Case 1:20-cv-01580-LPS Document 1-10 Filed 11/23/20 Page 1 of 18 PageID #: 445
`Case 1:20-cv-01580-LPS Document 1-10 Filed 11/23/20 Page 1 of 18 PageID #: 445
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`EXHIBIT 10
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`EXHIBIT 10
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`Case 1:20-cv-01580-LPS Document 1-10 Filed 11/23/20 Page 2 of 18 PageID #: 446
`The Journal of Molecular Diagnostics, Vol. 20, No. 5, September 2018
`
`jmd.amjpathol.org
`
`Analytical Validation of a Hybrid
`CaptureeBased Next-Generation Sequencing
`Clinical Assay for Genomic Profiling of Cell-Free Circulating
`Tumor DNA
`
`Travis A. Clark,* Jon H. Chung,* Mark Kennedy,* Jason D. Hughes,* Niru Chennagiri,* Daniel S. Lieber,* Bernard Fendler,*
`Lauren Young,* Mandy Zhao,* Michael Coyne,* Virginia Breese,* Geneva Young,* Amy Donahue,* Dean Pavlick,*
`Alyssa Tsiros,* Timothy Brennan,* Shan Zhong,* Tariq Mughal,* Mark Bailey,* Jie He,* Steven Roels,* Garrett M. Frampton,*
`y
`y
`y
`y
`y
`Steven Gendreau,
`Mark Lackner,
`Erica Schleifman,
`Eric Peters,
`Jeffrey S. Ross,* Siraj M. Ali,*
`Jill M. Spoerke,
`z
`Vincent A. Miller,* Jeffrey P. Gregg,
`Philip J. Stephens,* Allison Welsh,* Geoff A. Otto,* and Doron Lipson*
`
`y
`From Foundation Medicine, Inc.,* Cambridge, Massachusetts; Genentech, Inc.,
`z
`Sacramento, California
`Medical Center,
`
`South San Francisco, California; and the University of California Davis
`
`Accepted for publication
`May 18, 2018.
`
`Address correspondence to
`Jon H. Chung, Ph.D., or Doron
`Lipson, Ph.D., Foundation
`Medicine, Inc., 150 Second St,
`Cambridge, MA 02141.
`E-mail: jchung@
`foundationmedicine.com or
`dlipson@foundationmedicine.
`com.
`
`Genomic profiling of circulating tumor DNA derived from cell-free DNA (cfDNA) in blood can provide a
`noninvasive method for detecting genomic biomarkers to guide clinical decision making for cancer
`patients. We developed a hybrid captureebased next-generation sequencing assay for genomic
`profiling of circulating tumor DNA from blood (FoundationACT). High-sequencing coverage and
`molecular barcodeebased error detection enabled accurate detection of genomic alterations,
`including short variants (base substitutions, short insertions/deletions) and genomic re-arrange-
`ments at low allele frequencies (AFs), and copy number amplifications. Analytical validation was
`performed on 2666 reference alterations. The assay achieved >99% overall sensitivity (95% CI,
`99.1%e99.4%) for short variants at AF >0.5%, >95% sensitivity (95% CI, 94.2%e95.7%) for AF
`0.25% to 0.5%, and 70% sensitivity (95% CI, 68.2%e71.5%) for AF 0.125% to 0.25%. No false
`positives were detected in 62 samples from healthy volunteers. Genomic alterations detected by
`FoundationACT demonstrated high concordance with orthogonal assays run on the same clinical
`cfDNA samples. In 860 routine clinical FoundationACT cases, genomic alterations were detected in
`cfDNA at comparable frequencies to tissue; for the subset of cases with temporally matched tissue and
`blood samples, 75% of genomic alterations and 83% of short variant mutations detected in tissue
`were also detected in cfDNA. On the basis of analytical validation results, FoundationACT has been
`approved for use in our Clinical Laboratory Improvement Amendmentsecertified/College of American
`Pathologistseaccredited/New York Stateeapproved laboratory. (J Mol Diagn 2018, 20: 686e702;
`https://doi.org/10.1016/j.jmoldx.2018.05.004)
`
`Sequencing of cancer genomes has yielded insights into the
`genomic alterations that drive different cancer types, and
`has led to the development of numerous therapies that target
`genetic vulnerabilities of
`tumors. With the increasing
`number of genomic alterations that are either predictive
`biomarkers for approved targeted therapies or used as in-
`clusion criteria for genomically matched clinical
`trials,
`comprehensive genomic profiling of tissue samples using
`
`T.A.C., J.H.C., M.K., and J.D.H. contributed equally to this work.
