DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Continued Examination Under 37 CFR 1.114
1. A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on August 28, 2025 has been entered.
Claims 1, 30-38, 40-45, 111-121 are now pending. Claims 1, 41, 118 are amended. Claims 119 are -121 new. Claims 34, 36, 37, 45, and 111 remain withdrawn. Claims 1, 30-33, 35, 38, 40-44, 112-121 are being examined as drawn to the elected species of:
A: (a) detecting whether the thyroid nodule comprises follicular thyroid carcinoma (FTC) (claim 35).
B: statistical classifier built from reference profiles obtained from one or a combination of tissue samples and fine needle biopsy samples by MS methods (claims 30-31, 41).
C: statistical classifier comprises a two-class classifier that can identify thyroid nodules as benign thyroid or malignant thyroid carcinomas, (claims 32 and 33).
D: (a) method further requiring performing histopathology on the FNA biopsy sample (claim 42).
New Rejections
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.
2. Claims 1, 30-33, 35, 38, 40-44, 112-121 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature/ a natural phenomenon) without significantly more. The claim(s) recite(s) identifying whether a thyroid nodule in a subject is benign or malignant based on statistical classification probability results for mass spectrometry data using a statistical classifier, the method comprising: (a) obtaining a fine-needle aspirate (FNA) biopsy sample from the thyroid nodule of a subject; (b) performing DESI-MS on the FNA biopsy sample to generate mass spectrometry data of metabolites and lipids from one or more pixels of the FNA biopsy sample that contains one or more cells, wherein the mass spectrometry data comprises a plurality of mass-to-charge (m/z) ratios; and (c) identifying a thyroid nodule in a subject as benign when the mass spectrometry profile comprises one or more m/z selected from the list recited in claim 1, or identifying the nodule as malignant FTC when the mass spectrometry profile comprises one or more m/z selected from the list recited in claim 1. Thus, the claims are directed to the judicial exception of correlating the presence of metabolites and lipids identified by mass spectrometry to malignant or benign thyroid tissue. This judicial exception is not integrated into a practical application because the claims recite only the detection or observation of a naturally occurring phenomenon/law of nature, which is data gathering to observe the naturally occurring phenomenon/law of nature without applying the data to a practical application. The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the claims recite use of routine laboratory procedures to detect and observe naturally occurring presence of metabolites and lipids in thyroid nodule biopsy. The steps of detecting the presence of metabolites and lipids in thyroid nodule, including by FNA biopsy and DESI-MS as claimed, are considered known, routine steps and are typically taken by those in the field to perform testing of a sample and are not elements that are sufficient to amount to significantly more than the judicial exception (see MPEP 2106.05(d)). For example, WO 2016/142691, Takats et al, published September 2016; Jarmusch (2016). Ambient ionization-mass spectrometry: Advances toward intrasurgical cancer detection (Doctoral dissertation, Purdue University); Zhang et al (Cancer Research, 2016, 76:6588-6597) and Zhang Supplementary Figures and Tables; Guo et al (Anal. Bioannal. Chem., 2014, 406:4357-4370) teach or demonstrate detecting the presence of metabolites and lipids in FNA biopsy, including thyroid nodule, and utilizing DESI-MS negative ion mode to produce a mass spectrometry profile of m/z values, as well as using statistical classifier to correlate m/z values to the presence of cancer or benign tissue (see prior art rejections below). Zhang demonstrates detection of several of the claimed m/z values for correlation to benign or carcinoma thyroid tissues. Zhang identified the presence of fatty acid (FA) m/z 303.233 in oncocytic tumor and non-oncocytic tumor (Figure 2 and Supportive Figure 6). CL m/z 724.486 was increased in non-oncocytic versus normal, and increased in oncocytic tumors versus non-oncocytic (Figure 5B; Supporting Table 1 Exact m/z). Biomarker m/z 303.233 and 255.233 were detected in oncocytic and non-oncocytic samples (Supporting Figure 1A and B). Biomarker (CL) m/z 724.485 and 885.550 were detected in oncoytic thyroid samples (Supporting Figures 1A and 8A; Table 1). Biomarker m/z 723.480 was found in non-oncocytic thyroid (Supporting Figure 1B). Biomarker m/z 281.248 was found in normal thyroid (Supporting Figure 1C). Zhang further detected biomarkers (CL) m/z 279.233, 281.248, 307.264, and 255.233 in thyroid samples (Supporting Figure 3B and 3C; p. 6590, col. 2). Zhang detected biomarkers m/z 788.546 and 857.519 in normal thyroid (Supportive Figure 8C). Zhang detected biomarker (CL) m/z 736.487 in oncocytic tumors (Table 1).
