DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Status
Claims 66, 69-71, 81-82, 85, and 103-104 are pending and under examination.
Claims 66, 69-71, 81-82, 85, and 103-104 are rejected.
Claim 66 is independent.
Claims 66, 82, and 103 are amended.
Claims 1-65, 67-68, 72-80, 83-84, and 86-102 are canceled.
No claims are new or withdrawn.
Office Action Overview
Rejections applied
Abbreviations
x
112/b Indefiniteness
PHOSITA
"a Person Having Ordinary Skill In The Art before the effective filing date of the claimed invention"
112/b "Means for"
BRI
Broadest Reasonable Interpretation
112/a Enablement,
Written description
CRM
"Computer-Readable Media" and equivalent language
112 Other
IDS
Information Disclosure Statement
x
102, 103
JE
Judicial Exception
101 JE(s)
112/a
35 USC 112(a) and similarly for 112/b, etc.
101 Other
N:N
page:line
Double Patenting
MM/DD/YYYY
date format
Priority
As detailed in the 06/04/2021 filing receipt, this application is a continuation of application PCT/US2019/033574, filed 05/22/2019, which claims the benefit of priority to Provisional Application No. 62/675,590, filed 05/23/2018.
Overview of Withdrawal/Revision of Objections/Rejections
In view of the amendment and remarks received 12/30/2025:
• The claim objections are withdrawn.
• The 112(b) rejections are withdrawn because amended claim 66 recites active steps of "deriving annotations..." and "obtaining metadata..." Relatedly, the product-by-process interpretations are no longer asserted for the same reasoning.
• A new 112(b) rejection is applied below for a clarity issue in claim 66(c).
• The 103 rejection is maintained for reasons given below in "Response to Applicant Arguments - 35 USC 103." Briefly: It is not yet clear that the instant claims recite a method that is distinct from deriving annotations de novo in the Anczuków reference.
Rejections and/or objections not maintained from previous office actions are withdrawn. The following rejections and/or objections are either maintained or newly applied. They constitute the complete set applied to the instant application.
Claim Interpretation
Claim 66 step (a) recites the term a "subject". There is no limiting definition of "subject" in the specification. The term will be interpreted to include animals and humans.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 66, 69-71, 81-82, 85, and 103-104 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention. Claims depending from rejected claims are rejected similarly, unless otherwise noted, and any amendments in response to the following rejections should be applied throughout the claims, as appropriate.
In step (c) of claim 66, the relationships are unclear among the clauses, specifically among:
"deriving annotations of a plurality of types of alternative splicing based on public RNA-seq data or other biological data"
"both with and without the splicing factor error de novo"
"from the RNA-seq data or the other biological data" and
"based on assembly of exon duos and/or exon trios."
It is unclear whether "deriving annotations" is based on the two "based on" clauses, or if "deriving annotations" is based on the first "based on" clause. This leads to further questions as to what the second "based on" clause refers to, be it RNA-seq data or the other biological data, or both. It is unclear whether the clause "both with and without the splicing factor error de novo..." refers to the derived annotations or to the RNA-seq data or other biological data. It is suggested to amend at least by reciting "de novo" with "deriving annotations," i.e., "deriving annotations de novo," to avoid ambiguity with "de novo," while amending to clarify the rest of the claim appropriately. For compact examination, step (c) of claim 66 will be interpreted as deriving annotations of a plurality of types of alternative splicing from RNA-seq data or other biological data.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 66, 69-71, 81, 82, 85, 103, and 104 are rejected under 35 U.S.C. 103 as being unpatentable over Anczuków, (Molecular cell, vol. 60(1), pages 105-117 (2015); 98 pages total with Supplemental Material file (p.15-41 of 98) and Table S1 file (p.42-98 of 98); cited on the Form PTO-892 mailed 06/22/2023), in view of Hao, (Cell reports, vol. 12(2), pages 183-189 (2015); 32 pages total with the Supplemental Information file (p.9-32); cited on the Form PTO-892 mailed 06/22/2023), in view of Xiong. (Bioinformatics, vol. 27(18), pages 2554-2562 (2011); cited on the Form PTO-892 mailed 06/05/2024), in view of Klein, (Current gene therapy, vol. 15(4), pages 329-337 (2015); cited on the Form PTO-892 mailed 01/12/2024).
