Distinct biological subtypes and patterns of genome evolution in ...

SC I ENCE TRANS LAT IONAL MED I C I N E | R E S EARCH ART I C L E

CANCER

1Division of Oncology, Department of Medicine, Stanford University, Stanford, CA94305, USA. 2Department of Bioengineering, Stanford University, Stanford, CA94305, USA. 3Division of Hematology, Department of Medicine, Stanford Univer-sity, Stanford, CA 94305, USA. 4Institute for Stem Cell Biology and RegenerativeMedicine, Stanford University, Stanford, CA 94305, USA. 5Stanford Cancer Insti-tute, Stanford University, Stanford, CA 94305, USA. 6Department of Radiation On-cology, Stanford University, Stanford, CA 94305, USA. 7Division of SurgicalOncology, 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

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Distinct biological subtypes and patterns of genomeevolution in lymphoma revealed by circulatingtumor DNAFlorian 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‡

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 differ-ences is incompletely understood. We hypothesized that characterization of mutational heterogeneity and ge-nomic evolution using circulating tumor DNA (ctDNA) profiling could reveal molecular determinants of adverseoutcomes. 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. Atdiagnosis, the amount of ctDNA was found to strongly correlate with clinical indices and was independentlypredictive of patient outcomes. We demonstrate that ctDNA genotyping can classify transcriptionally definedtumor subtypes, including DLBCL cell of origin, directly from plasma. By simultaneously tracking multiple so-matic mutations in ctDNA, our approach outperformed immunoglobulin sequencing and radiographic imagingfor the detection of minimal residual disease and facilitated noninvasive identification of emergent resistancemutations to targeted therapies. In addition, we identified distinct patterns of clonal evolution distinguishingindolent follicular lymphomas from those that transformed into DLBCL, allowing for potential noninvasive pre-diction of histological transformation. Collectively, our results demonstrate that ctDNA analysis revealsbiological factors that underlie lymphoma clinical outcomes and could facilitate individualized therapy.
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INTRODUCTIONDiffuse large B cell lymphoma (DLBCL), the most common type ofnon-Hodgkin’s lymphoma (NHL), displays remarkable clinical andbiological heterogeneity (1). Although therapy is curative in mostcases, 30 to 40% of patients ultimately relapse or become refractoryto treatment (2, 3). Accurate prediction of patient outcomes wouldfacilitate individualized treatments, yet conventionalmethods for riskstratification and personalized therapy selection are limited. Forexample, the International Prognostic Index (IPI) classifies patientsinto risk groups based on clinical parameters but has failed to dem-onstrate utility for directing therapy (4, 5). In addition, metabolic im-aging with positron emission tomography/computed tomography(PET/CT) has failed to improve survival in patients who relapse afterinitial response to therapy, in part because of low specificity (6–8).

Biomarkers based on tumormolecular features hold great promisefor risk stratification and therapeutic targeting but are currently dif-

ficult to measure in clinical settings. For example, most DLBCLtumors can be classified into two transcriptionally distinct molecularsubtypes, each derived from a specific B cell differentiation state [cellof origin (COO)]: germinal center B cell–like (GCB) and activated Bcell–like (ABC) DLBCL (9–11). These subtypes are prognostic andmay 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 expressionprofiling, which is clinically impractical because of its reliance onfresh frozen tissues (10, 11). In contrast, immunohistochemistry isroutinely used for COO classification on fixed clinical samples butsuffers from low reproducibility and accuracy. Although newermethods can overcome some of these issues (16), all existing ap-proaches rely on the availability of invasive tumor biopsies (16–19).

Separately, a subset of patients are diagnosed with DLBCL afterhistological transformation from an otherwise indolent and low-grade follicular lymphoma (FL); these patients represent anotherbiologically defined risk group in need of improved biomarkers(20, 21). Although several genetic aberrations have been linked to thisevent, no single factor has been shown to accurately predict trans-formation. In addition, the molecular properties of transformationremain poorly understood (22–25).

High-throughput sequencing (HTS) of circulating tumor DNA(ctDNA) in peripheral blood has recently emerged as a promisingnoninvasive approach for analyzing tumor genetic diversity andclonal evolution (26–32). Using cancer personalized profiling bydeep sequencing (CAPP-Seq), an ultrasensitive capture-based tar-geted sequencing method, we performed deep molecular profiling

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of lymphoma tissue and cell-free DNA to define key biologicalfeatures predictive of clinical outcomes (Fig. 1) (33, 34). Our find-ings reveal distinct patterns of genetic variation linked to adverseoutcomes and emphasize the promise of noninvasive characteriza-tion of risk for managing patients with lymphoma.