Routine data gathering in order to observe a natural phenomenon/ natural principle does not add a meaningful limitation to the method as it would be routinely used by those of ordinary skill in the art in order to observe the natural phenomenon/ natural principle, and it fails to narrow the scope of the claims such that others are not foreclosed from using the law of nature/natural phenomenon. Methods of detecting natural phenomenon preempt all practical uses of it as others must use/detect the natural phenomenon to apply it to any other correlations, diagnosis, prognosis, therapeutic response, monitoring, etc.
To obviate the rejection, there must be at least one additional element or physical step that applies, relies on, or uses the natural principle so that the claim amounts to significantly more than the judicial exception itself. The claimed method currently fails to provide a practical application of the judicial exception and fails to add any elements that amount to significantly more than the judicial exception.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
3. Claims 1, 30-33, 35, 38, 40-44, 112-121 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. Claim 1 recites identifying whether a thyroid nodule in a subject is benign or malignant based on statistical classification probability results for mass spectrometry using a statistical classifier, wherein the statistical classifier was developed using molecular signature of lipids and metabolites from reference profiles obtained by DESI-MS….and (c) identifying whether the thyroid nodule is benign or malignant using the statistical classifier;
wherein when the mass spectrometry profile comprises one or more mass-to-charge ratios (m/z) selected from the group consisting of 157.122, …then the thyroid nodule I identified as benign; and
when the mass spectrometry profile comprises one or more m/z selected from the group consisting of 174.040, …then the thyroid nodule is identified as a malignancy selected form the group consisting of follicular thyroid carcinoma, medullary thyroid cancer, anaplastic thyroid cancer, or papillary thyroid cancer.
Claim 1 recites “using” a statistical classifier to identify whether the thyroid nodule is benign or malignant but does not set forth any steps or limitations indicating how the statistical classifier is used. Further, the claim recites the step of identifying whether the thyroid nodule is benign or malignant when specific m/z values are present in the subject MS profile, therefore, the claimed method does not appear to implement or even require a statistical classifier to be “used” during the step of identifying whether the thyroid nodule is benign or malignant, therefore, it is unclear how the statistical classifier is “used” and what that use encompasses. The metes and bounds of the claims cannot be determined.
Claim Rejections - 35 USC § 103
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.
4. Claim(s) 1, 30-33, 35, 38, 40-44, 113-117 are rejected under 35 U.S.C. 103 as being unpatentable over WO 2016/142691, Takats et al, published September 2016; in view of Jarmusch (2016). Ambient ionization-mass spectrometry: Advances toward intrasurgical cancer detection (Doctoral dissertation, Purdue University); Zhang et al (Cancer Research, 2016, 76:6588-6597) and Zhang Supplementary Figures and Tables; Guo et al (Anal. Bioannal. Chem., 2014, 406:4357-4370); Xu et al (Journal of Proteome Research, 2015, 14:3315-3321); and Wojakowska et al (Int. J. Endocrinol., 2015, Vol. 2015; Article ID 258763, internet pages 1-13).
Takats teaches an assay method to diagnose or characterize cancer comprising:
(a) obtaining a tissue or fine needle aspirate (FNA) biopsy sample (p. 85, line 35 to p. 86, line 18; p. 88, lines 1-22; p. 101, lines 21-31), wherein the sample is taken from thyroid tissue (p. 102, lines 12-23);
(b) performing desorption electrospray ionization mass spectrometry (DESI-MS) on the FNA biopsy sample to generate MS data, wherein the MS data is a “fingerprint”, “profile”, or molecular signature of some or all of the lipids and metabolites in the sample that can serve as biomarkers (p. 85, line 35 to p. 87, line 36; p. 88, line 1 to p. 92, line 22; p. 107, line 31 to p. 109, line 42);
(c) using a statistical classifier to determine whether the FNA is benign or malignant based on the MS data, or to identify a subtype of cancer, (p. 85, line 35 to p. 87, line 5; p. 92-101; p. 104, lines 7-35; p. 106, lines 13-31);
wherein the statistical classifier comprises a reference library or database of molecular signatures of lipids and metabolites obtained by the DESI-MS that is used to train a classifier, and is compared to the MS data obtained from the biopsy sample, and a diagnosis is made based on altered expression of the MS data, wherein the classifier comprises a cutoff or threshold classification score (p. 7, line 18 to p. 8, line 7; p. 38, lines 3 to p. 39, line 38; p. 98-108; claims 54-57; Figures 28, 34, 37, and 38);
wherein the statistical classifier comprises a two-class classifier (p. 94, lines 7-26), and is derived from thyroid tissues that are (i) healthy or normal tissue and (ii) cancerous or abnormal tissue (p. 102, lines 12-23);
wherein the cancer analyzed is carcinoma or thyroid cancer (p. 103, lines 1-3 and 30);
wherein the abnormal tissue analyzed is adenoma (p. 103, line 6); and
performing histopathology on the FNA biopsy sample (p. 88, lines 1-28).