`Disclosures: T.A.C., J.H.C., M.K., J.D.H., N.C., D.S.L., B.F., L.Y.,
`M.Z., M.C., V.B., G.Y., A.D., D.P., A.T., T.B., S.Z., T.M., M.B., J.H.,
`S.R., G.M.F., J.S.R., S.M.A., V.A.M., P.J.S., A.W., G.A.O., and D.L. are
`employees of Foundation Medicine, Inc. and own stock in Foundation
`Medicine, Inc.; J.S., S.G., M.L., E.S., and E.P. are employees of Genentech,
`Inc.; J.P.G. is a speaker for Foundation Medicine, Inc. and AstraZeneca and
`serves on advisory boards for AstraZeneca and Bristol-Myers Squibb.
`
`Copyright ª 2018 American Society for Investigative Pathology and the Association for Molecular Pathology. Published by Elsevier Inc.
`This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0).
`https://doi.org/10.1016/j.jmoldx.2018.05.004
`
`

`

`Case 1:20-cv-01580-LPS Document 1-10 Filed 11/23/20 Page 3 of 18 PageID #: 447
`
`next-generation sequencing (NGS) to evaluate hundreds of
`cancer-related genes has transitioned from the research
`setting into an important tool for routine clinical manage-
`ment of patients with cancer.1e4
`Several tumor tissueebased companion diagnostic NGS
`assays have been approved by the US Food and Drug
`Administration for the identification of genomic biomarkers
`to guide treatment with targeted therapies,4e6 and on the
`basis of extensive studies using tissue samples to define the
`genomic landscape of cancer, tissue-based testing represents
`the gold standard for genomic profiling.2,3 However,
`in
`some cases, obtaining a tissue sample may not be possible
`because of inaccessibility of the tumor, risk of complica-
`tions from the tissue biopsy, or insufficient tissue.7 Because
`approximately 80% of metastatic solid tumors release cell-
`free circulating tumor DNA (ctDNA) into the circulation,8
`sequencing of cell-free DNA (cfDNA) from blood could
`provide an alternative method for
`identifying genomic
`changes in the tumor tissue. Recently, a plasma-based PCR
`test for epidermal growth factor receptor (EGFR) mutations
`in patients with nonesmall-cell lung cancer (NSCLC) was
`US Food and Drug Administration approved as a compan-
`ion diagnostic for EGFR tyrosine kinase inhibitors (TKIs).9
`Therefore, ctDNA provides an opportunity to perform
`noninvasive blood-based genomic profiling should a tissue
`sample be unavailable.
`Blood-based testing of ctDNA offers the advantage of
`simple and rapid sample collection and may be particularly
`suited to serial genomic profiling for identifying resistance
`mutations and monitoring disease burden. An understanding
`of acquired genomic alterations that mediate resistance to
`first-line targeted therapies has led to the development of
`subsequent targeted therapies that are designed to be active
`against resistance mutations, such as the EGFR TKI osi-
`mertinib for the EGFR T790M mutation in NSCLC10; serial
`genomic profiling assessments of ctDNA may provide a
`convenient method to monitor emergence of resistant clones
`and identify mechanisms of resistance to guide selection of
`later-line targeted therapies. Furthermore, because the
`abundance of ctDNA in blood is associated with tumor size,
`serial genomic profiling of ctDNA may be used for longi-
`tudinal assessment of disease burden to detect minimal
`residual disease, identify relapse, and monitor response to
`therapy.11e13
`The development of NGS-based gene panels to sequence
`ctDNA has allowed blood-based genomic profiling of early-
`and late-stage cancers.11,14e17 Because ctDNA typically
`comprises a small fraction of the total cfDNA, sensitive
`techniques are required to detect sequence alterations in
`ctDNA that frequently exist at low abundance.15 In this
`study, we describe the development and analytical valida-
`tion of a hybrid captureebased NGS clinical assay of
`ctDNA in blood (FoundationACT). High-sequencing
`coverage and molecular barcodeebased error detection
`allowed for accurate and sensitive detection of genomic
`alterations in ctDNA,
`including base substitutions, short
`
`Analytical Validation of FoundationACT
`
`insertions/deletions (indels), and re-arrangements/fusions at
`low allele frequencies (AFs), as well as copy number
`amplifications (CNAs).
