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
This action is a second NON-FINAL, in response to the Pre-Appeal Brief Conference (3/30/2026).
Claim Status
Claims 1-34 are under examination (2/5/2026).
Priority Status
Claims 1-34 receive a US priority date of 12/2/2020, the filing date of US Provisional No. 63/120,636.
Claim Interpretations
Claim Interpretation - 35 USC § 112(f)
The interpretation of claim 34 under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph is maintained in view of Applicant’s acknowledgement of the interpretation (8/1/2025).
New Rejections
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-34 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
The claims recite methods and systems for identifying target mutations in nucleic acid samples by performing sequencing reactions, determining statistical measures, comparing read coverage to thresholds, and making decisions about whether to perform additional sequencing based on these comparisons.
The integration of the judicial exception into the claims does not render them patent eligible because the claims are written at a high level of generality and merely use well-known, routine, and conventional techniques in the field.
Subject Matter Eligibility Test for Products and Processes
Step 1 - Is the Claim to a Process, Machine, Manufacture or Composition of Matter? YES.
The claims provide for a method comprising:
performing a first sequencing reaction to determine sample specific properties of a nucleic acid sample;
determining, based on the sample specific properties, a first statistical measure relating to the target mutation;
determining if a first read coverage for the target mutation from the first sequencing reaction is above or below a threshold reference to the first statistical measure;
if the determined read coverage does not exceed the threshold, determining if a sufficient amount of sample nucleic acid is available to perform a second sequencing reaction to increase the first read coverage above the threshold;
if a sufficient amount of sample nucleic acid is available, calculating a sample amount required to achieve a second effective read coverage and re-sequencing the sample nucleic acid to achieve a second read coverage exceeding the threshold; and
implementing these methods through a system comprising a sequencer, processor, and memory configured to process the sample nucleic acid and identify target mutations.
Thus, the claims are directed to statutory categories (i.e., processes and machine).
Step 2A, Prong One — Does the Claim Recite an Abstract Idea, Law of Nature, or Natural Phenomenon? YES.
Abstract ideas have been identified by the courts by way of example, including fundamental economic practices, certain methods of organizing human activities, an idea ‘of itself,’ and mathematical relationships/formulas. The claims recite a judicial exception. The “mental process” of determining if values exceed thresholds and making decisions based on statistical measures corresponds “an abstraction” (an idea having no particular concrete or tangible form). The mathematical concepts involving statistical measures and threshold comparisons are abstract ideas. Thus, the claimed invention describes a judicial exception, which correspond to abstractions (ideas, having no particular concrete or tangible form) and mathematical relationships.
Step 2A, Prong Two — Does the Claim Recite an Additional Elements that Integrate the Judicial Exception into a Practical Application? NO.
The Supreme Court has long distinguished between principles themselves, which are not patent eligible, and the integration of those principles into practical applications, which are patent eligible. However, absent are any additional elements recited in the claim beyond the judicial exceptions which integrate the exception into a practical application of the exception. The “integration into a practical application” requires an additional element or a combination of additional elements in the claim to apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that it is more than a drafting effort designed to monopolize the exception.
The claim limitations are considered to be; (a) a mental process of evaluating/interpreting statistical measures and comparing read coverage to thresholds, and (b) mathematical calculations for determining statistical relationships between sequencing depth and target mutation detection (i.e., abstract ideas).
While the claims recite steps of “performing sequencing relations”, “determining statistical measures”, and “calculating sample amounts,” these steps are recited at a high level of generality and amount to mere data gathering steps, including the sequencing of nucleic acids. There are no additional steps which apply either of the identified judicial exceptions into a practical application. Thus, the claims do not provide for any element/step that integrates the law of nature into a practical application.
Step 2B - Does the Claim Recite Additional Elements that Amount to Significantly More than the Judicial Exception? NO.
The Supreme Court has identified a number of considerations for determining whether a claim with additional elements amounts to “significantly more” than the judicial exception(s) itself. The claims as a whole are analyzed to determine whether any additional element/step, or combination of additional elements/steps, in addition to the identified judicial exception(s) is sufficient to ensure that the claim amounts to “significantly more” than the exception(s).
However, the additional elements of the instant application, individually and in combination, do not amount to “significantly more.” Under the Step 2B analysis, the “physical” elements/steps of, “performing sequencing reactions”, “determining statistical measures”, and “calculating sample amounts for resequencing” are “physical” steps telling a practitioner to simply implement the abstract idea and are considered to be within the purview of one in the art as being routine and conventional in the art when investigating detection of target mutations in nucleic acid samples.
