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Clinical impact of small subclones harboring
NOTCH1, SF3B1 or BIRC3 mutations in chronic
lymphocytic leukemia
Large-scale sequencing approaches have shown that
chronic lymphocytic leukemia (CLL) is composed of a
mosaic of leukemic subpopulations, each defined by dis-
tinct genetic lesions.1-4
The application of ultra-deep-next
generation sequencing (NGS) to track TP53 mutated sub-
clones in CLL has provided a proof-of-concept that small
tumor cell populations of very low clonal abundance can
drive the overall disease course, and may represent
informative and highly sensitive biomarkers of chemore-
fractoriness.5
In addition to TP53, other cancer genes known to be
recurrently mutated in CLL and significantly associated
with poor survival include NOTCH1, SF3B1, and BIRC3.6
The clinical impact of mutations in the NOTCH1, SF3B1,
and BIRC3 genes has so far only been documented in
CLL series investigated by conventional sequencing that
has a sensitivity limit of 10%-15%. Here we assessed the
frequency, prognostic impact and evolution during CLL
course of small subclones mutated in the NOTCH1,
SF3B1, and BIRC3 genes.
The study population was a consecutive series of 304
newly diagnosed CLL patients (Online Supplementary
Table 1S) who were prospectively registered in the
Amedeo Avogadro University CLL database.5
CLL diag-
nosis had been made according to IWCLL-NCI criteria.
Median follow up of patients alive at the time of the
study was seven years. Patients provided informed con-
sent in accordance with local IRB requirements and the
Declaration of Helsinki. The study was approved by the
ethical committee of the Ospedale Maggiore della Carità
di Novara associated with the Amedeo Avogadro
University of Eastern Piedmont (Protocol Code 59/CE;
Study Number CE 8/11).
We applied an ultra-deep-NGS strategy coupled with a
robust bioinformatic algorithm for highly sensitive detec-
tion of small mutated subclones in the NOTCH1, SF3B1,
and BIRC3 genes. NOTCH1, SF3B1, and BIRC3 mutation
hotspots (Online Supplementary Table 2S) were investigat-
ed on genomic DNA extracted from peripheral blood
mononuclear cell samples collected at CLL diagnosis,
progression requiring treatment, relapse, and last follow
up. In all cases, the fraction of tumor cells corresponded
to 70%-98% as assessed by flow cytometry. Ultra-deep-
NGS was performed using the 454 chemistry, was based
on amplicon libraries, and was tailored at an approxi-
mately 200-fold coverage per amplicon (average coverage
haematologica 2016; 101:e135
LETTERS TO THE EDITOR
Figure 1. Molecular profile of subclonal NOTCH1, SF3B1, and BIRC3 mutations. Allele frequency of NOTCH1 (A), SF3B1 (B), and BIRC3 (C) mutations identified
by ultra-deep-next generation sequencing. Mutations are ordered according to their allelic abundance. Mutations that tested positive (gray bars) and negative
(red bars) by Sanger sequencing are indicated. Schematic diagram of the NOTCH1 (D), SF3B1 (E), and BIRC3 (F) proteins with their conserved functional
domains. Color-coded shapes indicate the position of small subclonal mutations from the CLL study cohort (red shapes) and mutations detectable by Sanger
sequencing in CLL from our internal dataset (gray shapes). Hot spot codons recurrently affected by both subclonal and clonal mutations are highlighted.
A
B
C
D
E
F
©FerrataStortiFoundation
2437x; range 1003x-8584x). A previously developed
bioinformatic algorithm was applied to call non-silent
variants out of background NGS noise.5
The sensitivity of
the ultra-deep-NGS approach allowed mutant allele frac-
tions down to 0.3% to be detected (3 mutant alleles in a
background of ~1000 wild type alleles) with a 95% con-
fidence interval of 0.2%-0.5%. Variant calling by the
algorithm was validated by Sanger sequencing or, if the
variant was below the sensitivity threshold of Sanger
sequencing, by both duplicate ultra-deep-NGS and allele
specific PCR (AS-PCR) (Online Supplementary Figure 1S).