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RESULTSImproved noninvasive profiling of tumor geneticheterogeneity in DLBCLWe and others previously showed that clonotypic immunoglobulin(Ig) V(D)J rearrangements can be detected and monitored in theperipheral blood of most DLBCL patients by HTS (IgHTS) (26, 27).However, IgHTS tracks a single tumor-specific genetic aberrationand cannot capture the complex landscape of somatic variation inlymphoma. To overcome this shortcoming, we implemented aDLBCL-focused sequencing panel targeting recurrent single-nucleotide


variants (SNVs), insertions/deletions, and breakpoints involving genesthat participate in canonical fusions (BCL2, BCL6, MYC, and IGH).We also included Ig heavy-chain variable regions (IgVH) and the Igheavy-chain joining cluster (IgJH) (table S1) (33–42). By profiling 92human subjects at various disease milestones, we evaluated the techni-cal performance of this targeted sequencing approach and the clinicalutility of ctDNA for capturing DLBCL tumor genotypes.

We started by analyzing 76 diagnostic DLBCL tumor biopsiesand 144 longitudinal plasma samples, 45 of which were obtainedbefore treatment (figs. S1 to S6 and table S2). We identified somaticalterations in 100% of tumors with a median of 134 variants, in-cluding driver mutations in well-known DLBCL hotspot genes,IgH V(D)J rearrangements, and 89% of all chromosomal transloca-tions previously identified by fluorescence in situ hybridization(FISH; fig. S1 and table S3). Applied to pretreatment plasma, ourassay detected ctDNA in 100% of patients with 99.8% specificity whentumor genotypes were known (fig. S2). In addition, 91% of tumor-

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confirmed SNVs in driver genes couldbenoninvasively genotypeddirectly frompretreatment plasma, and this detectionrate was directly correlated with ctDNAconcentrations (fig. S3). At least onetumor-confirmed variant was identifiedby noninvasive genotyping in 87% of pre-treatment plasma samples (39 of 45) andin all cases with ctDNA concentrationsabove 5 haploid genome equivalents(hGE)/ml (fig. S3B). Over this threshold,95% of FISH-confirmed translocations inBCL2, BCL6, and MYC were detected bybiopsy-free genotyping. This included apatient harboring a clinically importantdouble hit lymphoma involving BCL2and MYC, which is associated with poorprognosis (fig. S4A) (43–47). Because ourpanel targets multiple genomic regionsand aberration types, we reasoned that itshould have advantages over IgHTS fortumor genotyping and ctDNA assess-ment. In both historic studies of IgHTSand paired analyses in our own cohort,CAPP-Seq achieved higher sensitivity(Fig. 2, A and B) (26, 27). Thus, capture-based targeted sequencing can effectivelydetect somatic alterations in DLBCLtumors and plasma samples.

Because our approach can interro-gate many mutations simultaneously,we next assessed whether the mutationalarchitecture of DLBCL tumors is faith-fully maintained in the plasma. We there-fore determined and compared ctDNAburden serially over time, using mutationsidentified from either tumor biopsies orpaired pretreatment plasma samples.Regardless of the source, the amount ofctDNA was highly concordant in serialplasma time points, both within indi-vidual patients and across all patients

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Fig. 1. Framework for noninvasive identification of DLBCL poor-risk groups. Schematic illustrating the
application of ultrasensitive ctDNA assessment for the identification of adverse risk in DLBCL at different diseasemilestones and as a navigation aid to remaining figures. A lymphoma patient is imagined as experiencing thesedisease milestones over time, depicted as an arrow progressing from left to right. During this temporal sequence,ctDNA can inform risk at diagnosis, during therapy, in surveillance of disease, and at progression or diseasetransformation, as illustrated in the corresponding figures associated with each milestone. At diagnosis, profilingof tumor DNA obtained from either tissue biopsies (indicated by a scalpel) or plasma (depicted as blood collectiontubes) allows for the identification of patients with high tumor burden, non-GCB subtypes, and “double hit” lym-phoma. Assessment of ctDNA during and after lymphoma treatment facilitates the detection of both emergingresistance mutations and minimal residual disease (MRD) before progression, with potential for noninvasive pre-diction of relapse and histological transformation. Tumor evolution in an anecdotal DLBCL patient is illustrated,showing tumor response and clonal evolution over the course of the disease (detectable subclones at diagnosisare shown in blue/gray; an emergent subclone after therapy is shown in red). The profiling of tumor DNA andctDNA at each milestone is shown by a double-stranded DNA molecule.
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(figs. S4B and S5). Moreover, in nearly every patient, allele frequen-cies (AFs) of individual mutations found in both the primary tumorand the paired plasma were highly correlated (fig. S6). These datasuggest that, in most DLBCL patients, ctDNA is a robust surrogatefor direct assessment of primary tumor genotypes.