Independent claim 66 recites a method for treating a disease associated with a splicing factor error by: (a) obtaining sequence data; (b) identifying the splicing error; (c) deriving annotations of a plurality of types of alternative splicing based on public RNA-seq data or other biological data both with and without the splicing factor error de novo from the RNA-seq data or the other biological data based on assembly of exon duos and/or exon trios; (d) obtaining metadata from the annotations; (e) obtaining a reference transcriptome by generating features information stored in a database which comprises the metadata of step (d); (f) obtaining from the reference transcriptome alternative splicing event(s) associated with the splicing factor error; (g) quantitatively estimating the probabilities of such splicing events of damaging protein structures, protein functions, RNA stability, RNA integrity, or pathways; (h) applying a neural network to predict the functional impact based on the probabilities, the neural network is trained with a training set, each data point of the training set comprising a feature of the plurality of features, and a label which is positive, negative, and unlabeled; (i) generating a list of biologically relevant splicing events; (j) outputting the list of alternative splicing events; and (k) administering to the subject an antisense oligonucleotide (ASO) associated with an alternative splicing event from the list, thereby treating the disease.
Dependent claim 69 further recites the training set comprises no less than 50 training data points. Dependent claim 70 further recites the plurality of features is selected from: RNA-based features, protein domain features, evolutionary features, mutability features, and/or splicing regulatory features. Dependent claim 71 further recites quantitatively estimating probabilities of the alternative splicing event(s) of damaging the protein structures, protein functions, RNA stability, RNA integrity, or biological pathways comprises quantitatively estimating damage caused by: removal of a functional protein domain by alternative splicing; nonsense-mediated decay (NMD) and translation frameshifting (FS) by alternative splicing; mutability of alternative splicing events; and/or weighted closeness centrality of alternative splicing.
Dependent claim 81 further recites the disease condition is selected from the group consisting of cancer, leukemia, a disease of the central nervous system, muscular dystrophy, a hormonal disorder, chronic inflammation and abnormal inflammation. Dependent claim 82 further recites the disease condition is selected from the group consisting of familial dysautonomia (FD), Spinal muscular atrophy (SMA), Medium-chain acyl-CoA dehydrogenase (MCAD) deficiency, Hutchinson-Gilford progeria syndrome (HGPS), Myotonic dystrophy Type 1 (DMl), Myotonic dystrophy Type 2 (DM2), Autosomal dominant retinitis pigmentosa (RP), Duchenne muscular dystrophy (DMD), Microcephalic osteodysplastic primordial dwarfism type 1 (MOPDl), Taybi-Linder syndrome (TALS), Frontotemporal dementia with parkinsonism-17 (FTDP-17), Fukuyama congenital muscular dystrophy (FCMD), Amyotrophic lateral sclerosis (ALS), Hypercholesterolemia, and Cystic Fibrosis (CF). Dependent claim 85 further recites the list of alternative splicing events comprises at least one gene of a group comprising: BRCAl, BRCA2, EZH2, BINl, BCL2Ll, BCL2Lll, CASP2, CCNDl, CD44, ENAH, FAS, FGRF, HER2, HRAS, KLF6, MCLl, MKNK2, MSTRl, PKM, RACl, RPS6KB1, VEGFA, IKBKAP, SMN2, MCAD, LMNA, DMPK, ZNF9, PRPF31, PRPF8, PRPF3, RP9, MAPT, TKTN, TPD-43, LDLR, CFTR, DMD, ATF2, and the gene encoding U4atac snRNA.
Dependent claim 103 further recites the splicing factor error is selected from the group consisting of: a mutation of a splicing factor; abnormal expression of a splicing factor; aberrant splicing; and a splicing factor error associated with RNA destabilization. Dependent claim 104 further recites a step of predicting one or more specific points of therapeutic intervention.
