DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Status
Claims 1-20 are pending and examined herein.
Claims 1-20 are rejected.
Claims 1 and 11 are objected to
Priority
Claims 1-20 are granted the claim to the benefit of priority to U.S. Provisional application 63/312823 filed 22 February 2022. Thus, the effective filling date of claims 1-20 is 22 February 2022.
Claim Objections
Claims 1 and 11 are objected to because of the following informalities:
Claims 1 and 11 recite “generating from the genome copy number profile a chromosome instability (CI) scores for each chromosome” but should read “generating from the genome copy number profile a chromosome instability (CI) score for each chromosome”.
Appropriate correction is required.
Drawings
The drawings received 22 February 2023 are accepted.
Claim Rejections - 35 USC § 112
112/b
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-10 and 12-20 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.
Claims 1 and 12 recite “determining that the risk score is greater than a threshold number and identified the patient as having a risk of deficiency in the DNA homologous recombination pathway” and claim 13 recites “determining that the number of LGBs is greater than a threshold number of LGBs and identified the patient as having a risk of deficiency in the DNA homologous recombination pathway” which renders the metes and bounds of the claim indefinite. The indefiniteness arises because it is unclear if “identified the patient as having a risk of deficiency…” is meant to be an active step of the methods or if “identified the patient as having a risk of deficiency…” is meant to be the result produced by the active step of determining a value is greater than a threshold number. Dependent claims 2-10 and 14-20 are rejected by virtue of their dependency on a rejected claim without alleviating the indefiniteness. For the sake of furthering examination, the limitations in claims 1 and 12 are interpreted as “and determining that the risk score is greater than a threshold number thereby identifying the patient as having a risk of deficiency in the DNA homologous recombination pathway” and claim 13 is interpreted as “and determining that the number of LGBs is greater than a threshold number of LGBs thereby identifying the patient as having a risk of deficiency in the DNA homologous recombination pathway” which provides the identification of the patient as having a risk of deficiency is a result of the active step of determining a value is greater than a threshold number.
Claims 6 and 17 recite “wherein two adjacent genomic segments have significantly different numbers of read counts” which renders the metes and bounds of the claim indefinite. The term “significantly” in claims 6 and 17 is a relative term which renders the claim indefinite. The term “significantly” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. The limitation of “different numbers of read counts” is rendered indefinite by the use of the term. For the sake of furthering examination this limitation will be interpreted as “wherein two adjacent genomic segments have a different number of read counts”.
Claim 13 recites “wherein the LGB is a breakpoint between two adjacent genomic segments of a LGB such as different copy number” which renders the metes and bounds of the claim indefinite. the phrase "such as" renders the claim indefinite because it is unclear whether the limitations following the phrase are part of the claimed invention. See MPEP § 2173.05(d). Dependent claims 14-20 are rejected by virtue of their dependency on a rejected claim without alleviating the indefiniteness. It is noted that claims 18 and 19 are included in this rejection due to the claim interpretation made in the rejection immediately below. For the sake of furthering examination, this limitation will be interpreted as “wherein the LGB is a breakpoint between two adjacent genomic segments that have different copy number”.
Claim 18 recites “A method for treating cancer in a patient, the method comprising… wherein the patient has been identified as having a risk of deficiency in the DNA homologous recombination pathway by the method of claim 12” which renders the metes and bounds of the claim indefinite. The indefiniteness arises because claim 12 is not a method, it is a non-transitory computer readable medium which performs a method. Dependent claim 19 is rejected by virtue of its dependency on a rejected claim without alleviating the indefiniteness. For the sake of furthering examination, this limitation in claim 18 will be interpreted as “wherein the patient has been identified as having a risk of deficiency in the DNA homologous recombination pathway by the method of claim 13”.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-8, 11-17, and 20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
(Step 1)
Claims 1-8 and 13-17 fall under the statutory category of a process and claims 11, 12, and 20 fall under the statutory category of a machine.
(Step 2A Prong 1)
Under the BRI, the instant claims recite judicial exceptions that are an abstract idea of the type that is in the grouping of a “mental process”, such as procedures for evaluating, analyzing or organizing information, and forming judgement or an opinion. The instant claims further recite judicial exceptions that are an abstract idea of the type that is in the grouping of a “mathematical concept”, such as mathematical relationships and mathematical equations.
