DETAILED ACTION
Applicant’s response, filed03/09/2026, has been fully considered. Rejections and/or objections not reiterated from previous Office Actions are hereby withdrawn. The following rejections and/or objections are either reiterated or newly applied. They constitute the complete set presently being applied to the instant application.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
Request for Response After Final
Applicant's request for reconsideration of the finality of the rejection of the last Office action is persuasive and, therefore, the finality of that action is withdrawn.
Claim Status
Claims 1-11 are pending.
Claims 1-11 are rejected.
Priority
The instant Application claims the benefit of priority as a Continuation of US non-provisional application 16/475,752, filed 07/03/2019 PAT 11150238, which is a 371 of PCT/US2018/012564, filed on 01/05/2018, which claims benefit of US provisional application 62/442,557, filed on 01/05/2017. The claim to the benefit of priority is acknowledged. Accordingly, each of claims 1-11 are afforded the effective filing date of the 01/05/2017.
Drawings
The Drawings submitted 10/06/2021 are accepted.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
For the following rejections, underlined text indicates newly recited portions necessitated by claim amendment.
Claims 1-11 are rejected under 35 U.S.C. 101 because the claimed invention is directed to one or more judicial exceptions without significantly more. Any newly recited portions are necessitated by claim amendment.
MPEP 2106 organizes judicial exception analysis into Steps 1, 2A (Prongs One and Two) and 2B as follows below. MPEP 2106 and the following USPTO website provide further explanation and case law citations: uspto.gov/patent/laws-and-regulations/examination-policy/examination-guidance-and-training-materials.
Framework with which to Evaluate Subject Matter Eligibility:
Step 1: Are the claims directed to a process, machine, manufacture, or composition of matter;
Step 2A, Prong One: Do the claims recite a judicially recognized exception, i.e. a law of nature, a natural phenomenon, or an abstract idea;
Step 2A, Prong Two: If the claims recite a judicial exception under Prong One, then is the judicial exception integrated into a practical application (Prong Two); and
Step 2B: If the claims do not integrate the judicial exception, do the claims provide an inventive concept.
Framework Analysis as Pertains to the Instant Claims:
Step 1
With respect to Step 1: yes, the claims are directed to methods, i.e., a process, machine, or manufacture within the above 101 categories [Step 1: YES; See MPEP § 2106.03].
Step 2A, Prong One
With respect to Step 2A, Prong One, the claims recite judicial exceptions in the form of abstract ideas. The MPEP at 2106.04(a)(2) further explains that abstract ideas are defined as:
mathematical concepts (mathematical formulas or equations, mathematical relationships and mathematical calculations);
certain methods of organizing human activity (fundamental economic practices or principles, managing personal behavior or relationships or interactions between people); and/or
mental processes (procedures for observing, evaluating, analyzing/ judging and organizing information).
With respect to the instant claims, under the Step 2A, Prong One evaluation, the claims are found to recite abstract ideas that fall into the mathematical concepts (in particular mathematical relationships and formulas are as follows:
Independent claims 1, 4, and 7:
the integrated intensity values are processed in a first stage classification algorithm using a training set comprising class-labeled spectra produced from blood-based samples from the same type of cancer patients to identify the patient as being in a class of patients determined to be a poor prognosis subgroup corresponding to a class label for the blood-based sample
responsive to identification of the patient as being in the class of patients determined to be a poor prognosis subgroup, data from the mass spectrometer test with a programmed computer implementing a second stage classification algorithm, wherein the programmed computer compares the integrated intensity values with feature values of a reference set of class-labeled mass spectral data obtained from blood-based samples from a multitude of patients having the same type of cancer treated with an immunotherapy drug and revising the class label for the blood-based sample
Dependent claim 10:
changing the class label for the sample responsive to operating on the mass spectral data with the programmed computer implementing the second stage classification algorithm from a first class label to a second class label.
Dependent claim 11 recite further steps that limit the judicial exceptions in independent claim 1 , as such, also are directed to those abstract ideas. For example, claim 11 further limits the training set of claim 1.
The abstract ideas recited in the claims are evaluated under the Broadest Reasonable Interpretation (BRI) and determined to each cover performance by mathematical operation because the method only requires a user to manually identify, operating, and determine, . Without further detail as to the methodology involved in “to identify(ing) the patient”, “operating on the mass spectral data with a programmed computer implementing a second stage classification algorithm“, and “determining that the patient having a class label of Late “, and “changing the class label for the sample responsive to operating on the mass spectral data with the programmed computer implementing the second stage classification algorithm from a first class label to a second class label”; under the BRI, one may simply, for example, use pen and paper to identify of cancer patients with durable benefit from immunotherapy. Those steps and those recited in the dependent claims require mathematical techniques disclosed in the specification “classifiers” as programmed computers with stored reference data and classification algorithms [0008], mini-classifiers execute a supervised learning classification algorithm, such ask-nearest neighbors (kNN) [0086], and alternative supervised classification algorithms could be used, such as linear discriminants, decision trees, probabilistic classification methods, margin-based classifiers like support vector machines [0087] that require mathematical calculations with mathematical functions.
Therefore, claims 1, 4, and 7 and those claims dependent therefrom recite an abstract idea and a law of nature/natural phenomenon [Step 2A, Prong 1: YES; See MPEP § 2106.04].
Step 2A, Prong Two
Because the claims do recite judicial exceptions, direction under Step 2A, Prong Two, provides that the claims must be examined further to determine whether they integrate the judicial exceptions into a practical application (MPEP 2106.04(d)). A claim can be said to integrate a judicial exception into a practical application when it applies, relies on, or uses the judicial exception in a manner that imposes a meaningful limit on the judicial exception. This is performed by analyzing the additional elements of the claim to determine if the judicial exceptions are integrated into a practical application (MPEP 2106.04(d).I.; MPEP 2106.05(a-h)). If the claim contains no additional elements beyond the judicial exceptions, the claim is said to fail to integrate the judicial exceptions into a practical application (MPEP 2106.04(d).III).
Additional elements, Step 2A, Prong Two
With respect to the instant recitations, the claims recite the following additional elements:
Independent claims 1, 4, and 7:
conducting a mass spectrometer test on a blood-based sample of the cancer patient to obtain a mass spectrum comprising integrated intensity values of selected features in the mass spectrum at one or more m/z ranges from a multitude of mass-spectral features listed in Table 25, wherein the mass spectrometer test is conducted using a mass spectrometer.
administering, responsive to the class label being Late or the equivalent, the immunotherapy drug to the cancer patient.
Dependent claims 2-3, 5-6, and 8-9 recite steps that further limit the recited additional elements in the claims. For example, claims 2-3, 5-6, and 8-9 further limit the immunotherapy drug of claims 1, 4, and 7.
The claims also include non-abstract computing elements. For example, independent claim 1 includes a programmed computer.
Considerations under Step 2A, Prong Two
With respect to Step 2A, Prong Two, the additional elements of the claims do not integrate the judicial exceptions into a practical application for the following reasons. Those steps directed to data gathering, such as “conduct a mass spectrometer test”, perform functions of collecting the data needed to carry out the judicial exceptions. Data gathering and outputting do not impose any meaningful limitation on the judicial exceptions, or on how the judicial exceptions are performed. Data gathering and outputting steps are not sufficient to integrate judicial exceptions into a practical application (MPEP 2106.05(g)).
Further steps directed to additional non-abstract elements of “a programmed computer” do not describe any specific computational steps by which the “computer parts” perform or carry out the judicial exceptions, nor do they provide any details of how specific structures of the computer, such as the computer-readable recording media, are used to implement these functions. The claims state nothing more than a generic computer which performs the functions that constitute the judicial exceptions. Hence, these are mere instructions to apply the judicial exceptions using a computer, and therefore the claim does not integrate that judicial exceptions into a practical application. The courts have weighed in and consistently maintained that when, for example, a memory, display, processor, machine, etc.… are recited so generically (i.e., no details are provided) that they represent no more than mere instructions to apply the judicial exception on a computer, and these limitations may be viewed as nothing more than generally linking the use of the judicial exception to the technological environment of a computer (MPEP 2106.05(f)).
