Enhancing Data Completeness in Early Detection Pathway of Prostate Cancer: Integration of a Dashboard-Driven Feedback Tool to Improve Quality of Care.

: Quality assurance in data collection is essential as data quality directly impacts the accuracy and reliability of outcomes. In the context of early detection of prostate cancer, improving data completeness is a key focus for enhancing patient care. This study aimed to evaluate the effectiveness of a data-driven feedback tool, visualized through a dashboard, in improving the completeness of data collection by healthcare professionals.

A pan-cancer screen identifies drug combination benefit in cancer cell lines at the individual and population level.

Combining drugs can enhance their clinical efficacy, but the number of possible combinations and inter-tumor heterogeneity make identifying effective combinations challenging, while existing approaches often overlook clinically relevant activity. We screen one of the largest cell line panels (N = 757) with 51 clinically relevant combinations and identify responses at the level of individual cell lines and tissue populations. We establish three response classes to model cellular effects beyond monotherapy: synergy, Bliss additivity, and independent drug action (IDA).

Effective drug combinations in breast, colon and pancreatic cancer cells.

Combinations of anti-cancer drugs can overcome resistance and provide new treatments. The number of possible drug combinations vastly exceeds what could be tested clinically. Efforts to systematically identify active combinations and the tissues and molecular contexts in which they are most effective could accelerate the development of combination treatments.

Inhibition of the Replication Stress Response Is a Synthetic Vulnerability in SCLC That Acts Synergistically in Combination with Cisplatin.

Small cell lung cancer (SCLC) is generally regarded as very difficult to treat, mostly due to the development of metastases early in the disease and a quick relapse with resistant disease. SCLC patients initially show a good response to treatment with the DNA damaging agents cisplatin and etoposide. This is, however, quickly followed by the development of resistant disease, which urges the development of novel therapies for this type of cancer.

Principal component analysis of binary genomics data.

Motivation: Genome-wide measurements of genetic and epigenetic alterations are generating more and more high-dimensional binary data. The special mathematical characteristics of binary data make the direct use of the classical principal component analysis (PCA) model to explore low-dimensional structures less obvious. Although there are several PCA alternatives for binary data in the psychometric, data analysis and machine learning literature, they are not well known to the bioinformatics community.

iTOP: inferring the topology of omics data.

Motivation: In biology, we are often faced with multiple datasets recorded on the same set of objects, such as multi-omics and phenotypic data of the same tumors. These datasets are typically not independent from each other. For example, methylation may influence gene expression, which may, in turn, influence drug response.

Mortality in Severe Human Immunodeficiency Virus-Tuberculosis Associates With Innate Immune Activation and Dysfunction of Monocytes.

Background: Case fatality rates among hospitalized patients diagnosed with human immunodeficiency virus (HIV)-associated tuberculosis remain high, and tuberculosis mycobacteremia is common. Our aim was to define the nature of innate immune responses associated with 12-week mortality in this population.

Methods: This prospective cohort study was conducted at Khayelitsha Hospital, Cape Town, South Africa.

Highlights from the ISCB Student Council Symposia in 2016.

This editorial provides a brief overview of the 12th International Society for Computational Biology (ISCB) Student Council Symposium and the 4th European Student Council Symposium held in Florida, USA and The Hague, Netherlands, respectively. Further, the role of the ISCB Student Council in promoting education and networking in the field of computational biology is also highlighted.

TANDEM: a two-stage approach to maximize interpretability of drug response models based on multiple molecular data types.

Motivation: Clinical response to anti-cancer drugs varies between patients. A large portion of this variation can be explained by differences in molecular features, such as mutation status, copy number alterations, methylation and gene expression profiles. We show that the classic approach for combining these molecular features (Elastic Net regression on all molecular features simultaneously) results in models that are almost exclusively based on gene expression.

A Landscape of Pharmacogenomic Interactions in Cancer.

Systematic studies of cancer genomes have provided unprecedented insights into the molecular nature of cancer. Using this information to guide the development and application of therapies in the clinic is challenging. Here, we report how cancer-driven alterations identified in 11,289 tumors from 29 tissues (integrating somatic mutations, copy number alterations, DNA methylation, and gene expression) can be mapped onto 1,001 molecularly annotated human cancer cell lines and correlated with sensitivity to 265 drugs.