Abstract 579: Immune features and neoantigen recognition in mismatch repair-proficient colorectal cancer liver metastases
Bibliographic record
Abstract
Abstract Immunotherapy for patients with non-highly mutated, mismatch repair-proficient (MMRprof) metastatic colorectal cancer has largely been unsuccessful thus far. Here, we used transcriptomics, genomics and in vitro analyses to study the adaptive immune features of colorectal cancer liver metastases (CRLMs), aiming to provide insights into the development of immunotherapies for this common malignancy. Analysis of the RNAseq data from 63 core biopsies obtained in patients enrolled in the Q-CROC-01 trial (NCT00984048) revealed 17 (27%) immune-reactive (IR) CRLMs, defined by concurrent relative high expression of antigen processing, immune cell, immune checkpoint, interferon-gamma response, cytokine, and chemokine transcripts. More T-cell receptor (TCR) sequences were found in IR vs. non-IR CRLMs (mean 89.8 ± 13.3 vs. 19.5 ± 4.0 CDR3 counts, p<0.0001), consistent with higher density of tumor-infiltrating T lymphocytes (TILs) confirmed by immunofluorescence. By whole exome analysis, no significant difference was found in non-synonymous somatic mutation (median 76) and neoantigen (neoAg) (median 18) counts comparing IR vs. non-IR CRLMs. High mutation or neoAg loads did not correlate with higher T cell infiltration. TILs were expanded in vitro from five CRLMs, one classified as high IR, two as moderate IR, and two as non-IR by immune gene expression profiling. TIL recognition of predicted neoAgs was tested in co-culture with autologous antigen-presenting cells pulsed with synthetic peptides. Only in the most immune reactive CRLM did we find CD8+ and CD4+ TIL clones (n=8) reactive to neoAg peptides derived from three single nucleotide variant genes, and none reactive to the wild-type peptide counterparts. In this case, by deep sequencing of intratumoral TCRs, the average frequency of neoAg-reactive TIL clones was estimated at 0.95% (range 0.003% to 3.3%), which represented a 3625 fold enrichment compared to the circulating blood. In general, expression of immune checkpoints was more highly correlated in IR vs. non-IR CRLMs. The following T cell-related conceptual pairs of immune checkpoint receptors & ligands were expressed at significantly higher levels in IR compared to non-IR CRLMs: HVEM/LTBR & BTLA/LIGHT, A2A & CD73, TIGIT & PVRL3, FAS & FASL, CD2/2B4 & CD48. Our results support that naturally occurring neoAg-reactive T cells may be therapeutically harnessed in a subgroup of metastatic MMRprof colorectal cancer patients selected based on the level of intratumoral immune reactivity, and potentially enhanced by targeting immune checkpoints more biologically relevant than PD-1/PD-L1 and CTLA-4/CD80. Citation Format: Steven Hébert, Mélissa Mathieu, David Henault, Maud Marques, Éric Audemard, Mathieu Courcelles, Scott D. Brown, Pratyaksha Wirapati, Sabine Tejpar, Michael I. D'Angelica, Robert A. Holt, Sylvie Mader, Pierre Thibault, Gerald Batist, Claudia Kleinman, Simon Turcotte. Immune features and neoantigen recognition in mismatch repair-proficient colorectal cancer liver metastases [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 579.
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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 | 0.000 |
| 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".