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Large-scale cancer genome projects, such as the Cancer Genome Atlas (TCGA) project, are comprehensive molecular characterization efforts to accelerate our understanding of cancer biology and the discovery of new therapeutic targets. The accumulating wealth of multidimensional data provides a new paradigm for important research problems including cancer subtype discovery. The current standard approach relies on separate clustering analyses followed by manual integration. Results can be highly data type dependent, restricting the ability to discover new insights from multidimensional data. In this study, we present an integrative subtype analysis of the TCGA glioblastoma (GBM) data set. Our analysis revealed new insights through integrated subtype characterization. We found three distinct integrated tumor subtypes. Subtype 1 lacks the classical GBM events of chr 7 gain and chr 10 loss. This subclass is enriched for the G-CIMP phenotype and shows hypermethylation of genes involved in brain development and neuronal differentiation. The tumors in this subclass display a Proneural expression profile. Subtype 2 is characterized by a near complete association with EGFR amplification, overrepresentation of promoter methylation of homeobox and G-protein signaling genes, and a Classical expression profile. Subtype 3 is characterized by NF1 and PTEN alterations and exhibits a Mesenchymal-like expression profile. The data analysis workflow we propose provides a unified and computationally scalable framework to harness the full potential of large-scale integrated cancer genomic data for integrative subtype discovery.

Individual bacteria and shifts in the composition of the microbiome have been associated with human diseases including cancer. To investigate changes in the microbiome associated with oral cancers, we profiled cancers and anatomically matched contralateral normal tissue from the same patient by sequencing 16S rDNA hypervariable region amplicons. In cancer samples from both a discovery and a subsequent confirmation cohort, abundance of Firmicutes (especially Streptococcus) and Actinobacteria (especially Rothia) was significantly decreased relative to contralateral normal samples from the same patient. Significant decreases in abundance of these phyla were observed for pre-cancers, but not when comparing samples from contralateral sites (tongue and floor of mouth) from healthy individuals. Weighted UniFrac principal coordinates analysis based on 12 taxa separated most cancers from other samples with greatest separation of node positive cases. These studies begin to develop a framework for exploiting the oral microbiome for monitoring oral cancer development, progression and recurrence.

Detection of recurrently mutated nucleotides identifies novel cancer hotspots in an analysis of >11,000 human tumor samples. Mutational hotspots indicate selective pressure across a population of tumor samples, but their prevalence within and across cancer types is incompletely characterized. An approach to detect significantly mutated residues, rather than methods that identify recurrently mutated genes, may uncover new biologically and therapeutically relevant driver mutations. Here, we developed a statistical algorithm to identify recurrently mutated residues in tumor samples. We applied the algorithm to 11,119 human tumors, spanning 41 cancer types, and identified 470 somatic substitution hotspots in 275 genes. We find that half of all human tumors possess one or more mutational hotspots with widespread lineage-, position- and mutant allele–specific differences, many of which are likely functional. In total, 243 hotspots were novel and appeared to affect a broad spectrum of molecular function, including hotspots at paralogous residues of Ras-related small GTPases RAC1 and RRAS2 . Redefining hotspots at mutant amino acid resolution will help elucidate the allele-specific differences in their function and could have important therapeutic implications.

Large-scale integrated cancer genome characterization efforts including the cancer genome atlas and the cancer cell line encyclopedia have created unprecedented opportunities to study cancer biology in the context of knowing the entire catalog of genetic alterations. A clinically important challenge is to discover cancer subtypes and their molecular drivers in a comprehensive genetic context. Curtis et al. [ Nature (2012) 486(7403):346–352] has recently shown that integrative clustering of copy number and gene expression in 2,000 breast tumors reveals novel subgroups beyond the classic expression subtypes that show distinct clinical outcomes. To extend the scope of integrative analysis for the inclusion of somatic mutation data by massively parallel sequencing, we propose a framework for joint modeling of discrete and continuous variables that arise from integrated genomic, epigenomic, and transcriptomic profiling. The core idea is motivated by the hypothesis that diverse molecular phenotypes can be predicted by a set of orthogonal latent variables that represent distinct molecular drivers, and thus can reveal tumor subgroups of biological and clinical importance. Using the cancer cell line encyclopedia dataset, we demonstrate our method can accurately group cell lines by their cell-of-origin for several cancer types, and precisely pinpoint their known and potential cancer driver genes. Our integrative analysis also demonstrates the power for revealing subgroups that are not lineage-dependent, but consist of different cancer types driven by a common genetic alteration. Application of the cancer genome atlas colorectal cancer data reveals distinct integrated tumor subtypes, suggesting different genetic pathways in colon cancer progression.