With respect to claims 1, 4, and 7, the additional elements of administering the immunotherapy drug does not integrate the judicial exceptions into a practical application for the following reasons. The step directed to administering the immunotherapy drug to the cancer patient do not impose any meaningful limitations on the abstract idea, or on how the abstract idea is performed. Also, the limitation is contingent and thus the claims do not require the administration to occur in the BRI of the claim These steps are insignificant extra-solution activity to the judicial exception (MPEP 2106.05(g)(2))
Thus, none of the claims recite additional elements which would integrate a judicial exception into a practical application, and the claims are directed to one or more judicial exceptions [Step 2A, Prong 2: NO; See MPEP § 2106.04(d)].
Step 2B (MPEP 2106.05.A i-vi)
According to analysis so far, the additional elements described above do not provide significantly more than the judicial exception. A determination of whether additional elements provide significantly more also rests on whether the additional elements or a combination of elements represents other than what is well-understood, routine, and conventional. Conventionality is a question of fact and may be evidenced as: a citation to an express statement in the specification or to a statement made by an applicant during prosecution that demonstrates a well-understood, routine or conventional nature of the additional element(s); a citation to one or more of the court decisions as discussed in MPEP 2106(d)(II) as noting the well-understood, routine, conventional nature of the additional element(s); a citation to a publication that demonstrates the well-understood, routine, conventional nature of the additional element(s); and/or a statement that the examiner is taking official notice with respect to the well-understood, routine, conventional nature of the additional element(s).
With respect to claim 1 and those dependent therefrom, the limitations of “conducting a mass spectrometer test on a blood-based sample of the cancer patient” is well-understood, routine, and conventional in the art. Karpova et al. (Karpova, M. A., et al. "Cancer-specific MALDI-TOF profiles of blood serum and plasma: biological meaning and perspectives "Journal of proteomics 73.3 (2010), newly cited) discloses cancer-specific MALDI-TOF profiles of blood serum and plasma [title]. Karpova further discloses the present review describes different methodologies of direct MALDI-TOF profiling of blood serum/plasma and analyzes the results of their application to cancer biomarker discovery [p. 1, col. 2, par. 2].
With respect to claim 1 and those claims dependent therefrom, the computer-related elements or the general purpose computer do not rise to the level of significantly more than the judicial exception. The claims state nothing more than a generic computer which performs the functions that constitute the judicial exceptions. Hence, these are mere instructions to apply the judicial exceptions using a computer, which the courts have found to not provide significantly more when recited in a claim with a judicial exception (see MPEP 2106.06(A)). The specification as published also notes that computer processors and systems, as example, are commercially available or widely used at [0168]. The additional elements are set forth at such a high level of generality that they can be met by a general purpose computer. Therefore, the computer components constitute no more than a general link to a technological environment, which is insufficient to constitute an inventive concept that would render the claims significantly more than the judicial exceptions (see MPEP 2106.05(b)I-III).
With respect to claims 1, 4, and 7 and those claims dependent therefrom, the administering step does not rise to the level of significantly more than the judicial exception. As stated above the administering steps are contingent and thus are not required in combination.
Taken alone, the additional elements do not amount to significantly more than the above-identified judicial exception(s). Even when viewed as a combination, the additional elements fail to transform the exception into a patent-eligible application of that exception. Thus, the claims as a whole do not amount to significantly more than the exception itself [Step 2B: NO; See MPEP § 2106.05].
Therefore, the instant claims are not drawn to eligible subject matter as they are directed to one or more judicial exceptions without significantly more. For additional guidance, applicant is directed generally to the MPEP § 2106.
Response to Applicant Arguments
Applicant submits no person of ordinary skill, or any person, can practically perform this process in the human mind or with pen and paper [p. 39, par. 4]. It is respectfully found persuasive. The amended claims require conducting a mass spectrometer test on a blood-based sample, processing the resulting high-dimensional mass spectral data through a first-stage classification algorithm trained on class-labeled spectra from cancer patients to identify a poor-prognosis subgroup, and then processing that data with a programmed computer implementing a second-stage classification algorithm that compares the integrated intensity values against a reference set of class labeled mass spectral data from a multitude of cancer patients treated with immunotherapy to generate a class label can nit be performed in the mind.
Applicant notes that the classification steps as claimed are tied to specific physical inputs (mass spectral data from a blood-based sample) and specific physical outputs (a class label directing patient treatment), and do not constitute a mathematical concept divorced from application. [p. 40, par. 2].
It is respectfully found not persuasive. The instant claims recite mathematical concepts of “a first stage classification algorithm” and “a second stage classification algorithm”. MPEP 2106.04(a)(2) notes that a mathematical concept need not be expressed in mathematical symbols, because “words used in a claim operating on data to solve a problem can serve the same purpose as a formula”. A review of the published specification provides support for detecting a class label in a patient using mathematical techniques as the only embodiments at [00086-00087]. Therefore, the claimed calculations are not merely only based on or involve a mathematical concept, and, as such, recite a judicial exception at Step 2A, Prong One.
Applicant submits the amended independent claims include a treatment step requiring that, responsive to the class label being late or the equivalent, an immunotherapy drug is administered to the cancer patient. [p. 40, par. 5].
It is respectively found not persuasive. The instant claims recite an administering step, however, the it is still written as a contingent claim without an active step of administering immunotherapy to the patient identified as a Late or equivalent class label. In order to qualify as a "treatment" or "prophylaxis" limitation for purposes of this consideration, the claim limitation in question must affirmatively recite an action that effects a particular treatment or prophylaxis for a disease or medical condition MPEP 2106.04(d)(2), which in this case the treatment is contingent and therefore not required.
Applicant submits the amended claim does not recite a generic computer performing generic functions; it recites a programmed computer implementing a specific classifier architecture applied to specific mass-spectral features to stratify cancer patients for immunotherapy, followed by administration of treatment based on the result. That combination is not found in the prior art, is not well-understood, routine, or conventional, and no evidence that this combination is well-understood, routine, or conventional in the field is presented [p. 41, par. 4].
It is respectfully found not persuasive. The specification as published discloses the laboratory test center or system includes a mass spectrometer(e.g., MALDI time of flight) and a general purpose computer having a CPU implementing a classifier (or hierarchical arrangement of classifiers) coded as machine-readable instructions implanting a final classifier [p.44, par. 1].
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
A. Claims 1-2, 4-5, and 7-8 are rejected under 35 U.S.C. 103 as being unpatentable over Roder et al. (PG Pub US 2015/0102216 A1, published 04/16/2015, cited on IDS 10/06/2021).
Claim 1 is directed to a method of detecting a class label in a lung cancer patient, a renal cell carcinoma patient, or a melanoma patient comprising:
Roder discloses another example generates a classifier for classification of mass spectra from blood-based samples into one of three classes to guide treatment of non-small cell lung cancer (NSCLC) patients [0018].
(a) conducting a mass spectrometer test on a blood-based sample of the cancer patient to obtain a mass spectrum comprising integrated intensity values of selected features in the mass spectrum at one or more m/z ranges from a multitude of mass-spectral features listed in a table below:
Feature definitions..
