SCIENCE TRANSLATIONAL MEDICINE | RESEARCH ARTICLE
CANCER
Distinct biological subtypes and patterns of genome
evolution in lymphoma revealed by circulating
tumor DNA
2016 © The Authors,
some rights reserved;
exclusive licensee
American Association
for the Advancement
of Science.
Florian Scherer,1* David M. Kurtz,1,2,3* Aaron M. Newman,1,4* Henning Stehr,5
Alexander F. M. Craig,1 Mohammad Shahrokh Esfahani,1 Alexander F. Lovejoy,4,5,6†
Jacob J. Chabon,4 Daniel M. Klass,1,4,5† Chih Long Liu,1,4 Li Zhou,5 Cynthia Glover,1
Brendan C. Visser,7 George A. Poultsides,7 Ranjana H. Advani,1 Lauren S. Maeda,1,3
Neel K. Gupta,1,3 Ronald Levy,1 Robert S. Ohgami,8 Christian A. Kunder,8
Maximilian Diehn,4,5,6‡ Ash A. Alizadeh1,3,4,5‡
INTRODUCTION
Diffuse large B cell lymphoma (DLBCL), the most common type of
non-Hodgkin’s lymphoma (NHL), displays remarkable clinical and
biological heterogeneity (1). Although therapy is curative in most
cases, 30 to 40% of patients ultimately relapse or become refractory
to treatment (2, 3). Accurate prediction of patient outcomes would
facilitate individualized treatments, yet conventional methods for risk
stratification and personalized therapy selection are limited. For
example, the International Prognostic Index (IPI) classifies patients
into risk groups based on clinical parameters but has failed to demonstrate utility for directing therapy (4, 5). In addition, metabolic imaging with positron emission tomography/computed tomography
(PET/CT) has failed to improve survival in patients who relapse after
initial response to therapy, in part because of low specificity (6–8).
Biomarkers based on tumor molecular features hold great promise
for risk stratification and therapeutic targeting but are currently dif1
Division of Oncology, Department of Medicine, Stanford University, Stanford, CA
94305, USA. 2Department of Bioengineering, Stanford University, Stanford, CA
94305, USA. 3Division of Hematology, Department of Medicine, Stanford University, Stanford, CA 94305, USA. 4Institute for Stem Cell Biology and Regenerative
Medicine, Stanford University, Stanford, CA 94305, USA. 5Stanford Cancer Institute, Stanford University, Stanford, CA 94305, USA. 6Department of Radiation Oncology, Stanford University, Stanford, CA 94305, USA. 7Division of Surgical
Oncology, Department of Surgery, Stanford University, Stanford, CA 94305,
USA. 8Department of Pathology, Stanford University, Stanford, CA 94305, USA.
*These authors contributed equally to this work.
†Present address: Roche Molecular Systems, Pleasanton, CA 94588, USA.
‡Corresponding author. Email: [email protected] (A.A.A.); [email protected]
(M.D.)
Scherer et al., Sci. Transl. Med. 8, 364ra155 (2016)
9 November 2016
ficult to measure in clinical settings. For example, most DLBCL
tumors can be classified into two transcriptionally distinct molecular
subtypes, each derived from a specific B cell differentiation state [cell
of origin (COO)]: germinal center B cell–like (GCB) and activated B
cell–like (ABC) DLBCL (9–11). These subtypes are prognostic and
may also predict sensitivity to emerging targeted therapies (12–15).
Although several methods for COO assessment have been developed,
the current gold standard is based on microarray gene expression
profiling, which is clinically impractical because of its reliance on
fresh frozen tissues (10, 11). In contrast, immunohistochemistry is
routinely used for COO classification on fixed clinical samples but
suffers from low reproducibility and accuracy. Although newer
methods can overcome some of these issues (16), all existing approaches rely on the availability of invasive tumor biopsies (16–19).
Separately, a subset of patients are diagnosed with DLBCL after
histological transformation from an otherwise indolent and lowgrade follicular lymphoma (FL); these patients represent another
biologically defined risk group in need of improved biomarkers
(20, 21). Although several genetic aberrations have been linked to this
event, no single factor has been shown to accurately predict transformation. In addition, the molecular properties of transformation
remain poorly understood (22–25).
High-throughput sequencing (HTS) of circulating tumor DNA
(ctDNA) in peripheral blood has recently emerged as a promising
noninvasive approach for analyzing tumor genetic diversity and
clonal evolution (26–32). Using cancer personalized profiling by
deep sequencing (CAPP-Seq), an ultrasensitive capture-based targeted sequencing method, we performed deep molecular profiling
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Patients with diffuse large B cell lymphoma (DLBCL) exhibit marked diversity in tumor behavior and outcomes,
yet the identification of poor-risk groups remains challenging. In addition, the biology underlying these differences is incompletely understood. We hypothesized that characterization of mutational heterogeneity and genomic evolution using circulating tumor DNA (ctDNA) profiling could reveal molecular determinants of adverse
outcomes. To address this hypothesis, we applied cancer personalized profiling by deep sequencing (CAPP-Seq)
analysis to tumor biopsies and cell-free DNA samples from 92 lymphoma patients and 24 healthy subjects. At
diagnosis, the amount of ctDNA was found to strongly correlate with clinical indices and was independently
predictive of patient outcomes. We demonstrate that ctDNA genotyping can classify transcriptionally defined
tumor subtypes, including DLBCL cell of origin, directly from plasma. By simultaneously tracking multiple somatic mutations in ctDNA, our approach outperformed immunoglobulin sequencing and radiographic imaging
for the detection of minimal residual disease and facilitated noninvasive identification of emergent resistance
mutations to targeted therapies. In addition, we identified distinct patterns of clonal evolution distinguishing
indolent follicular lymphomas from those that transformed into DLBCL, allowing for potential noninvasive prediction of histological transformation. Collectively, our results demonstrate that ctDNA analysis reveals
biological factors that underlie lymphoma clinical outcomes and could facilitate individualized therapy.
