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 .
Applicant’s election without traverse of Group I, claims 1-16 and 21-24 in the reply filed on 6/3/2026 is acknowledged.
Claims 17-20 have been canceled.
Claims 1-16 and 21-24 are under examination.
This application is a CON of US 15/838,203, which claimed priority to two provisional applications. The currently pending claims appear to have the effective filing date of the earliest provisional application filed 12/9/2016.
This application has published as US PG Pub US 2022/0228208 A1.
The Drawings as filed are suitable for examination.
The sequence listing and associated filings have been entered.
Eight separate IDS statements have been entered and considered.
Claim Interpretation
The claims in this application are given their broadest reasonable interpretation (BRI) using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-17, 21-24 is/are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea of mental steps, mathematic concepts, organizing human activity, or a natural law without significantly more.
Applicant is directed to MPEP 2106 for the most current and complete guidelines in the analysis of patent- eligible subject matter. The current MPEP is the primary source for the USPTO’s patent eligibility guidance.
With respect to step (1): YES, the claims are drawn to statutory categories: Processes.
With respect to step (2A) (1): YES, the claims recite an abstract idea, law of nature and/or natural phenomenon. The claims explicitly recite elements that, individually and in combination, constitute one or more judicial exceptions (JE).
Mathematic concepts, Mental Processes or Elements in Addition (EIA) in the claim(s) include:
1. (Previously Presented) A method for identifying a T-cell receptor (TCR), comprising:
(Preamble, stating a method and the goal of the method.)
a) sequencing, using a high-throughput sequencing device, reads of RNA obtained from a T-cell and storing, in a system memory of a computing device, a sequence data structure comprising the reads and a reference data structure comprising a reference sequence that does not contain a TCR gene sequence;
(EIA- data gathering step, performing sequencing of a sample T cell, using a routine sequencing device; AND EIA- routine data storage, and a description of the data gathered, in a computing system.)
b) aligning, by the computing device, the reads in the sequence data structure with the reference sequence in the reference data structure, thereby generating, in the sequence data structure, mapped reads and unmapped reads;
(Mental Process of observing the sequence reads, comparing them to the reference reads, and matching the sample reads to the reference reads; The classification of “mapped” or “unmapped” is the step of judgement as to whether one sequence matches the reference, or not. MPEP 2106.04(a).)
c) discarding, by the computing device, the mapped reads from the sequence data structure; and
(Mental Process of observing the mapped reads and deleting, masking or removing them. MPEP 2106.04(a).)
d) identifying, by the computing device, a first mapped read in the sequence data structure that aligns to a TCR V gene reference sequence as a candidate TCR V gene sequence and a second mapped read in the sequence data structure that aligns to a TCR J gene reference sequence as a candidate TCR J gene sequence,
wherein the candidate TCR V gene sequence combined with the candidate TCR J gene sequence comprise a TCR sequence.
(Mental process of observing the remaining reads, aligning them to candidate TCR V or J reference sequences, and making a judgement as to whether they represent a TCR sequence. MPEP 2106.04(a).)
2. (Previously Presented) The method of claim 1, wherein the reads comprise short reads of less than about 100 base pairs.
(EIA- related to the data gathered in claim 1, specifying a type of sequencing: short read sequencing.)
3. (Previously Presented) The method of claim 1, further comprising:
assembling, by the computing device, the unmapped short reads into one or more long reads by aligning the unmapped short reads in the sequence data structure to one or more reference TCR sequences from a reference database of TCR sequences; and
translating, by the computing device, the one or more long reads into corresponding amino acid sequences.
(Mental process of observing unmapped reads, aligning them against one another/ and reference sequences (a step of matching), and making a judgement as to whether they can be combined into a long read. ALSO a mental process of observing the nucleic acid sequence of the long read, and translating it into an amino acid sequence. MPEP 2106.04(a).)
4. (Previously Presented) The method of claim 3, wherein identifying, by the computing device, a first mapped read in the sequence data structure that aligns to a TCR V gene reference sequence as a candidate TCR V gene sequence and a second mapped read in the sequence data structure that aligns to a TCR J gene reference sequence as a candidate TCR J gene sequence comprises:
fractioning, by the computing device, TCR V region and TCR J region amino acid reference sequences, from the reference database of TCR sequences, into k-strings of about six amino acids;
(Mathematic concept of dividing a string into substrings)
aligning, by the computing device, the k-strings with the corresponding amino acid sequences;
(Mental process of observing the substring and matching them with other strings)
detecting, by the computing device, one or more conserved TCR CDR3 residues in the k- strings that map to the corresponding amino acid sequences;
(Mental process of observing certain residues at certain positions, and making a judgement as to whether it is conserved, or meets a conservation threshold.)
scoring, by the computing device, based on the one or more conserved TCR CDR3 residues that map to the corresponding amino acid sequences, a level of conservation for each of the corresponding amino acid sequences;
(Mathematic concept of calculating a score.)
selecting, by the computing device, one or more of the corresponding amino acid sequences, wherein the level of conservation for the one or more corresponding amino acid sequences is above a threshold conservation score; and
(Mental process and mathematic concept of identifying and selecting a sequence when a calculated value exceeds a threshold)
detecting, by the computing device, a candidate CDR3 region amino acid sequence in the selected corresponding amino acid sequences.
(Mental process of observing sequences meeting a condition)
5. (Previously Presented) The method of claim 4, further comprising: identifying, by the computing device, a nucleic acid sequence of the candidate CDR3 region amino acid sequence in the one or more long reads as a candidate CDR3 region nucleic acid sequence.
(Mental process of observation and selection of a nucleic acid sequence that meets a condition.)
6. (Previously Presented) The method of claim 5, further comprising:
aligning, by the computing device, a nucleic acid sequence of the one or more long reads, that is upstream of the candidate CDR3 region nucleic acid sequence with one or more TCR V gene reference sequences from the reference database of TCR sequences;
(Mental process of comparison and matching between nucleic acid sequences)
scoring, by the computing device, a degree of the alignment of the nucleic acid sequence of the one or more long reads that is upstream of the candidate CDR3 region nucleic acid sequence with the one or more TCR V gene reference sequences from the reference database of TCR sequences; and
(Mathematic concept of calculating a score: a degree of alignment)
identifying, by the computing device, at least one portion of the one or more long reads as comprising a candidate TCR V gene sequence, wherein the scored degree of alignment for the at least one portion of the one or more long reads that is upstream of the candidate CDR3 region nucleic acid sequence is above a threshold alignment score.
(Mental process of observing the calculated degree of alignment value, and comparing it to a threshold score to make a judgment of whether it meets or exceeds the threshold)
7. (Previously Presented) The method of claim 6, further comprising:
aligning, by the computing device, a nucleic acid sequence of the one or more long reads that is downstream of the candidate CDR3 region nucleic acid sequence with one or more TCR J gene reference sequences from the reference database of TCR sequences;
(Mental process of comparison and matching between nucleic acid sequences)
scoring a degree of the alignment of the nucleic acid sequence of the one or more long reads that is downstream of the candidate CDR3 region nucleic acid sequence with the one or more TCR J gene reference sequences from the reference database of TCR sequences; and
(Mental process of observing the calculated degree of alignment value, and comparing it to a threshold score to make a judgment of whether it meets or exceeds the threshold)
identifying at least one portion of the one or more long reads as comprising a candidate TCR J gene sequence, wherein the scored degree of alignment for the at least one portion of the one or more long reads, in the sequence data structure in the system memory, that is downstream of the candidate CDR3 region nucleic acid sequence is above the threshold alignment score.
(Mental process of observing the calculated degree of alignment value, and comparing it to a threshold score to make a judgment of whether it meets or exceeds the threshold)
8. (Previously Presented) The method of claim 1, wherein discarding the mapped reads from the sequence data structure further comprises discarding unmapped reads that are less than about 35 base pairs.
(Mental process of observing mapped reads meeting a condition, and deleting, masking or removing them.)
9. (Previously Presented) The method of claim 1 further comprising, prior to sequencing the reads of RNA obtained from the T cell, administering an immunotherapy to a subject from which the T cell is obtained.
(EIA- a step related to data gathering, performed outside the bounds of the claim, treating the source of the T cell sample.)
10. (Previously Presented) The method of claim 9, wherein the immunotherapy comprises a monotherapy or a combination therapy.
(EIA- a step related to data gathering, performed outside the bounds of the claim, treating the source of the T cell sample.)
11. (Previously Presented) The method of claim 10, wherein the combination therapy comprises a costimulatory agonist and a coinhibitory antagonist.
(EIA- a step related to data gathering, performed outside the bounds of the claim, treating the source of the T cell sample.)
