DETAILED ACTION
The Applicant’s filing, received 26 September 2022, has been fully considered. The following rejections and/or objections constitute the complete set presently being applied to the instant application.
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 .
Status of the Claims
The preliminary amendment received 26 September 2022 has been entered.
Claims 1-13, 15, 16, 19-21, 33, and 34 are pending.
Claims 1-13, 15, 16, 19-21, 33, and 34 are rejected.
Priority
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
This application is a 371 of PCT/US2021/022921, filed 18 March 2021, which claims benefit of U.S. provisional application number 63/000,426, filed 26 March 2020.
Information Disclosure Statement
The information disclosure statement (IDS) received 26 September 2022 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement has been considered by the examiner.
The listing of references in the specification is not a proper information disclosure statement. 37 CFR 1.98(b) requires a list of all patents, publications, or other information submitted for consideration by the Office, and MPEP § 609.04(a) states, "the list may not be incorporated into the specification but must be submitted in a separate paper." Therefore, unless the references have been cited by the examiner on form PTO-892, they have not been considered.
Drawings
The drawings were received 26 September 2022. These drawings are accepted.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 20 and 21 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 20 is indefinite for reciting “acquiring, from the sequencing device, sequence reads corresponding to cfDNA fragments…” and “generating, by the one or more processors, using the sequence reads from the sequencing device, a tumor fragment size profile …” because the claim recites a system as well as method steps of using the system, i.e., acquiring sequence reads and generating size profiles (see MPEP 2173.05(p) II.).
Claim 21 is indefinite for reciting “obtaining a trend line…” and “defining in the trend line one or more…” because the claim recites a system as well as method steps of using the system, i.e., obtaining a trend line and defining regions of interest (see MPEP 2173.05(p) II.).
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 1-13, 15, 16, 19-21, 33, and 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: (a) mathematical concepts, (e.g., mathematical relationships, formulas or equations, mathematical calculations); and (b) mental processes, i.e., concepts performed in the human mind, (e.g., observation, evaluation, judgement, opinion).
Claim Interpretations:
Claim 4 recites “…based on analysis of cfDNA samples of a plurality of training subjects.” The limitation of “cfDNA samples of a plurality of training subjects” is interpreted as a product-by-process limitation, with the product being the cfDNA data from the samples, and further interpreted to not require the active steps of performing the process of gathering the plurality of samples and generating the data from the samples.
Subject matter eligibility evaluation in accordance with MPEP 2106.
Eligibility Step 1: Step 1 of the eligibility analysis asks: Is the claim to a process, machine, manufacture or composition of matter?
Claims 1-13, 15, and 16 recite a method (i.e., a process) of employing machine learning to distinguish tumor-derived mutations from clonal hematopoietic derived mutations in cell-free DNA (cfDNA); claims 19-21 recite a computing system comprising one or more processors (i.e., a machine or manufacture) for distinguishing tumor-derived mutations from clonal hematopoietic derived mutations in cell-free DNA (cfDNA) through machine learning; and claims 33-34 recite a method (i.e., a process) for extracting and analyzing cfDNA fragments in a sample of a patient using tumor and clonal hematopoietic derived regions of interest (ROIs) for the cfDNA fragments.
Therefore, these claims are encompassed by the categories of statutory subject matter, and thus, satisfy the subject matter eligibility requirements under step 1.
[Step 1: YES]
Eligibility Step 2A: First it is determined in Prong One whether a claim recites a judicial exception, and if so, then it is determined in Prong Two whether the recited judicial exception is integrated into a practical application of that exception.
Eligibility Step 2A Prong One: In determining whether a claim is directed to a judicial exception, examination is performed that analyzes whether the claim recites a judicial exception, i.e., whether a law of nature, natural phenomenon, or abstract idea is set forth or described in the claim.