Independent claim 1 recite mental processes of generating from the WGS data a genomic alteration profile including genome copy number profile, fusion profile, translocation profile, or inversion profile, generating from the genome copy number profile a chromosome instability (CI) score for each chromosome, thus generating a set of CI scores, generating from the set of CI scores a risk score of deficiency in the DNA homologous recombination pathway, determining that the risk score is greater than a threshold thereby identifying the patient as having a risk of deficiency in the DNA homologous recombination pathway.
Independent claim 11 recites mental processes of generating from the WGS data a genomic alteration profile including genome copy number profile, fusion profile, translocation profile, or inversion profile, generating from the genome copy number profile a chromosome instability score for each chromosome thus generating a set of CI scores, and generating from the set of CI scores a risk score of deficiency in the DNA homologous recombination pathway.
Independent claims 1 and 11 recite a mathematical concept of generating from the set of CI scores a risk score of deficiency in the DNA homologous recombination pathway.
Independent claims 13 and 20 recite mental processes of generating from the WGS data a genome copy number profile, generating from the genome copy number profile a number, per genome, of large-scale genomic breakpoints (LGBs), wherein the LGB is a breakpoint between two adjacent genomic segments having a different copy number, each such genomic segment being at least 10 megabases long, determining that the number of LGBs is greater than a threshold number of LGBs thereby identifying the patient as having a risk of deficiency in the DNA homologous recombination pathway.
Independent claims 13 and 20 recite a mathematical concept of generating from the genome copy number profile a number, per genome, of large-scale genomic breakpoints (LGBs), wherein the LGB is a breakpoint between two adjacent genomic segments having a different copy number, each such genomic segment being at least 10 megabases long.
Dependent claims 6 and 17 recite mental processes of aligning the WGS data to a reference genome, thereby generating a group of aligned WGS reads along the genome, counting the group of aligned WGS reads along the genome, and generating genomic segments along the genome, wherein two adjacent genomic segments have a different number of read counts. Dependent claims 6 and 17 recite a mathematical concept of counting the group of aligned WGS reads along the genome. Dependent claim 12 recites mental processes of determining that the risk score is greater than a threshold number thereby identifying the patient as having a risk of deficiency in the DNA homologous recombination pathway.
The claims recite mental processes of generating from the WGS data a genomic alteration profile including genome copy number profile, fusion profile, translocation profile, or inversion profile (which encompasses analyzing genomic alterations present in the WGS data through steps of aligning/comparing sequence reads to a reference genome, counting groups of aligned WGS reads along the genome, and partitioning the genome into segments based on criteria of two adjacent genomic segments have a different number of read counts), generating from the genome copy number profile a chromosome instability (CI) score for each chromosome, thus generating a set of CI scores (which encompasses assigning a chromosome instability score for each chromosome by analyzing the copy number profile generated from the WGS data), generating from the set of CI scores a risk score of deficiency in the DNA homologous recombination pathway (which encompasses calculating an average using the set of numerical CI scores), determining that the risk score (or number of LGBs) is greater than a threshold thereby identifying the patient as having a risk of deficiency in the DNA homologous recombination pathway (which encompasses comparing a numerical value to a threshold to determine that the numerical risk score is greater than a numerical threshold), and generating from the genome copy number profile a number, per genome, of large-scale genomic breakpoints (LGBs), wherein the LGB is a breakpoint between two adjacent genomic segments having a different copy number, each such genomic segment being at least 10 megabases long (which encompasses calculating a sum of copy number alteration breaks for all 22 chromosomes). The human mind is capable of analyzing WGS sequencing data, aligning/comparing sequence reads to a reference genome (which encompasses any process of performing alignment/comparing sequence reads to the reference including look up tables), counting groups of aligned reads, partitioning the genome into segments based on criteria, assigning scores to chromosomes based on analyzing copy number profiles, calculating an average using numerical values, comparing numerical values to a numerical threshold, and calculating a sum of copy number alteration breaks for all 22 chromosomes. Thus, the claims recite mental processes.
