Abstract 2138: Whole genome cfDNA profiles reveal common signatures of Immune checkpoint inhibition response in kidney, melanoma, and lung cancers

Immune checkpoint inhibition (ICI) via anti-PD-1/PD-L1 and anti-CTLA4, has improved clinical outcomes for multiple cancers, including non-small cell lung cancer (NSCLC), kidney cancer, and melanoma. Despite this success, response rates remain low, highlighting the need for more robust predictive biomarkers. In this study, we used cell-free DNA (cfDNA) profiles from patients undergoing ICI therapy to identify common signatures associated with responses shared among the three cancers. Whole-genome sequencing (mean coverage=18X) of cfDNA was performed on pre-treatment plasma samples from 126 patients across three cancers (NSCLC, n=91; kidney, n=21; melanoma, n=14). Transcriptional activation for protein-coding genes was then inferred by modeling fragment distribution around each transcription start site (TSS-GAP). Transcription factor binding activity (TFBA) was estimated by measuring binding site accessibility across the genome.

To delineate potential signatures of response, we performed a gene set enrichment analysis (GSEA) based on weighted average effect sizes of TSS-GAP levels between response groups across the three cancer types. GSEA results showed an enrichment of DNA repair and cell cycle genes in non-responders and of EMT processing genes in responders. To identify shared signatures of ICI response, we performed a meta-analysis by (1) computing the effect size between response groups for TFBA and TSS-GAP features in each cancer type; (2) calculating a weighted-average effect size across cancer types; (3) performing gene selection based on the degree of heterogeneity among cancer types, followed by false discovery rate correction and assessment of significance through Monte Carlo permutation testing. This analysis identified 13 transcription factors and 269 genes consistently enriched in response groups across these cancer types. Notably, this analysis revealed significantly higher accessibility in STAT5A (p=0.02), JUN (p=0.02) and JUNB (p=0.03) in non-responders, suggesting JAK/STAT-pathway dependency as a common feature in ICI resistance.

Using our platform that detects both tumor and non-tumor-derived signals, we identified common signatures of ICI response, revealing a potential pathway of resistance through JAK/STAT signaling and a possible EMT signature of response in kidney, melanoma, and NSCLC. These findings warrant further investigation into using cfDNA signatures for patient stratification and response monitoring.

Citation Format: Hayley Donnella, Yue Zhang, Irving Wang, Francesco Vallania, Maggie Louie, C. Jimmy Lin. Whole genome cfDNA profiles reveal common signatures of Immune checkpoint inhibition response in kidney, melanoma, and lung cancers [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 2138.

Cellular senescence impairs the reversibility of pulmonary arterial hypertension

Pulmonary arterial hypertension (PAH) in congenital cardiac shunts can be reversed by hemodynamic unloading (HU) through shunt closure. However, this reversibility potential is lost beyond a certain point in time. The reason why PAH becomes irreversible is unknown. In this study, we used MCT+shunt-induced PAH in rats to identify a dichotomous reversibility response to HU, similar to the human situation. We compared vascular profiles of reversible and irreversible PAH using RNA sequencing. Cumulatively, we report that loss of reversibility is associated with a switch from a proliferative to a senescent vascular phenotype and confirmed markers of senescence in human PAH-CHD tissue. In vitro, we showed that human pulmonary endothelial cells of patients with PAH are more vulnerable to senescence than controls in response to shear stress and confirmed that the senolytic ABT263 induces apoptosis in senescent, but not in normal, endothelial cells. To support the concept that vascular cell senescence is causal to the irreversible nature of end-stage PAH, we targeted senescence using ABT263 and induced reversal of the hemodynamic and structural changes associated with severe PAH refractory to HU. The factors that drive the transition from a reversible to irreversible pulmonary vascular phenotype could also explain the irreversible nature of other PAH etiologies and provide new leads for pharmacological reversal of end-stage PAH.

