DETAILED ACTION
Notice of Pre-AIA or AIA Status
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim status
2. Claims 1-20 are currently pending and under exam herein.
Claims 1-20 are rejected.
Claims 1, 13-15 and 17 are objected to.
Priority
3. This application claims domestic benefit of provisional Application No. 63/215877 filed on 06/28/2021. Acknowledgment is made of applicant’s claim for domestic priority and the effective filing date will be considered to be 06/28/2021.
Information Disclosure Statement
4. The information disclosure statement (IDS) submitted on 08/31/2022 is being considered by the examiner.
Drawings
5. The drawings are objected to because the text in Fig. 2A-4B and 7A-7B is illegible. Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. The figure or figure number of an amended drawing should not be labeled as “amended.” If a drawing figure is to be canceled, the appropriate figure must be removed from the replacement sheet, and where necessary, the remaining figures must be renumbered and appropriate changes made to the brief description of the several views of the drawings for consistency. Additional replacement sheets may be necessary to show the renumbering of the remaining figures. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
Specification
6. The abstract of the disclosure is objected to because the acronym DLBCL is not defined. A corrected abstract of the disclosure is required and must be presented on a separate sheet, apart from any other text. See MPEP § 608.01(b).
The disclosure is objected to because of the following informalities: a few grammatical errors were noted. For example, it is written that “DLBCL cases were classified into the either group” (para. 0005) and “In another yet aspect of the invention…” (para. 0012). Appropriate review of the specification for identification and correction of these and other errors is required.
Claim Objections
7. Claims 1, 13-15, and 17 are objected to because of the following informalities listed below. Appropriate correction is required.
Claims 1 and 17 use periods in their step identifiers instead of (a), (b), etc. Periods should only be used at the end of claims or in abbreviations (see MPEP 608.01(m)).
Claim 13 recites: “from initial set of available RNA-based biomarkers”, which should be corrected to: “from an initial set of available RNA-based biomarkers”.
Claim 14 recites: “ for the individual RNA-based biomarker for one of the subsets”, which should be corrected to: “for the individual RNA-based biomarkers for one of the subsets”.
Claim 15 recites: “for each of the subdividing step”, which should be corrected to either “for the subdividing step” or “for each of the subdividing steps”.
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.
8. Claims 1-20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Independent claim 1 and dependent claims 2-16 are rejected for being indefinite. Claim 1 is directed to “providing a mathematical algorithm”, and the process by which the mathematical algorithm is made. It is unclear if the claim is intended to require performing the training process (described in limitations of claims 1a, 2-3, 5-6, 8-9, and 13-16) or if they are merely product-by-process limitations that are not performed in the metes and bounds of the claim. For the purpose of review, the broadest reasonable interpretation will be applied and the claim will be interpreted to not include the steps in developing the algorithm. Therefore, differences in the claimed steps that would result in an equivalent mathematical algorithm could be used for the method of treating a subject. Claims dependent on claim 1 (claims 2-16) do not resolve the indefiniteness issue and thus are rejected for the same reason.
Independent claim 1, and dependent claims 2-16 are further rejected due to lack of antecedent basis. Claim 1b refers to “the subset of individual RNA-based biomarkers” but prior to this, only “individual RNA-based biomarkers” and “a subset of RNA-based biomarkers” have been introduced. Therefore, there is insufficient antecedent basis for this limitation in the claim. For the purpose of review, the subset of individual RNA-based biomarkers will be considered to be “the individual RNA-based biomarkers corresponding to the subset of RNA-based biomarkers”. Claims dependent on claim 1 (claims 2-16) do not resolve the antecedent basis issue and thus are rejected for the same reason.
Dependent claim 4 is rejected as being indefinite. Claim 4 is directed to “a second group of low responders”. It is unclear if this is a new second group of low responders or if it is referring to “the second group of low responders” introduced in claim 3, on which claim 4 depends. With broadest reasonable interpretation, for the purpose of review, the claim will be interpreted to mean a second group of low responders, which could be the second group introduced in claim 3 or another group.
Dependent claims 15 and 16 are rejected as being indefinite. Claim 15 refers to “the first group and the second group, the third group and the fourth group and the fifth group and the sixth group” and claim 16 refers to “the first group and the second group and the third group, and the fourth group”, but there is no previous recitation of a first group, second group, third group, fourth group, fifth group or sixth group in these claims nor in their parent claims. Therefore, there is insufficient antecedent basis for these limitations in the claims.
Claims 15 and 16 are further rejected as being indefinite. refer to 'high responders’ and ‘low responders’. These are relative terms that are not defined in the claims nor in the parent claims and no adequate limiting definition is provided in the specification. For the purpose of review, the broadest reasonable interpretation will be applied and the groups compared will be considered to be any groups of subjects separated by survival time in response to a known therapy.
Independent claims 17-19 are rejected as being indefinite. Independent claims 17 and 19 recite limitations wherein subjects are grouped or defined as “high responders” or “low responders”, but the terms 'high responders’ and ‘low responders’ are relative terms that have not been defined in the claims and no adequate limiting definition is provided in the specification. Claim 18, which is dependent on claim 17, does not resolve the indefiniteness issue and thus is rejected for the same reason. For the purpose of review, the broadest reasonable interpretation will be applied and high and low responders will be considered to be patients that have lesser or greater response to a known treatment.
Claim 20 is further rejected due to lack of antecedent basis for depending on “the method as in claim 76”, as claim 76 is not a claim in the application.
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.
9. Claims 1-19 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea of mental steps, mathematic concepts, organizing human activity, or a natural law without significantly more.
Step 2A, Prong 1
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:
Claim 1c recites forecasting clinical course for the subject using the subset of individual RNA-based biomarkers obtained from the subject
Claim 17b recites based on survival time, dividing all subjects from the training set into a first group of high responders and a second group of low responders
Claim 17c recites using machine learning, identifying a first subset of one or more individual RNA-based biomarkers from a plurality of individual RNA-based biomarkers
Claim 17c recites wherein the first subset of one or more individual RNA-based biomarkers is identified as correlating to dividing the subjects into the first group and the second group
Claim 18 recites dividing the first group of high responders into a third group of high responders and a fourth group of high responders
Claim 18 recites wherein the third group of high responders is characterized by survival time longer than average survival time for the entire first group of high responders, the fourth group of high responders is characterized by survival time shorter than average survival time for the entire first group of high responders.
