Rapid single cell evaluation of human disease and disorder targets using REVEAL: SingleCell™.

BACKGROUND: Single-cell (sc) sequencing performs unbiased profiling of individual cells and enables evaluation of less prevalent cellular populations, often missed using bulk sequencing. However, the scale and the complexity of the sc datasets poses a great challenge in its utility and this problem is further exacerbated when working with larger datasets typically generated by consortium efforts. As the scale of single cell datasets continues to increase exponentially, there is an unmet technological need to develop database platforms that can evaluate key biological hypotheses by querying extensive single-cell datasets. Large single-cell datasets like Human Cell Atlas and COVID-19 cell atlas (collection of annotated sc datasets from various human organs) are excellent resources for profiling target genes involved in human diseases and disorders ranging from oncology, auto-immunity, as well as infectious diseases like COVID-19 caused by SARS-CoV-2 virus. SARS-CoV-2 infections have led to a worldwide pandemic with massive loss of lives, infections exceeding 7 million cases. The virus uses ACE2 and TMPRSS2 as key viral entry associated proteins expressed in human cells for infections. Evaluating the expression profile of key genes in large single-cell datasets can facilitate testing for diagnostics, therapeutics, and vaccine targets, as the world struggles to cope with the on-going spread of COVID-19 infections. MAIN BODY: In this manuscript we describe REVEAL: SingleCell, which enables storage, retrieval, and rapid query of single-cell datasets inclusive of millions of cells. The array native database described here enables selecting and analyzing cells across multiple studies. Cells can be selected using individual metadata tags, more complex hierarchical ontology filtering, and gene expression threshold ranges, including co-expression of multiple genes. The tags on selected cells can be further evaluated for testing biological hypotheses. One such example includes identifying the most prevalent cell type annotation tag on returned cells. We used REVEAL: SingleCell to evaluate the expression of key SARS-CoV-2 entry associated genes, and queried the current database (2.2 Million cells, 32 projects) to obtain the results in < 60 s. We highlighted cells expressing COVID-19 associated genes are expressed on multiple tissue types, thus in part explains the multi-organ involvement in infected patients observed worldwide during the on-going COVID-19 pandemic. CONCLUSION: In this paper, we introduce the REVEAL: SingleCell database that addresses immediate needs for SARS-CoV-2 research and has the potential to be used more broadly for many precision medicine applications. We used the REVEAL: SingleCell database as a reference to ask questions relevant to drug development and precision medicine regarding cell type and co-expression for genes that encode proteins necessary for SARS-CoV-2 to enter and reproduce in cells.

Progressive increase in mtDNA 3243A>G heteroplasmy causes abrupt transcriptional reprogramming.

Variation in the intracellular percentage of normal and mutant mitochondrial DNAs (mtDNA) (heteroplasmy) can be associated with phenotypic heterogeneity in mtDNA diseases. Individuals that inherit the common disease-causing mtDNA tRNA(Leu(UUR)) 3243A>G mutation and harbor ∼10-30% 3243G mutant mtDNAs manifest diabetes and occasionally autism; individuals with ∼50-90% mutant mtDNAs manifest encephalomyopathies; and individuals with ∼90-100% mutant mtDNAs face perinatal lethality. To determine the basis of these abrupt phenotypic changes, we generated somatic cell cybrids harboring increasing levels of the 3243G mutant and analyzed the associated cellular phenotypes and nuclear DNA (nDNA) and mtDNA transcriptional profiles by RNA sequencing. Small increases in mutant mtDNAs caused relatively modest defects in oxidative capacity but resulted in sharp transitions in cellular phenotype and gene expression. Cybrids harboring 20-30% 3243G mtDNAs had reduced mtDNA mRNA levels, rounded mitochondria, and small cell size. Cybrids with 50-90% 3243G mtDNAs manifest induction of glycolytic genes, mitochondrial elongation, increased mtDNA mRNA levels, and alterations in expression of signal transduction, epigenomic regulatory, and neurodegenerative disease-associated genes. Finally, cybrids with 100% 3243G experienced reduced mtDNA transcripts, rounded mitochondria, and concomitant changes in nuclear gene expression. Thus, striking phase changes occurred in nDNA and mtDNA gene expression in response to the modest changes of the mtDNA 3243G mutant levels. Hence, a major factor in the phenotypic variation in heteroplasmic mtDNA mutations is the limited number of states that the nucleus can acquire in response to progressive changes in mitochondrial retrograde signaling.

