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 .
This Application has been transferred to AU 1686, Examiner Mary K Zeman.
Applicant’s election of Group I, claims 1-17, 32-33 in the reply filed on 12/3/2024 is acknowledged. Because applicant did not distinctly and specifically point out the supposed errors in the restriction requirement, the election has been treated as an election without traverse (MPEP § 818.01(a)).
The supplemental election of species of 1) packed bed columns comprising resin beads having c18 functional groups on the surface and 2) the truncated proteoform COA6A3 (aa 2781-2792) (SEQ ID NO:1), without traverse on 6/9/2025, is acknowledged. However, upon initial search and consideration this election of species requirement is WITHDRAWN.
In view of the withdrawal of the election of species requirement, applicant(s) are advised that if any claim presented in a divisional application is anticipated by, or includes all the limitations of, a claim that is allowable in the present application, such claim may be subject to provisional statutory and/or nonstatutory double patenting rejections over the claims of the instant application. Once the restriction requirement is withdrawn, the provisions of 35 U.S.C. 121 are no longer applicable. See In re Ziegler, 443 F.2d 1211, 1215, 170 USPQ 129, 131-32 (CCPA 1971). See also MPEP § 804.01.
Claims 18-31 have been canceled by the amendment filed 6/9/2025.
Claims 1-17, 32-33 are under examination, as amended 12/3/2024.
This application was filed 8/11/2022. This application claims priority as a CIP of a US non-provisional application (17/827708), which is a CIP of a PCT application (PCT/US22/22964), that claims priority to three separate provisional applications. The prosecution history of the parent has been reviewed. The ‘708 application fails to provide support for predicting a respiratory tract infection, using a composite score, or confusion matrices as is required for the pending claims in this application. Therefore, the Effective Filing Date for the pending claims is 8/11/2022.
This application has a PG-Pub Number of 2022/0386893 A1, published 12/8/2022.
Multiple IDS statements have been entered and considered in this application.
The Drawings as filed are suitable for examination.
The files related to the sequence listing, filed 6/10/2025 have been entered.
The amendment to the specification regarding the sequencing listing has been entered.
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, 32-33 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 and the Federal Register notice (FR89, no 137 (7/17/2024) p 58128-58138) 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: methods.
With respect to step (2A) (1): YES. The claims recite an abstract idea, law of nature and/or natural phenomenon. The claims recite an abstract idea of identifying subsets of proteoforms from exhaled breath condensate samples from patients, statistically analyzing the samples after LC/MS processes, and identifying which subsets of proteins or proteoforms are used to identify or diagnose a disease, such as a respiratory tract infection. (See MPEP 2106.07(a)). The claims also embrace the natural law describing the naturally occurring correlations between naturally occurring proteoforms in exhaled breath, and naturally occurring phenotypes, such as disease. (MPEP 2106.04). 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 independent claim(s) 1, 9 and 32 include:
1. (Previously Presented) A method for predicting a respiratory tract infection (RTI) in intubated patients breathing with the assistance of a ventilator, the method comprising:
(EIA- preamble, setting forth the goal of the method.)
diagnosing the presence or absence of the RTI by culturing at least one of sputum samples, endotracheal tube samples (ET), and bronchoalveolar lavage (BAL) for each patient in a group of patients with and without the RTI participating in clinical laboratory trials to obtain baseline data;
(EIA- data gathering step, by obtaining certain samples, culturing them, and determining the presence or absence of infection to provide reference or baseline data. [0056, 0071])
selectively capturing truncated proteoforms in the exhaled breath aerosols produced by each patient using a packed bed column removably connected to the exhaled air tubing of the ventilator;
(EIA- data gathering step of capturing a type of sample [0033-0035])
extracting the truncated proteoforms from the packed bed column into one or more collected liquid samples corresponding to each patient;
(EIA- laboratory process step to recover the desired sample component [0038, 0052, 0065, 0072)
analyzing the one or more collected liquid samples including truncated proteoforms using mass spectrometry to obtain raw mass spectra;
(EIA- data gathering step of routine mass spectrometry analysis to obtain data for the statistical analysis. [0019, 0039, 0041, 0055, 0067-0068, 0073])
identifying a statistically significant subset of the truncated proteoforms characteristic of the RTI; and
(Mental Process in a computing environment, or using a computer as a tool, of observing truncated proteoforms, and making a judgement as to whether they are “characteristic” of RTI. [0039, 0049] Alternatively, a mathematic concept, of performing Significance Analysis of Microarrays, or t-test calculations, [0056, 0078] “In step 402, a class of statistically significant truncated proteoforms characteristic of a respiratory tract infection are identified using mass spectra feature selection comprising at least one of SAM (Significance Analysis of Microarray) 403 and t-test 404.”)
predicting the presence of RTI using at least one of calculating a composite score representative of the statistically significant subset of the truncated proteoforms, and calculating the area under the curve (AUC) of the receiver operating characteristic curve (ROC) representative of the statistically significant subset.