For example, Li et al. discloses (“CONTRA: copy number analysis for targeted resequencing”, Bioinformatics, published 4/2/2012, from IDS 4/21/2022), targeted resequencing (TR), including whole-exome sequencing, is becoming widely adopted as a cost-effective way to interrogate specific genomic regions across a large number of samples, a technique particularly useful for the study of genetic causes of cancer and other diseases (Introduction: Paragraph 1). Further, Li discloses that the primary objective of TR is the detection of single-nucleotide variants (SNVs) and short (<50 bp) insertions and deletions (indels) within the targeted regions, overcoming inherent limitations on sequence alignment of short reads that prohibit the detection of larger indels and, therefore, many potential disease-causing copy number variations (CNVs) are not accessible from TR data (Introduction: Paragraphs 2-3). Li also discloses that through using either a matched control or a robust baseline, the first step of this specialized TR method is to compute base-level log-ratios, establishing these forms of statistical analyses as conventional techniques.
Further, Chen et al. (“Targeted resequencing of the microRNAome and 3′UTRome reveals functional germline DNA variants with altered prevalence in epithelial ovarian cancer”, Oncogene, published 6/9/2014, from IDS 4/21/2022) discloses, the existence of relatively rare, functional variants in miRNAs and their binding sites in target genes, via systematically sequencing germline genomic DNA obtained from ovarian cancer patients to discover additional functional variants associated with cancer in the miRNA regions and 3′UTRs of cancer-related genes through capturing these regions using NimbleGen™’s sequence capture technology using a custom developed hybridization array followed by high-throughput paired-end sequencing to identify genetic variations using individual genomic DNA samples from ovarian cancer patients (Introduction: Paragraphs 2-3). Further Chen discloses that based on strong evidence that 3′UTRs and miRNAs have a critical role in oncogenesis via a hypothesis-driven investigation of these regions through sequencing of the non-protein-coding regions/3′UTRs of ~6000 cancer genes and ~700 validated miRNA genes in 31 ovarian cancer patients focusing only on those genes with known varied expression in ovarian cancer, and applied bioinformatics to identify variants in predicted miRNA-binding sites (Discussion: Paragraph 2). These detailed and specified sequencing analyses demonstrate that practitioners were well-versed in analyzing complex interaction data from mutated systems.
Further, Yang et al. (“Technical Validation of a Next-Generation Sequencing Assay for Detecting Clinically Relevant Levels of Breast Cancer–Related Single-Nucleotide Variants and Copy Number Variants Using Simulated Cell-Free DNA”, The Journal of Molecular Diagnostics, published 6//2017, from IDS 4/21/2022) discloses that next-generation sequencing (NGS) is commonly used in a clinical setting for diagnostic and prognostic testing of genetic mutations to select optimal targeted therapies (Abstract). Further, Yang discloses issue-based NGS is hampered by several limitations that bottleneck its potential clinical use, including, biopsy samples may be of insufficient quantity or be unavailable, and initial biopsy specimens may not reflect a tumor's progressive genetic status over time, as well as intertumor and intratumor heterogeneity may limit the sensitivity of tissue-based NGS to detect genomic alterations (Introduction: Paragraphs 2-3). Also, Yang discloses that these challenges highlighted the pressing need to develop noninvasive assays that broadly and accurately detect actionable genomic alterations (Introduction: Paragraphs 3-4), thus establishing a high level of routine and convention.
Therefore, performing sequencing reactions, determining statistical measures, comparing read coverage to thresholds, and calculating required sample amounts was routine and conventional before the effective filing date of the claimed invention.
Simply appending routine and conventional activities previously known to the industry specified at a high level of generality to the judicial exception and/or generally linking the use of the judicial exception(s) to a particular technological environment or field of use, are not found to be enough to qualify as “significantly more.” Nothing is added by identifying the techniques to be used (i.e., “performing sequencing reactions”, “determining statistical measures”, “comparing read coverage to thresholds”, “calculating sample amounts”, and “performing additional sequencing”) because those techniques were well-understood, routine, and conventional techniques that a practitioner would have thought of when instructed to process and analyze nucleic acid samples for mutation detection. In context with the other recited claim limitations, the language “determining if a first read coverage for the target mutation from the first sequencing reaction is above or below a threshold b reference to the first statistical measure; if the determined first read coverage does not exceed the threshold, determining if a sufficient amount of sample nucleic acid is available to perform a second sequence reaction to increase the first read coverage above the threshold; and if a sufficient amount of sample nucleic acid is available, calculating a sample amount required to achieve a second effective read coverage and re-sequencing the sample nucleic acid to achieve a second read coverage exceeding the threshold” indicates whether or not the relationship/correlation between sequencing overage and mutation detection capability exists.
This information simply tells a practitioner about the relevant statistical relationships between sequencing depth and mutation detection capability, at most adding a suggestion that the genetic testing practitioner should take those relationships into account. Thus, when viewed both individually and as an ordered combination, the claimed elements/steps in addition to the identified judicial exception are found insufficient to supply an inventive concept because the elements/steps are considered conventional and specified at a high level of generality. The claim limitations do not transform the abstract idea that they recite into patent-eligible subject matter because “the claims simply instruct the practitioner to implement the abstract idea with routine, conventional activity.”
