Abstract LB-362: A TGF-beta linked ECM-dysregulation programme in CAFs drives immune evasion and immunotherapy resistance
Bibliographic record
Abstract
Abstract Background One of the key hallmarks of cancer is metastasis, which involves extensive extracellular matrix remodelling to permit extravasation from the primary tumour site before colonisation of distant sites. In this study, we defined the landscape of ECM-associated transcriptional dysregulation and performed in-silico associative analyses in terms of multiple -omics platforms, cellular composition and immunological parameters. Methods We defined the landscape of ECM gene dysregulation by integrating curated Gene Ontology ECM genes with genes differentially expressed between cancer and normal tissues from TCGA. ssGSEA scores for this set were then correlated with purity (ABSOLUTE), tested for stromal association using microdissected tumours and MethylCIBERSORT, and neoepitope counts derived using Topiary. Single-cell RNAseq data was used to validate CAF-origin. Linear models were used to identify transcriptomic, proteomic and genomic correlates, and finally, RNAseq data from three cohorts of PD1-blockade treated patients and 0.632 bootstrap evaluation of logistic regression or Random Forest models based on ECM genes, Cytolytic Activity, Mutational burden or their combinations was performed. Results A core set of 58 ECM genes is dysregulated and significantly enriched amongst genes differentially expressed between cancer and normal tissues pan-cancer. Those upregulated in cancers were found to be a negative prognostic factor pan-cancer and through integrative analysis of microdissected samples, deconvolution and single-cell RNAseq, were found to originate in Cancer Associated Fibroblasts. Transcriptome, proteome and genomics analyses implicated TGF-beta as a driver of this ECM-dysregulation signature and most notably, this programme was associated with heavily-mutated, neoantigen-high, CD8 high cancers, M1-macrophage-skewed tumours suggesting a role as a putative adaptation permitting immune evasion. Indeed, ECM dysregulation scores were significantly better predictors of response to PD1-blockade in pretreatment biopsies compared to cytolytic activity, an interferon gene signature, and mutational-burden, while TGF-beta expression alone was not, implicating a key role for CAF-specific ECM gene dysregulation in immune evasion. Conclusion A TGF-beta associated, CAF-derived signature of ECM-gene dysregulation broadly in operation across cancer types constitutes an immune evasion mechanism that indicates refractoriness to PD1-blockade. Citation Format: Ankur Ravinarayana Chakravarthy, Lubaba Khan, Nathan P. Bensler, Pinaki Bose, Daniel Diniz De Carvalho. A TGF-beta linked ECM-dysregulation programme in CAFs drives immune evasion and immunotherapy resistance [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr LB-362.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".