DETAILED ACTION
Applicant’s response, filed 09/22/2025, has been fully considered. Rejections and/or objections not reiterated from previous Office Actions are hereby withdrawn. The following rejections and/or objections are either reiterated or newly applied. They constitute the complete set presently being applied to the instant application.
CONTINUED EXAMINATION
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 09/22/2025 has been entered.
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 .
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 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.
Claim Status
Claims 1, 3, 5-22 are pending.
Claims 2 and 4 is canceled.
Claims 22 is newly added.
Claims 1, 3, 5-22 are rejected.
Priority
Applicant's claim as a CON of a prior-filed application, PCT/EP2019/086781, filed 12/20/2019, which claims domestic benefit to US provisional application 62/784486, filed 12/23/2018 and US provisional application 62/822690, filed 03/22/2019. Accordingly, each of claims 1-20 are afforded the effective filing date of the 12/23/2018.
Information Disclosure Statement
The information disclosure statement (IDS) filed on 07/02/2024 is in compliance with the provisions of 37 CFR 1.97 and has therefore been considered. A signed copy of the IDS document is included with this Office Action.
Drawings
The Drawings submitted 06/30/2025 are accepted.
Claim Rejections - 35 USC § 112
The outstanding rejections to the claims are withdrawn in view of the amendments submitted herein.
35 USC § 112(b)
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.
Claims 5-13 and 18-19 are rejected under 35 U.S.C. 112(b) as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor, regards as the invention. The instant rejection is newly stated.
Claim 5 recites “wherein the computer-executable instructions, when executed by the one or more processors, cause the system to identify non- synonymous mutations within the obtained sequencing data”. It is unclear if this is a further limitation of claim 1(b) or is an additional step requiring a new TMB. Please amend this limitation to clarify its intention.
Claim 5: The relationship between the recitation of "the tumor mutational burden" in claim 5 and the previous recitation of " the tumor mutational burden " in claim 1(b) and (c) is not clear as claim 5 is related to non-synonymous mutations not just somatic mutations. It is not clear if the recitations are intended to be related or if they are intended to be distinct from one another. For compact examination, it is assumed that the recitations are related.
Claims 6 and 18 recites “wherein the computer-executable instructions, when executed by the one or more processors, cause the system to identify non- synonymous mutations and synonymous mutations within the obtained sequencing data”. It is unclear if this is a further limitation of claim 1(b) or is an additional step requiring a new TMB. Please amend this limitation to clarify its intention. Claim(s) 7-13 is/are rejected for the same reason because they depend from claim 1, and does not resolve the indefiniteness issue in those claims.
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.
For the following rejections, underlined text indicates newly recited portions necessitated by claim amendment.
1. Claims 1, 3 and 5-21 are rejected under 35 U.S.C. 101 because the claimed invention is directed to one or more judicial exceptions without significantly more. Any newly recited portions are necessitated by claim amendment.
MPEP 2106 organizes judicial exception analysis into Steps 1, 2A (Prongs One and Two) and 2B as follows below. MPEP 2106 and the following USPTO website provide further explanation and case law citations: uspto.gov/patent/laws-and-regulations/examination-policy/examination-guidance-and-training-materials.
Framework with which to Evaluate Subject Matter Eligibility:
Step 1: Are the claims directed to a process, machine, manufacture, or composition of matter;
Step 2A, Prong One: Do the claims recite a judicially recognized exception, i.e. a law of nature, a natural phenomenon, or an abstract idea;
Step 2A, Prong Two: If the claims recite a judicial exception under Prong One, then is the judicial exception integrated into a practical application (Prong Two); and
Step 2B: If the claims do not integrate the judicial exception, do the claims provide an inventive concept.
Framework Analysis as Pertains to the Instant Claims:
Step 1
With respect to Step 1: yes, the claims are directed to system, i.e., a process, machine, or manufacture within the above 101 categories [Step 1: YES; See MPEP § 2106.03].
Step 2A, Prong One
With respect to Step 2A, Prong One, the claims recite judicial exceptions in the form of abstract ideas. The MPEP at 2106.04(a)(2) further explains that abstract ideas are defined as:
mathematical concepts (mathematical formulas or equations, mathematical relationships and mathematical calculations);
certain methods of organizing human activity (fundamental economic practices or principles, managing personal behavior or relationships or interactions between people); and/or
mental processes (procedures for observing, evaluating, analyzing/ judging and With respect to the instant claims, under the Step 2A, Prong One evaluation, the claims are found to recite abstract ideas that fall into the grouping of mental processes (in particular procedures for observing, analyzing and organizing information) and mathematical concepts (in particular mathematical relationships and formulas) are as follows:
Independent claim 1:
identifying an identification of somatic mutations within obtained sequencing data, the sequencing data derived from the tumor sample
estimating a tumor mutational burden from a tumor sample based on the received identified somatic mutations; and
assigning a cancer subtype to the tumor sample based on a transformation of the estimated tumor mutational burden.
modeling the transformation of the estimated tumor mutational burden as a Gaussian mixture model, where each Kth component of the Gaussian mixture model represents one cancer subtype;
computing an assignment score for each Kth component of the Gaussian mixture model;
identifying a Kth component having a highest assignment score; and
determining and assigning the cancer subtype associated with the identified Kth component having the highest assignment score as the cancer subtype of the tumor sample.