SCIENCE TRANSLATIONAL MEDICINE | RESEARCH ARTICLE
of lymphoma tissue and cell-free DNA to define key biological
features predictive of clinical outcomes (Fig. 1) (33, 34). Our findings reveal distinct patterns of genetic variation linked to adverse
outcomes and emphasize the promise of noninvasive characterization of risk for managing patients with lymphoma.
Scherer et al., Sci. Transl. Med. 8, 364ra155 (2016)
9 November 2016
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Subclones
variants (SNVs), insertions/deletions, and breakpoints involving genes
that participate in canonical fusions (BCL2, BCL6, MYC, and IGH).
We also included Ig heavy-chain variable regions (IgVH) and the Ig
heavy-chain joining cluster (IgJH) (table S1) (33–42). By profiling 92
human subjects at various disease milestones, we evaluated the technical performance of this targeted sequencing approach and the clinical
utility of ctDNA for capturing DLBCL tumor genotypes.
RESULTS
We started by analyzing 76 diagnostic DLBCL tumor biopsies
Improved noninvasive profiling of tumor genetic
and 144 longitudinal plasma samples, 45 of which were obtained
heterogeneity in DLBCL
before treatment (figs. S1 to S6 and table S2). We identified somatic
We and others previously showed that clonotypic immunoglobulin alterations in 100% of tumors with a median of 134 variants, in(Ig) V(D)J rearrangements can be detected and monitored in the cluding driver mutations in well-known DLBCL hotspot genes,
peripheral blood of most DLBCL patients by HTS (IgHTS) (26, 27). IgH V(D)J rearrangements, and 89% of all chromosomal translocaHowever, IgHTS tracks a single tumor-specific genetic aberration tions previously identified by fluorescence in situ hybridization
and cannot capture the complex landscape of somatic variation in (FISH; fig. S1 and table S3). Applied to pretreatment plasma, our
lymphoma. To overcome this shortcoming, we implemented a assay detected ctDNA in 100% of patients with 99.8% specificity when
DLBCL-focused sequencing panel targeting recurrent single-nucleotide tumor genotypes were known (fig. S2). In addition, 91% of tumorconfirmed SNVs in driver genes could
be noninvasively genotyped directly from
pretreatment plasma, and this detection
Tumor biopsy
Plasma sample
rate was directly correlated with ctDNA
concentrations (fig. S3). At least one
tumor-confirmed variant was identified
by noninvasive genotyping in 87% of pretreatment plasma samples (39 of 45) and
DNA
in all cases with ctDNA concentrations
above 5 haploid genome equivalents
(hGE)/ml (fig. S3B). Over this threshold,
95% of FISH-confirmed translocations in
BCL2, BCL6, and MYC were detected by
Treatment
biopsy-free genotyping. This included a
patient harboring a clinically important
Diagnosis
Surveillance
double hit lymphoma involving BCL2
and MYC, which is associated with poor
prognosis (fig. S4A) (43–47). Because our
Detection of
Progression/
panel targets multiple genomic regions
resistance mutations
relapse
and aberration types, we reasoned that it
Fig. 2, C and D
should have advantages over IgHTS for
High tumor burden
Relapse detection
figs. S7 and S8
tumor genotyping and ctDNA assessFig. 3, A to C, and fig. S9
Fig. 3, D to F
ment. In both historic studies of IgHTS
Relapse prediction
table S4
and paired analyses in our own cohort,
Transformation
detection
Fig. 3, D to F
CAPP-Seq achieved higher sensitivity
Fig. 5, C to E
Non-GCB subtype
Transformation prediction
(Fig. 2, A and B) (26, 27). Thus, captureFig. 4
Fig. 5E
based targeted sequencing can effectively
Double hit lymphoma
detect somatic alterations in DLBCL
fig. S4A
tumors and plasma samples.
Because our approach can interroFig. 1. Framework for noninvasive identification of DLBCL poor-risk groups. Schematic illustrating the
gate many mutations simultaneously,
application of ultrasensitive ctDNA assessment for the identification of adverse risk in DLBCL at different disease
milestones and as a navigation aid to remaining figures. A lymphoma patient is imagined as experiencing these
we next assessed whether the mutational
disease milestones over time, depicted as an arrow progressing from left to right. During this temporal sequence,
architecture of DLBCL tumors is faithctDNA can inform risk at diagnosis, during therapy, in surveillance of disease, and at progression or disease
fully maintained in the plasma. We theretransformation, as illustrated in the corresponding figures associated with each milestone. At diagnosis, profiling
fore determined and compared ctDNA
of tumor DNA obtained from either tissue biopsies (indicated by a scalpel) or plasma (depicted as blood collection
burden serially over time, using mutations
tubes) allows for the identification of patients with high tumor burden, non-GCB subtypes, and “double hit” lymidentified from either tumor biopsies or
phoma. Assessment of ctDNA during and after lymphoma treatment facilitates the detection of both emerging
paired pretreatment plasma samples.
resistance mutations and minimal residual disease (MRD) before progression, with potential for noninvasive preRegardless of the source, the amount of
diction of relapse and histological transformation. Tumor evolution in an anecdotal DLBCL patient is illustrated,
ctDNA was highly concordant in serial
showing tumor response and clonal evolution over the course of the disease (detectable subclones at diagnosis
plasma time points, both within indiare shown in blue/gray; an emergent subclone after therapy is shown in red). The profiling of tumor DNA and
ctDNA at each milestone is shown by a double-stranded DNA molecule.
