Biology of Human Tumors
`
`Combination Approach for Detecting Different
`Types of Alterations in Circulating Tumor DNA
`in Leiomyosarcoma
`Joanna Przybyl1, Jacob J. Chabon2,3, Lien Spans4, Kristen N. Ganjoo5, Sujay Vennam1,
`Aaron M. Newman2,3, Erna Forgo1, Sushama Varma1, Shirley Zhu1, Maria Debiec-Rychter4,
`Ash A. Alizadeh2,3, Maximilian Diehn2,3, and Matt van de Rijn1
`
`Clinical
`Cancer
`Research
`
`Downloaded from http://aacrjournals.org/clincancerres/article-pdf/24/11/2688/2043900/2688.pdf by guest on 07 September 2023
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`Abstract
`
`Purpose: The clinical utility of circulating tumor DNA (ctDNA)
`monitoring has been shown in tumors that harbor highly recur-
`rent mutations. Leiomyosarcoma represents a type of tumor with
`a wide spectrum of heterogeneous genomic abnormalities; thus,
`targeting hotspot mutations or a narrow genomic region for
`ctDNA detection may not be practical. Here, we demonstrate a
`combinatorial approach that
`integrates different sequencing
`protocols for the orthogonal detection of single-nucleotide
`variants (SNV), small indels, and copy-number alterations (CNA)
`in ctDNA.
`Experimental Design: We employed Cancer Personalized Pro-
`filing by deep Sequencing (CAPP-Seq) for the analysis of SNVs
`and indels, together with a genome-wide interrogation of CNAs
`by Genome Representation Profiling (GRP). We profiled 28
`longitudinal plasma samples and 25 tumor specimens from 7
`patients with leiomyosarcoma.
`
`Results: We detected ctDNA in 6 of 7 of these patients with
`>98% specificity for mutant allele fractions down to a level of
`0.01%. We show that results from CAPP-Seq and GRP are
`highly concordant, and the combination of these methods
`allows for more comprehensive monitoring of ctDNA by pro-
`filing a wide spectrum of tumor-specific markers. By analyzing
`multiple tumor specimens in individual patients obtained from
`different sites and at different times during treatment, we
`observed clonal evolution of these tumors that was reflected
`by ctDNA profiles.
`Conclusions: Our strategy allows for the comprehensive
`monitoring of a broad spectrum of tumor-specific markers in
`plasma. Our approach may be clinically useful not only
`in leiomyosarcoma but also in other tumor types that lack
`recurrent genomic alterations. Clin Cancer Res; 24(11); 2688–99.
`Ó2018 AACR.
`
`Introduction
`Recent improvements in next-generation sequencing plat-
`forms have paved the way for the highly sensitive detection of
`circulating tumor DNA (ctDNA) in plasma specimens. Current
`strategies for ctDNA analysis may be divided into three cate-
`gories: (1) patient-specific approaches that utilize personalized
`assays (1–4); (2) tumor type–specific targeted sequencing that
`does not require patient-specific optimization (5–7); and (3)
`tumor type–independent genome-wide analyses (8–11). The
`
`1Department of Pathology, Stanford University School of Medicine, Stanford,
`California. 2Institute for Stem Cell Biology and Regenerative Medicine, Stanford
`University, Stanford, California. 3Stanford Cancer Institute, Stanford University,
`Stanford, California. 4Department of Human Genetics, KU Leuven and University
`Hospitals Leuven, Leuven, Belgium. 5Department of Medicine, Stanford Univer-
`sity School of Medicine, Stanford, California.
`
`Note: Supplementary data for this article are available at Clinical Cancer
`Research Online (http://clincancerres.aacrjournals.org/).
`
`J. Przybyl and J.J. Chabon are first coauthors of this article.
`
`A.A. Alizadeh, M. Diehn, and M. van de Rijn are senior coauthors of this article.
`
`Corresponding Author: J. Przybyl, Stanford University, Stanford, CA 94305.
`Phone: 650-725-7742; E-mail: jprzybyl@stanford.edu
`
`doi: 10.1158/1078-0432.CCR-17-3704
`Ó2018 American Association for Cancer Research.