Dependent claim 3:
estimated using an expectation-maximization algorithm based on training data.
Dependent claim 5:
identify non- synonymous mutations within the obtained sequencing data
tumor mutational burden is estimated by dividing a total number of identified non-synonymous mutations by a pre-determined genome size.
Dependent claim 6:
identify non- synonymous mutations within the obtained sequencing data
tumor mutational burden is estimated by dividing a total number of identified non-synonymous mutations by a pre-determined genome size.
Dependent claim 7:
estimated by performing a maximum likelihood estimation using the identified non-synonymous and synonymous mutations and a plurality of pre-determined mutation rate parameters.
Dependent claim 10:
derived by modeling an observed number of mutations for each gene in a training sample derived from whole-exome sequencing.
Dependent claim 11:
estimating a background mutation rate using one of a negative binomial regression, a Poisson regression, a zero-inflated Poisson regression, or a zero-inflated negative binomial regression with consideration of only known influencing factors;
estimating a background mutation rate using single gene analysis with consideration of unknown influencing factors; and
combining the estimates of (i) and (ii) within a Bayesian framework.
Dependent claim 16:
calculated by performing a log transform on the estimated tumor mutational burden.
Independent claim 17:
identifying an identification of somatic mutations within obtained whole exome sequencing data
estimating a tumor mutational burden from the tumor sample based on the identified somatic mutations;
computing a log-transform of the estimated tumor mutational burden to provide a log-transformed estimated tumor mutational burden; and
identifying the cancer subtypes by modeling the log-transformed estimated tumor mutational burden as a Gaussian mixture model, wherein the assignment of the cancer subtype comprises (i) modeling the transformation of the estimated tumor mutational burden as a Gaussian mixture model, where each of a plurality of Kth components of the Gaussian mixture model represents one cancer subtype respectively; (ii) computing an assignment score for each of the plurality of Kth components of the Gaussian mixture model: (iii) identifying one of the K components of the plurality of Kth components as having a highest assignment score; and (iv) determining and assigning the cancer subtype associated with the identified K component having the highest assignment score as the cancer subtype of the tumor sample; and(e) generating a report of the identified cancer subtypes.
Dependent claim 18:
identify non- synonymous mutations and synonymous mutations within the obtained sequencing data
the tumor mutational burden is estimated using the identified non-synonymous mutations and identified synonymous mutations.
Dependent claim 19:
estimated by performing a maximum likelihood estimation using the identified non-synonymous and synonymous mutations and a plurality of pre-determined mutation rate parameters.
Dependent claim 21:
recommend a particular type of immunotherapy treatment based on the determined cancer type.
Dependent claims 6, 8-9, 12-13, 15, 18, and 20 recite further steps that limit the judicial exceptions in independent claims 1 and 17 and, as such, also are directed to those abstract ideas. For example, claim 6 further limits the tumor mutational burden of claim 1, claim 8 further limits parameters of claim 7, claim 9 further limits the context-specific mutation rates of claim 8, claim 12 further limits the Poisson regression of claim 11, claim 13 further limits the binomial regression of claim 11, claim 15 further limits the identified somatic mutations of claim 1, claim 18 further limits the tumor mutational burden of claim 17, and claim 20 further limits the cancer subtypes of claim 17.
The abstract ideas recited in the claims are evaluated under the Broadest Reasonable Interpretation (BRI) and determined to each cover performance in the mind because the method only requires a user to manually receive, assign and identify. Without further detail as to the methodology involved in “receiving an identification of somatic mutations “, “assigning a cancer subtype” and “identifying a Kth component “,under the BRI, one may simply, for example, use pen and paper to assign a cancer subtype.