To account for tumor representation, the frequency of the
mutant alleles provided by ultra-deep-NGS was corrected
for the proportion of CD19+
/CD5+
cells in each sample.
Overall survival (OS) from diagnosis was the primary end
point and this was measured from date of initial presen-
tation to date of death from any cause (event) or last fol-
low up (censoring). Survival analysis was performed
using the Kaplan-Meier method.7
To identify the optimal
cut off of the variant allele frequency to predict CLL OS,
we used the outcome-driven maximally selected rank sta-
tistics implemented in the Maxstat package of R.8
Continuous variables from related samples were com-
pared using the Wilcoxon test. Further details are avail-
able in the Online Supplementary Appendix.
Ultra-deep-NGS identified 46 NOTCH1 mutations
(median allele frequency 24%; range 1.4%-64%) in 14%
(43 of 304) of CLL patients, 43 SF3B1 mutations (median
allele frequency 16%; range 0.5%-48%) in 11% (35 of
304), and 37 BIRC3 mutations (median allele frequency
5.6%; range 0.2%-47%) in 8% (26 of 304). All mutations
that had been detected by Sanger sequencing (i.e. Sanger
sequencing positive mutations) were also identified by
ultra-deep-NGS (Figure 1). Ultra-deep-NGS identified
additional small subclonal mutations that, because of
their very low abundance, were missed by Sanger
sequencing (NOTCH1 n=14; median allele frequency
3.9%; range 1.4%-9%; SF3B1 n=25; median allele fre-
quency 4.3%; range 0.5%-17%; BIRC3 n=30; median
allele frequency 1.4%; range 0.2%-6%) (Figure 1A-C and
Online Supplementary Tables S3-S5). NOTCH1 mutations
were more represented in the leukemic cell population
(Online Supplementary Figure S2) and, therefore, more fre-
quently detectable by Sanger sequencing than SF3B1 and
BIRC3 variants. This is consistent with the view that
NOTCH1 mutations are an early event, while SF3B1 and
BIRC3 lesions are late events in CLL evolution.2,9,10
These
data indicate that a fraction of SF3B1, BIRC3, and to a
lesser extent NOTCH1 mutations may be restricted to
small subclones not detectable by conventional sequenc-
ing at the time of CLL presentation.
The molecular spectrum (i.e. type and hot spot distri-
bution) of small subclonal mutations of NOTCH1, SF3B1,
and BIRC3 was highly consistent with that of variants
that were detectable by Sanger sequencing in CLL (Figure
1D-F). These data indicate that small subclonal mutations
of NOTCH1, SF3B1, and BIRC3 do not represent biolog-
ically irrelevant random passenger events, but instead are
predicted to have a similar impact on the proteins as that
of clonal variants.
There was no statistically significant difference in OS
between patients harboring small subclonal mutations of
NOTCH1, SF3B1, or BIRC3 and wild-type patients
(Figure 2A-C), even after adjusting for confounding fac-
tors (Online Supplementary Table S6). The study, however,
was not fully powered to support non-inferiority.
Conversely, patients harboring small subclonal mutations
of NOTCH1, SF3B1, or BIRC3 showed a trend towards a
longer OS than patients having a mutation that was
detectable by Sanger sequencing (Figure 2A-C).
Consistently, outcome-driven approaches documented
that NOTCH1, SF3B1, and BIRC3 mutations should be
represented in at least 25%, 35% and 1% of the alleles,
respectively, to have the maximum impact on CLL OS
(Online Supplementary Figure S3). Application of these
approaches to TP53 mutations failed to identify a clear
haematologica 2016; 101:e136
LETTERS TO THE EDITOR
Figure 2. Estimates of overall survival (OS) of patients harboring small mutated subclones. Comparison of OS from chronic lymphocytic leukemia (CLL) diag-
nosis between patients harboring small subclonal mutations not detectable by Sanger sequencing (yellow line), cases harboring Sanger sequencing positive
mutations (red line), and cases harboring an unmutated gene (blue line) of the NOTCH1 (A), SF3B1 (B), and BIRC3 (C) genes. P: P values by log rank test; M:
mutation.