Accordingly, the claims do not qualify as patent-eligible subject matter.
Claim Rejections - 35 USC § 103
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 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-34 are rejected under 35 U.S.C. 103 as being unpatentable over Rabinowitz et al. (US Patent No. 9163282 B2; issued 10/20/2015) and Lo et al. (US PGPub 2017/0073774 A1; published 3/16/2017), in view of Illumina (“Estimating Sequencing Coverage: Before starting a sequencing experiment, you should know the depth of sequencing you want to achieve. This Technical Note helps you estimate that coverage”, published 2014) and in further view of Potapov et al. (“Examining Sources of Error in PCR by Single-Molecule Sequencing”, PLOS One, published 2017).
Regarding claim 1, Rabinowitz teaches methods for determining the ploidy status of a chromosome in a gestating fetus from genotypic data measured from a mixed sample of DNA comprising DNA from both the mother of the fetus and from the fetus, and optionally from genotypic data from the mother and father via determination of a ploidy state using a joint distribution model (Abstract). Further, Rabinowitz teaches the ploidy state of the fetus also includes combining the relative probabilities of each of the ploidy hypotheses determined using the joint distribution model and the allele count probabilities with relative probabilities of each of the ploidy hypotheses that are calculated using statistical techniques taken from a group consisting of a read count analysis, comparing heterozygosity rates (Column 5, lines 55-65). Further, Rabinowitz teaches the probability of normalized genotype signals for certain parent contexts, a statistic that is calculated using an estimated fetal fraction of the first sample or the prepared sample (Column 6, lines 1-5). Rabinowitz teaches determining whether the distribution of observed allele measurements is indicative of a euploid or an aneuploid fetus using a maximum likelihood technique for determining maximum likelihood techniques and cutoff thresholds (Column 14, lines 50-65). Further, Rabinowitz teaches that when a set of probabilities that were determined by a first expert technique or threshold on a first sample, each of which are associated with one of the hypotheses in the first set, are combined with a set of probabilities that were determined by a second expert technique or threshold on a second sample, each of which are associated with the same set of hypotheses, then the two sets of probabilities are multiplied, creating for each hypothesis in the set, the two probabilities that are associated with that hypothesis, are multiplied together, and the corresponding product is the output probability (Column 29, lines 20-40).
Regarding claims 2-3, Rabinowitz teaches that the previously described method of processing a targeted mutation via statistical sequencing relationships includes an observed allele ratio or fetal fraction that may not converge with a sufficiently high depth of read to the expected allele ratio or fetal fraction due to amplification bias (Column 88, lines 10-20). Rabinowitz further teaches that the allele ration or tumor fraction can include cancer patient plasma and tumors: mix between health and cancer DNA (Column 61, lines 30-40).
Regarding claim 4, Rabinowitz teaches that the previously described method of processing a targeted mutation via statistical sequencing relationships is aimed towards a target set of DNA that is mixed with an amount of contaminating DNA (Column 61, lines 1-10).
Regarding claim 5, Rabinowitz teaches that the previously described method of processing a targeted mutation via statistical sequencing relationships includes calculating a confidence metric, indicating reporting false negative results when there is insufficient fetal
cfDNA to make a call prior to resequencing (Column 113, lines 55-65).
Regarding claim 6, Rabinowitz teaches that the previously described method of processing a targeted mutation via statistical sequencing relationships includes determining whether the distribution of observed allele measurements is indicative of a euploid or an aneuploid fetus without comparing any metrics to observed allele measurements on a reference chromosome that is expected to be disomic (Column 12, lines 40-45).
Regarding claim 7, Rabinowitz teaches that the previously described method of processing a targeted mutation via statistical sequencing relationships includes sequencing a first fraction of the prepared sample of DNA to give a first set of measurements, the method further comprising: making a first relative probability determination for each of the ploidy hypotheses for each of the fetuses, given the first set of DNA measurements; resequencing a second fraction of the prepared sample from those fetuses where the first relative probability determination for each of the ploidy hypotheses indicates that a ploidy hypothesis corresponding to an aneuploid fetus (Column 112, lines 1-10).
Regarding claim 8, Rabinowitz teaches that the previously described method of processing a targeted mutation via statistical sequencing relationships includes a calculation where each molecule sampled has a probability of being read correctly or determining if the sufficient amount of nucleic acid is available, in which case it will show up correctly as allele A or B or C or D, where p is the true amount of volume of the reference DNA for each allele or sequencing reaction, where Pr+Pm+p0 =l (Column 86, lines 60-65).
Regarding claim 9, Rabinowitz teaches that the previously described method of processing a targeted mutation via statistical sequencing relationships includes once a mixture has been preferentially enriched at the set of target loci, it may be sequenced using any one of the previous, current, or next generation of sequencing instruments that sequences a clonal sample (Column 45, lines 55-65).