(Mental Process and Mathematic Concept steps: first either a composite score is calculated, or an AUC is calculated. Subsequently, the results are observed, and a judgement is made as to whether the scores represent the presence or absence of infection. [0027, Fig 5, 0057-0062, 0081-0083])
9. (Previously Presented) A method for diagnosing a respiratory tract infection (RTI) in intubated patients by capturing truncated proteoforms in exhaled breath aerosols, the method comprising:
(EIA- Preamble, noting the goal of the method, and intended patient group.)
selectively capturing truncated proteoforms in the exhaled breath aerosols produced by each patient using a packed bed column removably connected to the exhaled air tubing of the ventilator;
(EIA- data gathering step of capturing a type of sample [0033-0035])
extracting the truncated proteoforms into one or more collected liquid samples corresponding to each patient;
(EIA- laboratory process step to recover the desired sample component [0038, 0052, 0065, 0072)
analyzing the collected samples corresponding to each patient comprising truncated proteoforms using mass spectrometry to obtain raw mass spectra;
(EIA- data gathering step of routine mass spectrometry analysis to obtain data for the statistical analysis. [0019, 0039, 0041, 0055, 0067-0068, 0073])
calculating a composite score for the statistically significant proteoforms in the samples, wherein the statistically significant proteoforms are provided by the reference data of claim 5;
(Mathematic Concept of calculating a composite score, for proteoforms identified in a separate process. [0027, Fig 5, 0057-0062, 0081-0083])
and diagnosing the presence of RTI if the composite score is greater than or equal to the composite score in the referenced data that predicts RTI with an accuracy of greater than at least 90%.
(Mathematic concept and Mental Process of calculating RTI in reference data, then a mental comparison of the composite score from the previous step with the reference data, and making a judgement as to whether RTI is present. [0027, 0058, 0062, 0081])
32. (Previously Presented) A method for predicting a disease by capturing truncated proteoforms in exhaled breath aerosols, the method comprising:
(EIA- preamble setting forth the goal of the method)
diagnosing the presence or absence of the disease by culturing at least one of sputum samples, endotracheal tube samples (ET), and bronchoalveolar lavage (BAL) for each patient in a group of patients with and without the disease participating in clinical laboratory trials to obtain baseline data;
(EIA- data gathering step, by obtaining certain samples, culturing them, and determining the presence or absence of infection to provide reference or baseline data. [0056, 0071])
selectively capturing truncated proteoforms in the exhaled breath aerosols produced by each patient using a packed bed column;
(EIA- data gathering step of capturing a type of sample [0033-0035])
extracting the truncated proteoforms from the packed bed column into one or more collected liquid samples corresponding to each patient;
(EIA- laboratory process step to recover the desired sample component [0038, 0052, 0065, 0072)
analyzing the one or more collected liquid samples comprising truncated proteoforms using mass spectrometry to obtain raw mass spectra;
(EIA- data gathering step of routine mass spectrometry analysis to obtain data for the statistical analysis. [0019, 0039, 0041, 0055, 0067-0068, 0073])
identifying a statistically significant subset of the truncated proteoforms characteristic of the disease; and
(Mental Process in a computing environment, or using a computer as a tool, of observing truncated proteoforms, and making a judgement as to whether they are “characteristic” of RTI. [0039, 0049] Alternatively, a mathematic concept, of performing Significance Analysis of Microarrays, or t-test calculations, [0056, 0078] “In step 402, a class of statistically significant truncated proteoforms characteristic of a respiratory tract infection are identified using mass spectra feature selection comprising at least one of SAM (Significance Analysis of Microarray) 403 and t-test 404.”)
predicting the presence of the disease using at least one of calculating a composite score representative of the statistically significant subset of the truncated proteoforms and calculating the area under the curve (AUC) of the receiver operating characteristic curve (ROC) representative of the statistically significant subset.
(Mental Process and Mathematic Concept steps: first either a composite score is calculated, or an AUC is calculated. Subsequently, the results are observed, and a judgement is made as to whether the scores represent the presence or absence of infection. [0027, Fig 5, 0057-0062, 0081-0083])
Natural law embraced by independent claim(s) 1, 9 and 32:
The claims describe a naturally occurring relationship between naturally occurring proteoforms from a patient sample, and a naturally occurring disease, this is a genotype/ phenotype relationship.