A B C
©FerrataStortiFoundation
cut off, suggesting that, in contrast to NOTCH1, SF3B1,
and BIRC3 mutations, TP53 lesions are relevant to the
outcome of CLL independent of their abundance (Online
Supplementary Figure S4).5
Longitudinal ultra-deep-NGS of sequential samples
was carried out in: i) 7 cases harboring small subclonal
NOTCH1 mutations (including 2 patients who required
therapy and 5 who were managed with a “watch and
wait” approach); ii) in 12 cases harboring small subclonal
SF3B1 mutations (including 4 patients who required ther-
apy and 8 who were managed with a “watch and wait”
approach); and iii) in 6 cases harboring small subclonal
BIRC3 mutations (including 4 patients who required ther-
apy and 2 who were managed with a “watch and wait”
approach). By longitudinal analysis, the abundance of
small subclonal mutations of NOTCH1, SF3B1, and
BIRC3 did not significantly change in those patients who
were managed with a “watch and wait” approach, with
only scattered mutations displaying an outgrowth (Figure
3 and Online Supplementary Figure S5). Upon treatment,
small subclones harboring NOTCH1 or BIRC3 mutations
either shrank below the sensitivity threshold of our ultra-
deep-NGS (Figure 3 and Online Supplementary Figure S5)
or persisted at low abundance, suggesting that they
maintained sensitivity to chemotherapy and did not gain
a competitive advantage over the wild-type clones. This
observation suggests that the enrichment of BIRC3 muta-
tions that had previously been documented in advanced
relapsed/refractory CLL11
might be related to other fac-
tors that contribute to the fitness of the clones harboring
BIRC3 lesions, rather than a differential sensitivity to
therapy. Small subclonal SF3B1 mutations were more fre-
quently selected than counter-selected (Figure 3 and
Online Supplementary Figure S5).
The clinical impact of small subclones harboring
NOTCH1, SF3B1, or BIRC3 mutations appears to be less
pronounced than that of small TP53 mutated subclones.
Also, upon treatment, NOTCH1, SF3B1, and BIRC3
mutations to do not provide small subclones with the
same strength of fitness advantage as TP53 mutations.
However, many homogeneously treated CLL patients
and large-scale genotyping of sequential samples collect-
ed in a timely fashion are required to quantify precisely
the specific fitness of small NOTCH1, SF3B1, and BIRC3
mutated subclones in the context of therapy.
Silvia Rasi,1*
Hossein Khiabanian,2*§
Carmela Ciardullo,1
Lodovico Terzi-di-Bergamo,1
Sara Monti,1
Valeria Spina,1
Alessio Bruscaggin,1
Michaela Cerri,1
Clara Deambrogi,1
Lavinia Martuscelli,1
Alessandra Biasi,1
Elisa Spaccarotella,1
Lorenzo De Paoli,1
Valter Gattei,3
Robin Foà,4
Raul Rabadan,2
Gianluca Gaidano,1§
and Davide Rossi1§
haematologica 2016; 101:e137
LETTERS TO THE EDITOR
Figure 3. Longitudinal analysis of patients harboring small mutated subclones. Changes of the allele frequency of the NOTCH1 (A), SF3B1 (B), and BIRC3 (C)
mutated subclones upon “watch and wait”. The upper line graph represents the allele frequency modification of each individual mutation. The lower box plot
graph shows in aggregate the mean, interquartile, and extreme values of the allele frequency at diagnosis and last follow up. P values by Wilcoxon rank test.
Changes of the allele frequency of the NOTCH1 (D), SF3B1 (E), and BIRC3 (F) mutated subclones upon treatment. The upper line graph represents the allele
frequency modification of each individual mutation after each individual treatment. The lower box plot graph shows in aggregate the mean, interquartile, and
extreme values of the allele frequency before treatment and at relapse. P: P values by Wilcoxon rank test.