The claims recite mathematical concepts as mathematical calculations of generating from the set of CI scores a risk score of deficiency in the DNA homologous recombination pathway which encompasses a process of calculating an average of CI scores (see dependent claim 8 and instant disclosure [0074]), counting the group of aligned WGS reads along the genome (which encompasses a mathematical operation of summing the group of aligned WGS reads along the genome) and generating from the genome copy number profile a number, per genome, of large-scale genomic breakpoints (LGBs), wherein the LGB is a breakpoint between two adjacent genomic segments having a different copy number, each such genomic segment being at least 10 megabases long which encompass a mathematical operation of summing the number of copy number alteration breaks for all 22 chromosomes (see instant disclosure [0098]). The MPEP states that “there is no particular word or set of words that indicates a claim recites a mathematical calculation. That is, a claim does not have to recite the word "calculating" in order to be considered a mathematical calculation. For example, a step of "determining" a variable or number using mathematical methods or "performing" a mathematical operation may also be considered mathematical calculations when the broadest reasonable interpretation of the claim in light of the specification encompasses a mathematical calculation” (MPEP 2106.04(a)(2)(I)(C)). Thus, the claims recite mathematical concepts as mathematical calculations.
Dependent claims 2-5, 8, and 14-16 further limit the mental process/mathematical concept recited in the independent claim but do not change their nature as a mental process/mathematical concept. Thus, claims 1-8, 11-17 and 20 recite abstract ideas.
(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). Integration into a practical application is evaluated by identifying whether there are any additional elements recited in the claim and evaluating those additional elements to determine whether they integrate the exception into a practical application.
The additional element in claims 1 and 11 of obtaining a whole genome sequencing (WGS) data of a tumor sample from a patient and the additional element in claim 13 of obtaining a whole genome sequencing (WGS) data of a tumor sample from the patient, wherein the WGS data has a sequencing depth of 0.05 to 0.5 do not integrate the judicial exceptions into a practical application because these steps constitute as insignificant extra solution activity of data gathering (see MPEP 2106.05(g)). These additional elements constitute as data gathering because these additional elements only interact with the judicial exceptions by providing data as input to the abstract data analysis. Further, these additional elements encompass receiving whole genome sequencing data (which was previously sequenced) in a computer environment. Thus, the active step of these additional elements encompass receiving data in a computer environment. It is noted that the content of the data (i.e., the data being whole genome sequencing (WGS) data of a tumor sample from a patient and wherein the WGS data has a sequencing depth of 0.05 to 0.5) falls under the judicial exception itself and does not limit the active step of receiving data in a computer environment.
The additional element in claims 1 and 11 of using a deep learning model, the additional elements in claim 7 of wherein the deep learning model comprises one or more layers of long short-term memory or convolutional neural network and a fully connected network, and the additional element in claims 11 and 20 of a non-transitory computer readable medium having instructions stored thereon, wherein the instructions, when executed by a processor, cause the processor to perform judicial exceptions do not integrate the judicial exceptions into a practical application because these additional elements constitute as mere instructions to apply the judicial exceptions to a computer without an improvement to computer technology (see MPEP 2106.04(d)(1), MPEP 2106.05(f), and Example 47). These additional elements only interact with the judicial exceptions in a manner where a computer is invoked as a tool to perform the judicial exceptions. It is interpreted that the additional elements and the judicial exceptions do not interact in a manner which improves the capabilities of a computer itself but rather the computer is invoked as a tool to implement the abstract data analysis (see MPEP 2106.05(a)). Further the additional elements in claims 1 and 11 of using a deep learning model and the additional elements in claim 7 of wherein the deep learning model comprises one or more layers of long short-term memory or convolutional neural network and a fully connected network do not integrate the judicial exceptions into a practical application because they constitute as generally linking the judicial exceptions (i.e., generating from the genome copy number profile a chromosome instability scores for each chromosome) to the technological environment of deep learning models and deep learning models with one or more layers of long short-term memory or convolutional neural network and a fully connected network (see MPEP 2106.05(h)).
Thus, the additional elements do not integrate the judicial exceptions into a practical application and claims 1-8, 11-17 and 20 are directed to the abstract idea.