`
`first approach is highly sensitive but is technically challenging
`and expensive. The second approach involves
`targeted
`sequencing methods such as Cancer Personalized Profiling by
`deep Sequencing (CAPP-Seq; refs. 5, 6). It is highly sensitive,
`but is most practical in patients with tumor types that harbor
`highly recurrent aberrations that can be sequenced with a
`capture panel of a relatively limited size. The third approach,
`although broadly applicable, does not reach the sensitivity of
`the first two targeted approaches. In cancer types that are
`characterized by intermediate levels of
`recurrent
`single-
`nucleotide variants (SNV), small
`insertions and deletions
`(indels), or copy-number alterations (CNA), a combination
`of multiple assays may improve the sensitivity of ctDNA
`detection while still retaining the benefits of broad applica-
`bility and cost-effectiveness. Leiomyosarcoma is a suitable
`disease to explore the potential of such a combination
`approach for the orthogonal detection of multiple classes of
`alterations because these tumors are characterized by a wide
`range of DNA abnormalities spread across the whole genome.
`These include complex CNAs, as well as point mutations
`affecting multiple tumor-suppressor genes such as TP53, RB1,
`ATM, ATR, ATRX, and PTEN (12, 13).
`Leiomyosarcoma patients, like many other cancer patients,
`could greatly benefit from a noninvasive monitoring of tumor
`burden by liquid biopsies. Currently, the decision to initiate
`adjuvant treatment in leiomyosarcoma patients is based on the
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`Translational Relevance
`The detection of circulating tumor DNA (ctDNA) has poten-
`tial to improve prognostication, molecular profiling, and
`surveillance, especially in cancer types with highly recurrent
`genomic alterations. Leiomyosarcoma represents a type of
`tumor that harbors a wide spectrum of heterogeneous geno-
`mic abnormalities; thus, targeting a narrow genomic region for
`ctDNA monitoring may not be practical. In this study, we
`demonstrate a combination approach that integrates different
`sequencing protocols for the orthogonal detection of multiple
`types of alterations in ctDNA. This strategy substantially
`increases the number of molecular markers that can be tracked
`in plasma and improves the confidence of ctDNA detection in
`leiomyosarcoma patients and could be applied to a wide
`range of tumors characterized by genomic profiles of compa-
`rable complexity.
`
`assessment of multiple prognostic factors related to patient per-
`formance, stage of the disease, and type of surgery, as well as the
`potential benefits and side effects of the treatment. Leiomyosar-
`coma ctDNA testing may improve the patients' clinical outcome
`through earlier identification of candidates for adjuvant therapy.
`Longitudinal monitoring of ctDNA may also complement imag-
`ing-based regimens for long-term surveillance of leiomyosarcoma
`patients for disease recurrence.
`Here, we describe a proof-of-principle study to determine the
`feasibility of ctDNA analysis in patients diagnosed with tumors of
`moderate genomic complexity, through a simultaneous applica-
`tion of two separate methods, CAPP-Seq and Genome Represen-
`tation Profiling (GRP) in leiomyosarcoma. The former is a deep,
`targeted sequencing approach optimized for ctDNA detection,
`which is ideal for the ultrasensitive quantitative analysis of SNVs,
`indels, and fusion breakpoints. The clinical utility of monitoring
`ctDNA by CAPP-Seq has been previously demonstrated in
`patients with lung cancer and diffuse large B-cell lymphoma
`(5, 6, 14–16). The second approach, GRP, is based on shallow
`whole-genome sequencing for the assessment of genome-wide
`CNAs and has been shown to detect ctDNA in patients with
`ovarian carcinoma, Hodgkin lymphoma, and follicular lympho-
`ma (9). Successful monitoring of CNAs in plasma has been also
`described previously in prostate cancer patients (11). In the
`present study, we demonstrate that the combination of these two
`techniques enables the reliable monitoring of a wide spectrum of
`molecular markers in ctDNA, and this approach has a significant
`translational potential in leiomyosarcoma and other cancer types
`characterized with a comparable genomic complexity.
`
`Materials and Methods
`Leiomyosarcoma patient cohort
`Nine leiomyosarcoma patients treated at the Stanford Cancer
`Institute provided informed consent to participate in the study
`and donated serial blood samples throughout the course of their
`treatment. The study was approved by the Stanford University
`Institutional Review Board (approvals IRB-31067 and IRB-
`31596). Clinical features of the patients included in this study
`are summarized in Supplementary Table S1. Data from two
`patients have been excluded from the analysis due to failed quality
`
`Combination Approach for Liquid Biopsies
`
`control (QC) or the absence of SNV/indels in tumor within the
`genomic region covered by CAPP-Seq panel. The data from the
`remaining 7 leiomyosarcoma patients have been used for the final
`analysis comparing CAPP-Seq and GRP. All leiomyosarcoma
`patients in the ctDNA monitoring analysis had either a primary
`tumor or metastatic disease confirmed by imaging at all blood
`collection time points.
