Prediction of cancer treatment response from histopathology images through imputed transcriptomics.

Advances in artificial intelligence have paved the way for leveraging hematoxylin and eosin (H&E)-stained tumor slides for precision oncology. We present ENLIGHT-DeepPT, an approach for predicting response to multiple targeted and immunotherapies from H&E-slides. In difference from existing approaches that aim to predict treatment response directly from the slides, ENLIGHT-DeepPT is an indirect two-step approach consisting of (1) DeepPT, a new deep-learning framework that predicts genome-wide tumor mRNA expression from slides, and (2) ENLIGHT, which predicts response based on the DeepPT inferred expression values. DeepPT successfully predicts transcriptomics in all 16 TCGA cohorts tested and generalizes well to two independent datasets. Our key contribution is showing that ENLIGHT-DeepPT successfully predicts true responders in five independent patients' cohorts involving four different treatments spanning six cancer types with an overall odds ratio of 2.44, increasing the baseline response rate by 43.47% among predicted responders, without the need for any treatment data for training. Furthermore, its prediction accuracy on these datasets is comparable to a supervised approach predicting the response directly from the images, which needs to be trained and tested on the same cohort. ENLIGHT-DeepPT future application could provide clinicians with rapid treatment recommendations to an array of different therapies and importantly, may contribute to advancing precision oncology in developing countries.

Genomic and transcriptomic analyses identify a prognostic gene signature and predict response to therapy in pleural and peritoneal mesothelioma.

Malignant mesothelioma is an aggressive cancer with limited treatment options and poor prognosis. A better understanding of mesothelioma genomics and transcriptomics could advance therapies. Here, we present a mesothelioma cohort of 122 patients along with their germline and tumor whole-exome and tumor RNA sequencing data as well as phenotypic and drug response information. We identify a 48-gene prognostic signature that is highly predictive of mesothelioma patient survival, including CCNB1, the expression of which is highly predictive of patient survival on its own. In addition, we analyze the transcriptomics data to study the tumor immune microenvironment and identify synthetic-lethality-based signatures predictive of response to therapy. This germline and somatic whole-exome sequencing as well as transcriptomics data from the same patient are a valuable resource to address important biological questions, including prognostic biomarkers and determinants of treatment response in mesothelioma.

Predicting cancer prognosis and drug response from the tumor microbiome.

Tumor gene expression is predictive of patient prognosis in some cancers. However, RNA-seq and whole genome sequencing data contain not only reads from host tumor and normal tissue, but also reads from the tumor microbiome, which can be used to infer the microbial abundances in each tumor. Here, we show that tumor microbial abundances, alone or in combination with tumor gene expression, can predict cancer prognosis and drug response to some extent-microbial abundances are significantly less predictive of prognosis than gene expression, although similarly as predictive of drug response, but in mostly different cancer-drug combinations. Thus, it appears possible to leverage existing sequencing technology, or develop new protocols, to obtain more non-redundant information about prognosis and drug response from RNA-seq and whole genome sequencing experiments than could be obtained from tumor gene expression or genomic data alone.

Characterization of HLH-like manifestations as a CRS variant in patients receiving CD22 CAR T cells.

Chimeric antigen receptor (CAR) T-cell toxicities resembling hemophagocytic lymphohistiocytosis (HLH) occur in a subset of patients with cytokine release syndrome (CRS). As a variant of conventional CRS, a comprehensive characterization of CAR T-cell-associated HLH (carHLH) and investigations into associated risk factors are lacking. In the context of 59 patients infused with CD22 CAR T cells where a substantial proportion developed carHLH, we comprehensively describe the manifestations and timing of carHLH as a CRS variant and explore factors associated with this clinical profile. Among 52 subjects with CRS, 21 (40.4%) developed carHLH. Clinical features of carHLH included hyperferritinemia, hypertriglyceridemia, hypofibrinogenemia, coagulopathy, hepatic transaminitis, hyperbilirubinemia, severe neutropenia, elevated lactate dehydrogenase, and occasionally hemophagocytosis. Development of carHLH was associated with preinfusion natural killer(NK) cell lymphopenia and higher bone marrow T-cell:NK cell ratio, which was further amplified with CAR T-cell expansion. Following CRS, more robust CAR T-cell and CD8 T-cell expansion in concert with pronounced NK cell lymphopenia amplified preinfusion differences in those with carHLH without evidence for defects in NK cell mediated cytotoxicity. CarHLH was further characterized by persistent elevation of HLH-associated inflammatory cytokines, which contrasted with declining levels in those without carHLH. In the setting of CAR T-cell mediated expansion, clinical manifestations and immunophenotypic profiling in those with carHLH overlap with features of secondary HLH, prompting consideration of an alternative framework for identification and management of this toxicity profile to optimize outcomes following CAR T-cell infusion.

