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Supplementary Information

Materials/Subjects and Methods

Patients

The study was approved by the local ethics committee (REC No: 09/H0606/5). All

patients gave written informed consent in accordance with the Declaration of

Helsinki. Patients followed up at Liverpool University Hospital, Bournemouth Hospital

and the Oxford Radcliffe Cancer and Haematology Centre were recruited into the

study. Patient characteristics are summarized in Table 1. Overall, the patient cohort

was that of a tertiary referral centre with a high proportion of unmutated and

treatment refractory cases.

Sample collection

To minimize ambiguities in OncoSNP1 data interpretation as a result of sample

contamination, only samples from patients with lymphocytes contributing >90% of the

total white blood cell count (WBC) were collected. To achieve this, samples were

analyzed by multiparameter flow cytometry and expert hematopathological review.

Samples with <90% circulating B-CLL cells were excluded. These represented <10%

of patients, including patients with small lymphocytic lymphoma and early stage

patients. In total, 93 pre-treatment samples were collected.16 patients did not require

any treatment, 18 had chemotherapy refractory disease and for 15 patients, follow-up

was <1 year. 48 patients relapsed and for 42 of these, follow-up samples before

treatment of subsequent relapse were also available. Time to relapse varied from

patient to patient (6 months to 5 years), but follow-up samples were always from the

next relapse.

DNA was extracted from vital frozen cells using the QIAamp DNA Midi Kit (Qiagen,

Hilden, Germany) according to the manufacturer’s protocol.

A flowchart documenting the overall study design, the numbers of samples involved

and where they fit into the work undertaken is given in Supplementary Fig 1.

Clinical Risk Score and Statistical Analyses

To investigate the clinical significance of CNAs/cnLOHs, we assigned a clinical risk

score to each clinically evaluable patient in our cohort (n=76). Scores assigned were

were: 0=on watch and wait (W&W), 1=progression free survival (PFS) >1year,

2=PFS<1year and 3=chemotherapy refractory). We also categorized additional

covariates for testing (see ‘Summary Table for Statistical Testing’ below). We used

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the total length of CNAs noted rather than the area under the curve (AUC) because

B-CLL is relatively stable and CNAs are comparatively sparse, confined mainly to

single copy number deletions or gains. Thus the AUC for B-CLL data reduces down

to a measure of total aberration length. This is in contrast to highly unstable cancers

such as polyploidy breast cancer or chromosomally unstable colon cancer, where

high values of AUC will result from long abnormalities with high copy number.

Logistic regression was used to evaluate the association between one response and

several prognostic categories using the appropriate statistical test for ordinal

variables eg Kruskal-Wallis test2, Cochran-Armitage trend exact test3,4, Chi-square

test5. The significance level for all statistical tests was 0.05. The analyses were

performed using the statistical software SAS 9.2.

Summary Table for Statistical Testing

Covariate Description Type CategoriesPatient No ID - -

Age Age in years Continuous -

Sex Sex Categorical Male, Female

CRS Clinical Risk Score Categorical 0, 1, 2, 3 (W&W, PFS>1year,

PFS<1year, chemotherapy

refractory)

Length of CNAs/cnLOHs Total Length of CNAs/cnLOHs Categorical <1Mb, 1-5Mb, >5 Mb

Number of CNAs/cnLOHs Total Number of CNAs/cnLOHs Categorical 0, 1, 2, 3, >3

del11q22.3 Known Abnormality Categorical Yes, no

Trisomy12 Known Abnormality Categorical Yes, no

del13q14.3 Known Abnormality Categorical Yes, no

del17p13.1 Known Abnormality Categorical Yes, no

TP53 gene Mutation status Categorical Negative, Positive

del17p13.1 or TP53 Known Abnormality Categorical Yes, No

IGHV gene status Variable Gene Sequence Categorical Unmutated, Mutated

RB1 gene RB1 deletion or cnLOH Categorical Yes, No

Clonal evolution Clonal evolution Categorical Yes, No

CRS = Clinical Risk Score

CNA = Copy Number Alteration

IGHV = immunoglobulin heavy chain variable gene

PFS = Progression Free Survival

Fluorescence in situ hybridization (FISH)

Interphase cells from CLL cultures were analyzed by FISH using the Vysis CLL FISH

panel probe set (Abbott Molecular, Illinois, USA) according to manufacturer’s

instructions. The slides were viewed using an Axioscope fluorescence microscope

(Zeiss, New York, USA) equipped with the appropriate filters and imaging software.

A total of 200 nuclei were scored for each probe.

Array Studies using the Agilent Technologies 244K genome-wide oligonucleotide

platform

These hybridizations were carried out according to Agilent protocols found on the

Agilent Technologies website

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(http://www.genomics.agilent.com/GenericA.aspx?PageType=Product&SubPageTyp

e=ProductLiterature&PageID=1465).

Illumina Genome-wide SNP Platform hybridizations

Hybridization to Illumina 1M-Duo SNP chips was performed according to

manufacturer’s protocols found on registration at

http://www.illumina.com/products/human1m_duo_dna_analysis_beadchip_kits.ilmn.

Due to product discontinuation, 22 samples were hybridized to alternative

HumanOmni1-Quad SNP chips according to manufacturer’s protocols found on

registration at http://www.illumina.com/support/array/array_kits/humanomni1-

quad_beadchip_kit/documentation.ilmn. In brief, patient DNA was denatured,

amplified and enzymatically fragmented and then hybridized onto the BeadChips by

rocking in an Illumina Hybridization Oven at 48˚C for 16-24hrs. The BeadChips were

washed according to the Illumina Inc protocol and the hybridized DNA detected by

primer extension with labeled nucleotides followed by detection using fluorescent

antibodies. The data were processed using GenomeStudioV2009.2 (Illumina, Inc.,

San Diego, California, USA) and then analyzed using OncoSNP v1.0 (see next

section). For visual comparisons, the data were processed also using Nexus 5

Discovery Edition (BioDiscovery, Inc., El Segundo, California, USA) with the following

settings in the first instance (SNPRank Segmentation): Significance Threshold 1x 10-

5; Max Contiguous Probe Spacing (Kbp) 1000.0; Min number of probes per segment

5; High Gain 0.6; Gain 0.2; Loss -0.2; Big Loss -1.0; 3:1 sex chromosome gain 1.2;

Homozygous Frequency threshold 0.95; Homozygous Value Threshold 0.8;

Heterozygous Imbalance Threshold 0.4; Minimum LOH Length (Kb) 20; percentage

outliers to remove 3%. All Nexus processed plots were also inspected visually to

scan for changes not identified using these analysis settings.

Statistical Computational Tool for Data Analysis

A number of dedicated cancer tools have emerged recently for SNP array analysis6-

10. However, not all approaches are best suited for our study. For example, Navin et

al. (2010)9 and Letouze et al. (2010)10 developed explicit modeling strategies and

algorithms to model the sequence of events leading to the genomic aberrations

observed in tumor samples. These methods rely on obtaining multiple spatio-

temporal samples (e.g. pre-treatment biopsies, sections from different parts of a

tumour, metastases, etc) from an individual patient and using the information to infer

possible models of tumour progression. In our study, these approaches would be

less useful since we have data generated from two time points only and spatial

http://www.illumina.com/products/human1m_duo_dna_analysis_beadchip_kits.ilmn

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heterogeneity is not applicable in this scenario. Furthermore, the degree of genomic

complexity in B-CLL is substantially lower than in the breast and bladder cancers

considered in other studies. Importantly, we were interested not only in the

accumulation of genomic aberrations over time (which in B-CLL is much rarer than

the other cancers), but we were also interested particularly in the expansion of

distinct sub-clones. For these reasons we selected OncoSNP to detect genomic

aberrations and quantify cellular admixture at a per-SNP level. We did not develop

formal algorithms for elucidating the actual sub-clonal structure; this will be

addressed in future work that will be aided by high-depth next generation sequencing

technology.

OncoSNP Analysis

Detailed methods have been described previously1 (see also

http://sites.google.com/site/oncosnp/). OncoSNP was run for 15 EM iterations in

intra-tumor heterogeneity mode using a thinning setting of 30 for training. Before

analysis, we removed all SNP and monomorphic probes in WTCCC2 germ-line copy

number variant regions. In addition, we also removed probes showing high levels of

signal variation (greater than three times the average median absolute deviation

across all probes) in the WTCCC2 samples. We used natural logarithms when

calculating the log Bayes Factor. Putative genomic aberrations detected by

OncoSNP with log Bayes Factor less than 30 were removed from consideration and

considered to be insignificant. The log Bayes Factor threshold value was required

due to unexplained, spatially correlated error artifacts in the data that resemble and

occur on the scale of real genomic aberrations (particularly when we allow tumor

heterogeneity and small signal deflections that may correspond to aberrations

present at low quantities in the sample). As described previously for QuantiSNP11,

the threshold value of 30 allows us to achieve low false positive rates whilst retaining

good detection power based on simulation and real data testing.

Reference data

To help identify and exclude germline CNVs of unlikely relevance from our data we

excluded SNP and monomorphic copy number probes in known germline CNV

regions from the OncoSNP analysis and we also used the Database of Genomic

Variants (DGV), a comprehensive catalog of structural variation noted in control data

from many studies and representing data from over 3000 samples12. This allowed us

to define somatic gains and losses >20Kb in size. Copy number changes that

encompassed fully changes noted in the DGV were excluded from further analysis.

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However, the DGV does not inform upon cnLOH. To determine whether regions of

cnLOH >2Mb noted in our B-CLL samples were observed commonly in controls, we

used data from 150 controls, selected randomly from 6000 control sample data sets.

The latter were generated from 3000 samples from the 1958 British Birth Cohort and

3000 from the UK Blood Service Control Group by the Wellcome Trust Case Control

Consortium using an Illumina 1.2M custom chip. Although the occurrence of a region

of cnLOH both in WTCCC2 controls and in B-CLL samples could not rule out the

existence of relevant gene mutations in these regions, the information did provide an

element of stringency for cnLOH selection/exclusion. The main criterion for

excluding a cnLOH region >2Mb from our data was if it occurred/overlapped

relatively frequently in the control samples. By contrast, if a region of cnLOH was not

observed at all, then we included it in our data because we considered it unlikely to

occur at appreciable frequency in the rest of the WTCCC2 cohort. Therefore, for our

purposes, 150 data sets (one and a half times the number of pre-treatment samples)

were considered sufficient.

In addition, we considered CNA analysis against patient constitutional DNA.

However, this was not feasible for this study as germline samples were not available;

routine collection has started only recently. Furthermore, additional tests would be

limited severely by financial constraints

Array Data Analysis

The Illumina raw data generated using the B-CLL samples are available on request

from the Corresponding Author.

All CNAs ≥20Kb and all cnLOHs ≥2Mb were included in the analysis; both were

easily verified visually affording a high level of confidence in the results. The

comparatively higher ≥2Mb size threshold for cnLOH was applied because very small

regions of cnLOH are extremely common and current lack of knowledge would

render further investigation of putative clinical relevance unviable. Smaller

CNAs/cnLOHs were only included if they were in specific regions of interest and

visually verified. Imbalances that had been repeatedly observed previously either in

control data deposited in the DGV or in the WTCCC2 samples were not pursued

further. Similarly, cnLOH regions identified repeatedly (identical or overlapping) in

the WTCCC2 samples were not pursued further.

Where possible, the start and end point positions of CNA/cnLOH regions were

defined using OncoSNP. Of interest, we observed that OncoSNP and Nexus calls

were highly comparable; the start and end base positions differed subtly, but were

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too small to alter the genes mapping within the minimally deleted regions (MDRs)

and minimally overlapping regions (MORs). Where there were apparent

discrepancies, the events and positions were manually verified using Nexus.

In order to delineate MDRs/MORs, OncoSNP (or in some instances, Nexus) start

and end positions of all CNA/cnLOH regions were entered first into Microsoft Excel

2007 spreadsheets. Second, CNAs noted in control samples in the DGV or cnLOH

regions occurring commonly in the WTCCC2 data were removed. Third, regions of

overlap involving the remaining CNAs/cnLOHs were identified using Microsoft Excel

2007 functions and the start and end positions of MDRs/MORs identified according

to the start/end positions of the changes noted in informative samples.

To be classified as recurrent, CNA/cnLOH regions had to occur at least two times,

either in different pre-treatment samples or once in a pre-treatment sample and once

newly emerging in a different relapse sample. Where relapse samples showed an

increased percentage of cells compared with the pre-treatment samples, this was

also noted. To increase the stringency for identifying MORs harboring putative driver

genes, MORs >5Mb were excluded. To avoid any inclusion of germ-line variants,

CNAs 20-500kb were only included if they showed mosaicism, if they were

homozygous deletions involving at least one exon, or if they mapped to a known

region of interest or had newly emerged/showed increase in percentage at relapse.

Results are given in Table 2.

Pathway Analysis

We identified 706 genes within the 62 MORs presented in Table 2. After exclusion of

genes mapping to regions with known copy number variants reported in the DGV,

542 genes remained and were tested for pathway enrichment using R/Bioconductor

org.Hs.eg.db and GOstats packages against a universe of 45,469 possible IDs. We

noted KEGG Pathways significantly over-represented at p≤0.05. Results were

validated using the DAVID web-based interface13. These analyses were then

repeated adding RB1, ATM, TP53 and DLEU2 to the previous list of genes.

