as) United States
`a2) Patent Application Publication (10) Pub. No.: US 2014/0296081 Al
`Oct. 2, 2014
`Diehn et al.
`(43) Pub. Date:
`
`US 20140296081A1
`
`(54)
`
`(71)
`
`(72)
`
`IDENTIFICATION AND USE OF
`CIRCULATING TUMOR MARKERS
`
`Publication Classification
`
`61)
`
`Int. Cl.
`
`Applicant: The Boardof Trustees of the Leland
`Stanford Junior University, Palo Alto,
`CA (US)
`
`Inventors: Maximilian Diehn, Stanford, CA (US);
`Arash Ash Alizadeh, San Mateo, CA
`(US); Aaron M. Newman,Palo Alto,
`CA (US); Scott V. Bratman,Palo Alto,
`CA (US)
`
`(52)
`
`(57)
`
`(2006.01)
`(2006.01)
`
`GO6F 19/22
`C120 1/68
`US. Cl
`CPC veeceeccsceee GO6F 19/22 (2013.01); C12Q 1/6886
`(2013.01)
`USPC oieccsssssssssssssssesssssssssseesesssssessesnseees 506/2; 506/8
`
`ABSTRACT
`
`(21)
`
`Appl. No.: 14/209,807
`
`(22)
`
`Filed:
`
`Mar. 13, 2014
`
`Related U.S. Application Data
`
`(60)
`
`Provisional application No. 61/798,925, filed on Mar.
`15, 2013.
`
`Methods for creating a library of recurrently mutated
`genomic regions and for using the library to analyze cancer-
`specific and patient-specific genetic alterations in a patient
`are provided. The methods can be used to measure tumor-
`derived nucleic acids in patient blood and thus to monitor the
`progression of disease. The methods can also be used for
`cancer screening.
`
`00001
`
`EX1026
`
`

`

`Patent Application Publication
`
`Oct. 2,2014 Sheet 1 of 19
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`US 2014/0296081 Al
`
`
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`Figure 1
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`00002
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`Patent Application Publication
`
`Oct. 2,2014 Sheet 2 of 19
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`Figure 1 (cont.)
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`00003
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`Patent Application Publication
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`Oct. 2,2014 Sheet 3 of 19
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`US 2014/0296081 Al
`
`SNV/indel
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`00004
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`Patent Application Publication
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`Oct. 2,2014 Sheet 4 of 19
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`US 2014/0296081 Al
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`

`

`Patent Application Publication
`
`Oct. 2,2014 Sheet 5 of 19
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`US 2014/0296081 Al
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`Patent Application Publication
`
`Oct. 2,2014 Sheet 6 of 19
`
`US 2014/0296081 Al
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`GGYGTEI
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`Figure 5
`
`00007
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`

`

`Patent Application Publication
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`Oct. 2,2014 Sheet 7 of 19
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`US 2014/0296081 Al
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`a
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`Patent Application Publication
`
`Oct. 2,2014 Sheet 8 of 19
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`US 2014/0296081 Al
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`Figure 7
`
`00009
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`Patent Application Publication
`
`Oct. 2,2014 Sheet 9 of 19
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`US 2014/0296081 Al
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`Patent Application Publication
`
`Oct. 2,2014 Sheet 10 of 19
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`US 2014/0296081 Al
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`Patent Application Publication
`
`Oct. 2,2014 Sheet 11 of 19
`
`US 2014/0296081 Al
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`Figure 10
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`00012
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`

`

`Patent Application Publication
`
`Oct. 2,2014 Sheet 12 of 19
`
`US 2014/0296081 Al
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`Patent Application Publication
`
`Oct. 2,2014 Sheet 13 of 19
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`US 2014/0296081 Al
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`00014
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`

`

`Patent Application Publication
`
`Oct. 2,2014 Sheet 14 of 19
`
`US 2014/0296081 Al
`
`ALK(chr2)
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`Patent Application Publication
`
`Oct. 2,2014 Sheet 15 of 19
`
`US 2014/0296081 Al
`
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`00016
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`

`Patent Application Publication
`
`Oct. 2,2014 Sheet 16 of 19
`
`US 2014/0296081 Al
`
`Crizotinib initiated
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`00017
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`