Increased frequency of de novo copy number variants in congenital heart disease by integrative analysis of single nucleotide polymorphism array and exome sequence data.

RATIONALE: Congenital heart disease (CHD) is among the most common birth defects. Most cases are of unknown pathogenesis. OBJECTIVE: To determine the contribution of de novo copy number variants (CNVs) in the pathogenesis of sporadic CHD. METHODS AND RESULTS: We studied 538 CHD trios using genome-wide dense single nucleotide polymorphism arrays and whole exome sequencing. Results were experimentally validated using digital droplet polymerase chain reaction. We compared validated CNVs in CHD cases with CNVs in 1301 healthy control trios. The 2 complementary high-resolution technologies identified 63 validated de novo CNVs in 51 CHD cases. A significant increase in CNV burden was observed when comparing CHD trios with healthy trios, using either single nucleotide polymorphism array (P=7×10(-5); odds ratio, 4.6) or whole exome sequencing data (P=6×10(-4); odds ratio, 3.5) and remained after removing 16% of de novo CNV loci previously reported as pathogenic (P=0.02; odds ratio, 2.7). We observed recurrent de novo CNVs on 15q11.2 encompassing CYFIP1, NIPA1, and NIPA2 and single de novo CNVs encompassing DUSP1, JUN, JUP, MED15, MED9, PTPRE SREBF1, TOP2A, and ZEB2, genes that interact with established CHD proteins NKX2-5 and GATA4. Integrating de novo variants in whole exome sequencing and CNV data suggests that ETS1 is the pathogenic gene altered by 11q24.2-q25 deletions in Jacobsen syndrome and that CTBP2 is the pathogenic gene in 10q subtelomeric deletions. CONCLUSIONS: We demonstrate a significantly increased frequency of rare de novo CNVs in CHD patients compared with healthy controls and suggest several novel genetic loci for CHD.

Cancer-associated fibroblasts are the main contributors to epithelial-to-mesenchymal signatures in the tumor microenvironment.

Epithelial-to-mesenchymal transition (EMT) is associated with tumor initiation, metastasis, and drug resistance. However, the mechanisms underlying these associations are largely unknown. We studied several tumor types to identify the source of EMT gene expression signals and a potential mechanism of resistance to immuno-oncology treatment. Across tumor types, EMT-related gene expression was strongly associated with expression of stroma-related genes. Based on RNA sequencing of multiple patient-derived xenograft models, EMT-related gene expression was enriched in the stroma versus parenchyma. EMT-related markers were predominantly expressed by cancer-associated fibroblasts (CAFs), cells of mesenchymal origin which produce a variety of matrix proteins and growth factors. Scores derived from a 3-gene CAF transcriptional signature (COL1A1, COL1A2, COL3A1) were sufficient to reproduce association between EMT-related markers and disease prognosis. Our results suggest that CAFs are the primary source of EMT signaling and have potential roles as biomarkers and targets for immuno-oncology therapies.

Assessment of spatial transcriptomics for oncology discovery.

Tumor heterogeneity is a major challenge for oncology drug discovery and development. Understanding of the spatial tumor landscape is key to identifying new targets and impactful model systems. Here, we test the utility of spatial transcriptomics (ST) for oncology discovery by profiling 40 tissue sections and 80,024 capture spots across a diverse set of tissue types, sample formats, and RNA capture chemistries. We verify the accuracy and fidelity of ST by leveraging matched pathology analysis, which provides a ground truth for tissue section composition. We then use spatial data to demonstrate the capture of key tumor depth features, identifying hypoxia, necrosis, vasculature, and extracellular matrix variation. We also leverage spatial context to identify relative cell-type locations showing the anti-correlation of tumor and immune cells in syngeneic cancer models. Lastly, we demonstrate target identification approaches in clinical pancreatic adenocarcinoma samples, highlighting tumor intrinsic biomarkers and paracrine signaling.

Modeling performance of sample collection sites using whole exome sequencing metrics.

