Growth dynamics in naturally progressing chronic lymphocytic leukaemia

Abstract

How the genomic features of a patient’s cancer relate to individual disease kinetics remains poorly understood. Here we used the indolent growth dynamics of chronic lymphocytic leukaemia (CLL) to analyse the growth rates and corresponding genomic patterns of leukaemia cells from 107 patients with CLL, spanning decades-long disease courses. We found that CLL commonly demonstrates not only exponential expansion but also logistic growth, which is sigmoidal and reaches a certain steady-state level. Each growth pattern was associated with marked differences in genetic composition, the pace of disease progression and the extent of clonal evolution. In a subset of patients, whose serial samples underwent next-generation sequencing, we found that dynamic changes in the disease course of CLL were shaped by the genetic events that were already present in the early slow-growing stages. Finally, by analysing the growth rates of subclones compared with their parental clones, we quantified the growth advantage conferred by putative CLL drivers in vivo.

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Fig. 1: Growth dynamics and genetic changes in naturally progressing CLL.
Fig. 2: Integrative analysis of global CLL growth patterns and genetic attributes in an extension cohort.
Fig. 3: Treatment-associated evolution in relation to patterns of leukaemia growth.
Fig. 4: Subclonal growth patterns in untreated CLLs.
Fig. 5: Selective growth advantage of subclones in CLL.

Data availability

All relevant data are available from the authors and/or are included with the manuscript. Clinical data about patients and samples analysed in the discovery cohort are listed in Supplementary Table 1a, b; sequencing metrics and somatic mutations are provided in Supplementary Tables 24. WES data are in dbGaP under accession code phs001431.v1.p. For the extension cohort, patient and sample characteristics as well as sequencing data are available from a previous publication15, and clinical data are summarized in Extended Data Table and Supplementary Tables 7 and 8. Further data to assess WBC dynamics were collected from these patients for this study and are illustrated in Extended Data Fig. 2, with clinical characteristics of patients with additional relapse samples provided separately in Supplementary Table 1c.

Code availability

PhylogicNDT package18 is available at https://github.com/broadinstitute/PhylogicNDT. PhylogicNDT uses Python 2.7.13 and the following Python modules available from pypi.org: bottle 0.12.13, dill 0.2.7.1, et-xmlfile 1.0.1, intervaltree 2.1.0, jsonschema 2.6.0, lxml 3.7.3, more-itertools 2.5.0, mpmath 0.19, networkx 1.11, openpyxl 2.4.1, pdfkit 0.6.1, pydotplus 2.0.2, pymc 2.3.6, pymc3 3.0, python-dateutil 2.6.1, rpy2 2.8.5, seaborn 0.7.1, simplejson 3.10.0, svgwrite 1.1.9, scikit-learn 0.18.1, biopython 1.68. In addition, pyemd (https://github.com/garydoranjr/pyemd) and sselogsumexp (https://github.com/rmcgibbo/logsumexp) modules were used. Code for the Bayesian modelling of growth patterns is available at: https://github.com/ivbozic/Bayesian-Growth-Pattern-Modeling.

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Acknowledgements

We are grateful to P. dal Cin, D.-A. Landau, S. Shukla and U. Jäger for discussions. We also appreciate the efforts of all study nurses and clinical staff that made this study feasible, and the patients who generously provided their samples for this research. This work was supported in part by the NCI (5P01CA081534-14, 1R01CA155010-01A1, P01CA206978, U10CA180861), the CLL Global Research Foundation, and by NHLBI (1RO1HL103532-01). M.G. was supported by a Marie-Curie International Outgoing Fellowship from the European Union (PIOF-2013-624924). G.G. is partially supported by the Paul C. Zemecnik Chair in Oncology at the Massachusetts General Hospital Cancer Center. C.J.W. is a Scholar of the Leukemia and Lymphoma Society.

Author information

M.G., I.B., I.L., D.L., D.N., M.A.N., G.G. and C.J.W. designed the study, analysed and interpreted data. I.B. modelled CLL growth patterns across patients. I.L., D.L., I.B. and G.G. developed methods for the analysis of genomic data and modelled the clonal structure, trees and growth dynamics of individual clones. I.L., D.L., M.G. and G.G. performed analysis of genomic data. M.G., L.R., S.M.F., O.O. and R.G. collected samples and annotations. J.G.G., K.R.R., M.J.K., J.R.B. and T.J.K. oversaw patient care. M.G., L.R., W.Z., A.W. and C.C. performed sample isolation and analysis. K.S. and D.N. performed statistical analysis. D.R., C.S., J.S., J.G.R., J.M.G. and A.T.-W. contributed to the analysis of genomic data. M.G., I.B., I.L., D.L., K.S., D.N., G.G. and C.J.W. wrote the manuscript. All authors read and approved the final manuscript.

