Jacquelyn Dillon, MD (she/her/hers)
Breast Service, Department of Surgery
Memorial Sloan Kettering Cancer Center
NY, New York, United States
Xuechun Bai, MA
Computational Oncology, Department of Epidemiology and Biostatistics
Memorial Sloan Kettering Cancer Center
New York, New York, United States
Yashasvini Sampathkumar, MD (she/her/hers)
Breast Service, Department of Surgery
Memorial Sloan Kettering Cancer Center
Montclair, New Jersey, United States
Risa Kiernan, DO
Breast Service, Department of Surgery
Memorial Sloan Kettering Cancer Center
New York, New York, United States
Robert James, MD
Breast Service, Department of Surgery
Memorial Sloan-Kettering Cancer Center
NY, New York, United States
Michele Waters, PhD
Computational Oncology, Department of Epidemiology and Biostatistics
Memorial Sloan Kettering Cancer Center
New York, New York, United States
Jian Carrot-Zhang, PhD
Clinical Genetics, Department of Medicine
Memorial Sloan Kettering Cancer Center
New York, New York, United States
Monica Morrow, MD (she/her/hers)
Chief, Breast Service, Department of Surgery; Anne Burnett Windfohr Chair of Clinical Oncology; Vice President, Women in Science and Medicine, MSKCC; Professor of Surgery, Weill Cornell Medical College of Cornell University
Memorial Sloan Kettering Cancer Center
New York, New York, United States
Neha Goel, MD, MPH
Breast Service, Department of Surgery
Memorial Sloan Kettering Cancer Center
New York, New York, United States
We analyzed data from 7374 patients (10,118 samples) who underwent targeted sequencing as part of routine clinical care at a tertiary cancer center. 1926 samples were excluded. ADMIXTURE sequencing software estimated proportions of AFR (African), EUR (European), EAS (East Asian), NAM (Native American), and SAS (South Asian) ancestry. We assigned ancestry labels if any ancestral fraction was ≥ 0.8 or an admixed label if all populations were < 0.8. Sequencing data were linked to clinical characteristics, and logistic regression analyzed associations between genetic ancestry, tumor characteristics, and somatic mutations.
Results:
Median age was 60 years. Most patients (57.5%) had stage 1-3 BC, while 23.0% had stage 4, and 19.4% had unknown stage (Table). 60.1% of patients had ER+/HER2- disease. EUR ancestry was most prevalent (61.0%), followed by admixed (13.0%), AFR (6.8%), EAS (4.6%), SAS (2.1%), and NAM (0.4%). On multivariable analysis adjusting for BC stage and subtype, significant ancestry-associated somatic mutations were identified. AFR was associated with higher odds of TP53 and GATA3 mutations, and lower odds of ERBB2, PIK3CA, CDH1, and TBX3 mutations. EAS and NAM were associated with lower odds of CDH1 mutations. NAM was associated with higher odds of PTEN and STK11 mutations.
Conclusions: We identified both known and novel associations between diverse, genetic ancestries and somatic mutations in a real-world BC cohort. Notably, AFR ancestry was associated with lower odds of actionable mutations (ERRB2 and PIK3CA). As precision oncology expands, ensuring equitable sequencing access across diverse populations by incorporating targeted clinical panels into routine diagnostic workflows is essential to uncover actionable mutations in diverse populations.