TP53 mutation analysis

TP53 was screened for mutations using four high-resolution melting (HRM) assays

covering exons 5-8. Primers sequences are provided in the Table below. Each 10ul

PCR reaction contained 1x HotShot Diamond (Clent Life Sciences, Stourbridge, UK)

1x LCGreen (Clent Life Sciences), 1x Q solution (Qiagen), 1uM each primers and

40ng DNA. The PCR amplification consisted of an initial enzyme activation step of

95°C for 10 min, followed by 45 cycles of 95°C for 15sec, 60°C for 20sec, 72°C for

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15sec. This was followed by the HRM which increased from 72°C to 95°C by 0.1°C

every 2sec. Both amplification and HRM were performed on a Rotorgene 6000

(Qiagen). Following HRM results were analyzed on the HRM module of the Rotor-

Gene 6000 software 1.7. Positive PCR products were purified then sequenced from

both strands. The products were analyzed on the CEQ 8000 (Beckman Coulter,

California, USA) using the Sequence Investigator software.

Primers used for TP53 sequencing

Primer Sequence (5'-3')

TP53 exon 5 (F) TGCCGTCTTCCAGTTGCT

TP53 exon 5 (R) CATCGCTATCTGAGCAGC

TP53 exon 6 (F) CAGGCCTCTGATTCCTCACTGATTGCTC

TP53 exon 6 (R) CTCACCTGGAGGGCCACTGACAA

TP53 exon 7 (F) CAAGGCGCACTGGCCTCATCT

TP53 exon 7 (R) CAGGCCAGTGTGCAGGGT

TP53 exon 8 (F) GGTAGGACCTGATTTCCTTACT

TP53 exon 8 (R) AGGCATAACTGCACCCTTGG

Analysis of Immunoglobulin heavy chain variable (IGHV) gene rearrangements

To identify clonal rearrangements of the immunoglobulin heavy (IGH) chain gene and

determine the somatic mutation status of the variable (V) gene sequence in patients

we used the IGH Somatic Hypermutation (SHM) Assay v2.0 (Invivoscribe, La Coutat,

France) according to the manufacturer’s instructions. Sequences were run on the

CEQ 8000 (Beckman Coulter) and analyzed by Vquest

(http://www.imgt.org/IMGT_vquest/share/textes/). The IGHV gene status of a patient

was categorized as mutated if there was <98% homology with germline and

unmutated if there was ≥98% homology with germline or presence of the V3-21 re-

arrangement14.

BLIMP1 sequencing

11 assays were designed to cover the whole of the coding region of the BLIMP1

gene. Primer sequences are provided in the Table below. Each primer had a M13

tail; forward: 5’GTAAAACGACGGCCAGT 3’ and reverse: 5’

CAGGAAACAGCTATGAC 3’. Each 25µl reaction contained 1x Qiagen MasterMix

(Qiagen) and 10mM of each primer. All PCR conditions, except for 5b, consisted of

an initial enzyme activation step of 95°C for 15 min, followed by 35 cycles of 95°C for

30sec, 60°C for 90sec, 72°C for 90sec, followed by a final 10min extension at 72°C.

Conditions for 5b were the same except the annealing temperature was 57°C.

Purified amplicons were sequenced from both strands using the M13 tails, then

http://www.imgt.org/IMGT_vquest/share/textes/

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analyzed on the CEQ 8000 (Beckman Coulter, California, USA) using the Sequence

Investigator software and compared with the corresponding germ-line sequences.

Primers used for BLIMP1 sequencing


BLIMP1 exon 1 (F) GCCGAGTGGCTAAGGAAATC

BLIMP1 exon 1 (R) GCAGAAGCATGTTTCTACTGC

BLIMP1 exon 2 (F) CATAGCCTCTCAGAAGGAGC

BLIMP1 exon 2 (R) GTAGGGAGATTTGGCACCAG

BLIMP1 exon 3 (F) GGCATCATTAATGTCTGTTTACTTATC

BLIMP1 exon 3 (R) CGCACCTTAGTCCCAGCTAC

BLIMP1 exon 4 (F) CTTACCTGTTTCCGCCCTG

BLIMP1 exon 4 (R) CAGCATGTCTGGACCAATCC

BLIMP1 exon 5a (F) CTAGCCCTCTGTGTAATCGC

BLIMP1 exon 5a (R) GTTGTTGATGCCATTCATGC

BLIMP1 exon 5b (F) AGTACGCTCACTACCCCAAGTTC

BLIMP1 exon 5b (R) CTCACAGCCCCTTGGACTG

BLIMP1 exon 6 (F) GAGCCAGCTTGAGAGCAGAG

BLIMP1 exon 6 (R) TGGGAGGGTGACTCACAGAC

BLIMP1 exon 7a (F) CCCGTTGGCAACTCTTAATC

BLIMP1 exon 7a (R) GCCTTTGCCTTGTTCATGC

BLIMP1 exon 7b (F) AAATGGTTTCCCCTCACCTC

BLIMP1 exon 7b (R) TGGGGAAATTTTCGCAGTG

BLIMP1 exon 7c (F) AGTCCTGTGGCCATTCAGAG

BLIMP1 exon 7c (R) ACCGACGTGATTGTGAGGTC

BLIMP1 exon 7d (F) TGAGCCAAGCCATGTAAAAG

BLIMP1 exon 7d (R) TGGAGATGTCGCTCACTGAC

ATG5 sequencing

10 assays were designed to cover the whole of the coding region of the ATG5 gene.

Primer sequences are provided in the Table below. Each primer had a M13 tail;

forward: 5’GTAAAACGACGGCCAGT 3’ and reverse: 5’ CAGGAAACAGCTATGAC

3’. Each 25µl reaction contained 1x Qiagen MasterMix (Qiagen) and 10mM of each

primer. All PCR conditions consisted of an initial enzyme activation step of 97°C for

15 min, followed by 35 cycles of 97°C for 30sec, 60°C for 90sec, 72°C for 2 min,

followed by a final 10min extension at 72°C. Purified amplicons were sequenced

from both strands using the M13 tails, then analyzed on the CEQ 8000 (Beckman

Coulter, California, USA) using the Sequence Investigator software and compared

with the corresponding germ-line sequences.

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Primers used for ATG5 sequencing


ATG5 exon 1 (F) CTGGGTTAGGCAGAACACG

ATG5 exon 1 (R) ACAAGGTGGACACACACACG

ATG5 exon 2 (F) TGCAAGGATCTGACTAATGCTC

ATG5 exon 2 (R) CCCATTTGCCACAATCAATG

ATG5 exon 3 (F) AAGAACACGGCTGTTTTTCC

ATG5 exon 3 (R) TGCTTAATAATGCAGAAAAATTCAC

ATG5 exon 4 (F) TTAAAGCCCCTGACATTTGG

ATG5 exon 4 (R) AATGGGACGAAGGAGAAATG

ATG5 exon 5 (F) TTGAAAAACTGGGGGATATAGTTC

ATG5 exon 5 (R) AATCTGGGCACAGAGGCTAC

ATG5 exon 6 (F) TTTCATCTCTTCATGTGAGGTATTC

ATG5 exon 6 (R) TGTCTGAGGCTTTCATAAATGG

ATG5 exon 7 (F) AAAAGGCACCTAATGCCAAC

ATG5 exon 7 (R) GAAATGTTTTAATGTTGCTGATTG

ATG5 exon 8a (F) TTGTTGGGTTTCTTTCTTGG

ATG5 exon 8a (R) TTCGTTAAGGAAAGATGGGTTTAC

ATG5 exon 8b (F) TTGAACTTTAGCTCATGAAAGTGG

ATG5 exon 8b (R) TCAGTGAAAATCGCAAAAGG

ATG5 exon 8c (F) CGATCATGGTTTTAGATCCCATA

ATG5 exon 8c (R) TTTTAAATAAAGACGGACACAACA

Results

Comparison between FISH and Agilent Technologies 244K and Illumina 1M-Duo

array platforms

As part of the validation of array based platforms for the detection of CNAs in CLL,

we tested 31 patient DNAs using both the Agilent 244K and the Illumina 1M-Duo

platforms and compared the results with those obtained by routine FISH

(Supplementary Table 1). Both array platforms detected all of the CNAs seen by

FISH. Importantly, one case presented in Supplementary Table 1 (CLL034) and an

additional case tested only on the Illumina Platform (CLL118) had small 11q

deletions encompassing the ATM gene that were not noted in the patients’ FISH

results. The array data show that the ~732kb region interrogated by the probe from

the Vysis ATM/CEP11 FISH Probe Kit overlaps proximally with a non-deleted region

in both cases, thereby allowing probe binding (Supplementary Fig. 2). Both array

platforms also detected five 13q deletions that were not reported in the FISH results

(Supplementary Table 1). In addition, the use of arrays enabled the extent and

boundaries of the abnormalities to be defined more accurately.

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Choice of Platform

Whilst both Agilent and Illumina platforms allowed the reliable detection of CNAs,

only the SNP-based Illumina platform was capable of identifying regions of cnLOH

that might be important in B-CLL evaluation. For example, three out of 93 patients,

CLL071, CLL075 and MCLL148, had cnLOH encompassing TP53. Mutation analysis

of these patients revealed TP53 mutations on both alleles (Supplementary Table 2,

Supplementary Fig. 3). When considering which SNP platform to choose for the

study, the Illumina platform was preferred to the Affymetrix because of the superior

signal to noise ratio. The content of the 1M-Duo chip (the highest density chip

available for use at the time) matched, in large part, the 1M-Duo design used for the

WTCCC2 study (1,109,332 probes in common). Therefore, this platform was chosen

to extend our studies.

Validation of OncoSNP Quantification

Spiking experiments have shown previously the excellent precision of the

quantitative data obtained with OncoSNP using promyelocytic leukemia cancer,

colon cancer and breast cancer samples1. To further validate the quantifications

obtained, we compared the OncoSNP output for heterozygous losses with visual

inspection of Nexus plots; the percentage calls were consistent with the observations

(Supplementary Fig. 4). Furthermore, we performed blindly a series of interphase

FISH experiments using cells from a subset of patients with known cytogenetic

abnormalities involving 11q22.3, 13q14.3, or 17p13.1 (Supplementary Table 3). This

comparison showed good correlation in 18/20 samples tested. One of the two

potentially inconsistent cases was CLL071, which involved a homozygous

del13q14.3. Here the OncoSNP call of 100% was not stated in Supplementary Table

3 because formal OncoSNP calibration for homozygous losses is pending. Thus,

inaccurate calibration may be the reason for the inconsistency in this case. The

second inconsistent result identified was for CLL084 where OncoSNP did not detect

the 15% 13q14.3 loss reported by FISH. On visual inspection of the array data, we

were unable to confirm the CNA despite the fact that a low-level CNA of 15% would

be expected to be visible. As FISH background levels of 10%-20% are allowable, we

believe that the most likely explanation here is that the FISH result may reflect non-

specific background.

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Limitations of OncoSNP in this Study

Ultimately, the accuracy with which we can quantify cellular admixture using

OncoSNP or any other computational tool is determined by the noise variances of

the SNP data. Whilst OncoSNP was essential for our study, the performance is

subject to technological limitations which mean it is not proven accurate enough to

report mixed cell populations of >90% or <10% and has not been calibrated formally

for homozygous deletions. However, for some patient samples we did record values

>90% (but<100%), or of apparently 100% based on visual inspection of Nexus B-

allele frequency plots (Supplementary Fig. 4). For six cases, an aberration change

from 90% to >90% or vice versa was noted between paired pre-treatment and

relapse samples, potentially impacting on interpretation of clonal evolution. For these

cases, the possibility of variation in sampling (namely variation in the percentage of

B-cells harvested from paired pre-treatment sample and relapse samples) had to be

taken into consideration as a possible explanation for the observations. For three of

the six cases noted it was not possible to rule out sampling variation on the basis of

the array data alone. However, for the remaining three cases, comparison with CNAs

from other chromosomal regions of the samples suggested strongly that the

differences in percentages were not attributable to sampling. For example, in both

the pre-treatment and relapse samples for CLL068, the percentage of cells carrying

the chromosome 13q loss remained unchanged, whilst the percentage of cells

carrying the chromosome 2 loss increased visibly from 90% in the pre-treatment

sample to >90% in the relapse sample (data not shown).

Finally, we observed that some cnLOH regions visualized manually and reported

here, were interpreted by OncoSNP as germ-line variants.

We expect that the fundamental limits of ‘analogue’ array based technology will be

addressed ultimately by ‘digital’ high resolution next generation sequencing

technologies. However, for this study, our experience was that the data were best

interpreted by using the output of OncoSNP in combination with manual visualization

and verification of results using software such as Nexus.

Analysis of well-recognized CNAs in 93 pre-treatment samples

11q22.3 and 17p13.1 abnormalities

We identified a total of 17 out of 93 patients with deletions ≥500kb involving 11q22.3

(one of these, in CLL084, is newly visible at relapse) and two with cnLOH >2Mb). As

outlined above, for two cases, CLL034 and CLL118, the deletions were missed by

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FISH using the standard Abbott/Vysis probes (Supplementary Fig. 2). Including these

cases, the MDR in our sample set was ~577kb involving CUL5, ACAT1, NPAT, ATM,

C11orf65, KDELC2, EXPH5 and DDX10. In both patient and control groups there

were small regions of cnLOH that included ATM, but two patients showed large

regions of cnLOH that were not observed in controls; CLL090 gave a mosaic pattern

showing 50% cnLOH 11qcen-qter, whereas CLL108 showed complete cnLOH for

11q12.3-qter. Although we did not formally prove the presence of ATM mutations in

these patients, the results point to a possible role of cnLOH in CLL.

Nine of the 93 samples tested carried single copy losses involving 17p13.1. Eight of

these had TP53 mutations of the non-deleted allele; three of the 93 had mono-allelic

mutations. A further three patients had bi-allelic mutations with cnLOH encompassing

TP53 (Supplementary Table 2, Supplementary Fig. 3). None of the control samples

showed CNAs or cnLOH of this region. This data provides further evidence that

areas of cnLOH can indicate hotspots for pathogenically important mutations. A

further 2/93 patients (CLL093 and CLL108) had mono-allelic mutations.