`
`Healthy donors
`Blood specimens from 24 healthy donors used for CAPP-
`Seq analysis were collected into ethylenediaminetetraacetic acid
`(EDTA) tubes (Beckton Dickinson). Plasma specimens from 428
`volunteers (214 females and 214 males) used for GRP analysis
`were collected into cell-free DNA BCT tubes (Streck). Collection of
`plasma from these asymptomatic donors was approved by the
`local Institutional Review Boards.
`
`Leiomyosarcoma-specific CAPP-Seq selector design
`Whole-exome sequencing data from 77 matched tumor-nor-
`mal specimens from leiomyosarcoma patients from The Cancer
`Genome Atlas (TCGA) were used to design a leiomyosarcoma-
`specific CAPP-Seq capture panel. The analyses presented in the
`current publication are based on the use of study data down-
`loaded from the dbGaP web site, under phs000178.v8.p7 (17).
`Paired-end sequencing reads were aligned to the human reference
`genome (GRCh37/hg19) using BWA-MEM (version 0.7.13) with
`the default settings (18). SAMtools (version 1.3) was used for
`converting SAM to BAM format, sorting, and indexing the align-
`ments (19). Picard (version 1.96) was used for the removal of
`duplicate reads (20). The GATK framework (version 3.3-0) was
`used for the local realignment and base call recalibration (20).
`SNVs and indels were identified using VarScan2 (version 2.3.7;
`ref. 21). Variants identified by Varscan2 were annotated with
`ANNOVAR (22) and filtered for exonic or splice site nonsynon-
`ymous SNVs, frameshift indels, stopgain, and stoploss variants,
`requiring 0% variant allele fraction (VAF) in the matched germ-
`line DNA. All variants were required to be present on at least one
`forward and one reverse sequencing read. Variants reported at
`>0.01% frequency in 1000Genomes and ExAC databases were
`excluded from the analysis. In addition, genes that are likely to
`produce false-positive variant calls in next-generation sequencing
`data were filtered out (including MUC, GOLG, NBP, ZNF, OR, and
`WDR family genes). Somatic variants obtained after this prefilter-
`ing were used to identify all exons mutated in at least 2 leiomyo-
`sarcoma patients. This output was further filtered based on the
`effective size of each exon, i.e., based on the recurrence index (5)
`defined as the number of patients with the mutation divided by
`the exon length in kb. Only exons with recurrence index > 0.5 were
`retained. Exons with poor mappability according to the Unique-
`ness of 35 bp Windows from ENCODE/OpenChrom (Duke) track
`in UCSC Genome Browser GRCh37/hg19 were excluded (23).
`This analysis yielded a panel of 281 exons from 82 genes that were
`recurrently mutated in TCGA leiomyosarcoma cases. This panel
`covered 98.7% (76/77 cases) of the TCGA cohort with a median of
`3 SNVs/indels per patient. This panel was extended with 25 exons
`from 7 genes (MED12, KRAS, CDKN2A, CDH1, KIT, HRAS, and
`KDM6A) that carried mutations reported in 114 leiomyosarcoma
`patients in the COSMIC database v74 (accessed on October 23,
`2015). The final leiomyosarcoma-specific selector for CAPP-Seq
`covered the region of 184 kb and included 306 exons from 89
`genes (Supplementary Table S2). A custom SeqCap EZ Choice
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`Przybyl et al.
`
`Library (Roche) capture panel was designed using NimbleDesign
`software version 3.0 (Roche), with the maximum mismatches set
`to 1. The estimated coverage of the input genomic region of
`184,871 bp was 98.1%.
`
`Blood sample collection and processing
`Peripheral blood was collected into EDTA tubes (Beckton Dick-
`inson), and plasma was separated by centrifugation at 2,500 g for
`20 minutes at room temperature, and stored at 80
`
`C. The cell
`pellet containing white blood cells was frozen and banked for the
`germline DNA extraction. cfDNA was extracted from a median of
`5 mL plasma (range, 3–5 mL) using a QIAamp Circulating Nucleic
`Acid kit (Qiagen). Germline DNA was extracted from 200 mL of
`PBLs using a DNeasy Blood & Tissue kit (Qiagen). cfDNA and
`germline DNA were quantified using Quant-iT dsDNA High
`Sensitivity Assay (ThermoFisher Scientific).
`
`Tumor specimens
`Twenty-eight formalin-fixed paraffin-embedded (FFPE) tumor
`specimens (a median of 3 specimens per patient) from 9 leio-
`myosarcoma patients, collected between 2013 and 2015, were
`used for extraction of genomic DNA (Supplementary Table S3).