Claim 19 recites using a Bayesian classifier to define the subject as a high responder or a low responder to chemotherapy using one or more of individual RNA-based biomarkers selected from a group consisting of PPP2R1B, GOLGA5, LINGO2, HMGA1, SIN3A, ARID1A, BCL7A, CDK5RAP2, MAGED1, CREB3L1, AMER1, DLL1, GSTT1, GPR34, DNM2, CCNB1IP1, MUTYH, RET, CDH1, POFUT1, XRCC6, KIT, RALGDS, SS18, CD22, BRCA2, HDAC3, LHX4, FAM19A2, PRG2, PRCC, TBL1XR1, HIF1A, EDIL3, ROS1, DKK4, CDC25A, WNT7B, MYBL1, MLLT10, SLCO1B3, TACC2, CANT1, NCAM1, FGF3, FGF19, PPP3R2, CRADD, ETV6, SPP1, SDHB, FGF2, SUZ12, MB21D2, MYC, BAX, CEP57, ITGA5, ABCC3, and HECW1
The limitations in claims 1c, 17 and 18 directed to forecasting clinical course for the subject, and dividing subjects into groups, equate to evaluating data and making a decision based on that evaluation which, under the broadest reasonable interpretation, can be practically performed in the human mind. Therefore, these limitations fall within the “mental process” grouping of abstract ideas because they cover concepts performed in the human mind including observation, evaluation, judgment, and opinion (MPEP 2106.04(a)(2), subsection III).
The limitations in claims 17 and 19 directed to using machine learning for identifying RNA biomarkers and to using a Bayesian classifier to define a subject as a high responder or a low responder are mathematical calculations. These include applying arithmetic calculations and functions (division, multiplication, addition, subtraction, log and square root calculations), and applying these functions to probability and correlation functions. Therefore, these limitations recite “mathematical calculations”, and fall into the “mathematical concepts” grouping of abstract ideas. These limitations also fall into “mental process” grouping because the mathematical calculations are simple enough to be practically performed in the human mind. Even if most humans would use a physical aid, like a pen and paper or a calculator, to make such calculations, the use of a physical aid would not negate the mental nature of this limitation See MPEP 2106.04(a)(2), subsection III.B.
Additionally, the limitations discussed above also fall into the category of natural phenomenon and laws of nature. Claims 1c and 17-19 that are directed to identifying correlations between survival time in response to therapy or disease with the presence of RNA biomarkers and using the correlations to forecast the clinical course of a subject are natural phenomena because they describe consequence of natural processes in the human body, e.g., the naturally-occurring relationship between the presence of RNA biomarkers and response to therapy. Claims 17 and 18 also embrace a product of nature as they group subjects based on natural course of a disease or natural response to a therapy (See MPEP 2106.04(b)).
Therefore, the claims explicitly recite elements that, individually and in combination, constitute one or more judicial exceptions. As such, claims 1-19 recite an abstract idea (Step 2A, Prong 1: YES).
Step 2A, Prong 2
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) (MPEP 2106.04(d)). The claimed additional elements are analyzed alone, or in combination to determine if the judicial exception is integrated into a practical application. This judicial exception is not integrated into a practical application because the claims do not recite an additional element that reflects an improvement to the diagnostic field and do not effect a particular treatment for either a heterogeneous disease or diffuse large B-cell lymphoma. Rather, the instant claims recite additional elements that amount to insignificant extra-solution activity and mere instructions to "apply" the exception in a generic way. Specifically, the claims recite the following additional elements:
Claim 1a recites providing a mathematical algorithm for forecasting clinical course of the subject with the heterogeneous disease by classifying the subject into one of several predetermined survival groups based on response to a known therapy
Claim 1b recites obtaining the subset of individual RNA-based biomarkers defined in step (a) for the subject
Claim 1d recites treating the subject forecasted in step (c) with the known therapy.
Claim 17a recites providing a training set of subjects with the heterogenous disease with known plurality of individual RNA-based biomarkers and known survival time
Limitations in claims 1a, and 2-10 and 12-16 further describe the provided mathematical algorithm
The limitation in claim 11 further limits treating the subjects (wherein treating the subject in step (d) comprises: a step of treating the subject forecasted in step (c) as a high responder with the known therapy; a step of treating the subject forecasted in step (c) as a low responder with a further therapy or an additional therapy; or a combination thereof)
The limitations in claims 1a, 1b and 17a are directed to providing a mathematical algorithm, obtaining RNA biomarkers and providing subjects, merely serve to gather data that is used an input for the judicial exception. Therefore, these limitations are mere data gathering activities. As set forth in MPEP 2106.05(g), mere data gathering activity has been identified by the courts as insignificant extra-solution activity that does not provide a practical application. Note that as indicated in section 8 of this office action pertaining to claim 1 and dependent claims 2-16, the steps of generating the provided mathematical algorithm are not considered necessary aspects of the invention as the mathematical algorithm is simply provided. Therefore, the limitations in claims 1a, 2-10 and 12-16 that describe the features used by the provided mathematical algorithm and how the algorithm is trained, are being treated as simply a description of the data gathered that do not change the significance of data gathering activity. Therefore, the data gathering activities do not impose any meaningful limitation on the judicial exception, or how the judicial exception is performed.
The limitation in claim 1d reciting the non-abstract additional element of treating the subject also fails to integrate the judicial exception into a practical application as the step does not “affirmatively recite an action that effects a particular treatment or prophylaxis for a disease or medical condition”, instead, the claims are directed to treating with a “known therapy”. This equates to "administering a suitable medication to a patient." This administration step is not particular, and is instead merely instructions to "apply" the exception in a generic way (see MPEP § 2106.05(f) and 2106.04(d)(2)). Furthermore, there is no specific disease indicated for the treatment other than “a heterologous disease”, thus there is no specific process of adjusting the treatment of patients with a specific disease to improve patient outcome, thus the limitations do not include other meaningful limitations (see MPEP § 2106.05(e)). Claim 11 further limits the treating the subject, and introduces conditional treatments with “further therapy”, “additional therapy” or “a combination thereof”, but does not change the generic nature of the treatment. Note that although claim 19 recites a method for treating a subject with diffuse large B-cell lymphoma, there is no treatment step in the claimed method. Therefore there is no treatment step that applies the abstract ideas and natural relationships in a manner that is integrated it into a practical application.