`

`Patent Application Publication
`
`Oct. 2,2014 Sheet 17 of 19
`
`US 2014/0296081 Al
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`Patent Application Publication
`
`Oct. 2,2014 Sheet 18 of 19
`
`US 2014/0296081 Al
`
`
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`Figure 16
`
`00019
`
`

`

`Patent Application Publication
`
`Oct. 2,2014 Sheet 19 of 19
`
`US 2014/0296081 Al
`
`(0) Pre-filter ————————_ (1) Germlinefilter ————— (2) cfDNA backgroundfilter 3 (3) Outlier detection
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`

`

`US 2014/0296081 Al
`
`Oct. 2, 2014
`
`IDENTIFICATION AND USE OF
`CIRCULATING TUMOR MARKERS
`
`STATEMENT OF GOVERNMENTAL SUPPORT
`
`[0001] This invention was made with government support
`under grant number W81XWH-12-1-0285 awarded by the
`Department of Defense. The governmenthascertain rights in
`the invention.
`
`BACKGROUND OF THE INVENTION
`
`[0002] Analysis of cancer-derived cell-free DNA (cfDNA)
`hasthepotential to revolutionize detection and monitoring of
`cancer. Noninvasive access to malignant DNAisparticularly
`attractive for solid tumors, which cannot be repeatedly
`sampled without invasive procedures. In non-small cell lung
`cancer (NSCLC), PCR-based assays have been used previ-
`ously to detect recurrent point mutations in genes such as
`KRASor EGFRin plasma DNA(Taniguchietal. (2011) Clin.
`Cancer Res. 17:7808-7815; Gautschi et al. (2007) Cancer
`Lett. 254:265-273; Kuang et al. (2009) Clin. Cancer Res.
`15:2630-2636; Rosell et al. (2009) N. Engl. J. Med. 361:958-
`967), but the majority of patients lack mutations in these
`genes. Other studies have proposed identifying patient-spe-
`cific chromosomal rearrangements in tumors via whole
`genome sequencing (WGS), followed by breakpoint qPCR
`from cf{DNA(Leary et al. (2010) Sci. Transl. Med. 2:20ra14;
`McBrideet al. (2010) Genes Chrom. Cancer 49:1062-1069).
`While sensitive,
`such methods require optimization of
`molecular assays for each patient, limiting their widespread
`clinical application. More recently, several groups have
`reported amplicon-based deep sequencing methodsto detect
`cfDNA mutations in up to 6 recurrently mutated genes (For-
`shew et al. (2012) Sci. Transl. Med. 4:136ra168; Narayan et
`al. (2012) Cancer Res. 72:3492-3498; Kinde et al. (2011)
`Proc. Natl Acad. Sci. USA 108:9530-9535). While powerful,
`these approachesare limited by the number of mutationsthat
`can be interrogated (Rachlin et al. (2005) BMC Genomics
`6:102) and the inability to detect genomic fusions.
`[0003]
`PCT International Patent Publication No. 2011/
`103236 describes methods for
`identifying personalized
`tumor markers in a cancer patient using “mate-paired”librar-
`ies. The methodsare limited to monitoring somatic chromo-
`somalrearrangements, however, and must be personalized for
`eachpatient, thus limiting their applicability and increasing
`their cost.
`
`[0004] U.S. Patent Application Publication No. 2010/
`0041048 Al describes the quantitation oftumor-specific cell-
`free DNA in colorectal cancer patients using the “BEAMing”
`technique (Beads, Emulsion, Amplification, and Magnetics).
`While this technique provideshigh sensitivity and specificity,
`this method is for single mutations and thus any given assay
`can only be applied to a subset of patients and/or requires
`patient-specific optimization. U.S. Patent Application Publi-
`cation No. 2012/0183967 Al describes additional methods to
`
`identify and quantify genetic variations, including the analy-
`sis ofminor variants ina DNA population, using the “BEAM-
`ing” technique.
`[0005] U.S. Patent Application Publication No. 2012/
`0214678 Al describes methods and compositions for detect-
`ing fetal nucleic acids and determining the fraction of cell-
`free fetal nucleic acid circulating in a maternal sample. While
`sensitive, these methods analyze polymorphisms occurring
`between maternalandfetal nucleic acids rather than polymor-
`
`phismsthat result from somatic mutations in tumorcells. In
`addition, methods that detect fetal nucleic acids in maternal
`circulation require much less sensitivity than methods that
`detect tumor nucleic acids in cancer patient circulation,
`because fetal nucleic acids are much more abundant than
`tumornucleic acids.
`
`[0006] US. Patent Application Publication Nos. 2012/
`0237928 Al and 2013/0034546 describe methodsfor deter-
`mining copy numbervariations of a sequenceof interest in a
`test sample comprising a mixture of nucleic acids. While
`potentially applicable to the analysis of cancer, these methods
`are directed to measuring major structural changes in nucleic
