Ying Hu

Bio: Ying Hu is an academic researcher from National Institutes of Health. The author has contributed to research in topics: Azoxymethane & Medicine. The author has an hindex of 46, co-authored 156 publications receiving 14842 citations. Previous affiliations of Ying Hu include University of Maryland, Baltimore & Thomas Jefferson University.

Comprehensive molecular portraits of human breast tumours

Abstract: We analysed primary breast cancers by genomic DNA copy number arrays, DNA methylation, exome sequencing, messenger RNA arrays, microRNA sequencing and reverse-phase protein arrays. Our ability to integrate information across platforms provided key insights into previously defined gene expression subtypes and demonstrated the existence of four main breast cancer classes when combining data from five platforms, each of which shows significant molecular heterogeneity. Somatic mutations in only three genes (TP53, PIK3CA and GATA3) occurred at >10% incidence across all breast cancers; however, there were numerous subtype-associated and novel gene mutations including the enrichment of specific mutations in GATA3, PIK3CA and MAP3K1 with the luminal A subtype. We identified two novel protein-expression-defined subgroups, possibly produced by stromal/microenvironmental elements, and integrated analyses identified specific signalling pathways dominant in each molecular subtype including a HER2/phosphorylated HER2/EGFR/phosphorylated EGFR signature within the HER2-enriched expression subtype. Comparison of basal-like breast tumours with high-grade serous ovarian tumours showed many molecular commonalities, indicating a related aetiology and similar therapeutic opportunities. The biological finding of the four main breast cancer subtypes caused by different subsets of genetic and epigenetic abnormalities raises the hypothesis that much of the clinically observable plasticity and heterogeneity occurs within, and not across, these major biological subtypes of breast cancer.

Allelic Variation in Gene Expression Is Common in the Human Genome

Abstract: Variations in gene sequence and expression underlie much of human variability. Despite the known biological roles of differential allelic gene expression resulting from X-chromosome inactivation and genomic imprinting, a large-scale analysis of allelic gene expression in human is lacking. We examined allele-specific gene expression of 1063 transcribed single-nucleotide polymorphisms (SNPs) by using Affymetrix HuSNP oligo arrays. Among the 602 genes that were heterozygous and expressed in kidney or liver tissues from seven individuals, 326 (54%) showed preferential expression of one allele in at least one individual, and 170 of those showed greater than fourfold difference between the two alleles. The allelic variation has been confirmed by real-time quantitative PCR experiments. Some of these 170 genes are known to be imprinted, such as SNRPN, IPW, HTR2A, and PEG3. Most of the differentially expressed genes are not in known imprinting domains but instead are distributed throughout the genome. Our studies demonstrate that variation of gene expression between alleles is common, and this variation may contribute to human variability.

Susceptibility to hepatocellular carcinoma is associated with genetic variation in the enzymatic detoxification of aflatoxin B1

Abstract: Aflatoxin B1 (AFB1) has been postulated to be a hepatocarcinogen in humans, possibly by causing p53 mutations at codon 249. AFB1 is metabolized via the phase I and II detoxification pathways; hence, genetic variation at those loci may predict susceptibility to the effects of AFB1. To test this hypothesis, genetic variation in two AFB1 detoxification genes, epoxide hydrolase (EPHX) and glutathione S-transferase M1 (GSTM1), was contrasted with the presence of serum AFB1-albumin adducts, the presence of hepatocellular carcinoma (HCC), and with p53 codon 249 mutations. Mutant alleles at both loci were significantly overrepresented in individuals with serum AFB1-albumin adducts in a cross-sectional study. Mutant alleles of EPHX were significantly overrepresented in persons with HCC, also in a case-control study. The relationship of EPHX to HCC varied by hepatitis B surface antigen status and indicated that a synergistic effect may exist. p53 codon 249 mutations were observed only among HCC patients with one or both high-risk genotypes. These results indicate that individuals with mutant genotypes at EPHX and GSTM1 may be at greater risk of developing AFB1 adducts, p53 mutations, and HCC when exposed to AFB1. Hepatitis B carriers with the high-risk genotypes may be an even greater risk than carriers with low-risk genotypes. These findings support the existence of genetic susceptibility in humans to the environmental carcinogen AFB1 and indicate that there is a synergistic increase in risk of HCC with the combination of hepatitis B virus infection and susceptible genotype.

Integrative analysis of complex cancer genomics and clinical profiles using the cBioPortal

Abstract: The cBioPortal for Cancer Genomics (http://cbioportal.org) provides a Web resource for exploring, visualizing, and analyzing multidimensional cancer genomics data. The portal reduces molecular profiling data from cancer tissues and cell lines into readily understandable genetic, epigenetic, gene expression, and proteomic events. The query interface combined with customized data storage enables researchers to interactively explore genetic alterations across samples, genes, and pathways and, when available in the underlying data, to link these to clinical outcomes. The portal provides graphical summaries of gene-level data from multiple platforms, network visualization and analysis, survival analysis, patient-centric queries, and software programmatic access. The intuitive Web interface of the portal makes complex cancer genomics profiles accessible to researchers and clinicians without requiring bioinformatics expertise, thus facilitating biological discoveries. Here, we provide a practical guide to the analysis and visualization features of the cBioPortal for Cancer Genomics.

Cancer Genome Landscapes

Abstract: Over the past decade, comprehensive sequencing efforts have revealed the genomic landscapes of common forms of human cancer. For most cancer types, this landscape consists of a small number of “mountains” (genes altered in a high percentage of tumors) and a much larger number of “hills” (genes altered infrequently). To date, these studies have revealed ~140 genes that, when altered by intragenic mutations, can promote or “drive” tumorigenesis. A typical tumor contains two to eight of these “driver gene” mutations; the remaining mutations are passengers that confer no selective growth advantage. Driver genes can be classified into 12 signaling pathways that regulate three core cellular processes: cell fate, cell survival, and genome maintenance. A better understanding of these pathways is one of the most pressing needs in basic cancer research. Even now, however, our knowledge of cancer genomes is sufficient to guide the development of more effective approaches for reducing cancer morbidity and mortality.

PD-1 and PD-L1 Immune Checkpoint Blockade to Treat Breast Cancer

Abstract: Immune checkpoint inhibition represents a major recent breakthrough in the treatment of malignant diseases including breast cancer. Blocking the programmed death receptor-1 (PD-1) and its ligand, PD-L1, has shown impressive antitumor activity and may lead to durable long-term disease control, especially in the triple-negative subtypes of breast cancer (TNBC). Although immune checkpoint blockade is generally well tolerated, specific immune-related adverse events (irAEs) may occur. This review summarizes the clinical efficacy, perspectives, and future challenges of using PD-1/PD-L1-directed antibodies in the treatment of breast cancer.