A B C
D E F
©FerrataStortiFoundation
1
Division of Hematology, Department of Translational Medicine,
Amedeo Avogadro University of Eastern Piedmont, Novara, Italy;
2
Center for Topology of Cancer Evolution and Heterogeneity,
Department of Biomedical Informatics and Center for Computational
Biology and Bioinformatics, Columbia University, New York, NY,
USA; 3
Clinical and Experimental Onco-Hematology, Centro di
Riferimento Oncologico, Aviano, Italy; and 4
Hematology, Department
of Cellular Biotechnologies and Hematology, Sapienza University,
Rome, Italy
§
Current address: Center for Systems and Computational Biology,
Rutgers Cancer Institute, Rutgers University, New Brunswick, NJ,
USA
*SR and HK contributed equally; §
GG and DR contributed equally.
Funding: this study was supported by Special Program Molecular
Clinical Oncology 5 x 1000 No. 10007, My First AIRC Grant
No. 13470, Associazione Italiana per la Ricerca sul Cancro Foundation
Milan, Italy; Fondazione Cariplo, Grant No. 2012-0689; Ricerca
Finalizzata, RF-2010-2307262 and RF-2011-02349712, and
Progetto Giovani Ricercatori GR-2010-2317594, Ministero della
Salute, Rome, Italy; Compagnia di San Paolo, Grant No.
PMN_call_2012_0071, Turin, Italy; Futuro in Ricerca 2012 Grant
No. RBFR12D1CB, Ministero dell'Istruzione, dell'Università e della
Ricerca, Rome, Italy; National Cancer Institute Grant
U54-CA193313.
The online version of this letter has a Supplementary Appendix.
Correspondence: rossidav@med.unipmn.it
doi:10.3324/haematol.2015.136051
Key words: chronic lymphocytic leukemia, subclones, NOTCH1,
SF3B1, BIRC3, TP53, frequency, prognostic impact, evolution.
Information on authorship, contributions, and financial & other disclo-
sures was provided by the authors and is available with the online version
of this article at www.haematologica.org.
References
1. Schuh A, Becq J, Humphray S, et al. Monitoring chronic lymphocytic
leukemia progression by whole genome sequencing reveals hetero-
geneous clonal evolution patterns. Blood. 2012;120(20):4191-4196.
2. Landau DA, Carter SL, Stojanov P, et al. Evolution and impact of sub-
clonal mutations in chronic lymphocytic leukemia. Cell. 2013;
152(4):714-726.
3. Ouillette P, Saiya-Cork K, Seymour E, Li C, Shedden K, Malek SN.
Clonal evolution, genomic drivers, and effects of therapy in chronic
lymphocytic leukemia. Clin Cancer Res. 2013;19(11):2893-2904.
4. Jethwa A, Hüllein J, Stolz T, et al. Targeted resequencing for analysis
of clonal composition of recurrent gene mutations in chronic lym-
phocytic leukaemia. Br J Haematol. 2013;163(4):496-500.
5. Rossi D, Khiabanian H, Spina V, et al. Clinical impact of small TP53
mutated subclones in chronic lymphocytic leukemia. Blood.
2014;123(14):2139-2147.
6. Foà R, Del Giudice I, Guarini A, Rossi D, Gaidano G. Clinical impli-
cations of the molecular genetics of chronic lymphocytic leukemia.
Haematologica. 2013;98(5):675-685.
7. Kaplan EL, Meier P. Nonparametric estimation from incomplete
observations. Am Stat Assoc. 1958;53(282):457-481.
8. Lausen B, Hothorn T, Bretz F, Schmacher M. Assessment of
Optimally Selected Prognostic Factors. Biometrical Journal.
2004;46(3), 364-374.
9. Wang J, Khiabanian H, Rossi D, et al. Tumor evolutionary direct-
ed graphs and the history of chronic lymphocytic leukemia. Elife.
2014; 3.
10. Landau DA, Tausch E, Taylor-Weiner AN, et al. Mutations driving
CLL and their evolution in progression and relapse. Nature.
2015;526(7574):525-530.