Correspondence to Gad Getz or Catherine J. Wu.

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Competing interests

C.J.W. is founder of Neon Therapeutics and a member of its scientific advisory board. G.G. receives research funds from IBM and Pharmacyclics. G.G. is an inventor of several bioinformatics-related patents, including patents related to MuTect and ABSOLUTE. C.J.W., D.N. and T.J.K. receive research funding from Pharmacyclics. J.S. is a current employee of Moderna Therapeutics. J.G.G. receives grant funding from Janssen, Acerta, Celgene; and received honoraria from Abbvie, AZ, Celgene, Kite, Janssen, Pharmacyclics, Roche and Novartis. K.R.R. is on Medical Advisory Boards of Pharmacyclics, Roche/Genentech and Cellectis. J.R.B. is a consultant for Abbvie, Acerta, Beigene, Genentech/Roche, Gilead, Juno/Celgene, Kite, Loxo, Novartis, Pfizer, Pharmacyclics, Sunesis, TG Therapeutics and Verastem; received honoraria from Janssen and Teva; received research funding from Gilead, Loxo, Sun and Verastem; and served on data safety monitoring committees for Morphosys and Invectys. The other authors declare no potential conflicts of interest.

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Extended data figures and tables

Extended Data Fig. 1 Growth kinetics of naturally progressing CLLs from the discovery cohort.

a, Time courses of the discovery CLL cohort (Supplementary Table 1). Circles indicate time points of samples analysed by WES. Dotted lines represent course of CLL from diagnosis (left vertical line) until last follow-up (arrows) or death (right vertical line), and solid lines indicate timeframe covered by the analysis of serial samples by WES, coloured by growth pattern. b, Cumulative distribution function (CDF) of posterior probabilities for carrying capacity K obtained from the Bayesian model based on a logistic growth pattern for patients. Categorizations of the growth pattern of the individual patients are marked. c, Classification of patients based on the probability that their carrying capacity K is below 1,000 (×109 cells per litre) (red numbers in top left corner). Also shown are the posterior probability distributions for all model parameters (carrying capacity K, growth rate r, WBC count at diagnosis X0 and variance of the noise σ2). Far right panels per patient: leukaemia burden information provided by WBC measurements (blue dots), with ten random fits from the Bayesian model. Red numbers in top left corners indicate time (years) from diagnosis to first treatment.

Extended Data Fig. 2 Growth kinetics of CLLs from the extension cohort.

ac, Shown are samples displaying: logistic growth (n = 43) (a), indeterminate growth (n = 30) (b) or exponential growth (n = 12) (c). See Supplementary Table 8 for information on growth pattern fitting. Blue dots denote WBC measurements; coloured lines denote ten random growth model fits (see Supplementary Methods). Red numbers indicate years from diagnosis to first treatment for patients who progressed to treatment.

Extended Data Fig. 3 Clonal shifts and growth rates in untreated CLL patients.

a, The increase in the numbers of total (top), clonal (middle) and subclonal (bottom) drivers is associated with overall leukaemia growth patterns. P values were determined by Kruskal–Wallis test. b, A trend towards increased maximal change in the CCF of a driver event is observed between the first and last pre-treatment samples of a given patient based on growth pattern. P values were determined by Kruskal–Wallis test. c, Top, probability of having a carrying capacity K of WBC of less than 1,000 × 109 cells per litre (blue dots) for patients with logistic, indeterminate or exponential growth patterns. Bottom, growth rates (small circles) together with 70% credible intervals (lines) across the discovery and extension samples, ordered based on the probability of logistic growth with samples classified as displaying logistic, indeterminate or exponential growth.

Extended Data Fig. 4 Assessment of evolutionary dynamics using sample pairs.

