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 .
Election/Restrictions
Upon further consideration the restriction dated 5/2/2025 is withdrawn. Pursuant to the procedures set forth in MPEP § 821.04(B), claims 1-3, 5-6, 8-10, 12, 14, 16-23, and 25-26, previously withdrawn from consideration (in Applicant’s response received 9/26/2025) as a result of a restriction requirement, are hereby rejoined and fully examined for patentability under 37 CFR 1.104, as presented in the amended claims dated 8/15/2022.
Because all claims previously withdrawn from consideration under 37 CFR 1.142 have been rejoined, the restriction requirement as set forth in the Office action mailed on 5/2/2025 is hereby withdrawn. In view of the withdrawal of the restriction requirement as to the rejoined inventions, applicant(s) are advised that if any claim presented in a divisional application is anticipated by, or includes all the limitations of, a claim that is allowable in the present application, such claim may be subject to provisional statutory and/or nonstatutory double patenting rejections over the claims of the instant application. Once the restriction requirement is withdrawn, the provisions of 35 U.S.C. 121 are no longer applicable. See In re Ziegler, 443 F.2d 1211, 1215, 170 USPQ 129, 131-32 (CCPA 1971). See also MPEP § 804.01.
Status of Claims
Claims 1-3, 5-6, 8-10, 12, 14, 16-23, and 25-26 are pending and examined on the merits.
Claims 4, 7, 11, 13, 15, and 24 are cancelled.
Priority
The instant application filed on 2/28/2022 is a 371 national stage entry of PCT/US2020/048612 having an international filing date of 8/28/2020, and claims the benefit of priority to provisional U.S. Application No. 62/893,119 filed on 8/28/2019. Thus, the effective filing date of the claims is 8/28/2019. The applicant is reminded that amendments to the claims and specification must comply with 35 U.S.C. § 120 and 37 C.F.R. § 1.121 to maintain priority to an earlier-filed application. Claim amendments may impact the effective filing date if new subject matter is introduced that lacks support in the originally filed disclosure. If an amendment adds limitations that were not adequately described in the parent application, the claim may no longer be entitled to the priority date of the earlier filing.
Information Disclosure Statement
The information disclosure statements (IDS) filed on 2/28/2022 and 2/13/2023 have been entered and considered. A signed copy of corresponding 1449 forms have been included with this Office action.
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 1, 17-20, 22-23, and 25-26 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.
Claims 1 and 25-26 recite "the set of criteria includes at least one criterion that is met when the predicted TMB is high". It is not clear what the metes and bounds of a high predicted TMB are. However, the specification para.0005 mentions "the predicted TMB is determined to be high when the predicted TMB exceeds a predetermined value". To further prosecution, it is interpreted that a high predicted TMB is determined to be high when the predicted TMB exceeds a predetermined value.
Claims 1, 18, 23, and 25-26 recite "determining that the subject is likely to respond to the treatment". It is not clear what the metes and bounds of "likely to respond" are, and given that there is no specific tumor or disease type claimed it is also not clear how any likelihood of a response would be measured against (symptoms, biomarkers, disease progression, etc.) or to what degree a likelihood of response is considered to have been responding to a treatment (nonresponsive, partial, complete, remission, and/or recovery). To further prosecution, and under the broadest reasonable interpretation, a likely to respond is interpreted as any improvement in symptoms, biomarkers, or disease progression as would be reasonably acknowledged by one skilled in the art, and a nonresponse (or "not likely to respond") is interpreted as the absence of any improvement in symptoms, biomarkers, or disease progression.
Claim 17 recites "a predicted tumoral heterogeneity (TH)". It is not clear what the term "tumoral heterogeneity" is meant to encompass (any difference between cancer cells within a tumor, or differences between multiple tumors in a single patient, or both). To further prosecution, and according to the disclosure specification para.0083, the limitation is interpreted as differences between cancer cells within a tumor or within multiple tumors in a single patient.
Claims 17-20 recite using "a predicted tumoral heterogeneity (TH)". It is not clear if the predicted TH is being predicted by the TMB prediction model, or a new model specific for predicting TH. To further prosecution, it is interpreted that "a predicted TH" is predicted using a separate model specific for predicting TH, as explicitly trained in claim 21, and used in claims 19-20.
Claim 22 recites "a tumor fraction (TF) computed based on the sequence data is low". It is not clear what the metes and bounds of a low TF are. However, the specification para.0147 mentions "In some cases, the computed TF is indicative of a positive treatment response when the computed TF is a high TF (e.g., >=1%,>=0.05%) and the disease state is stage III lung cancer. The computed TF can be compared to a threshold TF value or score to determine whether the computed TF is low or high. The threshold TF value or score can depend on a sequencing method or panel used for generating the cfDNA data, or vary for different cancer types or stages being assessed". To further prosecution, it is interpreted that a TF is determined to be low when the TF is below a predetermined value.