Regarding claims 10-12, Rabinowitz teaches that the previously described method of processing a targeted mutation via statistical sequencing relationships includes samples with longer fragments by benefit from fragmentation prior to sequencing library preparation and enrichment maximum specificity may be achieved relatively few sequences reads failing to overlap the critical region of interest (Column 51, lines 40-50). Specifically, Rabinowitz teaches that some DNA samples such as plasma samples are already fragmented due to biological processes that take place in vivo (Column 51, lines 35-40) or involves measuring maternal blood levels of other hormones or maternal serum markers as a targeted sample (Column 103, lines 25-30).
Regarding claims 13-16, Rabinowitz teaches that the previously described method of processing a targeted mutation via statistical sequencing relationships includes the previously described threshold, as described above, and further implementing these methods to determine the ploidy status of the fetus using a single hypothesis rejection method. However, they suffer from some significant shortcomings since these methods for determining ploidy in the fetus are invariant according to the percentage of fetal DNA in the sample, they use one cut off value and the result of this is that the accuracies of the determinations are not optimal, and those cases where the percentage of fetal DNA in the mixture are relatively low or negative and will suffer the worst accuracies (Column 91, lines 1-10). Further, Rabinowitz teaches that one reason is that single hypothesis rejection techniques set various cut off thresholds based on only one measurement distribution rather than two, meaning that the thresholds are usually not optimal (Column 14, lines 60-65). Further, Rabinowitz teaches that another reason is that the maximum likelihood technique allows the optimization of the cut off threshold for each individual sample instead of determining a cut off threshold to be used for all samples regardless of the particular characteristics (i.e., positive or negative findings) of each individual sample (Column 14, lines 60-65).
Regarding claims 17-19, Rabinowitz also teaches that the previously described method of processing a targeted mutation via statistical sequencing relationships includes determining whether the distribution of observed allele measurements is indicative of a euploid or an aneuploid fetus without comparing any metrics to observed allele measurements on a reference chromosome that is expected to be disomic (Column 12, lines 40-45) following resequencing a second fraction of the prepared sample from those fetuses where the first relative probability determination for each of the ploidy hypotheses indicates that making a second relative probability determination for ploidy hypotheses for the fetuses using the second set of measurements and optionally also the first set of measurements; and calling the ploidy states of the fetuses whose second sample was resequenced by selecting the ploidy state corresponding to the hypothesis with the greatest probability as determined by the second relative probability determination (Column 112, lines 10-20).
Regarding claim 20, Rabinowitz also teaches that the previously described method of processing a targeted mutation via statistical sequencing relationships includes a method that uses aspects of the present set of data to determine parameters for the estimated allele frequency distribution for that set of data; an improvement over methods that utilize training set of data or prior sets of data to set parameters for the present expected allele frequency distributions, or possibly expected allele ratios or fetal fractions following re-sequencing because there are different sets of conditions involved in the collection and measurement of every genetic sample, and thus a method that uses data from the instant set of data to determine the parameters for the joint distribution model that is to be used in the ploidy determination for that sample will tend to be more accurate (Column 14, lines 40-50).
Regarding claim 21, Rabinowitz also teaches that the previously described method of processing a targeted mutation via statistical sequencing relationships includes using a targeted sequencing to obtain mixed maternal-fetal genotypes and optionally mother and/or father genotypes at a plurality of SNPs to first establish the various expected allele frequency distributions under the different hypotheses, and then observing the quantitative allele information obtained on the maternal-fetal mixture and evaluating which hypothesis fits the data best (Column 13, lines 1-10).
Regarding claims 22-23, Rabinowitz also teaches that the previously described method of processing a targeted mutation via statistical sequencing relationships includes the log likelihood of a hypothesis may be determined on a per SNP basis where i, assuming fetal ploidy hypothesis H and percent fetal DNA or true and false positivity rates cf, the log likelihood of observed data Dis defined as where m are possible true mother genotypes, fare possible true father genotypes, where m,f E{ AA,AB,BB}, and where c are possible child genotypes given the hypothesis H (Column 96, lines 20-40). Rabinowitz further teaches that for monosomy c { A,B}, for disomy cE{ AA,AB,BB}, fortrisomy cE{AAA,AAB,ABB, BBB} (Column 96, lines 20-40) as shown in
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[AltContent: textbox (Figure 1: Rabinowitz’s log-likelihood method of determination for maternal (host) and fetus (guest) nucleic acid samples. )]Figure 1 below.
Regarding claim 24, Rabinowitz also teaches that the previously described method of processing a targeted mutation via statistical sequencing relationships includes after centrifugation, maternal DNA extracted from the buffy coat and cell-free DNA was extracted from plasma (Column 114, lines 40-50).
