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.

Advancing human genetics research and drug discovery through exome sequencing of the UK Biobank.

The UK Biobank Exome Sequencing Consortium (UKB-ESC) is a private-public partnership between the UK Biobank (UKB) and eight biopharmaceutical companies that will complete the sequencing of exomes for all ~500,000 UKB participants. Here, we describe the early results from ~200,000 UKB participants and the features of this project that enabled its success. The biopharmaceutical industry has increasingly used human genetics to improve success in drug discovery. Recognizing the need for large-scale human genetics data, as well as the unique value of the data access and contribution terms of the UKB, the UKB-ESC was formed. As a result, exome data from 200,643 UKB enrollees are now available. These data include ~10 million exonic variants-a rich resource of rare coding variation that is particularly valuable for drug discovery. The UKB-ESC precompetitive collaboration has further strengthened academic and industry ties and has provided teams with an opportunity to interact with and learn from the wider research community.

Pan-ancestry exome-wide association analyses of COVID-19 outcomes in 586,157 individuals.

Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) causes coronavirus disease 2019 (COVID-19), a respiratory illness that can result in hospitalization or death. We used exome sequence data to investigate associations between rare genetic variants and seven COVID-19 outcomes in 586,157 individuals, including 20,952 with COVID-19. After accounting for multiple testing, we did not identify any clear associations with rare variants either exome wide or when specifically focusing on (1) 13 interferon pathway genes in which rare deleterious variants have been reported in individuals with severe COVID-19, (2) 281 genes located in susceptibility loci identified by the COVID-19 Host Genetics Initiative, or (3) 32 additional genes of immunologic relevance and/or therapeutic potential. Our analyses indicate there are no significant associations with rare protein-coding variants with detectable effect sizes at our current sample sizes. Analyses will be updated as additional data become available, and results are publicly available through the Regeneron Genetics Center COVID-19 Results Browser.

First-Line Nivolumab Plus Ipilimumab in Advanced Non-Small-Cell Lung Cancer (CheckMate 568): Outcomes by Programmed Death Ligand 1 and Tumor Mutational Burden as Biomarkers.

PURPOSE: CheckMate 568 is an open-label phase II trial that evaluated the efficacy and safety of nivolumab plus low-dose ipilimumab as first-line treatment of advanced/metastatic non-small-cell lung cancer (NSCLC). We assessed the association of efficacy with programmed death ligand 1 (PD-L1) expression and tumor mutational burden (TMB). PATIENTS AND METHODS: Two hundred eighty-eight patients with previously untreated, recurrent stage IIIB/IV NSCLC received nivolumab 3 mg/kg every 2 weeks plus ipilimumab 1 mg/kg every 6 weeks. The primary end point was objective response rate (ORR) in patients with 1% or more and less than 1% tumor PD-L1 expression. Efficacy on the basis of TMB (FoundationOne CDx assay) was a secondary end point. RESULTS: Of treated patients with tumor available for testing, 252 patients (88%) of 288 were evaluable for PD-L1 expression and 98 patients (82%) of 120 for TMB. ORR was 30% overall and 41% and 15% in patients with 1% or greater and less than 1% tumor PD-L1 expression, respectively. ORR increased with higher TMB, plateauing at 10 or more mutations/megabase (mut/Mb). Regardless of PD-L1 expression, ORRs were higher in patients with TMB of 10 or more mut/Mb (n = 48: PD-L1, ≥ 1%, 48%; PD-L1, < 1%, 47%) versus TMB of fewer than 10 mut/Mb (n = 50: PD-L1, ≥ 1%, 18%; PD-L1, < 1%, 5%), and progression-free survival was longer in patients with TMB of 10 or more mut/Mb versus TMB of fewer than 10 mut/Mb (median, 7.1 v 2.6 months). Grade 3 to 4 treatment-related adverse events occurred in 29% of patients. CONCLUSION: Nivolumab plus low-dose ipilimumab was effective and tolerable as a first-line treatment of advanced/metastatic NSCLC. TMB of 10 or more mut/Mb was associated with improved response and prolonged progression-free survival in both tumor PD-L1 expression 1% or greater and less than 1% subgroups and was thus identified as a potentially relevant cutoff in the assessment of TMB as a biomarker for first-line nivolumab plus ipilimumab.

Tumor Mutational Burden and Efficacy of Nivolumab Monotherapy and in Combination with Ipilimumab in Small-Cell Lung Cancer.