12. (Previously Presented) The method of claim 1, further comprising:
performing steps a-d for a first plurality of T cells of a subject, wherein the T cells are collected prior to administration of a treatment;
(Mental process/mathematic concepts: repeating the JE’s of step 1, on a sample, pre-treatment of the source of the sample.)
determining a number of occurrences of unique TCR sequences present in the first plurality of T cells;
(Mathematic concept of counting)
administering the treatment to the subject;
(EIA- generic treatment step, that fails to effect a particular treatment or prophylaxis for a disease or medical condition. MPEP 2106.04(d).)
performing steps a-d for a second plurality of T cells of the subject, wherein the T cells are collected after the administration of the treatment;
(Mental process/mathematic concepts: repeating the JE’s of step 1, on a sample post-treatment of the source of the sample)
determining a number of occurrences of unique TCR sequences present in the second plurality of T cells; and
(Mathematic concept of counting)
determining, based on the number of occurrences of unique TCR sequences present in the first plurality of T cells being less than the number of occurrences of unique TCR sequences present in the second plurality of T cells, one or more unique TCR sequences that experienced clonal expansion.
(Mathematic concept of counting and mental processes of comparison and judgement.)
13. (Previously Presented) The method of claim 12, further comprising determining a T cell clonal expansion signature based on the one or more unique TCR sequences that experienced clonal expansion.
(Mental concept of observing a signature)
14. (Previously Presented) The method of claim 13, further comprising: querying a database of T cell clonal expansion signatures and corresponding treatment responses using the T cell clonal expansion signature; and determining, based on the query, the subject's likelihood of responding to the treatment.
(Mental concept of comparing the signature to a database, analyzing any similarity, making a judgement as to what treatment corresponds, and the mathematic concept of calculating a likelihood.)
15. (Previously Presented) The method of claim 13, further comprising: determining the subject's response to the treatment; storing the T cell clonal expansion signature in a database; and associating the subject's response to the treatment with the T cell clonal expansion signature in the database.
(Mental concept of observation of the subject; the EIA of routine data storage, and the EIA of routine data annotation.)
16. (Previously Presented) The method of claim 1, further comprising: determining that the TCR sequence is present in a T cell clone that expands in response to a treatment; producing one or more T cells containing the TCR sequence; administering the one or more T cells to a subject; and administering the treatment to the subject.
(Mental process of observation of a cell culture that expands under a condition; EIA of “producing” T cells comprising the sequence, and two EIA: administration steps which fail to effect a particular treatment or prophylaxis for a disease or medical condition)
21. (New) The method of claim 1, wherein the reads are obtained from random-priming of RNA.
(EIA related to data gathering or a description of the data gathered.)
22. (New) The method of claim 1, wherein the T cell is obtained from a human or mouse.
(EIA- related to data gathering, describing the source of the sample.)
23. (New) The method of claim 1, wherein the reference sequence comprises a human genome, a mouse genome, a human transcriptome, or a mouse transcriptome.
(EIA- related to sequences gathered for comparison)
24. (New) The method of claim 1, further comprising appending a TCR C region nucleic acid sequence to the TCR sequence.
(Mental process of data modification)
With respect to step 2A (2): NO, the claims do not integrate the JE into a practical application (MPEP 2106.04(d)):
“Examiners evaluate integration into a practical application by: (1) identifying whether there are any additional elements recited in the claim beyond the judicial exception(s); and (2) evaluating those additional elements individually and in combination to determine whether they integrate the exception into a practical application, using one or more of the considerations introduced in subsection I supra, and discussed in more detail in MPEP §§ 2106.04(d)(1), 2106.04(d)(2), 2106.05(a) through (c) and 2106.05(e) through (h).”
Claim(s) 1-2, 9-11, 21-23 recite the additional non-abstract element(s) of data gathering, or a description of the data gathered.
Data gathering steps are not an abstract idea, they are extra-solution activity, as they collect the data necessary to carry out the JE. MPEP 2106.05(g).
The data gathering does not impose any meaningful limitation on the JE, or how the JE is performed. MPEP 2106.05(g).
The data gathering steps constitute a general link to a technological environment: DNA sequence analysis of T cell receptors. (MPEP 2106.05(h), citing Mayo, Bilski, electric Power Group, Genetic Techs Ltd v Merial LLC.)
The additional limitation (data gathering) must have more than a nominal or insignificant relationship to the identified judicial exception to provide integration into a practical application. (MPEP 2106.05(g) citing Mayo, PerkinElmer, Inc. v. Interna Ltd, Intellectual Ventures LLC v. Erie Indem. Co., Electric Power Group LLC v. Alstom S.A.).
Claim(s) 9-12, 16 recite the additional non-abstract element (EIA) of a treatment or prophylaxis: Claims 9-11 describe treatment of the source of the sample as a step related to data gathering; claims 12, 16 relate to treatment of the source of the sample.
The identified treatment step fails to integrate the JE into a practical application, as the step does not “affirmatively recite an action that effects a particular treatment or prophylaxis for a disease or medical condition” see (MPEP 2106.04(d)(2)).
Claim(s) 1, 15 recite the additional non-abstract element (EIA) of a general-purpose computer system or parts thereof.
The claims do not provide any details of how specific structures of the computer elements are used to implement the JE. MPEP 2106.05(a), contrasting decisions identifying how the computer implements an abstract idea, such as in McRo to decisions which found no specific interaction with the computer, such as in Affinity Labs of Tex v. DirecTV, LLC.
The computer elements of the claims do not provide improvements to the functioning of the computer itself. MPEP 2106.05(a) I, contrasting decisions indicating an improvement to the computer, such as DDR Holdings, LLC v. Hotels.com LP, with decisions that did not identify an improvement to the computer, such as FairWarning IP, LLC v. Iatrix Sys.
The computer elements of the claims do not provide improvements to any other technology or technical field. MPEP 2106.05(a) II: contrasting decisions indicating an improvement to the technology, such as Diamond v. Diehr, Trading Techs. Int’l v. CQG Inc, or Intellectual Ventures I v. Symantec Corp, with decisions that did not identify an improvement to the technology, such as Alice Corp, Versata Dev. Group, Inc. v. SAP AM. Inc, or TLI Communications.
The computer elements of the claims do not utilize a particular machine. MPEP 2106.05(b): contrasting decisions wherein a particular machine was identified, such as MacKay Radio & Tel. Co. v. Radio Corp. of America, Eibel Process Co. v. Minn. & Ont. Paper Co., with decisions where a general-purpose computer does not qualify as a particular machine, such as Ultramercial, Inc. v. Hulu, LLC, TLI communications, or Eon Corp. IP holdings LLC v. AT&T Mobility LLC.
Hence, these are mere instructions to apply the JE using a computer, and therefore the claim does not recite integrate that JE into a practical application.
Dependent claim(s) 3-8, 12-16, 24 recite(s) an abstract limitation to the JE reciting additional mathematic concepts, or mental processes. Additional abstract limitations cannot provide a practical application of the JE as they are a part of that JE.
In combination, the limitations of data gathering, for the purpose of carrying out the JE, using a general-purpose computer merely provide extra-solution activity, and fail to integrate the JE into a practical application.
With respect to step 2B: NO, the claims do not recite a specific inventive concept. The judicial exception alone cannot provide that inventive concept or practical application (MPEP 2106.05).
“… an "inventive concept" is furnished by an element or combination of elements that is recited in the claim in addition to (beyond) the judicial exception, and is sufficient to ensure that the claim, as a whole, amounts to significantly more than the judicial exception itself. Alice Corp…”
With respect to claim(s) 1-2, 9-11, 21-23: The limitation(s) identified above as non-abstract elements (EIA) related to data gathering do not rise to the level of significantly more than the judicial exception.
Freeman, (Freeman, J. D. et al. Profiling the T cell receptor beta chain repertoire by massively parallel sequencing. 2009 Genome Research 19:1817-1824; PTO-1449) Freeman provides high throughput sequencing of CDR3 regions from TCRs from cDNA libraries made from the RNA of T cells.
Li, (Li, B et al. Landscape of tumor-infiltrating T cell repertoire of human cancers. (July 2016) Nature Genetics V 48:7 p725, and some supplemental material; PTO-1449) provided performing short read sequencing of T cells and TCR sequences.
Li 2 (Li, B. et al (September 2016) Ultrasensitive detection of TCR hypervariable region in solid-tissue RNA-seq data. bioRxiv, 15 pages.) performed short read sequencing on RNA-containing tissues including TCR hypervariable regions.
The specification also notes that systems for carrying out RNA short read sequencing are commercially available or widely used at [0055] including those sold by Illumina, Roche, and Pacific Biosciences.
These elements meet the BRI of the identified data gathering limitations. As such, the prior art recognizes that this data gathering element is routine, well understood and conventional in the art. MPEP 2106.05(d): “If, however, the additional element (or combination of elements) is no more than well-understood, routine, conventional activities previously known to the industry, which is recited at a high level of generality, then this consideration does not favor eligibility.”
Data gathering steps are not an abstract idea, they are extra-solution activity, as they collect the data necessary to carry out the JE. MPEP 2106.05(g).
The data gathering does not impose any meaningful limitation on the JE, or how the JE is performed. MPEP 2106.05(g).
The additional limitation (data gathering) must have more than a nominal or insignificant relationship to the identified judicial exception to provide an inventive concept. (MPEP 2106.05(g) citing Mayo, PerkinElmer, Inc. v. Interna Ltd, Intellectual Ventures LLC v. Erie Indem. Co., Electric Power Group LLC v. Alstom S.A.)
The data gathering steps constitute a general link to a technological environment: the analysis of TCR sequence data. (MPEP 2106.05(h), citing Mayo, Bilski, electric Power Group, Genetic Techs Ltd v Merial LLC.)
