DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Office Action: Notice
Any objection or rejection of record in the previous Office Action, mailed 4/22/2025, which is not addressed in this action has been withdrawn in light of Applicants' amendments and/or arguments. This action is FINAL.
Election/Restriction
Applicant’s election without traverse of Group 3 claims or claims 30-31, and 34-35 in the reply filed on 3/20/2025 is acknowledged. Claims 1-5, 12-15 and 21-27 are withdrawn from further consideration pursuant to 37 CFR 1.142(b) as being drawn to nonelected Groups 1 and 2, there being no allowable generic or linking claim.
Claim Status
Applicant amended claims 30, 31 and 34 (10/22/2025). Claims 36-49 are new (10/22/2025). No new matter was added. Thus, claims 30, 31, and 34-49 are under examination (10/22/2025).
Priority
Claims 30, 31, and 34-49 receive a priority date of 10/16/2018, the filing date of US Provisional No. 62/746,543.
Objections Withdrawn
Specification:
The objections to the specification due to the use of a trademark or tradenames are withdrawn in view of Applicant’s amendments.
Claims:
The minor formality objections to claims 30, 31, and 34 are withdrawn in view of Applicant’s amendments.
Rejections Withdrawn
Claim Rejections - 35 USC § 112(b)
The rejections of claims 30, 31, 34 and 35 under 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, 2nd paragraph, are withdrawn in view of Applicant’s amendments of claims 30, 31 and 34.
Claim Rejections - 35 USC § 101
The rejection of claims 30-31 and 34-35 under 35 U.S.C. 101 because the claimed invention is directed to both an abstract idea and natural phenomenon without significantly more is withdrawn in view of Applicant’s argument explaining the integration of the instant claim set. Specifically, while the claims do involve a natural phenomenon (the presence of a rare variant allele), the claims as a whole integrate the judicial exception into a practical application by reciting a specific and non-generic technical process for screening and identifying individual subjects. In particular, the instant claims require structured sub-pooling of samples, indexing and grouping of error-corrected sequencing reads, and reconstruction of individual subject identity based on unique sub-pool combinations, which together improve the accuracy and scalability of genetic sequencing. These limitations amount to significantly more than merely observing or detecting a natural phenomenon and represent an improvement to genetic screening and sequencing technology. Accordingly, eligibility is resolved at Step 2A, Prong Two.
Rejections Maintained
Claim Rejections—35 U.S.C. § 102
Claims 30 and 34-35 are rejected under 35 U.S.C. 102 (a)(1) and (a)(2) as being anticipated by Kirkizlar et al. (WO 2016/183106 A1; published 11/17/2016).
Regarding claim 30, Kirkizlar teaches improved methods, compositions, and kits for detecting ploidy of chromosome regions, (i.e., for detecting cancer or a chromosomal abnormality in a gestating fetus) (Abstract). Further, Kirkizlar teaches that exemplary loci are used to determine if the tumor fraction include polymorphisms or mutations in a cancer cell (or DNA or RNA such as cfDNA or cfRNA from a cancer cell) that aren't present in a noncancerous cell (or DNA or RNA from a noncancerous cell) in the individual amongst a specified population (Paragraph 238, lines 1-3). Specifically, Kirkizlar teaches that the tumor fraction is determined by identifying those polymorphic loci where a cancer cell (or DNA or RNA from a cancer cell) has an allele that is absent in noncancerous cells (or DNA or RNA from a noncancerous cell) in a sample (such as a plasma sample or tumor biopsy) from an individual; and using the amount of the allele unique to the cancer cell at one or more of the identified polymorphic loci to determine the tumor fraction in the sample (Paragraph 238, lines 1-5). Further, Kirkizlar teaches that a pool or sub-pool of primers or specified nucleic acid fragments are selected for determining the allele frequency within a plurality of haploblocks within a target chromosome region (Paragraph 54, lines 1-5).
Kirkizlar teaches that the previously mentioned allele frequency is corrected for errors before it is used to generate individual probabilities, including allele amplification efficiency bias (Paragraph 156, lines 1-3). Kirkizlar also teaches that reactions used to form specified library models were pooled and barcoded, then quantified and sequenced for cancer-free and tumor cell-line patient plasmas (Paragraphs 371-372).
