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Translational Cancer Mechanisms and Therapy

Low-pass Whole-genome Sequencing ofCirculating Cell-free DNA Demonstrates DynamicChanges in Genomic Copy Number in a SquamousLung Cancer Clinical CohortXiaoji Chen1, Ching-Wei Chang2, Jill M. Spoerke1, Kathryn E. Yoh1, Vidushi Kapoor1,Charles Baudo1, Junko Aimi1, Mamie Yu3, May M.Y. Liang-Chu3, Rebecca Suttmann1,Ling-Yuh Huw1, Steven Gendreau1, Craig Cummings1, and Mark R. Lackner1

Abstract

Purpose:We developed amethod tomonitor copy numbervariations (CNV) in plasma cell-free DNA (cfDNA) frompatients withmetastatic squamous non–small cell lung cancer(NSCLC). We aimed to explore the association betweentumor-derived cfDNA and clinical outcomes, and soughtCNVs that may suggest potential resistance mechanisms.

ExperimentalDesign: Sensitivity and specificity of low-passwhole-genome sequencing (LP-WGS) were first determinedusing cell line DNA and cfDNA. LP-WGS was performed onbaseline and longitudinal cfDNA of 152 patients with squa-mous NSCLC treated with chemotherapy, or in combinationwith pictilisib, a pan-PI3K inhibitor. cfDNA tumor fractionand detected CNVs were analyzed in association with clinicaloutcomes.

Results: LP-WGS successfully detected CNVs in cfDNAwith tumor fraction �10%, which represented approximate-ly 30% of the first-line NSCLC patients in this study. The

most frequent CNVs were gains in chromosome 3q, whichharbors the PIK3CA and SOX2 oncogenes. The CNV land-scape in cfDNA with a high tumor fraction generallymatched that of corresponding tumor tissue. Tumor fractionin cfDNA was dynamic during treatment, and increases intumor fraction and corresponding CNVs could be detectedbefore radiographic progression in 7 of 12 patients. Recur-rent CNVs, such as MYC amplification, were enriched incfDNA from posttreatment samples compared with thebaseline, suggesting a potential resistance mechanism topictilisib.

Conclusions: LP-WGS offers an unbiased and high-throughput way to investigate CNVs and tumor fraction incfDNA of patients with cancer. It may also be valuable formonitoring treatment response, detecting disease progressionearly, and identifying emergent clones associated with thera-peutic resistance.

IntroductionNon–small cell lung cancer (NSCLC) accounts for 85% of lung

cancers andhas apredicted 5-year survival of 15.9%(1, 2).NSCLCis a heterogeneous disease and consists of two major histologicsubtypes: adenocarcinoma (50%) and squamous cell carcinoma(40%; ref. 2) that are characterized by distinct genomic events. Inaddition to TP53 mutations, the adenocarcinoma subtype exhi-bits frequentmutational alterations inEGFR, KRAS, and LKB1 (1),

whereas the squamous subtype is characterized by frequentamplifications in PIK3CA, SOX2, MET, and EGFR, as well as lossof the tumor suppressor PTEN (3).

The frequent activation of the PI3K signaling pathway inNSCLCprovided a rationale for testing agents targeting PI3K, AKT, andmTOR in clinical trials of patients with NSCLC. Pictilisib (GDC-0941) is a potent, selective inhibitor of PI3K that has demonstratedstrong antitumor activity in preclinical models and was shown inphase I studies tohave adose-proportionalpharmacokinetic profileand on-target pharmacodynamic activity at the recommendedphase II dose(4), andcouldbesafely combinedwithchemotherapyin NSCLC (5). The phase II FIGARO study (GO27912,NCT01493843) was designed to evaluate the efficacy and safetyof pictilisib plus carboplatin and paclitaxel, with or without bev-acizumab in NSCLC (6). The FIGARO trial did not demonstratesubstantial benefit from the addition of pictilisib to standardchemotherapy regimen, but biological samples collected frompatients in this study offer a rich repository for biomarker studies.In particular, the collectionof serial plasma samples at baseline andover the course of treatment was implemented to enable theassessment of baseline clinical features and the effects of treatmenton tumor-derived circulating cell-free DNA (cfDNA) dynamics.

cfDNA can be released from healthy tissues, hematopoieticcells, or tumor cells through apoptosis or necrosis into theblood stream, and previous studies have demonstrated the

1Department of Oncology Biomarker Development, Genentech, South SanFrancisco, California. 2Department of Biostatistics, Genentech, South SanFrancisco, California. 3Department of Discovery Oncology, Genentech, SouthSan Francisco, California.

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

Current address for M.R. Lackner: IDEAYA Biosciences, South San Francisco, CA;and current address for X. Chen: GRAIL, Menlo Park, CA.

Corresponding Authors: Mark R. Lackner, Genentech, Inc., 1 DNA Way, SouthSan Francisco, CA 94080. Phone: 650-467-1846; Fax: 650-467-7571; E-mail:[email protected]; and Xiaoji Chen, Phone: 650-467-0834; Fax: 650-225-1998; E-mail: [email protected]

doi: 10.1158/1078-0432.CCR-18-1593

�2019 American Association for Cancer Research.

