Inferring clonal heterogeneity in cancer using SNP arrays and whole genome sequencing

Topological Structures in the Space of Treatment-Naïve Patients with Chronic Lymphocytic Leukemia

Patients are complex and heterogeneous; clinical data sets are complicated by noise, missing data, and the presence of mixed-type data. Using such data sets requires understanding the high-dimensional "space of patients", composed of all measurements that define all relevant phenotypes. The current state-of-the-art merely defines spatial groupings of patients using cluster analyses. Our goal is to apply topological data analysis (TDA), a new unsupervised technique, to obtain a more complete understanding of patient space. We applied TDA to a space of 266 previously untreated patients with Chronic Lymphocytic Leukemia (CLL), using the "daisy" metric to compute distances between clinical records. We found clear evidence for both loops and voids in the CLL data. To interpret these structures, we developed novel computational and graphical methods. The most persistent loop and the most persistent void can be explained using three dichotomized, prognostically important factors in CLL: IGHV somatic mutation status, beta-2 microglobulin, and Rai stage. In conclusion, patient space turns out to be richer and more complex than current models suggest. TDA could become a powerful tool in a researcher's arsenal for interpreting high-dimensional data by providing novel insights into biological processes and improving our understanding of clinical and biological data sets.

The prognostic significance of genomic complexity in patients with CLL

Chromosomal aberrations are a common feature of cancer and can fuel cancer progression and treatment resistance. In chronic lymphocytic leukemia (CLL), the presence of multiple chromosomal aberrations is commonly referred to as "genomic complexity" or "complex karyotype"- (CKT). In the context of chemo- and chemoimmunotherapy, genomic complexity is associated with poor response to treatment and short survival, while some targeted therapies are able to mitigate its adverse prognostic impact. This article reviews currently available data and literature on the role of genomic complexity in CLL. The currently established tools to measure genomic complexity in patients with CLL are summarized and their strengths and weaknesses for routine diagnostics are evaluated. Moreover, possible definitions of CKT as an indicator for genomic complexity are discussed. Finally, data on the impact of CKT on clinical outcomes of patients with CLL are reviewed and the implications for patient stratification are presented.

Cytogenetics in Chronic Lymphocytic Leukemia: ERIC Perspectives and Recommendations

Mounting evidence underscores the clinical value of cytogenetic analysis in chronic lymphocytic leukemia (CLL), particularly as it allows the identification of complex karyotype, that has recently emerged as a prognostic and potentially predictive biomarker. That said, explicit recommendations regarding the methodology and clinical interpretation of either chromosome banding analysis (CBA) or chromosome microarray analysis (CMA) are still lacking. We herein present the consensus of the Cytogenetic Steering Scientific Committee of ERIC, the European Research Initiative on CLL, regarding methodological issues as well as clinical interpretation of CBA/CMA and discuss their relevance in CLL. ERIC considers CBA standardized and feasible for CLL on the condition that standards are met, extending from the use of novel mitogens to the accurate interpretation of the findings. On the other hand, CMA, is also standardized, however, robust data on its clinical utility are still scarce. In conclusion, cytogenetic analysis is not yet mature enough to guide treatment choices in CLL. That notwithstanding, ERIC encourages the wide application of CBA, and potentially also CMA, in clinical trials in order to obtain robust evidence regarding the predictive value of specific cytogenetic profiles towards refining risk stratification and improving the management of patients with CLL.

A statistical approach for tracking clonal dynamics in cancer using longitudinal next-generation sequencing data

MOTIVATION

Tumours are composed of distinct cancer cell populations (clones), which continuously adapt to their local micro-environment. Standard methods for clonal deconvolution seek to identify groups of mutations and estimate the prevalence of each group in the tumour, while considering its purity and copy number profile. These methods have been applied on cross-sectional data and on longitudinal data after discarding information on the timing of sample collection. Two key questions are how can we incorporate such information in our analyses and is there any benefit in doing so?

RESULTS

We developed a clonal deconvolution method, which incorporates explicitly the temporal spacing of longitudinally sampled tumours. By merging a Dirichlet Process Mixture Model with Gaussian Process priors and using as input a sequence of several sparsely collected samples, our method can reconstruct the temporal profile of the abundance of any mutation cluster supported by the data as a continuous function of time. We benchmarked our method on whole genome, whole exome and targeted sequencing data from patients with chronic lymphocytic leukaemia, on liquid biopsy data from a patient with melanoma and on synthetic data and we found that incorporating information on the timing of tissue collection improves model performance, as long as data of sufficient volume and complexity are available for estimating free model parameters. Thus, our approach is particularly useful when collecting a relatively long sequence of tumour samples is feasible, as in liquid cancers (e.g. leukaemia) and liquid biopsies.

AVAILABILITY AND IMPLEMENTATION

The statistical methodology presented in this paper is freely available at github.com/dvav/clonosGP.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.