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 .
Status of Application
This action is in reply to the correspondence received through June 16, 2025.
Claims 1-49 are canceled.
Claims 50-69 are new.
Claims 50-69 are pending.
Information Disclosure Statement
The information disclosure statement submitted May 9, 2025 and its contents have been considered.
Claim Rejections - 35 U.S.C. § 112
The following is a quotation of 35 U.S.C. 112(d):
(d) REFERENCE IN DEPENDENT FORMS.—Subject to subsection (e), a claim in dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers.
The following is a quotation of pre-AIA 35 U.S.C. 112, fourth paragraph:
Subject to the following paragraph [i.e., the fifth paragraph of pre-AIA 35 U.S.C. 112], a claim in dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers.
Claims 54, 55, and 62 are rejected under 35 U.S.C. 112(d) or pre-AIA 35 U.S.C. 112, 4th paragraph, as being of improper dependent form for failing to further limit the subject matter of the claim upon which it depends, or for failing to include all the limitations of the claim upon which it depends.
Claims 54 and 62 depend from claims 50 and 57, respectively, but in a manner that makes the features of their parent claims optional, and thus, not required.
Applicant may cancel the claim(s), amend the claim(s) to place the claim(s) in proper dependent form, rewrite the claim(s) in independent form, or present a sufficient showing that the dependent claim(s) complies with the statutory requirements.
Claim 55 is rejected for incorporating the deficiencies of the rejected claims on which it depends.
Claim Rejections - 35 U.S.C. § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. § 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 50-63 are rejected under 35 U.S.C. § 102(a)(1) as being anticipated by Djirackor et al. (“Intraoperative DNA methylation classification of brain tumors impacts neurosurgical strategy.” Neuro-Oncology Advances 3.1 (2021): vdab149) (hereinafter “Djirackor”).
Claims 50 and 57: Djirackor, as shown, discloses the following limitations:
A computer-implemented method for identifying a DNA modification, preferably DNA methylation, in DNA sequencing data, for the intraoperative classification of a disease and/or disorder, preferably a tumor, more preferably a brain tumor, of a subject on which the operation is performed (see at least Abstract: “Brain tumor surgery must balance the benefit of maximal resection against the risk of inflicting severe damage. The impact of increased resection is diagnosis-specific. However, the precise diagnosis is typically uncertain at surgery due to limitations of imaging and intraoperative histomorphological methods. Novel and accurate strategies for brain tumor classification are necessary to support personalized intraoperative neurosurgical treatment decisions. Here, we describe a fast and cost-efficient workflow for intraoperative classification of brain tumors based on DNA methylation profiles generated by low coverage nanopore sequencing and machine learning algorithms”; see also at least p. 2), the method comprising the steps of:
a) obtaining DNA sequencing data of the subject, preferably wherein the DNA sequencing data is obtained by using a sequencing method capable of directly sensing a DNA modification, more preferably nanopore sequencing, even more preferably wherein the DNA sequencing data is obtained by whole genome sequencing (see at least Abstract: “Brain tumor surgery must balance the benefit of maximal resection against the risk of inflicting severe damage. The impact of increased resection is diagnosis-specific. However, the precise diagnosis is typically uncertain at surgery due to limitations of imaging and intraoperative histomorphological methods. Novel and accurate strategies for brain tumor classification are necessary to support personalized intraoperative neurosurgical treatment decisions. Here, we describe a fast and cost-efficient workflow for intraoperative classification of brain tumors based on DNA methylation profiles generated by low coverage nanopore sequencing and machine learning algorithms”; see also at least p. 3: “Nanopore sequencing can be used to detect base modifications such as 5-methylcytosine (5mC) in native DNA without the need for bisulfite conversion.17 This provides an ideal platform for rapid generation of tumor methylation profiles. We have previously demonstrated the potential of nanopore DNA methylation analysis (NDMA) for multimodal and rapid molecular diagnostics in brain cancer.18 This was used for subclassification of the tumor entities into over 80 subgroups according to the Heidelberg cohort.6 Here, we demonstrate how low-pass whole-genome nanopore sequencing can be used to generate DNA methylation profiles of a tumor biopsy that is sufficient for accurate classification in less than 2 h”)
b) identifying the DNA modification status of the DNA sequencing data, thereby obtaining a DNA modification profile (see at least p. 3: “Nanopore sequencing can be used to detect base modifications such as 5-methylcytosine (5mC) in native DNA without the need for bisulfite conversion.17 This provides an ideal platform for rapid generation of tumor methylation profiles. We have previously demonstrated the potential of nanopore DNA methylation analysis (NDMA) for multimodal and rapid molecular diagnostics in brain cancer.18 This was used for subclassification of the tumor entities into over 80 subgroups according to the Heidelberg cohort.6 Here, we demonstrate how low-pass whole-genome nanopore sequencing can be used to generate DNA methylation profiles of a tumor biopsy that is sufficient for accurate classification in less than 2 h”); see also at least pp. 3-4: “Within this pipeline, reads were aligned to the hg19 human reference genome (minimap2 v2.15),21 and copy number profiles were generated. The methylation status of the genome-wide CpG sites (5mC) was called (nanopolish v0.11.0)17 and binarized (cutoff beta value >0.6) together with 5mC signals for overlapping sites probed by the Illumina BeadChip 450K array”; see also at least p. 4: “The 450k array was used to obtain genome-wide DNA methylation profiles for tumor samples, according to the manufacturer’s instructions (Illumina)”);
c) classifying the DNA modification profile using a DNA modification based deep learning (DL)-classifier thereby obtaining a classification score for a disease and/or disorder (see at least p. 3: “DNA methylation-based classification of CNS tumors can improve diagnostic precision. This can revise the WHO grading of a tumor and refine patient management.6 It has been shown to impact treatment of children by providing additional molecular subtyping and amending final diagnosis. 14 The basis of this classification is the DNA methylation pattern of cancer cells which is unique for each entity.15 Machine learning algorithms can be trained to identify these patterns and use them to discriminate the type of tumor entity as well as the subgroups, which are now included in the WHO tumor grading system.16”; see also at least pp. 3-4: “Equipped with the manufacturer software MinKNOW (v20.06.17), the MinIT can obtain raw data (FAST5) in real time, separate barcoded reads and filter low-quality reads while simultaneously base-calling using the built-in proprietary software guppy (v4.0.11, GPU based, fast mode, Oxford Nanopore Technologies). FAST5 and FASTQ files were analyzed using an adapted snakemake19 v5.4.0 workflow from the nanoDx pipeline20 that enabled analysis on a local laptop computer. Within this pipeline, reads were aligned to the hg19 human reference genome (minimap2 v2.15),21 and copy number profiles were generated. The methylation status of the genome-wide CpG sites (5mC) was called (nanopolish v0.11.0)17 and binarized (cutoff beta value >0.6) together with 5mC signals for overlapping sites probed by the Illumina BeadChip 450K array. For each sample, an ad hoc random forest classifier (R/ranger package v0.10.1)22 was trained using the Heidelberg reference cohort of brain tumor methylation profiles, and a final report containing sequencing statistics and classification results was generated.”; see also at least p. 4: “In total, we performed NDMA on 105 individual patient samples using public reference data for more than 80 CNS tumor entities and compared methylation-based classification to the definite WHO integrated diagnosis obtained by full neuropathological workup. NDMA was concordant with final neuropathological diagnosis in 93 of the 105 cases (89%) (Figure 1; Supplementary Table 1). Most adult cases were classified as gliomas (20 glioblastomas, 14 oligodendrogliomas, and 10 astrocytomas) by NDMA (Figure 1A). Among the pediatric cases in the study (Figure 1B), medulloblastomas were the most common tumor type (23 cases) followed by pilocytic astrocytomas (10 cases). To initially evaluate the robustness of NDMA we analyzed 79 samples from 4 independent cohorts and compared these results to the final neuropathological diagnosis (Supplementary Table 1; Supplementary Figure 1). NDMA afforded classification of all cases, including cases not subclassified by standard diagnostic evaluation. Overall, this demonstrated a robust pipeline for NDMA with low coverage sequencing and methylation profiling”);
wherein the DNA modification based DL-classifier is trained with a training set, wherein the training is performed in a pre-operative training routine; and wherein the pre-operative training routine comprises training the DNA modification based DL-classifier on a training set generated from reference data comprising DNA modification data, preferably wherein the DNA modification data is any one or more selected from the group of microarray data, preferably hybridization microarray data, whole genome sequencing data, whole genome bisulfite sequencing data, PacBio SMRT sequencing data, TAPS- sequencing data or nanopore sequencing data (see at least p. 4: “For each sample, an ad hoc random forest classifier (R/ranger package v0.10.1)22 was trained using the Heidelberg reference cohort of brain tumor methylation profiles, and a final report containing sequencing statistics and classification results was generated”; see also at least Abstract: “We evaluated 6 independent cohorts containing 105 patients, including 50 pediatric and 55 adult patients. Ultra-low coverage whole-genome sequencing was performed on nanopore flow cells. Data were analyzed using copy number variation and ad hoc random forest classifier for the genome-wide methylation-based classification of the tumor”; see also at least pp. 6-7 and Figure 2).
