Single-Cell Analysis Identifies NOTCH3-Mediated Interactions between Stromal Cells That Promote Microenvironment Remodeling and Invasion in Lung Adenocarcinoma.

Cancer immunotherapy has revolutionized the treatment of lung adenocarcinoma (LUAD); however, a significant proportion of patients do not respond. Recent transcriptomic studies to understand determinants of immunotherapy response have pinpointed stromal-mediated resistance mechanisms. To gain a better understanding of stromal biology at the cellular and molecular level in LUAD, we performed single-cell RNA sequencing of 256,379 cells, including 13,857 mesenchymal cells, from 9 treatment-naïve patients. Among the mesenchymal cell subsets, FAP+PDPN+ cancer-associated fibroblasts (CAF) and ACTA2+MCAM+ pericytes were enriched in tumors and differentiated from lung-resident fibroblasts. Imaging mass cytometry revealed that both subsets were topographically adjacent to the perivascular niche and had close spatial interactions with endothelial cells (EC). Modeling of ligand and receptor interactomes between mesenchymal and ECs identified that NOTCH signaling drives these cell-to-cell interactions in tumors, with pericytes and CAFs as the signal receivers and arterial and PLVAPhigh immature neovascular ECs as the signal senders. Either pharmacologically blocking NOTCH signaling or genetically depleting NOTCH3 levels in mesenchymal cells significantly reduced collagen production and suppressed cell invasion. Bulk RNA sequencing data demonstrated that NOTCH3 expression correlated with poor survival in stroma-rich patients and that a T cell-inflamed gene signature only predicted survival in patients with low NOTCH3. Collectively, this study provides valuable insights into the role of NOTCH3 in regulating tumor stroma biology, warranting further studies to elucidate the clinical implications of targeting NOTCH3 signaling. SIGNIFICANCE: NOTCH3 signaling activates tumor-associated mesenchymal cells, increases collagen production, and augments cell invasion in lung adenocarcinoma, suggesting its critical role in remodeling tumor stroma.

Parallel single-cell and bulk transcriptome analyses reveal key features of the gastric tumor microenvironment.

BACKGROUND: The tumor microenvironment (TME) has been shown to strongly influence treatment outcome for cancer patients in various indications and to influence the overall survival. However, the cells forming the TME in gastric cancer have not been extensively characterized. RESULTS: We combine bulk and single-cell RNA sequencing from tumors and matched normal tissue of 24 treatment-naïve GC patients to better understand which cell types and transcriptional programs are associated with malignant transformation of the stomach. Clustering 96,623 cells of non-epithelial origin reveals 81 well-defined TME cell types. We find that activated fibroblasts and endothelial cells are most prominently overrepresented in tumors. Intercellular network reconstruction and survival analysis of an independent cohort imply the importance of these cell types together with immunosuppressive myeloid cell subsets and regulatory T cells in establishing an immunosuppressive microenvironment that correlates with worsened prognosis and lack of response in anti-PD1-treated patients. In contrast, we find a subset of IFNγ activated T cells and HLA-II expressing macrophages that are linked to treatment response and increased overall survival. CONCLUSIONS: Our gastric cancer single-cell TME compendium together with the matched bulk transcriptome data provides a unique resource for the identification of new potential biomarkers for patient stratification. This study helps further to elucidate the mechanism of gastric cancer and provides insights for therapy.

Reverse Translating Molecular Determinants of Anti-Programmed Death 1 Immunotherapy Response in Mouse Syngeneic Tumor Models.