When we tested statistically the status of TP53 mutations vs CRSs we found that

patients who were TP53 mutation positive showed significantly higher CRSs (80.0%

≥ 2 CRS) than TP53 mutation negative patients (46.6% ≥ 2 CRS) (Cochran-Armitage

trend exact test, p-value=0.0154; Supplementary Table 5). Statistically significant

differences were also found with respect to the total length of CNAs/cnLOHs

between TP53 mutation groups (Cochran-Armitage trend exact test, p-value=0.0081;

Supplementary Table 5).

13q14.3 abnormalities

The most common abnormalities were CNAs/cnLOHs involving 13q14.3, detected in

47/93 of the pre-treatment patients. The ~7kb MDR in these cases involved DLEU2.

However, no single patient had a 13q abnormality restricted to DLEU2

(Supplementary Fig. 5).

33 patients had heterozygous deletions encompassing the MDR whereas 14 had a

homozygous deletion involving this region (Supplementary Table 6). In 24 patients,

the deletion included the retinoblastoma gene RB1 at 13q14.2. Seven patients with

13q abnormalities showed large regions of cnLOH. One of them (CLL074) showed

isolated cnLOH involving the 13q14.3 MDR. Quantification of the subclonal

distribution of CNAs/cnLOHs involving the 13q locus revealed that whilst the MDR

was deleted in the majority of leukemic cells in individual patients, regions next to the

MDR were deleted only in subpopulations of the same leukemia (eg: CLL060).

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Together, these data confirm previous observations that the 13q14.3 locus is highly

heterogeneous in CLL patients. Furthermore, the results suggest that apparently

smaller abnormalities involving the MDR can gradually extend through clonal

evolution from an initially small proportion of cells carrying the extended abnormality

(Supplementary Fig. 6).

13q abnormalities and clinical outcome

There has been accumulating evidence that the size of 13q deletions has prognostic

relevance with regards to depth and duration of response to treatment15-17. However,

recent genetic evidence from conditional knock-out mice suggests that deletion of the

MDR containing the mir15a/16-1 locus is sufficient to control the expansion of the

mature B-cell pool in mice leading to a CLL-like disease albeit with low penetrance

and after a long period of latency18. In an attempt to establish whether there was an

association between the size of the 13q deletion and clinical outcome, we compared

the size of 13q deletions with the clinical risk score. To this end, we defined Type I

and Type II del13q14.3 as previously described on the basis of aberrations affecting

the RB1 gene (16). We excluded patients with additional known CNAs/cnLOHs in

addition to 13q deletions (in order to eliminate compounding factors) and patients for

whom no CRS was available and analyzed only patients who had del13q as their

sole abnormality (21/47 patients). When we looked at CRSs in patients without RB1

deletions or cnLOH compared with those with RB1 deletions or cnLOH we found that

patients without RB1 deletions or cnLOHs showed higher CRSs (73.7% ≥2 CRS)

than patients who carried RB1 deletions or cnLOHs (42.1% ≥2 CRS) (Cochran-

Armitage trend exact test, p-value=0.0466; Supplementary Table 7).

Thus, in our small cohort, large Type II 13q deletions were not associated with a

worse clinical outcome.

When we looked at IGHV gene status (unmutated/mutated) in patient samples with

isolated 13q anomalies we noted that patients without RB1 deletions or cnLOHs

showed a slightly higher percentage of cases with mutated IGHV genes (60.0%)

compared with patients with RB1 deletions or cnLOHs (41.2%). However, this did not

reach statistical significance (Chi-square test, p-value=0.2879; Supplementary Table

7).

Finally, we also found recurrent deletions of 22q involving the PRAME locus19.

However, both large and small deletions involving this locus were also observed in

the WTCCC samples indicating that this deletion is a common variant. Therefore it

was excluded from further analysis.

14

Patients without apparent genomic complexity

Recent studies of acute myeloid leukemia have shown that a large proportion of

cases exhibit low genomic instability, with 50% cases showing no acquired CNAs or

cnLOH at the resolution tested20. By contrast, when we investigated infrequent

CNAs/cnLOH events in our B-CLL pre-treatment cases (using 20kb and 2Mb

thresholds respectively), we found that only ~4% cases had no CNAs/cnLOH events

identified, ~18% cases had one CNA (including three cases of trisomy 12) and ~17%

had two CNAs (including three different cases with trisomy 12).

Pathway Analysis

When we tested the 542 genes from the MDRs/MORs of interest using the KEGG

pathway analysis, we obtained ten pathways over-represented at a P-value<0.05.

When ATM, RB1, DLEU2 and TP53 were added to the gene list we obtained 17

pathways over-represented at a P-value<0.05, many of these with a strong link to

cancer (Supplementary Table 10). Other pathways of interest were also identified,

though did not reach statistical significance in this analysis. The results for the

DAVID analyses were similar in each case.

15

Supplementary References

1. Yau C, Mouradov D, Jorissen RN, Colella S, Mirza G, Steers G, et al. A statistical approach for detecting genomic aberrations in heterogeneous tumor samples from single nucleotide polymorphism genotyping data. Genome Biol 2010 Sep 21; 11(9): R92.

2. Kruskal WH and Allen Wallis, W. Use of ranks in one-criterion variance analysis. Journal of the American Statistical Association 1952 Dec; 47(260): 583–621

3. Cochran, WG Some methods for strengthening the common chi-square tests. Biometrics 1954 Dec; 10 (4): 417–451.

4. Armitage, P. Tests for Linear Trends in Proportions and Frequencies. Biometrics 1955 Sep; 11 (3): 375–386.

5. Pearson K. On the criterion that a given system of deviations from the probable in the case of a correlated system of variables is such that it can be reasonably supposed to have arisen from random sampling. Philosophical Magazine, Series 5. 1900; 50 (302): 157–175.

6. Greenman CD, Bignell G, Butler A, Edkins S, Hinton J, Beare D, et al. PICNIC: an algorithm to predict absolute allelic copy number variation with microarray cancer data. Biostatistics 2010 Jan; 11(1): 164-175.

7. Sun W, Wright FA, Tang Z, Nordgard SH, Van Loo P, Yu T, et al. Integrated

study of copy number states and genotype calls using high-density SNP arrays. Nucleic Acids Res 2009 Sep; 37(16): 5365-5377.

8. Van Loo P, Nordgard SH, Lingjaerde OC, Russnes HG, Rye IH, Sun W, et al.

Allele-specific copy number analysis of tumors. Proc Natl Acad Sci U S A 2010 Sep 28; 107(39): 16910-16915.

9. Navin N, Krasnitz A, Rodgers L, Cook K, Meth J, Kendall J, et al. Inferring

tumor progression from genomic heterogeneity. Genome Res 2010 Jan; 20(1): 68-80.

10. Letouze E, Allory Y, Bollet MA, Radvanyi F, Guyon F. Analysis of the copy

number profiles of several tumor samples from the same patient reveals the successive steps in tumorigenesis. Genome Biol 2010; 11(7): R76.

11. Colella S, Yau C, Taylor JM, Mirza G, Butler H, Clouston P, et al. QuantiSNP:

an Objective Bayes Hidden-Markov Model to detect and accurately map copy number variation using SNP genotyping data. Nucleic Acids Res 2007; 35(6): 2013-2025.

12. Iafrate AJ, Feuk L, Rivera MN, Listewnik ML, Donahoe PK, Qi Y, et al.

Detection of large-scale variation in the human genome. Nat Genet 2004 Sep; 36(9): 949-951.

16

13. Wei Huang D, Sherman BT, Lempicki RA. Systematic and integrative

analysis of large gene lists using DAVID bioinformatics resources. Nature Protocols 2009; 4(1):44 & Genome Biology 2003; 4(5):P3 ).

14. Oscier DG, Gardiner AC, Mould SJ, Glide S, Davis ZA, Ibbotson RE, et al.

Multivariate analysis of prognostic factors in CLL: clinical stage, IGVH gene mutational status, and loss or mutation of the p53 gene are independent prognostic factors. Blood 2002 Aug 15; 100(4): 1177-1184.

15. Ouillette P, Erba H, Kujawski L, Kaminski M, Shedden K, Malek SN.

Integrated genomic profiling of chronic lymphocytic leukemia identifies subtypes of deletion 13q14. Cancer Res 2008 Feb 15; 68(4): 1012-1021.

16. Parker H, Rose-Zerilli MJ, Parker A, Chaplin T, Wade R, Gardiner A, et al.

13q deletion anatomy and disease progression in patients with chronic lymphocytic leukemia. Leukemia 2011 Mar; 25(3): 489-497.

17. Ouillette P, Collins R, Shakhan S, Li J, Li C, Shedden K, et al. The prognostic

significance of various 13q14 deletions in chronic lymphocytic leukemia. Clin Cancer Res Sep 2.

18. Klein U, Lia M, Crespo M, Siegel R, Shen Q, Mo T, et al. The DLEU2/miR-

15a/16-1 cluster controls B cell proliferation and its deletion leads to chronic lymphocytic leukemia. Cancer Cell Jan 19; 17(1): 28-40.

19. Gunn SR, Bolla AR, Barron LL, Gorre ME, Mohammed MS, Bahler DW, et al.

Array CGH analysis of chronic lymphocytic leukemia reveals frequent cryptic monoallelic and biallelic deletions of chromosome 22q11 that include the PRAME gene. Leuk Res 2009 Sep; 33(9): 1276-1281.

20. Walter MJ, Payton JE, Ries RE, Shannon WD, Deshmukh H et al. Acquired

copy number alterations in adult acute myeloid leukemia genomes. Proc Natl Acad Sci. 2009 Aug 4; 106(31):12950-12955.

17

Supplementary Table 1. Comparison between FISH and Agilent Technologies and Illumina Inc. array results*

Patient No Reported FISH result Agilent 244K Illumina 1M-Duo

CLL010 normal normal normal


CLL034 del13q14.3 del11q22.3; del13q14.2-q14.3 del11q22.3; del13q14.2-q14.3

CLL035 trisomy 12 trisomy 12 trisomy 12

CLL038 del11q22.3 del11q22.3-23.2 del11q22.3-23.2

CLL041 del13q14.3 homo del13q14.12-q14.3 (homo del13q14.2-q14.3) del13q14.12-q14.3 (homo del13q14.2-q14.3)


CLL043 del17p13.1 del17pcen-pter 90% del17pcen-pter

CLL044 del17p13.1 del17pcen-pter; homo del13q14.3 del17pcen-pter; homo del13q14.3

CLL046 normal del13q14.2-q14.3 del13q14.2-q14.3



CLL050 del13q14.3 del13q14.2-q14.3 del13q14.2-q14.3

CLL055 del11q22.3 del11q14.1-q23.2 90% del11q14.1-q23.2

CLL056 del13q14.3 del13q14.3 90% del13q14.3

CLL058 del17p13.1 del17p13.1; del13q14.2-q14.2 with homo

del13q14.2-q14.3

90% del17p11.2-pter; del13q14.2-q14.2 with homo del13q14.2-q14.3

CLL060 del13q14.3 del13q14.3 80% del13q14.3

CLL063† del11q22.3 homo del13q14.3; del11q22.3 cnLOH chr13q12.11-qter wth homo del13q14.3; del11q14.1-24.1

CLL064 normal del13q14.3 (mosaic) 50% del13q14.3

CLL071† homo del13q14.3 homo del13q14.3 (MDR) 70% del13q14.2-q14.3 with homo del13q14.3 (MDR); 30-70% cnLOH

17p11.2-pter

CLL076 trisomy 12 trisomy 12 80% trisomy 12



CLL080 del13q14.3 del13q14.3 (mosaic) with del13q14.3 MDR ~50% del13q14.3 with del13q14.3 MDR

CLL081 del17p13.1;del13q14.3 del17pcen-pter; del13q14.3 (mosiac) 90% del17pcen-pter; 50% del13q14.3

CLL082 homo del13q14.3 del13q13.2-21.2 with homo del13q14.3 del13q13.2-21.2 with homo del13q14.3



CLL085 del13q14.3 del13q14.11-q21.2 (mosaic) 50% del13q14.11-q21.2

CLL087 trisomy 12 trisomy 12 80% trisomy 12

CLL088 del13q14.3 del13q14.3 >90% del13q14.3

* Results given are for the 11q22.3, 13q13.4 and 17p13.1 FISH probe regions of pre-treatment samples and 'normal' refers only to these regions.

All deletions are heterozygous unless stated as homozygous ('homo').

Illumina percentages represent CNA/cnLOH levels determined using OncoSNP.

