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Tumors with mismatch repair deficiency (MMR-d) are characterized by sequence alterations in microsatellites and can accumulate thousands of mutations. This high mutational burden renders tumors immunogenic and sensitive to programmed cell death–1 (PD-1) immune checkpoint inhibitors. Yet, despite their tumor immunogenicity, patients with MMR-deficient tumors experience highly variable responses, and roughly half are refractory to treatment. We present experimental and clinical evidence showing that the degree of microsatellite instability (MSI) and resultant mutational load, in part, underlies the variable response to PD-1 blockade immunotherapy in MMR-d human and mouse tumors. The extent of response is particularly associated with the accumulation of insertion-deletion (indel) mutational load. This study provides a rationale for the genome-wide characterization of MSI intensity and mutational load to better profile responses to anti–PD-1 immunotherapy across MMR-deficient human cancers.

Immunohistochemistry for mismatch repair protein expression is widely used as a surrogate for microsatellite instability status—an important signature for immunotherapy and germline testing. There are no systematic analyses examining the sensitivity of immunohistochemistry for microsatellite instability-high status. Mismatch repair immunohistochemistry and microsatellite instability testing were performed routinely as clinically validated assays. We classified germline/somatic mutation types as truncating (nonsense, frameshift, and in/del) versus missense and predicted pathogenicity of the latter. Discordant cases were compared with concordant groups: microsatellite instability-high/mismatch repair-deficient for mutation comparison and microsatellite stable/mismatch repair-proficient for immunohistochemical comparison. 32 of 443 (7%) microsatellite instability-high cases had immunohistochemistry. Four additional microsatellite instability-high research cases had discordant immunohistochemistry. Of 36 microsatellite instability-high cases with discordant immunohistochemistry, 30 were mismatch repair-proficient, while six (five MLH1 and one MSH2) retained expression of the defective mismatch repair protein and lost its partner. In microsatellite instability-high tumors with discordant immunohistochemistry, we observed an enrichment in deleterious missense mutations over truncating mutations, with 69% (25/36) of cases having pathogenic germline or somatic missense mutations, as opposed to only 19% (7/36) in a matched microsatellite instability-high group with concordant immunohistochemistry ( p  = 0.0007).  In microsatellite instability-high cases with discordant immunohistochemistry and MLH1 or PMS2 abnormalities, less cells showed expression ( p  = 0.015 and p  = 0.00095, respectively) compared with microsatellite stable/mismatch repair-proficient cases. Tumor mutation burden, MSIsensor score, and truncating mismatch repair gene mutations were similar between microsatellite instability-high cases with concordant versus discordant immunohistochemical expression. Approximately 6% of microsatellite instability-high cases have retained mismatch repair protein expression and would be missed by immunohistochemistry-based testing, hindering patient access to immunotherapy. Another 1% of microsatellite instability-high cases show isolated loss of the defective gene’s dimerization partner, which may lead to germline testing of the wrong gene. These cases are enriched for pathogenic mismatch repair missense mutations.

The Cancer Genome Atlas data on colorectal carcinoma have provided a comprehensive view of the tumor's genomic alterations and their tumorigenic roles. Tumor morphology, however, has not been fully integrated into the analysis. The aim of this study was to explore relevant associations between tumor morphology and the newly characterized genomic alterations in colorectal carcinoma. Two hundred and seven colorectal carcinomas that had undergone whole-exome sequencing as part of The Cancer Genome Atlas project and had adequate virtual images in the cBioPortal for Cancer Genomics constituted our study population. Upon analysis, a tight association between ‘microsatellite instability-high histology' and microsatellite instability-high (P<0.001) was readily detected and helped validate our image-based histology evaluation. Further, we showed, (1) among all histologies, the not otherwise specified type had the lowest overall mutation count (P<0.001 for entire cohort, P<0.03 for the microsatellite-instable group), and among the microsatellite-instable tumors, this type also correlated with fewer frameshift mutations in coding mononucleotide repeats of a defined set of relevant genes (P<0.01); (2) cytosine phosphate guanine island methylator phenotype-high colorectal cancers with or without microsatellite instability tended to have different histological patterns: the former more often mucinous and the latter more often not otherwise specified; (3) mucinous histology was associated with more frequent alterations in BRAF, PIK3CA, and the transforming growth factor-β pathway when compared with non-mucinous histologies (P<0.001, P=0.01, and P<0.001, respectively); and (4) few colorectal cancers (<9%) exhibited upregulation of immune-inhibitory genes including major immune checkpoints; these tumors were primarily microsatellite-instable (up to 43%, vs <3% in microsatellite-stable group) and had distinctly non-mucinous histologies with a solid growth. These morphology–molecular associations are interesting and propose important clinical implications. The morphological patterns associated with alterations of immune checkpoint genes bear the potential to guide patient selection for clinical trials that target immune checkpoints in colorectal cancer, and provide directions for future studies.