Multicohort Analysis Identifies Monocyte Gene Signatures to Accurately Monitor Subset-Specific Changes in Human Diseases

Monocytes are crucial regulators of inflammation, and are characterized by three distinct subsets in humans, of which classical and non-classical are the most abundant. Different subsets carry out different functions and have been previously associated with multiple inflammatory conditions. Dissecting the contribution of different monocyte subsets to disease is currently limited by samples and cohorts, often resulting in underpowered studies and poor reproducibility. Publicly available transcriptome profiles provide an alternative source of data characterized by high statistical power and real-world heterogeneity. However, most transcriptome datasets profile bulk blood or tissue samples, requiring the use of in silico approaches to quantify changes in cell levels. Here, we integrated 853 publicly available microarray expression profiles of sorted human monocyte subsets from 45 independent studies to identify robust and parsimonious gene expression signatures, consisting of 10 genes specific to each subset. These signatures maintain their accuracy regardless of disease state in an independent cohort profiled by RNA-sequencing and are specific to their respective subset when compared to other immune cells from both myeloid and lymphoid lineages profiled across 6160 transcriptome profiles. Consequently, we show that these signatures can be used to quantify changes in monocyte subsets levels in expression profiles from patients in clinical trials. Finally, we show that proteins encoded by our signature genes can be used in cytometry-based assays to specifically sort monocyte subsets. Our results demonstrate the robustness, versatility, and utility of our computational approach and provide a framework for the discovery of new cellular markers.

Abstract 2433: Multiomic plasma profiling identifies potential signatures of disease progression in early-stage NSCLC

Blood-based markers can be used to non-invasively predict cancer progression after treatment. Here, cell-free DNA (cfDNA) and plasma proteins were evaluated to explore biological signatures of progression in non-small cell lung cancer (NSCLC). Baseline plasma samples (n=24; 16 progressors, 8 non-progressors) were from patients diagnosed in 2004 with stage I-III NSCLC, collected prior to surgical resection, and retrospectively analyzed. Six patients were treated with neoadjuvant therapies, one with adjuvant therapy, and 17 with surgery alone. Progression was defined as a relapse event or death by any cause. Whole-genome sequencing was performed to characterize cfDNA fragments, which reflect nucleosome protection and chromatin state. Transcriptional activation for protein-coding genes was inferred by modeling fragment distribution around each transcription start site. Univariate comparisons of gene activation between progressors and non-progressors and Cox proportional hazard ratios (HRs) were calculated by grouping patients above or below the median of the marker of interest. This analysis revealed IL-1RN, the gene encoding for the IL-1RA antagonist to the IL-1 receptor complex, as the gene most negatively correlated with progression-free survival (PFS) (r = -0.76, p < 0.0001; Cox HR = 13.77, p < 0.001). This gene was also significantly more active in progressors than in non-progressors (p < 0.005). The binding activity of ~500 transcription factors was also inferred by measuring chromatin accessibility across the genome, revealing SOX-9 to be significantly associated with progression (p < 0.0001, FDR = 1.1%) and the factor most negatively correlated with PFS (r = -0.72, p < 0.001, FDR = 16.9%). Both IL-1RN and SOX-9 have been previously reported to affect survival in NSCLC patients.In addition, the abundances of ~450 proteins including cytokines, receptors, and enzymes were measured. Six proteins were identified as differentially abundant between progression groups. Among these, IL-1α was more abundant in progressors vs. non-progressors (effect size = 0.92, p < 0.05). Notably, IL-1RA abundance did not differ between these groups. Both IL-1α (r = -0.61, p < 0.01; Cox HR = 3.78, p < 0.05) and IL-1RA (r = -0.75, p < 0.001; Cox HR = 1.13, p = 0.78) were negatively correlated with PFS in progressors. Finally, all features and analytes were integrated to identify biological signatures that may be shared among proteins and cfDNA. These signatures differed significantly (p < 0.05) between progressors and non-progressors, suggesting differences in cytokine signaling. The multiomics platform described here integrates biological signals with computational featurization to reveal clinically relevant signatures. Specifically, findings from a small cohort of early-stage NSCLC patients demonstrated the potential of this platform to reveal signatures of progression in NSCLC.

Citation Format: Francesco Vallania, Hayley Warsinske, Peter Ulz, Tzu-Yu Liu, Karen Assayag, Krishnan K. Palaniappan, Mitch Bailey, Irving Wang, David E. Weinberg, Riley Ennis, C Jimmy Lin, Anne-Marie Martin, Nancy Krunic. Multiomic plasma profiling identifies potential signatures of disease progression in early-stage NSCLC [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 2433.

