Oncogenic ERBB3 Mutations in Human Cancers

SummaryThe human epidermal growth factor receptor (HER) family of tyrosine kinases is deregulated in multiple cancers either through amplification, overexpression, or mutation. ERBB3/HER3, the only member with an impaired kinase domain, although amplified or overexpressed in some cancers, has not been reported to carry oncogenic mutations. Here, we report the identification of ERBB3 somatic mutations in ∼11% of colon and gastric cancers. We found that the ERBB3 mutants transformed colonic and breast epithelial cells in a ligand-independent manner. However, the mutant ERBB3 oncogenic activity was dependent on kinase-active ERBB2. Furthermore, we found that anti-ERBB antibodies and small molecule inhibitors effectively blocked mutant ERBB3-mediated oncogenic signaling and disease progression in vivo

A case-based reasoning system for genotypic prediction of HIV-1 co-receptor tropism.

Accurate co-receptor tropism (CRT) determination is critical for making treatment decisions in HIV management. We created a genotypic tropism prediction tool by utilizing the case-based reasoning (CBR) technique that attempts to solve new problems through applying the solution from similar past problems. V3 loop sequences from 732 clinical samples with diverse characteristics were used to build a case library. Additional sequence and molecular properties of the V3 loop were examined and used for similarity assessment. A similarity metric was defined based on each attribute's frequency in the CXCR4-using viruses. We implemented three other genotype-based tropism predictors, support vector machines (SVM), position specific scoring matrices (PSSM), and the 11/25 rule, and evaluated their performance as the ability to predict CRT compared to Monogram's enhanced sensitivity Trofile(®) assay (ESTA). Overall concordance of the CBR based tropism prediction algorithm was 81%, as compared to ESTA. Sensitivity to detect CXCR4 usage was 90% and specificity was at 73%. In comparison, sensitivity of the SVM, PSSM, and the 11/25 rule were 85%, 81%, and 36% respectively while achieving a specificity of 90% by SVM, 75% by PSSM, and 97% by the 11/25 rule. When we evaluated these predictors in an unseen dataset, higher sensitivity was achieved by the CBR algorithm (87%), compared to SVM (82%), PSSM (76%), and the 11/25 rule (33%), while maintaining similar level of specificity. Overall this study suggests that CBR can be utilized as a genotypic tropism prediction tool, and can achieve improved performance in independent datasets compared to model or rule based methods

An Evolutionary Model-Based Algorithm for Accurate Phylogenetic Breakpoint Mapping and Subtype Prediction in HIV-1

Genetically diverse pathogens (such as Human Immunodeficiency virus type 1, HIV-1) are frequently stratified into phylogenetically or immunologically defined subtypes for classification purposes. Computational identification of such subtypes is helpful in surveillance, epidemiological analysis and detection of novel variants, e.g., circulating recombinant forms in HIV-1. A number of conceptually and technically different techniques have been proposed for determining the subtype of a query sequence, but there is not a universally optimal approach. We present a model-based phylogenetic method for automatically subtyping an HIV-1 (or other viral or bacterial) sequence, mapping the location of breakpoints and assigning parental sequences in recombinant strains as well as computing confidence levels for the inferred quantities. Our Subtype Classification Using Evolutionary ALgorithms (SCUEAL) procedure is shown to perform very well in a variety of simulation scenarios, runs in parallel when multiple sequences are being screened, and matches or exceeds the performance of existing approaches on typical empirical cases. We applied SCUEAL to all available polymerase (pol) sequences from two large databases, the Stanford Drug Resistance database and the UK HIV Drug Resistance Database. Comparing with subtypes which had previously been assigned revealed that a minor but substantial (≈5%) fraction of pure subtype sequences may in fact be within- or inter-subtype recombinants. A free implementation of SCUEAL is provided as a module for the HyPhy package and the Datamonkey web server. Our method is especially useful when an accurate automatic classification of an unknown strain is desired, and is positioned to complement and extend faster but less accurate methods. Given the increasingly frequent use of HIV subtype information in studies focusing on the effect of subtype on treatment, clinical outcome, pathogenicity and vaccine design, the importance of accurate, robust and extensible subtyping procedures is clear

SCINA: A Semi-Supervised Subtyping Algorithm of Single Cells and Bulk Samples

Advances in single-cell RNA sequencing (scRNA-Seq) have allowed for comprehensive analyses of single cell data. However, current analyses of scRNA-Seq data usually start from unsupervised clustering or visualization. These methods ignore prior knowledge of transcriptomes and the probable structures of the data. Moreover, cell identification heavily relies on subjective and possibly inaccurate human inspection afterwards. To address these analytical challenges, we developed SCINA (Semi-supervised Category Identification and Assignment), a semi-supervised model that exploits previously established gene signatures using an expectation–maximization (EM) algorithm. SCINA is applicable to scRNA-Seq and flow cytometry/CyTOF data, as well as other data of similar format. We applied SCINA to a wide range of datasets, and showed its accuracy, stability and efficiency, which exceeded most popular unsupervised approaches. SCINA discovered an intermediate stage of oligodendrocytes from mouse brain scRNA-Seq data. SCINA also detected immune cell population changes in cytometry data in a genetically-engineered mouse model. Furthermore, SCINA performed well with bulk gene expression data. Specifically, we identified a new kidney tumor clade with similarity to FH-deficient tumors (FHD), which we refer to as FHD-like tumors (FHDL). Overall, SCINA provides both methodological advances and biological insights from perspectives different from traditional analytical methods

Diverse modes of genomic alteration in hepatocellular carcinoma

Background: Hepatocellular carcinoma (HCC) is a heterogeneous disease with high mortality rate. Recent genomic studies have identified TP53, AXIN1, and CTNNB1 as the most frequently mutated genes. Lower frequency mutations have been reported in ARID1A, ARID2 and JAK1. In addition, hepatitis B virus (HBV) integrations into the human genome have been associated with HCC.Results: Here, we deep-sequence 42 HCC patients with a combination of whole genome, exome and transcriptome sequencing to identify the mutational landscape of HCC using a reasonably large discovery cohort. We find frequent mutations in TP53, CTNNB1 and AXIN1, and rare but likely functional mutations in BAP1 and IDH1. Besides frequent hepatitis B virus integrations at TERT, we identify translocations at the boundaries of TERT. A novel deletion is identified in CTNNB1 in a region that is heavily mutated in multiple cancers. We also find multiple high-allelic frequency mutations in the extracellular matrix protein LAMA2. Lower expression levels of LAMA2 correlate with a proliferative signature, and predict poor survival and higher chance of cancer recurrence in HCC patients, suggesting an important role of the extracellular matrix and cell adhesion in tumor progression of a subgroup of HCC patients.Conclusions: The heterogeneous disease of HCC features diverse modes of genomic alteration. In addition to common point mutations, structural variations and methylation changes, there are several virus-associated changes, including gene disruption or activation, formation of chimeric viral-human transcripts, and DNA copy number changes. Such a multitude of genomic events likely contributes to the heterogeneous nature of HCC