With respect to step 2A (2): NO. The claims were examined further to determine whether they integrated any JE into a practical application (MPEP 2106.04(d)). The claimed additional elements are analyzed alone, or in combination to determine if the JE is integrated into a practical application (MPEP 2106.05(a-c, e, f and h)).
Independent claim(s) 1, 9 and 32 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 needed to carry out the JE. The data gathering does not impose any meaningful limitation on the JE, or how the JE is performed. The additional limitation (data gathering) must have more than a nominal or insignificant relationship to the identified judicial exception. (MPEP 2106.04/.05, citing Intellectual Ventures LLC v. Symantec Corp, McRO, TLI communications, OIP Techs. Inc. v. Amason.com Inc., Electric Power Group LLC v. Alstrom S.A.).
Independent claim(s) 1, 9 and 32 recite the additional non-abstract element (EIA) of elements of a ventilator system, and packed bed chromatography columns.
These EIA represent routine hospital ventilator systems, and routine liquid chromatography elements used to collect the samples, to provide the elements for the judicial exception.
The EIA do not provide any details of how specific structures of the elements are used to implement the JE. Use of these elements does not change how the JE is performed, nor does performing the JE change the ventilator or column elements. they do not provide improvements to the functioning of the computer itself (as in DDR Holdings, LLC v. Hotels.com LP); they do not provide improvements to any other technology or technical field (as in Diamond v. Diehr); nor do they utilize a particular machine (as in Eibel Process Co. v. Minn. & Ont. Paper Co.). 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) 2-8, 10-17, 33 have been analyzed with respect to 2A-2. See MPEP 2106.05(a, b, c, e and h).
Dependent claim(s) 2-8, 10, 33 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.
Dependent claim(s) 11-17 recite(s) non-abstract limitations (EIA) which are directed to the data gathering, aspects of the data gathered, or to the output of results. As set forth above data gathering is pre-solution insignificant activity (MPEP 2106.05(g) citing OIP Tech Inc v. Amazon.com, Inc.) Describing aspects of the data gathered are similarly considered pre-solution insignificant activity. The output of results is post-solution insignificant activity (MPEP 2106.05(g) citing Apple v. Ameranth Inc.). Collectively they are extra-solution activity and insufficient to integrate the JE into a practical application.
Dependent claim(s) 11-17 recite(s) non-abstract limitations (EIA) directed to limitations to the routine chromatography column. These elements of the claims do not provide improvements to the functioning of a computer itself (as in DDR Holdings, LLC v. Hotels.com LP); they do not provide improvements to any other technology or technical field (as in Diamond v. Diehr); nor do they utilize a particular machine (as in Eibel Process Co. v. Minn. & Ont. Paper Co.). Therefore, the computer system elements fail to integrate the JE into a practical application.
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 recite a JE, do not integrate that JE into a practical application, and thus are probed for a specific inventive concept. The judicial exception alone cannot provide that inventive concept or practical application (MPEP 2106.05). The additional elements were considered individually and in combination to determine if they provide significantly more than the judicial exception. (MPEP 2106.05.A i-vi).
With respect to independent claim(s) 1, 9 and 32: 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.
With respect to obtaining reference samples including BAL, ETA, or sputum for baseline disease status determination:
QU obtains sputum samples from patients, to obtain baseline data regarding infection status.
Bardet obtains samples from patients to obtain baseline data regarding status of infection.
Lopez-Sanchez obtains ETA samples from patients to determine disease status.
With respect to obtaining exhaled breath aspirates or aerosols from patients, including those on a ventilator:
Bardet obtains exhaled breath aspirates or aerosols from patients on a ventilator.
Lopez-Sanchez obtains ETA samples from lung cancer patients.
Amann obtains exhaled breath aspirates from patients.
Bregy obtains exhaled breath aspirates from patients.
With respect to the extraction of the proteoforms, and routine mass spectrometry analysis limitations, including use of packed bed columns:
Bardet performs extraction of proteoforms, and routine MS analysis to obtain mass spectra.
Lopez-Sanchez performs extraction of proteoforms, and routine MS analysis to obtain mass spectra.
These elements meet the BRI of the identified data gathering limitations. As such, the prior art recognizes that this data gathering element was routine, well understood and conventional in the art (as in Alice Corp., CyberSource v. Retail Decisions, Parker v. Flook).
In the specification at [0035-0041, 50] it is disclosed that the packed bed columns are available from SigmaAldritch, or other vendors, and comprise known resin beads such as those provided by Boca Scientific. GE Healthcare Life Sciences, or JKC Corp. Multiple routine, and commercially available LC, MS, or LC- MS analyzers and equipment can be used as set forth at [0068, 0073] including Thermo Fisher Scientific equipment.
