Genomic and Transcriptomic Characterization of Relapsed SCLC Through Rapid Research Autopsy.

INTRODUCTION: Relapsed SCLC is characterized by therapeutic resistance and high mortality rate. Despite decades of research, mechanisms responsible for therapeutic resistance have remained elusive owing to limited tissues available for molecular studies. Thus, an unmet need remains for molecular characterization of relapsed SCLC to facilitate development of effective therapies. METHODS: We performed whole-exome and transcriptome sequencing of metastatic tumor samples procured from research autopsies of five patients with relapsed SCLC. We implemented bioinformatics tools to infer subclonal phylogeny and identify recurrent genomic alterations. We implemented immune cell signature and single-sample gene set enrichment analyses on tumor and normal transcriptome data from autopsy and additional primary and relapsed SCLC data sets. Furthermore, we evaluated T cell-inflamed gene expression profiles in neuroendocrine (ASCL1, NEUROD1) and non-neuroendocrine (YAP1, POU2F3) SCLC subtypes. RESULTS: Exome sequencing revealed clonal heterogeneity (intertumor and intratumor) arising from branched evolution and identified resistance-associated truncal and subclonal alterations in relapsed SCLC. Transcriptome analyses further revealed a noninflamed phenotype in neuroendocrine SCLC subtypes (ASCL1, NEUROD1) associated with decreased expression of genes involved in adaptive antitumor immunity whereas non-neuroendocrine subtypes (YAP1, POU2F3) revealed a more inflamed phenotype. CONCLUSIONS: Our results reveal substantial tumor heterogeneity and complex clonal evolution in relapsed SCLC. Furthermore, we report that neuroendocrine SCLC subtypes are immunologically cold, thus explaining decreased responsiveness to immune checkpoint blockade. These results suggest that the mechanisms of innate and acquired therapeutic resistances are subtype-specific in SCLC and highlight the need for continued investigation to bolster therapy selection and development for this cancer.

Characterization of Clonal Evolution in Microsatellite Unstable Metastatic Cancers through Multiregional Tumor Sequencing.

Microsatellites are short, repetitive segments of DNA, which are dysregulated in mismatch repair-deficient (MMRd) tumors resulting in microsatellite instability (MSI). MSI has been identified in many human cancer types with varying incidence, and microsatellite instability-high (MSI-H) tumors often exhibit increased sensitivity to immune-enhancing therapies such as PD-1/PD-L1 inhibition. Next-generation sequencing (NGS) has permitted advancements in MSI detection, and recent computational advances have enabled characterization of tumor heterogeneity via NGS. However, the evolution and heterogeneity of microsatellite changes in MSI-positive tumors remains poorly described. We determined MSI status in 6 patients using our previously published algorithm, MANTIS, and inferred subclonal composition and phylogeny with Canopy and SuperFreq. We developed a simulated annealing-based method to characterize microsatellite length distributions in specific subclones and assessed the evolution of MSI in the context of tumor heterogeneity. We identified three to eight tumor subclones per patient, and each subclone exhibited MMRd-associated base substitution signatures. We noted that microsatellites tend to shorten over time, and that MMRd fosters heterogeneity by introducing novel mutations throughout the disease course. Some microsatellites are altered among all subclones in a patient, whereas other loci are only altered in particular subclones corresponding to subclonal phylogenetic relationships. Overall, our results indicate that MMRd is a substantial driver of heterogeneity, leading to both MSI and subclonal divergence. IMPLICATIONS: We leveraged subclonal inference to assess clonal evolution based on somatic mutations and microsatellites, which provides insight into MMRd as a dynamic mutagenic process in MSI-H malignancies.

Detection of Microsatellite Instability Biomarkers via Next-Generation Sequencing.

A high level of microsatellite instability (MSI-H+) is an emerging predictive and prognostic biomarker for immunotherapy response in cancer. Recently, MSI-H+ has been detected in a variety of cancer types, in addition to the classical cancers associated with Lynch Syndrome. Clinical testing for MSI-H+ is currently performed primarily through traditional polymerase chain reaction (PCR) or immunohistochemistry (IHC) assays. However, next-generation sequencing (NGS)-based approaches have been developed which have multiple advantages over traditional assays. For instance, NGS has the ability to interrogate thousands of microsatellite loci compared with just 5-7 loci that are detected by PCR. In this chapter, we detail the biochemical and computational steps to detect MSI-H+ from analysis of paired tumor and normal samples through NGS. We begin with DNA extraction, describe sequencing library preparation and quality control (QC), and outline the bioinformatics steps necessary for sequence alignment, preprocessing, and MSI-H+ detection using the software tool MANTIS. This workflow is intended to facilitate more widespread usage and adaptation of NGS-powered MSI detection, which can be eventually standardized for routine clinical testing.

