Indel sensitive and comprehensive variant/mutation detection from RNA sequencing data for precision medicine

Abstract Background RNA-seq is the most commonly used sequencing application. Not only does it measure gene expression but it is also an excellent media to detect important structural variants such as single nucleotide variants (SNVs), insertion/deletion (Indels) or fusion transcripts. However, detection of these variants is challenging and complex from RNA-seq. Here we describe a sensitive and accurate analytical pipeline which detects various mutations at once for translational precision medicine. Methods The pipeline incorporates most sensitive aligners for Indels in RNA-Seq, the best practice for data preprocessing and variant calling, and STAR-fusion is for chimeric transcripts. Variants/mutations are annotated, and key genes can be extracted for further investigation and clinical actions. Three datasets were used to evaluate the performance of the pipeline for SNVs, indels and fusion transcripts. Results For the well-defined variants from NA12878 by GIAB project, about 95% and 80% of sensitivities were obtained for SNVs and indels, respectively, in matching RNA-seq. Comparison with other variant specific tools showed good performance of the pipeline. For the lung cancer dataset with 41 known and oncogenic mutations, 39 were detected by the pipeline with STAR aligner and all by the GSNAP aligner. An actionable EML4 and ALK fusion was also detected in one of the tumors, which also demonstrated outlier ALK expression. For 9 fusions spiked-into RNA-seq libraries with different concentrations, the pipeline was able to detect all in unfiltered results although some at very low concentrations may be missed when filtering was applied. Conclusions The new RNA-seq workflow is an accurate and comprehensive mutation profiler from RNA-seq. Key or actionable mutations are reliably detected from RNA-seq, which makes it a practical alternative source for personalized medicine

Robust hierarchical density estimation and regression for re-stained histological whole slide image co-registration.

For many disease conditions, tissue samples are colored with multiple dyes and stains to add contrast and location information for specific proteins to accurately identify and diagnose disease. This presents a computational challenge for digital pathology, as whole-slide images (WSIs) need to be properly overlaid (i.e. registered) to identify co-localized features. Traditional image registration methods sometimes fail due to the high variation of cell density and insufficient texture information in WSIs-particularly at high magnifications. In this paper, we proposed a robust image registration strategy to align re-stained WSIs precisely and efficiently. This method is applied to 30 pairs of immunohistochemical (IHC) stains and their hematoxylin and eosin (H&E) counterparts. Our approach advances the existing methods in three key ways. First, we introduce refinements to existing image registration methods. Second, we present an effective weighting strategy using kernel density estimation to mitigate registration errors. Third, we account for the linear relationship across WSI levels to improve accuracy. Our experiments show significant decreases in registration errors when matching IHC and H&E pairs, enabling subcellular-level analysis on stained and re-stained histological images. We also provide a tool to allow users to develop their own registration benchmarking experiments

Abstract B101: NFAT regulates a gene expression program associated with invasiveness and poor prognosis in colorectal cancer

Abstract Colorectal cancer is the second leading cause of cancer-related death in the United States. In order to understand the regulatory mechanisms underlying poor prognosis in colorectal cancer, we analyzed fourteen human colorectal cancer microarray data sets and identified co-expressed modules using network analysis. We next filtered these modules using gene expression data from a mouse model of metastatic colon cancer, narrowing down to a candidate metastasis-related module, and identified NFAT as its potential transcriptional regulator. The NFAT family and their identified targets were found to be upregulated in human colorectal cancer patients. Analysis of NFAT family members expression in mouse and human microarray datasets, revealed NFATc1 to be differentially expressed between metastatic and non-metastatic, and between disease progression and no disease progression, respectively. We found that high NFATc1 expression correlated with significantly increased invasion (p&amp;lt;0.0001) and migration (p&amp;lt;0.005) in mouse colon cancer cells. We show that RNAi- based knockdown of NFATc1 and functional inhibition by the calcineurin inhibitor FK506 resulted in downregulation of predicted NFAT target genes from the metastatic module and decreased cancer cell invasiveness. Finally, we showed that the expression of NFAT target genes was significantly correlated with both disease-specific and disease-free survival in Stage II and III colorectal cancer patients. Our studies suggest a role for NFATs in colon cancer cell invasion and a potential application for the NFAT driven program as a biologically anchored prognostic gene expression signature. Citation Format: Manish K. Tripathi, Shinji Mima, Zhiao Shi, Naresh Prodduturi, Zhu Jing, Kristen K. Ciombor, Xi Chen, Natasha Deane, Robert D. Beauchamp, Bing Zhang. NFAT regulates a gene expression program associated with invasiveness and poor prognosis in colorectal cancer. [abstract]. In: Proceedings of the AACR Special Conference on Tumor Invasion and Metastasis; Jan 20-23, 2013; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2013;73(3 Suppl):Abstract nr B101.</jats:p

Nuclear Factor of Activated T-cell Activity Is Associated with Metastatic Capacity in Colon Cancer

Metastatic recurrence is the leading cause of cancer death in patients with colorectal carcinoma. In order to capture the molecular underpinnings for metastasis and tumor progression, we performed integrative network analysis on 11 independent human colorectal cancer gene expression data sets and applied expression data from an immunocompetent mouse model of metastasis as an additional filter for this biological process. In silico analysis of one metastasis-related co-expression module predicted Nuclear Factor of Activated T-cell (NFAT) transcription factors as potential regulators for the module. Cells selected for invasiveness and metastatic capability expressed higher levels of NFATc1 as compared with poorly metastatic and less invasive parental cells. We found that inhibition of NFATc1 in human and mouse colon cancer cells resulted in decreased invasiveness in culture and down-regulation of metastasis-related network genes. Overexpression of NFATc1 significantly increased the metastatic potential of colon cancer cells while inhibition of NFATc1 reduced metastasis growth in an immunocompetent mouse model. Finally, we found that an 8-gene signature comprising genes up-regulated by NFATc1 significantly correlated with worse clinical outcomes in Stage II and III colorectal cancer patients. Thus, NFATc1 regulates colon cancer cell behavior and its transcriptional targets constitute a novel, biologically-anchored gene expression signature for the identification of colon cancers with high risk of metastatic recurrence