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presence of elevated cfDNA levels in the plasma of patients withlate-stage cancer (7, 8). A number of recent reports havesuggested that tumor-specific genetic alterations, such as sin-gle-nucleotide variants (SNV) and copy number variations(CNV) can been detected in cfDNA of patients with can-cer (9–14), and also that overall tumor-derived cfDNA levelsmay be correlated with tumor burden (14–16). Compared withtissue biopsies, cfDNA collection and analysis have a number ofbenefits that make them ideal for assessing cancer biomarkersand studying drug resistance. First, cfDNA collection is mini-mally invasive, and can be easily obtained by blood draw,allowing for serial sampling. Second, cfDNA has a short half-life of 16 minutes to 2.5 hours; therefore, tumor status cantheoretically be captured in real time (17). These features allowfor continuous monitoring of tumor evolution. Furthermore,unlike a single tissue biopsy, cfDNA is derived from differenttumor sites, thus providing a comprehensive reflection ofintrapatient tumor heterogeneity (18).

CNVs play a critical role in cancer biology. However, tradi-tional CNV analyses of cfDNA using droplet digital PCR(ddPCR) or qRT-PCR can only assess a small number of genesdue to the low abundance of cfDNA in the majority of patientsamples. Here, we explored the use of next-generation sequenc-ing (NGS) methodologies, which might provide a more effi-cient, cost-effective, and high-throughput way to study CNVsacross the genome in cfDNA. Over the past 15 years, NGStechnology has evolved rapidly and is playing an increasinglyimportant role in cancer research and clinical studies (19). It hasbeen shown that low-pass whole-genome sequencing (LP-WGS)was capable of identifying both focal and broad CNVs in cfDNAfrom patients with cancer (15, 20).

In this study, we analyzed CNVs and tumor fraction in cfDNAfrom the patients with squamous NSCLC in the FIGARO trialusing LP-WGS. We found a general concordance between tissueand cfDNA samples, and showed that dynamic changes in tumor-derived cfDNA levels occur over treatment, with dramatic short-term posttreatment decreases in almost all patients, followed by alater rise that in some cases occurs before radiographic progres-sion. In addition, we identified candidate resistance mechanisms,

including c-Myc amplification, in clones that emerged after long-term treatment.

Materials and MethodsStudy design

FIGARO (GO27912, NCT01493843) is a phase II double-blind, randomized trial of the PI3K inhibitor pictilisib (GDC-0941) in combinationwith carboplatin andpaclitaxel (squamousNSCLC) and bevacizumab (nonsquamous NSCLC) in patientswith metastatic NSCLC (6). FERGI (GO00769, NCT01437566)is a phase II double-blind, randomized trial of pictilisib incombination with fulvestrant in patients with estrogen recep-tor–positive, HER2-negative, and aromatase inhibitor–resistantmetastatic breast cancer. These studies were performed in accor-dance with the FDA regulations, the International Council onHarmonisation E6 Guideline for Good Clinical Practice, and theDeclaration of Helsinki. Approval for the protocol, supportinginformation, and patient material was obtained from the Insti-tutional Review Board or ethics committee at each site. Informedconsent was obtained from all patients.

SamplesApproximately 10 mL of blood was collected at baseline, on

treatment at 6-week intervals, and at the end of treatment(EOT) in labeled Vacutainer K2EDTA tubes (BD Biosciences)for patients enrolled in the FIGARO study. Blood samples weremixed thoroughly by slowly inverting the collection tubes atleast 10 times. Samples were kept chilled and processed within2 hours of collection. Plasma was obtained by centrifugingwhole blood at approximately 2,000 � g at 2–8�C for 15minutes. Plasma was then transferred to labeled polypropylenescrew-cap cryovials without disturbing the buffy coat layer, andfrozen at �70 to �80�C until processing. DNA was isolatedusing the QIAamp Circulating Nucleic Acid Kit (QIAGEN).DNA yield was determined by ddPCR targeting EFTUD2 andTRAK2 (ddPCR, Bio-Rad), with a range from 4 to 8,000 ng andmedian yield of 59 ng. In addition, genomic DNA was extractedfrom available formalin-fixed paraffin-embedded (FFPE) tumorsections using the QIAamp DNA FFPE Tissue Kit (QIAGEN) asdescribed previously (21). DNA from FFPE tissue was fragmen-ted with Covaris S220, according to the manufacturer's instruc-tions to around 150 bp with size confirmed by Bioanalyzer(Agilent).

As a reference standard, blood was collected from 17 healthydonors through the Genentech Employee Donation Program.Peripheral bloodmononuclear cells (PBMC) were obtained fromthe buffy coat by centrifuging the whole blood at 1,000 � g atroom temperature for 15minutes. Red blood cells were lysedwithammonium-chloride-potassium lysis buffer at room temperaturefor 20 minutes, followed by PBS wash. Genomic DNA fromPBMCs was isolated using the AllPrep DNA/RNA kit (QIAGEN),and fragmented with Covaris S220 to around 150 bp. Fragmentsize was confirmed by Bioanalyzer (Agilent).