The value and suitability of cytology specimens for molecular diagnosis has been demonstrated by numerous studies. In practice, however, the success rates vary widely across institutions depending on the disease setting, institutional practices of acquisition, handling/processing, and testing methodologies. As the number of clinically relevant biomarkers continues to increase, more laboratories are turning to next-generation sequencing platforms for testing. Although amplicon-based next-generation sequencing assays, interrogating a limited genomic territory, can be performed with minimal input material, broader-based next-generation sequencing assays have higher DNA input requirements that may not be met if the small tissue samples are not acquired and handled appropriately. We briefly describe some of the process changes we have instituted in our laboratories when handling cytologic material to maximize the tissue available for broad hybrid-capture–based next-generation sequencing assays. Among the key changes established were the consolidation and preservation of previously discarded supernatant material in cytologic samples, the introduction of mineral oil for deparaffinization of cell blocks, and adjustments in the molecular laboratory process and bioinformatics pipelines. We emphasize that even minimal changes can have broad implications for test performance, highlighting the importance of a cohesive group-based approach among clinical, cytopathology, surgical pathology, molecular, and bioinformatics teams.

Microsatellite instability (MSI) is a critical phenotype of cancer genomes and an FDA-recognized biomarker that can guide treatment with immune checkpoint inhibitors. Previous work has demonstrated that next-generation sequencing data can be used to identify samples with MSI-high phenotype. However, low tumor purity, as frequently observed in routine clinical samples, poses a challenge to the sensitivity of existing algorithms. To overcome this critical issue, we developed MiMSI, an MSI classifier based on deep neural networks and trained using a dataset that included low tumor purity MSI cases in a multiple instance learning framework. On a challenging yet representative set of cases, MiMSI showed higher sensitivity (0.895) and auROC (0.971) than MSISensor (sensitivity: 0.67; auROC: 0.907), an open-source software previously validated for clinical use at our institution using MSK-IMPACT large panel targeted NGS data. In a separate, prospective cohort, MiMSI confirmed that it outperforms MSISensor in low purity cases ( P  = 8.244e-07). Identifying microsatellite instability (MSI) from routine next generation sequencing assays is an important part of clinical patient care. Here, authors develop a deep-learning based algorithm, highlighting its performance in a large validation cohort.

Background Massive parallel sequencing has the potential to replace microarrays as the method for transcriptome profiling. Currently there are two protocols: full-length RNA sequencing (RNA-SEQ) and 3'-tag digital gene expression (DGE). In this preliminary effort, we evaluated the 3' DGE approach using two reference RNA samples from the MicroArray Quality Control Consortium (MAQC). Results Using Brain RNA sample from multiple runs, we demonstrated that the transcript profiles from 3' DGE were highly reproducible between technical and biological replicates from libraries constructed by the same lab and even by different labs, and between two generations of Illumina's Genome Analyzers. Approximately 65% of all sequence reads mapped to mitochondrial genes, ribosomal RNAs, and canonical transcripts. The expression profiles of brain RNA and universal human reference RNA were compared which demonstrated that DGE was also highly quantitative with excellent correlation of differential expression with quantitative real-time PCR. Furthermore, one lane of 3' DGE sequencing, using the current sequencing chemistry and image processing software, had wider dynamic range for transcriptome profiling and was able to detect lower expressed genes which are normally below the detection threshold of microarrays. Conclusion 3' tag DGE profiling with massive parallel sequencing achieved high sensitivity and reproducibility for transcriptome profiling. Although it lacks the ability of detecting alternative splicing events compared to RNA-SEQ, it is much more affordable and clearly out-performed microarrays (Affymetrix) in detecting lower abundant transcripts.