Therefore, simply appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception are insufficient to provide significantly more (as discussed in Alice Corp.,).
With respect to claim(s) 1, 15: the limitations identified above as non-abstract elements (EIA) related to general-purpose computer systems do not rise to the level of significantly more than the judicial exception.
Freeman, Li and Li 2 disclosed computer systems or computing elements which meet the BRI of the claimed computer system or computer system elements, comprising input, output/ display, a processor, and memory.
As such, the prior art recognizes that these computing elements are routine, well understood and conventional in the art.
The specification, at [0048-0050, 0107-0108] discloses the use of routine general-purpose computers for carrying out the invention, and/or the use of commercially available computer system elements.
The claims do not provide any details of how specific structures of the computer elements are used to implement the JE. MPEP 2106.05(a), contrasting decisions identifying how the computer implements an abstract idea, such as in McRo to decisions which found no specific interaction with the computer, such as in Affinity Labs of Tex v. DirecTV, LLC.
The computer elements of the claims do not provide improvements to the functioning of the computer itself. MPEP 2106.05(a) I, contrasting decisions indicating an improvement to the computer, such as DDR Holdings, LLC v. Hotels.com LP, with decisions that did not identify an improvement to the computer, such as FairWarning IP, LLC v. Iatrix Sys.
The computer elements of the claims do not provide improvements to any other technology or technical field. MPEP 2106.05(a) II: contrasting decisions indicating an improvement to the technology, such as Diamond v. Diehr, Trading Techs. Int’l v. CQG Inc, or Intellectual Ventures I v. Symantec Corp, with decisions that did not identify an improvement to the technology, such as Alice Corp, Versata Dev. Group, Inc. v. SAP AM. Inc, or TLI Communications.
The computer elements of the claims do not utilize a particular machine. MPEP 2106.05(b): contrasting decisions wherein a particular machine was identified, such as MacKay Radio & Tel. Co. v. Radio Corp. of America, Eibel Process Co. v. Minn. & Ont. Paper Co., with decisions where a general-purpose computer does not qualify as a particular machine, such as Ultramercial, Inc. v. Hulu, LLC, TLI communications, or Eon Corp. IP holdings LLC v. AT&T Mobility LLC.
Hence, these are mere instructions to apply the JE using a computer, and therefore the claim does not provide significantly more.
Dependent claim(s) 3-8, 12-16, 24 each recite a limitation requiring additional mathematic concepts or mental processes. Additional abstract limitations cannot provide significantly more than the JE as they are a part of that JE (MPEP 2106.05).
In combination, the data gathering steps providing the information required to be acted upon by the JE, performed in a generic computer or generic computing environment fail to rise to the level of significantly more than that JE. The data gathering steps provide the data for the JE, which is carried out by the general-purpose computers. No non-routine step or element has clearly been identified.
The claims have all been examined to identify the presence of one or more judicial exceptions. Each additional limitation in the claims has been addressed, alone and in combination, to determine whether the additional limitations integrate the judicial exception into a practical application. Each additional limitation in the claims has been addressed, alone and in combination, to determine whether those additional limitations provide an inventive concept which provides significantly more than those exceptions. For these reasons, the claims, when the limitations are considered individually and as a whole, are rejected under 35 USC § 101 as being directed to non-statutory subject matter.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
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 3, 4 and 6 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
MPEP 2161.01 states, with respect to computer-implemented inventions, and rejections made under 35 USC 112(a): “As stated by the Federal Circuit, "[a]lthough many original claims will satisfy the written description requirement, certain claims may not." Id. at 1349, 94 USPQ2d at 1170-71; see also LizardTech, Inc. v. Earth Res. Mapping, Inc., 424 F.3d 1336, 1343-46, 76 USPQ2d 1724, 1730-33 (Fed. Cir. 2005); Regents of the Univ. of Cal. v. Eli Lilly & Co., 119 F.3d 1559, 1568, 43 USPQ2d 1398, 1405-06 (Fed. Cir. 1997)("The description requirement of the patent statute requires a description of an invention, not an indication of a result that one might achieve if one made that invention."). Problems satisfying the written description requirement for original claims often occur when claim language is generic or functional, or both. Ariad, 593 F.3d at 1349, 94 USPQ2d at 1171…
[O]riginal claims may lack written description when the claims define the invention in functional language specifying a desired result but the specification does not sufficiently describe how the function is performed or the result is achieved. For software, this can occur when the algorithm or steps/procedure. for performing the computer function are not explained at all or are not explained in sufficient detail (simply restating the function recited in the claim is not necessarily sufficient). In other words, the algorithm or steps/procedure taken to perform the function must be described with sufficient detail so that one of ordinary skill in the art would understand how the inventor intended the function to be performed. See MPEP §§ 2163.02 and 2181, subsection IV...
An algorithm is defined, for example, as "a finite sequence of steps for solving a logical or mathematical problem or performing a task." Microsoft Computer Dictionary (5th ed., 2002). Applicant may "express that algorithm in any understandable terms including as a mathematical formula, in prose, or as a flow chart, or in any other manner that provides sufficient structure." Finisar Corp. v. DirecTV Grp., Inc., 523 F.3d 1323, 1340 (Fed. Cir. 2008) (internal citation omitted). It is not enough that one skilled in the art could write a program to achieve the claimed function because the specification must explain how the inventor intends to achieve the claimed function to satisfy the written description requirement. See, e.g., Vasudevan Software, Inc. v. MicroStrategy, Inc., 782 F.3d 671, 681-683, 114 USPQ2d 1349, 1356, 1357 (Fed. Cir. 2015)”
In claim 3, Claim limitation “assembling, by the computing device, the unmapped short reads…” invokes 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. However, the written description fails to disclose the corresponding structure, material, or acts for performing the entire claimed function and to clearly link the structure, material, or acts to the function. While original claims are generally considered to provide written description, there are times they do not, especially in regards to functional language. The specification and original disclosure fail to set forth particular structures, algorithms and/or step-by step actions required to perform the assembly of unmapped reads into a long read. Unmapped reads are those left over from an algorithmic mapping procedure. One does not expect leftover, unmapped reads to all be related to one sequence. The specification does not set forth how disparate, unmapped reads, which do not correspond to the reference, are to be assembled for further study.
In claim 4, Claim limitation “fractioning… the TCR V region and TCR J region amino acid reference sequences into k-strings…aligning … the K-strings with the…sequences, scoring… the level of conservation detected and selecting… corresponding amino acid sequences with a conservation score above a threshold conservation score and detecting… a candidate CDR3 region…” invokes 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. However, the written description fails to disclose the corresponding structure, material, or acts for performing the entire claimed function and to clearly link the structure, material, or acts to the function. The specification fails to set forth how to score the alignments of the amino acid sequences from the long reads of the test to the short K-strings of the reference, to arrive at a score that is appropriate for further processing. It is unclear if this requires steps beyond a simple alignment score between amino acid sequences, or whether other steps are required, such as backtranslation or error correction or instructions for sequences which align to one or more locations.
In claim 6, Claim limitation “aligning… the nucleic acid sequence of the one or more long reads upstream of the candidate CDR3 region nucleic acid sequence with one or more TCR V gene references sequences, scoring … the degree of alignment, and identifying … long reads above a threshold alignment score as comprising a candidate TCR gene sequence” invokes 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. However, the written description fails to disclose the corresponding structure, material, or acts for performing the entire claimed function and to clearly link the structure, material, or acts to the function. It is first unclear and not disclosed how to identify “upstream reads” as there is no clear step of classifying the long reads this way. No specialized structure, algorithm or set of steps are disclosed to carry out this function. The specification further fails to set forth how to score the degree of alignment, to arrive at a score that is appropriate for further processing. It is unclear if this requires steps beyond a simple alignment score between amino acid sequences, or whether other steps are required, such as backtranslation or error correction or instructions for sequences which align to one or more locations. The specification does not disclose if this alignment is done with the same steps and/or parameters as that in step g).
In claim 7, Claim limitation “aligning… the nucleic acid sequence of the one or more long reads downstream of the candidate CDR3 region nucleic acid sequence with one or more TCR V gene references sequences, scoring… the degree of alignment, and identifying … long reads above a threshold alignment score as comprising a candidate TCR gene sequence” invokes 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. However, the written description fails to disclose the corresponding structure, material, or acts for performing the entire claimed function and to clearly link the structure, material, or acts to the function. It is first unclear and not disclosed how to identify “downstream reads” as there is no clear step of classifying the long reads this way. No specialized structure, algorithm or set of steps are disclosed to carry out this function. The specification further fails to set forth how to score the degree of alignment, to arrive at a score that is appropriate for further processing. It is unclear if this requires steps beyond a simple alignment score between amino acid sequences, or whether other steps are required, such as backtranslation or error correction or instructions for sequences which align to one or more locations. The specification does not disclose if this alignment is done with the same steps and/or parameters as that in step g) and/ or h).