Identification of bacteria-derived HLA-bound peptides in melanoma.

A variety of species of bacteria are known to colonize human tumours1-11, proliferate within them and modulate immune function, which ultimately affects the survival of patients with cancer and their responses to treatment12-14. However, it is not known whether antigens derived from intracellular bacteria are presented by the human leukocyte antigen class I and II (HLA-I and HLA-II, respectively) molecules of tumour cells, or whether such antigens elicit a tumour-infiltrating T cell immune response. Here we used 16S rRNA gene sequencing and HLA peptidomics to identify a peptide repertoire derived from intracellular bacteria that was presented on HLA-I and HLA-II molecules in melanoma tumours. Our analysis of 17 melanoma metastases (derived from 9 patients) revealed 248 and 35 unique HLA-I and HLA-II peptides, respectively, that were derived from 41 species of bacteria. We identified recurrent bacterial peptides in tumours from different patients, as well as in different tumours from the same patient. Our study reveals that peptides derived from intracellular bacteria can be presented by tumour cells and elicit immune reactivity, and thus provides insight into a mechanism by which bacteria influence activation of the immune system and responses to therapy.

Enhancing KDM5A and TLR activity improves the response to immune checkpoint blockade.

Immune checkpoint blockade (ICB) therapies are now established as first-line treatments for multiple cancers, but many patients do not derive long-term benefit from ICB. Here, we report that increased amounts of histone 3 lysine 4 demethylase KDM5A in tumors markedly improved response to the treatment with the programmed cell death protein 1 (PD-1) antibody in mouse cancer models. In a screen for molecules that increased KDM5A abundance, we identified one (D18) that increased the efficacy of various ICB agents in three murine cancer models when used as a combination therapy. D18 potentiated ICB efficacy through two orthogonal mechanisms: (i) increasing KDM5A abundance, which suppressed expression of the gene PTEN (encoding phosphatase and tensin homolog) and increased programmed cell death ligand 1 abundance through a pathway involving PI3K-AKT-S6K1, and (ii) activating Toll-like receptors 7 and 8 (TLR7/8) signaling pathways. Combination treatment increased T cell activation and expansion, CD103+ tumor-infiltrating dendritic cells, and tumor-associated M1 macrophages, ultimately enhancing the overall recruitment of activated CD8+ T cells to tumors. In patients with melanoma, a high KDM5A gene signature correlated with KDM5A expression and could potentially serve as a marker of response to anti-PD-1 immunotherapy. Furthermore, our results indicated that bifunctional agents that enhance both KDM5A and TLR activity warrant investigation as combination therapies with ICB agents.

Publisher Correction: The molecular landscape of pediatric acute myeloid leukemia reveals recurrent structural alterations and age-specific mutational interactions.

In the version of this article originally published, the color key in Fig. 1a was wrong. In the Cytogenetics key, the box over t(8;21) originally was green. It should have been red, matching the color of the sections of the pie graphs below the key that were labeled with 15% and 19%.

The genetic basis and cell of origin of mixed phenotype acute leukaemia.

