Comprehensive Genomic Profile of Heterogeneous Long Follow-Up Triple-Negative Breast Cancer and Its Clinical Characteristics Shows DNA Repair Deficiency Has Better Prognostic
Bibliographic record
Abstract
Triple-negative breast cancer (TNBC) presents a marked diversity at the molecular level, which promotes a clinical heterogeneity that further complicates treatment. We performed a detailed whole exome sequencing profile of 29 Mexican patients with long follow-up TNBC to identify genomic alterations associated with overall survival (OS), disease-free survival (DFS), and pathologic complete response (PCR), with the aim to define their role as molecular predictive factors of treatment response and prognosis. We detected 31 driver genes with pathogenic mutations in TP53 (53%), BRCA1/2 (27%), CDKN1B (9%), PIK3CA (9%), and PTEN (9%), and 16 operative mutational signatures. Moreover, tumors with mutations in BRCA1/2 showed a trend of sensitivity to platinum salts. We found an association between deficiency in DNA repair and surveillance genes and DFS. Across all analyzed tumors we consistently found a heterogeneous molecular complexity in terms of allelic composition and operative mutational processes, which hampered the definition of molecular traits with clinical utility. This work contributes to the elucidation of the global molecular alterations of TNBC by providing accurate genomic data that may help forthcoming studies to improve treatment and survival. This is the first study that integrates genomic alterations with a long follow-up of clinical variables in a Latin American population that is an underrepresented ethnicity in most of the genomic studies.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".