A community challenge to predict clinical outcomes after immune checkpoint blockade in non-small cell lung cancer.

BACKGROUND: Predictive biomarkers of immune checkpoint inhibitor (ICI) efficacy are currently lacking for non-small cell lung cancer (NSCLC). Here, we describe the results from the Anti-PD-1 Response Prediction DREAM Challenge, a crowdsourced initiative that enabled the assessment of predictive models by using data from two randomized controlled clinical trials (RCTs) of ICIs in first-line metastatic NSCLC. METHODS: Participants developed and trained models using public resources. These were evaluated with data from the CheckMate 026 trial (NCT02041533), according to the model-to-data paradigm to maintain patient confidentiality. The generalizability of the models with the best predictive performance was assessed using data from the CheckMate 227 trial (NCT02477826). Both trials were phase III RCTs with a chemotherapy control arm, which supported the differentiation between predictive and prognostic models. Isolated model containers were evaluated using a bespoke strategy that considered the challenges of handling transcriptome data from clinical trials. RESULTS: A total of 59 teams participated, with 417 models submitted. Multiple predictive models, as opposed to a prognostic model, were generated for predicting overall survival, progression-free survival, and progressive disease status with ICIs. Variables within the models submitted by participants included tumor mutational burden (TMB), programmed death ligand 1 (PD-L1) expression, and gene-expression-based signatures. The best-performing models showed improved predictive power over reference variables, including TMB or PD-L1. CONCLUSIONS: This DREAM Challenge is the first successful attempt to use protected phase III clinical data for a crowdsourced effort towards generating predictive models for ICI clinical outcomes and could serve as a blueprint for similar efforts in other tumor types and disease states, setting a benchmark for future studies aiming to identify biomarkers predictive of ICI efficacy. TRIAL REGISTRATION: CheckMate 026; NCT02041533, registered January 22, 2014. CheckMate 227; NCT02477826, registered June 23, 2015.

Bayesian multi-source regression and monocyte-associated gene expression predict BCL-2 inhibitor resistance in acute myeloid leukemia.

The FDA recently approved eight targeted therapies for acute myeloid leukemia (AML), including the BCL-2 inhibitor venetoclax. Maximizing efficacy of these treatments requires refining patient selection. To this end, we analyzed two recent AML studies profiling the gene expression and ex vivo drug response of primary patient samples. We find that ex vivo samples often exhibit a general sensitivity to (any) drug exposure, independent of drug target. We observe that this "general response across drugs" (GRD) is associated with FLT3-ITD mutations, clinical response to standard induction chemotherapy, and overall survival. Further, incorporating GRD into expression-based regression models trained on one of the studies improved their performance in predicting ex vivo response in the second study, thus signifying its relevance to precision oncology efforts. We find that venetoclax response is independent of GRD but instead show that it is linked to expression of monocyte-associated genes by developing and applying a multi-source Bayesian regression approach. The method shares information across studies to robustly identify biomarkers of drug response and is broadly applicable in integrative analyses.

Genetic program activity delineates risk, relapse, and therapy responsiveness in multiple myeloma.

Despite recent advancements in the treatment of multiple myeloma (MM), nearly all patients ultimately relapse and many become refractory to multiple lines of therapies. Therefore, we not only need the ability to predict which patients are at high risk for disease progression but also a means to understand the mechanisms underlying their risk. Here, we report a transcriptional regulatory network (TRN) for MM inferred from cross-sectional multi-omics data from 881 patients that predicts how 124 chromosomal abnormalities and somatic mutations causally perturb 392 transcription regulators of 8549 genes to manifest in distinct clinical phenotypes and outcomes. We identified 141 genetic programs whose activity profiles stratify patients into 25 distinct transcriptional states and proved to be more predictive of outcomes than did mutations. The coherence of these programs and accuracy of our network-based risk prediction was validated in two independent datasets. We observed subtype-specific vulnerabilities to interventions with existing drugs and revealed plausible mechanisms for relapse, including the establishment of an immunosuppressive microenvironment. Investigation of the t(4;14) clinical subtype using the TRN revealed that 16% of these patients exhibit an extreme-risk combination of genetic programs (median progression-free survival of 5 months) that create a distinct phenotype with targetable genes and pathways.