Activities such as data gathering do not improve the functioning of a computer, or comprise an improvement to any other technical field. The limitations do not require or set forth a particular machine, they do not effect a transformation of matter, nor do they provide an unconventional step (citing McRO and Trading Technologies Int’l v. IBG). Data gathering steps constitute a general link to a technological environment. 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.,).
Dependent claim(s) 2-8, 10-17, 32 have been analyzed with respect to step 2B. MPEP 2106.06(a-h).
Dependent claim(s) 2-8, 10 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).
Dependent claim(s) 11-17 recite non-abstract limitations (EIA) which are directed to the data gathering, aspects of the data gathered, or routine output steps which are insufficient to provide significantly more than the JE (citing McRO, Alice Corp. and Trading Technologies Int’l v. IBG).
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 1-17, 32-33 are rejected under 35 U.S.C. 112, first paragraph, because the specification, while being enabling for identification of cellular markers of infection of the respiratory tract in exhaled breath aerosols, from intubated patients on a ventilator, using a particular collection device, and a particular set of truncated proteoforms, does not reasonably provide enablement for diagnosing or predicting any disease, including all respiratory diseases or infections, in those patients using undefined or unlisted proteoforms. The specification does not enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make or use the invention commensurate in scope with these claims.
In In re Wands (8 USPQ2d 1400 (CAFC 1988)) the CAFC considered the issue of enablement in molecular biology. The CAFC summarized eight factors to be considered in a determination of "undue experimentation". These factors include: (a) the quantity of experimentation necessary; (b) the amount of direction or guidance presented; (c) the presence or absence of working examples; (d) the nature of the invention; (e) the state of the prior art; (f) the relative skill of those in the art; (g) the predictability of the art; and (h) the breadth of the claims.
In considering the factors for the instant claims:
a) In order to practice the claimed invention one of skill in the art identify a specific set of proteoforms, or truncated proteoforms that are associated with all diseases, or that predict all diseases, in particular respiratory tract infections; all in a specific population. For the reasons discussed below, there would be an unpredictable amount of experimentation required to practice the claimed invention.
b) The specification provides generalized guidance for obtaining exhaled breath aerosols from patients that are intubated and, on a ventilator, using a packed bed column removably connected to the exhaled air tubing of the ventilator [0018, Fig 1A, 0033-0037]. The elected aspect is a C18 functionalized column. The specification provides guidance for analyzing the exhaled breath aerosols using MS, to identify the presence of proteoforms [0019, 0038-0063]. An elected proteoforms is CO6A3, and the disclosed set of proteoforms is a set of 6 truncated proteins. The specification provides guidance for obtaining samples from patients with and without a respiratory tract infection, culturing the samples, and identifying the presence or absence of the infection from that data, and clinical findings, as baseline information [0020, 0038-0040]. An open-ended list of pathogens to be identified is set forth at [0040].
c) The specification provides an example of identifying proteoforms related to the presence of COVID-19, at Example 1, [0064-0069, Table 1]. The specification provides an example of diagnosing, identifying or predicting the presence of a generic “respiratory tract infection” by the specific identification of a subset of specific proteoforms at Example 2 [0070-0083, Table 3]. In this example, the baseline positive or negative generic RTI information is obtained from culture samples and physician criteria.
This example does not identify what particular respiratory tract pathogen, or diagnosed disease was present in each RTI+ patient’s samples.
After collection, purification, and analysis, and after the statistical analysis steps of the claims, a set of six specific truncated proteoforms were able to distinguish between the overall classes of 1) the presence of cellular markers of infection or 2) no cellular markers associated with infection. The particular proteoforms were identified as portions of: CO6A3, MMP9, PHTF2, IRAK4, CYTA and DEN2B. These proteoforms are from host cellular processes, and not any particular pathogen. These truncated proteoforms do not overlap with those identified for COVID-19 in Example 1. (Compare Table 3, to Table 1.).
d) The invention is drawn to diagnosing the presence or absence of any respiratory tract infection (Claim 1) or any disease (claim 32) by the analysis of exhaled breath aerosols from patients that are intubated and, on a ventilator, using unspecified and unlimited truncated proteoform information. The independent claims are unlimited as to what proteoforms must be detected, or how many.
The specification does not link any one pathogen of the respiratory tract to the pattern of proteoforms identified in Table 3. The actual pathogens infecting the intubated patients are not disclosed. At best, the specific subset of six truncated proteoforms is associated with the generic conditions of 1) cellular markers of infection present; and 2) no cellular markers of infection present. The six identified proteoforms of claim 17 are not truncated proteins or proteoforms of any particular virus, or pathogen but of cellular proteins of the host.
