The clinical impact of advances in the genetic understanding of cancer


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Gareth Morgan
The Institute of Cancer Research, London, UK

Abstract

Much has been learnt about cancer biology by systematically examining patients within the clinical trial setting using novel technologies such as gene expression profiling, DNA copy number analysis and next generation sequencing.

Studies have shown that myeloma is a good disease in which to model some of these genetic findings as it is not a single disease, but is rather a number of related diseases, each of which has a distinct molecular pathogenesis. The related diseases include monoclonal gammopathy of undetermined significance (MGUS), smouldering myeloma (SMM), multiple myeloma (MM) and plasma cell leukaemia (PCL). It is thought that the sequential acquisition of ‘genetic hits’ results in the transition from a benign condition to these more malignant phenotypes causing the classical hallmarks of cancer. Key genetic events include ploidy, translocations involving the immunoglobulin gene on chromosome 14, copy number abnormalities, mutations and epigenetic modifications. Underlying this transition, the existence and competition between different tumour clones, also termed intraclonal heterogeneity, has been recently highlighted.

Using next generation sequencing (NGS) and single cell analysis techniques, we have characterised some of the complex intraclonal relationships in MM. Our results suggest that all of the genetic deregulation necessary to give rise to an aggressive clinical state is already present in asymptomatic stages of the disease, but is masked by the existence of a predominant clone with a more benign pattern of behaviour that may compete for access to a putative myeloma niche. In addition, different patterns of ‘clonal tides’ are seen in paired tumour samples consistent with a Darwinian process of tumour evolution.

As well as utilising this information to understand myeloma biology, the genetic information can be used to identify biological subgroups of disease with distinct clinical behaviour. By studying patients treated within the NCRI Myeloma IX study of newly diagnosed myeloma patients, we have identified a high and low risk genetic signature (based on t(x;14), 17p, 1q and 1p abnormalities). Importantly this FISH-based signature adds prognostic information to standard staging systems in myeloma, and can be combined/complementary with more complicated biological data such as gene expression profiling to improve prognostification.

In the future this biological information will be used in two main ways to influence clinical decisions: Risk adapted therapy where myeloma patient subgroups are defined based on clinical outcome and treatment tailored accordingly; and targeted therapy where a target or biomarker is used to define a patient subgroup that will benefit from a specific drug.