Tumor heterogeneity and acquired drug resistance in FGFR2-fusion-positive cholangiocarcinoma through rapid research autopsy.

Cholangiocarcinoma is a highly aggressive and lethal malignancy, with limited treatment options available. Recently, FGFR inhibitors have been developed and utilized in FGFR-mutant cholangiocarcinoma; however, resistance often develops and the genomic determinants of resistance are not fully characterized. We completed whole-exome sequencing (WES) of 11 unique tumor samples obtained from a rapid research autopsy on a patient with FGFR-fusion-positive cholangiocarcinoma who initially responded to the pan-FGFR inhibitor, INCB054828. In vitro studies were carried out to characterize the novel FGFR alteration and secondary FGFR2 mutation identified. Multisite WES and analysis of tumor heterogeneity through subclonal inference identified four genetically distinct cancer cell populations, two of which were only observed after treatment. Additionally, WES revealed an FGFR2 N549H mutation hypothesized to confer resistance to the FGFR inhibitor INCB054828 in a single tumor sample. This hypothesis was corroborated with in vitro cell-based studies in which cells expressing FGFR2-CLIP1 fusion were sensitive to INCB054828 (IC50 value of 10.16 nM), whereas cells with the addition of the N549H mutation were resistant to INCB054828 (IC50 value of 1527.57 nM). Furthermore, the FGFR2 N549H secondary mutation displayed cross-resistance to other selective FGFR inhibitors, but remained sensitive to the nonselective inhibitor, ponatinib. Rapid research autopsy has the potential to provide unprecedented insights into the clonal evolution of cancer throughout the course of the disease. In this study, we demonstrate the emergence of a drug resistance mutation and characterize the evolution of tumor subclones within a cholangiocarcinoma disease course.

Characterization of a KLK2-FGFR2 fusion gene in two cases of metastatic prostate cancer.

BACKGROUND: The fibroblast growth factor receptor (FGFR) signaling pathway is activated in multiple tumor types through gene amplifications, single base substitutions, or gene fusions. Multiple small molecule kinase inhibitors targeting FGFR are currently being evaluated in clinical trials for patients with FGFR chromosomal translocations. Patients with novel gene fusions involving FGFR may represent candidates for kinase inhibitors. METHODS: A targeted RNA-sequencing assay identified a KLK2-FGFR2 fusion gene in two patients with metastatic prostate cancer. NIH3T3 cells were transduced to express the KLK2-FGFR2 fusion. Migration assays, Western blots, and drug sensitivity assays were performed to functionally characterize the fusion. RESULTS: Expression of the KLK2-FGFR2 fusion protein in NIH3T3 cells induced a profound morphological change promoting enhanced migration and activation of downstream proteins in FGFR signaling pathways. The KLK2-FGFR2 fusion protein was determined to be highly sensitive to the selective FGFR inhibitors AZD-4547, BGJ398, JNJ-42756943, the irreversible inhibitor TAS-120, and the non-selective inhibitor Ponatinib. The KLK2-FGFR2 fusion did not exhibit sensitivity to the non-selective inhibitor Dovitinib. CONCLUSIONS: Importantly, the KLK2-FGFR2 fusion represents a novel target for precision therapies and should be screened for in men with prostate cancer.

Genomic characterization of metastatic ultra-hypermutated interdigitating dendritic cell sarcoma through rapid research autopsy.

Interdigitating dendritic cell sarcoma (IDCS) is an extremely rare cancer of dendritic cell origin that lacks a standardized treatment approach. Here, we performed genomic characterization of metastatic IDCS through whole exome sequencing (WES) of tumor tissues procured from a patient who underwent research autopsy. WES was also performed on a treatment-naïve tumor biopsy sample obtained from prior surgical resection. Our analyses revealed ultra-hypermutation, defined as >100 mutations per megabase, in this patient's cancer, which was further characterized by the presence of three distinct mutational signatures including UV radiation and APOBEC signatures. To characterize clonal heterogeneity, we used the bioinformatics tool Canopy to leverage single nucleotide and copy number variants to catalog six subclones across various metastatic tumors. Truncal alterations, defined as being present in all clonal tumor cell populations, in this patient's cancer include point mutations in TP53 and CDKN2A and amplifications of c-KIT and APOBEC3A-H, which are likely driver mutations. In summary, we have performed genomic characterization evaluating tumor mutational burden (TMB) and heterogeneity in a patient with metastatic IDCS. Despite ultra-hypermutation, this patient's cancer was not responsive to treatment with PD-1 inhibition. Our results underscore the importance of characterizing clonal heterogeneity in TMB-high cancers.