The specification does not link any other specific diseases, disease processes, or infections not in the respiratory tract with any specific pattern of proteoforms. The specification indicates that all 6 truncated proteoforms are required for identification of RTI, however the claims are unlimited, and dependent claim 17 requires only a single proteoforms for identification of RTI. The specification does not identify what individual proteoforms or set of proteoforms are necessary and sufficient to identify any other specific disease, or generalized category of disease. The specification does not identify what diseases, other than RTI, can be identified by one of the proteoforms of claim 17. The specification does not identify what diseases can be specifically diagnosed using the 6 proteoforms, or any proteoforms from exhaled breath.
e) The prior art can be represented by Dupree (2020), Amann (2014), Lopez-Sanchez (2017) Bregy (2018), Qu (2010) and Ross.
Around the time of filing, Dupree (2020) reviews the use of MS and proteoforms analysis. Dupree reviews the general processes in proteomics, that can use Mass Spectrometry in their workflow, as in Figure 1. Dupree notes that while MS analysis of protein samples are widely used for sequence analysis, protein-protein interactions and identifying PTM’s; issues with sample preparation, experimental variability, contamination, and the sheer abundance of data generated, all can lead to incorrectly assigned spectra, false positives/negatives, or failed experiments. Dupree notes that:
“Overall, the complexity, dynamic range of biological samples and low abundance of disease-specific biomarkers remains a major challenge for proteomic biomarker discovery, and there is no MS instrument that can simultaneously address these challenges efficiently.” (p11).
Dupree also discusses the analysis of MS data, and certain software pipelines. Dupree points out that it is important to “understand the basics of peptide fragmentation before one can grasp a firm understanding of the current problems in proteomic data analysis, since most problems arise from lacking complete sequence information for many proteins.” (p11) A key factor in understanding the results is the “predictability of peptide fragmentation” and the use of complementary fragmentation processes. Section 6.1 of Dupree reviews available algorithms, programs, or software workflows used to analyze proteome MS data. Available workflows or programs such as MapQuant, OpenMS et al are useful for pilot and preliminary studies, but findings require confirmation by more precise, reliable methods. (p14). In Section 6.2 Dupree points out that proteoforms or PTMs are not easily identifiable from genetic data, or even protein databases. De novo sequencing of the isolated PTM’s is a possible workaround, but can be difficult and inaccurate. Section 7 of Dupree tackles the biggest problems in the identification of proteoforms present in a sample: false positives, false negatives, and unassigned spectra. Dupree identifies known algorithms for protein inference, and quality assessment, and recent improvements in the field. However, multiple reasons can still confound analysis of any identified proteoforms. Post translational modifications to a proteoforms can obscure the identity of the related protein. Experimental issues can lead to false-negative identification of the peptide. The nature of the proteoforms can influence whether it is detected, such as whether it is more hydrophobic, or hydrophilic. Incomplete protein fragmentation can lead to erroneous results and false negative identifications. Dupree notes that
“… in a proteomics experiment, about 60-70% of the spectra are not assigned with confidence to any peptide…”
Around the time of Applicant’s effective filing date, Smith (2021) analyzes the Human Proteoform Project, attempting to define the human proteome.
“We propose here an ambitious initiative to define the human proteome, that is, to generate a definitive reference set of the proteoforms produced from the genome. Several examples of the power and importance of proteoform-level knowledge in disease-based research are presented…” (Abstract).
The complete human proteome has yet to be obtained. As reviewed by Smith, the identification of a definitive set of reference proteoforms is more complex than completing the human genome, due to the presence of alternative splicing, SNP, post translation modification, and unknown degradation processes. (Fig 1).
“Only direct analysis of the proteoforms themselves can reveal their composition, enabling studies of their spatial distributions and temporal dynamics in biological systems.” (Introduction.)
Figure 2 illustrates five clinical areas of interest, and specific instances where a proteoform has been identified and linked to the progression of human disease.
“Proteoform expression varies across cells and tissues, and studies of proteoform expression can be either global or targeted. The expression of rare proteoforms is stochastic in nature.” (p3).
Figure 4 illustrates the most studied proteoforms or proteins, which do not overlap with the set of genes in claim 17. The closest protein of Smith Fig 4 is CYTC, as opposed to CYTA in claim 17.
The majority of diagnosis signatures identified in the prior art appear to be from tissue, sera, or plasma samples, particularly for non-respiratory diseases (heart disease, neurological diseases, cancers, liver cirrhosis, transplant monitoring et al.) Exhaled breath samples used for prior art diagnosis methods were largely based on the identification of volatile compounds, metabolites and small compounds.
