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`EXHIBIT 59
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`EXHIBIT 59
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`RESEARCH ARTICLE
`
`CANCER GENOMICS
`
`Noninvasive Identification and Monitoring of
`Cancer Mutations by Targeted Deep
`Sequencing of Plasma DNA
`
`Tim Forshew,‘* Muhammed Murtaza,"2* Christine Parkinson,"2'3* Davina Gale,”
`Dana W. Y. Tsui,'* Fiona Kaper,4+ Sarah-Jane Dawson,""'3 Anna M. Piskorz,”2
`Mercedes Jimenez-Linanf"s David Bentley,6 James Hadfield,1 Andrew P. May,‘ Carlos Caldas,
`James D. Brenton,"2'3'7* Nitzan Rosenfeldm"
`
`‘I 3,3,7
`
`Plasma of cancer patients contains cell-free tumor DNA that carries information on tumor mutations and tumor
`burden. Individual mutations have been probed using allele-specific assays, but sequencing of entire genes to de-
`tect cancer mutations in circulating DNA has not been demonstrated. We developed a method for mgged-amplicon
`deep sequencing (TAm-Seq) and screened 5995 genomic bases for low-frequency mutations. Using this method, we
`identified cancer mutations present in circulating DNA at allele frequencies as low as 2%, with sensitivity and spec-
`ificity of >97%. We identified mutations throughout the tumor suppressor gene TP53 in circulating DNA from 46
`plasma samples of advanced ovarian cancer patients. We demonstrated use of TAm-Seq to noninvasively identify
`the origin of metastatic relapse in a patient with multiple primary tumors. In another case, we identified in plasma
`an EGFI-‘t mutation not found in an initial ovarian biopsy. We further used TAm-Seq to monitor tumor dynamics, and
`tracked 10 concomitant mutations in plasma of a metastatic breast cancer patient over 16 months. This low-cost,
`high-throughput method could facilitate analysis of circulating DNA as a noninvasive "liquid biopsy” for person-
`alized cancer genomics.
`
`INTRODUCTION
`
`Circulating cell-free DNA extracted from plasma or other body fluids
`has potentially transformative applications in cancer management
`(1-7). Characterization of tumor mutation profiles is required for in-
`formed choice of therapy, given that biological agents target specific
`pathways and efiéctiveness may be modulated by specific mutations
`(8—11). Yet, mutation profiles in different metastatic clones can differ
`significantly from each other or from the parent primary tumor (12. 13).
`Evolutionary changes within the cancer can alter the mutational spec-
`trum of the disease and its responsiveness to therapies. which may
`necessitate repeat biopsies (14—17). Biopsies are invasive and costly and
`only provide a snapshot of mutations present at a given time and lo—
`cation. For some applications. mutation detection in plasma DNA as a
`“liquid biopsy” could potentially replace invasive biopsies as a means
`to assess tumor genetic characteristics (2—7). Sensitive methods for de—
`tecting cancer mutations in plasma may [ind use in early detection
`screening (1), prognosis, monitoring tumor dynamics over time or de—
`tection of minimal residual disease (3, 18, 19). In high—grade serous
`
`
`its String Centre, Robin-son
`‘Cancei Regearcn Uif. Cairibridge Research institute, Li
`Way, Cambridge C82 OHi, Ult, iDepartnienl or Oncology, University ol Cambridoe,
`Addenbinoie‘s Hospital, Hill‘? Road, Cambridge C82 OQQ, UK. :‘Addenhroolre‘s Hos-e
`rural, Cambridge Unwersrty Hospital NHS foundation Trust and National lnslilrite tor
`i‘lealtl'r Research Cambridge Biomedical Researcri Centre. Cambridge CBZ ZQQ UK
`“Filildigrr: Corporation, 7000 Shoreline Court. finite ltit‘l, Sotitn San Francisco, CA 9403!].
`USA Department of Histopatnology, Aclclenbroolre's Honpital, Cambridge CB2 000,
`UK.
`t‘illurnina Cambridge, Chestertorrl Research Part Little Ctresrertord, Cambridge
`CBIO tilt, UK. "Cambridge Experimental Cancer Medicine Centre. rilartibrrclge “.82
`ORE. UK
`*Tl’iese authors contributed equally to this worlc
`tPreserit address, lliumrria, inc. 5200 illtiiriina Way, Lian Diego. CA 92122, USA
`1T0 whom correspondence inotrld be addressed Email" i’rinan rcruenirrldfarcancer orci.
`mi. (N R): iJineebientonrorancerzorgiii. U D B)
`
`ovarian carcinomas (HGSOC), mutations in the tumor suppressor
`gene TP53 have been observed in 97% of cases (20, 21), but these are
`located throughout the gene and are difficult to assay. A cost-efiective
`method that could detect and measure allele frequency (AF) of TP53
`mutations in plasma may be highly applicable as a biomarker for
`HGSOC (22).
