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The mutational landscape of a US Midwestern breast cancer cohort reveals subtype-specific cancer drivers and prognostic markers

BACKGROUND: Female breast cancer remains the second leading cause of cancer-related death in the USA. The heterogeneity in the tumor morphology across the cohort and within patients can lead to unpredictable therapy resistance, metastasis, and clinical outcome. Hence, supplementing classic pathologi...

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Autores principales: Vellichirammal, Neetha Nanoth, Tan, Yuan-De, Xiao, Peng, Eudy, James, Shats, Oleg, Kelly, David, Desler, Michelle, Cowan, Kenneth, Guda, Chittibabu
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10349437/
https://www.ncbi.nlm.nih.gov/pubmed/37454130
http://dx.doi.org/10.1186/s40246-023-00511-6
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author Vellichirammal, Neetha Nanoth
Tan, Yuan-De
Xiao, Peng
Eudy, James
Shats, Oleg
Kelly, David
Desler, Michelle
Cowan, Kenneth
Guda, Chittibabu
author_facet Vellichirammal, Neetha Nanoth
Tan, Yuan-De
Xiao, Peng
Eudy, James
Shats, Oleg
Kelly, David
Desler, Michelle
Cowan, Kenneth
Guda, Chittibabu
author_sort Vellichirammal, Neetha Nanoth
collection PubMed
description BACKGROUND: Female breast cancer remains the second leading cause of cancer-related death in the USA. The heterogeneity in the tumor morphology across the cohort and within patients can lead to unpredictable therapy resistance, metastasis, and clinical outcome. Hence, supplementing classic pathological markers with intrinsic tumor molecular markers can help identify novel molecular subtypes and the discovery of actionable biomarkers. METHODS: We conducted a large multi-institutional genomic analysis of paired normal and tumor samples from breast cancer patients to profile the complex genomic architecture of breast tumors. Long-term patient follow-up, therapeutic regimens, and treatment response for this cohort are documented using the Breast Cancer Collaborative Registry. The majority of the patients in this study were at tumor stage 1 (51.4%) and stage 2 (36.3%) at the time of diagnosis. Whole-exome sequencing data from 554 patients were used for mutational profiling and identifying cancer drivers. RESULTS: We identified 54 tumors having at least 1000 mutations and 185 tumors with less than 100 mutations. Tumor mutational burden varied across the classified subtypes, and the top ten mutated genes include MUC4, MUC16, PIK3CA, TTN, TP53, NBPF10, NBPF1, CDC27, AHNAK2, and MUC2. Patients were classified based on seven biological and tumor-specific parameters, including grade, stage, hormone receptor status, histological subtype, Ki67 expression, lymph node status, race, and mutational profiles compared across different subtypes. Mutual exclusion of mutations in PIK3CA and TP53 was pronounced across different tumor grades. Cancer drivers specific to each subtype include TP53, PIK3CA, CDC27, CDH1, STK39, CBFB, MAP3K1, and GATA3, and mutations associated with patient survival were identified in our cohort. CONCLUSIONS: This extensive study has revealed tumor burden, driver genes, co-occurrence, mutual exclusivity, and survival effects of mutations on a US Midwestern breast cancer cohort, paving the way for developing personalized therapeutic strategies. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s40246-023-00511-6.
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spelling pubmed-103494372023-07-16 The mutational landscape of a US Midwestern breast cancer cohort reveals subtype-specific cancer drivers and prognostic markers Vellichirammal, Neetha Nanoth Tan, Yuan-De Xiao, Peng Eudy, James Shats, Oleg Kelly, David Desler, Michelle Cowan, Kenneth Guda, Chittibabu Hum Genomics Research BACKGROUND: Female breast cancer remains the second leading cause of cancer-related death in the USA. The heterogeneity in the tumor morphology across the cohort and within patients can lead to unpredictable therapy resistance, metastasis, and clinical outcome. Hence, supplementing classic pathological markers with intrinsic tumor molecular markers can help identify novel molecular subtypes and the discovery of actionable biomarkers. METHODS: We conducted a large multi-institutional genomic analysis of paired normal and tumor samples from breast cancer patients to profile the complex genomic architecture of breast tumors. Long-term patient follow-up, therapeutic regimens, and treatment response for this cohort are documented using the Breast Cancer Collaborative Registry. The majority of the patients in this study were at tumor stage 1 (51.4%) and stage 2 (36.3%) at the time of diagnosis. Whole-exome sequencing data from 554 patients were used for mutational profiling and identifying cancer drivers. RESULTS: We identified 54 tumors having at least 1000 mutations and 185 tumors with less than 100 mutations. Tumor mutational burden varied across the classified subtypes, and the top ten mutated genes include MUC4, MUC16, PIK3CA, TTN, TP53, NBPF10, NBPF1, CDC27, AHNAK2, and MUC2. Patients were classified based on seven biological and tumor-specific parameters, including grade, stage, hormone receptor status, histological subtype, Ki67 expression, lymph node status, race, and mutational profiles compared across different subtypes. Mutual exclusion of mutations in PIK3CA and TP53 was pronounced across different tumor grades. Cancer drivers specific to each subtype include TP53, PIK3CA, CDC27, CDH1, STK39, CBFB, MAP3K1, and GATA3, and mutations associated with patient survival were identified in our cohort. CONCLUSIONS: This extensive study has revealed tumor burden, driver genes, co-occurrence, mutual exclusivity, and survival effects of mutations on a US Midwestern breast cancer cohort, paving the way for developing personalized therapeutic strategies. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s40246-023-00511-6. BioMed Central 2023-07-15 /pmc/articles/PMC10349437/ /pubmed/37454130 http://dx.doi.org/10.1186/s40246-023-00511-6 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
Vellichirammal, Neetha Nanoth
Tan, Yuan-De
Xiao, Peng
Eudy, James
Shats, Oleg
Kelly, David
Desler, Michelle
Cowan, Kenneth
Guda, Chittibabu
The mutational landscape of a US Midwestern breast cancer cohort reveals subtype-specific cancer drivers and prognostic markers
title The mutational landscape of a US Midwestern breast cancer cohort reveals subtype-specific cancer drivers and prognostic markers
title_full The mutational landscape of a US Midwestern breast cancer cohort reveals subtype-specific cancer drivers and prognostic markers
title_fullStr The mutational landscape of a US Midwestern breast cancer cohort reveals subtype-specific cancer drivers and prognostic markers
title_full_unstemmed The mutational landscape of a US Midwestern breast cancer cohort reveals subtype-specific cancer drivers and prognostic markers
title_short The mutational landscape of a US Midwestern breast cancer cohort reveals subtype-specific cancer drivers and prognostic markers
title_sort mutational landscape of a us midwestern breast cancer cohort reveals subtype-specific cancer drivers and prognostic markers
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10349437/
https://www.ncbi.nlm.nih.gov/pubmed/37454130
http://dx.doi.org/10.1186/s40246-023-00511-6
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