The above recited additional elements do not provide a practical application of the recited judicial exception. As such, claims 1-19 are directed to an abstract idea (Step 2A, Prong 2: NO).
Step 2B
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 equate to mere instructions to apply the recited exception in a generic way or are well-understood, routine and conventional activities.
The additional elements identified above that amount to data gathering (including providing an algorithm, providing a training set of subjects and obtaining biomarkers (in claims 1a, 1b and 17a)) and the limitations that simply describe the provided algorithm (in claims 1a, 2-10 and 12-16) do not rise to the level of significantly more than the judicial exception. These data gathering elements are routine, well understood and conventional as recognized by the court decisions listed in MPEP 2106.05(d). Furthermore, as evidenced by Xi et al. (Xi et al., Non-coding RNA, 3(1), p. 9.1-9.17, 2017), acquiring patient samples from blood or saliva, and isolating, amplifying and measuring RNA biomarkers using PCR or sequencing is routine, well understood and conventional in the art (sections 4, 7 and 8). Additionally the specification of the instant application disclosed that the data gathering step of RNA quantification can be done using a variety of known techniques including next-generation sequencing (para. 0023). Simply appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception are insufficient to provide significantly more (as discussed in Alice Corp.).
The additional element in claim 1d directed to treating the subject forecasted in step (c) with the known therapy (and the limitation in claim 11 that further limits the treatment) do not rise to the level of significantly more than the judicial exception, because treating the subject uses well-understood, routine, and conventional activity in the field. For example, as evidenced by Grzegorz et al. (Grzegorz et al., 2015, disclosed in the IDS), treating a patient having lymphoma with a known therapy (chemoimmunotherapy) is considered a standard treatment. Also, as evidenced by Alam et al. (Alam et al., Open Access journal of Toxicology, 2(5), p. 3641-3647, 2018), cancer patients are frequently treated with known therapies to control for residual tumor cells and reduce the risk of recurrence. Furthermore, the application of transcriptomic profiling to identify RNA biomarkers and the utilization of these data to make treatment decisions for patients with heterologous diseases were well-understood, routine, conventional activities at the time of the invention as evidenced by Buzdin et al. (Buzdin, 2020 Seminars in Cancer biology, Vol. 60, p. 311-323) (section 13). In 2020, several regulatory-approved multivariate gene expression signatures were used in the clinic to guide treatment (foremost, adjuvant chemotherapy) in breast cancer, including MammaPrint (with prognostic power to differentiate low- and high-risk patients), Oncotype Dx, Prosigna, and Endopredict. The prognostic value of these signatures to prevent tumor relapse is well documented (sect 13, para.2). As such, the combination of additional elements recited in the claims is 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 1-19 are not patent eligible.
Note that claim 20 (which recites treating the subject in step (d) comprising: a step of treating the subject forecasted as a high responder with the known therapy; a step of treating the subject forecasted as a low responder with a further therapy or an additional therapy; or a combination thereof) was not rejected under 35 USC 101 because it is only directed to treating a subject does not depend on other claims and thus is not directed to an abstract idea. However, if the dependency issue noted in section 8 of this office action above is corrected, its assessment under 35 USC 101 could change.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
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.
10. Claims 1 and 20 are rejected under 35 U.S.C. 102(a)(1) and 102(a)(2) as being unpatentable over Gutin et al. (US 2020/0283855 A1). The italicized text corresponds to the instant claim limitations.
With respect to claim 1, Gutin et al. discloses a method for predicting chemotherapy benefit for subjects with breast cancer and treating more or less aggressively based on the test scores. Breast cancer is a heterogeneous disease with multiple underlying mutations, differently affecting expression of genes like HER2/neu and estrogen receptor expression (para. 0004, 0007, 0011 and 0113), a method for treating a subject with a heterogeneous disease).
Regarding claim 1a, Gutin et al. provides mathematical algorithms for predicting clinical course of a subject with breast cancer in response to known chemotherapies. The method comprises: (a) measuring RNA biomarkers in a tumor sample from the patient; (b) generating an expression score of the RNA biomarkers; (c) generating a clinical values score; and (d) mathematically combining the expression score with the clinical values score by applying an algorithm to generate a combined score used to predict a prognosis for the patient. In some embodiments, one, two, or more thresholds are determined for the combined score and patients are discriminated into high and low risk, high, intermediate and low risk, or more risk or survival groups by applying the threshold on the combined score. (Gutin et al., para. 0011, 0025, 0037 and 0061, providing a mathematical algorithm for predicting clinical course of a subject with heterogeneous disease by classifying the subject into one of several predetermined survival groups based on response to a known therapy).
With respect to claim 1a, Gutin et al. teaches the mathematical algorithm is trained using ML by analyzing multiple RNA biomarkers with a training set of subjects treated with the known therapy. The method uses a “discrimination function”, which is a ML algorithm to classify a patient into multiple categories based on survival time (or other metrics) according to RNA biomarkers or other data from the patient. Gutin et al. teaches that ML algorithms include support vector machines (SVM), k-nearest neighbors (kNN), (naive) Bayes models or linear regression models. The method of generating the model incudes a training data set in which the correct class assignment of each object is known. (Gutin et al., para. 0020, 0040, 0061, and 0091, wherein the mathematical algorithm is trained using ML by analyzing multiple RNA biomarkers and survival times within a training set of subjects with the disease treated with the known therapy).
