistance to treatments or the appearance of adverse effects. In this context, the fibroblast growth factor/fibroblast growth factor receptor (FGF/FGFR) system represents an emerging transduction pathway and therapeutic target to be fully investigated among the diverse anti-cancer settings in breast cancer. Here, we have recapitulated previous studies dealing with FGFR molecular aberrations, such as the gene amplification, point mutations, and chromosomal translocations that occur in breast cancer. Furthermore, alterations in the FGF/FGFR signaling across the different subtypes of breast cancer have been described. Next, we discussed the functional interplay between the FGF/FGFR axis and important components of the breast tumor microenvironment. Lastly, we pointed out the therapeutic usefulness of FGF/FGFR inhibitors, as revealed by preclinical and clinical models of breast cancer.
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Affiliation(s)
| | - Marcello Maggiolini
- Department of Pharmacy, Health and Nutritional Sciences, University of Calabria, 87036 Rende, Italy;
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25
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Hassan MK, Kumar D, Patel SA, Dixit M. EEF1A2 triggers stronger ERK mediated metastatic program in ER negative breast cancer cells than in ER positive cells. Life Sci 2020; 262:118553. [PMID: 33035587 DOI: 10.1016/j.lfs.2020.118553] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2020] [Revised: 08/18/2020] [Accepted: 09/30/2020] [Indexed: 12/11/2022]
Abstract
AIMS Ever since EEF1A2's identification as a putative oncogene in breast cancer, it has stimulated curiosity due to its contrasting role in predicting the prognostic values in breast cancer patients. Contradicting reports suggest it to be playing a pro-survival as well as a negative role in the survival of patients. This prompted us to find the association of this protein with molecular subtypes in breast cancer and its effect on EMT in representative cell lines. MAIN METHODS Data-mining was carried out to ascertain the correlation of EEF1A2 with molecular subtypes in breast cancer patients. Scratch wound healing and transwell invasion assays were carried out to assess its role in migration and invasion. Western blot, qRT-PCR, and ELISA were carried out to determine key signalling pathways, cytokines, and EMT factors responsible for the observed phenotype. KEY FINDINGS EEF1A2 was associated with ER receptor positivity in breast cancer and was involved in its transcriptional regulation. It induced a robust metastatic program in MDA-MB-231 (a triple-negative cell line), and induced significant changes in its invasive and migratory properties via activation of the ERK pathway. This was not the case in MCF7 which is an ER-positive cell line. SIGNIFICANCE We highlight the specific tendency of EEF1A2 to enhance invasive properties of cell lines in particular molecular subtype only. This sheds light on its selective role in regulating oncogenic processes in breast cancer and could explain its contradicting association with good survival, despite being an oncogene in a certain cohort of breast cancer patients.
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Affiliation(s)
- Md Khurshidul Hassan
- School of Biological Sciences, National Institute of Science Education and Research, HBNI, PO-Bhimpur-Padanpur, Jatni, Khurda 752050, Odisha, India
| | - Dinesh Kumar
- School of Biological Sciences, National Institute of Science Education and Research, HBNI, PO-Bhimpur-Padanpur, Jatni, Khurda 752050, Odisha, India
| | - Saket Awadhesbhai Patel
- School of Biological Sciences, National Institute of Science Education and Research, HBNI, PO-Bhimpur-Padanpur, Jatni, Khurda 752050, Odisha, India
| | - Manjusha Dixit
- School of Biological Sciences, National Institute of Science Education and Research, HBNI, PO-Bhimpur-Padanpur, Jatni, Khurda 752050, Odisha, India.
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26
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Cascianelli S, Molineris I, Isella C, Masseroli M, Medico E. Machine learning for RNA sequencing-based intrinsic subtyping of breast cancer. Sci Rep 2020; 10:14071. [PMID: 32826944 PMCID: PMC7442834 DOI: 10.1038/s41598-020-70832-2] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2020] [Accepted: 07/23/2020] [Indexed: 01/01/2023] Open
Abstract
Stratification of breast cancer (BC) into molecular subtypes by multigene expression assays is of demonstrated clinical utility. In principle, global RNA-sequencing (RNA-seq) should enable reconstructing existing transcriptional classifications of BC samples. Yet, it is not clear whether adaptation to RNA-seq of classifiers originally developed using PCR or microarrays, or reconstruction through machine learning (ML) is preferable. Hence, we focused on robustness and portability of PAM50, a nearest-centroid classifier developed on microarray data to identify five BC "intrinsic subtypes". We found that standard PAM50 is profoundly affected by the composition of the sample cohort used for reference construction, and we propose a strategy, named AWCA, to mitigate this issue, improving classification robustness, with over 90% of concordance, and prognostic ability; we also show that AWCA-based PAM50 can even be applied as single-sample method. Furthermore, we explored five supervised learners to build robust, single-sample intrinsic subtype callers via RNA-seq. From our ML-based survey, regularized multiclass logistic regression (mLR) displayed the best performance, further increased by ad-hoc gene selection on the global transcriptome. On external test sets, mLR classifications reached 90% concordance with PAM50-based calls, without need of reference sample; mLR proven robustness and prognostic ability make it an equally valuable single-sample method to strengthen BC subtyping.