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 1-3, 5-6, 8-10, 12, 14, 16-23, and 25-26 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea of a mental process, a mathematical concept, organizing human activity, or a law of nature or natural phenomenon without significantly more. In accordance with MPEP § 2106, claims found to recite statutory subject matter (Step 1: YES) are then analyzed to determine if the claims recite any concepts that equate to an abstract idea, law of nature or natural phenomenon (Step 2A, Prong 1). In the instant application, the claims recite the following limitations that equate to an abstract idea:
Claims 1 and 25-26: “determining whether a set of criteria has been met, wherein the set of criteria includes at least one criterion that is met when the predicted TMB is high; in accordance with a determination that the set of criteria has been met, determining that the subject is likely to respond to the treatment; and in accordance with a determination that the set of criteria has not been met, determining that the subject is not likely to respond to the treatment” provides an evaluation or comparison (determining that some set of criteria has or has not been met, and determining likelihood of a subject responding to treatment) that may be performed in the human mind and is therefore considered a mental process, which is an abstract idea.
Claim 12: “correlating the predicted TMB with the corresponding ground truth TMB” provides a mathematical calculation (deriving a correlation involves mathematical calculations) that is considered a mathematical concept, which is an abstract idea.
Claim 18: “determining whether the predicted TH is indicative of homogeneous or heterogeneous tissue; in accordance with a determination that the predicted TH is indicative of the homogeneous tissue, determining that the subject is likely to respond to the treatment; and in accordance with a determination that the predicted TH is indicative of the heterogeneous tissue, determining that the subject is not likely to respond to the treatment” provides an evaluation or comparison (determining that some set of criteria has or has not been met, and determining likelihood of a subject responding to treatment) that may be performed in the human mind and is therefore considered a mental process, which is an abstract idea.
Claim 20: “determining, with the TH prediction model, a coefficient of variation of the allele frequency of SNV calls based on the set of features” provides a mathematical calculation (calculating a coefficient of variation involves mathematical calculations) that is considered a mathematical concept, which is an abstract idea.
“in accordance with a determination that the coefficient of variation is low, determining that the predicted TH is indicative of homogeneous tissue; and in accordance with a determination that the coefficient of variation is high, determining that the predicted TH is indicative of heterogeneous tissue” provides an evaluation or comparison (determining an indication) that may be performed in the human mind and is therefore considered a mental process, which is an abstract idea.
Claim 23: “determining whether the TF is low, wherein the tumor fraction comprises a fraction of tumor-derived cfDNA over a total amount of cfDNA in the cfDNA sample” provides a mathematical calculation (calculating a fraction involves mathematical calculations) that is considered a mathematical concept, which is an abstract idea. The limitation also provides an evaluation or comparison (determining that the TF is low) that may be performed in the human mind and is therefore considered a mental process, which is an abstract idea.
“in accordance with a determination that the TF is low, determining that the subject is likely to respond to the treatment; and in accordance with a determination that the TF is not low, determining that the subject is not likely to respond to the treatment” provides an evaluation or comparison (determining that some set of criteria has or has not been met, and determining likelihood of a subject responding to treatment) that may be performed in the human mind and is therefore considered a mental process, which is an abstract idea.
These recitations are similar to the concepts of collecting information, analyzing it, and displaying certain results of the collection and analysis in Electric Power Group, LLC, v. Alstom (830 F.3d 1350, 119 USPQ2d 1739 (Fed. Cir. 2016)), organizing and manipulating information through mathematical correlations in Digitech Image Techs., LLC v Electronics for Imaging, Inc. (758 F.3d 1344, 111 U.S.P.Q.2d 1717 (Fed. Cir. 2014)) and comparing information regarding a sample or test to a control or target data in Univ. of Utah Research Found. v. Ambry Genetics Corp. (774 F.3d 755, 113 U.S.P.Q.2d 1241 (Fed. Cir. 2014)) and Association for Molecular Pathology v. USPTO (689 F.3d 1303, 103 U.S.P.Q.2d 1681 (Fed. Cir. 2012)) that the courts have identified as concepts that can be practically performed in the human mind or are mathematical relationships. Therefore, these limitations fall under the “Mental process” and “Mathematical concepts” groupings of abstract ideas. Additionally, while claims 25-26 recite performing some aspects of the analysis on “A non-transitory computer-readable medium storing executable instructions [] the instructions, when executed by a hardware processor, configured to cause the hardware processor to perform steps” or “An electronic device, comprising: one or more processors; and a non-transitory computer-readable storage medium storing executable instructions [] the instructions, when executed by the one or more processors, configured to cause the electronic device to perform steps”, there are no additional limitations that indicate that this requires anything other than carrying out the recited mental processes or mathematical concepts in a generic computer environment. Merely reciting that a mental process is being performed in a generic computer environment does not preclude the steps from being performed practically in the human mind or with pen and paper as claimed. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental processes” grouping of abstract ideas. As such, claims 1-3, 5-6, 8-10, 12, 14, 16-23, and 25-26 recite an abstract idea (Step 2A, Prong 1: YES).