Roder discloses the classification generation system may also include a mass spectrometer for obtaining the data for use in classification [0021]. Roder further discloses a laboratory test center is described which includes a measurement system for conducting a physical testing process on a test sample and obtain data for classification ( e.g., mass spectrometer, or gene expression assay platform), and a programmed computer implementing a final classifier as described herein, wherein the programmed computer is operative to classify the data for classification obtained from the test sample [0022]. Roder also discloses feature definitions and feature tables [0097]. Roder further discloses to define possible candidates for peaks that can differentiate between clinical groups we located peaks in the pre-processed spectra and defined a range in m/Z around each peak's maximum [0098]. Roder also discloses these ranges in m/Z define features that are used for all further analysis [0098]. Roder further discloses selecting 655 features as possible candidates for differentiating between groups and calculated the integrated intensity of each of these features for each spectrum to obtain a feature value for each feature for each spectrum where the tabular listing, rows are spectra, columns are features, of these integrated intensities (feature values) is the feature table [0098]. Roder does not discloses the exact features of the instant claims, but uses the same process that results in features and definitions representative of the input data.
wherein the mass spectrometer test is conducted using a mass spectrometer and wherein the integrated intensity values are processed in a first stage classification algorithm using a training set comprising class-labeled spectra produced from blood-based samples from the same type of cancer patients to identify the patient as being in a class of patients determined to be a poor prognosis subgroup corresponding to a class label for the blood-based sample, and
Roder discloses a method for classifier generation includes a step of obtaining data for classification of a multitude of samples, the data for each of the samples consisting of a multitude of physical measurement feature values and a class label [abstract]. Roder further discloses predicting patient benefit from anti-cancer drugs in the context of non-small cell lung cancer (NSCLC) [p. 37, col. 2, par. 4], which reads on blood-based samples from the same type of cancer patients. Roder also discloses obtaining a multitude of samples, e.g., blood-based samples (serum or plasma) from cancer patients [p. 51, col. 1, par. 5]. Roder further discloses the blood sample being processed through mass spectrometry to acquire a mass spectrum and obtaining integrated intensity values over predefined m/z ranges [p. 30, fig. 26]. Roder discloses selecting 400 features according to expression differences between early and late groups [p. 31, fig. 27]. Roder also discloses applying classifier using training set of class-labeled specta from the integrated intensity values to produce a class label [p. 30, fig. 26] which reads on a first stage classifier. Roder further discloses a distribution of values with some good prognosis patients having poor outcomes and some poor prognosis patients having good outcomes [p. 47, col. 1, par. 6]. Roder also discloses produce the same class label, the module reports the class label as indicated at 2666 (i.e., "early”, “late', '+', '-'. “good”, “poor' or the equivalent) [p. 51, col. 2, par. 1]. Roder is silent on the exact ranges of table 25, but it would be obvious to modify the table to the specific datasets you are classifying.
(b) responsive to identification of the cancer patient as being in the class of patients determined to be a poor prognosis subgroup, processing data from the mass spectrometer test with a programmed computer implementing a second stage classification algorithm, wherein the programmed computer the classifier compares the integrated intensity values with feature values of a reference set of class-labeled mass spectral data obtained from blood-based samples from a multitude of patients having the same type of cancer treated with an immunotherapy drug and revising the class label for the blood-based sample; and
Roder discloses another example of the generation of a CMC/D classifier and use thereof to guide treatment of NSCLC patients by the generation of the classifier largely follows the method described in Fig. 26 and Fig. 11, however, the processing of a test sample to make a prediction using the CMC/D classifier in this example makes use of reference spectra [p. 51, col. 2, par. 4], which reads on a second classifer. Roder further discloses the new classifier described in this document explains how to identify if a NSCLC patient is a member of this subset of patients that are likely to obtain more benefit from an EGFR-I such as erlotinib than chemotherapy [p. 52, col. 1, par. 5], which reads on patients having the same type of cancer treated with an immunotherapy drug. The classification algorithm detected class labels of good and poor. Roder also discloses restricting the classifier generation process to samples that yielded an original “VeriStrat Good classification, i.e. to design a classifier that splits the VeriStrat Good samples into patients with better or worse outcomes on EGFR-Is [p. 52, col. 2, par. 6], which reads on detecting a class label. Although Roder does not specifically disclose the use of both types of classifiers together it would have been obvious that one could have therefore combined the elements as claimed by the known methods of Roder example 1 and Roder example 3, and that in combination, each element merely would have performed the same function as it did separately for the predictable result of splitting the VeriStrat Good or Poor samples into subgroups.
(c) administering, responsive to the class label being Late or an equivalent, the immunotherapy drug to the cancer patient.
Roder discloses a further embodiment of the method is a method of treating a pancreatic cancer patient comprising the step of administering GI-4000+gemcitabine to the patient, the patient being selected for such treatment by means of a classifier operating on a mass spectrum of a blood based sample of the patient, wherein the classifier is generated by the CMC/D method described herein [p. 49, col. 2, par. 5].
Claim 2 is directed to wherein the immunotherapy drug comprises an antibody drug blocking ligand activation of the PD-i checkpoint protein, anti-CTLA4 drugs, high dose interleukin-2, and combination therapies.
Roder discloses results and methods used in the development of a predictive test for patient benefit for GI-4000+gemcitabine as an example of the generation and use of the classifier development methodology [p. 37, col. 2, par. 3], which reads on a combination therapy.
Claim 4 is directed to a method of guiding treatment of a lung cancer patient, a renal cell carcinoma patient, or a melanoma patient comprising the steps of:
Roder discloses another example generates a classifier for classification of mass spectra from blood-based samples into one of three classes to guide treatment of non-small cell lung cancer (NSCLC) patients [0018].
(a) conducting a mass spectrometer test on a blood-based sample of the cancer patient to obtain a mass spectrum comprising integrated intensity values of selected features in the mass spectrum at one or more m/z ranges from a multitude of mass-spectral features listed in a table below:
Feature definitions..
Roder discloses the classification generation system may also include a mass spectrometer for obtaining the data for use in classification [0021]. Roder further discloses a laboratory test center is described which includes a measurement system for conducting a physical testing process on a test sample and obtain data for classification ( e.g., mass spectrometer, or gene expression assay platform), and a programmed computer implementing a final classifier as described herein, wherein the programmed computer is operative to classify the data for classification obtained from the test sample [0022]. Roder also discloses feature definitions and feature tables [0097]. Roder further discloses to define possible candidates for peaks that can differentiate between clinical groups we located peaks in the pre-processed spectra and defined a range in m/Z around each peak's maximum [0098]. Roder also discloses these ranges in m/Z define features that are used for all further analysis [0098]. Roder further discloses selecting 655 features as possible candidates for differentiating between groups and calculated the integrated intensity of each of these features for each spectrum to obtain a feature value for each feature for each spectrum where the tabular listing, rows are spectra, columns are features, of these integrated intensities (feature values) is the feature table [0098]. Roder does not discloses the exact features of the instant claims, but uses the same process that results in features and definitions representative of the input data.
wherein the mass spectrometer test is conducted using a mass spectrometer and wherein the integrated intensity values are processed in a first stage classification algorithm using a training set comprising class-labeled spectra produced from blood-based samples from the same type of cancer patients to identify the patient as being in a class of patients determined to be a poor prognosis subgroup corresponding to a class label for the blood-based sample, and
Roder discloses a method for classifier generation includes a step of obtaining data for classification of a multitude of samples, the data for each of the samples consisting of a multitude of physical measurement feature values and a class label [abstract]. Roder further discloses predicting patient benefit from anti-cancer drugs in the context of non-small cell lung cancer (NSCLC) [p. 37, col. 2, par. 4], which reads on blood-based samples from the same type of cancer patients. Roder also discloses obtaining a multitude of samples, e.g., blood-based samples (serum or plasma) from cancer patients [p. 51, col. 1, par. 5]. Roder further discloses the blood sample being processed through mass spectrometry to acquire a mass spectrum and obtaining integrated intensity values over predefined m/z ranges [p. 30, fig. 26]. Roder discloses selecting 400 features according to expression differences between early and late groups [p. 31, fig. 27]. Roder also discloses applying classifier using training set of class-labeled specta from the integrated intensity values to produce a class label [p. 30, fig. 26] which reads on a first stage classifier. Roder further discloses a distribution of values with some good prognosis patients having poor outcomes and some poor prognosis patients having good outcomes [p. 47, col. 1, par. 6]. Roder also discloses produce the same class label, the module reports the class label as indicated at 2666 (i.e., "early”, “late', '+', '-'. “good”, “poor' or the equivalent) [p. 51, col. 2, par. 1]. Roder is silent on the exact ranges of table 25, but it would be obvious to modify the table to the specific datasets you are classifying.
