Early Reduction in ctDNA Predicts Survival in Patients ... · therapy has been reported in colorectal cancer (34). In a study of 49 patients with NSCLC, targeted next-generation sequencing

Precision Medicine and Imaging

Early Reduction in ctDNA Predicts Survival inPatients with Lung and Bladder Cancer Treatedwith DurvalumabRajiv Raja1, Michael Kuziora1, Philip Z. Brohawn1, Brandon W. Higgs1, Ashok Gupta2,Phillip A. Dennis2, and Koustubh Ranade1

Abstract

Purpose: Immunotherapy has transformed the treatmentof many solid tumors, with some patients deriving long-term benefit, but how to identify such patients remainsunclear. Somatic mutations detected in circulating tumorDNA (ctDNA) from plasma can be an indicator of diseaseprogression, response to therapy, and clonality of primaryand metastatic lesions. Hence, ctDNA analysis can provide avaluable noninvasive and tumor-specific marker for longi-tudinal monitoring of tumor burden. We explored the use ofctDNA to predict survival on durvalumab, an anti-PD-L1therapy.

Experimental Design: Variant allele frequencies (VAF) ofsomatic mutations in 73 genes were assessed in ctDNA usingtargeted sequencing in a discovery cohort consisting of 28patients with non–small cell lung cancer (NSCLC) and twovalidation NSCLC and urothelial cancer (UC) cohorts of 72

and 29 patients, respectively, to correlate ctDNA changes withclinical outcomes.

Results: Somatic variants were detected in 96% of patients.Changes in VAFpreceded radiographic responses, and patientswith reduction in VAF at 6 weeks had significantly greaterreduction in tumor volume, with longer progression-free andoverall survival.

Conclusions: ctDNA VAF changes are strongly correlatedwith duration of treatment, antitumor activity, and clinicaloutcomes in NSCLC and UC. Early on-treatment reduction inctDNA VAF may be a useful predictor of long-term benefitfrom immunotherapy. Prospective studies should validatethesefindings and the value ofutilizing early changes in ctDNAfor therapeutic decisionmaking by identifying nonrespondersto checkpoint inhibitor monotherapies and guiding combi-nation therapies. Clin Cancer Res; 24(24); 6212–22. �2018 AACR.

IntroductionCirculating cell-free DNA (cfDNA) is present in plasma and

serum of healthy individuals as well as patients with cancer. Inpatients with cancer, cfDNA is seen in markedly higher quantitiescompared with those found in healthy individuals (1). Circulat-ing tumor DNA (ctDNA) is the fraction of cfDNA that is derivedfrom tumor tissues (2). ctDNA is thought to be shed into circu-lation by apoptotic and necrotic tumor cells in patients withcancer (3, 4), highly prevalent in most advanced solid tumorswith the exception of brain tumors (5), and has a half-life rangingfrom 16minutes to a few hours (6–8). Because advanced tumors,either pretreatedor at progression, have ahighermitotic index andundergo more rapid cell cycling compared with normal tissue orearlier stage tumors, ctDNA constitutes a larger proportion ofcfDNA in metastatic disease (9, 10). Patients with high tumorburden and aggressive disease have higher proportions of ctDNA,

whichmay rise above 90%of cfDNA, where it becomes a negativeprognostic indicator.

Analysis of ctDNA provides a significant opportunity to studytumor growth dynamics and the evolving genomic landscape oftumors. The advantages of using liquid biopsies (e.g., plasma)for genomic analysis in patients with cancer include: (i) nonin-vasive nature of sample collection (i.e., blood draws); (ii) theability to obtain repeat samples throughout the duration oftreatment; (iii) representation of intra- and intertumor hetero-geneity in patients; and (iv) continuous monitoring of geneticalterations during therapy as a surrogate of tumor burden andtumor clonal structure in the emergence of resistance to treat-ment (11). Numerous studies have shown that levels of ctDNAin plasma are correlated with tumor burden and that response tocertain therapies correlate with decreased levels of ctDNA in on-treatment samples. It has been shown that patients with non–small cell lung cancer (NSCLC) with somatic alterations of >5%variant allele frequency (VAF) have shortened survival (12). Astudy that evaluated 162 plasma samples from 18 patients withcolorectal cancer demonstrated that higher ctDNA levels wereassociated with higher tumor burden and that ctDNA dynamicsmay be more sensitive than carcinoembryonic antigen (CEA)levels in monitoring tumor burden (10). In other reports, tumorprogression after chemotherapy was shown to be associated withincreasing plasma (13, 14) and serum (15) DNA concentrationsin NSCLC. Similar associations between ctDNA and tumorburden have been reported in metastatic melanoma (16), breastcancer (9), gynecologic malignancies (17), and metastatic colo-rectal cancer (18), and associations with response to various

1Translational Medicine Oncology, MedImmune, Gaithersburg, Maryland. 2GlobalMedicines Development, AstraZeneca, Gaithersburg, Maryland.

Note: Supplementary data for this article are available at Clinical CancerResearch Online (http://clincancerres.aacrjournals.org/).

Corresponding Author: Rajiv Raja, MedImmune, One MedImmune Way,Gaithersburg, MD 20878. Phone: 301-398-4505; E-mail:[email protected]

doi: 10.1158/1078-0432.CCR-18-0386

�2018 American Association for Cancer Research.

ClinicalCancerResearch

Clin Cancer Res; 24(24) December 15, 20186212

on October 5, 2020. © 2018 American Association for Cancer Research. clincancerres.aacrjournals.org Downloaded from

Published OnlineFirst August 9, 2018; DOI: 10.1158/1078-0432.CCR-18-0386

http://crossmark.crossref.org/dialog/?doi=10.1158/1078-0432.CCR-18-0386&domain=pdf&date_stamp=2018-11-28

http://clincancerres.aacrjournals.org/

targeted and systemic therapies have been shown (19–23). Arecent ctDNA study demonstrated the ability to observe emer-gence of resistance to adjuvant chemotherapy and the ability toperform phylogenetic ctDNA profiling to track subclones duringrelapse and metastasis (24).