All cell lines were obtained from the Genentech cellbank (22) and cell line identities were confirmed by SNP geno-typing.Mycoplasma test was performed using theMycoAlertMyco-plasma Detection Kit (Lonza) or the MycoSensor QPCR Assay Kit(Agilent). Cells were cultured with RPMI1640 medium supple-mented with 10% FBS, 2 mmol/L L-glutamine, and penicillin/streptomycin. Genomic DNA was isolated by the QIAamp DNA

Translational Relevance

Cell-free DNA (cfDNA) released by tumor cells into theblood stream provides a noninvasive way to continuouslystudy genetic alterations in patients with cancer. Here, we usedlow-passwhole-genome sequencing (LP-WGS)of cfDNA froma cohort of patients with non–small cell lung cancer (NSCLC)enrolled in a phase II clinical trial of the PI3K inhibitor,pictilisib. We show that tumor DNA fraction in the blood canbe estimated fromLP-WGS analysis and that it shows dynamicchanges over time and treatment. In some patients, a rise intumor-derived DNA was observed before clinical progression.Moreover, by comparing baseline and posttreatment cfDNA,we identifiedCNVs andmutations thatmay be associatedwithresistance to PI3K inhibition. These results hold promise forcomprehensive, real-time monitoring of tumor dynamics inpatients with cancer, the potential to develop approaches toidentify resistance mechanisms, and to inform rational treat-ment strategies.

ctDNA Analysis in Metastatic Lung Cancer Patients

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Mini kit (QIAGEN) and fragmented with Covaris S220 to around150 bp. Fragment size was confirmed by TapeStation (Agilent).

LP-WGSSequencing libraries were prepared using 4–50 ng DNA by the

ThruPLEX Tag-Seq Kit (Rubicon Genomics) and purified byAgencourt AMPure XP beads (Beckman Coulter). Libraries werequantified by the KAPA Library Quantification Kit (Kapa Biosys-tems) and the size was confirmed using Bioanalyzer (Agilent).Sequencing libraries were pooled at equal amount. WGS at anaverage coverage of 0.5� was performed on Illumina NextSequsing 2 � 75 bp paired-end sequencing.

Sequencing data analysisUnique molecular identifiers (UMI) were trimmed from

sequencing reads in PartekFlow (Partek, Inc). High-quality andnonduplicate (�85%) reads were aligned to human referencegenome GRCh37. Copy number analysis was performed usingNexus CopyNumber 9.0 (BioDiscovery). First, a normal referencewas created from LP-WGS of PBMC-derived genomic DNA of 17healthy donors sequenced at 0.5� coverage using the BAMMulti-Scale Reference Builder module. An adjustable dynamic bin sizewas applied, with 500 target reads per bin. Reads were correctedfor GC content. Log2 ratios of the bin groups between the sampleand the normal reference were calculated. CNVs were called usingthe Hidden Markov Model–based fast adaptive states segmenta-tion technique (SNP-FASST2) algorithm. Default cutoffs wereapplied with high copy number calls at 0.6, copy number gainat 0.18, copy number loss at�0.18, and high copy number loss at�1.0. GISTIC (23) analysis was performed in Nexus to determinethe CNVs of statistically significant high frequency. Tumor frac-tion of cfDNA and ploidy were estimated using the ichorCNAsoftware (15).

Circulating tumor cell enumerationTen milliliters of blood was drawn from patients into CellSave

preservative tubes (Janssen Diagnostics) using standard veni-puncture techniques. Sampleswere shipped directly toGenentechfor circulating tumor cell (CTC) processing and 7.5mLwas testedfor CTC enumeration using the CellSearch System according tomanufacturer's instructions. Only samples processed within 96hours of blood draw were included in the analysis. cfDNA wasisolated from the residual plasma for a subset of samples from theFERGI study.

Exome sequencingWGS libraries were first prepared using 30–50 ng cfDNA by the

ThruPLEX Tag-Seq Kit (Rubicon Genomics), which containsUMIs, and quantified by the KAPA Library Quantification Kit(Kapa Biosystems). Hybridization-based exome capture was per-formed with 750 ng of each library mixed with xGen UniversalBlocking Oligos (Integrated DNA Technologies) using the Sur-eSelectXT Focused Exome baits (Agilent Technologies), whichcovers 12 Mb of the human genome and over 6,000 genes.Postcapture libraries were amplified according to manufacturer'sinstructions, and sequenced on Illumina NextSeq with an averageunique molecular coverage of 350�. UMI deduplication wasconducted and deduplicated reads were aligned to human refer-ence genome GRCh37 in PartekFlow 6.0.17.0602 (Partek, Inc).Picard CollectHsMetrics (http://broadinstitute.github.io/picard)was used to assess target coverage. Baseline and EOT cfDNA

samples of the same patient were analyzed as a pair. Strelka1.0.14 somatic variant caller (24) was used to identify SNVs andsmall indels that were significantly increased in the EOT cfDNA.Default parameters of Strelka were applied, except for the isSkip-DepthFilters, which was set to 1 to skip depth filtration. VariantEffect Predictor (VEP, https://www.ensembl.org/vep; ref. 25) wasused to annotate variants.