`acids, such as translocations, deletions, and amplifications,
`rather than single nucleotide variations.
`[0007] U.S. Patent Application Publication No. 2012/
`0264121 Al describes methods for estimating a genomic
`fraction, for example, a fetal fraction, from polymorphisms
`such as small base variations or insertions-deletions. These
`methods do not, however, make use of optimized libraries of
`polymorphisms, such as, for example, libraries containing
`recurrently-mutated genomic regions.
`[0008] U.S. Patent Application Publication No. 2013/
`0024127 Al describes computer-implemented methods for
`calculating a percent contribution of cell-free nucleic acids
`from a major source and a minor source in a mixed sample.
`The methods do not, however, provide any advantages in
`identifying or making use of optimized libraries of polymor-
`phismsin the analysis.
`[0009]
`PCT International Publication No. WO 2010/
`141955 A2 describes methodsof detecting cancer by analyz-
`ing panels of genes from a patient-obtained sample and deter-
`mining the mutational status of the genes in the panel. The
`methodsrely ona relatively small number of knowncancer
`genes, however, and they do not provide any ranking of the
`genes according to effectiveness in detection of relevant
`mutations. In addition, the methods were unable to detect the
`presence of mutations in the majority of serum samples from
`actual cancerpatients.
`[0010] There is thus a need for new and improved methods
`to detect and monitor tumor-related nucleic acids in cancer
`patients.
`
`SUMMARY OF THE INVENTION
`
`[0011] The present invention addresses these and other
`problemsby providing novel methodsand systemsrelating to
`the characterization, diagnosis, and monitoring of cancer. In
`particular, according to one aspect, the invention provides
`methodsfor creating a library ofrecurrently mutated genomic
`regions comprising:
`[0012]
`identifying a plurality of genomic regions from a
`group of genomic regions that are recurrently mutated in a
`specific cancer;
`[0013] wherein the library comprises the plurality of
`genomic regions;
`[0014]
`the plurality of genomic regions comprisesat least
`10 different genomic regions; and
`[0015]
`atleast one mutation within the plurality ofgenomic
`regions is present in at least 60% of all subjects with the
`specific cancer.
`[0016]
`In specitic embodiments of these methods,the plu-
`rality of genomic regions comprisesat least 25, at least 50, at
`least 100, at least 150, at least 200, or at least 500 different
`genomic regions.
`
`00021
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`Oct. 2, 2014
`
`mutations within the plurality ofgenomic regionsis present in
`at least 60% ofall subjects with the specific cancer.
`[0034]
`In still other specific embodiments, at least one
`mutation within the plurality of genomicregionsis present in
`at least 60%, 70%, 80%, 90%, 95%, 98%, 99%, or 99.9% of
`all subjects with the specific cancer.
`[0035]
`In some embodiments, each genomic regionin the
`plurality of genomic regions is identified by ranking the
`genomic region to maximize the numberofall subjects with
`the specific cancer having at least one mutation within the
`genomic region.
`[0036]
`In other embodiments, each genomic region in the
`plurality of genomic regions is identified by ranking the
`genomic region to maximize the ratio between the number of
`all subjects with the specific cancer having at least one muta-
`tion within the genomic region andthe length of the genomic
`region.
`In some embodiments, the plurality of genomic
`[0037]
`regions comprises genomic regions encoding a plurality of
`driver sequences, more specifically known driver sequences
`or driver sequencesthat are recurrently mutatedin the specific
`cancer.
`
`In some embodiments, the plurality of genomic
`[0038]
`regions comprises genomic regionsthat are recurrently rear-
`ranged in the specific cancer.
`[0039]
`In preferred embodiments, the specific cancer is a
`carcinoma, and in more preferred embodiments, the carci-
`nomais an adenocarcinoma,a non-small cell lung cancer, or
`a squamouscell carcinoma.
`[0040]
`In specific embodiments, the cumulative length of
`the plurality of genomic regionsis at most 30 Mb, 20 Mb, 10
`Mb, 5 Mb, 2 Mb, 1 Mb, 500 kb, 200 kb, 100 kb, 50 kb, 20 kb,
`or 10 kb.
`
`In other specific method embodiments, at least two
`[0017]
`mutations within the plurality of genomic regionsorat least
`three mutations within the plurality of genomic regions is
`presentin at least 60% of all subjects with the specific cancer.
`[0018]
`In still other specific method embodiments, at least