With respect to claim 1a, Gutin et al. teaches the mathematical algorithm is further trained to divide all subjects from the training set into survival groups and defines a subset of RNA biomarkers corresponding to the groups. The method uses a “discrimination function”, which is a ML algorithm, to classify a patient into one of multiple categories according to data from the patient including risk of patient death or survival using RNA biomarkers or other molecular markers. The method of generating the model incudes a training data set, in which the correct class assignment (for example survival time) of each object is known. The training determines whether the RNA biomarker genes (or other molecular indicators) are indicative of a good outcome or a bad outcome in a patient receiving chemotherapy. The algorithm including its threshold to discriminate low risk from high risk based on biomarker expression is constructed based on the training data set. (Gutin et al., para. 0025, 0040, 0059, 0091, and 0097, wherein the mathematical algorithm is further trained to divide all subjects from the training set into survival groups and to define a subset of RNA biomarkers corresponding to the groups).
With respect to claim 1b, Gutin et al. teaches obtaining the subset of individual RNA biomarkers for the subject. Gutin et al. teaches that markers such as target polynucleotide molecules (i.e. RNA biomarkers) or proteins, can be extracted from a sample taken from an individual afflicted with a condition such as breast cancer. The markers might be isolated from any type of tumor sample, e.g., biopsy samples, smear samples, resected tumor material, fresh frozen tumor tissue or from paraffin embedded and formalin fixed tumor tissue. In an embodiment, gene expression (RNA biomarker) level is determined by at least one of a PCR based method, a microarray-based method, or a hybridization-based method, a sequencing and/or next generation sequencing approach (Gutin et al., para. 0011 and 0078, obtaining the subset of individual RNA biomarkers for the subject).
With respect to claim 1c, Gutin et al. teaches predicting the clinical course of a subject using the subset of individual RNA biomarkers. Gutin et al. discloses a method for predicting a response to and/or a benefit of chemotherapy in a patient suffering from or at risk of developing recurrent neoplastic disease including: (a) determining RNA expression level values of four or more of the following 8 genes in a tumor sample from the patient: UBE2C, BIRC5, DHCR7, STC2, AZGP1, RBBP8, IL6ST and MGP; (b) generating an expression score from the RNA expression levels; (c) generating a clinical values score; and (d) combining the expression score with the clinical values score to generate a combined score, wherein the combined score is indicative of a prognosis for the patient (Gutin et al., para. 0011, Forecasting clinical course of the subject using the subset of individual RNA biomarkers).
With respect to claim 1d, Gutin et al. teaches treating the subject with the known therapy. Gutin et al., teaches that the levels of one or more analyte biomarkers (RNA biomarkers) or the levels of a specific panel of analyte biomarkers and clinical variables in a sample are compared to a reference standard in order to direct treatment decisions. In some applications, a patient is treated more or less aggressively than a reference therapy based on the difference of scores. Gutin et al. teaches that the predictive methods disclosed can be used clinically to make treatment decisions by choosing the most appropriate treatment modalities for any particular patient the therapies, and that treatments can be known chemotherapies includes an anthracyclin-based therapy, 5-fluorouracil, epirubicin, or cyclophosphamide (FEC). (Gutin et al., para. 0011, 00221, 0108, 0113, treating the subject with the known therapy).
With respect to claim 20, Gutin et al. discloses a method that can classify a patient as “high risk” or “low risk” or classify as “in need of treatment” or “not in need of treatment” according to the analysis of RNA biomarkers from the patient and that the treatment can include a variety of known chemotherapy treatments including cytotoxic chemotherapy (including alkylating agents or antimetabolites), neoadjuvant chemotherapy etc. Gutin et al. further teaches treating the high responder subject differently than the low responder and recommending specific therapeutic regimes (including withdrawal from treatment) depending on the algorithm scores. In various embodiments, the practitioner discontinues a therapy if a score is low, or changing therapy by administering a different drug depending on the score (para. 0040-0047, 0116, wherein treating the subject in step (d) comprises: a step of treating the subject forecasted as a high responder or low responder with the known therapy, or other therapy, respectively).
11. Claims 17 is rejected under 35 U.S.C. 102(a)(1) and 102(a)(2) as being unpatentable over Smyth et al. (US 2020/0239968 A1). The italicized text corresponds to the instant claim limitations.
Concerning claim 17, Smyth et al. teaches a method to predict response to treatment for gastric cancer, a heterologous cancer with subtypes. Smyth et al. further teaches providing a training set divided into low-risk and high-risk groups based on survival times following tumor resection. Smyth et al. further teaches a training set of gene expression data is used to construct a statistical model that predicts correctly the subgroups of each sample (para. 0005-0006, 0026, 0140, claim 17a providing a training set of subjects with a heterogeneous disease with known plurality of individual RNA-based biomarkers and known survival time; claim 17b based on survival time, dividing all subjects from the training set into two groups.
Regarding claim 17c, Smyth et al., further teaches that a gene set of 7 RNA biomarkers correlates with dividing the subjects into two groups based on median survival time following tumor resection. Smyth et al., further teaches that supervised machine learning is used to learn and quantify the multivariate boundaries to characterize and separate each subtype in terms of it intrinsic gene expression profile (i.e. the RNA biomarkers) (para. 0026, 0140, claim 17c using machine learning, identify a first subset of RNA biomarkers that correlates to dividing the subjects into the first and second groups).
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.
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.
12. Claims 2-3 and 11 are rejected under 35 U.S.C. 103 as being unpatentable over Gutin et al. (US 2020/0283855 A1) as applied to claim 1 above, in view of Smyth et al. (US 2020/0239968 A1). The italicized text corresponds to the instant claim limitations.
The limitations of claim 1 have been taught by Gutin et al. above.
With respect to claim 2, Gutin et al. teaches training a machine learning classifier to discriminate patients with cancer who are low risk from those that are high risk of metastasis in response to treatment using a training set for which the correct class assignments are known. Gutin et al. further teaches that response to treatment can be measured by overall survival in response to chemotherapy (a known therapy). (Gutin et al. para. 0011, 0020, 0025, 0091 and 0097, the method of claim 1 wherein in step (a) the mathematical algorithm is further trained to divide training set subjects into two groups depending on the level of response to a known therapy measured by survival time).