Each of the specialized functions lack corresponding structures, algorithms or step-by step procedures which are necessary and sufficient for carrying out the claimed invention and to obtain the claimed results. Merely stating the goal of the method does not clearly set forth and particularly claim the actual algorithmic steps which are performed to achieve the goal. Figures 1 and 17 purportedly showing the pipeline for the system and computer program product merely recites the same limitations in a flow chart format, without actually disclosing how the steps are actually performed.
Therefor the original disclosure fails to provide adequate written description for the claims. “MPEP2106.01: For computer-implemented inventions, the determination of the sufficiency of disclosure will require an inquiry into the sufficiency of both the disclosed hardware and the disclosed software due to the interrelationship and interdependence of computer hardware and software. The critical inquiry is whether the disclosure of the application relied upon reasonably conveys to those skilled in the art that the inventor had possession of the claimed subject matter as of the filing date. Vasudevan Software, Inc. v. MicroStrategy, Inc., 782 F.3d 671, 682. 114 USPQ2d 1349, 1356 (citing Ariad Pharm., Inc. V. Eli Lilly & Co, 598 F.3d 1336, 1351, 94 USPQ2d 1161, 1172 (Fed. Cir. 2010)”
Claims 1-20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention. Claims 1, 19 and 20 are independent claims largely using the same structure and limitations.
In claim 3, “assembling … the unmapped short reads” invokes 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. However, the claim fails to clearly point out and distinctly claim the corresponding structure, material, or acts for performing the entire claimed function and to clearly link the structure, material, or acts to the function. The claims fail to set forth particular structures, algorithms and/or step-by step actions required to perform the assembly of unmapped reads into a long read. Unmapped reads are those left over from an algorithmic mapping procedure. One does not expect leftover, unmapped reads to all be related to one sequence. The specification does not set forth how disparate, unmapped reads, which do not correspond to the reference, are to be assembled for further study. If there is no disclosure of structure, material or acts for performing the recited function, the claim fails to satisfy the requirements of 35 U.S.C. 112(b).
In claim 4, Claim limitation “fractioning… the TCR V region and TCR J region amino acid reference sequences into k-strings…aligning… the K-strings with the…sequences from step e), scoring … the level of conservation detected and selecting corresponding amino acid sequences with a conservation score above a threshold conservation score and detecting… a candidate CDR3 region…” invokes 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. However, the claim fails to specifically point out and distinctly claim the corresponding structure, material, or acts for performing the entire claimed function and to clearly link the structure, material, or acts to the function. The claim fails to set forth how to score the alignments of the amino acid sequences from the long reads of the test to the short K-strings of the reference, to arrive at a score that is appropriate for further processing. It is unclear if this requires steps beyond a simple alignment score between amino acid sequences, or whether other steps are required, such as backtranslation or error correction or instructions for sequences which align to one or more locations. If there is no disclosure of structure, material or acts for performing the recited function, the claim fails to satisfy the requirements of 35 U.S.C. 112(b).
In claim 6, Claim limitation “aligning… the nucleic acid sequence of the one or more long reads upstream of the candidate CDR3 region nucleic acid sequence with one or more TCR V gene references sequences, scoring… the degree of alignment, and identifying… long reads above a threshold alignment score as comprising a candidate TCR gene sequence” invokes 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. However, the claim fails to disclose the corresponding structure, material, or acts for performing the entire claimed function and to clearly link the structure, material, or acts to the function. It is first unclear and not disclosed how to identify “upstream reads” as there is no clear step of classifying the long reads this way. The claim provides no specialized structure, algorithm or set of steps are disclosed to carry out this function. The claim further fails to specifically point out and distinctly claim how to score the degree of alignment, to arrive at a score that is appropriate for further processing. It is unclear if this requires steps beyond a simple alignment score between amino acid sequences, or whether other steps are required, such as backtranslation or error correction or instructions for sequences which align to one or more locations. The specification does not disclose if this alignment is done with the same steps and/or parameters as that in step g). If there is no disclosure of structure, material or acts for performing the recited function, the claim fails to satisfy the requirements of 35 U.S.C. 112(b).
In claim 7, Claim limitation “aligning… the nucleic acid sequence of the one or more long reads downstream of the candidate CDR3 region nucleic acid sequence with one or more TCR V gene references sequences, scoring… the degree of alignment, and identifying … long reads above a threshold alignment score as comprising a candidate TCR gene sequence” invokes 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. However, the claim fails to disclose the corresponding structure, material, or acts for performing the entire claimed function and to clearly link the structure, material, or acts to the function. It is first unclear and not disclosed how to identify “downstream reads” as there is no clear step of classifying the long reads this way. The claim provides no specialized structure, algorithm or set of steps are disclosed to carry out this function. The claim further fails to clearly point out and distinctly claim how to score the degree of alignment, to arrive at a score that is appropriate for further processing. It is unclear if this requires steps beyond a simple alignment score between amino acid sequences, or whether other steps are required, such as backtranslation or error correction or instructions for sequences which align to one or more locations. The specification does not disclose if this alignment is done with the same steps and/or parameters as that in step g) and/ or h). If there is no disclosure of structure, material or acts for performing the recited function, the claim fails to satisfy the requirements of 35 U.S.C. 112(b).
In claim 1, the metes and bounds of the phrase “a candidate TCR V gene” and “a candidate TCR J gene” are unclear. It is unclear what elements a long read nucleic acid sequence must comprise to be considered a candidate gene. Sequences characterized as genes generally have a set of predictable elements such as an open reading frame, start and stop codons, splice site sequences, as well as promotor and/or enhancer sequences. It is entirely unclear how an alignment score between a reference TCR gene, alone, is sufficient to identify a sequence as a candidate gene. The requirement to select long reads which align over a given threshold value does not appear to provide all the necessary and sufficient steps to carry out these actions.
The metes and bounds of claim 3 are unclear. Claim 3 states that the assembly comprises mapping the unmapped reads to a particular reference sequence, and then “assembling” the short reads into a long read based on the alignment. The claim fails to clearly point out and distinctly claim the structures, algorithms, or step-by-step process for assembly of these unmapped reads into long reads. The unmapped short reads would generally comprise unmapped sequences from a variety of disparate places in a genome, and do not necessarily lend themselves to simple end to end joining methods. The specification does not provide a specific algorithm, structure, or set of steps to perform this procedure.
The metes and bounds of claim 12 are unclear. The claim fails to particularly point out and distinctly claim what makes a candidate TCR V or J sequence which results from claim 1, a “unique TCR sequence” in either pre or post treatment T cells. Further, the claim fails to particularly point out and distinctly claim the algorithms, structures or step-by-step actions which are necessary and sufficient to “determine…one or more unique TCR sequences that experienced clonal expansion.” The claim makes this determination “based on the number of occurrences” but it is unclear whether this is merely a counting step or not, and it is not clear how high a count must be to be classified as having experienced clonal expansion.
The term "unique TCR sequences" in claim 13 is a relative term which renders the claim indefinite. The term "unique TCR sequence" is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. As this parameter is not discussed or calculated in this claim, or claim 1 from which it depends, it is unclear how to determine the boundary between a unique TCR sequence and a “not-unique” TCR sequence.
This is further complicated by Claim 14, depending from claim 13, which then identifies a “T cell clonal expansion signature” based on the unique sequences. If each sequence is unique from any other, then the particular steps for identifying some sort of consensus signature are not readily apparent. The claim fails to particularly point out and distinctly claim what elements of a unique TCR sequence are necessary and sufficient to be designated a “signature.” The term “T cell clonal expansion signature” does not appear to have a limited definition in the specification. Nor does the specification make clear all the steps required to make this determination.
The metes and bounds of claim 14 are unclear, and fail to resolve the issues present in claims 12-13. It is entirely unclear from claim 14, what a query of a database of T cell expansion signatures and corresponding treatment signatures actually provides in term of information used to then “determine, based on the query” the subject’s likelihood of responding to the treatment. Claim 14 fails to particularly point out and distinctly claim the particular structures, algorithms or step/by/step procedures required to identify some set of information in the initial query, and then utilize that set of information to somehow provide a risk score, or a likelihood a subject would “respond” to the treatment. It is entirely unclear how any information obtained using an alignment, or some other search comparing the test “T cell clonal expansion signature” to the recited database relates to “response to a treatment.”
The term "responding to treatment" in claim 14 is a relative term which renders the claim indefinite. The term "responding" is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. The opposite of a response is not a “null” or no response: a “response” can be both positive (getting better), or negative (getting much worse or developing other problems, but does not encompass a “null” state.) The claim fails to particularly point out and distinctly claim how a positive or negative response is identified in the query database, such that any sort of likelihood can be calculated. As this parameter is not discussed or calculated in this claim, or claim 1 from which it depends, it is unclear how to determine the boundary between a positive response and a “not positive response” which encompasses poor outcomes.
The metes and bounds of claim 15 are unclear. As set forth in the rejection of the term “responding to treatment” in claim 14, the metes and bounds of the term “determining the subject’s response to the treatment” are unclear. The claim fails to particularly point out and distinctly claim how a response to a treatment is identified in a subject, which encompasses positive, negative, or null outcomes, such that any association between a “T cell expansion signature” can be associated with any particular outcome of any kind. The claim fails to particularly point out and distinctly claim how a positive or negative response is identified in the query database, such that any sort of likelihood can be calculated. As this parameter is not discussed or calculated in this claim, or claims from which it depends, it is unclear how to determine the boundary between a positive response and a “not positive response” which encompasses poor outcomes in any statistically significant manner such that the “associating” step has any scientific meaning.