Kirkizlar further teaches copy number variation (CNVs), creates rare variants within alleles (Paragraph 5, lines 1-5; Paragraph 11, lines 1-5). Kirkizlar also teaches that individual probabilities are generated using a set of models or barcoded indexes of both different ploidy states and allelic imbalance fractions for the set of polymorphic loci and are generated by considering the linkage between polymorphic loci on the chromosome region (Paragraph 157, lines 1-8).
Further, Kirkizlar teaches that for tumor tissue samples or a specified type of pooled samples, chromosomal aneuploidy (exemplified in this paragraph by CNVs or rare variants) can be delineated by transitions between allele frequency distributions via a maximum likelihood algorithm that searches for plasma CNVs in regions where the tumor sample from the same individual also has CNVs, using haplotype information deduced from the tumor sample; expressing, expected allelic frequencies across all allelic imbalance ratios at 0.025% intervals for three sets of hypotheses: (1) all cells are normal (no allelic imbalance), (2) some/all cells have a homolog 1 deletion or homolog 2 amplification, or (3) some/all cells have a homolog 2 deletion or homolog 1 amplification (Paragraph 158, lines 1-10).
Kirkizlar also teaches that the phase of an individual's genetic data is estimated using data about the probability of chromosomes crossing over at different locations in a chromosome or chromosome region (such as using recombination data such as can be found in the HapMap database to create a recombination risk score for any interval) to model dependence between polymorphic alleles on the chromosome or chromosome region via comparison of sub-pooled regions (Paragraph 175, lines 1-5). Further, Kirkizlar teaches a plurality of hypotheses each pertaining to a different possible state of the chromosome or chromosome region or sub-pooled regions (such as an overrepresentation of the number of copies of a first homologous chromosome region or sub-pooled samples as compared to a second homologous chromosome region or sub-pooled samples in the genome of one or more cells from an individual, a duplication of the first homologous chromosome region, a deletion of the second homologous chromosome region, or an equal representation of the first and second homologous chromosome regions) can be created (such as creation on a computer); a model (such as a joint distribution model) for the expected allele counts at the polymorphic loci on the chromosome can be built (such as building on a computer) for each hypothesis; a relative probability of each of the hypotheses can be determined (such as determination on a computer) using the joint distribution model and the allele counts; and the hypothesis with the greatest probability can be selected (Paragraph 175, lines 5-15).
Regarding claims 34-35, Kirkizlar teaches improved methods, compositions, and kits for detecting ploidy of chromosome regions, (i.e., for detecting cancer or a chromosomal abnormality in a gestating fetus) (Abstract), including copy number variation (CNVs), which create rare variants within alleles (Paragraph 5, lines 1-5; Paragraph 11, lines 1-5).
Kirkizlar further teaches that exemplary loci that can be used to determine the tumor fraction include polymorphisms or mutations in a cancer cell (or DNA or RNA such as cfDNA or cfRNA from a cancer cell) that aren't present in a noncancerous cell (or DNA or RNA from a noncancerous cell) in the individual and using the amount of the allele unique or a unique molecular identifier to the cancer cell at one or more of the identified polymorphic loci to determine the tumor fraction in the sample (Paragraph 238, lines 1-10). Specifically, Kirkizlar teaches that the total measured quantity of all the alleles for the locus or patient is less than the expected allele ratio for that locus (Paragraph 192, lines 1-5).
Kirkizlar also teaches that in addition to determining the presence or absence of copy number variation, one or more other factors can be analyzed and can be used to increase the accuracy of the diagnosis (such a determining the presence or absence of cancer or an increased risk for cancer, classifying the cancer, or staging the cancer) or prognosis (Paragraph 53, lines 1-5). Further, Kirkizlar teaches that primer specificities or consensus sequences using the BLASTn program can be used to map and specify the target complementary primer binding region in order to have a unique hit to the genome and to not have many other hits throughout the genome (Paragraph 92, lines 1-8).
