All cancers are caused by somatic mutations; however, understanding of the biological processes generating these mutations is limited. The catalogue of somatic mutations from a cancer genome bears the signatures of the mutational processes that have been operative. Here we analysed 4,938,362 mutations from 7,042 cancers and extracted more than 20 distinct mutational signatures. Some are present in many cancer types, notably a signature attributed to the APOBEC family of cytidine deaminases, whereas others are confined to a single cancer class. Certain signatures are associated with age of the patient at cancer diagnosis, known mutagenic exposures or defects in DNA maintenance, but many are of cryptic origin. In addition to these genome-wide mutational signatures, hypermutation localized to small genomic regions, 'kataegis', is found in many cancer types. The results reveal the diversity of mutational processes underlying the development of cancer, with potential implications for understanding of cancer aetiology, prevention and therapy.

/pdf/signatures-of-mutational-processes-in-human-cancer-j3duru54oq.pdf

Signatures of mutational processes in human cancer

BackgroundSomatic mutations have the potential to encode “non-self” immunogenic antigens. We hypothesized that tumors with a large number of somatic mutations due to mismatch-repair defects may be susceptible to immune checkpoint blockade. MethodsWe conducted a phase 2 study to evaluate the clinical activity of pembrolizumab, an anti–programmed death 1 immune checkpoint inhibitor, in 41 patients with progressive metastatic carcinoma with or without mismatch-repair deficiency. Pembrolizumab was administered intravenously at a dose of 10 mg per kilogram of body weight every 14 days in patients with mismatch repair–deficient colorectal cancers, patients with mismatch repair–proficient colorectal cancers, and patients with mismatch repair–deficient cancers that were not colorectal. The coprimary end points were the immune-related objective response rate and the 20-week immune-related progression-free survival rate. ResultsThe immune-related objective response rate and immune-related progression-free survival ...

/pdf/pd-1-blockade-in-tumors-with-mismatch-repair-deficiency-1eb0ztv7j7.pdf

PD-1 Blockade in Tumors with Mismatch-Repair Deficiency

The Cancer Genome Atlas (TCGA) Research Network has profiled and analyzed large numbers of human tumors to discover molecular aberrations at the DNA, RNA, protein and epigenetic levels. The resulting rich data provide a major opportunity to develop an integrated picture of commonalities, differences and emergent themes across tumor lineages. The Pan-Cancer initiative compares the first 12 tumor types profiled by TCGA. Analysis of the molecular aberrations and their functional roles across tumor types will teach us how to extend therapies effective in one cancer type to others with a similar genomic profile.

/pdf/the-cancer-genome-atlas-pan-cancer-analysis-project-gw3srsikvs.pdf

The cancer genome atlas pan-cancer analysis project

Infiltrating stromal and immune cells form the major fraction of normal cells in tumour tissue and not only perturb the tumour signal in molecular studies but also have an important role in cancer biology. Here we describe 'Estimation of STromal and Immune cells in MAlignant Tumours using Expression data' (ESTIMATE)--a method that uses gene expression signatures to infer the fraction of stromal and immune cells in tumour samples. ESTIMATE scores correlate with DNA copy number-based tumour purity across samples from 11 different tumour types, profiled on Agilent, Affymetrix platforms or based on RNA sequencing and available through The Cancer Genome Atlas. The prediction accuracy is further corroborated using 3,809 transcriptional profiles available elsewhere in the public domain. The ESTIMATE method allows consideration of tumour-associated normal cells in genomic and transcriptomic studies. An R-library is available on https://sourceforge.net/projects/estimateproject/.