Takats teaches the molecular signature (fingerprint) of biomarkers detected include fatty acids, glycerolipids, sterol lipids, sphingolipids, prenol lipids, saccharolipids, and/or phospholipids (claim 32; p. 5, lines 32-34). Takats teach cancers can be associated with, or characterized by, altered expression or accumulation of lipids (such as glycolipids or phospholipids), carbohydrates, lipoproteins, lipopeptides, amino acids, organic chemical compounds and metabolites (p. 103, line 41 to p. 104, line 10). Cancer type, subtype, malignancy, stage, grade, and phenotype can be analyzed by the above methods (p. 104, lines 25-26). Takats teaches: a biomarker may optionally be an increased or decreased level of one or more compounds, e.g., a metabolite, a lipopeptide and/or lipid species, which may optionally manifest itself as an increase and/or decrease in the intensity of two or more mass spectral signals at two or more m/z. The presence, absence and relative abundance of a variety of compounds may be referred to as a molecular "fingerprint" or "profile". The totality of the lipids of a cell may be referred to as a lipidomic fingerprint/profile, whereas the totality of metabolites produced by a cell may be referred to as a metabolomic fingerprint/profile. Thus, the biomarker may be a molecular fingerprint, e.g., a lipid fingerprint and/or a metabolomic fingerprint, more particularly e.g., a (i) a lipidomic profile; (ii) a fatty acid profile; (iii) a phospholipid profile; (iv) a phosphatidic acid (PA) profile; (v) a
phosphatidylethanolamine (PE) profile; (vi) a phosphatidylglycerol (PG) profile; (vii) a phosphatidylserines (PS) profile; (viii) a phosphatidylinositol (PI) profile; or (ix) a triglyceride (TG) profile. A lipid biomarker may optionally be selected from, e.g., fatty acids, glycerolipids, sterol lipids, sphingolipids, prenol lipids, saccharolipids and/or phospholipids (p. 109, lines 7-36).
Takats teaches detecting a range of 2 or more metabolites, lipopeptides, and/or lipid species at two or more mass spectral signals at two or more m/z, including subsets of metabolome biomarkers comprising 3, 4, 5, …10, 15, 20, 25, ..50, … or 100 (p. 109), which encompasses 4 or more, 10 or more, and 50 or more biomarker m/z signals.
Takats demonstrates successfully collecting mass spectrometry (DESI-MS) data of lipids over a mass-to charge (m/z) range from 150 to 1000 in negative ion mode in a wide variety of biological samples and statistically analyzing the data to extract specific ion patterns, wherein the lipids detected include glycerophosphoethanolamine (PE), glycophosphoserine (PS), glycophosphoinositol (PI), (p. 66-71; Figures 10-16).
Takats does not teach:
the FNA sample is taken from a nodule on the thyroid and identifying the nodule as benign (such as adenoma or normal tissue) when at least one m/z listed in claim 1 (including m/z 281.248) is present in the mass spectrometry profile, or identifying the nodule as malignant when at least one m/z listed in claim 1 (including m/z 723.480) is present in the mass spectrometry profile (claim 1);
Digitally imaging the FNA biopsy sample MS data on glass slide, identifying the pixels by histopathology; or
Building a statistical classifier from a reference library of MS profiles derived from thyroid FNA and/or tissue biopsy samples (claims 30 and 31).
Takats demonstrates successfully collecting mass spectrometry (DESI-MS) data of lipids over a mass-to charge (m/z) range from 150 to 1,000 in negative ion mode but does not teach conducting DESI-MS in a range from m/z 100-1,500 (claim 113).