Next, we evaluated our method’s capability for biopsy-free detec-tion of somatic alterations emerging during therapy or disease surveil-lance (Fig. 2, C and D, and figs. S7 and S8). We applied noninvasivegenotyping to three patients with progressive disease receiving ibruti-


nib, an inhibitor of B cell receptor (BCR) signaling targeting Brutontyrosine kinase (BTK). Resistance mutations in BTK have exclusivelybeen described in tumor cells of patients with ibrutinib–refractorychronic lymphocytic leukemia and mantle cell lymphoma (48, 49).However, it remains unclear whether these mutations also occur inaggressive lymphomas, such as DLBCL, and whether they can be de-tected in plasma. By using ctDNA, we identified emergent resistancemutations in BTK that displayed distinct clonal dynamics in two ofthree patients (Fig. 2, C and D, and fig. S7, A and B). In one DLBCL

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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. Shownis 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) overserial 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 routineclinical 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 twoindependent subclones emerging during therapy. RBL, rituximab, bendamustine, and lenalidomide. (D) Schematic illustrating the two acquired BTK C481S resistancemutations 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 hereare 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 thetop. At the bottom, germline bases and amino acid sequences are depicted.
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patient, two adjacentBTKmutations encoding an identical amino acidsubstitution (BTK C481S) were found, but they were never observedwithin the same ctDNA molecule, demonstrating convergent evolu-tion of independent resistant subclones (Fig. 2, C and D, and fig.S7A). These results suggest that tumor genotyping from plasma canfacilitate monitoring of BTK-targeted therapy, regardless of histology.Thus, ctDNA profiling with CAPP-Seq has utility for real-time assess-ment of dynamic tumor processes, including clonal evolution and theacquisition of molecular resistance.


Prognostic value of ctDNA in DLBCLHaving demonstrated the technical performance of the assay, wenext determined whether ctDNA analysis could facilitate earlyidentification of clinically relevant risk groups in DLBCL. Westarted by comparing total ctDNA burden at diagnosis with stan-dard clinical indices and risk of radiographic progression (Fig. 3and fig. S9) (33). The amount of ctDNA was significantly correlatedwith serum lactate dehydrogenase (LDH; P < 1 × 10−4), the mostcommonly used biomarker for DLBCL (Fig. 3A and fig. S9A) (50).

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Fig. 3. Quantification of ctDNA in relation to DLBCL clinical indices and treatment response. (A) Relationship between LDH and ctDNA concentration from pre-
treatment 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 timeas 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) Associationbetween ctDNA concentration at diagnosis and Ann Arbor stage. Statistical comparison between early-stage (I + II) and late-stage (III + IV) patients was performed usingMann-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 withdetectable ctDNA as a function of time before relapse. Bottom: Patient level data demonstrating ctDNA detection before relapse (n = 11). Clinical relapses wereconfirmed 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 de-tected; 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 andbefore 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 patientswithout detectable ctDNA after the end of curative therapy. Significance was assessed using the log-rank test.
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Notably, whereas 100% of pretreatment samples had detectablectDNA, only 37% of samples had abnormally high LDH, demonstrat-ing superior sensitivity of ctDNA. Pretreatment ctDNA levels werestrongly associated with metabolic tumor volumes (MTVs) measuredusing [18F]fluorodeoxyglucose PET/CT scans (Fig. 3B) (33). ctDNAconcentrations at initial diagnosis were also significantly correlatedwith Ann Arbor stage (P = 3 × 10−4; Fig. 3C) and IPI (P < 1 × 10−4;fig. S9B) (5). Furthermore, we testedwhether ctDNA concentrations atdiagnosis were linked with the risk of future disease progression. Inmultivariate analyses incorporating key clinical parameters, higherctDNA levels were continuously and independently correlated with in-ferior progression-free survival (PFS; table S4). Thus, pretreatmentctDNA in DLBCL can complement traditional clinical indices andserve as an independent prognostic biomarker.