Multiple Myeloma DREAM Challenge reveals epigenetic regulator PHF19 as marker of aggressive disease.

While the past decade has seen meaningful improvements in clinical outcomes for multiple myeloma patients, a subset of patients does not benefit from current therapeutics for unclear reasons. Many gene expression-based models of risk have been developed, but each model uses a different combination of genes and often involves assaying many genes making them difficult to implement. We organized the Multiple Myeloma DREAM Challenge, a crowdsourced effort to develop models of rapid progression in newly diagnosed myeloma patients and to benchmark these against previously published models. This effort lead to more robust predictors and found that incorporating specific demographic and clinical features improved gene expression-based models of high risk. Furthermore, post-challenge analysis identified a novel expression-based risk marker, PHF19, which has recently been found to have an important biological role in multiple myeloma. Lastly, we show that a simple four feature predictor composed of age, ISS, and expression of PHF19 and MMSET performs similarly to more complex models with many more gene expression features included.

Community assessment to advance computational prediction of cancer drug combinations in a pharmacogenomic screen.

The effectiveness of most cancer targeted therapies is short-lived. Tumors often develop resistance that might be overcome with drug combinations. However, the number of possible combinations is vast, necessitating data-driven approaches to find optimal patient-specific treatments. Here we report AstraZeneca's large drug combination dataset, consisting of 11,576 experiments from 910 combinations across 85 molecularly characterized cancer cell lines, and results of a DREAM Challenge to evaluate computational strategies for predicting synergistic drug pairs and biomarkers. 160 teams participated to provide a comprehensive methodological development and benchmarking. Winning methods incorporate prior knowledge of drug-target interactions. Synergy is predicted with an accuracy matching biological replicates for >60% of combinations. However, 20% of drug combinations are poorly predicted by all methods. Genomic rationale for synergy predictions are identified, including ADAM17 inhibitor antagonism when combined with PIK3CB/D inhibition contrasting to synergy when combined with other PI3K-pathway inhibitors in PIK3CA mutant cells.

KRAS Mutation and Consensus Molecular Subtypes 2 and 3 Are Independently Associated with Reduced Immune Infiltration and Reactivity in Colorectal Cancer.

Purpose:KRAS mutation is a common canonical mutation in colorectal cancer, found at differing frequencies in all consensus molecular subtypes (CMS). The independent immunobiological impacts of RAS mutation and CMS are unknown. Thus, we explored the immunobiological effects of KRAS mutation across the CMS spectrum.Experimental Design: Expression analysis of immune genes/signatures was performed using The Cancer Genome Atlas (TCGA) RNA-seq and the KFSYSCC microarray datasets. Multivariate analysis included KRAS status, CMS, tumor location, MSI status, and neoantigen load. Protein expression of STAT1, HLA-class II, and CXCL10 was analyzed by digital IHC.Results: The Th1-centric co-ordinate immune response cluster (CIRC) was significantly, albeit modestly, reduced in KRAS-mutant colorectal cancer in both datasets. Cytotoxic T cells, neutrophils, and the IFNγ pathway were suppressed in KRAS-mutant samples. The expressions of STAT1 and CXCL10 were reduced at the mRNA and protein levels. In multivariate analysis, KRAS mutation, CMS2, and CMS3 were independently predictive of reduced CIRC expression. Immune response was heterogeneous across KRAS-mutant colorectal cancer: KRAS-mutant CMS2 samples have the lowest CIRC expression, reduced expression of the IFNγ pathway, STAT1 and CXCL10, and reduced infiltration of cytotoxic cells and neutrophils relative to CMS1 and CMS4 and to KRAS wild-type CMS2 samples in the TCGA. These trends held in the KFSYSCC dataset.Conclusions:KRAS mutation is associated with suppressed Th1/cytotoxic immunity in colorectal cancer, the extent of the effect being modulated by CMS subtype. These results add a novel immunobiological dimension to the biological heterogeneity of colorectal cancer. Clin Cancer Res; 24(1); 224-33. ©2017 AACR.