Changes in the CCF of subclones represented as two-dimensional pair-wise plots of multi-sample clustering results. Samples at a time point (TP) closest to diagnosis (first) versus the last sample before treatment (preTx) are shown in the left column; samples at the last time point before and the first time point after treatment are shown in the right column. a, b, Patients are grouped based on those having: subclones with significant evolution (a) or subclones that maintain interclonal balance (b). Significantly evolving subclones are indicated in orange (Supplementary Methods); expanding CLL driver mutations are coloured magenta. c, Examples of genetic evolution from the first to last pre-treatment time points, and from pre-treatment to relapse samples for patient 6 (with significant evolution) and patient 10 (not evolving). Shown are the two-dimensional distributions that reflect the average of the positional distributions of the cluster centres along the MCMC iterations, rather than the final posterior for the cluster centre, which is determined by the normalized product of the pre-clustered distributions of the mutations that were finally assigned to each cluster. Marginal distributions (on the x and y axes) depict the CCF distributions before clustering for each individual mutation. Final cluster assignment is indicated by the colour.

Extended Data Fig. 5 Detecting subclones and construction of evolutionary phylogenies using simulated data.

a, Bar plots showing the fraction of clustering results on simulated samples that are concordant with the ground truth (or differ by ∆n clusters). Simulations are grouped by low (2) and high (3–8) numbers of samples per case as well as low (2–9) and high (≥10) numbers of mutation per subclone. b, Similar CCF accuracy after clustering between simulated WES and WGS data. c, Simulation of a case with 5 samples and 5 subclones present at different CCF levels per sample (black lines denote ground truth). The predicted CCF distributions for each cluster are plotted as a function of the number of mutations in the subclone (from 2 to 100). When the number of mutations exceeds approximately 15–20, the CCF predictions become stable and accurate (low bias and variance). d, Examples of PhylogicNDT BuildTree algorithm results applied to simulated data. Grey shading highlights the correct tree, with percentage of MCMC iterations supporting the trees indicated. e, Analysis of prior selection for clustering. For a range of priors with varying mean number of clusters, K, the prior for α is computed, and the Dirichlet process posteriors for α and K illustrate how the choice of prior affects the estimation of K. f, Pigeon-hole principle: for two clusters, A and B (top), the convolution (middle) and difference (bottom) is illustrated. The area above 1.0 CCF of the convolution is consistent with the probability that they are parent–child rather than siblings. The area below 0.0 CCF of the difference represents the probability that cluster B is more prevalent than cluster A.

Extended Data Fig. 6 Subclonal genetic evolutionary dynamics in the discovery cohort.

a, Subclonal dynamics for each patient in the discovery cohort in relation to tumour load over time in the observed disease course (represented by WBC, with dots indicating an available WBC measurement). Arrows denote time of sampling with WES. Distinguishable subclones meeting the criteria for confident detection (>10% CCF, in at least one sequenced sample) are coloured. CCFs in time periods between sequenced time points were inferred from the closest sequenced sample. b, Subclonal growth patterns of additional patients analogous to Fig. 4.

Extended Data Fig. 7 Subclonal growth rate estimates of patients with non-bounded growth.

For 15 patients with non-bounded growth (EXP and IND) and at least one macroscopic subclone, we show the following: first column: selected complete phylogenetic trees of subclones; yellow boxes indicate branches that were detectable only in relapse samples; second column: cluster CCF dynamics over time with 95% credible intervals based on uncertainty of mutation assignment; third column: pre-treatment growth rates for each generated clone within the most likely phylogeny; fourth column: relative pre-treatment growth rates of subclones compared to their respective parent subclone.

Extended Data Fig. 8 Somatic copy number alteration calling from WES, WGS and SNP array data showing highly concordant results.

a, WES and WGS of CLLs from patients 1 and 4. b, Patient 1 data before and after capture bias correction via tangent normalization32. c, TCGA samples with available paired WES and single nucleotide polymorphism (SNP) array data.

Extended Data Fig. 9 Comparison of PhylogicNDT clustering results between WES and WGS data and growth of selected subclones.

a, In patient 1, paired results of WES and WGS data were available for all four time points and demonstrate matching CCFs throughout. b, c, CCF posterior distributions for the cluster centres (b) and individual mutations (c) for the corresponding subclones found in WES and WGS data of patient 1. d, e, For patients 4 and 6, two-dimensional comparisons are illustrated. f, g, Examples for subclones (magenta boxes) with a significant growth advantage relative to their parent and known driver (f), one subclone with significantly accelerated growth but no driver (g), and subclones with driver and no growth acceleration (h).

Extended Data Table 1 Exact logistic regression modelling of the probability of treatment

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Supplementary Tables 1-11.

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