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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 4/10/2026 has been entered.
Status of Claims
Currently, claims 1 and 7 are pending in the instant application. All the amendments and arguments have been thoroughly reviewed but are deemed insufficient to place this application in condition for allowance. The following rejections constitute the complete set being presently applied to the instant Application. This action is Non-FINAL.
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
Any rejection not reiterated is withdrawn in view of the amendments to the claims.
Claim Rejections - 35 USC § 103
Claim 1 and 7 are rejected under 35 U.S.C. 103 as being unpatentable over Sharma (Sharma et al; US2013/0116132) in view of Scherzer (Scherzer et al; PNAS, vol 104, pages 955-960; 2007), and Bolotina (Bolotina, V.; US 2019/0049466), and further in view of Costa (Costa et al; Journal of Biomedicine and Biotechnology, doi:10.1155/2010/853916; pages 1-19; 2010), Leggio (Leggio et al; International Journal of Molecular Sciences; 2017; 18, pages 1-30), Majidinia (Majidinia et al; Mol Biol Rep, vol 43, pages 1193-1204; 2016), Kumar (Kumar et al; Mol Neurbiol; vol 54, pages 7224-7234; 2017), and Koch (Koch et al; Am J Respir Cell Mol Biol, vol 59, pages 145-157, 2018).
With regard to claim 1 , Sharma teaches diagnostic methods for detecting early stage neurodegenerative disease, including Parkinson’s disease (PD) (para 0115). Sharma teaches that the diagnostic method comprises isolating mRNA from a whole blood sample, hybridizing cDNA reverse transcribed from the mRNA to a set of oligonucleotides specific for a specific stage of neurodegenerative disease, producing a characteristic transcript pattern reflecting the level of gene expression in the sample, and comparing the pattern to a standard diagnostic pattern to determine the degree of correlation between the patterns so as to indicate the presence of a specific stage of the neurodegenerative disease or condition (para 0145-0149). Sharma teaches fitting data of expression patterns to a classification model to determine the pattern that is reflective of a specific stage or progression of a neurodegenerative disease or condition (para 0188). Additionally, Scherzer exemplifies an assay which analyzed genome wide expression in whole blood samples of patients with early PD (see whole document), as well as healthy controls (page 959). Scherzer teaches that a window of opportunity exists for interventions designed to prevent PD or delay the onset of the clinical disease with a reliable risk predictor of PD (see page 958). Scherzer teaches using gene expression in cellular blood and clinical data to rapidly characterize laboratory biomarkers for PD risk. Scherzer teaches designing a continuous score to identify individuals with higher or lower risk of PD that accounts for the complex biology of the disease. Therefore, it would have have been prima facie obvious to one of ordinary skill in the art prior to the effective filing date, to have applied the method of Sharma specifically for the detection of PD because Scherzer teaches that a window of opportunity exists for interventions designed to prevent PD or delay the onset of the clinical disease with a reliable risk predictor of PD as well as using gene expression in cellular blood and clinical data to rapidly characterize laboratory biomarkers for PD risk.
Sharma and Scherzer do not teach administering treatment for PD. However Bolotina teaches that suitable PD treatment includes L-dopa, anticholinergics, MAO-B inhibitors, COMT inhibitors, etc (see para 0344-0349). Therefore it would have been prima facie obvious to the ordinary artisan prior to the effective filing date to have treated preclinical PD patients in the method of Sharma and Scherzer because Bolotina teaches that it is desirable to be able to identify PD patients without symptoms (eg: preclinical) to be able to administer a suitable PD treatment to prevent further decline and Scherzer teaches that a window of opportunity exists for interventions designed to prevent PD or delay the onset of the clinical disease with a reliable risk predictor of PD.
While Scherzer teaches microarray based analysis of RNA expression, with regard to claim 1, neither Sharma, Scherzer nor Bolotina teach preparing an RNA library from a whole blood sample and sequencing that RNA library which comprises non-coding RNA.