`Circulating DNA is fragmented to an average length of 140 to
`170 base pairs (bp) and is present in only a few thousand amplie
`fiable copies per milliliter of blood, of which only a fraction may be
`diagnostically relevant (2, 3. 23—25). Recent advances in noninvasive
`prenatal diagnostics highlight the clinical potential of circulating
`DNA (25—28), but also the challenges involved in analysis of circulating
`tumor DNA (ctDNA). where mutated loci and AFs may be more var—
`iable. Various methods have been optimized to detect extremely rare
`alleles (1, 2. 6, 7, 29—31), and can assay for predefined or hotspol
`mutations. These methods, however, interrogate individual or few
`loci and have limited ability to identify mutations in genes that lack
`mutation hotspots, such as the TP53 and PTEN tumor suppressor
`genes (32). In patients with more advanced cancers, ctDNA can com~
`prise as much as 1% to 10% or more of circulating DNA (2), presenting
`an opportunity for more extensive genomic analysis- Targeted
`resequencing has been recently used to identify mutalions in selected
`genes at AFs as low as 5% (33—35). However, identifying mutations
`across size-able genomic regions spanning entire genes at an AF as
`low as 2%, or in few nanograms of fragmented template from circu-
`lating DNA. has been more challenging.
`In response, we describe a tool for noninvasive mutation analysis
`on the basis of tagged—amplicon deep sequencing (TAm—Seq), which
`allows amplification and deep sequencing of genomic regions span-
`ning thousands of bases from as little as individual copies of fragmented
`DNA. We applied this technique for detection of both abundant and
`
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`RESEARCH ARTICLE
`
`rare mutations in circulating DNA from blood plasma of ovarian and
`breast cancer patients. This sequencing approach allowed us to
`monitor changes in tumor burden by sampling only patient plasma
`over time. Combined with faster. more accurate sequencing technolo-
`gies or rare allele amplification strategies, this approach could poten—
`tially be used for personalized medicine at point of care.
`
`RESULTS
`
`Targeted deep sequencing of fragmented DNA by TAm-Seq
`To amplify and sequence fragmented DNA1 we designed primers to
`generate amplicons that tile regions of interest in short segments of
`about 150 to 200 bases (Fig. 1A and table 51). incorporating universal
`
`
`
`1200
`
`1000
`
`300
`
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`
`400
`
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`
`
`
`Numberofnonreferencebases
`
`1200
`
`1000
`
`800
`
`600
`
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`
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`
`
`
`0.002
`
`0.004
`
`0.006
`
`Frequency of nonreference allele
`
`0
`
`0.02
`
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`
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`
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`
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`
`Frequency of nonreference allele
`
`
`
`2000
`1000
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`1000
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`1000
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`
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`4000
`2000
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`2000
`
`A 6
`
`50bpb——————————————————————————————————————q
`
`1
`
`a
`—..
`
`TP53——-———————
`Exon 6
`been 5
`
`DNA (dilute or degraded)
`
`1
`Preamplification
`
`i 1 1 V
`V2 E ’1‘
`
`
`
`
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`1 l
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`Pool and sequence
`
`
`
`Numberofnonreferencebases
`
`0
`
`0.005
`
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`
`0005
`
`001
`
`0
`
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`
`0.01
`
`400
`200
`
`2000
`1000
`
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`
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`
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`
`0.01
`
`0
`
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`
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`
`)
`-000
`1000
`
`A>T 1000
`500
`
`l
`OT 4001
`2000
`
`4000
`000
`
`be
`
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`
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`
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`
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`
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`
`0.01
`
`0
`
`0.005
`
`0.01
`
`0.005
`
`0.01
`
`Frequency of nonreference allele
`
`Fig. 'I. Overview of tagged amplicon sequencing (TAm-Seq). (A) Illustration
`of amplicon design. Primers were designed to amplify regions of interest in
`overlapping short ampllcons (table Si). Amplicon design is illustrated for a
`region covering exons 5 to 6 of TP53. Colored bars, segmented into forward
`and reverse reads, show regions covered by different amplicons (excluding
`primer regions). Sequencing adaptors are attached at either end, such that a
`single-end read generates separate sets of forward and reverse reads (fig. Si ).
