MétaCan
Menu
← Back to cohort

Molecular profiling of advanced pancreatic ductal adenocarcinoma (PDAC): Role of ctDNA.

2021· article· en· W3123446117 on OpenAlexaff
Ángela Lamarca, Mairéad G. McNamara, Richard Hubner, Juan W. Valle

Bibliographic record

VenueJournal of Clinical Oncology · 2021
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCancer Genomics and Diagnostics
Canadian institutionsPrincess Margaret Cancer Centre
Fundersnot available
KeywordsMedicineKRASInternal medicinePancreatic cancerOncologyChemotherapyPancreatic ductal adenocarcinomaPopulationPathologyCancerColorectal cancer

Abstract

fetched live from OpenAlex

425 Background: Molecular profiling of tumour samples and circulating tumour DNA (ctDNA) may inform treatment of advanced cancer; the role of ctDNA to predict progression-free-survival (PFS) and overall survival (OS) in advanced PDAC is not fully understood. Methods: Eligible patients: those diagnosed with advanced PDAC undergoing molecular profiling [tumour (Foundation Medicine CDx/Caris) or ctDNA (FoundationMedicine Liquid (72 cancer-related genes))]. Baseline patient characteristics and molecular profiling outcomes, including mutant allele frequency (MAF) for pathological alterations were extracted. The primary aim was to assess the impact of presence of ctDNA at time of systemic chemotherapy initiation on PFS and OS. Results: Total of 26 samples (ctDNA 18 samples and 8 tumour samples) from 25 patients diagnosed with advanced PDAC underwent molecular profiling. When the whole population was analysed, the rate of sample analysis failure seemed to be higher when tumour tissue was tested (37.5%) compared to ctDNA (5.56%); p-value 0.072. The overall rate of identification of pathological findings was 72.73%, with 18.18% of patients having targetable findings [EGFRmut (1 patient), KRAS G12C mut (1 patient), FGFR2 fusion (1 patient), RNF43 mut (1 patient)]; these findings impacted treatment management in one patient only (RNF43 mutation; Wnt inhibitor). Variants of unknown significance were identified in 63.64% of samples. Patients with ctDNA analysis at time of palliative chemotherapy initiation (16 samples; 15 patients) were analysed [6 female (40.00%), median age 69.57 years (range 51.61-81.49), metastatic disease (66.67%), 80% first-line (80%), 20% second-line]. Pathological mutations were identified in 9/15 (60.00%) of these patients (KRAS mutation identified in 6/9). After median follow-up of 8.33 months from sample acquisition, 80% and 53.33% of patients had progressed and died, respectively. Median estimated PFS and OS were 5.65 months (95% CI 1.59-8.17) and 7.80 months (95% CI 4.13-not reached). Presence (vs absence) of pathological alterations in ctDNA showed a trend towards shorter PFS (2.91 vs 6.51 months; HR 1.38 (95% CI 0.40-4.77)) and OS (6.12 vs 9.72 months; HR 2.03 (95% CI 0.60-6.82)). Conclusions: This pilot study demonstrates the feasibility of ctDNA analysis in patients with advanced PDAC prior to initiation of palliative therapy. The presence of pathological alterations in ctDNA may prognosticate for worse PFS and OS. Larger studies are required to confirm these findings.

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)0.002
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 score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
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 head0.027
GPT teacher head0.372
Teacher spread0.345 · 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

Explore more

Same venueJournal of Clinical Oncology→Same topicCancer Genomics and Diagnostics→French-language works237,207→