SNP genotypingHigh-throughput SNP genotyping was performed for cfDNA

and tumor tissue DNA using Fluidigm multiplexed assays asdescribed previously (22). Forty-eight SNPs that were analyzedare as follows: rs11746396, rs16928965, rs2172614, rs10050093,rs10828176, rs16888998, rs16999576, rs1912640, rs2355988,rs3125842, rs10018359, rs10410468, rs10834627, rs11083145,rs11100847, rs11638893, rs12537, rs1956898, rs2069492,rs10740186, rs12486048, rs13032222, rs1635191, rs17174920,rs2590442, rs2714679, rs2928432, rs2999156, rs10461909,rs11180435, rs1784232, rs3783412, rs10885378, rs1726254,rs2391691, rs3739422, rs10108245, rs1425916, rs1325922,rs1709795, rs1934395, rs2280916, rs2563263, rs10755578,rs1529192, rs2927899, rs2848745, and rs10977980.

Statistical analysisThe Catalogue Of Somatic Mutations In Cancer (COSMIC;

ref. 26) genes with CNVs not detected in cfDNA at the baseline,but in the matched last time point were extracted, and the log2ratio of the last time point over the baseline was calculated. CNVgains in the last time point with log2 ratio >0.2 and CNV losses inthe last time point with log2 ratio <�0.2 compared with thebaselinewere identified. Linear regressionwas applied to evaluatethe relationship between cfDNA tumor fraction and baselineclinical characteristics. The tumor fraction in continuous scalewas treated as response variable. Simple linear regression wasapplied to baseline clinical characteristics in continuous scale, andlinear regression with dummy variable was applied to categoricalbaseline clinical characteristics. Pearson correlation values for theNSCLC plasma and tumor pairs were calculated using the unionof all genes showing copy number gain (log2 ratio�0.18 over thehealthy reference) and copy number loss (log2 ratio��0.18 overthe healthy reference). All analyses were carried out in R 3.4.2.

ResultsEvaluation of LP-WGS to detect CNVs in cell line DNA andpatient cfDNA

To determine the sensitivity of detecting tumor-derivedDNA incfDNA from plasma by LP-WGS, we first performed a spike-inexperiment by mixing genomic DNA from four lung cancer celllines having known genomic profiles with DNA from healthyPBMCs at different ratios. Tumor fraction was estimated from LP-WGS (15). For the LXFL-529 cell line, which has the highestploidy, the estimated tumor fraction matched the spike-in tumorfraction when it was�10%. For other cell lines with lower ploidy,the estimated tumor fraction was somewhat lower than the actualspike-in levels (Supplementary Fig. S1A and S1B). The identifiedCNV fold change, normalized to the healthy reference, alsoincreased with tumor fraction (Supplementary Fig. S1C andS1D). We also hypothesized that the tumor fraction estimationmay be more accurate for cell lines with a higher degree andproportion of copy number gains, as gains are easier to detect with

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LP-WGS. Median copy number gain and percent genome ampli-fied was highest for LXFL-529 and lowest for A549 (Supplemen-tary Fig. S1E), which is consistent with the Wilcoxon signed-ranktest P values (Supplementary Fig. S1A).

Next, to determine the feasibility of LP-WGS to detect CNVs incfDNA samples from patients with cancer, we validated the assayusing cfDNA from 12 patients with metastatic breast cancerenrolled in the FERGI trial (27) where the copy number statusfor 35 genes in tumor tissue, which were not contemporaneouslycollected with cfDNA, had previously been analyzed byqPCR (28). Tumor tissue was collected as long as 15 years priorto plasma collection. In 6 patients with estimated tumor fraction>5%, we successfully detected known copy number gains incfDNA at comparable levels to tumor tissue (Fig.1; Supplemen-tary Fig. S2A and S2B). PIK3CA is an early driver mutation inbreast cancer and thus may serve as a surrogate marker for theoverall quantity of circulating tumorDNA.We found that somaticallele frequency for PIK3CA mutations in cfDNA determined bydigital PCR (29) correlatedwith tumor fraction determined by LP-WGS (Supplementary Fig. S2C). We also compared tumor frac-

tion to the number of CTCs isolated from the same time points. Ingeneral, the largest number of CTCs was found in plasma withhigh tumor fraction in the FERGI patients (Supplementary Fig.S2D). Moreover, we found that higher WGS coverage resulted inbetter resolution of the CNV landscape and lower background atindividual gene level, although 0.5� coverage provides an appro-priate balance between sequencing depth and ability to detectalterations (Fig. 2A; Supplementary Fig. S3). cfDNA tumor frac-tions estimated from WGS were similar regardless of sequencingcoverage (Fig. 2B).