Anczuków teaches a study identifying and analyzing effects of alternative splicing events regulated by splicing factor SRSF1 in breast cancer cells. Anczuków shows the basis of the oncogenic impact of SRFS 1 in breast cancer, by investigating the biological functions and molecular pathways deregulated upon SRSF1 overexpression (i.e., a splicing factor error) in the mammary-gland context (article, p.112, col.2). Anczuków shows to determine which SRSF1 regulated alternative splicing events may play a role in breast cancer, they applied the SpliceTrap/SpliceDuo pipeline to a collection of human breast tumors, and detected 2,181 alternative splicing events (i.e., biologically relevant alternative splicing events) associated with SRSF1 overexpression (i.e., analyzing sequencing data and identifying a splicing factor error), reproducible in~ 5 tumors (article, p.113, col. I). Anczuków shows a list of pathways and functions (i.e., outputting a list) enriched in SRSF1-splicing targets in 3-D and 2-D MCF-l0A cultures (Supplemental Material, p.24/98 of Anczuków document, Figure S6C and S6D) (i.e., showing functional impact and probabilities of damaging protein, RNA, or pathways). Anczuków shows the systematic identification of human SRSF1-regulated AS splicing events, using next-generation RNA-sequencing (RNA-seq) in a cell-culture system relevant to breast-cancer pathogenesis (article, p.106, col.1). Anczuków reveals they mapped paired-end reads to the human exon-trio TXdb database (i.e., a reference transcriptome) and quantified exon inclusion using their SpliceTrap/SpliceDuo pipeline (article, p.106, col.2). Anczuków shows on p.107, fig.1E, the number of alternative splicing events in each category (i.e. quantitatively estimating probabilities based on features; and splicing error groups) as well as the alternative splicing events downregulated or upregulated in SRSF1-OE cells (i.e., prediction of functional impact). Anczuków shows a list of all SRSF1-regulated alternative splicing events identified by RNA-sequencing in the supplemental table S1 (Table S1, p.41/98). Anczuków teaches de novo discovery of the SRSF1 Binding Motif, (showing de novo derived annotations of claim 66) and found 44 of the CA-exons they identified overlap with previously detected SRSF1 CLIP-tags in HEK293 cells (article, p.109, col.1). Anczuków shows training and control sets used in de novo motif discovery (Supplemental Material, bridging p.33-34/98 of document). Anczuków shows alternative splicing (AS) events (CA, AD, AA and IR) were identified and quantified using SpliceTrap, a tool which combines RNA-seq data with prebuilt transcript models to quantify the level of inclusion of every exon in a transcript. The transcript models are exon trios, composed of alternative-exon candidates with their annotated flanking exons, and derived from the hg18 TXdb database (p.27/98 of document). (This shows the obtained sequence data, identifying the splicing error, obtaining one or more slicing events, de novo derived annotations, RNA-seq data, exon trios, quantitatively estimating probabilities, generating and outputting a list of alternative splicing events (ASE) limitations of claim 66 (steps (a), (b), (c), (e), (g), (i) (functional impact), and (j), and splicing error groups of claim 103.)
Anczuków teaches obtaining metadata of claim 66, step (d) by analyzing SMN2 and ADAR2 for every mutant, and at every nucleotide position, calculating creation/loss scores of SRSF1 binding motif as an aggregated difference (Supplemental Material, p.37/98 of Anczuków document)
Anczuków shows using the RNA-seq data, they derived an SRFS1 binding consensus. This motif was predicted from reproducible splicing changes associated with SRSF1, trained with MEME, RSAT, and in-house methods, and tested with independent data (article, p.115, col.2). This shows obtaining from the reference transcriptome one or more alternative splicing
events associated with the splicing factor error of claim 66, step (f).
Anczuków shows using Bayes inference to derive posterior probabilities of SRSF1 binding at each sequence position (article, bridging p.109 and p.111). This shows functional impact based on probabilities, and a training set (but not specifically a neural network) of claim 66, step (h).