`Because amplicons are mostly shorter than 200 bp, the forward and reverse
`reads also partially overlap. Figure adapted from University of California, Santa
`Cruz, Genome BrOWSer (htth/genomeucscedu/i. (B) Workflow overview. Mul-
`tiple regions were amplified In parallel. An initial preamplification step was
`
`performed for 15 cycles using a pool of the brget—specific primer pairs to pre-
`serve representation ofall alleles in the template material‘l‘he schematic diagram
`shows DNA molecules that carry mutations (red stars) being amplified alongside
`wild-type molecules. Regions of interest in the pream plified material were then
`selectively amplified in individual (singleplex) PCR. thus excluding nonspecific
`products. Finally, sequencing adaptors and sample-specific barcocles were
`attached to the harvested amplicons in a further PCR. (C) Distribution of ob-
`served nonreference read frequencies, averaged over 47 FFPE samples, across
`all loci and all nonreference bases. Inset expands the low-frequency range. (D)
`Distribution of the observed background nonreference read frequencirfi aver-
`aged over 47 FFPE samples for the i2 different A/C/G/l’ base substitutions.
`
`www.5dente‘rranslatinnalMedicineorg
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`RESEARCH ARTICLE
`
`adaptors at 5’ ends (fig. Si ). Performing single—pleat amplification with
`each of these primer pairs would require dispersing the initial sam—
`ple into many separate reactions. considerably increasing the prob—
`ability of sampling errors and allelic loss. Multiplex amplification
`using a large set of primers could result in nonspecific amplification
`products and biased coverage. We therefore applied a two-step ampli-
`flcation process: a limited—cycle preamplification step where all primer
`sets were used together to capture the starting molecules present in
`the template, followed by individual amplification to purify and select
`for intended targets (Fig. IB) (Supplementary Methods). The final
`concentration of each primer in the preamplification reaction was
`50 nM, reducing the potential for interprimer interactions, and 15 cy—
`cles of long-extension (4 min) polymerase chain reaction (PCR) were
`used to remain in the exponential phase of amplification We used a
`mio‘ofluidic system (Access Array, Fluidigm) to perform parallel single-
`plex amplification from multiple preamplified samples using multiple
`primer sets. An additional PCR step attached sequencing adaptors
`(fig. 51) and tagged each sample by a unique molecular identifier
`or “barcode” (table 82). Sequencing adaptors were separately attached
`at either end and the products mixed together, such that singleend
`sequencing generated separate sets of forward and reverse reads. We
`performed 100-base single—end sequencing (GAIIx sequencer, Illurnina),
`with an additional 10 cycles using the barcode sequencing primer.
`generating ~30 million reads per lane. This produced an average read
`depth of 3250 for each of 96 barcoded samples for 48 amplicons read
`in two possible orientations.
`
`Validation and sensitivity for mutation identification in
`ovarian tumor samples
`We designed a set of 48 primer pairs to amplify 5995 bases of genomic
`sequence covering coding regions (exons and exon junctions) of TP53
`and PTEN, and selected regions in EGPR. BRAF, KRAS, and PIKBCA
`(table 51) by overlapping short amplicous (Fig. 1A). The sequenced
`regions cover mutations that account for 38% of all point mutations
`in the COSMIC database (v55) (32). We used TAm-Seq to sequence
`DNA extracted from 47 fortnalin‘fixed, paladin-embedded (FFPE)
`tumor specimens of ovarian cancers (table 83), which were also se-
`quenced for T1353 by Sanger sequencing (36) (Supplementary Meth-
`ods). DNA extracted from FFPE samples is generally degraded and
`fragmented as a result of fixation and long-term ambient storage. We
`amplified DNA from each sample in duplicate, tagging each replicate
`with a different barcode. Using a single lane of sequencing, we gen—
`erated 3.5 gigabases of data passing signal purity filters, producing
`mean read depth of 3200 above Q30 for each of the 9024 expected
`read groups (48 amplicons x 2 directions x 94 barcoded samples). Back-
`ground fi'equencies of nonreference reads were ~0.1% (median, 0.03%;
`mean, 0.2%; in keeping with Q30 quality threshold applied), yet varied
`substantially between loci and base substitutions (Fig. 1C) and showed
`a clear bias toward purine/pyrimidine conservation (Fig. 1D). Sixty—six
`percent of loci had mean background rate of <O.1%, and 96% of loci
`had backgt‘ormd rate of <0.6%.
`The data set interrogated nearly 18,000 possible single—base substi-
`tutions for each sample, which introduces a risk of false detection To
`control for sporadic PCR errors and reduce false positives, we called
`point mutations in a sample only if noru'eterence AFs were above the
`respective substitution—specific background distribution at a high con-
`fidence margin (0.9995 or greater), and ranked high in the list of non-
`reference AFs, in both replicates (Supplementary Methods). Duplicate
`
`sequencing data were obtained for 44 samples, and 43 single-base 51le
`stitutions were called (table S3). These matched 100% of mutations
`identified by Sanger sequencing and included three additional muta-
`tions at low AFs that were below detection thresholds of Sanger sequenc-
`ing (fig. 82). The upper bound of AFs that may have been missed was
`estimated (Supplementary Methods) at <5% for 36 of 44 FFPE sam—
`ples (82%) and <10% for 42 of 44 samples (95%), with median value
`of 1.3% and mean value of 2.7%. Mutant AFs were highly reproduc~
`ible in duplicate samples. For 42 of 43 mutations called, the dilference
`in measured frequency between duplicates was less than 0.08, and the
`relative difference was 25% or less (Fig 2A). Mutant AFs correlated
`significantly with tumor cellularity in the FFPE block (correlation
`coefficient = 0.422; P = 0.0049, t test) (Fig. 28).