vidual patients and across all patients
SCIENCE TRANSLATIONAL MEDICINE | RESEARCH ARTICLE
Tumor
Pretreatment
genotyping ctDNA detection
PD
CR
Relapse
p
PD
0
84
140
223
487
572
Days
74
60
40
20
100
R-CHOP
10
1
Brentuximab,
DeVIC
80
R-DHAP +
SCT
100
0.1
0.01
ND
n = 54
n = 23
CAPP-Seq
Days
R-CHOP
PET/CT PET/CT
PD
CR
Ibrutinib
100
RBL
10
1
0.1
ND
ND
61 124 166 222 271 439 572 628
Tumor samples
Mean AF
BTK C481S A > T
BTK C481S C > G
Plasma samples
473
571
Days from
ibrutinib
585
Integrative Genomics Viewer (IGV)
2.3%
27.9%
3.3%
5.8%
C>G
Emergence of BTK C481S
resistance mutations
A>T
Plasma sample
Day 222
217
224
IgHTS
D
PET/CT
PD
0.1
−6
154
Tumor sample
Day 217
1
112
Mutant AF (%)
10
31
CAPP-Seq
PET/CT
PD
100
0
IgHTS
DLBCL007
1000
100
10
1
0.1
0.01
ND
BTK C481S
Fig. 2. Improved noninvasive genotyping and monitoring of DLBCL tumor heterogeneity. (A) Direct comparison of CAPP-Seq with IgHTS for tumor genotyping
and ctDNA detection in DLBCL. (B) Change of ctDNA disease burden in response to treatment and during clinical progression in a patient with stage IIAX DLBCL. Shown
is the mean AF of all SNVs detected by CAPP-Seq (left y axis) and the number of lymphoma DNA molecules per milliliter of plasma identified by IgHTS (right y axis) over
serial time points (x axis). The black arrows highlight ctDNA detection by CAPP-Seq, at which time ctDNA by IgHTS was below the limit of detection (false negative,
open circles). In (A) and (B), CAPP-Seq and IgHTS were performed from the same specimens (tissue biopsy or blood draw). IgHTS was performed as part of routine
clinical practice by an independent laboratory. ND, not detected; PR, partial response; PD, progressive disease; R-CHOP, rituximab, cyclophosphamide, doxorubicin,
vincristine, and prednisone; R-DHAP, rituximab, dexamethasone, high-dose cytarabine, and cisplatin; SCT, stem cell transplantation; DeVIC, dexamethasone, etoposide,
ifosfamide, and carboplatin. (C) Noninvasive detection of ibrutinib resistance mutations in BTK (C481S, arrows) in a patient with progressive lymphoma, reflecting two
independent subclones emerging during therapy. RBL, rituximab, bendamustine, and lenalidomide. (D) Schematic illustrating the two acquired BTK C481S resistance
mutations in the patient from (C). Read pileups were rendered with Integrative Genomics Viewer. A major resistance clone harboring the BTK C481S A > T mutation (red,
arrow) and a minor clone carrying the BTK C481S C > G mutation (dark green, arrow) were detected during ibrutinib therapy and after disease progression. Shown here
are the progression tumor and plasma samples taken at days 217 and 222 with their respective AFs. Germline bases are represented by light green and blue bars at the
top. At the bottom, germline bases and amino acid sequences are depicted.
(figs. S4B and S5). Moreover, in nearly every patient, allele frequencies (AFs) of individual mutations found in both the primary tumor
and the paired plasma were highly correlated (fig. S6). These data
suggest that, in most DLBCL patients, ctDNA is a robust surrogate
for direct assessment of primary tumor genotypes.
Next, we evaluated our method’s capability for biopsy-free detection of somatic alterations emerging during therapy or disease surveillance (Fig. 2, C and D, and figs. S7 and S8). We applied noninvasive
genotyping to three patients with progressive disease receiving ibrutiScherer et al., Sci. Transl. Med. 8, 364ra155 (2016)
9 November 2016
nib, an inhibitor of B cell receptor (BCR) signaling targeting Bruton
tyrosine kinase (BTK). Resistance mutations in BTK have exclusively
been described in tumor cells of patients with ibrutinib–refractory
chronic lymphocytic leukemia and mantle cell lymphoma (48, 49).
However, it remains unclear whether these mutations also occur in
aggressive lymphomas, such as DLBCL, and whether they can be detected in plasma. By using ctDNA, we identified emergent resistance
mutations in BTK that displayed distinct clonal dynamics in two of
three patients (Fig. 2, C and D, and fig. S7, A and B). In one DLBCL
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C
Mutant AF (%)
PR
PET/CT
100
80
0
Diagnosis
g
IgHTS - lymphoma
DNA molecules per ml
Success rate (%)
100
B
CAPP-Seq
Seq mean
ctDNA
A AF
A (%)
A
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patient, two adjacent BTK mutations encoding an identical amino acid
substitution (BTK C481S) were found, but they were never observed
within the same ctDNA molecule, demonstrating convergent evolution of independent resistant subclones (Fig. 2, C and D, and fig.
S7A). These results suggest that tumor genotyping from plasma can
facilitate monitoring of BTK-targeted therapy, regardless of histology.
Thus, ctDNA profiling with CAPP-Seq has utility for real-time assessment of dynamic tumor processes, including clonal evolution and the
acquisition of molecular resistance.