Plasma-derived cfDNA to reveal potential biomarkers of response prediction and monitoring in non-small cell lung cancer (NSCLC) patients on immunotherapy

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Background: Immune checkpoint inhibitors have shown promising results in many advanced cancers, but the response rate remains low. Various molecular and cellular biomarkers, such as elevated tumor-infiltrating cytotoxic T cells and Natural Killer (NK) cells at baseline, are associated with response. Blood-based biomarkers to predict or monitor response remain challenging to develop. Here we investigate the potential of cell-free DNA (cfDNA) biomarkers to predict response to the PD-1 immune checkpoint inhibitor nivolumab in patients with refractory metastatic non-small cell lung cancer (NSCLC). Methods: Plasma from stage IV NSCLC patients enrolled in ALCINA (NCT02866149) was collected before (baseline, BL, n = 30) and at week 8 (W8, n = 17) of nivolumab therapy. Response was determined using RECIST 1.1 (responders n = 5; non-responders n = 25). Whole-genome sequencing was performed to characterize cfDNA fragments. Tumor fraction (TF) was assessed using ichorCNA. Cellular composition was estimated by deconvolution of cfDNA co-fragmentation patterns, and transcription factor activity was estimated by measuring binding site accessibility across the genome. Results: Although estimated TF at baseline did not predict response to nivolumab, NK cell levels estimated by cell-mixture deconvolution were significantly higher in responders at BL (p < 0.05). Furthermore, estimated monocyte levels at W8 strongly correlated with overall survival (r = 0.75, p < 0.0005, HR = 15.02) and were significantly higher in responders (p < 0.05). By evaluating changes in transcription factor binding activity, we identified factors with greater accessibility in non-responders at baseline (DEAF1, THAP11) and W8 (DUX4, PDX-1). Conclusions: Plasma cfDNA signatures may be useful for response prediction and monitoring in NSCLC patients on immunotherapy. Our results suggest that changes in the immune system, as reflected by cellular composition and transcriptional activity inferred from cfDNA, may provide biological insights beyond TF alone that may benefit biomarker discovery and drug target identification.

Exploratory longitudinal analysis of cfDNA to reveal potential biomarkers of CRC progression and treatment response

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Background: Blood-based tests can predict drug response and disease progression. Many tests rely on detecting tumor DNA, which represents only a fraction of all cell-free DNA (cfDNA). Here, we used a novel methodology to infer gene activation from cfDNA. This unique platform offers the ability to identify potential non-tumor derived biomarkers that may be associated with clinical outcomes in colorectal cancer (CRC). Methods: Longitudinal plasma samples from metastatic CRC patients enrolled in NCT01803282 (andecaliximab/chemotherapy/bevacizumab; 92 samples from 12 patients) were evaluated. Patients were classified as progressors (SD+PD) or responders (CR+PR) based on best objective response. Gene activation was inferred from cfDNA fragment length and counts around transcription start sites using whole-genome sequencing. Transcription factor activity was estimated by measuring binding site accessibility across the genome. Tumor fraction (TF) was estimated using ichorCNA. Results: Gene activation profiles inferred from cfDNA across the entire genome identified several genes differentially expressed in progressors or responders. Specifically, all patients with elevated KIR2DL1, an inhibitory NK cell receptor, progressed (p = 6.35e-14). Additionally, BMPR1A activation decreased in responders (p = 0.002) while the DNA- binding activity of SMAD1, which functions directly downstream of BMPR1A in the BMP2 pathway, increased in responders post-therapy (p = 0.03). These 3 genes (i.e., KIR2DL1, BMPR1A and SMAD1) are related to NK cell maturation, suggesting an immunological mechanism. Notably, pre-therapy TF did not predict response. Conclusions: In this pilot study, we demonstrated the ability of a unique cfDNA platform to interrogate multiple features to reveal genes associated with drug response and their underlying mechanism. We identified that KIR2DL1 is associated with progression, and BMPR1A and SMAD1 are associated with response. This work highlights the potential of cfDNA to provide biological insights beyond TF and that identification of non-tumor-derived signals may benefit biomarker discovery and drug target identification.

Increased monocyte count as a cellular biomarker for poor outcomes in fibrotic diseases: a retrospective, multicentre cohort study

BACKGROUND

There is an urgent need for biomarkers to better stratify patients with idiopathic pulmonary fibrosis by risk for lung transplantation allocation who have the same clinical presentation. We aimed to investigate whether a specific immune cell type from patients with idiopathic pulmonary fibrosis could identify those at higher risk of poor outcomes. We then sought to validate our findings using cytometry and electronic health records.