Targeting the programmed death 1/programmed death ligand 1 (PD-1/PD-L1) pathway with immunotherapy has revolutionized the treatment of many cancers. Somatic tumor mutational burden (TMB) and T-cell-inflamed gene expression profile (GEP) are clinically validated pan-tumor genomic biomarkers that can predict responsiveness to anti-PD-1/PD-L1 monotherapy in many tumor types. We analyzed the association between these biomarkers and the efficacy of PD-1 inhibitor in 11 commonly used preclinical syngeneic tumor mouse models using murinized rat anti-mouse PD-1 DX400 antibody muDX400, a surrogate for pembrolizumab. Response to muDX400 treatment was broadly classified into three categories: highly responsive, partially responsive, and intrinsically resistant to therapy. Molecular and cellular profiling validated differences in immune cell infiltration and activation in the tumor microenvironment of muDX400-responsive tumors. Baseline and on-treatment genomic analysis showed an association between TMB, murine T-cell-inflamed gene expression profile (murine-GEP), and response to muDX400 treatment. We extended our analysis to investigate a canonical set of cancer and immune biology-related gene signatures, including signatures of angiogenesis, myeloid-derived suppressor cells, and stromal/epithelial-to-mesenchymal transition/TGFß biology previously shown to be inversely associated with the clinical efficacy of immune checkpoint blockade. Finally, we evaluated the association between murine-GEP and preclinical efficacy with standard-of-care chemotherapy or antiangiogenic agents that previously demonstrated promising clinical activity, in combination with muDX400. Our profiling studies begin to elucidate the underlying biological mechanisms of response and resistance to PD-1/PD-L1 blockade represented by these models, thereby providing insight into which models are most appropriate for the evaluation of orthogonal combination strategies.

ILT3 (LILRB4) Promotes the Immunosuppressive Function of Tumor-Educated Human Monocytic Myeloid-Derived Suppressor Cells.

Myeloid-derived suppressor cells (MDSC) are immature myeloid cells that accumulate in the tumor microenvironment (TME). MDSCs have been shown to dampen antitumor immune responses and promote tumor growth; however, the mechanisms of MDSC induction and their role in promoting immune suppression in cancer remain poorly understood. Here, we characterized the phenotype and function of monocytic MDSCs (M-MDSC) generated by coculture of human peripheral blood mononuclear cells with SK-MEL-5 cancer cells in vitro. We selected the SK-MEL-5 human melanoma cell line to generate M-MDSCs because these cells form subcutaneous tumors rich in myeloid cells in humanized mice. M-MDSCs generated via SK-MEL-5 coculture expressed low levels of human leukocyte antigen (HLA)-DR, high levels of CD33 and CD11b, and suppressed both CD8+ T-cell proliferation and IFNγ secretion. M-MDSCs also expressed higher levels of immunoglobulin-like transcript 3 (ILT3, also known as LILRB4) and immunoglobulin-like transcript 4 (ILT4, also known as LILRB2) on the cell surface compared with monocytes. Therefore, we investigated how ILT3 targeting could modulate M-MDSC cell function. Treatment with an anti-ILT3 antibody impaired the acquisition of the M-MDSC suppressor phenotype and reduced the capacity of M-MDSCs to cause T-cell suppression. Finally, in combination with anti-programmed cell death protein 1 (PD1), ILT3 blockade enhanced T-cell activation as assessed by IFNγ secretion. IMPLICATIONS: These results suggest that ILT3 expressed on M-MDSCs has a role in inducing immunosuppression in cancer and that antagonism of ILT3 may be useful to reverse the immunosuppressive function of M-MDSCs and enhance the efficacy of immune checkpoint inhibitors.

A community-based lung cancer rapid tissue donation protocol provides high-quality drug-resistant specimens for proteogenomic analyses.