`one mutation within the plurality of genomic regions is
`present in at least 60%, 70%, 80%, 90%, 95%, 98%, 99%, or
`99.9% of all subjects with the specific cancer.
`[0019]
`In some embodiments, the identifying step com-
`prises for each genomic region in the plurality of genomic
`regions, ranking the genomic region to maximize the number
`of all subjects with the specific cancer having at least one
`mutation within the genomicregion.
`[0020]
`In other embodiments, the identifying step com-
`prises for each genomic region in the plurality of genomic
`regions, ranking the genomic region to maximize the ratio
`between the numberofall subjects with the specific cancer
`having at least one mutation within the genomic region and
`the length of the genomic region.
`[0021]
`In some embodiments,the library comprises a plu-
`rality of genomic regions encoding a plurality of driver
`sequences, more specifically known driver sequences or
`driver sequences that are recurrently mutated in the specific
`cancer.
`
`In some embodiments, the library comprises a plu-
`[0022]
`rality of genomic regions that are recurrently rearranged in
`the specific cancer.
`[0023]
`In preferred embodiments, the specific cancer is a
`carcinoma, and in more preferred embodiments, the carci-
`nomais an adenocarcinoma,a non-small cell lung cancer, or
`a squamouscell carcinoma.
`[0024]
`In specific embodiments, the cumulative length of
`the plurality of genomic regions is at most 30 Mb, 20 Mb, 10
`Mb, 5 Mb, 2 Mb, 1 Mb, 500 kb, 200 kb, 100 kb, 50 kb, 20 kb,
`or 10 kb.
`
`In another aspect, the invention provides methods
`[0025]
`for analyzing a cancer-specific genetic alteration in a subject
`comprising the steps of:
`[0026]
`obtaining a tumor nucleic acid sample and a
`genomic nucleic acid sample from a subject with a specific
`cancer;
`
`In some embodiments, the methods further com-
`[0041]
`prising thesteps of:
`[0042]
`obtaining a cell-free nucleic acid sample from the
`subject; and
`[0043]
`identifying the patient-specific genetic alteration in
`the cell-free nucleic acid sample.
`[0044]
`Inspecific embodiments,the step of identifying the
`patient-specific genetic alteration in the cell-free nucleic acid
`sample comprises sequencing a genomic region comprising
`[0027] sequencingaplurality of target regions in the tumor
`
`the patient-specific genetic alteration in the cell-free sample.
`nucleic acid sample and in the genomic nucleic acid sample to
`[0045]
`Inother specific embodiments, the step of obtaining
`obtain a plurality of tumor nucleic acid sequences and a
`a tumor nucleic acid sample and a genomic nucleic acid
`plurality of genomic nucleic acid sequences; and
`sample comprises the step of enrichingthe plurality of target
`[0028]
`comparing the plurality of tumor nucleic acid
`regions in the tumor nucleic acid sample and the genomic
`sequencesto the plurality of genomic nucleic acid sequences
`nucleic acid sample, and in more specific embodiments, the
`to identify a patient-specific genetic alteration in the tumor
`enriching step comprises use of a custom library of biotiny-
`lated DNA.
`nucleic acid sample;
`[0029] wherein the plurality of target regions are selected
`from a plurality of genomic regions that are recurrently
`mutated in the specific cancer;
`[0030]
`the plurality of genomic regions comprisesat least
`10 different genomic regions; and
`[0031]
`atleast one mutation within the plurality ofgenomic
`regions is present in at least 60% of all subjects with the
`specific cancer.
`[0032]
`In specific embodimentsofthis aspect of the inven-
`tion,the plurality ofgenomic regions comprises at least 25, at
`least 50, at least 100, at least 150, at least 200, or at least 500
`different genomic regions.
`[0033]
`In other specific embodiments, at least two muta-
`tions within the plurality of genomic regionsorat least three
`
`the step of
`In still other specific embodiments,
`[0046]
`obtaining a cell-free nucleic acid sample comprises the step
`of enriching the plurality of target regions in the cell-free
`nucleic acid sample, andin still more specific embodiments,
`the enriching step comprises use of a custom library ofbicti-
`nylated DNA.
`[0047]
`In some embodiments, the methods further com-
`prise the step of quantifying the cancer-specific genetic alter-
`ation in the cell-free sample.
`[0048]
`In yet another aspect, the invention provides meth-
`ods for screening a cancer-specific genetic alteration in a
`subject comprising the stepsof:
`[0049]
`obtaining a cell-free nucleic acid sample from a
`subject;
`
`00022
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`