Some of these steps and those recited in the dependent claims require performing mathematical techniques or mathematical operations such as “estimating a tumor mutational burden”, “modeling the transformation of the estimated tumor mutational burden as a Gaussian mixture model”, “computing an assignment score”, “estimated using an expectation-maximization algorithm”, “estimated by dividing”, “estimated by performing a maximum likelihood estimation”, “derived by modeling”, “estimating a background mutation rate using one of a negative binomial regression”, “estimating a background mutation rate”, “combining the estimates”, “calculated by performing a log transform”, “estimating a tumor mutational burden”, “computing a log-transform”, and “estimated by performing a maximum likelihood estimation”.
Therefore, claims 1 and 17 and those claims dependent therefrom recite an abstract idea [Step 2A, Prong 1: YES; See MPEP § 2106.04].
Step 2A, Prong Two
Because the claims do recite judicial exceptions, direction under Step 2A, Prong Two, provides that the claims must be examined further to determine whether they integrate the judicial exceptions into a practical application (MPEP 2106.04(d)). A claim can be said to integrate a judicial exception into a practical application when it applies, relies on, or uses the judicial exception in a manner that imposes a meaningful limit on the judicial exception. This is performed by analyzing the additional elements of the claim to determine if the judicial exceptions are integrated into a practical application (MPEP 2106.04(d).I.; MPEP 2106.05(a-h)). If the claim contains no additional elements beyond the judicial exceptions, the claim is said to fail to integrate the judicial exceptions into a practical application (MPEP 2106.04(d).III).
Additional elements, Step 2A, Prong Two
With respect to the instant recitations, the claims recite the following additional elements:
Independent claim 1:
generating a report of the assigned cancer subtype.[AltContent: rect]
The claims also include non-abstract computing elements. For example, independent claims 1 and 17 include a system comprising: (i) one or more processors and (ii) one or more memories coupled to the one or more processors.
Considerations under Step 2A, Prong Two
With respect to Step 2A, Prong Two, the additional elements of the claims do not integrate the judicial exceptions into a practical application for the following reasons. Those steps directed to data outputting, such as “generating an output”, perform functions of collecting the data needed to carry out the judicial exceptions. Data gathering and outputting do not impose any meaningful limitation on the judicial exceptions, or on how the judicial exceptions are performed. Data gathering and outputting steps are not sufficient to integrate judicial exceptions into a practical application (MPEP 2106.05(g)).
Further steps directed to additional non-abstract elements of “a system ” do not describe any specific computational steps by which the “computer parts” perform or carry out the judicial exceptions, nor do they provide any details of how specific structures of the computer, such as the computer-readable recording media, are used to implement these functions. The claims state nothing more than a generic computer which performs the functions that constitute the judicial exceptions. Hence, these are mere instructions to apply the judicial exceptions using a computer, and therefore the claim does not integrate that judicial exceptions into a practical application. The courts have weighed in and consistently maintained that when, for example, a memory, display, processor, machine, etc.… are recited so generically (i.e., no details are provided) that they represent no more than mere instructions to apply the judicial exception on a computer, and these limitations may be viewed as nothing more than generally linking the use of the judicial exception to the technological environment of a computer (MPEP 2106.05(f)).
Thus, none of the claims recite additional elements which would integrate a judicial exception into a practical application, and the claims are directed to one or more judicial exceptions [Step 2A, Prong 2: NO; See MPEP § 2106.04(d)].
Step 2B (MPEP 2106.05.A i-vi)
According to analysis so far, the additional elements described above do not provide significantly more than the judicial exception. A determination of whether additional elements provide significantly more also rests on whether the additional elements or a combination of elements represents other than what is well-understood, routine, and conventional. Conventionality is a question of fact and may be evidenced as: a citation to an express statement in the specification or to a statement made by an applicant during prosecution that demonstrates a well-understood, routine or conventional nature of the additional element(s); a citation to one or more of the court decisions as discussed in MPEP 2106(d)(II) as noting the well-understood, routine, conventional nature of the additional element(s); a citation to a publication that demonstrates the well-understood, routine, conventional nature of the additional element(s); and/or a statement that the examiner is taking official notice with respect to the well-understood, routine, conventional nature of the additional element(s).
With respect to the instant claims, the courts have found that outputting data are well-understood, routine, and conventional functions of a computer when claimed in a merely generic manner or as insignificant extra-solution activity (see Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information), buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network), Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015), and OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93, as discussed in MPEP 2106.05(d)(II)(i)).
As such, the claims simply append well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception (MPEP2106.05(d)). The data gathering steps as recited in the instant claims constitute a general link to a technological environment which is insufficient to constitute an inventive concept which would render the claims significantly more than the judicial exception (MPEP2106.05(g)&(h)).