Independent claim 1 recites the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas:
detecting, using the sequence reads corresponding to the cfDNA fragments, a gene mutation in the cfDNA (i.e., mental processes);
generating a size profile for a set of cfDNA fragments with the gene mutation of specific origins, the size profile identifying how many cfDNA fragments are detected for each fragment length in a plurality of fragment lengths (i.e., mental processes and mathematical concepts);
classifying, in the set of cfDNA fragments in the cfDNA sample, a first subset of cfDNA fragments as having a tumor origin and a second subset of cfDNA fragments as having a CH origin by feeding the size profile as an input to a mutation-specific predictive machine-learning model that is configured to generate a first set of ranges of fragment lengths for fragments with the tumor origin and a second set of ranges of fragment lengths for fragments of the CH origin, wherein the first subset of cfDNA fragments have lengths falling in the first set of ranges and the second subset of cfDNA fragments have lengths falling in the second set of ranges (i.e., mental processes); and
generating a characterization of the mutation based on the classifying of cfDNA fragments (i.e., mental processes).
Independent claim 19 recites the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas:
detect, using the sequence reads corresponding to the cfDNA fragments, a gene mutation in the cfDNA (i.e., mental processes);
generate a size profile for a set of cfDNA fragments with the gene mutation, the size profile identifying how many cfDNA fragments are detected for each fragment length in a plurality of fragment lengths (i.e., mental processes and mathematical concepts);
classify, in the set of cfDNA fragments in the cfDNA sample, a first subset of cfDNA fragments as having a tumor origin and a second subset of cfDNA fragments as having a CH origin by feeding the size profile as an input to a predictive machine-learning model that is configured to generate, for the gene mutation, a first set of one or more ranges of fragment lengths for fragments with the tumor origin and a second set of one or more ranges of fragment lengths for fragments of the CH origin, wherein the first subset of cfDNA fragments have lengths falling in the first set of ranges and the second subset of cfDNA fragments have lengths falling in the second set of ranges (i.e., mental processes); and
generate, using a metric threshold, a characterization of the mutation based on the classifying of cfDNA fragments (i.e., mental processes).
Independent claim 33 recites the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas:
at step (b) producing one or more tumor regions of interest (ROIs) and one or more CH ROIs for the cfDNA fragments of (a) by:
(i) generating a tumor fragment size profile and a CH fragment size profile (i.e., mental processes);
(ii) applying a smoothing operation to a difference between the tumor fragment size profile and the CH fragment size profile to obtain a trend line with a set of extrema comprising one or more maximums and one or more minimums (i.e., mental processes and mathematical concepts); and
(iii) defining the tumor and CH ROIs as sets of ranges of cfDNA fragment sizes based on the maximums and minimums (i.e., mental processes); and
analyzing cfDNA fragments in a sample of a patient using the tumor and CH ROIs (i.e., mental processes).
Dependent claims 2-13, 15, 16, 20, 21, and 34 further recite the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas, as noted below.
Dependent claim 2 further recites:
generating a metric based on the size profile, and wherein generating the characterization comprises identifying an origin of the gene mutation based on a comparison of the metric with a metric threshold (i.e., mental processes).
Dependent claim 3 further recites:
the metric is a proportion of fragments in one of the subsets of cfDNA fragments to fragments in both of the subsets of cfDNA fragments (i.e., mental processes and mathematical concepts).
Dependent claim 4 further recites:
the predictive machine-learning model is further configured to generate the metric threshold based on an analysis of cfDNA samples of a plurality of training subjects (i.e., mental processes and mathematical concepts).
Dependent claim 5 further recites:
training the predictive machine-learning model (i.e., mental processes and mathematical concepts) by:
generating, using the sequence reads from the sequencing device, a tumor fragment size profile and a CH fragment size profile (i.e., mental processes).
Dependent claim 6 further recites:
training the predictive machine-learning model (i.e., mental processes and mathematical concepts) by:
obtaining a trend line, wherein obtaining the trend line comprises applying a smoothing operation to the tumor fragment size profile and the CH fragment size profile (i.e., mental processes and mathematical concepts); and
defining in the trend line one or more tumor regions of interest (ROI) and one or more CH ROIs, the tumor ROIs corresponding with the first set of ranges of fragment lengths and the CH ROIs corresponding with the second set of ranges of fragment lengths (i.e., mental processes).