Anczuków shows SRSF1 overexpression promoted the inclusion of the 168 nt long CASC4 exon 9, which encodes a longer protein isoform comprising 56 unique amino acids in the C-terminal region, and predicted to have several phosphorylation and myristoylation sites (article, p.114, col.1; and p.113, figs. 6C and 6D). Anczuków shows they characterized the role of CASC4 isoforms, assessed changes in acinar morphology in cultured cells, and found acini overexpressing CASC4-FL exhibited increased proliferation, as well as decreased apoptosis (article, p.114, cols.1-2). (This shows the quantitatively estimating damage limitations of claim 71.)
Anczuków shows a study analyzing SRSF1-regulated alternative splicing in breast cancer (breast cancer is recited in the group of diseases in claim 81) (entire document). Anczuków shows the altering of SRSF1-regulated cassette exon (CA) events between 2D and 3D culture, with CD44 in top right of figure 2, p.108 (CD44 is recited in claim 85). Anczuków shows their study uncovered SRSF1 positive and negative regulatory mechanisms, and oncogenic alternative splicing (AS) events that represent potential targets for therapeutics development (article, p.105, summary). (This shows breast cancer of claim 81; CD44 of claim 85; and predicting therapeutic intervention points of claim 104.)
Anczuków does not specifically show a neural network, features with labels that are positive, negative, or unlabeled of claim 66, step (h).
Anczuków does not show administering an agent associated with alternative splicing events to treat a disease of claim 66, step (k).
Anczuków does not show the training set is at least 50 data points of claim 69.
Anczuków does not specifically show the categories of features of claim 70.
Anczuków does not show a disease from the group recited in claim 82.
Hao presents a semi-supervised learning algorithm, "positive unlabeled learning for splicing elucidation" (PULSE), to predict the likelihood of yielding stably folded protein isoforms from alternative splicing events and its use in their method to quantitatively predict ‘‘exon skipping’’ alternative splicing events in producing stable proteins (p.183, col.1; p.184, fig.1; p.185, fig.2). Hao shows a list of predictive features, most of them structural and splicing features (i.e., alternative splicing events), organized by relative importance score (RI) and category (p.186, table 1), the categories showing functional impact; and feature importance analysis (p.25/32, Figure S3). Hao shows the PULSE algorithm uses a both a positive and unlabeled set for training. Hao shows PULSE first trains a binary classifier wherein unlabeled data points are treated as negatives (p.184, col.2). Hao shows they use a Random Forest algorithm for their underlying classifiers (p.16/32). Hao discloses treating a subset of isoforms containing 145 experimentally validated alternative isoforms as the positive set to train their model (i.e., the training set is at least 50 data points) (p.188, col.2; also see p.22/32 [= supplemental info., p.14, fig S1A, Heygi positive set]). Hao shows a list of predictive features, which are categorized into splicing, structure, evolution and regulatory features (p.186, table 1). (This shows functional impact, the training data set with pos/neg/unlabeled labels, and the features of claim 66, step (f) and the data points of claim 69, and the features of claim 70.)
Xiong shows use of a Bayesian neural network to predict splicing patterns (p.2555, fig. 2a&b). Xiong shows the training and test sets, features, and algorithms, (p.2556, col.2-p.2558, col.1). (This shows a neural network trained with a training sets and features of claim 66, step (f).)
Klein presents a review on of the different strategies that were developed to neutralize the RNA toxicity due to alternative splicing misregulations in Myotonic Dystrophy 1 and 2 (DM1 and DM2) (p.329-330), and shows models evaluating different approaches including antisense oligonucleotide technologies (p.333, cols.1-2) and gene therapies using TALENs and CRISPRs (p.334, col.1) were tested and validated in cellular and animal models. (This shows the therapy limitation (ASO) of claim 66 step (k), diseases of claim 82.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method identifying, quantifying, and analyzing effects of alternative splicing events using training data and de novo derivation of Anczuków, with the method using features, training points, and labels of Hao, the neural network of Xiong, and the therapeutic approaches of Klein, to come to a better performing method of quantifying and analyzing effects of alternate splicing, because the method of Hao provides a benefit for base classification with its built-in functionality to calculate the variable importance of the predictive features (Hao, supplemental info, p.9), while Anczuków shows Splice-Duo addresses the challenge of assigning statistical significance to PSI (percent spliced in, or exon inclusion ratio) changes using the variance of the data (Anczuków, p.106, col.2) and further reveals out of 141 tested CA (cassette exons) events, 104 were successfully validated, including 58 exon-inclusion and 46 exon-skipping events across the different cell lines (Anczuków, p.108, col.1). Adding motivation, Xiong shows their Bayesian neural network technique performs significantly better than all other analyzed techniques (Xiong, p.2555, col.1), while Klein. states the recent development of genome editing technologies using molecules like TALENs and CRISPRs opens new therapeutic avenues to cure genetic diseases (Klein, p.334, col.1). One would have had a reasonable expectation of success in doing so because the references are generally drawn to related teaching, and one of ordinary skill in the art would have understood how to and would have been motivated to modify the teaching of Anczuków, with the related teachings of Hao, Xiong, and Klein, and as such, the combination would have been obvious.