Regarding claims 25-26, Rabinowitz teaches methods for determining the ploidy status of a chromosome in a gestating fetus from genotypic data measured from a mixed sample of DNA comprising DNA from both the mother of the fetus and from the fetus, and optionally from genotypic data from the mother and father via determination of a ploidy state using a joint distribution model (Abstract). Further, Rabinowitz teaches the ploidy state of the fetus also includes combining the relative probabilities of each of the ploidy hypotheses determined using the joint distribution model and the allele count probabilities with relative probabilities of each of the ploidy hypotheses that are calculated using statistical techniques taken from a group consisting of a read count analysis, comparing heterozygosity rates (Column 5, lines 55-65). Further, Rabinowitz teaches that targeted nucleic acid samples can include fetal death (Column 2, lines 30-35) and the presence or absence of fetal aneuploidy in a maternal tissue sample comprising fetal and maternal genomic DNA (Column 6, lines 30-35).
Regarding claim 27, Rabinowitz also teaches that the previously described method of processing a targeted mutation via statistical sequencing relationships includes other situations where these previously described methods would be particularly advantageous would be in the case of cancer testing where only one or a small number of cells were present among a larger amount of normal cells (Column 16, lines 35-40).
Regarding claims 28-29, Rabinowitz teaches methods for determining the ploidy status of a chromosome in a gestating fetus from genotypic data measured from a mixed sample of DNA comprising DNA from both the mother of the fetus and from the fetus, and optionally from genotypic data from the mother and father via determination of a ploidy state using a joint distribution model (Abstract). Further, Rabinowitz teaches the ploidy state of the fetus also includes combining the relative probabilities of each of the ploidy hypotheses determined using the joint distribution model and the allele count probabilities with relative probabilities of each of the ploidy hypotheses that are calculated using statistical techniques taken from a group consisting of a read count analysis, comparing heterozygosity rates (Column 5, lines 55-65). Further Rabinowitz teaches that the previously discussed method, may be implemented in digital electronic circuitry, integrated circuitry, specially designed ASI Cs ( application-specific integrated circuits), computer hardware, firmware, software, or can be implemented in a computer program product tangibly embodied in a machine-readable storage device for execution by a programmable processor; and method steps of the presently disclosed embodiments can be performed by a programmable processor executing a program of instructions to generate output from input data (Column 40, lines 30-40).
Specifically, Rabinowitz teaches there is a composition comprising a mixture of DNA of fetal origin, and DNA of maternal origin, wherein the percent of sequences that uniquely map to a chromosome and that contain at least one single nucleotide polymorphism from a set of single nucleotide polymorphisms is greater than 0.1% to 10% (Column 76, lines 1-15).
Regarding claim 30, Rabinowitz also teaches that the previously described method of processing a targeted mutation via statistical sequencing relationships includes ideally, uniform depth of read (DOR) where each locus will have a similar number of representative sequence reads where it is desirable to minimize the DOR variance or minimal read coverage to not exceed a specific threshold or cut off (Column 71, lines 20-30) that is made based on a threshold that is optimized for the case where there is a higher percent fetal DNA (Column 92, lines 1-5) and does not does not use genetic measurements of the maternal plasma or target mutation (Column 96, lines 1-10).
Regarding claim 31, Rabinowitz also teaches that the previously described method of processing a targeted mutation via statistical sequencing relationships includes separate reactions, thereby reducing the complexity of the reaction and preventing dimer formation of forward and reverse primers (Column 68, lines 20-25).
Regarding claim 32, Rabinowitz also teaches that the previously described method of processing a targeted mutation via statistical sequencing relationships includes has been reported that the distribution of length of sequences differ for maternal and fetal DNA, with fetal generally being shorter and it is possible to use previous knowledge in the form of empirical data, and construct prior distribution for expected length of both mother(P(Xlmaternal)) and fetal DNA (P(X I fetal)) (Column 97, lines 30-60). Further, Rabinowitz teaches that given new unidentified DNA sequence of length x, it is possible to assign a probability that a given sequence of DNA is either maternal or fetal DNA, based on prior likelihood of x given either maternal or fetal; where P(xlmaternal)>P(xlfetal), then the DNA sequence can be classified as maternal, with P(xlmaternal)=P(xlmaternal)/[(P(xlmaternal)+P(xlfetal)], and if p(xlmaternal)<p(xlfetal), then the DNA sequence can be classified as fetal, P(xlfetal) =P(xlfetal)/[(P(xlmaternal)+P(xlfetal)] Column 97, lines 30-60). Further, Rabinowitz teaches that in the previously described method, distributions of maternal and fetal sequence lengths can be determined that is specific for that sample by considering the sequences that can be assigned as maternal or fetal with high probability, and then that sample specific distribution can be used as the expected size distribution for that sample (Column 97, lines 30-60).