METHODS

We first did a discovery analysis with transcriptome data from the Gene Expression Omnibus at the National Center for Biotechnology Information for 120 peripheral blood mononuclear cell (PBMC) samples of patients with idiopathic pulmonary fibrosis. We estimated percentages of 13 immune cell types using statistical deconvolution, and investigated the association of these cell types with transplant-free survival. We validated these results using PBMC samples from patients with idiopathic pulmonary fibrosis in two independent cohorts (COMET and Yale). COMET profiled monocyte counts in 45 patients with idiopathic pulmonary fibrosis from March 12, 2010, to March 10, 2011, using flow cytometry; we tested if increased monocyte count was associated with the primary outcome of disease progression. In the Yale cohort, 15 patients with idiopathic pulmonary fibrosis (with five healthy controls) were classed as high risk or low risk from April 28, 2014, to Aug 20, 2015, using a 52-gene signature, and we assessed whether monocyte percentage (measured by cytometry by time of flight) was higher in high-risk patients. We then examined complete blood count values in the electronic health records (EHR) of 45 068 patients with idiopathic pulmonary fibrosis, systemic sclerosis, hypertrophic cardiomyopathy, or myelofibrosis from Stanford (Jan 01, 2008, to Dec 31, 2015), Northwestern (Feb 15, 2001 to July 31, 2017), Vanderbilt (Jan 01, 2008, to Dec 31, 2016), and Optum Clinformatics DataMart (Jan 01, 2004, to Dec 31, 2016) cohorts, and examined whether absolute monocyte counts of 0·95 K/μL or greater were associated with all-cause mortality in these patients.

FINDINGS

In the discovery analysis, estimated CD14+ classical monocyte percentages above the mean were associated with shorter transplant-free survival times (hazard ratio [HR] 1·82, 95% CI 1·05-3·14), whereas higher percentages of T cells and B cells were not (0·97, 0·59-1·66; and 0·78, 0·45-1·34 respectively). In two validation cohorts (COMET trial and the Yale cohort), patients with higher monocyte counts were at higher risk for poor outcomes (COMET Wilcoxon p=0·025; Yale Wilcoxon p=0·049). Monocyte counts of 0·95 K/μL or greater were associated with mortality after adjusting for forced vital capacity (HR 2·47, 95% CI 1·48-4·15; p=0·0063), and the gender, age, and physiology index (HR 2·06, 95% CI 1·22-3·47; p=0·0068) across the COMET, Stanford, and Northwestern datasets). Analysis of medical records of 7459 patients with idiopathic pulmonary fibrosis showed that patients with monocyte counts of 0·95 K/μL or greater were at increased risk of mortality with lung transplantation as a censoring event, after adjusting for age at diagnosis and sex (Stanford HR=2·30, 95% CI 0·94-5·63; Vanderbilt 1·52, 1·21-1·89; Optum 1·74, 1·33-2·27). Likewise, higher absolute monocyte count was associated with shortened survival in patients with hypertrophic cardiomyopathy across all three cohorts, and in patients with systemic sclerosis or myelofibrosis in two of the three cohorts.

INTERPRETATION

Monocyte count could be incorporated into the clinical assessment of patients with idiopathic pulmonary fibrosis and other fibrotic disorders. Further investigation into the mechanistic role of monocytes in fibrosis might lead to insights that assist the development of new therapies.

FUNDING

Bill & Melinda Gates Foundation, US National Institute of Allergy and Infectious Diseases, and US National Library of Medicine.

Cell-centred meta-analysis reveals baseline predictors of anti-TNFα non-response in biopsy and blood of patients with IBD

OBJECTIVE

Although anti-tumour necrosis factor alpha (anti-TNFα) therapies represent a major breakthrough in IBD therapy, their cost-benefit ratio is hampered by an overall 30% non-response rate, adverse side effects and high costs. Thus, finding predictive biomarkers of non-response prior to commencing anti-TNFα therapy is of high value.

DESIGN

We analysed publicly available whole-genome expression profiles of colon biopsies obtained from multiple cohorts of patients with IBD using a combined computational deconvolution-meta-analysis paradigm which allows to estimate immune cell contribution to the measured expression and capture differential regulatory programmes otherwise masked due to variation in cellular composition. Insights from this in silico approach were experimentally validated in biopsies and blood samples of three independent test cohorts.