`The analyzed specimens included 8 primary tumors and 20
`metastatic/recurrent tumors. Diagnosis was confirmed by a sur-
`gical pathologist (M. van de Rijn). Tumor specimens selected for
`CAPP-Seq (n ¼ 24) and SNP array (n ¼ 26) analysis contained a
`median of 95% tumor cells (range, 60%–100%). Genomic DNA
`was extracted using the AllPrep DNA/RNA FFPE Kit (Qiagen).
`Twenty and 22 samples from 7 leiomyosarcoma patients
`passed the QC by CAPP-Seq and SNP array analysis, respectively
`(Supplementary Table S3).
`
`RNA sequencing of leiomyosarcoma tumors used for validation
`of CAPP-Seq selector
`RNA-seq was performed on tumor specimens collected from 20
`leiomyosarcoma patients treated at Stanford University Medical
`Center (19 FFPE and 1 frozen tumor specimen). These specimens
`included 8 uterine, 5 extremity, and 7 thoracic/abdominal/
`retroperitoneal leiomyosarcoma from 11 female and 9 male
`patients. Libraries were prepared using TruSeq RNA Sample Prep
`Kit V2 (Illumina) and sequenced on a HiSeq2000 instrument
`(Illumina) in 2  101 bp mode.
`Paired-end reads were mapped to the human reference genome
`(GRCh37/hg19) using STAR (version 2.3.0.1; ref. 24). Duplicate
`reads were removed using Picard (version 1.96; ref. 20). The GATK
`framework (version 3.3-0) was used for Split'N'Trim,
`indel
`realignment, and base recalibration (20). SNPs and indels were
`identified using HaplotypeCaller in the RNA-seq mode. Variants
`were annotated using ANNOVAR (22) and filtered to identify
`exonic, nonsynonymous SNPs, and indels.
`Within the 184 kb region of the leiomyosarcoma-specific
`CAPP-Seq panel, we identified a median of 7 SNVs/indels per
`patient in 18 of 20 patients from our validation cohort. The higher
`number of variants identified in the RNA-seq data compared with
`exome sequencing data from TCGA is most likely due to calling
`both germline and somatic mutations, as no germline control was
`analyzed by RNA-seq.
`
`CAPP-Seq library construction
`CAPP-Seq libraries were prepared from 32 ng of cfDNA (if less
`than 32 ng was available, all the cfDNA obtained was input into
`
`library preparation, input range: 18.3–32 ng; Supplementary
`Table S6) and 50 ng of genomic tumor DNA (with the exception
`of 14.6–100 ng input DNA used from 4 tumor specimens). For
`CAPP-Seq libraries from germline DNA, the whole yield of DNA
`obtained from 200 mL of PBLs was used (input range, 28.8–64.6
`ng). Tumor and germline DNA was sheared before library con-
`struction using Covaris S2 instrument to obtain approximately
`170 bp fragments. Libraries were prepared using unique molec-
`ular identifiers as described before (6). Postcapture enrichment
`was evaluated by qPCR using Power SYBR Green PCR Master Mix
`(ThermoFisher Scientific) with primers specific for ARID1A, ATRX
`(genes included in the selector), B2M (as a negative control), and
`internal quality controls for the NimbleGen SeqCap capture
`panel. Seven to 12 libraries were pooled and sequenced using
`2  101 bp mode on HiSeq2000 or HiSeq4000 instruments
`(Illumina).
`
`CAPP-Seq data analysis
`Sequencing data were processed using a custom bioinfor-
`matics pipeline, and SNV/indel calling was performed as previ-
`ously described with minor modifications (6). Briefly, sequencing
`reads were demultiplexed using a 4 bp sample index and dedu-
`plicated using molecular barcodes. For cfDNA samples, back-
`ground polishing was performed to reduce stereotypical base
`substitution errors. SNV/indel calling was performed as previous-
`ly described (6) with the following modifications. We defined
`a "blacklist" as the genes in our panel that were found to
`be recurrently mutated in the plasma sequencing data from
`24 healthy controls. Alterations were recurrently observed in four
`genes in our panel: MLL2, APOBR, PPR21, and DSPP. One or more
`alterations were observed in each of these genes in >90% of
`healthy plasma samples, and thus these genes were removed
`from consideration for variant calling in tumor or plasma samples
`in leiomyosarcoma patients. For tumor genotyping, we applied
`additional requirements to identify high-confidence somatic var-
`iant calls, to account for the possible artifacts in DNA extracted
`from FFPE tissue (25, 26). We required 3 supporting duplex
`reads, a positional depth in tumor and germline  25% of the
`selector wide median depth, 5% mutant AF in the tumor, 1
`read in matched germline, and no overlap with the UCSC Repeat-
`Masker track (23, 27).