`US 2014/0296081 Al
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`
`N=229) and an independent lung adenocarcinoma WESdata
`sequencing a plurality of target regionsin the cell-
`[0050]
`set (Validation; N=183) (Imielinski et al. (2012) Cell 150:
`free sample to obtain a plurality of cell-free nucleic acid
`1107-1120). (d) Number of SNVsperpatient identified by the
`sequences; and
`NSCLC CAPP-Seq selector in WES data from three adeno-
`[0051]
`identifying a cancer-specific genetic alteration in
`carcinomas from TCGA,colon (COAD), rectal (READ), and
`the cell-free sample;
`endometrioid (UCEC)cancers. (e-f) Quality parameters from
`[0052] wherein the plurality of target regions are selected
`a representative CAPP-Seq analysis of plasma cfDNA,
`from a plurality of genomic regions that are recurrently
`including length distribution of sequenced cfDNA fragments
`mutated in the specific cancer;
`(e), and depth of sequencing coverage across all genomic
`[0053]
`the plurality of genomic regions comprisesat least
`regionsin the selector (f). (g) Variation in sequencing depth
`10 different genomic regions; and
`
`[0054] across cfDNA samples from4patients.at least one mutation withinthe plurality ofgenomic
`
`regions is present in at least 60% of all subjects with the
`[0066]
`FIG. 2. CAPP-Seq computational pipeline. Major
`specific cancer.
`steps of the bioinformatics pipeline for mutation discovery
`[0055]
`In specific embodiments, the plurality of genomic
`and quantitation in plasma are schematically illustrated.
`regions comprisesat least 25, at least 50, at least 100,at least
`[0067]
`FIG.
`3. Statistical enrichment of recurrently
`150, at least 200, or at least 500 different genomic regions.
`mutated NSCLC exons captures knowndrivers.
`[0056]
`In other specific embodiments, at least two muta-
`[0068]
`FIG. 4. Development of the FACTERA algorithm.
`tions within the plurality of genomic regionsorat least three
`Major steps used by FACTERA (see Detailed Methods) to
`mutations within the plurality ofgenomic regionsis present in
`precisely identify genomic breakpoints from aligned paired-
`at least 60% ofall subjects with the specific cancer.
`end sequencing data are anecdotally illustrated using two
`[0057]
`In still other specific embodiments, at least one
`hypothetical genes, w andv. (a) Improperly paired,or “‘dis-
`mutation within the plurality of genomic regions is present in
`cordant,” reads (indicated in yellow) are used to locate genes
`at least 60%, 70%, 80%, 90%, 95%, 98%, 99%, or 99.9% of
`involved in a potential fusion (in this case, w and v). (b)
`all subjects with the specific cancer.
`Because truncated (i.e., soft-clipped) reads may indicate a
`[0058]
`In particular embodiments, each genomic region in
`fusion breakpoint, any such reads within genomic regions
`the plurality of genomic regionsis identified by ranking the
`delineated by w andvare also further analyzed. (c) Consider
`genomic region to maximize the numberofall subjects with
`soft-clipped reads, R1 and R2, whose non-clipped segments
`the specific cancer having at least one mutation within the
`map to w and v, respectively. If R1 and R2 derive from a
`genomic region.
`fragment encompassing a true fusion between w andv, then
`[0059]
`In other particular embodiments, each genomic
`the mappedportion of R1 should match the soft-clipped por-
`region in the plurality of genomic regions is identified by