Gutin et al. does not explicitly disclose the limitation in claim 2 wherein the groups are characterized by survival time that is longer or shorter than average survival time of the training set. However, this limitation was known in the art at the time of the effective filing date of the invention, as taught by Smyth et al.
Regarding claim 2, Smyth et al. teaches training a classifier using a training set of gastroesophageal cancer patients separated into two groups by survival time following tumor resection (i.e. the treatment) that are classified as having greater or shorter survival time than is typical for gastroesophageal cancer patients in the general population. Smyth et al. further teaches that classes can be “good prognosis” versus “bad prognosis” corresponding to overall survival that is longer or shorter than average for that stage of cancer and cancer type (Smyth et al., para. 0062-0068, 0080 and 0151-0152, wherein the groups are characterized by survival time that is longer or shorter than average survival time of the training set).
An invention would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention if some motivation in the prior art would have led that person to combine the prior art teachings to arrive at the claimed invention. Smyth et al. taught modelling with a training set containing classes defined by average survival times after tumor resection are useful to predict treatment response and survival of gastric cancer patients (para. 0010). Therefore, one of ordinary skill in the art would have been motivated to apply training the classifier with two groups defined by survival time after a known therapy taught by Smyth et al. to the modeling method taught by Gutin et al. in order to enable predicting treatment response of gastric cancer patients. Furthermore, one of ordinary skill in the art would predict that the training classes taught by Smyth et al. could readily be added to the classification method taught by Gutin et al. with reasonable expectation of success because the both pertain to developing classifiers to predict response of cancer patients to treatment. The invention is therefore prima facie obvious. (see MPEP 2143(I)(C)).
With respect to claim 3, Gutin et al. teaches training a “discriminant function” to define subsets of RNA-based biomarkers corresponding to dividing all training set subjects into two subgroups based on response to treatment. Gutin et al. further teaches that training the classifier determines whether the marker genes (i.e. RNA biomarkers) are indicative of a good outcome or a bad outcome in a patient receiving chemotherapy. (para. 0011, 0025, 0040, 0059, claim 21, the algorithm is further trained to define a first subset of RNA-based biomarkers corresponding to dividing all training set subjects into two groups of low and high responders.)
With respect to claims 11, Gutin et al. discloses a method that can classify a patient as “high risk” or “low risk” or classify as “in need of treatment” or “not in need of treatment” according to the analysis of RNA biomarkers from the patient and that the treatment can include a variety of known chemotherapy treatments including cytotoxic chemotherapy (including alkylating agents or antimetabolites), neoadjuvant chemotherapy etc. Gutin et al. further teaches treating the high responder subject differently than the low responder and recommending specific therapeutic regimes (including withdrawal from treatment) depending on the algorithm scores. In various embodiments, the practitioner discontinues a therapy if a score is low, or changing therapy by administering a different drug depending on the score (para. 0040-0047, 0116, wherein treating the subject in step (d) comprises: a step of treating the subject forecasted as a high responder or low responder with the known therapy, or other therapy, respectively).
13. Claim 4 is rejected under 35 U.S.C. 103 as being unpatentable over Gutin et al. (US 2020/0283855 A1) and Smyth et al. (US 2020/0239968 A1) as applied to claims 2-3 above and further in view of Chapuy et al. (Nature Medicine, vol 24, p679-690, 2018 in the IDS). The italicized text corresponds to the instant claim limitations.
Gutin et al. and Smyth et al. teach the limitations of claims 1-3 above.
With respect to claim 4, Gutin et al. and Smyth et al. are silent to where a TP53 mutation is a predictor for a second group of low responders.
Regarding claim 4, Chapuy et al. teaches integrating genetic data with gene expression data (genetic and RNA-based biomarkers) from patients with diffuse large B-cell lymphoma (DLBCL) that have outcome data after treatment with R-CHOP to determine prognostic value of the biomarkers. Using consensus clustering of the integrated data, they identified five DCBC subsets that had different biomarker patterns and different overall survival and progression free survival trajectories. Cluster 2 had enrichment of patients with TP53 alterations, down-regulation of TP53 targets (RNA-based biomarkers) and lower progression free survival than clusters 1 and 4 without these biomarkers. (Abstract, Fig. 5, Fig. 6n, para. 6 p. 8, para. 2, p.6, wherein a presence of a TP53 mutation is a predictor for a second group of low responders).
An invention would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention if some motivation in the prior art would have led that person to combine the prior art teachings to arrive at the claimed invention. Chapuy et al. taught that combining the RNA-based survival classifier of Gutin with the DLBCL outcome-associated genetic signatures taught by Chapuy et al. would guide development of rational single-agent and combination therapies in DLBCL patients with the greatest need (p. 11, para. 3). Therefore, one of ordinary skill in the art would have been motivated to apply the genetic based grouping taught by Chapuy et al. with method taught by Gutin et al. in order to enable predicting treatment response of DLBCL. Furthermore, one of ordinary skill in the art would predict that the genetic combined with RNA-based biomarker approach could readily be added to the classification method taught by Gutin et al. with reasonable expectation of success because the both pertain to developing classifiers to predict response of cancer patients to treatment and p53 is known prognostic indicator for breast and other cancers (Friedrichs et al., 1993). The invention is therefore prima facie obvious. (see MPEP 2143(I)(C)).
14. Claims 5-10 are rejected under 35 U.S.C. 103 as being unpatentable over Gutin et al. (US 2020/0283855 A1) and Smyth et al. (US 2020/0239968 A1) as applied to claims 1-3 and 11 above, and further in view of Roder et al. (US 2017/0039345 A1). The italicized text corresponds to the instant claim limitations.
The limitations of claims 1-3 have been taught by Gutin et al. and Smyth et al. above.
Regarding claims 5 and 8, Gutin et al. further discloses that subjects can be divided into one, two or more classes to discriminate in to high and low risk, high, intermediate and low risk, or more risk groups, and that survival time may be applied to distinguish clinically relevant subgroups (para. 0011, 0039-0040, 0075, wherein the mathematical algorithm is further trained to subdivide the first group of high responders into a third and fourth group characterized by survival time and to subdivide the second group of low responders into a fifth and sixth group characterized by survival time).