Durable responses and encouraging survival have been demonstrated with immune checkpoint inhibitors in small-cell lung cancer (SCLC), but predictive markers are unknown. We used whole exome sequencing to evaluate the impact of tumor mutational burden on efficacy of nivolumab monotherapy or combined with ipilimumab in patients with SCLC from the nonrandomized or randomized cohorts of CheckMate 032. Patients received nivolumab (3 mg/kg every 2 weeks) or nivolumab plus ipilimumab (1 mg/kg plus 3 mg/kg every 3 weeks for four cycles, followed by nivolumab 3 mg/kg every 2 weeks). Efficacy of nivolumab ± ipilimumab was enhanced in patients with high tumor mutational burden. Nivolumab plus ipilimumab appeared to provide a greater clinical benefit than nivolumab monotherapy in the high tumor mutational burden tertile.

Genome-wide association analysis identifies genetic correlates of immune infiltrates in solid tumors.

Therapeutic options for the treatment of an increasing variety of cancers have been expanded by the introduction of a new class of drugs, commonly referred to as checkpoint blocking agents, that target the host immune system to positively modulate anti-tumor immune response. Although efficacy of these agents has been linked to a pre-existing level of tumor immune infiltrate, it remains unclear why some patients exhibit deep and durable responses to these agents while others do not benefit. To examine the influence of tumor genetics on tumor immune state, we interrogated the relationship between somatic mutation and copy number alteration with infiltration levels of 7 immune cell types across 40 tumor cohorts in The Cancer Genome Atlas. Levels of cytotoxic T, regulatory T, total T, natural killer, and B cells, as well as monocytes and M2 macrophages, were estimated using a novel set of transcriptional signatures that were designed to resist interference from the cellular heterogeneity of tumors. Tumor mutational load and estimates of tumor purity were included in our association models to adjust for biases in multi-modal genomic data. Copy number alterations, mutations summarized at the gene level, and position-specific mutations were evaluated for association with tumor immune infiltration. We observed a strong relationship between copy number loss of a large region of chromosome 9p and decreased lymphocyte estimates in melanoma, pancreatic, and head/neck cancers. Mutations in the oncogenes PIK3CA, FGFR3, and RAS/RAF family members, as well as the tumor suppressor TP53, were linked to changes in immune infiltration, usually in restricted tumor types. Associations of specific WNT/beta-catenin pathway genetic changes with immune state were limited, but we noted a link between 9p loss and the expression of the WNT receptor FZD3, suggesting that there are interactions between 9p alteration and WNT pathways. Finally, two different cell death regulators, CASP8 and DIDO1, were often mutated in head/neck tumors that had higher lymphocyte infiltrates. In summary, our study supports the relevance of tumor genetics to questions of efficacy and resistance in checkpoint blockade therapies. It also highlights the need to assess genome-wide influences during exploration of any specific tumor pathway hypothesized to be relevant to therapeutic response. Some of the observed genetic links to immune state, like 9p loss, may influence response to cancer immune therapies. Others, like mutations in cell death pathways, may help guide combination therapeutic approaches.

Mutations in TRAF3IP1/IFT54 reveal a new role for IFT proteins in microtubule stabilization.

Ciliopathies are a large group of clinically and genetically heterogeneous disorders caused by defects in primary cilia. Here we identified mutations in TRAF3IP1 (TNF Receptor-Associated Factor Interacting Protein 1) in eight patients from five families with nephronophthisis (NPH) and retinal degeneration, two of the most common manifestations of ciliopathies. TRAF3IP1 encodes IFT54, a subunit of the IFT-B complex required for ciliogenesis. The identified mutations result in mild ciliary defects in patients but also reveal an unexpected role of IFT54 as a negative regulator of microtubule stability via MAP4 (microtubule-associated protein 4). Microtubule defects are associated with altered epithelialization/polarity in renal cells and with pronephric cysts and microphthalmia in zebrafish embryos. Our findings highlight the regulation of cytoplasmic microtubule dynamics as a role of the IFT54 protein beyond the cilium, contributing to the development of NPH-related ciliopathies.

The use of haplotype-specific transcripts improves sample annotation consistency.