Early detection of DLBCL relapseAmong the most promising clinical applications of ctDNA is itspotential use for the detection of radiographically occult MRD(26, 27). We profiled plasma samples at times of radiographic com-plete response (n = 30) or recurrence (n = 8) from 11 patients, all ofwhom ultimately experienced disease progression despite therapywith curative intent. Whereas ctDNA was identified in all patientsat the time of clinical relapse (Fig. 3D), it was also detectable asMRD 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 timepoint and clinical relapse was 188 days, and all blood collections upto 3 months before relapse had ctDNA above the detection limit ofour assay (Fig. 3D). When directly compared to IgHTS, our methoddetected 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 inplasma samples from 10 patients who were disease-free for at least24 months after therapy (51) and in 24 healthy adult subjects, demon-strating 100% specificity. Finally, we found that patients with ctDNAdetected in plasma showed significantly inferior PFS compared tothose with undetectable ctDNA (P = 3 × 10−4, log-rank test; Fig.3F). This remained significant when controlling for “guarantee-timebias” (P= 8× 10−5, likelihood ratio test), a potential confounding effectof comparing survival between groupswhen the classifying event (thatis, ctDNA measurement) occurs during follow-up (52, 53). We ob-served a similar, though not significant, trend for overall survival (P =0.056, log-rank test; fig. S11). Collectively, these results illustrate thepromise of ctDNA profiling by targeted sequencing for improvedMRD assessment and early relapse detection.

COO classificationCOOclassification ofDLBCL is one of the strongest prognostic factorsand a potential biomarker for future personalized therapies, yet accu-rate subtyping remains challenging in clinical settings (12–16, 19).Wetherefore used multiplexed somatic mutation profiling to develop atool for COO classification from tumor or pretreatment plasma. Byconsidering mutations enriched in GCB or non-GCB (ABC) DLBCLand targeted by our capture panel, we built a probabilistic classifierusing a Bayesian approach (23, 54, 55). Patients in the training cohortwere previously subtyped bymicroarray-based gene expression profil-ing of frozen tissues, currently considered the gold standard even if notclinically practical (fig. S12 and table S5) (23, 55). We then bench-marked the classifier performance using our cohort of 76 lymphoma


tumor biopsies, predicting 44 patients as GCB and 32 as non-GCB(Fig. 4A). By comparing our results to a blinded, centralized immuno-histochemical classification using the Hans algorithm (the current clin-ical standard), we observed a concordance rate of almost 80% (Fig.4A) (17, 19). Patients identified as having GCB DLBCL by our classi-fication approach had superior PFS over those identified as havingnon-GCB DLBCL (P = 0.02, log-rank test; Fig. 4B), consistent withprevious descriptions of survival differences between COO subtypes(11). In addition, COO classifier scores were continuously associatedwith improved PFS (P = 3 × 10−3; Fig. 4C). Among patients analyzedby both immunohistochemistry and DNA genotyping, the Hansalgorithm failed to stratify patient clinical outcomes, suggesting moreaccurate 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 concordancebetween COO predictions from tumor tissue and biopsy-free plasmagenotyping was 88% (Fig. 4E). Moreover, DLBCL molecular subtypespredicted directly from plasma were significantly associated with PFSin continuousmodels (P= 0.02; Fig. 4C). Thus, biopsy-free assessmentof ctDNAhas considerable potential for the classification of transcrip-tionally defined DLBCL subtypes.

Patterns of genome evolution in patients withhistological transformationPatients with aggressive DLBCL arising from histological trans-formation of an indolent FL represent another biologically definedrisk group associated with poor prognosis (56, 57). We hypothesizedthat a comparative genomic analysis of paired tumor specimensmightreveal biological features distinguishing histological transformationof FL (tFL), progression without transformation [nontransformedFL (ntFL)], and progression of DLBCL. Accordingly, we appliedCAPP-Seq to three groups of paired tumor samples: (i) diagnosticFL 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 comparedthe evolutionary history of these sequential tumor pairs by defininggenetic alterations that were either common to both tumors or privateto each (fig. S13A).