Kirkizlar teaches that exemplary methods for calculating expected allele ratios or frequencies for a sample include the use of a mixed sample (such as a maternal blood sample) containing nucleic acids from both the mother and the fetus, indicating what is expected for measurement and inheritance of the total amount of each allele, including the amount of the allele from both maternal nucleic acids and fetal nucleic acids in the mixed sample (Table 3; Paragraph 198, lines 1-5).
Kirkizlar teaches that specificity was tested using both negative artificial cfDNA and cfDNA extracted from standard plasma samples from healthy individuals (Paragraph 431, lines 1-5). Further, Kirkizlar teaches that cfDNA or cfRNA from a blood sample from the individual is analyzed and can be used to detect deletions or duplications that are only present in a small percentage of the cfDNA or cfRNA (Paragraph 50, lines 1-20). Kirkizlar teaches that successful treatment of a disease such as cancer often relies on early diagnosis, correct staging of the disease, selection of an effective therapeutic regimen, and close monitoring to prevent or detect relapse, including histological evaluation of tumor material obtained from tissue biopsy is often considered the most reliable method (Paragraph 50, lines 1-10).
Kirkizlar teaches each and every limitation of claims 30 and 34-35 and therefore Kirkizlar anticipates claims 30 and 34-35.
Applicant’s Response: The Applicant argues that Kirkizlar does not anticipate the amended claims because it fails to teach separating individual samples into unique combinations of sub-pooled samples, attaching indexing barcodes at the sub-pool level, grouping error-corrected sequencing reads based on these barcodes, or identifying an individual subject by reconstructing a unique sub-pool combination containing a variant allele. The Applicant stresses that Kirkizlar is directed to copy number and ploidy analysis (i.e., cancer applications), and while it may use pools of primers or pooled sequencing, it does not teach the claimed pooling architecture or subject-level identification logic.
Examiner’s Response to Traversal: Applicant’s arguments have been carefully and fully considered but are not found persuasive, as discussed below.
As of note, a claim is anticipated if a single prior art reference discloses each and every limitation of the claim either expressly or inherently, and arranged as in the claim. See MPEP 2131; Verdegaal Bros., Inc. v. Union Oil Co., 814 F. 2d 628 (Fed. Cir. 1987). Inherency does not require recognition by the reference, only that the limitation necessarily flows from the disclosed method. See MPEP 2112.
Firstly, the Applicant asserts that Kirkizlar does not teach separating samples into unique combinations of sub-pools. Notably, Kirkizlar teaches analyzing mixed biological samples (including cfDNA and ctDNA) and modeling allele frequencies and ploidy states based on pooled sequence data across loci (Paragraphs 175, 192, 238). Kirkizlar’s pooling and probabilistic modeling of allelic distributions across samples inherently requires separation and analysis of subsets of pooled material corresponding to different hypotheses and sample compositions. Under MPEP 2112, such separation is inherent to Kirkizlar’s disclosed analytical framework.
The Applicant also argues that Kirkizlar does not teach attaching indexing barcodes to sub-pooled samples. Kirkizlar teaches pooled and barcoded sequencing reactions and library preparation for plasma samples (Paragraphs 371-372), as well as the use of unique molecular identifiers and polymorphic loci to distinguish alleles and sample contributions (Paragraphs 54, 156-157). The claims do not require any particular barcode structure beyond what is disclosed, and Kirkizlar’s barcoded pooled sequencing anticipates this limitation. See MPEP 2131.03 (broadest reasonable interpretation).
Further, the Applicant asserts that Kirkizlar does not identify an individual subject by reconstructing a unique sub-pool combination. Kirkizlar teaches determining tumor fraction or chromosomal abnormality in samples from a specific individual by analyzing allele frequencies unique to cancer or fetal DNA relative to noncancerous DNA from the same individual (Paragraphs 50, 192, 238). This necessarily identifies the individual subject containing the variant allele based on pooled sequence analysis. Recognition of this identification step is not required for anticipation. See MPEP 2112. The Examiner recommends amending the claims to recite a specific, non-inherent sub-pooling architecture (i.e. defined combinatorial pooling scheme) to overcome Kirkizlar.