Although next-generation sequencing assays are routinely carried out using samples from cancer trials, the sequencing data are not always of the required quality. There is a need to evaluate the performance of tissue collection sites and provide feedback about the quality of next-generation sequencing data. This study used a modeling approach based on whole exome sequencing quality control (QC) metrics to evaluate the relative performance of sites participating in the Bristol Myers Squibb Immuno-Oncology clinical trials sample collection. We identified several events for the sample swap. Overall, most sites performed well and few showed poor performance. These findings can increase awareness of sample failure and improve the quality of samples.

Bioinformatic Methods and Bridging of Assay Results for Reliable Tumor Mutational Burden Assessment in Non-Small-Cell Lung Cancer.

INTRODUCTION: Tumor mutational burden (TMB) has emerged as a clinically relevant biomarker that may be associated with immune checkpoint inhibitor efficacy. Standardization of TMB measurement is essential for implementing diagnostic tools to guide treatment. OBJECTIVE: Here we describe the in-depth evaluation of bioinformatic TMB analysis by whole exome sequencing (WES) in formalin-fixed, paraffin-embedded samples from a phase III clinical trial. METHODS: In the CheckMate 026 clinical trial, TMB was retrospectively assessed in 312 patients with non-small-cell lung cancer (58% of the intent-to-treat population) who received first-line nivolumab treatment or standard-of-care chemotherapy. We examined the sensitivity of TMB assessment to bioinformatic filtering methods and assessed concordance between TMB data derived by WES and the FoundationOne® CDx assay. RESULTS: TMB scores comprising synonymous, indel, frameshift, and nonsense mutations (all mutations) were 3.1-fold higher than data including missense mutations only, but values were highly correlated (Spearman's r = 0.99). Scores from CheckMate 026 samples including missense mutations only were similar to those generated from data in The Cancer Genome Atlas, but those including all mutations were generally higher. Using databases for germline subtraction (instead of matched controls) showed a trend for race-dependent increases in TMB scores. WES and FoundationOne CDx outputs were highly correlated (Spearman's r = 0.90). CONCLUSIONS: Parameter variation can impact TMB calculations, highlighting the need for standardization. Encouragingly, differences between assays could be accounted for by empirical calibration, suggesting that reliable TMB assessment across assays, platforms, and centers is achievable.

Circulating T Cell Subpopulations Correlate With Immune Responses at the Tumor Site and Clinical Response to PD1 Inhibition in Non-Small Cell Lung Cancer.

Agents targeting the PD1-PDL1 axis have transformed cancer therapy. Factors that influence clinical response to PD1-PDL1 inhibitors include tumor mutational burden, immune infiltration of the tumor, and local PDL1 expression. To identify peripheral correlates of the anti-tumor immune response in the absence of checkpoint blockade, we performed a retrospective study of circulating T cell subpopulations and matched tumor gene expression in melanoma and non-small cell lung cancer (NSCLC) patients. Notably, both melanoma and NSCLC patients whose tumors exhibited increased inflammatory gene transcripts presented high CD4+ and CD8+ central memory T cell (CM) to effector T cell (Eff) ratios in blood. Consequently, we evaluated CM/Eff T cell ratios in a second cohort of NSCLC. The data showed that high CM/Eff T cell ratios correlated with increased tumor PDL1 expression. Furthermore, of the 22 patients within this NSCLC cohort who received nivolumab, those with high CM/Eff T cell ratios, had longer progression-free survival (PFS) (median survival: 91 vs. 215 days). These findings show that by providing a window into the state of the immune system, peripheral T cell subpopulations inform about the state of the anti-tumor immune response and identify potential blood biomarkers of clinical response to checkpoint inhibitors in melanoma and NSCLC.

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.

NaV channel variants in patients with painful and nonpainful peripheral neuropathy.