Claims 51 and 59: Djirackor discloses the limitations as shown in the rejections above. Further, Djirackor, as shown, discloses the following limitations:
wherein the pre-operative training routine comprises:
a) providing reference data comprising DNA modification data, preferably whole genome sequencing data, as input in a simulation module, preferably wherein the DNA modification data comprises a DNA modification profile associated with a cell type and/or cell state of interest (see at least pp. 3-4: “Within this pipeline, reads were aligned to the hg19 human reference genome (minimap2 v2.15),21 and copy number profiles were generated. The methylation status of the genome-wide CpG sites (5mC) was called (nanopolish v0.11.0)17 and binarized (cutoff beta value >0.6) together with 5mC signals for overlapping sites probed by the Illumina BeadChip 450K array”);
b) using the simulation module to generate a training set from the reference data, wherein the training set comprises data having a DNA modification profile, preferably a nanopore profile (see at least pp. 3-4: “Within this pipeline, reads were aligned to the hg19 human reference genome (minimap2 v2.15),21 and copy number profiles were generated. The methylation status of the genome-wide CpG sites (5mC) was called (nanopolish v0.11.0)17 and binarized (cutoff beta value >0.6) together with 5mC signals for overlapping sites probed by the Illumina BeadChip 450K array. For each sample, an ad hoc random forest classifier (R/ranger package v0.10.1)22 was trained using the Heidelberg reference cohort of brain tumor methylation profiles, and a final report containing sequencing statistics and classification results was generated.”; see also at least p. 4: “In total, we performed NDMA on 105 individual patient samples using public reference data for more than 80 CNS tumor entities and compared methylation-based classification to the definite WHO integrated diagnosis obtained by full neuropathological workup. NDMA was concordant with final neuropathological diagnosis in 93 of the 105 cases (89%) (Figure 1; Supplementary Table 1). Most adult cases were classified as gliomas (20 glioblastomas, 14 oligodendrogliomas, and 10 astrocytomas) by NDMA (Figure 1A). Among the pediatric cases in the study (Figure 1B), medulloblastomas were the most common tumor type (23 cases) followed by pilocytic astrocytomas (10 cases). To initially evaluate the robustness of NDMA we analyzed 79 samples from 4 independent cohorts and compared these results to the final neuropathological diagnosis (Supplementary Table 1; Supplementary Figure 1). NDMA afforded classification of all cases, including cases not subclassified by standard diagnostic evaluation. Overall, this demonstrated a robust pipeline for NDMA with low coverage sequencing and methylation profiling”); and
c) training the DNA modification based DL-classifier on the training set, preferably wherein the pre-operative training routine further comprises after c) the step d), wherein step d) comprises an intraoperative validation routine comprising validation of the DNA modification based DL-classifier on whole genome sequencing data, preferably nanopore sequencing data (pp. 3-4: “Equipped with the manufacturer software MinKNOW (v20.06.17), the MinIT can obtain raw data (FAST5) in real time, separate barcoded reads and filter low-quality reads while simultaneously base-calling using the built-in proprietary software guppy (v4.0.11, GPU based, fast mode, Oxford Nanopore Technologies). FAST5 and FASTQ files were analyzed using an adapted snakemake19 v5.4.0 workflow from the nanoDx pipeline20 that enabled analysis on a local laptop computer. Within this pipeline, reads were aligned to the hg19 human reference genome (minimap2 v2.15),21 and copy number profiles were generated. The methylation status of the genome-wide CpG sites (5mC) was called (nanopolish v0.11.0)17 and binarized (cutoff beta value >0.6) together with 5mC signals for overlapping sites probed by the Illumina BeadChip 450K array. For each sample, an ad hoc random forest classifier (R/ranger package v0.10.1)22 was trained using the Heidelberg reference cohort of brain tumor methylation profiles, and a final report containing sequencing statistics and classification results was generated.”; see also at least p. 4: “In total, we performed NDMA on 105 individual patient samples using public reference data for more than 80 CNS tumor entities and compared methylation-based classification to the definite WHO integrated diagnosis obtained by full neuropathological workup. NDMA was concordant with final neuropathological diagnosis in 93 of the 105 cases (89%) (Figure 1; Supplementary Table 1). Most adult cases were classified as gliomas (20 glioblastomas, 14 oligodendrogliomas, and 10 astrocytomas) by NDMA (Figure 1A). Among the pediatric cases in the study (Figure 1B), medulloblastomas were the most common tumor type (23 cases) followed by pilocytic astrocytomas (10 cases). To initially evaluate the robustness of NDMA we analyzed 79 samples from 4 independent cohorts and compared these results to the final neuropathological diagnosis (Supplementary Table 1; Supplementary Figure 1). NDMA afforded classification of all cases, including cases not subclassified by standard diagnostic evaluation. Overall, this demonstrated a robust pipeline for NDMA with low coverage sequencing and methylation profiling”).
Claims 52 and 60: Djirackor discloses the limitations as shown in the rejections above. Further, Djirackor, as shown, discloses the following limitations:
wherein generating the training set from the reference data comprises any one or more of:
- Binarization;
- Non-uniform subsampling; and
- Error simulation.
preferably, wherein non-uniform subsampling comprises the random selection of CpG sites and the extension of the size of the sequencing reads and/or the sequencing time, and/or, preferably wherein error simulation comprises the generating of an error- rate of at least 10% in DNA modification-calling (pp. 3-4: “Equipped with the manufacturer software MinKNOW (v20.06.17), the MinIT can obtain raw data (FAST5) in real time, separate barcoded reads and filter low-quality reads while simultaneously base-calling using the built-in proprietary software guppy (v4.0.11, GPU based, fast mode, Oxford Nanopore Technologies). FAST5 and FASTQ files were analyzed using an adapted snakemake19 v5.4.0 workflow from the nanoDx pipeline20 that enabled analysis on a local laptop computer. Within this pipeline, reads were aligned to the hg19 human reference genome (minimap2 v2.15),21 and copy number profiles were generated. The methylation status of the genome-wide CpG sites (5mC) was called (nanopolish v0.11.0)17 and binarized (cutoff beta value >0.6) together with 5mC signals for overlapping sites probed by the Illumina BeadChip 450K array. For each sample, an ad hoc random forest classifier (R/ranger package v0.10.1)22 was trained using the Heidelberg reference cohort of brain tumor methylation profiles, and a final report containing sequencing statistics and classification results was generated.”; see also at least p. 4: “In total, we performed NDMA on 105 individual patient samples using public reference data for more than 80 CNS tumor entities and compared methylation-based classification to the definite WHO integrated diagnosis obtained by full neuropathological workup. NDMA was concordant with final neuropathological diagnosis in 93 of the 105 cases (89%) (Figure 1; Supplementary Table 1). Most adult cases were classified as gliomas (20 glioblastomas, 14 oligodendrogliomas, and 10 astrocytomas) by NDMA (Figure 1A). Among the pediatric cases in the study (Figure 1B), medulloblastomas were the most common tumor type (23 cases) followed by pilocytic astrocytomas (10 cases). To initially evaluate the robustness of NDMA we analyzed 79 samples from 4 independent cohorts and compared these results to the final neuropathological diagnosis (Supplementary Table 1; Supplementary Figure 1). NDMA afforded classification of all cases, including cases not subclassified by standard diagnostic evaluation. Overall, this demonstrated a robust pipeline for NDMA with low coverage sequencing and methylation profiling”).
Claims 53 and 61: Djirackor discloses the limitations as shown in the rejections above. Further, Djirackor, as shown, discloses the following limitations:
wherein the pre-operative training routine comprises down sampling of the DNA modification data, preferably whole genome sequencing data and/or wherein the classifying of the DNA modification profile comprises up sampling of the whole genome sequencing data, preferably nanopore sequencing data, preferably wherein the DNA modification classification provides a diagnosis of a tumor species, preferably of a brain tumor species (pp. 3-4: “Equipped with the manufacturer software MinKNOW (v20.06.17), the MinIT can obtain raw data (FAST5) in real time, separate barcoded reads and filter low-quality reads while simultaneously base-calling using the built-in proprietary software guppy (v4.0.11, GPU based, fast mode, Oxford Nanopore Technologies). FAST5 and FASTQ files were analyzed using an adapted snakemake19 v5.4.0 workflow from the nanoDx pipeline20 that enabled analysis on a local laptop computer. Within this pipeline, reads were aligned to the hg19 human reference genome (minimap2 v2.15),21 and copy number profiles were generated. The methylation status of the genome-wide CpG sites (5mC) was called (nanopolish v0.11.0)17 and binarized (cutoff beta value >0.6) together with 5mC signals for overlapping sites probed by the Illumina BeadChip 450K array. For each sample, an ad hoc random forest classifier (R/ranger package v0.10.1)22 was trained using the Heidelberg reference cohort of brain tumor methylation profiles, and a final report containing sequencing statistics and classification results was generated.”; see also at least p. 4: “In total, we performed NDMA on 105 individual patient samples using public reference data for more than 80 CNS tumor entities and compared methylation-based classification to the definite WHO integrated diagnosis obtained by full neuropathological workup. NDMA was concordant with final neuropathological diagnosis in 93 of the 105 cases (89%) (Figure 1; Supplementary Table 1). Most adult cases were classified as gliomas (20 glioblastomas, 14 oligodendrogliomas, and 10 astrocytomas) by NDMA (Figure 1A). Among the pediatric cases in the study (Figure 1B), medulloblastomas were the most common tumor type (23 cases) followed by pilocytic astrocytomas (10 cases). To initially evaluate the robustness of NDMA we analyzed 79 samples from 4 independent cohorts and compared these results to the final neuropathological diagnosis (Supplementary Table 1; Supplementary Figure 1). NDMA afforded classification of all cases, including cases not subclassified by standard diagnostic evaluation. Overall, this demonstrated a robust pipeline for NDMA with low coverage sequencing and methylation profiling”).