Among the three classes, we observed the greatest evolutionarydistance among tumor pairs associated with histological trans-formation (Fig. 5, A and B, and figs. S13, B to D, and S14). This pat-tern wasmost pronounced when examining the fraction ofmutationsunique to the tumor biopsy at progression, which served to distin-guish all three tumor subtypes (Fig. 5A and fig. S13D). Genomicdivergence was independent of both the time to progression ortransformation and the number of previous therapies, suggesting thatthis simple index could have utility as a biomarker of histologicaltransformation (fig. S13E).

We therefore analyzed tumor biopsies obtained at diagnosis,along with follow-up plasma samples from patients with indolentlymphomas experiencing transformation (n = 8), progression with-out transformation (n = 7), or rrDLBCL (n = 11). In four patients,we additionally profiled follow-up plasma samples obtained beforeclinical evidence of transformation. Plasma genotyping resultslargely matched those from sequential tumors, with a higher frac-tion of emergent variants distinguishing tFL from other histologies(Fig. 5C). Separately, higher amounts of ctDNA were found to dis-tinguish tFL and rrDLBCL from ntFL (Fig. 5D), suggesting thataggressive lymphomas display similar tumor cell proliferation

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and turnover kinetics, despite their separate origins. When consid-ering these discriminatory features within a logistic regression frame-work incorporating leave-one-out cross validation, we were able tononinvasively classify tFL from ntFL with 83% sensitivity and 89%specificity (Fig. 5E). Moreover, our model successfully predicted tFLin three of four plasma samples collected on an average of 66 daysbefore clinical diagnosis. Together, these results demonstrate key ge-nomic differences between the three lymphoma subtypes and high-light the potential of ctDNA as a noninvasive biomarker for earlydetection of transformation.

Most of the patients who experienced histological transformationshowed similar mutations in tumor/plasma pairs obtained atmatching time points. However, in one patient, we observed amarkeddiscordance between a diagnostic FL tumor biopsy (left inguinal) and


corresponding plasma sample (Fig. 5F,left).Mostmutations from the latter weresharedwith the patient’s tFL tumor biopsy(retroperitoneum), obtained 9 monthslater and after unusual refractoriness torituximab (Fig. 5F, right). These observa-tions suggest that both indolent and ag-gressive clones were already present beforeclinical diagnosis of transformation, evenif spatially separated (Fig. 5F, left). Insupport of this hypothesis, when we ap-plied our logistic regressionmodel to thispatient’s pretreatment plasma at FL diag-nosis, we classified the tumor subtype astFL and, thus, poorly suited for rituximabmonotherapy. These data further dem-onstrate the value of plasma genotypingfor capturing clinically relevant tumorheterogeneity and emphasize the impor-tance of sampling genomic informationfrom spatially distinct tumor deposits.

DISCUSSIONClinical and biological heterogeneityare key factors contributing to adverserisk and treatment failure in many can-cers, including lymphomas. To addressthese challenges for patients withDLBCL, we applied CAPP-Seq, a highlysensitive targeted sequencing method,to analyze genetic profiles in 118 biop-sies and 166 plasma samples from ma-jor disease milestones. In comparisonto IgHTS, this approach achieved high-er analytical and clinical sensitivity incapturing the mutational landscape oflymphoma and its clonal evolution. Inaddition, capture-based ctDNA analysiscomplemented cross-sectional imagingand facilitated the discovery of tumormolecular features and candidate bio-markers associated with high diseaseburden, relapse, non-GCB DLBCL, andhistological transformation. Together,

our findings highlight the advantages of ctDNA as a noninvasive bio-marker and provide a number of risk stratification strategies forclinical translation (Fig. 1).

For example, some patients with recurrent DLBCL undergo po-tentially curative subsequent therapies, including autologous stemcell transplantation (58). Although early detection of relapse has apotential for improving outcomes, surveillance imaging is consideredto be largely ineffective for disease monitoring because of high false-positive rates (6, 7, 59, 60). We detected ctDNA in 100% of the ana-lyzed patients at the time of radiographic relapse, and 73% of patientsundergoing surveillance had detectable ctDNA before clinical pro-gression, with a mean lead time of more than 6 months. These resultscould inform clinical trial designs examining treatment paradigmsbased on early intervention directed by ctDNA detection.