Immune checkpoint inhibitors, such as anti-PD-1 [pembroli-zumab (25), nivolumab (26)] and anti–PD-L1 [atezolizumab(27), durvalumab (28), and avelumab (29)], have shown clinicalbenefit in multiple tumor types. Monitoring responses toimmune checkpoint inhibitors can be challenging due to theirmechanism of action being significantly different from othertypes of therapies (30). Because these therapies seek to activatethe immune system, response to therapy and subsequenttumor regression can be delayed compared with chemotherapy,radiotherapy, or targeted therapies (31). ctDNA analysis ofearly time-course samples could be used to gauge antitumorresponse to immunotherapy, opening up unique opportunitiesin immuno-oncology. Such assessment of patient responsescould inform decision making in indications where image-based response analysis may not be reliable, to enable earlytreatment or treatment combination decisions before availabil-ity of radiographic response, and to quickly inform whetherresponses to certain treatment combinations (e.g., anti-PD-L1and chemotherapy) are transient or durable.

ctDNA levels have been shown to correlate with response toimmunotherapies. Somatic hotspot mutations in BRAF, cKIT,NRAS, and TERT were analyzed from ctDNA in 12 patients withmetastatic melanoma receiving anti-CTLA-4 or anti-PD-L1 ther-apies. The study showed that mutant allele frequencies in hotspotgenes correlated with clinical and radiologic outcomes and in onecase, preceded manifestation of relapse (16). In a study of 48patients with metastatic melanoma receiving adoptive transfer ofautologous tumor-infiltrating lymphocytes, a strong correlationwas seen between the clearance of BRAFV600E mutation in serumand complete response over the next 1 to 2 years. Amajority of thepatients showing no clearance failed to achieve objective response(32). In another study of patients with metastatic melanomareceiving anti-PD-1 therapy, ctDNA levels at baseline correlated

with lactate dehydrogenase levels, tumor burden and EasternCooperative Oncology Group (ECOG) scores, and longitudinalassessment of ctDNA levels correlated with tumor response,progression-free survival (PFS), and overall survival (OS; ref. 33).A similar association between ctDNA and response to immuno-therapy has been reported in colorectal cancer (34). In a study of49 patients with NSCLC, targeted next-generation sequencing(NGS) of 43 hotspots in 24 genes found 57% of baseline samplespositive for ctDNA. A 50% reduction in the variant with thehighest variant allele fraction was seen in patients on anti-PD-1or anti-PD-L1 checkpoint inhibitors at a median of 24.5 days onctDNA but response was not observed by imaging until a medianof 72.5 days. The decline in ctDNA was strongly associated withtime on treatment, PFS, and OS (35).

These studies are limited in that they have focused on one or afew specific mutations or a specific cancer type, have a limitednumber of patients in the analysis, and do not provide indepen-dent cohorts to confirm initial findings.

Here, using a broad NGS-based mutation panel, we demon-strate a strong relationship between clinical outcome inmetastaticNSCLC and changes in ctDNA VAF from baseline to 6 weeks afterinitiation of treatment with durvalumab. We validated this find-ing in independent sets of patients with NSCLC and urothelialcarcinoma (UC) treated with durvalumab.

Materials and MethodsStudy design and patients

Study 1108 (NCT01693562) is a phase I/II, first-in-human,multicenter, open-label, dose-escalation and dose-expansionstudy being conducted at 70 centers worldwide. Eligible patientswere �18 years of age with histologically or cytologically con-firmed inoperable or metastatic transitional-cell UC or NSCLCwhohadprogressedon, been ineligible for, or refused anynumberof prior therapies. Patients had an ECOGperformance status scoreof 0 or 1, adequate organ and hematologic functions, and freshtumor biopsy and/or archival tumor tissue available for PD-L1testing. Key exclusion criteria were active autoimmune disease orinflammatory bowel disease, prior severe or persistent immune-relatedadverseevents(AE),previousexposure toanti-PD-1oranti-PD-L1 therapy, requirement for >10 mg/day of prednisone orequivalent, and untreated central nervous system (CNS) metasta-ses.AsofApril29,2016,304patientswithNSCLCand191patientswith UC had received durvalumab (10 mg/kg i.v. twice weekly).

ATLANTIC (NCT02087423) is a multicenter, phase II open-label study enrolling patients with stage IIIB/IV NSCLC withdisease progression following two or more systemic treatments,including one platinum-based chemotherapy and one tyrosinekinase inhibitor (TKI) for EGFRmut/ALKþ patients. As of June 3,2016, 444 patients had received durvalumab (10mg/kg i.v. twiceweekly) for up to 12 months.

For the purpose of discovery of ctDNA-based biomarkers inplasma, we used samples from 28 patients with NSCLC fromStudy 1108. The findings from these samples were independentlyvalidated in samples from 72 patients with EGFR-wild-typeNSCLC (cohort 2) of ATLANTIC and 29 patients with UC inStudy 1108 for confirmation.

The studies were conducted in accordance with Good ClinicalPractices, the Declaration of Helsinki, and approval by eachInstitution's Ethical Review Board. Patients provided writteninformed consent.

Translational Relevance

Levels of circulating tumor DNA (ctDNA) in plasma areknown to correlate with tumor burden and changes frombaseline correlate with response to some immunotherapies.Here, we demonstrate that somatic mutations in ctDNAare detectable in plasma from most patients with advanced/metastatic non–small cell lung cancer and urothelial cancer.Targeted genomic sequencing shows that during treatmentwith durvalumab, an anti-PD-L1 immunotherapy, a reductionin ctDNA variant allele frequency at 6 weeks is associatedwith,and could often precede, tumor shrinkage. The reduction isalso associated with improved progression-free and overallsurvival. This early change may be a useful and noninvasiveway to predict long-term benefit from immunotherapy, open-ing up unique opportunities to support decision making inindications where image-based response analysis may not bereliable, and to enable early treatment decisions before avail-ability of radiographic response.

ctDNA Predicts Survival with Durvalumab in NSCLC and UC

www.aacrjournals.org Clin Cancer Res; 24(24) December 15, 2018 6213




Plasma collection and cfDNA isolationAs ctDNAanalysiswas not preplanned, our analysiswas limited

to available samples frompatientswhoconsented toparticipate inan optional biomarker component of the study.We used pretreat-ment samples that were collected either at screening or before firstdose depending on sample availability. Post-dose samples werecollected prior to the fourth treatment (6 weeks after first dose).Briefly, venous blood was collected in K2-EDTA tubes duringroutine phlebotomy and 10mL of blood was processed to isolateplasma by centrifugation at 1,300 g for 10 minutes. Plasma wasimmediately aliquoted and stored at �20 �C or colder. Cell-freeDNA was extracted from 1-mL aliquots of plasma using theQIAamp Circulating Nucleic Acid Kit (Qiagen), concentratedusing Agencourt Ampure XP beads (BeckmanCoulter), and quan-tified by Qubit fluorometer (Life Technologies). All cell-free DNAisolation and sequencing was performed at Guardant Health.