BACKGROUND: Exact sample annotation in expression microarray datasets is essential for any type of pharmacogenomics research. RESULTS: Candidate markers were explored through the application of Hartigans' dip test statistics to a publically available human whole genome microarray dataset. The marker performance was tested on 188 serial samples from 53 donors and of variable tissue origin from five public microarray datasets. A qualified transcript marker panel consisting of three probe sets for human leukocyte antigens HLA-DQA1 (2 probe sets) and HLA-DRB4 identified sample donor identifier inconsistencies in six of the 188 test samples. About 3% of the test samples require root-cause analysis due to unresolvable inaccuracies. CONCLUSIONS: The transcript marker panel consisting of HLA-DQA1 and HLA-DRB4 represents a robust, tissue-independent composite marker to assist control donor annotation concordance at the transcript level. Allele-selectivity of HLA genes renders them good candidates for "fingerprinting" with donor specific expression pattern.

Whole-exome sequencing reveals overlap between macrophage activation syndrome in systemic juvenile idiopathic arthritis and familial hemophagocytic lymphohistiocytosis.

OBJECTIVE: Macrophage activation syndrome (MAS), a life-threatening complication of systemic juvenile idiopathic arthritis (JIA), resembles familial hemophagocytic lymphohistiocytosis (HLH), a constellation of autosomal-recessive immune disorders resulting from deficiency in cytolytic pathway proteins. We undertook this study to test our hypothesis that MAS predisposition in systemic JIA could be attributed to rare gene sequence variants affecting the cytotolytic pathway. METHODS: Whole-exome sequencing was used in 14 patients with systemic JIA and MAS and in their parents to identify protein-altering single-nucleotide polymorphisms/indels in known HLH-associated genes. To discover new candidate genes, the entire whole-exome sequencing data were filtered to identify protein-altering, rare recessive homozygous, compound heterozygous, and de novo variants with the potential to affect the cytolytic pathway. RESULTS: Heterozygous protein-altering rare variants in the known genes (LYST,MUNC13-4, and STXBP2) were found in 5 of 14 patients with systemic JIA and MAS (35.7%). This was in contrast to only 4 variants in 4 of 29 patients with systemic JIA without MAS (13.8%). Homozygosity and compound heterozygosity analysis applied to the entire whole-exome sequencing data in systemic JIA/MAS revealed 3 recessive pairs in 3 genes and compound heterozygotes in 73 genes. We also identified 20 heterozygous rare protein-altering variants that occurred in at least 2 patients. Many of the identified genes encoded proteins with a role in actin and microtubule reorganization and vesicle-mediated transport. "Cellular assembly and organization" was the top cellular function category based on Ingenuity Pathways Analysis (P < 3.10 × 10(-5) ). CONCLUSION: Whole-exome sequencing performed in patients with systemic JIA and MAS identified rare protein-altering variants in known HLH-associated genes as well as in new candidate genes.

LMX1B mutations cause hereditary FSGS without extrarenal involvement.

LMX1B encodes a homeodomain-containing transcription factor that is essential during development. Mutations in LMX1B cause nail-patella syndrome, characterized by dysplasia of the patellae, nails, and elbows and FSGS with specific ultrastructural lesions of the glomerular basement membrane (GBM). By linkage analysis and exome sequencing, we unexpectedly identified an LMX1B mutation segregating with disease in a pedigree of five patients with autosomal dominant FSGS but without either extrarenal features or ultrastructural abnormalities of the GBM suggestive of nail-patella-like renal disease. Subsequently, we screened 73 additional unrelated families with FSGS and found mutations involving the same amino acid (R246) in 2 families. An LMX1B in silico homology model suggested that the mutated residue plays an important role in strengthening the interaction between the LMX1B homeodomain and DNA; both identified mutations would be expected to diminish such interactions. In summary, these results suggest that isolated FSGS could result from mutations in genes that are also involved in syndromic forms of FSGS. This highlights the need to include these genes in all diagnostic approaches to FSGS that involve next-generation sequencing.

DeconRNASeq: a statistical framework for deconvolution of heterogeneous tissue samples based on mRNA-Seq data.

SUMMARY: For heterogeneous tissues, measurements of gene expression through mRNA-Seq data are confounded by relative proportions of cell types involved. In this note, we introduce an efficient pipeline: DeconRNASeq, an R package for deconvolution of heterogeneous tissues based on mRNA-Seq data. It adopts a globally optimized non-negative decomposition algorithm through quadratic programming for estimating the mixing proportions of distinctive tissue types in next-generation sequencing data. We demonstrated the feasibility and validity of DeconRNASeq across a range of mixing levels and sources using mRNA-Seq data mixed in silico at known concentrations. We validated our computational approach for various benchmark data, with high correlation between our predicted cell proportions and the real fractions of tissues. Our study provides a rigorous, quantitative and high-resolution tool as a prerequisite to use mRNA-Seq data. The modularity of package design allows an easy deployment of custom analytical pipelines for data from other high-throughput platforms. AVAILABILITY: DeconRNASeq is written in R, and is freely available at http://bioconductor.org/packages. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

RAMP: a bioinformatics framework for researching imaging agents through molecular pathways.

