Deciphering primary and acquired immunotherapy resistance with whole genome and transcriptome analysis (WGTA).
Bibliographic record
Abstract
2602 Background: Most cancer patients (pts) do not respond to antiPD1/PDL1 immune checkpoint inhibitors (ICI) or experience only a temporary benefit. Comprehensive molecular profiling with WGTA could broaden our understanding of the mechanisms contributing to primary (Pr) and acquired resistance (Ar) to ICI and identify novel predictive biomarkers. Methods: The Princess Margaret investigator-initiated Immune Resistance Interrogation Study (NCT04243720) aims to comprehensively characterize ICI-resistant cancers (Genta et. al. ASCO 2021). Fresh tumor biopsies and blood samples were obtained from solid tumor pts at the time of PD on ICI-based treatment. DNA/RNA was extracted from tumor tissue and paired buffy coat and sequenced using an Illumina NovaSeq6000 system (2x150 paired reads for WG and 2X100 paired reads for WT). Copy number alterations (CNA), structural variants (SVs), mutation signatures and genes expression were assessed. Wald test in univariable and multivariable logistic regression model was used for comparison. Results: As of January 2023, 73 pts (45 Pr/28 Ar) were enrolled. WGTA was completed in 22 pts (14 Pr/8 Ar). Key pt characteristics included: median age 60.5 years (range 26-79); male 68%/female 32%; melanoma 50%, squamous cell cancer of the head and neck 36%, and other tumor types 14%. Forty-one percent of the pts were treated with PD-1/PD-L1 inhibitor monotherapy and 59% with PD-1/PD-L1 inhibitor combinations. There were no significant differences in median tumor mutation burden (TMB, coding; 2.8, range 0.6-30.5 for Pr vs 4.4, range 1.6-12.8 for Ar), median percent genome altered by CNA (43% Pr vs 65% Ar) or median number of SVs (144, range 9-775, for Pr vs 76, range 25-924, for Ar pts). After performing RNA sequencing we observed a significantly higher expression of the epithelial mesenchymal transition (EMT) hallmark gene-set from msigDB and of 3 previously reported ICI-resistance gene expression signatures in Pr vs Ar ( p≤0.05) in univariable analysis. The signatures included genes involved in the regulation of EMT, cellular de-differentiation and a cancer associated fibroblast (CAF) signature (IPRES signature, Hugo et al. Cell 2016; melanocytic plasticity signature [MPS], Pérez-Guijarro et al. Nat Med 2020; LRRC15 CAF signature, Dominguez et al. Cancer Discov. 2020). After adjusting for cancer type, the MPS signature was significantly associated with Pr vs Ar resistance in multivariable analysis ( p = 0.048). Conclusions: No significant differences between Ar and Pr were observed in genomic features including TMB, CNAs and number of SVs. Cancers primary resistant to ICI were characterized by a higher expression of EMT, cell de-differentiation and CAF related genes, detected with transcriptome analysis. Our findings might indicate these features as possible driver of early ICI progression. Analysis of additional samples is ongoing.
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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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".