Claim 54: Djirackor discloses the limitations as shown in the rejections above. Further, Djirackor, as shown, discloses the following limitations:
A method of the pre-operative configuring of a DNA modification based DL- classifier, preferably a DNA modification based DL-classifier as used in Claim 50, to receive a sample comprising DNA and/or to receive DNA sequencing data and to generate a classification score for a disease and/or disorder, preferably for a tumor species (see at least Abstract: “Brain tumor surgery must balance the benefit of maximal resection against the risk of inflicting severe damage. The impact of increased resection is diagnosis-specific. However, the precise diagnosis is typically uncertain at surgery due to limitations of imaging and intraoperative histomorphological methods. Novel and accurate strategies for brain tumor classification are necessary to support personalized intraoperative neurosurgical treatment decisions. Here, we describe a fast and cost-efficient workflow for intraoperative classification of brain tumors based on DNA methylation profiles generated by low coverage nanopore sequencing and machine learning algorithms”; see also at least p. 2), the method comprising:
training a DNA modification based DL-classifier, which runs on a processor coupled to memory, comprising a pre-operative training routine using as input reference data (pp. 3-4: “Equipped with the manufacturer software MinKNOW (v20.06.17), the MinIT can obtain raw data (FAST5) in real time, separate barcoded reads and filter low-quality reads while simultaneously base-calling using the built-in proprietary software guppy (v4.0.11, GPU based, fast mode, Oxford Nanopore Technologies). FAST5 and FASTQ files were analyzed using an adapted snakemake19 v5.4.0 workflow from the nanoDx pipeline20 that enabled analysis on a local laptop computer. Within this pipeline, reads were aligned to the hg19 human reference genome (minimap2 v2.15),21 and copy number profiles were generated. The methylation status of the genome-wide CpG sites (5mC) was called (nanopolish v0.11.0)17 and binarized (cutoff beta value >0.6) together with 5mC signals for overlapping sites probed by the Illumina BeadChip 450K array. For each sample, an ad hoc random forest classifier (R/ranger package v0.10.1)22 was trained using the Heidelberg reference cohort of brain tumor methylation profiles, and a final report containing sequencing statistics and classification results was generated.”; see also at least p. 4: “In total, we performed NDMA on 105 individual patient samples using public reference data for more than 80 CNS tumor entities and compared methylation-based classification to the definite WHO integrated diagnosis obtained by full neuropathological workup. NDMA was concordant with final neuropathological diagnosis in 93 of the 105 cases (89%) (Figure 1; Supplementary Table 1). Most adult cases were classified as gliomas (20 glioblastomas, 14 oligodendrogliomas, and 10 astrocytomas) by NDMA (Figure 1A). Among the pediatric cases in the study (Figure 1B), medulloblastomas were the most common tumor type (23 cases) followed by pilocytic astrocytomas (10 cases). To initially evaluate the robustness of NDMA we analyzed 79 samples from 4 independent cohorts and compared these results to the final neuropathological diagnosis (Supplementary Table 1; Supplementary Figure 1). NDMA afforded classification of all cases, including cases not subclassified by standard diagnostic evaluation. Overall, this demonstrated a robust pipeline for NDMA with low coverage sequencing and methylation profiling”),
wherein the reference data comprises DNA modification data (see at least p. 3: “Nanopore sequencing can be used to detect base modifications such as 5-methylcytosine (5mC) in native DNA without the need for bisulfite conversion.17 This provides an ideal platform for rapid generation of tumor methylation profiles. We have previously demonstrated the potential of nanopore DNA methylation analysis (NDMA) for multimodal and rapid molecular diagnostics in brain cancer.18 This was used for subclassification of the tumor entities into over 80 subgroups according to the Heidelberg cohort.6 Here, we demonstrate how low-pass whole-genome nanopore sequencing can be used to generate DNA methylation profiles of a tumor biopsy that is sufficient for accurate classification in less than 2 h”); see also at least pp. 3-4: “Within this pipeline, reads were aligned to the hg19 human reference genome (minimap2 v2.15),21 and copy number profiles were generated. The methylation status of the genome-wide CpG sites (5mC) was called (nanopolish v0.11.0)17 and binarized (cutoff beta value >0.6) together with 5mC signals for overlapping sites probed by the Illumina BeadChip 450K array”; see also at least p. 4: “The 450k array was used to obtain genome-wide DNA methylation profiles for tumor samples, according to the manufacturer’s instructions (Illumina)”);
wherein the DNA modification based DL-classifier after training is configured to receive a DNA modification profile (pp. 3-4: “For each sample, an ad hoc random forest classifier (R/ranger package v0.10.1)22 was trained using the Heidelberg reference cohort of brain tumor methylation profiles, and a final report containing sequencing statistics and classification results was generated.”; see also at least p. 4: “In total, we performed NDMA on 105 individual patient samples using public reference data for more than 80 CNS tumor entities and compared methylation-based classification to the definite WHO integrated diagnosis obtained by full neuropathological workup. NDMA was concordant with final neuropathological diagnosis in 93 of the 105 cases (89%) (Figure 1; Supplementary Table 1). Most adult cases were classified as gliomas (20 glioblastomas, 14 oligodendrogliomas, and 10 astrocytomas) by NDMA (Figure 1A). Among the pediatric cases in the study (Figure 1B), medulloblastomas were the most common tumor type (23 cases) followed by pilocytic astrocytomas (10 cases). To initially evaluate the robustness of NDMA we analyzed 79 samples from 4 independent cohorts and compared these results to the final neuropathological diagnosis (Supplementary Table 1; Supplementary Figure 1). NDMA afforded classification of all cases, including cases not subclassified by standard diagnostic evaluation. Overall, this demonstrated a robust pipeline for NDMA with low coverage sequencing and methylation profiling”); and
wherein the DNA modification based DL-classifier generates a classification score output that indicates whether the DNA modification of the DNA modification profile is indicative for a disease and/or disorder, preferably wherein the DNA modification data comprises any one or more selected from the group of microarray data, preferably hybridization microarray data, whole genome bisulfite sequencing data, TAPS-sequencing data or nanopore sequencing data (see at least p. 3: “DNA methylation-based classification of CNS tumors can improve diagnostic precision. This can revise the WHO grading of a tumor and refine patient management.6 It has been shown to impact treatment of children by providing additional molecular subtyping and amending final diagnosis. 14 The basis of this classification is the DNA methylation pattern of cancer cells which is unique for each entity.15 Machine learning algorithms can be trained to identify these patterns and use them to discriminate the type of tumor entity as well as the subgroups, which are now included in the WHO tumor grading system.16”; see also at least pp. 3-4: “Equipped with the manufacturer software MinKNOW (v20.06.17), the MinIT can obtain raw data (FAST5) in real time, separate barcoded reads and filter low-quality reads while simultaneously base-calling using the built-in proprietary software guppy (v4.0.11, GPU based, fast mode, Oxford Nanopore Technologies). FAST5 and FASTQ files were analyzed using an adapted snakemake19 v5.4.0 workflow from the nanoDx pipeline20 that enabled analysis on a local laptop computer. Within this pipeline, reads were aligned to the hg19 human reference genome (minimap2 v2.15),21 and copy number profiles were generated. The methylation status of the genome-wide CpG sites (5mC) was called (nanopolish v0.11.0)17 and binarized (cutoff beta value >0.6) together with 5mC signals for overlapping sites probed by the Illumina BeadChip 450K array. For each sample, an ad hoc random forest classifier (R/ranger package v0.10.1)22 was trained using the Heidelberg reference cohort of brain tumor methylation profiles, and a final report containing sequencing statistics and classification results was generated.”; see also at least p. 4: “In total, we performed NDMA on 105 individual patient samples using public reference data for more than 80 CNS tumor entities and compared methylation-based classification to the definite WHO integrated diagnosis obtained by full neuropathological workup. NDMA was concordant with final neuropathological diagnosis in 93 of the 105 cases (89%) (Figure 1; Supplementary Table 1). Most adult cases were classified as gliomas (20 glioblastomas, 14 oligodendrogliomas, and 10 astrocytomas) by NDMA (Figure 1A). Among the pediatric cases in the study (Figure 1B), medulloblastomas were the most common tumor type (23 cases) followed by pilocytic astrocytomas (10 cases). To initially evaluate the robustness of NDMA we analyzed 79 samples from 4 independent cohorts and compared these results to the final neuropathological diagnosis (Supplementary Table 1; Supplementary Figure 1). NDMA afforded classification of all cases, including cases not subclassified by standard diagnostic evaluation. Overall, this demonstrated a robust pipeline for NDMA with low coverage sequencing and methylation profiling”).
Claim 55: Djirackor discloses the limitations as shown in the rejections above. Further, Djirackor, as shown, discloses the following limitations:
wherein the DNA sequencing data is obtained by using a sequencing method capable of directly sensing a DNA modification, more preferably nanopore sequencing, further preferably wherein the reference data comprising DNA modification data is obtained by using a technique that differs from the technique used for obtaining DNA sequencing data of the subject and/or of the sample, preferably wherein said sample is obtained intraoperatively (see at least p. 9: “we demonstrate the accuracy, sensitivity, feasibility, and impact of ultra-low coverage nanopore whole-genome sequencing for intraoperative neuropathological classification. Even within our moderately sized cohort, we identified a substantial number of patients where improved intraoperative diagnostic accuracy would have impacted surgical decision making in real time. Importantly, methylation-based classification is a diagnostic approach generalizable far beyond neuro-oncology, and ongoing efforts to include a wider range of malignancies hold promise for pan-cancer classification”; see also at least pp. 3-4).
