Project description:These clinical specimens were collected were collected as part of patients' routine care at Memorial Sloan Kettering Cancer Center. They were sequenced on the targeted platform MSK-IMPACT. Here, we anonymized germline variant calling on these specimens for comprehensive exploration of the landscape of pathogenic germline variants in patients with advanced cancer.
Project description:The study cohort comprises samples from patients whose tumors have been prospectively characterized as part of their care at Memorial Sloan Kettering Cancer Center. This includes a variety of distinct tumor type and molecular subtypes.
Project description:A key constraint in genomic testing in oncology is that matched normal specimens are not commonly obtained in clinical practice. Thus, while well-characterized genomic alterations do not require normal tissue for interpretation, a significant number of alterations will be unknown in whether they are germline or somatic, in the absence of a matched normal control. We introduce SGZ (somatic-germline-zygosity), a computational method for predicting somatic vs. germline origin and homozygous vs. heterozygous or sub-clonal state of variants identified from deep massively parallel sequencing (MPS) of cancer specimens. The method does not require a patient matched normal control, enabling broad application in clinical research. SGZ predicts the somatic vs. germline status of each alteration identified by modeling the alteration's allele frequency (AF), taking into account the tumor content, tumor ploidy, and the local copy number. Accuracy of the prediction depends on the depth of sequencing and copy number model fit, which are achieved in our clinical assay by sequencing to high depth (>500x) using MPS, covering 394 cancer-related genes and over 3,500 genome-wide single nucleotide polymorphisms (SNPs). Calls are made using a statistic based on read depth and local variability of SNP AF. To validate the method, we first evaluated performance on samples from 30 lung and colon cancer patients, where we sequenced tumors and matched normal tissue. We examined predictions for 17 somatic hotspot mutations and 20 common germline SNPs in 20,182 clinical cancer specimens. To assess the impact of stromal admixture, we examined three cell lines, which were titrated with their matched normal to six levels (10-75%). Overall, predictions were made in 85% of cases, with 95-99% of variants predicted correctly, a significantly superior performance compared to a basic approach based on AF alone. We then applied the SGZ method to the COSMIC database of known somatic variants in cancer and found >50 that are in fact more likely to be germline.
Project description:SUMMARY:CharGer (Characterization of Germline variants) is a software tool for interpreting and predicting clinical pathogenicity of germline variants. CharGer gathers evidence from databases and annotations, provided by local tools and files or via ReST APIs, and classifies variants according to ACMG guidelines for assessing variant pathogenicity. User-designed pathogenicity criteria can be incorporated into CharGer's flexible framework, thereby allowing users to create a customized classification protocol. AVAILABILITY AND IMPLEMENTATION:Source code is freely available at https://github.com/ding-lab/CharGer and is distributed under the GNU GPL-v3.0 license. Software is also distributed through the Python Package Index (PyPI) repository. CharGer is implemented in Python 2.7 and is supported on Unix-based operating systems. SUPPLEMENTARY INFORMATION:Supplementary data are available at Bioinformatics online.
Project description:Cancer is characterized by diverse genetic alterations in both germline and somatic genomes that disrupt normal biology and provide a selective advantage to cells during tumorigenesis. Germline and somatic genomes have been extensively studied independently, leading to numerous biological insights. Analyses integrating data from both genomes have identified genetic variants impacting somatic events in tumors, including hotspot driver mutations. Interactions among specific germline variants and somatic events influence cancer subtypes, treatment response, and clinical outcomes. Investigation of these complex interactions is increasing our understanding of aberrant pathways in tumors that may uncover novel therapeutic targets. Here, we review the literature describing the role of germline genetic variants in promoting the selection and generation of specific mutations during tumorigenesis.
Project description:Germline pathogenic variants in BRCA1/2 account for one-third of familial breast cancers. The majority of BRCA1 function requires heterodimerization with BARD1. In contrast to BRCA1, BARD1 is a low-penetrance gene with an unclear clinical relevance, partly because of limited functional evidence. Using patient-derived lymphoblastoid cells, we functionally characterized two pathogenic variants (c.1833dupT, c.2099delG) and three variants of uncertain significance (VUSs) (c.73G>C, c.1217G>A, c.1918C>A). Three of these patients had breast cancers, whereas the remaining had colorectal cancers (n = 3). Both patients with pathogenic variants (c.1833dupT, c.2099delG) developed breast cancers with aggressive disease phenotypes such as triple-negative breast cancer and high cancer grades. As BARD1 encompasses multiple functional domains, including those of apoptosis and homologous recombination repair, we hypothesized that the function being impaired would correspond with the domain where the variant was located. Variants c.1918C>A, c.1833dupT, c.1217G>A, and c.2099delG, located within and proximal to apoptotic domains of ankyrin and BRCT, were associated with impaired apoptosis. Conversely, apoptosis function was preserved in c.73G>C, which was distant from the ankyrin domain. All variants displayed normal BRCA1 heterodimerization and RAD51 colocalization, consistent with their location being distal to BRCA1-and RAD51-binding domains. In view of deficient apoptosis, VUSs (c.1217G>A and c.1918C>A) may be pathogenic or likely pathogenic variants. In summary, functional analysis of BARD1 VUSs requires a combination of assays and, more importantly, the use of appropriate functional assays with consideration to the variant's location.
Project description:PURPOSE:The genomic landscape of gliomas has been characterized and now contributes to disease classification, yet the relationship between molecular profile and disease progression and treatment response remain poorly understood.Experimental Design: We integrated prospective clinical sequencing of 1,004 primary and recurrent tumors from 923 glioma patients with clinical and treatment phenotypes. RESULTS:Thirteen percent of glioma patients harbored a pathogenic germline variant, including a subset associated with heritable genetic syndromes and variants mediating DNA repair dysfunctions (29% of the total) that were associated with somatic biallelic inactivation and mechanism-specific somatic phenotypes. In astrocytomas, genomic alterations in effectors of cell-cycle progression correlated with aggressive disease independent of IDH mutation status, arose preferentially in enhancing tumors (44% vs. 8%, P < 0.001), were associated with rapid disease progression following tumor recurrence (HR = 2.6, P = 0.02), and likely preceded the acquisition of alkylating therapy-associated somatic hypermutation. Thirty-two percent of patients harbored a potentially therapeutically actionable lesion, of whom 11% received targeted therapies. In BRAF-mutant gliomas, response to agents targeting the RAF/MEK/ERK signaling axis was influenced by the type of mutation, its clonality, and its cellular and genomic context. CONCLUSIONS:These data reveal genomic correlates of disease progression and treatment response in diverse types of glioma and highlight the potential utility of incorporating genomic information into the clinical decision-making for patients with glioma.