Comparison of CNV landscapes in cfDNA and matched tumortissue

To evaluate how well the CNV landscape in cfDNA reflects thatof matched tumor tissue, we performed LP-WGS for baselinecfDNA and matched archival tissue DNA from 12 FIGAROpatients with squamous NSCLC (Fig. 3A; Supplementary FigS4A). CNVs were identified in all tissue samples by LP-WGS. Inpatients with high tumor fraction (>25%) in the plasma, CNVpatterns in cfDNA and tissue generally showed high concordance,

Figure 1.

Detection of CNVs in cfDNA by LP-WGS. Representative whole-genome views of a healthy sample(A), cfDNA sample frommetastaticbreast cancer (mBC) patient 6101with known copy number gains atZNF703, PAK1, RSF1, and CCND1 inthe tumor tissue (B), and cfDNAsample frommBC patient 1753 withknown copy number gains atMYCand IGF1R (C). D, Representativechromosomal views of a cfDNAsample frommBC patient 5955 withknown copy number gains at IGF1R,MDM2, andMYC in the tumor tissue.E, Comparisons of gene copynumbers detected in tissue by qPCRand in cfDNA by LP-WGS. Tumortissue collected 13 years (6101), 7years (1753), and 16 months (5955)prior to plasma collection.






with Pearson correlation values greater than 0.75 (SupplementaryFig S4B). Pearson correlation values were low (0.3–0.6) whencfDNA tumor fraction was less than 25%, and below 0.2 whencfDNA tumor fraction was 0%. Tumor samples were collectedwithin 60 days of the plasma collection with the exception toplasma tissue pair three collected 10 months prior to plasmacollection.

In one patient (3101) with 38% tumor fraction in the plasma,the cfDNA CNV pattern was found to be quite distinct from thematched tissue DNA pattern (Pearson correlation 0.28; Supple-mentary Fig. S4C). This plasma was obtained at baseline, 25 daysafter tissue collection. SNP array analysis confirmed that theplasma and tumor samples were from the same patient (Supple-mentary Fig. S4D and S4E), suggesting that the discordance waslikely caused by tumor heterogeneity as cfDNA could derive frommultiple metastases.

At the level of individual genes, PIK3CA gain and PTEN lossfound in cfDNA of FIGARO patients by LP-WGS were also

compared with available dCISH and IHC results from tissue,respectively. Most PIK3CA gains and some PTEN losses couldbe detected in cfDNA with tumor fraction greater than 10%(Fig. 3B). Specifically, in 17 of 19 dCISH tissue-positivepatients, PIK3CA gain was also detected in cfDNA by LP-WGSfor samples with greater than 10% tumor fraction (Fig. 3C).However, only 7 of 16 of the patients with tissue IHC-detectedPTEN loss (H score < 100) had LP-WGS detectable PTEN loss incfDNA. This observation is likely due to the lower dynamicrange (a maximum of two copies) for loss, as compared withgain for a gene, suggesting that copy number loss analysesmay require a higher tumor fraction for detection (Fig. 3B).Consistent with this hypothesis, PIK3CA gain was detected in4 patients' cfDNA where PTEN loss was missed. In 6 patients,CNVs in PIK3CA or PTEN were only detected in cfDNA, whichmay be due to spatial/temporal tumor heterogeneity, posttran-scriptional regulation or differences in the sensitivity, andperformance of the assay formats.

Figure 2.

Comparison ofWGS at differentsequencing coverage. A, CNVs onchromosome 17 of cfDNA frommBCpatient 7452. Arrows indicateERBB2, which has known copynumber gain in the correspondingtumor tissue. B, Estimated tumorfraction fromWGS of cfDNA from 3mBC patients.

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Baseline CNV landscape of cfDNA in patients with NSCLCNext, we used LP-WGS to analyze cfDNA fromplasma collected

at baseline from 225 patients with metastatic NSCLC enrolled inthe FIGARO trial, 152 with squamous and 73 with nonsquamoushistology (Supplementary Fig S5A and S5B). Distinct CNV land-scapes were observed in patients with squamous versus nonsqua-mous histology (Fig. 4A; Supplementary Fig. S5C). Tumor frac-tion in cfDNA ranged from0% to 80% (Supplementary Fig. S5D).Twenty-eight percent (43/152) of the squamous patient cfDNAsamples, and 16% (12/73) of the nonsquamous patient cfDNAsamples had tumor fraction 10% or greater. Consistent withprevious findings (30, 31), the most frequent CNVs identified inpatients with squamous NSCLC were copy number gains inchromosome 3q encompassing oncogenes such as PIK3CA andSOX2 (q < 10�10 from GISTIC analysis; Fig. 4A). The 3q copynumber gains were found at a much lower frequency in non-squamous patients (Supplementary Fig. S5C).