MPEP 2181.II "If one employs means plus function language in a claim, one must set forth in the specification an adequate disclosure showing what is meant by that language. If an applicant fails to set forth an adequate disclosure, the applicant has in effect failed to particularly point out and distinctly claim the invention as required by the 35 U.S.C. 112(b) [or the second paragraph of pre-AIA section 112 ]." In re Donaldson Co., 16 F.3d 1189, 1195, 29 USPQ2d 1845, 1850 (Fed. Cir. 1994) (en banc)”
Further in 2161.IIB: “For a computer-implemented 35 U.S.C. 112(f) claim limitation, the specification must disclose an algorithm for performing the claimed specific computer function, or else the claim is indefinite under 35 U.S.C. 112(b) (b). See Net MoneyIN, Inc. v. Verisign. Inc., 545 F.3d 1359, 1367 (Fed. Cir. 2008). See also In re Aoyama, 656 F.3d 1293, 1297, 99 USPQ2d 1936, 1939 (Fed. Cir. 2011) ("[W]hen the disclosed structure is a computer programmed to carry out an algorithm, ‘the disclosed structure is not the general purpose computer, but rather that special purpose computer programmed to perform the disclosed algorithm.’") (quoting WMS Gaming, Inc. v. Int’l Game Tech., 184 F.3d 1339, 1349, 51 USPQ2d 1385, 1391 (Fed. Cir. 1999)).”
Therefore, the claim is indefinite and is rejected under 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, second paragraph.
To address rejections based on 35USC 112(f), Applicant may:
(a) Amend the claim so that the claim limitation will no longer be interpreted as a limitation under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph;
(b) Amend the written description of the specification such that it expressly recites what structure, material, or acts perform the entire claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(c) Amend the written description of the specification such that it clearly links the structure, material, or acts disclosed therein to the function recited in the claim, without introducing any new matter (35 U.S.C. 132(a)).
If applicant is of the opinion that the written description of the specification already implicitly or inherently discloses the corresponding structure, material, or acts and clearly links them to the function so that one of ordinary skill in the art would recognize what structure, material, or acts perform the claimed function, applicant should clarify the record by either:
(a) Amending the written description of the specification such that it expressly recites the corresponding structure, material, or acts for performing the claimed function and clearly links or associates the structure, material, or acts to the claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(b) Stating on the record what the corresponding structure, material, or acts, which are implicitly or inherently set forth in the written description of the specification, perform the claimed function. For more information, see 37 CFR 1.75(d) and MPEP §§ 608.01(o) and 2181.
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 1-10, 12-13, 22-23 is/are rejected under 35 U.S.C. 103 as being unpatentable over Li et al. (2016: pto-1449), in view of Gongora-Castillo (2013; PTO-1449) and further in view of Brueffer (published online 04 May 2016; PTO-1449).
The claims set forth a set of data analysis steps for processing next generation sequencing data from the RNA of T cells in a sample. The sequencing provides sequence reads that are stored in a system memory, with reference data which lacks a TCR sequence. The reads are aligned with the reference data, “mapping” the reads to a set of reference sequences lacking the TCR genes, to create two datasets, one of mapped reads, and one of unmapped reads. The mapped reads are discarded, and the unmapped reads are then aligned against reference TCR sequences to identify “candidate V /J genes.”
Li et al. (Li, B et al. Landscape of tumor-infiltrating T cell repertoire of human cancers. (July 2016) Nature Genetics V 48:7 p725) discloses methods of obtaining candidate CDR3 sequences from T cell short read sequences. Li obtains RNA-seq data from T cells from subjects which were generated using high throughput sequencing. (p2) After the sequencing procedure, the reads are mapped to a reference genome, then informative read-pairs are extracted from the TCR regions (supplemental Figure 1). The legend to Supplemental Figure 1 reads: “Paired-end short-read RNA-seq data were mapped to human reference genome hg19, and unmapped reads in the TCR regions were
extracted for pairwise comparison. CDR3 sequences were assembled from disjoint read sets and annotated using IMGT nomenclatures.” The assembled sequences represent a candidate CDR3 sequence. Two separate pipelines are disclosed in supplemental figure 4 and the Online Methods. The long read Candidate CDR3 sequences were translated to possible amino acid sequences (online Methods, “annotation of CDR3 sequences”) and short reference TCR gene sequences were aligned against the translated long reads. Sequences comprising either the V or J amino acid sequences were kept. The original long read is obtained, and a further alignment against TCR V or J sequences was performed, to obtain the final “candidate TCR gene sequence.” Li et looks at unmapped reads as containing possible CDR3 sequences which simply didn’t get mapped due to error. As set forth in the Online Methods, Li does analyze those unmapped reads and use them in de novo assembly of CDR3 sequences (Online Methods Data Preparation and preprocessing). This differs from deliberately creating an artificial reference genome lacking the TCR sequence, however in one experiment of Li, a reference sample was obtained that naturally does not express TCR transcript (Online methods, validation based on in silico simulations). The tests of simulated short reads to this reference sample are essentially the same as the methods of the rejected claims.
With respect to using unmapped reads, wherein the reads which do not map to a particular set of reference sequences are used for analysis, this process has been performed previously. Gongora-Castillo (Gongora-Castillo et al. Bioinformatics challenges in de novo transcriptome assembly using short read sequences in the absence of a reference genome sequence. 2013 Nat Prod Rep vol 30:490) discloses the creation of artificial reference genome, lacking certain gene regions, followed by a TopHat mapping step using short read sequences. Gongora-Castillo looks specifically to the analysis of unmapped short reads, to obtain information otherwise overlooked in the mapping process. Figure 1 of Gongora-Castillo shows that in step (6) reads which do not align to their artificial genome (similar to the artificial reference sequence of the claims which lacks TCR sequences) are then sent to a de-novo assembly process (#2) using TopHat. The assembled short reads are then processed by alignment and annotation in BLAST which the assembled reads are aligned against reference sequences, in order to obtain candidate gene sequences.
A technical difficulty exists in analyzing unmapped short read sequences from the mapping steps of Li. The files for unmapped reads which are produced by Li et al or other methods are not formatted for easy analysis in additional steps. Brueffer (Brueffer et al. TopHat-Recondition: a post-processor for TopHat unaligned reads. (4 May 2016) BMC Bioinformatics 17:199) created this additional module specifically in response to the need to reformat the unmapped read files generated by TopHat processes. TopHat-Recondition performs a series of steps which restore certain information to the file, and applies the proper formatting to the files. Once the files are reconditioned, they are readily available for downstream analysis by various software platforms.
In KSR Int 'l v. Teleflex, the Supreme Court, in rejecting the rigid application of the teaching, suggestion, and motivation test by the Federal Circuit, indicated that “The principles underlying [earlier] cases are instructive when the question is whether a patent claiming the combination of elements of prior art is obvious. When a work is available in one field of endeavor, design incentives and other market forces can prompt variations of it, either in the same field or a different one. If a person of ordinary skill can implement a predictable variation, § 103 likely bars its patentability.” KSR Int'l v. Teleflex lnc., 127 S. Ct. 1727, 1740 (2007).
Applying the KSR standard of obviousness to Gongora-Castillo, Brueffer and Li, the Examiner concludes that the combination of the software package and TCR sequencing protocols of Li with the method of selecting unmapped reads for further analysis by Gongora-Castillo, in view of the technical advancement provided by Brueffer is a use of known techniques to improve similar methods.
The nature of the problem to be solved may lead inventors to look at references relating to possible solutions to that problem. The problem is how to obtain a set of reads which are only related to the genes of interest, from a short read mapping sequencing procedure. One of skill would have considered constructing a particular set of references lacking the genes in question, so that the largest possible pool of related sequences of interest are obtained in that unmapped file as set forth by Gongora-Castillo, and also the experiment of Li using a reference sequence which was from a sample which did not express TCR genes. One would have been motivated to pursue this strategy in view of the technical advance of Brueffer which specifically formats unmapped reads from its TopHat mapping processes for further analysis. Therefore, it would have been obvious to use the unmapped reads left over from a TopHat mapping step (As performed by Gongora-Castillo and Li) to assemble long reads for further analysis (as disclosed by Gongora-Castillo) which was made technically feasible by the additional algorithms of Brueffer to prepare the leftover, unmapped TopHat files for use in other software platforms. Short read sequencing information was known to be challenging to analyze as discussed by Gongora-Castillo, in view of a lack of appropriate non-model gene reference sequences, challenges in assembly such as known high error rates of high throughput short read sequencing, unknown quality scores for short read sequences, the length of the short reads can limit specificity, and in view of computing resource issues. Gongora-Castillo demonstrated one way to address some of these issues by constructing an artificial reference genome, which lacked certain genes. The unmapped genes were therefore rescued, and used to assemble the genes of interest. One of skill in the art would have had an expectation of success in this strategy as algorithmic tools existed specifically to recondition unmapped read files for further downstream analysis by Brueffer. The remaining steps are all taught by Li, as set forth above.