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Affiliation(s)
- Silvia Cascianelli
- Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Piazza Leonardo da Vinci 32, 20133, Milan, Italy.
| | - Ivan Molineris
- Candiolo Cancer Institute, FPO-IRCCS, S.P. 142, km 3.95, 10060, Candiolo, TO, Italy
| | - Claudio Isella
- Candiolo Cancer Institute, FPO-IRCCS, S.P. 142, km 3.95, 10060, Candiolo, TO, Italy
| | - Marco Masseroli
- Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Piazza Leonardo da Vinci 32, 20133, Milan, Italy
| | - Enzo Medico
- Candiolo Cancer Institute, FPO-IRCCS, S.P. 142, km 3.95, 10060, Candiolo, TO, Italy
- Department of Oncology, University of Torino, S.P. 142, km 3.95, 10060, Candiolo, TO, Italy
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27
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UDP-glucose 6-dehydrogenase knockout impairs migration and decreases in vivo metastatic ability of breast cancer cells. Cancer Lett 2020; 492:21-30. [PMID: 32768525 DOI: 10.1016/j.canlet.2020.07.031] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2020] [Revised: 07/14/2020] [Accepted: 07/24/2020] [Indexed: 12/24/2022]
Abstract
Dysregulated metabolism is a hallmark of cancer that supports tumor growth and metastasis. One understudied aspect of cancer metabolism is altered nucleotide sugar biosynthesis, which drives aberrant cell surface glycosylation known to support various aspects of cancer cell behavior including migration and signaling. We examined clinical association of nucleotide sugar pathway gene expression and found that UGDH, encoding UDP-glucose 6-dehydrogenase which catalyzes production of UDP-glucuronate, is associated with worse breast cancer patient survival. Knocking out the mouse homolog Ugdh in highly-metastatic 6DT1 breast cancer cells impaired migration ability without affecting in vitro proliferation. Further, Ugdh-KO resulted in significantly decreased metastatic capacity in vivo when the cells were orthotopically injected in syngeneic mice. Our experiments show that UDP-glucuronate biosynthesis is critical for metastasis in a mouse model of breast cancer.
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28
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Yoon S, Won HS, Kang K, Qiu K, Park WJ, Ko YH. Hormone Receptor-Status Prediction in Breast Cancer Using Gene Expression Profiles and Their Macroscopic Landscape. Cancers (Basel) 2020; 12:cancers12051165. [PMID: 32380759 PMCID: PMC7281553 DOI: 10.3390/cancers12051165] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2020] [Revised: 04/26/2020] [Accepted: 05/03/2020] [Indexed: 11/24/2022] Open
Abstract
The cost of next-generation sequencing technologies is rapidly declining, making RNA-seq-based gene expression profiling (GEP) an affordable technique for predicting receptor expression status and intrinsic subtypes in breast cancer patients. Based on the expression levels of co-expressed genes, GEP-based receptor-status prediction can classify clinical subtypes more accurately than can immunohistochemistry (IHC). Using data from The Cancer Genome Atlas Breast Invasive Carcinoma (TCGA BRCA) and Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) datasets, we identified common predictor genes found in both datasets and performed receptor-status prediction based on these genes. By assessing the survival outcomes of patients classified using GEP- or IHC-based receptor status, we compared the prognostic value of the two methods. We found that GEP-based HR prediction provided higher concordance with the intrinsic subtypes and a stronger association with treatment outcomes than did IHC-based hormone receptor (HR) status. GEP-based prediction improved the identification of patients who could benefit from hormone therapy, even in patients with non-luminal breast cancer. We also confirmed that non-matching subgroup classification affected the survival of breast cancer patients and that this could be largely overcome by GEP-based receptor-status prediction. In conclusion, GEP-based prediction provides more reliable classification of HR status, improving therapeutic decision making for breast cancer patients.
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Affiliation(s)
- Seokhyun Yoon
- Department of Electronics Eng., College of Engineering, Dankook University, Yongin-si 16890, Korea; (S.Y.); (K.Q.)
| | - Hye Sung Won
- Department of Internal Medicine, College of Medicine, The Catholic University of Korea, Seoul 06591, Korea;
| | - Keunsoo Kang
- Department of Microbiology, College of Natural Sciences, Dankook University, Cheonan-si 31116, Korea;
| | - Kexin Qiu
- Department of Electronics Eng., College of Engineering, Dankook University, Yongin-si 16890, Korea; (S.Y.); (K.Q.)
| | - Woong June Park
- Department of Molecular Biology, College of Natural Sciences, Dankook University, Cheonan-si 31116, Korea;
| | - Yoon Ho Ko
- Department of Internal Medicine, College of Medicine, The Catholic University of Korea, Seoul 06591, Korea;
- Cancer Research Institute, College of Medicine, The Catholic University of Korea, Seoul 06591, Korea
- Correspondence:
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