Dependent claim 7 further recites:
determining a difference between the tumor fragment size profile and the CH fragment size profile, wherein obtaining the trend line comprises applying the smoothing operation to the difference to obtain the trend line (i.e., mental processes and mathematical concepts).
Dependent claim 8 further recites:
the predictive machine-learning model is further trained by generating, for each mutation, a metric based on the proportion of fragments in the tumor and CH ROIs (i.e., mental processes and mathematical concepts).
Dependent claim 9 further recites:
the metric is a number of cfDNA fragments with lengths in one of the tumor ROIs or the CH ROIs, divided by a total number of cfDNA fragments with lengths in both the tumor ROIs and the CH ROIs (i.e., mental processes and mathematical concepts).
Dependent claim 10 further recites:
selecting a metric threshold for use in classifying cfDNA fragments as having a tumor-derived mutation or a CH-derived mutation (i.e., mental processes).
Dependent claim 11 further recites:
the predictive machine-learning model is trained on a tumor fragment size profile and a CH fragment size profile (i.e., mental processes and mathematical concepts).
Dependent claim 12 further recites:
the trend line includes a set of one or more extrema, wherein the tumor and CH ROIs are centered about extrema in the set of extrema (i.e., mental processes).
Dependent claim 13 further recites:
the tumor ROI is a first number of base pairs on one or both sides of a first extremum, and wherein the CH ROI is a second number of base pairs on one or both sides of a second extremum (i.e., mental processes and mathematical concepts).
Dependent claim 15 further recites:
the gene mutation is in one or more cancer-related genes (i.e., mental processes).
Dependent claim 16 further recites:
the predictive machine-learning model is trained on a tumor fragment size profile and a CH fragment size profile using unsupervised learning (i.e., mental processes and mathematical concepts).
Dependent claim 20 further recites:
train the predictive machine-learning model (i.e., mental processes and mathematical concepts) by:
generating, using the sequence reads from the sequencing device, a tumor fragment size profile and a CH fragment size profile (i.e., mental processes).
Dependent claim 21 further recites:
train the predictive machine-learning model (i.e., mental processes and mathematical concepts) by:
obtaining a trend line by applying a smoothing operation to the tumor fragment size profile and the CH fragment size profile (i.e., mental processes and mathematical concepts); and
defining in the trend line one or more tumor regions of interest (ROIs) and one or more CH ROIs, the tumor ROIs corresponding with the first set of ranges of fragment lengths and the CH ROIs corresponding with the second set of ranges of fragment lengths (i.e., mental processes).
Dependent claim 34 further recites:
generating a metric threshold using the samples of the plurality of subjects, determining a metric for the sample of the patient, and characterizing the cfDNA fragments in the sample of the patient by comparing the metric with the metric threshold (i.e., mental processes).
The abstract ideas recited in the claims are evaluated under the broadest reasonable interpretation (BRI) of the claim limitations when read in light of and consistent with the specification. As noted in the foregoing section, the claims are determined to contain limitations that can practically be performed in the human mind with the aid of a pen and paper (e.g., detecting, using the sequence reads corresponding to the cfDNA fragments, a gene mutation in the cfDNA), and therefore recite judicial exceptions from the mental process grouping of abstract ideas. Additionally, the recited limitations that are identified as judicial exceptions from the mathematical concepts grouping of abstract ideas (e.g., applying a smoothing operation to a difference between the tumor fragment size profile and the CH fragment size profile to obtain a trend line with a set of extrema comprising one or more maximums and one or more minimums) are abstract ideas irrespective of whether or not the limitations are practical to perform in the human mind.
Therefore, claims 1-13, 15, 16, 19-21, 33, and 34 recite an abstract idea.
[Step 2A Prong One: YES]
Eligibility Step 2A Prong Two: In determining whether a claim is directed to a judicial exception, further examination is performed that analyzes if the claim recites additional elements that when examined as a whole integrates the judicial exception(s) into a practical application (MPEP 2106.04(d)). A claim that integrates a judicial exception into a practical application will apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception. The claimed additional elements are analyzed to determine if the abstract idea is integrated into a practical application (MPEP 2106.04(d)(I); MPEP 2106.05(a-h)). If the claim contains no additional elements beyond the abstract idea, the claim fails to integrate the abstract idea into a practical application (MPEP 2106.04(d)(III)).