In addition, we analyzed a number of individual genes whoseCNVs have been shown to play key roles in NSCLC (1, 30, 31)

from LP-WGS data (Fig. 4A; Supplementary Fig. S5E). For exam-ple,MYC gain was found in the baseline plasma samples of 15%patients, loss of the tumor suppressor RASFF1 was detected in12% of the patients, and PTEN loss was detected in 7% of thepatients. Most CNVs were found in samples with tumor fraction�10%.

Tumor fraction �10% in baseline cfDNA was prognostic foroverall survival

To evaluate the prognostic role of cfDNA tumor fraction,patients from the pictilisib and control arms of the squamousstudy were pooled as there was no significant difference inprogression-free survival (PFS) and overall survival (OS) betweenthe two arms (6), and included 107 patients (Supplementary FigS5A). Similarly, there was no significant difference in PFS and OSfor the nonsquamous study (6), but only the pictilisib arm wasincluded in the analysis due to limited control arm plasmasamples, (Supplementary Fig S5B; 72 patients). We found thattumor fraction �10% in baseline cfDNA was a poor prognostic

Figure 3.

Comparison of CNV landscapes intumor tissue and cfDNA of theFIGARO patients with squamousNSCLC. A, Representative LP-WGSprofiles of archival tissue (T1-T5)and matched baseline cfDNA (P1-P5). Blue, copy number gain; red,copy number loss. Bar graph showsestimated tumor fraction for eachsample with dashed line at 10%. B,Tumor fraction in cfDNAwherePIK3CA gain and PTEN loss wasdetected versus not detected byLP-WGS. Median tumor fraction ofeach group was indicated. C,Overlap of PIK3CA gain and PTENloss identified in cfDNA by LP-WGSand in tissue by dCISH and IHC,respectively. Samples with cfDNAtumor fraction�10%.






marker for OS but not for PFS for both squamous (Fig. 4B and C)and nonsquamous patients (Supplementary Fig. S6A and S6B).The concentration of total cfDNA in baseline plasma was notassociated with survival (Supplementary Fig. S6C and S6D). Inaddition, we analyzed the correlation between tumor fraction inbaseline cfDNA and other clinical characteristics in the squamouspopulation (Supplementary Fig. S6E). Two factors related tosurvival—the number of days patients remained in the trial andwhether treatment was discontinued due to patients' death—were significantly correlated with cfDNA tumor fraction at base-line (P < 0.05). Other characteristics such as age, race, sex, andbodymass index, were not correlated with cfDNA tumor fraction.

Dynamic changes in cfDNA tumor fraction during treatmentNext, we evaluated the ability of LP-WGS tomonitor changes in

cfDNA tumor fraction andCNV landscape during treatment. First,18 patientswith tumor fraction�10%at baseline andwith at leasttwo longitudinal plasma samples available were analyzed by LP-WGS (Supplementary Fig. S7A). We found that tumor fraction inthe plasma decreased to <10% after the first two cycles of treat-ments for the majority of the patients. At later time points, tumorfraction rebounded to above 10% for all but 5 patients. Three ofthese 5 patients experienced disease progression at day 254,whereas the remaining 2 patients had stable disease at their lasttumor assessment at data cutoff (day 85 and 296). Of the 13patients with tumor fraction rebound, 12 experienced diseaseprogression with a median PFS of 127 days (range from 126–305

days), and one had stable disease at their last tumor assessment atdata cutoff (day 85) and death was reported on day 163. Inaddition, 32 patients had tumor fraction <10% at baselineand all but 6 remained below 10% at the EOT (SupplementaryFig. S7B). For the 12patientswith tumor fraction above 10%at theEOTanddata available for at least twoon-treatment timepoints, arise in cfDNA tumor fractionwas observed before (n¼ 7) or at theday of (n ¼ 3) clinical progression (Supplementary Fig. S7C).Tumor fraction increased to over 10% up to 93 days beforeprogression. In the remaining 2 patients, tumor fraction reboundwas detected after progression (Supplementary Fig. S7C).

Identification of potential resistance mechanisms to PI3Kinhibition

To identify potential resistance mechanisms to PI3 kinaseinhibition in patients with squamous NSCLC, we investigatedCNVs that appeared late in the pictilisib-treated patients, but werenot detected at baseline. To do this, we comparedCNV landscapesof baseline versus matched EOT cfDNA samples in 8 patients andbaseline versus matched last time points that were over 30 weeksin 3patients.We required that all patients hadhigh tumor fractionin both time points. To ensure the CNVs identified are pictilisibspecific, we performed the same comparisons for patients treatedwith chemotherapy only (n¼ 6).We found recurring CNVs in thecfDNA obtained late in the treatment in pictilisib-treated patients(Fig. 5A), including copy number gains of MYC, NDRG1, andEXT1 on chromosome 8q24, copy number gains of SRSF2 and

Figure 4.