Claims found to recite a judicial exception under Step 2A, Prong 1 are then further analyzed to determine if the claims as a whole integrate the recited judicial exception into a practical application or not (Step 2A, Prong 2). The judicial exceptions listed above are not integrated into a practical application because the claims do not recite an additional element or elements that reflects an improvement to technology. Specifically, the claims recite the following additional elements:
Claims 1 and 25-26: “receiving sequence data gathered from sequencing the cfDNA sample” provides insignificant extra-solution activities (receiving data is a pre-solution activity involving data gathering steps) that do not serve to integrate the judicial exceptions into a practical application.
“generating a feature matrix comprising feature values corresponding to synonymous and nonsynonymous mutations in the sequence data” provides insignificant extra-solution activities (generating a feature matrix is a pre-solution activity involving data manipulation steps) that do not serve to integrate the judicial exceptions into a practical application.
“predicting a tumor mutational burden (TMB) for a tissue of interest at the subject using a TMB prediction model that receives the feature matrix as input and outputs a predicted TMB” provides insignificant extra-solution activities (using a statistical model by inputting and outputting data are pre- and post-solution activities involving data gathering and manipulation steps) that do not serve to integrate the judicial exceptions into a practical application.
Claim 8: “continuing administration of the treatment to the subject” and “altering administration of the treatment to the subject” provides insignificant extra-solution activities (continuing or altering treatment is a routine post-solution activity) that do not serve to integrate the judicial exceptions into a practical application. Furthermore, although this is a recitation of a treatment based on the analysis method of claim 1, it is not a particular treatment. MPEP 2106.04(d)(2) states that in order to qualify as a "treatment" or "prophylaxis" limitation for purposes of consideration as a physical "real world" treatment step, the claim limitation in question must affirmatively recite an action that effects a particular treatment or prophylaxis for a disease or medical condition. An example of such a limitation is a step of "administering amazonic acid to a patient" or a step of "administering a course of plasmapheresis to a patient." If the limitation does not actually provide a treatment or prophylaxis, e.g., it is merely an intended use of the claimed invention or a field of use limitation, then it cannot integrate a judicial exception under the "treatment or prophylaxis" consideration.
Claims 9-10: “training data obtained from sequencing a plurality of train samples of cfDNA collected from a plurality of subjects, wherein the training data obtained from each train sample corresponds to matched tissue data obtained from a tumoral tissue sample collected from the same subject” and “the training data is obtained from targeted sequencing of the plurality of train samples, and wherein the matched tissue data is obtained from whole exome sequencing of the tumoral tissue sample” provide insignificant extra-solution activities (obtaining training data from genome sequencing is a pre-solution activity involving data gathering steps) that do not serve to integrate the judicial exceptions into a practical application.
Claim 12: “labeling the training data with a corresponding ground truth TMB determined from the corresponding matched tissue data; generating a predicted TMB from the labeled training data using the statistical model” provides insignificant extra-solution activities (labeling training data and using a statistical model are a pre- and post-solution activities involving data gathering and manipulation steps) that do not serve to integrate the judicial exceptions into a practical application.
Claim 18: “predicting, based on the sequence data, the TH for the tissue of interest at the subject” provides insignificant extra-solution activities (using a statistical model is a pre-solution activity involving data manipulation steps) that do not serve to integrate the judicial exceptions into a practical application.
Claim 19: “determining the predicted TH using a TH prediction model that receives a set of features in the sequence data as input and outputs the predicted TH” provides insignificant extra-solution activities (using a statistical model by inputting and outputting data are pre- and post-solution activities involving data gathering and manipulation steps) that do not serve to integrate the judicial exceptions into a practical application.
Claim 21: “the TH prediction model comprises a statistical model trained on a training set comprising a plurality of training samples that are derived from cfDNA samples having matched tissue data from tumoral tissue samples” provides insignificant extra-solution activities (obtaining training data from samples and training a statistical model are pre-solution activities involving data gathering and manipulation steps) that do not serve to integrate the judicial exceptions into a practical application.