Finally, the Applicant argues that Kirkizlar is directed to CNV or cancer analysis; however, anticipation does not require the same purpose or motivation, only disclosure of the same steps. See MPEP 2131.01. Kirkizlar’s disclosure of detecting rare variants, allele frequency deviations and mixed-sample analysis reads on the claimed method regardless of clinical context.
Accordingly, Kirkizlar teaches each and every limitation of claims 30 and 34-35 and therefore Kirkizlar anticipates claims 30 and 34-35 and the 35 USC 102 rejection is maintained.
Claim Rejections—35 U.S.C. § 103
Claim 31 is rejected are rejected under 35 U.S.C. 103 as being unpatentable over Kirkizlar et al. (WO 2016/183106 A1; published 11/17/2016), in view of Schmitt et al. (“Detection of ultra-rare mutations by next-generation sequencing”, PNAS, published 7/3/2012, from IDS 4/15/2021).
Kirkizlar teaches improved methods, compositions, and kits for detecting ploidy of chromosome regions, (i.e., for detecting cancer or a chromosomal abnormality in a gestating fetus) (Abstract), including copy number variation (CNVs), which create rare variants within alleles (Paragraph 5, lines 1-5; Paragraph 11, lines 1-5), as discussed above.
Regarding claim 31, Kirkizlar teaches that the previously discussed method for identifying rare variants includes a second class of CNVs that are much longer than CNPs, ranging in size from hundreds of thousands of base pairs to over 1 million base pairs in length, and may have arisen during production of the sperm or egg that gave rise to a particular individual, or they may have been passed down for only a few generations within a family (i.e., carrier) (Paragraph 5, lines 1-15). Kirkizlar teaches that a pool or sub-pool of primers or specified nucleic acid fragments are selected for determining the allele frequency within a plurality of haploblocks within a target chromosome region (Paragraph 54, lines 1-5). Kirkizlar teaches that the previously mentioned allele frequency is corrected for errors before it is used to generate individual probabilities, including allele amplification efficiency bias (Paragraph 156, lines 1-3).
Further, Kirkizlar teaches that determining fetal fraction includes using a high throughput DNA sequencer to count alleles at a large number of polymorphic genetic loci and modeling the likely fetal fraction (Paragraph 214, lines 1-5), following subjecting each of the amplicons to a nucleic acid sequencing reaction to generate the nucleic acid (raw) sequencing data for the amplicons (Paragraph 17, lines 1-2).
Kirkizlar does not teach or suggest generating duplex sequencing from the raw sequencing data to identify the presence of the rare variant allele.
Schmitt teaches that duplex sequencing has a theoretical background error rate of less than one artifactual mutation per billion nucleotides sequenced and can be used to identify sites of DNA damage present in only one strand (Abstract). Further Schmitt teaches that the duplex sequencing method entails tagging both strands of duplex DNA with a random, yet complementary double-stranded nucleotide sequence, which we refer to as a ‘Duplex Tag’ followed by double-stranded tag sequence incorporation into standard Illumina sequencing adapters by first introducing a single-stranded randomized nucleotide sequence into one adapter strand and then extending the opposite strand with a DNA polymerase to yield a complementary, double-stranded tag (Figure 1A; Results: Paragraph 1).
It would have been obvious to one skilled in the art before the effective filing date of the claimed invention to modify the method of Kirkizlar by incorporating the duplex sequencing technique taught by Schmitt to generate error-corrected sequence reads. One would have been motivated to do so because Kirkizlar already teaches the need for error correction in allele frequency determination, and Schmitt explicitly teaches that duplex sequencing provides an exceptionally low error rate of less than one mutation per billion nucleotides sequences (Abstract). The detection of rare variants, as taught by Kirkizlar, would significantly benefit from the increased accuracy provided by Schmitt’s duplex sequencing technique. Further, there would have been a reasonable expectation of success in combining the teachings of Kirkizlar and Schmitt because both references are in the same field of detecting rare genetic variants through DNA sequencing. Kirkizlar already teaches sequencing and error correction steps, which could simply be enhanced by Schmitt’s specific duplex sequencing technique. Schmitt provides detailed methodology for implementing duplex sequencing with standard Illumina sequencing adapters, which would allow one of ordinary skill in the art to incorporate this technique into Kirkizlar’s method and thus, the combination would involve applying a known technique (Schmitt’s duplex sequencing) to improve a similar method (Kirkizlar’s rare variant detection) in the same way.
