Abstract 2631: Restrictions on access to systemic therapy limit the application of whole genome sequencing in clinical care
Bibliographic record
Abstract
Abstract Background: Whole genome analyses have the potential to identify the full landscape of activating and inactivating genomic abnormalities at work within cancers, and can thus be used to provide rationales for selection of treatment agents or clinical trials in a broad range of patients. Patients & Methods: Eligible patients (pts) with metastatic cancers were recruited within a general oncology practice across the province of B.C., Canada. Each pt underwent a fresh tumor biopsy and a blood sample and had comprehensive DNA (80X) and RNA sequencing. In-depth bioinformatic analyses were preformed to identify genomic changes that may be cancer “drivers” or therapeutically actionable targets. Aberrant pathways were matched to drug databases and manual literature reviews undertaken to identify drugs or clinical trials of potential utility for the individual pt. Results: Between July 2012 - Oct 2015: 380 pts (358 adult + 22 peds) consented; 227 have completed whole DNA and RNA sequencing and analysis to date (remainder ongoing). For this analysis, data is available on 160 pts. Genome bioinformaticians assessed the genomic data to be potentially druggable in all cases. Medical Oncologists assessed this data to be directly clinically actionable in 135 (84%); the difference being that clinicians did not agree that some putative “druggable” drivers (such as p53) or pathways with no current drugs available (MMP, AURA, WNT) were “actionable”. Of the 135 cases, 58 (43%) pts received therapy based directly on this genomic information; 6 on a phase 1 clinical trial. The most common reasons for 77 pts defined as actionable but who have not received genomically-informed therapy were: drug only available on a clinical trial but trial not available to pt - 22 (26%); drug approved but not available off label - 18 (23%); pt presently on first-line therapy that is working - 14 (18%); or death/too unwell 13 (17%). The limited availability of clinical trials was primarily because of highly restrictive trials entry criteria, primarily limiting patients to one primary tumour type or narrowly defined biomarker entry criteria. The most commonly mutated cancer genes identified by the genome analysts were: p53 as the predominant driver in 42%; APC in 16%; KRAS and PI3KCA mutations in 14%. Going forward it is essential to distinguish what driver mutations might be clinically actionable from those that are still only theoretically druggable; and similarly learn to distinguish which targets are actionable but not truly drivers. Conclusions: Genomic DNA and RNA sequencing data were found to be clinically actionable in 84% of pts with advanced cancers in a population cancer care setting. However, the ability to act on this information is limited by the restrictive nature of clinical trials and the lack of accessibility of off-label drugs despite an identified biomarker. As genome sequencing becomes integrated into cancer management these drug access issues need to be addressed. Citation Format: Janessa J. Laskin, Yaoqing Shen, Daniel Renouf, Martin Jones, Howard Lim, Alexandra Fok, Cheryl Ho, Balvir Deol, Karen A. Gelmon, Stephen Chia, Richard Moore, Andrew Mungall, Stephen Yip, Steven Jones, Marco Marra. Restrictions on access to systemic therapy limit the application of whole genome sequencing in clinical care. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 2631.
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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 | 0.015 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.102 | 0.042 |
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".