Genomic analysisGenomic alterations (mutations, insertions, deletions, and

amplifications) were detected from cfDNA extracted from plas-ma samples using a broad targeted NGS-based 73-gene panel(Guardant360), including coverage of the most prevalenttumor suppressor genes in human cancers. After isolation ofcfDNA by hybrid capture, the assay is performed using molec-ular barcoding and proprietary bioinformatics algorithms withmassively parallel sequencing on an Illumina Hi-Seq 2500

platform in a CLIA/CAP accredited laboratory (GuardantHealth). Variants in plasma ctDNA were assessed in samplescollected at predose and 6 weeks after first dose of treatmentwith durvalumab.

PD-L1 stainingPD-L1 status was determined by IHC, using a cutoff of PD-L1

expression on�25%of tumor cells at any intensity inNSCLC andexpression on�25% of tumor or immune cells at any intensity inUC. PD-L1 expression level was determined by IHC using theSP263 anti–PD-L1 antibody assay (Ventana Medical Systems) asdescribed previously (36, 37). Samples were classified as having�25%or<25%of tumor cellmembranes or immune cells (forUConly) staining positive for PD-L1 at any intensity. This cutoff waschosen based on a number of considerations, including theprevalence of PD-L1 expression in the population, ease of scoringby pathologists, optimizing for higher negative predictive value,and delineating between responders and nonresponders (38).

Statistical analysisSomatic variants of unknown significance as well as variants

known to be associated with cancer including single nucleotidevariants (SNV), insertions/deletions (indels), and fusions weresummarized per patient by calculating the mean allele frequencyof all genes with a VAF �0.3% at predose. Synonymous andnonsynonymous variants were included in calculation of VAF.

Table 1. Patient demographics, baseline characteristics, and prior therapies

Study 1108 lung ATLANTIC Study 1108 bladder

N 28 72 29Age Mean (SD) 62.21 (13.27) 61.43 (9.79) 65.86 (8.19)

Range 31–87 23–78 49–81

Gender (%) F 8 (28.6) 30 (41.7) 9 (31.0)M 20 (71.4) 42 (58.3) 20 (69.0)

Race (%) Asian 6 (21.4) 36 (50.0) 3 (10.3)African American – – 1 (3.4)White 21 (75.0) 36 (50.0) 22 (75.9)Other 1 (3.6) – 3 (10.3)

Previous lines of therapy (%) 0 7 (25.0) – 1 (3.4)1 4 (14.3) – 16 (55.2)2 17 (60.7) – 6 (20.7)3 – 31 (43.1) 5 (17.2)4 – 18 (25.0) 1 (3.4)>4 – 23 (31.9) –

Smoking history (%) Nonsmoker 6 (21.4) 13 (22.0) 13 (44.8)Smoker 22 (78.6) 59 (78.0) 16 (55.2)

Stage at entry (%) III 3 (10.7) 16 (18.1) –

IV 25 (89.3) 56 (66.7) 29 (100.0)

ECOG/WHO PS at baseline (%) 0 10 (35.7) 27 (37.5) 11 (37.9)1 18 (64.3) 45 (62.5) 18 (62.1)

PDL1 status (%) Negative 13 (46.4) 11 (15.3) 6 (20.7)Positive 13 (46.4) 58 (80.6) 23 (79.3)Unknown 2 (7.1) 3 (4.2) –

Histology (%) Nonsquamous 10 (35.7) 57 (79.2) –

Squamous 18 (64.3) 15 (20.8) –

Bladder – – 29 (100.0)

Raja et al.

Clin Cancer Res; 24(24) December 15, 2018 Clinical Cancer Research6214




Variants with VAF < 0.3% were not included in the mean VAFcalculation based on 95% to 100% limits of detection of 0.2% to0.25% for SNVs, indels, and fusions for this technology (39).Onlyvariants observed at predose were used for the 6-week mean VAFcalculation. For variants detected at predose but not 6 weeks, the6-week VAF was set to 0. The change in mean VAF (dVAF) wascalculated as (mean VAFweek 6) – (mean VAFpretreatment), hence adVAF < 0 indicates a decrease at week 6. We compared dVAFbetween baseline and week 6 in plasma samples using a pairedStudent t test. To test the stability of VAF measurements, weanalyzed replicate samples from the same patient at screeningand predose in a limited number of cases and found that VAF aswell as dVAF values calculated from these replicates were highlycorrelated (Supplementary Fig. S1).

For two NSCLC samples in the discovery set, and sevenNSCLC and two UC samples in the validation sets, either nomutations were detected or allele frequencies of variants werebelow the 0.3% cutoff in predose samples. This left 26 NSCLC,65 NSCLC, and 27 UC samples, respectively, for analysis ofdVAF. The prevalence of mutations in The Cancer GenomeAtlas for lung adenocarcinoma, lung squamous cell carcinoma,and bladder cancer indications were calculated using MUTECT2variant calling data downloaded on May 26, 2016.

AFisherexact testwasusedtodeterminetheassociationbetweendVAF � 0 or dVAF < 0 and PD-L1 expression �25% or <25%.

Objective response rate was calculated according to RECISTv1.1, and a Cox proportional hazards model was calculated

adjusting for baseline ECOG score, gender, age, smoking status,previous lines of therapy, and histology.

The median follow-up time for patients with NSCLC and UCwas 15 and 12months, respectively, in Study 1108 and 9monthsfor ATLANTIC.