`In a separate run, we sequenced libraries prepared from six differ»
`ent diluted mixtures of six FFPF. samples, with a different known point
`mutation in TP53 in each, to mean read depth of 5600. Of more than
`100,000 possible non—SNP (single-nucleotide polymorphism) substitu-
`tions, we identified all 33 expected point mutations present at AF >1%,
`including 6 mutations present at AF <2%, with one false-positive called
`with AF : 1.9%. Using less stringent parameters (Supplementary Meth-
`ods), we identified three additional mutations present at AF 2 0.6%
`(Fig. BC), with no additional false positives. Thus, we obtained 100%
`sensitivity, identifying mutations at AFs as low as 0.6%. A positive pre-
`dictive value (PPV) of 100% was calculated for mutations at AF >2%,
`and 3 PPV of 90% for mutations identified at AF <2% (Fig. 2D).
`
`Quantitative limitations of mutation detection
`When applying TAm»Seq to measure a predefined mutation (as op-
`posed to screening thousands of possible substitutions), the frequency
`of the mutant allele can be read out directly from the data at the
`desired locus. False detection is less likely, and criteria for confident
`mutation detection for a predefined substitution can be less stringent
`than those described above for de novo mutation identification (Sup—
`plementary Methods). The minimal nonreference AFs that could be
`detected depend on the read depth and background rates of nonrefer—
`ence reads, which vary per locus and substitution type Minimal de—
`tectable frequencies increase when higher confidence margins are used
`(Supplementary Methods) and had a median value of 0.14% at con-
`fidence margin of 0.95 and 0.18% at confidence margin of 0.99 (fig.
`S3). The minimal detectable frequency would also be limited if a min-
`imal number of reads is applied for confident mutation detection; for
`example, a minimum of 10 reads implies that sequencing depth of
`5000 would be required to detect mutations at AF as low as 0.2%.
`For alleles present at ~10 or fewer copies in the starting template, rev
`producibility would also be limited by sampling noise, because these
`alleles may be over— or underrepresented in any particular reaction.
`To characterize the quantitative accuracy of TAm-Seq as applied to
`circulating DNA, we simulated rare circulating tumor mutations by
`mixing plasma DNA from two healthy individuals. Using the same
`set of primers as used for the FFPE experiment, we identified that
`these two individuals differ-ed at five known SNP loci (table S4). Total
`amplifiable copies in both plasma DNA samples were detennined by
`digital PCR and mixed to obtain minor AFs ranging from 0.16% to
`40% (Supplementary Methods). We sequenced diluted templates
`containing between 250 and <1 expected copy of the minor allele (ta~
`ble $5). The coefficient of variation (CV) of the observed AFs was
`equal on average to the inverse square root (thi) of the expected
`number of copies of the rare allele (Fig. 3A). which is the theoretical
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`RESEARCH ARTICLE
`
` A B
`
`
`
`to
`Coimlatificoefficlént: 0.422.
`
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`
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`
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`Mutations (sorted by allele frequency)
`
`to 120 p1 of plasma, we performed du-
`plicate pleamplilication reactions for each
`sample. For all seven patients, TP53 tu—
`mor mutations were identified in the cir-
`
`milating DNA at frequencies of4% to 44%
`(Table 1). In one plasma sample collected
`from an ovarian mncer patient at relapse.
`we also identified a de novo mutation in the
`
`tyrosine kinase domain of EGFR (exon 2i).