`Rigorous validation studies are required to demonstrate
`robust analytical performance. Therefore, extensive valida-
`tion was performed by: i) constructing a validation set of
`2666 genomic alterations encompassing all tested alteration
`types across the spectrum of genes targeted by the assay; ii)
`assessing performance across a broad range of allele
`frequencies; iii) validating performance at sequencing cov-
`erages that are reflective of the range routinely achieved in
`clinical samples; iv) demonstrating, using clinical cfDNA
`samples, that the results of the assay are concordant with
`orthogonal methods; and v) establishing that the genomic
`profiling results
`from the FoundationACT assay are
`consistent with tissue-based genomic profiling. On the basis
`of the analytical validation studies, FoundationACT has
`been approved for use
`in our Clinical Laboratory
`Improvement Amendments (CLIA)ecertified, College of
`American Pathologists (CAP)eaccredited, New York (NY)
`Stateeapproved laboratory.
`
`Materials and Methods
`Whole Blood Collection, Plasma Isolation, and cfDNA
`Extraction
`
`Clinical samples for analytical validation and comparison
`with orthogonal approaches were received as whole blood or
`archival frozen plasma stored at 80
`
`C. For blood samples,
`16 to 20 mL peripheral blood was collected in Cell Free DNA
`Blood Collection Tubes (Roche, Pleasanton, CA) or Cell-
`Free DNA BCT tubes (Streck Inc., La Vista, NE). To isolate
`plasma: i) whole blood was centrifuged at 1600  g for 20
`minutes at room temperature, ii) supernatant was collected
`and centrifuged at 16,000  g for 20 minutes at 4
`
`C, and iii)
`supernatant was collected as plasma that underwent cfDNA
`extraction. Plasma was treated with proteinase K for 20 mi-
`C and mixed with 1.25  volume of cfDNA
`
`nutes at 60
`binding solution (Thermo Fisher Scientific, Waltham, MA)
`and 500 ng/mL of paramagnetic MyOne SILANE beads
`(Thermo Fisher Scientific). Beads were washed twice with
`cfDNA wash solution (Thermo Fisher Scientific) and twice
`with 80% ethanol, and they were eluted in cfDNA elution
`solution (Thermo Fisher Scientific). cfDNA concentration
`was determined using the D1000 ScreenTape assay on the
`4200 TapeStation (Agilent Technologies, Santa Clara, CA).
`cfDNA (20 to 100 ng) was used for library construction.
`
`Library Construction
`
`Library construction was performed on the Bravo Benchbot
`(Agilent Technologies) automation system with NEBNext
`reagents (NEB, Ipswich, MA) containing mixes for end
`repair, dA addition, and ligation using the with-bead pro-
`tocol to maximize library yield and complexity. A set of 12
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`Clark et al
`
`fragment-level indexed adaptors with variable 6-bp DNA
`barcodes were ligated randomly onto both ends of each
`input duplex cfDNA fragment. Ligated sequencing libraries
`were PCR amplified with a universal PCR primer and an
`indexed PCR primer with a high-fidelity polymerase (Kapa
`Biosystems, Wilmington, MA) for 10 cycles, 1.8  Solid
`Phase Reversible Immobilization purified, and quantified by
`PicoGreen (Invitrogen, Carlsbad, CA). Samples yielding
`500 to 2000 ng of sequencing library proceeded to hybrid
`capture.