11. Rossi D, Fangazio M, Rasi S, et al. Disruption of BIRC3 associates
with fludarabine chemorefractoriness in TP53 wild-type chronic
lymphocytic leukemia. Blood. 2012;119(12):2854-2862.
haematologica 2016; 101:e138
LETTERS TO THE EDITOR
©FerrataStortiFoundation

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Article_Subclones_BIG

  • 1. Clinical impact of small subclones harboring NOTCH1, SF3B1 or BIRC3 mutations in chronic lymphocytic leukemia Large-scale sequencing approaches have shown that chronic lymphocytic leukemia (CLL) is composed of a mosaic of leukemic subpopulations, each defined by dis- tinct genetic lesions.1-4 The application of ultra-deep-next generation sequencing (NGS) to track TP53 mutated sub- clones in CLL has provided a proof-of-concept that small tumor cell populations of very low clonal abundance can drive the overall disease course, and may represent informative and highly sensitive biomarkers of chemore- fractoriness.5 In addition to TP53, other cancer genes known to be recurrently mutated in CLL and significantly associated with poor survival include NOTCH1, SF3B1, and BIRC3.6 The clinical impact of mutations in the NOTCH1, SF3B1, and BIRC3 genes has so far only been documented in CLL series investigated by conventional sequencing that has a sensitivity limit of 10%-15%. Here we assessed the frequency, prognostic impact and evolution during CLL course of small subclones mutated in the NOTCH1, SF3B1, and BIRC3 genes. The study population was a consecutive series of 304 newly diagnosed CLL patients (Online Supplementary Table 1S) who were prospectively registered in the Amedeo Avogadro University CLL database.5 CLL diag- nosis had been made according to IWCLL-NCI criteria. Median follow up of patients alive at the time of the study was seven years. Patients provided informed con- sent in accordance with local IRB requirements and the Declaration of Helsinki. The study was approved by the ethical committee of the Ospedale Maggiore della Carità di Novara associated with the Amedeo Avogadro University of Eastern Piedmont (Protocol Code 59/CE; Study Number CE 8/11). We applied an ultra-deep-NGS strategy coupled with a robust bioinformatic algorithm for highly sensitive detec- tion of small mutated subclones in the NOTCH1, SF3B1, and BIRC3 genes. NOTCH1, SF3B1, and BIRC3 mutation hotspots (Online Supplementary Table 2S) were investigat- ed on genomic DNA extracted from peripheral blood mononuclear cell samples collected at CLL diagnosis, progression requiring treatment, relapse, and last follow up. In all cases, the fraction of tumor cells corresponded to 70%-98% as assessed by flow cytometry. Ultra-deep- NGS was performed using the 454 chemistry, was based on amplicon libraries, and was tailored at an approxi- mately 200-fold coverage per amplicon (average coverage haematologica 2016; 101:e135 LETTERS TO THE EDITOR Figure 1. Molecular profile of subclonal NOTCH1, SF3B1, and BIRC3 mutations. Allele frequency of NOTCH1 (A), SF3B1 (B), and BIRC3 (C) mutations identified by ultra-deep-next generation sequencing. Mutations are ordered according to their allelic abundance. Mutations that tested positive (gray bars) and negative (red bars) by Sanger sequencing are indicated. Schematic diagram of the NOTCH1 (D), SF3B1 (E), and BIRC3 (F) proteins with their conserved functional domains. Color-coded shapes indicate the position of small subclonal mutations from the CLL study cohort (red shapes) and mutations detectable by Sanger sequencing in CLL from our internal dataset (gray shapes). Hot spot codons recurrently affected by both subclonal and clonal mutations are highlighted. A B C D E F ©FerrataStortiFoundation