OBJECTIVE: To examine the incidence of nonsynonymous missense variants in SCN9A (NaV1.7), SCN10A (NaV1.8), and SCN11A (NaV1.9) in patients with painful and nonpainful peripheral neuropathy. METHODS: Next-generation sequencing was performed on 457 patient DNA samples provided by the Peripheral Neuropathy Research Registry (PNRR). The patient diagnosis was as follows: 278 idiopathic peripheral neuropathy (67% painful and 33% nonpainful) and 179 diabetic distal polyneuropathy (77% painful and 23% nonpainful). RESULTS: We identified 36 (SCN9A), 31 (SCN10A), and 15 (SCN11A) nonsynonymous missense variants, with 47.7% of patients carrying a low-frequency (minor allele frequency <5%) missense variant in at least 1 gene. The incidence of previously reported gain-of-function missense variants was low (≤3%), and these were detected in patients with and without pain. There were no significant differences in missense variant allele frequencies of any gene, or SCN9A haplotype frequencies, between PNRR patients with painful or nonpainful peripheral neuropathy. PNRR patient SCN9A and SCN11A missense variant allele frequencies were not significantly different from the Exome Variant Server, European American (EVS-EA) reference population. For SCN10A, there was a significant increase in the alternate allele frequency of the common variant p.V1073A and low-frequency variant pS509P in PNRR patients compared with EVS-EA and the 1000 Genomes European reference populations. CONCLUSIONS: These results suggest that identification of a genetically defined subpopulation for testing of NaV1.7 inhibitors in patients with peripheral neuropathy is unlikely and that additional factors, beyond expression of previously reported disease "mutations," are more important for the development of painful neuropathy than previously discussed.

Application of Whole Exome Sequencing in Six Families with an Initial Diagnosis of Autosomal Dominant Retinitis Pigmentosa: Lessons Learned.

This study aimed to identify the genetics underlying dominant forms of inherited retinal dystrophies using whole exome sequencing (WES) in six families extensively screened for known mutations or genes. Thirty-eight individuals were subjected to WES. Causative variants were searched among single nucleotide variants (SNVs) and insertion/deletion variants (indels) and whenever no potential candidate emerged, copy number variant (CNV) analysis was performed. Variants or regions harboring a candidate variant were prioritized and segregation of the variant with the disease was further assessed using Sanger sequencing in case of SNVs and indels, and quantitative PCR (qPCR) for CNVs. SNV and indel analysis led to the identification of a previously reported mutation in PRPH2. Two additional mutations linked to different forms of retinal dystrophies were identified in two families: a known frameshift deletion in RPGR, a gene responsible for X-linked retinitis pigmentosa and p.Ser163Arg in C1QTNF5 associated with Late-Onset Retinal Degeneration. A novel heterozygous deletion spanning the entire region of PRPF31 was also identified in the affected members of a fourth family, which was confirmed with qPCR. This study allowed the identification of the genetic cause of the retinal dystrophy and the establishment of a correct diagnosis in four families, including a large heterozygous deletion in PRPF31, typically considered one of the pitfalls of this method. Since all findings in this study are restricted to known genes, we propose that targeted sequencing using gene-panel is an optimal first approach for the genetic screening and that once known genetic causes are ruled out, WES might be used to uncover new genes involved in inherited retinal dystrophies.

Whole-genome sequencing in an autism multiplex family.

BACKGROUND: Autism spectrum disorders (ASDs) represent a group of childhood neurodevelopmental disorders that affect 1 in 88 children in the US. Previous exome sequencing studies on family trios have implicated a role for rare, de-novo mutations in the pathogenesis of autism. METHODS: To examine the utility of whole-genome sequencing to identify inherited disease candidate variants and genes, we sequenced two probands from a large pedigree, including two parents and eight children. We evaluated multiple analytical strategies to identify a prioritized list of candidate genes. RESULTS: By assuming a recessive model of inheritance, we identified seven candidate genes shared by the two probands. We also evaluated a different analytical strategy that does not require the assumption of disease model, and identified a list of 59 candidate variants that may increase susceptibility to autism. Manual examination of this list identified ANK3 as the most likely candidate gene. Finally, we identified 33 prioritized non-coding variants such as those near SMG6 and COQ5, based on evolutionary constraint and experimental evidence from ENCODE. Although we were unable to confirm rigorously whether any of these genes indeed contribute to the disease, our analysis provides a prioritized shortlist for further validation studies. CONCLUSIONS: Our study represents one of the first whole-genome sequencing studies in autism leveraging a large family-based pedigree. These results provide for a discussion on the relative merits of finding de-novo mutations in sporadic cases versus finding inherited mutations in large pedigrees, in the context of neuropsychiatric and neurodevelopmental diseases.

