Chavarriaga (2025) — ASCO GU 2025: Evolution of ctDNA Detection with Next Generation Technology

Bibliographic Reference

Chavarriaga, J. (2025, February 15). ASCO GU 2025: Evolution of ctDNA detection with next generation technology. UroToday. https://www.urotoday.com/conference-highlights/asco-gu-2025/asco-gu-2025-bladder-cancer/158294-asco-gu-2025-evolution-of-ctdna-detection-with-next-generation-technology.html

Core Argument

This article reports on a presentation by Dr. Alexander Wyatt at the 2025 ASCO GU symposium (San Francisco, February 13–15, 2025) titled “Panning for Gold: The Role of ctDNA as a Biomarker for Bladder Cancer.” Wyatt argues that circulating tumor DNA (ctDNA) detection technology is evolving rapidly through next-generation approaches, particularly leveraging epigenomic features (methylation marks, fragmentomics, and plasma ChIP-seq) to improve sensitivity. However, he emphasizes that “one size does not fit all” when selecting a ctDNA test: test design must be matched to the clinical goal of either detecting cancer (screening, MRD) or characterizing it (genotyping, treatment monitoring). Key technical challenges include confounding by clonal hematopoiesis (CH), variability in ctDNA shedding across cancer types, and the lack of standardized head-to-head platform comparisons.

Methods

This is a conference report summarizing an expert lecture. As such, it draws on Dr. Wyatt’s presentation of previously published ctDNA research, unpublished data from his group (a study of ~300 patients with metastatic renal cell carcinoma involving synchronous plasma ctDNA and clonal hematopoiesis profiling), and a review of emerging epigenomic ctDNA technologies. No systematic review methodology or formal evidence synthesis is described. The article is a secondary journalistic account (by Julian Chavarriaga, MD) of Wyatt’s oral presentation, not a peer-reviewed primary research publication.

Key Findings

• “New technology is advancing the long-standing concept of the ‘liquid biopsy,’ a promise first recognized over a century ago,” with the earliest documented evidence of solid tumor material in peripheral blood reported in Melbourne, Australia, in 1869.
• “One size does not fit all when selecting a ctDNA test” — test design varies by clinical goal: DETECT (improving cancer screening, detecting minimal residual disease) or CHARACTERIZE (identifying targetable gene alterations, assessing resistance mechanisms, characterizing tumor biology and evolution).
• Clonal hematopoiesis (CH) is “pervasive in the elderly cancer population” and “can complicate the interpretation of cfDNA in cancer diagnostics.” In Wyatt’s unpublished data from ~300 metastatic renal cell carcinoma patients, “around 50% of the variants fell into both the ctDNA and clonal hematopoiesis compartments,” underscoring the difficulty of distinguishing tumor-derived from CH-derived variants without matched white blood cell sequencing.
• “Sensitivity and specificity remain sub-optimal in certain contexts,” and “there are no head-to-head comparisons between platforms, and the sensitivity and lower limits of detection are unknown and will likely vary by factors such as phenotype, background signal in blood, the number of ‘targets’ interrogated, and the ctDNA%.”
• New epigenomic technologies (DNA methylation marks on ctDNA, ctDNA fragment information/nucleosome positioning, and plasma ChIP-seq for histone modifications) “are enhancing the sensitivity of detection tests and enabling ctDNA lineage phenotyping,” with all three approaches showing ability to indicate “ctDNA cell-of-origin and identify small cell cancers.”

Concepts Introduced or Used

• Circulating tumor DNA (ctDNA) — Tumor-derived fraction of cell-free DNA in the bloodstream, identifiable by mutations, structural rearrangements, lineage-specific methylation marks, and nucleosome patterns (fragmentomics). The percentage of ctDNA correlates with volume of active and proliferative cancer. ctdna
• Cell-free DNA (cfDNA) — DNA fragments in blood plasma, predominantly 167 bp (matching nucleosome unit length), shed by apoptotic cells. Most normal cfDNA originates from the blood lineage. cell-free-dna
• Clonal hematopoiesis (CH) — Condition in which hematopoietic stem cells acquire somatic mutations, leading to expansion of clonal blood cell populations. CH variants in plasma cfDNA can confound ctDNA interpretation, particularly in elderly cancer patients. clonal-hematopoiesis
• Minimal residual disease (MRD) — Small remaining cancer cell population after treatment, detectable by ctDNA assays with greater sensitivity than current clinical tools. minimal-residual-disease
• Fragmentomics — Analysis of ctDNA fragment size distribution and nucleosome positioning patterns to infer gene expression and cell-of-origin.
• Tumor-informed vs. tumor-naive ctDNA assays — Tumor-informed assays use prior tissue sequencing information (specific, but time- and cost-intensive); tumor-naive assays detect ctDNA de novo (quick, can screen, but lower specificity). New tumor-informed assays incorporate additional features from tissue whole genome sequencing and ctDNA fragment features; next-generation tumor-naive assays focus on epigenomic features.
• Liquid biopsy — Non-invasive sampling of body fluids (typically blood) to detect tumor-derived material. The concept was first recognized over a century ago (1869). liquid-biopsy
• Urine tumor DNA (utDNA) — DNA from urine containing both ctDNA and cellular DNA including exfoliated urothelial cells, relevant for urothelial carcinoma detection. Standardization of urine collection and processing remains a work in progress.
• ctDNA lineage phenotyping — Using epigenomic features (methylation, fragment patterns, histone modifications) carried by ctDNA to infer the cell type-of-origin, including identification of small cell cancers and neuroendocrine differentiation.
• Field cancerization — A challenge specific to urine DNA analysis, where the specificity of somatic mutations is unclear because the entire urothelial field may carry pre-neoplastic alterations.