`tion of R2, and vice versa. This is assessed by FACTERA
`ranking the genomic region to maximizetheratio between the
`using fast k-mer indexing and comparison. (d) Four possible
`numberofall subjects with the specific cancer having at least
`orientations of R1 and R2 are depicted. However, only Cases
`one mutation within the genomic region and the length of the
`la and 2a can generate valid fusions (see Detailed Methods).
`genomic region.
`Thus, prior to k-mer comparison (panel c),
`the reverse
`[0060]
`In still other particular embodiments, the plurality
`complement of R1 is taken for Cases 1b and 2b, respectively,
`of genomic regions comprises genomic regions encoding a
`converting them into Cases 1a and2a. (e) In some cases, short
`plurality of driver sequences, and, more particularly, the
`sequences immediately flanking the breakpointare identical,
`driver sequences are knowndriver sequences or are recur-
`preventing unambiguous determination ofthe breakpoint. Let
`rently mutated in the specific cancer.
`iterators i and j denotethe first matching sequence positions
`[0061]
`In yet still other particular embodiments,the plural-
`between R1 and R2. To reconcile sequence overlap,
`ity of genomic regions comprises genomic regions that are
`FACTERA arbitrarily adjusts the breakpoint in R2 (i.e., bp2)
`recurrently rearranged in the specific cancer.
`to match R1 (.e., bp1) using the sequenceoffset determined
`[0062]
`In some embodiments, the specific cancer is a car-
`by differences in distance between bp2 and i, and bp! andj.
`cinoma, including, for example, an adenocarcinoma, a non-
`Twocasesare illustrated, corresponding to sequenceorienta-
`smallcell lung cancer, or a squamouscell carcinoma.
`tions described in (d).
`[0063]
`In specific embodiments, the cumulative length of
`[0069]
`FIG. 5. Application of FACTERA to NSCLC cell
`the plurality of genomic regionsis at most 30 Mb, 20 Mb, 10
`lines NCI-H3122 and HCC78, and Sanger-validation of
`Mb, 5 Mb, 2 Mb, 1 Mb, 500 kb, 200 kb, 100 kb, 50 kb, 20 kb,
`breakpoints. (a) Pile-up of a subset of soft-clipped reads
`or 10 kb.
`mapping to the EML4-ALK fusion identified in NCI-H3122
`along with the corresponding Sanger chromatogram.
`(b)
`Sameas(a), but for the SLC34A2-ROS1 translocation iden-
`tified in HCC78.
`
`Inother specific embodiments, the step of obtaining
`[0064]
`a cell-free nucleic acid sample comprises the step of enrich-
`ing the plurality of target regionsin the cell-free nucleic acid
`sample, and, in some embodiments, the enriching step com-
`prises use of a custom library of biotinylated DNA.
`
`BRIEF DESCRIPTION OF THE DRAWINGS
`
`FIG. 1. Development of CAncer Personalized Pro-
`[0065]
`filing by Deep Sequencing (CAPP-Seq).
`(a) Schematic
`depicting design of CAPP-Seqselectors andtheir application
`for assessing circulating tumor DNA.(b) Multi-phase design
`ofthe NSCLC CAPP-Segselector. (c) Analysis ofthe number
`of SNVs per lung adenocarcinoma covered by the NSCLC
`CAPP-Seq selector in the TCGA WEScohort (Training;
`
`FIG. 6. Improvements in CAPP-Seq performance
`[0070]
`with optimized library preparation procedures.
`[0071]
`FIG. 7. Optimizing allele recovery from low input
`cfDNAduring Illuminalibrary preparation.
`[0072]
`FIG. 8. CAPP-Seq performance with various
`amounts ofinput cfDNA.
`[0073]
`FIG. 9. Analysis of CAPP-Seq background, allele
`detection threshold, and linearity. (a) Analysis of background
`rate for 6 NSCLC patient plasma samples and a healthy