`For CAPP-Seq–based ctDNA analysis, cfDNA samples were
`sequenced to a median deduplicated depth of 2,031 (Supple-
`mentary Table S4), and only somatic mutations that were present
`in one or more tumor samples were considered.
`
`GRP of plasma specimens
`Sequencing libraries were prepared with the TruSeq Chip
`preparation Kit (Illumina), indexed, and 23 samples were pooled
`for multiplex sequencing across both lanes of an Illumina
`HiSeq2500 flow cell. Sequencing was performed in a 1  50 bp
`mode, and at least 10 million reads per sample were required from
`the leiomyosarcoma plasma specimens.
`Sequencing reads were mapped to the GRCh37/hg19 reference
`genome using BWA-MEM with the default settings (version
`0.7.10; ref. 18). The pseudo-autosomal region on chromosome
`Y was masked in the reference genome. Duplicate reads were
`removed using SAMtools (version 0.1.18; refs. 19, 28). The
`sequencing summary statistics are included in Supplementary
`Table S5. Copy-number variants in cfDNA were identified using
`the depth-of-coverage Plasma-Seq algorithm version 0.6 (11),
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`

`Combination Approach for Liquid Biopsies
`
`A
`
`CAPP-Seq
`LMS-specific
`capture panel
`
`SNVs
`indels
`
`CAPP-Seq
`LMS-specific
`capture panel
`
`SNVs
`indels
`
`SNP array
`
`CNAs
`
`GRP
`CNAs
`Low-pass whole
`genome sequencing
`
`B
`
`Patient ID Total # of
`samples
`
`LMS1
`LMS2
`LMS3
`LMS4
`LMS5
`LMS6
`LMS7
`Total
`
`5
`8
`9
`10
`13
`7
`8
`60
`
`1
`1
`1
`1
`1
`1
`1
`7
`
`Germline
`DNA
`
`Tumor
`samples
`
`CAPPseq
`[somatic SNV/indels]
`Longitudinal
`plasma
`samples
`3
`3
`5
`6
`5
`2
`4
`28
`
`Tumor
`samples
`
`GRP/SNP array
`[CNAs]
`Longitudinal
`plasma
`samples
`2
`3
`5
`6
`5
`2
`4
`27
`
`1
`4
`3
`3
`7
`4
`3
`25
`
`1
`2
`3
`3
`7
`4
`2
`22
`
`Figure 1.
`Study design and a summary of the
`analyzed specimens. A, Types of
`assays applied to study tumor and
`plasma specimens. B, Summary of
`all tumor, plasma, and peripheral
`blood cell specimens analyzed by
`CAPP-Seq, GRP, and SNP arrays.
`
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`
`with the following modifications: (1) we used sequencing reads of
`50 bp; (2) genome was divided into 100,000 windows, where
`each window contains the same amount of mappable reads; (3)
`the average length of the bins was 28 kb; (4) data from 189 female
`and 189 male healthy donors were used as the nontumor controls.
`Next we applied Plasma-Seq algorithm with these settings to an
`independent group of 50 healthy donors, to define the genome-
`wide segmented Z-scores that set the specificity for leiomyosar-
`coma analysis at 98% (allowing 1 of 50 healthy donors to carry a
`CNA in cfDNA). The genome-wide Z-score was set at < 5.44 and
`>5.44 for the significantly under- and overrepresented regions in
`leiomyosarcoma specimens, respectively.
`
`Copy-number profiling in leiomyosarcoma tumor specimens
`Seventy-five nanograms of genomic DNA were used for
`genome-wide copy-number and allelic ratio profiling with the
`OncoScan FFPE Assay by the Affymetrix Research Services Labo-
`ratory (Affymetrix). Results were visualized and analyzed with
`NexusExpress for OncoScan3 software using SNP-FASST2 algo-
`rithm (BioDiscovery). Specimens with the Median Absolute Pair-
`wise Difference value above 0.3 were excluded from the analysis.
`Diploid recentering was performed manually in all samples, and
`copy-number gain and loss was defined as log2 ratio > 0.25 and
`<0.25, respectively. A minimum of 200 probes in the segment was
`used to call CNAs.
`
`Availability of data
`GRP sequencing data have been deposited in the European
`Nucleotide Archive and have study accession number PRJEB22076
`
`(www.ebi.ac.uk/ena/data/view/PRJEB22076). Raw data from
`CAPP-Seq experiments are available upon request.