`
`Panel Design, Hybrid Capture, and Sequencing
`
`Solution hybridization was performed using a >50-fold
`molar excess of a pool of 2695 individually synthesized
`0
`-biotinylated single-stranded DNA oligonucleotide
`120-bp 5
`baits (assay baitset version CF2; Integrated DNA Technol-
`ogy, Coralville, IA). The baitset targeted 140,419 bp of the
`human genome, including all exons of 27 genes, selected
`exons of an additional 33 genes (133 exons), selected introns
`of 6 genes frequently involved in genomic re-arrangements in
`cancer (12 introns), and the TERT promoter region that is
`recurrently mutated in cancer (Supplemental Table S1). The
`baitset also targeted 96 single-nucleotide polymorphisms
`(SNPs) that serve as a patient-specific signature to allow
`confirmation of the same subject in longitudinal test com-
`parison. Bait design and hybridization capture were per-
`formed as described previously.1,18 Briefly, 500 to 2000 ng of
`sequencing library was lyophilized with human Cot-1 DNA,
`sheared salmon sperm DNA, and adaptor-specific blocking
`oligonucleotides; resuspended in water; heat denatured at
`
`
`95
`C for 5 minutes; and incubated at 68
`C, with the final
`addition of the baitset into hybridization buffer. The hybrid-
`
`C for 12 to 24 hours, and
`ization reaction was incubated at 68
`library-baitset duplexes were captured on paramagnetic
`MyOne streptavidin beads (Invitrogen). Off-target library
`was removed by washing once with 1  saline-sodium citrate
`C and four times with 0.25  saline-sodium citrate at
`
`at 25
`C. The 1  KAPA HiFi Hotstart ReadyMix PCR mas-
`
`55
`termix (number KK2602; Kapa Biosystems) was added
`directly to the beads to amplify the captured library. Samples
`were 1.8  Solid Phase Reversible Immobilization purified
`and quantified by PicoGreen (Invitrogen). Libraries were
`normalized to 1.05 nmol/L, pooled, and loaded onto an Illu-
`mina cBot for the template extension reaction directly on the
`flow cell that was loaded onto an Illumina HiSeq 4000 with
`2  151 bp or HiSeq 2500 with 2  176 bp paired-end
`sequencing protocol (Illumina, San Diego, CA).
`Process-matched normal control DNA was run in parallel
`with each batch of test samples to observe variation across
`assays and to serve as normal reference for CNA analysis.
`Purified normal control DNA from two individuals was
`obtained from the International HapMap Project (Coriell
`Institute, Camden, NJ), combined in a 99:1 ratio, and
`sheared by ultrasonication to generate approximately 200-bp
`fragments (Covaris, Woburn, MA).
`
`Sequence Data Processing
`
`Read Processing
`The following steps were used to process raw sequence
`data: i) Reads pairs were demultiplexed by sample barcode
`to yield sets of reads deriving from distinct patient samples.
`ii) For each sample, read pairs were sorted into subsets on
`the basis of the fragment barcodes found at the start of each
`read in the pair, segregating read pairs that cannot have
`derived from the same fragment. iii) Read pairs within each
`fragment barcode pair subset were mapped to the reference
`genome (hg19) using BWA version 0.7.1519 and clustered
`into subsets corresponding to distinct fragments. iv) Read
`pairs corresponding to each distinct fragment were aligned
`to each other (read 1 versus read 2) as well as to all other
`read pairs in the set to identify any experimentally intro-
`duced sequence errors.20 A merged complete fragment
`sequence was generated when possible (typically when the
`fragment size was <250 bp), whereas a paired representa-
`tion was retained for larger fragments. Any errors identified
`were marked as such.
`
`Variant Calling
`A set of candidate variants was generated by parsing all
`alignments found in the consensus representation of the
`sequences determined for each fragment, avoiding sections
`marked as containing errors.1,21 Every read in the original
`raw data mapping to the region of the putative variant was
`realigned to the candidate variant haplotypes and assessed to
`determine which allele was supported.20 Read-level support
`within each cluster was evaluated to derive an allele
`assignment for the associated fragment or to determine that
`no such assignment could be made reliably. Given the set of
`allele assignments for all fragments covering the locus, a
`statistical model
`incorporating the observed redundancy
`level and error rate was used to determine the expected
`noise level for the putative variant. Given that noise level,
`the Poisson distribution was used to determine the proba-
`bility of observing the obtained number of fragments sup-
`porting the variant. A threshold was applied for variant
`calling: for most variants, variant calls were made when the
`number of fragments unambiguously supporting the variant
`was greater than five; for variants at noisier loci, higher
`thresholds were set on the basis of the level of redundancy at
`the locus and the number of error-containing fragments
`identified.
`
`Variant Filtering
`Final variant calls were annotated for predicted protein
`impact and biological significance. Germline variants were
`removed by referencing dbSNP (release 135) and 1000
`Genomes Project,22 except for known pathogenic germline
`variants, such as certain BRCA1/2 mutations that were
`considered as reportable. Reportable genomic alterations
`were called as known/likely functional driver alterations on
`the basis of presence of the specific variant in the Catalogue
`
`688
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`Case 1:20-cv-01580-LPS Document 1-10 Filed 11/23/20 Page 5 of 18 PageID #: 449
`
`of Somatic Mutations in Cancer23 or more general knowl-
`edge about the gene affected (eg, truncations and deletions
`in known tumor suppressor genes or mutations that have
`been characterized as pathogenic in the scientific literature);
`all other uncharacterized alterations were classified as var-
`iants of unknown significance.