The judicial exceptions identified in Eligibility Step 2A Prong One are not integrated into a practical application because of the reasons noted below.
Dependent claims 2-4, 9-13, 15, 16, and 34 do not recite any elements in addition to the judicial exception, and thus are part of the judicial exception.
The additional elements in independent claim 1 include:
a computer;
one or more processors; and
acquiring, from a sequencing device, sequence reads corresponding to cfDNA fragments in a sample of a test subject.
The additional elements in independent claim 19 include:
a computing system comprising one or more processors; and
acquire, from a sequencing device, sequence reads corresponding to cfDNA fragments in a sample of a test subject.
The additional elements in independent claim 33 include:
extracting cell-free DNA (cfDNA) comprising tumor-origin cfDNA fragments and CH-origin cfDNA fragments from substantially cell-free samples of blood plasma and/or blood serum of a plurality of subjects; and
extracting cfDNA fragments in a sample of a patient.
The additional elements in dependent claims 5-8, 20, and 21 include:
one or more processors (claims 5-8, 20, and 21); and
acquiring, from the sequencing device, sequence reads corresponding to cfDNA fragments in samples of a plurality of subjects with known tumor mutations and/or known CH mutations (claims 5 and 20).
The additional elements of a computer (claim 1); a computing system (claim 19); and one or more processors (claims 1, 5-8, and 19-21); invoke a computer and/or computer-related components merely as tools for use in the claimed process, and therefore are not an improvement to computer functionality itself, or an improvement to any other technology or technical field, and thus, do not integrate the judicial exceptions into a practical application (MPEP 2106.04(d)(1)).
The additional element of acquiring, from a sequencing device, sequence reads corresponding to cfDNA fragments in a sample of a test subject (claims 1 and 19); and acquiring, from the sequencing device, sequence reads corresponding to cfDNA fragments in samples of a plurality of subjects with known tumor mutations and/or known CH mutations (claims 5 and 20); is merely a pre-solution activity of gathering data for use in the claimed process – a nominal addition to the claims that does not meaningfully limit the claims, and therefore does not add more than insignificant extra-solution activity to the judicial exceptions (MPEP 2106.05(g)).
The additional elements of extracting cell-free DNA (cfDNA) comprising tumor-origin cfDNA fragments and CH-origin cfDNA fragments from substantially cell-free samples of blood plasma and/or blood serum of a plurality of subjects (claim 33); and extracting cfDNA fragments in a sample of a patient (claim 33); is merely a pre-solution activity in the process of gathering data for use in the claimed process – a nominal addition to the claims that does not meaningfully limit the claims, and therefore does not add more than insignificant extra-solution activity to the judicial exceptions (MPEP 2106.05(g)).
Thus, the additionally recited elements merely invoke a computer and/or computer related components as tools; and/or amount to insignificant extra-solution activity; and as such, when all limitations in claims 1-13, 15, 16, 19-21, 33, and 34 have been considered as a whole, the claims are deemed to not recite any additional elements that would integrate a judicial exception into a practical application, and therefore claims 1-13, 15, 16, 19-21, 33, and 34 are directed to an abstract idea (MPEP 2106.04(d)).
[Step 2A Prong Two: NO]
Eligibility Step 2B: Because the claims recite an abstract idea, and do not integrate that abstract idea into a practical application, the claims are probed for a specific inventive concept. The judicial exception alone cannot provide that inventive concept or practical application (MPEP 2106.05). Identifying whether the additional elements beyond the abstract idea amount to such an inventive concept requires considering the additional elements individually and in combination to determine if they amount to significantly more than the judicial exception (MPEP 2106.05A i-vi).
The claims do not include any additional elements that are sufficient to amount to significantly more than the judicial exception(s) because of the reasons noted below.
Dependent claims 2-4, 9-13, 15, 16, and 34 do not recite any elements in addition to the judicial exception(s).