Gutin et al. is silent to the specific binary decision tree model of first dividing samples into high and low responders and then subdividing each group based on survival time. However, these limitations were known in the art at the time of the effective filing date of the invention, as taught by Roder et al.
Roder et al. teaches training a multi-stage classifier for predicting response to therapy including a first stage classifier for stratifying patients into either an early or late group; a second stage classifier for further stratifying the early group of the first stage classifier into early and late groups; and a third stage classifier for further stratifying the late group of the first stage classifier into early and late groups. Roder et al. further teaches that each early and late group can correspond to shorter and longer overall survival, respectively (para. 0002, 0044 0014, wherein the mathematical algorithm is further trained to subdivide the first group of high responders into a third and fourth group characterized by survival time and to subdivide the second group of low responders into a fifth and sixth group characterized by survival time).
An invention would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention if some motivation in the prior art would have led that person to combine the prior art teachings to arrive at the claimed invention. Roder et al. teaches that immunohistochemistry staining of tumor biopsies does not work well for predicting response to treatment due to lack of standardization and that combining the RNA biomarker- based model development taught by Gutin et al. and Smyth et al. with the partitioning approach taught by Roder et al. would enable making predictions based on molecular profiling data to provide better standardization in diagnostics (Roder et al., para. 0008 – 0009). Therefore, one of ordinary skill in the art would have been motivated to utilize the sample partitioning method taught by Roder et al. for the methods to predict response to cancer treatment taught by Gutin et al. and Smyth et al., in order to improve standardization in prediction of response to treatment for cancer patients. Furthermore, one of ordinary skill in the art would predict that the multi-stage classification approach taught by Roder et al. could be readily combined with the system of Gutin et al. and Smyth et al. with a reasonable expectation of success because they both pertain to analysis of molecular data to predict response of cancer patients to treatment. The invention is therefore prima facie obvious.
Regarding claims 6 and 9, Gutin et al. teaches defining a set of RNA biomarkers from tumor samples for each model for predicting response to therapy. The RNA biomarkers are used to generate expression scores that can classify samples into one of a plurality of categories (which can be based on outcome/survival) (para. 0011, 0016, 0025, 0036, 0040 and 0079, wherein the mathematical algorithm is further trained to define a second subset of RNA-based biomarkers corresponding to dividing the third and fourth group and to define a third subset of RNA-based biomarkers corresponding to dividing the fifth and sixth group).
Regarding claim 7 and 10, Gutin et al. is silent to the limitation wherein the second and third subset of RNA-based biomarkers is different from the first subset. However, these limitations were known in the art at the time of the effective filing date of the invention, as taught by Roder et al., 2017.
[AltContent: textbox (Figure 1. Two stage classifier of Roder at al.)]Roder et al. describes creation of a multitude of different classifiers, each using different feature subsets related to different protein subsets to stratify patients with melanoma/nivolumab in response to treatment. For example, they train a first level classifier to split patients into early (poor performing) and late (good performing) using features associated with acute response function. A sample which tests Late (or good performing) on the first level classifier is then classified by the second level classifier, which is trained using a different set of features (associated with wound healing protein function (but not associated with acute response) to stratify patients into better (late) or worse (early)
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time to progression (Fig. 1 of this office action). They then defined a final classifier as a hierarchical combination of classifiers 1 and 2. (para. 0456, 0499-0503, appendix D, Fig 46 (shown in Fig. 1 of this office action).
An invention would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention if some motivation in the prior art would have led that person to combine the prior art teachings to arrive at the claimed invention. Roder et al. teaches that immunohistochemistry staining of tumor biopsies does not work well for predicting response to treatment due to lack of standardization and that combining the teachings of Gutin et al. and Smyth et al. to make predictions based on molecular profiling data would provide better standardization (Roder et al., para. 0008 – 0009). Therefore, one of ordinary skill in the art would have been motivated to utilize the hierarchical modeling approach taught by Roder et al. with the methods to predict response to cancer treatment taught by Gutin et al. and Smyth et al., in order to improve standardization in prediction of response to treatment for cancer patients. Furthermore, one of ordinary skill in the art would predict that the multi-stage classification approach taught by Roder et al. could be readily added to the system of Gutin et al. with a reasonable expectation of success because they both pertain to analysis of molecular data to predict response of cancer patients to treatment. The invention is therefore prima facie obvious.
15. Claims 12-14 are rejected under 35 U.S.C. 103 as being unpatentable over Gutin et al. (US 2020/0283855 A1) as applied to claim 1 above, and further in view of Cohan (Proc. of the 12th Intl. Conference on Natural Language Processing, pages 118–123, 2015).
The limitations of claim 1 have been taught by Gutin et al. above.
With respect to claim 12, Gutin et al. teach using a ‘discriminant function’ for classification, including for example, support vector machines (SVM), k-nearest neighbors (KNN), naïve Bayes models or linear regression models (para. 0040 and 0059, wherein the mathematical algorithm is based on a naïve Bayesian classifier that is a generalized naïve Bayesian classifier defined by applying a geometric mean to a likelihood product).
Gutin et al. is silent to using a generalized naïve Bayesian classifier defined by applying a geometric mean to a likelihood product in claim 12. However, these limitations were known in the art at the time of the effective filing date of the invention, as taught by Cohan.
With respect to claim 12, Cohan discloses a Perplexed Bayes Classifier that applies a geometric mean to a likelihood product. The classifier makes decisions that are identical to those of a Naive Bayes classifier without assuming that the features used are class-conditionally independent, by combining the class-conditional feature probabilities into posterior probabilities using their geometric mean unlike the Naive Bayes classifier that takes their product. Equation 7 describing the algorithm (shown below in Figure 2 of this office action) is effectively the same as the equation shown in the instant application in paragraph 0061 (p. 5, section 6, para. 1, Equation 7, wherein the mathematical algorithm is based on a naïve Bayesian classifier that is a generalized naïve Bayesian classifier defined by applying a geometric mean to a likelihood product).