MTV (ml)
1000
500 ULN (340 U/L)
r = 0.93
250
r = 0.4
101
100
-2
0
2
4
10
10
10
10
Mean ctDNA conc. (hGE/ml)
P = 0.0003
10 6
10 4
10 2
10 0
10 −2
10–2
6
100
102
104
106
I
n=5
Mean ctDNA conc. (hGE/ml)
II
n=5
III
IV
I + II III + IV
n = 3 n = 18 n = 10 n = 21
Stage
100
80
Clinical relapse
detection
E
60
100
40
Detection rate (%)
Proportion of patients
detected over time
102
10–1
10
20
0
Time to 18 − 36
Relapse
mo
12 − 18
mo
9 − 12
mo
6−9
mo
3−6
mo
1−3
mo
0−1
mo
Relapse
DLBCL088
DLBCL012
Early relapse
detection
100
80
75
60
CAPP-Seq
67
IgHTS
40
38
20
DLBCL005*
0
DLBCL006
F
DLBCL002
DLBCL007
DLBCL008
DLBCL071
DLBCL033
DLBCL075
DLBCL020*
Treatment:
Isolated brain
relapse: *
Radiographic outcome:
CR
Relapse
ctDNA:
neg
pos
Progression-free survival (%)
Relapsers with blood samples at radiographic CR
C
P < 0.0001
r = 0.67
n = 37
n=6
n=8
100
P = 0.0003
80
ctDNA never positive
after treatment (n = 10)
60
ctDNA ever positive
after treatment (n = 15)
40
20
0
0
1000 2000 3000 4000 5000
Days from end of therapy
Fig. 3. Quantification of ctDNA in relation to DLBCL clinical indices and treatment response. (A) Relationship between LDH and ctDNA concentration from pretreatment plasma time points. Correlations were determined separately above and below the upper limit of normal (ULN; 340 U/liter). (B) Correlation between MTV,
measured from PET/CT imaging, and ctDNA concentrations from pretreatment plasma. Pretreatment LDH and MTV values in (A) and (B) were obtained as close in time
as possible to blood draws used for plasma cell–free DNA sequencing (median, 6 days for LDH and 4 days for MTV). r, Pearson correlation coefficient. (C) Association
between ctDNA concentration at diagnosis and Ann Arbor stage. Statistical comparison between early-stage (I + II) and late-stage (III + IV) patients was performed using
Mann-Whitney U test. Means and SEMs are indicated. (D) Detection of ctDNA in relapsing patients as a function of time. Top: Cumulative fraction of patients with
detectable ctDNA as a function of time before relapse. Bottom: Patient level data demonstrating ctDNA detection before relapse (n = 11). Clinical relapses were
confirmed radiographically, and corresponding blood draws were taken within 30 days of diagnostic imaging, except for patients DLBCL088 (43 days) and DLBCL071
(78 days). All other blood draws were obtained between radiographic complete response and relapse (14 to 983 days before clinical relapse). Red circle, ctDNA detected; open circle, ctDNA not detected; black bars, imaging studies demonstrating complete response; red bars, imaging studies demonstrating detection of disease.
Asterisks highlight patients with an isolated brain relapse. mo, months. (E) Direct comparison of CAPP-Seq and IgHTS for relapse detection at the time of relapse and
before relapse. (F) Kaplan-Meier analysis of PFS in patients with at least one ctDNA-positive plasma sample after the end of curative therapy compared to patients
without detectable ctDNA after the end of curative therapy. Significance was assessed using the log-rank test.
Scherer et al., Sci. Transl. Med. 8, 364ra155 (2016)
9 November 2016
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LDH (U/liter)
103
100
D
104
B
P < 0.0001
2500 r = 0.94
n = 43
Mean ctDNA conc.
(hGE/ml) at diagnosis
5000
A
Prognostic value of ctDNA in DLBCL
Having demonstrated the technical performance of the assay, we
next determined whether ctDNA analysis could facilitate early
identification of clinically relevant risk groups in DLBCL. We
started by comparing total ctDNA burden at diagnosis with standard clinical indices and risk of radiographic progression (Fig. 3
and fig. S9) (33). The amount of ctDNA was significantly correlated
with serum lactate dehydrogenase (LDH; P < 1 × 10−4), the most
commonly used biomarker for DLBCL (Fig. 3A and fig. S9A) (50).
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Notably, whereas 100% of pretreatment samples had detectable
ctDNA, only 37% of samples had abnormally high LDH, demonstrating superior sensitivity of ctDNA. Pretreatment ctDNA levels were
strongly associated with metabolic tumor volumes (MTVs) measured
using [18F]fluorodeoxyglucose PET/CT scans (Fig. 3B) (33). ctDNA
concentrations at initial diagnosis were also significantly correlated
with Ann Arbor stage (P = 3 × 10−4; Fig. 3C) and IPI (P < 1 × 10−4;
fig. S9B) (5). Furthermore, we tested whether ctDNA concentrations at
diagnosis were linked with the risk of future disease progression. In
multivariate analyses incorporating key clinical parameters, higher
ctDNA levels were continuously and independently correlated with inferior progression-free survival (PFS; table S4). Thus, pretreatment
ctDNA in DLBCL can complement traditional clinical indices and
serve as an independent prognostic biomarker.