BACKGROUND: For the advancement of cancer research, the collection of tissue specimens from drug-resistant tumors after targeted therapy is crucial. Although patients with lung cancer are often provided targeted therapy, post-therapy specimens are not routinely collected due to the risks of collection, limiting the study of targeted therapy resistance mechanisms. Posthumous rapid tissue donation (RTD) is an expedient collection process that provides an opportunity to understand treatment-resistant lung cancers. METHODS: Consent to participate in the thoracic RTD protocol was obtained during patient care. When death occurred, tumor and paired non-tumor, cytology, and blood specimens were collected within 48 hours and preserved as formalin-fixed and frozen specimens. Tissue sections were evaluated with hematoxylin and eosin staining and immunohistochemistry (IHC) against multiple biomarkers, including various programmed death ligand 1 (PD-L1) clones. Next-generation sequencing was performed on 13 specimens from 5 patients. RESULTS: Postmortem specimens (N = 180) were well preserved from 9 patients with lung cancer. PD-L1 IHC revealed heterogeneity within and between tumors. An AGK-BRAF fusion was newly identified in tumor from a donor with a known echinoderm microtubule-associated protein-like 4 to anaplastic lymphoma kinase (EML4-ALK) fusion and history of anaplastic lymphoma kinase (ALK) inhibitor therapy. RNA expression analysis revealed a clonal genetic origin of metastatic cancer cells. CONCLUSIONS: Post-therapy specimens demonstrated PD-L1 heterogeneity and an acyl glycerol kinase to B-rapidly accelerated fibrosarcoma (AGK-BRAF) fusion in a patient with an EML4-ALK-positive lung adenocarcinoma as a potential resistance mechanism to ALK inhibitor therapy. Rapid tissue donation collection of postmortem tissue from lung cancer patients is a novel approach to cancer research that enables studies of molecular evolution and drug resistance.

Elucidating therapeutic molecular targets in premenopausal Asian women with recurrent breast cancers.

Breast cancer is an increasing problem in Asia, with a higher proportion of premenopausal patients who are at higher risk of recurrence. Targeted sequencing was performed on DNA extracted from primary tumor specimens of 63 premenopausal Asian patients who relapsed after initial diagnosis of non-metastatic breast cancer. The most prevalent alterations included: TP53 (65%); PIK3CA (32%); GATA3 (29%); ERBB2 (27%); MYC (25%); KMT2C (21%); MCL1 (17%); PRKDC, TPR, BRIP1 (14%); MDM4, PCDH15, PRKAR1A, CDKN1B (13%); CCND1, KMT2D, STK11, and MLH1 (11%). Sixty of the 63 patients (95%) had at least one genetic alteration in a signaling pathway related to cell cycle or p53 signaling. The presence of MCL1 amplification, HIF-1-alpha transcription factor network pathway alterations, and direct p53 effectors pathway alterations were independent predictors of inferior overall survival from initial diagnosis. Comparison with non-Asian premenopausal tumors in The Cancer Genome Atlas (TCGA) revealed a higher prevalence of TP53 mutations among HER2-positive cancers, and more frequent TP53, TET2, and CDK12 mutations among hormone receptor-positive HER2-negative cancers in our cohort. Given the limited number of non-Asian premenopausal breast cancers that had relapsed in TCGA, we compared the frequency of mutations in our cohort with 43 premenopausal specimens from both TCGA and International Cancer Genome Consortium that had relapsed. There was a trend toward higher prevalence of TP53 mutations in our cohort. Certain genomic aberrations may be enriched in tumors of poor-prognosis premenopausal Asian breast cancers. The development of novel therapies targeting these aberrations merit further research.

Genomic Analysis of Nasopharyngeal Carcinoma Reveals TME-Based Subtypes.

Nasopharyngeal carcinoma (NPC) is an Epstein-Barr virus (EBV) associated cancer characterized by a poor prognosis and a high level of lymphocyte infiltrate. Genetic hallmarks of NPC are not completely known but include deletion of the p16 (CDKN2A) locus and mutations in NF-κB pathway components, with a relatively low total mutational load. To better understand the genetic landscape, an integrated genomic analysis was performed using a large clinical cohort of treatment-naïve NPC tumor specimens. This genomic analysis was generally concordant with previous studies; however, three subtypes of NPC were identified by differences in immune cell gene expression, prognosis, tumor cell morphology, and genetic characteristics. A gene expression signature of proliferation was poorly prognostic and associated with either higher mutation load or specific EBV gene expression patterns in a subtype-specific manner. Finally, higher levels of stromal tumor-infiltrating lymphocytes associated with good prognosis and lower expression of a WNT and TGFß pathway activation signature.Implications: This study represents the first integrated analysis of mutation, copy number, and gene expression data in NPC and suggests how tumor genetics and EBV infection influence the tumor microenvironment in this disease. These insights should be considered for guiding immunotherapy treatment strategies in this disease. Mol Cancer Res; 15(12); 1722-32. ©2017 AACR.