`at AF of 6% (patient 27. Table 1). We sub—
`sequently validated the presence of this
`mutation in plasma by performing repli‘
`cate Sanger sequencing reactions of highly
`diluted template (Supplementary MeLh~
`ods), and 4 of91 wells that were sunsessful—
`ly Sanger—sequenced contained the EGFR
`mutation (fig. S4). We fiu'ther validated
`the presence of this mutation by design—
`ing a sequence-specific TaqMan probe
`targeting this mutation and performing
`digital PCR (Table l). The mutation was
`also identified by TAm—Seq in additional
`plasma collected from the same individual
`(sample 16, Table 2). This mutation in
`EGFR was not found in the ovarian mass
`
`removed by interval debulking surgery
`15 months before the blood sample was
`collected, although the same sample did
`contain the concomitant TP53 mutation
`
`found in the same patients plasma. at AF
`of 85% (patient 27, table S3). We subse
`quently used TAm—Seq to sequence seven
`additional samples collected at the time
`of initial surgery including deposits in
`right and left ovaries and omentum. The
`EGFR mutation was detected in the two
`
`
`
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`Frequency of mutant allele (repeat 1)
`
`Fig. 2. Identification of mutations in ovarian cancer FFPE samples by TAm-Seq. (A) Concordance be-
`tween duplicate measurements of AFs of mutations identified in fragmented DNA extracted from
`FFPE samples. The mutatlon frequency in each library was calculated as the fraction of reads with
`the mutant (nonreference) base. Solid line indicates equality. Dotted lines indicate a difference in
`AF of 0.05. (8) Correlation of AF with FFPE tumor cellularityi The measured mutant AF (average of
`both repeats) Correlated significantly with the cellularity, estimated from histology (table 53). (C) Con-
`cordance between duplicate measurements of AFs of mutations identified in a mixture of DNA
`extracted from different FFPE samples. (D) Summary of mutations called in FFPE using TAm—Seq.
`sorted by increasing AF. Dotted line indicates AF of 2%.
`
`limit of accuracy set by the Poisson distribution for independently
`segregating molecules. We compared the observed AP to Ihe expected
`AF for cases where more than six copies of the minor allele were
`expected. 0124 such cases, the root mean square (RMS) relative error
`between the expected and the observed frequency was 14%. with on—
`ly 2 of 24 cases exhibiting more than 20% discrepancy. For samples
`with expected minor AF of 0.025, the RMS error was 23% (Fig. 3B).
`
`Noninvasive identification of cancer mutations
`
`in plasma circulating DNA
`We applied TAm-Seq to directly identify mutations in plasma of can—
`cer patients. We studied a cohort of samples from individuals with
`HGSOC. These samples were first analyzed for tumor—specific muta-
`tions using digital PCR (Supplementary Methods), a method that is
`highly accurate (2, 3, 7, 37) but requires design and validation of
`a different assay for every mutation screened and relies on previous
`identification of mutations in tumor samples from the same patients
`(2. 3). We initially selected for analysis seven cases that had relatively
`high levels of circulating mutant TP53 DNA in the plasma (as assessed
`by digital PCR). Using the equivalent amount of DNA present in 30
`
`omental samples above the 0.99 confi—
`dence margin (fig. 53) at AF of 0.7%, but
`was not detected in the six ovarian samples (below the 0.8 confidence
`margin). Without previous identification in plasma, this mutation
`would not have been directly identified on screening those samples
`using high—specificity mutation identification criteria owing to its
`low AP. In contrast, the TP53 mutation was identifiable in all biopsy
`and plasma samples (Fig. 4A). The frequency of mutant alleles in the
`relapsed tumor could not be directly assessed because a biopsy at re-
`lapse was not available.
`We validated the TAm-Seq method on a larger panel of plasma
`samples in which levels of tumor-specific mutations were measured
`in parallel using patient-specific digital PCR assays. DNA extracted
`from 62 additional plasma samples collected at different time points
`from 37 patients with advanced HGSOC was amplified in duplicate
`(table 56), using DNA present in ~0.15 ml of plasma per reaction
`(range, 0.06 to 0.2 ml). Amplicon libraries were tagged and pooled
`together for sequencing with libraries- prepared from 24 control sam~
`pies. This generated an average sequencing depth of 650 for 62 plasma
`samples, sufficient to detect mutations present at AFs of 1% to 2%. Of
`>l.5 million possible substitutions. 42 mutations were called using
`the parameters previously optimized for FFPE analysis (table S6).
`
`www.5denceTranslatinnalflledicine.org
`
`30 May 2012 Vol 4 Issue 136 136ra68
`A0898
`
`4
`
`FM1616-00510152
`
`A0898
`
`

`

`Case 1:20-cv-01580-LPS Document 40-2 Filed 03/05/21 Page 6 of 191 PageID #: 8417
`Case 1:20-cv-01580-LPS Document 40-2
`Filed 03/05/21 Page 6 Of 191 PageID #: 8417
`
`RESEARCH ARTICLE
`
`
`> C
`
`C
`
`a
`5
`i}
`7‘;
`3
`<
`
`u
`'0
`
`'0'
`
`w;
`
`Fig. 3. Noninvasive identification and
`quantification of cancer mutations in plasma
`DNA by TAm—Seq. (A) Sampling noise In
`sequencing of sparse DNA using dilutions
`of plasma DNA from healthy individuals.