Regarding claims 33-34, Rabinowitz teaches methods for determining the ploidy status of a chromosome in a gestating fetus from genotypic data measured from a mixed sample of DNA comprising DNA from both the mother of the fetus and from the fetus, and optionally from genotypic data from the mother and father via determination of a ploidy state using a joint distribution model (Abstract); including through comparing the relative number of sequence reads for the sex chromosomes to one or a plurality of reference chromosomes that are assumed to be euploid (Column 108, lines 1-5). Further, Rabinowitz teaches the ploidy state of the fetus also includes combining the relative probabilities of each of the ploidy hypotheses determined using the joint distribution model and the allele count probabilities with relative probabilities of each of the ploidy hypotheses that are calculated using statistical techniques taken from a group consisting of a read count analysis, comparing heterozygosity rates (Column 5, lines 55-65). Further Rabinowitz teaches that the previously discussed method, may be implemented in digital electronic circuitry, integrated circuitry, specially designed ASI Cs ( application-specific integrated circuits), computer hardware, firmware, software, or can be implemented in a computer program product tangibly embodied in a machine-readable storage device for execution by a programmable processor; and method steps of the presently disclosed embodiments can be performed by a programmable processor executing a program of instructions to generate output from input data (Column 40, lines 30-40).
Rabinowitz does not teach or suggest determining a sample or sufficient amount required to achieve a second read coverage that exceeds the threshold when the initial sequencing coverage is insufficient. Specifically, Rabinowitz does not teach or suggest estimating the quantity of sample nucleic acid or number of informative molecules needed to obtain additional sequencing coverage sufficient to satisfy a desired mutation detection criterion via resequencing.
Lo teaches embodiments that are related to the accurate detection of somatic mutations in the plasma (or other samples containing cell-free DNA) of cancer patients and for subjects being screened for cancer, where the detection of these molecular markers would be useful for the screening, detection, monitoring, management, and prognostication of cancer patients and for example, a mutational load can be determined from the identified somatic mutations, and the mutational load can be used to screen for any or various types of cancers, where no prior knowledge about a tumor or possible cancer of the subject may be required (Abstract; Figure 7). that the depth of the survey also matters, depending on the number of mutations detected per tumor, multiple plasma DNA fragments that bore that mutation would need to be detected to reach a specified threshold, e.g., 500 informative cancer DNA fragments for each genome equivalent of cancer cell, where for example, if only one mutation is identified in a particular tumor, then 500 plasma DNA fragments covering that mutation would be needed (Paragraph 128, lines 1-10). Further, Lo teaches that on the other hand, if 50 different mutations are present in the tumor, on average, one would need to detect at least 10 informative cancer DNA fragments covering each one of those 50 mutations (Paragraph 128, lines 1-10). Lo teaches that tumor DNA typically represents a minor DNA population in plasma and furthermore, some cancer-associated changes are heterozygous in nature (i.e. with one change per diploid genome) (Paragraph 129, lines 1-5). Lo also teaches that therefore to detect 10 copies of informative cancer DNA fragment (i.e. plasma DNA fragments that carry at least one cancer-associated change) per locus, one would need to analyze at least 100 molecules from the locus in a plasma sample with 20% tumor DNA fraction and hence, the ability to detect multiple plasma DNA fragments covering any single mutation site is dependent on how deep the plasma sample is surveyed and yet, there is only a finite number of cancer cell genomes in the plasma sample, which affects both the required depth and breadth of the plasma DNA analysis (Paragraph 129, lines 5-10). Lo also teaches that for illustration of the detection of early cancers, assume one aims to develop a test or protocol that could detect a tumor fraction of 1 % in a sample and given that there are typically 1,000 genome-equivalents of DNA in every milliliter of plasma, there would be 10 cancer cell-equivalent of DNA in a milliliter sample with 1 % tumor DNA fraction where this means that even if one could detect every single cancer-specific DNA fragment in the sample, there would only be a maximum of 10 genome-equivalents of any one cancer-associated change that would be available for detection (Paragraph 130, lines 1-5). Further, Lo teaches that accordingly, even if one has prior knowledge that a particular mutation is present in a tumor, its targeted detection would only provide a signal of 10 genome-equivalents in the best-case scenario, which may lack the analytical sensitivity for robust detection of a cancer at 1 % fractional concentration and if the mutation to be detected is heterozygous, there would only be 5 plasma DNA fragments showing this mutation (Paragraph 130, lines 5-10). Also, Lo teaches that in the best-case scenario with 1 % tumor DNA fraction, the depth of the analysis at this mutation site would need to be covered at least 1,000 times to be able to detect the 10 genome-equivalents of plasma DNA with the mutation and in this situation, the breadth of the analysis would need to make up for the relatively low number of copies detected per mutation site, where the selective detection of a handful or even just hundreds of mutation sites is unlikely to be able to achieve the sensitivity required for a screening test to detect early cancer (Paragraph 131, lines 1-10).
