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Exploitation of treatment induced tumor lysis to enhance sensitivity of ctDNA analysis: A first-in-human pilot study.

2020· article· en· W3032359244 on OpenAlexaff
Daniel Breadner, Mark Vincent, Rohann Correa, Morgan Black, Andrew Warner, Melody Qu, Diane Logan, Michael Sanatani, Jawaid Younus, Brian Yaremko, George Rodrigues, Phillip Blanchette, Joanna Laba, Vasudeva Bhat, Clive Morris, Emma Green, Greg Jones, Alison L. Allan, David A. Palma, Jacques Raphael

Bibliographic record

VenueJournal of Clinical Oncology · 2020
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCancer Genomics and Diagnostics
Canadian institutionsHealth Sciences CentreLondon Health Sciences CentreSunnybrook Health Science CentreWestern UniversityCancer Care Ontario
Fundersnot available
KeywordsMedicineLung cancerInternal medicineChemotherapyOncologyProspective cohort studyCohortCancerGastroenterology

Abstract

fetched live from OpenAlex

3530 Background: Blood based liquid biopsies examining circulating tumour DNA (ctDNA) have increasing applications in non-small cell lung cancer (NSCLC). Limitations in sensitivity remains a barrier to ctDNA replacing tissue-based testing. There is a paucity of data regarding the dynamics of ctDNA levels in the hours to days following a new treatment. We hypothesize that chemotherapy or radiation will yield an increased abundance of ctDNA in plasma by inducing tumor lysis, allowing for the detection of genetic alterations that were occult in baseline testing. Methods: Two prospective cohorts of 20 patients (pts) with stage III/IV NSCLC were enrolled. Cohort 1 (C1) contained pts starting the first cycle of platinum doublet chemoradiation (C1a, n=10) or the first cycle of platinum doublet cytotoxic chemotherapy ± immunotherapy without radiation (RT) (C1b, n=10). Cohort 2 (C2) contained pts receiving palliative RT alone. Two baseline samples were collected, the first ≤ 14 days prior to starting treatment and one immediately prior to treatment. In C1, subsequent samples were collected 3, 6, 24 and 48 hours post initiation of chemotherapy. Pts in C2 had samples collected immediately prior to RT fractions 2, 3, and 4. Samples were analyzed for ctDNA using the 36-gene amplicon-based NGS Inivata InVisionFirst-Lung assay. Results: Complete results were available for the first 35 of 40 enrolled pts, C1a – 10 pts, C1b – 9 pts, C2 – 16 pts. Detectable ctDNA was present at baseline in 27 pts (77%), 4 additional pts (11%) had detectable ctDNA in post treatment samples. Four of the patients with detectable ctDNA at baseline (15%) had new genetic alterations detected in post treatment samples. A total of 8/35 pts (23%) had new genetic alterations detected in the post treatment samples. Mutant molecule numbers increased with treatment in 23 of 31 (74%) pts with detectable ctDNA, C1 - 13 of 19 pts (68%) and C2 - 10 of 16 pts (63%). ctDNA levels peaked a median of 2.2 hours (IQR: 1.5 – 2.9 hours) after the initiation of chemotherapy and a median of 1 day (IQR: 1-2 days) after radiation was commenced. The percentage increase in ctDNA levels was a median of 29% (IQR: -18 to +112%) in C1. C2 had a median increase of 16% (IQR: 0 to +131%). Conclusions: ctDNA levels increase in the hours to days after starting treatment. ctDNA testing in the acute post treatment phase can yield results that were not evident in pretreatment testing. Application of this principle could improve ctDNA utility as an alternate to tissue-based testing and improve sensitivity for the detection of treatment-resistant clones.

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)0.003
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0050.001

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.

Opus teacher head0.107
GPT teacher head0.442
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 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

Citations3
Published2020
Admission routes1
Has abstractyes

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