`
`Results
`Overview of patient cohort
`In this study, we report the CAPP-Seq, GRP, and SNP array
`results from 60 samples from 7 female patients treated for
`leiomyosarcoma at Stanford University Medical Center (Fig. 1A
`and B; Supplementary Table S1). These included 28 longitudinal
`plasma samples (median of 4 samples per patient), 25 tumor
`specimens (median of 3 samples per patient), and 7 specimens of
`peripheral blood leukocytes (PBL) for germline DNA analysis. In
`this proof-of-principle study, we focused primarily on those
`genomic aberrations that could be detected in matching tumor
`and plasma specimens.
`
`Development of leiomyosarcoma-specific CAPP-Seq approach
`for ctDNA monitoring
`To assess the feasibility of detecting ctDNA in leiomyosarcoma
`patients using CAPP-Seq, we designed a custom hybrid capture
`panel (i.e., "selector") to target the most frequently mutated
`genomic regions in leiomyosarcoma based on the analysis of
`matched tumor and germline whole-exome sequencing data from
`77 leiomyosarcoma patients from TCGA (data downloaded from
`the dbGaP web site, under phs000178.v8.p7; Fig. 2A; ref. 17). In
`addition, our selector included exons with mutations reported in
`114 leiomyosarcoma patients in the COSMIC database v74 (29).
`The final leiomyosarcoma-specific selector targets 184 kb and
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`Pediatric Rhabdoid
`Pediatric Medulloblastoma
`NSCLC - Never smokers
`Breast
`Pancreatic Adenocarcinoma
`Gastric
`Esophageal (EAC)
`Head and Neck
`Non-Hodgkin Lymphoma
`Esophageal (ESCC)
`LMS
`Melanoma
`NSCLC
`SCLC
`
`250
`
`200
`
`150
`
`100
`
`50
`
`0
`
`C
`
`mutations per tumor
`Non-synonymous
`
`Przybyl et al.
`
`A
`
`B
`
`RECURRENT SNVs/INDELs IN LMS
`
`TCGA exome seq - 77 LMS (tumor/normal)
`COSMIC v74
`
`CAPP-SEQ CAPTURE PANEL DESIGN
`
`306 exons in 89 genes, 184 kb 76/77 TCGA LMS
`with >1 mutation (median of 3 mutations/tumor)
`
`CAPP-SEQ CAPTURE PANEL VALIDATION
`
`RNA-seq data from 20 MS Stanford patients 18/20
`LMS with > 1 mutation (median of 7 mutations/tumor)
`
`Top genes mutated in LMS in TCGA
`TP53
`RB1
`APOBR
`ATRX
`DSPP
`CHIT1
`PRR21
`DMKN
`PTEN
`ATM
`ARID1A
`0% 10% 20% 30% 40% 50%
`Patients
`
`D
`
`TP53
`RB1
`ATRX
`
`SPEN
`PPRC1
`
`E
`
`Aggregate % of tumors
`
`with CNA [n = 22]
`
`Primary/Metastatic/Recurrent tumor
`
`Pb
`Pa
`M1f
`M1e
`M1d
`M1c
`M1b
`M1a
`
`M PM
`
`R
`
`M2
`M2
`M1
`M2
`
`1
`M PM M
`
`2 2 2
`
`Variant effect:
`Missense
`Splice
`Stop-gain
`Indel Frameshift
`
`LMS7-T2
`LMS7-T1
`LMS6-T4
`LMS6-T2
`LMS6-T1
`LMS5-T7
`LMS5-T6
`LMS5-T5
`LMS5-T4
`LMS5-T3
`LMS5-T2
`LMS5-T1
`LMS4-T3
`LMS4-T2
`LMS4-T1
`LMS3-T3
`LMS3-T2
`LMS2-T2
`LMS2-T1
`LMS1-T1
`
`1
`
`2
`
`3
`
`4
`
`5
`
`6
`
`7
`
`8
`
`9
`
`10
`
`11
`
`12
`
`13
`
`14
`
`15
`
`16 17 18
`
`19
`
`20
`
`
`
`22 X21
`
`100%
`
`50%
`
`50%
`
`100%
`CN gain
`CN loss
`
`Figure 2.
`Mutational landscape of leiomyosarcoma and design of leiomyosarcoma-specific CAPP-Seq selector. A, Design and validation of leiomyosarcoma-specific CAPP-Seq
`capture panel based on TCGA, COSMIC, and Stanford sequencing data. B, The most frequently mutated genes in 77 leiomyosarcoma TCGA cases, according
`to the analysis described in the present study. C, The median number of exonic somatic mutations in leiomyosarcoma based on the analysis of TCGA cohort (n ¼ 77)
`compared with selected types of cancer based on the studies reviewed by Vogelstein and colleagues (33). Horizontal bars indicate 25% and 75% quartiles.