(b) responsive to identification of the cancer patient as being in the class of patients determined to be a poor prognosis subgroup, processing data from the mass spectrometer test with a programmed computer implementing a second stage classification algorithm, wherein the programmed computer the classifier compares the integrated intensity values with feature values of a reference set of class-labeled mass spectral data obtained from blood-based samples from a multitude of patients having the same type of cancer treated with an immunotherapy drug and revising the class label for the blood-based sample; and
Roder discloses another example of the generation of a CMC/D classifier and use thereof to guide treatment of NSCLC patients by the generation of the classifier largely follows the method described in Fig. 26 and Fig. 11, however, the processing of a test sample to make a prediction using the CMC/D classifier in this example makes use of reference spectra [p. 51, col. 2, par. 4], which reads on a second classifer. Roder further discloses the new classifier described in this document explains how to identify if a NSCLC patient is a member of this subset of patients that are likely to obtain more benefit from an EGFR-I such as erlotinib than chemotherapy [p. 52, col. 1, par. 5], which reads on patients having the same type of cancer treated with an immunotherapy drug. The classification algorithm detected class labels of good and poor. Roder also discloses restricting the classifier generation process to samples that yielded an original “VeriStrat Good classification, i.e. to design a classifier that splits the VeriStrat Good samples into patients with better or worse outcomes on EGFR-Is [p. 52, col. 2, par. 6], which reads on detecting a class label. Although Roder does not specifically disclose the use of both types of classifiers together it would have been obvious that one could have therefore combined the elements as claimed by the known methods of Roder example 1 and Roder example 3, and that in combination, each element merely would have performed the same function as it did separately for the predictable result of splitting the VeriStrat Good or Poor samples into subgroups.
(c) administering, responsive to the class label being Late or an equivalent, the immunotherapy drug to the cancer patient.
Roder discloses a further embodiment of the method is a method of treating a pancreatic cancer patient comprising the step of administering GI-4000+gemcitabine to the patient, the patient being selected for such treatment by means of a classifier operating on a mass spectrum of a blood based sample of the patient, wherein the classifier is generated by the CMC/D method described herein [p. 49, col. 2, par. 5].
Claim 5 is directed to wherein the immunotherapy drug comprises an antibody drug blocking ligand activation of the PD-i checkpoint protein, anti-CTLA4 drugs, high dose interleukin-2, and combination therapies.
Roder discloses results and methods used in the development of a predictive test for patient benefit for GI-4000+gemcitabine as an example of the generation and use of the classifier development methodology [p. 37, col. 2, par. 3], which reads on a combination therapy.
Claim 7 is directed to a method indicating the relative likelihood of success of an immunotherapy treatment for a lung cancer patient, a renal cell carcinoma patient, or a melanoma patient comprising the steps of: (a) conducting a mass spectrometer test on a blood-based sample of the cancer patient to obtain a mass spectrum comprising integrated intensity values of selected features in the mass spectrum at one or more m/z ranges from a multitude of mass-spectral features listed in a table below:
Feature definitions..
Roder discloses the classification generation system may also include a mass spectrometer for obtaining the data for use in classification [0021]. Roder further discloses a laboratory test center is described which includes a measurement system for conducting a physical testing process on a test sample and obtain data for classification ( e.g., mass spectrometer, or gene expression assay platform), and a programmed computer implementing a final classifier as described herein, wherein the programmed computer is operative to classify the data for classification obtained from the test sample [0022]. Roder also discloses feature definitions and feature tables [0097]. Roder further discloses to define possible candidates for peaks that can differentiate between clinical groups we located peaks in the pre-processed spectra and defined a range in m/Z around each peak's maximum [0098]. Roder also discloses these ranges in m/Z define features that are used for all further analysis [0098]. Roder further discloses selecting 655 features as possible candidates for differentiating between groups and calculated the integrated intensity of each of these features for each spectrum to obtain a feature value for each feature for each spectrum where the tabular listing, rows are spectra, columns are features, of these integrated intensities (feature values) is the feature table [0098]. Roder does not discloses the exact features of the instant claims, but uses the same process that results in features and definitions representative of the input data.
wherein the mass spectrometer test is conducted using a mass spectrometer and wherein the integrated intensity values are processed in a first stage classification algorithm using a training set comprising class-labeled spectra produced from blood-based samples from the same type of cancer patients to identify the patient as being in a class of patients determined to be a poor prognosis subgroup corresponding to a class label for the blood-based sample, and
Roder discloses a method for classifier generation includes a step of obtaining data for classification of a multitude of samples, the data for each of the samples consisting of a multitude of physical measurement feature values and a class label [abstract]. Roder further discloses predicting patient benefit from anti-cancer drugs in the context of non-small cell lung cancer (NSCLC) [p. 37, col. 2, par. 4], which reads on blood-based samples from the same type of cancer patients. Roder also discloses obtaining a multitude of samples, e.g., blood-based samples (serum or plasma) from cancer patients [p. 51, col. 1, par. 5]. Roder further discloses the blood sample being processed through mass spectrometry to acquire a mass spectrum and obtaining integrated intensity values over predefined m/z ranges [p. 30, fig. 26]. Roder discloses selecting 400 features according to expression differences between early and late groups [p. 31, fig. 27]. Roder also discloses applying classifier using training set of class-labeled specta from the integrated intensity values to produce a class label [p. 30, fig. 26] which reads on a first stage classifier. Roder further discloses a distribution of values with some good prognosis patients having poor outcomes and some poor prognosis patients having good outcomes [p. 47, col. 1, par. 6]. Roder also discloses produce the same class label, the module reports the class label as indicated at 2666 (i.e., "early”, “late', '+', '-'. “good”, “poor' or the equivalent) [p. 51, col. 2, par. 1]. Roder is silent on the exact ranges of table 25, but it would be obvious to modify the table to the specific datasets you are classifying.
(b) responsive to identification of the cancer patient as being in the class of patients determined to be a poor prognosis subgroup, processing data from the mass spectrometer test with a programmed computer implementing a second stage classification algorithm, wherein the programmed computer the classifier compares the integrated intensity values with feature values of a reference set of class-labeled mass spectral data obtained from blood-based samples from a multitude of patients having the same type of cancer treated with an immunotherapy drug and revising the class label for the blood-based sample; and
Roder discloses another example of the generation of a CMC/D classifier and use thereof to guide treatment of NSCLC patients by the generation of the classifier largely follows the method described in Fig. 26 and Fig. 11, however, the processing of a test sample to make a prediction using the CMC/D classifier in this example makes use of reference spectra [p. 51, col. 2, par. 4], which reads on a second classifer. Roder further discloses the new classifier described in this document explains how to identify if a NSCLC patient is a member of this subset of patients that are likely to obtain more benefit from an EGFR-I such as erlotinib than chemotherapy [p. 52, col. 1, par. 5], which reads on patients having the same type of cancer treated with an immunotherapy drug. The classification algorithm detected class labels of good and poor. Roder also discloses restricting the classifier generation process to samples that yielded an original “VeriStrat Good classification, i.e. to design a classifier that splits the VeriStrat Good samples into patients with better or worse outcomes on EGFR-Is [p. 52, col. 2, par. 6], which reads on detecting a class label. Although Roder does not specifically disclose the use of both types of classifiers together it would have been obvious that one could have therefore combined the elements as claimed by the known methods of Roder example 1 and Roder example 3, and that in combination, each element merely would have performed the same function as it did separately for the predictable result of splitting the VeriStrat Good or Poor samples into subgroups.