Jarmusch teaches utilizing DEMI-MS imaging to produce lipid profiles diagnostic of cancer using statistical classifiers. Jarmusch teaches using MS to assist molecular cancer diagnosis of patient biopsies, leveraging mass spectrometry’s sensitivity and molecular specificity, and improve patient care via rapid chemical measurement (p. 6).
Jarmusch explains: “DESI‐MS imaging (DESI‐ MSI) is commonly performed in a line‐by‐line fashion by continuously scanning the DESI spot (i.e. area covered by the thin‐film) laterally across the sample in the x‐dimension, and then stepping a defined distance in the y‐dimension, repeatedly. When performed under ambient conditions has significant advantages over other MS imaging techniques which the full scan mode, every MS image pixel contains a mass spectrum that spans a user defined m/z range of a single ionization mode (i.e. positive or negative)” (p. 4-5).
Jarmusch successfully produced lipid mass spectrometry profiles from surgical biopsy tissue and from FNA biopsy of non-Hodgkin’s lymphoma subjects using DESI-MS analysis (detecting PI, PS, PE, PG), negative ion mode, additional histopathologic analysis of samples, and applying statistical analysis to m/z values in the range of m/z 700-1,000 to differentiate disease state (i.e., normal vs. tumor), and successfully producing a list of m/z values that differed in abundance in cancer versus normal samples (p. 40-55). Jarmusch teaches FNA biopsy is a less invasive alternative to surgical biopsy and can be used for DESI-MS to provide an objective prediction of disease state, i.e., normal versus tumor (p. 15, p. 51). Jarmusch teaches:
“DESI provides a key connection between the information from histopathology and the characteristic mass spectra of each disease state. DESI‐MS can be operated in an imaging mode, collecting mass spectra, pixel by pixel, over two dimensional space to create a 2D molecular image, i.e. ion image. DESI‐MS imaging has been previously used to study human prostate (3, 4) and kidney (5, 6) cancers which reported the lipid profiles of normal and cancerous tissue” (p. 15).
Jarmusch teaches:
“A promising method for improving the diagnostic yield of FNA biopsies is desorption electrospray ionization – mass spectrometry (DESI‐MS), an ambient ionization technique which allows for chemical analysis of surfaces, including tissue sections and tissue smears, at native atmospheric conditions (temperature, pressure, and humidity). The mechanism of analyte desorption and ion generation has been extensively studied. (9, 10) DESI‐MS imaging of tissue sections has been applied previously in canine bladder cancer (11) and human cancers, including those of the liver, (12) brain, (13, 14) kidney, (15) bladder, (16) and other organs. (15, 17) Further, DESI‐ MS has been applied to human brain tissue smears which are similar to FNA smears, e.g. used for rapid analysis. Tissue smears, another alternative to tissue sectioning, differ from fine‐needle aspirate on the method of collection (needle biopsy versus incisional/excisional biopsy) and the consistency of the cellular material – the latter tends to be more aqueous and contain a suspension of cells. Analysis of tissue smears allowed differentiation of normal brain tissue and gliomas (14) and has been implemented for intrasurgical analysis, discussed later in this dissertation.
The analysis of lipids that compose cells, structurally and functionally, and tissue has been the focus of the previously mentioned studies, allowing for differentiation of cancer from normal tissue without exception. The lipid profile varies with cell metabolism and signaling and is indicative of disease state. The use of multivariate statistics for pattern recognition, such as principal component analysis (PCA) followed by supervised classification techniques (e.g. linear discriminant analysis, LDA), allowed visualization and classification of differences between samples and complex relationships within large datasets” (p. 38-39).
Jarmusch teaches DESI-MS has successfully been used to subtype tumors, and demonstrates successfully subtyping tumor tissues using a statistical classifier (p. 50-51).
Jarmusch demonstrated that DESI-MS analysis of FNA biopsies provided molecular information that was similar to that of tissue sections, and teaches FNA analysis can enhance diagnostic capability (p. 51-54).