Claims 56 and 63: Djirackor discloses the limitations as shown in the rejections above. Further, Djirackor, as shown, discloses the following limitations:
wherein the disease and/or disorder is a cancer, preferably a tumor, preferably a brain tumor (see at least p. 4: “In total, we performed NDMA on 105 individual patient samples using public reference data for more than 80 CNS tumor entities and compared methylation-based classification to the definite WHO integrated diagnosis obtained by full neuropathological workup. NDMA was concordant with final neuropathological diagnosis in 93 of the 105 cases (89%) (Figure 1; Supplementary Table 1). Most adult cases were classified as gliomas (20 glioblastomas, 14 oligodendrogliomas, and 10 astrocytomas) by NDMA (Figure 1A). Among the pediatric cases in the study (Figure 1B), medulloblastomas were the most common tumor type (23 cases) followed by pilocytic astrocytomas (10 cases). To initially evaluate the robustness of NDMA we analyzed 79 samples from 4 independent cohorts and compared these results to the final neuropathological diagnosis (Supplementary Table 1; Supplementary Figure 1). NDMA afforded classification of all cases, including cases not subclassified by standard diagnostic evaluation. Overall, this demonstrated a robust pipeline for NDMA with low coverage sequencing and methylation profiling.”).
Claim 58: Djirackor discloses the limitations as shown in the rejections above. Further, Djirackor, as shown, discloses the following limitations:
wherein the reference data comprising DNA modification data is obtained by using a technique that differs from the technique used for obtaining DNA sequencing data, preferably wherein the DNA modification data comprises a DNA modification selected from the group consisting of: methylation or oxidation (see at least p. 3: “DNA was extracted using spin columns (QIAamp DNA Micro Kit, Qiagen, NL) according to the manufacturer’s protocol with ~15 mg of tumor tissue. Quantification of eluted DNA was performed on a Qubit 4.0 fluorometer using a dsDNA BR Assay (Thermo Fisher, USA) and assessed for purity using the NanoDrop 260/280 ratio (NanoDrop, Thermo Fisher). For intraoperative NDMA analysis, the tumor biopsy was immediately placed in lysis buffer (Qiagen) at the operating room, allowing digestion to begin during transport to the laboratory.10 Upon arrival, the biopsy lysate was homogenized for 30 s (TissueLyser, Qiagen) and digested for 10 min at 56°C and 5 min at 70°C. This reduced the total sample handling and DNA extraction time to 30 min. The DNA quantity and quality were still above the required standards”).
Claim 62: Djirackor discloses the limitations as shown in the rejections above. Further, Djirackor, as shown, discloses the following limitations:
A method of the pre-operative configuring of a DNA modification based DL- classifier, preferably a DNA modification based DL-classifier as used in Clam 57, to receive a sample comprising DNA and/or to receive DNA sequencing data and to generate a classification score for a disease and/or disorder, preferably for a tumor species (see at least Abstract: “Brain tumor surgery must balance the benefit of maximal resection against the risk of inflicting severe damage. The impact of increased resection is diagnosis-specific. However, the precise diagnosis is typically uncertain at surgery due to limitations of imaging and intraoperative histomorphological methods. Novel and accurate strategies for brain tumor classification are necessary to support personalized intraoperative neurosurgical treatment decisions. Here, we describe a fast and cost-efficient workflow for intraoperative classification of brain tumors based on DNA methylation profiles generated by low coverage nanopore sequencing and machine learning algorithms”; see also at least p. 2), the method comprising:
training a DNA modification based DL-classifier, which runs on a processor coupled to memory, comprising a pre- operative training routine using as input reference data (pp. 3-4: “Equipped with the manufacturer software MinKNOW (v20.06.17), the MinIT can obtain raw data (FAST5) in real time, separate barcoded reads and filter low-quality reads while simultaneously base-calling using the built-in proprietary software guppy (v4.0.11, GPU based, fast mode, Oxford Nanopore Technologies). FAST5 and FASTQ files were analyzed using an adapted snakemake19 v5.4.0 workflow from the nanoDx pipeline20 that enabled analysis on a local laptop computer. Within this pipeline, reads were aligned to the hg19 human reference genome (minimap2 v2.15),21 and copy number profiles were generated. The methylation status of the genome-wide CpG sites (5mC) was called (nanopolish v0.11.0)17 and binarized (cutoff beta value >0.6) together with 5mC signals for overlapping sites probed by the Illumina BeadChip 450K array. For each sample, an ad hoc random forest classifier (R/ranger package v0.10.1)22 was trained using the Heidelberg reference cohort of brain tumor methylation profiles, and a final report containing sequencing statistics and classification results was generated.”; see also at least p. 4: “In total, we performed NDMA on 105 individual patient samples using public reference data for more than 80 CNS tumor entities and compared methylation-based classification to the definite WHO integrated diagnosis obtained by full neuropathological workup. NDMA was concordant with final neuropathological diagnosis in 93 of the 105 cases (89%) (Figure 1; Supplementary Table 1). Most adult cases were classified as gliomas (20 glioblastomas, 14 oligodendrogliomas, and 10 astrocytomas) by NDMA (Figure 1A). Among the pediatric cases in the study (Figure 1B), medulloblastomas were the most common tumor type (23 cases) followed by pilocytic astrocytomas (10 cases). To initially evaluate the robustness of NDMA we analyzed 79 samples from 4 independent cohorts and compared these results to the final neuropathological diagnosis (Supplementary Table 1; Supplementary Figure 1). NDMA afforded classification of all cases, including cases not subclassified by standard diagnostic evaluation. Overall, this demonstrated a robust pipeline for NDMA with low coverage sequencing and methylation profiling”),
wherein the reference data comprises DNA modification data (see at least p. 3: “Nanopore sequencing can be used to detect base modifications such as 5-methylcytosine (5mC) in native DNA without the need for bisulfite conversion.17 This provides an ideal platform for rapid generation of tumor methylation profiles. We have previously demonstrated the potential of nanopore DNA methylation analysis (NDMA) for multimodal and rapid molecular diagnostics in brain cancer.18 This was used for subclassification of the tumor entities into over 80 subgroups according to the Heidelberg cohort.6 Here, we demonstrate how low-pass whole-genome nanopore sequencing can be used to generate DNA methylation profiles of a tumor biopsy that is sufficient for accurate classification in less than 2 h”); see also at least pp. 3-4: “Within this pipeline, reads were aligned to the hg19 human reference genome (minimap2 v2.15),21 and copy number profiles were generated. The methylation status of the genome-wide CpG sites (5mC) was called (nanopolish v0.11.0)17 and binarized (cutoff beta value >0.6) together with 5mC signals for overlapping sites probed by the Illumina BeadChip 450K array”; see also at least p. 4: “The 450k array was used to obtain genome-wide DNA methylation profiles for tumor samples, according to the manufacturer’s instructions (Illumina)”);
wherein the DNA modification based DL-classifier after training is configured to receive a DNA modification profile (pp. 3-4: “For each sample, an ad hoc random forest classifier (R/ranger package v0.10.1)22 was trained using the Heidelberg reference cohort of brain tumor methylation profiles, and a final report containing sequencing statistics and classification results was generated.”; see also at least p. 4: “In total, we performed NDMA on 105 individual patient samples using public reference data for more than 80 CNS tumor entities and compared methylation-based classification to the definite WHO integrated diagnosis obtained by full neuropathological workup. NDMA was concordant with final neuropathological diagnosis in 93 of the 105 cases (89%) (Figure 1; Supplementary Table 1). Most adult cases were classified as gliomas (20 glioblastomas, 14 oligodendrogliomas, and 10 astrocytomas) by NDMA (Figure 1A). Among the pediatric cases in the study (Figure 1B), medulloblastomas were the most common tumor type (23 cases) followed by pilocytic astrocytomas (10 cases). To initially evaluate the robustness of NDMA we analyzed 79 samples from 4 independent cohorts and compared these results to the final neuropathological diagnosis (Supplementary Table 1; Supplementary Figure 1). NDMA afforded classification of all cases, including cases not subclassified by standard diagnostic evaluation. Overall, this demonstrated a robust pipeline for NDMA with low coverage sequencing and methylation profiling”); and