COO classification
COO classification of DLBCL is one of the strongest prognostic factors
and a potential biomarker for future personalized therapies, yet accurate subtyping remains challenging in clinical settings (12–16, 19). We
therefore used multiplexed somatic mutation profiling to develop a
tool for COO classification from tumor or pretreatment plasma. By
considering mutations enriched in GCB or non-GCB (ABC) DLBCL
and targeted by our capture panel, we built a probabilistic classifier
using a Bayesian approach (23, 54, 55). Patients in the training cohort
were previously subtyped by microarray-based gene expression profiling of frozen tissues, currently considered the gold standard even if not
clinically practical (fig. S12 and table S5) (23, 55). We then benchmarked the classifier performance using our cohort of 76 lymphoma
Scherer et al., Sci. Transl. Med. 8, 364ra155 (2016)
9 November 2016
Patterns of genome evolution in patients with
histological transformation
Patients with aggressive DLBCL arising from histological transformation of an indolent FL represent another biologically defined
risk group associated with poor prognosis (56, 57). We hypothesized
that a comparative genomic analysis of paired tumor specimens might
reveal biological features distinguishing histological transformation
of FL (tFL), progression without transformation [nontransformed
FL (ntFL)], and progression of DLBCL. Accordingly, we applied
CAPP-Seq to three groups of paired tumor samples: (i) diagnostic
FL versus tFL (n = 12), (ii) diagnostic FL versus ntFL (n = 12), and
(iii) diagnostic de novo DLBCL versus relapsed/refractory DLBCL
(rrDLBCL) (n = 7; Fig. 5 and figs. S13 and S14). We then compared
the evolutionary history of these sequential tumor pairs by defining
genetic alterations that were either common to both tumors or private
to each (fig. S13A).
Among the three classes, we observed the greatest evolutionary
distance among tumor pairs associated with histological transformation (Fig. 5, A and B, and figs. S13, B to D, and S14). This pattern was most pronounced when examining the fraction of mutations
unique to the tumor biopsy at progression, which served to distinguish all three tumor subtypes (Fig. 5A and fig. S13D). Genomic
divergence was independent of both the time to progression or
transformation and the number of previous therapies, suggesting that
this simple index could have utility as a biomarker of histological
transformation (fig. S13E).
We therefore analyzed tumor biopsies obtained at diagnosis,
along with follow-up plasma samples from patients with indolent
lymphomas experiencing transformation (n = 8), progression without transformation (n = 7), or rrDLBCL (n = 11). In four patients,
we additionally profiled follow-up plasma samples obtained before
clinical evidence of transformation. Plasma genotyping results
largely matched those from sequential tumors, with a higher fraction of emergent variants distinguishing tFL from other histologies
(Fig. 5C). Separately, higher amounts of ctDNA were found to distinguish tFL and rrDLBCL from ntFL (Fig. 5D), suggesting that
aggressive lymphomas display similar tumor cell proliferation
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Early detection of DLBCL relapse
Among the most promising clinical applications of ctDNA is its
potential use for the detection of radiographically occult MRD
(26, 27). We profiled plasma samples at times of radiographic complete response (n = 30) or recurrence (n = 8) from 11 patients, all of
whom ultimately experienced disease progression despite therapy
with curative intent. Whereas ctDNA was identified in all patients
at the time of clinical relapse (Fig. 3D), it was also detectable as
MRD before relapse in at least one plasma sample in 8 of 11 patients
(73%), with ctDNA concentrations as low as 0.003% AF (0.11 hGE/
ml). The mean elapsed time between the first ctDNA-positive time
point and clinical relapse was 188 days, and all blood collections up
to 3 months before relapse had ctDNA above the detection limit of
our assay (Fig. 3D). When directly compared to IgHTS, our method
detected MRD in twice as many patients with a mean lead time of
>2 months, suggesting potential advantages in the surveillance setting
(Fig. 3E and fig. S10) (26, 27). In contrast, ctDNA was undetectable in
plasma samples from 10 patients who were disease-free for at least
24 months after therapy (51) and in 24 healthy adult subjects, demonstrating 100% specificity. Finally, we found that patients with ctDNA
detected in plasma showed significantly inferior PFS compared to
those with undetectable ctDNA (P = 3 × 10−4, log-rank test; Fig.
3F). This remained significant when controlling for “guarantee-time
bias” (P = 8 × 10−5, likelihood ratio test), a potential confounding effect
of comparing survival between groups when the classifying event (that
is, ctDNA measurement) occurs during follow-up (52, 53). We observed a similar, though not significant, trend for overall survival (P =
0.056, log-rank test; fig. S11). Collectively, these results illustrate the
promise of ctDNA profiling by targeted sequencing for improved
MRD assessment and early relapse detection.
tumor biopsies, predicting 44 patients as GCB and 32 as non-GCB
(Fig. 4A). By comparing our results to a blinded, centralized immunohistochemical classification using the Hans algorithm (the current clinical standard), we observed a concordance rate of almost 80% (Fig.
4A) (17, 19). Patients identified as having GCB DLBCL by our classification approach had superior PFS over those identified as having
non-GCB DLBCL (P = 0.02, log-rank test; Fig. 4B), consistent with
previous descriptions of survival differences between COO subtypes
(11). In addition, COO classifier scores were continuously associated
with improved PFS (P = 3 × 10−3; Fig. 4C). Among patients analyzed
by both immunohistochemistry and DNA genotyping, the Hans
algorithm failed to stratify patient clinical outcomes, suggesting more
accurate classification by our approach (Fig. 4D).
We next tested the COO classifier without knowledge of the tumor,
using pretreatment plasma ctDNA (n = 41). The overall concordance
between COO predictions from tumor tissue and biopsy-free plasma
genotyping was 88% (Fig. 4E). Moreover, DLBCL molecular subtypes
predicted directly from plasma were significantly associated with PFS
in continuous models (P = 0.02; Fig. 4C). Thus, biopsy-free assessment
of ctDNA has considerable potential for the classification of transcriptionally defined DLBCL subtypes.
