Abstract A26: Defining VALUE: Routine liquid biopsy in NSCLC diagnosis—a Canadian trial in progress
Bibliographic record
Abstract
Abstract Background: Genotyping tumor tissue in time for clinical treatment decision-making has been challenging in advanced non-small cell lung cancer (NSCLC). Next-generation sequencing (NGS) of cell-free DNA (cfDNA) obtained from blood samples may improve diagnostic testing, with faster turnaround time (TAT) and potential cost savings. This study defines the added value of cfDNA versus tumor tissue genotyping in patients with advanced NSCLC in the Canadian public health care system. Methods: Patients with advanced non-squamous NSCLC patients at 6 cancer centers across Canada are being recruited (BC, Alberta, Ontario, Quebec). Two cohorts are included: 1) treatment-naïve patients with ≤10 pack year smoking history (N=150) and 2) patients with known oncogenic drivers (e.g., EGFR, ALK, ROS1, BRAF) that have progressed on tyrosine kinase inhibitors (N=60). Consenting patients undergo peripheral blood draw and cfDNA NGS analysis using Guardant360™ (Guardant Health), a validated assay that detects alterations in 74 known cancer-associated genes, prior to starting treatment (Cohort 1) or next line of treatment (Cohort 2). Standard-of-care (SOC) tissue profiling is completed per institutional standards. Endpoints include response, progression-free survival, time-to-treatment failure, as well as time to treatment initiation, number of actionable genomic alterations identified, result TAT, patient-reported quality of life (EQ-5D), and willingness to pay. A decision-analytic model will be developed to perform a cost-consequence analysis of cfDNA versus tissue-based diagnostics. Results: Between February and October 3, 2019, 49 patients (32 female, 17 male) were recruited to Cohort 1 and 31 (21 female, 10 male) to Cohort 2. Forty patients in Cohort 1 (81.6%) had ≥1 alteration detected, with a total of 145 genetic alterations detected in 36 genes. Of these, 68 were actionable with FDA-approved drugs and/or clinical trials available. The most frequent actionable alterations were in EGFR (33.8%), MET Exon 14 Skipping (7.4%), and EML4-ALK fusion (2.9%). Additional alterations included TP53 (29.4%), KRAS (8.8%), PIK3CA (4.4%), and STK11 (2.9%). In Cohort 2, 29 patients (93.5%, 29/31) had ≥1 alteration detected, with a total of 130 alterations in 33 genes. The most frequent alterations were in EGFR (46.0%), TP53 (9.5%), EML4-ALK fusion (6.3%), ALK mutations (4.8%), and BRAF, BRCA2, GNAS, KRAS, MET Exon 14 Skipping, NRAS, and PTEN (each 3.2%). In samples with alterations detected, the median number of alterations/patient was 3 (range 1-17). The median time to Guardant report was 8 days (range 5-27). Mutations detected in tissue, concordance, and time to result for SOC tissue profiling will be updated, along with incremental treatment options for patients. Conclusion: Over 86% of advanced NSCLC patients had detectable cfDNA and at least 61 had actionable mutations. Blood-based testing may be an important cost-efficient addition to tissue-based testing in lung cancer to determine optimal treatment options. Citation Format: Jason Agulnik, Jennifer H. Law, Rosalyn Juergens, Janessa Laskin, Scott Laurie, Desiree Hao, Doreen A. Ezeife, Lisa W. Le, Lesli A. Kiedrowski, Richard B. Lanman, Natasha B. Leighl. Defining VALUE: Routine liquid biopsy in NSCLC diagnosis—a Canadian trial in progress [abstract]. In: Proceedings of the AACR Special Conference on Advances in Liquid Biopsies; Jan 13-16, 2020; Miami, FL. Philadelphia (PA): AACR; Clin Cancer Res 2020;26(11_Suppl):Abstract nr A26.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".