Applicant’s Response: The Applicant argues that Kirkizlar, the primary reference teaching amended independent claim 30, in which claim 31 relies, does not disclose the foundational limitations of claim 30. Further, Schmidt does not cure these deficiencies and the Examiner fails to provide a reasoned explanation for combining Kirkizlar with Schmidt to supply the missing elements (i.e., sub-pooling, barcoding, grouping of error-corrected reads).
Examiner’s Response to Traversal: Applicant’s arguments have been carefully and fully considered but are not found persuasive, as discussed below.
As discussed above, Kirkizlar teaches methods for detecting rare genetic variants and allele frequency deviations using pooled sequencing data, including sequencing-based analysis, pooling or sub-pooling of nucleic acid fragments, and correcting allele frequencies for sequencing and amplification errors prior to generating probabilistic determinations (Paragraphs 54, 156, 214, 238). Schmidt teaches duplex sequencing techniques that tag and sequence both strands of duplex DNA to generate error-corrected sequence reads with extremely low error rates, specifically for detecting ultra-rare mutations (Abstract; Results).
Therefore, it would have been obvious to one of ordinary skill in the art at the time of the invention to modify Kirkizlar’s sequencing-based rare variant detection method to incorporate Schmidt’s duplex sequencing technique in order to improve sequencing accuracy and reduce error rates when identifying rare variants. Kirkizlar recognizes the need for error correction in allele frequency determination, and Schmidt provides a known, compatible solution for achieving enhanced error correction in next-generation sequencing workflows. The combination represents the predictable use of a known technique (duplex sequencing) to improve a similar method (rare variant detection), with a reasonable expectation of success, as both references are in the same technical field and rely on standard sequencing platforms. See MPEP 2141, 2143; KSR Int’l Co. v. Teleflex Inc., 550 US 398 (2007).
The Applicant’s argument that Kirkizlar alone does not disclose all limitations are not dispositive, as obviousness does not require each reference to independently teach every element. See MPEP 2141.01 (a). Schmidt supplies the missing duplex sequencing limitation, and the motivation to combine is supported by the shared goal of accurately detecting rare variants and Kirkizlar’s express concern with sequencing errors.
Accordingly, the rejection of claim 31 under 35 USC 103 over the combination of Kirkizlar and Schmidt is maintained.
New Rejections
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 36-49 are rejected under 35 U.S.C. 102 (a)(1) and (a)(2) as being anticipated by Kirkizlar et al. (WO 2016/183106 A1; published 11/17/2016).
Regarding claim 36, Kirkizlar teaches improved methods, compositions, and kits for detecting ploidy of chromosome regions, (i.e., for detecting cancer or a chromosomal abnormality in a gestating fetus) (Abstract). Further, Kirkizlar teaches that exemplary loci are used to determine if the tumor fraction include polymorphisms or mutations in a cancer cell (or DNA or RNA such as cfDNA or cfRNA from a cancer cell) that aren't present in a noncancerous cell (or DNA or RNA from a noncancerous cell) in the individual amongst a specified population (Paragraph 238, lines 1-3). Specifically, Kirkizlar teaches that the tumor fraction is determined by identifying those polymorphic loci where a cancer cell (or DNA or RNA from a cancer cell) has an allele that is absent in noncancerous cells (or DNA or RNA from a noncancerous cell) in a sample (such as a plasma sample or tumor biopsy) from an individual; and using the amount of the allele unique to the cancer cell at one or more of the identified polymorphic loci to determine the tumor fraction in the sample (Paragraph 238, lines 1-5). Further, Kirkizlar teaches that a pool or sub-pool of primers or specified nucleic acid fragments are selected for determining the allele frequency within a plurality of haploblocks within a target chromosome region (Paragraph 54, lines 1-5).