Rapid Research Autopsy: Piecing the Puzzle of Tumor Heterogeneity.

Tumor heterogeneity decreases the effectiveness of anticancer therapies and is an important topic in translational cancer research, given its relevance in clinical oncology. Here, we discuss how rapid research autopsy of cancer patients can elucidate heterogeneity-associated processes including cancer evolution and acquired therapeutic resistance. In practice, rapid research autopsy is performed shortly after a patient's passing to procure multiple metastatic tumor samples for genomic studies through next-generation sequencing and development of patient-derived xenografts or organoids. Mechanistic insights gained from research autopsy studies of cancer patients can help identify new targets for therapeutic intervention. Finally, the success of research autopsy programs is bolstered by collaboration across different medical and scientific disciplines in addition to support from patients and families.

Implementing precision cancer medicine in the genomic era.

The utilization of genomic data to direct treatment for cancer patients represents the central tenet in precision oncology, in which a patient is matched to a specific drug or therapy based on the genetic drivers detected in his or her tumor rather than the tumor's histologic classification. The expected but not always realized outcomes of molecularly matched therapies include increased response rates, more durable responses, deeper responses, and decreased number of therapy-related side effects. In this review, we will discuss different facets of utilizing genomic data to direct the increasingly complex care of cancer patients. We discuss the enlarging compendium of actionable genomic alterations and the development of novel molecular diagnostic assays for clinical application. Finally, we present an overview of the growing number of genomics-driven clinical trials and conclude with a discussion of future challenges in the implementation of precision oncology.

IntLIM: integration using linear models of metabolomics and gene expression data.

BACKGROUND: Integration of transcriptomic and metabolomic data improves functional interpretation of disease-related metabolomic phenotypes, and facilitates discovery of putative metabolite biomarkers and gene targets. For this reason, these data are increasingly collected in large (> 100 participants) cohorts, thereby driving a need for the development of user-friendly and open-source methods/tools for their integration. Of note, clinical/translational studies typically provide snapshot (e.g. one time point) gene and metabolite profiles and, oftentimes, most metabolites measured are not identified. Thus, in these types of studies, pathway/network approaches that take into account the complexity of transcript-metabolite relationships may neither be applicable nor readily uncover novel relationships. With this in mind, we propose a simple linear modeling approach to capture disease-(or other phenotype) specific gene-metabolite associations, with the assumption that co-regulation patterns reflect functionally related genes and metabolites. RESULTS: The proposed linear model, metabolite ~ gene + phenotype + gene:phenotype, specifically evaluates whether gene-metabolite relationships differ by phenotype, by testing whether the relationship in one phenotype is significantly different from the relationship in another phenotype (via a statistical interaction gene:phenotype p-value). Statistical interaction p-values for all possible gene-metabolite pairs are computed and significant pairs are then clustered by the directionality of associations (e.g. strong positive association in one phenotype, strong negative association in another phenotype). We implemented our approach as an R package, IntLIM, which includes a user-friendly R Shiny web interface, thereby making the integrative analyses accessible to non-computational experts. We applied IntLIM to two previously published datasets, collected in the NCI-60 cancer cell lines and in human breast tumor and non-tumor tissue, for which transcriptomic and metabolomic data are available. We demonstrate that IntLIM captures relevant tumor-specific gene-metabolite associations involved in known cancer-related pathways, including glutamine metabolism. Using IntLIM, we also uncover biologically relevant novel relationships that could be further tested experimentally. CONCLUSIONS: IntLIM provides a user-friendly, reproducible framework to integrate transcriptomic and metabolomic data and help interpret metabolomic data and uncover novel gene-metabolite relationships. The IntLIM R package is publicly available in GitHub ( https://github.com/mathelab/IntLIM ) and includes a user-friendly web application, vignettes, sample data and data/code to reproduce results.

Transcription factor-associated combinatorial epigenetic pattern reveals higher transcriptional activity of TCF7L2-regulated intragenic enhancers.