Where a percentage is not noted for the Illumina result, then all cells appear to carry the anomaly

Grey shading highlights samples showing differences between FISH and array analysis.† Samples with cnLOHs identifiable only using Illumina Platform

18

Supplementary Table 2. Clinical and Molecular Characteristics of B-CLL patients included in the study

Patient No Age

(yrs)

Sex

(M/F)

IGHV

gene

status

TP53

mutation

status

Current

management

PFS CRS CNAs and cnLOH events *

CLL034 73 M NK Negative W&W N/A 0 47,XY, arr 70% 2pterp14(0-65,091,911)x3, 11q22.3(107,477,140-108,054,389)x1,

13q14.2q14.3(48,737,980-50,553,577)x1

CLL035 71 F Mutated Negative W&W N/A 0 47,XX, arr 3p24.3(21,592,730-22,039,016)x1†, 12(0-132,146,663)x3

CLL041 68 F Unmutated Negative W&W N/A 0 46,XX, arr 6p21.32p22.1(25,791,596-32,491,015)x2 hmz‡, 80% 10q24.32(103,708,264-

104,851,056)x1, 13q14.12q14.3(45,763,393-47,874,856)x1, 13q14.2q14.3(47,874,856-

50,427,794)x0, 13q14.3(50,427,794-50,514,234)x1

CLL042 71 M Unmutated Negative W&W N/A 0 47,XY, arr 12(0-132,146,663)x3

CLL047 49 M Mutated NK W&W N/A 0 None noted

CLL048 54 M Mutated Negative W&W N/A 0 49,XY, arr 3p14.2(59,416,360-59,688,740)x3†, 12(0-132,146,663)x3, 18(0-76,116,152)x3, 19(0-

63,507,665)x3, Xp21.1(31,700,590-31,763,529)x0†

CLL052 63 M Mutated Negative W&W N/A 0 47,XY, arr 5p13.1(40,238,262-40,263,075)x1†,12(0-132,146,663)x3

CLL053 56 M NK Negative W&W N/A 0 46,XY, arr 13q12.11qter(22,060,201-114,121,252)x2 hmz, 13q14.3(49,460,089-50,493,338)x0

CLL055 51 M Unmutated Negative W&W N/A 0 46,XY, arr 90% 11q14.1q23.2 (78,697,937-114,120,247)x1

CLL060 75 M NK Negative W&W N/A 0 46,XY, arr 1p33(49,687,041-49,772,789),x1†, 7p22.1(4,932,547-5,123,570)x3

†, 11p13(33,083,227-

33,228,359)x3†, 50% 13q14.12q14.13(44,532,138-45,941,312)x1, <10% 13q14.2(45,941,312-

47,522,891)x1† , 50% 13q14.2(47,522,891-48,463,998)x1

†, 80% 13q14.3(48,463,998-

50,493,338)x1† ,

<10% 16p13.3(3,196,980-4,598,585)x1†

CLL061 36 M Mutated Negative W&W N/A 0 46,XY, arr 80% 6q21 (106,208,162-112,080,443)x1, 17q22q23.1(54,877,659-55,032,870)x3†

CLL062 55 M Mutated Negative W&W N/A 0 47,XY, arr 17q22q23.1(54,877,659-55,032,870)x3†

CLL064 45 M Mutated NK W&W N/A 0 46,XY, arr 3q13.32(120,256,979-120,302,958)x3† , 50% 13q14.2q14.3(46,776,954-50,012,183)x1

†,

40% 13q14.3(50,012,183-52,794,521)x1†

CLL070 82 M NK Negative W&W N/A 0 46,XY, arr 13q14.2-q14.3(48,660,957-51,331,565)x1

CLL072 NK M Mutated Negative W&W N/A 0 46,XY, arr 13q14.2q14.3 (48,381,735-50,599,548)x1

CLL110 62 F NK Negative W&W N/A 0 46,XX, arr 13q14.3(49,358,002-50,499,548)x1, 90% 13q14.3(49,468,192-50,355,810)x0

CLL010 63 F Unmutated Negative pre-Flu/Cy >1year 1 None noted

CLL023 70 F Unmutated Negative pre-Chlorambucil >1year 1 46,XX, arr 4q24(107,162,437-107,237,623)x3†

, 40% 10p12.23(17,833,779-18,398,113)x1, 40%

10q24.32q25.2 (103,562,915-112,637,185)x1,

CLL026 68 M Unmutated NK pre-Flu/Cy >1year 1 47,XY, arr 18p11.32(146,787-290,422)x3

CLL050 84 F Unmutated Negative pre-Chlorambucil >1year 1 46, XX, arr 6q14.1 (83,165,917-83,265,032)x1† , 7p21.1(18,576,853-18,602,061)x3

† ,

11q12.3(62,082,265-62,330,255)x1† , 13q14.2q14.3(48,608,987-51,326,824)x1

CLL068 71 M Mutated Negative pre-Chlorambucil >1year 1 46,XY, arr 90% 2q22.2q24.1(144,240,156-157,961,129)x1, 13q14.2q14.3(47,595,821-50,072,312)x1

CLL080 62 F Mutated NK pre-Chlorambucil >1year 1 46,XX, arr 3q25.31(156,553,159-156,572,680)x1† , 30% 13q14.11q14.3(40,674,362-49,342,922)x1

†,

~50% 13q14.3(49,342,923-50,332,192)x1†

CLL083 NK M Unmutated Negative pre-Flu/Cy >1year 1 46,XX, arr 4p14(39,001,471-39,049,059)x3† , 11q25(131,123,910-131,186,533)x3

† , <10%

12q14.3(64,809,667-64,853,817)x1† , 12q23.1(99,175,388-99,224,488)x1† ,10%

16p12.2(20,710,305-20,790,857)x3† , 17q12(34,817,937-34,948,998)x3

†

CLL085 54 M Unmutated Negative pre-Flu/Cy >1year 1 46,XY, arr 10q23.1q23.31(85,670,734-89,454,971)x2 hmz, 50% 13q14.11q21.31(43,934,565-

61,368,181)x1

CLL089 NK M Unmutated Negative pre-Flu/Cy >1year 1 46, XY, arr 1q21.1(144,335,130-144,614,943)x3†

, 1q25.2(174,495,962-174,547,429)x3†

CLL090 79 F Mutated Negative pre-Chlorambucil >1year 1 47,XX, arr 90% 7q33q34(134,397,353-139,656,577)x1, 90% 7q36 (156,694,855-156,824,049)x1†

,

50% 11q12.1qter(60,468,463-134,445,626)x2 hmz, 80% 12(0-132,146,663)x3, 80%

13q14.3(49,296,907-50,580,804)x1

CLL091 NK M Mutated Negative pre-Chlorambucil >1year 1 46,XY, arr 14q24.3q31.1(77,490,883-80,987,659)x2 hmz‡

CLL093 65 M Mutated HRM

Positive

pre-Chlorambucil >1year 1 46,XY, arr 80% 6p25.3p25.2-(94,609-4,294,807)x3, 6q14.1-qter(83,755,489-170,753,209)x1, 70%

10q25.1(109,682,821-109,770,215)x3† , 13q14.2-q14.3(47,556,552-50,610,384)x1, 70%

20q13.13(47,292,495-47,500,772)x3†

CLL096 51 F Mutated Negative pre-Flu/Cy >1year 1 46,XX arr 40% 13(0-114,121,252)x2 hmz, 13q14.3(49,425,974-49,522,141)x1, 13q14.3(49,522,141-

50,380,713)x0

MCLL189 63 F Unmutated Negative pre-FCM-miniR >1 year 1 46,XX, arr 4q24(107,259,869-107,283,621)x1†, 12q13.11q24.33(47,384,207-132,289,191)x2 hmz

CLL097 NK M Mutated NK NK >1year 1 46, XY, arr >90% 13q14.11q14.3(40,332,062-51,453,289)x1

CLL098 NK M Unmutated Negative NK >1year 1 None noted

CLL099 NK F Unmutated Negative NK >1year 1 46,XY, arr 70% 1q21.1(144,099,494-144,757,265)x3†

CLL100 NK M Unmutated Negative NK >1year 1 46,XY, arr 6q23.3(136,941,101-137,303,525)x1†, 11q21q23.3(95,807,200-116,159,645)x1,

13q14.12q14.3(45,803,275-50,435,049)x1

CLL101 NK M Unmutated Negative NK >1year 1 46,XY, arr 11q14.1qter(77,080,067-134,445,626)x1, 60-70% 13q14.2q21.32(47,781,909-

64,410,990)x1†, 50% 13q21.32q33.2(64,350,990-103,707,225)x3

†, 70% 22q11.21qter(17,403,754-

49,582,267)x3

CLL105 NK M Unmutated Negative NK >1year 1 46,XY, arr 80% 10q24.31q33(102,616,568-105,675,986)x1, 90% 11q14.1q24.2(78,235,887-

126,814,884)x1, 90% 13q14.2q14.3(47,373,566-51,972,428)x1, 13q14.3(49,425,974-49,953,648)x0

CLL107 NK M Unmutated Negative NK >1year 1 46,XY, arr 50% 3p21.31(46,827,053-50,133,778)x1, 50% 3p21.1(52,038,099-52,553,933)x1†, >90%

11q14.3q24.3(91,864,846-127,805,084)x1, >90% 13q14.11(40,389,170-40,761,341)x1† , >90%

13q14.11(41,687,912-41,826,016)x1†, >90% 13q14.2q14.3(47,515,748-51,333,756)x1

CLL108 NK F Unmutated Phe212-

SerfsX3

NK >1year 1 46,XX, arr 3q22.3(137,678,290-137,832,637)x1† , 80% 6q14.1q22.33(81,463,977-129,051,286)x1,

90% 11q12.3qter(62,398,575-134,445,626)x2 hmz, 80% 12pterp12.2(0-20,356,457)x2 hmz, 80%

12p12.2q21.31(20,356,457-84,251,286)x3, 80% 12q21.31qter(84,251,286-132,146,663)x2 hmz

* using size thresholds of >20kb for CNAs and 2Mb for cnLOH unless the anomaly is in a recognized region of interest.

All co-ordinates are hg36 and determined using OncoSNP except † and

‡ (see below)

† Co-ordinates determined using Nexus (either due to variable CNA boundaries less well defined by OncoSNP or outside OncoSNP thresholds)

‡ called by OncoSNP as possible germline variant and manually verified as probable somatic event. Co-ordinates determined using Nexus

Percentages represent CNA/cnLOH levels determined using OncoSNP. Percentages <10% and >90% are outside the OncoSNP thresholds.

Approximate percentages derived using OncoSNP and visual inspection.

NK= Not Known prior to testing; HRM= High Resolution Melting curve analysis; PFS= Progression Free Survival; W&W= Watch and Wait; CRS=Clinical Risk Score; N/A= Not Applicable; FU=Follow Up

Flu(or F)=Fludarabine; Cy(orC)= Cyclophosphamide; M=Mitozantrone; miniR=mini-Rituximab

19

Supplementary Table 2 (cont) . Clinical and Molecular Characteristics of B-CLL patients included in the study

Patient

No

Age

(yrs)

Sex

(M/F)

IGHV

gene

status

TP53

mutation

status

Current

management


CLL003 62 M Unmutated Negative pre-Chlorambucil <1year 2 46,XY, arr 11q22.3(104,158,306-109,733,134)x1,11q23.1q23.2(110,556,535-

114,089,910)x1,11q23.3(chr11:115,527,657-116,182,933)x1, 13q14.3(49,038,530-50,906,239)x1,

17q21.33q22(45,720,750-49,017,380)x2 hmz‡

CLL005 63 M Unmutated Negative pre-Chlorambucil <1year 2 46,XY, arr 5q12.1(59,747,698-59,804,164)x3†, 9q32(115,160,057-115,182,884)x3

† , 40%

12q12qter(37,473,675-132,088,962)x3, 80% 13q14.2q14.3 (47,443,974-50,755,648)x1, 80%

22q11.21(17,245,391-17,832,230)x1†

CLL006 64 M Unmutated Negative pre-Flu/Cy <1year 2 47,XY, arr 90% 4q31.1(153,631,568-153,935,927)x1†, 70% 12(0-132,146,663)x3 ,

17p12(13,118,349-13,171,201)x3†

CLL044 79 M Mutated Cys135Ser pre-Ofatumumab <1year 2 46,XY, arr 46,XY, arr 2q36.2 (225,094,102-225,617,560)x1, ~20% 9p23p21.1(11,986,163-

32,324,220)x1† , 50% 9q13qter(70,223,358-139,946,600)x3, 13q14.3(49,515,641-51,327,198)x0,

17pterp11.2(0-21,430,683)x1, 19q13.12q13.2(41,320,887-43,412,844)x2 hmz‡

CLL054 67 M NK Negative pre-Chlorambucil <1year 2 47,XY, arr 12q14.1(60,235,935-60,327,516)x1†

CLL056 67 M NK Negative pre-Flu/Cy <1year 2 46,XY, arr 80% 5p13.3(37,408,772-37,784,135)x3†, 80% 9pter9p11.2(0-43,254,687)x2 hmz

‡, 70%

13q14.12q14.2(45,377,644-45,760,335)x3, 70% 13q14.2(46,403,477-47,574,445)x3, >90%

13q14.2(47,583,917-48,099,556)x1, 90% 13q14.2q14.3(48,719,245-50,523,975)x1, 70%

13q14.3qter(50,523,975-114,121,252)x3

CLL063 62 F Unmutated Negative pre-Flu/Cy < 1year 2 46, XX, arr 11q14.1q24.1(77,652,729-122,488,138)x1, 13q14.11(39,758,892-39,909,885)x0†,

13q14.3(49,425,974-50,389,210)x0†, 13qcenqter(19,075,593-114,142,980)x2 hmz

‡

CLL079 73 F Unmutated Negative pre-Chlorambucil <1year 2 None noted

CLL082 65 F Mutated Negative pre-Chlorambucil 1year 2 46, XX, arr 13q13.2-21.2(33,726,200-53,619,700)x1, 13q14.3(49,461,448-50,635,061)x0

CLL084 60 M Unmutated Negative pre-Flu/Cy <1year 2 46,XY, arr 4q34.1(172,446,717-172,482,835)x1†

CLL086 76 M Mutated Negative pre-Chlorambucil <1year 2 46,XY, arr 3p26.3p26.1(0-7,571,446)x1, >90% Xq27.1qter(139,443,330-154,582,606)x2

CLL087 NK M Unmutated Negative pre-Chlorambucil <1year 2 47,XY, arr 80% 12(0-132,146,663)x3, Xp11.3p11.4(41,074,455-43,983,367)x0