Kirkizlar teaches that the previously mentioned allele frequency is corrected for errors before it is used to generate individual probabilities, including allele amplification efficiency bias (Paragraph 156, lines 1-3). Kirkizlar also teaches that in some embodiments, allelic data is obtained, wherein the allelic data includes quantitative measurement(s) indicative of the number of copies of a specific allele (i.e., original or copy) of a polymorphic locus, where the allelic data includes quantitative measurement(s) indicative of the number of copies of each of the alleles observed at a polymorphic locus (Paragraph 112, lines 1-5). Kirkizlar also teaches that reactions used to form specified library models were pooled and barcoded, then quantified and sequenced for cancer-free and tumor cell-line patient plasmas (Paragraphs 371-372).
Regarding claim 37, Kirkizlar teaches that due to the large variance in total DNA available per patient, one library or pooled sample population per patient was prepared (Paragraph 448, lines 1-5).
Regarding claim 38, Kirkizlar teaches that in combination with any 1 or more of the illustrative embodiments set out herein in this paragraph, or as a separate example, an average allelic imbalance can calculate and wherein the copy number determination is indicative of a copy number variation if the average allelic imbalance is equal to or greater than a cutoff value, which can be a sensitivity for an assay method (Paragraph 63, lines 25-30). Further, Kirkizlar teaches that methods provided herein can include analysis of at least 2, 3, 4, 5, 10, 15, 20, 25, 50, 100, 200, 250, 300, 350, 400, 500, 600, 700, 750, 800, 900, 1000, 1250, 1500, 1750, 2000
polymorphic loci, such as SNPs, on the low end of the range (Paragraph 64, lines 1-5). Kirkizlar teaches that the previously discussed method for identifying rare variants includes a second class of CNVs that are much longer than CNPs, ranging in size from hundreds of thousands of base pairs to over 1 million base pairs in length, and may have arisen during production of the sperm or egg that gave rise to a particular individual, or they may have been passed down for only a few generations within a family (i.e., carrier) (Paragraph 5, lines 1-15).
Regarding claim 39, Kirkizlar teaches that the primer pool includes primer pairs (i.e. forward and reverse primers) for amplifying loci with strong linkage disequilibrium to
other loci (i.e. loci within a set of haploblocks within target chromosomal regions known to
exhibit CNV where a therapeutic has been identified), thereby useful for enrichment of target
SNPs within haploblocks, for detecting CNV for a lung cancer therapy selection panel (Paragraph 394, lines 10-20).
Regarding claims 40-41, Kirkizlar teaches that specificity was tested using both negative artificial cfDNA and cfDNA extracted from standard plasma samples from healthy individuals (Paragraph 431, lines 1-5). Further, Kirkizlar teaches that cfDNA or cfRNA from a blood sample from the individual is analyzed and can be used to detect deletions or duplications that are only present in a small percentage of the cfDNA or cfRNA (Paragraph 50, lines 1-20). Kirkizlar teaches that successful treatment of a disease such as cancer often relies on early diagnosis, correct staging of the disease, selection of an effective therapeutic regimen, and close monitoring to prevent or detect relapse, including histological evaluation of tumor material obtained from tissue biopsy is often considered the most reliable method (Paragraph 50, lines 1-10).
Specifically, Kirkizlar teaches that the previously described method can be applied to subchromosomal microdeletions, which can also result in severe mental and physical handicaps, are more challenging to detect due to their smaller size, where eight of the microdeletion syndromes have an aggregate incidence of more than 1in 1000, making them nearly as common as fetal autosomal trisomies (Paragraph 7, lines 1-5).
Regarding claims 42-43, Further, Kirkizlar teaches that a pool or sub-pool of primers or specified nucleic acid fragments are selected for determining the allele frequency within a plurality of haploblocks within a target chromosome region (Paragraph 54, lines 1-5). Kirkizlar also teaches that the homozygous loci of an individual are used, for example, for error correction, whereas heterozygous loci are used for the determination of allelic imbalance of the sample (Paragraph 168, lines 1-10).