(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 additional element in claims 1 and 11 of obtaining a whole genome sequencing (WGS) data of a tumor sample from a patient and the additional element in claim 13 of obtaining a whole genome sequencing (WGS) data of a tumor sample from the patient, wherein the WGS data has a sequencing depth of 0.05 to 0.5 encompass receiving whole genome sequencing data (which was previously sequenced) in a computer environment. Thus, the active step of these additional elements encompass receiving data in a computer environment. It is noted that the content of the data (i.e., the data being whole genome sequencing (WGS) data of a tumor sample from a patient and wherein the WGS data has a sequencing depth of 0.05 to 0.5) falls under the judicial exception itself and does not limit the active step of receiving data in a computer environment. The additional elements of receiving data in a computer environment is conventional as shown by MPEP 2106.05(b) and MPEP 2106.05(d)(II).
The additional element in claims 1 and 11 of using a deep learning model, the additional elements in claim 7 of wherein the deep learning model comprises one or more layers of long short-term memory or convolutional neural network and a fully connected network, and the additional element in claims 11 and 20 of a non-transitory computer readable medium having instructions stored thereon, wherein the instructions, when executed by a processor, cause the processor to perform judicial exceptions which encompasses utilizing a generic computer to perform judicial exceptions is conventional as shown by MPEP 2106.05(b) and MPEP 2106.05(d)(II). Further, the deep learning model comprises one or more layers of long short-term memory or convolutional neural network and a fully connected network is conventional as shown by [0091]-[0093] of Pozzorini et al. (US 20220028481 A1) and page 3 - page 4 and Fig. 3 of Park et al. (Sci Rep 9, 3644 (2019)) which both show the combination of a convolutional neural network and a fully connected neural network.
Thus, the additional elements are not sufficient to amount to significantly more than the judicial exception because they are conventional.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-7 and 9-12 are rejected under 35 U.S.C. 103 as being unpatentable over Pozzorini et al. (US 20220028481 A1) in view of Park et al. (Sci Rep 9, 3644 (2019)).
Claim 1 is directed to a method for detecting deficiency in a DNA homologous recombination pathway in a patient having cancer, the method comprising: obtaining a whole genome sequencing (WGS) data of a tumor sample from the patient,
Pozzorini et al. shows a process of determining a homologous recombination deficiency status of a subject using whole genome sequencing data of a tumor sample from the patient
generating from the WGS data a genomic alteration profile including genome copy number profile, fusion profile, translocation profile, or inversion profile
The BRI of this limitation only requires that the genomic alteration profile include one of a genome copy number profile, fusion profile, translocation profile, or inversion profile because these profiles are recited in the alternative form. Pozzorini et al. shows generating a copy number profile by processing the raw coverage data signal from the whole genome sequencing data to produce a set of 1D vectors which each correspond to a chromosome (Pozzorini et al. [0064]- [0066]). Pozzorini et al. shows the size of the vectors may be variable, reflecting the fact that different chromosomes have different sizes (Pozzorini et al. [0066]).
generating from the genome copy number profile a chromosome instability (CI) score for each chromosome using a deep learning model, thus generating a set of CI scores
Pozzorini et al. shows processing these 1D vectors using a convolutional neural network which produces a chromosome spatial instability score (Pozzorini et al. [0064]-[0066] and [0092]).
generating from the set of CI scores a risk score of deficiency in the DNA homologous recombination pathway and determining that the risk score is greater than a threshold number thereby identifying the patient as having a risk of deficiency in the DNA homologous recombination pathway.
Pozzorini et al. shows a first convolutional neural network which quantifies a set of intermediary features which is itself input to a second neural network classifier in charge with extract two scores one for HRD negative and HRD positive as its two outputs which then derive an HRD status by comparing and thresholding the HRD negative and HRD positive output values (Pozzorini et al. [0091]-[0093]). Pozzorini et al. shows the model computes a CSI score of the subject DNA sample and uses this CSI score to determine a HRD status of the subject DNA sample belonging to a group of samples (Pozzorini et al. [0107]).
Pozzorini et al. does not show generating a CI score for each chromosome thus generating a set of chromosome instability scores.
Like Pozzorini et al., Park et al. shows utilizing a convolutional neural network to processes copy number signals from chromosomes which are formatted as 1D vectors. Park et al. shows an ensemble model which includes multiple 1D convolutional models that process copy number signals from separate chromosomes to produce multiple outputs which are used as input into a full connected layer to produce a classification (Park et al. page 3 - page 4 and page 4 Fig. 3).