The steps for receiving, generating, labeling, inputting, and outputting data, and training and using statistical models are insignificant extra-solution activities that do not serve to integrate the recited judicial exceptions into a practical application because they are pre- and post-solution activities involving data gathering. Additionally, the steps for continuing or altering treatment restricts use to a particular environment or application without adding significant innovation that does not serve to integrate the judicial exceptions into a practical application because they are post-solution activities involving a mere field of use (see MPEP 2106.04(d)(2) - Integration of a Judicial Exception Into A Practical Application; MPEP 2106.05(g) - Insignificant Extra-Solution Activity; and MPEP 2106.05(h) - Field of Use and Technological Environment). Furthermore, the limitations regarding implementing program instructions do not indicate that they require anything other than mere instructions to implement the abstract idea in a generic way or in a generic computing environment. As such, this limitation equates to mere instructions to implement the abstract idea on a generic computer that the courts have stated does not render an abstract idea eligible in Alice Corp., 573 U.S. at 223, 110 USPQ2d at 1983. See also 573 U.S. at 224, 110 USPQ2d at 1984. Therefore, claims 1-3, 5-6, 8-10, 12, 14, 16-23, and 25-26 are directed to an abstract idea (Step 2A, Prong 2: NO).
Claims found to be directed to a judicial exception are then further evaluated to determine if the claims recite an inventive concept that provides significantly more than the judicial exception itself (Step 2B). The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the claims recite additional elements that are insignificant extra-solution activities that do not serve to integrate the recited judicial exceptions into a practical application, or equate to mere instructions to apply the recited exception in a generic way or in a generic computing environment.
As discussed above, there are no additional elements to indicate that the claimed “A non-transitory computer-readable medium storing executable instructions [] the instructions, when executed by a hardware processor, configured to cause the hardware processor to perform steps” or “An electronic device, comprising: one or more processors; and a non-transitory computer-readable storage medium storing executable instructions [] the instructions, when executed by the one or more processors, configured to cause the electronic device to perform steps” requires anything other than generic computer components in order to carry out the recited abstract idea in the claims. Claims that amount to nothing more than an instruction to apply the abstract idea using a generic computer do not render an abstract idea eligible. MPEP 2106.05(f) discloses that mere instructions to apply the judicial exception cannot provide an inventive concept to the claims. Additionally, the limitations for receiving, generating, labeling, inputting, and outputting data, and training and using statistical models are insignificant extra-solution activities that do not serve to integrate the recited judicial exceptions into a practical application. Furthermore, no inventive concept is claimed by these limitations as they are well-understood, routine, and conventional.
The additional elements do not comprise an inventive concept when considered individually or as an ordered combination that transforms the claimed judicial exception into a patent-eligible application of the judicial exception. Therefore, the claims do not amount to significantly more than the judicial exception itself (Step 2B: No). As such, claims 1-3, 5-6, 8-10, 12, 14, 16-23, and 25-26 are not patent eligible.
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.
Claims 1-3, 5-6, 8-10, 12, 14, 16, 22-23, and 25-26 rejected under 35 U.S.C. 103 as being unpatentable over Cummings et al. (US-20190025308) in view of Lyu et al. (Lyu et al. "Mutation load estimation model as a predictor of the response to cancer immunotherapy." NPJ genomic medicine 3.1 (2018): 12).
Regarding claims 1-2 and 25-26, Cummings teaches a method for determining a subject’s likelihood of responding to a treatment by assessing a cell-free DNA (cfDNA) sample collected from the subject (Abstract "The invention provides methods of treating a cancer, methods of determining whether an individual having a cancer is likely to respond to a treatment including an immune checkpoint inhibitor").
Cummings also teaches receiving sequence data gathered from sequencing the cfDNA sample (Para.0112 "the bTMB [blood tumor mutational burden] score is determined from measuring the number of somatic mutations in cell-free DNA (cfDNA) in a sample").
Cummings also teaches predicting a tumor mutational burden (TMB) for a tissue of interest at the subject using a TMB prediction model that receives the feature matrix as input and outputs a predicted TMB (Para.0024 "the invention features a method of predicting disease progression in an individual having a cancer, the method comprising determining a bTMB score in a sample obtained from the individual, wherein a bTMB score in the sample that is at or above a reference bTMB score identifies the individual as one who is more likely to exhibit disease progression").
Cummings also teaches subsequent to determining the predicted TMB, determining whether a set of criteria has been met, wherein the set of criteria includes at least one criterion that is met when the predicted TMB is high, or as interpreted above, a high predicted TMB is determined to be high when the predicted TMB exceeds a predetermined value – also the limitation of Claim 2 (Para.0588 "In patients with high TMB (>30 mutations) in both blood and tissue, on average one-third of the variants were unique to the blood sample and one-fourth of the total variants were unique to the tissue sample, with the remaining variants identified in both (FIG. 11K)").
