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Immunogenomic features of pathologic response to neoadjuvant immune checkpoint blockade in esophageal cancer.

2021· article· en· W3168443498 on OpenAlexfundno aff
Zineb Belcaid, Archana Balan, Christopher Cherry, Mara Lanis, Kristen A. Marrone, Benjamin Levy, Heather Schneider, Hanika Rodavia, Blair A. Jobe, Elizabeth D. Thompson, Victor E. Velculescu, Richard J. Battafarano, Stephen C. Yang, Stephen Broderick, Jinny S. Ha, Ali H. Zaidi, Ronan J. Kelly, Josephine Feliciano, Valsamo Anagnostou, Vincent K. Lam

Bibliographic record

VenueJournal of Clinical Oncology · 2021
Typearticle
Languageen
FieldMedicine
TopicEsophageal Cancer Research and Treatment
Canadian institutionsnot available
FundersBristol-Myers Squibb Canada
KeywordsImmune checkpointMedicineNivolumabCancer researchNeoadjuvant therapyCancerImmunotherapyEsophageal cancerImmune systemBladder cancerOncologyInternal medicineImmunologyBreast cancer

Abstract

fetched live from OpenAlex

4042 Background: Improving immunotherapy efficacy remains an unmet need in esophagogastric cancer and a deeper understanding of tumor and immune system dynamics during therapy may tailor immuno-oncology approaches. Methods: We performed whole exome sequencing (WES) and bulk RNA sequencing (RNAseq) of 70 serial tumor samples from 23 patients with stage II/III esophageal/gastroesophageal junction (E/GEJ) cancer treated on a phase 1B clinical trial with neoadjuvant nivolumab with or without relatlimab (anti-LAG-3) and chemoradiation followed by surgery (NCT03044613; CA209-906). Pathologic response was measured by tumor regression at the time of resection. Median follow up was 23 months post-surgery. Serial tumor samples were collected prior to therapy, after 2 cycles of induction immune checkpoint blockade (ICB), and at the time of resection. Twenty-two baseline tumor/normal DNA pairs were analyzed by WES and 48 serial tumor samples were analyzed by RNAseq. WES data was analyzed to identify somatic mutations, generate tumor mutation burden (TMB) estimates and assess the fraction of expressed mutations in conjunction with RNAseq data. Immune cell subset composition was determined by RNAseq data deconvolution by CIBERSORT and gene set enrichment analyses were performed utilizing GSEA. B-cell density was inferred by immunoglobulin rearrangements detected by RNAseq. Results: Gene set enrichment expression analyses revealed an upregulation of effector pro-inflammatory cytokines after induction ICB. Interferon-gamma, interferon-alpha and TNF-alpha related genes were significantly upregulated after induction ICB compared to baseline (p < 0.0001). In contrast, significant downregulation of E2F targets (p = 0.002), G2M checkpoint genes (p = 0.005) and DNA damage repair genes (p = 0.004) was observed post ICB; enrichment analyses were independent of response to therapy and treatment arm. While TMB was not predictive of pathologic response (p = 0.22), patients with tumors harboring a higher number of expressed mutations were more likely to achieve a pathologic complete response (pCR; p = 0.026). RNAseq deconvolution analyses revealed a higher B-cell density post ICB induction in tumors with pCR (p = 0.018). Furthermore, an increased baseline content of intra-tumoral activated M1 macrophages differentiated tumors from patients achieving a pCR (p = 0.0034), which was further exemplified post induction ICB. Conclusions: Neoadjuvant immunotherapy induces an inflammatory immune response in the tumor microenvironment that is linked with tumor elimination and pathologic response. Our findings highlight the importance of nuanced multi-omics analyses to understand the wiring of response to immunotherapy and guide therapy for E/GEJ cancer.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.090
GPT teacher head0.484
Teacher spread0.394 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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".

Quick stats

Citations0
Published2021
Admission routes1
Has abstractyes

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