With respect to claims 1 and 17 and those claims dependent therefrom, the computer-related elements or the general purpose computer do not rise to the level of significantly more than the judicial exception. The claims state nothing more than a generic computer which performs the functions that constitute the judicial exceptions. Hence, these are mere instructions to apply the judicial exceptions using a computer, which the courts have found to not provide significantly more when recited in a claim with a judicial exception (see MPEP 2106.06(A)). The specification also notes that computer processors and systems, as example, are commercially available or widely used at [0098]. The additional elements are set forth at such a high level of generality that they can be met by a general purpose computer. Therefore, the computer components constitute no more than a general link to a technological environment, which is insufficient to constitute an inventive concept that would render the claims significantly more than the judicial exceptions (see MPEP 2106.05(b)I-III).
Taken alone, the additional elements do not amount to significantly more than the above-identified judicial exception(s). Even when viewed as a combination, the additional elements fail to transform the exception into a patent-eligible application of that exception. Thus, the claims as a whole do not amount to significantly more than the exception itself [Step 2B: NO; See MPEP § 2106.05].
Therefore, the instant claims are not drawn to eligible subject matter as they are directed to one or more judicial exceptions without significantly more. For additional guidance, applicant is directed generally to the MPEP § 2106.
2. Claim 22 is not rejected under 35 U.S.C. 101 because the additional elements of claim 22 integrate the judicial exceptions into a practical application for the following reasons. Those steps directed to identifying a particular type of immunotherapy treatment selected to reduce the estimated tumor mutational burden of the patient; and administering the identified immunotherapy treatment to the patient provide a practical application at Step 2A, Prong 2 because the identified immunotherapy may be used the treatment of a subject afflicted with, or at risk of contracting or suffering a recurrence of, a disease by a method comprising inducing, enhancing, suppressing or otherwise modifying the immune system or an immune response [0076]. The specification continues to add the immunotherapy comprises administering an antibody to a subject. In other embodiments, the immunotherapy comprises administering a small molecule to a subject [0077], which supports the immunotherapy treatment being a practical application of the judicial exceptions.
Response to Applicant Arguments
Applicant submits there is no "practical" or reasonably timely manner the claimed system could be implemented to process a patient's molecular sequencing data, typically involving millions of sequenced (e.g., DNA) molecules, with mere "mental steps" or "pen and paper" in order to achieve the recited estimation of mutational burdens and identification of cancer subtypes [p. 8, par. 5-p. 9, par. 1].
It is respectfully found not persuasive. As set forth in MPEP 2106.04(a)(2).III.C, merely claiming that a concept that can be performed in the human mind is performed on a generic computer does not negate that the claim is still considered to recite a mental process. In addition, MPEP 2106.05(f) sets forth that simply adding a general purpose computer after the fact to an abstract idea does not integrate a judicial exception into a practical application or provide significantly more. Since the claims do not provide any specific computational structures, it appears that the processor equates to a generic computer component to implement the abstract idea, which does not change the fact that the claims recite a mental process or mathematical concept.
Applicant submits the present claims are directed to improvements in diagnostic sequencing technology that direct clinicians to better assess patients, and to select and execute lifesaving treatment options [p. 10, par. 1].
It is respectively found not persuasive. It is important to keep in mind that an improvement in the abstract idea itself (e.g. a recited fundamental economic concept) is not an improvement in technology. Furthermore, it is important to note, the judicial exception alone cannot provide the improvement. The improvement can be provided by one or more additional elements or by the additional element(s) in combination with the recited judicial exception. See MPEP 2106.05(a). The instant claims do not recite any additional elements that provide the improvement and/or that the improvement is in the abstract idea, which is not a technology.
Applicant submits the present claims include reporting predicted outcomes (e.g., survival rate) and and/or include recommending or performing particular treatments (e.g., immunotherapy). Thus, by the USPTO's own guidelines, the present claims are "integrated into a practical application" and are patent eligible under U.S.C. § 101 [p. 10, par. 3].
It is respectfully found not persuasive. In regards to example 49 being deemed patent eligible, it is the administering step that integrates the judicial exception into a practical application. The instant claims recite “recommend a particular type of immunotherapy treatment based on the determined cancer type” which is an abstract idea. The practical application must be found among the additional elements of the claims; ie elements that are not judicial exceptions (MPEP 2106.04(d)). Mathematical calculations are judicial exceptions, so no matter how useful or beneficial those calculations are, they are not a practical application.
Claim Rejections - 35 USC § 102
The outstanding 102 rejection to the claims is withdrawn in view of the amended claims. Chaudhary does not disclose a Gaussian mixed model as included in the amended claims.
Claim Rejections - 35 USC § 103
The outstanding 103 rejection to the claims is withdrawn in view of the amended claims. Chaudhary in view of Kapoor do not disclose a Gaussian mixed model as included in the amended claims.
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 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.