With respect to claim 2, Li generates short reads of less than about 100bp.
With respect to claim 3, The pair-wise reads of Li are clustered into groups, then assembled within each group to a long-read candidate CDR3 sequence. Two separate pipelines are disclosed in supplemental figure 4 and the Online Methods. The long read Candidate CDR3 sequences were translated to possible amino acid sequences (online Methods, “annotation of CDR3 sequences”) and short reference TCR gene sequences were aligned against the translated long reads. Sequences comprising either the V or J amino acid sequences were kept. Gongora-Castillo looks specifically to the analysis of unmapped short reads, to obtain information otherwise overlooked in the mapping process. Figure 1 of Gongora-Castillo shows that in step (6) reads which do not align to their artificial genome (similar to the artificial reference sequence of the claims which lacks TCR sequences) are then sent to a de-novo assembly process (#2) using TopHat. The assembled short reads are then processed by alignment and annotation in BLAST which the assembled reads are aligned against reference sequences, in order to obtain candidate gene sequences.
With respect to claims 4-7, Li provided the following:
a) sequencing short reads of RNA from a T cell in the online methods section, including RNA-seq data from T cells from TCGA (data preparation and pre-processing).
b) These were aligned to a reference sequence, to obtain mapped and unmapped sequences (Data preparation and pre-processing). One references sequence is HG19. However, another reference genome lacking TCR gene sequences is provided in the section “validation based on in silico simulations” RNA-seq short read data is obtained from K562 cell line ENCFF002DK1 and ENCFF002DKF. These are a leukemia cell line that do not express TCR transcript.
c) Li separates mapped and unmapped reads (Data preparation and preprocessing). The unmapped read set therefore has “discarded” the mapped reads.
d) Li takes the unmapped reads and performs de novo assembly of the short reads into long reads (CDR3 de novo assembly and annotation workflow).
e) Li translates the long reads into corresponding amino acid sequences at the section named “Annotation of CDR3 sequences”.
f)-g) Li “fractions” the amino acid sequences and references sequences of the translated long reads, and reference genes from IMGT (annotation of CDR3 sequences). K-strings such as CAXS, CASX, CSVE, FGXG, LGGG et al. were used in the alignments, and matches meeting a threshold were retained as possible CDR3, V or J sequences (annotation of CDR3 sequences).
h)- i) Li extends or extracts upstream and downstream sequences to identify V and J sequences, further aligning the sequences to IMGT reference sequences, reporting the alignments with the highest scores (annotation of CDR3 sequences).
With respect to claim 8, these steps are within the disclosure of Li. Short read data comprises transcripts of varying length, and various trimming and quality control processing steps are applied. The referenced source for iSSAKE also specifically uses reads greater than 35nt in their alignments (referenced source for iSSAKE, Warren et al.)
With respect to claims 9-10, 12-13, Li et al disclose analyzing T cells from subject pre and post treatment, as well as obtaining unique TCR sequences from those populations, and determining a clonal expansion signature. Li specifically identifies at least one signature as potentially useful as an anti-cancer vaccine. Li also discloses parallel processing of portions of the process, as this is how their pipeline, the pipeline of iSAKKE and many others manage their computing resources.
With respect to claim 22-23, the references are human genomes and human transcriptomes.
Claims 9-10, 12-13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Li, Gongora-Castillo and Brueffer as applied to claims 1-10, 12-13, 22-23 above, and further in view of Ryan (2016).
Ryan et al. Successful immunotherapy induces previously unidentified allergen specific CD4+ T cell subsets. (January 2016) PNAS e1286-1295 and supplemental information.
Claims 9-10, add limitations that the T cell sample be from someone who has been treated with an immunotherapy prior to T cell collection. Claims 12-14 adds limitations that T cells are analyzed prior to treatment after treatment, and a treatment signature is determined of unique TCR sequences.
As set forth above, Li et al. disclose methods of obtaining candidate CDR3 sequences from T cell short read sequences which meet claim 1. RNA-seq data from any source including TCGA can be subjected to the methods of Li- including RNA-seq data from cells which have undergone a treatment. (See TCGA description; clinical data may include treatment information).
With respect to claims 1 and 4, Li sets forth a) sequencing short reads of RNA from a T cell in the online methods section, including RNA-seq data from T cells from TCGA (data preparation and pre-processing). b) These were aligned to a reference sequence, to obtain mapped and unmapped sequences (Data preparation and pre-processing). One references sequence is HG19. However, another reference genome lacking TCR gene sequences is provided in the section “validation based on in silico simulations” RNA-seq short read data is obtained from K562 cell line ENCFF002DK1 and ENCFF002DKF. These are a leukemia cell line that do not express TCR transcript. c) Li separates mapped and unmapped reads (Data preparation and preprocessing). The unmapped read set therefore has “discarded” the mapped reads. d) Li takes the unmapped reads and performs de novo assembly of the short reads into long reads (CDR3 de novo assembly and annotation workflow). e) Li translates the long reads into corresponding amino acid sequences at the section named “Annotation of CDR3 sequences”. f)-g) Li “fractions” the amino acid sequences and references sequences of the translated long reads, and reference genes from IMGT (annotation of CDR3 sequences). K-strings such as CAXS, CASX, CSVE, FGXG, LGGG et al. were used in the alignments, and matches meeting a threshold were retained as possible CDR3, V or J sequences (annotation of CDR3 sequences). h)- i) Li extends or extracts upstream and downstream sequences to identify V and J sequences, further aligning the sequences to IMGT reference sequences, reporting the alignments with the highest scores (annotation of CDR3 sequences).
With respect to using unmapped reads, wherein the reads which do not map to a particular set of reference sequences are used for analysis, this process has been performed previously. Gongora-Castillo (Gongora-Castillo et al. Bioinformatics challenges in de novo transcriptome assembly using short read sequences in the absence of a reference genome sequence. 2013 Nat Prod Rep vol 30:490) discloses the creation of artificial reference genome, lacking certain gene regions, followed by a TopHat mapping step using short read sequences. Gongora-Castillo looks specifically to the analysis of unmapped short reads, to obtain information otherwise overlooked in the mapping process. Figure 1 of Gongora-Castillo shows that in step (6) reads which do not align to their artificial genome (similar to the artificial reference sequence of the claims which lacks TCR sequences) are then sent to a de-novo assembly process (#2) using TopHat. The assembled short reads are then processed by alignment and annotation in BLAST which the assembled reads are aligned against reference sequences, in order to obtain candidate gene sequences.
A technical difficulty exists in analyzing unmapped short read sequences from the mapping steps of Li. The files for unmapped reads which are produced by Li et al or other methods are not formatted for easy analysis in additional steps. Brueffer (Brueffer et al. TopHat-Recondition: a post-processor for TopHat unaligned reads. (4 May 2016) BMC Bioinformatics 17:199) created this additional module specifically in response to the need to reformat the unmapped read files generated by TopHat processes. TopHat-Recondition performs a series of steps which restore certain information to the file, and applies the proper formatting to the files. Once the files are reconditioned, they are readily available for downstream analysis by various software platforms.
Ryan et al. discloses that patients who have undergone immunotherapy against an antigen have additional gene expression in individual antigen-specific and non-specific CD4+ T cells (p1290 CD4+ T cell TCR and gene expression) and sequencing of TCRs showed that these samples express certain CDR3 sequences at a higher level than expected. Ryan also provides experiments which analyze T cell samples before treatment, during treatment and after treatment (Fig 1, legend, Fig 4, legend, Fig 6).
Applying the KSR standard of obviousness to methods of Li, Gongora-Castillo, Bruffer and Ryan et al, substitution of the TCR sequencing and analysis of T cells which have previously undergone an immunotherapy as in Ryan is no more than "the simple substitution of one known element for another or the mere application of a known technique to a piece of prior art ready for improvement." Thus, it would have been obvious to one of ordinary skill in the art to replace the TCR sequencing of Ryan with the improved processes of Li, because one of ordinary skill in the art would have been able to carry out such a substitution, and the results were reasonably predictable. One of skill would have been motivated to use the methods of Li to obtain the most complete picture of TCR sequence data in cells which have undergone a treatment, and cells which have not undergone a treatment. Using the data analysis on T cell samples from patients at all phases of the experiment such as pre-treatment, during treatment and after treatment would have similarly been obvious to one of skill in the art at the time of the invention. Further, the Li reference itself uses RNA-seq data from TCGA, which comprises RNA-seq data from normal and matched tumor samples, which may or may not have already undergone a treatment. The treatment of the data is the same.
Claims 11 and 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Li, Gongora-Castillo and Brueffer as applied to claims 1-10, 12-13, 22-23 above, and further in view of Radvanyi (2012).
Radvani et al. (2012) Specific Lymphocyte Subsets Predict Response to Adoptive Cell Therapy Using Expanded Autologous Tumor-Infiltrating Lymphocytes in Metastatic Melanoma Patients. Clinical Cancer Research, vol 18 no 24, p6758-6570.
Claim 11 sets forth that the combination treatment is a costimulatory agonist and a coinhibitory antagonist, and claim 16 sets forth autologous immunotherapy using a T cell expanded clone, which expanded in response to a treatment.