`
`Copy Number Amplification Calling
`CNAs were identified by modeling both coverage variation
`and allele frequencies for common germline polymorphisms
`as a function of amplification at targeted loci, tumor ploidy,
`and overall tumor purity for a sample. Sequence coverage at
`all targets was normalized against a process-matched normal
`control sample and subsequently GC normalized. Measured
`targets were composed of exons, introns, and SNPs, which
`are designed to improve copy number modeling by use of
`allele imbalance, as previously described.1 Thresholds were
`applied to the resulting CNA model on the basis of esti-
`mated tumor purity and ploidy, with the goal of reporting
`amplifications of at least eight copies while avoiding low-
`level gains.
`
`Re-Arrangement Detection
`Re-arrangements were detected by searching for chimeric
`alignments, where one portion of a read was aligned to a
`targeted gene, and the other portion was aligned to another
`location in the genome. Filters were applied to ensure high-
`quality alignments, and a minimum number of reads sup-
`porting the re-arrangement were required, as described.1
`
`Reference Cell Lines, Synthetic Gene Fusions, and
`Clinical cfDNA Samples
`
`For validation of base substitution and indel variant calls,
`purified DNA from 20 lymphoblastoid cell lines from the
`International Hapmap Project (HapMap cell lines) and 26
`cancer cell lines were used to generate reference samples
`(Supplemental Table S2). Various mixtures of cell
`line
`DNA were generated, including one mixture of DNA from
`20 HapMap cell lines and five mixtures of DNA derived
`from 26 cancer cell lines (Supplemental Table S2); cell line
`DNA mixtures were diluted with normal HapMap DNA
`(HapMap NA12878) at varying ratios to generate reference
`samples for validation. Mixtures were generated by pooling
`in equal parts using a Biomek NX (Beckman Coulter,
`Pasadena, CA) and making dilutions with normal HapMap
`DNA; final expected mutant allele frequencies (MAFs) were
`calculated on the actual mixing ratios using a linear
`regression of SNP alternate AFs in the pools (Supplemental
`Table S3).
`For re-arrangement validation, reference samples were
`generated from two mixtures of DNA derived from cancer cell
`lines (Supplemental Tables S4 and S5) that were diluted with
`normal HapMap DNA at varying ratios and five synthetic 1-kbp
`dsDNA gBlock gene fusion constructs (Integrated DNA
`Technology) spiked in to fusion-negative cfDNA isolated from
`
`Analytical Validation of FoundationACT
`
`clinical samples at varying ratios (Supplemental Tables S4
`and S5).
`For CNA validation, DNA from three tumor-normal
`paired cell lines was used (Supplemental Table S6); DNA
`from each cell line was individually diluted into its paired
`normal DNA at varying ratios to generate reference sam-
`ples. A further 29 reference clinical cfDNA samples with
`confirmed amplification by orthogonal assays were included
`in the analysis (Supplemental Table S6).
`Reference DNA samples were sheared to cfDNA-sized
`fragments
`(approximately 200 bp) by ultrasonication
`(Covaris), and 100 ng DNA was analyzed by the Founda-
`tionACT assay.
`
`Genomic Profiling to Determine Reference Variants in
`Cell Lines
`
`Cell line DNA samples were sequenced individually using
`the FoundationOne NGS assay1 to determine the reference
`variants present, including base substitutions and indels in
`dbSNP for HapMap cell lines and base substitutions, indels,
`re-arrangements, and CNAs for cancer cell
`lines. The
`expected MAF for each variant in the pooled reference
`samples was calculated on the basis of the allele frequency
`in the original cell line and the composition/dilution of the
`reference DNA mixtures (Supplemental Tables S3 and S5).
`
`Samples from Healthy Individuals
`
`cfDNA was extracted from blood samples from volunteers,
`aged 18 to 65 years, who all self-reported as healthy without
`history of cancer (Research Blood Components, Boston,
`MA).
`
`Comparison with Orthogonal Assays Used for Clinical
`Samples
`
`For concordance analyses, clinical cfDNA samples were
`processed by FoundationACT, and select genomic alter-
`ations were also evaluated using orthogonal confirmatory
`assays,
`including droplet digital PCR (ddPCR), beads,
`emulsions,
`amplification,
`and magnetics
`(BEAMing),
`FoundationOne NGS, and breakpoint PCR, as outlined
`below. All primers/probes used for ddPCR and breakpoint
`PCR assays are listed in Supplemental Table S7.