CLL088 69 F Mutated Negative pre-Flu/Cy <1year 2 46,XX, arr >90% 13q14.12q14.3(45,817,763-49,547,499)x1

CLL095 69 M NK NK pre-Chlorambucil <1year 2 47,XY, arr 80% 2q14.3qter(129,537,170-242,497,808)x2 hmz, 6q14.1q21(79,968,009-

106,917,920)x1, 70% 12(0-132,146,663)x3, >90% 20q11.2qter (30,155,027-62,435,964)x2 hmz‡,

22q11.23(35,010,463-35,020,688)x0†

CLL102 NK M Unmutated Negative NK <1year 2 47,XY, arr 80% 12(0-132,146,663)x3

CLL103 NK F Unmutated Negative NK <1year 2 47,XX, arr 1q25.3(183,360,633-183,397,169)x3†, >90% 2q22.1q31.1(141,094,077-171,777,386)x2

hmz, >90% 9p24.1p22.3(9,000,336-14,313,346)x2 hmz, 10q26.3(135,089,371-135,374,737)x3†, 80%

12(0-132,146,663)x3, >90% 13q33.3q34(108,839,643-112,014,298)x2 hmz, 20p11.23(19,723,376-

19,823,725)x1†, 70% 22q11.21q12(17,295,655-19,791,274)x3

CLL106 NK M Unmutated Arg267Pro NK <1year 2 46,XY, arr 90% 1p34.2(41,538,823-42,112,410)x1, 90% 6p25.3p25.1(1,376,135-6,173,214)x1, 90%

6p25.1p24.3(6,471,929-7,578,657)x1, 90% 6p24.3p24.1(10,155,458-11,223,334)x1, 90%

6p24.1p22.3(12,754,522-18,670,995)x1, 90% 6p21.2(38,889,727-39,544,026)x1, 90%

6p21.1p12.3(43,689,563-46,299,596)x1,90% 7p15.3(23,346,710-24,400,934)x1, 90% 9q33.3

(127,381,099-127,900,525)x1, 90% 12p13.33(977,044-1,959,491)x1, 17pterpcen(0-22,125,162)x1,

18pterp11.31(0-6,572,104)x1, 18p11.31p11.23(6,802,509-7,333,763)x3, 18p11.22p11.21(8,926,107-

13,281,880)x1, 18p11.21(13,536,937-15,348,275)x1, 90% 22q11.21(16,712,510-17,055,914)x1†,

90% 22q11.23q12.1(23,409,925-27,331,789)x1, 90% 22q12.1q12.2(27,573,410-27,918,140)x1†

CLL145 71 M Unmutated Arg273His pre-FCR <1year 2 46,XY, arr <10% 3p25.1p14.3(12,516,876- 55.709725)x1† , <10% 5q14.3q23.3(87,285,075-

129,855,075)x1† , >90% 6p24.3p22.3(10,220,342-16,992,531)x2 hmz

†, 6q16.3(100,876,399-

101,445,547)x1, 80% 8q22.3q24.21(106.058,337-128,286,818)x2 hmz, 80% 8q24.21(128,286,819-

128,297,901)x3†, 80% 8q24.21-q24.3(128,297,902-147,274,826)x2 hmz

†, 12q14.1q14.2(56,925,673-

62,754,801)x2 hmz†, 16q22.3q23.1(72,290,299-76,525,557)x2 hmz

†, <10%-50% 17p13.3p11.2(0-

18,485,225)x2 hmz†, ~30% 18q11q23(22,045,087-76,115,172)x2 hmz

†, 80% 22q11.23(23,991,557-

24,250,622)x1

CLL156 55 M NK Negative pre-Alemtuzumab <1year 2 46,XY, arr 80% 5q23.3 (124,282,026-124,531,381)x3†, ~10% 19q13.12q13.13(41,440,201-

43,149,438)x1 hmz†

ARR003 71 M Unmutated Negative pre-Bendamustine <1year 2 46,XY, 13q14.2q14.3(49,983,042-51,331,787)x1†

CLL037 51 M Mutated Negative chemotherapy

refractory

N/A 3 46,XY, arr 9q32qter(114,777,580-140,273,252)x2 hmz‡, 13q14.3(49,448,465-49,958,586)x1,

<50%13q14.3(49,958,586-50,470,735)x1†

CLL040 72 F NK NK chemotherapy

refractory

N/A 3 46,XX, arr 1p36.22(10,166,938-10,240,277)x3†, 30% 3p25.3(9,610,471-10,363,762)x1, 30%

3p21.33p21.31(44,436,042-47,426,525)x1, 40% 4pterp14(0-39462168)x1, 30-40% 5pterp13.3(0-

33,225,747)x3, 40% 5q31.3q33.3(141,585,436-157,102,574)x1, 40% 5q33.3q34(159,368,739-

160,909,332)x1, 6q16.1(94,130,248-94,158,093)x3†, 70% 8q21.3qter(88,785,896-146,263,890)x3,

<10% 12p13.2p13.1(10,176,350-14,248,326)x1; 80% 13q12.11q12.13(17,920,393-25,846,844)x1,

13q14.2q21.1(46,976,461-52,641,590)x1, ~10% 15q15.1(38,975,579-40,758,191)x1, 90%

17pterp11.2(0-22,684,316)x1, 80% 17q12(30,750,407-31,140,027)x1†, 18pterp11.21(0-

14,078,284)x1, <10% 18q11.2q21.2(20,999,500-49,134,000)x1†, 90% 19p13.3(212,341-

4,841,219)x1†, 80% 19p13.3(4,841,219-5,635,144)x1

†, 70% 19p13.2(9,104,258-10,926,409)x3,

<10% Xq12qter(66,869,806-154,913,750)x1†

CLL043 75 M Unmutated Tyr205Cys chemotherapy

refractory

N/A 3 46,XY, arr ~30% 1p36.31p36.23(6,508,255-7,966,180)x3†, 1p21.1(105,011,801-105,118,455)x3

†,

70% 2q36.3q37.1(230,780,105-231,367,190)x1, 90% 3pter3p12.2(0-83,717,142)x1, 90%

3p12.1pcen(83,723,698-90,587,335)x4†, 80% 3qcen3qter(94,988,002-199,302,161)x3, 50%

7q36.1qter(151,287,397-158,803,229)x1, 90% 8pterp21.1(0-31,094,002)x1, 90% 8p12(33,488,001-

36,433,467)x1, 90% 8p11.22p11.21(39,796,895-40,860,746)x1, 90% 8p11.21pcen(40,947,586-

43,533,112)x1, 70% 11q13.4(73,346,490-74,926,833)x1, 80% 15q15.1q23(40,832,872-

66,297,824)x3, 50% 15q23qter (66,297,824-100,176,830)x3 (partial tetrasomy?),

16q23.1(77,240,683-77,338,055)x1†, 90% 17pterpcen(0-22,125,162)x1,70% 17qcenqter(22,496,341-

78,503,256)x3, ~90% Xp22.33(0-2,709,234)x2 hmz‡, ~90% Xq21.31q21.32(83,356,463--

92,208,942)x2 hmz‡

, ~90% Xq28(154,570,121-154,913,754)x2 hmz‡

CLL046 82 M Unmutated Negative chemotherapy

refractory

N/A 3 46,XY, arr 5pterp15.1(0-15,968,582)x2 hmz†, 13q14.12q14.2(45,353,865-46,421,078)x1,

13q14.2q14.3(47,387,659-50,923,001)x1, 13q21.2q33.3(58,195,136-107,914,455)x2 hmz,

20p12.1(12,586,831-15,008,138)x2 hmz‡

CLL058 61 F Unmutated Pro153-

AlafsX28

chemotherapy

refractory

N/A 3 46,XX, arr 12q24.31(119,578,493-121,172,890)x1, 13q12.2(26,955,745-27,052,913)x1†,

13q12.2(27,304,454-27,383,456)x1, 13q12.2(27,461,395-27,710,289)x1†, 13q12.3(27,878,812-

28,049,348)x1†, 13q14.2(46,713,975-47,895,694)x1, 13q14.2(47,895,694-48,559,998)x0,

13q14.2(48,559,998-48,704,022)x1, 13q14.2q14.3(48,704,022-50,191,807)x0, 13q14.3(50,191,807-

50,302,346)x1, 70% 15q11.2(20,302,387-20,657,533)x3†, 80% 15q14qter(33,100,118-

100,176,830)x3, 90% 17pterp11.2(0-16,508,847)x1

CLL071 63 M Mutated Glu271Lys chemotherapy

refractory

N/A 3 46,XY, arr <10% 13q13.3q34(36,483,665-114,123,125)x1†, 70% 13q14.2q14.3(48,713,406-

50,479,598)x1†, 13q14.3(49,229,284-50,423,376)x0, 30-70% 17pterp11.2(0-21,100,678)x2 hmz



‡ (see below)





NK= Not Known prior to testing; PFS= Progression Free Survival; W&W= Watch and Wait; CRS=Clinical Risk Score; N/A= Not Applicable;FU=Follow Up

Flu(or F)=Fludarabine; Cy(or C)= Cyclophosphamide; R=Rituximab

20

Supplementary Table 2 (cont) . Clinical and Molecular Characteristics of B-CLL patients included in the study

Patient No Age

(yrs)

Sex

(M/F)

IGHV

gene

status

TP53

mutation

status

Current

management


CLL074 NK F NK NK chemotherapy

refractory

N/A 3 47,XX, arr 80% 3(0-199,264,624)x3, 4q13.3(71,086,180-71,455,900)x1†, 4q13.3(71,736,694-

71,822,824)x1†, 11p15.4(9,745,400-10,316,737)x1, 11p13(32,809,601-33,299,316)x1

†,

11p13(33,682,105-34,179,798)x1†, 13q14.2qter(47,632,520-114,121,252)x2 hmz,

19p13.13p13.12(12,921,191-14,497,928)x1, 19p13.12p13.11(15,839,641-16,595,885)x1,

19p13.11(18,336,675-19,169,002)x1, 19q13.32(51,526,523-53,157,140)x1

CLL075 NK M NK HRM Positive chemotherapy

refractory

N/A 3 46,XY, arr 50% 1p36.23p36.32(2,396,446-8,492,493)x1, 50% 1p35.1p35.3(28,122,270-

32,692,291)x1, 50% 1p32.21p32.3(55,244,056-56,962,381)x1, 90% 1q24.2q24.3(167,889,551-

170,636,321)x2 hmz, 50% 1q32.2q41(212,087,837-215,475,787)x1,<10% 2p22.2p21(37,413,860-

43,848,439)x1†, 80% 9p24.19p13.3(5,944,274-33,693,234)x1, 80% 9q13q21.31(70,174,192-

80,333,445)x1, 80% 9q22.33q32(101,069,612-115,321,935)x1, 80% 9q33.1(119,325,172-

121,100,178)x1, 80% 9q33.1q33.2(121,461,281-122,566,552)x1, 80% 9q34.13q34.3(134,588,424-

139,101,448)x1, 80% 13q14.12q14.2(45,128,861-47,516,428)x1† , 90% 13q14.2q14.3(47,516,428-

49,482,152)x1† , 13q14.3 (49,482,152-49,732,093)x0, 90% 13q14.3(49,732,09-49,941,789)x1,

13q14.3 (49,941,789-50,302,346)x0, 90% 13q14.3(50,302,346-50,504,198)x1, 30%

14q21.3qter(47,690,870-104,581,072)x2 hmz, 70% 17pterp13.1(0-8,074,364)x2 hmz†, Xq27.3

(142,536,091-144,254,333)x0

CLL076 65 F NK Negative chemotherapy

refractory

N/A 3 47,XX, arr ~80% 1p36.12p36.11(21,423,975-23,960,71)x2 hmz†, ~50% 1pterp36.12(0-21,423,975)x2

hmz†, 2q37.1q37.1(230,940,929-231,540,187)x2 hmz

‡, 80%12(0-132,146,663)x3


refractory

N/A 3 46,XY, arr 90% 20q11.21q13.12(31,081,079-43,771,286)x2 hmz, arr 20q13.12(43,780,732-

43,812,931)x0†, 90% 20q13.12,qter(43,812,931- 62,385,675)x2 hmz

CLL078 81 F Unmutated Negative chemotherapy

refractory

N/A 3 46,XX, arr 60% 4q13.1q22.2(59,939,995-95,374,528)x1, 60% 8p22p21.3(16,324,942-20,039,997)x1,

11q22.1(100,699,657-101,935,421)x1, 11q22.3q23.2(107,417,165-114,576,024)x1, 90%

Xq28(153,861,143-154,095,678)x0†

CLL081 59 M Unmutated Cys135Ser chemotherapy

refractory

<1year 3 46,XY, arr <10% 2pterp14(0-67,981,586)x3†, 90% 2q11.1q11.2(95,597,687-97,676,314)x1, 90%

2q14.2q14.3(121,572,920-126,531,524)x1, 90% 2q14.3(127,036,406-128,569,251)x1, 90%

2q21.2q24.1(134,752,138-155,542,922)x1, 40% 8q22.2qter(100,925,155-146,263,890)x3, 90%

9pterp11.2(0-33,318,812)x1, 90% 9p13.3p11.2(33,831,777-43,548,708)x1, 9q21.13(74,372,294-

74,516,002)x1†, <10% 10q23.2q23.31(89,306,467-93,720,035)x1

†, 90% 11q12.2q13.1(61,231,809-

64,790,450)x1, 70% 12q13.11q13.12(47,308,669-48,029,106)x3, 50% 13q11q13.1(17,920,393-

32,659,455)x1, 50% 13q14.3(48,898,975-50,805,219)x1, 90% 17pterpcen(0-22,125,162)x1, 70%