Response to Applicant Arguments - 35 USC 103
Applicant's arguments filed 12/30/2025 have been fully and respectfully considered, but they are not persuasive.
Applicant asserts (12/30/2025 remarks, p.7):
• "The Office rejected claims 66, 69-71, 81, 82, 85, 103, and 104 for alleged obviousness over Anczuków in view of Hao, Xiong, and Klein, stating:
Amending [ claim 66] to recite active steps involving obtaining the reference transcriptome (e.g. for obtaining metadata, for deriving annotations de novo, etc.) taking care not to recite new matter, may be a step toward overcoming the 103 and l 12(b) rejections."
• "Applicant has amended claim 66 to recite active steps of obtaining metadata
and deriving annotations de novo."
• "None of the cited publications...teach or suggest a method presently amended claim 66...Therefore, the present claims are nonobvious in view of the cited publications, and the instant rejection of the noted claims is overcome."
Additionally, Applicant's previous remarks, filed 05/12/2025, included the assertion:
• A key technical advantage of the presently claimed method is the ability to capture previously unidentified splicing events using by obtaining a reference transcriptome from de novo-derived annotations based on assembly of exon duos and exon trios." (p.9, para.1)
• In contrast to the presently claimed method, Anczuków 's method is not capable of obtaining a reference transcriptome from de novo-derived annotations based on assembly of exon duos and exon trios. Indeed, Anczuków applies a method and employs the same database and mapping as taught in Wu et al.,2 which is only capable of analyzing previously-identified splice isoforms... The methods taught by Wu and used in Anczuków require that analyzed transcripts be mapped to TXdb...the method of Wu is incapable identifying novel or certain complex splicing events." (p.10, para. 2, through p.11, para.1).
The arguments (both 12/30/2025 and 05/12/2025) are not yet persuasive.
Applicant's amending of claim 66 to recite active steps of obtaining metadata and deriving annotations de novo is acknowledged, however the aspect of obtaining metadata and deriving annotations de novo remains interpreted as reading on Anczuków in the 103 rejection above; briefly the essential teachings are listed here:
Anczuków teaches de novo discovery of the SRSF1 Binding Motif, (showing de novo derived annotations of claim 66) and found 44 of the CA-exons they identified overlap with previously detected SRSF1 CLIP-tags in HEK293 cells (article, p.109, col.1).
Anczuków shows training and control sets used in de novo motif discovery (Supplemental Material, bridging p.33-34/98 of document).
Anczuków teaches obtaining metadata by analyzing SMN2 and ADAR2 for every mutant, and at every nucleotide position, calculating creation/loss scores of SRSF1 binding motif as an aggregated difference (Supplemental Material, p.37/98 of Anczuków document).
To summarize, it is not yet clear that Anczuków in view of Hao, Xiong, and Klein, does not teach the deriving de novo annotations and obtaining metadata limitations of claim 66. The Applicant is encouraged to request an interview to discuss the pending rejections and/or potential amendments.
Conclusion
No claim is allowed.
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Meredith A Vassell whose telephone number is (571)272-1771. The examiner can normally be reached 8:30 - 4:30.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, KARLHEINZ SKOWRONEK can be reached at (571)272-9047. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/M.A.V./Examiner, Art Unit 1687
/G. STEVEN VANNI/Primary patents examiner, Art Unit 1686
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