`D, SNVs and indels detected by CAPP-Seq in 20 leiomyosarcoma tumor specimens analyzed in the present study. Arrows indicate tumor specimens with subclonal
`SNVs. Index 2 indicates two different somatic mutations in the same gene. M1 and M2 indicate two different metastatic tumors. Pa, Pb and M1a, M1b, etc.
`indicate different regions of the same tumor. E, Cumulative representation of CNAs identified by SNP array in 22 leiomyosarcoma tumor specimens analyzed in the
`present study. CN, copy number; EAC, esophageal adenocarcinoma; ESCC, esophageal squamous cell carcinoma; NSCLC, non–small cell lung cancer; SCLC,
`small cell lung cancer.
`
`includes 306 exons from 89 genes (Supplementary Table S2). This
`panel covered 98.7% (76/77) of the patients in the TCGA cohort
`with a median of 3 SNVs/indels per patient. Consistent with prior
`leiomyosarcoma studies (12, 30–32), the most frequently mutat-
`ed genes in the TCGA cohort included TP53, RB1, and ATRX
`(Fig. 2B).
`
`Previous studies have shown a wide spectrum of the prevalence
`of somatic mutations across human cancer types (33, 34). Based
`on TCGA data, we calculated that leiomyosarcoma patients car-
`ried a median of 96 nonsynonymous mutations per tumor, which
`classifies leiomyosarcoma as a tumor type with an intermediate
`level of SNVs and indels, as compared with the other cancer types
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`(Fig. 2C; ref. 34). As such, we anticipated that the number of SNVs/
`indels detectable in ctDNA of leiomyosarcoma patients may be
`substantially lower than it would be for a cancer with higher
`mutational burden, such as lung cancer, for which a similar
`approach to selector design and comparable selector size yielded
`a panel covering an average of 8 mutations per patient (6).
`
`Performance of CAPP-Seq in leiomyosarcoma patients
`To assess the performance of our leiomyosarcoma-focused
`CAPP-Seq approach, we first analyzed 22 DNA samples obtained
`from spatially and temporally distinct sites of tumors from 7
`leiomyosarcoma patients. We identified a median of 1 (range,
`0–4) nonsynonymous SNV/indel in 20 of 22 tumor specimens
`(Fig. 2D). We identified point mutations in numerous genes
`previously implicated in leiomyosarcoma including TP53, RB1,
`ATRX, SPEN, and PPRC1. TP53, RB1, and ATRX are among the
`most frequently mutated genes in leiomyosarcoma (Fig. 2B), and
`all mutations identified in these genes were present in each tumor
`specimen analyzed from each patient (Fig. 2D), suggesting
`that these are truncal genetic events, i.e., shared early drivers in
`the development of leiomyosarcoma. We also found evidence
`for intrapatient tumor heterogeneity in 2 patients. In LMS5
`and LMS6, we identified mutation in SPEN and PPRC1 that were
`unique to individual metastatic sites in these patients. Mutations
`in these genes have been less frequently reported in leiomyosar-
`coma and likely represent subclonal events in these patients
`(Fig. 2D).
`We next applied leiomyosarcoma-focused CAPP-Seq to a total
`of 28 serial plasma samples from the 7 patients, as well as plasma
`samples from 24 healthy controls to assess specificity of ctDNA
`detection. Data from cfDNA of healthy donors were used to
`characterize selector-specific error profiles and perform digital
`error suppression as described before (6). CAPP-Seq demonstrat-
`ed a baseline sensitivity of ctDNA detection of 86% (defined as
`detection of ctDNA at the first blood draw in a patient with known
`disease, Table 1; Supplementary Fig. S1) with a specificity of
`98.91% (determined using plasma from the 24 healthy donors;
`Supplementary Fig. S2). The overall sensitivity of ctDNA detection
`across all analyzed samples was 68% (19/28 positive samples)
`with a median VAF of 0.27% (range, 0%–31.89%), indicating that
`ctDNA levels are relatively low in leiomyosarcoma as compared
`with other solid tumors (7). The CAPP-Seq protocol was initially
`optimized for the input of 32 ng cfDNA, but it has been previously
`shown to perform well with the input as low as 4 ng cfDNA (5).