CNVs and tumor fraction of baselinecfDNA in patients with squamousNSCLC. A,Whole-genome CNVlandscape of 43 cfDNA samples withtumor fraction�10%. Top trackshows CNV regions of significantlyhigh frequency (q < 0.05). Peakswithin each region was highlighted.Bottom tracks show the CNV patternof each sample. Heatmap densityrepresents CNV log2 ratios betweencfDNA sample and the normalreference, ranging between��0.5 to�0.5. Genes with CNVs known to beassociated with NSCLC wereannotated. Blue, copy number gain;red, copy number loss. OS (B) andPFS (C) of patients with squamousNSCLC with baseline cfDNA tumorfraction�10% versus <10%.

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PRKAR1A on chromosome 17q25 and 17q24, respectively, andcopy number losses of GOPC and ROS1 on chromosome 6q22.PIK3CA gain was detected in the last time point of 2 pictilisibpatients but also 1 chemotherapy-only patient. A number ofadditional genes were also identified and varied in differentpatients (Supplementary Fig. S8A). For example, in patient1306, in addition to the same CNVs detected at baseline, newCNVs emerged at EOT in chromosomes 2, 6, and 8, includingelevation of theMYC locus on chromosome 8q (Fig. 5B), suggest-ing that this might be a newly acquired CNV or an existingresistant clone that expanded during treatment.

To further explore resistance mechanisms to pictilisib, weanalyzed somatic SNVs by focused exome sequencing in 3patients who had very high tumor fraction (>25%) in bothbaseline and EOT cfDNA. We found genes that had significantlyhigher mutation allele frequency in EOT compared with thebaseline in 2 patients (Supplementary Fig. S8B). They are poten-tially involved in pictilisib resistance through various mechan-isms (Supplementary Fig. S8C), although prospective validationand larger datasets would be required to confirm these observa-tions. Overall the data provide proof of concept that LP-WGSmaybe an efficient method to identify samples with sufficient tumorfraction for exome or targeted sequencing, but further work isrequired to fully realize the potential of this approach.

DiscussioncfDNA was first discovered in human blood 70 years ago (17,

18). However, it was not until recently that it was widely used inclinical studies based on the development of sensitive and specificplatforms for genomic analysis. In 2016, the FDA granted the firstgenetic test in cfDNA on EGFR mutation as a companion diag-nostic for Tarceva in patients with NSCLC (32). Because of a greatexpansion in NGS technologies, cfDNA can be analyzed using

variousNGS approaches to address different biological questions.Here, we demonstrated the feasibility of using LP-WGS to detectCNVs in cfDNA and estimate tumor fraction in the plasma ofpatients with NSCLC receiving standard chemotherapy with orwithout the PI3K inhibitor, pictilisib. Our data show that squa-mous and nonsquamous subtypes ofNSCLCdisplay uniqueCNVlandscapes, and that in themajority of patients, tumor genomes incfDNA are similar to corresponding tumor tissue profiles.Although we acknowledge that plasma tumor fraction, tumorheterogeneity, and time between sample collection may play arole in plasma versus tissue discordance. Tumor fraction in cfDNAat baseline was associated with OS in patients with NSCLC, anddisplayed dynamic changes over time and treatment, whereasnovel CNVs that emerged later in treatment may indicate poten-tial resistance mechanisms to PI3K inhibition.

The LP-WGS approach has numerous applications in clinicalstudies. First, genome instability is one of the hallmarks ofcancer (33). CNV status has been found to correlate with clinicaloutcomes of primary breast cancer (34) and primary prostatecancer (35) patients and response to anti-CTLA-4 immunother-apy in melanoma (36). LP-WGS offers an unbiased and high-throughput way to study genome-wide copy number alterationsthat are cancer-related. Previously, LP-WGS has been used tostudy CNVs in cfDNA in multiple cancer types such as lungcancer, ovarian cancer, prostate cancer, and neuroblastoma (12,15, 37–39). Tumor-derived CNVs were detected in cfDNA usingthis approach and were concordant with that in tumor tissueDNA (38, 39). This unbiased approach is also valuable foridentifying novel CNVs as prognostic or predictive biomarkers.

In addition, tumor fraction in the plasma can be calculatedfrom cfDNA LP-WGS (15). Similarly to our findings, a recentstudy has shown that baseline cfDNA tumor fraction �10% wasassociated with poor metastatic survival in patients with meta-static triple-negative breast cancer (40). Moreover, it allows us to

Figure 5.

Identification of CNVs emerginglate in treatment.A, Recurring geneCNVs found in plasma samples fromthe last time point but not thebaseline, in patients treated withpictilisib plus chemotherapy but notchemotherapy alone. Log2 foldchange between CNVs in the lasttime point and the baseline for eachpatient was plotted. B, CNVlandscapes of plasma samplescollected at different time points(week 6, 12, 18, 24, EOT) frompatient 1306. COSMIC genes withCNVs not found in the baseline butin EOT were annotated. Blue, copynumber gain; red, copy numberloss.






continuously monitor the disease throughout and after treat-ments. Our study shows that an increase in tumor fraction in theplasma by LP-WGS likely reflects tumor progression. Because it istime- and cost-effective, LP-WGS can also be used as an initialquality control step to determine tumor fraction and select onlythe samples with high tumor fraction to perform target enrich-ment and deep sequencing (15, 20).

