The Immune Landscape of Cancer.

We performed an extensive immunogenomic analysis of more than 10,000 tumors comprising 33 diverse cancer types by utilizing data compiled by TCGA. Across cancer types, we identified six immune subtypes-wound healing, IFN-γ dominant, inflammatory, lymphocyte depleted, immunologically quiet, and TGF-ß dominant-characterized by differences in macrophage or lymphocyte signatures, Th1:Th2 cell ratio, extent of intratumoral heterogeneity, aneuploidy, extent of neoantigen load, overall cell proliferation, expression of immunomodulatory genes, and prognosis. Specific driver mutations correlated with lower (CTNNB1, NRAS, or IDH1) or higher (BRAF, TP53, or CASP8) leukocyte levels across all cancers. Multiple control modalities of the intracellular and extracellular networks (transcription, microRNAs, copy number, and epigenetic processes) were involved in tumor-immune cell interactions, both across and within immune subtypes. Our immunogenomics pipeline to characterize these heterogeneous tumors and the resulting data are intended to serve as a resource for future targeted studies to further advance the field.

A RSC/nucleosome complex determines chromatin architecture and facilitates activator binding.

How is chromatin architecture established and what role does it play in transcription? We show that the yeast regulatory locus UASg bears, in addition to binding sites for the activator Gal4, sites bound by the RSC complex. RSC positions a nucleosome, evidently partially unwound, in a structure that facilitates Gal4 binding to its sites. The complex comprises a barrier that imposes characteristic features of chromatin architecture. In the absence of RSC, ordinary nucleosomes encroach over the UASg and compete with Gal4 for binding. Taken with our previous work, the results show that both prior to and following induction, specific DNA-binding proteins are the predominant determinants of chromatin architecture at the GAL1/10 genes. RSC/nucleosome complexes are also found scattered around the yeast genome. Higher eukaryotic RSC lacks the specific DNA-binding determinants found on yeast RSC, and evidently Gal4 works in those organisms despite whatever obstacle broadly positioned nucleosomes present.

An oncogenomics-based in vivo RNAi screen identifies tumor suppressors in liver cancer.

Cancers are highly heterogeneous and contain many passenger and driver mutations. To functionally identify tumor suppressor genes relevant to human cancer, we compiled pools of short hairpin RNAs (shRNAs) targeting the mouse orthologs of genes recurrently deleted in a series of human hepatocellular carcinomas and tested their ability to promote tumorigenesis in a mosaic mouse model. In contrast to randomly selected shRNA pools, many deletion-specific pools accelerated hepatocarcinogenesis in mice. Through further analysis, we identified and validated 13 tumor suppressor genes, 12 of which had not been linked to cancer before. One gene, XPO4, encodes a nuclear export protein whose substrate, EIF5A2, is amplified in human tumors, is required for proliferation of XPO4-deficient tumor cells, and promotes hepatocellular carcinoma in mice. Our results establish the feasibility of in vivo RNAi screens and illustrate how combining cancer genomics, RNA interference, and mosaic mouse models can facilitate the functional annotation of the cancer genome.

Optimizing sparse sequencing of single cells for highly multiplex copy number profiling.

Genome-wide analysis at the level of single cells has recently emerged as a powerful tool to dissect genome heterogeneity in cancer, neurobiology, and development. To be truly transformative, single-cell approaches must affordably accommodate large numbers of single cells. This is feasible in the case of copy number variation (CNV), because CNV determination requires only sparse sequence coverage. We have used a combination of bioinformatic and molecular approaches to optimize single-cell DNA amplification and library preparation for highly multiplexed sequencing, yielding a method that can produce genome-wide CNV profiles of up to a hundred individual cells on a single lane of an Illumina HiSeq instrument. We apply the method to human cancer cell lines and biopsied cancer tissue, thereby illustrating its efficiency, reproducibility, and power to reveal underlying genetic heterogeneity and clonal phylogeny. The capacity of the method to facilitate the rapid profiling of hundreds to thousands of single-cell genomes represents a key step in making single-cell profiling an easily accessible tool for studying cell lineage.