Whole-genome DNA/RNA sequencing identifies truncating mutations in RBCK1 in a novel Mendelian disease with neuromuscular and cardiac involvement.

BACKGROUND: Whole-exome sequencing has identified the causes of several Mendelian diseases by analyzing multiple unrelated cases, but it is more challenging to resolve the cause of extremely rare and suspected Mendelian diseases from individual families. We identified a family quartet with two children, both affected with a previously unreported disease, characterized by progressive muscular weakness and cardiomyopathy, with normal intelligence. During the course of the study, we identified one additional unrelated patient with a comparable phenotype. METHODS: We performed whole-genome sequencing (Complete Genomics platform), whole-exome sequencing (Agilent SureSelect exon capture and Illumina Genome Analyzer II platform), SNP genotyping (Illumina HumanHap550 SNP array) and Sanger sequencing on blood samples, as well as RNA-Seq (Illumina HiSeq platform) on transformed lymphoblastoid cell lines. RESULTS: From whole-genome sequence data, we identified RBCK1, a gene encoding an E3 ubiquitin-protein ligase, as the most likely candidate gene, with two protein-truncating mutations in probands in the first family. However, exome data failed to nominate RBCK1 as a candidate gene, due to poor regional coverage. Sanger sequencing identified a private homozygous splice variant in RBCK1 in the proband in the second family, yet SNP genotyping revealed a 1.2Mb copy-neutral region of homozygosity covering RBCK1. RNA-Seq confirmed aberrant splicing of RBCK1 transcripts, resulting in truncated protein products. CONCLUSIONS: While the exact mechanism by which these mutations cause disease is unknown, our study represents an example of how the combined use of whole-genome DNA and RNA sequencing can identify a disease-predisposing gene for a novel and extremely rare Mendelian disease.

Low p53 binding protein 1 (53BP1) expression is associated with increased local recurrence in breast cancer patients treated with breast-conserving surgery and radiotherapy.

PURPOSE: To investigate whether the expression of p53 binding protein 1 (53BP1) has prognostic significance in a cohort of early-stage breast cancer patients treated with breast-conserving surgery and radiotherapy (BCS+RT). METHODS AND MATERIALS: A tissue microarray of early-stage breast cancer treated with BCS+RT from a cohort of 514 women was assayed for 53BP1, estrogen receptor, progesterone receptor, and HER2 expression by immunohistochemistry. Through log-rank tests and univariate and multivariate models, the staining profile of each tumor was correlated with clinical endpoints, including ipsilateral breast recurrence-free survival (IBRFS), distant metastasis-free survival (DMFS), cause-specific survival (CSS), recurrence-free survival (RFS), and overall survival (OS). RESULTS: Of the 477 (93%) evaluable tumors, 63 (13%) were scored as low. Low expression of 53BP1 was associated with worse outcomes for all endpoints studied, including 10-year IBRFS (76.8% vs. 90.5%; P=.01), OS (66.4% vs. 81.7%; P=.02), CSS (66.0% vs. 87.4%; P<.01), DMFS (55.9% vs. 87.0%; P<.01), and RFS (45.2% vs. 80.6%; P<.01). Multivariate analysis incorporating various clinico-pathologic markers and 53BP1 expression found that 53BP1 expression was again an independent predictor of all endpoints (IBRFS: P=.0254; OS: P=.0094; CSS: P=.0033; DMFS: P=.0006; RFS: P=.0002). Low 53BP1 expression was also found to correlate with triple-negative (TN) phenotype (P<.01). Furthermore, in subset analysis of all TN breast cancer, negative 53BP1 expression trended for lower IBRFS (72.3% vs. 93.9%; P=.0361) and was significant for worse DMFS (48.2% vs. 86.8%; P=.0035) and RFS (37.8% vs. 83.7%; P=.0014). CONCLUSION: Our data indicate that low 53BP1 expression is an independent prognostic indicator for local relapse among other endpoints in early-stage breast cancer and TN breast cancer patients treated with BCS+RT. These results should be verified in larger cohorts of patients to validate their clinical significance.