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Tumor 50 0.003 0.59 (0.42–0.84)

Plasma 25 0.02 0.47 (0.25–0.89)

CAPP-Seq classifier prognostic performance in primary DLBCL: PFS from diagnosis

E

Fig. 4. DLBCL COO classification by tumor and plasma sequencing. (A) Top: CAPP-Seq COO classifier scores are
shown for each patient’s DLBCL tumor sample (n = 76), ordered on the basis of decreasing log odds scores. Bottom:COO classification of patients in (A) using the Hans immunohistochemistry (IHC) algorithm (n = 59). Cases classified asGCB or non-GCB are shown in orange and blue, respectively. Empty spaces indicate cases with no IHC classificationavailable. (B) PFS from diagnosis in DLBCL cases, as determined by the CAPP-Seq COO classifier on all analyzed DLBCLtumor samples (n = 50). (C) The results of applying univariate Cox proportional hazards regression to analyze PFS intumor and plasma samples from DLBCL patients. HR, hazards ratio; CI, confidence interval. (D) PFS from diagnosis inDLBCL cases, as determined by Hans algorithm (n = 38). The log-rank test was used in (B) and (D) to determinestatistical significance. n.s., not significant. (E) Concordance between COO assignments of the CAPP-Seq classifierapplied to tumor samples and applied to corresponding plasma samples (n = 41). Primary central nervous systemlymphoma and transformed lymphoma cases were excluded from the patient cohort for the analyses in (B) to (D).
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In addition, accurate classification of GCB- and ABC-like mo-lecular subtypes is important for determining prognosis in DLBCLpatients. Here, we report a method for DLBCL classification basedon integrating diverse somatic mutation profiles. This approach isboth accurate and practical, allowing input material from eitherfixed tumor tissue or plasma samples, with high tumor-plasmaconcordance rates. Our noninvasive classification results were asso-ciated with clinical outcomes, suggesting a viable alternative to cur-rent methods that are limited by the requirement for invasivebiopsies and suboptimal assay performance (11, 17, 61, 62). More-


over, the recent development of subtype-directed therapy hasincreased the importance of simultaneous disease classificationand tumor genotyping (12–15). For example, patients classifiedas having ABC-like DLBCL by expression-based subtyping, andparticularly those with ABC-like tumors that harbor gain-of-function mutations in BCR pathway genes (CD79B with or with-outMYD88), demonstrated a higher rate of ibrutinib efficacy (12).In this cohort, we detected nine such patients by deep sequencing (ta-ble S3). Thus, our integrative approach could support future clinicaltrials through the identification of poor-risk groups at diagnosis and

ntFLn = 7

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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 SNVsspecific 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 highlightpatients with different histologies, excluding outliers. (B) Network depiction of the mutational divergence between each tumor 1 and tumor 2 pair analyzed in (A). Thecentral 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 tumor1 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) Evolutionof different types of NHL as determined by comparing diagnostic tumor samples from (A) (“tumor 1”) with follow-up plasma samples. The percentage of SNVs founduniquely 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 ofhistological transformation from plasma. Sn, sensitivity; Sp, specificity; PPV, positive predictive value; NPV, negative predictive value. (F) Biopsy-free detection of an occultaggressive lymphoma subclone (tFL) in a patient histologically diagnosed with ntFL from a left inguinal lymph node biopsy (left, blue solid circle). The tumor site harboringthe aggressive subclone (tFL, green dashed circle) was later identified in a retroperitoneal lymph node biopsy (right, green solid circle). Bottom: Venn diagram analysis ofmutations found in tumor/plasma pairs at FL and tFL diagnosis. Mutations in key driver genes, such as CARD11 or PIM1, are indicated.
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could also guide therapy selection and improve treatment decisions bycombining COO subtyping and assessment of favorable mutationalpatterns in a single assay (12). Separately, the framework we describefor disease classification using somatic alterations could extend to thenoninvasive classification of many tumor types.

Finally, histological transformation of FL to DLBCL is charac-terized by a change from indolent to aggressive clinical behavior,associated with an unfavorable prognosis (56). We demonstratethat different NHL types, including tFL, exhibit distinct patternsof genome evolution. Among the subtypes that we evaluated,paired FL and tFL tumors showed the greatest evolutionary dis-tance, on average, from their last common clonal progenitor, afinding that mirrors the marked shift in clinical presentation thataccompanies transformation. By incorporating these genomic dif-ferences within a model, we found that FL transformation could bepredicted with high sensitivity and specificity from ctDNA.