A. Claims 1, 4-6, 15, and 22 are rejected under 35 U.S.C. 103 as being unpatentable over Chaudhary et al. (US 2018/0165410 Al, filed on 12/07/2017, cited on IDS dated 07/02/2024) in view of Lithwick et al. (WO 2012/131670 A2, published 10/04/2012, cited on IDS dated 07/02/2024).
Claim 1 is directed to a system for reducing a computational burden of classifying a tumor sample derived from a patient, the system comprising: (i) one or more processors, and (ii) one or more memories coupled to the one or more processors, the one or more memories to store computer-executable instructions that, when executed by the one or more processors, cause the system to perform operations comprising:
Chaudhary discloses the targeted panel and method for estimating tumor mutation load described herein provide improvements to the technology over whole exome sequencing (WES). Chaudhary further discloses sequence assembly methods must be able to assemble and/or map a large number of reads efficiently, such as by minimizing use of computational resources [0067]. Chaudhary also discloses a system for analyzing a tumor sample genome for a mutation load, comprising a processor and a data store communicatively connected with the processor, the processor [0004].
(a) identifying of somatic mutations within obtained sequencing data, the sequencing data derived from the tumor sample; (b) estimating a tumor mutational burden based on the received identified somatic mutations; and
Chaudhary discloses the method, including detecting variants in nucleic acid sequence reads corresponding to targeted locations in the tumor sample genome; annotating detected variants with an annotation information from a population database; filtering the detected variants, wherein the filtering rule set retains the somatic variants and removes germ-line variants; counting the identified somatic variants to give a number of somatic variants; determining a number of bases in covered regions of the targeted locations in the tumor sample genome; and calculating a number of somatic variants per megabase, provides an estimate of the mutation load per megabase in the tumor sample genome [abstract]. Chaudhary further discloses analyzing a tumor sample genome for a mutation load, including: (1) detecting variants in a plurality of nucleic acid sequence reads to produce a plurality of detected variants, wherein the nucleic acid sequence reads correspond to a plurality of targeted locations in the tumor sample genome, wherein the detected variants include somatic variants [0005].
(c) assigning a cancer subtype to the tumor sample based on a transformation of the estimated tumor mutational burden, wherein the assignment of the cancer subtype comprises (i) modeling the transformation of the estimated tumor mutational burden as a Gaussian mixture model, where each of a plurality of Kth components of the Gaussian mixture model represents one cancer subtype respectively: (ii) computing an assignment score foreach of the plurality of Kth components of the Gaussian mixture model: (iii) identifying one of the Kth Components of the plurality of Kth components as having a highest assignment score: and (iv) determining and assigning the cancer subtype associated with the identified Kth component having the highest assignment score as the cancer subtype of the tumor sample: and (d) generating a report of the assigned cancer subtype.
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Chaudhary discloses counting the number of bases in the covered regions to produce the covered base count in megabases (Mb). Chaudhary further discloses in the calculating step, the processor divides the somatic mutation count by the covered base count to form an estimate of the tumor mutation load in number of somatic mutations per Mb for the tumor sample genome [0053]. Chaudhary also discloses all called variants that are potentially mutually interacting can be grouped and annotated together to give researchers/clinicians greater insight into the synergistic or antagonistic interplay between variants [0099]. Chaudhary further discloses analyzing a set of variants to identify groups of synergistic or antagonistic variants [0099] which reads on cancer subtypes, because the specification says that “distinguishable mutation profiles” is a way of defining cancer subtypes (para. 0011). Chaudhary also discloses transforming TML values into a determination of high MSI or low MSI [0061] which are additional cancer subtypes. Chaudry is silent on using a Gaussian mixture model.
However, Lithwick discloses methods for lung cancer classification [title]. Lithwick further discloses the pathologic diagnosis used to classify the tumor taken together with the stage of the cancer is then used to predict prognosis and direct therapy [p. 1, par. 5]. Lithwick also discloses the classification method of the present invention further comprises a classifier algorithm, said classifier algorithm is selected from the group consisting of K nearest neighbors classifier (KNN), logistic regression classifier, linear regression classifier, nearest neighbor classifier, neural network classifier, Gaussian mixture model (GMM) classifier and Support Vector Machine (SVM) classifier [p. 5, par. 6]. Lithwick further discloses "Classification" as used herein refers to a procedure and/or algorithm in which individual items are placed into groups or classes based on quantitative information on one or more characteristics inherent in the items (referred to as traits, variables, characters, features, etc) and based on a statistical model and/or a training set of previously labeled items [p. 12, par. 2]. Lithwick also discloses "1D/2D threshold classifier" used herein may mean an algorithm for classifying a case or sample such as a cancer sample into one of two possible types such as two types of cancer or two types of prognosis (e.g. good and bad). Lithwick also discloses for a lD threshold classifier, the decision is based on one variable and one predetermined threshold value; the sample is assigned to one class if the variable exceeds the threshold and to the other class if the variable is less than the threshold. A 2D threshold classifier is an algorithm for classifying into one of two types based on the values of two variables [p. 16, par. 4]. Lithwick further discloses a score may be calculated as a function (usually a continuous :function) of the two variables; the decision is then reached by comparing the score to the predetermined threshold, similar to the ID threshold classifier [p. 16, par. 4] which reads on a assignment score and identifying the highest score.