With respect to claims 1 and 4, Li sets forth a) sequencing short reads of RNA from a T cell in the online methods section, including RNA-seq data from T cells from TCGA (data preparation and pre-processing). b) These were aligned to a reference sequence, to obtain mapped and unmapped sequences (Data preparation and pre-processing). One references sequence is HG19. However, another reference genome lacking TCR gene sequences is provided in the section “validation based on in silico simulations” RNA-seq short read data is obtained from K562 cell line ENCFF002DK1 and ENCFF002DKF. These are a leukemia cell line that do not express TCR transcript. c) Li separates mapped and unmapped reads (Data preparation and preprocessing). The unmapped read set therefore has “discarded” the mapped reads. d) Li takes the unmapped reads and performs de novo assembly of the short reads into long reads (CDR3 de novo assembly and annotation workflow). e) Li translates the long reads into corresponding amino acid sequences at the section named “Annotation of CDR3 sequences”. f)-g) Li “fractions” the amino acid sequences and references sequences of the translated long reads, and reference genes from IMGT (annotation of CDR3 sequences). K-strings such as CAXS, CASX, CSVE, FGXG, LGGG et al. were used in the alignments, and matches meeting a threshold were retained as possible CDR3, V or J sequences (annotation of CDR3 sequences). h)- i) Li extends or extracts upstream and downstream sequences to identify V and J sequences, further aligning the sequences to IMGT reference sequences, reporting the alignments with the highest scores (annotation of CDR3 sequences).
With respect to using unmapped reads, wherein the reads which do not map to a particular set of reference sequences are used for analysis, this process has been performed previously. Gongora-Castillo (Gongora-Castillo et al. Bioinformatics challenges in de novo transcriptome assembly using short read sequences in the absence of a reference genome sequence. 2013 Nat Prod Rep vol 30:490) discloses the creation of artificial reference genome, lacking certain gene regions, followed by a TopHat mapping step using short read sequences. Gongora-Castillo looks specifically to the analysis of unmapped short reads, to obtain information otherwise overlooked in the mapping process. Figure 1 of Gongora-Castillo shows that in step (6) reads which do not align to their artificial genome (similar to the artificial reference sequence of the claims which lacks TCR sequences) are then sent to a de-novo assembly process (#2) using TopHat. The assembled short reads are then processed by alignment and annotation in BLAST which the assembled reads are aligned against reference sequences, in order to obtain candidate gene sequences.
A technical difficulty exists in analyzing unmapped short read sequences from the mapping steps of Li. The files for unmapped reads which are produced by Li et al or other methods are not formatted for easy analysis in additional steps. Brueffer (Brueffer et al. TopHat-Recondition: a post-processor for TopHat unaligned reads. (4 May 2016) BMC Bioinformatics 17:199) created this additional module specifically in response to the need to reformat the unmapped read files generated by TopHat processes. TopHat-Recondition performs a series of steps which restore certain information to the file, and applies the proper formatting to the files. Once the files are reconditioned, they are readily available for downstream analysis by various software platforms.
Radvanyi obtains T cells from patients with metastatic melanoma. The T cells underwent clonal expansion, T cells exhibiting certain characteristics were selected, and that cloned, expanded group of cells is administered back to the patient, followed by treatment with IL-2 (a combination therapy.) p6759.
“Data presented in this article are from the nonrandomized component whose objective is to determine clinical response rates and predictive biomarkers associated with clinical response in 50 patients. The analysis in this article is on the first 31 treated patients.”
“Hematologic and biochemical parameters were monitored daily during IL-2 administration. Intravenous blood samples (10 mL) were collected from patients before and after lymphodepletion on day 7 and day 0 before chemotherapy and before TIL infusion, respectively. Subsequent blood samples (50 mL) were collected on days 7, 14, 21, 35, and 70 after TIL infusion. The samples were analyzed for total white blood cell (WBC) count, absolute lymphocyte count (ALC), and absolute neutrophil count (ANC) in the Division of Pathology and Laboratory Medicine at MD Anderson Cancer Center.” P6761
Radvanyi measures tumor response to therapy as set forth at page 6761 and 6763.
The T cells from the samples were expanded, and each clonotype was analyzed for the presence of certain cell surface antigens as set forth at p 6761-6762.
“Patients of any HLA subtype with stage IIIc to IV (M1a M1c) disease were recruited into the study. TILs were first expanded from tumor fragments with IL-2 for 5 weeks and cryopreserved for further expansion in the REP if a minimum of 48 x 106 cells was reached. This was required so that at least 40 x 106 cells were available for the REP after samples for quality control and antitumor analysis (8 x 106 cells) were removed.” p6762
“We have found that IL-2- expanded TIL were functional and responded to polyclonal TCR using anti-CD3 stimulation in both IFN-g and cytotoxic T lymphocyte (CTL) assays (data not shown).” P6764
“both the percentage and total number of CD8Beta T cells infused were significantly associated with clinical response (P = 0.001 and 0.0003, respectively; …)”
Radvanyi investigates TCR V-beta genes in TIL or the patients’ PBMC as set forth at p6761.
“For TCR Vb gene analysis, the CDR3 region of 96 TCR Vb–positive DNA samples were
prepared and confirmed and then sequenced. To characterize each individual TCR Vb clonotype, sequence data from each sample was further analyzed using the international ImMunoGeneTics information system (IMGT) program. The frequency of each dominant Vb clonotype was calculated by determining the percentage of each specific CDR3 sequence found within the 96 clones picked and sequenced.” P6761
“We used a TCR Vb cloning and CDR3 sequencing approach used previously to track the changes in dominant TCR Vb clonotypes in PBMC in 5 responding patients up to 22 months after TIL infusion (patients #2131, #2150/2153, #2258, #2124, and #2180). As shown in Supplementary Table S5, some dominant Vb clonotypes in the original TIL persisted long-term over the entire 22-month period (e.g., Vb24-1 and Vb12-3 in patient #2150/2153, and Vb4-1 and Vb29 inpatient#2131)”
Radvanyi concludes: “In summary, we have found that the adoptive transfer of TIL expanded ex vivo can induce a high rate of objective tumor regression in unresectable metastatic melanoma
patients and that these responses can be long lasting in a significant fraction of treated patients.”
Applying the KSR standard of obviousness to methods of Li, Gongora-Castillo, Brueffer and Radvanyi et al, expansion and analysis of T cells in the presence of an immunotherapy (IL-2) as in Radvanyi, then the readministration of the expanded T cells to the patient followed by the immunotherapy (IL-2), including the analysis of the TCR V and J genes, after performing the methods of Li is the use of a known technique to improve similar methods. The nature of the problem to be solved may lead inventors to look at references relating to possible solutions to that problem. Melanoma was known to be difficult to treat using only immunotherapy. The concept of adoptive immunotherapy using T cells from the patient, after ex-vivo expansion in the presence of IL-2, followed by reintroduction of the expanded T cell clones, and the therapy (IL-2), was well known as illustrated by Radvanyi. Therefore, it would have been obvious to use the process of Li to identify T cell clones responsive to a therapy then to readminister T cell clones responsive to that therapy to the patient, followed by treatment with that therapy as shown in Radvanyi to treat the melanoma.
Thus, it would have been obvious to one of ordinary skill in the art to add the TCR sequencing of Li to identify clones possibly useful in adoptive immunotherapy, to the methods of Radvanyi, because one of ordinary skill in the art would have been able to carry out such a step, and the results were reasonably predictable. One of skill would have been motivated to use the methods of Li to obtain the most complete picture of TCR sequence data in cells which have undergone a treatment, and cells which have not undergone a treatment. Using the data analysis on T cell samples from patients at all phases of the experiment such as pre-treatment, during treatment and after treatment would have similarly been obvious to one of skill in the art at the time of the invention. Further, the Li reference itself uses RNA-seq data from TCGA, which comprises RNA-seq data from normal and matched tumor samples, which may or may not have already undergone a treatment. The treatment of the data is the same.
Claims 12-15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Li, Gongora-Castillo and Brueffer as applied to claims 1-10, 12-13, 22-23 above, and further in view of Fang (2014).
Fang et al. (2014) Quantitative T cell Repertoire analysis by deep cDNA sequencing of T cell receptor alpha and beta chains using next generation sequencing.
Claims 12-15 adds limitations that T cells are analyzed prior to treatment after treatment, and a treatment signature is determined of unique TCR sequences, and determine a response to a treatment, and associating that response with the TCR signature in a database..
As set forth above, Li et al. disclose methods of obtaining candidate CDR3 sequences from T cell short read sequences which meet claim 1. RNA-seq data from any source including TCGA can be subjected to the methods of Li- including RNA-seq data from cells which have undergone a treatment. (See TCGA description; clinical data may include treatment information).