Background Chromatin immunoprecipitation (ChIP) followed by next-generation sequencing (ChIP-Seq) has been widely used to identify genomic loci of transcription factor (TF) binding and histone modifications. ChIP-Seq data analysis involves multiple steps from read mapping and peak calling to data integration and interpretation. It remains challenging and time-consuming to process large amounts of ChIP-Seq data derived from different antibodies or experimental designs using the same approach. To address this challenge, there is a need for a comprehensive analysis pipeline with flexible settings to accelerate the utilization of this powerful technology in epigenetics research. Results We have developed a highly integrative pipeline, termed HiChIP for systematic analysis of ChIP-Seq data. HiChIP incorporates several open source software packages selected based on internal assessments and published comparisons. It also includes a set of tools developed in-house. This workflow enables the analysis of both paired-end and single-end ChIP-Seq reads, with or without replicates for the characterization and annotation of both punctate and diffuse binding sites. The main functionality of HiChIP includes: (a) read quality checking; (b) read mapping and filtering; (c) peak calling and peak consistency analysis; and (d) result visualization. In addition, this pipeline contains modules for generating binding profiles over selected genomic features, de novo motif finding from transcription factor (TF) binding sites and functional annotation of peak associated genes. Conclusions HiChIP is a comprehensive analysis pipeline that can be configured to analyze ChIP-Seq data derived from varying antibodies and experiment designs. Using public ChIP-Seq data we demonstrate that HiChIP is a fast and reliable pipeline for processing large amounts of ChIP-Seq data.

MicroRNAs play a role in regulating diverse biological processes and have considerable utility as molecular markers for diagnosis and monitoring of human disease. Several technologies are available commercially for measuring microRNA expression. However, cross-platform comparisons do not necessarily correlate well, making it difficult to determine which platform most closely represents the true microRNA expression level in a tissue. To address this issue, we have analyzed RNA derived from cell lines, as well as fresh frozen and formalin-fixed paraffin embedded tissues, using Affymetrix, Agilent, and Illumina microRNA arrays, NanoString counting, and Illumina Next Generation Sequencing. We compared the performance within- and between the different platforms, and then verified these results with those of quantitative PCR data. Our results demonstrate that the within-platform reproducibility for each method is consistently high and although the gene expression profiles from each platform show unique traits, comparison of genes that were commonly detectable showed that detection of microRNA transcripts was similar across multiple platforms.

Background Glutathione S-transferase omega-1 and 2 genes ( GSTO1 , GSTO2 ), residing within an Alzheimer and Parkinson disease (AD and PD) linkage region, have diverse functions including mitigation of oxidative stress and may underlie the pathophysiology of both diseases. GSTO polymorphisms were previously reported to associate with risk and age-at-onset of these diseases, although inconsistent follow-up study designs make interpretation of results difficult. We assessed two previously reported SNPs, GSTO1 rs4925 and GSTO2 rs156697, in AD (3,493 ADs vs. 4,617 controls) and PD (678 PDs vs. 712 controls) for association with disease risk (case-controls), age-at-diagnosis (cases) and brain gene expression levels (autopsied subjects). Results We found that rs156697 minor allele associates with significantly increased risk (odds ratio = 1.14, p = 0.038) in the older ADs with age-at-diagnosis > 80 years. The minor allele of GSTO1 rs4925 associates with decreased risk in familial PD (odds ratio = 0.78, p = 0.034). There was no other association with disease risk or age-at-diagnosis. The minor alleles of both GSTO SNPs associate with lower brain levels of GSTO2 ( p = 4.7 × 10 -11 -1.9 × 10 -27 ), but not GSTO1 . Pathway analysis of significant genes in our brain expression GWAS, identified significant enrichment for glutathione metabolism genes ( p = 0.003). Conclusion These results suggest that GSTO locus variants may lower brain GSTO2 levels and consequently confer AD risk in older age. Other glutathione metabolism genes should be assessed for their effects on AD and other chronic, neurologic diseases.

As the cost of whole genome sequencing (WGS) decreases, clinical laboratories will be looking at broadly adopting this technology to screen for variants of clinical significance. To fully leverage this technology in a clinical setting, results need to be reported quickly, as the turnaround rate could potentially impact patient care. The latest sequencers can sequence a whole human genome in about 24 hours. However, depending on the computing infrastructure available, the processing of data can take several days, with the majority of computing time devoted to aligning reads to genomics regions that are to date not clinically interpretable. In an attempt to accelerate the reporting of clinically actionable variants, we have investigated the utility of a multi-step alignment algorithm focused on aligning reads and calling variants in genomic regions of clinical relevance prior to processing the remaining reads on the whole genome. This iterative workflow significantly accelerates the reporting of clinically actionable variants with no loss of accuracy when compared to genotypes obtained with the OMNI SNP platform or to variants detected with a standard workflow that combines Novoalign and GATK.