Signaling pathways are the fundamental grammar of cellular communication, yet few frameworks are available to analyze molecular imaging probes in the context of signaling pathways. Such a framework would aid in the design and selection of imaging probes for measuring specific signaling pathways and, vice versa, help illuminate which pathways are being assayed by a given probe. RAMP (Researching imaging Agents through Molecular Pathways) is a bioinformatics framework for connecting signaling pathways and imaging probes using a controlled vocabulary of the imaging targets. RAMP contains signaling pathway data from MetaCore, the Kyoto Encyclopedia of Genes and Genomes, and the Gene Ontology project; imaging probe data from the Molecular Imaging and Contrast Agent Database (MICAD); and tissue protein expression data from The Human Protein Atlas. The RAMP search tool is available at . Examples are presented to demonstrate the utility of RAMP for pathway-based searches of molecular imaging probes.

Optimal deconvolution of transcriptional profiling data using quadratic programming with application to complex clinical blood samples.

Large-scale molecular profiling technologies have assisted the identification of disease biomarkers and facilitated the basic understanding of cellular processes. However, samples collected from human subjects in clinical trials possess a level of complexity, arising from multiple cell types, that can obfuscate the analysis of data derived from them. Failure to identify, quantify, and incorporate sources of heterogeneity into an analysis can have widespread and detrimental effects on subsequent statistical studies.We describe an approach that builds upon a linear latent variable model, in which expression levels from mixed cell populations are modeled as the weighted average of expression from different cell types. We solve these equations using quadratic programming, which efficiently identifies the globally optimal solution while preserving non-negativity of the fraction of the cells. We applied our method to various existing platforms to estimate proportions of different pure cell or tissue types and gene expression profilings of distinct phenotypes, with a focus on complex samples collected in clinical trials. We tested our methods on several well controlled benchmark data sets with known mixing fractions of pure cell or tissue types and mRNA expression profiling data from samples collected in a clinical trial. Accurate agreement between predicted and actual mixing fractions was observed. In addition, our method was able to predict mixing fractions for more than ten species of circulating cells and to provide accurate estimates for relatively rare cell types (<10% total population). Furthermore, accurate changes in leukocyte trafficking associated with Fingolomid (FTY720) treatment were identified that were consistent with previous results generated by both cell counts and flow cytometry. These data suggest that our method can solve one of the open questions regarding the analysis of complex transcriptional data: namely, how to identify the optimal mixing fractions in a given experiment.

Identification of novel genes and pathways regulating SREBP transcriptional activity.

BACKGROUND: Lipid metabolism in mammals is orchestrated by a family of transcription factors called sterol regulatory element-binding proteins (SREBPs) that control the expression of genes required for the uptake and synthesis of cholesterol, fatty acids, and triglycerides. SREBPs are thus essential for insulin-induced lipogenesis and for cellular membrane homeostasis and biogenesis. Although multiple players have been identified that control the expression and activation of SREBPs, gaps remain in our understanding of how SREBPs are coordinated with other physiological pathways. METHODOLOGY: To identify novel regulators of SREBPs, we performed a genome-wide cDNA over-expression screen to identify proteins that might modulate the transcription of a luciferase gene driven from an SREBP-specific promoter. The results were verified through secondary biological assays and expression data were analyzed by a novel application of the Gene Set Enrichment Analysis (GSEA) method. CONCLUSIONS/SIGNIFICANCE: We screened 10,000 different cDNAs and identified a number of genes and pathways that have previously not been implicated in SREBP control and cellular cholesterol homeostasis. These findings further our understanding of lipid biology and should lead to new insights into lipid associated disorders.

Transcriptome changes in renal allograft protocol biopsies at 3 months precede the onset of interstitial fibrosis/tubular atrophy (IF/TA) at 6 months.

