Abstract 6494: Personalize My Treatment (PMT): A pan-Canadian initiative integrating precision oncology across Canada - PMT-001 pilot project
Bibliographic record
Abstract
Abstract More than 90% of cancer therapeutics in development are targeted therapies. Although the number of patients who can benefit from targeted therapies is increasing, many of the biomarkers are identified in only a small fraction of patients. Identifying and enrolling these rare cancer patient sub-populations into clinical trials presents an enormous challenge to both pharmaceutical companies and clinicians. Furthermore, in Canada, the lack of harmonized standard of care molecular testing for cancer patients results in a discordance in the number of genes tested across Hospital sites. Identifying patients with biomarkers of interest across Canada is essential to attract precision oncology clinical trials to Canada and to offer cancer patients better therapeutic options and improved outcomes. Founded in 2014, Exactis Innovation is a non-profit Academic Research Organization which aims to integrate precision oncology across Canada. Exactis has consolidated a pan-Canadian network of 13 cancer care centre sites and 4 federated laboratories. Network sites host an REB-approved molecular cancer patient registry (PMT), through which patients can be profiled, identified as carrying a biomarker, followed throughout their disease trajectory and be re-contacted if their molecular profile matches a clinical trial or an approved therapy. Here we report the results of Exactis' first profiling initiative (PMT-001), performed across 8 Exactis Network sites within 3 Canadian provinces in a four months' time period. In summary, 447 individual tumors specimens were collected from breast, colorectal, lung and ovary cancer patients and 382 were profiled using the Oncomine comprehensive assay v3 (OCAv3) panel DNA (n=365) and OCAv3 DNA/RNA (n=151). At least one aberration was identified in 346 participants. As expected, the top mutated genes across the 4 cohorts were TP53 (50%), PIK3CA(15%), KRAS (12.5%) and BRCA1/2 (12%/11%). At the fusion level, we identified EML4-ALK (5.2%), KIF5B-RET(1.7%) and MET exon 14 skipping (3.4%) in NSCLC participants and we identified RSPO3 (3%) and RSPO2 (2%) fusion in colorectal cancer participants. The profiling pilot of the PMT initiative shows how strong foundations within the Exactis pan-Canadian network is resulting in fast, high quality and meaningful molecular data for Canadian cancer patients. Citation Format: Maud Marques, Karen Gambaro, Mathilde Couetoux de Tertre, Suzan McNamara, Cyrla Hoffer, The Exactis Network, Richard Fajzel, Gerald Batist. Personalize My Treatment (PMT): A pan-Canadian initiative integrating precision oncology across Canada - PMT-001 pilot project [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 6494.
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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 | 0.010 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".