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Immune Resistance Interrogation Study (IRIS): A prospective comprehensive multi-omic analysis in patients with intrinsic and acquired resistance to immunotherapy.

2021· article· en· W3171092377 on OpenAlexaff
Sofia Genta, Ming‐Sound Tsao, Ben X. Wang, Aaron R. Hansen, Trevor J. Pugh, Mathieu Lupien, Bryan Coburn, Javier Diaz-Mejia, Marcus O. Butler, Philippe L. Bédard, Albiruni Ryan Abdul Razak, Sevan Hakgor, Vanessa Speers, Heidi Wagner, Laura Tamblyn, Troy Ketela, Dax Torti, Nikolina Radulovich, Lillian L. Siu, Anna Spreafico

Bibliographic record

VenueJournal of Clinical Oncology · 2021
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCancer Genomics and Diagnostics
Canadian institutionsToronto General HospitalPrincess Margaret Cancer CentreOntario Institute for Cancer ResearchUniversity of TorontoUniversity Health Network
Fundersnot available
KeywordsMedicineImmunotherapyBiopsyImmune checkpointProspective cohort studyImmune systemTumor microenvironmentOncologyInternal medicineImmunology

Abstract

fetched live from OpenAlex

TPS2679 Background: Immune checkpoint inhibitors (ICI) have demonstrated efficacy in a wide variety of cancers. Nevertheless, only a small proportion of patients derive a durable benefit. Mechanisms underlying primary and acquired resistance are still incompletely understood. They comprise tumor-intrinsic factors such as genomic and transcriptomic changes; upregulation of immunosuppressive subsets; T cell exhaustion; and promotion of an immune-tolerant tumor microenvironment. The collection of tumor biopsy at disease progression (PD) is challenging both in clinical and research settings as this often occurs at the time of treatment discontinuation. However, the analysis of these samples can lead to novel strategies to prevent or reverse immune resistance. Thus, the current approach to begin a profiling study with patients at the time of PD on ICI enables access and interrogation of such samples. Methods: IRIS is a prospective, investigator-initiated trial at the Princess Margaret Cancer Centre that aims to extensively characterize the genomic, transcriptomic, epigenetic and immunophenotypic profiles of tumors with primary versus acquired resistance to ICI-based therapy. Primary resistance is defined as PD at the first on-treatment imaging, whereas acquired resistance is defined as PD occurring after an initial partial or complete response or following disease stability lasting ≥6 months. Additional objectives include the evaluation of radiomic parameters on standard radiological imaging, investigation of fecal microbiome, generation of patient-derived organoids and facilitation of data and sample sharing with the research community. The planned samples size is 100 patients. A one-time fresh tumor biopsy, blood and stool samples and archival tissue (when available) are collected at the time of PD on ICI (baseline) from all the participants. Longitudinal blood samples are obtained every 2-3 months (around the time of tumor imaging) until PD in patients receiving a subsequent treatment. Subjects who are not amenable for therapy undergo blood collections at the time of further PD. Molecular characterization of tumor samples includes: DNA/RNA sequencing, Assay of Transposase Accessible Chromatin (ATAC)-sequencing, Cellular Indexing of Transcriptomes and Epitopes (CITE)-sequencing, multiplexed immunohistochemistry and flow cytometry. Results of NGS performed on the first biopsy core are returned to patient and physician. Key eligibility criteria include diagnosis of solid tumor, progression to ICI as the most recent line of treatment and disease amenable to core needle biopsy. The IRIS trial, activated in October 2020, is currently open to enrollment. As of January 2021, 21 patients have been enrolled and a total of 92 tissue cores, 42 blood and 20 stool samples have been collected. Clinical trial information: NCT04243720.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.026
GPT teacher head0.366
Teacher spread0.340 · 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
Published2021
Admission routes1
Has abstractyes

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