wherein the DNA modification based DL-classifier generates a classification score output that indicates whether the DNA modification of the DNA modification profile is indicative for a disease and/or disorder, preferably wherein the DNA modification data comprises any one or more selected from the group of microarray data, preferably hybridization microarray data, whole genome bisulfite sequencing data, TAPS-sequencing data or nanopore sequencing data (see at least p. 3: “DNA methylation-based classification of CNS tumors can improve diagnostic precision. This can revise the WHO grading of a tumor and refine patient management.6 It has been shown to impact treatment of children by providing additional molecular subtyping and amending final diagnosis. 14 The basis of this classification is the DNA methylation pattern of cancer cells which is unique for each entity.15 Machine learning algorithms can be trained to identify these patterns and use them to discriminate the type of tumor entity as well as the subgroups, which are now included in the WHO tumor grading system.16”; see also at least pp. 3-4: “Equipped with the manufacturer software MinKNOW (v20.06.17), the MinIT can obtain raw data (FAST5) in real time, separate barcoded reads and filter low-quality reads while simultaneously base-calling using the built-in proprietary software guppy (v4.0.11, GPU based, fast mode, Oxford Nanopore Technologies). FAST5 and FASTQ files were analyzed using an adapted snakemake19 v5.4.0 workflow from the nanoDx pipeline20 that enabled analysis on a local laptop computer. Within this pipeline, reads were aligned to the hg19 human reference genome (minimap2 v2.15),21 and copy number profiles were generated. The methylation status of the genome-wide CpG sites (5mC) was called (nanopolish v0.11.0)17 and binarized (cutoff beta value >0.6) together with 5mC signals for overlapping sites probed by the Illumina BeadChip 450K array. For each sample, an ad hoc random forest classifier (R/ranger package v0.10.1)22 was trained using the Heidelberg reference cohort of brain tumor methylation profiles, and a final report containing sequencing statistics and classification results was generated.”; see also at least p. 4: “In total, we performed NDMA on 105 individual patient samples using public reference data for more than 80 CNS tumor entities and compared methylation-based classification to the definite WHO integrated diagnosis obtained by full neuropathological workup. NDMA was concordant with final neuropathological diagnosis in 93 of the 105 cases (89%) (Figure 1; Supplementary Table 1). Most adult cases were classified as gliomas (20 glioblastomas, 14 oligodendrogliomas, and 10 astrocytomas) by NDMA (Figure 1A). Among the pediatric cases in the study (Figure 1B), medulloblastomas were the most common tumor type (23 cases) followed by pilocytic astrocytomas (10 cases). To initially evaluate the robustness of NDMA we analyzed 79 samples from 4 independent cohorts and compared these results to the final neuropathological diagnosis (Supplementary Table 1; Supplementary Figure 1). NDMA afforded classification of all cases, including cases not subclassified by standard diagnostic evaluation. Overall, this demonstrated a robust pipeline for NDMA with low coverage sequencing and methylation profiling”),
wherein the DNA sequencing data is obtained by using a sequencing method capable of directly sensing a DNA modification, more preferably nanopore sequencing, further preferably wherein the reference data comprising DNA modification data is obtained by using a technique that differs from the technique used for obtaining DNA sequencing data of the subject and/or of the sample, preferably wherein said sample is obtained intraoperatively (see at least p. 9: “we demonstrate the accuracy, sensitivity, feasibility, and impact of ultra-low coverage nanopore whole-genome sequencing for intraoperative neuropathological classification. Even within our moderately sized cohort, we identified a substantial number of patients where improved intraoperative diagnostic accuracy would have impacted surgical decision making in real time. Importantly, methylation-based classification is a diagnostic approach generalizable far beyond neuro-oncology, and ongoing efforts to include a wider range of malignancies hold promise for pan-cancer classification”; see also at least pp. 3-4).
Claim Rejections - 35 U.S.C. § 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 set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied 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.
Claims 64-69 are rejected under AIA 35 U.S.C. § 103 as being unpatentable over Djirackor et al. (“Intraoperative DNA methylation classification of brain tumors impacts neurosurgical strategy.” Neuro-Oncology Advances 3.1 (2021): vdab149) (hereinafter “Djirackor”) in view of Maher et al. (U.S. Pub. No. 2021/0310075 A1) (hereinafter “Maher”).
Claim 64: Djirackor, as shown, discloses the following limitations:
A method for identifying a type of tumor in a patient based on DNA methylation status of a subset of a plurality of candidate DNA methylation sites, preferably wherein the tumor is a central nervous system (CNS) tumor (see at least Abstract: “Brain tumor surgery must balance the benefit of maximal resection against the risk of inflicting severe damage. The impact of increased resection is diagnosis-specific. However, the precise diagnosis is typically uncertain at surgery due to limitations of imaging and intraoperative histomorphological methods. Novel and accurate strategies for brain tumor classification are necessary to support personalized intraoperative neurosurgical treatment decisions. Here, we describe a fast and cost-efficient workflow for intraoperative classification of brain tumors based on DNA methylation profiles generated by low coverage nanopore sequencing and machine learning algorithms”; see also at least p. 2), the method comprising:
using at least one computer hardware processor (see at least p. 3: “DNA methylation-based classification of CNS tumors can improve diagnostic precision. This can revise the WHO grading of a tumor and refine patient management.6 It has been shown to impact treatment of children by providing additional molecular subtyping and amending final diagnosis. 14 The basis of this classification is the DNA methylation pattern of cancer cells which is unique for each entity.15 Machine learning algorithms can be trained to identify these patterns and use them to discriminate the type of tumor entity as well as the subgroups, which are now included in the WHO tumor grading system.16”; see also at least pp. 3-4: “Equipped with the manufacturer software MinKNOW (v20.06.17), the MinIT can obtain raw data (FAST5) in real time, separate barcoded reads and filter low-quality reads while simultaneously base-calling using the built-in proprietary software guppy (v4.0.11, GPU based, fast mode, Oxford Nanopore Technologies). FAST5 and FASTQ files were analyzed using an adapted snakemake19 v5.4.0 workflow from the nanoDx pipeline20 that enabled analysis on a local laptop computer. Within this pipeline, reads were aligned to the hg19 human reference genome (minimap2 v2.15),21 and copy number profiles were generated) to perform:
(A) obtaining DNA sequencing data previously obtained in part by sequencing a biological sample obtained from the patient (see at least Abstract: “Brain tumor surgery must balance the benefit of maximal resection against the risk of inflicting severe damage. The impact of increased resection is diagnosis-specific. However, the precise diagnosis is typically uncertain at surgery due to limitations of imaging and intraoperative histomorphological methods. Novel and accurate strategies for brain tumor classification are necessary to support personalized intraoperative neurosurgical treatment decisions. Here, we describe a fast and cost-efficient workflow for intraoperative classification of brain tumors based on DNA methylation profiles generated by low coverage nanopore sequencing and machine learning algorithms”; see also at least p. 3: “Nanopore sequencing can be used to detect base modifications such as 5-methylcytosine (5mC) in native DNA without the need for bisulfite conversion.17 This provides an ideal platform for rapid generation of tumor methylation profiles. We have previously demonstrated the potential of nanopore DNA methylation analysis (NDMA) for multimodal and rapid molecular diagnostics in brain cancer.18 This was used for subclassification of the tumor entities into over 80 subgroups according to the Heidelberg cohort.6 Here, we demonstrate how low-pass whole-genome nanopore sequencing can be used to generate DNA methylation profiles of a tumor biopsy that is sufficient for accurate classification in less than 2 h”);
(B) identifying, using the DNA sequencing data, the subset of the plurality of candidate DNA methylation sites as those DNA methylation sites for which the DNA sequencing data indicates DNA methylation status, preferably wherein: the DNA sequencing data indicates DNA methylation status only for sites in the identified subset of plurality of candidate DNA methylation sites, and the identified subset consists of between 0.001% and 4.0% of sites in the plurality of candidate DNA methylation sites (see at least p. 3: “Nanopore sequencing can be used to detect base modifications such as 5-methylcytosine (5mC) in native DNA without the need for bisulfite conversion.17 This provides an ideal platform for rapid generation of tumor methylation profiles. We have previously demonstrated the potential of nanopore DNA methylation analysis (NDMA) for multimodal and rapid molecular diagnostics in brain cancer.18 This was used for subclassification of the tumor entities into over 80 subgroups according to the Heidelberg cohort.6 Here, we demonstrate how low-pass whole-genome nanopore sequencing can be used to generate DNA methylation profiles of a tumor biopsy that is sufficient for accurate classification in less than 2 h”); see also at least pp. 3-4: “Within this pipeline, reads were aligned to the hg19 human reference genome (minimap2 v2.15),21 and copy number profiles were generated. The methylation status of the genome-wide CpG sites (5mC) was called (nanopolish v0.11.0)17 and binarized (cutoff beta value >0.6) together with 5mC signals for overlapping sites probed by the Illumina BeadChip 450K array”; see also at least p. 4: “The 450k array was used to obtain genome-wide DNA methylation profiles for tumor samples, according to the manufacturer’s instructions (Illumina)”);