Mixed phenotype acute leukaemia (MPAL) is a high-risk subtype of leukaemia with myeloid and lymphoid features, limited genetic characterization, and a lack of consensus regarding appropriate therapy. Here we show that the two principal subtypes of MPAL, T/myeloid (T/M) and B/myeloid (B/M), are genetically distinct. Rearrangement of ZNF384 is common in B/M MPAL, and biallelic WT1 alterations are common in T/M MPAL, which shares genomic features with early T-cell precursor acute lymphoblastic leukaemia. We show that the intratumoral immunophenotypic heterogeneity characteristic of MPAL is independent of somatic genetic variation, that founding lesions arise in primitive haematopoietic progenitors, and that individual phenotypic subpopulations can reconstitute the immunophenotypic diversity in vivo. These findings indicate that the cell of origin and founding lesions, rather than an accumulation of distinct genomic alterations, prime tumour cells for lineage promiscuity. Moreover, these findings position MPAL in the spectrum of immature leukaemias and provide a genetically informed framework for future clinical trials of potential treatments for MPAL.

Erratum: The molecular landscape of pediatric acute myeloid leukemia reveals recurrent structural alterations and age-specific mutational interactions.

This corrects the article DOI: 10.1038/nm.4439.

Pan-cancer genome and transcriptome analyses of 1,699 paediatric leukaemias and solid tumours.

Analysis of molecular aberrations across multiple cancer types, known as pan-cancer analysis, identifies commonalities and differences in key biological processes that are dysregulated in cancer cells from diverse lineages. Pan-cancer analyses have been performed for adult but not paediatric cancers, which commonly occur in developing mesodermic rather than adult epithelial tissues. Here we present a pan-cancer study of somatic alterations, including single nucleotide variants, small insertions or deletions, structural variations, copy number alterations, gene fusions and internal tandem duplications in 1,699 paediatric leukaemias and solid tumours across six histotypes, with whole-genome, whole-exome and transcriptome sequencing data processed under a uniform analytical framework. We report 142 driver genes in paediatric cancers, of which only 45% match those found in adult pan-cancer studies; copy number alterations and structural variants constituted the majority (62%) of events. Eleven genome-wide mutational signatures were identified, including one attributed to ultraviolet-light exposure in eight aneuploid leukaemias. Transcription of the mutant allele was detectable for 34% of protein-coding mutations, and 20% exhibited allele-specific expression. These data provide a comprehensive genomic architecture for paediatric cancers and emphasize the need for paediatric cancer-specific development of precision therapies.

The molecular landscape of pediatric acute myeloid leukemia reveals recurrent structural alterations and age-specific mutational interactions.

We present the molecular landscape of pediatric acute myeloid leukemia (AML) and characterize nearly 1,000 participants in Children's Oncology Group (COG) AML trials. The COG-National Cancer Institute (NCI) TARGET AML initiative assessed cases by whole-genome, targeted DNA, mRNA and microRNA sequencing and CpG methylation profiling. Validated DNA variants corresponded to diverse, infrequent mutations, with fewer than 40 genes mutated in >2% of cases. In contrast, somatic structural variants, including new gene fusions and focal deletions of MBNL1, ZEB2 and ELF1, were disproportionately prevalent in young individuals as compared to adults. Conversely, mutations in DNMT3A and TP53, which were common in adults, were conspicuously absent from virtually all pediatric cases. New mutations in GATA2, FLT3 and CBL and recurrent mutations in MYC-ITD, NRAS, KRAS and WT1 were frequent in pediatric AML. Deletions, mutations and promoter DNA hypermethylation convergently impacted Wnt signaling, Polycomb repression, innate immune cell interactions and a cluster of zinc finger-encoding genes associated with KMT2A rearrangements. These results highlight the need for and facilitate the development of age-tailored targeted therapies for the treatment of pediatric AML.

CTD2 Dashboard: a searchable web interface to connect validated results from the Cancer Target Discovery and Development Network.

Database URL: https://ctd2-dashboard.nci.nih.gov/.

A Children's Oncology Group and TARGET initiative exploring the genetic landscape of Wilms tumor.