BACKGROUND: Recent studies have suggested that combinations of multiple epigenetic modifications are essential for controlling gene expression. Despite numerous computational approaches have been developed to decipher the combinatorial epigenetic patterns or "epigenetic code", none of them has explicitly addressed the relationship between a specific transcription factor (TF) and the patterns. METHODS: Here, we developed a novel computational method, T-cep, for annotating chromatin states associated with a specific TF. T-cep is composed of three key consecutive modules: (i) Data preprocessing, (ii) HMM training, and (iii) Potential TF-states calling. RESULTS: We evaluated T-cep on a TCF7L2-omics data. Unexpectedly, our method has uncovered a novel set of TCF7L2-regulated intragenic enhancers missed by other software tools, where the associated genes exert the highest gene expression. We further used siRNA knockdown, Co-transfection, RT-qPCR and Luciferase Reporter Assay not only to validate the accuracy and efficiency of prediction by T-cep, but also to confirm the functionality of TCF7L2-regulated enhancers in both MCF7 and PANC1 cells respectively. CONCLUSIONS: Our study for the first time at a genome-wide scale reveals the enhanced transcriptional activity of cell-type-specific TCF7L2 intragenic enhancers in regulating gene expression.

Landscape of Microsatellite Instability Across 39 Cancer Types.

PURPOSE: Microsatellite instability (MSI) is a pattern of hypermutation that occurs at genomic microsatellites and is caused by defects in the mismatch repair system. Mismatch repair deficiency that leads to MSI has been well described in several types of human cancer, most frequently in colorectal, endometrial, and gastric adenocarcinomas. MSI is known to be both predictive and prognostic, especially in colorectal cancer; however, current clinical guidelines only recommend MSI testing for colorectal and endometrial cancers. Therefore, less is known about the prevalence and extent of MSI among other types of cancer. METHODS: Using our recently published MSI-calling software, MANTIS, we analyzed whole-exome data from 11,139 tumor-normal pairs from The Cancer Genome Atlas and Therapeutically Applicable Research to Generate Effective Treatments projects and external data sources across 39 cancer types. Within a subset of these cancer types, we assessed mutation burden, mutational signatures, and somatic variants associated with MSI. RESULTS: We identified MSI in 3.8% of all cancers assessed-present in 27 of tumor types-most notably adrenocortical carcinoma (ACC), cervical cancer (CESC), and mesothelioma, in which MSI has not yet been well described. In addition, MSI-high ACC and CESC tumors were observed to have a higher average mutational burden than microsatellite-stable ACC and CESC tumors. CONCLUSION: We provide evidence of as-yet-unappreciated MSI in several types of cancer. These findings support an expanded role for clinical MSI testing across multiple cancer types as patients with MSI-positive tumors are predicted to benefit from novel immunotherapies in clinical trials.

Performance evaluation for rapid detection of pan-cancer microsatellite instability with MANTIS.

In current clinical practice, microsatellite instability (MSI) and mismatch repair deficiency detection is performed with MSI-PCR and immunohistochemistry. Recent research has produced several computational tools for MSI detection with next-generation sequencing (NGS) data; however a comprehensive analysis of computational methods has not yet been performed. In this study, we introduce a new MSI detection tool, MANTIS, and demonstrate its favorable performance compared to the previously published tools mSINGS and MSISensor. We evaluated 458 normal-tumor sample pairs across six cancer subtypes, testing classification performance on variable numbers of target loci ranging from 10 to 2539. All three computational methods were found to be accurate, with MANTIS exhibiting the highest accuracy with 98.91% of samples from all six diseases classified correctly. MANTIS displayed superior performance among the three tools, having the highest overall sensitivity (MANTIS 97.18%, MSISensor 96.48%, mSINGS 76.06%) and specificity (MANTIS 99.68%, mSINGS 99.68%, MSISensor 98.73%) across six cancer types, even with loci panels of varying size. Additionally, MANTIS also had the lowest resource consumption (<1% of the space and <7% of the memory required by mSINGS) and fastest running times (49.6% and 8.7% of the running times of MSISensor and mSINGS, respectively). This study highlights the potential utility of MANTIS in classifying samples by MSI-status, allowing its incorporation into existing NGS pipelines.

A hidden Markov model to identify combinatorial epigenetic regulation patterns for estrogen receptor α target genes.

