Yiqing Zhang1,2, Sarah Asad1,2, Zachary Weber3, David Tallman1, William Nock1,2, Meghan Wyse2,4, Jerome F Bey1, Kristin L Dean1, Elizabeth J Adams1,2, Sinclair Stockard1, Jasneet Singh1, Eric P Winer5, Nancy U Lin5, Yi-Zhou Jiang6, Ding Ma6, Peng Wang7, Leming Shi8, Wei Huang9, Zhi-Ming Shao6, Mathew Cherian1,2,4, Maryam B Lustberg1,2,4, Bhuvaneswari Ramaswamy1,2,4, Sagar Sardesai1,2,4, Jeffrey VanDeusen1,2,4, Nicole Williams1,2,4, Robert Wesolowski1,2,4, Samilia Obeng-Gyasi1,4, Gina M Sizemore1, Steven T Sizemore1, Claire Verschraegen1,2, Daniel G Stover10,11,12,13,14. 1. Ohio State University College of Medicine, 370 W 9th Ave, Columbus, OH, 43210, USA. 2. Division of Medical Oncology, Ohio State University Comprehensive Cancer Center, 460 W 10th Ave, Columbus, OH, 43210, USA. 3. Department of Biomedical Informatics, The Ohio State University, Columbus, OH, 43210, USA. 4. Stefanie Spielman Comprehensive Breast Center, 1145 Olentangy River Rd, Columbus, OH, 43212, USA. 5. Department of Medical Oncology, Susan F. Smith Center for Women's Cancers, Dana-Farber Cancer Institute, 450 Brookline Avenue, Boston, MA, 02215, USA. 6. Department of Breast Surgery, Precision Cancer Medicine Center, Fudan University Shanghai Cancer Center, 270 Dong'an Road, Shanghai, 200032, P.R. China. 7. Shanghai Institute of Nutrition and Health, Shanghai Institutes for Biological Sciences, University of Chinese Academy of Sciences, Chinese Academy of Sciences, 320 Yueyang Road, Shanghai, 200031, P.R. China. 8. State Key Laboratory of Genetic Engineering, School of Life Sciences and Human Phenome Institute, Fudan University, 2005 Songhu Road, Shanghai, 200438, P.R. China. 9. Shanghai-MOST Key Laboratory of Health and Disease Genomics, Chinese National Human Genome Center at Shanghai (CHGC) and Shanghai Industrial Technology Institute (SITI), 250 Bibo Road, Shanghai, 201203, P.R. China. 10. Ohio State University College of Medicine, 370 W 9th Ave, Columbus, OH, 43210, USA. daniel.stover@osumc.edu. 11. Division of Medical Oncology, Ohio State University Comprehensive Cancer Center, 460 W 10th Ave, Columbus, OH, 43210, USA. daniel.stover@osumc.edu. 12. Department of Biomedical Informatics, The Ohio State University, Columbus, OH, 43210, USA. daniel.stover@osumc.edu. 13. Stefanie Spielman Comprehensive Breast Center, 1145 Olentangy River Rd, Columbus, OH, 43212, USA. daniel.stover@osumc.edu. 14. Stefanie Spielman Comprehensive Breast Center, Ohio State University Comprehensive Cancer Center, Biomedical Research Tower, Room 512, Columbus, OH, 43210, USA. daniel.stover@osumc.edu.
Abstract
BACKGROUND: Triple-negative breast cancer (TNBC) is a heterogeneous disease and we have previously shown that rapid relapse of TNBC is associated with distinct sociodemographic features. We hypothesized that rapid versus late relapse in TNBC is also defined by distinct clinical and genomic features of primary tumors. METHODS: Using three publicly-available datasets, we identified 453 patients diagnosed with primary TNBC with adequate follow-up to be characterized as 'rapid relapse' (rrTNBC; distant relapse or death ≤2 years of diagnosis), 'late relapse' (lrTNBC; > 2 years) or 'no relapse' (nrTNBC: > 5 years no relapse/death). We explored basic clinical and primary tumor multi-omic data, including whole transcriptome (n = 453), and whole genome copy number and mutation data for 171 cancer-related genes (n = 317). Association of rapid relapse with clinical and genomic features were assessed using Pearson chi-squared tests, t-tests, ANOVA, and Fisher exact tests. We evaluated logistic regression models of clinical features with subtype versus two models that integrated significant genomic features. RESULTS: Relative to nrTNBC, both rrTNBC and lrTNBC had significantly lower immune signatures and immune signatures were highly correlated to anti-tumor CD8 T-cell, M1 macrophage, and gamma-delta T-cell CIBERSORT inferred immune subsets. Intriguingly, lrTNBCs were enriched for luminal signatures. There was no difference in tumor mutation burden or percent genome altered across groups. Logistic regression mModels that incorporate genomic features significantly outperformed standard clinical/subtype models in training (n = 63 patients), testing (n = 63) and independent validation (n = 34) cohorts, although performance of all models were overall modest. CONCLUSIONS: We identify clinical and genomic features associated with rapid relapse TNBC for further study of this aggressive TNBC subset.
BACKGROUND: Triple-negative breast cancer (TNBC) is a heterogeneous disease and we have previously shown that rapid relapse of TNBC is associated with distinct sociodemographic features. We hypothesized that rapid versus late relapse in TNBC is also defined by distinct clinical and genomic features of primary tumors. METHODS: Using three publicly-available datasets, we identified 453 patients diagnosed with primary TNBC with adequate follow-up to be characterized as 'rapid relapse' (rrTNBC; distant relapse or death ≤2 years of diagnosis), 'late relapse' (lrTNBC; > 2 years) or 'no relapse' (nrTNBC: > 5 years no relapse/death). We explored basic clinical and primary tumor multi-omic data, including whole transcriptome (n = 453), and whole genome copy number and mutation data for 171 cancer-related genes (n = 317). Association of rapid relapse with clinical and genomic features were assessed using Pearson chi-squared tests, t-tests, ANOVA, and Fisher exact tests. We evaluated logistic regression models of clinical features with subtype versus two models that integrated significant genomic features. RESULTS: Relative to nrTNBC, both rrTNBC and lrTNBC had significantly lower immune signatures and immune signatures were highly correlated to anti-tumorCD8 T-cell, M1 macrophage, and gamma-delta T-cell CIBERSORT inferred immune subsets. Intriguingly, lrTNBCs were enriched for luminal signatures. There was no difference in tumor mutation burden or percent genome altered across groups. Logistic regression mModels that incorporate genomic features significantly outperformed standard clinical/subtype models in training (n = 63 patients), testing (n = 63) and independent validation (n = 34) cohorts, although performance of all models were overall modest. CONCLUSIONS: We identify clinical and genomic features associated with rapid relapse TNBC for further study of this aggressive TNBC subset.
Entities:
Keywords:
Breast Cancer; Machine learning; Triple-negative breast cancer
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