`CV of triplicate AF readings was calculated
`for each of the five SNPs in each of the
`mixes, which had varying numbers of copies
`of the minor allele (n) (blue dots). Bin av-
`erages (red diamonds) are the mean CVs
`calculated for each bin (bin edges denoted
`by the dotted vertical lines). A linear fit to
`the log; of the mean CV as a function of
`the log; expected copy number was cal-
`culated (black line). Two data points, with
`(n = 100. CV = 0.0064) and (n = 32, CV =
`0.0185), were omitted from the figure for
`enhanced scaling. Three data points with
`minor allele copies of (0.8 were omitted
`from the analysis (n = 0.51. CV = 0.62; n =
`0.41. CV = 0.86: n = 0.20, CV = 0.99). (B)
`Expected versus observed frequency of
`rare alleles in a dilution series of circulating
`DNA. Mean observed frequency was calcu-
`lated for each of five SNPs for samples.
`where expected initial number of minor
`allele copies was greater than 6. Expected
`frequencies were calculated on the basis
`of quantification by digital PCR. Dotted
`lines represent 20% deviation from the ex-
`pected frequencies. Inset highlights cases
`with expected minor AF <0.025. (C) Muta-
`tions identified in 62 plasma samples from patients with advanced HGSOC
`using TAm-Seq. AFs are based on digital PCR measurement for con-
`firmed mutations (identified or missed by TAm—Seq), and on TAm-Seq
`for the false positives called using parameters optimized for analysis
`
`(CV)
`
`oefficientofvariation
`
`
`Number of copies of the minor allele (n)
`
`esp
`unmet”
`
`jam“:
`J:
`012m”
`Dis-13°
`d1
`
`6 Hwy,“
`a Missed
`’ False ”“5"“
`-------------
`.
`‘
`|
`4o
`30
`20
`10
`Mutaiions (sorted by allele frequency)
`
`9"?
`J- -
`-
`c:
`
`51625595
`151800899
`317337350
`51050171
`
`510241451 Observed
`
`frequency01minorallele
`
`0
`
`0,1
`
`0.2
`
`03
`
`0.4
`
`05
`
`Expected frequency of minor allele
`
`if
`é
`5
`5‘
`E
`‘5;
`g
`.3
`i
`
`
`
`
`
`“oz'91ieqtuaoaauoisanfiliq(SJO'BBLueouaiosLuiS//tdnuwonpepEOIUMOCI
`
`10 a
`
`10..
`Allele frequency by digital PCR
`
`10a
`
`of FFPE samples. The dashed horizontal line indicates AF of 2%. Mu-
`rations detected by digital PCR at AF <1% are not shown. (D) AFs
`measured by TAm—Seq versus digital PCR for mutations identified in
`plasma DNA.
`
`embedded (FFPE) sample was not included in the TAm-Seq set and the
`Table 'i. Mutations identified by TAm-Seq in plasma samples from seven
`mutation was validated in FFPE by Sanger sequencing. CA125 was
`ovarian cancer patients. TAm-Seq was used to sequence DNA extracted
`measured at time of plasma collection. Mean depth of coverage at the'mu-
`from plasma of subjects with HGSOC (stage Ill/lV at diagnosis). Plasma
`tation locus in the TAm-Seq data was averaged over the repeats (RMS
`was collected when patients presented with relapse disease, before initia-
`
`tion of chemotherapy. For patient 46, DNA from a formalin-fixed, paraffin— deviation = 850). AF, allele frequency; N, no; Y, yes.
`
`Patient Age at
`.
`ID
`diagnosis
`
`8
`
`12
`14
`
`25
`
`27"
`
`31
`46
`
`60
`
`62
`58
`
`61
`
`68
`
`64
`56
`
`Time elapsed
`Mean
`Mean
`Mutation
`Plasma per
`since surgery
`A.F
`Mea." AF
`depth
`Protein Detsdea
`and base
`amplification
`(months);
`using
`usmg
`.
`In
`change
`.
`number of
`.
`.
`(sequencing
`change
`reaction
`.
`prevrous
`(
`I)
`(genome
`FFPE
`reads)
`TAm-Seq digital
`lines of
`"
`build h919)
`PCR
`
`chemotherapy
`
`CA‘I'25
`(Wml)
`
`Gene
`
`13; 1
`
`27; 3
`50; 3
`
`9:
`
`1
`
`15; 1
`
`12; 'l
`30; 2
`
`2122
`
`365
`260
`
`944
`
`1051
`
`313
`1509
`
`50
`
`50
`120
`
`110
`
`90
`
`30
`30
`
`TP53
`
`1727577120
`
`C>T p.R273H
`
`TP53
`TP53
`
`TP53
`
`1727577579
`1717578212
`
`Fifi/234‘
`G>T
`G>A p.R213‘
`
`1717578404
`
`A>T
`
`p.C176$
`
`1727578262
`TP53
`EGFR 7155259437
`
`C>G p.R196P
`G>A p.R832H
`
`Y
`
`Y
`Y
`
`Y
`
`Y
`N
`
`5000
`
`5000
`5800
`
`4800
`
`7700
`5700
`
`0.09
`
`0.10
`0.15
`
`0.04
`
`0.06
`0.06
`
`0.10
`
`0.08
`0.12
`
`0.08
`
`0.14
`0.05
`
`TPB 1717578406
`TP53
`1717578406
`
`0.56
`0.44
`4500
`Y
`C>T p.R‘l 75H
`
`C>T p.R175H
`Y
`4200
`0.23
`0.30
`
`‘indlcares stop codon.
