Abstract S1-02: Prognostic effects of gene mutation in estrogen receptor positive breast cancer
Bibliographic record
Abstract
Abstract Background: Relationships between recurrent somatic mutations and outcome in estrogen receptor positive (ER+) breast cancer has not been extensively studied as the original discovery efforts were from either heterogeneously treated patients or follow up was too brief. Targeted massively parallel sequencing (MPS) analysis was therefore conducted on DNA extracted from archived formalin-fixed breast primaries from a cohort of over 600 patents from British Columbia treated with five years of adjuvant tamoxifen monotherapy and followed for over 10 years (Nielsen et al CCR 16:5222, 2010). Methods: Genes were selected for targeted sequencing by meta-analysis of five large-scale breast cancer sequencing studies and manual review of breast cancer literature. In total 83 genes were identified and 3286 probes were designed to tile across all known exons. Minimum starting input DNA was 50ng (mean=189.1ng). Illumina sequencing libraries were constructed, indexed, pooled, and enriched for target sequences by hybrid-capture followed by paired-end 100bp reads. The Genome Modeling System was used to perform single-tumor somatic variant prediction. Variant calls were filtered to include only targeted regions and exclude variants with global mutant allele frequencies greater than 0.1% in 1000 genomes or NHLBI exome datasets. Kaplan-Meier analysis and multivariable analysis (clinical features and intrinsic subtype by qPCR) was performed for breast-cancer-specific and relapse free survival. Results: A total of 638 samples met minimum quality controls of 80% targeted space covered at 20X or greater. On average each sample had 332M of aligned bases and a mean coverage of 134.3X. In total 7,159 variants were identified including 3,696 missense, 494 nonsense, and 1,047 frameshift insertions or deletions. Preliminary results indicate significant associations between mutation status and improved survival for PIK3CA, ARID1B, ERBB3, MAP3K1 and GATA3 or worse survival for PTEN, DDR1, TP53 and JAK2. Five Y537N/C, two E380Q and 5 potentially novel ligand-binding-domain mutations were identified in ESR1. Such mutations were recently reported to be associated with resistance to hormone therapy but were discovered here in as much as 1.9% of pre-treatment samples. Analysis will be presented regarding the use of relapse events to differentiate passenger from driver mutations. Conclusion. Multiple recurrently mutated genes have both positive and negative associations with prognosis in tamoxifen montherapy treated breast cancer populations. Associations with poor outcome suggest that PTEN, DDR1, and JAK2 are high priorities for pharmacological interventions. Table 1: Mutations and survival in ER+ breast cancerGeneP-ValueHazard RatioPTEN0.0022.11PIK3CA0.0070.69DDR10.0082.41ARID1B0.010.57ERBB30.020.36MAP3K10.010.5TP530.041.48GATA30.040.44JAK20.052.1 Citation Format: Obi L Griffith, Malachi Griffith, Jingqin Luo, Jasreet Hundall, Christopher A Miller, David E Larson, Robert Fulton, Richard K Wilson, Shuzhen Liu, Samuel Leung, Torsten O Nielsen, Elaine R Mardis, Matthew J Ellis. Prognostic effects of gene mutation in estrogen receptor positive breast cancer [abstract]. In: Proceedings of the Thirty-Seventh Annual CTRC-AACR San Antonio Breast Cancer Symposium: 2014 Dec 9-13; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2015;75(9 Suppl):Abstract nr S1-02.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.020 | 0.002 |
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".