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GATA6 expression in advanced pancreatic ductal adenocarcinoma (PDAC) as a prognostic and predictive biomarker in the Canadian COMPASS trial and the Comprehensive Cancer Center, Fondazione Policlinico Universitario Agostino Gemelli IRCCS.

2024· article· en· W4399881081 on OpenAlexafffundabout
Ronan Andrew Mc Laughlin, Lisa Salvatore, Diletta Barone, Yangqing Deng, Faiyaz Notta, Gun Ho Jang, Sergio Alfieri, Giuseppe Quero, Geny Piro, Carmine Carbone, Marta Chiaravalli, Maria Bensi, Cinzia Bagalá, Shawn Hutchinson, Anna Dodd, Sandra E. Fischer, Steven Gallinger, Jennifer J. Knox, Giampaolo Tortora, Grainne M. O’Kane

Bibliographic record

VenueJournal of Clinical Oncology · 2024
Typearticle
Languageen
FieldMedicine
TopicPancreatic and Hepatic Oncology Research
Canadian institutionsToronto General HospitalUniversity Health NetworkUniversity of TorontoOntario Institute for Cancer ResearchPrincess Margaret Cancer Centre
FundersTerry Fox Research InstitutePancreatic Cancer Canada FoundationPrincess Margaret Hospital Foundation
KeywordsMedicinePancreatic ductal adenocarcinomaBiomarkerCancerCompassPancreatic cancerOncologyInternal medicineCartography

Abstract

fetched live from OpenAlex

4150 Background: Modified FOLFIRINOX (mFFX) and gemcitabine + nab-paclitaxel (GnP) are current 1st line options in advanced PDAC. GATA6 expression, a transcription factor identifies the classical phenotype and is associated with superior overall survival (OS), low GATA6 predicts a basal-like phenotype, associated with a poorer prognosis. We have previously established high concordance between GATA6 expression by RNAseq and in situ hybridization (ISH). Recent studies suggest a survival difference between primary tumor anatomical locations, it is unknown if GATA6 expression influences this. This multicentre study evaluates GATA6 as a prognostic and predictive biomarker and determines its impact on primary tumor location. Methods: Patients were included from COMPASS and a retrospective IRCCS cohort. COMPASS participants had GATA6 by RNAseq and IRCCS cohort by ISH, all treated in 1st line metastatic setting with mFFX or GnP. The primary endpoint was OS by GATA6 expression and by 1st line treatment. We investigate if there are OS differences between primary tumor anatomical locations. The Kaplan Meier method was used to assess OS. The log-rank test was used to examine whether there is a significant difference between the OS in different groups. GATA6 as a predictive biomarker to response to therapy was assessed by the interaction term for GATA6 and treatment group in a Cox proportional hazards model. Results: The combined analysis included 465 pts (253 COMPASS and 212 IRCCS). Baseline characteristics were similar. The combined median age was 64 yrs (28, 84); 402 (86%) had metastatic PDAC and 63 (14%) locally advanced. 56 (12%) had prior surgery, 40 (9%) had prior adjuvant therapy. In total, 296 (64%) and 169 (36%) were GATA6 high and low respectively. OS in months (m) analyses by GATA6 expression are summarized (Table). We demonstrate no difference between OS based on primary tumor location (p=0.09), however, regardless of location, GATA6 high tumors show statistically significant better OS. We confirm, by interaction term, GATA6 as a predictive biomarker, with GATA6 high predictive of response to mFFX while low expression demonstrates less benefit with mFFX (p=0.003). Conclusions: GATA6 is a highly prognostic biomarker. We did not find an association with tumor location, which also was not prognostic in this study. Importantly GATA6 was predictive of survival in patients receiving mFFX, suggesting GATA6 low tumors should receive 1st line GnP based regimens. Clinical trial information: NCT02750657 . [Table: see text]

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 score0.202
Threshold uncertainty score0.401

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.127
GPT teacher head0.463
Teacher spread0.336 · 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

Citations4
Published2024
Admission routes3
Has abstractyes

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