Low HERV-K(C4) copy number is associated with type 1 diabetes.

Complement component C4 (C4) is a highly variable complement pathway gene situated ∼500 kb from DRB1 and DQB1, the genes most strongly associated with many autoimmune diseases. Variations in C4 copy number (CN), length, and isotype create a highly diverse gene cluster in which insertion of an endogenous retrovirus in the ninth intron of C4, termed HERV-K(C4), is a notable component. We investigated the relationship between C4 variation/CN and type 1 diabetes. We found that individuals with type 1 diabetes have significantly fewer copies of HERV-K(C4) and that this effect is not solely due to linkage with known major histocompatibility complex class II susceptibility alleles. We show that HERV-K(C4) is a novel marker of type 1 diabetes that accounts for the disease association previously attributed to some key HLA-DQB1 alleles, raising the possibility that this retroviral insertion element contributes to functional protection against type 1 diabetes.

Identification of context-dependent motifs by contrasting ChIP binding data.

MOTIVATION: DNA binding proteins play crucial roles in the regulation of gene expression. Transcription factors (TFs) activate or repress genes directly while other proteins influence chromatin structure for transcription. Binding sites of a TF exhibit a similar sequence pattern called a motif. However, a one-to-one map does not exist between each TF and motif. Many TFs in a protein family may recognize the same motif with subtle nucleotide differences leading to different binding affinities. Additionally, a particular TF may bind different motifs under certain conditions, for example in the presence of different co-regulators. The availability of genome-wide binding data of multiple collaborative TFs makes it possible to detect such context-dependent motifs. RESULTS: We developed a contrast motif finder (CMF) for the de novo identification of motifs that are differentially enriched in two sets of sequences. Applying this method to a number of TF binding datasets from mouse embryonic stem cells, we demonstrate that CMF achieves substantially higher accuracy than several well-known motif finding methods. By contrasting sequences bound by distinct sets of TFs, CMF identified two different motifs that may be recognized by Oct4 dependent on the presence of another co-regulator and detected subtle motif signals that may be associated with potential competitive binding between Sox2 and Tcf3. AVAILABILITY: The software CMF is freely available for academic use at www.stat.ucla.edu/∼zhou/CMF.

Derivation of burn scar depths and estimation of carbon emissions with LIDAR in Indonesian peatlands.

During the 1997/98 El Niño-induced drought peatland fires in Indonesia may have released 13-40% of the mean annual global carbon emissions from fossil fuels. One major unknown in current peatland emission estimations is how much peat is combusted by fire. Using a light detection and ranging data set acquired in Central Kalimantan, Borneo, in 2007, one year after the severe peatland fires of 2006, we determined an average burn scar depth of 0.33 +/- 0.18 m. Based on this result and the burned area determined from satellite imagery, we estimate that within the 2.79 million hectare study area 49.15 +/- 26.81 megatons of carbon were released during the 2006 El Niño episode. This represents 10-33% of all carbon emissions from transport for the European Community in the year 2006. These emissions, originating from a comparatively small area (approximately 13% of the Indonesian peatland area), underline the importance of peat fires in the context of green house gas emissions and global warming. In the past decade severe peat fires occurred during El Niño-induced droughts in 1997, 2002, 2004, 2006, and 2009. Currently, this important source of carbon emissions is not included in IPCC carbon accounting or in regional and global carbon emission models. Precise spatial measurements of peat combusted and potential avoided emissions in tropical peat swamp forests will also be required for future emission trading schemes in the framework of Reduced Emissions from Deforestation and Degradation in developing countries.

Chd1 regulates open chromatin and pluripotency of embryonic stem cells.