Cummings also teaches in accordance with a determination that the set of criteria has been met, determining that the subject is (or is not) likely to respond to the treatment (Para.0253 "the method including determining a bTMB score from a sample from the individual, wherein a bTMB score from the sample that is at or above a reference bTMB score identifies the individual as one who may benefit from a treatment comprising an immune checkpoint inhibitor" and Para.0193 "The term “biomarker” as used herein refers to an indicator, e.g., predictive, diagnostic, and/or prognostic, which can be detected in a sample, e.g., a bTMB score, a tTMB [tissue TMB] score, or PD-L1. The biomarker may serve as an indicator of a particular subtype of a disease or disorder (e.g., cancer) characterized by certain, molecular, pathological, histological, and/or clinical features (e.g., responsiveness to therapy including a PD-L1 axis binding antagonist). In some embodiments, a biomarker is a collection of genes or a collective number of mutations/alterations (e.g., somatic mutations) in a collection of genes. Biomarkers include, but are not limited to, polynucleotides (e.g., DNA and/or RNA), polynucleotide alterations (e.g., polynucleotide copy number alterations, e.g., DNA copy number alterations), polypeptides, polypeptide and polynucleotide modifications (e.g., post-translational modifications), carbohydrates, and/or glycolipid-based molecular markers").
Cummings also teaches feature values corresponding to synonymous and nonsynonymous mutations in the sequence data (Para.0123 "In certain embodiments, the somatic alteration is a silent mutation (e.g., a synonymous alteration). In other embodiments, the somatic alteration is a non-synonymous single nucleotide variant (SNV)").
Cummings does not explicitly teach generating a feature matrix comprising feature values corresponding to synonymous and nonsynonymous mutations in the sequence data.
However, Lyu teaches generating a matrix comprising feature values of nonsynonymous point mutations (Page 2 col 2 paragraph 2 "After selecting nonsynonymous point mutations, the mutation matrix with 13,526 genes and 230 patients was generated").
Therefore, it would have been obvious to one of ordinary skill in the art as of the effective filing date of the claimed invention to modify the input of Cummings with the matrix as taught by Lyu in order to order to predict immunotherapy treatment response using mutation load (Abstract " the objective of this study was to construct a mutation load estimation model for lung adenocarcinoma, using a small set of genes, as a predictor of the immunotherapy treatment response"). One skilled in the art would have a reasonable expectation of success because both approaches are using TMB data to predict treatment responses.
Regarding claims 3 and 5, Cummings in view of Lyu teach the methods of Claim 1 on which this claim depends/these claims depend, respectively. Cummings also teaches the feature values comprise one or more of: a number of nonsynonymous somatic mutations for each region of a plurality of regions included in an assay used to sequence the cfDNA sample, a total number of somatic mutations in the cfDNA sample, and a total number of nonsynonymous somatic mutations in the cfDNA sample; and the predicted TMB represents an estimated total number of nonsynonymous somatic mutations for the tissue of interest at the subject (Para.0112 "the bTMB [blood tumor mutational burden] score is determined from measuring the number of somatic mutations in cell-free DNA (cfDNA) in a sample" and Para.0123 "the somatic alteration is a silent mutation (e.g., a synonymous alteration). In other embodiments, the somatic alteration is a non-synonymous single nucleotide variant (SNV)").
Regarding claim 6, Cummings in view of Lyu teach the methods of Claim 1 on which this claim depends/these claims depend, respectively. Cummings also teaches the treatment comprises one or both of an immunotherapy treatment and an immune oncology treatment (Abstract "The invention provides methods of treating a cancer, methods of determining whether an individual having a cancer is likely to respond to a treatment including an immune checkpoint inhibitor").
Regarding claim 8, Cummings in view of Lyu teach the methods of Claim 1 on which this claim depends/these claims depend, respectively. Cummings also teaches in accordance with the determination that the subject is (and is not) likely to respond to the treatment, continuing (and altering) administration of the treatment to the subject (respectively) (Para.0288 "provided herein is a method of monitoring a response of an individual having a cancer to treatment with an anti-cancer therapy that includes an immune checkpoint inhibitor, [], the method including: (a) determining a bTMB score in a sample obtained from an individual at a time point following administration of the anti-cancer therapy to the individual; and (b) comparing the bTMB score in the sample to a reference bTMB score, thereby monitoring the response in the individual to the treatment with the anti-cancer therapy. In some embodiments, the method further comprises administering one or more additional doses of the anti-cancer therapy if the bTMB score in the sample decreases relative to the reference bTMB score. In other embodiments, the method may further include selecting an anti-cancer therapy that does not include an immune checkpoint inhibitor for the individual if the bTMB score in the sample increases relative to the reference bTMB score. In other embodiments, the method may further include selecting an anti-cancer therapy that includes an immune checkpoint inhibitor in combination with an additional therapeutic agent for the individual if the bTMB score in the sample increases or remains the same relative to the reference bTMB score. [and so on]").