A tumour suppressor network relying on the polyamine-hypusine axis.

Tumour suppressor genes encode a broad class of molecules whose mutational attenuation contributes to malignant progression. In the canonical situation, the tumour suppressor is completely inactivated through a two-hit process involving a point mutation in one allele and chromosomal deletion of the other. Here, to identify tumour suppressor genes in lymphoma, we screen a short hairpin RNA library targeting genes deleted in human lymphomas. We functionally identify those genes whose suppression promotes tumorigenesis in a mouse lymphoma model. Of the nine tumour suppressors we identified, eight correspond to genes occurring in three physically linked 'clusters', suggesting that the common occurrence of large chromosomal deletions in human tumours reflects selective pressure to attenuate multiple genes. Among the new tumour suppressors are adenosylmethionine decarboxylase 1 (AMD1) and eukaryotic translation initiation factor 5A (eIF5A), two genes associated with hypusine, a unique amino acid produced as a product of polyamine metabolism through a highly conserved pathway. Through a secondary screen surveying the impact of all polyamine enzymes on tumorigenesis, we establish the polyamine-hypusine axis as a new tumour suppressor network regulating apoptosis. Unexpectedly, heterozygous deletions encompassing AMD1 and eIF5A often occur together in human lymphomas and co-suppression of both genes promotes lymphomagenesis in mice. Thus, some tumour suppressor functions can be disabled through a two-step process targeting different genes acting in the same pathway.

Target inference from collections of genomic intervals.

Finding regions of the genome that are significantly recurrent in noisy data are a common but difficult problem in present day computational biology. Cores of recurrent events (CORE) is a computational approach to solving this problem that is based on a formalized notion by which "core" intervals explain the observed data, where the number of cores is the "depth" of the explanation. Given that formalization, we implement CORE as a combinatorial optimization procedure with depth chosen from considerations of statistical significance. An important feature of CORE is its ability to explain data with cores of widely varying lengths. We examine the performance of this system with synthetic data, and then provide two demonstrations of its utility with actual data. Applying CORE to a collection of DNA copy number profiles from single cells of a given tumor, we determine tumor population phylogeny and find the features that separate subpopulations. Applying CORE to comparative genomic hybridization data from a large set of tumor samples, we define regions of recurrent copy number aberration in breast cancer.

A cluster of cooperating tumor-suppressor gene candidates in chromosomal deletions.

The large chromosomal deletions frequently observed in cancer genomes are often thought to arise as a "two-hit" mechanism in the process of tumor-suppressor gene (TSG) inactivation. Using a murine model system of hepatocellular carcinoma (HCC) and in vivo RNAi, we test an alternative hypothesis, that such deletions can arise from selective pressure to attenuate the activity of multiple genes. By targeting the mouse orthologs of genes frequently deleted on human 8p22 and adjacent regions, which are lost in approximately half of several other major epithelial cancers, we provide evidence suggesting that multiple genes on chromosome 8p can cooperatively inhibit tumorigenesis in mice, and that their cosuppression can synergistically promote tumor growth. In addition, in human HCC patients, the combined down-regulation of functionally validated 8p TSGs is associated with poor survival, in contrast to the down-regulation of any individual gene. Our data imply that large cancer-associated deletions can produce phenotypes distinct from those arising through loss of a single TSG, and as such should be considered and studied as distinct mutational events.

Significant distinct branches of hierarchical trees: a framework for statistical analysis and applications to biological data.

