SMuRF: portable and accurate ensemble prediction of somatic mutations.

SUMMARY: Somatic Mutation calling method using a Random Forest (SMuRF) integrates predictions and auxiliary features from multiple somatic mutation callers using a supervised machine learning approach. SMuRF is trained on community-curated matched tumor and normal whole genome sequencing data. SMuRF predicts both SNVs and indels with high accuracy in genome or exome-level sequencing data. Furthermore, the method is robust across multiple tested cancer types and predicts low allele frequency variants with high accuracy. In contrast to existing ensemble-based somatic mutation calling approaches, SMuRF works out-of-the-box and is orders of magnitudes faster. AVAILABILITY AND IMPLEMENTATION: The method is implemented in R and available at https://github.com/skandlab/SMuRF. SMuRF operates as an add-on to the community-developed bcbio-nextgen somatic variant calling pipeline. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Transcriptome Deconvolution Reveals Absence of Cancer Cell Expression Signature in Immune Checkpoint Blockade Response.

Immune checkpoint therapy (ICB) has conferred significant and durable clinical benefit to some patients with cancer. However, most patients do not respond to ICB, and reliable biomarkers of ICB response are needed to improve patient stratification. Here, we performed a transcriptome-wide meta-analysis across 1,486 tumors from ICB-treated patients and tumors with expected ICB outcomes based on microsatellite status. Using a robust transcriptome deconvolution approach, we inferred cancer- and stroma-specific gene expression differences and identified cell-type specific features of ICB response across cancer types. Consistent with current knowledge, stromal expression of CXCL9, CXCL13, and IFNG were the top determinants of favorable ICB response. In addition, we identified a group of potential immune-suppressive genes, including FCER1A, associated with poor response to ICB. Strikingly, PD-L1 expression in stromal cells, but not cancer cells, is correlated with ICB response across cancer types. Furthermore, the unbiased transcriptome-wide analysis failed to identify cancer-cell intrinsic expression signatures of ICB response conserved across tumor types, suggesting that cancer cells lack tissue-agnostic transcriptomic features of ICB response. SIGNIFICANCE: Our results challenge the prevailing dogma that cancer cells present tissue-agnostic molecular markers that modulate immune activity and ICB response, which has implications on the development of improved ICB diagnostics and treatments.

Dynamic phenotypic heterogeneity and the evolution of multiple RNA subtypes in hepatocellular carcinoma: the PLANET study.

Intra-tumor heterogeneity (ITH) is a key challenge in cancer treatment, but previous studies have focused mainly on the genomic alterations without exploring phenotypic (transcriptomic and immune) heterogeneity. Using one of the largest prospective surgical cohorts for hepatocellular carcinoma (HCC) with multi-region sampling, we sequenced whole genomes and paired transcriptomes from 67 HCC patients (331 samples). We found that while genomic ITH was rather constant across stages, phenotypic ITH had a very different trajectory and quickly diversified in stage II patients. Most strikingly, 30% of patients were found to contain more than one transcriptomic subtype within a single tumor. Such phenotypic ITH was found to be much more informative in predicting patient survival than genomic ITH and explains the poor efficacy of single-target systemic therapies in HCC. Taken together, we not only revealed an unprecedentedly dynamic landscape of phenotypic heterogeneity in HCC, but also highlighted the importance of studying phenotypic evolution across cancer types.

Tissue-specific cell-free DNA degradation quantifies circulating tumor DNA burden.

Profiling of circulating tumor DNA (ctDNA) may offer a non-invasive approach to monitor disease progression. Here, we develop a quantitative method, exploiting local tissue-specific cell-free DNA (cfDNA) degradation patterns, that accurately estimates ctDNA burden independent of genomic aberrations. Nucleosome-dependent cfDNA degradation at promoters and first exon-intron junctions is strongly associated with differential transcriptional activity in tumors and blood. A quantitative model, based on just 6 regulatory regions, could accurately predict ctDNA levels in colorectal cancer patients. Strikingly, a model restricted to blood-specific regulatory regions could predict ctDNA levels across both colorectal and breast cancer patients. Using compact targeted sequencing (<25 kb) of predictive regions, we demonstrate how the approach could enable quantitative low-cost tracking of ctDNA dynamics and disease progression.