(d) generating a report of the assigned cancer subtype.
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the summary report generated by the reporter module.
Chaudhary discloses a summary report generated by the reporter module [0105].
Claim 4 is directed to the system of claim 1, wherein the tumor mutational burden is estimated using identified non-synonymous mutations.
Chaudhary discloses filtering for: variants that are nonsynonymous and fall within a particular gene, variants that are associated with a particular disease condition, variants that have a functional score of greater or less than a selected value, novel variants that are not present in a functional type annotations source, variants that fall in gene panel regions (defined by user), etc. [0105].
Claim 5 is directed to the system of claim 4, wherein the tumor mutational burden is estimated by dividing a total number of identified non-synonymous mutations by a pre-determined genome size.
Chaudhary discloses calculating a number of somatic variants per mega base by dividing the number of identified somatic variants by the number of bases in the covered regions to produce the mutation load for the tumor sample genome [0005].
Claim 6 is directed to the system of claim 1, the tumor mutational burden is estimated using identified non- synonymous mutations and identified synonymous mutations.
Chaudhary discloses filtering for: variants that are nonsynonymous and fall within a particular gene, variants that are associated with a particular disease condition, variants that have a functional score of greater or less than a selected value, novel variants that are not present in a functional type annotations source, variants that fall in gene panel regions (defined by user), etc. [0105]. As the filtering allows for selecting just nonsynonymous mutations, it would be inherent that the synonymous mutations are also used if a filter is not applied.
Claim 15 is directed to the system of claim 1, wherein the received identified somatic mutations are derived from targeted panel sequencing of nucleic acids derived from the tumor sample
Chaudhary discloses targeted panel with low sample input requirements from a tumor sample may be used to estimate mutation load in a tumor sample genome [0002].
Claim 22 is directed to a method for treating cancer subtypes identified from sequencing a tumor sample, the method comprising: sequencing a tumor sample obtained from a patient to generate molecular sequencing data representing the tumor sample; executing programming instructions through one or more processors that cause the one or more processors to: estimate a tumor mutational burden from the tumor sample based on the identified somatic mutations; assign a cancer subtype of the tumor sample based on a transformation of the estimated tumor mutational burden, wherein the assignment of the cancer subtype comprises (i) modeling the transformation of the estimated tumor mutational burden as a Gaussian mixture model, where each of a plurality of KI components of the Gaussian mixture model represents one cancer subtype respectively; (ii) computing an assignment score for each of the plurality of K components of the Gaussian mixture model; (iii) identifying one of the K components of the plurality of Kth components as having a highest assignment score; and (iv) determining and assigning the cancer subtype associated with the identified K component having the highest assignment score as the cancer subtype of the tumor sample; based on the assigned cancer subtype, identifying a particular type of immunotherapy treatment selected to reduce the estimated tumor mutational burden of the patient; and administering the identified immunotherapy treatment to the patient.
Chaudhary discloses genomic variants can be detected using a nucleic acid sequencing system and/or analysis of sequencing data [0036]. Chaudhary discloses the method, including detecting variants in nucleic acid sequence reads corresponding to targeted locations in the tumor sample genome; annotating detected variants with an annotation information from a population database; filtering the detected variants, wherein the filtering rule set retains the somatic variants and removes germ-line variants; counting the identified somatic variants to give a number of somatic variants; determining a number of bases in covered regions of the targeted locations in the tumor sample genome; and calculating a number of somatic variants per megabase, provides an estimate of the mutation load per megabase in the tumor sample genome [abstract]. Chaudhary further discloses analyzing a tumor sample genome for a mutation load, including: (1) detecting variants in a plurality of nucleic acid sequence reads to produce a plurality of detected variants, wherein the nucleic acid sequence reads correspond to a plurality of targeted locations in the tumor sample genome, wherein the detected variants include somatic variants [0005].
