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Genome-wide mutational signatures in low-coverage whole genome sequencing of cell-free DNA.

ABSTRACT: Mutational signatures accumulate in somatic cells as an admixture of endogenous and exogenous processes that occur during an individual's lifetime. Since dividing cells release cell-free DNA (cfDNA) fragments into the circulation, we hypothesize that plasma cfDNA might reflect mutational signatures. Point mutations in plasma whole genome sequencing (WGS) are challenging to identify through conventional mutation calling due to low sequencing coverage and low mutant allele fractions. In this proof of concept study of plasma WGS at 0.3-1.5x coverage from 215 patients and 227 healthy individuals, we show that both pathological and physiological mutational signatures may be identified in plasma. By applying machine learning to mutation profiles, patients with stage I-IV cancer can be distinguished from healthy individuals with an Area Under the Curve of 0.96. Interrogating mutational processes in plasma may enable earlier cancer detection, and might enable the assessment of cancer risk and etiology.

SUBMITTER: Wan JCM

PROVIDER: S-EPMC9399180 | biostudies-literature | 2022 Aug

REPOSITORIES: biostudies-literature

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Genome-wide mutational signatures in low-coverage whole genome sequencing of cell-free DNA.

Wan Jonathan C M JCM Stephens Dennis D Luo Lingqi L White James R JR Stewart Caitlin M CM Rousseau Benoît B Tsui Dana W Y DWY Diaz Luis A LA

Nature communications 20220823 1

Mutational signatures accumulate in somatic cells as an admixture of endogenous and exogenous processes that occur during an individual's lifetime. Since dividing cells release cell-free DNA (cfDNA) fragments into the circulation, we hypothesize that plasma cfDNA might reflect mutational signatures. Point mutations in plasma whole genome sequencing (WGS) are challenging to identify through conventional mutation calling due to low sequencing coverage and low mutant allele fractions. In this proof ...[more]

PMID: 35999207

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Project description:BACKGROUND:Inherited susceptibility to common, complex diseases may be caused by rare, pathogenic variants ("monogenic") or by the cumulative effect of numerous common variants ("polygenic"). Comprehensive genome interpretation should enable assessment for both monogenic and polygenic components of inherited risk. The traditional approach requires two distinct genetic testing technologies-high coverage sequencing of known genes to detect monogenic variants and a genome-wide genotyping array followed by imputation to calculate genome-wide polygenic scores (GPSs). We assessed the feasibility and accuracy of using low coverage whole genome sequencing (lcWGS) as an alternative to genotyping arrays to calculate GPSs. METHODS:First, we performed downsampling and imputation of WGS data from ten individuals to assess concordance with known genotypes. Second, we assessed the correlation between GPSs for 3 common diseases-coronary artery disease (CAD), breast cancer (BC), and atrial fibrillation (AF)-calculated using lcWGS and genotyping array in 184 samples. Third, we assessed concordance of lcWGS-based genotype calls and GPS calculation in 120 individuals with known genotypes, selected to reflect diverse ancestral backgrounds. Fourth, we assessed the relationship between GPSs calculated using lcWGS and disease phenotypes in a cohort of 11,502 individuals of European ancestry. RESULTS:We found imputation accuracy r2 values of greater than 0.90 for all ten samples-including those of African and Ashkenazi Jewish ancestry-with lcWGS data at 0.5×. GPSs calculated using lcWGS and genotyping array followed by imputation in 184 individuals were highly correlated for each of the 3 common diseases (r2 = 0.93-0.97) with similar score distributions. Using lcWGS data from 120 individuals of diverse ancestral backgrounds, we found similar results with respect to imputation accuracy and GPS correlations. Finally, we calculated GPSs for CAD, BC, and AF using lcWGS in 11,502 individuals of European ancestry, confirming odds ratios per standard deviation increment ranging 1.28 to 1.59, consistent with previous studies. CONCLUSIONS:lcWGS is an alternative technology to genotyping arrays for common genetic variant assessment and GPS calculation. lcWGS provides comparable imputation accuracy while also overcoming the ascertainment bias inherent to variant selection in genotyping array design.

Project description:BackgroundOutcomes under anti-PD-(L)1 therapy have been variable in advanced non-small cell lung cancer (NSCLC) without reliable predictive biomarkers so far. Targeted next-generation sequencing (NGS) of circulating tumor DNA (ctDNA) has demonstrated potential clinical utility to support clinical decisions, but requires prior tumor genetic profiling for proper interpretation, and wide adoption remains limited due to high costs.MethodsTumor-agnostic low-coverage ctDNA whole genome sequencing (lcWGS) was used to longitudinally track genome-wide copy number variations (CNVs) and fragmentation features in advanced NSCLC patients (n = 118 samples from 49 patients) and healthy controls (n = 57). Tumor PD-L1 expression was available for comparison.FindingsFragmentation features and CNVs were complementary indicators, whose combination significantly increased ctDNA detection compared to single-marker assessments (+ 20.3% compared to CNV analysis alone). Baseline fragment length alterations, but not CNVs, were significantly associated with subsequent progression-free survival (PFS; hazard ratio [HR] = 4.10, p = 6.58e-05) and could improve PFS predictions based on tumor PD-L1 expression alone (HR = 2.70, p = 0.019). Residual CNVs or aberrant fragmentation of ctDNA under ongoing therapy could stratify patients according to the subsequent response duration (median 5.8 vs. 47.0 months, p = 1.13e-06). The integrative analysis of ctDNA fragment characteristics at baseline, tumor PD-L1 expression, and residual ctDNA under ongoing treatment constituted the strongest independent predictor of PFS (p = 6.25e-05) and overall survival (p = 1.3e-03) in multivariable analyses along with other clinicopathologic variables.InterpretationThis study demonstrates the feasibility and potential clinical utility of lcWGS for the tumor-agnostic stratification and monitoring of advanced NSCLC under PD-(L)1 blockade based on CNV and fragmentomic profiling.

