Comprehensive whole genome and transcriptome analysis of patients with advanced solid tumor treated with immune checkpoint inhibitor therapy in the pan-cancer cohorts from the Marathon of Hope Cancer Centres Network Study (MOHCCN).
Bibliographic record
Abstract
2645 Background: Immune checkpoint inhibitors (ICI) improve survival in multiple advanced solid tumors but many patients do not benefit. We conducted a comprehensive whole genome transcriptome sequencing (WGTS) analysis to identify predictors of immune sensitivity. Methods: Clinical and molecular data from archival or pre-treatment FFPE tumor tissue were available for analysis from The Marathon of Hope Cancer Centres Network Study (MOHCCN). It includes patients (Pts) with advanced solid tumors and ECOG PS 0 or 1 treated with ICIs targeting PD-1, PD-L1, or CTLA-4 in the INSPIRE (NCT02644369) and OCTANE (NCT02906943) cohorts. Response (R) to ICI was defined as radiological and clinical response without PFS event at 6 months; versus non-response (NR) as radiological or clinical progression within 6 months. Responders without evidence of progression for >12 months were categorized as durable responders (DR). Nucleic acids were sequenced using Illumina NovaSeq 6000 system targeting minimum coverage of 80X and 30X for tumour and normal WGS, respectively and 80M reads for tumour RNA-seq. WGTS data were integrated with clinical data to identify associations with response to ICIs. Also, an analysis of immune cell types was conducted using multiplexed IHC and RNA-Seq deconvolution (CIBERSORT). Results: 59 Pts were included in this analysis: 28 (R) and 31 (NR). The most common tumor types were head and neck (n = 19) and melanoma (n = 7). Median age was 62 years (range 24-81); male 58% and median follow-up was 58 months (range 11-280). The most frequent ICI was pembrolizumab 76%, with 90% of all pts receiving ICI monotherapy and 10% combination. 75% of pts received chemotherapy prior to ICI. Higher tumor mutation burden (TMB) was observed in R vs NR (median 13 vs 5 coding mut/Mb, p=0.001), with the highest TMB in patients with DR (median 14 mut/Mb ). Differential RNA gene expression analysis showed NR had increased expression of several oncogenic pathway signatures, including MYC targets, G2M checkpoint, and E2F targets. Differential abundance analysis of immune cell types did not yield any differences of immune cell populations amongst R versus NR after correction for multiple comparisons. Responders had significant enrichment in mutations in WNT/β-catenin pathway genes in both coding (APC, AMER1, LZTR1, TCF7L2) and promoter regions (CTNNB1). Notably, the 6 Pts with CTNNB1 promoter mutations had significantly increased CTNNB1 gene expression (p = 0.005). Conclusions: ICI responders showed enrichment in coding mutations of several negative regulators of the Wnt/β-catenin pathway and non-coding promoter mutations in CTNNB1 compared with non-responders. Further investigation is ongoing to biologically validate how these mutations in the Wnt/β-catenin pathway may lead to improved response to ICI.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".