Additional file 1 of MHC-I genotype and tumor mutational burden predict response to immunotherapy
Bibliographic record
Abstract
Additional file 1: Table S1. List of patients who underwent immunotherapy at UCSD (N = 83). Table S2. Univariate analysis of factors affecting outcome for patients with TMB > 10 mutations/mb treated with immune checkpoint blockade (N = 39 with TMB ≥ 10 mutations/mb). Table S3. Multivariate analysis of factors affecting outcome for patients treated with immunotherapy (N = 39 with TMB ≥10 mutations/mb). Table S4. Validation cohort of 32 patients with NSCLC treated with pembrolizumab. Table S5. Validation cohort patient demographics by PHBR score (< 0.5 vs. ≥0.5) for 32 patients with NSCLC treated with pembrolizumab. Table S6. Univariate analysis of factors affecting outcome for validation patients treated with immune checkpoint blockade (N = 32). Table S7. Overall response rate and PFS, segregated by TMB low/high and PHBR low/high among validation patients (N = 32). Table S8. Covariates retained after the backwards selection process. The coefficients and respective p-values for the covariates including TMB and PHBR in the final model are shown. Figure S1. Overview of tumor type distribution for the discovery cohort. Figure S2. CONSORT Diagram. Figure S3. Overview of minimum PHBR score distribution and TMB distribution for the discovery (A-B) and validation (C-D) cohorts. Figure S4. Kaplan and Meier PFS and OS for patients treated with immunotherapy, excluding patients with TMB = 0. Figure S5. Additional PFS and OS for patients treated with immunotherapy (N = 77 with TMB available. Figure S6. Correlation between PHBR score and TMB (N = 77 with TMB available. Figure S7. Area under the receiver operating characteristic curve (AUROC) for predicting OBR in the discovery cohort using the covariates obtained from the backward selection process, with the addition of PHBR (A), TMB (B) and the combination of PHBR and TMB (C). Figure S8. Kaplan Meier PFS dichotomized by both PHBR < 0.5 and ≥ 0.5 and TMB < 10 and ≥ 10 mutations/mb for histologies with ≥5 patients; NSCLC (A), SCC (B), Head and Neck (C), and Breast (D). Figure S9. Kaplan Meier PFS dichotomized by both PHBR < 0.5 and ≥ 0.5 and TMB < 10 and ≥ 10 mutations/mb excluding NSCLC (A), SCC (B), Head and Neck (C), Breast (D) and both NSCLC and SCC, the most common histologies in our cohort (E). Fig. S10: Area under the receiver operating characteristic curve (AUROC) for predicting OBR from PHBR and TMB in the discovery cohort training on NSCLC and SCC patients (A) and testing on patients in the remaining tumor types (B).
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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 | 0.002 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.846 | 0.121 |
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".