  • 2. 2437x; range 1003x-8584x). A previously developed bioinformatic algorithm was applied to call non-silent variants out of background NGS noise.5 The sensitivity of the ultra-deep-NGS approach allowed mutant allele frac- tions down to 0.3% to be detected (3 mutant alleles in a background of ~1000 wild type alleles) with a 95% con- fidence interval of 0.2%-0.5%. Variant calling by the algorithm was validated by Sanger sequencing or, if the variant was below the sensitivity threshold of Sanger sequencing, by both duplicate ultra-deep-NGS and allele specific PCR (AS-PCR) (Online Supplementary Figure 1S). To account for tumor representation, the frequency of the mutant alleles provided by ultra-deep-NGS was corrected for the proportion of CD19+ /CD5+ cells in each sample. Overall survival (OS) from diagnosis was the primary end point and this was measured from date of initial presen- tation to date of death from any cause (event) or last fol- low up (censoring). Survival analysis was performed using the Kaplan-Meier method.7 To identify the optimal cut off of the variant allele frequency to predict CLL OS, we used the outcome-driven maximally selected rank sta- tistics implemented in the Maxstat package of R.8 Continuous variables from related samples were com- pared using the Wilcoxon test. Further details are avail- able in the Online Supplementary Appendix. Ultra-deep-NGS identified 46 NOTCH1 mutations (median allele frequency 24%; range 1.4%-64%) in 14% (43 of 304) of CLL patients, 43 SF3B1 mutations (median allele frequency 16%; range 0.5%-48%) in 11% (35 of 304), and 37 BIRC3 mutations (median allele frequency 5.6%; range 0.2%-47%) in 8% (26 of 304). All mutations that had been detected by Sanger sequencing (i.e. Sanger sequencing positive mutations) were also identified by ultra-deep-NGS (Figure 1). Ultra-deep-NGS identified additional small subclonal mutations that, because of their very low abundance, were missed by Sanger sequencing (NOTCH1 n=14; median allele frequency 3.9%; range 1.4%-9%; SF3B1 n=25; median allele fre- quency 4.3%; range 0.5%-17%; BIRC3 n=30; median allele frequency 1.4%; range 0.2%-6%) (Figure 1A-C and Online Supplementary Tables S3-S5). NOTCH1 mutations were more represented in the leukemic cell population (Online Supplementary Figure S2) and, therefore, more fre- quently detectable by Sanger sequencing than SF3B1 and BIRC3 variants. This is consistent with the view that NOTCH1 mutations are an early event, while SF3B1 and BIRC3 lesions are late events in CLL evolution.2,9,10 These data indicate that a fraction of SF3B1, BIRC3, and to a lesser extent NOTCH1 mutations may be restricted to small subclones not detectable by conventional sequenc- ing at the time of CLL presentation. The molecular spectrum (i.e. type and hot spot distri- bution) of small subclonal mutations of NOTCH1, SF3B1, and BIRC3 was highly consistent with that of variants that were detectable by Sanger sequencing in CLL (Figure 1D-F). These data indicate that small subclonal mutations of NOTCH1, SF3B1, and BIRC3 do not represent biolog- ically irrelevant random passenger events, but instead are predicted to have a similar impact on the proteins as that of clonal variants. There was no statistically significant difference in OS between patients harboring small subclonal mutations of NOTCH1, SF3B1, or BIRC3 and wild-type patients (Figure 2A-C), even after adjusting for confounding fac- tors (Online Supplementary Table S6). The study, however, was not fully powered to support non-inferiority. Conversely, patients harboring small subclonal mutations of NOTCH1, SF3B1, or BIRC3 showed a trend towards a longer OS than patients having a mutation that was detectable by Sanger sequencing (Figure 2A-C). Consistently, outcome-driven approaches documented that NOTCH1, SF3B1, and BIRC3 mutations should be represented in at least 25%, 35% and 1% of the alleles, respectively, to have the maximum impact on CLL OS (Online Supplementary Figure S3). Application of these approaches to TP53 mutations failed to identify a clear haematologica 2016; 101:e136 LETTERS TO THE EDITOR Figure 2. Estimates of overall survival (OS) of patients harboring small mutated subclones. Comparison of OS from chronic lymphocytic leukemia (CLL) diag- nosis between patients harboring small subclonal mutations not detectable by Sanger sequencing (yellow line), cases harboring Sanger sequencing positive mutations (red line), and cases harboring an unmutated gene (blue line) of the NOTCH1 (A), SF3B1 (B), and BIRC3 (C) genes. P: P values by log rank test; M: mutation. A B C ©FerrataStortiFoundation