(C) generating a sparse DNA methylation profile for the patient using the DNA sequencing data, the sparse DNA methylation profile indicating DNA methylation status of sites in the identified subset of the plurality of candidate DNA methylation sites (see at least p. 3: “DNA methylation-based classification of CNS tumors can improve diagnostic precision. This can revise the WHO grading of a tumor and refine patient management.6 It has been shown to impact treatment of children by providing additional molecular subtyping and amending final diagnosis. 14 The basis of this classification is the DNA methylation pattern of cancer cells which is unique for each entity.15 Machine learning algorithms can be trained to identify these patterns and use them to discriminate the type of tumor entity as well as the subgroups, which are now included in the WHO tumor grading system.16”; see also at least pp. 3-4: “Equipped with the manufacturer software MinKNOW (v20.06.17), the MinIT can obtain raw data (FAST5) in real time, separate barcoded reads and filter low-quality reads while simultaneously base-calling using the built-in proprietary software guppy (v4.0.11, GPU based, fast mode, Oxford Nanopore Technologies). FAST5 and FASTQ files were analyzed using an adapted snakemake19 v5.4.0 workflow from the nanoDx pipeline20 that enabled analysis on a local laptop computer. Within this pipeline, reads were aligned to the hg19 human reference genome (minimap2 v2.15),21 and copy number profiles were generated. The methylation status of the genome-wide CpG sites (5mC) was called (nanopolish v0.11.0)17 and binarized (cutoff beta value >0.6) together with 5mC signals for overlapping sites probed by the Illumina BeadChip 450K array. For each sample, an ad hoc random forest classifier (R/ranger package v0.10.1)22 was trained using the Heidelberg reference cohort of brain tumor methylation profiles, and a final report containing sequencing statistics and classification results was generated.”; see also at least p. 4: “In total, we performed NDMA on 105 individual patient samples using public reference data for more than 80 CNS tumor entities and compared methylation-based classification to the definite WHO integrated diagnosis obtained by full neuropathological workup. NDMA was concordant with final neuropathological diagnosis in 93 of the 105 cases (89%) (Figure 1; Supplementary Table 1). Most adult cases were classified as gliomas (20 glioblastomas, 14 oligodendrogliomas, and 10 astrocytomas) by NDMA (Figure 1A). Among the pediatric cases in the study (Figure 1B), medulloblastomas were the most common tumor type (23 cases) followed by pilocytic astrocytomas (10 cases). To initially evaluate the robustness of NDMA we analyzed 79 samples from 4 independent cohorts and compared these results to the final neuropathological diagnosis (Supplementary Table 1; Supplementary Figure 1). NDMA afforded classification of all cases, including cases not subclassified by standard diagnostic evaluation. Overall, this demonstrated a robust pipeline for NDMA with low coverage sequencing and methylation profiling”); and
(D)identifying the type of tumor in the patient by processing the sparse DNA methylation profile using a trained […] model to obtain output indicative of the type of the tumor in the patient, preferably further comprising: prior to performing (A), sequencing the biological sample obtained from the patient to obtain raw sequencing data and performing base calling on the raw sequencing data to obtain the DNA sequencing data, preferably wherein sequencing the biological sample comprises sequencing the biological sample using nanopore sequencing, more preferably wherein sequencing the biological sample consists of sequencing the biological sample for an amount of time between 10 and 45 minutes to obtain the DNA sequencing data (see at least p. 3: “DNA methylation-based classification of CNS tumors can improve diagnostic precision. This can revise the WHO grading of a tumor and refine patient management.6 It has been shown to impact treatment of children by providing additional molecular subtyping and amending final diagnosis. 14 The basis of this classification is the DNA methylation pattern of cancer cells which is unique for each entity.15 Machine learning algorithms can be trained to identify these patterns and use them to discriminate the type of tumor entity as well as the subgroups, which are now included in the WHO tumor grading system.16”; see also at least pp. 3-4: “Equipped with the manufacturer software MinKNOW (v20.06.17), the MinIT can obtain raw data (FAST5) in real time, separate barcoded reads and filter low-quality reads while simultaneously base-calling using the built-in proprietary software guppy (v4.0.11, GPU based, fast mode, Oxford Nanopore Technologies). FAST5 and FASTQ files were analyzed using an adapted snakemake19 v5.4.0 workflow from the nanoDx pipeline20 that enabled analysis on a local laptop computer. Within this pipeline, reads were aligned to the hg19 human reference genome (minimap2 v2.15),21 and copy number profiles were generated. The methylation status of the genome-wide CpG sites (5mC) was called (nanopolish v0.11.0)17 and binarized (cutoff beta value >0.6) together with 5mC signals for overlapping sites probed by the Illumina BeadChip 450K array. For each sample, an ad hoc random forest classifier (R/ranger package v0.10.1)22 was trained using the Heidelberg reference cohort of brain tumor methylation profiles, and a final report containing sequencing statistics and classification results was generated.”; see also at least p. 4: “In total, we performed NDMA on 105 individual patient samples using public reference data for more than 80 CNS tumor entities and compared methylation-based classification to the definite WHO integrated diagnosis obtained by full neuropathological workup. NDMA was concordant with final neuropathological diagnosis in 93 of the 105 cases (89%) (Figure 1; Supplementary Table 1). Most adult cases were classified as gliomas (20 glioblastomas, 14 oligodendrogliomas, and 10 astrocytomas) by NDMA (Figure 1A). Among the pediatric cases in the study (Figure 1B), medulloblastomas were the most common tumor type (23 cases) followed by pilocytic astrocytomas (10 cases). To initially evaluate the robustness of NDMA we analyzed 79 samples from 4 independent cohorts and compared these results to the final neuropathological diagnosis (Supplementary Table 1; Supplementary Figure 1). NDMA afforded classification of all cases, including cases not subclassified by standard diagnostic evaluation. Overall, this demonstrated a robust pipeline for NDMA with low coverage sequencing and methylation profiling”).
Djirackor does not explicitly disclose, but Maher, as shown, teaches that the trained model is a trained neural network model (see at least ¶ [0058]: FIG. 1A is an exemplary flowchart describing a process 100 of sequencing a fragment of cell-free (cf) DNA to obtain a methylation state vector, according to one or more embodiments. In order to analyze DNA methylation, an analytics system first obtains 110 a sample from an individual comprising a plurality of cfDNA molecules; see also at least ¶ [0077]: with the identified anomalous fragments, the analytics system may filter the set of methylation state vectors for a sample for use in other processes, e.g., for use in training and deploying a cancer classifier; see also at least ¶ [0203]: the classifier can include a logistic regression algorithm, a neural network algorithm, a support vector machine algorithm, a Naive Bayes algorithm, a nearest neighbor algorithm, a boosted trees algorithm, a random forest algorithm, a decision tree algorithm, a multinomial logistic regression algorithm, a linear model, or a linear regression algorithm; see also at least ¶ [0212]).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to combine the machine learning diagnostic techniques taught by Maher with the machine learning diagnostic systems disclosed by Djirackor, because Maher teaches at ¶ [0005] that its techniques provide “improved systems and methods for making use of existing data in order to improve the performance of classifiers that discriminate disease conditions.” See M.P.E.P. § 2143(I)(G).
Moreover, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to combine the machine learning diagnostic techniques taught by Maher with the machine learning diagnostic systems disclosed by Djirackor, because the claimed invention is merely a combination of old elements (the machine learning diagnostic techniques taught by Maher and the machine learning diagnostic systems disclosed by Djirackor), in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. See M.P.E.P. § 2143(I)(A).
Claim 65: The combination of Djirackor and Maher teaches the limitations as shown in the rejections above. Further, Djirackor, as shown, discloses the following limitations:
while the patient is undergoing surgery:
obtaining the biological sample from the patient (see at least pp. 6-7: “We further optimized the NDMA workflow to obtain rapid classification results within a surgically relevant time period, typically below 120 min. Benchmarking of rapid NDMA was established on biopsies from 20 independent surgical procedures. The median transport time from the operating room to the laboratory was 6 min (range 5-24 min); the median time for DNA isolation, purification, and library preparation was 41 min (22-61 min); real-time computational analysis was initiated after 20-60 min of sequencing, and the median runtime of each bioinformatics analysis cycle was 28 min (17-60 min) (Figure 2A). Reports could be returned to the operating room as fast as 91 min from the time when the sample was obtained. Fifteen of the 20 surgeries were still ongoing when the results were ready. A median of 4140 (696-10 803) CpG methylation sites were detected after 30 min of sequencing (Figure 2B, left) and ad hoc random forest classifiers with an average out-of-bag error rate of 7.6% were generated (Figure 2B, right). The error rate was reduced to 3.5% when tumors were classified on the level of clinically relevant methylation class families (MCF) rather than all subclasses (Figure 2C). Using the MCF classifier and a minimum of 3500 CpG sites as cutoff for reproducible classification20 yielded correct classification in 20/20 patients (Supplementary Table 4). This cutoff was reached after a median of 30 min of sequencing (Supplementary Table 4). In summary, NDMA can provide accurate classification of CNS tumors within a timeframe relevant for intraoperative decision making”);
