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Abstract B011: Immunoediting in untreated mismatch repair deficient colorectal cancer

2018· article· en· W2786998403 on OpenAlexaff
Catherine S. Grasso, Marios Giannakis, Daniel K. Wells, David A. Wheeler, Eve Shinbrot, Syed Hassan Ejaz Zaidi, Jeroen R. Huyghe, Milan S. Geybels, Stephen J. Salipante, Gabriel Abril-Rodríguez, Helena Escuin-Ordinas, Cristina Puig-Saus, Daniel Sanghoon Shin, Shuji Ogino, Antoni Ribas, Ulrike Peters

Bibliographic record

VenueMolecular Cancer Therapeutics · 2018
Typearticle
Languageen
FieldMedicine
TopicGenetic factors in colorectal cancer
Canadian institutionsInstitute of Cancer ResearchOntario Institute for Cancer Research
Fundersnot available
KeywordsImmunoeditingMicrosatellite instabilityDNA mismatch repairColorectal cancerCancerExomeBiologyImmune checkpointImmune systemExome sequencingCancer researchMedicineGeneticsGeneMicrosatelliteImmunotherapyMutationAllele

Abstract

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Abstract A subset of colorectal carcinomas (CRC) with high microsatellite instability (MSI-high) have been shown to respond to immune checkpoint blockade, while the much more common microsatellite stable (MSS) CRC do not usually respond to these immunotherapies. Most MSI-high tumors grow progressively despite a high mutational load, suggesting that the cancer may have developed means to escape from immune recognition, which is termed immunoediting. This immunoediting process includes three phases: an initial phase of nascent cancer cell elimination due to high immunogenicity, then a period of equilibrium with the immune response, finally leading to escape of the cancer cells that have developed genetic or epigenetic mechanisms allowing them to grow progressively in an immunocompetent host. It is likely that MSI-high cancers would go through similar processes to eventually escape from immune recognition. To better understand the differential immunoediting in MSI-high and MSS CRC, we combined tumor exome data from 592 cases from The Cancer Genome Atlas (TCGA) with previously published tumor exome data from 619 cases from the Nurses’ Health Study (NHS) and the Health Professionals Follow-up Study to yield an unprecedented sample of 1,211 CRC molecularly characterized cases. The large number of samples, including a very large number of MSI-high cases (N = 179), gives us the power to identify the prevalence of the mechanisms of resistance discussed and thereby prioritize downstream efforts. We utilized the MutSigCV algorithm to identify significantly mutated genes in MSS and MSI-high tumors in the TCGA cohort (496 MSS and 75 MSI-high cases), and the NHS/HPFS cohorts (438 MSS and 104 MSI-high cases). We identified a total of 62 significantly mutated genes, of which 9 were identified in MSS tumors only, 40 in MSI-high tumors only, and 13 in both. Twenty-seven of the 53 significantly mutated genes in MSI-high were novel, likely resulting from a large increase in the number of MSI-high tumors relative to previous studies. We identified 13 significantly mutated genes with a documented role in the immune system, specifically in MSI-high tumors, a subtype shown previously to have a high level of T-cell infiltration; ZFP36L2 and B2M were significantly mutated in both MSS and MSI-high. B2M, HLA-A, and HLA-B are significantly mutated antigen presenting genes that were previously reported to be recurrently mutated in MSI-high. Eleven of the significantly mutated immune-related genes had roles in modulating diverse hematopoietic cell types and effects beyond antigen presentation; those genes include XYLT2, a dendritic cell trafficking gene; ZBTB20, a Toll-like receptor mediating immune response; RNF128, a regulator of IL2 and IL4 mediated T-cell maturation; KLF3, a gene involved in B-cell development; ZFP36L2, a gene involved in thymocyte development; CASP8, a gene involved in innate immunity; and CD58, a gene involved in the activation of both T lymphocytes and NK cells. Together, these mutations indicate positive selection for immune escape through other mechanisms beyond mutations in antigen-presenting machinery. Citation Format: Catherine Grasso, Marios Giannakis, Daniel Wells, David Wheeler, Eve Shinbrot, Syed Zaidi, Jeroen Huyghe, Milan Geybels, Stephen Salipante, Gabriel Abril-Rodriguez, Helena Escuin-Ordinas, Cristina Puig-Saus, Daniel Sanghoon Shin, Shuji Ogino, Antoni Ribas, Ulrike Peters. Immunoediting in untreated mismatch repair deficient colorectal cancer [abstract]. In: Proceedings of the AACR-NCI-EORTC International Conference: Molecular Targets and Cancer Therapeutics; 2017 Oct 26-30; Philadelphia, PA. Philadelphia (PA): AACR; Mol Cancer Ther 2018;17(1 Suppl):Abstract nr B011.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.156
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.027
GPT teacher head0.314
Teacher spread0.287 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designBench or experimental
Domainnot available
GenreEmpirical

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".

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Citations0
Published2018
Admission routes1
Has abstractyes

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