Regarding claim 9, Cummings in view of Lyu teach the methods of Claim 1 on which this claim depends/these claims depend, respectively. Lyu and Cummings also teach the TMB prediction model comprises a statistical model trained with a training set comprising training data obtained from sequencing a plurality of train samples of cfDNA collected from a plurality of subjects, wherein the training data obtained from each train sample corresponds to matched tissue data obtained from a tumoral tissue sample collected from the same subject (Lyu Page 2 col 2 paragraph 1 "a simple linear model was used for the construction of mutation load estimation model. Least squares parameter estimation method was employed for parameter identification and Bayesian information criterion (BIC) was used for model selection. After the selection of the most appropriate model, the performance of the mutation load estimation model was evaluated and verified using the mutation information obtained from the independent validation data" and Cummings Para.0589 "To determine if factors associated with the samples themselves contributed to the variation observed in the pairwise comparison of tTMB and bTMB in POPLAR and OAK, we evaluated a series of sample characteristics including sample type (biopsy versus resection), stage at diagnosis, fraction of cfDNA that is ctDNA [circulating tumor DNA] as measured by the maximum somatic allele frequency (MSAF) detected in a sample, sample collection time (blood versus tissue), baseline tumor burden as determined by RECIST v1.1, and tumor purity, as well as other factors").
Regarding claim 10, Cummings in view of Lyu teach the methods of Claim 9 on which this claim depends/these claims depend, respectively. Lyu also teaches the training data is obtained from targeted sequencing of the plurality of train samples, and wherein the matched tissue data is obtained from whole exome sequencing of the tumoral tissue sample (Page 2 col 1 last paragraph "We generated the mutation matrix with the somatic mutation data downloaded from The Cancer Genome Atlas (TCGA) as the training data" as TCGA extensively uses exome sequence data to identify mutations and other genetic alterations in cancer).
Regarding claim 12, Cummings in view of Lyu teach the methods of Claim 9 on which this claim depends/these claims depend, respectively. Cummings also teaches labeling the training data with a corresponding ground truth TMB determined from the corresponding matched tissue data (Para.0589 "To determine if factors associated with the samples themselves contributed to the variation observed in the pairwise comparison of tTMB and bTMB in POPLAR and OAK, we evaluated a series of sample characteristics including sample type (biopsy versus resection), stage at diagnosis, fraction of cfDNA that is ctDNA [circulating tumor DNA] as measured by the maximum somatic allele frequency (MSAF) detected in a sample, sample collection time (blood versus tissue), baseline tumor burden as determined by RECIST v1.1, and tumor purity, as well as other factors").
Cummings also teaches generating a predicted TMB from the labeled training data using the statistical model (Para.0256 "The methods provided herein may include determining a bTMB score from a sample (e.g., a whole blood sample, a plasma sample, a serum sample, or a combination thereof) from an individual. The sample from the individual may be an archival sample, a fresh sample, or a frozen sample. The determination step may include determining the total number of somatic mutations (e.g., a base substitution in a coding region and/or an indel mutation in a coding region) occurring in a pre-determined set of genes to derive a bTMB score from the sample from the individual. In some embodiments, the number of somatic mutations is the number of single nucleotide variants (SNVs) counted or a sum of the number of SNVs and the number of indel mutations counted").
Cummings does not explicitly teach correlating the predicted TMB with the corresponding ground truth TMB.
However, Lyu also teaches correlating the predicted TMB with the corresponding ground truth TMB (Page 3 col 1 paragraph 2 "For the performance evaluation of the constructed model for lung adenocarcinoma, the mutation load for all patients in the training data from TCGA (n = 230) was estimated using this model. R2 between the estimated and actual mutation load was shown to be 0.9336 (Supplementary Fig. 2), indicating that the estimated mutation loads highly correlate with the actual mutation loads").
Regarding claims 14 and 16, Cummings in view of Lyu teach the methods of Claim 9 on which this claim depends/these claims depend, respectively. Cummings also teaches each train sample corresponds to a cancer stage III or stage IV condition, and wherein each train sample of cfDNA has a tumor faction that exceeds a minimum tumor fraction; and the tumor fraction comprises a maximum allele frequency of all mutations in the train sample (Para.0224 "The terms “cancer” and “cancerous” refer to or describe the physiological condition in mammals that is typically characterized by unregulated cell growth. Included in this definition are benign and malignant cancers. [] In particular examples, the lung cancer is NSCLC, for example a locally advanced or metastatic NSCLC (e.g., stage IIIB NSCLC, stage IV NSCLC, or recurrent NSCLC)" and Para.547 "The estimation of tumor fraction by the MSAF is defined according to the highest allele fraction for confirmed somatic base substitutions <20%, regardless of their driver status. This threshold was chosen to minimize the chance of rare germline events in highly aneuploid tumors affecting the estimation").
