SNOW: Sitravatinib and nivolumab in oral cavity cancer (OCC) window of opportunity study.
Bibliographic record
Abstract
6569 Background: Sitravatinib (receptor TKI against TYRO3, AXL, MERTK and VEGF family of receptors) is predicted to increase M1-type tumor-associated macrophages (TAMs) and decrease MDSCs in the tumor microenvironment. SNOW is a window-of-opportunity study evaluating the immunogenic and antitumor effects of preoperative sitravatinib and nivolumab in patients (pts) with OCC. Early results demonstrated the combination was safe and active (Oliva et al, SITC 2019). Biomarker analyses and updated results are presented. Methods: Pts with untreated T2-4a, N0-2 or T1>1cm-N2 OCC are eligible. All pts receive oral sitravatinib 120mg daily from day (D) 1 up to 48h pre-surgery and 1 dose of Nivolumab 240mg on D15. Surgery planned between D23-D30. Standard of care adjuvant radiotherapy given based on clinical stage. Tumor pictures, fresh tumor biopsies, blood samples taken at baseline, D15 and pre-surgery. Tumor flow cytometry and multiplex immunofluorescence staining performed on all biopsies to study changes in immune-cell populations. Tumor whole-exome sequencing (WES) performed on baseline biopsies. Results: As of Jan 31st 2020, 10 out of 12 planned pts were enrolled. Study treatment was well-tolerated: only 1 pt had grade (G) >3 toxicity (hypertension) and 1 pt required surgery delay due to G2 thrombocytopenia. None had intraoperative complications. 1 pt had wound infection and tracheostomy bleeding 11 days post-surgery, possibly-related to study drugs. All pts had tumor reduction, 9/10 had pathological downstaging, including 1 complete response (Table). All had clear margins with no extranodal extension; none required adjuvant chemotherapy. All pts are alive with no recurrence (median follow-up= 69 weeks). Lower % of MDSCs and increased % of M1-TAMs and M1:M2 ratio trend was seen at D15 and pre-surgery, with stronger effect in major responders. Best responders (Pts S1-S2) had higher % of PD-L1+ TAMs at baseline. Tumor WES revealed an HRAS G12D mutation in pt S2 and a BLM mutation (DNA repair) in pt S6 (no downstaging). Conclusions: Pharmacodynamic analyses support the antitumor and immune effects of sitravatinib and nivolumab in OCC. Immune pathological response assessment and transcriptomics are on-going. Clinical trial information: NCT03575598 . [Table: see text]
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".