sequencing the biological sample using nanopore sequencing to generate the DNA sequencing data (see also at least pp. 3-4: “Equipped with the manufacturer software MinKNOW (v20.06.17), the MinIT can obtain raw data (FAST5) in real time, separate barcoded reads and filter low-quality reads while simultaneously base-calling using the built-in proprietary software guppy (v4.0.11, GPU based, fast mode, Oxford Nanopore Technologies). FAST5 and FASTQ files were analyzed using an adapted snakemake19 v5.4.0 workflow from the nanoDx pipeline20 that enabled analysis on a local laptop computer”; see also at least Abstract: “We evaluated 6 independent cohorts containing 105 patients, including 50 pediatric and 55 adult patients. Ultra-low coverage whole-genome sequencing was performed on nanopore flow cells. Data were analyzed using copy number variation and ad hoc random forest classifier for the genome-wide methylation-based classification of the tumor”; see also at least pp. 6-7 and Figure 2); and
performing (A), (B), (C), and (D) (see the analysis of these steps in the rejection of claim 64, which is incorporated herein), preferably wherein the output indicative of the type of tumor in the patient includes a confidence associated with the type of tumor indicated,
the method further comprising: while the patient is undergoing the surgery and when the confidence is below a predetermined threshold confidence (see at least p. 7: “In this intraoperative cohort, surgery was stopped for 2 out of 20 patients (Figure 3A and B) due to uncertain tumor classification and for 1 patient (Figure 3C) due to classification as a possible lymphoma based on frozen section evaluation; NDMA correctly classified all 3 samples and the results would have warranted further resection. Conversely, another patient (Figure 3D) was returned to surgery after intraoperative imaging for resection of a small tumor remnant, while NDMA correctly classified the tumor as a WNT-activated subgroup medulloblastoma, for which further surgery was unnecessary. Precise classification of the tumor entity and subtype would have supported modification of the surgical strategy in 12 out of 20 patients (Figure 3E; Supplementary Table 4). In the complete set of analyses, all instances of NDMA discordance with the final neuropathological diagnosis would not have negatively impacted the surgical strategy if the result was combined with preoperative imaging and previous medical history (Supplementary Table 2). In summary, NDMA can accurately discriminate between tumor entities intraoperatively and guide surgical procedures when preoperative imaging and frozen section evaluation are ambiguous”; see also at least p. 5):
continuing to sequence the biological sample using the nanopore sequencing to generate additional DNA sequencing data (see at least pp. 5-6: “Out of the total 105 samples, discordant results between NDMA and neuropathological evaluation were observed in 12 cases (11%). These made up 4 of the 55 adult cases (7.3%), and 8 of the 50 pediatric cases (16%). Discordance was primarily observed in diagnostically challenging cases (8 cases) and 1 case with low tumor cell content (Supplementary Table 2). Clear misclassification by NDMA was observed for 3/105 samples (2.8%). Of note, 5/12 (42%) discordant cases were from recurring tumors despite only eight recurring tumors being found in the complete data (7.6%). All cases that produced results that were discordant between NDMA and standard neuropathology were further analyzed by Illumina Infinium HumanMethylation450 Bead Chip (450k) array (Table 2). The classification results of the Illumina sequencing matched NDMA in 3 cases and neuropathology in another 3 cases. A methylation classification could not be made in 4 cases due to DNA quality or quantity insufficiencies while a new tumor subtype was concluded for the remaining 2 cases (Supplementary Table 2)”; see also at least pp. 3-4);
augmenting the sparse DNA methylation profile using information in the additional DNA sequencing data to obtain an augmented sparse DNA methylation profile (see at least pp. 5-6 and the analysis above; see also pp. 3-4: “Equipped with the manufacturer software MinKNOW (v20.06.17), the MinIT can obtain raw data (FAST5) in real time, separate barcoded reads and filter low-quality reads while simultaneously base-calling using the built-in proprietary software guppy (v4.0.11, GPU based, fast mode, Oxford Nanopore Technologies). FAST5 and FASTQ files were analyzed using an adapted snakemake19 v5.4.0 workflow from the nanoDx pipeline20 that enabled analysis on a local laptop computer. Within this pipeline, reads were aligned to the hg19 human reference genome (minimap2 v2.15),21 and copy number profiles were generated. The methylation status of the genome-wide CpG sites (5mC) was called (nanopolish v0.11.0)17 and binarized (cutoff beta value >0.6) together with 5mC signals for overlapping sites probed by the Illumina BeadChip 450K array. For each sample, an ad hoc random forest classifier (R/ranger package v0.10.1)22 was trained using the Heidelberg reference cohort of brain tumor methylation profiles, and a final report containing sequencing statistics and classification results was generated.” See also p. 4); and
identifying the type of tumor in the patient by processing the augmented sparse DNA methylation profile using the trained […] model to obtain a second output indicative of the type of the tumor in the patient, more preferably further comprising: stopping the surgery based on the output indicative of the type of tumor in the patient; or performing the surgery in a manner that depends on the output indicative of the type of the tumor in the patient, preferably wherein the plurality of candidate DNA methylation sites consists of between 400,000 and 500,000 sites or between 800,000 and 900,000 sites, more preferably wherein: the DNA sequencing data was obtained by nanopore sequencing; and the plurality of candidate DNA methylation sites consists of a number of sites substantially equal to a number of CpG probes in a methylation profiling microarray (see at least pp. 5-6 and the analysis above; see also see at least p. 3: “DNA methylation-based classification of CNS tumors can improve diagnostic precision. This can revise the WHO grading of a tumor and refine patient management.6 It has been shown to impact treatment of children by providing additional molecular subtyping and amending final diagnosis. 14 The basis of this classification is the DNA methylation pattern of cancer cells which is unique for each entity.15 Machine learning algorithms can be trained to identify these patterns and use them to discriminate the type of tumor entity as well as the subgroups, which are now included in the WHO tumor grading system.16”; see also at least pp. 3-4: “Equipped with the manufacturer software MinKNOW (v20.06.17), the MinIT can obtain raw data (FAST5) in real time, separate barcoded reads and filter low-quality reads while simultaneously base-calling using the built-in proprietary software guppy (v4.0.11, GPU based, fast mode, Oxford Nanopore Technologies). FAST5 and FASTQ files were analyzed using an adapted snakemake19 v5.4.0 workflow from the nanoDx pipeline20 that enabled analysis on a local laptop computer. Within this pipeline, reads were aligned to the hg19 human reference genome (minimap2 v2.15),21 and copy number profiles were generated. The methylation status of the genome-wide CpG sites (5mC) was called (nanopolish v0.11.0)17 and binarized (cutoff beta value >0.6) together with 5mC signals for overlapping sites probed by the Illumina BeadChip 450K array. For each sample, an ad hoc random forest classifier (R/ranger package v0.10.1)22 was trained using the Heidelberg reference cohort of brain tumor methylation profiles, and a final report containing sequencing statistics and classification results was generated.”; see also at least p. 4: “In total, we performed NDMA on 105 individual patient samples using public reference data for more than 80 CNS tumor entities and compared methylation-based classification to the definite WHO integrated diagnosis obtained by full neuropathological workup. NDMA was concordant with final neuropathological diagnosis in 93 of the 105 cases (89%) (Figure 1; Supplementary Table 1). Most adult cases were classified as gliomas (20 glioblastomas, 14 oligodendrogliomas, and 10 astrocytomas) by NDMA (Figure 1A). Among the pediatric cases in the study (Figure 1B), medulloblastomas were the most common tumor type (23 cases) followed by pilocytic astrocytomas (10 cases). To initially evaluate the robustness of NDMA we analyzed 79 samples from 4 independent cohorts and compared these results to the final neuropathological diagnosis (Supplementary Table 1; Supplementary Figure 1). NDMA afforded classification of all cases, including cases not subclassified by standard diagnostic evaluation. Overall, this demonstrated a robust pipeline for NDMA with low coverage sequencing and methylation profiling”; see also Tables 1 and 2).
Djirackor does not explicitly disclose, but Maher, as shown, teaches that the trained model is a trained neural network model (see at least ¶ [0058]: FIG. 1A is an exemplary flowchart describing a process 100 of sequencing a fragment of cell-free (cf) DNA to obtain a methylation state vector, according to one or more embodiments. In order to analyze DNA methylation, an analytics system first obtains 110 a sample from an individual comprising a plurality of cfDNA molecules; see also at least ¶ [0077]: with the identified anomalous fragments, the analytics system may filter the set of methylation state vectors for a sample for use in other processes, e.g., for use in training and deploying a cancer classifier; see also at least ¶ [0203]: the classifier can include a logistic regression algorithm, a neural network algorithm, a support vector machine algorithm, a Naive Bayes algorithm, a nearest neighbor algorithm, a boosted trees algorithm, a random forest algorithm, a decision tree algorithm, a multinomial logistic regression algorithm, a linear model, or a linear regression algorithm; see also at least ¶ [0212]).
The rationales to modify/combine the teachings of Djirackor to include the teachings of Maher are presented above regarding claim 64 and incorporated herein.
