Somatic mutations in Luminal HER2 negative tumors from young breast cancer patients.
Bibliographic record
Abstract
1544 Background: Early age patients less than 36 years represent 4% of breast cancer cases and have a higher probability of being BRCA1/2 mutation carriers. There are indications that tumors from young patients are biologically distinct from older women, however, these tumors have been less studied. Our aim was to identify somatic mutations in luminal tumors from BRCA1/2 wild type young breast cancer patients. Methods: Seventy-nine unselected young patients were enrolled. BRCA1/2 mutations were screened by Sanger sequencing and Multiplex Ligation-Dependent Probe amplification (MLPA). Tumor and blood samples from eight patients (hormone receptor positive, HER2 negative) were selected for whole exome sequencing using Nextera Rapid Capture Enrichment in an Illumina HiSeq 1000, analyzed through MuTect (v1.14), SomaticSniper (v1.0.2) and Strelka (v1.0.1.2). Results: Median age of the 79 patients was 32 years (22-35) and luminal subtype was the most frequent (63.1%). Deleterious mutation in BRCA1/2 genes was detected in 13 patients (BRCA1, n = 4 and BRCA2, n = 9). One novel mutation was detected in BRCA1 gene: a stop codon in exon 6 (c.483T > A; p.Cys161Ter). Somatic mutations were evaluated in eight luminal samples and a median of 60 alterations/tumor was detected, varying from 49 to 113. A total of 537 individual genomic alterations were found, comprising 77 non-synonymous and four nonsense base substitutions and 2 splice donor variants. Among these 83 mutations, 56 were detected in potentially driver genes, including genes involved in Hedgehog signaling pathway and GSK3B interactions. Five to ten driver genes were mutated per tumor sample. For non-synonymous point mutations, only PIK3CA was repeatedly mutated in three samples; TP53 was mutated in one sample. Conclusions: Besides PIK3CA mutation, which is the most frequent alteration in luminal tumors, and TP53 mutation, at least five other different driver genes may be mutated in tumors from young breast cancer patients.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".