B
GCB (n = 44)
Non-GCB (n = 32)
5
4
3
2
1
0
−1
−2
−3
Hans algorithm
classification
Patient tumor samples (n = 76)
C
CAPP-Seq
100
P = 0.02
CAPP-Seq classifier prognostic performance in
primary DLBCL: PFS from diagnosis
80
60
40
GCB (n = 26)
20
CAPP-Seq
classification
Sample type
n
P
HR (95% CI)
Tumor
50
0.003
0.59 (0.42–0.84)
Plasma
25
0.02
0.47 (0.25–0.89)
Non-GCB (n = 24)
0
0
1000
2000
3000
corresponding plasma sample (Fig. 5F,
left). Most mutations from the latter were
shared with the patient’s tFL tumor biopsy
(retroperitoneum), obtained 9 months
later and after unusual refractoriness to
rituximab (Fig. 5F, right). These observations suggest that both indolent and aggressive clones were already present before
clinical diagnosis of transformation, even
if spatially separated (Fig. 5F, left). In
support of this hypothesis, when we applied our logistic regression model to this
patient’s pretreatment plasma at FL diagnosis, we classified the tumor subtype as
tFL and, thus, poorly suited for rituximab
monotherapy. These data further demonstrate the value of plasma genotyping
for capturing clinically relevant tumor
heterogeneity and emphasize the importance of sampling genomic information
from spatially distinct tumor deposits.
D
Progression-free survival (%)
Days from diagnosis
Hans algorithm
100
E
CAPP-Seq classification concordance
DISCUSSION
100
80
Scherer et al., Sci. Transl. Med. 8, 364ra155 (2016)
Concordance (%)
Clinical and biological heterogeneity
are key factors contributing to adverse
83%
risk and treatment failure in many can60
60
cers, including lymphomas. To address
40
40
Non-GCB
these challenges for patients with
GCB (n = 21)
DLBCL, we applied CAPP-Seq, a highly
20
20
Non-GCB (n = 17)
sensitive targeted sequencing method,
0
0
to analyze genetic profiles in 118 biopTumor-defined GCB
Non-GCB
0
1000
2000
3000
sies and 166 plasma samples from masubtype
n = 24
n = 17
Days from diagnosis
jor disease milestones. In comparison
Fig. 4. DLBCL COO classification by tumor and plasma sequencing. (A) Top: CAPP-Seq COO classifier scores are
to IgHTS, this approach achieved highshown for each patient’s DLBCL tumor sample (n = 76), ordered on the basis of decreasing log odds scores. Bottom:
er analytical and clinical sensitivity in
COO classification of patients in (A) using the Hans immunohistochemistry (IHC) algorithm (n = 59). Cases classified as
capturing the mutational landscape of
GCB or non-GCB are shown in orange and blue, respectively. Empty spaces indicate cases with no IHC classification
lymphoma and its clonal evolution. In
available. (B) PFS from diagnosis in DLBCL cases, as determined by the CAPP-Seq COO classifier on all analyzed DLBCL
addition, capture-based ctDNA analysis
tumor samples (n = 50). (C) The results of applying univariate Cox proportional hazards regression to analyze PFS in
complemented cross-sectional imaging
tumor and plasma samples from DLBCL patients. HR, hazards ratio; CI, confidence interval. (D) PFS from diagnosis in
and facilitated the discovery of tumor
DLBCL cases, as determined by Hans algorithm (n = 38). The log-rank test was used in (B) and (D) to determine
statistical significance. n.s., not significant. (E) Concordance between COO assignments of the CAPP-Seq classifier
molecular features and candidate bioapplied to tumor samples and applied to corresponding plasma samples (n = 41). Primary central nervous system
markers associated with high disease
lymphoma and transformed lymphoma cases were excluded from the patient cohort for the analyses in (B) to (D).
burden, relapse, non-GCB DLBCL, and
histological transformation. Together,
and turnover kinetics, despite their separate origins. When consid- our findings highlight the advantages of ctDNA as a noninvasive bioering these discriminatory features within a logistic regression frame- marker and provide a number of risk stratification strategies for
work incorporating leave-one-out cross validation, we were able to clinical translation (Fig. 1).
noninvasively classify tFL from ntFL with 83% sensitivity and 89%
For example, some patients with recurrent DLBCL undergo pospecificity (Fig. 5E). Moreover, our model successfully predicted tFL tentially curative subsequent therapies, including autologous stem
in three of four plasma samples collected on an average of 66 days cell transplantation (58). Although early detection of relapse has a
before clinical diagnosis. Together, these results demonstrate key ge- potential for improving outcomes, surveillance imaging is considered
nomic differences between the three lymphoma subtypes and high- to be largely ineffective for disease monitoring because of high falselight the potential of ctDNA as a noninvasive biomarker for early positive rates (6, 7, 59, 60). We detected ctDNA in 100% of the anadetection of transformation.
lyzed patients at the time of radiographic relapse, and 73% of patients
Most of the patients who experienced histological transformation undergoing surveillance had detectable ctDNA before clinical proshowed similar mutations in tumor/plasma pairs obtained at gression, with a mean lead time of more than 6 months. These results
matching time points. However, in one patient, we observed a marked could inform clinical trial designs examining treatment paradigms
discordance between a diagnostic FL tumor biopsy (left inguinal) and based on early intervention directed by ctDNA detection.
N.S.