An open chromatin largely devoid of heterochromatin is a hallmark of stem cells. It remains unknown whether an open chromatin is necessary for the differentiation potential of stem cells, and which molecules are needed to maintain open chromatin. Here we show that the chromatin remodelling factor Chd1 is required to maintain the open chromatin of pluripotent mouse embryonic stem cells. Chd1 is a euchromatin protein that associates with the promoters of active genes, and downregulation of Chd1 leads to accumulation of heterochromatin. Chd1-deficient embryonic stem cells are no longer pluripotent, because they are incapable of giving rise to primitive endoderm and have a high propensity for neural differentiation. Furthermore, Chd1 is required for efficient reprogramming of fibroblasts to the pluripotent stem cell state. Our results indicate that Chd1 is essential for open chromatin and pluripotency of embryonic stem cells, and for somatic cell reprogramming to the pluripotent state.

Signed weighted gene co-expression network analysis of transcriptional regulation in murine embryonic stem cells.

BACKGROUND: Recent work has revealed that a core group of transcription factors (TFs) regulates the key characteristics of embryonic stem (ES) cells: pluripotency and self-renewal. Current efforts focus on identifying genes that play important roles in maintaining pluripotency and self-renewal in ES cells and aim to understand the interactions among these genes. To that end, we investigated the use of unsigned and signed network analysis to identify pluripotency and differentiation related genes. RESULTS: We show that signed networks provide a better systems level understanding of the regulatory mechanisms of ES cells than unsigned networks, using two independent murine ES cell expression data sets. Specifically, using signed weighted gene co-expression network analysis (WGCNA), we found a pluripotency module and a differentiation module, which are not identified in unsigned networks. We confirmed the importance of these modules by incorporating genome-wide TF binding data for key ES cell regulators. Interestingly, we find that the pluripotency module is enriched with genes related to DNA damage repair and mitochondrial function in addition to transcriptional regulation. Using a connectivity measure of module membership, we not only identify known regulators of ES cells but also show that Mrpl15, Msh6, Nrf1, Nup133, Ppif, Rbpj, Sh3gl2, and Zfp39, among other genes, have important roles in maintaining ES cell pluripotency and self-renewal. We also report highly significant relationships between module membership and epigenetic modifications (histone modifications and promoter CpG methylation status), which are known to play a role in controlling gene expression during ES cell self-renewal and differentiation. CONCLUSION: Our systems biologic re-analysis of gene expression, transcription factor binding, epigenetic and gene ontology data provides a novel integrative view of ES cell biology.

Induced pluripotent stem cells and embryonic stem cells are distinguished by gene expression signatures.

Induced pluripotent stem cells (iPSCs) outwardly appear to be indistinguishable from embryonic stem cells (ESCs). A study of gene expression profiles of mouse and human ESCs and iPSCs suggests that, while iPSCs are quite similar to their embryonic counterparts, a recurrent gene expression signature appears in iPSCs regardless of their origin or the method by which they were generated. Upon extended culture, hiPSCs adopt a gene expression profile more similar to hESCs; however, they still retain a gene expression signature unique from hESCs that extends to miRNA expression. Genome-wide data suggested that the iPSC signature gene expression differences are due to differential promoter binding by the reprogramming factors. High-resolution array profiling demonstrated that there is no common specific subkaryotypic alteration that is required for reprogramming and that reprogramming does not lead to genomic instability. Together, these data suggest that iPSCs should be considered a unique subtype of pluripotent cell.

Role of the murine reprogramming factors in the induction of pluripotency.

Induced pluripotent stem (iPS) cells can be obtained from fibroblasts upon expression of Oct4, Sox2, Klf4, and c-Myc. To understand how these factors induce pluripotency, we carried out genome-wide analyses of their promoter binding and expression in iPS and partially reprogrammed cells. We find that target genes of the four factors strongly overlap in iPS and embryonic stem (ES) cells. In partially reprogrammed cells, many genes co-occupied by c-Myc and any of the other three factors already show an ES cell-like binding and expression pattern. In contrast, genes that are specifically co-bound by Oct4, Sox2, and Klf4 in ES cells and encode pluripotency regulators severely lack binding and transcriptional activation. Among the four factors, c-Myc promotes the most ES cell-like transcription pattern when expressed individually in fibroblasts. These data uncover temporal and separable contributions of the four factors during the reprogramming process and indicate that ectopic c-Myc predominantly acts before pluripotency regulators are activated.