Amann (2014) provides a review of the used of exhaled breath components for disease detection. While the techniques have identified certain specific components may be linked to a specific disease, many issues in the field prevented the identification of discriminatory components for all diseases.
“In exhaled breath, ∼200 compounds have been identified on the basis of spectral library identification and retention time (55). For most of these compounds, the biochemical background is unclear. Many compounds are of exogenous origins. In particular, ∼80 volatile compounds are attributed to smoking (57). For well-characterized molecules, it is now necessary to postulate their biochemical origins and compare the breath concentration profiles with concentration patterns in the headspace of cell cultures (60, 63–65) or tissue samples (66).” (p458).
Amann reviews common methods for hypothesis testing for exhaled breath components, including paired comparisons pre and post exposure to a treatment/ pathogen/ pollutant; classical PK models to assess internal metabolism and distribution of known components; and physiologically based pharmacokinetic models for modeling target organ dose, receptor sites and damage/ repair functions. Each of these is limited to a particular hypothesis under test, for a particular pathogen/ component/ treatment/ pollutant, in a particular model system or particular disease model. As of the time of the review (2014) only 7 breath-related test had gained FDA approval, for various health processes, including correct placement of an endotracheal tube, for neonatal jaundice, for certain gastrointestinal diagnoses, certain asthma therapy monitoring processes, law enforcement test for blood alcohol concentration, detection of heart transplant rejection, and for infection of the gastric system by a particular pathogen, H. pylori. (p461).
Amann describes certain technological improvements in the developmental pipeline, and certain diseases under study for the use of exhaled breath components for diagnosis or disease detection at pages 461-462. Even in diseases of the respiratory system, such as lung cancer, the use of exhaled breath components was still “far from clinical testing.”
“During the past 10 years, more than 100 volatile biomarkers have been suggested as being related to cancer (134). It will be important to validate all these compounds carefully. In part, identification of these compounds is based only on spectral library matching without a comparison of the retention time of peaks in breath samples to the retention time of native standards.” (p463).
This indicates that no single pattern of volatile compounds in exhaled breath were considered to be indicative of all diseases, nor were any single compounds sufficient to detect or diagnose all diseases, even when limited to the respiratory tract.
In 2014 Amann summarized:
“Breath analysis is still in its infancy, and the biochemical bases of many volatile compounds have not yet been elucidated. As a single breath may contain several hundreds or even thousands of single compounds, statistical limitations (e.g., voodoo correlations) have to be taken into account (214, 215). Moreover, the breath sampling methodologies (216) remain controversial. To determine the biochemical origin of volatiles, it is interesting to look at the headspace of cancer cell cultures and of tissues. For cancer patients, the chemical signature of exhaled breath can be compared with the chemical signature of resected cancerous tissues (66). During the past decade, more than 100 volatile biomarkers appearing in exhaled breath have been noted as being related to cancer (134). The biochemical pathways for these biomarkers must be determined in the near future.” (p472).
In 2017, Lopez-Sanchez (LS) provided LC/ MS analysis of exhaled breath condensates to identify biomarkers or proteoforms related to lung cancer. LS identifies over 300 different proteoforms having a difference in patterning between four groups of patients, including healthy control. Lung cancer patients showed variability in the observed proteins, but in general, more proteoforms were present in lung cancer patient samples, compared to other groups, including patients with COPD, a different lung disease. LS performed statistical analysis on the data, including performing ROC curve analysis as summarized at page L667 and Fig 3. LS found the set using more than 12 proteoforms could identify lung cancer with 70% sensitivity and 67% specificity.
“In general, the protein profile observed in control and risk factor-smoking groups were different from those observed in COPD and lung cancer groups (Fig. 4). The most abundant proteins classified as structural proteins in the EBC samples were cytokeratins. Particularly, cytokeratins 1, 2, 9, and 10were found in 85–99% of samples, whereas cytokeratins 6, 14,16, and 17 were identified in 27– 61% of samples.” (pL668).
Even within the subtype of Lung Cancer, profiles could vary depending on tumor size, and type or subtype of lung cancer. (L670 and Fig 9) Simple Principal Component Analysis (PCA) of the profiles was unable to separate between cancer/ non cancer (Fig 10). Additional statistical analysis was able to determine a relative importance of each element of the lung cancer profile, as in Fig 11A.
This indicates that no single pattern of proteoforms, or peptide profile in exhaled breath was considered to be indicative of all diseases, all cancers, nor all lung cancers, nor was any single identified proteoform or proteoform profile sufficient to detect or diagnose all diseases, even when limited to the respiratory tract.