Independent claim 11 is directed to a non-transitory computer readable medium having instructions stored thereon, wherein the instructions, when executed by a processor, cause the processor to perform a method comprising: obtaining a whole genome sequencing (WGS) data of a tumor sample from a patient; generating from the WGS data a genomic alteration profile including genome copy number profile, fusion profile, translocation profile, or inversion profile; generating from the genome copy number profile a chromosome instability (CI) scores for each chromosome using a deep learning model, thus generating a set of CI scores; and generating from the set of CI scores a risk score of deficiency in the DNA homologous recombination pathway.
Pozzorini et al. shows that a computer system can be programed to perform HRD detection (Pozzorini et al. [0201]). It is noted that the method steps in the instant claim is obvious over Pozzorini et al. in view of Park et al. and are addressed above for the method of claim 1.
Claim 2 is directed to wherein (i) the threshold number is 0.4; or (ii) the risk of deficiency in the DNA homologous recombination pathway is high when the risk score is greater than 0.6; or (iii) the risk of deficiency in the DNA homologous recombination pathway is moderate when the risk score is greater than 0.4 and less than or equal to 0.6.
Pozzorini et al. shows a final output decision such as an HRD+ or an HRD− status analysis for each sample, by thresholding the output from the CNN and the threshold may be adapted depending on the application, so as the optimize the sensitivity and/or the specificity of the CSI analysis according to the end user needs, for instance for diagnosis, prognosis, or to guide the choice of the most efficient cancer treatment (Pozzorini et al. et al. [0093]). It would have been obvious to one of ordinary skill in the art before the effective filling date to have routinely optimized these threshold values based on application as shown by (Pozzorini et al. [0093]).
Dependent claim 3 is directed to wherein the tumor sample is an FFPE sample or a CTC sample.
Pozzorini et al. shows the tumor sample as a FFPE sample (Pozzorini et al. [0134]).
Dependent claim 4 is directed to wherein the cancer is selected from breast cancer, ovary cancer, pancreas cancer, head and neck carcinoma and melanoma.
Pozzorini et al. shows the patients have ovarian cancer, prostate cancer, and breast cancer (Pozzorini et al. [0135]).
Dependent claim 5 is directed to wherein the genome copy number profile comprises a parameter selected from genomic segments along the genome, copy number of the genomic segments, copy number alteration (CNA) breaks, number of CNA breaks, and a combination thereof.
Pozzorini et al. shows the 1D vectors represent coverage data from genomic segments along the genome which includes copy number information of the genomic segments (Pozzorini et al. [0064]- [0066]).
Dependent claim 6 is directed to wherein the genome copy number profile is generated by: aligning the WGS data to a reference genome, thereby generating a group of aligned WGS reads along the genome, counting the group of aligned WGS reads along the genome, and generating genomic segments along the genome, wherein two adjacent genomic segments have a different number of read counts.
Pozzorini et al. shows aligning the reads to a reference genome (Pozzorini et al. [0010] and [0048]). Pozzorini et al. shows A coverage signal for each chromosome or for each chromosome arm may be then obtained by counting the number of aligned reads in each bin of the fixed set of bins which is interpreted as counting the group of aligned WGS reads along the genome (Pozzorini et al. [0010] and [0058]). Pozzorini et al. shows decomposing the coverage profile into genomic segments within which the coverage is believed to be constant (Pozzorini et al. [0064]). It is interpreted that two adjacent genomic segments will have a different number of read counts because the segments are defined as having a constant coverage and two adjacent segments will be generated when there is a change in coverage (e.g., read counts for the segment) between the constant coverage of one region and the constant coverage of the adjacent region.
Dependent claim 7 is directed to wherein the deep learning model comprises one or more layers of long short-term memory or convolutional neural network and a fully connected network.
Pozzorini et al. shows a first convolutional neural network which quantifies a set of intermediary features which is itself input to a second neural network classifier in charge with extract two scores one for HRD negative and HRD positive as its two outputs which then derive an HRD status by comparing and thresholding the HRD negative and HRD positive output values (Pozzorini et al. [0091]-[0093]).
