Genomic complexity in patients with chronic lymphocytic leukaemia; Landscape, Definitions and Utility.

Chronic lymphocytic leukemia (CLL) is a B cell malignancy which has increasing prevalence in older people. Clinically, CLL is highly heterogenous with some patients requiring immediate treatment with dismal outcomes, whereas others are put on a watch and wait intervention. To improve patient outcome, many biomarkers have been developed which allow risk stratification of patients and can dictate treatment plans. These include serological markers such as beta-2-microglobulin expression, cell of origin markers such as IGHV mutational status and genetic alterations including ATM mutations and deletion of chromosome 17p. However, these biomarkers insufficiently predict patient outcome across the whole CLL population. A more holistic approach being suggested is genomic complexity (GC) which incorporates multiple established biomarkers. There is a great deal of evidence that indicates GC has utility to risk stratify patients, even within high risk subsets of the CLL population. Furthermore, evidence also suggests it can be used a predictive marker. The main limitation for application of this biomarker, is the lack of a definitive definition of GC that integrates multiple established biomarkers. Current literature investigates GC using various metrics such as the number of copy number aberrations (CNAs) per patient, number of mutations and CNAs per patient and the total length (Mb) of sub chromosomal losses and gains (dosage). Further variation is introduced through the variety of technology methods used to detect and measure GC, ranging from chromosomal banding analysis to targeted next generation sequencing (NGS). Furthermore, the work that has established GC as a useful predictive marker typically underrepresent patients treated with novel agents. Before the clinical application of this biomarker can be achieved, a unified approach to measure GC must be established with a validation across the wide variety of patients that make up the CLL population must be completed. The mechanism of action of GC is currently unknown but evidence indicates a critical role of TP53 mutation leading to genomic instability. However other genetic features associated with genomic instability haven't yet been fully investigated, such as ATM gene dysfunction and telomere erosion. It is likely that a better understanding of the molecular mechanisms that contribute to GC, will advance our understanding of disease relapse, provide more accurate rational for treatment selection, and result in more favorable outcomes and optimal clinical management.

Hypothesis: CLL patients identified with high GC, a novel prognostic and predictive biomarker, will also have other established high-risk biomarkers and poor overall survival.

Aim 1: To identify the GC metric that accurately captures biological and clinical trends within CLL patients.
Aim 2: To investigate the genetic drivers that lead to GC within CLL patients and how these genetic drivers impact the clinical outcome of patients.
Aim 3: To assess the validity of GC as a predictive biomarker within patients treated with novel agents.