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Abstract PR002: Antigen presentation and processing pathway is associated with early relapse after neoadjuvant immune checkpoint blockade (ICB) in dedifferentiated liposarcomas (DDLPS)

2022· article· en· W4296230819 on OpenAlexaboutno aff
Elise F. Nassif, Chia-Chin Wu, Kadir C. Akdemir, Russell G. Witt, Raymond Traweek, Brandon Cope, Prapassorn Thirasastr, Taylor Tate, Grace Mathew, Shadarra Crosby, Randy Chu, Mohammad Mohammad, Kenna Shaw, Ingram Davis, Khalida Wani, Alexander J. Lazar, Wei‐Lien Wang, Sheila Duncan, B. Ashleigh Guadagnolo, Andrew J. Bishop, Valerae O. Lewis, Justin E. Bird, Keila E. Torres, Kelly K. Hunt, Barry W. Feig, Christopher P. Scally, Ravin Ratan, Shreyaskumar Patel, Robert S. Benjamin, Robert L. Satcher, Kevin M. McBride, Wolf H. Fridman, Ignacio Wistuba, P. Andrew Futreal, Jennifer A. Wargo, Neeta Somaiah, Christina L. Roland, Emily Z. Keung

Bibliographic record

VenueClinical Cancer Research · 2022
Typearticle
Languageen
FieldMedicine
TopicSarcoma Diagnosis and Treatment
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineOncologyInternal medicineIpilimumabNivolumabImmune checkpointCancer researchImmunotherapyCancer

Abstract

fetched live from OpenAlex

Abstract Background: We evaluated the activity of neoadjuvant ICB in localized resectable DDLPS (n=17) and undifferentiated pleomorphic sarcomas (UPS; n=10). DDLPS and UPS patients were randomized to neoadjuvant nivolumab or ipilimumab+nivolumab, with UPS patients receiving concurrent radiotherapy. We assessed genomic markers of early relapse (progression before surgery or relapse within 52 weeks following surgery) using longitudinally acquired tumor samples. Methods: RNA sequencing (RNAseq) and whole genome sequencing (WGS) were performed on longitudinally acquired samples (baseline biopsies and surgical specimens). Differential gene expression between any two groups of patients (i.e., non-early relapse [non-relapsers] vs early relapse [relapsers]) were selected (fold change>1.5 and p value<0.05). Gene set enrichment analyses (GSEA) of KEGG pathways were performed and a network-based approach used to identify genes/pathways associated with MHC-I. Results: At a median follow-up of 23 months, 12 patients (9 DDLPS, 3 UPS) relapsed, including 7 early relapses (relapsers: 5 DDLPS, 2 UPS). The median relapse-free survival was 22 months in DDLPS patients (6 months in relapsers; not reached [NR] in non-relapsers) and NR in UPS patients. At baseline, the most differentially upregulated pathways in non-relapsers compared to relapsers were “graft versus host disease” (GSEA Normalized Enrichment Score[NES]=2.25; False Discovery Rate[FDR] q= 0.009), “natural killer cell mediated cytotoxicity” (NES=2.17; FDR q=0.009), “antigen processing and presentation” (NES=2.16; FDR q=0.009), “allograft rejection” (NES=1.99; FDR q=0.019) and “B-cell receptor signaling pathway” (NES=1.87; FDR q=0.018). In DDLPS patients, the antigen presentation and processing pathway was the most upregulated pathway in non-relapsers compared to relapsers (NES=2.01; FDR q=0.025) while it was not significantly upregulated in UPS (NES=1.15; FDR q=0.62). When looking at pathways longitudinally, the antigen presentation and processing pathway was significantly upregulated at surgery compared to baseline in DDLPS. As antigen presentation and processing was significantly upregulated in DDLPS patients and associated with relapse, we looked for expressed neoantigens that may be processed and presented. Using WGS, we detected 5712 rearrangements at baseline in DDLPS, of which 230 were found in more than one tumor specimen. We also sought to identify genes associated with MHC-I. We selected genes upregulated during ICB comparing baseline to surgical specimens in DDLPS relapsers and looked at the top 10% of genes associated with MHC-I in order to identify potential therapeutic targets for combination. We identified 41 genes upregulated during ICB and associated with MHC-I in relapsers, for which up to 275 inhibitory compounds were found in drug databases. Conclusion: Antigen presentation and processing is a major driver of response to immunotherapy. Future efforts should focus on identifying which antigens are presented to find synergizing compounds in order to increase the clinical benefit of ICB. Citation Format: Elise F. Nassif, Chia-Chin Wu, Kadir Akdemir, Russell G. Witt, Raymond Traweek, Brandon Cope, Prapassorn Thirasastr, Taylor Tate, Grace Mathew, Shadarra Crosby, Randy Chu, Mohammad Mohammad, Kenna Shaw, Ingram Davis, Khalida Wani, Alexander J. Lazar, Wei-Lien Wang, Sheila Duncan, Ashleigh B. Guadagnolo, Andrew J. Bishop, Valerae Lewis, Justin E. Bird, Keila E. Torres, Kelly K. Hunt, Barry W. Feig, Christopher P. Scally, Ravin Ratan, Shreyaskumar Patel, Robert S. Benjamin, Robert Satcher, Kevin McBride, Wolf H. Fridman, Ignacio Wistuba, Andrew Futreal, Jennifer A. Wargo, Neeta Somaiah, Christina L. Roland, Emily Z. Keung. Antigen presentation and processing pathway is associated with early relapse after neoadjuvant immune checkpoint blockade (ICB) in dedifferentiated liposarcomas (DDLPS) [abstract]. In: Proceedings of the AACR Special Conference: Sarcomas; 2022 May 9-12; Montreal, QC, Canada. Philadelphia (PA): AACR; Clin Cancer Res 2022;28(18_Suppl):Abstract nr PR002.

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 score0.004

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 head0.121
GPT teacher head0.430
Teacher spread0.309 · 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".

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Citations0
Published2022
Admission routes1
Has abstractyes

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