ResultsDemographics and characteristics of patients included in our

analysis are listed in Table 1. In Study 1108, the majority ofpatients had undergone fewer lines of therapy than those enrolledin ATLANTIC. Both validation datasets [patients with EGFR–wild-type NSCLC (cohort 2) of ATLANTIC and patients with UC inStudy 1108] had notably higher percentages of PD-L1–positivepatients compared with the discovery set (patients withNSCLC inStudy 1108).

Consistency in ctDNA detection rates and recurrently mutatedgenes across three patient cohorts of NSCLC or UC

We first evaluated the ability to detect ctDNA mutants inpretreatment plasma from patients with NSCLC in Study 1108.At least one somatic variant was observed in 27 of 28 (96%)NSCLC discovery, 29/29UC (100%), and 68 of 72NSCLC (94%)validation samples, suggesting that the method used has thesensitivity required for detecting ctDNA in most patients withthese advanced cancers. Figure 1 summarizes the variants detectedin ctDNA discovery and validation datasets noted above. The

Figure 1.

Mutations detected in ctDNA from 27 patients with NSCLC in the discovery cohort (A), 68 patients with NSCLC (B), and 29 patients with UC (C) in the validationcohorts, color-coded by the type of mutation.






5 most recurrent genes containing nonsynonymous variants orcopy number amplifications in the NSCLC discovery cohort wereTP53 (85%), PIK3CA (26%), FGFR1 (26%), ERBB2 (26%), andEGFR (22%; Fig. 1A). Within the NSCLC validation cohort, the 5most recurrent genes containing nonsynonymous variants orcopy number amplifications were TP53 (72%), KRAS (27%),EGFR (22%), BRAF (16%), and PIK3CA (16%; Fig. 1B). For theUC validation cohort, the most recurrent variants were TP53(69%), ARID1A (41%), TERT (41%), PIK3CA (31%), and ERBB2(31%; Fig. 1C).

Reduction in mean VAF at 6 weeks is associated with change intumor volume and time on study

We examined the correlation between dVAF and changes intumor volume in response to durvalumab by comparing thepercent change in tumor volume in patients showing dVAF < 0versus those showing dVAF � 0. We found that in the NSCLCdiscovery samples, mean tumor volume reduced by 39% inpatients with dVAF < 0, whereas it increased by 36% in patientswithdVAF �0 (Fig. 2A;P ¼0.0001). A similar trendwas observedin the validation sets: a decrease of 31% for dVAF < 0 and anincrease of 11% for dVAF� 0 inNSCLC (Fig. 2B, P¼ 0.0009), andadecrease of 38% for dVAF<0 and adecrease of 15% for dVAF�0in UC (Fig. 2C; P ¼ 0.18).

We also compared dVAFwith the length of time patients stayedon durvalumab. In the NSCLC discovery samples, patients withdVAF < 0 had a median duration of treatment of 22 months. Incontrast, duration of treatment for patients with dVAF � 0 wasonly 6.5months (Fig. 2D;P¼ 0.00001). This trendwas confirmedin the validation sets: in the NSCLC cohort, the median durationof treatment for patients with dVAF < 0 was 12 months versus 8months for dVAF�0 (Fig. 2E;P¼0.04). In theUCcohort,medianduration of treatment was 13 months for dVAF < 0 versus 7months for dVAF � 0 (Fig. 2F; P ¼ 0.03).

These observations suggest that a decrease in mean VAF within6 weeks of initiation of durvalumab treatment may be associatedwith better outcomes.

Decreases in mean VAF after 6 weeks of treatment correlatedwith complete/partial response

ctDNAdata were then evaluated for associations between dVAFand objective response by radiography. Each plot in Fig. 3 repre-sents mean VAF changes in the three groups defined by radio-graphic response: complete/partial response (CR/PR), stable dis-ease (SD), or progressive disease (PD). In the discovery NSCLCcohort (Fig. 3A), mean VAF decreased by 2.7% (P ¼ 0.005) forpatients with PR and 1.5% (P ¼ 0.14) for patients with SD. Incontrast, patients with PD showed an increase of 1.7% (P¼ 0.16).

Figure 2.

A–C, Changes in tumor volume plotted by dVAF < 0 versus dVAF � 0 for NSCLC discovery cohort (A), NSCLC (B), and UC (C) validation cohorts. D–F, Medianduration of treatment plotted by dVAF <0 versus dVAF�0 for NSCLCdiscovery cohort (A), NSCLC (B), andUC (C) validation cohorts. On all plots, the horizontal barrepresents the median, the box represents the interquartile range (IQR), and the whiskers represent 1.5 times the IQR above the upper quartile and below thelower quartile.

Raja et al.





In the validationNSCLC cohort (Fig. 3B), themeanVAFdecreasedby 4% (P¼ 0.0009) in patients with CR/PR and 1.1% (P¼ 0.2) inpatients with SD, whereas themean VAF increased 1.4% (P¼ 0.3)

in patients with PD. Similarly, in the validation UC cohort (Fig.3C), themeanVAFdecreased by 2.2%(P¼0.009) in patientswithCR/PR and1.1%(P¼0.36) inpatientswith SD,whereas themean

Figure 3.

Changes inmeanctDNAVAFat 6weeks comparedwith baseline, plotted by objective response statusfor NSCLC discovery cohort (A), NSCLC (B), andUC (C) validation cohorts (CR, complete response;PR, partial response; SD, stable disease; PD,progressive disease).






VAF increased 2.5% (P¼ 0.42) in patients with PD.Overall, of the37 responders in the three studies, 36 had a reduction in meanVAF at week 6; 23 of 46 patients with PD showed an increase inmean VAF, whereas 12 patients with SD showed an increase ofmean VAF and 22 patients showed a decrease.

Reduction in mean VAF after 6 weeks of treatment withdurvalumab may precede radiographic tumor shrinkage

The ability to detect early clinical response or nonresponsethrough the course of therapy can help inform clinical deci-sions. Therefore, we investigated whether a decrease in meanVAF after 6 weeks of treatment preceded radiographic determi-nation of a decrease in tumor volume by �30%. Figure 4 showseach patient who received durvalumab colored by outcome asdetermined by radiography. The plot also shows each patient'sduration of treatment with durvalumab, their mean VAF statusat 6 weeks (increase or decrease), and the time at which the firstradiographic response was recorded. In the discovery samples,out of the 10 patients who showed a �30% decrease in tumorvolume, 7 showed a corresponding decrease in mean VAF 1 to12 months prior to radiographic confirmation (Fig. 4A). Sim-ilar trends were noted in the validation samples. In the NSCLCcohort, 24 patients showed a �30% decrease in tumor volumeand 22 of them had a corresponding decrease in mean VAF. In 9of those 22 patients, the decreases in mean VAF precededradiographic change in tumor volume by 3.6 to 9 months (Fig.