17qcenqter(22,713,949-78,637,198)x3; 90% 19pterp13.2(0-10,071,486)x1; 70%

19p13.2p13.13(10,071,486-13,419,854)x3


refractory

<1year 3 46,XY, arr 2p16.1(55,330,268-55,352,382)x1†, 11q22.3q24.3(103,008,248-127,852,266)x1,

13q14.3(49,500,283-50,255,794)x1, ~20% 20q11.22qter(32,772,470-62,385,675)x2 hmz‡


refractory

<1year 3 46,XY, arr 30% 2q33.2q36.3(206,690,791-230,780,953)x1, 40% 17pterp11.2(0-20,067,160)x1†, 60%

17p11.2(20,067,161-21,471,049)x1†

CLL111 67 F NK Negative chemotherapy

refractory

N/A 3 46,XX, arr 60% 2pterp16.1(0-60,616,006)x3, 4pterp14(0-37,269,568)x1, 4p13q12(41,367,356-

58,152,039)x1, 4q13.1q13.2(64,455,242-66,975,683)x1, 4q13.2q13.3(69,847,111-71,783,789)x1,

60% 8pterq12.1(0-63,281,507)x1, 11q21q24.2(92,396,199-126,058,859)x1, 60% 18pterp11.21(0-

12,950,950)x1†, 40% 18p11.21(12,950,950-14,856,930)x1

†,

CLL112 58 M NK Negative chemotherapy

refractory

N/A 3 46,XYarr 90% 2q35(218,476,667-220,204,673)x1, 90% 2q36.3q37.1(230,239,060-231,446,513)x1,

8q21.11q21.13(78,181,345-80,471,188)x2 hmz‡, >90% 13q11.2qter(20,381,113-113,908,097)x2 hmz,

13q14.3(49,494,577-50,238,316)x0, 80% 16p12.1(21,860,290-22,350,261)x1

ARR002 68 M Unmutated Negative chemotherapy

refractory

N/A 3 46,XY, arr 50% 2p16.1p14(60,279,157-67,896,939)x3†, 70% 15q25.2q25.3(82,852,550-

83,573,559)x3†,~10% 18q23(75,210,150-76,116,152)x1

†, 20q11.23(34,645,412-34,974,353)x1

†

MCLL148 87 M NK Ile195del chemotherapy

refractory

<6 months 3 46,XY, arr 2p16.1(56,456,704-56,519,123)x1†, 50% 4q21.21-q21.23(82,819,454-84,544,925)x1,

30% 5q21.3q22.1(106,355,299-110,412,993)x1†, 30% 5q22.2q22.3(112,371,443-113,463,403)x1,

30% 11q22.1q22.2(99,750,235-102,130,166)x1†, 30% 11q22.3q23.2(104,918,208-113,505,937)x1

†,

30% 11q23.2q23.3(114,934,105-115,687,486)x1†,30%11q23.3(117,457,306-118,510,342)x1

†, 30%

11q23.3(119,441,500-120,562,032)x1†, 30% 11q24.2q24.3(126,353,410-128,613,424)x1

†, 30%

11q24.3(128,805,080-128,951,253)x1†, 50% 12q24.11(109,411,836-109,658,160)x1

†, ~10%

13q13.3q14.3(36,320,198-49,460.768)x1†, ~90% 13q14.3(49,460.769-49,493,361)x1

†,

13q14.3(49,493,362-49,639,921)x0†, ~90% 13q14.3(49,639,922-49,845,114)x1

†,

13q14.3(49,845,115-49,992,953)x0†, ~90% 13q14.3(49,992,954-50,209,795)x1

†,

13q14.3(50,209,796-50,309,646)x0†, ~10% 13q14.3(50,309,647-50,383,609)x1

†, ~10%

13q14.3q34(50,383,610-114,125,255)x1†, 60% 17p13.3-p11.2(0-17,522,348)x2 hmz

†,

Xp11.4p11.3(41,001,474-43,480,372)x0†

CLL039 NK F NK Negative NK NK NK 46,XX, arr 4q24(107,162,437-107,237,623)x3†, 40% 10p12.33(17,900,269-18,398,113)x1, 40%

10q24.32q25.2 (103,564,352-112,658,371)x1, ~10% 16q23.1(74,997,818-75,060,214)x1†

CLL069 52 M Unmutated Negative pre-Flu/Cy FU<1year NK None noted

CLL114 63 M NK Negative pre-Flu/CyR FU<1year NK 46,XY, arr 70% 11q14.1q23.3(77,741,166-116,465,523)x1

CLL116 48 M NK HRM Positive pre-Alemtuzumab FU<1year NK 46,XY, arr 80% 17pterp11.2(0-21,964,965)x1, 80% 20p13-p12.2(4,785,063-9,654,363)x1, 80%

20p12.1q11.21(17,106,967-29,434,300)x1, 80% 20q11.22q13.12 (32,667,050-42,799,625)x1

CLL117 76 M NK NK pre-BR FU<1year NK 46,XY, arr 12p13.33(1,302,036-1,460,642)x3†, 16p13.3(3,601,816-3,736,149)x1

†,

90%16q24.3(87,195,552-88,104,547)x1, 17q21.2(37,362,610-37,545,814)x1†

CLL118 45 M Unmutated Negative pre-Flu/CyR FU<1year NK 46,XY, arr 70% 1p34.1(46,022,981-46,260,143)x3†, 90% 1p22.2(88,981,462-89,083,655)x1†, 90%

1p22.2(89,319,559-89,741,569)x1†, 90% 1p13.1(116,804,159-117,487,515)x1, 2q34(214,363,194-

214,512,575)x1†, 2q34(214,639,267-214,706,974)x1†, 90% 10q11.23(50,320,838-50,505,621)x1

†,

90% 10q11.23(51,479,606-52,200,825)x1, 11q22.3(107,443,788-108,149,178)x1,

12q24.31(121,703,515-121,806,600)x1†, 90% 13q14.11(40,332,062-40,718,960)x1, 90%

13q14.11(41,349,414-42,201,691)x1, 90% 13q14.2q14.3(47,731,453-51,632,859)x1

CLL119 65 M NK Negative pre-Flu/CyR FU<1year NK 46,XY, arr 90% 5q14.3q23.1(88,216,865-118,359,270)x1, 9q22.31(94,191,198-94,271,909)x1†, 90%

11q22.3q23.3(103,276,131-116,197,768)x1

CLL120 60 M NK Arg267Pro pre-Flu/CyR FU<1year NK 46,XY, arr 30% 13q14.2q14.3(47,411,558-50,943,647)x1†

MCLL159 72 M NK Negative pre-ChlR FU<1year NK 47,XY, arr 70% 12(0-132,146,663)x3†

MCLL171 71 F NK Negative pre-Chlorambucil FU<1year NK 46,XX, arr 70% 12(0-132,146,663)x3, 70% 17q22(51,452,730-51,748,539)x3†

MCLL137 NK M NK NK NK NK NK 46,XY, arr 1q24.2q43(230,738,028-234,657,439)x2 hmz, 2q35q36.1(220,876,620-221,498,801)x1,

13q14.2q14.3(48,748,895-51,339,176)x1

MCLL138 72 M Mutated Negative pre-ChlO FU<1year NK 46,XY, arr 1q25.3q31.1(183,714,636-188,107,919)x2 hmz†, 50% 9q21.11q34.3(71,087,509-

140,233,916)x2 hmz, 80% 13q14.11q21.33(40,811,871-45,706,420)x1, ~90%

13q14.12q14.3(45,706,421-49,971,647)x1, >90% 13q14.3(49,971,648-51,240,377)x1, 80% MCLL169 72 M NK Negative pre-ChlR FU<1year NK 46,XY, arr 2q11.2q12.1(100,630,839-104,604,613 )x2 hmz, 40% 13q12.11q34(19,727,582-

114,125,259)x2 hmz, 80% 13q14.3(49,443,358-50,378,319)x1, 17q11.2(24,916,898-24,975,608)x3

hmz†

MCLL121 70 M Mutated Negative pre-BR FU<1year NK 46,XY, arr 15q26(100,010,941-100,338,915)x1†, 17q11.2(24,913,908-24,975,608)x3

†

MCLL100 61 F Mutated Negative pre-FCM-miniR FU<1year NK 46,XX, arr 80% 13q14.3(49,552,639-49,967,095)x1, 18q23(72,560,419-72,606,933)x1†

MCLL152 67 M Mutated Negative pre-BR FU<1year NK 46,XY, arr 13q14.3(49,428,032-50,557,052)x1†, 13q14.3(49,516,595-50,2557,94)x0

MCLL161 53 M Unmutated Negative pre-FCR FU<1year NK 46,XY, arr ~10% 4pterp15.1(0-28,404,856)x1†, ~10% 11q14.3q23.3(90,803,515-117,404,675)x1

†,

20% 13q14.2q(46,483,567-47,832,800)x3†, 20%13q14.2q21.33(46,483,568-70,853,744)x2 hmz(or

x1)†, 15q25.3(83,260,487-83,303,854)x1

†, 20q11.23(34,428,530-35,199,710)x2 hmz

‡,



‡ (see below)





NK= Not Known prior to testing; PFS= Progression Free Survival; W&W= Watch and Wait; CRS=Clinical Risk Score; N/A= Not Applicable;FU=Follow Up

Flu(or F)=Fludarabine; Cy(or C)= Cyclophosphamide; Chl=Chlorambucil; R=Rituximab; B=Bendamustine; O-Ofatumumab; M=Mitozantrone; miniR=mini-Rituximab

21

Supplementary Table 3. Validation of OncoSNP Quantification by comparison with FISH data

Sample IDa OncoSNP Resultb FISH OncoSNP vs FISH

Correlation (Yes/No)c

CLL041 homozygous del13q14.3 90% homozygous 10% heterozygous del13q14.3 Yes

CLL060 80% heterozygous del13q14.3 84% heterozygous del13q14.3 Yes

CLL071 homozygous del13q14.3 84% homozygous del13q14.3 No

CLL058 90% heterozygous del17p13.1 81% heterozygous del17p13.1 Yes

CLL080 50% heterozygous del13q14.3 58% homozygous del13q14.3 Yes


50% heterozygous del13q14.3 62% heterozygous del13q14.3 Yes

CLL082 homozygous del13q14.3 95% homozygous del13q14.3 Yes

CLL084 No change noted 15% heterozygous del13q14.3 No


CLL087 80% trisomy 12 82% trisomy 12 Yes


80% trisomy 12 66% trisomy 12 Yes



CLL092 (relapse) 90% heterozygous del11q22.3 97% heterozygous del11q22.3 Yes




CLL095 70% trisomy 12 57% trisomy 12 Yes

aAll samples are pretreatment unless otherwise statedbMean OncoSNP % is given where segmentation of the data results in multiple calls across a region (eg trisomy 12 examples)

In the case of homozygous losses, OncoSNP percentages of 100% are not stated in the Table as formal calibration testing for homozyous events is pendingcCorrelation was defined as less than 15% difference between FISH and OncoSNP

22

Supplementary Table 4. Statistical analyses of IgVH SHM status vs CRS, Number of CNAs/cnLOHs and Length

of CNAs/cnLOHs

Covariate Category n %1 n %1 P-value2

CRS 0: W&W 3 7.7 8 38.1

1: PFS>1year 15 38.5 7 33.3

2: PFS<1year 12 30.8 4 19.0 0.0135

3: Chemotherapy refractory 9 23.1 2 9.5

All 39 100.0 21 100.0

Total number of CNAs/cnLOHs 0 6 14.3 6 24.0

1 10 23.8 11 44.0

2 5 11.9 4 16.0 0.0032

3 7 16.7 1 4.0

>3 14 33.3 3 12.0

All 42 100.0 25 100.0

Total length of CNAs/cnLOHs <1Mb 15 35.7 13 52.0

1-5Mb 5 11.9 1 4.0 0.3569

>5Mb 22 52.4 11 44.0

All 42 100.0 25 100.01 Percentage calculated over the total number of patients for whom data were available2 Cochran-Armitage trend exact test

IGHV gene status

Unmutated Mutated

Supplementary Table 5. Statistical analyses of TP53 mutation status vs CRS, Number of CNAs/cnLOHs and Length of

of CNAs/cnLOHs


CRS 0: W&W 14 24.1 0 0.0

1: PFS>1year 17 29.3 2 20.0

2: PFS<1year 16 27.6 3 30.0 0.0154


All 58 100.0 10 100.0


1 21 29.2 1 9.1

2 13 18.1 0 0.0 0.1345

3 9 12.5 0 0.0

>3 15 20.8 9 81.8

All 72 100.0 11 100.0


1-5Mb 10 13.9 0 0.0 0.0081

>5Mb 32 44.4 10 90.9


Negative Positive

TP53 mutation status

23

Supplementary Table 6. Detailed results for patients with 13q14.3 abnormalities Patient No Chr13 abnormalities % Cells with anomaly

encompassing 13q14.3 MDR

Additional abnormalities (Pre-treatment)* Clinical

details

PFS Clinical risk

score

RB1 deletion

or cnLOH (Y/N)

MDR only

CLL037 del13q14.3 (MDR) 100 cnLOH 9q32-q34.3 chemotherapy

refractory

N/A 3 N

CLL090 80% del13q14.3 (MDR) 80 90% del7q33-q34; 50% cnLOH11q12.1-qter

(ATM ); 80% tri12

chemotherapy

refractory

N/A 3 N

CLL092 del13q14.3 (MDR) 100 del2p16.1(<500kb); del11q22.3-q24.3 (ATM );

