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. We find that SWI/SNF inactivation impairs coordinated occupancy of non-pioneer CRC members at enhancers within 1 hour, rapidly breaking their autoregulation. By precisely timing inhibitor treatment following synchronization, we show that SWI/SNF is dispensable for survival in S and G2/M, but becomes acutely essential only during G1 phase. We furthermore developed a new approach to analyze the oscillating patterns of genome-wide DNA accessibility across the cell cycle, which revealed that SWI/SNF-dependent CRC binding sites are enriched at enhancers with peak accessibility during G1 phase, where they activate genes involved in cell-cycle progression. SWI/SNF inhibition strongly impairs G1-S transition and potentiates the ability of retinoids used clinically to induce cell-cycle exit. Similar cell-cycle effects in diverse SWI/SNF-addicted settings highlight G1-S transition as a common cause of SWI/SNF dependency. Our results illustrate that deeper knowledge of the temporal patterns of enhancer-related dependencies may aid the rational targeting of addicted cancers.

Cancer cells driven by runaway transcription factor networks frequently depend on the cellular machinery that promotes DNA accessibility. For this reason, recently developed small molecules that impair SWI/SNF (or BAF) chromatin remodeling activity have been under active evaluation as anti-cancer agents. However, exactly when SWI/SNF activity is essential in dependent cancers has remained unknown. By combining live-cell imaging and genome-wide profiling in neuroblastoma cells, Cermakova et al. discover that SWI/SNF activity is needed for survival only during G1 phase of the cell cycle. The authors reveal that in several cancer settings, dependency on SWI/SNF arises from the need to reactivate factors involved in G1-S transition. Because of this role, authors find that SWI/SNF inhibition potentiates cell-cycle exit by retinoic acid.

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. We estimate and validate TmS in 6,590 patient tumors across 15 cancer types, identifying significant inter-tumor variability. Across cancers, high TmS is associated with increased risk of disease progression and death. TmS is influenced by cancer-specific patterns of gene alteration and intra-tumor genetic heterogeneity as well as by pan-cancer trends in metabolic dysregulation. Taken together, our results indicate that measuring cell-type-specific total mRNA expression in tumor cells predicts tumor phenotypes and clinical outcomes.

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. Here, we describe the method and provide a tutorial on the installation and application of MuSE.

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. Additionally, random forest models were used to determine the aggregate utility of common hematological tests in determining MS disease status and subtype. The most significant and relevant results were increased bilirubin and creatinine in MS cases. The random forest models achieved some success in differentiating between MS cases and controls (AUC values: 0.725 and 0.710, respectively) but were not successful in differentiating between subtypes. However, larger samples that adjust for possible confounding variables, such as treatment status, may reveal the value of these tests in differentiating between 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. To uncover the relationship between viral incidence and cancer risk in the context of autoimmune disease, we extracted electronic health records (EHR) from Vanderbilt University. ICD-9/10 codes and laboratory values were collected for hematological, lung, anal-vaginal, thyroid, hepatobiliary, bladder, prostate, and breast cancers; and viruses including Epstein Barr virus (EBV), Human papilloma virus (HPV), and Hepatitis A/B/C (Hep). Only viral infections that led to a physician visit or laboratory test were entered into the EMR; therefore, only clinically relevant cases were noted and considered positive in this study. The relationship between virus infection and cancer in an SLE cohort (SLE-cases n = 2,313, and SLE-controls n = 5,702) and an MS cohort (MS-case n = 7,277, MS-control n = 7,277) was examined by multilinear logistic regression. Viral infection was strongly associated with increased risk for cancer overall. SLE and MS patients were more susceptible to all viral infections. MS patients trended toward increased risk for cancers overall, while decreased risk for hormone-based cancers in SLE patients non-significantly reduced their risk for overall cancer. Both SLE and MS patients had increased clinically relevant EBV infection, which was associated with risk for hematological cancers. Preventing viral infections by vaccination may be especially helpful in controlling risk for cancer in SLE and MS patients.

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. We introduce Famdenovo.TP53 for Li-Fraumeni syndrome (LFS) and analyze 324 LFS family pedigrees from four US cohorts: a validation set of 186 pedigrees and a discovery set of 138 pedigrees. The concordance index for Famdenovo.TP53 prediction was 0.95 (95% CI: [0.92, 0.98]). Forty individuals (95% CI: [30, 50]) were predicted as DNM carriers, increasing the total number from 42 to 82. We compared clinical and biological features of FM versus DNM carriers: (1) cancer and mutation spectra along with parental ages were similarly distributed; (2) ascertainment criteria like early-onset breast cancer (age 20-35 yr) provides a condition for an unbiased estimate of the DNM rate: 48% (23 DNMs vs. 25 FMs); and (3) hotspot mutation R248W was not observed in DNMs, although it was as prevalent as hotspot mutation R248Q in FMs. Furthermore, we introduce Famdenovo.BRCA for hereditary breast and ovarian cancer syndrome and apply it to a small set of family data from the Cancer Genetics Network. In summary, we introduce a novel statistical approach to systematically evaluate deleterious DNMs in inherited cancer syndromes. Our approach may serve as a foundation for future studies evaluating how new deleterious mutations can be established in the germline, such as those in TP53.