Beyond basics: Key mutation selection features for successful tumor-informed ctDNA detection

Marijana Nesic; Mads H. Rasmussen; Tenna V. Henriksen; Christina Demuth; Amanda Frydendahl; Iver Nordentoft; Lars Dyrskjøt; Claus L. Andersen

doi:10.1002/ijc.34964

Beyond basics: Key mutation selection features for successful tumor-informed ctDNA detection

Marijana Nesic, Mads H. Rasmussen, Tenna V. Henriksen, Christina Demuth, Amanda Frydendahl, Iver Nordentoft, Lars Dyrskjøt, Claus L. Andersen^*

^*Corresponding author af dette arbejde

Institut for Klinisk Medicin - Molekylær Medicinsk afdeling (MOMA)

Publikation: Bidrag til tidsskrift/Konferencebidrag i tidsskrift /Bidrag til avis › Tidsskriftartikel › Forskning › peer review

2 Citationer (Scopus)

Abstract

Tumor-informed mutation-based approaches are frequently used for detection of circulating tumor DNA (ctDNA). Not all mutations make equally effective ctDNA markers. The objective was to explore if prioritizing mutations using mutational features—such as cancer cell fraction (CCF), multiplicity, and error rate—would improve the success rate of tumor-informed ctDNA analysis. Additionally, we aimed to develop a practical and easily implementable analysis pipeline for identifying and prioritizing candidate mutations from whole-exome sequencing (WES) data. We analyzed WES and ctDNA data from three tumor-informed ctDNA studies, one on bladder cancer (Cohort A) and two on colorectal cancer (Cohorts I and N). The studies included 390 patients. For each patient, a unique set of mutations (median mutations/patient: 6, interquartile 13, range: 1–46, total n = 4023) were used as markers of ctDNA. The tool PureCN was used to assess the CCF and multiplicity of each mutation. High-CCF mutations were detected more frequently than low-CCF mutations (Cohort A: odds ratio [OR] 20.6, 95% confidence interval [CI] 5.72–173, p = 1.73e−12; Cohort I: OR 2.24, 95% CI 1.44–3.52, p = 1.66e−04; and Cohort N: OR 1.78, 95% CI 1.14–2.79, p = 7.86e−03). The detection-likelihood was additionally improved by selecting mutations with multiplicity of two or above (Cohort A: OR 1.55, 95% CI 1. 14–2.11, p = 3.85e−03; Cohort I: OR 1.78, 95% CI 1.23–2.56, p = 1.34e−03; and Cohort N: OR 1.94, 95% CI 1.63–2.31, p = 2.83e−14). Furthermore, selecting the mutations for which the ctDNA detection method had the lowest error rates, additionally improved the detection-likelihood, particularly evident when plasma cell-free DNA tumor fractions were below 0.1% (p = 2.1e−07). Selecting mutational markers with high CCF, high multiplicity, and low error rate significantly improve ctDNA detection likelihood. We provide free access to the analysis pipeline enabling others to perform qualified prioritization of mutations for tumor-informed ctDNA analysis.

Originalsprog	Engelsk
Tidsskrift	International Journal of Cancer
Vol/bind	155
Nummer	5
Sider (fra-til)	925-933
Antal sider	9
ISSN	0020-7136
DOI	https://doi.org/10.1002/ijc.34964
Status	Udgivet - sep. 2024

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10.1002/ijc.34964Licens: CC BY-NC

Citationsformater

@article{0a5c59bce6d44397b8347dc0f159a71a,

title = "Beyond basics: Key mutation selection features for successful tumor-informed ctDNA detection",

abstract = "Tumor-informed mutation-based approaches are frequently used for detection of circulating tumor DNA (ctDNA). Not all mutations make equally effective ctDNA markers. The objective was to explore if prioritizing mutations using mutational features—such as cancer cell fraction (CCF), multiplicity, and error rate—would improve the success rate of tumor-informed ctDNA analysis. Additionally, we aimed to develop a practical and easily implementable analysis pipeline for identifying and prioritizing candidate mutations from whole-exome sequencing (WES) data. We analyzed WES and ctDNA data from three tumor-informed ctDNA studies, one on bladder cancer (Cohort A) and two on colorectal cancer (Cohorts I and N). The studies included 390 patients. For each patient, a unique set of mutations (median mutations/patient: 6, interquartile 13, range: 1–46, total n = 4023) were used as markers of ctDNA. The tool PureCN was used to assess the CCF and multiplicity of each mutation. High-CCF mutations were detected more frequently than low-CCF mutations (Cohort A: odds ratio [OR] 20.6, 95% confidence interval [CI] 5.72–173, p = 1.73e−12; Cohort I: OR 2.24, 95% CI 1.44–3.52, p = 1.66e−04; and Cohort N: OR 1.78, 95% CI 1.14–2.79, p = 7.86e−03). The detection-likelihood was additionally improved by selecting mutations with multiplicity of two or above (Cohort A: OR 1.55, 95% CI 1. 14–2.11, p = 3.85e−03; Cohort I: OR 1.78, 95% CI 1.23–2.56, p = 1.34e−03; and Cohort N: OR 1.94, 95% CI 1.63–2.31, p = 2.83e−14). Furthermore, selecting the mutations for which the ctDNA detection method had the lowest error rates, additionally improved the detection-likelihood, particularly evident when plasma cell-free DNA tumor fractions were below 0.1% (p = 2.1e−07). Selecting mutational markers with high CCF, high multiplicity, and low error rate significantly improve ctDNA detection likelihood. We provide free access to the analysis pipeline enabling others to perform qualified prioritization of mutations for tumor-informed ctDNA analysis.",

keywords = "clonality, ctDNA markers, error rate, multiplicity, somatic mutations, tumor-informed ctDNA analysis",

author = "Marijana Nesic and Rasmussen, {Mads H.} and Henriksen, {Tenna V.} and Christina Demuth and Amanda Frydendahl and Iver Nordentoft and Lars Dyrskj{\o}t and Andersen, {Claus L.}",

year = "2024",

month = sep,

doi = "10.1002/ijc.34964",

language = "English",

volume = "155",

pages = "925--933",

journal = "International Journal of Cancer",

issn = "0020-7136",

publisher = "John Wiley & Sons Inc.",

number = "5",

}

TY - JOUR

T1 - Beyond basics

T2 - Key mutation selection features for successful tumor-informed ctDNA detection

AU - Nesic, Marijana

AU - Rasmussen, Mads H.