Regarding claim 22, Cummings in view of Lyu teach the methods of Claim 1 on which this claim depends/these claims depend, respectively. Cummings also teaches a criterion that is met when the predicted TMB is high and a tumor fraction (TF) computed based on the sequence data is low (Para.0589 " fraction of cfDNA that is ctDNA [circulating tumor DNA] as measured by the maximum somatic allele frequency (MSAF) detected in a sample" and Para.0032-33 "the method further comprises determining an MSAF from a sample from the individual, wherein the MSAF from the sample has been determined to be greater than, or equal to, 1%, and the method further comprises administering to the individual an effective amount of an anti-cancer therapy other than, or in addition to, a PD-L1 axis binding antagonist. [] the method further comprises determining an MSAF from a sample from the individual, wherein the MSAF from the sample has been determined to be less than 1%, and the method further comprises administering an effective amount of a PD-L1 axis binding antagonist to the individual").
Regarding claim 23, Cummings in view of Lyu teach the methods of Claim 1 on which this claim depends/these claims depend, respectively. Cummings also teaches in accordance with a determination that the TF is low, determining that the subject is (or is not) likely to respond to the treatment (Para.0120 "Since methods and systems of the present disclosure can detect somatic SNVs from low tumor cfDNA fraction plasma samples, they can be applied to early cancer diagnosis with liquid biopsy and cancer post-treatment monitoring. With certain driver mutations detected, clinicians can determine cancer types, and provide personalized treatment to patients" suggests determining treatment responses from low tumor cfDNA).
Claims 17-18 rejected under 35 U.S.C. 103 as being unpatentable over Cummings et al. (US-20190025308) in view of Lyu et al. (Lyu et al. "Mutation load estimation model as a predictor of the response to cancer immunotherapy." NPJ genomic medicine 3.1 (2018): 12) as applied to claims 1-3, 5-6, 8-10, 12, 14, 16, 22-23, and 25-26 above, and further in view of Budczies et al. (Budczies et al. "Optimizing panel-based tumor mutational burden (TMB) measurement." Annals of Oncology 30.9 (2019): 1496-1506).
Cummings et al. in view of Lyu et al. are applied to claims 1-3, 5-6, 8-10, 12, 14, 16, 22-23, and 25-26.
Regarding claims 17 and 18, Cummings in view of Lyu teach the method of Claim 1 on which this claim depends/these claims depend.
Cummings nor Lyu explicitly teach a criterion that is met when the predicted TMB is high and corresponds to a predicted tumoral heterogeneity (TH) that is indicative of a homogeneous tissue; determining whether the predicted TH is indicative of homogeneous or heterogeneous tissue; nor in accordance with a determination that the predicted TH is indicative of the homogeneous tissue, determining that the subject is (or is not) likely to respond to the treatment.
However, Budczies teaches using quantifying variability in predicted TMB, and assessing tumor heterogeneity of TMB samples for determining treatment response (Page 2 paragraph 5 "First, using a random mutation model, we derive an algebraic formula for the coefficient of variation (CV) of psTMB as a function of panel size and number of detected mutations. Secondly, by the simulation of panel sequencing in WES data, we quantify psTMB variability, assess the degree of imprecision attributable to intratumoral heterogeneity, and analyze the capability of psTMB to predict response to ICB [immune checkpoint blockade]").
Therefore, it would have been obvious to one of ordinary skill in the art as of the effective filing date of the claimed invention to modify the methods of Cummings and Lyu as taught by Budczies in order to describe TH using TMB (page 1 Abstract "A mathematical law describing psTMB variability was derived using a random mutation model and complemented by the contributions of non-randomly mutated real-world cancer genomes and intratumoral heterogeneity through simulations in publicly available datasets). One skilled in the art would have a reasonable expectation of success because both approaches are using variant data from TCGA.
Claims 19-21 rejected under 35 U.S.C. 103 as being unpatentable over Cummings et al. (US-20190025308) in view of Lyu et al. (Lyu et al. "Mutation load estimation model as a predictor of the response to cancer immunotherapy." NPJ genomic medicine 3.1 (2018): 12) and Budczies et al. (Budczies et al. "Optimizing panel-based tumor mutational burden (TMB) measurement." Annals of Oncology 30.9 (2019): 1496-1506) as applied to claims 1-3, 5-6, 8-10, 12, 14, 16-18, 22-23, and 25-26 above, and further in view of Hardiman et al. (Hardiman et al. "Intra-tumor genetic heterogeneity in rectal cancer." Laboratory investigation 96.1 (2016): 4-15).
Cummings et al. in view of Lyu et al. and Budczies et al. are applied to claims 1-3, 5-6, 8-10, 12, 14, 16-18, 22-23, and 25-26.
Regarding claim 19, Cummings in view of Lyu and Budczies teach the method of Claim 17 on which this claim depends/these claims depend.