RESULTS

We found the proportion of plasma cells as a robust pretreatment biomarker of non-response to therapy, which we validated in two independent cohorts of immune-stained colon biopsies, where a plasma cellular score from inflamed biopsies was predictive of non-response with an area under the curve (AUC) of 82%. Meta-analysis of the cell proportion-adjusted gene expression data suggested that an increase in inflammatory macrophages in anti-TNFα non-responding individuals is associated with the upregulation of the triggering receptor expressed on myeloid cells 1 (TREM-1) and chemokine receptor type 2 (CCR2)-chemokine ligand 7 (CCL7) -axes. Blood gene expression analysis of an independent cohort, identified TREM-1 downregulation in non-responders at baseline, which was predictive of response with an AUC of 94%.

CONCLUSIONS

Our study proposes two clinically feasible assays, one in biopsy and one in blood, for predicting non-response to anti-TNFα therapy prior to initiation of treatment. Moreover, it suggests that mechanism-driven novel drugs for non-responders should be developed.

A 20-Gene Set Predictive of Progression to Severe Dengue

There is a need to identify biomarkers predictive of severe dengue. Single-cohort transcriptomics has not yielded generalizable results or parsimonious, predictive gene sets. We analyzed blood samples of dengue patients from seven gene expression datasets (446 samples, five countries) using an integrated multi-cohort analysis framework and identified a 20-gene set that predicts progression to severe dengue. We validated the predictive power of this 20-gene set in three retrospective dengue datasets (84 samples, three countries) and a prospective Colombia cohort (34 patients), with an area under the receiver operating characteristic curve of 0.89, 100% sensitivity, and 76% specificity. The 20-gene dengue severity scores declined during the disease course, suggesting an infection-triggered host response. This 20-gene set is strongly associated with the progression to severe dengue and represents a predictive signature, generalizable across ages, host genetic factors, and virus strains, with potential implications for the development of a host response-based dengue prognostic assay.

Single-cell epigenetics - Chromatin modification atlas unveiled by mass cytometry

Modifications of histone proteins are fundamental to the regulation of epigenetic phenotypes. Dysregulations of histone modifications have been linked to the pathogenesis of diverse human diseases. However, identifying differential histone modifications in patients with immune-mediated diseases has been challenging, in part due to the lack of a powerful analytic platform to study histone modifications in the complex human immune system. We recently developed a highly multiplexed platform, Epigenetic landscape profiling using cytometry by Time-Of-Flight (EpiTOF), to analyze the global levels of a broad array of histone modifications in single cells using mass cytometry. In this review, we summarize the development of EpiTOF and discuss its potential applications in biomedical research. We anticipate that this platform will provide new insights into the roles of epigenetic regulation in hematopoiesis, immune cell functions, and immune system aging, and reveal aberrant epigenetic patterns associated with immune-mediated diseases.

KLRD1-expressing natural killer cells predict influenza susceptibility

BACKGROUND

Influenza infects tens of millions of people every year in the USA. Other than notable risk groups, such as children and the elderly, it is difficult to predict what subpopulations are at higher risk of infection. Viral challenge studies, where healthy human volunteers are inoculated with live influenza virus, provide a unique opportunity to study infection susceptibility. Biomarkers predicting influenza susceptibility would be useful for identifying risk groups and designing vaccines.

METHODS

We applied cell mixture deconvolution to estimate immune cell proportions from whole blood transcriptome data in four independent influenza challenge studies. We compared immune cell proportions in the blood between symptomatic shedders and asymptomatic nonshedders across three discovery cohorts prior to influenza inoculation and tested results in a held-out validation challenge cohort.

RESULTS

Natural killer (NK) cells were significantly lower in symptomatic shedders at baseline in both discovery and validation cohorts. Hematopoietic stem and progenitor cells (HSPCs) were higher in symptomatic shedders at baseline in discovery cohorts. Although the HSPCs were higher in symptomatic shedders in the validation cohort, the increase was statistically nonsignificant. We observed that a gene associated with NK cells, KLRD1, which encodes CD94, was expressed at lower levels in symptomatic shedders at baseline in discovery and validation cohorts. KLRD1 expression in the blood at baseline negatively correlated with influenza infection symptom severity. KLRD1 expression 8 h post-infection in the nasal epithelium from a rhinovirus challenge study also negatively correlated with symptom severity.