(c) administering, responsive to the class label being Late or an equivalent, the immunotherapy drug to the cancer patient.
Roder discloses a further embodiment of the method is a method of treating a pancreatic cancer patient comprising the step of administering GI-4000+gemcitabine to the patient, the patient being selected for such treatment by means of a classifier operating on a mass spectrum of a blood based sample of the patient, wherein the classifier is generated by the CMC/D method described herein [p. 49, col. 2, par. 5].
Claim 8 is directed to wherein the immunotherapy drug comprises an antibody drug blocking ligand activation of the PD-i checkpoint protein, anti-CTLA4 drugs, high dose interleukin-2, and combination therapies.
Roder discloses results and methods used in the development of a predictive test for patient benefit for GI-4000+gemcitabine as an example of the generation and use of the classifier development methodology [p. 37, col. 2, par. 3], which reads on a combination therapy.
B. Claims 3, 6, and 9 are rejected under 35 U.S.C. 103 as being unpatentable over Roder as applied above to claims 1, 4, and 7 in view of McDermott et al. (McDermott, David, et al. "Durable benefit and the potential for long-term survival with immunotherapy in advanced melanoma." Cancer treatment reviews 40.9 (2014), cited on IDS dated, 10/06/2021).
Claims 3, 6, and 9 are directed to wherein the immunotherapy drug comprises a combination of two immunotherapy drugs.
Roder discloses the use of the immunotherapy drug GI-4000 alone and in combination with gemcitabine [p. 37, col. 2, par. 3] but is silent on a combination of two immunotherapy drugs.
However, McDermott discloses durable benefit and the potential for long-term survival with immunotherapy in advanced melanoma [title]. McDermott further discloses on-going immunotherapy trials in advanced melanoma including single-agent immunotherapy and dual immunotherapy such as Ipilimumab and nivolumab together [p.1058, table 1], which reads on a combination of two immunotherapy drugs.
Regarding claims 3, 6, and 9, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine, in the course of routine experimentation and with a reasonable expectation of success, the reference application with McDermott as they both disclose durable benefit with immunotherapy in advanced melanoma. The motivation would have been to combine the features of the models of the Roder and McDermott to improve the understanding of these novel treatments may improve survival outcomes in melanoma, increase the number of patients who experience this overall survival benefit, and inform the future use of these agents in the treatment of other cancer types as disclosed by McDermott [abstract].
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
A. Claims 1-2, 4-5, and 7-8 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1 and 8 of US Patent 10007766, (reference application) in view of Roder et al (US10007766B2, cited on IDs dated 10/06/2021). Although the claims at issue are not identical, they are not patentably distinct from each other because they both claim detecting a class label in a melanoma patient.
Instant Application
U.S. Patent 10007766
Claims
Limitations
Claims
Limitations
1
A method of detecting a class label in a lung cancer patient, a renal cell carcinoma patient, or a melanoma patient comprising:(a) conducting a mass spectrometer test on a blood-based sample of the cancer patient to obtain a mass spectrum; obtaining integrated intensity values of selected features in the mass spectrum at one or more m/z ranges from a multitude of mass-spectral features listed in Table 25; using the integrated intensity values in a first stage classification algorithm using a training set comprising class-labeled spectra produced from blood-based samples from the same type of cancer patients to identify the patient as being in a class of patients determined to be a poor prognosis subgroup,
1
A method of guiding detecting a class label in a melanoma patient, comprising: a) conducting mass spectrometry on a blood-based sample of the patient and obtaining mass spectrometry data; (b) obtaining integrated intensity values in the mass spectrometry data of a multitude of mass-spectral features, wherein the mass-spectral features include a multitude of features listed in Appendix A, Appendix B or Appendix C; and (c) operating on the mass spectral data with a programmed computer implementing a classifier; wherein in the operating step the classifier compares the integrated intensity values with feature values of a reference set of class-labeled mass spectral data obtained from blood-based samples obtained from a multitude of other melanoma patients treated with an antibody drug blocking ligand activation of programmed cell death 1 (PD-1) with a classification algorithm and detects a class label for the sample;
2
The method of claim 1, wherein the immunotherapy drug comprises an antibody drug blocking ligand activation of the PD-i checkpoint protein, anti-CTLA4 drugs, high dose interleukin-2, and combination therapies.
1
other melanoma patients treated with an antibody drug blocking ligand activation of programmed cell death 1 (PD-1)
3
The method of claim 1, wherein the immunotherapy drug comprises a combination of two immunotherapy drugs.
4
A method of guiding treatment of lung cancer patient, a renal cell carcinoma patient, or a melanoma patient comprising the steps of: (a) conducting a mass spectrometer test on a blood-based sample of the cancer patient to obtain a mass spectrum; obtaining integrated intensity values of selected features in the mass spectrum at one or more m/z ranges as shown in Table 25; using the integrated intensity values in a first stage classification algorithm using a training set comprising class-labeled spectra produced from blood-based samples from the same type of cancer patients to identify the patient as being in a class of patients determined to be a poor prognosis subgroup, and (b) identifying the patient as being in the class of patients determined to be a poor prognosis subgroup, obtaining integrated intensity values of selected features in the mass spectrum at one or more m/z ranges as shown in the table and using the integrated intensity values in a second stage classification algorithm using a training set comprising class-labeled spectra produced from blood-based samples from the same type of cancer patients treated with an immunotherapy drug to obtain a class label of Late or the equivalent which guides treatment of the patient to an immunotherapy drug.
1 and 8
A method of guiding detecting a class label in a melanoma patient, comprising: a) conducting mass spectrometry on a blood-based sample of the patient and obtaining mass spectrometry data; (b) obtaining integrated intensity values in the mass spectrometry data of a multitude of mass-spectral features, wherein the mass-spectral features include a multitude of features listed in Appendix A, Appendix B or Appendix C; and (c) operating on the mass spectral data with a programmed computer implementing a classifier; wherein in the operating step the classifier compares the integrated intensity values with feature values of a reference set of class-labeled mass spectral data obtained from blood-based samples obtained from a multitude of other melanoma patients treated with an antibody drug blocking ligand activation of programmed cell death 1 (PD-1) with a classification algorithm and detects a class label for the sample;
The method of claim 2, wherein the reference set comprise a set of class-labeled mass spectral data of a development set of samples having either the class label Early or the equivalent or Late or the equivalent, wherein the samples having the class label Early are comprised of samples having relatively shorter overall survival on treatment with nivolumab as compared to samples having the class label Late.
5
The method of claim 4, wherein the immunotherapy drug comprises an antibody drug blocking ligand activation of the PD-i checkpoint protein, anti-CTLA4 drugs, high dose interleukin-2, and combination therapies.
1
other melanoma patients treated with an antibody drug blocking ligand activation of programmed cell death 1 (PD-1)
6
The method of claim 4, wherein the immunotherapy drug comprises a combination of two immunotherapy drugs.
7
A method indicating the relative likelihood of success of an immunotherapy treatment for a lung cancer patient, a renal cell carcinoma patient, or a melanoma patient comprising the steps of: (a) conducting a mass spectrometer test on a blood-based sample of the cancer patient to obtain a mass spectrum; obtaining integrated intensity values of selected features in the mass spectrum at one or more m/z ranges as shown in Table 25 using the integrated intensity values in a first stage classification algorithm using a training set comprising class-labeled spectra produced from blood-based samples from the same type of cancer patients to identify the patient as being in a class of patients determined to be a poor prognosis subgroup, (b) identifying the patient as being in the class of patients determined to be a poor prognosis subgroup, using the integrated intensity values as shown in the table in a second stage classification algorithm using a training set comprising class-labeled spectra produced from blood-based samples from the same type of cancer patients treated with an immunotherapy drug to a class label of Late or the equivalent which identifies the patient as likely to have durable benefit from an immunotherapy drug; and (c) determining that the patient having a class label of Late or the equivalent is likely to respond to an immunotherapy drug.