The additional elements recited in independent claims 1, 19, and 33 and dependent claims 5-8, 20, and 21 are identified above, and carried over from Step 2A Prong Two along with their conclusions for analysis at Step 2B. Any additional element or combination of elements that was considered to be insignificant extra-solution activity at Step 2A Prong Two was re-evaluated at Step 2B, because if such re-evaluation finds that the element is unconventional or otherwise more than what is well-understood, routine, conventional activity in the field, this finding may indicate that the additional element is no longer considered to be insignificant; and all additional elements and combination of elements were evaluated to determine whether any additional elements or combination of elements are other than what is well-understood, routine, conventional activity in the field, or simply append well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception, per MPEP 2106.05(d).
The additional elements of a computer (claim 1); a computing system (claim 19); and one or more processors (claims 1, 5-8, and 19-21); are conventional computer components and/or functions (see MPEP at 2106.05(b) and 2106.05(d)(II) regarding conventionality of computer components and computer processes).
The additional elements of extracting cell-free DNA (cfDNA) comprising tumor-origin cfDNA fragments and CH-origin cfDNA fragments from substantially cell-free samples of blood plasma and/or blood serum of a plurality of subjects (claim 33); and extracting cfDNA fragments in a sample of a patient (claim 33); are conventional. Evidence of conventionality is shown by Volckmar et al. (“A field guide for cancer diagnostics using cell-free DNA: From principles to practice and clinical applications.” Genes Chromosomes Cancer, (2018), Vol. 57, pp. 123-139). Volckmar et al. reviews cancer diagnostics using cell-free DNA (Title) based on a review of the pertinent literature and discusses the advantages and limitations of available methodologies and their potential applications in molecular diagnostics (Abstract). Volckmar et al. shows that it is hypothesized that differences in the nucleosomal patterning between malignant and hematopoietic cells play a role (i.e., in the observation that tumor-derived DNA molecules are characterized by higher fragmentation than non-neoplastic cfDNA fragments) and may reflect the cells or tissues of origin (page 124, col. 2; and page 125, col. 1). Volckmar et al. further shows a workflow of different cfDNA extraction methods, e.g., a liquid biopsy (e.g., blood, urine, cerebrospinal fluid) is taken from a cancer patient and can be extracted by various manual or automatic methods (Figure 1; and Section 2, pp. 125-126).
The additional elements of acquiring, from a sequencing device, sequence reads corresponding to cfDNA fragments in a sample of a test subject (claims 1 and 19); and acquiring, from the sequencing device, sequence reads corresponding to cfDNA fragments in samples of a plurality of subjects with known tumor mutations and/or known CH mutations (claims 5 and 20); are conventional. Evidence of conventionality is shown by Volckmar et al. (Ibid.). Volckmar et al. reviews different methods for mutation detection in routine diagnostics, e.g., Sanger sequencing, quantitative PCR, dPCR and ddPCR, NGS or massive parallel sequencing, and whole-exome sequencing or whole-genome sequencing (Section 5, pp. 126-129).
Therefore, when taken alone, all additional elements in claims 1-13, 15, 16, 19-21, 33, and 34 do not amount to significantly more than the above-identified judicial exception(s). Even when evaluated as a combination, the additional elements fail to transform the exception(s) into a patent-eligible application of that exception. Thus, claims 1-13, 15, 16, 19-21, 33, and 34 are deemed to not contribute an inventive concept, i.e., amount to significantly more than the judicial exception(s) (MPEP 2106.05(II)).
[Step 2B: NO]
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.
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.
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.
Claims 1-13, 15, 16, 19-21, 33, and 34 are rejected under 35 U.S.C. 103 as being unpatentable over Jaimovich et al. (“Methods and systems for determining the cellular origin of cell-free nucleic acids.” WO 2019/236478, pp. 1-114) in view of Mouliere et al. (“Enhanced detection of circulating tumor DNA by fragment size analysis.” Science Translational Medicine, (2018), Vol. 10, eaat4921, pp. 1-13).