CONCLUSIONS

We identified KLRD1-expressing NK cells as a potential biomarker for influenza susceptibility. Expression of KLRD1 was inversely correlated with symptom severity. Our results support a model where an early response by KLRD1-expressing NK cells may control influenza infection.

Single-Cell Chromatin Modification Profiling Reveals Increased Epigenetic Variations with Aging

Post-translational modifications of histone proteins and exchanges of histone variants of chromatin are central to the regulation of nearly all DNA-templated biological processes. However, the degree and variability of chromatin modifications in specific human immune cells remain largely unknown. Here, we employ a highly multiplexed mass cytometry analysis to profile the global levels of a broad array of chromatin modifications in primary human immune cells at the single-cell level. Our data reveal markedly different cell-type- and hematopoietic-lineage-specific chromatin modification patterns. Differential analysis between younger and older adults shows that aging is associated with increased heterogeneity between individuals and elevated cell-to-cell variability in chromatin modifications. Analysis of a twin cohort unveils heritability of chromatin modifications and demonstrates that aging-related chromatin alterations are predominantly driven by non-heritable influences. Together, we present a powerful platform for chromatin and immunology research. Our discoveries highlight the profound impacts of aging on chromatin modifications.

Interpretation of biological experiments changes with evolution of the Gene Ontology and its annotations

Gene Ontology (GO) enrichment analysis is ubiquitously used for interpreting high throughput molecular data and generating hypotheses about underlying biological phenomena of experiments. However, the two building blocks of this analysis — the ontology and the annotations — evolve rapidly. We used gene signatures derived from 104 disease analyses to systematically evaluate how enrichment analysis results were affected by evolution of the GO over a decade. We found low consistency between enrichment analyses results obtained with early and more recent GO versions. Furthermore, there continues to be a strong annotation bias in the GO annotations where 58% of the annotations are for 16% of the human genes. Our analysis suggests that GO evolution may have affected the interpretation and possibly reproducibility of experiments over time. Hence, researchers must exercise caution when interpreting GO enrichment analyses and should reexamine previous analyses with the most recent GO version.

META-ANALYSIS OF CONTINUOUS PHENOTYPES IDENTIFIES A GENE SIGNATURE THAT CORRELATES WITH COPD DISEASE STATUS

The utility of multi-cohort two-class meta-analysis to identify robust differentially expressed gene signatures has been well established. However, many biomedical applications, such as gene signatures of disease progression, require one-class analysis. Here we describe an R package, MetaCorrelator, that can identify a reproducible transcriptional signature that is correlated with a continuous disease phenotype across multiple datasets. We successfully applied this framework to extract a pattern of gene expression that can predict lung function in patients with chronic obstructive pulmonary disease (COPD) in both peripheral blood mononuclear cells (PBMCs) and tissue. Our results point to a disregulation in the oxidation state of the lungs of patients with COPD, as well as underscore the classically recognized inammatory state that underlies this disease.

EMPOWERING MULTI-COHORT GENE EXPRESSION ANALYSIS TO INCREASE REPRODUCIBILITY

A major contributor to the scientific reproducibility crisis has been that the results from homogeneous, single-center studies do not generalize to heterogeneous, real world populations. Multi-cohort gene expression analysis has helped to increase reproducibility by aggregating data from diverse populations into a single analysis. To make the multi-cohort analysis process more feasible, we have assembled an analysis pipeline which implements rigorously studied meta-analysis best practices. We have compiled and made publicly available the results of our own multi-cohort gene expression analysis of 103 diseases, spanning 615 studies and 36,915 samples, through a novel and interactive web application. As a result, we have made both the process of and the results from multi-cohort gene expression analysis more approachable for non-technical users.