Claim 66: The combination of Djirackor and Maher teaches the limitations as shown in the rejections above. Further, Djirackor, as shown, discloses the following limitations:
wherein generating the sparse DNA methylation profile comprises:
generating a data structure representing the methylation profile, the data structure configured to store values for a plurality of entries corresponding to the plurality of candidate DNA methylation sites (see at least p. 3: “Nanopore sequencing can be used to detect base modifications such as 5-methylcytosine (5mC) in native DNA without the need for bisulfite conversion.17 This provides an ideal platform for rapid generation of tumor methylation profiles. We have previously demonstrated the potential of nanopore DNA methylation analysis (NDMA) for multimodal and rapid molecular diagnostics in brain cancer.18 This was used for subclassification of the tumor entities into over 80 subgroups according to the Heidelberg cohort.6 Here, we demonstrate how low-pass whole-genome nanopore sequencing can be used to generate DNA methylation profiles of a tumor biopsy that is sufficient for accurate classification in less than 2 h”); see also at least pp. 3-4: “Within this pipeline, reads were aligned to the hg19 human reference genome (minimap2 v2.15),21 and copy number profiles were generated. The methylation status of the genome-wide CpG sites (5mC) was called (nanopolish v0.11.0)17 and binarized (cutoff beta value >0.6) together with 5mC signals for overlapping sites probed by the Illumina BeadChip 450K array”; see also at least p. 4: “The 450k array was used to obtain genome-wide DNA methylation profiles for tumor samples, according to the manufacturer’s instructions (Illumina)”); and
setting, based on the DNA sequencing data, values for a subset of the plurality of entries that correspond to the identified subset of the plurality of candidate DNA methylation sites (see at least p. 3: “Nanopore sequencing can be used to detect base modifications such as 5-methylcytosine (5mC) in native DNA without the need for bisulfite conversion.17 This provides an ideal platform for rapid generation of tumor methylation profiles. We have previously demonstrated the potential of nanopore DNA methylation analysis (NDMA) for multimodal and rapid molecular diagnostics in brain cancer.18 This was used for subclassification of the tumor entities into over 80 subgroups according to the Heidelberg cohort.6 Here, we demonstrate how low-pass whole-genome nanopore sequencing can be used to generate DNA methylation profiles of a tumor biopsy that is sufficient for accurate classification in less than 2 h”); see also at least pp. 3-4: “Within this pipeline, reads were aligned to the hg19 human reference genome (minimap2 v2.15),21 and copy number profiles were generated. The methylation status of the genome-wide CpG sites (5mC) was called (nanopolish v0.11.0)17 and binarized (cutoff beta value >0.6) together with 5mC signals for overlapping sites probed by the Illumina BeadChip 450K array”; see also at least p. 4: “The 450k array was used to obtain genome-wide DNA methylation profiles for tumor samples, according to the manufacturer’s instructions (Illumina)”),
wherein a first value for a first entry in the subset of the plurality of entries indicates presence or absence of DNA methylation at a candidate DNA methylation site in the identified subset to which the first entry corresponds, preferably wherein values of entries, which are in the plurality of entries but not in the subset of the plurality of entries, indicate that DNA methylation status was not indicated by the DNA sequencing data for candidate DNA methylation sites to which the entries correspond, more preferably wherein processing the sparse DNA methylation profile using the trained neural network model comprises processing values stored in the data structure using the trained neural network model, and/or wherein the subset of the plurality of entries in the data structure consists of between 0.001 and 4% of all entries in the data structure (see at least p. 3: “Nanopore sequencing can be used to detect base modifications such as 5-methylcytosine (5mC) in native DNA without the need for bisulfite conversion.17 This provides an ideal platform for rapid generation of tumor methylation profiles. We have previously demonstrated the potential of nanopore DNA methylation analysis (NDMA) for multimodal and rapid molecular diagnostics in brain cancer.18 This was used for subclassification of the tumor entities into over 80 subgroups according to the Heidelberg cohort.6 Here, we demonstrate how low-pass whole-genome nanopore sequencing can be used to generate DNA methylation profiles of a tumor biopsy that is sufficient for accurate classification in less than 2 h”); see also at least pp. 3-4: “Within this pipeline, reads were aligned to the hg19 human reference genome (minimap2 v2.15),21 and copy number profiles were generated. The methylation status of the genome-wide CpG sites (5mC) was called (nanopolish v0.11.0)17 and binarized (cutoff beta value >0.6) together with 5mC signals for overlapping sites probed by the Illumina BeadChip 450K array”; see also at least p. 4: “The 450k array was used to obtain genome-wide DNA methylation profiles for tumor samples, according to the manufacturer’s instructions (Illumina)”); and/or
wherein the data structure represents a vector,
wherein, within the vector, each value for a particular entry in the subset of the plurality of entries that corresponds to the identified subset of the plurality of candidate DNA methylation sites is either a 1, indicating presence of a DNA methylation, or -1, which indicates absence of a DNA methylation at the candidate DNA methylation site to which the particular entry corresponds, and
wherein, within the vector, each value for a particular entry not in the subset of the plurality of entries that corresponds to the identified subset of the plurality of candidate DNA methylation sites, is set to 0 indicating that the DNA sequencing data provides no indication as to the DNA methylation status at the candidate DNA methylation site to which the particular entry corresponds.
Claim 67: The combination of Djirackor and Maher teaches the limitations as shown in the rejections above.
Djirackor does not explicitly disclose, but Maher, as shown, teaches the following limitations:
wherein the trained neural network comprises a plurality of fully connected layers with non-linear activations therebetween and a classification head and/or wherein the trained neural network comprises: a first fully connected layer having a first input size corresponding to a total number of sites in the plurality of candidate DNA methylation sites and a first output size smaller than the input size; a second fully connected layer having a second input size equal to the first output size and a second output size smaller than the second input size; a third fully connected layer having a third input size equal to the second output size and a third output size smaller than the third input size and corresponding to a number of candidate types of the tumor; and a first non-linear activation between the first and second fully connected layers and a second non-linear activation between the second and third fully connected layers; preferably wherein the trained neural network comprises at least 10 million parameters, and wherein processing the sparse DNA methylation profile using the trained neural network comprises determining the output indicative of the type of the tumor in the patient by using values in the sparse DNA methylation profile and values of the at least 10 million parameters; and/or wherein the output indicative of the type of tumor in the patient comprises a plurality of likelihoods corresponding to a respective plurality of tumor types, wherein each particular likelihood of the plurality of likelihoods indicates a likelihood that the patient has a respective particular type of tumor in the plurality of tumor types (see at least ¶ [0209]: the neurons of the embedding layer can be fully connected to each of the inputs of the input layer. Each neuron of the output layer can be fully connected to each neuron of the embedding layer. Each neuron of the output layer can be associated with a Softmax activation function; see also at least ¶ [).
The rationales to modify/combine the teachings of Djirackor to include the teachings of Maher are presented above regarding claim 64 and incorporated herein.
Claim 68: The combination of Djirackor and Maher teaches the limitations as shown in the rejections above. Further, Djirackor, as shown, discloses the following limitations:
wherein sequencing the biological sample using nanopore sequencing comprises sequencing the biological sample using nanopore sequence with adaptive sampling and/or wherein sequencing the biological sample using nanopore sequencing comprising: while a DNA strand is being sequenced using a nanopore part of a nanopore sequencing apparatus :obtaining a partial sequence of the DNA strand using measurements generated by the nanopore; determining whether the partial sequence maps to at least one of the plurality of candidate DNA methylation sites; when it is determined that the partial sequence does not map to at least one of the plurality of candidate DNA methylation sites, ejecting the DNA strand from the nanopore; and when it is determined that the partial sequence maps to the at least one of the plurality of candidate DNA methylation sites, continuing to sequence the DNA strand (pp. 3-4: “Equipped with the manufacturer software MinKNOW (v20.06.17), the MinIT can obtain raw data (FAST5) in real time, separate barcoded reads and filter low-quality reads while simultaneously base-calling using the built-in proprietary software guppy (v4.0.11, GPU based, fast mode, Oxford Nanopore Technologies). FAST5 and FASTQ files were analyzed using an adapted snakemake19 v5.4.0 workflow from the nanoDx pipeline20 that enabled analysis on a local laptop computer. Within this pipeline, reads were aligned to the hg19 human reference genome (minimap2 v2.15),21 and copy number profiles were generated. The methylation status of the genome-wide CpG sites (5mC) was called (nanopolish v0.11.0)17 and binarized (cutoff beta value >0.6) together with 5mC signals for overlapping sites probed by the Illumina BeadChip 450K array. For each sample, an ad hoc random forest classifier (R/ranger package v0.10.1)22 was trained using the Heidelberg reference cohort of brain tumor methylation profiles, and a final report containing sequencing statistics and classification results was generated.”; see also at least pp. 5-6 and 9).
Claim 69: The combination of Djirackor and Maher teaches the limitations as shown in the rejections above. Further, Djirackor, as shown, discloses the following limitations:
generating training data using microarray methylation data (see at least p. 4: “The 450k array was used to obtain genome-wide DNA methylation profiles for tumor samples, according to the manufacturer’s instructions (Illumina). DNA methylation data were generated at the Institute for Neuropathology, Charite Universitaets Medizin (Berlin, Germany) using >400 ng of DNA was used as input material. The tumor classification was performed according to the established protocol”).
Djirackor does not explicitly disclose, but Maher, as shown, teaches the following limitations:
training a neural network model using the training data to obtain the trained neural network model, preferably wherein generating the training data using microarray methylation data comprises generating, from microarray methylation data, sparse DNA methylation profiles representative of types of sparse DNA methylation profiles that would be generated using nanopore sequencing with a threshold amount of time (see at least ¶ [0058]: FIG. 1A is an exemplary flowchart describing a process 100 of sequencing a fragment of cell-free (cf) DNA to obtain a methylation state vector, according to one or more embodiments. In order to analyze DNA methylation, an analytics system first obtains 110 a sample from an individual comprising a plurality of cfDNA molecules; see also at least ¶ [0077]: with the identified anomalous fragments, the analytics system may filter the set of methylation state vectors for a sample for use in other processes, e.g., for use in training and deploying a cancer classifier; see also at least ¶ [0203]: the classifier can include a logistic regression algorithm, a neural network algorithm, a support vector machine algorithm, a Naive Bayes algorithm, a nearest neighbor algorithm, a boosted trees algorithm, a random forest algorithm, a decision tree algorithm, a multinomial logistic regression algorithm, a linear model, or a linear regression algorithm; see also at least ¶ [0212]).
The rationales to modify/combine the teachings of Djirackor to include the teachings of Maher are presented above regarding claim 64 and incorporated herein.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure. The following references have been cited to further show the state of the art with respect to genetic analyses and classification.
Mueller et al. (U.S. Pub. No. 2024/0379236 A1) (diagnosis and classification of disease in a subject); and
Reid et al. (U.S. Pub. No. 2017/0233804 A1) (analysis of a polymer, including using adaptive nanopore sampling).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Christopher Tokarczyk, whose telephone number is 571-272-9594. The examiner can normally be reached Monday-Thursday between 6:00 AM and 4:00 PM Eastern.
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/CHRISTOPHER B TOKARCZYK/ Primary Examiner, Art Unit 3687
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