We performed genome-wide sequencing and analyzed mRNA and miRNA expression, DNA copy number, and DNA methylation in 117 Wilms tumors, followed by targeted sequencing of 651 Wilms tumors. In addition to genes previously implicated in Wilms tumors (WT1, CTNNB1, AMER1, DROSHA, DGCR8, XPO5, DICER1, SIX1, SIX2, MLLT1, MYCN, and TP53), we identified mutations in genes not previously recognized as recurrently involved in Wilms tumors, the most frequent being BCOR, BCORL1, NONO, MAX, COL6A3, ASXL1, MAP3K4, and ARID1A. DNA copy number changes resulted in recurrent 1q gain, MYCN amplification, LIN28B gain, and MIRLET7A loss. Unexpected germline variants involved PALB2 and CHEK2. Integrated analyses support two major classes of genetic changes that preserve the progenitor state and/or interrupt normal development.

Genomic Profiling of Pediatric Acute Myeloid Leukemia Reveals a Changing Mutational Landscape from Disease Diagnosis to Relapse.

The genomic and clinical information used to develop and implement therapeutic approaches for acute myelogenous leukemia (AML) originated primarily from adult patients and has been generalized to patients with pediatric AML. However, age-specific molecular alterations are becoming more evident and may signify the need to age-stratify treatment regimens. The NCI/COG TARGET-AML initiative used whole exome capture sequencing (WXS) to interrogate the genomic landscape of matched trios representing specimens collected upon diagnosis, remission, and relapse from 20 cases of de novo childhood AML. One hundred forty-five somatic variants at diagnosis (median 6 mutations/patient) and 149 variants at relapse (median 6.5 mutations) were identified and verified by orthogonal methodologies. Recurrent somatic variants [in (greater than or equal to) 2 patients] were identified for 10 genes (FLT3, NRAS, PTPN11, WT1, TET2, DHX15, DHX30, KIT, ETV6, KRAS), with variable persistence at relapse. The variant allele fraction (VAF), used to measure the prevalence of somatic mutations, varied widely at diagnosis. Mutations that persisted from diagnosis to relapse had a significantly higher diagnostic VAF compared with those that resolved at relapse (median VAF 0.43 vs. 0.24, P < 0.001). Further analysis revealed that 90% of the diagnostic variants with VAF >0.4 persisted to relapse compared with 28% with VAF <0.2 (P < 0.001). This study demonstrates significant variability in the mutational profile and clonal evolution of pediatric AML from diagnosis to relapse. Furthermore, mutations with high VAF at diagnosis, representing variants shared across a leukemic clonal structure, may constrain the genomic landscape at relapse and help to define key pathways for therapeutic targeting. Cancer Res; 76(8); 2197-205. ©2016 AACR.

Confero: an integrated contrast data and gene set platform for computational analysis and biological interpretation of omics data.

BACKGROUND: High-throughput omics technologies such as microarrays and next-generation sequencing (NGS) have become indispensable tools in biological research. Computational analysis and biological interpretation of omics data can pose significant challenges due to a number of factors, in particular the systems integration required to fully exploit and compare data from different studies and/or technology platforms. In transcriptomics, the identification of differentially expressed genes when studying effect(s) or contrast(s) of interest constitutes the starting point for further downstream computational analysis (e.g. gene over-representation/enrichment analysis, reverse engineering) leading to mechanistic insights. Therefore, it is important to systematically store the full list of genes with their associated statistical analysis results (differential expression, t-statistics, p-value) corresponding to one or more effect(s) or contrast(s) of interest (shortly termed as " contrast data") in a comparable manner and extract gene sets in order to efficiently support downstream analyses and further leverage data on a long-term basis. Filling this gap would open new research perspectives for biologists to discover disease-related biomarkers and to support the understanding of molecular mechanisms underlying specific biological perturbation effects (e.g. disease, genetic, environmental, etc.). RESULTS: To address these challenges, we developed Confero, a contrast data and gene set platform for downstream analysis and biological interpretation of omics data. The Confero software platform provides storage of contrast data in a simple and standard format, data transformation to enable cross-study and platform data comparison, and automatic extraction and storage of gene sets to build new a priori knowledge which is leveraged by integrated and extensible downstream computational analysis tools. Gene Set Enrichment Analysis (GSEA) and Over-Representation Analysis (ORA) are currently integrated as an analysis module as well as additional tools to support biological interpretation. Confero is a standalone system that also integrates with Galaxy, an open-source workflow management and data integration system. To illustrate Confero platform functionality we walk through major aspects of the Confero workflow and results using the Bioconductor estrogen package dataset. CONCLUSION: Confero provides a unique and flexible platform to support downstream computational analysis facilitating biological interpretation. The system has been designed in order to provide the researcher with a simple, innovative, and extensible solution to store and exploit analyzed data in a sustainable and reproducible manner thereby accelerating knowledge-driven research. Confero source code is freely available from http://sourceforge.net/projects/confero/.

