No abstract

Hepatitis B virus (HBV) infection is endemic in some parts of Asia, Africa, and South America and remains to be a significant public health problem in these areas. It is known as a leading risk factor for the development of hepatocellular carcinoma, but epidemiological studies have also shown that the infection may increase the incidence of several types of B-cell lymphoma. Here, by characterizing altogether 275 Chinese diffuse large B-cell lymphoma (DLBCL) patients, we showed that patients with concomitant HBV infection (surface antigen positive [HBsAg]) are characterized by a younger age, a more advanced disease stage at diagnosis, and reduced overall survival. Furthermore, by whole-genome/exome sequencing of 96 tumors and the respective peripheral blood samples and targeted sequencing of 179 tumors from these patients, we observed an enhanced rate of mutagenesis and a distinct set of mutation targets in HBsAg DLBCL genomes, which could be partially explained by the activities of APOBEC and activation-induced cytidine deaminase. By transcriptome analysis, we further showed that the HBV-associated gene expression signature is contributed by the enrichment of genes regulated by BCL6, FOXO1, and ZFP36L1. Finally, by analysis of immunoglobulin heavy chain gene sequences, we showed that an antigen-independent mechanism, rather than a chronic antigenic simulation model, is favored in HBV-related lymphomagenesis. Taken together, we present the first comprehensive genomic and transcriptomic study that suggests a link between HBV infection and B-cell malignancy. The genetic alterations identified in this study may also provide opportunities for development of novel therapeutic strategies.

Most implementations of mass spectrometry-based proteomics involve enzymatic digestion of proteins, expanding the analysis to multiple proteolytic peptides for each protein. Currently, there is no consensus of how to summarize peptides' abundances to protein concentrations, and such efforts are complicated by the fact that error control normally is applied to the identification process, and do not directly control errors linking peptide abundance measures to protein concentration. Peptides resulting from suboptimal digestion or being partially modified are not representative of the protein concentration. Without a mechanism to remove such unrepresentative peptides, their abundance adversely impacts the estimation of their protein's concentration. Here, we present a relative quantification approach, Diffacto, that applies factor analysis to extract the covariation of peptides' abundances. The method enables a weighted geometrical average summarization and automatic elimination of incoherent peptides. We demonstrate, based on a set of controlled label-free experiments using standard mixtures of proteins, that the covariation structure extracted by the factor analysis accurately reflects protein concentrations. In the 1% peptide-spectrum match-level FDR data set, as many as 11% of the peptides have abundance differences incoherent with the other peptides attributed to the same protein. If not controlled, such contradicting peptide abundance have a severe impact on protein quantifications. When adding the quantities of each protein's three most abundant peptides, we note as many as 14% of the proteins being estimated as having a negative correlation with their actual concentration differences between samples. Diffacto reduced the amount of such obviously incorrectly quantified proteins to 1.6%. Furthermore, by analyzing clinical data sets from two breast cancer studies, our method revealed the persistent proteomic signatures linked to three subtypes of breast cancer. We conclude that Diffacto can facilitate the interpretation and enhance the utility of most types of proteomics data.