`
`thh a TP53 and an EGFR mutation were identified in this sample (Fig. 4A).
`
`www,Scien:eTranslationalMedicine.org
`
`30 May 2012 V014 Issue 136 136ra68
`A0899
`
`5
`
`FM1616-00510153
`
`A0899
`
`

`

`Case 1:20-cv-01580-LPS Document 40-2 Filed 03/05/21 Page 7 of 191 PageID #: 8418
`Case 1:20-cv-01580-LPS Document 40-2 Filed 03/05/21 Page 7 of 191 PagelD #: 8418
`
`RESEARCH ARTICLE
`
`Table 2. Mutations identified by TAm-Seq In a set of 62 plasma sam.
`ples from ovarian cancer patients. Forty mutations were identified by
`TAm~Seq using stringent parameters for mutation calling. Plasma sam-
`
`ples described in this table are distinct from those in Table 1, but pa-
`tients included overlap. Additional data on patients and mutations are
`provided In table 56.
`
`Plasma volume per
`Sample
`number amplification teaction (pl)
`1
`70
`
`Luis)
`
`‘OODNIUVUTA
`
`lo
`
`160
`
`150
`120
`
`120
`120
`
`190
`160
`
`160
`
`160
`
`DNA amount per
`amplification reaction ing)
`
`0.9
`4.2
`
`5.7
`9.9
`
`1 .4
`2.1
`
`1 7.9
`
`14.8
`
`10.7
`
`6.1
`
`Protein
`change
`
`p.R273C
`
`p.R24BQ
`
`13.112480
`
`p.R213X
`
`p.C141V
`
`p.C141Y
`p.1195N
`
`p.8175H
`p.R175H
`
`p.R175H
`
`Mean depth
`(sequencing teadsi’
`640
`
`340
`
`640
`
`810
`
`680
`
`720
`300
`
`510
`
`550
`
`Mean AF using Mean AF using
`TAm~Seq
`digital PCB
`
`0.260
`
`0.244
`0.507
`
`0.059
`
`0.021
`
`0.044
`
`0.091
`
`0.608
`
`0.526
`
`0.65 1
`
`0.167
`
`0.15.0
`0.410
`0.035
`
`0.013
`
`0.038
`
`0.081
`0.627
`
`0.604
`
`0.682
`
`4.9
`
`2.8
`
`2.5
`3.0
`
`3 .7
`
`4.2
`
`4.4
`
`5.2
`
`3.6
`
`p.R175H
`
`p.C135R
`
`p.C135R
`
`p.C135R
`13.11196?
`P.8832H
`
`p.C1765
`
`p.C176S
`P-R17SH
`
`p.R175H
`
`530
`490
`
`480
`
`610
`470
`
`1070
`614
`
`580
`
`620
`
`650
`
`650
`
`630
`
`0.526
`
`0.039
`
`0.046
`
`0.091
`
`0.088
`
`0.048
`
`0.1 1 3
`0.029
`
`0.201
`
`0.085
`
`0.081
`
`0.581
`
`0.045
`
`0.120
`
`0.068
`0.135
`
`0.050
`0.432
`
`0.108
`
`0.226
`
`0.074
`
`160
`160
`
`160
`160
`
`130
`
`160
`
`160
`
`140
`
`140
`
`11
`
`13
`
`m i
`
`s
`
`16'
`
`17
`18
`
`20
`
`21
`22
`23
`
`24
`25
`
`25
`
`27
`29
`
`31
`32
`
`33
`
`34
`
`140
`
`140
`
`140
`
`130
`
`160
`150
`150
`160
`
`140
`
`160
`
`4.1
`
`3.7
`
`7.1
`
`3.9
`
`5.7
`
`3.6
`
`9.5
`
`3.6
`
`2.4
`
`13.2
`
`p.R175H
`
`p.R17SH
`
`p.R17SH
`
`p.R273H
`
`p.R282W
`p.C141Y
`p.E2581<
`
`p.C135Y
`
`p.ES6X
`
`710
`
`760
`750
`
`640
`
`1180
`
`190
`620
`1480
`
`740
`
`0.074
`
`0.269
`
`0.094
`
`0.048
`
`0.32 1
`
`0.548
`
`0.040
`
`0.1 37
`0.216
`
`0.12.5
`
`0.106
`0.286
`0.099
`
`0.061
`
`0.3154.