BACKGROUND: One of the most common goals of hierarchical clustering is finding those branches of a tree that form quantifiably distinct data subtypes. Achieving this goal in a statistically meaningful way requires (a) a measure of distinctness of a branch and (b) a test to determine the significance of the observed measure, applicable to all branches and across multiple scales of dissimilarity. RESULTS: We formulate a method termed Tree Branches Evaluated Statistically for Tightness (TBEST) for identifying significantly distinct tree branches in hierarchical clusters. For each branch of the tree a measure of distinctness, or tightness, is defined as a rational function of heights, both of the branch and of its parent. A statistical procedure is then developed to determine the significance of the observed values of tightness. We test TBEST as a tool for tree-based data partitioning by applying it to five benchmark datasets, one of them synthetic and the other four each from a different area of biology. For each dataset there is a well-defined partition of the data into classes. In all test cases TBEST performs on par with or better than the existing techniques. CONCLUSIONS: Based on our benchmark analysis, TBEST is a tool of choice for detection of significantly distinct branches in hierarchical trees grown from biological data. An R language implementation of the method is available from the Comprehensive R Archive Network: http://www.cran.r-project.org/web/packages/TBEST/index.html.

Inferring tumor progression from genomic heterogeneity.

Cancer progression in humans is difficult to infer because we do not routinely sample patients at multiple stages of their disease. However, heterogeneous breast tumors provide a unique opportunity to study human tumor progression because they still contain evidence of early and intermediate subpopulations in the form of the phylogenetic relationships. We have developed a method we call Sector-Ploidy-Profiling (SPP) to study the clonal composition of breast tumors. SPP involves macro-dissecting tumors, flow-sorting genomic subpopulations by DNA content, and profiling genomes using comparative genomic hybridization (CGH). Breast carcinomas display two classes of genomic structural variation: (1) monogenomic and (2) polygenomic. Monogenomic tumors appear to contain a single major clonal subpopulation with a highly stable chromosome structure. Polygenomic tumors contain multiple clonal tumor subpopulations, which may occupy the same sectors, or separate anatomic locations. In polygenomic tumors, we show that heterogeneity can be ascribed to a few clonal subpopulations, rather than a series of gradual intermediates. By comparing multiple subpopulations from different anatomic locations, we have inferred pathways of cancer progression and the organization of tumor growth.

Genetic Ancestry Inference from Cancer-Derived Molecular Data across Genomic and Transcriptomic Platforms.

Genetic ancestry-oriented cancer research requires the ability to perform accurate and robust genetic ancestry inference from existing cancer-derived data, including whole-exome sequencing, transcriptome sequencing, and targeted gene panels, very often in the absence of matching cancer-free genomic data. Here we examined the feasibility and accuracy of computational inference of genetic ancestry relying exclusively on cancer-derived data. A data synthesis framework was developed to optimize and assess the performance of the ancestry inference for any given input cancer-derived molecular profile. In its core procedure, the ancestral background of the profiled patient is replaced with one of any number of individuals with known ancestry. The data synthesis framework is applicable to multiple profiling platforms, making it possible to assess the performance of inference specifically for a given molecular profile and separately for each continental-level ancestry; this ability extends to all ancestries, including those without statistically sufficient representation in the existing cancer data. The inference procedure was demonstrated to be accurate and robust in a wide range of sequencing depths. Testing of the approach in four representative cancer types and across three molecular profiling modalities showed that continental-level ancestry of patients can be inferred with high accuracy, as quantified by its agreement with the gold standard of deriving ancestry from matching cancer-free molecular data. This study demonstrates that vast amounts of existing cancer-derived molecular data are potentially amenable to ancestry-oriented studies of the disease without requiring matching cancer-free genomes or patient self-reported ancestry. SIGNIFICANCE: The development of a computational approach that enables accurate and robust ancestry inference from cancer-derived molecular profiles without matching cancer-free data provides a valuable methodology for genetic ancestry-oriented cancer research.

Brk is coamplified with ErbB2 to promote proliferation in breast cancer.