However Costa teaches next generation sequencing applications, such as RNA-Seq, allows for the analysis of the RNA transcriptome (page 2). Costa teaches that the discovery of endogenous small interfering RNA (siRNA) and microRNA (miRNA), long interspersed noncoding RNA (lincRNA), and many others represent part of the pieces of a complicated transcription puzzle. Costa teaches that discovering and interpreting the complexity of a transcriptome by analyzing the molecular constituents of cells and tissues, will allow for more complete knowledge of many biological issues such as the onset of disease and progression (page 2). Costa teaches: sample isolation allowing for enriched factions of mRNA and regulatory molecules such as miRNA, siRNA, small ncRNA, etc,; library preparation (see pages 4-5); sequencing the RNA library (page 6; figure 2); and computational analysis leading to differential expression data for transcripts (figure 3, pages 6-11). While Costa illustrates creating transcriptome libraries after depletion of ribosomal RNA, Koch provides a review of RNA sequencing. Koch teaches that transcriptome libraries can be constructed from total RNA, RNA enriched for mRNA, or RNA depleted of rRNA.
Additionally, the prior art provides motivation for analysis of both mRNA and non-coding RNA expression in PD analysis:
Leggio teaches that the long preclinical PD phase represents a fundamental diagnostic window allowing for an early PD diagnosis. Leggio reviews a number of studies that showed differential expression of miRNAs (non-coding RNA) in whole blood samples of patients with PD (see page 13-14, section 5.1), including in PBMCs and leukocytes. Leggio teaches that miRNAs have gained importance as potential biomarkers for the detection of the preclinical stage of the disease. Leggio teaches that the use of blood samples for miRNA profiling, which is easy to extract, represents a step forward in clinical application (see page 19, section 7). Leggio teaches that consistency was found between different studies performed on liquid biopsies, when comparing PD patients with their controls (page 15, last para).
Majidinia teaches that non-coding RNAs impact the pathogenesis and progress of PD (see abstract). Majidinia teaches that expression levels of both miRNA and lncRNA have been shown to be associated with PD (see whole document).
Kumar also teaches the functions of circRNAs are very diverse, including regulation of miRNA (see page 7229, col 1). Kumar teaches that the involvement of miRNA in PD warrants studies exploring the involvement of circRNAs in PD (see page 7230, col 1).
Therefore, it would have been prima facie obvious to the ordinary artisan prior to the effective filing date to analyze all of the RNA from whole blood, including mRNA and non-coding RNA: tRNA, rRNA, miRNA, siRNA, piRNA, snoRNA, snRNA, exRNA, scaRNA and lncRNA to detect and treat PD as early in the disease as possible, in the method of Sharma, Scherzer and Bolotina because Costa and Koch teach the ability to analyze transcripts from particular cell populations using RNA-Seq allows practitioners to arrive at a more complete assessment of biological issues such as the onset of disease and progression. Further, Leggio teaches that the long preclinical PD phase represents a fundamental diagnostic window allowing for an early PD diagnosis and reviews studies showing associations between PD and miRNA expression, for example, including in whole blood samples, while Majidinia and Kumar teach studies analyzing the involvement miRNAs, circRNAs, and lncRNA in PD.
Although the claims have been amended to recite determining the differential expression of a plurality of RNA sequences comprising mRNA, tRNA, rRNA, miRNA, siRNA, piRNA, snoRNA, snRNA, exRNA, scaRNA and lncRNA, in determining expression of the entire RNA transcriptome, the ordinary artisan would have necessarily determined the expression of mRNA, tRNA, rRNA, miRNA, siRNA, piRNA, snoRNA, snRNA, exRNA, scaRNA and lncRNA because these RNA species are necessarily contained within the RNA transcriptome
With regard to claim 7, given that Bolotina also teaches that some patients with PD also experience dementia, it would have been further prima facie obvious to one of ordinary skill in the art prior to the effective filing date to have included comparison of a subject’s blood RNA transcriptome profile to a reference profile from patients with cognitive impairment, including MCI and/or FTD.
Response to Arguments
The response traverses the rejections. The response addresses each reference in dividually, however one cannot show nonobviousness by attacking references individually where the rejections are based on combinations of references. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981); In re Merck & Co., 800 F.2d 1091, 231 USPQ 375 (Fed. Cir. 1986). The response asserts that Koch only teaches that starting material for RNA-SEQ can be from one of three sources rather than suggesting the creation of a total RNA transcriptome. The response asserts that Koch teaches in the “Methods” section the creation of a poly-A enriched library which is a significant difference from the claimed invention. This argument has been thoroughly reviewed but was not found persuasive because the rejection relies on a combination of references to address the obviousness of determining expression of a whole blood RNA transcriptome. The rejections are maintained.