To differentiate disease state using FNA biopsies, Jarmusch demonstrated successfully building a classification training set using tissue section data and evaluating the FNA data, and used Principal Component Analysis-Linear Discriminant Analysis (PCA-LDA), and teaches applying this method to rapidly identify tumor subtypes (p. 54-55; Figures 3-1 to 3-8; Tables 3-1 to 3-4). Figures 3-1 and 3-2 demonstrate DESI-MS ion images of tissue biopsy, histopathological analysis, and a PCA score plot of pixels. Jarmusch concluded that FNA biopsies provided the same chemical information relevant to disease state differentiation detected from tissue sections (p. 55). Jarmusch suggests applying their method to determine disease state for diagnosis of other cancers that typically collect FNA samples for diagnostics, including thyroid (p. 56). Jarmusch teaches the polar lipids detected in their negative ion mode represented only a fraction of metabolites, and suggest a further study of metabolome to reveal additional diagnostic information (p. 56).
Zhang teaches utilizing DEMI-MS imaging to produce lipid and metabolite profiles characteristic of thyroid tissues encompassing: benign tumors (follicular thyroid adenoma (FTA) and Hurthle cell adenoma); malignant tumors (follicular thyroid carcinoma (FTC), papillary thyroid carcinoma (PTC) and Hurthle cell carcinoma), and normal thyroid (benign thyroid tissue). Zhang assayed frozen thyroid tissue samples by DESI-MS in negative ion mode from m/z 100-1,500 (p. 6589, “DESI-MS imaging” in col. 1) and conducting histophathology (p. 6589, col. 1-2). Zhang discloses the resulting profiles with various m/z values representing the lipids and metabolites, including PS, PI, PE, PA, FA, DG, and CL (Results; Figures 1-2; Table 1, Supplementary information).
Zhang applied statistical analysis to the DESI-MS data to identify which lipid and metabolite biomarker levels were altered significantly between oncocytic tumors (Hurthle cell adenoma and Hurthle cell carcinoma), non-oncocytic tumors (PTC, FTA, FTC), and normal thyroid tissues, and identified specific lipid levels characteristic for each group (Figure 5; Materials and Methods “Statistical analysis”; p. 6593, col. 2 to pl. 6594 “Lipids are molecular markers of oncocytic tumors”). Zhang teaches (abstract) “A total of 219 molecular ions, including CLs, other glycerophospholipids, fatty acids, and metabolites, were found at increased or decreased abundance in oncocytic, nononcocytic, or normal thyroid tissues.” Zhang teaches (p. 6596, col. 1) that in using significance analysis of microarrays (SAM) statistical analysis of DESI-MS data, “219 distinct molecular ions (FDR < 5%), including various lipids and metabolites, were found at increased or decreased relative abundance in oncocytic, nononcocytic, or normal thyroid tissues. Besides CL, significant changes in FA abundances were also observed using statistical analysis.”
Zhang successfully identified 101 different CL-containing molecular ions directly from oncocytic thyroid tissues (p. 6595, col. 1). Zhang identified the presence of fatty acid (FA) m/z 303.233 in oncocytic tumor and non-oncocytic tumor (Figure 2 and Supportive Figure 6). CL m/z 724.486 was increased in non-oncocytic versus normal, and increased in oncocytic tumors versus non-oncocytic (Figure 5B; Supporting Table 1 Exact m/z). Biomarker m/z 303.233 and 255.233 were detected in oncocytic and non-oncocytic samples (Supporting Figure 1A and B). Biomarker (CL) m/z 724.485 and 885.550 were detected in oncoytic thyroid samples (Supporting Figures 1A and 8A; Table 1). Biomarker m/z 723.480 was found in non-oncocytic thyroid (Supporting Figure 1B). Zhang further detected biomarkers (CL) m/z 279.233, 281.248, 307.264, and 255.233 in thyroid samples (Supporting Figure 3B and 3C; p. 6590, col. 2). Zhang detected biomarkers m/z 788.546 and 857.519 in normal thyroid (Supportive Figure 8C). Zhang detected biomarker (CL) m/z 736.487 in oncocytic tumors (Table 1).
As summarized above, Zhang teaches biomarker m/z 723.480 was present in non-oncocytic tumors encompassing PTC, FTA, and FTC, therefore teaches m/z 723.480 is characteristic of FTC malignancy.
Zhang teaches biomarker m/z 281.248 was only found in normal thyroid (Supporting Figure 1C), therefore present in and characteristic of normal (benign) thyroid.
Zhang notes that PTC (malignant) presented higher relative abundances of CL for areas that stained positive for mitochondria (p. 6593, col. 1).
Zhang demonstrates DESI-MS digital image analysis for spatial distribution of lipids in the biopsy samples utilizing pixel measurements on glass slides (Figures 2-3).