`We confirm this in leiomyosarcoma patients, as for 7 of 28
`samples, we used less than 32 ng of cfDNA (range, 18.3–29.6
`ng; Supplementary Table S6), and ctDNA detection by CAPP-Seq
`was not correlated with the amount of input cfDNA (two-tailed
`Fisher exact test, P ¼ 0.65).
`
`Spectrum of CNAs and performance of GRP in leiomyosarcoma
`patients
`To obtain a more comprehensive picture of ctDNA profiles in
`leiomyosarcoma, we sought to increase the number of molecular
`markers queried in the cfDNA by including CNAs as an additional
`class of genomic alterations. We decided to study CNAs in the
`same group of patients because genomic instability resulting in
`complex chromosomal aberrations has been previously described
`in leiomyosarcoma (13). The analysis of 72 leiomyosarcoma
`cases within the AACR Genie project identified 15 recurrent CNAs
`in this entity (GENIE cBioPortal data accessed on June 3, 2017).
`
`Combination Approach for Liquid Biopsies
`
`Table 1. Summary of SNV/indels and CNAs detected in ctDNA of
`leiomyosarcoma patients
`
`Plasma sample ID
`LMS1-C1
`LMS1-C2
`LMS1-C3
`LMS2-C1
`LMS2-C2
`LMS2-C3
`LMS3-C1
`LMS3-C2
`LMS3-C3
`LMS3-C4
`LMS3-C5
`LMS4-C1
`LMS4-C2
`LMS4-C3
`LMS4-C4
`LMS4-C5
`LMS4-C6
`LMS5-C1
`LMS5-C2
`LMS5-C3
`LMS5-C4
`LMS5-C5
`LMS6-C1
`LMS6-C2
`LMS7-C1
`LMS7-C2
`LMS7-C3
`LMS7-C4
`
`CAPP-Seq
`SNV/indels
`RB1 splice site
`—
`RB1 splice site
`ATRX indel
`ATRX indel
`ATRX indel
`RB1 K154
`—
`RB1 K154
`RB1 K154
`RB1 K154
`TP53 V272L
`TP53 V272L
`—
`TP53 V272L
`TP53 V272L
`TP53 V272L
`TP53 L344P
`TP53 L344P
`—
`TP53 L344P
`TP53 L344P
`—
`—
`RB1 D604G
`—
`—
`—
`
`ctDNA monitoring
`Number of CNA
`regions per GRP
`—
`Not analyzed
`—
`—
`—
`3
`194
`—
`1
`16
`4
`—
`—
`—
`3
`—
`41
`40
`5
`1
`130
`—
`—
`—
`2
`—
`—
`—
`
`This roughly classifies leiomyosarcoma as having intermediate
`levels of CNAs, compared with the pan-cancer analysis of genomic
`aberrations published previously by Ciriello and colleagues (35).
`Copy-number calls from 22 leiomyosarcoma tumor specimens
`from 7 patients were filtered for the presence of large segmental
`CNAs (covered by  200 probes) with a copy-number gain and
`loss defined as log2 ratio > 0.25 and < 0.25, respectively. With
`these criteria, we identified extensive CNAs across all 22 tumor
`samples (median of 80 CNAs per tumor; range, 25–198; Fig. 2E;
`Supplementary Table S7; Supplementary Fig. S3). Consistent with
`previous leiomyosarcoma studies,
`the most
`frequent CNAs
`included loss of chromosome 13q with RB1 locus and gain of
`chromosome 17p encompassing the MYOCD gene (12, 32).
`CNAs in leiomyosarcoma were highly heterogeneous, and the
`vast majority of CNAs called with the above-described criteria
`appear to be subclonal events (Supplementary Fig. S3). The only
`consistently truncal CNA, found across all tumors specimens
`analyzed in patients LMS2, LMS3, LMS4, LMS5, and LMS7, was
`a 3.5 Mb copy-number loss on chromosome 11q24.3-q25 (Sup-
`plementary Fig. S3). This genomic region includes 24 genes
`(Supplementary Fig. S4),
`including ADAMTS8 gene (ADAM
`metallopeptidase with thrombospondin type 1 motif 8, also
`known as METH2) that has been previously described as a tumor
`suppressor in multiple types of cancer (36).
`GRP was performed at a median depth of 0.21 across the
`whole genome on 27 plasma specimens, all of which had been
`profiled also by CAPP-Seq (Supplementary Table S5). We also
`analyzed GRP data obtained from 378 healthy donors (189 males
`and 189 females) to calibrate the analysis algorithm, and the data
`from additional 50 healthy donors were used to set the genome-
`wide z-score cut-off at 5.44 that allowed for 98% specificity.
`
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