/pdf/inferring-tumour-purity-and-stromal-and-immune-cell-3qvnp398w9.pdf

Inferring tumour purity and stromal and immune cell admixture from expression data

The Cancer Genome Atlas Pan-Cancer analysis project

Four gene expression subtypes of high-grade serous ovarian cancer (HGSC) have been previously described. In these studies, a fraction of samples that did not fit well into the four subtype classifications were excluded. Therefore, we sought to systematically determine the concordance of transcriptomic HGSC subtypes across populations without removing any samples. We created a bioinformatics pipeline to independently cluster the five largest mRNA expression datasets using k-means and non-negative matrix factorization (NMF). We summarized differential expression patterns to compare clusters across studies. While previous studies reported four subtypes, our cross-population comparison does not support four. Because these results contrast with previous reports, we attempted to reproduce analyses performed in those studies. Our results suggest that early results favoring four subtypes may have been driven by including serous borderline tumors. In summary, our analysis suggests that either two or three, but not four, gene expression subtypes are most consistent across datasets.

https://www.biorxiv.org/content/biorxiv/early/2016/07/12/030239.full.pdf

Comprehensive cross-population analysis of high-grade serous ovarian cancer supports no more than three subtypes

The invention relates to an automatic-deviation-rectification, constant-speed and constant-tension rewinding machine. A magnetic powder brake and a cantilever unwinding inflation shaft are mounted on a rack; an unwinding membrane passes through an unwinding force sensor roll and an unwinding guide roll, and is delivered into a membrane processing device to form an unwinding tension control device; a screw rod sliding table and a stepping motor are fixed on the rack; a variable frequency motor, a magnetic powder clutch and a cantilever winding inflation shaft are fixed on the surface of the screw rod sliding table; a winding membrane sequentially passes through a photoelectric position sensor, a winding force sensor roll and an encoder roll, and finally passes into the membrane processing device to form a device for controlling winding tension, winding speed, deviation rectification and the like; and an unwinding force, winding force and deviation rectification controller with a program-controlled preset value is utilized to control constant tension of the membrane, and to rectify deviation automatically. The programmable logic controller (PLC), a frequency converter and the variable frequency motor are arranged to control the transmission of the membrane at the constant speed. The machine achieves multifunctional control, solves the problems of high stress deformation, non-uniform viscosity, the deviation and the like, is high in quality and efficiency, simple, reasonable in design, and convenient to operate and maintain, and is particularly applicable to rewinding of a polytetrafluoroethylene adhesive tape coil.

Automatic-deviation-rectification, constant-speed and constant-tension rewinding machine

Traditional statistical methods often fail to identify biologically meaningful biomarkers from expression data alone. In this paper, we develop a novel strategy, namely knowledge-driven multi-level Independent Component Analysis (ICA), to infer regulatory signals and identify biomarkers based on clustering results and partial prior knowledge. A statistical test is designed to evaluate significance of transcription factor enrichment for extracted gene set based on motif information. The experimental results on an Rsf-1 (HBXAP) induced microarray data set show that our method can successfully extract biologically meaningful biomarkers related to ovarian cancer compared to other gene selection methods with or without prior knowledge.

Biomarker identification by knowledge-driven multilevel ICA and motif analysis

Stability-Based Dimension Estimation of ICA with Application to Microarray Data Analysis.

In this study, we describe our efforts in building a clinical statistics and analysis application platform using an emerging clinical data standard, HL7 FHIR, and an open source web application framework, Shiny We designed two primary workflows that integrate a series of R packages to enable both patient-centered and cohort-based interactive analyses We leveraged Shiny with R to develop interactive interfaces on FHIR-based data and used ovarian cancer study datasets as a use case to implement a prototype Specifically, we implemented patient index, patient-centered data report and analysis, and cohort analysis The evaluation of our study was performed by testing the adaptability of the framework on two public FHIR servers We identify common research requirements and current outstanding issues, and discuss future enhancement work of the current studies Overall, our study demonstrated that it is feasible to use Shiny for implementing interactive analysis on FHIR-based standardized clinical data

Chen Wang

Papers

Comprehensive cross-population analysis of high-grade serous ovarian cancer supports no more than three subtypes

Automatic-deviation-rectification, constant-speed and constant-tension rewinding machine

Biomarker identification by knowledge-driven multilevel ICA and motif analysis

Stability-Based Dimension Estimation of ICA with Application to Microarray Data Analysis.

Shiny FHIR: An Integrated Framework Leveraging Shiny R and HL7 FHIR to Empower Standards-Based Clinical Data Applications.