Biology subjects

Peter J Campbell

Publications and source records attributed to Peter J Campbell.

3 recordsLinked to original sources

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

Mutational signatures associated with tobacco smoking in human cancer

Tobacco smoking increases the risk of at least 15 classes of cancer. We analyzed somatic mutations and DNA methylation in 5,243 cancers of types for which tobacco smoking confers an elevated risk. Smoking is associated with increased mutation burdens of multiple distinct mutational signatures, which contribute to different extents in different cancers. One of these signatures, mainly found in cancers derived from tissues directly exposed to tobacco smoke, is attributable to misreplication of DNA damage caused by tobacco carcinogens. Others likely reflect indirect activation of DNA editing by APOBEC cytidine deaminases and of an endogenous clock-like mutational process. The results are consistent with the proposition that smoking increases cancer risk by increasing the somatic mutation load, although direct evidence for this mechanism is lacking in some smoking-related cancer types.\n\nONE SENTENCE SUMMARYMultiple distinct mutational processes associate with tobacco smoking in cancer reflecting direct and indirect effects of tobacco smoke.

Cancer Biology↗

bioRxiv2014

Principles governing A-to-I RNA editing in the breast cancer transcriptome

ABSTRACTA-to-I editing substitutes inosines for adenosines at specific positions in mRNAs and can substantially alter a cells transcriptome. Currently, little is known about how RNA editing operates in cancer. Transcriptome analysis of 68 normal and cancerous breast tissues revealed that the editing enzyme ADAR acts uniformly, on the same loci, across tissues. Controlled ADAR expression experiments demonstrated that the editing frequency at all loci is proportional to both ADAR expression levels and the individual locus editability--a propensity to be edited determined by the surrounding nucleotide sequence. Comparison of tumor transcriptomes to those of normal breast and breast organoids, i.e. pure normal breast epithelial cells, demonstrated that the editing frequency is increased in tumor cells. This was consistent with ADAR immunohistochemistry. We also demonstrated that type I interferon response and ADAR DNA copy number explain together 53% of ADAR expression in breast cancers, an observation also valid in nearly all of 20 other cancer types in The Cancer Genome Atlas. Interferon exposure increased ADAR mRNA, protein expression and editing in four breast cell lines. Finally, ADAR silencing using shRNA lentivirus transduction in breast cancer cell lines led to more cell proliferation and less apoptosis. Our results reveal that A-to-I editing is a pervasive, yet reproducible, source of variation that is controlled by two factors, 1q amplification and inflammation, both highly prevalent among human cancers. This suggests the potential for a new class of therapeutic targets and an unexpected role for inflammation in cancers.

Genomics↗