1 and 8
A method of guiding detecting a class label in a melanoma patient, comprising: a) conducting mass spectrometry on a blood-based sample of the patient and obtaining mass spectrometry data; (b) obtaining integrated intensity values in the mass spectrometry data of a multitude of mass-spectral features, wherein the mass-spectral features include a multitude of features listed in Appendix A, Appendix B or Appendix C; and (c) operating on the mass spectral data with a programmed computer implementing a classifier; wherein in the operating step the classifier compares the integrated intensity values with feature values of a reference set of class-labeled mass spectral data obtained from blood-based samples obtained from a multitude of other melanoma patients treated with an antibody drug blocking ligand activation of programmed cell death 1 (PD-1) with a classification algorithm and detects a class label for the sample;
The method of claim 2, wherein the reference set comprise a set of class-labeled mass spectral data of a development set of samples having either the class label Early or the equivalent or Late or the equivalent, wherein the samples having the class label Early are comprised of samples having relatively shorter overall survival on treatment with nivolumab as compared to samples having the class label Late.
8
The method of claim 7, wherein the immunotherapy drug comprises an antibody drug blocking ligand activation of the PD-i checkpoint protein, anti-CTLA4 drugs, high dose interleukin-2, and combination therapies.
1
other melanoma patients treated with an antibody drug blocking ligand activation of programmed cell death 1 (PD-1)
9
The method of claim 7, wherein the immunotherapy drug comprises a combination of two immunotherapy drugs.
Claims 1, 4, and 7 discloses identifying the cancer patient as being in the class of patients determined to be a poor prognosis subgroup, and operating on the mass spectral data with a programmed computer implementing a second stage classification algorithm; wherein in the operating step the classifier compares the integrated intensity values with feature values of a reference set of class-labeled mass spectral data obtained from blood-based samples from a multitude of patients having the same type of cancer treated with an immunotherapy drug and detecting a class label for the sample.
The reference application is silent on a second stage classifier.
However, Roder discloses predictive test for melanoma patient benefit from antibody drug blocking ligand activation of the t-cell programmed cell death 1 (pd-1) checkpoint protein and classifier development methods [title]. Roder further discloses defining a final classifier from a combination of the first and second classifiers, where the second classifier is used to further stratify members of a classification group assigned by the first classifier [p. 153, col. 2, par. 6-14], which reads on a second classifier.
Claims 3, 6, and 9 are rejected as being unpatentable over US Patent 10007766, (reference application) in view of Roder et al, in further view of McDermott et al (McDermott, David, et al. "Durable benefit and the potential for long-term survival with immunotherapy in advanced melanoma." Cancer treatment reviews 40.9 (2014), cited on IDS dated, 10/06/2021).
Claims 3, 6, and 9 disclose wherein the immunotherapy drug comprises a combination of two immunotherapy drugs.
The reference application discloses patients treated with an antibody drug blocking ligand activation of programmed cell death 1 (PD-1), but is silent on a combination of two immunotherapy drugs.
However, McDermott discloses durable benefit and the potential for long-term survival with immunotherapy in advanced melanoma [title]. McDermott further discloses on-going immunotherapy trials in advanced melanoma including single-agent immunotherapy and dual immunotherapy such as Ipilimumab and nivolumab together [p.1058, table 1], which reads on a combination of two immunotherapy drugs.
Regarding claims 1, 4, and 7, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine, in the course of routine experimentation and with a reasonable expectation of success, the reference application with Roder as they both disclose detecting a class label in a melanoma patient. The motivation would have been to combine the features of the models of the reference application and Roder for a relatively greater benefit from the combination therapy label means significantly greater (longer) overall survival as compared to monotherapy. as disclosed by Roder [p.107, col. 2, par. 2].
Regarding claims 3, 6, and 9, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine, in the course of routine experimentation and with a reasonable expectation of success, the reference application with McDermott as they both disclose durable benefit with immunotherapy in advanced melanoma. The motivation would have been to combine the features of the models of the reference application and McDermott to improve the understanding of these novel treatments may improve survival outcomes in melanoma, increase the number of patients who experience this overall survival benefit, and inform the future use of these agents in the treatment of other cancer types as disclosed by McDermott [abstract].
B. Claims 1-2, 4-5, and 7-8 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1 and 4 of US Patent 11150238, (reference application) Although the claims at issue are not identical, they are not patentably distinct from each other because they both claim detecting a class label in a melanoma patient.
Instant Application
U.S. Patent 11150238
Claims
Limitations
Claims
Limitations
1
A method of detecting a class label in a lung cancer patient, a renal cell carcinoma patient, or a melanoma patient comprising:(a) conducting a mass spectrometer test on a blood-based sample of the cancer patient to obtain a mass spectrum; obtaining integrated intensity values of selected features in the mass spectrum at one or more m/z ranges from a multitude of mass-spectral features listed in Table 25; using the integrated intensity values in a first stage classification algorithm using a training set comprising class-labeled spectra produced from blood-based samples from the same type of cancer patients to identify the patient as being in a class of patients determined to be a poor prognosis subgroup, and (b) identifying the cancer patient as being in the class of patients determined to be a poor prognosis subgroup, and operating on the mass spectral data with a programmed computer implementing a second stage classification algorithm; wherein in the operating step the classifier compares the integrated intensity values with feature values of a reference set of class-labeled mass spectral data obtained from blood-based samples from a multitude of patients having the same type of cancer treated with an immunotherapy drug and detecting a class label for the sample.
1
A method for treatment a class label in a lung cancer patient, a renal cell carcinoma patient, or a melanoma patient comprising the steps of: (a) conducting a mass spectrometer test on a blood-based sample of the cancer patient to obtain a mass spectrum; performing one or more pre-processing steps on the mass spectrum; obtaining integrated intensity values of selected features in the mass spectrum at one or more m/z ranges; using the integrated intensity values in a first stage classification algorithm using a training set comprising class-labeled spectra produced from blood-based samples from the same type of cancer patients to identify the patient as being in a class of patients determined to be a poor prognosis subgroup, and further (b) identifying the patient as being in the class of patients determined to be a poor prognosis subgroup, using the integrated intensity values in a second stage classification algorithm using a training set comprising class-labeled spectra produced from blood-based samples from the same type of cancer patients treated with an immunotherapy drug to obtain a class label of Late or the equivalent which identifies the patient as likely to have durable benefit from an immunotherapy drug
2
The method of claim 1, wherein the immunotherapy drug comprises an antibody drug blocking ligand activation of the PD-i checkpoint protein, anti-CTLA4 drugs, high dose interleukin-2, and combination therapies.
4
The method of claim 1, wherein the immunotherapy drug comprises an antibody drug blocking ligand activation of the PD-1 checkpoint protein, anti-CTLA4 drugs, high dose interleukin-2, and combination therapies.
3
The method of claim 1, wherein the immunotherapy drug comprises a combination of two immunotherapy drugs.
4
A method of guiding treatment of lung cancer patient, a renal cell carcinoma patient, or a melanoma patient comprising the steps of: (a) conducting a mass spectrometer test on a blood-based sample of the cancer patient to obtain a mass spectrum; obtaining integrated intensity values of selected features in the mass spectrum at one or more m/z ranges as shown in Table 25; using the integrated intensity values in a first stage classification algorithm using a training set comprising class-labeled spectra produced from blood-based samples from the same type of cancer patients to identify the patient as being in a class of patients determined to be a poor prognosis subgroup, and (b) identifying the patient as being in the class of patients determined to be a poor prognosis subgroup, obtaining integrated intensity values of selected features in the mass spectrum at one or more m/z ranges as shown in the table and using the integrated intensity values in a second stage classification algorithm using a training set comprising class-labeled spectra produced from blood-based samples from the same type of cancer patients treated with an immunotherapy drug to obtain a class label of Late or the equivalent which guides treatment of the patient to an immunotherapy drug.