~20%cnLOH20q11.22-qter

chemotherapy

refractory

N/A 3 N

CLL110 homozygous del13q14.3 (MDR) 100 None noted chemotherapy

refractory

N/A 3 N

MCLL100 80% del13q14.3 80 del18q23(<500kb) pre-FCM-miniR FU<1year NK N

More than MDR N/A

CLL005 80% del13q14.2-q14.3 80 ampl5q12.1 (<500kb); 40% ampl12q12-qter; 80%

del22q11.21

W&W N/A 0 Y

CLL034 del13q14.2-q14.3 100 70% ampl2p14-pter; del11q22.3 (ATM ) W&W N/A 0 N

CLL041 del13q14.12-q14.3 with homozygous del13q14.2-

q14.3)

100 cnLOH6p22.1-21.32; 80% del10q24.32 W&W N/A 0 Y

CLL053 cnLOH13q12.11-qter with homozygous del13q14.3 100None noted

W&WN/A

0 N

CLL060 50% del13q14.12; <10% del13q14.2; 50%

del13q14.2; 80% del13q14.3

80 del1p33(<500kb) ampl7p22.1(<500kb), ampl11p13;

<10% del 16p13.3

W&W N/A 0 Y

CLL064 50% del13q14.2-q14.3; 40% del13q14.3 40 ampl3q13.32(<500Kb) W&W N/A 0 Y

CLL070 del13q14.2-q14.3 100 None noted W&W N/A 0 N

CLL080 30% del13q14.11-q14; ~50% del13q14.3 30 del3q25.3(<500kb) pre-Chl >1year 1 N

CLL085 50% del13q14.11-q21.31 50 cnLOH10q23.1-q23.31; pre-FC >1year 1 Y

CLL093 del13q14.2-q14.3; 100 80% ampl6p25.2-25.3; del6q14.1-qter; 70%

ampl10q25.1(<500kb); 70% ampl20q13.13

(<500kb)

pre-Chl >1year 1 Y

CLL096 40% cnLOHchr13 with homozygous del13q14.3 100 None noted pre-FC >1year 1 N

CLL097 >90% del13q14.11-q14.3 >90 None noted >1year 1 Y

CLL101 60-70% del13q14.2-q21.32; 50% ampl13q21.32-

q33.1

60-70 del11q14.1-qter (ATM ); 70% ampl22q11.21-qter >1year 1 Y

CLL105 90% del13q14.2-q14.3 with homozygous

del13q14.3

90 80% del10q24.31-33; 90% del11q14.1-q24.2

(ATM )

NK >1year 1 Y

CLL107 >90% del13q14.11(two regions <500kb);>90%

del13q14.2-q14.3

>90 50% del3p21.31;50% del 3p21.1, >90%

del11q14.3-q24.3 (ATM )

>1year 1 Y

CLL003 del13q14.3 100 del11q22.3 (ATM ); del11q23.1-q23.2; del11q23.3;

cnLOHq17q21.33-q22

pre-FC <1year 2 N

CLL046 del13q14.12-q14.2; cnLOH13q21.2-q33.3;

del13q14.2-q14.3

90 cnLOH5pterp15.1; cnLOH20p12.1 pre-Chl <1year 2 Y

CLL056 70% ampl13q14.12-q14.2; >90% del 13q14.2; 90%

del13q14.2-q14.3; 70% ampl13q14.3-13qter

90 80% 5p13.3 (<500kb); 80% cnLOH 9p11.2-9pter pre-Chl <1year 2 Y

CLL063 cnLOH13q12.11-qter with homozygous del13q14.3 100 del11q14.1-24.1 (ATM ) pre-FC <1year 2 N

CLL082 del13q13.2-21.2 with homozygous del13q14.3 100 None noted pre-Chl <1year 2 Y

CLL088 >90% del13q14.3 >90 None noted pre-FC <1year 2 Y

CLL100 del13q14.12-q14.3 100 del6q23.3(<500kb); del11q21-q23.3 (ATM ) NK <1year 2 Y

ARR003 del13q14.2q14.3 100 None noted pre-B <1year 2 N

CLL040 del13q14.2-q21.1 100 ampl1p36.22 (<500kb); 20% del3p21.31-p21.33;

20% del3p25.3; 40% del4p14-pter; 30-40%

ampl5p13.3-pter; 40% del5q31.3-q33.3; 40%

5q33.3-q34, ampl6q16.1(<500kb), 70%

ampl6q22.2(<500kb); ampl 8q21.3-qter; <10%

del12p13.1-p13.2; 80% del13q12.11-q12.13; ~10%

del15q.15.1, 90% del17p11.2-pter (TP53 );

del17q12(<500kb), del18p11.21-pter; <10%

del18q12.2-q21.2; 90% del19p13.3; 80%

del19p13.3, 70% ampl19p13.2;; <10% delXq12-qter

chemotherapy

refractory

N/A 3 Y

CLL044 homozygous del13q14.3 100 del2q36.2, 20% del9p21.1-p23; 50% ampl9q13-

qter; del17pcen-pter (TP53 ); cnLOH19q13.12-

q13.2

chemotherapy

refractory

N/A 3 N

CLL050 del13q14.2-q14.3 100 del6q14.1 (<500kb) intermittent; ampl7p21.1

(<500kb), del11q12.3 (<500kb)

chemotherapy

refractory

N/A 3 N

CLL058 del13q12.3(<500kb), del13q12.2(<500kb - two

regions), del13q14.2-q14.3; homozygous

del13q14.2-q14.3 (intermittent)

100 del12q24.31; 80% ampl15q14-qter; 90%

del17p11.2-pter (TP53 ), 70% ampl15q11.2

chemotherapy

refractory

N/A 3 Y

CLL071 <10% del<10% 13q13.3q34, 70% del13q14.2q14.3

with homozygous del13q14.3 (MDR)

100 30%-70% cnLOH17p11.2-pter (TP53 ) chemotherapy

refractory

N/A 3 N

CLL074 cnLOH13q14.2-qter 100 80% tri3; del11p15.4, del19p13.12-p13.13;

del19p13.11-p13.12, del19p13.11; del19q13.32

chemotherapy

refractory

N/A 3 N

CLL075 80% del13q14.12-q14.2; 90% del13q14.2-q14.3

intermittent with homozygous del13q14.3

100 50% del1p36.32-p36.23; 50% del1p35.3-p35.1;

50% del1p32.3-p32.2; 90% cnLOH1q24.2-q24.3;

50% del1q32.2-q41; <10% del2p21-p22.2; high

ampl6q23.3(<500kb); 80% del9p13.3-9p24.1; 80%

del9q13-q21.31; 80% del9q22.33-q32; 80%

del9q33.1; 80% del9q33.1-q33.2; 80% del9q34.13-

q34.3; 30% cnLOH14q21.3-14qter; 70%

cnLOH17p13.1-pter (TP53 );delXq27.3

chemotherapy

refractory

N/A 3 Y

CLL081 50% del13q12.11-q13.1; 50% del13q14.3 (MDR) 50 10% ampl2p14-pter; 90% del2q11.1-11.2; 90%

del2q14.2-q14.3, 90% del2q14.3; 90% del2q21.2-

q24.1; 40% ampl8q22.2-qter; 90% del9p11.1-pter;

del9q21.13(<500kb); <10% del10q10q23.2q23.31;

90% del11q12.2-q13.1; 70% ampl12q13.12; 90%

del17pcen-pter (TP53 ); 70% ampl17qcen-qter;

90% del19p13.2-pter; 70% ampl19p13.13-p13.2

chemotherapy

refractory

N/A 3 N

CLL112 >90% cnLOH13q12.11-qter with homozygous

del13q14.3

100 90% del2q35; 90% del2q36.3-q37.1; cnLOH

8q21.11q21.13, 80% del16p12.1

chemotherapy

refractory

N/A 3 N

MCLL148 ~90% del13q14.3 with intermittent homozygous

del13q14.3; ~10% del13q14.3q34

~90 del2p16.1(<500kb); 50% del4q21.21-q21.23; 30%

del5q21.3q22.1; 30% del5q22.2q22.3; 30%

del11q22.1q22.2; 30% del11q22.3q23.2 (ATM );

30% del11q23.2q23.3; 30% del11q23.3; 30%

del11q23.3; 30% del11q24.2q24.3; 30% del11q24.3

(<500kb); 50% 12q24.11(<500kb); 60%

cnLOH17p13.3-p11.2 (TP53 );

delXp11.4p11.3(41;001;474-43;480;372)

chemotherapy

refractory

<6 months 3 N

CLL068 del13q14.3 100 90% del2q22.2-24.1 pre-FC NK NK Y

CLL072 del13q14.2-q14.3 100 high ampl15q24.1(<500kb) NK NK NK N

CLL118 90% del13q14.11 (two regions); 90% del13q14.2-

q14.3

90 90% ampl1p34.1(<500kb); 90% del1p22.2(two

regoins <500kb); del2q34(two regions <500kb);

del1p13.1; 90% del10q11.23(<500kb); 90%

del10q11.23, del12q24.3(<500kb); del11q22.3

(ATM )

pre-FCR NK NK Y

CLL120 30% del13q14.2q14.3 30 None noted pre-Flu/Cy-R FU<1year NK Y

MCLL137 del 13q14.2q14.3 100 cnLOH1q24.2q43; del2q35q36.1 NK NK NK N

MCLL138 80% del 13q14,11q21.33, ~90% del

13q14.12q14.3, >90% del13q14.3

>9 cnLOH 1q25.3q31.1; amp9q21.11q34.3;

ampl16p11.2(<500kb)

pre-ChlO FU<1year NK Y

MCLL169 40% cnLOH13q12.11q34 with 80% del13q14.3 80 cnLOH2q11.2q12; ampl17q11.2(<500kb) pre-ChlR FU<1year NK N

MCLL152 del13q14.3 with homozygous del13q14.3 100 None noted pre-BR FU<1year NK N

MCLL161 20% ampl13q14.2; 20% cnLOH/del 13q14,2q21.33 20 ~10% del4pterp15.1, ~10% del11q14.3q23.3, 20%

ampl13q14.2, del15q25.3 (<500kb), 20%

cnLOH20q11.23

pre-FCR FU<1year NK Y

* deletions events are heterozygous unless otherwise stated. Percentages of 30-90% arefrom OncoSNP; all other calls are from visual interpretation.

CNAs are >500kb and cnLOH>2Mb unless otherwise stated; known regions of interest are highlighted in bold

MDR = Minimally Deleted Region; PFS = Progression Free Survival; RB1 - Retinoblastoma gene

NK=Not Known; Flu(or F)=Fludarabine; Cy(or C)= Cyclophosphamide; R= Rituximab; miniR=mini-Rituximab B=Bendamustine; Chl=Chlorambucil; O=Ofatumumab

24

Supplementary Table 7. Statistical analyses of RB1 deletions or cnLOH vs CRS and IGHV gene status in patients with

13q14.3 deletions

Covariate Category n %1 n %1 P-value

CRS 0: W&W 3 15.8 4 21.1

1: PFS>1year 2 10.5 7 36.8

2: PFS<1year 3 15.8 5 26.3 0.046622


All 19 100.0 19 100.0

IGHV gene status Unmutated 6 40.0 10 58.8

Mutated 9 60.0 7 41.2 0.28793

All 15 100.0 17 100.01 Percentage calculated over the total of patients for which the data were available2 Cochran-Armitage trend exact test 3 Chi-square test

RB1 deletion or cnLOH

No Yes

Supplementary Table 8. Statistical analyses of Clinical Risk Score vs Total Number and Total Length of CNAs/cnLOHs excluding and

including well-known abnormalities

Covariate Category n %1 n %1 n %1 n %1 P-value2

0 4 25.0 3 13.6 5 25.0 0 0.0

1 8 50.0 9 40.9 3 15.0 3 16.7

2 2 12.5 2 9.1 3 15.0 2 11.1 0.0016

3 0 0.0 4 18.2 3 15.0 1 5.6

>3 2 12.5 4 18.2 6 30.0 12 66.7

All 16 100.0 22 100.0 20 100.0 18 100.0

<1Mb 9 56.3 9 40.9 9 45.0 0 0.0

1-5Mb 2 12.5 4 18.2 2 10.0 0 0.0 0.0002

>5Mb 5 31.3 9 40.9 9 45.0 18 100.0

All 16 100.0 22 100.0 20 100.0 18 100.0

0 1 6.3 2 9.1 1 5.0 0 0.0

1 5 31.3 4 18.2 6 30.0 0 0.0

2 6 37.5 6 27.3 3 15.0 3 16.7 0.0029

3 2 12.5 3 13.6 1 5.0 2 11.1

>3 2 12.5 7 31.8 9 45.0 13 72.2

All 16 100.0 22 100.0 20 100.0 18 100.01 Percentage calculated over the total number of patients for whom data were available2 Cochran-Armitage trend exact test

2 (PFS<1year) 3 (Chemotherapy

Refractory)

Clinical Risk Score

Total number of

CNAs/cnLOHs

including known

abnormalities

Total number of

CNAs/cnLOHs

excluding known

abnormalities

Total length of

CNAs/cnLOHs

excluding known

abnormalities

0 (W&W) 1 (PFS>1year)

Supplementary Table 9. Statistical analyses of Clonal Evolution vs CRS, Number of CNAs/cnLOHs and Length of

of CNAs/cnLOHs


CRS 0: W&W 0 0.0 0 0.0

1: PFS>1year 9 52.9 10 40.0

2: PFS<1year 8 47.1 10 40.0 0.1696


All 17 100.0 25 100.0


1 4 23.5 7 28.0

2 3 17.6 2 8.0 0.1020

3 3 17.6 3 12.0

>3 2 11.8 10 40.0

All 17 100.0 25 100.0


1-5Mb 3 17.6 2 8.0

>5Mb 4 23.5 17 68.0 0.0075


Clonal Evolution

No Yes

25

Supplementary Table 10. KEGG Pathway AnalysisKEGG ID Pathway* P-value Odds Ratio Exp Count Count Size Genes