Integrative epigenomic and high-throughput functional enhancer profiling reveals determinants of enhancer heterogeneity in gastric cancer.

BACKGROUND: Enhancers are distal cis-regulatory elements required for cell-specific gene expression and cell fate determination. In cancer, enhancer variation has been proposed as a major cause of inter-patient heterogeneity-however, most predicted enhancer regions remain to be functionally tested. METHODS: We analyzed 132 epigenomic histone modification profiles of 18 primary gastric cancer (GC) samples, 18 normal gastric tissues, and 28 GC cell lines using Nano-ChIP-seq technology. We applied Capture-based Self-Transcribing Active Regulatory Region sequencing (CapSTARR-seq) to assess functional enhancer activity. An Activity-by-contact (ABC) model was employed to explore the effects of histone acetylation and CapSTARR-seq levels on enhancer-promoter interactions. RESULTS: We report a comprehensive catalog of 75,730 recurrent predicted enhancers, the majority of which are GC-associated in vivo (> 50,000) and associated with lower somatic mutation rates inferred by whole-genome sequencing. Applying CapSTARR-seq to the enhancer catalog, we observed significant correlations between CapSTARR-seq functional activity and H3K27ac/H3K4me1 levels. Super-enhancer regions exhibited increased CapSTARR-seq signals compared to regular enhancers, even when decoupled from native chromatin contexture. We show that combining histone modification and CapSTARR-seq functional enhancer data improves the prediction of enhancer-promoter interactions and pinpointing of germline single nucleotide polymorphisms (SNPs), somatic copy number alterations (SCNAs), and trans-acting TFs involved in GC expression. We identified cancer-relevant genes (ING1, ARL4C) whose expression between patients is influenced by enhancer differences in genomic copy number and germline SNPs, and HNF4α as a master trans-acting factor associated with GC enhancer heterogeneity. CONCLUSIONS: Our results indicate that combining histone modification and functional assay data may provide a more accurate metric to assess enhancer activity than either platform individually, providing insights into the relative contribution of genetic (cis) and regulatory (trans) mechanisms to GC enhancer functional heterogeneity.

MutSpot: detection of non-coding mutation hotspots in cancer genomes.

Recurrence and clustering of somatic mutations (hotspots) in cancer genomes may indicate positive selection and involvement in tumorigenesis. MutSpot performs genome-wide inference of mutation hotspots in non-coding and regulatory DNA of cancer genomes. MutSpot performs feature selection across hundreds of epigenetic and sequence features followed by estimation of position- and patient-specific background somatic mutation probabilities. MutSpot is user-friendly, works on a standard workstation, and scales to thousands of cancer genomes.

Ensemble-Based Somatic Mutation Calling in Cancer Genomes.

Identification of somatic mutations in tumor tissue is challenged by both technical artifacts, diverse somatic mutational processes, and genetic heterogeneity in the tumors. Indeed, recent independent benchmark studies have revealed low concordance between different somatic mutation callers. Here, we describe Somatic Mutation calling method using a Random Forest (SMuRF), a portable ensemble method that combines the predictions and auxiliary features from individual mutation callers using supervised machine learning. SMuRF has improved prediction accuracy for both somatic point mutations (single nucleotide variants; SNVs) and small insertions/deletions (indels) in cancer genomes and exomes. Here, we describe the method and provide a tutorial on the installation and application of SMuRF.

Genomic and epigenomic EBF1 alterations modulate TERT expression in gastric cancer.

Transcriptional reactivation of telomerase catalytic subunit (TERT) is a frequent hallmark of cancer, occurring in 90% of human malignancies. However, specific mechanisms driving TERT reactivation remain obscure for many tumor types and in particular gastric cancer (GC), a leading cause of global cancer mortality. Here, through comprehensive genomic and epigenomic analysis of primary GCs and GC cell lines, we identified the transcription factor early B cell factor 1 (EBF1) as a TERT transcriptional repressor and inactivation of EBF1 function as a major cause of TERT upregulation. Abolishment of EBF1 function occurs through 3 distinct (epi)genomic mechanisms. First, EBF1 is epigenetically silenced via DNA methyltransferase, polycomb-repressive complex 2 (PRC2), and histone deacetylase activity in GCs. Second, recurrent, somatic, and heterozygous EBF1 DNA-binding domain mutations result in the production of dominant-negative EBF1 isoforms. Third, more rarely, genomic deletions and rearrangements proximal to the TERT promoter remobilize or abolish EBF1-binding sites, derepressing TERT and leading to high TERT expression. EBF1 is also functionally required for various malignant phenotypes in vitro and in vivo, highlighting its importance for GC development. These results indicate that multimodal genomic and epigenomic alterations underpin TERT reactivation in GC, converging on transcriptional repressors such as EBF1.

