Real-world Data for Clinical Evidence Generation in Oncology

Cancers exhibit extensive mutational heterogeneity and the resulting long tail phenomenon complicates the discovery of the genes and pathways that are significantly mutated in cancer. We perform a Pan-Cancer analysis of mutated networks in 3281 samples from 12 cancer types from The Cancer Genome Atlas (TCGA) using HotNet2, a novel algorithm to find mutated subnetworks that overcomes limitations of existing single gene and pathway/network approaches.. We identify 14 significantly mutated subnetworks that include well-known cancer signaling pathways as well as subnetworks with less characterized roles in cancer including cohesin, condensin, and others. Many of these subnetworks exhibit co-occurring mutations across samples. These subnetworks contain dozens of genes with rare somatic mutations across multiple cancers; many of these genes have additional evidence supporting a role in cancer. By illuminating these rare combinations of mutations, Pan-Cancer network analyses provide a roadmap to investigate new diagnostic and therapeutic opportunities across cancer types.

The process of generating 'signals' of possible unrecognized hazards from spontaneous adverse drug reaction reporting data has been likened to looking for a needle in a haystack. However, statistical approaches to the data have been under-utilised. Using the UK Yellow Card database, we have developed and evaluated a statistical aid to signal generation called a Proportional Reporting Ratio (PRR). The proportion of all reactions to a drug which are for a particular medical condition of interest is compared to the same proportion for all drugs in the database, in a 2 x 2 table. We investigated a group of newly-marketed drugs using as minimum criteria for a signal, 3 or more cases, PRR at least 2, chi-squared of at least 4. The database was used to examine retrospectively 15 drugs newly-marketed in the UK, with the highest levels of ADR reporting. The method identified 481 signals meeting the minimum criteria during the period 1996-8. Further evaluation of these showed that 70% were known adverse reactions, 13% were events which were likely to be related to the underlying disease and 17% were signals requiring further evaluation. Proportional reporting ratios are a valuable aid to signal generation from spontaneous reporting data which are easy to calculate and interpret, and various refinements are possible.