`
`ddPCR
`For select base substitutions and indels, probes and primers
`were either predesigned PrimePCR Mutation Assays (Bio-
`Rad, Hercules, CA) or custom synthesized (Integrated DNA
`Technology) and designed according to the ddPCR Applica-
`tions Guide (Bio-Rad). Dual-quenched probes were synthe-
`0
`HEX or FAM reporter, an internal ZEN
`sized with 5
`0
`quencher. For CNAs,
`quencher, and an Iowa Black FQ 3
`probes and primers were predesigned PrimePCR Copy
`Number Variation Assays (Bio-Rad). Each reaction contained
`
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`
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`

`Case 1:20-cv-01580-LPS Document 1-10 Filed 11/23/20 Page 6 of 18 PageID #: 450
`
`Clark et al
`the following: 1  ddPCR Supermix for Probes (no dUTP;
`number 186-3026; Bio-Rad), 250 nmol/L of each probe, 450
`nmol/L (for base substitutions/indels) or 900 nmol/L (for
`CNAs) of each primer, and 30 ng of cfDNA library in a 20-mL
`reaction volume. Emulsion PCR amplifications were per-
`formed in the C1000 Touch Thermal Cycler (Bio-Rad), as
`
`
`follows: 1 cycle of 95
`C for 10 minutes, 40 cycles of 94
`C for
`
`C for 1 minute (base substitutions/indels)
`30 seconds and 55
`
`
`C for 1 minute (CNAs), and 1 cycle of 98
`C for 10
`or 60
`minutes. Droplets were read with the QX200 droplet reader
`(Bio-Rad). QuantaSoft version 1.7.4 (Bio-Rad) was used to
`calculate fractional abundance of base substitutions/indels and
`set thresholds for CNA calling. Positive CNAs were identified
`as greater than the average ddPCR ratio from wild-type sam-
`ples plus 3 SDs.
`
`BEAMing Digital PCR
`Plasma samples from a phase 2 study in hormone recep-
`torepositive, human epidermal growth factor receptor 2
`(HER2)enegative
`metastatic
`breast
`cancer
`(NCT01740336)24 were sent
`to an external
`laboratory
`(Sysmex-Inostics, Baltimore, MD) to perform BEAMing;
`ESR1 E380Q, Y537C/S/N, and D538G base substitutions
`were assessed for concordance.
`
`Hybrid CaptureeBased NGS with FoundationOne
`Extracted cfDNA (50 ng) was submitted for processing to
`the clinical laboratory at Foundation Medicine (Cambridge,
`MA) to be analyzed on the FoundationOne NGS-based
`clinical cancer test1 that includes all targeted territory of the
`FoundationACT assay baitset.
`
`Breakpoint PCR
`Each reaction contained the following: 1  PCR supermix, 450
`nmol/L of each primer, and 30 ng of cfDNA library in a 20-mL
`reaction volume. PCR amplifications were performed in the
`
`C1000 Touch Thermal Cycler as follows: 1 cycle of 95
`C for 10
`
`
`C for 30 seconds and 55
`C for 1
`minutes, 40 cycles of 94
`
`minute, and 1 cycle of 98
`C for 10 minutes. All
`samples, including no template controls, were run in triplicate.