PureCN: copy number calling and SNV classification using targeted short read sequencing.

BACKGROUND: Matched sequencing of both tumor and normal tissue is routinely used to classify variants of uncertain significance (VUS) into somatic vs. germline. However, assays used in molecular diagnostics focus on known somatic alterations in cancer genes and often only sequence tumors. Therefore, an algorithm that reliably classifies variants would be helpful for retrospective exploratory analyses. Contamination of tumor samples with normal cells results in differences in expected allelic fractions of germline and somatic variants, which can be exploited to accurately infer genotypes after adjusting for local copy number. However, existing algorithms for determining tumor purity, ploidy and copy number are not designed for unmatched short read sequencing data. RESULTS: We describe a methodology and corresponding open source software for estimating tumor purity, copy number, loss of heterozygosity (LOH), and contamination, and for classification of single nucleotide variants (SNVs) by somatic status and clonality. This R package, PureCN, is optimized for targeted short read sequencing data, integrates well with standard somatic variant detection pipelines, and has support for matched and unmatched tumor samples. Accuracy is demonstrated on simulated data and on real whole exome sequencing data. CONCLUSIONS: Our algorithm provides accurate estimates of tumor purity and ploidy, even if matched normal samples are not available. This in turn allows accurate classification of SNVs. The software is provided as open source (Artistic License 2.0) R/Bioconductor package PureCN (http://bioconductor.org/packages/PureCN/).

Integrative biomarker analyses indicate etiological variations in hepatocellular carcinoma.

BACKGROUND & AIMS: The purpose of this study was to determine whether biomarkers from baseline plasma and archival tissue specimens collected from patients enrolled in the EVOLVE-1 trial - a randomized phase 3 study of everolimus in hepatocellular carcinoma (HCC) - were associated with prognosis, etiology or ethnicity. METHODS: Circulating plasma levels of bFGF, PLGF, VEGF, VEGF-D, c-Kit, collagen IV, sVEGFR1 and VEGFR2 were measured by ELISA (N=503). Protein levels of IGF-1R, c-Met, mTOR, Tsc2 were assayed by immunohistochemistry (N=125). Genomic DNA sequencing was conducted on a panel of 287 cancer-related genes (N=69). RESULTS: Patients with baseline plasma concentrations of VEGF or sVEGFR1 above the cohort median had significantly shorter overall survival. These plasma biomarkers retained prognostic significance in a multivariate Cox regression model with geographic region, macroscopic vascular invasion and alpha fetoprotein AFP levels. Membranous c-Met protein levels were significantly lower for Asian patients, as well as for hepatitis B viral etiology. The prevalence of genetic changes were similar to previous reports, along with a trend towards higher PTEN and TSC2 mutations among Asians. CONCLUSIONS: The angiogenesis biomarkers VEGF and sVEGFR1 were independent prognostic predictors of survival in patients with advanced HCC. Potential differences in c-Met and mTOR pathway activation between Asian and non-Asian patients should be considered in future clinical trials. LAY SUMMARY: Our study demonstrates that circulating angiogenesis biomarkers can predict the survival outcome in patients with advanced hepatocellular carcinoma independent of the clinical variables. There is etiology and ethnicity variation in molecular pathway activation in hepatocellular carcinoma, which should be considered for future clinical trial design of targeted therapy. CLINICAL TRIAL REGISTRATION NUMBER: NCT01035229.

High-throughput screening using patient-derived tumor xenografts to predict clinical trial drug response.

Profiling candidate therapeutics with limited cancer models during preclinical development hinders predictions of clinical efficacy and identifying factors that underlie heterogeneous patient responses for patient-selection strategies. We established ∼1,000 patient-derived tumor xenograft models (PDXs) with a diverse set of driver mutations. With these PDXs, we performed in vivo compound screens using a 1 × 1 × 1 experimental design (PDX clinical trial or PCT) to assess the population responses to 62 treatments across six indications. We demonstrate both the reproducibility and the clinical translatability of this approach by identifying associations between a genotype and drug response, and established mechanisms of resistance. In addition, our results suggest that PCTs may represent a more accurate approach than cell line models for assessing the clinical potential of some therapeutic modalities. We therefore propose that this experimental paradigm could potentially improve preclinical evaluation of treatment modalities and enhance our ability to predict clinical trial responses.