Background Methods for inferring the three-dimensional (3D) configuration of chromatin from conformation capture assays that provide strictly pairwise interactions, notably Hi-C, utilize the attendant contact matrix as input. More recent assays, in particular split-pool recognition of interactions by tag extension (SPRITE), capture multi-way interactions instead of solely pairwise contacts. These assays yield contacts that straddle appreciably greater genomic distances than Hi-C, in addition to instances of exceptionally high-order chromatin interaction. Such attributes are anticipated to be consequential with respect to 3D genome reconstruction, a task yet to be undertaken with multi-way contact data. However, performing such 3D reconstruction using distance-based reconstruction techniques requires framing multi-way contacts as (pairwise) distances. Comparing approaches for so doing, and assessing the resultant impact of long-range and multi-way contacts, are the objectives of this study. Results We obtained 3D reconstructions via multi-dimensional scaling under a variety of weighting schemes for mapping SPRITE multi-way contacts to pairwise distances. Resultant configurations were compared following Procrustes alignment and relationships were assessed between associated Procrustes root mean square errors and key features such as the extent of multi-way and/or long-range contacts. We found that these features had surprisingly limited influence on 3D reconstruction, a finding we attribute to their influence being diminished by the preponderance of pairwise contacts. Conclusion Distance-based 3D genome reconstruction using SPRITE multi-way contact data is not appreciably affected by the weighting scheme used to convert multi-way interactions to pairwise distances.

Tumor recurrence is a leading cause of cancer mortality. Therapies for recurrent disease may fail, at least in part, because the genomic alterations driving the growth of recurrences are distinct from those in the initial tumor. To explore this hypothesis, we sequenced the exornes of 23 initial low-grade gliomas and recurrent tumors resected from the same patients. In 43% of cases, at least half of the mutations in the initial tumor were undetected at recurrence, including driver mutations in TP53, ATRX, SMARCA4, and BRAF; this suggests that recurrent tumors are often seeded by cells derived from the initial tumor at a very early stage of their evolution. Notably, tumors from 6 of 10 patients treated with the chemotherapeutic drug temozolomide (TMZ) followed an alternative evolutionary path to high-grade glioma. At recurrence, these tumors were hypermutated and harbored driver mutations in the RB (retinoblastoma) and Akt-mTOR (mammalian target of rapamycin) pathways that bore the signature of TMZ-induced mutagenesis.

There are currently no clinical strategies utilizing tumor gene expression to inform therapeutic selection for patients with head and neck squamous cell carcinoma (HNSCC). One of the challenges in developing predictive biomarkers is the limited characterization of preclinical HNSCC models. Patient-derived xenografts (PDXs) are increasingly recognized as translationally relevant preclinical avatars for human tumors; however, the overall transcriptomic concordance of HNSCC PDXs with primary human HNSCC is understudied, especially in human papillomavirus-associated (HPV+) disease. Here, we characterized 64 HNSCC PDXs (16 HPV+ and 48 HPV-) at the transcriptomic level using RNA-sequencing. The range of human-specific reads per PDX varied from 64.6%-96.5%, with a comparison of the most differentially expressed genes before and after removal of mouse transcripts revealing no significant benefit to filtering out mouse mRNA reads in this cohort. We demonstrate that four previously established HNSCC molecular subtypes found in The Cancer Genome Atlas (TCGA) are also clearly recapitulated in HNSCC PDXs. Unsupervised hierarchical clustering yielded a striking natural division of HNSCC PDXs by HPV status, with C19orf57 ( BRME1 ), a gene previously correlated with positive response to cisplatin in cervical cancer, among the most significantly differentially expressed genes between HPV+ and HPV- PDXs. In vivo experiments demonstrated a possible relationship between increased C19orf57 expression and superior anti-tumor responses of PDXs to cisplatin, which should be investigated further. These findings highlight the value of PDXs as models for HPV+ and HPV- HNSCC, providing a resource for future discovery of predictive biomarkers to guide treatment selection in HNSCC.