Given the clinical relevance of the reported results, further de-velopment and validation of our findings in larger patient cohortswill be needed. Such studies could lead to prospective clinical trialsthat test the utility of ctDNA profiling in lymphoma. In addition,we did not explicitly evaluate somatic copy number variants in thisstudy, although we have previously shown that clinically relevantcopy number changes can be sensitively detected in plasma (29).Targeting these aberrations in future panel designs may prove use-ful for DLBCL outcome prediction.

In summary, noninvasive genotyping and serial ctDNA moni-toring are promising approaches for uncovering biology andimproving patient management. We anticipate that ctDNA will havebroad utility for dissecting tumor heterogeneity within and betweenpatients with lymphomas and other cancer types, with applicationsfor the identification of adverse risk groups, the discovery of resistancemechanisms to diverse therapies, and the development of risk-adaptedtherapeutics.

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MATERIALS AND METHODSFor detailed Materials and Methods, please see the SupplementaryMaterials.

5, 2016
SUPPLEMENTARY MATERIALSwww.sciencetranslationalmedicine.org/cgi/content/full/8/364/364ra155/DC1Materials and MethodsFig. 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 plasmasamples.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 apatient with tFL.Fig. S9. Relationship between pretreatment ctDNA concentration and key DLBCL clinicalindices.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.

Table S3. Somatic mutations and V(D)J recombination sequences detected in tumor biopsiesand 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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Acknowledgments: We thank the patients and their families who participated in thisstudy. We would like to thank R. Tibshirani for the statistical advice related to the CAPP-SeqCOO classifier. We also thank L. Pasqualucci for providing detailed information aboutgenes 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 HematologyScholar 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 AmericanSociety of Clinical Oncology Young Investigator Award (to D. M. Kurtz), the National CancerInstitute (R01CA188298 and 1K99CA187192-01A1), the U.S. NIH Director’s New InnovatorAward Program (1-DP2-CA186569), and the Ludwig Institute for Cancer Research. Authorcontributions: F.S., D. M. Kurtz, A.M.N., M.D., and A.A.A. developed the concept, designed theexperiments, 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. contributedequally as senior authors. All authors commented on the manuscript at all stages. Competinginterests: A.M.N, D. M. Klass, M.D., and A.A.A. are coinventors on patent applications related toCAPP-Seq. A.M.N., M.D., and A.A.A. are consultants for Roche Molecular Systems and A.F.L. andD. 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 fornonprofit use (34).

Submitted 24 August 2016Accepted 19 October 2016Published 9 November 201610.1126/scitranslmed.aai8545

Citation: F. Scherer, D. M. Kurtz, A. M. Newman, H. Stehr, A. F. M. Craig, M. S. Esfahani,A. F. Lovejoy, J. J. Chabon, D. M. Klass, C. L. Liu, L. Zhou, C. Glover, B. C. Visser,G. A. Poultsides, R. H. Advani, L. S. Maeda, N. K. Gupta, R. Levy, R. S. Ohgami, C. A. Kunder,M. Diehn, A. A. Alizadeh, Distinct biological subtypes and patterns of genome evolution inlymphoma revealed by circulating tumor DNA. Sci. Transl. Med. 8, 364ra155 (2016).

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10.1126/scitranslmed.aai8545] (364), 364ra155. [doi:8Science Translational Medicine

Diehn and Ash A. Alizadeh (November 9, 2016) Ronald Levy, Robert S. Ohgami, Christian A. Kunder, Maximilian Poultsides, Ranjana H. Advani, Lauren S. Maeda, Neel K. Gupta,Long Liu, Li Zhou, Cynthia Glover, Brendan C. Visser, George A. Alexander F. Lovejoy, Jacob J. Chabon, Daniel M. Klass, ChihStehr, Alexander F. M. Craig, Mohammad Shahrokh Esfahani, Florian Scherer, David M. Kurtz, Aaron M. Newman, Henninglymphoma revealed by circulating tumor DNADistinct biological subtypes and patterns of genome evolution in

Editor's Summary

allowing for periodic monitoring of each patient without repeated invasive biopsies.

analysis,authors also demonstrated that circulating tumor DNA in the patients' blood is suitable for this are going to transform into more aggressive subtypes and may require more intensive treatment. Thethat specific genetic characteristics can determine each tumor's cell of origin and identify tumors that

. have shownet altreat. By analyzing DNA in tumor samples and blood of lymphoma patients, Scherer range of behaviors, from indolent and curable cancers to ones that are very aggressive and difficult to

Diffuse large B cell lymphoma is a relatively common type of tumor that can exhibit a wideThe telltale DNA in lymphoma

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