Project description:BackgroundPapillary thyroid microcarcinoma (PTMC) is defined as a papillary carcinoma measuring ≤ 10 mm. The current management of PTMC has become more conservative; however, there are high-risk tumor features that can be revealed only postoperatively. For thyroid cancer, BRAF mutations and somatic copy number variation (CNV) are the most common genetic events. Molecular testing may contribute to clinical decision-making by molecular risk stratification, for example predicting lymph node (LN) metastasis. Here, we build a risk stratification model based on molecular profiling of thyroid fine needle aspiration (FNA) washout DNA (wDNA) for the differential diagnosis of thyroid nodules.MethodsFifty-eight patients were recruited, FNA wDNA samples were analyzed using CNV profiling through low-coverage whole genome sequencing (LC-WGS) and BRAF mutation was analyzed using quantitative PCR. FNA pathology was reported as a Bethesda System for Reporting Thyroid Cytopathology (BSRTC) score. Ultrasound examination produced a Thyroid Imaging Reporting and Data System (TIRADS) score.ResultsIn total, 37 (63.8%) patients with a TIRADS score of 4A, 13 (22.4%) patients with a TIRADS score of 4B, and 8 (13.8%) patients with a TIRADS score of 4C were recruited after ultrasound examination. All patients underwent FNA with wDNA profiling. CNVs were identified in 17 (29.3%) patients. CNVs were frequent in patients with a BSRTC score of V or VI, including eight (47.1%) patients with a score of VI and five (29.4%) with a score of V, but not in patients with a score of III, II, or I (0%). BRAF mutation was not significantly correlated with BSRTC score. LN metastasis was found more frequently in CNV-positive (CNV+) than in CNV-negative (CNV-) patients (85.7% vs. 34.6%, odds ratio = 11.33, p = 0.002). In total, three molecular subtypes of thyroid nodules were identified in this study: 1) CNV+, 2) CNV- and BRAF positive (BRAF+), and 3) CNV- and BRAF negative (BRAF-). For the CNV+ subtype, 10 (83.3%) lesions with LN metastasis were found, including four (100%) small lesions (i.e. ≤ 5 mm). For the CNV- and BRAF+ nodules, LN metastases were detected in only seven (60.0%) larger tumors (i.e. > 5 mm). For CNV- and BRAF- tumors, LN metastasis was also frequently found in larger tumors only.ConclusionsIt is feasible to identify high-risk LN metastasis thyroid cancer from FNA washout samples preoperatively using wDNA CNV profiling using LC-WGS.

Project description:Cell-free DNA (cfDNA) analysis is a minimally invasive method that can be used to detect genomic abnormalities by directly testing a blood sample. This method is particularly useful for immunosuppressed patients, who are at high risk of complications from tissue biopsy. The cfDNA tumor fraction (TF) varies greatly across cancer type and between patients. Thus, the detection of molecular alterations is highly dependent on the circulating TF. In our study, we aimed to calculate the TF and characterize the copy number aberration (CNA) profile of cfDNA from patients with rare malignancies occurring in immunosuppressed environments or immune-privileged sites. To accomplish this, we recruited 36 patients: 19 patients with non-Hodgkin lymphoma (NHL) who were either human immunodeficiency virus (HIV)-positive or organ transplant recipients, 5 HIV-positive lung cancer patients, and 12 patients with glioma. cfDNA was extracted from the patients' plasma and sequenced using low-coverage whole genome sequencing (LC-WGS). The cfDNA TF was then calculated using the ichorCNA bioinformatic algorithm, based on the CNA profile. In parallel, we performed whole exome sequencing of patient tumor tissue and cfDNA samples with detectable TFs. We detected a cfDNA TF in 29% of immune-suppressed patients (one patient with lung cancer and six with systemic NHL), with a TF range from 8 to 70%. In these patients, the events detected in the CNA profile of cfDNA are well-known events associated with NHL and lung cancer. Moreover, cfDNA CNA profile correlated with the CNA profile of matched tumor tissue. No tumor-derived cfDNA was detected in the glioma patients. Our study shows that tumor genetic content is detectable in cfDNA from immunosuppressed patients with advanced NHL or lung cancer. LC-WGS is a time- and cost-effective method that can help select an appropriate strategy for performing extensive molecular analysis of cfDNA. This technique also enables characterization of CNAs in cfDNA when sufficient tumor content is available. Hence, this approach can be used to collect useful molecular information that is relevant to patient care.

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Genome-wide mutational signatures in low-coverage whole genome sequencing of cell-free DNA.

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Genome-wide mutational signatures in low-coverage whole genome sequencing of cell-free DNA.

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