Conclusion
No claims are allowed.
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/JEHANNE S SITTON/Primary Examiner, Art Unit 1682
Claims 1, 4, 6-7, and 21 are rejected under 35 U.S.C. 103 as being unpatentable over Shigemizu (Shigemizu et al; Alzheimer’s Research and Therapy, 12:87, pages 1-12; July 16, 2020) in view of Sharma (US 2013/0116132), Chaussabel (Chaussabel, D; Seminars in Immunology, vol 27, pages 58-66; 2015), Costa (Costa et al; Journal of Biomedicine and Biotechnology, doi:10.1155/2010/853916; 2010; pages 1-19), Koch (Koch et al; Am J Respir Cell Mol Biol; Vol 29, pages 145-157; 2019), Lauretti (Lauretti et al; Ageing Research Reviews; vol 71, 2021, pages 1-15), and Sevigny (Sevigny et al; Nature, vol 357, 1-21, including pages 50-56, extended data and figures, and addendum; 2016); as evidenced by Murillo (Murillo et al; Cell; vol 177, pages 463-477; 2019) and Deryusheva (Deryusheva et al; RNA; vol 25, pages 17-22, 2019).
Regarding claims 1, 7, and 21, Shigemizu teaches the need for blood based biomarkers for early diagnosis of AD (see abstract; whole document). Shigemizu teaches extracting a whole blood sample from patients with AD, mild cognitive impairment (MCI, other form of dementia) and normal controls, preparing an RNA library from the blood sample, sequencing the RNA library, determining differential expression of a plurality of RNA sequences comprised within the library, and creating a blood RNA transcriptome profile based on differential expression of the RNA (see pages 2-3, “Methods). Shigemizu teaches comparing the blood RNA transcriptome profile of patients with AD, MCI, and normal controls (page 7, column 2) and modeling the differential expression patterns and validating the model in a prospective cohort (subject at risk for developing AD) (page 3, col 2; pages 6-7) to diagnose patients with AD. With regard to claim 4, Shigemizu teaches a risk prediction model analysis of subject characteristics including age, sex, and APOE genotypes (see table 1; page 6-7).
The instant claims are different from Shigemizu in that they require: preparing a whole blood RNA library, analysis of all RNA sequences including coding (mRNA) and noncoding RNA in the whole blood sample, and treatment of AD with, for example, aducanumab.
Although Shigemizu extracted whole blood samples, Shigemizu does not teach transcript analysis of the entire whole blood sample. However, the art prior to the effective filing date provides motivation to analyze RNA transcripts in the whole blood sample.
Sharma teaches analysis of characteristic RNA transcript expression patterns for the diagnosis of neurological disease, including Alzheimer’s disease and other forms of dementia, including MCI (see abstract; para 0104-0115; tables 1-11). Sharma teaches building a classifier by training the data that is capable of discriminating between members (eg: diseased class) and non-members (eg: non-diseased class) of a given class (para 0225-0253). Sharma teaches that these characteristic expression patterns in biological samples are compared to a test individual to identify the presence of disease (para 0136-0149). Sharma specifically teaches that the sample is preferably whole blood (para 0118).
Chaussabel teaches that whole genome profiling holds promise for adding new perspective, breadth, context, and depth across a wide range of medical disciplines (see abstract). Chaussabel teaches blood transcriptome analysis includes analysis of unfractionated blood (page 59). Chaussabel teaches that blood collection tubes have been developed that contain a solution disrupting cells and precipitating RNA immediately upon homogenization, allowing the storage of samples indefinitely and with robust results. Chaussabel teaches that the simplicity of this sampling RNA profile stabilization makes whole blood transcriptomics stand out among other system approaches. Chaussabel teaches a review of the literature revealed an abundance of studies in transcriptome profiling of whole blood (figure 1, pages 60-61; references cited). Chaussabel teaches that interest in blood transcriptome profiling involving patients with neurological disorders has soared, including for Alzheimer’s disease, spurred by the prospect of the development of a predictive blood transcriptome diagnostic test.
