MétaCan
Menu
← Back to cohort

The value of defining molecular resistance in patients with progressive <i>EGFR</i> and <i>ALK</i>-driven lung cancer in a public system.

2021· article· en· W3167491954 on OpenAlexaffabout
Carly C. Barron, Tracy Stockley, Jennifer Law, Muqdas Shabir, Roxanne Fernandes, Tong Zhang, Lisa W. Le, Ming‐Sound Tsao, Suzanne Kamel‐Reid, Prodipto Pal, Michael Cabanero, Joerg Schwock, Hyang Mi Ko, Geoffrey Liu, Penelope Ann Bradbury, Adrian G. Sacher, Frances A. Shepherd, Natasha B. Leighl, Kirstin Perdrizet

Bibliographic record

VenueJournal of Clinical Oncology · 2021
Typearticle
Languageen
FieldMedicine
TopicLung Cancer Treatments and Mutations
Canadian institutionsPrincess Margaret Cancer CentreUniversity Health NetworkUniversity of Toronto
Fundersnot available
KeywordsMedicineOsimertinibT790MLung cancerInternal medicineOncologyKRASClinical trialCancerErlotinibEpidermal growth factor receptorGefitinib

Abstract

fetched live from OpenAlex

3126 Background: Repeat molecular profiling, except to detect EGFR T790M, is not routinely performed in Canadian patients with lung cancer progressing on EGFR tyrosine kinase inhibitors (TKIs). We performed genomic profiling on post-progression biopsies in patients with stage IV non-small cell lung cancer (NSCLC) and known EGFR/ ALK aberrations treated with TKIs to identify resistance mechanisms, evaluate options for subsequent treatment, and to assess clinical trial eligibility and costs. Methods: From Feb 2018-Aug 2020, post-progression tumour biopsies from consenting patients at a major cancer centre underwent genomic profiling (ThermoFisher OCA v3.0 including hotspots, fusions, and copy number variations in 161 cancer-associated genes). Outcomes of interest were the identification of resistance mutations, actionable targets, clinical trial eligibility (per clinicaltrials.gov), and costs. Results: Thirty-two patients consented to the study. Most, 84% (n = 27), had successful testing completed while 16% (n = 5) had insufficient tissue. Median age of the cohort was 56 yrs, 59% (n = 16) were female, 74% (n = 20) were never-smokers, 81% (n = 22) had ECOG performance status 0-1, and 67% (n = 18) were Asian. The majority, 81% (n = 22) had EGFR mutated NSCLC, and had progressed on EGFR TKIs (15 with previously identified T790M had progressed on osimertinib), and 19% (n = 5) had ALK fusions. Patients had received a median of 2 prior lines of targeted therapy prior to re-biopsy (IQR 1.5,3). One patient had evidence of small cell transformation and associated TP53 and RB1 mutations, 11% (n = 3) had acquired EGFR C797S mutations, and 11% (n = 3) had acquired ALK resistance point mutations (G1202R n = 2, I1171N n = 1). Genomic profiling identified additional actionable targets in 19% of patients (n = 5: MET exon 14 skip mutation n = 1, MET amplification n = 2, BRAF V600E n = 2). Overall, 33% (n = 9) patients had management-changing resistance mechanisms identified (small cell transformation n = 1, actionable targets n = 5, ALK inhibitor resistance = 3). New clinical trial options based on genomic profiling results were identified for 67% (n = 18) of patients. Incremental costs for repeat genomic profiling were approximately $880 CAD per case. Conclusions: Molecular profiling upon development of resistance to targeted therapy in our cohort revealed actionable resistance mechanisms for over a third of patients and clinical trial options for 67%. These incremental benefits for patients highlight the importance of routine molecular profiling in the setting of acquired TKI resistance in lung cancer.

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)0.006
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 score0.027
Threshold uncertainty score0.053

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.022
GPT teacher head0.422
Teacher spread0.400 · 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 routes2
Has abstractyes

Explore more

Same venueJournal of Clinical Oncology→Same topicLung Cancer Treatments and Mutations→French-language works237,207→