Further, Lo teaches that one or more dynamic cutoff filtering criteria can be used to distinguish single nucleotide variants, namely mutations and polymorphisms, from nucleotide changes due to sequencing error and depending on the context, mutations can be "de nova mutations" (e.g., new mutations in the constitutional genome of a fetus) or "somatic mutations" (e.g., mutations in a tumor), where various parameter values can be determined for each of a plurality of loci, where each parameter value is compared to a respective cutoff value (Paragraph 210, lines 1-10). Lo also teaches that a locus can be discarded as having a potential mutation if a
parameter value does not satisfy a cutoff (Paragraph 210, lines 1-10). Lo also teaches that for the identification of somatic mutations in cancer, the high-depth sequencing data from a person's constitutional DNA (e.g., buffy coat) and plasma DNA can be compared to identify sites that are heterozygous in the plasma DNA (AB) and homozygous (AA) in the constitutional DNA, where "A" and "B" denote the wildtype and mutant alleles, respectively (Paragraph 211, lines 1-5). Lo also illustrates one embodiment of implementing the dynamic cutoff strategy for mutation detection, where, the binomial and Poisson distribution models were used to calculate three parameters (Paragraph 211, lines 1-10). Lo teaches that regarding a first parameter, the accuracy of determining the homozygous sites (AA) in the constitutional DNA is affected by sequencing error where the sequencing error can be estimated by a number of methods known to those
skills in the art and the higher the value of Score!, the more confident that the actual genotype is AA (Paragraph 212, lines 1-10). Specifically, Lo teaches that a cut-off greater than 0.01 could be
used and this parameter can be used to control the influence of sequencing errors (Paragraph 212, lines 1-10). Lo further teaches that regarding a second parameter, there is a chance that the observed wildtype AA (homozygous) in the constitutional genome would be miscalled from the actual AB (heterozygous) genotype due to insufficient sequencing depth of the SNP loci and to minimize the influence of this type of error, we calculated the second parameter, Score2, as
Score2=ppois(b, D/2), where "b" is the number of sequenced counts covering the B allele, and "ppois" is the Poisson cumulative distribution function, where A is the average sequencing depth per strand (i.e.D/2); e is the base of the natural logs (-2.717828) and the lower the value of Score2, the more confident that the actual genotype is AA (Paragraph 213, lines 1-10). Lo teaches that for example, a cut-off of <0.001, 0.0001,10-10, etc. can be used and this parameter can be used to
control allele or variant drop out, which refers to heterozygous sites appearing like homozygous sites because one allele or variant could not be amplified, and thus this missing allele or variant has dropped out and certain data below uses cutoffs ofscorel>0.01 and score2 <0.001, where score! And score2 can be used to guarantee that the buffy coat is homozygous (Paragraph 213, lines 1-10). Also, Lo teaches that regarding a third parameter, there is a chance that the observed mutant AB would be miscalled from the actual AA genotype due to sequencing errors and to minimize the influence of this type of error, we calculated the third parameter, Score3, representing a mathematical combination function, i.e. the number of combinations selecting b times of the mutant allele from sequencing depth D, where the lower the Score3, the more confident that the actual genotype is AB and for example, a cut-off of <0.001, 0.0001, 10-10, etc., can be used (Paragraph 214, lines 1-10). Additionally, Lo teaches that Score! and Score2 can be applied to constitutional tissue, and Score 3 can be applied to mixture (tumor or plasma) and therefore the joint analysis between constitutional tissues and mixture samples by adjusting Score!, Score2,
and Score3 can be conducted to determine the potential mutations (Paragraph 215, lines 1-10). Specifically, Lo teaches that different thresholds for the calculation of each score can be used in the dynamic cutoff depending on the intended purpose and for example, a lower value for Score3
could be used if one prefers high specificity in the identification of somatic mutations and similarly, a higher value for Score3 could be used if one prefers to detect a greater total
sum of somatic mutations, where the specificity of the identified somatic mutations can be improved by using other filtering parameters, e.g., as described below (Paragraph 216, lines 1-10). Lo also teaches that other mathematical or statistical models can also be used, for example, Chi square distribution, Gamma distribution, normal distribution, and other types of mixture models and that this process could be similarly applied for the identification of fetal de nova mutations (Paragraph 216, lines 1-10).
Additionally, Lo teaches that to develop an effective cancer screening test for the early identification of cancer and the identification of cancer at early stages, one would ideally obtain as much cancer relevant information from the plasma sample as possible and there are a number of issues hindering one's ability to obtain cancer-relevant information from the plasma sample: (1) the sample to be analyzed has a finite volume; (2) the tumor fraction in a particular biological
sample may be low during early cancer; (3) the total amount of somatic mutations per tumor available for detection are on the order of 1,000 to 10,000; and ( 4) the analytical steps and technical processes would lead to a loss in information content and therefore, one should try to minimize the loss of any cancer-related information content in the plasma sample that is amenable for detection (Paragraph 158, lines 1-10). More so, Lo teaches that due to limitations in the sample preparation steps, sequencing library preparation steps, sequencing, base-calling and alignment, not all plasma DNA molecules in a sample would be analyzable or sequenceable and exhaustive
sequencing refers to procedures implemented to maximize the ability to transform as many of the informative DNA molecules ( e.g., ones with mutations) in a finite sample into analyzable or sequenceable molecules where several processes could be adopted to achieve exhaustive sequencing (Paragraph 159, lines 1-10).