Von Hippel-Lindau status influences phenotype of liver cancers arising from PTEN loss.

BACKGROUND: PTEN loss contributes to the development of liver diseases including hepatic steatosis and both hepatocellular carcinoma (HCC) and cholangiocarcinoma (CC). The factors that influence the penetrance of these conditions are unclear. We explored the influence of sustained hypoxia signaling through co-deletion of Pten and Vhl in a murine model. METHODS: We used a CreER-linked Keratin 18 mouse model to conditionally delete Pten, Vhl or both in somatic cells of adult mice, evaluating the resultant tumors by histology and gene expression microarray. Existing sets of gene expression data for human HCC and CC were examined for pathways related to those observed in the murine tumors, and a cohort of human CC samples was evaluated for relationships between HIF-1α expression and clinical outcomes. RESULTS: Both Pten deletion genotypes developed liver tumors, but with differing phenotypes. Pten deletion alone led to large hepatic tumors with widespread hepatosteatosis. Co-deletion of Pten and Vhl with the Keratin 18 promoter resulted in reduced steatosis and a reduced tumor burden that was characterized by a trabecular architecture similar to CC. Genes associated with hepatic steatosis were coordinately expressed in the human HCC dataset, while genes involved in hypoxia response were upregulated in tumors from the human CC dataset. HIF-1α expression and overall survival were examined in an independent cohort of human CC tumors with no statistical differences uncovered. CONCLUSION: Pten deletion in Keratin 18 expressing cells leads to aggressive tumor formation and widespread steatosis in mouse livers. Co-deletion of Vhl and Pten results in lower tumor burden with gene expression profiling suggesting a switch from a profile of lipid deposition to an expression profile more consistent with upregulation of the hypoxia response pathway. A relationship between tumor hypoxia signaling and altered hepatic steatotic response suggests that competing influences may alter tumor phenotypes.

Studying clonal dynamics in response to cancer therapy using high-complexity barcoding.

Resistance to cancer therapies presents a significant clinical challenge. Recent studies have revealed intratumoral heterogeneity as a source of therapeutic resistance. However, it is unclear whether resistance is driven predominantly by pre-existing or de novo alterations, in part because of the resolution limits of next-generation sequencing. To address this, we developed a high-complexity barcode library, ClonTracer, which enables the high-resolution tracking of more than 1 million cancer cells under drug treatment. In two clinically relevant models, ClonTracer studies showed that the majority of resistant clones were part of small, pre-existing subpopulations that selectively escaped under therapeutic challenge. Moreover, the ClonTracer approach enabled quantitative assessment of the ability of combination treatments to suppress resistant clones. These findings suggest that resistant clones are present before treatment, which would make up-front therapeutic combinations that target non-overlapping resistance a preferred approach. Thus, ClonTracer barcoding may be a valuable tool for optimizing therapeutic regimens with the goal of curative combination therapies for cancer.

Targeted next generation sequencing identifies clinically actionable mutations in patients with melanoma.

Somatic sequencing of cancers has produced new insight into tumorigenesis, tumor heterogeneity, and disease progression, but the vast majority of genetic events identified are of indeterminate clinical significance. Here, we describe a NextGen sequencing approach to fully analyzing 248 genes, including all those of known clinical significance in melanoma. This strategy features solution capture of DNA followed by multiplexed, high-throughput sequencing and was evaluated in 31 melanoma cell lines and 18 tumor tissues from patients with metastatic melanoma. Mutations in melanoma cell lines correlated with their sensitivity to corresponding small molecule inhibitors, confirming, for example, lapatinib sensitivity in ERBB4 mutant lines and identifying a novel activating mutation of BRAF. The latter event would not have been identified by clinical sequencing and was associated with responsiveness to a BRAF kinase inhibitor. This approach identified focal copy number changes of PTEN not found by standard methods, such as comparative genomic hybridization (CGH). Actionable mutations were found in 89% of the tumor tissues analyzed, 56% of which would not be identified by standard-of-care approaches. This work shows that targeted sequencing is an attractive approach for clinical use in melanoma.