  • 3. cut off, suggesting that, in contrast to NOTCH1, SF3B1, and BIRC3 mutations, TP53 lesions are relevant to the outcome of CLL independent of their abundance (Online Supplementary Figure S4).5 Longitudinal ultra-deep-NGS of sequential samples was carried out in: i) 7 cases harboring small subclonal NOTCH1 mutations (including 2 patients who required therapy and 5 who were managed with a “watch and wait” approach); ii) in 12 cases harboring small subclonal SF3B1 mutations (including 4 patients who required ther- apy and 8 who were managed with a “watch and wait” approach); and iii) in 6 cases harboring small subclonal BIRC3 mutations (including 4 patients who required ther- apy and 2 who were managed with a “watch and wait” approach). By longitudinal analysis, the abundance of small subclonal mutations of NOTCH1, SF3B1, and BIRC3 did not significantly change in those patients who were managed with a “watch and wait” approach, with only scattered mutations displaying an outgrowth (Figure 3 and Online Supplementary Figure S5). Upon treatment, small subclones harboring NOTCH1 or BIRC3 mutations either shrank below the sensitivity threshold of our ultra- deep-NGS (Figure 3 and Online Supplementary Figure S5) or persisted at low abundance, suggesting that they maintained sensitivity to chemotherapy and did not gain a competitive advantage over the wild-type clones. This observation suggests that the enrichment of BIRC3 muta- tions that had previously been documented in advanced relapsed/refractory CLL11 might be related to other fac- tors that contribute to the fitness of the clones harboring BIRC3 lesions, rather than a differential sensitivity to therapy. Small subclonal SF3B1 mutations were more fre- quently selected than counter-selected (Figure 3 and Online Supplementary Figure S5). The clinical impact of small subclones harboring NOTCH1, SF3B1, or BIRC3 mutations appears to be less pronounced than that of small TP53 mutated subclones. Also, upon treatment, NOTCH1, SF3B1, and BIRC3 mutations to do not provide small subclones with the same strength of fitness advantage as TP53 mutations. However, many homogeneously treated CLL patients and large-scale genotyping of sequential samples collect- ed in a timely fashion are required to quantify precisely the specific fitness of small NOTCH1, SF3B1, and BIRC3 mutated subclones in the context of therapy. Silvia Rasi,1* Hossein Khiabanian,2*§ Carmela Ciardullo,1 Lodovico Terzi-di-Bergamo,1 Sara Monti,1 Valeria Spina,1 Alessio Bruscaggin,1 Michaela Cerri,1 Clara Deambrogi,1 Lavinia Martuscelli,1 Alessandra Biasi,1 Elisa Spaccarotella,1 Lorenzo De Paoli,1 Valter Gattei,3 Robin Foà,4 Raul Rabadan,2 Gianluca Gaidano,1§ and Davide Rossi1§ haematologica 2016; 101:e137 LETTERS TO THE EDITOR Figure 3. Longitudinal analysis of patients harboring small mutated subclones. Changes of the allele frequency of the NOTCH1 (A), SF3B1 (B), and BIRC3 (C) mutated subclones upon “watch and wait”. The upper line graph represents the allele frequency modification of each individual mutation. The lower box plot graph shows in aggregate the mean, interquartile, and extreme values of the allele frequency at diagnosis and last follow up. P values by Wilcoxon rank test. Changes of the allele frequency of the NOTCH1 (D), SF3B1 (E), and BIRC3 (F) mutated subclones upon treatment. The upper line graph represents the allele frequency modification of each individual mutation after each individual treatment. The lower box plot graph shows in aggregate the mean, interquartile, and extreme values of the allele frequency before treatment and at relapse. P: P values by Wilcoxon rank test. A B C D E F ©FerrataStortiFoundation
