Framework for the Use of External Controls to Evaluate Treatment Outcomes in Precision Oncology Trials.

Advances in genomics have enabled anticancer therapies to be tailored to target specific genomic alterations. Single-arm trials (SATs), including those incorporated within umbrella, basket, and platform trials, are widely adopted when it is not feasible to conduct randomized controlled trials in rare biomarker-defined subpopulations. External controls (ECs), defined as control arm data derived outside the clinical trial, have gained renewed interest as a strategy to supplement evidence generated from SATs to allow comparative analysis.

Development and validation of a deep learning model for improving detection of nonmelanoma skin cancers treated with Mohs micrographic surgery.

Background: Real-time review of frozen sections underpins the quality of Mohs surgery. There is an unmet need for low-cost techniques that can improve Mohs surgery by reliably corroborating cancerous regions of interest and surgical margin proximity.

Objective: To test that deep learning models can identify nonmelanoma skin cancer regions in Mohs frozen section specimens.

Synonymous alterations of cancer-associated Trp53 CpG mutational hotspots cause fatal developmental jaw malocclusions but no tumors in knock-in mice.

Intragenic CpG dinucleotides are tightly conserved in evolution yet are also vulnerable to methylation-dependent mutation, raising the question as to why these functionally critical sites have not been deselected by more stable coding sequences. We previously showed in cell lines that altered exonic CpG methylation can modify promoter start sites, and hence protein isoform expression, for the human TP53 tumor suppressor gene. Here we extend this work to the in vivo setting by testing whether synonymous germline modifications of exonic CpG sites affect murine development, fertility, longevity, or cancer incidence.

Computational prediction of multidisciplinary team decision-making for adjuvant breast cancer drug therapies: a machine learning approach.

Background: Multidisciplinary team (MDT) meetings are used to optimise expert decision-making about treatment options, but such expertise is not digitally transferable between centres. To help standardise medical decision-making, we developed a machine learning model designed to predict MDT decisions about adjuvant breast cancer treatments.

Methods: We analysed MDT decisions regarding adjuvant systemic therapy for 1065 breast cancer cases over eight years.

Gene-disease association with literature based enrichment.

Motivation: Gene set enrichment analysis (GSEA) annotates gene microarray data with functional information from the biomedical literature to improve gene-disease association prediction. We hypothesize that supplementing GSEA with comprehensive gene function catalogs built automatically using information extracted from the scientific literature will significantly enhance GSEA prediction quality.

Methods: Gold standard gene sets for breast cancer (BrCa) and colorectal cancer (CRC) were derived from the literature.

BICEPP: an example-based statistical text mining method for predicting the binary characteristics of drugs.

Background: The identification of drug characteristics is a clinically important task, but it requires much expert knowledge and consumes substantial resources. We have developed a statistical text-mining approach (BInary Characteristics Extractor and biomedical Properties Predictor: BICEPP) to help experts screen drugs that may have important clinical characteristics of interest.

Results: BICEPP first retrieves MEDLINE abstracts containing drug names, then selects tokens that best predict the list of drugs which represents the characteristic of interest.

Perpetrators of pharmacokinetic drug-drug interactions arising from altered cytochrome P450 activity: a criteria-based assessment.

Aims: To catalogue the perpetrators of CYP-mediated pharmacokinetic drug-drug interactions (PK-DDIs) using clinically relevant criteria, and to compare this with an analogous catalogue.

Methods: Candidate inhibitors and inducers of CYP1A2, CYP2C9, CYP2C19, CYP2D6 and CYP3A ('perpetrators') were evaluated using published clinical pharmacokinetic interaction studies. Studies were selected on the basis of ≥six human subjects, use of a validated in vivo probe substrate for the CYP enzyme, and clinically relevant dosing.

In silico prioritisation of candidate genes for prokaryotic gene function discovery: an application of phylogenetic profiles.

Background: In silico candidate gene prioritisation (CGP) aids the discovery of gene functions by ranking genes according to an objective relevance score. While several CGP methods have been described for identifying human disease genes, corresponding methods for prokaryotic gene function discovery are lacking. Here we present two prokaryotic CGP methods, based on phylogenetic profiles, to assist with this task.