4B). Similarly, in the UC cohort, 14 patients had a �30%decrease in tumor volume; 11 of them had a correspondingdecrease in mean VAF. In 4 of those 11 patients, the decreases inmean VAF preceded radiographic reduction in tumor volumeby 1.3 to 6 months (Fig. 4C).

Interestingly, 2 patients with UC had an increase in meanVAF and a �30% increase in tumor volume at 6 weeks. Bothstayed on durvalumab and showed a �30% reduction in tumorvolume 2 and 6 months later, respectively. Overall, reductionsin mean VAF 6 weeks after initiation of treatment precededradiographic responses in 20 of 48 (42%) patients.

Patients with a reduction in VAF after 6 weeks of treatment withdurvalumab had improved survival

Next, we evaluated whether dVAF correlated with PFS and/orOS. Figure 5A shows that the patients with NSCLC in thediscovery cohort with dVAF � 0 had markedly shorter PFScompared with patients with dVAF < 0 [median PFS (mPFS)1.45 vs. 13.7 months, HR 0.008 (95% CI, 0.0007–0.09)]. Themedian OS (mOS) was also shorter in patients with dVAF � 0versus dVAF < 0: 9.07 months versus 28.13 months, respec-tively, HR 0.001 (95% CI, 0–0.09). Consistent with the dis-covery cohort, an increase in mean VAF correlated with shortermPFS for patients with NSCLC in the validation cohort (Fig.5B): mPFS 1.9 months for dVAF � 0 versus 5.6 months fordVAF < 0, HR 0.26 (95% CI, 0.12–0.54). Likewise, mOS for

Figure 4.

Individual swimmer plots for each patient showing the duration of treatment for NSCLC discovery cohort (A), NSCLC (B), and UC (C) validation cohorts. Lanes arecolored by objective responses PR/CR (&), SD (&), and PD (&). Increase (~) or decrease (!) in dVAF is marked for each patient. Timepoints at which a

radiographic response was confirmed ( ) following the RECIST criteria are marked on the plot.

Raja et al.





dVAF � 0 versus dVAF < 0 was 8.7 months versus NR, HR 0.23(95% CI, 0.09–0.61). Similarly, for the UC cohort (Fig. 5C),patients with an increase in mean VAF had markedly shorter

PFS [mPFS of 1.6 months for dVAF � 0 vs. 13.8 months fordVAF < 0, HR 0.21 (95% CI, 0.05–0.96)] and OS [mOS of 8.2months for dVAF � 0 vs. NR for dVAF < 0, HR 0.001 (95% CI,

Figure 5.

Kaplan–Meier curves of median PFS and OS in relationto increase or decrease in mean VAF: NSCLC discoverycohort (A), NSCLC (B), and urothelial carcinoma (UC)(C) validation cohorts.






0–0.28)]. Overall, patients with a decrease in mean VAF hadsignificantly longer PFS and OS compared with patients with anincrease in mean VAF.

The relationships observed between PFS, OS, and dVAF werenot significantly impacted by baseline ECOG score, gender, age,smoking status, previous lines of therapy, or histology.

Tumor PD-L1 expression can predict clinical outcomes fordurvalumab and other anti-PD-1/PD-L1 agents (40). For thisreason, we examined the correlation between pretreatment PD-L1 status (�25% or <25% as measured by IHC) and dVAF.Patients in all three discovery and validation cohorts showed nostatistical association between dVAF and PD-L1 status (P > 0.1 forall contrasts, data not shown).

Interestingly, the analysis of patients with PD after 6 weeks oftherapy showed the emergence of new EGFR mutations in 7patients and increase in mean VAF in 5 patients. These EGFRmutations include V765M, T638M, and R973Q, which can sen-sitize the tumors to approved EGFR-targeting agents like osimer-tinib and erlotinib (41–43).

DiscussionCirculating biomarkers such as ctDNA offer significant promise

as valuable tools for monitoring tumor burden and antitumorresponse, and providing a real-time snapshot of tumor evolutionin a metastatic setting as patients relapse and are treated withmultiple lines of therapy over time. Previous studies have shownthat ctDNA can be used to monitor responses to conventional aswell as targeted therapies (9, 16–23). In this study,we investigatedthe potential utility of changes in ctDNA as an early predictor ofefficacy during anti–PD-L1 therapy. Using an NGS-based 73-genepanel, we detected mutations in 124/129 (96%) patients. Muta-tion frequencies were generally consistent with frequenciesreported in COSMIC (44), except for PIK3CA and FGFR1 for lungcancer, and ARID1A and TERT in UC, which we detected morefrequently. These differences may be related to differences indisease stage, aswell as better coverageof these genesonourpanel.

Patients who had a decrease in mean VAF (dVAF < 0) after 6weeks of durvalumab stayed on therapy significantly longer thanpatients who had an increase (dVAF � 0). This suggests that achange in mean VAF is directly related to antitumor activity andmay have clinical significance. Decreases in mean VAF also cor-related well with tumor response (CR/PR). In addition, we foundthat patients with a decrease in mean VAF had a markedlyimproved PFS and OS compared with those with dVAF � 0, andthat dVAF does not significantly correlatewith PD-L1 status. Thesedata strongly support the potential use of ctDNA dVAF as anendpoint to inform drug development and/or treatmentdecisions.