  • 4. 1 Division of Hematology, Department of Translational Medicine, Amedeo Avogadro University of Eastern Piedmont, Novara, Italy; 2 Center for Topology of Cancer Evolution and Heterogeneity, Department of Biomedical Informatics and Center for Computational Biology and Bioinformatics, Columbia University, New York, NY, USA; 3 Clinical and Experimental Onco-Hematology, Centro di Riferimento Oncologico, Aviano, Italy; and 4 Hematology, Department of Cellular Biotechnologies and Hematology, Sapienza University, Rome, Italy § Current address: Center for Systems and Computational Biology, Rutgers Cancer Institute, Rutgers University, New Brunswick, NJ, USA *SR and HK contributed equally; § GG and DR contributed equally. Funding: this study was supported by Special Program Molecular Clinical Oncology 5 x 1000 No. 10007, My First AIRC Grant No. 13470, Associazione Italiana per la Ricerca sul Cancro Foundation Milan, Italy; Fondazione Cariplo, Grant No. 2012-0689; Ricerca Finalizzata, RF-2010-2307262 and RF-2011-02349712, and Progetto Giovani Ricercatori GR-2010-2317594, Ministero della Salute, Rome, Italy; Compagnia di San Paolo, Grant No. PMN_call_2012_0071, Turin, Italy; Futuro in Ricerca 2012 Grant No. RBFR12D1CB, Ministero dell'Istruzione, dell'Università e della Ricerca, Rome, Italy; National Cancer Institute Grant U54-CA193313. The online version of this letter has a Supplementary Appendix. Correspondence: rossidav@med.unipmn.it doi:10.3324/haematol.2015.136051 Key words: chronic lymphocytic leukemia, subclones, NOTCH1, SF3B1, BIRC3, TP53, frequency, prognostic impact, evolution. Information on authorship, contributions, and financial & other disclo- sures was provided by the authors and is available with the online version of this article at www.haematologica.org. References 1. Schuh A, Becq J, Humphray S, et al. Monitoring chronic lymphocytic leukemia progression by whole genome sequencing reveals hetero- geneous clonal evolution patterns. Blood. 2012;120(20):4191-4196. 2. Landau DA, Carter SL, Stojanov P, et al. Evolution and impact of sub- clonal mutations in chronic lymphocytic leukemia. Cell. 2013; 152(4):714-726. 3. Ouillette P, Saiya-Cork K, Seymour E, Li C, Shedden K, Malek SN. Clonal evolution, genomic drivers, and effects of therapy in chronic lymphocytic leukemia. Clin Cancer Res. 2013;19(11):2893-2904. 4. Jethwa A, Hüllein J, Stolz T, et al. Targeted resequencing for analysis of clonal composition of recurrent gene mutations in chronic lym- phocytic leukaemia. Br J Haematol. 2013;163(4):496-500. 5. Rossi D, Khiabanian H, Spina V, et al. Clinical impact of small TP53 mutated subclones in chronic lymphocytic leukemia. Blood. 2014;123(14):2139-2147. 6. Foà R, Del Giudice I, Guarini A, Rossi D, Gaidano G. Clinical impli- cations of the molecular genetics of chronic lymphocytic leukemia. Haematologica. 2013;98(5):675-685. 7. Kaplan EL, Meier P. Nonparametric estimation from incomplete observations. Am Stat Assoc. 1958;53(282):457-481. 8. Lausen B, Hothorn T, Bretz F, Schmacher M. Assessment of Optimally Selected Prognostic Factors. Biometrical Journal. 2004;46(3), 364-374. 9. Wang J, Khiabanian H, Rossi D, et al. Tumor evolutionary direct- ed graphs and the history of chronic lymphocytic leukemia. Elife. 2014; 3. 10. Landau DA, Tausch E, Taylor-Weiner AN, et al. Mutations driving CLL and their evolution in progression and relapse. Nature. 2015;526(7574):525-530. 11. Rossi D, Fangazio M, Rasi S, et al. Disruption of BIRC3 associates with fludarabine chemorefractoriness in TP53 wild-type chronic lymphocytic leukemia. Blood. 2012;119(12):2854-2862. haematologica 2016; 101:e138 LETTERS TO THE EDITOR ©FerrataStortiFoundation