80
9 November 2016
94%
Plasma-defined
subtype:
GCB
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Progression-free survival (%)
A
CAPP-Seq COO log odds score
SCIENCE TRANSLATIONAL MEDICINE | RESEARCH ARTICLE
SCIENCE TRANSLATIONAL MEDICINE | RESEARCH ARTICLE
A
P = 0.03
C
rrDLBCL
P = 0.005
20
SNVs specific to
follow-up plasma (%)
ntFL
tFL
75
50
15
10
5
25
0
0
ntFL
n=7
10
20
30
40
50
Tumor 2−specific SNVs (%)
DLBCL
021
B
10 2
10 1
10 0
Biopsy-free
transformation prediction
Transforming vs. nontransforming FL (n = 21)
FL044
ntFL
n=7
tFL
n=8
rrDLBCL
n = 11
Sn
Sp
PPV
NPV
83%
89%
91%
80%
FL049
FL051
FL050
Tumor 1
FL054
F
FL
tFL
diagnosis
diagnosis
Rituximab
FL048
Tumor 1
FL053
DLBCL
090
FL047
FL052
FL046
DLBCL
091
10 3
ND
rrDLBCL
n = 11
ntFL
DLBCL
063
DLBCL
076
10 4
Days 0
FL055
256
DLBCL
084
DLBCL
100
rrDLBCL
DLBCL
007b
DLBCL
012
Tumor 1
DLBCL
033
DLBCL
024
Median % of mutations
unique to each tumor
DLBCL
074
2
DLBCL
006
DLBCL
007a
Dashed circle: tumor not biopsied
30
20
10
0
tFL
ntFL rrDLBCL
Legend
Tumor 1
DLBCL
002
Solid circle: tumor biopsied
7
40
Tumor 2
Scaled by the fraction of mutations
unique to each tumor
CREBBP CARD11
R1446H
F115I
46%
23%
BCL2–
IGH
CREBBP
R1446H
31%
30%
70%
BCL2–
IGH
PIM1
G119D
CARD11
F115I
PIM1
G119D
DLBCL074
Fig. 5. Patterns of genome evolution in patients with histological transformation. (A) Comparison of mutation profiles from diagnostic tumor samples (“tumor 1”;
FL or DLBCL) and follow-up tumor samples (“tumor 2”; transformation or progression) in patients with three distinct NHL types: tFL, ntFL, and rrDLBCL. The fraction of SNVs
specific to tumor 2 (x axis) is compared with the proportion of SNVs shared between both tumors (y axis). Each dot represents a single patient. Shaded ovals highlight
patients with different histologies, excluding outliers. (B) Network depiction of the mutational divergence between each tumor 1 and tumor 2 pair analyzed in (A). The
central node represents tumor 1, and the distance between tumor 1 and each patient’s tumor 2 (edge) is expressed as the fraction of unique mutations to both tumor
1 and tumor 2. Bar graph: percentage of SNVs unique to both tumor 1 and tumor 2 (nonshared mutations) for the median patient in each histological group. (C) Evolution
of different types of NHL as determined by comparing diagnostic tumor samples from (A) (“tumor 1”) with follow-up plasma samples. The percentage of SNVs found
uniquely in follow-up plasma compared to tumor 1 is shown for the three histologies. (D) Comparison of ctDNA concentrations in follow-up plasma samples from (C).
Statistical comparisons in (C) and (D) were performed using the Mann-Whitney U test. Medians and ranges are indicated. (E) Performance metrics for the prediction of
histological transformation from plasma. Sn, sensitivity; Sp, specificity; PPV, positive predictive value; NPV, negative predictive value. (F) Biopsy-free detection of an occult
aggressive lymphoma subclone (tFL) in a patient histologically diagnosed with ntFL from a left inguinal lymph node biopsy (left, blue solid circle). The tumor site harboring
the aggressive subclone (tFL, green dashed circle) was later identified in a retroperitoneal lymph node biopsy (right, green solid circle). Bottom: Venn diagram analysis of
mutations found in tumor/plasma pairs at FL and tFL diagnosis. Mutations in key driver genes, such as CARD11 or PIM1, are indicated.
In addition, accurate classification of GCB- and ABC-like molecular subtypes is important for determining prognosis in DLBCL
patients. Here, we report a method for DLBCL classification based
on integrating diverse somatic mutation profiles. This approach is
both accurate and practical, allowing input material from either
fixed tumor tissue or plasma samples, with high tumor-plasma
concordance rates. Our noninvasive classification results were associated with clinical outcomes, suggesting a viable alternative to current methods that are limited by the requirement for invasive
biopsies and suboptimal assay performance (11, 17, 61, 62). MoreScherer et al., Sci. Transl. Med. 8, 364ra155 (2016)
9 November 2016
over, the recent development of subtype-directed therapy has
increased the importance of simultaneous disease classification
and tumor genotyping (12–15). For example, patients classified
as having ABC-like DLBCL by expression-based subtyping, and
particularly those with ABC-like tumors that harbor gain-offunction mutations in BCR pathway genes (CD79B with or without MYD88), demonstrated a higher rate of ibrutinib efficacy (12).
In this cohort, we detected nine such patients by deep sequencing (table S3). Thus, our integrative approach could support future clinical
trials through the identification of poor-risk groups at diagnosis and
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DLBCL
038
10 5
FL045
DLBCL
037
DLBCL
075
tFL
n=8
E
DLBCL
087
tFL
P = 0.01
10 6
ctDNA conc. (hGE/ml)
follow-up plasma
Shared SNVs between
tumor 1 and tumor 2 (%)
100
P = 0.006
D
SCIENCE TRANSLATIONAL MEDICINE | RESEARCH ARTICLE
MATERIALS AND METHODS
For detailed Materials and Methods, please see the Supplementary
Materials.
SUPPLEMENTARY MATERIALS
www.sciencetranslationalmedicine.org/cgi/content/full/8/364/364ra155/DC1
Materials and Methods
Fig. S1. Overview of DLBCL tumor genotyping results.
