A Network Approach to Developing Immuno-Oncology Combinations in Canada
Bibliographic record
Abstract
Immune checkpoint inhibitors have revolutionized care for many cancer indications, with considerable effort now being focused on increasing the rate, depth, and duration of patient response. One strategy is to combine immune strategies (for example, ctla-4 and PD-1/L1-directed agents) to harness additive or synergistic efficacy while minimizing toxicity. Despite encouraging results with such combinations in multiple tumour types, numerous clinical challenges remain, including a lack of biomarkers that reliably predict outcome, the emergence of therapeutic resistance, and optimal management of immune-related toxicities. Furthermore, the selection of ideal combinations from the myriad of immune, systemic, and locoregional therapies has yet to be determined. A longitudinal network-based approach could offer advantages in addressing those critical questions, including long-term follow-up of patients beyond individual trials. The molecular cancer registry Personalize My Treatment, managed by the Networks of Centres of Excellence nonprofit organization Exactis Innovation, is uniquely positioned to accelerate Canadian immuno-oncology (io) research efforts throughout its national network of cancer sites. To gain deeper insight into how a pan-Canadian network could advance research in io combinations, Exactis invited preeminent clinical and scientific advisors from across Canada to a roundtable event in November 2017. The present white paper captures the expert advice provided: leverage longitudinal patient data collection; facilitate network collaboration and assay harmonization; synergize with existing initiatives, networks, and biobanks; and develop an io combination trial based on Canadian discoveries.
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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 | 0.016 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 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".