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PASS-01: Pancreatic adenocarcinoma signature stratification for treatment–01.

2022· article· en· W4205476752 on OpenAlexaff
Jennifer J. Knox, Elizabeth M. Jaffee, Grainne M. O’Kane, Dennis Plenker, Amy Zhang, Stephanie Ramotar, Anna Dodd, Rebecca M. Prince, Dan Laheru, Kenneth H. Yu, Wasif M. Saif, Elena Elimova, Michael J. Pishvaian, Kimberly Perez, Andrew J. Aguirre, Sandra E. Fischer, Julie M. Wilson, Faiyaz Notta, David A. Tuveson, Steven Gallinger

Bibliographic record

VenueJournal of Clinical Oncology · 2022
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCancer Genomics and Diagnostics
Canadian institutionsToronto General HospitalUniversity of TorontoOntario Institute for Cancer ResearchPrincess Margaret Cancer CentreUniversity Health Network
Fundersnot available
KeywordsMedicineGemcitabineOncologyPancreatic cancerInternal medicinePancreatic ductal adenocarcinomaChemotherapyPaclitaxelAdenocarcinomaCancer

Abstract

fetched live from OpenAlex

TPS635 Background: Over 70% of patients with pancreatic ductal adenocarcinoma (PDAC) present with metastatic disease where the mainstay of treatment is combination chemotherapy. Two pivotal phase III trials showed survival benefit of mFOLFIRINOX (mFFX) and gemcitabine/nab-paclitaxel (GnP), respectively, compared to gemcitabine alone. Both are considered standard 1st line treatment options but have not been compared prospectively. Other than the BRCA phenotype there are no predictive molecular markers to identify which patients will benefit from mFFX versus GnP. Growing data suggests that RNA signatures and GATA6 expression may predict response to chemotherapy. Genomic platforms do identify small subsets of patients who may benefit from a targeted approach however, impact has been small. Patient-derived organoids (PDOs) are now feasible to passage for drug pharmacotyping that could inform drug therapy approaches. Combining all molecular strategies in real time including genomics, RNA signatures and adding PDO drug sensitivities could enable better precision choices for more patients with metastatic PDAC. Methods: PASS-01 is a multi-institutional randomized phase II trial evaluating the benefit of 1st line mFFX vs GnP in de novo metastatic PDAC patients with good PS who have undergone baseline tumor biopsies with tissue prepared for whole genome (WGS) and RNA sequencing and PDO generation/pharmacotyping using standard and novel drugs. The 1 0 objective is to determine the PFS benefit of mFFX compared to GnP as 1st line treatment with 80% power to detect a median PFS of 7 vs 5 months, favoring mFFX. 27 of a planned 150 patients have been accrued to date. Secondary endpoints include ORR (RECIST), DOR, OS by chemotherapy and biomarkers of therapy response including GATA-6 as a surrogate biomarker for the Moffit RNA classifier. Exploratory objectives include: to evaluate if each PDO DNA/RNA signature matches the patient and if the PDO chemotherapy sensitivities correlate to the patient’s 1st line response; to evaluate the benefit in switching patients to 2nd line treatment based on PDO drug sensitivity; to evaluate novel agents derived from PDO pharmacotyping and potential findings from profiling in 2nd/3rd line treatment; to explore retrospectively whether serial cell-free circulating tumor DNA analysis, circulating tumor cells and CA19.9 could reflect potential early predictors of emerging or de novo resistance and explore biomarkers of immune-oncologic sensitivity with multiplex immunohistochemistry. Each patient’s WGS and PDO data is discussed at a combined tumor board with study investigators immediately following their 1st 8-week CT and ongoing as data develops with the goal of recommending precision treatment choices back to their treating investigator. References: Conroy T et al. NEJM, 2011.; Von Hoff DD et al. NEJM,2013; Aung KL et al. CCR 2017; O’Kane G et al. CCR 2019; Tiriac H et al. Can Discov, 2018. Clinical trial information: NCT04469556.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.657
Threshold uncertainty score0.383

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.0000.000

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 head0.072
GPT teacher head0.407
Teacher spread0.335 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations17
Published2022
Admission routes1
Has abstractyes

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