Zhang teaches (Materials and Methods, “IHC, immunofluorescence, and confocal microscopy”):
“All the H&E- and IHC-stained slides were scanned using the Aperio ScanScope imaging platform (Aperio Technologies) with a 20x objective at a spatial sampling period of 0.47 mmper pixel. Whole-slides images were viewed and analyzed by using desktop personal computers equipped with the free ScanScope software. For immunofluorescence, formalin-fixed tissues were stained using Alexa Fluor 488–conjugated anti-mitochondrial antibody MAB1273A4 (Millipore), counterstained and mounted in ProLong Gold Antifade mounting media (Thermo Fisher). Immunofluorescence images were acquired on a Zeiss LSM880 confocal microscope.”
Zhang teaches (p. 6596, col. 1-2, bold emphasis added):
“We show that lipids are molecular markers of oncocytic tumors with statistical significance. Although direct MS imaging does not provide a quantitative assessment of molecules in tissues, using SAM, 219 distinct molecular ions (FDR < 5%), including various lipids and metabolites, were found at increased or decreased relative abundance in oncocytic, nononcocytic, or normal thyroid tissues. Besides CL, significant changes in FA abundances were also observed using statistical analysis. This rich lipid signature is characteristic and diagnostic of oncocytic phenotypes. Although our sample size is not sufficient for discriminating adenomas and carcinomas within the oncocytic tumor group, our pilot study gives further rationale to explore this problem using the molecular information obtained by ambient ionization MS” (p. 6596, col. 1).”
“As lipid signatures can be readily accessed from tissue samples using ambient ionization MS, we expect this method to be valuable for diagnosis of thyroid cancers and clinical use (18). Nondestructive DESI-MS can be adapted for fine needle aspiration biopsy analysis, the gold-standard method for preoperative diagnosis of thyroid lesions. With further increase in sample size and analysis of different tumor types, we expect to identify unique molecular signatures in various types of thyroid neoplasia to enhance diagnosis of nodules (especially those deemed indeterminate) and thus overcome current limitations of thyroid cytology.
Our work showcases the power of ambient ionization MS for CL imaging in biological tissues and is relevant to a variety of applications. Dysregulation of mitochondria occurs in many pathologies besides cancer. Lipids and their oxidized counterparts have been increasingly appreciated as important molecular markers and investigated to uncover biological pathways in disease (36–38). Further studies will be performed to extensively investigate alterations in small metabolites, FAs, CLs, and other GPs in thyroid tumors.”
Guo demonstrates applying MS analysis to thyroid tissue samples to generate lipid profiles characteristic of benign tumors, papillary cancer, follicular cancer, and adjacent normal thyroid tissues (Materials and Methods; Figure 1). Guo stained the thyroid tissue biopsies and digitally imaged lipid levels using commercially available software, and analyzed individual pixels on glass slides from different positions of each tissue type tested in order to produce a lipid profile including PC, PA, SM, LPC, CerP (Figure 1; p. 4359, col. 1-2 “MSI or mass spectrometry profiling”):
“MSI or mass spectrometry profiling was performed using a 9.4 T Apex Ultra™ Hybrid Qh-FTICR mass spectrometer (Bruker Daltonics, Billerica, MA, USA) equipped with a Smartbeam Nd/YAG laser (355 nm) providing a repetition rate of 200 Hz. Each thyroid tissue section was analyzed at a fixed laser at a raster step of 200 μm, with a laser spot size of 50 μm. A mass spectrum at each of pixel was accumulated from three full scans 50 laser shots each, in the positive-ion mode, over the mass range fromm/z 600 to 1,000, with a mass resolution of 400,000 at m/z 400. The entire MSI experiment for one tissue section took approximately 10 h, depending on the area of the section, and the resulting dataset was about 8 Gb in size. The imaging of lipid species was achieved using Bruker’s FlexImaging software (version 2.1, Bruker Daltonics, Germany). Profiling of lipid species in the tissue samples was acquired using the same program as MSI. More than 20 spectra (or pixels) from different positions of each type of tissue (ANT, MTC, or BTT) were acquired.”
Guo teaches applying known statistical methods to the MS data and optimizing specificity to identify diagnostic biomarkers significantly correlated to the presence or absence of thyroid cancer (p. 4360, col. 1-2, “Data handling and statistical analysis”).