BACKGROUND: Interstitial fibrosis and tubular atrophy (IF/TA) in renal transplants are the major morphological correlates of progressive graft deterioration. Early diagnosis of IF/TA is a pre-requisite for a timely therapeutic intervention in patients at risk. To evaluate events occurring before the overt onset of IF/TA, gene expression profiling of 3-month protocol biopsies from patients with IF/TA was performed in a patient group (n = 8) who developed mild IF/TA [chronic allograft nephropathy (CAN) grade I, by the Banff scoring system] in the subsequent 6-month protocol biopsy ('progressors'), and in 12 patients without IF/TA at 6 months ('non-progressors'). METHODS: RNA was extracted, labelled and hybridized to human specific genome wide DNA microarrays. Normalized data were subjected to gene-centric and pathway-centric statistical methods. RESULTS: Compared to the non-progressors, the 3-month biopsies of the progressor group showed overexpression of several genes that are important in the T- and B-cell activation and immune response. Genes involved in pro-fibrotic processes were identified in the biopsies of the progressors that preceded the observed IF/TA at 6 months. Furthermore, several genes with transporter and metabolic functions were underrepresented in the progressors in the 3-month biopsies. CONCLUSION: Gene expression profiling of early protocol biopsies identified changes in the transcriptome of grafts, which may be important for the development of IF/TA. Such early detection of transcriptome changes can facilitate the identification of patients at risk shifting the intervention time point well before the histological diagnosis of irreversible IF/TA.

Uncovering mechanisms of transcriptional regulations by systematic mining of cis regulatory elements with gene expression profiles.

BACKGROUND: Contrary to the traditional biology approach, where the expression patterns of a handful of genes are studied at a time, microarray experiments enable biologists to study the expression patterns of many genes simultaneously from gene expression profile data and decipher the underlying hidden biological mechanism from the observed gene expression changes. While the statistical significance of the gene expression data can be deduced by various methods, the biological interpretation of the data presents a challenge. RESULTS: A method, called CisTransMine, is proposed to help infer the underlying biological mechanisms for the observed gene expression changes in microarray experiments. Specifically, this method will predict potential cis-regulatory elements in promoter regions which could regulate gene expression changes. This approach builds on the MotifADE method published in 2004 and extends it with two modifications: up-regulated genes and down-regulated genes are tested separately and in addition, tests have been implemented to identify combinations of transcription factors that work synergistically. The method has been applied to a genome wide expression dataset intended to study myogenesis in a mouse C2C12 cell differentiation model. The results shown here both confirm the prior biological knowledge and facilitate the discovery of new biological insights. CONCLUSION: The results validate that the CisTransMine approach is a robust method to uncover the hidden transcriptional regulatory mechanisms that can facilitate the discovery of mechanisms of transcriptional regulation.

Dynamic resolution of functionally related gene sets in response to acute heat stress.

BACKGROUND: Using a gene clustering strategy we determined intracellular pathway relationships within skeletal myotubes in response to an acute heat stress stimuli. Following heat shock, the transcriptome was analyzed by microarray in a temporal fashion to characterize the dynamic relationship of signaling pathways. RESULTS: Bioinformatics analyses exposed coordination of functionally-related gene sets, depicting mechanism-based responses to heat shock. Protein turnover-related pathways were significantly affected including protein folding, pre-mRNA processing, mRNA splicing, proteolysis and proteasome-related pathways. Many responses were transient, tending to normalize within 24 hours. CONCLUSION: In summary, we show that the transcriptional response to acute cell stress is largely transient and proteosome-centric.

Extending the pathway analysis framework with a test for transcriptional variance implicates novel pathway modulation during myogenic differentiation.

MOTIVATION: We describe an extension of the pathway-based enrichment approach for analyzing microarray data via a robust test for transcriptional variance. The use of a variance test is intended to identify additional patterns of transcriptional regulation in which many genes in a pathway are up- and down-regulated. Such patterns may be indicative of the reciprocal regulation of pathway activators and inhibitors or of the differential regulation of separate biological sub-processes and should extend the number of detectable patterns of transcriptional modulation. RESULTS: We validated this new statistical approach on a microarray experiment that captures the temporal transcriptional profile of muscle differentiation in mouse C2C12 cells. Comparisons of the transcriptional state of myoblasts and differentiated myotubes via a robust variance test implicated several novel pathways in muscle cell differentiation previously overlooked by a standard enrichment analysis. Specifically, pathways involved in cell structure, calcium-mediated signaling and muscle-specific signaling were identified as differentially modulated based on their increased transcriptional variance. These biologically relevant results validate this approach and demonstrate the flexible nature of pathway-based methods of data analysis. AVAILABILITY: The software is available as Supplementary Material.