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 Claims
Currently, claims 1, 4, 6-7, and 21-23 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 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Any rejection not reiterated is withdrawn in view of the amendments to the claims.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 § 112
Claims 22 and 23 are rejected under 35 U.S.C. 112(b) as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor regards as the invention.
Newly added claims 22 and 23 require that the RNA in the RNA library comprising an A260/A280 ratio >2.0 and a 28S/18S ratio > 5.0. The response asserts that paragraphs 0091 and 0093 provide support for these amendments. However the specification at paragraph 0093 refers to the 28S/18S ratio as a parameter in the context of the sample, not the RNA library. Although the claim only recites the first two steps as “extracting a whole blood sample…” and “preparing an RNA library…” these are broadly recited limitations which actually encompass many individual steps. However, it is not clear as to which of the many steps required to meet these claimed limitations, these standards are applied. The prior art (see Chacko, S. Validating RNA Quantity and Quality: Analysis of RNA Yield, Integrity, and Purity; 2005, BioPharm International, vol 18, pages 1-11) teaches that the 28S/18S parameter is used to evaluate RNA integrity after isolation and that a ratio of 2:1 is a good indication of intact RNA. Additionally, Chomczynski (Chomczynski et al; PLOS One, 11(2); 2016; pages 1-15) teaches that analysis of whole blood RNA samples showed that the ratio ranges from 1.02 to 1.06 (see page 6) while an average of 35 samples was 0.96. Chomczynski teaches this appears to represent a ratio that is characteristic of RNA isolated from human whole blood and was similar to other reports of methods for RNA isolation from human whole blood. It is further noted that the examiner could not find any evidence that a ratio of 28S/18S greater than 5 for isolated RNA from a sample, let alone a sample of whole blood, is possible. The specification does not provide any guidance as to how measurement of this parameter with the required ratio is achieved. Accordingly, the metes and bounds of the claims are unclear.
Claim Rejections - 35 USC § 103
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), Sevigny (Sevigny et al; Nature, vol 357, 1-21, including pages 50-56, extended data and figures, and addendum; 2016), and Sims (Sims et al; Nature Reviews, vol 15, 2014; pages 121-132); 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, a transcriptome profile that comprises over 20 million aligned reads, 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 transcribed 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 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 the transcriptome profile of the subject comprises at least 20 million aligned reads, however Sims teaches a review of sequencing depth and coverage for next generation sequencing (see whole document). Sims teaches that one application of transcriptome sequencing is the identification of novel transcripts such as lncRNAs, which are expressed at low levels (see pages 125-126). Sims teaches that the discovery of novel rare transcripts is estimated to require >200 million paired end reads. Sims also teaches that if the expectation is that expression abundant transcript changes across conditions, than 36 million reads per sample may be sufficient (see page 126). Therefore, it would have been prima facie obvious to the ordinary artisan prior to the effective filing date to have constructed a profile which comprises at least 20 million aligned reads in view of the teachings of Sims.
Shigemizu, Sharma, Chaussabel, Costa, Koch, Lauretti, and Sims 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.
Response to Arguments
The response traverses the rejection. The response reiterates arguments from previous responses. These are not found persuasive for the reasons already made of record in previous office actions. The response asserts that with regard to Sharma, Sharma teaches “preferably the sample from blood is whole blood or a blood product… such as plasma or serum” and that in view of Sharma, the buffy coat of Shigemizu is fully acceptable and Sharma provides no motivation to make a change. This argument has been thoroughly reviewed but was not found persuasive because in teaching the use of whole blood, Sharma provides ample motivation to arrive at a method that assess RNA expression in whole blood. The fact that Sharma also teaches that blood products may be used is not considered a teaching away from using whole blood because Sharma specifically teaches that it is one of two options in gene expression analysis. This teaching, coupled with the teachings of Chaussabel, provide motivation to analyze expression in whole blood samples. The response repeatedly asserts, in the context of each reference cited, that the examiner has used applicants specification as a template. This argument has been thoroughly reviewed but was not found persuasive because it must be recognized that any judgment on obviousness is in a sense necessarily a reconstruction based upon hindsight reasoning. But so long as it takes into account only knowledge which was within the level of ordinary skill at the time the claimed invention was made, and does not include knowledge gleaned only from the applicant's disclosure, such a reconstruction is proper. See In re McLaughlin, 443 F.2d 1392, 170 USPQ 209 (CCPA 1971).
Conclusion
No claims are allowed herein.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/JEHANNE S SITTON/Primary Examiner, Art Unit 1682
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