By comparing CNVs and SNVs in cfDNA collected at baselineversus late in treatment, we found genes that are potentiallyinvolved in pictilisib resistance. We found the evidence of focalamplification ofMYC, consistent with previous reports that MYCamplification confers resistance to pictilisib in preclinical mod-els (41) and that bromodomain and extra terminal domaininhibitors, JQ1 and MS417, which decrease MYC expression,sensitize resistant cell lines to pictilisib (42). Our group haspreviously shown that acquired PIK3CA amplification is associ-ated with preclinical resistance to PI3K inhibition by increasingsignaling through the pathway (43), and here we found theevidence of increases in PIK3CA copy number in posttreatmentsamples. We found evidence of novel resistance mechanisms aswell, although these findings require prospective validation andfunctional validation. For instance, EN2 expression may play arole in PI3K pathway activation and PTEN inhibition in bladdercancer cells (44).BRSK2 expressionmay activate Akt and thereforeincrease the survival of pancreatic ductal adenocarcinomacells (45). FOXC2 may enhance cancer cell proliferation byactivating the PI3K pathway, and has been implicated in cisplatinresistance of ovarian cancer cells and 5-fluorouracil resistance ofcolorectal cancer cells (46).

It should be noted that there are several limitations to ourtechnique. First, tumor-derived cfDNA in the blood stream canvary from 0.01% to 93% (18), depending on the size andshedding ability of tumors. In early-stage or low-shedding tumors,such as glioblastoma, it ismore difficult to identify tumor-specificgenetic alterations among a vast majority of normal cfDNAsamples. Several studies have indicated that there was a smalldifference in size between cfDNA derived from the tumor andnormal cells (47, 48). Performing a size-selection before sequenc-ing may help enrich for the proper circulating tumor DNA.Second, cfDNA is released by dead cells. Therefore, more cfDNAis likely to come from tumor cells that are sensitive to treatmentthan resistant cells.Our approachmaybebiased in that it does notcapture the quiescent "cancer stem cells" in cfDNA (49). Recentstudies have also foundDNAamongwithproteins andRNA in theexosome vesicles in the blood (50). As the exosome vesicles aresecreted by live cells, they may be more representative of the

tumor cells that survived particular therapies. Third, LP-WGS isnot a highly sensitive assay and requires a relatively high tumorfraction to detect tumor-derived alterations, as compared withdeep sequencing. If a patient sample has low tumor fraction, thereis the potential to miss true CNVs by LP-WGS. To enable moreaccurate early detection of tumor progression and reduce falsenegatives, we will utilize targeted deep sequencing with theincorporation of UMIs. Finally, we are also limited in the typeof statistical analyses that we could perform due to small samplesize in this phase II study and insufficient plasma sample collec-tion for a large number of patients, factors that should beconsidered in trial design and sample collection plans for futurestudies of this nature.

In summary, we monitored the CNV landscape in cfDNA ofsquamousNSCLC patients by LP-WGS and identified novel CNVspotentially conferring resistance to pictilisib. We also found thattumor fraction �10% was prognostic for patient OS. This workhas implications for clinical monitoring of disease, identificationof biomarkers, as well as resistance mechanisms to targetedtherapies.

Disclosure of Potential Conflicts of InterestC. Chang, J.M. Spoerke, and S. Gendreau hold ownership interest (including

patents) in Roche. C.A. Cummings is an employee of and holds ownershipinterest (including patents) in Roche. No potential conflicts of interest weredisclosed by the other authors.

Authors' ContributionsConception and design: X. Chen, M.R. LacknerDevelopment of methodology: X. Chen, R. Suttman, L.Y. HuwAcquisition of data (provided animals, acquired and managed patients,provided facilities, etc.): X. Chen, K. Yoh, J. Aimi, M. Yu, R. SuttmanAnalysis and interpretation of data (e.g., statistical analysis, biostatistics,computational analysis): X. Chen, C.-W. Chang, J.M. Spoerke, V. Kapoor,C.D. Baudo, M.M.Y. Liang-Chu, L.Y. Huw, S. Gendreau, C.A. Cummings,M.R. LacknerWriting, review, and/or revision of the manuscript: X. Chen, C.-W. Chang,J.M. Spoerke, K. Yoh, S. Gendreau, C.A. Cummings, M.R. LacknerAdministrative, technical, or material support (i.e., reporting or organizingdata, constructing databases): R. SuttmanStudy supervision: M.R. Lackner

The costs of publication of this articlewere defrayed in part 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 May 22, 2018; revised October 1, 2018; accepted January 3, 2019;published first January 7, 2019.

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2019;25:2254-2263. Published OnlineFirst January 7, 2019.Clin Cancer Res Xiaoji Chen, Ching-Wei Chang, Jill M. Spoerke, et al. Squamous Lung Cancer Clinical CohortDemonstrates Dynamic Changes in Genomic Copy Number in a Low-pass Whole-genome Sequencing of Circulating Cell-free DNA

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