Modeling and managing experimental data using FuGE.

The Functional Genomics Experiment data model (FuGE) has been developed to increase the consistency and efficiency of experimental data modeling in the life sciences, and it has been adopted by a number of high-profile standardization organizations. FuGE can be used: (1) directly, whereby generic modeling constructs are used to represent concepts from specific experimental activities; or (2) as a framework within which method-specific models can be developed. FuGE is both rich and flexible, providing a considerable number of modeling constructs, which can be used in a range of different ways. However, such richness and flexibility also mean that modelers and application developers have choices to make when applying FuGE in a given context. This paper captures emerging best practice in the use of FuGE in the light of the experience of several groups by: (1) proposing guidelines for the use and extension of the FuGE data model; (2) presenting design patterns that reflect recurring requirements in experimental data modeling; and (3) describing a community software tool kit (STK) that supports application development using FuGE. We anticipate that these guidelines will encourage consistent usage of FuGE, and as such, will contribute to the development of convergent data standards in omics research.

MIMAS 3.0 is a Multiomics Information Management and Annotation System.

BACKGROUND: DNA sequence integrity, mRNA concentrations and protein-DNA interactions have been subject to genome-wide analyses based on microarrays with ever increasing efficiency and reliability over the past fifteen years. However, very recently novel technologies for Ultra High-Throughput DNA Sequencing (UHTS) have been harnessed to study these phenomena with unprecedented precision. As a consequence, the extensive bioinformatics environment available for array data management, analysis, interpretation and publication must be extended to include these novel sequencing data types. DESCRIPTION: MIMAS was originally conceived as a simple, convenient and local Microarray Information Management and Annotation System focused on GeneChips for expression profiling studies. MIMAS 3.0 enables users to manage data from high-density oligonucleotide SNP Chips, expression arrays (both 3'UTR and tiling) and promoter arrays, BeadArrays as well as UHTS data using MIAME-compliant standardized vocabulary. Importantly, researchers can export data in MAGE-TAB format and upload them to the EBI's ArrayExpress certified data repository using a one-step procedure. CONCLUSION: We have vastly extended the capability of the system such that it processes the data output of six types of GeneChips (Affymetrix), two different BeadArrays for mRNA and miRNA (Illumina) and the Genome Analyzer (a popular Ultra-High Throughput DNA Sequencer, Illumina), without compromising on its flexibility and user-friendliness. MIMAS, appropriately renamed into Multiomics Information Management and Annotation System, is currently used by scientists working in approximately 50 academic laboratories and genomics platforms in Switzerland and France. MIMAS 3.0 is freely available via http://multiomics.sourceforge.net/.

A mammalian microRNA expression atlas based on small RNA library sequencing.

MicroRNAs (miRNAs) are small noncoding regulatory RNAs that reduce stability and/or translation of fully or partially sequence-complementary target mRNAs. In order to identify miRNAs and to assess their expression patterns, we sequenced over 250 small RNA libraries from 26 different organ systems and cell types of human and rodents that were enriched in neuronal as well as normal and malignant hematopoietic cells and tissues. We present expression profiles derived from clone count data and provide computational tools for their analysis. Unexpectedly, a relatively small set of miRNAs, many of which are ubiquitously expressed, account for most of the differences in miRNA profiles between cell lineages and tissues. This broad survey also provides detailed and accurate information about mature sequences, precursors, genome locations, maturation processes, inferred transcriptional units, and conservation patterns. We also propose a subclassification scheme for miRNAs for assisting future experimental and computational functional analyses.