Amplification of the receptor tyrosine kinase ErbB2 is frequently observed in breast cancer. Amplification of erbB2 is also associated with multiple genomic gains and losses; however, the importance of these associated changes is largely unknown. We demonstrate that Brk, a cytoplasmic tyrosine kinase, is coamplified and coexpressed with ErbB2 in human breast cancers. ErbB2 interacts with Brk and increases its intrinsic kinase activity. Expression of Brk enhances the ErbB2-induced activation of Ras/MAPK signaling and cyclin E/cdk2 activity to induce cell proliferation of mammary 3-dimensional acini in culture. In a murine model of breast cancer, expression of Brk was found to shorten the latency of ErbB2-induced tumors by promoting cell proliferation, with no effect on protection from apoptosis. Furthermore, overexpression of Brk conferred resistance to the ability of Lapatinib, an ErbB2 kinase inhibitor, to inhibit ErbB2-induced proliferation. Thus, we identified Brk as a drug target for ErbB2-positive cancers.

Integrated Computational Pipeline for Single-Cell Genomic Profiling.

PURPOSE: Copy-number profiling of multiple individual cells from sparse sequencing may be used to reveal a detailed picture of genomic heterogeneity and clonal organization in a tissue biopsy specimen. We sought to provide a comprehensive computational pipeline for single-cell genomics, to facilitate adoption of this molecular technology for basic and translational research. MATERIALS AND METHODS: The pipeline comprises software tools programmed in Python and in R and depends on Bowtie, HISAT2, Matplotlib, and Qt. It is installed and used with Anaconda. RESULTS: Here we describe a complete pipeline for sparse single-cell genomic data, encompassing all steps of single-nucleus DNA copy-number profiling, from raw sequence processing to clonal structure analysis and visualization. For the latter, a specialized graphical user interface termed the single-cell genome viewer (SCGV) is provided. With applications to cancer diagnostics in mind, the SCGV allows for zooming and linkage to the University of California at Santa Cruz Genome Browser from each of the multiple integrated views of single-cell copy-number profiles. The latter can be organized by clonal substructure or by any of the associated metadata such as anatomic location and histologic characterization. CONCLUSION: The pipeline is available as open-source software for Linux and OS X. Its modular structure, extensive documentation, and ease of deployment using Anaconda facilitate its adoption by researchers and practitioners of single-cell genomics. With open-source availability and Massachusetts Institute of Technology licensing, it provides a basis for additional development by the cancer bioinformatics community.

SOAT1 promotes mevalonate pathway dependency in pancreatic cancer.

Pancreatic ductal adenocarcinoma (PDAC) has a dismal prognosis, and new therapies are needed. Altered metabolism is a cancer vulnerability, and several metabolic pathways have been shown to promote PDAC. However, the changes in cholesterol metabolism and their role during PDAC progression remain largely unknown. Here we used organoid and mouse models to determine the drivers of altered cholesterol metabolism in PDAC and the consequences of its disruption on tumor progression. We identified sterol O-acyltransferase 1 (SOAT1) as a key player in sustaining the mevalonate pathway by converting cholesterol to inert cholesterol esters, thereby preventing the negative feedback elicited by unesterified cholesterol. Genetic targeting of Soat1 impairs cell proliferation in vitro and tumor progression in vivo and reveals a mevalonate pathway dependency in p53 mutant PDAC cells that have undergone p53 loss of heterozygosity (LOH). In contrast, pancreatic organoids lacking p53 mutation and p53 LOH are insensitive to SOAT1 loss, indicating a potential therapeutic window for inhibiting SOAT1 in PDAC.

Intraductal Transplantation Models of Human Pancreatic Ductal Adenocarcinoma Reveal Progressive Transition of Molecular Subtypes.

Pancreatic ductal adenocarcinoma (PDAC) is the most lethal common malignancy, with little improvement in patient outcomes over the past decades. Recently, subtypes of pancreatic cancer with different prognoses have been elaborated; however, the inability to model these subtypes has precluded mechanistic investigation of their origins. Here, we present a xenotransplantation model of PDAC in which neoplasms originate from patient-derived organoids injected directly into murine pancreatic ducts. Our model enables distinction of the two main PDAC subtypes: intraepithelial neoplasms from this model progress in an indolent or invasive manner representing the classical or basal-like subtypes of PDAC, respectively. Parameters that influence PDAC subtype specification in this intraductal model include cell plasticity and hyperactivation of the RAS pathway. Finally, through intratumoral dissection and the direct manipulation of RAS gene dosage, we identify a suite of RAS-regulated secreted and membrane-bound proteins that may represent potential candidates for therapeutic intervention in patients with PDAC. SIGNIFICANCE: Accurate modeling of the molecular subtypes of pancreatic cancer is crucial to facilitate the generation of effective therapies. We report the development of an intraductal organoid transplantation model of pancreatic cancer that models the progressive switching of subtypes, and identify stochastic and RAS-driven mechanisms that determine subtype specification.See related commentary by Pickering and Morton, p. 1448.This article is highlighted in the In This Issue feature, p. 1426.