AU - Henriksen, Tenna V.

AU - Demuth, Christina

AU - Frydendahl, Amanda

AU - Nordentoft, Iver

AU - Dyrskjøt, Lars

AU - Andersen, Claus L.

PY - 2024/9

Y1 - 2024/9

N2 - Tumor-informed mutation-based approaches are frequently used for detection of circulating tumor DNA (ctDNA). Not all mutations make equally effective ctDNA markers. The objective was to explore if prioritizing mutations using mutational features—such as cancer cell fraction (CCF), multiplicity, and error rate—would improve the success rate of tumor-informed ctDNA analysis. Additionally, we aimed to develop a practical and easily implementable analysis pipeline for identifying and prioritizing candidate mutations from whole-exome sequencing (WES) data. We analyzed WES and ctDNA data from three tumor-informed ctDNA studies, one on bladder cancer (Cohort A) and two on colorectal cancer (Cohorts I and N). The studies included 390 patients. For each patient, a unique set of mutations (median mutations/patient: 6, interquartile 13, range: 1–46, total n = 4023) were used as markers of ctDNA. The tool PureCN was used to assess the CCF and multiplicity of each mutation. High-CCF mutations were detected more frequently than low-CCF mutations (Cohort A: odds ratio [OR] 20.6, 95% confidence interval [CI] 5.72–173, p = 1.73e−12; Cohort I: OR 2.24, 95% CI 1.44–3.52, p = 1.66e−04; and Cohort N: OR 1.78, 95% CI 1.14–2.79, p = 7.86e−03). The detection-likelihood was additionally improved by selecting mutations with multiplicity of two or above (Cohort A: OR 1.55, 95% CI 1. 14–2.11, p = 3.85e−03; Cohort I: OR 1.78, 95% CI 1.23–2.56, p = 1.34e−03; and Cohort N: OR 1.94, 95% CI 1.63–2.31, p = 2.83e−14). Furthermore, selecting the mutations for which the ctDNA detection method had the lowest error rates, additionally improved the detection-likelihood, particularly evident when plasma cell-free DNA tumor fractions were below 0.1% (p = 2.1e−07). Selecting mutational markers with high CCF, high multiplicity, and low error rate significantly improve ctDNA detection likelihood. We provide free access to the analysis pipeline enabling others to perform qualified prioritization of mutations for tumor-informed ctDNA analysis.

AB - Tumor-informed mutation-based approaches are frequently used for detection of circulating tumor DNA (ctDNA). Not all mutations make equally effective ctDNA markers. The objective was to explore if prioritizing mutations using mutational features—such as cancer cell fraction (CCF), multiplicity, and error rate—would improve the success rate of tumor-informed ctDNA analysis. Additionally, we aimed to develop a practical and easily implementable analysis pipeline for identifying and prioritizing candidate mutations from whole-exome sequencing (WES) data. We analyzed WES and ctDNA data from three tumor-informed ctDNA studies, one on bladder cancer (Cohort A) and two on colorectal cancer (Cohorts I and N). The studies included 390 patients. For each patient, a unique set of mutations (median mutations/patient: 6, interquartile 13, range: 1–46, total n = 4023) were used as markers of ctDNA. The tool PureCN was used to assess the CCF and multiplicity of each mutation. High-CCF mutations were detected more frequently than low-CCF mutations (Cohort A: odds ratio [OR] 20.6, 95% confidence interval [CI] 5.72–173, p = 1.73e−12; Cohort I: OR 2.24, 95% CI 1.44–3.52, p = 1.66e−04; and Cohort N: OR 1.78, 95% CI 1.14–2.79, p = 7.86e−03). The detection-likelihood was additionally improved by selecting mutations with multiplicity of two or above (Cohort A: OR 1.55, 95% CI 1. 14–2.11, p = 3.85e−03; Cohort I: OR 1.78, 95% CI 1.23–2.56, p = 1.34e−03; and Cohort N: OR 1.94, 95% CI 1.63–2.31, p = 2.83e−14). Furthermore, selecting the mutations for which the ctDNA detection method had the lowest error rates, additionally improved the detection-likelihood, particularly evident when plasma cell-free DNA tumor fractions were below 0.1% (p = 2.1e−07). Selecting mutational markers with high CCF, high multiplicity, and low error rate significantly improve ctDNA detection likelihood. We provide free access to the analysis pipeline enabling others to perform qualified prioritization of mutations for tumor-informed ctDNA analysis.

KW - clonality

KW - ctDNA markers

KW - error rate

KW - multiplicity

KW - somatic mutations

KW - tumor-informed ctDNA analysis

U2 - 10.1002/ijc.34964

DO - 10.1002/ijc.34964

M3 - Journal article

C2 - 38623608

AN - SCOPUS:85190950495

SN - 0020-7136

VL - 155

SP - 925

EP - 933

JO - International Journal of Cancer

JF - International Journal of Cancer

IS - 5

ER -

Beyond basics: Key mutation selection features for successful tumor-informed ctDNA detection

Abstract

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