Integrative tumor and immune cell multi-omic analyses to predict melanoma response to immune checkpoint blockade.
Bibliographic record
Abstract
10009 Background: The complex crosstalk between tumor and immune cells during immune checkpoint blockade mandates the development of integrated models to interpret the antitumor immune response and predict clinical outcome. Methods: We performed comprehensive genomic, transcriptomic and T cell repertoire analyses on tumor biopsies from 64 patients with advanced melanoma receiving nivolumab +/- ipilimumab on CheckMate-038 (NCT01621490). Tumor biopsies were obtained at baseline and 2-4 weeks on therapy. Machine learning and Cox proportional hazards regression analyses were employed to integrate multi-omics features in predictive models of response, defined by RECISTv1.1 as complete and partial response, and survival (PFS and OS). Results: Responding patients had a higher tumor mutation burden (TMB) than non-responders. Expressed TMB more accurately predicted overall survival than genomic TMB (log rank p = 0.028 vs 0.078). High tumor aneuploidy was associated with worse prognosis especially for the patients in the nivolumab + ipilimumab group (log rank p = 0.01). TCR sequencing of paired tumors before and on-treatment revealed that responders had a significantly higher number of unique TCR clones at baseline and more clonotypic shifts on-treatment (p = 0.0018). Gene rearrangement analyses using transcriptome data identified a higher number of rearrangements involving immunoglobulin (Ig) genes in baseline tumors from responders. Deconvolution of transcriptomic data confirmed an enrichment in tumor associated B cells in baseline tumors of responders, suggesting that pre-existing B cell infiltration is a predictor of clinical outcome. Random forests were utilized to integrate Ig rearrangements, expressed TMB and tumor aneuploidy, into a predictive model of response that was superior to TMB (AUC = 0.89 and 0.65 respectively). Multivariate Cox proportional hazards analysis incorporating the same features was utilized to generate a risk score for each patient; those with high risk scores had a significantly shorter PFS compared to low risk patients (median PFS 1.45 months vs 29.01 months, log rank p = 3.4e-06, HR = 9.18, 95% CI: 3.14-26.85). Conclusions: Our findings highlight the multi-faceted interactions between the tumor and the immune system and the importance of pre-existing T and B cell immunity in driving clinical response and PFS after immune checkpoint blockade, laying the groundwork for integration of genomic and immune features into predictive models that may ultimately optimize therapeutic decisions.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".