CORE: A Software Tool for Delineating Regions of Recurrent DNA Copy Number Alteration in Cancer.

Collections of genomic intervals are a common data type across many areas of computational biology. In cancer genomics, in particular, the intervals often represent regions with altered DNA copy number, and their collections exhibit recurrent features, characteristic of a given cancer type. Cores of Recurrent Events (CORE) is a versatile computational tool for identification of such recurrent features. Here we provide practical guidance for the use of CORE, implemented as an eponymous R package.

Statistically Supported Identification of Tumor Subtypes.

Identification of biologically and clinically consequential subtypes within tumor types is a long-standing goal of cancer bioinformatics. Here we provide practical guidance to the use of a recently developed statistical subtyping tool, termed Tree Branches Evaluated Statistically for Tightness (TBEST), and its eponymous R language implementation. TBEST employs hierarchical clustering to partition the data at a user-specified level of significance. Functionalities of the package are illustrated using as an example a benchmark data set of mRNA expression levels in leukemia.

Utility of Single-Cell Genomics in Diagnostic Evaluation of Prostate Cancer.

A distinction between indolent and aggressive disease is a major challenge in diagnostics of prostate cancer. As genetic heterogeneity and complexity may influence clinical outcome, we have initiated studies on single tumor cell genomics. In this study, we demonstrate that sparse DNA sequencing of single-cell nuclei from prostate core biopsies is a rich source of quantitative parameters for evaluating neoplastic growth and aggressiveness. These include the presence of clonal populations, the phylogenetic structure of those populations, the degree of the complexity of copy-number changes in those populations, and measures of the proportion of cells with clonal copy-number signatures. The parameters all showed good correlation to the measure of prostatic malignancy, the Gleason score, derived from individual prostate biopsy tissue cores. Remarkably, a more accurate histopathologic measure of malignancy, the surgical Gleason score, agrees better with these genomic parameters of diagnostic biopsy than it does with the diagnostic Gleason score and related measures of diagnostic histopathology. This is highly relevant because primary treatment decisions are dependent upon the biopsy and not the surgical specimen. Thus, single-cell analysis has the potential to augment traditional core histopathology, improving both the objectivity and accuracy of risk assessment and inform treatment decisions.Significance: Genomic analysis of multiple individual cells harvested from prostate biopsies provides an indepth view of cell populations comprising a prostate neoplasm, yielding novel genomic measures with the potential to improve the accuracy of diagnosis and prognosis in prostate cancer. Cancer Res; 78(2); 348-58. ©2017 AACR.

Early Detection of Cancer in Blood Using Single-Cell Analysis: A Proposal.

Here, we explore the potential of single-cell genomic analysis in blood for early detection of cancer; we consider a method that screens the presence of recurrent patterns of copy number (CN) alterations using sparse single-cell sequencing. We argue for feasibility, based on in silico analysis of existing single-cell data and cancer CN profiles. Sampling procedures from existing diploid single cells can render data for a cell with any given profile. Sampling from multiple published tumor profiles can interrogate cancer clonality via an algorithm that tests the multiplicity of close pairwise similarities among single-cell cancer genomes. The majority of common solid cancers would be detectable in this manner. As any early detection method must be verifiable and actionable, we describe how further analysis of suspect cells can aid in determining risk and anatomic origin. Future affordability rests on currently available procedures for tumor cell enrichment and inexpensive methods for single-cell analysis.