1
A method for treatment a class label in a lung cancer patient, a renal cell carcinoma patient, or a melanoma patient comprising the steps of: (a) conducting a mass spectrometer test on a blood-based sample of the cancer patient to obtain a mass spectrum; performing one or more pre-processing steps on the mass spectrum; obtaining integrated intensity values of selected features in the mass spectrum at one or more m/z ranges; using the integrated intensity values in a first stage classification algorithm using a training set comprising class-labeled spectra produced from blood-based samples from the same type of cancer patients to identify the patient as being in a class of patients determined to be a poor prognosis subgroup, and further (b) identifying the patient as being in the class of patients determined to be a poor prognosis subgroup, using the integrated intensity values in a second stage classification algorithm using a training set comprising class-labeled spectra produced from blood-based samples from the same type of cancer patients treated with an immunotherapy drug to obtain a class label of Late or the equivalent which identifies the patient as likely to have durable benefit from an immunotherapy drug
5
The method of claim 4, wherein the immunotherapy drug comprises an antibody drug blocking ligand activation of the PD-i checkpoint protein, anti-CTLA4 drugs, high dose interleukin-2, and combination therapies.
4
other melanoma patients treated with an antibody drug blocking ligand activation of programmed cell death 1 (PD-1)
6
The method of claim 4, wherein the immunotherapy drug comprises a combination of two immunotherapy drugs.
7
A method indicating the relative likelihood of success of an immunotherapy treatment for a lung cancer patient, a renal cell carcinoma patient, or a melanoma patient comprising the steps of: (a) conducting a mass spectrometer test on a blood-based sample of the cancer patient to obtain a mass spectrum; obtaining integrated intensity values of selected features in the mass spectrum at one or more m/z ranges as shown in Table 25 using the integrated intensity values in a first stage classification algorithm using a training set comprising class-labeled spectra produced from blood-based samples from the same type of cancer patients to identify the patient as being in a class of patients determined to be a poor prognosis subgroup, (b) identifying the patient as being in the class of patients determined to be a poor prognosis subgroup, using the integrated intensity values as shown in the table in a second stage classification algorithm using a training set comprising class-labeled spectra produced from blood-based samples from the same type of cancer patients treated with an immunotherapy drug to a class label of Late or the equivalent which identifies the patient as likely to have durable benefit from an immunotherapy drug; and (c) determining that the patient having a class label of Late or the equivalent is likely to respond to an immunotherapy drug.
1
A method for treatment a class label in a lung cancer patient, a renal cell carcinoma patient, or a melanoma patient comprising the steps of: (a) conducting a mass spectrometer test on a blood-based sample of the cancer patient to obtain a mass spectrum; performing one or more pre-processing steps on the mass spectrum; obtaining integrated intensity values of selected features in the mass spectrum at one or more m/z ranges; using the integrated intensity values in a first stage classification algorithm using a training set comprising class-labeled spectra produced from blood-based samples from the same type of cancer patients to identify the patient as being in a class of patients determined to be a poor prognosis subgroup, and further (b) identifying the patient as being in the class of patients determined to be a poor prognosis subgroup, using the integrated intensity values in a second stage classification algorithm using a training set comprising class-labeled spectra produced from blood-based samples from the same type of cancer patients treated with an immunotherapy drug to obtain a class label of Late or the equivalent which identifies the patient as likely to have durable benefit from an immunotherapy drug
8
The method of claim 7, wherein the immunotherapy drug comprises an antibody drug blocking ligand activation of the PD-i checkpoint protein, anti-CTLA4 drugs, high dose interleukin-2, and combination therapies.
4
other melanoma patients treated with an antibody drug blocking ligand activation of programmed cell death 1 (PD-1)
9
The method of claim 7, wherein the immunotherapy drug comprises a combination of two immunotherapy drugs.
Claims 3, 6, and 9 are rejected as being unpatentable over US Patent 11150238, (reference application) in view of McDermott et al (McDermott, David, et al. "Durable benefit and the potential for long-term survival with immunotherapy in advanced melanoma." Cancer treatment reviews 40.9 (2014), cited on IDS dated, 10/06/2021).
Claims 3, 6, and 9 disclose wherein the immunotherapy drug comprises a combination of two immunotherapy drugs.
The reference application discloses patients treated with an antibody drug blocking ligand activation of programmed cell death 1 (PD-1), but is silent on a combination of two immunotherapy drugs.
However, McDermott discloses durable benefit and the potential for long-term survival with immunotherapy in advanced melanoma [title]. McDermott further discloses on-going immunotherapy trials in advanced melanoma including single-agent immunotherapy and dual immunotherapy such as Ipilimumab and nivolumab together [p.1058, table 1], which reads on a combination of two immunotherapy drugs.
Regarding claims 3, 6, and 9, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine, in the course of routine experimentation and with a reasonable expectation of success, the reference application with McDermott as they both disclose durable benefit with immunotherapy in advanced melanoma. The motivation would have been to combine the features of the models of the reference application and McDermott to improve the understanding of these novel treatments may improve survival outcomes in melanoma, increase the number of patients who experience this overall survival benefit, and inform the future use of these agents in the treatment of other cancer types as disclosed by McDermott [abstract].
C. Claims 1-2, 4-5, and 7-8 are rejected on the ground of nonstatutory double patenting as being unpatentable over claim 1 of US Patent 11710539, (reference application). Although the claims at issue are not identical, they are not patentably distinct from each other because they both claim detecting a class label in a melanoma patient.
Instant Application
U.S. Patent 11710539
Claims
Limitations
Claims
Limitations
1
A method of detecting a class label in a lung cancer patient, a renal cell carcinoma patient, or a melanoma patient comprising:(a) conducting a mass spectrometer test on a blood-based sample of the cancer patient to obtain a mass spectrum; obtaining integrated intensity values of selected features in the mass spectrum at one or more m/z ranges from a multitude of mass-spectral features listed in Table 25; using the integrated intensity values in a first stage classification algorithm using a training set comprising class-labeled spectra produced from blood-based samples from the same type of cancer patients to identify the patient as being in a class of patients determined to be a poor prognosis subgroup, and (b) identifying the cancer patient as being in the class of patients determined to be a poor prognosis subgroup, and operating on the mass spectral data with a programmed computer implementing a second stage classification algorithm; wherein in the operating step the classifier compares the integrated intensity values with feature values of a reference set of class-labeled mass spectral data obtained from blood-based samples from a multitude of patients having the same type of cancer treated with an immunotherapy drug and detecting a class label for the sample.
1
A method for predicting whether a melanoma patient is likely to benefit from high dose IL2 therapy, comprising the steps of: a) performing, by a mass spectrometer, mass spectrometry on a blood-based sample of the patient and obtaining mass spectrometry data of the sample;b) performing, by a computer implementing a classifier, a classification of the mass spectrometry data obtained by the mass spectrometer, wherein the classifier is developed from a development set of samples from melanoma patients treated with the high dose IL2 therapy comprising: iteratively training, by the computer, Classifier 1 from the development set of samples and a set of mass spectral features identified as being associated with an acute response biological function to generate an Early class label, a Late class label, or the equivalent for each of a subset of samples, and iteratively training, by the computer, Classifier 2 from a subset of samples classified with the Late class label by Classifier 1 in the development set of samples to generate an Early class label, a Late class label or the equivalent, c) supplying, from the computer to a programmed computer trained to predict whether the melanoma patient is likely to benefit from treatment with high dose IL2 therapy, the mass spectrometry data of the sample obtained in step a) to a classifier using Classifier 1 and Classifier 2, wherein: the classifier represents a k-nearest neighbor (kNN) classification algorithm implemented by the programmed computer and is arranged as a hierarchical combination of (a) the Classifier 1 classifying the patient into either a first Late group or a first Early group or the equivalent, and (b) the Classifier 2 further classifying the first [[late]] Late group into a second Late group or a second Early group, wherein the patient classified into the first Late group or the second Late group is predicted to be likely to benefit from high dose IL2 therapy
2
The method of claim 1, wherein the immunotherapy drug comprises an antibody drug blocking ligand activation of the PD-i checkpoint protein, anti-CTLA4 drugs, high dose interleukin-2, and combination therapies.
