Pan-cancer subclonal mutation analysis of 7,827 tumors predicts clinical outcome.

Intra-tumor heterogeneity is an important driver of tumor evolution and therapy response. Advances in precision cancer treatment will require understanding of mutation clonality and subclonal architecture. Currently the slow computational speed of subclonal reconstruction hinders large cohort studies.

Reactivation of the G1 enhancer landscape underlies core circuitry addiction to SWI/SNF.

Several cancer core regulatory circuitries (CRCs) depend on the sustained generation of DNA accessibility by SWI/SNF chromatin remodelers. However, the window when SWI/SNF is acutely essential in these settings has not been identified. Here we used neuroblastoma (NB) cells to model and dissect the relationship between cell-cycle progression and SWI/SNF ATPase activity.

MuSE: A Novel Approach to Mutation Calling with Sample-Specific Error Modeling.

Accurate detection of somatic mutations in genetically heterogeneous tumor cell populations using next-generation sequencing remains challenging. We have developed MuSE, Mutation calling using a Markov Substitution model for Evolution, a novel approach for modeling the evolution of the allelic composition of tumor and normal tissue at each reference base. It adopts a sample-specific error model to depict inter-tumor heterogeneity, which greatly improves the overall accuracy.

Estimation of tumor cell total mRNA expression in 15 cancer types predicts disease progression.

Single-cell RNA sequencing studies have suggested that total mRNA content correlates with tumor phenotypes. Technical and analytical challenges, however, have so far impeded at-scale pan-cancer examination of total mRNA content. Here we present a method to quantify tumor-specific total mRNA expression (TmS) from bulk sequencing data, taking into account tumor transcript proportion, purity and ploidy, which are estimated through transcriptomic/genomic deconvolution.

The Impact of Multiple Sclerosis Disease Status and Subtype on Hematological Profile.

Multiple sclerosis (MS) is an immune-mediated, demyelinating disease of the central nervous system. In this study, an MS cohort and healthy controls were stratified into Caucasian and African American groups. Patient hematological profiles-composed of complete blood count (CBC) and complete metabolic panel (CMP) test values-were analyzed to identify differences between MS cases and controls and between patients with different MS subtypes.

Contribution of viral infection to risk for cancer in systemic lupus erythematosus and multiple sclerosis.

Patients with autoimmune disorders (AD) have altered cancer risks compared to the general population. Systemic lupus erythematosus and multiple sclerosis lead to a heightened risk for hematological malignancies and decreased risk for breast, ovarian, and prostate malignancies. Often patients with autoimmune disease have dysregulated antiviral immune responses, including against oncogenic viruses.

A pedigree-based prediction model identifies carriers of deleterious de novo mutations in families with Li-Fraumeni syndrome.

De novo mutations (DNMs) are increasingly recognized as rare disease causal factors. Identifying DNM carriers will allow researchers to study the likely distinct molecular mechanisms of DNMs. We developed Famdenovo to predict DNM status (DNM or familial mutation [FM]) of deleterious autosomal dominant germline mutations for any syndrome.