`PCRs were analyzed with the Agilent TapeStation D1000
`assay (Agilent Technologies) for expected product size in the
`library and positive control with no amplification in the wild-
`type DNA and no template control. Breakpoint PCR primers
`0
`were as follows: ROS1 re-arrangement (forward, 5
`-CAT-
`0
`0
`;
`reverse,
`5
`-CCCAAAT-
`GACTGTCTTGGGCAATG-3
`0
`GAGGCAACTGTCTA-3
`), SMO re-arrangement (forward,
`0
`0
`0
`-GCAGATGTGCAAATATCTGGT-3
`; reverse, 5
`-CAG-
`5
`0
`), MYC re-arrangement (for-
`GAAGCCAAAAATGCCTG-3
`0
`0
`0
`ward, 5
`-CGTTAGCTTCACCAACAGGA-3
`;
`reverse, 5
`-
`0
`), VEGFA re-arrangement
`TCATTTCCCACTTGCCACAT-3
`0
`0
`0
`(forward, 5
`-AGGAAGAGTAGCTCGCCG-3
`; reverse, 5
`-
`0
`ACAGCTGCTTTCTCACAGAG-3
`), KIF5B-RET (forward,
`0
`0
`0
`-TCACCAAACCCAATATCACCT-3
`;
`reverse,
`5
`-
`5
`0
`ACTGCTCCGGATGCCTTC-3
`), EML4-ALK number 1
`0
`0
`(forward, 5
`-CAGGCTGGAATGCTGTAGAA-3
`;
`reverse,
`
`0
`0
`), EML4-ALK number
`-TAAGAGCTGGTTGGGACCAC-3
`5
`0
`0
`2 (forward, 5
`-GCCAGAAATTGTTTGAAGTGC-3
`; reverse,
`0
`0
`5
`-CCTGATCAGCCAGGAGGATA-3
`), EML4-ALK number
`0
`0
`0
`3 (forward, 5
`-AGGCTGCATGGAATCTGAA-3
`; reverse, 5
`-
`0
`), EML4-ALK number 4
`GTAGGGCAGCTTCAGTGCAA-3
`0
`0
`0
`(forward, 5
`-TGTTTTCACCGAAATGTGGA-3
`; reverse, 5
`-
`0
`), EML4-ALK number 5
`AGGAATTGGCCTGCCTTAGT-3
`0
`0
`0
`-CTGGAGGCAGGGAGGAATA-3
`; reverse, 5
`-
`(forward, 5
`0
`), EML4-ALK number
`TACATAGGGTGGGAGCCAAA-3
`0
`0
`6
`(forward,
`5
`-CAGGCACCATGTATAAAATTGCT-3
`;
`0
`0
`-ACAGAGTTGGAGAAGAGCCA-3
`), EML4-
`reverse, 5
`0
`-TCAGGGGCGCTAAT-
`ALK number
`7
`(forward,
`5
`0
`0
`0
`; reverse, 5
`-TGCTCAGCTTGTACTCAGGG-3
`),
`GAACA-3
`0
`-ACACCTGAGA-
`EML4-ALK number
`8
`(forward,
`5
`0
`0
`TAACTGTCCCA-3
`;
`reverse, 5
`-TCTGGAGCCAAAGT-
`0
`0
`), and EML4-ALK number 9 (forward, 5
`-
`CAGTCA-3
`0
`0
`; reverse, 5
`-GGGACT-
`TACGTGCTCGGCAATTTACA-3
`0
`GATCAAAGCAGAA-3
`).
`
`Calculation of Performance Statistics
`
`the reference alteration set was
`For sensitivity analysis,
`defined on the basis of FoundationOne NGS results from
`component cell lines analyzed individually. Each variant
`line at >15% allele frequency was
`found in any cell
`included in the reference set (a conservative threshold
`chosen to ensure a high-quality allele frequency estimate).
`Expected allele frequencies for all variants in the cell line
`mixes were determined on the basis of mixing ratios: mixing
`ratios were adjusted to account for variability in the mixing
`process and calculated on the basis of the observed allele
`frequency of variants that were unique to each component
`cell line in the mixture. All on-target variants from reference
`samples with an expected MAF 0.125% were assigned
`either a true positive (TP) if detected or false negative (FN)
`if not detected (Supplemental Tables S3 and S5). Sensitivity
`was calculated as follows: TP/(TP þ FN).
`For positive predictive value (PPV) analysis, each called
`variant was classified as a TP if a matching alteration was
`detected in the reference sample or as a false positive (FP) if
`a matching alteration was not detected. PPV was calculated
`as follows: TP/(TP þ FP).
`One variant (ERBB2 P232T chromosome 17:37866389
`C>A) that was observed by FoundationACT at low allele
`frequency was confirmed to be present in the reference
`samples by ddPCR and was excluded as an FP from anal-
`ysis. Calls made at the top dilutions within a dilution series
`at low allele frequency were excluded from the analysis as
`unconfirmed, but were likely true positives as any variant at
`<0.5% allele frequency in the top-dilution cell line mixture
`could reasonably have been present at an allele frequency of
`<15% in a component cell line.