`individual (Detailed Methods). (b) Analysis of biological
`background in (a) focusing on 107 recurrent somatic muta-
`
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`
`tions from a previously reported SNaPshot panel (Suetal.
`(2011) J Mol. Diagn. 13:74-84). Mutations foundin a given
`patient’s tumor were excluded. The mean frequency for each
`patient (horizontal red line) was within confidencelimits of
`the mean background limit of 0.007% (horizontal blueline;
`panela). A single outlier mutation (TP53 R175H)is indicated
`by an orange diamond. (c) Individual mutations from (b)
`ranked by mostto least recurrent, according to median fre-
`quency across the 7 samples. (d) Dilution series analysis of
`expected versus observed frequencies of mutantalleles using
`CAPP-Seq. Dilution series were generated by spiking frag-
`mented HCC78 DNAinto control cf{DNA.(e) Analysis of the
`effect of the number of SNVs considered onthe estimates of
`fractional abundance (95% confidence intervals shown in
`gray). (f) Analysis of the effect of the number of SNVscon-
`sidered on the meancorrelation coefficient between expected
`and observed cancer fractions (blue dashed line) using data
`from panel (d). 95% confidence intervals are shownfor (a)-
`(c). Statistical variation for (d) is shownas s.e.m.
`[0074]
`FIG. 10. Empirical spiking analysis of CAPP-Seq
`using two NSCLCcell lines. (a) Expected and observed (by
`CAPP-Seq) fractions ofNCI-H3122 DNA spikedinto control
`HCC78 DNA arelinearforall fractions tested (0.1%, 1%, and
`10%; R?=1). (b) Using data from (a), analysis ofthe effect of
`the number of SNVsconsidered onthe estimatesof fractional
`abundance (95% confidence intervals shown in gray). (c)
`Analysis of the effect of the number of SNVs considered on
`the mean correlation coefficient and coefficient of variation
`between expected and observed cancer fractions (dashed
`lines) using data from panel (a). (d) Expected and observed
`fractions of the EML4-ALK fusion present in HCC78 are
`linear (R?=0.995) overall spiking concentrations tested (see
`FIG. 5() for breakpointverification). The observed EML4-
`ALKfractions were normalized based on the relative abun-
`danceofthe fusion in 100% H3122 DNA(see Detailed Meth-
`ods for details). Moreover, a single heterozygous insertion
`(indel) discovered within the selector space of NCI-H3122
`(chr7: 107416855, +T) was concordant with defined concen-
`trations (shown areobservedfractions adjusted for zygosity).
`[0075]
`FIG. 11. Application of CAPP-Seq for noninvasive
`detection and monitoring ofcirculating tumor DNA.(a) Char-
`acteristics of 11 patients included in this study (Table 3).
`P-values reflect a two-sided paired t-test for patients with
`reporter SNVs detected at both time points; other p-values
`were determined as described in Methods. ND, mutant DNA
`was not detected above background. Dashes, plasma sample
`not available. Smoking history, =20 pack years (heavy), >0
`pack years (light). (b-d) Disease monitoring using CAPP-
`Seq. Mutantallele frequencies(left y-axis) and absolute con-
`centrations (right y-axis) are shown. The lowerlimit of detec-
`tion (defined in FIG. 2(a)-(6)) is indicated by the dashedlines.
`(b) Pre- and post-surgery circulating tumor DNAlevels quan-
`tified by CAPP-Seq in a Stage IB and a Stage IIIA NSCLC
`patient. Complete resections were achieved in both cases. (c)
`Disease burden changes in response to chemotherapy in a
`Stage