Genomic landscape of lung adenocarcinoma in East Asians.

Lung cancer is the world's leading cause of cancer death and shows strong ancestry disparities. By sequencing and assembling a large genomic and transcriptomic dataset of lung adenocarcinoma (LUAD) in individuals of East Asian ancestry (EAS; n = 305), we found that East Asian LUADs had more stable genomes characterized by fewer mutations and fewer copy number alterations than LUADs from individuals of European ancestry. This difference is much stronger in smokers as compared to nonsmokers. Transcriptomic clustering identified a new EAS-specific LUAD subgroup with a less complex genomic profile and upregulated immune-related genes, allowing the possibility of immunotherapy-based approaches. Integrative analysis across clinical and molecular features showed the importance of molecular phenotypes in patient prognostic stratification. EAS LUADs had better prediction accuracy than those of European ancestry, potentially due to their less complex genomic architecture. This study elucidated a comprehensive genomic landscape of EAS LUADs and highlighted important ancestry differences between the two cohorts.

Mutation hotspots at CTCF binding sites coupled to chromosomal instability in gastrointestinal cancers.

Tissue-specific driver mutations in non-coding genomic regions remain undefined for most cancer types. Here, we unbiasedly analyze 212 gastric cancer (GC) whole genomes to identify recurrently mutated non-coding regions in GC. Applying comprehensive statistical approaches to accurately model background mutational processes, we observe significant enrichment of non-coding indels (insertions/deletions) in three gastric lineage-specific genes. We further identify 34 mutation hotspots, of which 11 overlap CTCF binding sites (CBSs). These CBS hotspots remain significant even after controlling for a genome-wide elevated mutation rate at CBSs. In 3 out of 4 tested CBS hotspots, mutations are nominally associated with expression change of neighboring genes. CBS hotspot mutations are enriched in tumors showing chromosomal instability, co-occur with neighboring chromosomal aberrations, and are common in gastric (25%) and colorectal (19%) tumors but rare in other cancer types. Mutational disruption of specific CBSs may thus represent a tissue-specific mechanism of tumorigenesis conserved across gastrointestinal cancers.

Long noncoding RNA EGFR-AS1 mediates epidermal growth factor receptor addiction and modulates treatment response in squamous cell carcinoma.

Targeting EGFR is a validated approach in the treatment of squamous-cell cancers (SCCs), although there are no established biomarkers for predicting response. We have identified a synonymous mutation in EGFR, c.2361G>A (encoding p.Gln787Gln), in two patients with head and neck SCC (HNSCC) who were exceptional responders to gefitinib, and we showed in patient-derived cultures that the A/A genotype was associated with greater sensitivity to tyrosine kinase inhibitors (TKIs) as compared to the G/A and G/G genotypes. Remarkably, single-copy G>A nucleotide editing in isogenic models conferred a 70-fold increase in sensitivity due to decreased stability of the EGFR-AS1 long noncoding RNA (lncRNA). In the appropriate context, sensitivity could be recapitulated through EGFR-AS1 knockdown in vitro and in vivo, whereas overexpression was sufficient to induce resistance to TKIs. Reduced EGFR-AS1 levels shifted splicing toward EGFR isoform D, leading to ligand-mediated pathway activation. In co-clinical trials involving patients and patient-derived xenograft (PDX) models, tumor shrinkage was most pronounced in the context of the A/A genotype for EGFR-Q787Q, low expression of EGFR-AS1 and high expression of EGFR isoform D. Our study reveals how a 'silent' mutation influences the levels of a lncRNA, resulting in noncanonical EGFR addiction, and delineates a new predictive biomarker suite for response to EGFR TKIs.