`The unique coverage obtained for the validation experi-
`ments was biased toward the top end of the range of coverages
`observed for clinical samples (Supplemental Figure S1A). To
`determine performance measures that match the full spectrum
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`of coverage of routine clinical samples, including those at the
`lower bounds of coverage, the validation data sets were down-
`sampled to lower
`levels of
`coverage
`(Supplemental
`Figure S1B). For clinical samples,
`lower fragment-level
`coverage is accompanied by increased redundancy (read
`pairs per fragment). It is not possible to simulate increased
`redundancy or the higher quality of data that comes with it,
`meaning that the sampled data set represents a lower limit of
`performance. To prevent additional loss of redundancy in the
`simulation, the sampling was performed at the fragment level,
`retaining all reads associated with each selected fragment. The
`overall coverage distribution that we observe in clinical
`practice was approximated well by an equally weighted
`combination of samplings to 40%, 50%, 60%, 70%, 80%,
`90%, and 100% of
`the initial experimental data set
`(Supplemental Figure S1). The final reported performance
`statistics reflect this averaged sampled data set.
`
`Prospective Clinical Genomic Profiling Results of
`FoundationACT
`
`Approval for this study, including a waiver of informed
`consent and a Health Insurance Portability and Account-
`ability Act of 1996 waiver of authorization, was obtained
`from the Western Institutional Review Board (protocol
`20152817). Samples (16 to 20 mL whole blood) were
`submitted by clinicians for genomic profiling in the course
`of routine clinical care and processed in our CLIA-certified,
`CAP-accredited, NY Stateeapproved laboratory using the
`FoundationACT assay, as described above. Data are pre-
`sented from 884 consecutive clinical samples analyzed by
`FoundationACT. For
`the most common cancer
`types
`sequenced, the frequency of genomic alterations observed
`by genomic profiling of cfDNA in this study were compared
`with the corresponding frequency in the Foundation Medi-
`cine database of genomic profiling results from tissue
`
`Analytical Validation of FoundationACT
`
`samples sequenced using the FoundationOne assay,2 con-
`taining >10,000 cases of NSCLC, breast cancer, and colo-
`rectal cancer and >2000 cases of prostate cancer. For 36
`patients, temporally matched tissue samples were sequenced
`using the FoundationOne assay and assessed for concor-
`dance with blood samples analyzed by FoundationACT;
`concordance analysis was limited to reportable genomic
`alterations that are covered by both assays.
`
`Results
`Hybrid CaptureeBased NGS Assay for Genomic
`Profiling of ctDNA from Blood
`
`The FoundationACT assay was developed to identify genomic
`alterations from ctDNA in the blood of patients with cancer. A
`summary of the assay workflow is outlined in Figure 1. In brief,
`20 ng of cfDNA was extracted from plasma and underwent
`library construction, where input cfDNA fragments were tag-
`ged with molecular fragment barcodes. Sequencing libraries
`underwent hybridization capture using a custom gene panel
`and were sequenced to generate >50 million read pairs of raw
`data, which typically correspond to a raw on-target coverage of
`>25,000. Fragment barcodeebased error detection enabled
`detection of genomic alterations, including short variant mu-
`tations (base substitutions and indels) and re-arrangements at
`low AFs, as well as CNAs.
`
`Validation Approach
`
`To estimate the accuracy of the test, reference samples with
`defined variants in a diversity of assayed genes were
`generated using DNA from normal HapMap cell
`lines,
`cancer cell lines, synthetic DNA constructs, and clinical
`cfDNA samples; 100 ng of each reference sample was
`analyzed by FoundationACT. Sensitivity and PPV were
`
`Figure 1
`ctDNA genomic profiling assay workflow and fragment molecular barcodeebased sequencing and error detection approach. A: Peripheral whole
`blood (16 to 20 mL) is collected in cfDNA collection tubes, plasma is isolated, and cfDNA is extracted. B: cfDNA (20 to 100 ng) undergoes library construction,
`tagging with fragment barcodes, library amplification, and hybridization capture. C: Sequencing is performed using the Illumina HiSeq 4000 platform (2  151
`bp paired-end sequencing) to generate 50 to 100 million read pairs. Fragment barcodes are used to identify multiple reads originating from the same unique
`input cfDNA fragment for subsequent error detection. D: Base substitutions, insertions/deletions, gene re-arrangements, and copy number amplification are
`called, considering detected errors. Benign germline variants are filtered (dbSNP and 1000 Genomes Project). Driver alterations are called as known and
`clinically annotated to highlight potential matching approved targeted therapies and clinical trials.
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`Clark et al
`
`determined by comparing the variants detected by the
`FoundationACT assay with expected variants