Entities Referenced

• Alexander William Wyatt, PhD — Presenter; Vancouver Prostate Centre, University of British Columbia.
• FGFR3 — Fibroblast growth factor receptor 3 gene; targetable alteration that ctDNA can detect for prognostic and therapeutic guidance.
• ERBB2 (HER2) — Gene encoding human epidermal growth factor receptor 2; targetable alteration identifiable through ctDNA characterization.
• TP53 — Tumor suppressor gene; commonly mutated in bladder cancer. CH-related TP53 variants can confound plasma-only ctDNA interpretation.
• ATM — DNA damage response gene; CH-related ATM variants can also confound ctDNA interpretation.
• TRACERx — Large longitudinal study of lung cancer evolution, cited in related ctDNA source summaries. TRACERx
• UroToday — Online urology news and conference coverage platform (publication venue).
• Society of Urologic Oncology (SUO) — Professional organization; author’s fellowship affiliation.
• University of Toronto — Author’s institutional affiliation.
• ASCO GU — American Society of Clinical Oncology Genitourinary Cancers Symposium.
• ChIP-seq (plasma cfChIP-seq) — Method for identifying histone modifications on cell-free chromatin; used to infer transcriptional activity and detect signals from non-cancer tissues (e.g., identifying rectal invasion via GI tract H3K4me2 signal in plasma from a metastatic prostate cancer patient).

Limitations (as stated by authors)

Dr. Wyatt explicitly noted several limitations of current ctDNA technology:

• The technology “is still in its early stages and is immature compared to genomic tests.”
• “While it shows promise as a biology probe, its clinical utility remains to be determined.”
• “There are no head-to-head comparisons between platforms.”
• “Sensitivity and lower limits of detection are unknown and will likely vary by factors such as phenotype, background signal in blood, the number of ‘targets’ interrogated, and the ctDNA%.”
• “Unpredictable patient-specific biology and non-tumor cell confounders complicate the interpretation.”
• A key open question: “why do some cancers shed ctDNA more effectively than others?”
• For urine tumor DNA: “there is no consensus on how urine should be collected and processed, what preservatives and volumes are required, or the optimal methods for DNA extraction.”

Relevance to Clonal Evolution

This article has moderate-to-low direct relevance to the core theory of clonal-evolution as developed in this wiki. It is a clinical update piece, not a theoretical or mechanistic contribution. However, several points connect to wiki concepts:

• Tumor evolution monitoring: ctDNA characterization offers “insights into resistance mechanisms and new tumor biology, offering a view of cancer evolution across serial blood collections.” The ability to track subclonal shifts via ctDNA directly supports the study of subclonal-architecture and branching-evolution during treatment.
• Clonal hematopoiesis as confounder: The article’s emphasis on CH as a major confounder in ctDNA interpretation directly parallels the wiki’s interest in distinguishing true tumor evolution from hematopoietic background mutations — a practical challenge for any ctDNA-based evolutionary analysis.
• Intratumor heterogeneity: ctDNA can capture alterations across multiple metastatic sites simultaneously, potentially providing a more integrated view of intratumor-heterogeneity than single-site biopsies.
• Therapy resistance: The article notes ctDNA’s utility in “identifying resistance mechanisms” and detecting “subclonal shifts,” connecting to the wiki’s therapy-resistance concept pages.
• Clinical gap: The article explicitly states that ctDNA’s “clinical utility remains to be determined” and that sensitivity varies by context — important caveats for any wiki content that relies on ctDNA as an evolutionary readout.

The article also identifies a significant gap in the wiki’s current corpus: there is no dedicated concept page for ctdna, clonal-hematopoiesis, cell-free-dna, minimal-residual-disease, or liquid-biopsy, all of which are referenced across multiple existing source summaries (abbosh2017-ctdna-tracerx, khatami2018-ctdna-personalized-medicine, stejskal2023-ctdna-biology-review).