Biology subjects

Ultan McDermott

Publications and source records attributed to Ultan McDermott.

4 recordsLinked to original sources

bioRxiv2016

Population-level characterization of pathway alterations with SLAPenrich dissects heterogeneity of cancer hallmark acquisition

Cancer hallmarks are evolutionary traits required by a tumour to develop. While extensively characterised, the way these traits are achieved through the accumulation of somatic mutations in key biological pathways is not fully understood. To shed light on this subject, we characterised the landscape of pathway alterations associated with somatic mutations observed in 4,415 patients across ten cancer types, using 374 orthogonal pathway gene-sets mapped onto canonical cancer hallmarks. Towards this end, we developed SLAPenrich: a computational method based on population-level statistics, freely available as an open source R package. Assembling the identified pathway alterations into sets of hallmark signatures allowed us to connect somatic mutations to clinically interpretable cancer mechanisms. Further, we explored the heterogeneity of these signatures, in terms of ratio of altered pathways associated with each individual hallmark, assuming that this is reflective of the extent of selective advantage provided to the cancer type under consideration. Our analysis revealed the predominance of certain hallmarks in specific cancer types, thus suggesting different evolutionary trajectories across cancer lineages.\n\nFinally, although many pathway alteration enrichments are guided by somatic mutations in frequently altered high-confidence cancer genes, excluding these driver mutations preserves the hallmark heterogeneity signatures, thus the detected hallmarks predominance across cancer types. As a consequence, we propose the hallmark signatures as a ground truth to characterise tails of infrequent genomic alterations and identify potential novel cancer driver genes and networks.

Bioinformatics↗

bioRxiv2016

Genome-wide chemical mutagenesis screens allow unbiased saturation of the cancer genome and identification of drug resistance mutations.

Drug resistance is an almost inevitable consequence of cancer therapy and ultimately proves fatal for the majority of patients. In many cases this is the consequence of specific gene mutations that have the potential to be targeted to re-sensitize the tumor. The ability to uniformly saturate the genome with point mutations without chromosome or nucleotide sequence context bias would open the door to identify all putative drug resistance mutations in cancer models. Here we describe such a method for elucidating drug resistance mechanisms using genome-wide chemical mutagenesis allied to next-generation sequencing. We show that chemically mutagenizing the genome of cancer cells dramatically increases the number of drug-resistant clones and allows the detection of both known and novel drug resistance mutations. We have developed an efficient computational process that allows for the rapid identification of involved pathways and druggable targets. Such a priori knowledge would greatly empower serial monitoring strategies for drug resistance in the clinic as well as the development of trials for drug resistant patients.

Genetics↗

bioRxiv2016

Personally tailored cancer management based on knowledge banks of genomic and clinical data

Sequencing of cancer genomes, or parts thereof, has become widespread and will soon be implemented as part of routine clinical diagnostics. However the clinical ramifications of this have not been fully assessed. Here we assess the utility of sequencing large and clinically well-annotated cancer cohorts to derive personalized predictions about treatment outcome. To this end we study a cohort of 1,540 patients with AML (acute myeloid leukemia) with genetic profiles from 111 cancer genes, cytogenetic data and diagnostic blood counts. We test existing and develop new models to compute the probability of six different clinical outcomes based on more than 100 genetic and clinical variables. The predictions derived from our knowledge bank are more detailed and outperform strata currently used in clinical practice (concordance C=72% v C=64%), and are validated on three cohorts and data from TCGA (C=70%). Our prognostic algorithm is available as an online tool (http://cancer.sanger.ac.uk/aml-multistage). A simulation of different treatment scenarios indicates that a refined risk stratification could reduce the number of bonemarrow transplants by up to 25%, while achieving the same survival. Power calculation show that the inclusion of further genes most likely has small effects on the prognostic accuracy; increasing the number of cases will further reduce the error of personalized predictions.

Cancer Biology↗

bioRxiv2016

Logic models to predict continuous outputs based on binary inputs with an application to personalized cancer therapy

Mining large datasets using machine learning approaches often leads to models that are hard to interpret and not amenable to the generation of hypotheses that can be experimentally tested. Finding actionable knowledge is becoming more important, but also more challenging as datasets grow in size and complexity. We present Logic Optimization for Binary Input to Continuous Output (LOBICO), a computational approach that infers small and easily interpretable logic models of binary input features that explain a binarized continuous output variable. Although the continuous output variable is binarized prior to optimization, the continuous information is retained to find the optimal logic model. Applying LOBICO to a large cancer cell line panel, we find that logic combinations of multiple mutations are more predictive of drug response than single gene predictors. Importantly, we show that the use of the continuous information leads to robust and more accurate logic models. LOBICO is formulated as an integer programming problem, which enables rapid computation on large datasets. Moreover, LOBICO implements the ability to uncover logic models around predefined operating points in terms of sensitivity and specificity. As such, it represents an important step towards practical application of interpretable logic models.

Bioinformatics↗