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Figure 2. Equation 7 from Cohan
An invention would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention if some motivation in the prior art would have led that person to combine the prior art teachings to arrive at the claimed invention. Cohan taught that Perplexed bayes classifier (i.e. combining class conditional feature probabilities using the geometric mean) improves the classifier’s posterior probability estimates without affecting its performance and can produce better calibrated posterior probabilities than a Naive Bayes classifier for datasets with higher feature counts. (Abstract, para. 2, Conclusion p. 122). Therefore, one of ordinary skill in the art would have been motivated to use the algorithm taught by Cohan in the process to predict response to treatment for cancer patients taught by Gutin et al. in order to improve probability estimates because RNA-based datasets used by Gutin et al. typically have high feature counts. Furthermore, one of ordinary skill in the art would predict that the algorithm taught by Cohan could readily be added to the process of Gutin et al. with reasonable expectation of success because the algorithm of Cohan is a simple improvement of the Naïve Bayes classifier, which is already used in the method of Gutin et al. as discussed above.
With respect to claims 13-14, Gutin et al., teaches that RNA biomarker data is generated using an array or matrix to assay several to at least hundreds of thousands of features. Gutin et al., further teaches classifier development steps for selecting (ranking) features and reducing the number of the features in model development to minimize overfitting. Gutin et al., also teaches using a training set and cross-validation such as leave-one-out and 10-fold cross validation (para. 0053, 0091 and 0094, claim 13: wherein the naïve Bayesian classifier is trained to rank individual RNA-based biomarkers from an initial set of available RNA-based biomarkers that includes at least 500 individual genes; claim 14: wherein some of the individual RNA biomarkers are cross-validated).
16. Claims 15 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Gutin et al. (US 2020/0283855 A1) and Cohan (Proc. of the 12th Intl. Conference on Natural Language Processing, pages 118–123, 2015), as applied to claims 1 and 12-14 above, and further in view of Rosenwald et al. (N Engl. J Med. 2002 346(25):1937-47) as evidenced by Staudt et al. (biomarker database Immunol Rev. 2006 210:67-85). The italicized text corresponds to the instant claim limitations.
Claims 1 and 12-14 are taught by Gutin et al. and Cohan as discussed above.
With respect to claims 15-16, Gutin et al. is silent to the presence of 50-70 ranked RNA-based biomarkers after cross validation and is also silent to the specific RNA biomarkers for dividing the training set into groups based on survival time in response to treatment. However, these limitations were known in the art at the time of the effective filing date of the invention, as taught by Rosenwald et al.
Regarding claim 15, Rosenwald et al. teaches a method of training and validating a classifier for response of cancer patients to treatment whereby ranking/selecting RNA features for classification using the preliminary set (training set) is done using a cox proportional hazard model combined with hierarchical clustering. Rosenwald et al., further teaches that using the cox model, 670 genes with a good or bad outcome are identified from the training set (P<0.01). Rosenwald et al., further teaches that there is redundancy in the gene expression readout and that genes similar signatures (and similar functions) as identified by hierarchical clustering can be either pruned or combined into a representative feature by averaging their expression. This method was used to reduce feature number down to 16 genes, which were used for generating a final model (page 1942 para. 2-5, Table 2, wherein after cross-validation, the number of ranked RNA-based biomarkers is between 50 and 70 for each pairwise comparison).
Regarding claim 16, Rosenwald et al. teaches using a training set (preliminary group) to identify microarray gene expression features (RNA features) to identify those that are predictive of survival time. Rosenwald et al. identified 892 genes with significant correlation with either a good or bad outcome (overall survival after chemotherapy) using the Cox proportional-hazards model. Therefore, the 892 identified genes are capable of separating patients into two groups based on response to treatment. These 892 genes have been curated into a database curated by Staudt et al. Of the genes identified by Rosenwald et al., 5 are in the first gene set of claim 16 (first group versus second group) including HDAC3, MYBL1, SDHB, MYC, ITGA5; 9 are in the second gene set of claim 16 (third group versus fourth group) including CTNNB1, FH, HMGA1, COL3A1, SIN3A, RPL22, MELK, MMP9, YY1AP1; and 4 are in the third gene set of claim 16 (fifth group versus sixth group) including CTNNB1, TOP1, HRAS, and CDK7 (Rosenwald et al. page 1942 para. 2-5, as evidenced by Staudt et al. spreadsheet signatureDB annotation (version updated Jan 21, 2026), wherein sets of RNA-based biomarkers for separating patients that are high responders from low responders are selected from specific lists of gene names).
An invention would have been prima facie obvious to one of ordinary skill in the art at the effective filing date of the invention if some motivation in the prior art would have led that person to combine the prior art teaching to arrive at the claimed invention. Rosenwald et al. taught that the identification of RNA features for predicting response to treatment using a training set applied to patients with diffuse large-B-cell lymphoma could improve treatment plans, since these patients have molecularly distinct diseases that may require individualized and molecularly targeted therapies (p. 1946, para. 6). Therefore, one of ordinary skill in the art would have been motivated to combine the RNA biomarkers for large-B-cell lymphoma taught by Rosenwald et al. to the methods for predicting chemotherapy outcome taught by Gutin et al. in order to improve prediction of response to therapy and develop individualized treatment plans for patients with heterogeneous cancers (breast and large-B-cell lymphoma). Furthermore, one of ordinary skill in the art would predict that the RNA biomarkers for predicting response to treatment of large-B-cell lymphoma taught by Rosenwald et al. could be combined with the classifier development methods developed by Gutin for breast cancer could be applied with a reasonable expectation of success because they both develop multivariate RNA-based classifiers for heterogeneous cancers with heterogeneous responses to treatment. The invention is therefore prima facie obvious.
17. Claims 18 is rejected under 35 U.S.C. 103 as being unpatentable over Smyth et al. (US 2020/0239968 A1), as applied to claim 17 above, and further in view of Roder et al. (US 2017/0039345 A1). The italicized text corresponds to the instant claim limitations.
The limitations of Claim 17 have been taught by Smyth et al. above in section 11.