Methods to increase reproducibility in differential gene expression via meta-analysis

Findings from clinical and biological studies are often not reproducible when tested in independent cohorts. Due to the testing of a large number of hypotheses and relatively small sample sizes, results from whole-genome expression studies in particular are often not reproducible. Compared to single-study analysis, gene expression meta-analysis can improve reproducibility by integrating data from multiple studies. However, there are multiple choices in designing and carrying out a meta-analysis. Yet, clear guidelines on best practices are scarce. Here, we hypothesized that studying subsets of very large meta-analyses would allow for systematic identification of best practices to improve reproducibility. We therefore constructed three very large gene expression meta-analyses from clinical samples, and then examined meta-analyses of subsets of the datasets (all combinations of datasets with up to N/2 samples and K/2 datasets) compared to a ‘silver standard’ of differentially expressed genes found in the entire cohort. We tested three random-effects meta-analysis models using this procedure. We showed relatively greater reproducibility with more-stringent effect size thresholds with relaxed significance thresholds; relatively lower reproducibility when imposing extraneous constraints on residual heterogeneity; and an underestimation of actual false positive rate by Benjamini–Hochberg correction. In addition, multivariate regression showed that the accuracy of a meta-analysis increased significantly with more included datasets even when controlling for sample size.

Performance of common analysis methods for detecting low-frequency single nucleotide variants in targeted next-generation sequence data

Next-generation sequencing (NGS) is becoming a common approach for clinical testing of oncology specimens for mutations in cancer genes. Unlike inherited variants, cancer mutations may occur at low frequencies because of contamination from normal cells or tumor heterogeneity and can therefore be challenging to detect using common NGS analysis tools, which are often designed for constitutional genomic studies. We generated high-coverage (>1000×) NGS data from synthetic DNA mixtures with variant allele fractions (VAFs) of 25% to 2.5% to assess the performance of four variant callers, SAMtools, Genome Analysis Toolkit, VarScan2, and SPLINTER, in detecting low-frequency variants. SAMtools had the lowest sensitivity and detected only 49% of variants with VAFs of approximately 25%; whereas the Genome Analysis Toolkit, VarScan2, and SPLINTER detected at least 94% of variants with VAFs of approximately 10%. VarScan2 and SPLINTER achieved sensitivities of 97% and 89%, respectively, for variants with observed VAFs of 1% to 8%, with >98% sensitivity and >99% positive predictive value in coding regions. Coverage analysis demonstrated that >500× coverage was required for optimal performance. The specificity of SPLINTER improved with higher coverage, whereas VarScan2 yielded more false positive results at high coverage levels, although this effect was abrogated by removing low-quality reads before variant identification. Finally, we demonstrate the utility of high-sensitivity variant callers with data from 15 clinical lung cancers.

Detection of rare genomic variants from pooled sequencing using SPLINTER

As DNA sequencing technology has markedly advanced in recent years(2), it has become increasingly evident that the amount of genetic variation between any two individuals is greater than previously thought(3). In contrast, array-based genotyping has failed to identify a significant contribution of common sequence variants to the phenotypic variability of common disease(4,5). Taken together, these observations have led to the evolution of the Common Disease / Rare Variant hypothesis suggesting that the majority of the "missing heritability" in common and complex phenotypes is instead due to an individual's personal profile of rare or private DNA variants(6-8). However, characterizing how rare variation impacts complex phenotypes requires the analysis of many affected individuals at many genomic loci, and is ideally compared to a similar survey in an unaffected cohort. Despite the sequencing power offered by today's platforms, a population-based survey of many genomic loci and the subsequent computational analysis required remains prohibitive for many investigators. To address this need, we have developed a pooled sequencing approach(1,9) and a novel software package(1) for highly accurate rare variant detection from the resulting data. The ability to pool genomes from entire populations of affected individuals and survey the degree of genetic variation at multiple targeted regions in a single sequencing library provides excellent cost and time savings to traditional single-sample sequencing methodology. With a mean sequencing coverage per allele of 25-fold, our custom algorithm, SPLINTER, uses an internal variant calling control strategy to call insertions, deletions and substitutions up to four base pairs in length with high sensitivity and specificity from pools of up to 1 mutant allele in 500 individuals. Here we describe the method for preparing the pooled sequencing library followed by step-by-step instructions on how to use the SPLINTER package for pooled sequencing analysis (http://www.ibridgenetwork.org/wustl/splinter). We show a comparison between pooled sequencing of 947 individuals, all of whom also underwent genome-wide array, at over 20kb of sequencing per person. Concordance between genotyping of tagged and novel variants called in the pooled sample were excellent. This method can be easily scaled up to any number of genomic loci and any number of individuals. By incorporating the internal positive and negative amplicon controls at ratios that mimic the population under study, the algorithm can be calibrated for optimal performance. This strategy can also be modified for use with hybridization capture or individual-specific barcodes and can be applied to the sequencing of naturally heterogeneous samples, such as tumor DNA.