Novel liquid biopsy CNV biomarkers in malignant melanoma.

Malignant melanoma (MM) is known for its abundance of genetic alterations and a tendency for rapid metastasizing. Identification of novel plasma biomarkers may enhance non-invasive diagnostics and disease monitoring. Initially, we examined copy number variations (CNV) in CDK genes (CDKN2A, CDKN2B, CDK4) using MLPA (gDNA) and ddPCR (ctDNA) analysis. Subsequently, low-coverage whole genome sequencing (lcWGS) was used to identify the most common CNV in plasma samples, followed by ddPCR verification of chosen biomarkers. CNV alterations in CDK genes were identified in 33.3% of FFPE samples (Clark IV, V only). Detection of the same genes in MM plasma showed no significance, neither compared to healthy plasmas nor between pre- versus post-surgery plasma. Sequencing data showed the most common CNV occurring in 6q27, 4p16.1, 10p15.3, 10q22.3, 13q34, 18q23, 20q11.21-q13.12 and 22q13.33. CNV in four chosen genes (KIF25, E2F1, DIP2C and TFG) were verified by ddPCR using 2 models of interpretation. Model 1 was concordant with lcWGS results in 54% of samples, for model 2 it was 46%. Although CDK genes have not been proven to be suitable CNV liquid biopsy biomarkers, lcWGS defined the most frequently affected chromosomal regions by CNV. Among chosen genes, DIP2C demonstrated a potential for further analysis.

Genotype error due to low-coverage sequencing induces uncertainty in polygenic scoring.

Polygenic scores (PGSs) have emerged as a standard approach to predict phenotypes from genotype data in a wide array of applications from socio-genomics to personalized medicine. Traditional PGSs assume genotype data to be error-free, ignoring possible errors and uncertainties introduced from genotyping, sequencing, and/or imputation. In this work, we investigate the effects of genotyping error due to low coverage sequencing on PGS estimation. We leverage SNP array and low-coverage whole-genome sequencing data (lcWGS, median coverage 0.04×) of 802 individuals from the Dana-Farber PROFILE cohort to show that PGS error correlates with sequencing depth (p = 1.2 × 10-7). We develop a probabilistic approach that incorporates genotype error in PGS estimation to produce well-calibrated PGS credible intervals and show that the probabilistic approach increases classification accuracy by up to 6% as compared to traditional PGSs that ignore genotyping error. Finally, we use simulations to explore the combined effect of genotyping and effect size errors and their implication on PGS-based risk-stratification. Our results illustrate the importance of considering genotyping error as a source of PGS error especially for cohorts with varying genotyping technologies and/or low-coverage sequencing.

Low-coverage and cost-effective whole-genome sequencing assay for glioma risk stratification.

PURPOSE: To investigate chromosomal instability (CIN) as a biomarker for glioma risk stratifications, with cost-effective, low-coverage whole-genome sequencing assay (WGS). METHODS: Thirty-five formalin-fixed paraffin-embedded glioma samples were collected from Huashan Hospital. DNA was sent for WGS by Illumina X10 at low (median) genome coverage of 1.86x (range: 1.03-3.17×), followed by copy number analyses, using a customized bioinformatics workflow-Ultrasensitive Copy number Aberration Detector. RESULTS: Among the 35 glioma patients, 12 were grade IV, 10 grade III, 11 grade II, and 2 Grade I cases, with high chromosomal instability (CIN +) in 24 (68.6%) of the glioma patients. The other 11 (31.4%) had lower chromosomal instability (CIN-). CIN significantly correlates with overall survival (P = 0.00029). Patients with CIN + /7p11.2 + (12 grade IV and 3 grade III) had the worst survival ratio (hazard ratio:16.2, 95% CI:6.3-41.6) with a median overall survival of 24 months. Ten (66.7%) patients died during the first two follow-up years. In the CIN + patients without 7p11.2 + (6 grade III, 3 grade II), 3 (33.3%) patients died during follow-up, and the estimated overall survival was around 65 months. No deaths were reported in the 11 CIN- patients (2 grade I, 8 grade II, 1 grade III) during the 80-month follow-up period. In this study, chromosomal instability served as a prognosis factor for gliomas independent of tumor grades. CONCLUSION: It is feasible to use cost-effective, low-coverage WGS for risk stratification of glioma. Elevated chromosomal instability is associated with poor prognosis.

Washout DNA copy number analysis by low-coverage whole genome sequencing for assessment of thyroid FNAs.

Background: Papillary 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. Methods: Fifty-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. Results: In 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. Conclusions: It is feasible to identify high-risk LN metastasis thyroid cancer from FNA washout samples preoperatively using wDNA CNV profiling using LC-WGS.