Therefore it would have been prima facie obvious to one of ordinary skill in the art prior to the effective filing date, to have modified the analysis of the samples taught by Shigemizu to include the whole blood sample because Sharma specifically teaches to do so in methods of detecting RNA expression patterns diagnostic of different neurological diseases, including Alzheimer’s disease. Further, Chaussabel teaches that the simplicity of RNA profile stabilization (from blood collection tubes that contain a solution disrupting cells and precipitating RNA immediately upon homogenization) allowing for storage of samples indefinitely and with robust data makes whole blood transcriptomics stand out among other system approaches.
Shigemizu in view of Sharma and Chaussabel do not describe whether the expression analysis included all RNA from the sample, including coding and non-coding RNA, however, the art prior to the effective filing date provides motivation to modify the method of Shigemizu in view of Sharma and Chaussabel to include analysis of all RNA transcripts present in whole blood:
Costa teaches next generation sequencing applications, such as RNA-Seq, allows for the analysis of the RNA transcriptome (page 2). Costa teaches that the discovery of endogenous small interfering RNA (siRNA) and microRNA (miRNA), long interspersed noncoding RNA (lincRNA), and many others represent part of the pieces of a complicated transcription puzzle. Costa teaches that discovering and interpreting the complexity of a transcriptome by analyzing the molecular constituents of cells and tissues, will allow for more complete knowledge of many biological issues such as the onset of disease and progression (page 2). Costa teaches: sample isolation allowing for enriched factions of mRNA and regulatory molecules such as miRNA, siRNA, small ncRNA, etc,; library preparation (see pages 4-5); sequencing the RNA library (page 6; figure 2); and computational analysis leading to differential expression data for transcripts (figure 3, pages 6-11).
Koch reviews the steps of typical RNA-seq analysis. Koch teaches “The protocol of RNA-seq starts with the conversion of RNA, either total, enriched for mRNA, or depleted of rRNA, into cDNA.” (page 145). Therefore, Koch confirms that RNA-seq analysis can be carried out on total RNA in a sample, without enriching for mRNA or depleting rRNA.
Lauretti (see whole document) teaches advances in sequencing technology have led to the identification of thousands of RNA which are transcribe but not translated into proteins (pages 1-2). Lauretti teaches that aberrant expression of non-coding RNAs and mRNAs have been observed in the CNS and serum of AD patients, suggesting their involvement in AD (page 2, whole document). Lauretti teaches that the RNA transcriptome includes tRNA, rRNA, miRNA, siRNA, piRNA, snoRNA, snRNA, and lncRNA (see page 2).
Therefore, it would have been prima facie obvious to one of ordinary skill in the art prior to the effective filing date, to modify the method taught by Shigemizu, Sharma, and Chaussabel, to include analysis of all RNA transcripts in the sample, including mRNA and non-coding RNAs including tRNA, rRNA, miRNA, siRNA, piRNA, snoRNA, snRNA, exRNA, scaRNA, and lncRNA to arrive at analysis of the entire transcriptome because Costa teaches that discovering and interpreting the complexity of a transcriptome by analyzing the molecular constituents of cells and tissues, will allow for more complete knowledge of many biological issues such as the onset of disease and progression. Further, Koch Koch confirms that RNA-seq analysis can be carried out on total RNA in a sample while Lauretti teach that non coding RNAs are differentially expressed in patients with Alzheimer’s disease and that it is important to determine non coding RNA changes in Alzheimer’s disease patients for precise distinction of stage of disease (see page 11, col 1). It is noted that neither Costa, Koch, nor Lauretti teach exRNA or scaRNA, however, as evidenced by each of Murillo and Deryusheva respectively, the species of exRNA and scaRNA are necessarily components of whole RNA transcriptomes.
Shigemizu, Sharma, Chaussabel, Costa, Koch, and Lauretti do not teach do not teach administering aducanumab to a patient with AD, however Sevigny teaches that aducanumab reduced soluble and insoluble Aβ plaques in a dose dependent matter in a transgenic mouse model of AD (abstract). Sevigny teaches that in patients with prodromal or mild AD, aducanumab reduced brain Aβ in a dose and time dependent manner. Therefore, it would have been prima facie obvious to the ordinary artisan prior to the effective filing date to administer aducanumab in patients with AD, for the obvious benefit of treating patients as early in the course of the disease as possible.