4742 Taste transduction 0.0022 5.0156 1.3594 6 52 ADCY6,GNG3,KCNB1,TAS2R7,TAS2R8,TAS2R9

4145 Phagosome 0.0024 2.9666 4.0781 11 156 COLEC12,COMP,MRC1,MRC1L1,OLR1,TLR4,TUBA1

A,TUBA1B,TUBA1C,TUBB2B,VAMP3

5130 Pathogenic Escherichia coli infection 0.0038 4.4322 1.5162 6 58 ARPC4,TLR4,TUBA1A,TUBA1B,TUBA1C,TUBB2B

4210 Apoptosis 0.0079 3.3251 2.3005 7 88 ATM,BAD,CAPN1,CHP,IKBKB,IRAK2,TP53

5014 Amyotrophic lateral sclerosis (ALS) 0.0120 3.9772 1.3855 5 53 BAD,CHP,GRIN2B,PRPH,TP53

232 Caffeine metabolism 0.0131 15.0842 0.1830 2 7 NAT1,NAT2

5222 Small cell lung cancer 0.0232 2.9035 2.2220 6 85 CDKN1B,COL4A1,COL4A2,IKBKB,RB1,TP53

4970 Salivary secretion 0.0284 2.7616 2.3266 6 89 ADCY6,ATP2B2,HTN1,MUC7,PLCB3,STATH

5215 Prostate cancer 0.0284 2.7616 2.3266 6 89 BAD,CDKN1B,CREBBP,IKBKB,RB1,TP53

4540 Gap junction 0.0298 2.7283 2.3527 6 90 ADCY6,PLCB3,TUBA1A,TUBA1B,TUBA1C,TUBB2B

4720 Long-term potentiation 0.0355 2.9282 1.8299 5 70 CHP,CREBBP,GRIN2B,PLCB3,PPP1CC

5212 Pancreatic cancer 0.0355 2.9282 1.8299 5 70 BAD,IKBKB,RALGDS,RB1,TP53

5220 Chronic myeloid leukemia 0.0414 2.7976 1.9083 5 73 BAD,CDKN1B,IKBKB,RB1,TP53

5144 Malaria 0.0434 3.2284 1.3332 4 51 COMP,ICAM1,KLRK1,TLR4

4310 Wnt signaling pathway 0.0436 2.1435 3.9474 8 151 CHP,CREBBP,DKK4,LRP6,PLCB3,PPP2R5B,SFRP1,

TP53

920 Sulfur metabolism 0.0438 6.8493 0.3398 2 13 PAPSS2,SULT1E1

4110 Cell cycle 0.0497 2.2102 3.3461 7 128 ANAPC5,ATM,CDKN1B,CDKN2D,CREBBP,RB1,TP53

3410 Base excision repair 0.0540 3.7795 0.8627 3 33 FEN1,OGG1,POLB

4140 Regulation of autophagy 0.0581 3.6569 0.8888 3 34 ATG4D,ATG5,GABARAPL1

4120 Ubiquitin mediated proteolysis 0.0711 2.0220 3.6337 7 139 ANAPC5,CUL3,KEAP1,SYVN1,UBE2NL,UBE3C,VHL

4114 Oocyte meiosis 0.0773 2.1130 2.9801 6 114 ADCY6,ANAPC5,CHP,PPP1CC,PPP2R5B,SPDYC

5200 Pathways in cancer 0.0850 1.5923 8.5483 13 327 BAD,CDKN1B,COL4A1,COL4A2,CREBBP,ETS1,IKBK

B,NFKB2,RALGDS,RB1,SUFU,TP53,VHL592 alpha-Linolenic acid metabolism 0.0949 4.1806 0.5228 2 20 FADS2,PLA2G4E

860 Porphyrin and chlorophyll metabolism 0.1014 2.8296 1.1241 3 43 FTH1,HMOX2,UGT2A1

1040 Biosynthesis of unsaturated fatty acids 0.1032 3.9598 0.5490 2 21 FADS1,FADS2

4320 Dorso-ventral axis formation 0.1380 3.2689 0.6535 2 25 ETS1,ETV6

5146 Amoebiasis 0.1435 1.8725 2.7710 5 106 COL4A1,COL4A2,COL5A1,PLCB3,TLR4

983 Drug metabolism - other enzymes 0.1540 2.3063 1.3594 3 52 NAT1,NAT2,UGT2A1

5223 Non-small cell lung cancer 0.1667 2.2150 1.4116 3 54 BAD,RB1,TP53

130 Ubiquinone and other terpenoid-

quinone biosynthesis

0.1693 6.2429 0.1830 1 7 HPD

140 Steroid hormone biosynthesis 0.1797 2.1307 1.4639 3 56 CYP17A1,SULT1E1,UGT2A1

4020 Calcium signaling pathway 0.1802 1.5594 4.6271 7 177 ATP2B2,CHP,P2RX4,P2RX7,PLCB3,TNNC1,VDAC3

4512 ECM-receptor interaction 0.1818 1.8621 2.2220 4 85 COL4A1,COL4A2,COL5A1,COMP

5131 Shigellosis 0.2201 1.9120 1.6208 3 62 ARPC4,ATG5,IKBKB

5210 Colorectal cancer 0.2201 1.9120 1.6208 3 62 BAD,RALGDS,TP53

5143 African trypanosomiasis 0.2324 2.2743 0.9150 2 35 ICAM1,PLCB3

4722 Neurotrophin signaling pathway 0.2380 1.5444 3.3200 5 127 BAD,IKBKB,IRAK2,RPS6KA4,TP53

4130 SNARE interactions in vesicular

transport

0.2422 2.2070 0.9411 2 36 SNAP23,VAMP3

4360 Axon guidance 0.2527 1.5066 3.3984 5 130 CHP,EFNA5,EPHA5,RND1,UNC5D

4920 Adipocytokine signaling pathway 0.2622 1.7337 1.7776 3 68 ADIPOR2,CAMKK2,IKBKB

5145 Toxoplasmosis 0.2677 1.4705 3.4768 5 133 BAD,IKBKB,PLA2G4E,TLR4,TYK2

4510 Focal adhesion 0.2682 1.3679 5.2283 7 200 BAD,COL4A1,COL4A2,COL5A1,COMP,PPP1CC,VAV

24115 p53 signaling pathway 0.2693 1.7071 1.8038 3 69 ATM,SESN1,TP53

4972 Pancreatic secretion 0.2710 1.5505 2.6403 4 101 ADCY6,ATP2B2,PLA2G4E,PLCB3

5010 Alzheimer's disease 0.2712 1.4041 4.3656 6 167 BAD,CAPN1,CHP,COX8A,GRIN2B,PLCB3

5211 Renal cell carcinoma 0.2765 1.6813 1.8299 3 70 CREBBP,ETS1,VHL

4650 Natural killer cell mediated cytotoxicity 0.2828 1.4360 3.5553 5 136 CHP,ICAM1,KLRD1,KLRK1,VAV2

3018 RNA degradation 0.2836 1.6563 1.8561 3 71 CNOT7,DCP1B,DCP2

5218 Melanoma 0.2836 1.6563 1.8561 3 71 BAD,RB1,TP53

350 Tyrosine metabolism 0.2910 1.9224 1.0718 2 41 DBH,HPD

4520 Adherens junction 0.2980 1.6084 1.9083 3 73 CREBBP,FER,YES1

5219 Bladder cancer 0.3008 1.8740 1.0979 2 42 RB1,TP53

4971 Gastric acid secretion 0.3052 1.5855 1.9345 3 74 ADCY6,KCNJ1,PLCB3

603 Glycosphingolipid biosynthesis - globo

series

0.3101 2.8778 0.3660 1 14 GBGT1

3450 Non-homologous end-joining 0.3101 2.8778 0.3660 1 14 FEN1

4662 B cell receptor signaling pathway 0.3124 1.5632 1.9606 3 75 CHP,IKBKB,VAV2

4370 VEGF signaling pathway 0.3196 1.5415 1.9868 3 76 BAD,CHP,PLA2G4E

310 Lysine degradation 0.3202 1.7841 1.1502 2 44 SETD1B,SETD2

4260 Cardiac muscle contraction 0.3268 1.5204 2.0129 3 77 CACNA2D4,COX8A,TNNC1

5016 Huntington's disease 0.3450 1.2735 4.7839 6 183 COX8A,CREBBP,GRIN2B,PLCB3,TP53,VDAC3

564 Glycerophospholipid metabolism 0.3485 1.4604 2.0913 3 80 AGPAT6,DGKH,PLA2G4E

4330 Notch signaling pathway 0.3491 1.6643 1.2287 2 47 CREBBP,DLL4

4974 Protein digestion and absorption 0.3557 1.4414 2.1175 3 81 COL4A1,COL4A2,COL5A1

4270 Vascular smooth muscle contraction 0.3601 1.3393 3.0324 4 116 ADCY6,PLA2G4E,PLCB3,PPP1CC

360 Phenylalanine metabolism 0.3630 2.3370 0.4444 1 17 HPD

450 Selenocompound metabolism 0.3630 2.3370 0.4444 1 17 PAPSS2

3015 mRNA surveillance pathway 0.3700 1.4049 2.1698 3 83 MAGOHB,PPP2R5B,UPF1

561 Glycerolipid metabolism 0.3775 1.5595 1.3071 2 50 AGPAT6,DGKH

100 Steroid biosynthesis 0.3959 2.0766 0.4967 1 19 TM7SF2

5213 Endometrial cancer 0.3962 1.4966 1.3594 2 52 BAD,TP53

* 'Top' 70 pathways only listed

26

Paired Pre-treatment and Relapse samples (n=42 pairs)

Pre-treatment samples only (n=93)

All samples investigated for intra-clonal

heterogeneity using

arrays and OncoSNP

All Pre-treatment and Relapse pairs investigated for clonal

evolution over time using arrays and OncoSNP

B-CLL Study patients (n=93)B-CLL Study samples (93+42=135)

IGHV gene mutation status (n=67) and TP53 mutation status (n=83) established

Supplementary Figure 1. Flowchart documenting numbers of samples and

overview of study.

27

Supplementary Figure 2. The Vysis LSI ATM FISH probe (boundaries

indicated by yellow arrow and dashed lines) binds to non-deleted regions in

two B-CLL patients with small 11q22.3 MDR deletions (indicated by red bars).

28

Mutated TP53

Wild-type TP53

a) cnLOH detected by Illumina chip b) High resolution melting curve c) Sanger sequencing of TP53

CLL071Fluorescenceintensityratio

B-allelefrequency

Supplementary Figure 3. TP53 Mutation screening of the sample from CLL071 with cnLOH encompassing this gene. a) Nexus image showing

cnLOH across the region b) high resolution melting curve analysis shows deviation from the TP53 reference sequence c) Sanger sequencing

confirms the presence of a GAG>AAG mutation in this patient sample.

29

a)

b)

B-allelefrequency

Logfluorescence

ratio

Normal ~10% ~20% 30% 45%35% 55%40% 75% 80% 85% 90% 95% 100%50% 60% 70%

OncoSNP ManualManual

Chromosome 6 position (Mb)

B-a

llele

fre

qu

en

cy

Supplementary Figure 4: a) Nexus image showing examples of B-allele

frequency plots for mosaic copy number losses called by OncoSNP and called

manually. Here, the OncoSNP output percentages are given to the nearest

5%; however, for accuracy, the percentages in the data tables are rounded to

the nearest 10% (1 significant figure). Currently, OncoSNP is considered

accurate for calls between 10-90%, though this depends ultimately on the

noise variances of the SNP data. Additional calls were made when mosaic

changes were evident on visual inspection of plots b) Example of Nexus log

fluorescence ratio plot and B-allele frequency plot for CLL084_R,

demonstrating putative <10%6pterp22.3 loss that was not identified by

OncoSNP. Region of loss is shown by the red double-ended arrow.

30

47

B-C

LL p

atie

nts

with

de

letio

ns/c

nL

OH

invo

lvin

g 1

3q

13

.4

MDR (~7kb)

involves DLEU2

1

47

50%

0%

50%

100%

100%

1

47

Chromosome 13

CNVs

Exons

Genes

miRNA

Supplementary Figure 5. Nexus (BioDiscovery) image showing Illumina 1M-Duo data calls from 47 patients with deletions/cnLOH involving 13q13.4. Red bar = CNA loss (thick bar indicates homozygous loss); green bar = CNA gain; mustard bar = LOH; purple bar = allelic imbalance.

.

31

Supplementary Figure 6. a) The B-allele frequency plot shows clear evidence of

mosaicism involving chromosome 13q14.1-q14.3 (red arrow) in a pre-treatment

sample from CLL080 b) clonal evolution of a chromosome 13q14.3 deletion in a

pre-treatment sample from patient CLL071 - the red, dashed arrows indicate the

apparent boundary extension involving one allele.

32

Pre-treatment

Relapse

~20% ~30% ~80%

Proportion of sub-clones with CNA/cnLOH

CNAs/cnLOHs identified

11q22.3q24.3(x1)

20q11.22qter(x2) hmz

Leukemic Progenitor(s)

13q14.3(x1)

<5% >90%~60%

2p16.1(x1)

11q22.1(x1)

~10%

Supplementary Figure 7. Schematic representation of the possible clonal architecture

pre-treatment and at relapse for patient CLL092. Percentages below 10% and above

90% and cell populations without identifiable CNAs/cnLOHs are inferred. The precision

of the percentage contribution of sub-clones with CNAs/cnLOH is +/- 10%.

Documents

Materials/Subjects and Methods - Nature · high values of AUC will result from long abnormalities with high copy number. ... Statistical Computational Tool for Data Analysis A number