Dependent claim 9 is directed to a method for treating cancer in a patient, the method comprising administering to the patient a therapeutically effective amount of a PARP inhibitor and/or an alkylating agent, wherein the patient has been identified as having a risk of deficiency in the DNA homologous recombination pathway by the method of claim 1. Dependent claim 10 is directed to wherein the PARP inhibitor and/or alkylating agent is selected from iniparib, Olaparib, rucaparib, CEP9722, MK4827, BMN673, 3-aininobenzaide, platinum complexes, chlormethine, chloranbucil, melphalan, cyclophosphamide, ifosfamide, estramustine, carmustine, lomustine, fotemustine, streptozocin, busulfan, pipobroman, procarbazine, dacarbazine, thiotepa and temozolomide.
Pozzorini et al. shows when it is determined that the DNA cancer sample is homologous recombination deficient, treating the cancer by administering a PARP inhibitor to the test subject (Pozzorini et al. [0133]). Pozzorini et al. shows a PARP inhibitor may be used alone or in combination with other treatments (Pozzorini et al. [0131]). Pozzorini et al. further shows cancer treatment regimen may be selected from the group consisting of an alkylating agent, a platinum-based chemotherapeutic agent, chlormethine, chlorambucil, melphalan, cyclophosphamide, ifosfamide, estramustine, carmustine, lomustine, fotemustine, streptozocin, busulfan, pipobroman, procarbazine, dacarbazine, thiotepa, temozolomide and/or olaparib, rucaparib, iniparib, CEP 9722, MK 4827, BMN-673, and 3-aminobenzamide (Pozzorini et al. [0011]).
Claim 12 is directed to the non-transitory computer readable medium of claim 11, wherein the method further comprises: determining that the risk score is greater than a threshold number thereby identifying the patient as having a risk of deficiency in the DNA homologous recombination pathway.
Pozzorini et al. shows that a computer system can be programed to perform HRD detection (Pozzorini et al. [0201]). It is noted that the method steps in the instant claim is obvious over Pozzorini et al. in view of Park et al. and are addressed above for the method of claim 1.
An invention would have been obvious to one or ordinary skill in the art if some motivation in the prior art would have led that person to modify reference teachings to arrive at the claimed invention. It would have been obvious to one of ordinary skill in the art before the effective filling date of the invention to have modified the architecture of the convolutional neural network model which processes 1D vectors representing copy number coverage signals from whole genome sequencing data to produce a CSI score which is used to make a classification on HRD status of a sample in Pozzorini et al. to be an ensemble model which includes multiple 1D convolutional neural networks which separately processes variable sized 1D vectors representing copy number signals of a chromosome to produce a feature vector for each processed chromosome data which is then used as input into a fully connected neural network to make a final classification of Park et al. because this would allow for an ensemble model which processes the set of 1D vector each representing a chromosome to produce a feature value (as a CSI score) for each chromosome which are combined into an input for a fully connected layer to produce a classification on HRD status of the sample which provides the use of an ensemble method which obtain improved results (Park et al. page 3 para. 3). One would have a reasonable expectation of success because the process of Pozzorini et al. produces 1D vectors of copy number coverage signals which are processed by a CNN to produce a CSI score which is used in the detection of HRD status while Park et al. shows a particular ensemble method implementing multiple 1D convolutional neural network models which each process a 1D vector of copy number signal vectors for different chromosomes to produce feature values for separate chromosome data which is then combined as input into a fully connected neural network to produce a final classification.
Claims 13-20 are rejected under 35 U.S.C. 103 as being unpatentable over Manie et al. (US 20170260588 A1) in view of Pozzorini et al. (US 20220028481 A1).