Mutations of isocitrate dehydrogenase 1 and 2 in intrahepatic cholangiocarcinoma.

PURPOSE OF REVIEW: Exome sequencing studies have recently expanded the genetic characterization of intrahepatic cholangiocarcinomas. Among a number of novel genes, isocitrate dehydrogenase (IDH) is recurrently mutated in intrahepatic cholangiocarcinomas. We review the effects of these mutations on several biochemical pathways, as well as potential changes to downstream signaling pathways. RECENT FINDINGS: Hotspot mutations in IDH isoforms 1 or 2 occur in approximately 15% of intrahepatic cholangiocarcinomas. These mutations result in elevated levels of an oncometabolite, 2-hydroxyglutarate, which is associated with higher DNA CpG methylation and altered histone methylation that accompany a block in cellular differentiation. Exploratory studies have suggested additional phenotypes associated with IDH1/2 mutations. SUMMARY: Tumors with IDH1 or IDH2 mutations may represent a distinct subtype of cholangiocarcinomas. Further studies are required to elucidate the exact role that mutant IDH1/2 and 2-hydroxyglutarate play in tumorigenesis, and what are the best strategies to target these tumor types.

Identification of driver genes in hepatocellular carcinoma by exome sequencing.

UNLABELLED: Genetic alterations in specific driver genes lead to disruption of cellular pathways and are critical events in the instigation and progression of hepatocellular carcinoma (HCC). As a prerequisite for individualized cancer treatment, we sought to characterize the landscape of recurrent somatic mutations in HCC. We performed whole-exome sequencing on 87 HCCs and matched normal adjacent tissues to an average coverage of 59×. The overall mutation rate was roughly two mutations per Mb, with a median of 45 nonsynonymous mutations that altered the amino acid sequence (range, 2-381). We found recurrent mutations in several genes with high transcript levels: TP53 (18%); CTNNB1 (10%); KEAP1 (8%); C16orf62 (8%); MLL4 (7%); and RAC2 (5%). Significantly affected gene families include the nucleotide-binding domain and leucine-rich repeat-containing family, calcium channel subunits, and histone methyltransferases. In particular, the MLL family of methyltransferases for histone H3 lysine 4 were mutated in 20% of tumors. CONCLUSION: The NFE2L2-KEAP1 and MLL pathways are recurrently mutated in multiple cohorts of HCC.

A robust method for transcript quantification with RNA-seq data.

The advent of high throughput RNA-seq technology allows deep sampling of the transcriptome, making it possible to characterize both the diversity and the abundance of transcript isoforms. Accurate abundance estimation or transcript quantification of isoforms is critical for downstream differential analysis (e.g., healthy vs. diseased cells) but remains a challenging problem for several reasons. First, while various types of algorithms have been developed for abundance estimation, short reads often do not uniquely identify the transcript isoforms from which they were sampled. As a result, the quantification problem may not be identifiable, i.e., lacks a unique transcript solution even if the read maps uniquely to the reference genome. In this article, we develop a general linear model for transcript quantification that leverages reads spanning multiple splice junctions to ameliorate identifiability. Second, RNA-seq reads sampled from the transcriptome exhibit unknown position-specific and sequence-specific biases. We extend our method to simultaneously learn bias parameters during transcript quantification to improve accuracy. Third, transcript quantification is often provided with a candidate set of isoforms, not all of which are likely to be significantly expressed in a given tissue type or condition. By resolving the linear system with LASSO, our approach can infer an accurate set of dominantly expressed transcripts while existing methods tend to assign positive expression to every candidate isoform. Using simulated RNA-seq datasets, our method demonstrated better quantification accuracy and the inference of dominant set of transcripts than existing methods. The application of our method on real data experimentally demonstrated that transcript quantification is effective for differential analysis of transcriptomes.

iReckon: simultaneous isoform discovery and abundance estimation from RNA-seq data.