Although PFS HRs can be obtained by conventional radio-graphic response (CR/PR/SD vs. PD), in this study we demon-strated that in 20 of 48 (42%) patients, a reduction in VAF at 6weeks is an early indicator of patient response or nonresponse todurvalumab therapy. Hence, changes in VAF at 6 weeks couldpotentially be used to guide early treatment decisions. Thisfinding is consistent with earlier reports utilizing limited ctDNAtesting (35). Patients showing an increase in VAF at 6 weeks couldbe moved on to combination therapies or other treatments inindications where multiple options are available. Such informa-tionmay also aid in early decisionmaking in pan-tumor trials andtrials with adaptive design. Furthermore, ctDNA dVAF data and

imagingdatamaynot necessarily provide redundant information,and the two are likely to complement each other in future clinicalpractice. ctDNA dVAF assessment may aid in the identification ofpseudoprogressions, may be particularly relevant in indicationswhere evaluation of radiographic response is challenging (e.g.,pancreas and liver), and may help predict relapse in an adjuvantsetting (24, 45).

It is noteworthy that the dVAF and objective response were notconcordant in a minority of patients in our study. These patientsrepresentaparticularly interestingarea for future research, tobetterunderstandwhetherctDNAassessmenthelpsimprovetheaccuracyof clinical response assessments in such cases. Longitudinal mon-itoring of changes in tumor burden with liquid biopsies throughthe course of therapy may be particularly relevant for developingcombination therapies in immuno-oncology, as it may predictand/or confirm radiographic responses by an orthogonal methodand may help to distinguish durable versus transient responses.

In our study, we also saw the emergence of new EGFR muta-tions in patients with PD at week 6, which is consistent withactivating mutations in EGFR conferring resistance to immuno-therapy (46–50). Such patients, therefore, could potentially ben-efit from combination therapies with EGFR-targeted agents.

In conclusion, we have shown strong correlations betweenctDNA dVAF and duration of treatment, clinical activity, PFS, andOS. This supports the use of ctDNA dVAF to monitor antitumoractivity and clinical response to immunotherapies. Future studiesshould validate our findings through prospective analysis. Iden-tification of ctDNAmutations associatedwith emerging resistancemay provide an opportunity to test rational combinations.

Disclosure of Potential Conflicts of InterestR. Raja, M. Kuziora, P. Z. Brohawn, B.W.Higgs, andK. Ranade are employees

of MedImmune and have ownership interest (including patents) in AstraZe-neca. P.A. Dennis holds ownership interest (including patents) in AstraZeneca.A. Gupta holds ownership interest (including patents) in AstraZeneca andBristol Myers Squibb.

Authors' ContributionsConceptionanddesign:R.Raja,M.Kuziora, P. Z. Brohawn,A.Gupta, K. RanadeDevelopment of methodology: R. Raja, M. Kuziora, B.W. HiggsAcquisition of data (provided animals, acquired and managed patients,provided facilities, etc.): R. Raja, P. Z. Brohawn, A. GuptaAnalysis and interpretation of data (e.g., statistical analysis, biostatistics,computational analysis): R. Raja, M. Kuziora, P. Z. Brohawn, B.W. Higgs,A. Gupta, P.A. Dennis, K. RanadeWriting, review, and/or revision of the manuscript: R. Raja, M. Kuziora,P. Z. Brohawn, B.W. Higgs, A. Gupta, P.A. Dennis, K. RanadeAdministrative, technical, or material support (i.e., reporting or organizingdata, constructing databases): B.W. HiggsStudy supervision: R. Raja, A. Gupta, K. Ranade

AcknowledgmentsWe thank the patients who participated in this study and their families.

Editorial support, which was in accordance with Good Publication Practice(GPP3) guidelines, was provided by Susanne Gilbert, MA, of Cirrus Commu-nications and was funded by MedImmune. This study was funded by MedIm-mune/AstraZeneca.

The costs of publication of this articlewere defrayed inpart by the payment ofpage charges. This article must therefore be hereby marked advertisement inaccordance with 18 U.S.C. Section 1734 solely to indicate this fact.

Received February 1, 2018; revised June 15, 2018; accepted August 6, 2018;published first August 9, 2018.

Raja et al.





References1. Leon SA, Shapiro B, SklaroffDM, YarosMJ. FreeDNA in the serumof cancer

patients and the effect of therapy. Cancer Res 1977;37:646–50.2. ShuY,WuX, TongX,WangX,ChangZ,MaoY, et al. Circulating tumorDNA

mutation profiling by targeted next generation sequencing provides guid-ance for personalized treatments in multiple cancer types. Sci Rep 2017;7:583.

3. Snyder MW, Kircher M, Hill AJ, Daza RM, Shendure J. Cell-free DNAcomprises an in vivonucleosome footprint that informs its tissues-of-origin.Cell 2016;164:57–68.

4. StrounM, Lyautey J, Lederrey C,Olson-SandA, Anker P. About the possibleorigin and mechanism of circulating DNA apoptosis and active DNArelease. Clin Chim Acta 2001;313:139–42.

5. Bettegowda C, Sausen M, Leary RJ, Kinde I, Wang Y, Agrawal N, et al.Detection of circulating tumor DNA in early- and late-stage humanmalignancies. Sci Transl Med 2014;6:224ra224.

6. Lo YM, Zhang J, Leung TN, Lau TK, Chang AM, HjelmNM. Rapid clearanceof fetal DNA from maternal plasma. Am J Hum Genet 1999;64:218–24.

7. Minchin RF, Carpenter D, Orr RJ. Polyinosinic acid and polycationicliposomes attenuate the hepatic clearance of circulating plasmid DNA.J Pharmacol Exp Ther 2001;296:1006–12.

8. Yu SC, Lee SW, Jiang P, Leung TY, ChanKC, Chiu RW, et al. High-resolutionprofiling of fetal DNA clearance from maternal plasma by massivelyparallel sequencing. Clin Chem 2013;59:1228–37.

9. Dawson SJ, Tsui DW, Murtaza M, Biggs H, Rueda OM, Chin SF, et al.Analysis of circulating tumor DNA to monitor metastatic breast cancer.N Engl J Med 2013;368:1199–209.

10. Diehl F, Schmidt K, Choti MA, Romans K, Goodman S, Li M, et al.Circulating mutant DNA to assess tumor dynamics. Nat Med 2008;14:985–90.

11. Jamal-Hanjani M, Wilson GA, McGranahan N, Birkbak NJ, Watkins TBK,Veeriah S, et al. Tracking the evolution of non-small-cell lung cancer.N Engl J Med 2017;376:2109–21.