`
`0.253
`0.034
`
`0.122
`
`0.206
`
`
`
`1.102'91JeqtuaoacluoisanBAqISJO'BaLueouaios‘tuiS/izduu1.1.10.1)papeinMoa
`
`
`
`
`
`36
`
`37
`
`3s
`39
`
`4o
`41
`42
`43
`
`44:
`
`60
`
`160
`
`160
`160
`160
`
`160
`
`160
`160
`150
`
`170
`
`5.3
`
`5.8
`
`9.4
`
`10.1
`16.4
`
`19.7
`
`15.0
`
`8.5
`3.6
`
`5.2
`
`p.K132N
`p.K132N
`
`p.K132N
`
`p.K132N
`
`p.K132N
`
`p.K132N
`
`p.K132N
`
`p.K132N
`
`p.K132N
`
`Splicing
`
`p.C238R
`
`TP53
`
`TP53
`
`TP53
`
`570
`620
`
`530
`
`590
`
`700
`
`830
`
`730
`560
`680
`
`1543
`
`0.1 51
`
`0.1 91
`0.287
`
`0.275
`
`0.315
`0.435
`
`0.452
`0.1 85
`
`0.1 43
`0.07 1
`
`0.201
`0.275
`
`0.362
`0.331
`0.323
`
`0.482
`0.445
`
`0.245
`0.121
`
`0.073
`
`mom .3 W5] and an [GFR mutation were identified in this sample collected from patient 27 (Table 1), 25 months aftev lnltial surgery (Fig. 4A).
`amplification in this sample in the initial expenrnem and Wasidemifled successfully in repeat analysis.
`
`#The amplicon containing themutanon failed
`
`www.Science‘rranslalioualflledicineorg
`
`30 May 2012 V014 Issue 136 136r368
`A0900
`
`6
`
`FM1615-00510154
`
`A0900
`
`

`

`Case 1:20-cv-01580-LPS Document 40-2 Filed 03/05/21 Page 8 of 191 PageID #: 8419
`Case 1:20-cv-01580-LPS Document 40-2 Filed 03/05/21 Page 8 of 191 PagelD #: 8419
`
`RESEARCH ARTICLE
`
`Thirty-nine ofihese matched mutations detected by digital PCR in those
`samples (Fig. 3C). Three potential false positives were called. at AF
`of 3.1%, 1.3%, and 0.7% (the latter in a control sample). Using higher-
`
`stringency parameters for mutation identification (Supplementary
`Melhods), we retained only the 39 validated mutations called. with
`no False positives (Table 2).
`
`“
`
`\
`
`J
`
`1'
`k
`B .13)
`.g '3.
`
`‘
`
`l
`
`l
`
`l
`
`p,
`>1» :1. A
`
`.-
`
`~
`
`Plasma
`
`Wh
`”6
`blood
`cells
`
`- _- - - ~0mental
`mass
`
`~ --,__-_ Left
`
`-
`
`.
`
`ovary
`
`‘»
`
`Right
`ovary
`
`; “
`E CI:
`Z
`r?
`5 s
`~ e
`g
`to
`’
`LL:
`15m. - \ a.-
`5% 590
`'6
`U 9.
`MI I ,3 a
`9% 5%
`i '73
`DE] 3“
`U
`ND ND
`~
`‘ \
`‘
`.
`‘3
`43% 0.7%
`[A I B 75 8
`'5 c
`(U
`q, or
`35% ND >13; 6
`-— m
`2 _
`o —
`U ‘1
`E
`
`‘
`
`A
`3
`
`B
`
`A
`B
`C
`D
`
`
`
`ND: not detected
`
`8
`
`PR
`
`SD
`
`PD
`
`0.4
`
`+11 6e
`
`4000
`
`35% ND ,1
`
`C
`
`I
`
`’_/
`
`Plasma
`
`White
`blood cells [
`P |
`.
`evrc mass
`(relapsed
`[
`afterSy)
`Ovary
`primary
`Bowel
`primary
`
`[
`
`
`
`
`“
`
`D
`
`SD
`
`s
`5
`= e S
`5 :2: 2%“:
`g 3 gen.
`3' E. z E‘
`- -:' a a
`E E 1x l‘
`t—Y—l
`5y Sm. D
`29:, ND
`sum. [:1
`1% ND
`eyomlij
`1% ND
`B El
`ND ND
`Bio 5
`not avapiigble
`
`it;
`,,
`1,
`8 8.
`'32
`= 3
`8
`
`\
`
`v,
`17
`*5 o
`.0 S»
`u '—
`-3 m
`'o
`g _
`o g
`_ U .E
`'—
`
`1. [:l
`88% ND
`El I
`