Abstract P2-10-04: Targeted sequencing in early breast cancer: Identification of novel candidate mutations predictive of anthracycline benefit
Bibliographic record
Abstract
Abstract Background The use of chemotherapies such as anthracyclines and taxanes have improved overall and disease free survival in breast cancer. For all patients, anthracyclines can have significant toxicities including cardiotoxicity and leukemia. It is therefore essential to select the subset of patients who will receive the optimal overall benefit from anthracycline therapy and to identify molecular pathways driving resistance. To fully understand the impact of mutations in the context of current breast cancer therapy, requires a comprehensive mapping of key molecular events in the context of treatment. We sequenced 101 genes, that were prioritized based on not only gene frequency, but also taking into account the importance of amino acid substitution, type of mutation and network connectivity, in 692 primary tumours to both identify driver genes and pathway cassettes and to understand their clinical significance in response to anthracycline treatment. Methods We performed targeted sequencing in patients from the BR9601 (n=374) and CCTG MA.5 (n=703) clinical trials. The BR9601 and MA.5 clinical trials examined the effectiveness of combination chemotherapy consisting of CMF (cyclophosphamide, methotrexate and 5-fluorouracil) with or without epirubicin. DNA was extracted, samples were sequenced using AmpliSeq Technology adapted to Illumina and somatic mutations were called using a novel mutation calling pipeline (ISOWN). A priori analyses were performed using distant recurrence free survival (DRFS) as the primary endpoint. Results: In 692 successfully analysed samples 509 (73.6%) samples exhibited at least one single nucleotide mutation (range 0-54). 94/101 genes were mutated in at least one patient. Only variants in PIK3CA, TP53, CDH1, TLE6, MLL3 and USH2A were detected in 5% or more of samples. TSC22D1, RB1 and ZNF565 were associated with increased risk of distant relapse in multivariate analyses corrected for clinic-pathological variables. No single genes were predictive of anthracycline treatment compared to CMF in multivariate analyses corrected for clinic-pathological variables. Signaling cassettes/modules were designed based on the pathway database, Reactome. Within the signaling cassettes one module was predictive of anthracycline failure. Patients with one or more mutations in this module had an increased risk of distant relapse (HR 0.52, 95% CI 0.29-0.95, p=0.034) when treated with an anthracycline containing chemotherapy regimen compared to CMF (HR 1.34 95% CI 1.05-1.72, p=0.019). Conclusions: We successfully performed a signaling pathway-based targeted sequencing analysis within predefined signaling modules. We identified a single signaling cassette linked to anthracycline resistance in early breast cancer. However, further work to validate this study in a separate clinical trial is warranted. Citation Format: Spears M, Kalatskaya I, Trinh QM, Liao L, Chong TM, Crozier C, Dion D, Heisler L, Timms L, Stein LD, Pritchard KI, Levine MN, Shepherd L, Twelves CJ, Bartlett JMS. Targeted sequencing in early breast cancer: Identification of novel candidate mutations predictive of anthracycline benefit [abstract]. In: Proceedings of the 2017 San Antonio Breast Cancer Symposium; 2017 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2018;78(4 Suppl):Abstract nr P2-10-04.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.004 | 0.001 |
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".