Bibliographic record
Abstract
Data for our paper titled "Leveraging Big Data of Immune Checkpoint Blockade Response Identifies Novel Potential Targets". Bareche et al., Annals of Oncology (2022); https://doi.org/10.1016/j.annonc.2022.08.084 ---------------------------------------------------------------------------------------------------------------------------------------------- ---------------------------------------------------------------------------------------------------------------------------------------------- Background: The development of immune checkpoint blockade (ICB) has changed the way we treat various cancers. While ICB produces durable survival benefits in a number of malignancies, a large proportion of treated patients do not derive clinical benefit. Recent clinical profiling studies have shed light on molecular features and mechanisms that modulate response to ICB. Nevertheless, none of these identified molecular features were investigated in large enough cohorts to be of clinical value. Materials and methods: Literature review was performed to identify relevant studies including clinical dataset of patient treated with ICB (anti-PD1/L1, anti-CTLA4 or the combo) and available sequencing data. Tumor mutational burden (TMB) and 37 previously reported gene expression (GE) signature were computed with respect to the original publication. Biomarker association with ICB response (IR) and survival (PFS/OS) was investigated separately within each study and combined together for meta-analysis. Results: We performed a comparative meta-analysis of genomic and transcriptomic biomarkers of immune-checkpoint blockade (ICB) responses in over 3,600 patients across 12 tumor types and implemented an open-source web-application (predictIO.ca) for exploration. Tumor mutation burden (TMB) and 21/37 gene signatures were predictive of ICB responses across tumor types. We next developed a de novo gene expression signature (PredictIO) from our pan-cancer analysis and demonstrated its superior predictive value over other biomarkers. To identify novel targets, we computed the T-cell dysfunction score for each gene within PredictIO and their ability to predict dual PD-1/CTLA-4 blockade in mice. Two genes, F2RL1 (encoding protease-activated receptor-2) and RBFOX2 (encoding RNA-binding motif protein 9), were concurrently associated with worse ICB clinical outcomes, T cell dysfunction in ICB-naive patients and resistance to dual PD-1/CTLA-4 blockade in preclinical models. Conclusions: Our study highlights the potential of large-scale meta-analyses in identifying novel biomarkers and potential therapeutic targets for cancer immunotherapy. ---------------------------------------------------------------------------------------------------------------------------------------------- ---------------------------------------------------------------------------------------------------------------------------------------------- Data description mouseModel: Chen: Expression data of the TNBC mouse model study from Chen et al. (PMID:32907939) Meskini: Expression data of the Melanoma mouse model study from Meskini et al. (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE172320) Zemek: Expression data of the AB1 & Renca mouse model study from Zemek et al. (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE117358) Discovery_cohort: Expression and SNV data of the discovery cohort Validation_cohort: Expression and SNV data of the validation cohort
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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 | 0.005 | 0.063 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.435 | 0.372 |
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".