MOTIVATION: Many studies have shown that epigenetic changes, such as altered DNA methylation and histone modifications, are linked to estrogen receptor α (ERα)-positive tumors and disease prognoses. Several recent studies have applied high-throughput technologies such as ChIP-seq and MBD-seq to interrogate the altered architectures of ERα regulation in tamoxifen (Tam)-resistant breast cancer cells. However, the details of combinatorial epigenetic regulation of ERα target genes in breast cancers with acquired Tam resistance have not yet been fully examined. RESULTS: We developed a computational approach to identify and analyze epigenetic patterns associated with Tam resistance in the MCF7-T cell line as opposed to the Tam-sensitive MCF7 cell line, with the goal of understanding the underlying mechanisms of epigenetic regulatory influence on resistance to Tam treatment in breast cancer. In this study, we used ChIP-seq of ERα, RNA polymerase II, three histone modifications and MBD-seq data of DNA methylation in MCF7 and MCF7-T cells to train hidden Markov models (HMMs). We applied the Bayesian information criterion to determine that a 20-state HMM was best, which was reduced to a 14-state HMM with a Bayesian information criterion score of 1.21291 × 10(7). We further identified four classes of biologically meaningful states in this breast cancer cell model system, and a set of ERα combinatorial epigenetic regulated target genes. The correlated gene expression level and gene ontology analyses showed that different gene ontology terms were enriched with Tam-resistant versus sensitive breast cancer cells. Our study illustrates the applicability of HMM-based analysis of genome-wide high-throughput genomic data to study epigenetic influences on E2/ERα regulation in breast cancer.

Hierarchical modularity in ERα transcriptional network is associated with distinct functions and implicates clinical outcomes.

Recent genome-wide profiling reveals highly complex regulation networks among ERα and its targets. We integrated estrogen (E2)-stimulated time-series ERα ChIP-seq and gene expression data to identify the ERα-centered transcription factor (TF) hubs and their target genes, and inferred the time-variant hierarchical network structures using a Bayesian multivariate modeling approach. With its recurrent motif patterns, we determined three embedded regulatory modules from the ERα core transcriptional network. The GO analyses revealed the distinct biological function associated with each of three embedded modules. The survival analysis showed the genes in each module were able to render a significant survival correlation in breast cancer patient cohorts. In summary, our Bayesian statistical modeling and modularity analysis not only reveals the dynamic properties of the ERα-centered regulatory network and associated distinct biological functions, but also provides a reliable and effective genomic analytical approach for the analysis of dynamic regulatory network for any given TF.

High resolution detection and analysis of CpG dinucleotides methylation using MBD-Seq technology.

Methyl-CpG binding domain protein sequencing (MBD-seq) is widely used to survey DNA methylation patterns. However, the optimal experimental parameters for MBD-seq remain unclear and the data analysis remains challenging. In this study, we generated high depth MBD-seq data in MCF-7 cell and developed a bi-asymmetric-Laplace model (BALM) to perform data analysis. We found that optimal efficiency of MBD-seq experiments was achieved by sequencing ∼100 million unique mapped tags from a combination of 500 mM and 1000 mM salt concentration elution in MCF-7 cells. Clonal bisulfite sequencing results showed that the methylation status of each CpG dinucleotides in the tested regions was accurately detected with high resolution using the proposed model. These results demonstrated the combination of MBD-seq and BALM could serve as a useful tool to investigate DNA methylome due to its low cost, high specificity, efficiency and resolution.

W-ChIPeaks: a comprehensive web application tool for processing ChIP-chip and ChIP-seq data.

UNLABELLED: ChIP-based technology is becoming the leading technology to globally profile thousands of transcription factors and elucidate the transcriptional regulation mechanisms in living cells. It has evolved rapidly in recent years, from hybridization with spotted or tiling microarray (ChIP-chip), to pair-end tag sequencing (ChIP-PET), to current massively parallel sequencing (ChIP-seq). Although there are many tools available for identifying binding sites (peaks) for ChIP-chip and ChIP-seq, few of them are available as easy-accessible online web tools for processing both ChIP-chip and ChIP-seq data for the ChIP-based user community. As such, we have developed a comprehensive web application tool for processing ChIP-chip and ChIP-seq data. Our web tool W-ChIPeaks employed a probe-based (or bin-based) enrichment threshold to define peaks and applied statistical methods to control false discovery rate for identified peaks. The web tool includes two different web interfaces: PELT for ChIP-chip, BELT for ChIP-seq, where both were tested on previously published experimental data. The novel features of our tool include a comprehensive output for identified peaks with GFF, BED, bedGraph and .wig formats, annotated genes to which these peaks are related, a graphical interpretation and visualization of the results via a user-friendly web interface. AVAILABILITY: http://motif.bmi.ohio-state.edu/W-ChIPeaks/.