On the frequency of copy number variants

Genomic signatures in plasma circulating tumor DNA reveal treatment response and prognostic insights in mantel cell lymphoma

BACKGROUND

Mantle cell lymphoma (MCL) is an aggressive subtype of B-cell non-Hodgkin's lymphoma. The applicability of circulating tumor DNA (ctDNA) for predicting treatment response and prognosis in MCL remains underexplored.

METHODS

This study included 34 MCL patients receiving first-line chemoimmunotherapy. We assessed the ability of plasma ctDNA to detect tumor-specific genetic alterations and explored its potential as a noninvasive biomarker for treatment response and prognosis in MCL.

RESULTS

Commonly mutated genes in MCL included CCND1 (93.5%), ATM (48.4%), KMT2D (25.8%), and TP53 (25.8%). Subgroup analysis of tissue samples showed that CDKN2A mutations (P = 0.028), along with alterations in BCR and TCR signaling (P = 0.004) and the PI3K pathway (P = 0.008), were enriched in the blastoid subtype. ATM mutations (P = 0.041) were more prevalent in MIPI-low patients, while epigenetic chromatin remodeling pathway alterations (P = 0.028) were more common in MIPI-high patients. Plasma ctDNA demonstrated high sensitivity for detecting structural variants (96.6%), followed by mutations (71.3%) and copy number variants (30.0%). 75% of patients exhibited moderate-to-high concordance in detecting genomic variants between plasma and tissue samples. Pretreatment ctDNA levels exhibited high specificity in predicting clinical efficacy but had a suboptimal sensitivity of 68.2%. Higher ctDNA levels were significantly associated with shorter progression-free survival (PFS; P = 0.002) and overall survival (OS; P = 0.009). Additional ctDNA-based genetic features associated with shorter PFS included TP53 (P = 0.002), TRAF2 (P = 0.023), and SMARCA4 (P = 0.023) mutations, while TP53 (P = 0.006) and TERT (P = 0.031) mutations predicted shorter OS. Persistent positive ctDNA in post-treatment plasma samples indicated molecular relapse and poor prognosis, whereas undetectable ctDNA defined a subset of patients with favorable survival outcomes.

CONCLUSIONS

This study identified plasma ctDNA as a promising biomarker that noninvasively captures tumor-derived genetic variants associated with treatment response and survival outcomes in MCL, highlighting the clinical value of ctDNA for diagnosis, recurrence prediction, and surveillance monitoring.

Estimation of correlations between copy-number variants in non-coding DNA

Allelic DNA aberrations across our genome have been associated with normal human genetic heterogeneity as well as with a number of diseases and disorders. When copy-number variations (CNVs) occur in gene-coding regions, known relationships between genes may help us understand correlations between CNVs. However, a large number of these aberrations occur in non-coding, extragenic regions and their correlations may be characterized only quantitatively, e.g., probabilistically, but not functionally. Using a signal processing approach to CNV detection, we identified distributed CNVs in short, non-coding regions across chromosomes and investigated their potential correlations. We estimated predominantly local correlations between CNVs within the same chromosome, and a small number of apparently random long-distance correlations.

A novel signal processing approach for the detection of copy number variations in the human genome

MOTIVATION

Human genomic variability occurs at different scales, from single nucleotide polymorphisms (SNPs) to large DNA segments. Copy number variations (CNVs) represent a significant part of our genetic heterogeneity and have also been associated with many diseases and disorders. Short, localized CNVs, which may play an important role in human disease, may be undetectable in noisy genomic data. Therefore, robust methodologies are needed for their detection. Furthermore, for meaningful identification of pathological CNVs, estimation of normal allelic aberrations is necessary.

RESULTS

We developed a signal processing-based methodology for sequence denoising followed by pattern matching, to increase SNR in genomic data and improve CNV detection. We applied this signal-decomposition-matched filtering (SDMF) methodology to 429 normal genomic sequences, and compared detected CNVs to those in the Database of Genomic Variants. SDMF successfully detected a significant number of previously identified CNVs with frequencies of occurrence ≥10%, as well as unreported short CNVs. Its performance was also compared to circular binary segmentation (CBS). through simulations. SDMF had a significantly lower false detection rate and was significantly faster than CBS, an important advantage for handling large datasets generated with high-resolution arrays. By focusing on improving SNR (instead of the robustness of the detection algorithm), SDMF is a very promising methodology for identifying CNVs at all genomic spatial scales.

AVAILABILITY

The data are available at http://tcga-data.nci.nih.gov/tcga/ The software and list of analyzed sequence IDs are available at http://www.hsph.harvard.edu/~betensky/ A Matlab code for Empirical Mode Decomposition may be found at: http://www.clear.rice.edu/elec301/Projects02/empiricalMode/code.html

CONTACT

caterina@mit.edu.

Elucidating the genetic architecture of familial schizophrenia using rare copy number variant and linkage scans

To elucidate the genetic architecture of familial schizophrenia we combine linkage analysis with studies of fine-level chromosomal variation in families recruited from the Afrikaner population in South Africa. We demonstrate that individually rare inherited copy number variants (CNVs) are more frequent in cases with familial schizophrenia as compared to unaffected controls and affect almost exclusively genic regions. Interestingly, we find that while the prevalence of rare structural variants is similar in familial and sporadic cases, the type of variants is markedly different. In addition, using a high-density linkage scan with a panel of nearly 2,000 markers, we identify a region on chromosome 13q34 that shows genome-wide significant linkage to schizophrenia and show that in the families not linked to this locus, there is evidence for linkage to chromosome 1p36. No causative CNVs were identified in either locus. Overall, our results from approaches designed to detect risk variants with relatively low frequency and high penetrance in a well-defined and relatively homogeneous population, provide strong empirical evidence supporting the notion that multiple genetic variants, including individually rare ones, that affect many different genes contribute to the genetic risk of familial schizophrenia. They also highlight differences in the genetic architecture of the familial and sporadic forms of the disease.