Illumina teaches that it is very easy to increase the coverage or sequence depth, if you later decide you need more data and provided you still have your original sample, you can just sequence
more, and combine the sequencing output from different flow cells (When to Sequence More). Specifically, Illumina teaches that there are a number of reasons to sequence more than the originally estimated coverage, these include:
The effects you see are not statistically significant. Sequencing
more reads will generally increase the power of your assay.
You are investigating events that are very rare. For example,
you may want to look at transcripts that are expressed at a
very low level in RNA Sequencing, or look at very low binding
activities in ChIP Sequencing.
Certain journals or fields may require a higher level of coverage
for your particular application.
Certain genomes may need more sequencing. For example,
certain regions may be hard to sequence requiring more
coverage, or the genome may be polyploid (When to Sequence More).
Potapov teaches that next-generation sequencing technology has enabled the detection of rare genetic or somatic mutations and contributed to our understanding of disease progression and evolution, however, many next-generation sequencing technologies first rely on DNA amplification, via the Polymerase Chain Reaction (PCR), as part of sample preparation workflows. Mistakes made during PCR appear in sequencing data and contribute to false mutations that can ultimately confound genetic analysis and in this report, a single-molecule sequencing assay was used to comprehensively catalog the different types of errors introduced during PCR, including polymerase misincorporation, structure-induced template-switching, PCR-mediated recombination and DNA damage (Abstract). Further, Potapov teaches that an assay was developed to measure template-switching between different DNA molecules, frequently described as PCR-mediated recombination and during amplification of a mixed population of related sequences, recombination is thought to occur when a partially extended primer from one template anneals to a different (but closely related) template molecule in a later round of PCR, where further extension results in a chimeric product, where a single-molecule sequencing assay was developed to mimic amplification of closely related sequences, using pairs of artificial genes that differ by point mutations spaced at regular intervals across the gene and after amplification of the mixed templates, recombination events can be detected by SMRT sequencing when a sequencing read has markers from both templates (Fig 3) (PCR mediated recombination).
One of ordinary skill in the art would have been motivated to combine Rabinowitz with Lo, Illumina and Potapov because each reference addresses the common problem of accurately identifying low-frequency mutations from sequencing data. Lo provides quantitative techniques for determining whether sufficient sample material and sequencing depth exist to achieve a desired mutation-calling confidence threshold. Illumina provides a known solution for increasing coverage through additional sequencing when initial coverage is inadequate. Potapov reinforces the need for sufficient sequencing depth and confidence metrics to distinguish true mutations from sequencing and amplification artifacts. Combining these teachings would have predictably improved the accuracy and reliability of mutation detection while avoiding unnecessary sequencing of samples lacking sufficient remaining material.
More so, although Rabinowitz does not teach or suggest calculating whether a sufficient amount of sample nucleic acid remains to achieve a second read coverage above a threshold based upon sample-specific properties and statistical confidence measures relating to mutation detection, Lo teaches this determination through its disclosure of finite sample volume constraints, mutation-detection thresholds, sequencing-depth requirements, informative DNA molecule counts, dynamic cut-off values, and statistical measures used to determine whether sufficient sequencing information exists to reliably identify a mutation. In a similar vein, Rabinowitz does teach performing additional sequencing analyses and obtaining additional sequencing information, however Illumina is cited merely as evidence that increasing coverage through resequencing the same sample when additional data are required was a well-known and conventional technique in the art. Therefore, even if Rabinowitz’s disclosure of additional sequencing were deemed incomplete, Illumina confirms that increasing coverage through resequencing represented a routine optimization available to the ordinarily skilled artisan.
Additionally, Lo recognizes that mutation detection is ultimately constrained by the number of informative DNA molecules available within finite biological sample and teaches determining sequencing depth and coverage requirements based on the available molecules and desired mutation-detection confidence. Consistent with this understanding, Illumina further teaches that additional sequencing may be performed provided the original sample remains available, while Potapov teaches that a single template molecule is sufficient for sequencing and mutation analysis. Thus, one of ordinary skill in the art would have understood that the relevant consideration is whether sufficient sample nucleic acid molecules remain to support additional sequencing needed to achieve the desired coverage threshold and mutation-calling confidence.
Notably, Applicant is advised that the cited prior art teaches resequencing to increase coverage and teaches statistical and sequencing-depth-based determinations of mutation-detection confidence. Amendment of independent clam 1 to further define a specific calculation for the recited sufficient sample amount required to achieve read coverage (i.e., claims 7 and 8) is recommended.
Conclusion
No claim is allowed, but Applicant is recommended to incorporate additional elements of claims 7 and 8 into independent claim 1 for allowability.
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/ELIZABETH ROSE LAFAVE/
Examiner, Art Unit 1684
/HEATHER CALAMITA/Supervisory Patent Examiner, Art Unit 1684
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