MétaCan
Menu
Back to cohort

Abstract A18: Plasma circulating tumor DNA is scarce and confounded by clonal hematopoiesis in metastatic renal cell carcinoma

2020· article· en· W3036776981 on OpenAlexaff
Jack V. W. Bacon, Matti Annala, Maryam Soleimani, Jean‐Michel Lavoie, Alan So, Martin Gleave, Ladan Fazli, Kim N., Christian Kollmannsberger, Alexander W. Wyatt, Lucia Nappi

Bibliographic record

VenueClinical Cancer Research · 2020
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCancer Genomics and Diagnostics
Canadian institutionsSpinal Cord Injury BC
Fundersnot available
KeywordsRenal cell carcinomaMedicineOncologyBAP1ConcordanceInternal medicineCancerLiquid biopsySomatic cellCell-free fetal DNACarcinomaCancer researchGeneBiologyGenetics

Abstract

fetched live from OpenAlex

Abstract Despite the recent implementation of novel targeted therapies, the 5-year survival rate for metastatic renal cell carcinoma (mRCC) remains dismal. Prognostic and predictive biomarkers are urgently required to avoid unnecessary patient morbidity and financial toxicity. Exploratory studies leveraging archival primary tumor tissue are considered suboptimal in this setting due to the considerable heterogeneity of metastatic lesions. Circulating tumor DNA (ctDNA) permits the noninvasive characterization of metastatic cancers using a simple blood draw. In this study, we sought to establish the utility of a ctDNA-based assay as a tool to profile the somatic genome of patients with mRCC. We collected whole blood from 55 progressing mRCC patients. All patients were systemic therapy naïve at the time of sample collection. Plasma cell-free DNA (cfDNA) and matched leukocyte DNA were subjected to targeted sequencing across 981 cancer-associated genes. Matched tumor tissue from 14 patients was also analyzed. The median cfDNA sequencing depth for this cohort was 938x. 33% of patients had evidence for RCC-derived ctDNA above 1% of total cfDNA; this was significantly lower than prostate or bladder cancer patients analyzed using the same approach. Among ctDNA-positive patients, ctDNA fraction averaged only 3.9% and showed no association with clinical variables or cfDNA yield. In these patients, the most commonly mutated genes were VHL, BAP1, and PBRM1, and matched tissue concordance was 77%. Evidence of somatic expansions unrelated to RCC, such as clonal hematopoiesis of indeterminate potential (CHIP), was detected in 43% of patients. Pathogenic germline mutations in DNA repair genes were detected in 11% of patients. Patients with ctDNA above 1% had shorter overall survival and progression-free survival on first-line therapy. Patients with evidence of CHIP but not ctDNA had an intermediate prognosis compared to ctDNA-positive and ctDNA-negative patients. CfDNA sequencing enables characterization of the somatic RCC genome in only a minority of metastatic RCC patients. Due to low ctDNA abundance and presence of non-RCC derived somatic clones in circulation, cfDNA sequencing may not be a simple pan-patient alternative to tissue biopsy in metastatic RCC. Citation Format: Jack V. W. Bacon, Matti Annala, Maryam Soleimani, Jean-Michel Lavoie, Alan I. So, Martin E. Gleave, Ladan Fazli, Kim N. Chi, Christian K. Kollmannsberger, Alexander W. Wyatt, Lucia Nappi. Plasma circulating tumor DNA is scarce and confounded by clonal hematopoiesis in metastatic renal cell carcinoma [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 A18.

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 score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.128
GPT teacher head0.420
Teacher spread0.292 · 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

Citations2
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueClinical Cancer ResearchSame topicCancer Genomics and DiagnosticsFrench-language works237,207