Cummings, Lyu, nor Budczies explicitly teach determining the predicted TH using a TH prediction model that receives a set of features in the sequence data as input and outputs the predicted TH.
However, Hardiman teaches calculating a mutant-allele tumor heterogeneity score (Abstract "Mutant-allele tumor heterogeneity (MATH) scores, mutant allele frequency correlation, and mutation percent concordance were calculated, and copy number analysis including measurement of correlation between samples was performed").
Therefore, it would have been obvious to one of ordinary skill in the art as of the effective filing date of the claimed invention to modify the methods of Cummings, Lyu, and Budczies as taught by Hardiman in order to explore the clinical significance of TH (Page 11 sol 2 last paragraph "The potential clinical significance of intra-tumor genetic heterogeneity for cancer prognosis or for treatment of colorectal cancer patients has yet to be explored"). One skilled in the art would have a reasonable expectation of success because both approaches are using variant data from TCGA.
Cummings also teaches the set of features comprising an allele frequency of single nucleotide variant (SNV) calls in the cfDNA sample (Para.0117 "The allele frequency for somatic mutations may be calculated by dividing the number of sequence reads indicating a somatic mutation against the total reads aligned to a particular region of the human genome. In some instances, the MSAF [maximum somatic allele frequency] is derived from the largest somatic allele frequency less than about 20% in a sample. In some embodiments, the value is the fraction of all cfDNA in the sample from the subject that carries that allele").
Regarding claim 20, Cummings in view of Lyu, Budczies, and Hardiman teach the method of Claim 19 on which this claim depends/these claims depend.
Cummings, Budczies, nor Hardiman teach the TH prediction model comprises a linear regression model.
However, Lyu teaches a linear model for mutation load estimation (Page 2 col 2 paragraph 1 "a simple linear model was used for the construction of mutation load estimation model").
Cummings, Lyu, nor Hardiman teach determining, with the TH prediction model, a coefficient of variation of the allele frequency of SNV calls based on the set of features, in accordance with a determination that the coefficient of variation is low; nor determining that the predicted TH is indicative of homogeneous (or heterogeneous) tissue.
However, Budczies teaches calculating and using a coefficient of variation for characterizing tumor heterogeneity (Page 6 Figure 3 "Inhomogeneity of tumor mutational burden (TMB) across different regions of lung tumors TRACERx 100 data [27]. (A) TMB of 323 regions of 100 lung carcinoma measured by WES and classification using a cut-point of 199 mutations. Regions of the same tumor were classified inconsistently for 11 tumors (red dots). (B–D) The coefficient of variation (CV) of TMB across regions was calculated for each of the tumors. The CV was substantially higher in simulated panel sequencing data (TSO500, QIAseq and OTML panels) compared with the WES data").
Regarding claim 21, Cummings in view of Lyu, Budczies, and Hardiman teach the method of Claim 19 on which this claim depends/these claims depend.
Budczies, Lyu, nor Hardiman teach the TH prediction model comprises a linear regression model.
However, Cummings teaches (similar to claim 9) the TH prediction model comprises a statistical model trained on a training set comprising a plurality of training samples that are derived from cfDNA samples having matched tissue data from tumoral tissue samples (Para.0589 "To determine if factors associated with the samples themselves contributed to the variation observed in the pairwise comparison of tTMB and bTMB in POPLAR and OAK, we evaluated a series of sample characteristics including sample type (biopsy versus resection), stage at diagnosis, fraction of cfDNA that is ctDNA [circulating tumor DNA] as measured by the maximum somatic allele frequency (MSAF) detected in a sample, sample collection time (blood versus tissue), baseline tumor burden as determined by RECIST v1.1, and tumor purity, as well as other factors").
Cummings, Lyu, nor Hardiman teach the TH prediction model comprises a linear regression model.
However, Budczies teaches training samples having high cfDNA-tissue concordance correspond to low (high) coefficient of variation of cfDNA variant allele frequencies and are homogeneous (heterogeneous) (Page 6 Figure 3 "Inhomogeneity of tumor mutational burden (TMB) across different regions of lung tumors TRACERx 100 data [27]. (A) TMB of 323 regions of 100 lung carcinoma measured by WES and classification using a cut-point of 199 mutations. Regions of the same tumor were classified inconsistently for 11 tumors (red dots). (B–D) The coefficient of variation (CV) of TMB across regions was calculated for each of the tumors. The CV was substantially higher in simulated panel sequencing data (TSO500, QIAseq and OTML panels) compared with the WES data”, suggests homogeneity characterization using CoV).
Conclusion
No claims are allowed.
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/R.A.P./Examiner, Art Unit 1686
/LARRY D RIGGS II/Supervisory Patent Examiner, Art Unit 1686