The mutational landscape of a US Midwestern breast cancer cohort reveals subtype-specific cancer drivers and prognostic markers
Abstract 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 p...
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| Format: | Article |
| Language: | English |
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BMC
2023-07-01
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| Series: | Human Genomics |
| Online Access: | https://doi.org/10.1186/s40246-023-00511-6 |
| _version_ | 1797778804000686080 |
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| author | Neetha Nanoth Vellichirammal Yuan-De Tan Peng Xiao James Eudy Oleg Shats David Kelly Michelle Desler Kenneth Cowan Chittibabu Guda |
| author_facet | Neetha Nanoth Vellichirammal Yuan-De Tan Peng Xiao James Eudy Oleg Shats David Kelly Michelle Desler Kenneth Cowan Chittibabu Guda |
| author_sort | Neetha Nanoth Vellichirammal |
| collection | DOAJ |
| description | Abstract 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. |
| first_indexed | 2024-03-12T23:22:49Z |
| format | Article |
| id | doaj.art-f68281a503d34e2cb213e5bdc13be65e |
| institution | Directory Open Access Journal |
| issn | 1479-7364 |
| language | English |
| last_indexed | 2024-03-12T23:22:49Z |
| publishDate | 2023-07-01 |
| publisher | BMC |
| record_format | Article |
| series | Human Genomics |
| spelling | doaj.art-f68281a503d34e2cb213e5bdc13be65e2023-07-16T11:22:44ZengBMCHuman Genomics1479-73642023-07-0117111710.1186/s40246-023-00511-6The mutational landscape of a US Midwestern breast cancer cohort reveals subtype-specific cancer drivers and prognostic markersNeetha Nanoth Vellichirammal0Yuan-De Tan1Peng Xiao2James Eudy3Oleg Shats4David Kelly5Michelle Desler6Kenneth Cowan7Chittibabu Guda8Department of Genetics, Cell Biology and Anatomy, University of Nebraska Medical CenterDepartment of Genetics, Cell Biology and Anatomy, University of Nebraska Medical CenterDepartment of Genetics, Cell Biology and Anatomy, University of Nebraska Medical CenterDepartment of Genetics, Cell Biology and Anatomy, University of Nebraska Medical CenterEppley Institute for Research in Cancer and Allied Diseases, University of Nebraska Medical CenterEppley Institute for Research in Cancer and Allied Diseases, University of Nebraska Medical CenterEppley Institute for Research in Cancer and Allied Diseases, University of Nebraska Medical CenterEppley Institute for Research in Cancer and Allied Diseases, University of Nebraska Medical CenterDepartment of Genetics, Cell Biology and Anatomy, University of Nebraska Medical CenterAbstract 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.https://doi.org/10.1186/s40246-023-00511-6 |
| spellingShingle | Neetha Nanoth Vellichirammal Yuan-De Tan Peng Xiao James Eudy Oleg Shats David Kelly Michelle Desler Kenneth Cowan Chittibabu Guda The mutational landscape of a US Midwestern breast cancer cohort reveals subtype-specific cancer drivers and prognostic markers Human Genomics |
| 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 |
| url | https://doi.org/10.1186/s40246-023-00511-6 |
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