12. Schwaederl�eMC, Patel SP, HusainH, IkedaM, Lanman RB, Banks KC, et al.Utility of genomic assessment of blood-derived circulating tumor DNA(ctDNA) in patients with advanced lung adenocarcinoma. Clin Cancer Res2017;23:5101–11.

13. Kumar S, Guleria R, Singh V, Bharti AC, Mohan A, Das BC. Plasma DNAlevel in predicting therapeutic efficacy in advanced nonsmall cell lungcancer. Eur Respir J 2010;36:885–92.

14. Pan S, Xia W, Ding Q, Shu Y, Xu T, Geng Y, et al. Can plasma DNAmonitoring be employed in personalized chemotherapy for patients withadvanced lung cancer? Biomed Pharmacother 2012;66:131–7.

15. Gautschi O, Bigosch C, Huegli B, Jermann M, Marx A, Chass�e E, et al.Circulating deoxyribonucleic acid as prognostic marker in non–small-celllung cancer patients undergoing chemotherapy. J Clin Oncol 2004;22:4157–64.

16. Lipson EJ, Velculescu VE, Pritchard TS, SausenM, Pardoll DM, Topalian SL,et al. Circulating tumorDNAanalysis as a real-timemethod formonitoringtumor burden in melanoma patients undergoing treatment with immunecheckpoint blockade. J Immunother Cancer 2014;2:42.

17. Pereira E, Camacho-Vanegas O, Anand S, Sebra R, Catalina Camacho S,Garnar-Wortzel L, et al. Personalized circulating tumor DNA biomarkersdynamically predict treatment response and survival in gynecologic can-cers. PLoS One 2015;10:e0145754.

18. Tie J, Kinde I, Wang Y, Wong HL, Roebert J, Christie M, et al. Circulatingtumor DNA as an early marker of therapeutic response in patients withmetastatic colorectal cancer. Ann Oncol 2015;26:1715–22.

19. Frenel JS, Carreira S, Goodall J, Roda D, Perez-Lopez R, Tunariu N, et al.Serial next-generation sequencing of circulating cell-free DNA evaluatingtumor clone response to molecularly targeted drug administration.Clin Cancer Res 2015;21:4586–96.

20. Gray ES, Rizos H, Reid AL, Boyd SC, Pereira MR, Lo J, et al. Circulatingtumor DNA to monitor treatment response and detect acquired resistancein patients with metastatic melanoma. Oncotarget 2015;6:42008–18.

21. Momtaz P, Gaskell AA, Merghoub T, Viale A, Chapman PB. Correlation oftumor-derived circulating cell free DNA (cfDNA) measured by digital PCR(DigPCR) with tumor burden measured radiographically in patients (pts)with BRAFV600E mutated melanoma (mel) treated with RAF inhibitor(RAFi) and/or ipilimumab (Ipi). J Clin Oncol 32:15s, 2014 (suppl; abstr9085).

22. Murtaza M, Dawson SJ, Tsui DW, Gale D, Forshew T, Piskorz AM, et al.Non-invasive analysis of acquired resistance to cancer therapy by sequenc-ing of plasma DNA. Nature 2013;497:108–12.

23. Panka DJ, Buchbinder E, Giobbie-Hurder A, Schalck AP, Montaser-Kouhsari L, Sepehr A, et al. Clinical utility of a blood-based BRAF(V600E) mutation assay in melanoma. Mol Cancer Ther 2014;13:3210–8.

24. Abbosh C, Birkbak NJ, Wilson GA, Jamal-Hanjani M, Constantin T, SalariR, et al. Phylogenetic ctDNA analysis depicts early-stage lung cancerevolution. Nature 2017;545:446–51.

25. Farina MS, Lundgren KT, Bellmunt J. Immunotherapy in urothelial cancer:recent results and future perspectives. Drugs 2017;77:1077–89.

26. Sharma P, Callahan MK, Bono P, Kim J, Spiliopoulou P, Calvo E, et al.Nivolumab monotherapy in recurrent metastatic urothelial carcinoma(CheckMate 032): a multicentre, open-label, two-stage, multi-arm, phase1/2 trial. Lancet Oncol 2016;17:1590–8.

27. Powles T, Eder JP, Fine GD, Braiteh FS, Loriot Y, Cruz C, et al. MPDL3280A(anti-PD-L1) treatment leads to clinical activity in metastatic bladdercancer. Nature 2014;515:558–62.

28. Mehta K, Patel K, Parikh RA. Immunotherapy in genitourinary malignan-cies. J Hematol Oncol 2017;10:95.

29. Katz H, Wassie E, Alsharedi M. Checkpoint inhibitors: the new treatmentparadigm for urothelial bladder cancer. Med Oncol 2017;34:170.

30. Pardoll DM. The blockade of immune checkpoints in cancer immuno-therapy. Nat Rev Cancer 2012;12:252–64.

31. Wolchok JD, Hoos A, O'Day S, Weber JS, Hamid O, Lebb�e C, et al.Guidelines for the evaluation of immune therapy activity in solidtumors: immune-related response criteria. Clin Cancer Res 2009;15:7412–20.

32. Xi L, Pham TH, Payabyab EC, Sherry RM, Rosenberg SA, Raffeld M, et al.Circulating tumor DNA as an early indicator of response to t-celltransfer immunotherapy in metastatic melanoma. Clin Cancer Res2016;22:5480–6.

33. Lee JH, Long GV, Boyd S, Lo S, Menzies AM, Tembe V, et al. Circulatingtumour DNA predicts response to anti-PD-1 antibodies in metastaticmelanoma. Ann Oncol 2017;28:1130–6.

34. Kitahara M, Hazama S, Tsunedomi R, Takenouchi H, Kanekiyo S, Inoue Y,et al. Prediction of the efficacy of immunotherapy by measuring theintegrity of cell-free DNA in plasma in colorectal cancer. Cancer Sci2016;107:1825–9.

35. Goldberg SB, Narayan A, Kole AJ, Decker RH, Teysir J, Carriero NJ, et al.Early assessment of lung cancer immunotherapy response via circulatingtumor DNA. Clin Cancer Res 2018 April 15 [Epub ahead of print].