Independent claim 1 is broadly directed to a computer-implemented method of using a machine learning classifier to distinguish tumor-derived mutations from clonal hematopoietic derived mutations in cell-free DNA (cfDNA). The steps of claim 1 require using a sequencing device to obtain sequence reads corresponding to cfDNA fragments in a sample of a test subject; using the sequence reads corresponding to the cfDNA fragments to detect a gene mutation in the cfDNA; identifying how many cfDNA fragments are detected for each fragment length in a plurality of fragment lengths by generating a size profile for a set of cfDNA fragments with the gene mutation of specific origins; using a mutation-specific predictive machine-learning model to classify a first subset of cfDNA fragments as having a tumor origin and a second subset of cfDNA fragments as having a clonal hematopoietic (CH) origin, wherein the machine-learning model is configured to generate a first set of ranges of fragment lengths for fragments with the tumor origin and a second set of ranges of fragment lengths for fragments of the CH origin, wherein the first subset of cfDNA fragments have lengths falling in the first set of ranges and the second subset of cfDNA fragments have lengths falling in the second set of ranges; and generating a characterization of the mutation based on the classifying of cfDNA fragments. Independent claim 19 recites a computing system for performing the method of claim 1, and further expands on the step of generating a characterization of the mutation based on the classifying of cfDNA fragments by adding the limitation of using a metric threshold. Independent claim 33 recites a method for extracting and analyzing cfDNA fragments in a sample of a patient using defined sets of ranges of interest (ROIs) of cfDNA fragment sizes derived from tumor and clonal hematopoietic (CH) cfDNA, wherein the tumor and CH ROIs are derived from method steps of: (a) extracting cell-free DNA (cfDNA) comprising tumor-origin cfDNA fragments and CH-origin cfDNA fragments from substantially cell-free samples of blood plasma and/or blood serum of a plurality of subjects; (b) producing one or more tumor regions of interest (ROIs) and one or more CH ROIs for the cfDNA fragments of (a) by: (i) generating a tumor fragment size profile and a CH fragment size profile; (ii) applying a smoothing operation to a difference between the tumor fragment size profile and the CH fragment size profile to obtain a trend line with a set of extrema comprising one or more maximums and one or more minimums; and (iii) defining the tumor and CH ROIs as sets of ranges of cfDNA fragment sizes based on the maximums and minimums.
Dependent claims 2-13, 15, 16, 21, 21, and 34 further define the bioinformatic techniques used in the fragment size analysis to distinguish clonal hematopoiesis from tumor-derived mutations in cell-free DNA.
Jaimovich et al. is directed to methods and systems for determining the cellular origin of cell-free nucleic acid (cfNA) fragments from cfDNA samples, such as liquid biopsy samples, the disclosed methods typically improving the specificity and/or sensitivity of assays for detecting diseased cell nucleic acids (e.g., cancer cell DNA) in cfNA samples by identifying variant alleles produced by non-target cells, such as hematopoietic stems cells (Abstract). Jaimovich et al. further shows that hematopoietic stem cells can contain genetic variants in regions of the genome associated with cancer, even though the hematopoietic stem cells are not cancerous, and therefore it is of interest to identify alleles that are predominantly present in hematopoietic stem cells, but absent in cancer cells that contribute to sampled cfDNA populations (para. [003]).
Mouliere et al. is directed to enhanced detection of circulating tumor DNA (ctDNA) by fragment size analysis, and shows that current strategies to improve ctDNA detection rely on increasing depth of sequencing coupled with various error correction methods, however, approaches that focus only on genomic alterations do not take advantage of the potential differences in chromatic organization or fragment sizes of ctDNA, and further shows that the results of ever-deeper sequencing are also confounded by the likelihood of false-positive results from detection of mutations from noncancerous cells, clonal expansions in normal epithelia, or clonal hematopoiesis of indeterminate potential (CHIP) (page 1, col. 2, para. 1).