Claim 13 is directed to method for detecting deficiency in the DNA homologous recombination pathway in a patient having cancer, the method comprising:
Manie et al. shows a method for detecting deficiency in the DNA homologous recombination pathway in a patient having cancer (Manie et al. [0018]).
generating from the genome copy number profile a number, per genome, of large-scale genomic breakpoints (LGBs), wherein the LGB is a breakpoint between two adjacent genomic segments that have different copy numbers, each such genomic segment being at least 10 megabases long
Manie et al. shows quantifying the number of rearrangements in genomic DNA, such as Large-Scale Transitions, of a tumor sample obtained from a patient, wherein the number of rearrangements corresponds to the number, per genome, of breakpoints resulting in segments of at least 10 megabases (Manie et al. [0008]). Manie et al. shows the rearrangement as a large-scale transition which refers to any somatic copy number transition, such as a breakpoint, along the length of a chromosome (Manie et al. [0040]). Manie et al. shows an LST was defined as a chromosomal breakpoint (change in copy number) between adjacent regions each of at least 10 megabases (Mb) (Manie et al. [0215]).
determining that the number of LGBs is greater than a threshold number of LGBs thereby identifying the patient as having a risk of deficiency in the DNA homologous recombination pathway.
Manie et al. shows determining that the patient has one or more cancer cells having the homologous recombination deficiency status, wherein the presence of more than a reference number of rearrangements, such as Large-Scale Transitions, in the genomic DNA of the tumor sample indicates that the cancer cells have the homologous recombination deficiency status (Manie et al. [0018]). Manie et al. shows comparing the number of rearrangements to a reference number to determine a likelihood of a deficiency in homologous recombination in the cancer cells which is interpreted as being a risk of deficiency (Manie et al. [0025]).
Manie et al. does not show obtaining a whole genome sequencing (WGS) data of a tumor sample from the patient, wherein the WGS data has a sequencing depth of 0.05 to 0.5, generating from the WGS data a genome copy number profile
Like Manie et al., Pozzorini et al. shows detecting a homologous recombination deficiency status of a subject. Pozzorini et al. shows wherein a low-pass whole genome sequencing coverage is at least 0.1x and up to 30×, such as between 0.1x to 1x (Pozzorini et al. [0110]). Pozzorini et al. shows generating a copy number profile from the whole genome sequencing data (Pozzorini et al. [0064]-[0065]). The WGS data having a sequencing depth of 0.05 to 0.5 is obvious as shown by the overlapping range of between 0.1x and 1x of Pozzorini et al. (see MPEP 2144.05(I) for a discussion on obviousness of overlapping ranges).
Claim 20 is directed to a non-transitory computer readable medium having instructions stored thereon, wherein the instructions, when executed by a processor, cause the processor to perform the method of claim 13.
Manie et al. shows a system with a computer subsystem programed to perform the steps of the method (Manie et al. [0025]).
Claim 14 is directed to wherein (i) the threshold number of LGBs is 25; or (ii) the risk of deficiency in the DNA homologous recombination pathway is high when the number of LGBs is greater than 35; or (iii) the risk of deficiency in the DNA homologous recombination pathway is moderate when the number of LGBs is greater than 25 and less than or equal to 35.
The BRI of the claim only requires one of the thresholds of LGBs is 25, the risk of deficiency in the DNA homologous recombination pathway is high when the number of LGBs is greater than 35, and the risk of deficiency in the DNA homologous recombination pathway is moderate when the number of LGBs is greater than 25 and less than or equal to 35 because these limitations are recited in the alternative form. Manie et al. shows a reference number (reference number of LSTs) is or has been previously derived from a relevant reference population. Such reference populations may include patients with the same cancer as the patient being tested, with the same cancer sub-type, with cancer having similar genetic or other clinical or molecular features (Manie et al. [0073]). It would have been obvious to one of ordinary skill in the art before the effective filling date to have routinely optimized these threshold values based on factors such as the reference populations used to determine a reference number of Large-Scale Transitions to be used for making a determination of the likelihood of HRD.
Claim 15 is directed to wherein the tumor sample is an FFPE sample or a CTC sample.
Manie et al. shows that the sample can be formalin-fixed paraffin embedded (FFPE) issue samples containing cancer cells (Manie et al. [0068]).
Claim 16 is directed to wherein the cancer is selected from breast cancer, ovary cancer, pancreas cancer, head and neck carcinoma and melanoma.
Manie et al. shows the cancer is selected from the group consisting of breast cancer, ovary cancer, pancreas cancer, head and neck cancer and melanoma (Manie et al. [0056]).
Claim 17 is directed to wherein the genome copy number profile is generated by: aligning the WGS data to a reference genome, thereby generating a group of aligned WGS reads along the genome, counting the group of aligned WGS reads along the genome, and generating genomic segments along the genome, wherein two adjacent genomic segments have different numbers of read counts.