Fig. S2. Sensitivity and specificity of ctDNA detection in DLBCL pretreatment plasma samples.
Fig. S3. Performance assessment of biopsy-free tumor genotyping from DLBCL plasma
samples.
Fig. S4. Utility of biopsy-free genotyping for translocation detection and ctDNA monitoring.
Fig. S5. Analysis of biopsy-free ctDNA monitoring in serial plasma samples.
Fig. S6. Correlation of mutant AF from pretreatment tumor/plasma pairs.
Fig. S7. Noninvasive detection of ibrutinib resistance mutations in lymphoma patients.
Fig. S8. Noninvasive detection of an emergent somatic alteration after targeted therapy in a
patient with tFL.
Fig. S9. Relationship between pretreatment ctDNA concentration and key DLBCL clinical
indices.
Fig. S10. Performance comparison of CAPP-Seq and IgHTS for DLBCL relapse detection.
Fig. S11. Association between ctDNA positivity after curative therapy and overall survival.
Fig. S12. Genomic features incorporated into the DLBCL COO classifier.
Fig. S13. Analysis of mutation evolution in serial lymphoma tumor biopsies.
Fig. S14. Evolutionary patterns distinguishing lymphoma histologies.
Table S1. DLBCL selector design with references and final coordinates.
Table S2. Overview of patients, samples, and clinical characteristics.
Scherer et al., Sci. Transl. Med. 8, 364ra155 (2016)
9 November 2016
Table S3. Somatic mutations and V(D)J recombination sequences detected in tumor biopsies
and a list of driver genes used in this work.
Table S4. Univariate and multivariate outcome analysis.
Table S5. Illustrative example of DLBCL subtype determination.
References (63–82)
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9 November 2016
Acknowledgments: We thank the patients and their families who participated in this
study. We would like to thank R. Tibshirani for the statistical advice related to the CAPP-Seq
COO classifier. We also thank L. Pasqualucci for providing detailed information about
genes informative for COO classification and J. Kress for assistance with the graphic design.
Funding: This work was supported by the Damon Runyon Cancer Research Foundation
[DR-CI#71-14 (to A.A.A.) and PST#09-16 (to D. M. Kurtz)], the American Society of Hematology
Scholar Award (to A.A.A), the V Foundation for Cancer Research Abeloff Scholar Award
(to A.A.A.), the German Research Foundation [SCHE 1870/1-1 (to F.S.)], the Stanford TRAM
(Translational Research and Applied Medicine) Pilot Grant (to A.A.A. and F.S.), the American
Society of Clinical Oncology Young Investigator Award (to D. M. Kurtz), the National Cancer
Institute (R01CA188298 and 1K99CA187192-01A1), the U.S. NIH Director’s New Innovator
Award Program (1-DP2-CA186569), and the Ludwig Institute for Cancer Research. Author
contributions: F.S., D. M. Kurtz, A.M.N., M.D., and A.A.A. developed the concept, designed the
experiments, analyzed the data, and wrote the manuscript. F.S., D. M. Kurtz, A.M.N, H.S., M.S.E.,
and C.L.L. performed the bioinformatics analyses. F.S., D. M. Kurtz, A.F.M.C., A.F.L., J.J.C.,
D. M. Klass, and L.Z. performed the molecular biology experiments related to CAPP-Seq. C.A.K.
performed all Hans immunohistochemistry analyses. C.G., B.C.V., G.A.P., R.H.A., L.S.M., N.K.G.,
R.L., and R.S.O. provided patient specimens and/or clinical data. M.D. and A.A.A. contributed
equally as senior authors. All authors commented on the manuscript at all stages. Competing
interests: A.M.N, D. M. Klass, M.D., and A.A.A. are coinventors on patent applications related to
CAPP-Seq. A.M.N., M.D., and A.A.A. are consultants for Roche Molecular Systems and A.F.L. and
D. M. Klass are employed by Roche Molecular Systems. Data and materials availability:
Custom software used in this work was previously published and is available by request for
nonprofit use (34).
Submitted 24 August 2016
Accepted 19 October 2016
Published 9 November 2016
10.1126/scitranslmed.aai8545
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Distinct biological subtypes and patterns of genome evolution in
lymphoma revealed by circulating tumor DNA
Florian Scherer, David M. Kurtz, Aaron M. Newman, Henning
Stehr, Alexander F. M. Craig, Mohammad Shahrokh Esfahani,
Alexander F. Lovejoy, Jacob J. Chabon, Daniel M. Klass, Chih
Long Liu, Li Zhou, Cynthia Glover, Brendan C. Visser, George A.
Poultsides, Ranjana H. Advani, Lauren S. Maeda, Neel K. Gupta,
Ronald Levy, Robert S. Ohgami, Christian A. Kunder, Maximilian
Diehn and Ash A. Alizadeh (November 9, 2016)
Science Translational Medicine 8 (364), 364ra155. [doi:
10.1126/scitranslmed.aai8545]
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The telltale DNA in lymphoma
Diffuse large B cell lymphoma is a relatively common type of tumor that can exhibit a wide
range of behaviors, from indolent and curable cancers to ones that are very aggressive and difficult to
treat. By analyzing DNA in tumor samples and blood of lymphoma patients, Scherer et al. have shown
that specific genetic characteristics can determine each tumor's cell of origin and identify tumors that
are going to transform into more aggressive subtypes and may require more intensive treatment. The
authors also demonstrated that circulating tumor DNA in the patients' blood is suitable for this analysis,
allowing for periodic monitoring of each patient without repeated invasive biopsies.