Regarding independent claims 1, 19, and 33, Jaimovich et al. shows obtaining cell-free nucleic acids from bodily fluids (para. [0127]); a plurality of loci are sequenced so as to detect the allelic variants of the loci and the allele frequency at each of those loci (para. [0102]); the DNA can come from a variety of cellular sources each producing cell free DNA, thereby producing a mixture of cell free DNA derived from different genomic sources for the same locus (para. [0102]); the DNA source may be a tumor cell, including several different clonally different tumor cell variants present in the same subject, and non-tumor cell, especially blood cells (para. [0102]); regions of the genome are targeted for sequencing (in contrast to whole genome sequencing), and by using high throughput DNA sequencers multiple fragments of cfDNA from the sample may be concurrently sequenced so as to detect multiple alleles at the same locus and provide for the allele frequence at that locus (para. [0102]); clonal hematopoiesis of indeterminate potential (CHIP) is a common age-related phenomenon in which hematopoietic stem cells contribute to the formation of a genetically distinct subpopulation of blood cells, and these hematopoietic stem cells can produce cell free DNA allelic information that may be confused with the allelic variants produced in cancerous cells (para. [0102]); classifying an allelic variant in the test sequencing information that substantially matches at least one classification allele on a non-target nucleic acid variant filter list (i.e., non-tumor) as originating from a target cell (para. [015]); using a threshold cutoff value for classifying alleles from reference cfDNA fragments as originating from non-target cells and/or classifying alleles as originating from target cells (claim 19, pages 88-89); machine learning classifiers are trained based upon one or more features, including mutant allele frequency, subclonal ratio, gene type, variants associated with hematological malignancies, patient age, observation of other CHIP variants, cancer type, and/or the like (paras. [012] & [0110]); and generating classification criteria in addition to testing for the presence or absence of positive (i.e., of tumor origin) and negative (i.e., of non-tumor origin) alleles in a sample (paras. [0107] – [0109]).
Regarding independent claims 1, 19, and 33, Jaimovich et al. does not show generating a size profile for a set of cfDNA fragments with the gene mutation of specific origins; or generating sets of ranges of fragment lengths for fragments with the tumor origin and for fragments of CH origin.
Regarding independent claims 1, 19, and 33, Mouliere et al. shows using tumor-guided personalized deep sequencing for establishing the size distribution of mutant ctDNA fragments (Abstract); selection of different size ranges and features in the distribution of fragment sizes (Figure 3; and Figure 7, and in particular 7(A)); and a characterization of the detection of somatic alterations in multiple cancer types with fragment size selection (Figure 6, and in particular 6(D)).
Therefore, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the method shown by Jaimovich et al. by incorporating methods for detecting circulating tumor DNA by fragment size selection, as shown by Mouliere et al. and discussed above. One of ordinary skill in the art would have been motivated to combine the methods of Jaimovich et al. with the methods of Mouliere et al., because Mouliere et al. shows that results of ever-deeper sequencing can be confounded by the likelihood of false-positive results from detection of mutations from noncancerous cells (e.g., clonal hematopoietic stem cells), and further shows methods for selecting specific sizes of cfDNA fragments to improve the detection of ctDNA (page 2, col. 1, para. 2). This modification would have had a reasonable expectation of success given that both Jaimovich et al. and Mouliere et al. disclose methods for determining the cellular origin of cell-free nucleic acids.
Regarding dependent claims 2-13, 15, 16, 21, 21, and 34, Jaimovich et al. in view of Mouliere et al. show using bioinformatics data analysis techniques for the classification of cfDNA alleles, e.g., using metrics such as cutoff threshold values (Jaimovich et al., para. [0210] and throughout) and quantifying ctDNA fragment size selection using linear regression (Mouliere et al., Fig. 4). Jaimovich et al. in view of Mouliere et al. further show training a machine learning classifier, e.g., to exclude non-tumor derived alterations (Jaimovich et al., para. [0222]) and predicting the cfDNA fragmentation pattern for distinguishing cancer from healthy controls (Mouliere et al., page 7, col. 2).
Thus, the claimed invention as a whole was prima facie obvious.
Conclusion
No claims are allowed.
This Office action is a Non-Final action. A shortened statutory period for reply to this action is set to expire THREE MONTHS from the mailing date of this application.
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Any inquiry concerning this communication or earlier communications from the examiner should be directed to STEVEN W. BAILEY whose telephone number is (571)272-8170. The examiner can normally be reached Mon - Fri. 1000 - 1800.
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/S.W.B./Examiner, Art Unit 1687
/Joseph Woitach/Primary Examiner, Art Unit 1687