Chaudhary discloses counting the number of bases in the covered regions to produce the covered base count in megabases (Mb). Chaudhary further discloses in the calculating step, the processor divides the somatic mutation count by the covered base count to form an estimate of the tumor mutation load in number of somatic mutations per Mb for the tumor sample genome [0053]. Chaudhary also discloses all called variants that are potentially mutually interacting can be grouped and annotated together to give researchers/clinicians greater insight into the synergistic or antagonistic interplay between variants [0099]. Chaudhary further discloses analyzing a set of variants to identify groups of synergistic or antagonistic variants [0099] which reads on cancer subtypes, because the specification says that “distinguishable mutation profiles” is a way of defining cancer subtypes (para. 0011). Chaudhary also discloses transforming TML values into a determination of high MSI or low MSI [0061] which are additional cancer subtypes. Chaudry is silent on using a Gaussian mixture model.
However, Lithwick discloses methods for lung cancer classification [title]. Lithwick further discloses the pathologic diagnosis used to classify the tumor taken together with the stage of the cancer is then used to predict prognosis and direct therapy [p. 1, par. 5]. Lithwick also discloses the classification method of the present invention further comprises a classifier algorithm, said classifier algorithm is selected from the group consisting of K nearest neighbors classifier (KNN), logistic regression classifier, linear regression classifier, nearest neighbor classifier, neural network classifier, Gaussian mixture model (GMM) classifier and Support Vector Machine (SVM) classifier [p. 5, par. 6]. Lithwick further discloses "Classification" as used herein refers to a procedure and/or algorithm in which individual items are placed into groups or classes based on quantitative information on one or more characteristics inherent in the items (referred to as traits, variables, characters, features, etc) and based on a statistical model and/or a training set of previously labeled items [p. 12, par. 2]. Lithwick also discloses "1D/2D threshold classifier" used herein may mean an algorithm for classifying a case or sample such as a cancer sample into one of two possible types such as two types of cancer or two types of prognosis (e.g. good and bad). Lithwick also discloses for a lD threshold classifier, the decision is based on one variable and one predetermined threshold value; the sample is assigned to one class if the variable exceeds the threshold and to the other class if the variable is less than the threshold. A 2D threshold classifier is an algorithm for classifying into one of two types based on the values of two variables [p. 16, par. 4]. Lithwick further discloses a score may be calculated as a function (usually a continuous :function) of the two variables; the decision is then reached by comparing the score to the predetermined threshold, similar to the ID threshold classifier [p. 16, par. 4] which reads on a assignment score and identifying the highest score. Chaudhary discloses a summary report generated by the reporter module [0105].
Chaudhary discloses high tumor mutation load is associated with positive responses from immune checkpoint inhibitors [0043]. Chaudhary further discloses a high mutation load of a tumor may act as a predictive biomarker for immunotherapy [0043]. Although Chaudhary doesn’t explicitly say administer the immunotherapy it would be obvious to administer the best option for treatment as immunotherapies are well known in the art. Also, Lithwick discloses method is used to determine a course of treatment of the subject [p. 3, par. 6].
In regards to claim(s) 1, 4-6, 15, and 22 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 combine, Chaudhary and Lithwick as they both disclose methods for cancer classification. The motivation would have been to modify the method of Chaudhary to include the GMM of Lithwick to accurately classify cancers based on their miR expression profile without further manipulation as disclosed by Lithwick [p. 2, par. 3].
B. Claims 3 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Chaudhary in view of Lithwick, as applied to claims 1, 4-6, and 15 as above, and in further view of Ficklin et al. (Ficklin, Stephen P., et al. "Discovering condition-specific gene co-expression patterns using gaussian mixture models: a cancer case study." Scientific reports 7.1 (2017): 8617, newly cited).
Claim 3 is directed to the system of claim 1, wherein parameters for each Kth component are estimated using an expectation-maximization algorithm based on training data.
Chaudhary and Lithwick are silent on the details of a GNN.
However, Ficklin discloses discovering condition-specific gene co-expression patterns using gaussian mixture models: a cancer case study [title]. Ficklin further discloses use of mixture models with gene expression data is not new [p. 2, par. 4]. Ficklin also discloses a Poisson mixture model has been applied to pre-clustering of the input Gene Expression Matrix (GEM) (an n x m data set with n rows of transcripts and m columns of samples) into mixture components of genes with similar expression patterns where clusters of genes with high or low association with specific traits can be visualized [p. 2, par. 4]. Ficklin further discloses an overview of Gaussian Mixture Models, where finite mixture models are probabilistic models, or a mixture distribution, used for density estimation, identification of subpopulations and discriminant analysis and the GMM for identification of subpopulation (i.e. modes) [p. 8, par. 2]. Ficklin also discloses to estimate θ the expectation-maximization (EM) algorithm is used [p. 8, par. 4]. Ficklin further discloses it is an iterative method that finds the maximum likelihood for the parameters [p. 8, par. 4].