High-throughput RNA sequencing (RNA-seq) promises to revolutionize our understanding of genes and their role in human disease by characterizing the RNA content of tissues and cells. The realization of this promise, however, is conditional on the development of effective computational methods for the identification and quantification of transcripts from incomplete and noisy data. In this article, we introduce iReckon, a method for simultaneous determination of the isoforms and estimation of their abundances. Our probabilistic approach incorporates multiple biological and technical phenomena, including novel isoforms, intron retention, unspliced pre-mRNA, PCR amplification biases, and multimapped reads. iReckon utilizes regularized expectation-maximization to accurately estimate the abundances of known and novel isoforms. Our results on simulated and real data demonstrate a superior ability to discover novel isoforms with a significantly reduced number of false-positive predictions, and our abundance accuracy prediction outmatches that of other state-of-the-art tools. Furthermore, we have applied iReckon to two cancer transcriptome data sets, a triple-negative breast cancer patient sample and the MCF7 breast cancer cell line, and show that iReckon is able to reconstruct the complex splicing changes that were not previously identified. QT-PCR validations of the isoforms detected in the MCF7 cell line confirmed all of iReckon's predictions and also showed strong agreement (r(2) = 0.94) with the predicted abundances.

DiffSplice: the genome-wide detection of differential splicing events with RNA-seq.

The RNA transcriptome varies in response to cellular differentiation as well as environmental factors, and can be characterized by the diversity and abundance of transcript isoforms. Differential transcription analysis, the detection of differences between the transcriptomes of different cells, may improve understanding of cell differentiation and development and enable the identification of biomarkers that classify disease types. The availability of high-throughput short-read RNA sequencing technologies provides in-depth sampling of the transcriptome, making it possible to accurately detect the differences between transcriptomes. In this article, we present a new method for the detection and visualization of differential transcription. Our approach does not depend on transcript or gene annotations. It also circumvents the need for full transcript inference and quantification, which is a challenging problem because of short read lengths, as well as various sampling biases. Instead, our method takes a divide-and-conquer approach to localize the difference between transcriptomes in the form of alternative splicing modules (ASMs), where transcript isoforms diverge. Our approach starts with the identification of ASMs from the splice graph, constructed directly from the exons and introns predicted from RNA-seq read alignments. The abundance of alternative splicing isoforms residing in each ASM is estimated for each sample and is compared across sample groups. A non-parametric statistical test is applied to each ASM to detect significant differential transcription with a controlled false discovery rate. The sensitivity and specificity of the method have been assessed using simulated data sets and compared with other state-of-the-art approaches. Experimental validation using qRT-PCR confirmed a selected set of genes that are differentially expressed in a lung differentiation study and a breast cancer data set, demonstrating the utility of the approach applied on experimental biological data sets. The software of DiffSplice is available at http://www.netlab.uky.edu/p/bioinfo/DiffSplice.

Integrating prior knowledge in multiple testing under dependence with applications to detecting differential DNA methylation.

DNA methylation has emerged as an important hallmark of epigenetics. Numerous platforms including tiling arrays and next generation sequencing, and experimental protocols are available for profiling DNA methylation. Similar to other tiling array data, DNA methylation data shares the characteristics of inherent correlation structure among nearby probes. However, unlike gene expression or protein DNA binding data, the varying CpG density which gives rise to CpG island, shore and shelf definition provides exogenous information in detecting differential methylation. This article aims to introduce a robust testing and probe ranking procedure based on a nonhomogeneous hidden Markov model that incorporates the above-mentioned features for detecting differential methylation. We revisit the seminal work of Sun and Cai (2009, Journal of the Royal Statistical Society: Series B (Statistical Methodology)71, 393-424) and propose modeling the nonnull using a nonparametric symmetric distribution in two-sided hypothesis testing. We show that this model improves probe ranking and is robust to model misspecification based on extensive simulation studies. We further illustrate that our proposed framework achieves good operating characteristics as compared to commonly used methods in real DNA methylation data that aims to detect differential methylation sites.