Regarding claim 18, Smyth et al. teaches training a classifier using a training set of gastroesophageal cancer patients separated into two groups by survival time following tumor resection (i.e. the treatment) that are classified as having greater or shorter survival time than is typical for gastroesophageal cancer patients in the general population. Smyth et al., further teaches that classes can be “good prognosis” versus “bad prognosis” corresponding to overall survival that is longer or shorter than average for that stage of cancer and cancer type (Smyth et al., para. 0062-0068, 0080 and 0151-0152, dividing the first group of high responders into a third group and fourth group wherein the groups are characterized by survival time that is longer or shorter than average survival time of the training set).
Smyth et al. is silent to the specific binary decision tree structure of first dividing samples into high and low responders and then subdividing the first group based on survival time. However, this limitation was known in the art at the time of the effective filing date of the invention, as taught by Roder et al.
Roder et al. teaches training a multi-stage classifier for predicting response to therapy including a first stage classifier for stratifying patients into either an early or late group (which have shorter or longer overall survival), and a second stage classifier for further stratifying the early group of the first stage classifier into early and late groups (See Fig. 1 of this office action) (Roder et al., para. 0002, 0044 0014, further dividing the group of high responders into two subgroups using a threshold of average survival time for the group of high responders).
An invention would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention if some motivation in the prior art would have led that person to combine the prior art teachings to arrive at the claimed invention. Roder et al., teaches that immunohistochemistry staining of tumor biopsies does not work well for predicting response to treatment due to lack of standardization and that combining the teachings of Smyth et al. to make predictions based on molecular profiling data would provide better standardization (Roder et al., para. 0008 – 0009). Therefore, one of ordinary skill in the art would have been motivated to utilize the sample partitioning method taught by Roder et al. for the methods to predict response to gastroesophageal cancer treatment taught by Smyth et al, in order to improve standardization in prediction of response to treatment for cancer patients. Furthermore, one of ordinary skill in the art would predict that the multi-stage classification approach taught by Roder et al. could be readily added to the system of Smyth et al. with a reasonable expectation of success because they both pertain to analysis of molecular data to predict response of cancer patients to treatment. The invention is therefore prima facie obvious.
18. Claim 19 is rejected under 35 U.S.C. 103 as being unpatentable over Rosenwald et al. (N Engl. J Med. 2002 346(25):1937-47) as evidenced by (Staudt et al., biomarker database Immunol Rev. 2006 210:67-85) in view of Gutin et al. (US 2020/0283855 A1). The italicized text corresponds to the instant claim limitations.
Regarding Claim 19, Rosenwald et al. teaches predicting survival after chemotherapy for patients with diffuse large-B-cell lymphoma using gene expression features from patient biopsy samples. Rosenwald teaches generating microarray gene expression data and analyzing the data using a Cox proportional-hazards model to identify individual genes whose expression correlated with the outcome. Data from 670 of 7399 microarray features were significantly associated with a good or a bad outcome in the training group (P < 0.01). These 670 features are curated in a database of predictive biomarkers by Staudt et al. The 670- features include 10 RNA biomarkers selected from the group claimed in claim 19 of the instant application (HMGA1, SIN3A, BCL7A, ITGA5, XRCC6, RALGDS, HDAC3, MYBL1, SDHB, and MYC) (Staudt et al., spreadsheet signatureDB annotation, version updated January 21, 2026). Rosenwald teaches that molecular diagnosis by analyzing gene expression data (RNA biomarkers) can predict individualized and molecularly targeted therapies for diffuse large b-cell lymphoma (Rosenwald, results para. 6, discussion para. 7, methods A method for treating a subject with diffuse large B-cell lymphoma, comprising a step of using a Bayesian classifier to define the subject as a high responder or a low responder to chemotherapy using one or more of individual RNA-based biomarkers selected from a group consisting of PPP2R1B, GOLGA5, LINGO2, HMGA1, SIN3A, ARIDIA, BCL7A, CDK5RAP2, MAGEDI, CREB3L1, AMERI, DLL1, GSTT1, GPR34, DNM2, CCNB1IP1, MUTYH, RET, CDH1, POFUTI, XRCC6, KIT, RALGDS, SS18, CD22, BRCA2, HDAC3, LHX4, FAM19A2, PRG2, PRCC, TBL1XR1, HIF1A, EDIL3, ROS1,DKK4, CDC25A, WNT7B, MYBL1, MLLT10, SLCO1B3, TACC2, CANT1, NCAM1, FGF3, FGF19, PPP3R2, CRADD, ETV6, SPP1, SDHB, FGF2, SUZ12, MB21D2, MYC, BAX, CEP57, ITGA5, ABCC3, and HECW1).
Rosenwald et al. is silent to using a Bayesian classifier. However, this limitation was known in the art at the time of the effective filing date of the invention as taught by Gutin et al.
As to claim 19, Gutin et al. teaches using a (naïve) Bayes model or other model for classifying patients as high or low risk of metastasis of cancer or predicting response to chemotherapy (Gutin et al., para. 0011 and 0040, using a Bayesian classifier).
An invention would have been prima facie obvious to one of ordinary skill in the art at the effective filing date of the invention if some motivation in the prior art would have led that person to combine the prior art teachings to arrive at the claimed invention. Gutin et al. taught that developing models based on molecular biomarkers in combination with clinical factors allow better prediction of sensitivity to chemotherapy than clinical factors alone and allow a more tailored treatment strategy (para. 0010). Therefore, one of ordinary skill in the art would have been motivated to combine the method of treating diffuse large B-cell lymphoma taught by Rosenwald et al. with the Bayesian classifier taught by Gutin et al., in order to improve prediction of response to treatment with a molecular classifier instead of a clinical classifier. Furthermore, one of ordinary skill in the art would predict that modeling algorithm taught by Gutin et al., could readily be added to the method of treating large B-cell lymphoma taught by Rosenwald et al. with a reasonable expectation of success because they both pertain to prediction of response to therapy for patients with heterologous cancers. The invention is therefore prima facie obvious.
Conclusion
In conclusion, no claims are allowed.
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/J.J.S./ Examiner, Art Unit 1685
/OLIVIA M. WISE/ Supervisory Patent Examiner, Art Unit 1685