36. Powles T,O'Donnell PH,Massard C, ArkenauHT, Friedlander TW,HoimesCJ, et al. Efficacy and safety of durvalumab in locally advancedormetastaticurothelial carcinoma: updated results from a phase 1/2 open-label study.JAMA Oncol 2017;3:e172411.

37. Garassino MC, Cho BC, Kim JH, Mazieres J, Vansteenkiste J, Lena H, et al.Durvalumab as third-line or later treatment for advanced non-small-celllung cancer (ATLANTIC): an open-label, single-arm, phase 2 study. LancetOncol 2018;19:521–536.

38. Rebelatto MC, Midha A, Mistry A, Sabalos C, Schecter N, Li X, et al.Development of a programmed cell death ligand-1 immunohistochemis-try assay validated for analysis of non-small cell lung cancerand head andneck squamous cell carcinoma. Diag Pathol 2016;11:95.

39. Pal SK, Brooks C, Chudova D, Odegaard J, Gandara DR, Mack P, et al.Clinical implications of genomic variants identified in over 30,000advanced-stage cancer patients by next-generation sequencing of circulat-ing tumor DNA. Ann Oncol 2017;28:v595–v604.

40. Patel SP, Kurzrock R. PD-L1 expression as a predictive biomarker in cancerimmunotherapy. Mol Cancer Ther 2015;14:847–56.

41. Kim HS, Kim SM, Kim H, Pyo KH, Sun JM, Ahn MJ, et al. Phase II clinicaland exploratory biomarker study of dacomitinib in recurrent and/ormetastatic esophageal squamous cell carcinoma. Oncotarget 2015;6:44971–84.

42. Miron B, Peled N, Tarcic G, Barbash Z, Edelheit O, Vidne M, et al.Functional profiling of oncogenic mutations in lung cancer patients(NCT02274025) - interim results. Ann Oncol 2016;27:1189P.

43. Barretina J, Caponigro G, Stransky N, Venkatesan K, Margolin AA, Kim S,et al. The Cancer Cell Line Encyclopedia enables predictive modelling ofanticancer drug sensitivity. Nature 2012;483:603–7.






44. Forbes SA, Beare D, Gunasekaran P, Leung K, Bindal N, Boutselakis H, et al.COSMIC: exploring theworld's knowledge of somaticmutations inhumancancer. Nucleic Acids Res 2015;43:D805–11.

45. Chaudhuri A, Chabon JJ, Lovejoy AF, Newman AM, Stehr H, Azad TD.Analysis of circulating tumor DNA in localized lung cancer for detection ofmolecular residual disease and personalization of adjuvant strategies.J Clin Oncol 35:15s, 2017 (suppl; abstr 8519).

46. Borghaei H, Paz-Ares L, Horn L, Spigel DR, Steins M, Ready NE, et al.Nivolumab versus docetaxel in advanced nonsquamous non-small-celllung cancer. N Engl J Med 2015;373:1627–39.

47. Fehrenbacher L, Spira A, Ballinger M, Kowanetz M, Vansteenkiste J,Mazieres J, et al. Atezolizumab versus docetaxel for patients withpreviously treated non-small-cell lung cancer (POPLAR): a multicentre,

open-label, phase 2 randomised controlled trial. Lancet 2016;387:1837–46.

48. HellmannMD, RizviNA,Goldman JW,Gettinger SN, BorghaeiH, BrahmerJR, et al. Nivolumab plus ipilimumab as first-line treatment for advancednon-small-cell lung cancer (CheckMate 012): results of an open-label,phase 1, multicohort study. Lancet Oncol 2017;18:31–41.

49. Herbst RS, Baas P, Kim DW, Felip E, P�erez-Gracia JL, Han JY, et al.Pembrolizumab versus docetaxel for previously treated, PD-L1-positive,advanced non-small-cell lung cancer (KEYNOTE-010): a randomisedcontrolled trial. Lancet 2016;387:1540–50.

50. Lee CK, Man J, Lord S, Links M, Gebski V, Mok T, et al. Checkpointinhibitors in metastatic EGFR-mutated non-small cell lung cancer - ameta-analysis. J Thorac Oncol 2017;12:403–7.


Raja et al.




2018;24:6212-6222. Published OnlineFirst August 9, 2018.Clin Cancer Res Rajiv Raja, Michael Kuziora, Philip Z. Brohawn, et al. and Bladder Cancer Treated with DurvalumabEarly Reduction in ctDNA Predicts Survival in Patients with Lung

Updated version

10.1158/1078-0432.CCR-18-0386doi:

Access the most recent version of this article at:

Material

Supplementary

http://clincancerres.aacrjournals.org/content/suppl/2018/08/09/1078-0432.CCR-18-0386.DC1

Access the most recent supplemental material at:

Cited articles

http://clincancerres.aacrjournals.org/content/24/24/6212.full#ref-list-1

This article cites 48 articles, 11 of which you can access for free at:

Citing articles

http://clincancerres.aacrjournals.org/content/24/24/6212.full#related-urls

This article has been cited by 4 HighWire-hosted articles. Access the articles at:

E-mail alerts related to this article or journal.Sign up to receive free email-alerts

Subscriptions

Reprints and

[email protected]

To order reprints of this article or to subscribe to the journal, contact the AACR Publications Department at

Permissions

Rightslink site. Click on "Request Permissions" which will take you to the Copyright Clearance Center's (CCC)

.http://clincancerres.aacrjournals.org/content/24/24/6212To request permission to re-use all or part of this article, use this link



http://clincancerres.aacrjournals.org/lookup/doi/10.1158/1078-0432.CCR-18-0386

http://clincancerres.aacrjournals.org/content/suppl/2018/08/09/1078-0432.CCR-18-0386.DC1

http://clincancerres.aacrjournals.org/content/24/24/6212.full#ref-list-1

http://clincancerres.aacrjournals.org/content/24/24/6212.full#related-urls

http://clincancerres.aacrjournals.org/cgi/alerts

mailto:[email protected]

http://clincancerres.aacrjournals.org/content/24/24/6212


Documents

Early Reduction in ctDNA Predicts Survival in Patients ... · therapy has been reported in colorectal cancer (34). In a study of 49 patients with NSCLC, targeted next-generation sequencing