1
whether the melanoma patient is likely to benefit from treatment with high dose IL2 therapy
3
The method of claim 1, wherein the immunotherapy drug comprises a combination of two immunotherapy drugs.
4
A method of guiding treatment of lung cancer patient, a renal cell carcinoma patient, or a melanoma patient comprising the steps of: (a) conducting a mass spectrometer test on a blood-based sample of the cancer patient to obtain a mass spectrum; obtaining integrated intensity values of selected features in the mass spectrum at one or more m/z ranges as shown in Table 25; using the integrated intensity values in a first stage classification algorithm using a training set comprising class-labeled spectra produced from blood-based samples from the same type of cancer patients to identify the patient as being in a class of patients determined to be a poor prognosis subgroup, and (b) identifying the patient as being in the class of patients determined to be a poor prognosis subgroup, obtaining integrated intensity values of selected features in the mass spectrum at one or more m/z ranges as shown in the table and using the integrated intensity values in a second stage classification algorithm using a training set comprising class-labeled spectra produced from blood-based samples from the same type of cancer patients treated with an immunotherapy drug to obtain a class label of Late or the equivalent which guides treatment of the patient to an immunotherapy drug.
1
A method for predicting whether a melanoma patient is likely to benefit from high dose IL2 therapy, comprising the steps of: a) performing, by a mass spectrometer, mass spectrometry on a blood-based sample of the patient and obtaining mass spectrometry data of the sample;b) performing, by a computer implementing a classifier, a classification of the mass spectrometry data obtained by the mass spectrometer, wherein the classifier is developed from a development set of samples from melanoma patients treated with the high dose IL2 therapy comprising: iteratively training, by the computer, Classifier 1 from the development set of samples and a set of mass spectral features identified as being associated with an acute response biological function to generate an Early class label, a Late class label, or the equivalent for each of a subset of samples, and iteratively training, by the computer, Classifier 2 from a subset of samples classified with the Late class label by Classifier 1 in the development set of samples to generate an Early class label, a Late class label or the equivalent, c) supplying, from the computer to a programmed computer trained to predict whether the melanoma patient is likely to benefit from treatment with high dose IL2 therapy, the mass spectrometry data of the sample obtained in step a) to a classifier using Classifier 1 and Classifier 2, wherein: the classifier represents a k-nearest neighbor (kNN) classification algorithm implemented by the programmed computer and is arranged as a hierarchical combination of (a) the Classifier 1 classifying the patient into either a first Late group or a first Early group or the equivalent, and (b) the Classifier 2 further classifying the first [[late]] Late group into a second Late group or a second Early group, wherein the patient classified into the first Late group or the second Late group is predicted to be likely to benefit from high dose IL2 therapy
5
The method of claim 4, wherein the immunotherapy drug comprises an antibody drug blocking ligand activation of the PD-i checkpoint protein, anti-CTLA4 drugs, high dose interleukin-2, and combination therapies.
1
whether the melanoma patient is likely to benefit from treatment with high dose IL2 therapy
6
The method of claim 4, wherein the immunotherapy drug comprises a combination of two immunotherapy drugs.
7
A method indicating the relative likelihood of success of an immunotherapy treatment for a lung cancer patient, a renal cell carcinoma patient, or a melanoma patient comprising the steps of: (a) conducting a mass spectrometer test on a blood-based sample of the cancer patient to obtain a mass spectrum; obtaining integrated intensity values of selected features in the mass spectrum at one or more m/z ranges as shown in Table 25 using the integrated intensity values in a first stage classification algorithm using a training set comprising class-labeled spectra produced from blood-based samples from the same type of cancer patients to identify the patient as being in a class of patients determined to be a poor prognosis subgroup, (b) identifying the patient as being in the class of patients determined to be a poor prognosis subgroup, using the integrated intensity values as shown in the table in a second stage classification algorithm using a training set comprising class-labeled spectra produced from blood-based samples from the same type of cancer patients treated with an immunotherapy drug to a class label of Late or the equivalent which identifies the patient as likely to have durable benefit from an immunotherapy drug; and (c) determining that the patient having a class label of Late or the equivalent is likely to respond to an immunotherapy drug.
1
A method for predicting whether a melanoma patient is likely to benefit from high dose IL2 therapy, comprising the steps of: a) performing, by a mass spectrometer, mass spectrometry on a blood-based sample of the patient and obtaining mass spectrometry data of the sample;b) performing, by a computer implementing a classifier, a classification of the mass spectrometry data obtained by the mass spectrometer, wherein the classifier is developed from a development set of samples from melanoma patients treated with the high dose IL2 therapy comprising: iteratively training, by the computer, Classifier 1 from the development set of samples and a set of mass spectral features identified as being associated with an acute response biological function to generate an Early class label, a Late class label, or the equivalent for each of a subset of samples, and iteratively training, by the computer, Classifier 2 from a subset of samples classified with the Late class label by Classifier 1 in the development set of samples to generate an Early class label, a Late class label or the equivalent, c) supplying, from the computer to a programmed computer trained to predict whether the melanoma patient is likely to benefit from treatment with high dose IL2 therapy, the mass spectrometry data of the sample obtained in step a) to a classifier using Classifier 1 and Classifier 2, wherein: the classifier represents a k-nearest neighbor (kNN) classification algorithm implemented by the programmed computer and is arranged as a hierarchical combination of (a) the Classifier 1 classifying the patient into either a first Late group or a first Early group or the equivalent, and (b) the Classifier 2 further classifying the first [[late]] Late group into a second Late group or a second Early group, wherein the patient classified into the first Late group or the second Late group is predicted to be likely to benefit from high dose IL2 therapy
8
The method of claim 7, wherein the immunotherapy drug comprises an antibody drug blocking ligand activation of the PD-i checkpoint protein, anti-CTLA4 drugs, high dose interleukin-2, and combination therapies.
1
whether the melanoma patient is likely to benefit from treatment with high dose IL2 therapy
9
The method of claim 7, wherein the immunotherapy drug comprises a combination of two immunotherapy drugs.
Claims 3, 6, and 9 are rejected as being unpatentable over US Patent 11150238, (reference application) in view of McDermott et al (McDermott, David, et al. "Durable benefit and the potential for long-term survival with immunotherapy in advanced melanoma." Cancer treatment reviews 40.9 (2014), cited on IDS dated, 10/06/2021).
Claims 3, 6, and 9 disclose wherein the immunotherapy drug comprises a combination of two immunotherapy drugs.
The reference application discloses patients treated with an antibody drug blocking ligand activation of programmed cell death 1 (PD-1), but is silent on a combination of two immunotherapy drugs.
However, McDermott discloses durable benefit and the potential for long-term survival with immunotherapy in advanced melanoma [title]. McDermott further discloses on-going immunotherapy trials in advanced melanoma including single-agent immunotherapy and dual immunotherapy such as Ipilimumab and nivolumab together [p.1058, table 1], which reads on a combination of two immunotherapy drugs.
Regarding claims 3, 6, and 9, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine, in the course of routine experimentation and with a reasonable expectation of success, the reference application with McDermott as they both disclose durable benefit with immunotherapy in advanced melanoma. The motivation would have been to combine the features of the models of the reference application and McDermott to improve the understanding of these novel treatments may improve survival outcomes in melanoma, increase the number of patients who experience this overall survival benefit, and inform the future use of these agents in the treatment of other cancer types as disclosed by McDermott [abstract].
Conclusion
No claims are allowed.
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/D.M.B./ Examiner, Art Unit 1685
/OLIVIA M. WISE/ Supervisory Patent Examiner, Art Unit 1685