Overall, while the technical aspects of performing MS, including the use of C18 columns for separation, may be routine, it is the analysis of the generated data which represents the biggest difficulties in identifying disease-related proteins or peptides.
With respect to diagnosis of diseases, such as infections, using MS spectral analysis, the state of the art can be represented by Bregy and Qu and Ross.
Bregy (2018) obtained exhaled breath samples from patients with a lung disorder, COPD, and healthy individuals, analyzed them directly in real-time using mass spectrometry, to identify metabolites associated with COPD. The specifics are related in section 2.3, 2.4.
“The samples were analysed by UHPLC-HRMS and retention times were compared with those obtained from standards, where available. For analysis, EBC samples … were transferred to chromatographic vials without dilution, or any other sample preparation procedure, and were injected into the ACQUITY UPLC system (Waters, Boston, MA, USA) where separation took place on a C18 ACQUITY column 2.1mm×100 mm, 1.7 μm, Waters, MA, USA).”
This specific setup and workflow, provided the data analyzed in section 2.4 with the results provided in section 3.
“From this analysis, 14 features were found in common among the four sub-groups and were subjected to further compound identification (Fig. 4 and supplementary Table E5). 10 of the 14 features were chemically identified and are shown in Table 4; most of the compounds are associated with chemical families (i.e., aldehydes and fatty acids). All 14 features were subjected to a principal component analysis to better visualize their predictive value. Fig. 5 shows the first two principal components with a clear separation between healthy controls and COPD patients, except for one negative control outlier that localized to the COPD space.”
Some of the features were previously suggested to be altered in lung disease. Bregy found that:
“the levels of a number of fatty acids decreased in the breath of patients with COPD. These compounds (i.e., 11-hydroxyundecanoic acid, oxoheptadecanoic acid and dodecanedioic acid) are associated with oxidative, nitrosative and carbonyl stress processes produced by inflammatory processes in COPD diseased lungs [25]. Additionally, aspartic acid semialdehyde and 2-oxoglutaric acid semialdehyde, which are also related to oxidative stress processes, were significantly elevated.”
In the summary, Bregy notes they were able to predict COPD with an accuracy of 89%. Bregy enforced certain rules or guidelines on the participating patients, in an attempt to minimize the influence of biomarkers not related to COPD. Patients with any respiratory infections, other diseases, or co-existing morbidities were excluded: thus, this pattern was not shown to be predictive of any other disease.
Qu (2010) attempts to identify H. influenzae proteoforms present in human sputum sample cultures, using nano-LC/ MS, related to the presence of COPD.
“The proteome of sputum-grown H. influenzae was characterized and compared to that of H. influenzae grown in chemically defined medium alone. Identifying proteins that demonstrate increased expression during growth in pooled human sputum will help to identify potential virulence factors or abundantly expressed surface antigens that, with further study, could lead to an understanding of the mechanisms by which H. influenzae survives and causes infection in the human respiratory tract.” (p2).
Qu employs a particular sample treatment process, and “a chromatographic system with low void volume and high separation efficiency were employed with a shallow, long gradient (5 hour total separation time). A nano-LC, rather than a conventional LC, was used for peptide separation because of the significantly higher sensitivity…” (p3). Nearly 1402 unique proteoforms are identified, including 170 newly identified proteins of H. influenzae. 31 proteins were ultimately determined to be present in a higher ratio in the sputum samples, than the laboratory samples. Certain proteoforms were able to be correlated with COPD disease processes, such as co-occurring respiratory tract infection with H. influenzae, including stress and anti-oxidant responses. Peroxiredoxin-thioredoxin is an infection-associated protein that stimulates antibody response in the host. The identified factor pattern was not shown to predict or identify infection, or disease in this study.
Ross (2019) reviews the use of proteomics of the host to diagnose acute respiratory infections (ARI). Ross notes
“Advances are being made in all areas of host response-based diagnostics for ARIs. Specifically, there has been significant progress made in single protein biomarkers, as well as in various “omics” fields (including proteomics, metabolomics, and transcriptomics) and wearable technologies. There are many potential applications of a host response-based approach; a few key examples include the ability to discriminate bacterial and viral disease, presymptomatic diagnosis of infection, and pathogen-specific host response diagnostics, including modeling disease progression.” (Abstract).