Pozzorini et al. shows aligning the reads to a reference genome (Pozzorini et al. [0010] and [0048]). Pozzorini et al. shows A coverage signal for each chromosome or for each chromosome arm may be then obtained by counting the number of aligned reads in each bin of the fixed set of bins which is interpreted as counting the group of aligned WGS reads along the genome (Pozzorini et al. [0010] and [0058]). Pozzorini et al. shows decomposing the coverage profile into genomic segments within which the coverage is believed to be constant (Pozzorini et al. [0064]). It is interpreted that two adjacent genomic segments will have a different number of read counts because the segments are defined as having a constant coverage and two adjacent segments will be generated when there is a change in coverage (e.g., read counts for the segment) between the constant coverage of one region and the constant coverage of the adjacent region.
Claim 18 is directed to method for treating cancer in a patient, the method comprising administering to the patient a therapeutically effective amount of a PARP inhibitor and/or an alkylating agent, wherein the patient has been identified as having a risk of deficiency in the DNA homologous recombination pathway by the method of claim 13. Claim 19 is directed to wherein the PARP inhibitor and/or alkylating agent is selected from iniparib, Olaparib, rucaparib, CEP9722, MK4827, BMN673, 3-aminobenzaide, platinum complexes, chlormethine, chlorambucil, melphalan, cyclophosphamide, ifosfamide, estramustine, carmustine, lomustine, fotemustine, streptozocin, busulfan, pipobroman, procarbazine, dacarbazine, thiotepa and temozolomide.
Manie et al. shows administering a therapeutically effective amount of a PARP inhibitor and/or an alkylating agent, if said patient has a number of rearrangements superior to said reference (Manie et al. [0104]). Manie et al. shows that the PARP inhibitor according to the invention can be selected from the group consisting of iniparib, olaparib, rocaparib, CEP 9722, MK 4827, BMN-673, and 3-aminobenzamide (Manie et al. [0113]). Manie et al. shows that the alkylating agent according to the invention can be selected from platinum complexes such as chlormethine, chlorambucil, melphalan, cyclophosphamide, ifosfamide, estramustine, carmustine, lomustine, fotemustine, streptozocin, busulfan, pipobroman, procarbazine, dacarbazine, thiotepa and temozolomide (Manie et al. [0114]).
An invention would have been obvious to one or ordinary skill in the art if some motivation in the prior art would have led that person to modify reference teachings to arrive at the claimed invention. It would have been obvious to one or ordinary skill in the art before the effective filling date of the invention to have modified the sequencing data and copy number profile generation in the process of detecting homologous recombination deficiency of Manie et al. with the low-pass whole genome sequencing data and copy number profile generation using the low-pass whole genome sequencing data of Pozzorini et al. because this would allow for a process of detecting homologous recombination deficiency based on copy number profiles from low-pass genome sequencing data which is advantageous for clinical oncology due to lower costs of the analysis in clinical practice compared to analyses that require higher-coverage sequencing data for homologous recombination deficiency (HRD) detection (Pozzorini et al. [0007]). One would have a reasonable expectation of success because Manie et al. shows determining HRD by analyzing somatic copy number transition, such as a breakpoint, along the length of a chromosome while Pozzorini et al. shows a process of generating copy number profiles from low pass whole genome sequencing data and generates genomic segments that are defined as separate regions with constant coverage (e.g., adjacent segments will exhibit a change in coverage/sequence read counts which may be indicative of a copy number change between regions).
Conclusion
No claims are allowed.
Claims 9, 10, 18, and 19 are patent eligible under 35 U.S.C. 101 because they provide a particular treatment for patients having cancer with a risk of homologous recombination deficiency by administering a PARP inhibitor and/or alkylating agent which both treat patients with cancer that have a homologous recombination deficiency.
Claim 8 is free of the prior art of record. The closest prior art of record is Pozzorini et al. (US 20220028481) which shows generating a copy number profile from whole genome sequencing data and using a deep learning model to process this copy number profile to produce a chromosome instability score which is used to detect a HRD status of a patient. However, the prior art of record does not show wherein the risk score is an average of the set of CI scores weighted by base length of each chromosome. Thus, claim 8 is free of the prior art.
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