Gene-expression profiles from over 1,000 colorectal tumors define six subtypes with specific phenotypical features, responses to therapy and clinical progression. Colorectal cancer (CRC) is a major cause of cancer mortality. Whereas some patients respond well to therapy, others do not, and thus more precise, individualized treatment strategies are needed. To that end, we analyzed gene expression profiles from 1,290 CRC tumors using consensus-based unsupervised clustering. The resultant clusters were then associated with therapeutic response data to the epidermal growth factor receptor–targeted drug cetuximab in 80 patients. The results of these studies define six clinically relevant CRC subtypes. Each subtype shares similarities to distinct cell types within the normal colon crypt and shows differing degrees of 'stemness' and Wnt signaling. Subtype-specific gene signatures are proposed to identify these subtypes. Three subtypes have markedly better disease-free survival (DFS) after surgical resection, suggesting these patients might be spared from the adverse effects of chemotherapy when they have localized disease. One of these three subtypes, identified by filamin A expression, does not respond to cetuximab but may respond to cMET receptor tyrosine kinase inhibitors in the metastatic setting. Two other subtypes, with poor and intermediate DFS, associate with improved response to the chemotherapy regimen FOLFIRI 1 in adjuvant or metastatic settings. Development of clinically deployable assays for these subtypes and of subtype-specific therapies may contribute to more effective management of this challenging disease.

Background Comprehensive molecular profiling has revealed somatic variations in cancer at genomic, epigenomic, transcriptomic, and proteomic levels. The accumulating data has shown clearly that molecular phenotypes of cancer are complex and influenced by a multitude of factors. Conventional unsupervised clustering applied to a large patient population is inevitably driven by the dominant variation from major factors such as cell-of-origin or histology. Translation of these data into clinical relevance requires more effective extraction of information directly associated with patient outcome. Methods Drawing from ideas in supervised text classification, we developed survClust , an outcome-weighted clustering algorithm for integrative molecular stratification focusing on patient survival. survClust was performed on 18 cancer types across multiple data modalities including somatic mutation, DNA copy number, DNA methylation, and mRNA, miRNA, and protein expression from the Cancer Genome Atlas study to identify novel prognostic subtypes. Results Our analysis identified the prognostic role of high tumor mutation burden with concurrently high CD8 T cell immune marker expression and the aggressive clinical behavior associated with CDKN2A deletion across cancer types. Visualization of somatic alterations, at a genome-wide scale (total mutation burden, mutational signature, fraction genome altered) and at the individual gene level, using circomap further revealed indolent versus aggressive subgroups in a pan-cancer setting. Conclusions Our analysis has revealed prognostic molecular subtypes not previously identified by unsupervised clustering. The algorithm and tools we developed have direct utility toward patient stratification based on tumor genomics to inform clinical decision-making. The survClust software tool is available at https://github.com/arorarshi/survClust .

Juvenile myelomonocytic leukemia (JMML) is a myeloproliferative disorder of childhood caused by mutations in the Ras pathway. Outcomes in JMML vary markedly from spontaneous resolution to rapid relapse after hematopoietic stem cell transplantation. Here, we hypothesized that DNA methylation patterns would help predict disease outcome and therefore performed genome-wide DNA methylation profiling in a cohort of 39 patients. Unsupervised hierarchical clustering identifies three clusters of patients. Importantly, these clusters differ significantly in terms of 4-year event-free survival, with the lowest methylation cluster having the highest rates of survival. These findings were validated in an independent cohort of 40 patients. Notably, all but one of 14 patients experiencing spontaneous resolution cluster together and closer to 22 healthy controls than to other JMML cases. Thus, we show that DNA methylation patterns in JMML are predictive of outcome and can identify the patients most likely to experience spontaneous resolution. Juvenile myelomonocytic leukemia (JMML) is an aggressive disease with limited options for treatment. Here, the authors utilize DNA methylation based subgroups in JMML to predict clinical outcome.