Claim 16 discloses the system if claim 1, wherein the transformation of the estimated tumor mutational burden is calculated by performing a log transform on the estimated tumor mutational burden.
Chaudhary and Lithwick are silent on the details of a transformation.
However, Ficklin discloses in this GEM, all missing values were replaced with the word, ‘NA’, as is usually indicative of missing values in the R statistical package. A log2 transformation of the expression values was performed [p. 8, par. 6].
In regards to claim(s) 3 and 16, 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 combine, Chaudhary and Lithwick with Ficklin as they disclose methods and models for cancer classification. The motivation would have been to modify the method of Lithwick to include the GMM details of Ficklin to further address the problem of extrinsic noise as disclosed by Ficklin [p. 2, par. 3].
C. Claim(s) 7-11 is/are rejected under 35 U.S.C. 103 as being unpatentable over Chaudhary in view of Lithwick as applied to claim 1 in the above 103 rejection, in view of Fu et al. (WO 2017/181134, published on 10/19/2017, cited on IDS dated 07/24/2024).
Claim 7 is directed to the system of claim 6, wherein the tumor mutational burden is estimated by performing a maximum likelihood estimation using the identified non-synonymous and synonymous mutations and a plurality of pre-determined mutation rate parameters.
Chaudhary discloses dividing the somatic mutation count by the covered base count to form an estimate of the tumor mutation load in number of somatic mutations per Mb for the tumor sample genome [0053]. Chaudhary further discloses in various embodiments, the filtering component can be configured with a collection of filters to select for variants with a high likelihood of having possible functional significance [0106] which reads on being able to select which mutations are used.
Chaudhary and Lithwick are silent on maximum likelihood estimation using the identified non-synonymous and synonymous mutations and a plurality of pre-determined mutation rate parameters.
However, Fu discloses estimating background mutation rate using single gene analysis with consideration of unknown influencing factors [0077] which reads on both non-synonymous and synonymous mutations. Fu further discloses for each gene, the Maximum Likelihood Estimation (MLE) can be used to estimate gene-specific dispersion [0079]. Fu also discloses probability distribution of a gene-specific background mutation rate for the gene may be determined across the plurality of samples based on the expected silent mutation rate for the gene and the sample mutation rate for each sample [0104].
Claim 8 is directed to the system of claim 7, wherein the plurality of pre-determined mutation rate parameters comprise (i) gene-specific mutation rate factors, and (ii) context-specific mutation rates.
Chaudhary and Lithwick are silent on mutation rate parameters.
However, Fu discloses methods, systems, and apparatuses for detecting significantly mutated genes/pathways in a cancer cohort [abstract]. Fu further discloses a sample mutation rate for each sample across the plurality of genes and a context mutation rate for each mutation context across the plurality of genes and the plurality of samples may be calculated based on the sets of mutations in DNA measured for the plurality of genes in the plurality of samples [0057]. Fu also discloses each sample of the plurality of samples, determine a sample mutation rate (bs) based on a total number of mutations measured in the sample and each gene of the plurality of genes, determine a probability distribution of a gene specific background mutation rate across the plurality of samples for the gene, based on the expected silent mutation rate for the gene and the sample mutation rate for each sample [p. 60, fig.5] which reads on a predetermined gene-specific mutation rate factors, and context-specific mutation rates.
Claim 9 is directed to the system of claim 8, wherein the context-specific mutation rates are selected form the group consisting of (i) tri-nucleotide context specific mutation rates; (ii) di- nucleotide context specific mutation rates, and; (iii) mutation signatures.
Chaudhary and Lithwick are silent on context-specific mutation rates.
However, Fu discloses the mutation context rate may be determined based on the number of mutations in each context of flanking trinucleotide (mutation context), where each mutation context may correspond to a type of substitution or deletion [0057].
Claim 10 is directed to the system of claim 7, wherein the plurality of pre-determined mutation rate parameters are derived by modeling an observed number of mutations for each gene in a training sample derived from whole-exome sequencing.
Chaudhary and Lithwick are silent on pre-determined mutation rate parameters.
However, Fu discloses for each tri-nucleotide mutation context i of the, for example, 96 mutation contexts, the number of silent ni(silent) and non-silent ni(nonsilent) mutations observed across all tumor samples and the number of possible silent Ni(silent) and non-silent Ni(nonsilent) variants in an whole exome may be determined [0049-0050].
Claim 11 is directed to the system of claim 7, wherein the pre-determined mutation rate parameters are derived by: (i) estimating a background mutat