Ross reviews common prior art identified individual biomarkers associated with specific diseases beginning at p1924. These include the ESR profile, CRP and Procalcitonin (PCT). Ross notes however, that as the field matures, some biomarkers have been found not to be as useful as thought. Specifically:
“the recently published ProACT (Procalcitonin Antibiotic Consensus Trial) study suggests that PCT may not have as much clinical utility as was previously reported.27 In this study, patients with suspected lower respiratory tract infections in 14 US hospital EDs and hospital medicine departments were randomized to a PCT group or a usual care group (where PCT results remained blinded). Providing PCT results in this setting did not lower antibiotic prescribing rates. Reasons for this deviation from previous conclusions are likely multifactorial. They include increased antibiotic stewardship, leading to lower antibiotic rates in the control arm, as well as high rates of clinician overrule. This high rate of overruling may indicate that providers do not have sufficient confidence in the test to allow it to drive their decision-making or that the test is itself imperfect for differentiating bacterial and viral etiologies and is missing bacterial infections that are clinically identified.
High false-positive rates for PCT have been observed for patients with major trauma, cardiopulmonary bypass surgery, liver cirrhosis with ascites, chronic kidney disease, colonic ischemia, acute-onset Still's disease, and heatstroke, as well as burn patients.28e35 These limitations further restrict the clinical application of PCT for a diagnosis of ARI, as these are common comorbidities, particularly in the ED and hospital environment, where the product label for PCT indicates it should be used.” (p1925).
Ross reviews the attempt to use cytokines such as interleukins in distinguishing viral and bacterial etiologies in acute respiratory infections, however cytokines have an extremely short half-life, and intersubject variability provides too many clinical parameters to clearly discriminate between diagnoses. (p1925).
CD64, a receptor protein, has been shown to discriminate patients with acute respiratory failure based on an underlying infectious process, and as a relevant marker for bacterial infections in patients with acute exacerbations of COPD. MMP9, the protein that gives rise to MMP9 (673-691) of claim 17, is known to be elevated in the acute phase of ventilator-associated pneumonia compared with noninfected ventilated patients in plasma samples. (p1926).
“Although all of these individual biomarkers have shown some ability to distinguish between infected and noninfected states, most are inferior to PCT. Although PCT seems to be the best approach available to help manage antibiotic use in ARIs, it has limitations (as reviewed earlier). These limitations may be inherent to the use of a single biomarker approach; perhaps there is simply too little biology captured in a single biomarker. Consequently, integrating multiple complementary biomarkers from different biological pathways into a single classifier offers a new and exciting frontier in ID diagnostics.” (p1926.)
“Biomarkers for disease classification work by assigning patients to discrete phenotypic groups based on the biomarker measurements. In recent years, great strides have been made to bring “omics” based disease classifiers to the point of care for ARIs. Much of this research has focused on differentiating between bacterial, viral, and noninfectious illnesses in immunocompetent patients. The tests with the most clinical utility will be those that can differentiate bacterial infections from all other causes of acute respiratory symptoms. The ideal classifier will be able to identify bacterial infections with excellent sensitivity to avoid missing bacterial infections. To avoid antibacterial overuse, however, this test must also have high specificity. As one can imagine, the generation of such a classifier is a challenging task, but great progress has been made in multiple different “omics” fields in recent years. One challenge for host response biomarker discovery, whether single analyte biomarkers discussed earlier or multi-biomarker classifiers discussed later, is the absence of a gold standard. Indeed, if a gold standard for the diagnosis of infection existed, there would not be as pressing a need for alternative approaches.” (p1927)
Ross reviews multi-proteoform patterns used in diagnosis, and their limitations for clinical use, for various pathogens beginning at 1928.
“One limitation of these classifiers, and others like them, is that they were discovered in populations consisting only of patients with confirmed infection, and their control groups consisted of healthy patients. A more relevant discovery population would be one that more closely mimics the complete population a test of this type would be used in. This population would contain not only patients with infection but also patients with noninfectious causes of respiratory symptoms. For example, allergic rhinitis, asthma exacerbation, chronic obstructive pulmonary disease exacerbation, and postinfectious cough, as well as other cardiopulmonary diseases can mimic ARIs…”
Ross further notes that immunocompromised patients also represent a diagnostic challenge, with respect to proteomics and genomics. The state of being immunocompromised predisposes patients to bacterial, fungal and viral infections. The topic of pre-symptomatic diagnosis of acute respiratory infections is addressed beginning at p1931, Fig 2. Ross then summarizes advances in the diagnosis of two major pathogens that are related to acute respiratory infection, TB and RSV beginning at p1932.
f) The skill of those in the art of bioinformatics, proteomics, and Mass Spectrometry analysis is high.
g) The prior art predicts that any pattern of proteoforms or biomarkers that discriminate, identify or predict the presence of a particular infection, disease, or pathogen, would not be likely to be helpful in the identification, detection