With respect to claims 1 and 4, Li sets forth a) sequencing short reads of RNA from a T cell in the online methods section, including RNA-seq data from T cells from TCGA (data preparation and pre-processing). b) These were aligned to a reference sequence, to obtain mapped and unmapped sequences (Data preparation and pre-processing). One references sequence is HG19. However, another reference genome lacking TCR gene sequences is provided in the section “validation based on in silico simulations” RNA-seq short read data is obtained from K562 cell line ENCFF002DK1 and ENCFF002DKF. These are a leukemia cell line that do not express TCR transcript. c) Li separates mapped and unmapped reads (Data preparation and preprocessing). The unmapped read set therefore has “discarded” the mapped reads. d) Li takes the unmapped reads and performs de novo assembly of the short reads into long reads (CDR3 de novo assembly and annotation workflow). e) Li translates the long reads into corresponding amino acid sequences at the section named “Annotation of CDR3 sequences”. f)-g) Li “fractions” the amino acid sequences and references sequences of the translated long reads, and reference genes from IMGT (annotation of CDR3 sequences). K-strings such as CAXS, CASX, CSVE, FGXG, LGGG et al. were used in the alignments, and matches meeting a threshold were retained as possible CDR3, V or J sequences (annotation of CDR3 sequences). h)- i) Li extends or extracts upstream and downstream sequences to identify V and J sequences, further aligning the sequences to IMGT reference sequences, reporting the alignments with the highest scores (annotation of CDR3 sequences).
With respect to using unmapped reads, wherein the reads which do not map to a particular set of reference sequences are used for analysis, this process has been performed previously. Gongora-Castillo (Gongora-Castillo et al. Bioinformatics challenges in de novo transcriptome assembly using short read sequences in the absence of a reference genome sequence. 2013 Nat Prod Rep vol 30:490) discloses the creation of artificial reference genome, lacking certain gene regions, followed by a TopHat mapping step using short read sequences. Gongora-Castillo looks specifically to the analysis of unmapped short reads, to obtain information otherwise overlooked in the mapping process. Figure 1 of Gongora-Castillo shows that in step (6) reads which do not align to their artificial genome (similar to the artificial reference sequence of the claims which lacks TCR sequences) are then sent to a de-novo assembly process (#2) using TopHat. The assembled short reads are then processed by alignment and annotation in BLAST which the assembled reads are aligned against reference sequences, in order to obtain candidate gene sequences.
A technical difficulty exists in analyzing unmapped short read sequences from the mapping steps of Li. The files for unmapped reads which are produced by Li et al or other methods are not formatted for easy analysis in additional steps. Brueffer (Brueffer et al. TopHat-Recondition: a post-processor for TopHat unaligned reads. (4 May 2016) BMC Bioinformatics 17:199) created this additional module specifically in response to the need to reformat the unmapped read files generated by TopHat processes. TopHat-Recondition performs a series of steps which restore certain information to the file, and applies the proper formatting to the files. Once the files are reconditioned, they are readily available for downstream analysis by various software platforms.
In the same field of research, Fang analyzes TCR signatures in cancer patients, before and after treatment with a cancer peptide vaccine. Fang obtains T cell samples, generates cDNA from the RNA of the T cell sample, then performs next generation sequencing on the cDNA to obtain sequence reads. Both short (<120 bp) and long (>150 bp) reads are obtained and analyzed, to then assemble polynucleotides encoding the TCR V and J regions. (Results, p2).
Fang obtains T cell samples from patients prior to, and after a treatment: a mixture of three cancer peptide vaccines. (Results p2-3). The responses to the treatment were obtained.
“Patients' clinical characteristics and outcome are listed in Table 1. Among these five patients, strong CTL responses were detected in patients #1, #3, and #5 by ELISPOT assay along with the ex vivo peptide stimulation, and these patients showed good clinical outcome with prolonged overall survival (OS; average OS = 909 ± 278 days). On the other hand, patients #2 and #4, who had showed little CTL induction against these peptides, had shorter overall survival (133 ± 40 days; p = 0.033 by Student's t-test).” P2.
The response to the treatment was associated with the TCR signature. (Table 2). “We then quantitatively characterized the distinct V(D)J and CDR3 clones from the mapped reads. In patient #1, the top 10 V(D)J clones showing the largest differences in frequencies between two time points - before treatment (1-1) and after two cycles of vaccination (1-3) - were listed (Fig. 2a).” p3.
“Thus, our algorithm allowed us to detect and monitor every single clonotype (V(D)J or CDR3) quantitatively during treatment. In the patients' samples, we observed an increased TCR clonotype diversity with a more normal Gaussian distribution of the CDR3 length after vaccine treatment in patients showing better immune response but not in those patients showing poor immune response.” P4.
Fang also looks at unmapped reads, as set forth beginning at page 4.
“In the intronic group, the unmapped region contained either an intronic region adjacent to the J segment (subgroup 1: J Intron), some of which contained a 3' end of a further upstream J segment, or an intronic region adjacent to the D segment (subgroup 2: D Intron). In the non-intronic group, the unmapped regions contained sequences mapped to non-TRB regions of human genome reference sequence (subgroup 3: Unmapped). Furthermore, the majority of these "unmapped-J-C" reads contained intronic regions, ranging from 85.3% in sample 5-4B to 97.5% in sample 3-2B (Fig. 66, upper panel), and J intron was the dominant subgroup (Fig. 66, lower panel). In the nonintronic
group, subgroup 4 'Unmapped' was the dominant subgroup (Fig. 66, lower panel).”
Applying the KSR standard of obviousness to methods of Li, Gongora-Castillo, Bruffer and Fang et al, substitution of the TCR sequencing and analysis of T cells which have previously undergone an immunotherapy as in Fang, in the methods of Li is no more than "the simple substitution of one known element for another or the mere application of a known technique to a piece of prior art ready for improvement." Thus, it would have been obvious to one of ordinary skill in the art to replace the TCR sequencing of Fang with the improved processes of Li, because one of ordinary skill in the art would have been able to carry out such a substitution, and the results were reasonably predictable. One of skill would have been motivated to use the methods of Li to obtain the most complete picture of TCR sequence data in cells which have undergone a treatment, and cells which have not undergone a treatment. Using the data analysis on T cell samples from patients at all phases of the experiment such as pre-treatment, during treatment and after treatment would have similarly been obvious to one of skill in the art at the time of the invention. Further, the Li reference itself uses RNA-seq data from TCGA, which comprises RNA-seq data from normal and matched tumor samples, which may or may not have already undergone a treatment. The treatment of the data is the same.
Claim 21 is/are rejected under 35 U.S.C. 103 as being unpatentable over Li et al. (2016: pto-1449), in view of Gongora-Castillo (2013) and further in view of Brueffer (published online 04 May 2016) as applied to claims 1-3, 9-10, 12-13, 22-23 above further in view of Kasper (2010: pto-1449).
Claim 21 states that random priming is used in the production of the short reads. Li does not specifically address random priming of RNA for short read sequencing. However, this is a step performed in nearly any RNA based PCR process as discussed by Kasper. The conventional protocol for creating transcriptome cDNA comprises extracting total RNA, followed by polyA enrichment, then RNA fragmentation and reverse transcription into double stranded cDNA using random hexamers (“random priming”). This is used to generate reads across an entire transcriptome, not being limited to particular specific primers for known sequences. Both the process, the inherent bias in the process and methods of adjusting or reweighting the results were all shown to be well known by Kasper. It is assumed this was even a part of the disclosure of Li, wherein FFPE samples were used to obtain transcriptome DNA information and RNA-seq data (kidney cell carcinoma experiment and online methods).
It would have been prima facie obvious to one of skill in the art to have utilized random hexamer priming in the RNA processing steps to obtain cDNA representative of the majority of the transcriptome. This was a routine step in the production of transcriptome data as shown by Kasper. One would have had a reasonable expectation of success at using the random priming, as Kasper disclosed proper re-weighting schemes to balance any bias resulting from the random priming.
Claim 24 is/are rejected under 35 U.S.C. 103 as being unpatentable over Li et al. (2016: pto-1449), in view of Gongora-Castillo (2013) and further in view of Brueffer (published online 04 May 2016) as applied to claims 1-3, 9-10, 12-13, 22-23 above further in view of Glusman (2001; PTO-1449).
Claim 24 adds the limitation that a TCR C region nucleic acid sequence is appended to the TCR sequence.
Glusman et al. (Glusman, G. Comparative genomics of the human and mouse T cell receptor loci. 2001 Cell 15:337.) reviews the structure of TCR loci in humans and mice. Glusman discusses each region of a T cell receptor protein: the Variable region, the Joining region and the Constant region. Glusman notes that the C region is highly conserved between human and mouse. Glusman suggests that the C region plays a role in the proper folding of the produced product.
It would have been obvious to one of skill in the art at the time the invention was made to have appended a TCR c region nucleic acid sequence to the candidate TCR V or J nucleic acid sequences obtained from claim 1. One would have been motivated to perform this linkage, so that expression of that sequence would be more likely to fold appropriately and most similarly to native VJC rearrangements. One would have had a reasonable expectation of success at appending the sequence data of a c region to the sequence data of a V or J region as the sequences are both nucleic acid sequences. As such, the limitation would have been obvious to one of skill in the art at the time the invention was made.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 1-17, 21-24 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-20 of U.S. Patent No. 11,274,342 B2. Although the claims at issue are not identical, they are not patentably distinct from each other because the claims of the application are generic to the claims of the patent, for the same purposes. The patent combines the limitations of the applications claims 1 and 4. The dependent claims are reassorted but essentially the same between both.
Conclusion
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