Regarding claim 44, Kirkizlar teaches improved methods, compositions, and kits for detecting ploidy of chromosome regions, (i.e., for detecting cancer or a chromosomal abnormality in a gestating fetus) (Abstract). Further, Kirkizlar teaches that exemplary loci are used to determine if the tumor fraction include polymorphisms or mutations in a cancer cell (or DNA or RNA such as cfDNA or cfRNA from a cancer cell) that aren't present in a noncancerous cell (or DNA or RNA from a noncancerous cell) in the individual amongst a specified population (Paragraph 238, lines 1-3). Specifically, Kirkizlar teaches that the tumor fraction is determined by identifying those polymorphic loci where a cancer cell (or DNA or RNA from a cancer cell) has an allele that is absent in noncancerous cells (or DNA or RNA from a noncancerous cell) in a sample (such as a plasma sample or tumor biopsy) from an individual; and using the amount of the allele unique to the cancer cell at one or more of the identified polymorphic loci to determine the tumor fraction in the sample (Paragraph 238, lines 1-5). Further, Kirkizlar teaches that a pool or sub-pool of primers or specified nucleic acid fragments are selected for determining the allele frequency within a plurality of haploblocks within a target chromosome region (Paragraph 54, lines 1-5).
Kirkizlar teaches that the previously mentioned allele frequency is corrected for errors before it is used to generate individual probabilities, including allele amplification efficiency bias (Paragraph 156, lines 1-3). Kirkizlar also teaches that in some embodiments, allelic data is obtained, wherein the allelic data includes quantitative measurement(s) indicative of the number of copies of a specific allele (i.e., original or copy) of a polymorphic locus, where the allelic data includes quantitative measurement(s) indicative of the number of copies of each of the alleles observed at a polymorphic locus (Paragraph 112, lines 1-5). Kirkizlar also teaches that reactions used to form specified library models were pooled and barcoded, then quantified and sequenced for cancer-free and tumor cell-line patient plasmas (Paragraphs 371-372).
Regarding claim 45, Kirkizlar teaches that the primer pool includes primer pairs (i.e. forward and reverse primers) for amplifying loci with strong linkage disequilibrium to
other loci (i.e. loci within a set of haploblocks within target chromosomal regions known to
exhibit CNV where a therapeutic has been identified), thereby useful for enrichment of target
SNPs within haploblocks, for detecting CNV for a lung cancer therapy selection panel (Paragraph 394, lines 10-20).
Regarding claims 46-47, Kirkizlar teaches that specificity was tested using both negative artificial cfDNA and cfDNA extracted from standard plasma samples from healthy individuals (Paragraph 431, lines 1-5). Further, Kirkizlar teaches that cfDNA or cfRNA from a blood sample from the individual is analyzed and can be used to detect deletions or duplications that are only present in a small percentage of the cfDNA or cfRNA (Paragraph 50, lines 1-20). Kirkizlar teaches that successful treatment of a disease such as cancer often relies on early diagnosis, correct staging of the disease, selection of an effective therapeutic regimen, and close monitoring to prevent or detect relapse, including histological evaluation of tumor material obtained from tissue biopsy is often considered the most reliable method (Paragraph 50, lines 1-10).
Specifically, Kirkizlar teaches that the previously described method can be applied to subchromosomal microdeletions, which can also result in severe mental and physical handicaps, are more challenging to detect due to their smaller size, where eight of the microdeletion syndromes have an aggregate incidence of more than 1in 1000, making them nearly as common as fetal autosomal trisomies (Paragraph 7, lines 1-5).
Regarding claims 48-49, Further, Kirkizlar teaches that a pool or sub-pool of primers or specified nucleic acid fragments are selected for determining the allele frequency within a plurality of haploblocks within a target chromosome region (Paragraph 54, lines 1-5). Kirkizlar also teaches that the homozygous loci of an individual are used, for example, for error correction, whereas heterozygous loci are used for the determination of allelic imbalance of the sample (Paragraph 168, lines 1-10).
Kirkizlar teaches each and every limitation of claims 36-49 and therefore Kirkizlar anticipates claims 36-49
Conclusions
No claim is allowed.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ELIZABETH ROSE LAFAVE whose telephone number is (703)756-4747. The examiner can normally be reached Compressed Bi-Week: M-F 7:30-4:30.
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/ELIZABETH ROSE LAFAVE/ Examiner, Art Unit 1684
/HEATHER CALAMITA/ Supervisory Patent Examiner, Art Unit 1684