Xu demonstrates mass spectrometry analysis of thyroid tissue biopsies from papillary thyroid carcinoma, benign adenoma, and normal adjacent tissues and, using statistical analysis, identified profiles of metabolites (metabolomic profiling) and significant changes in metabolite levels characteristic of each type (Abstract; Materials and Methods; Figure 3; Results).
Wojakowska reviews several studies demonstrating detection of differentiating lipid and metabolite profiles for nodules assayed from papillary thyroid carcinoma, follicular thyroid carcinoma, and healthy thyroid tissue controls (Table 1). In general, significant differences have been observed between the metabolomes of normal thyroid tissues and neoplastic lesions, as well as between benign and malignant nodules (section 6). Wojakowska teaches (abstract):
“Thyroid cancer is the most common endocrine malignancy with four major types distinguished on the basis of histopathological features: papillary, follicular, medullary, and anaplastic. Classification of thyroid cancer is the primary step in the assessment of prognosis and selection of the treatment. However, in some cases, cytological and histological patterns are inconclusive; hence, classification based on histopathology could be supported by molecular biomarkers, including markers identified with the use of high-throughput “omics” techniques. Beside genomics, transcriptomics, and proteomics, metabolomic approach emerges as the most downstream attitude reflecting phenotypic changes and alterations in pathophysiological states of biological systems. Metabolomics using mass spectrometry and magnetic resonance spectroscopy techniques allows qualitative and quantitative profiling of small molecules present in biological systems. This approach can be applied to reveal metabolic differences between different types of thyroid cancer and to identify new potential candidates for molecular biomarkers.”
Wojakowska teaches (p. 1, col. 2):
“Metabolomics is one of the high-throughput “omics” techniques, which beside genomics, transcriptomics, and proteomics play an important role in systems biology. Metabolome is the final downstream product of gene expression and therefore reflects changes in the transcriptome (mRNA) and the proteome (proteins). Additionally, metabolomics reflects phenotypic changes and alterations in pathophysiological states of biological systems and therefore represents the most “downstream” level of molecular life of a cell.”
Wojakowska summarizes known methods for metabolomics cancer research including steps of collecting tissue samples, acquiring sample data by MS profiling; data processing statistics, identifying metabolites using database and library matching, followed by biomarker validation (Figure 2). Wojakowska teaches utilizing metabolomics for the diagnosis and classification of thyroid cancer (section 7).
Thyroid nodule FNA samples:
It would have been prima facie obvious to one of ordinary skill in the art at the time the invention was filed to utilize FNA biopsy samples from thyroid nodules in the method of Takats. One would have been motivated to, and have a reasonable expectation of success to, because: (1) Takats suggests assaying thyroid tissue and performing their method to generate lipid and metabolite profiles diagnostic of adenomas and cancer including thyroid cancer; (2) Jarmusch suggests utilizing FNA thyroid samples for DESI-MS analysis and distinguishing normal versus cancer, and demonstrates successfully distinguishing normal versus cancer utilizing FNA samples of another cancer type analyzed by DESI-MS lipid profiles; (3) Jarmusch suggests using FNA biopsy over surgical tissue biopsy for DESI-MS analysis in cancer diagnostics because it is less invasive, and demonstrates that FNA biopsies provided molecular information that was similar to that of tissue sections, and (4) all of Zhang, Guo, Xu, and Wojakowska teach and successfully demonstrate assaying thyroid nodule tissues to generate lipid and metabolite profiles in methods for diagnosing and characterizing normal and benign thyroid and cancers.
Identifying benign thyroid or malignant carcinoma:
It would have been prima facie obvious to one of ordinary skill in the art at the time the invention was filed to identify benign thyroid when m/z 281.248 is present in the MS profile, or to identify malignant follicular thyroid carcinoma (FTC) when m/z 723.480 is present in the MS profile in the method of Takats. One would have been motivated to, and have a reasonable expectation of success to, because: (1) Takats suggests performing their method to diagnose or classify subtypes of cancer, including thyroid cancer; (2) Jarmusch teaches and exemplifies successfully distinguishing benign from cancer by DESI-MS analysis of lipid profile in FNA samples and utilizing a statistical classifier and suggests including the metabolome in analysis; (3) all of Zhang, Guo, Xu, and Wojakowska teach the need to classify thyroid cancers including follicular thyroid cancer, and demonstrate lipid and metabolite profiles can successfully distinguish and characterize follicular thyroid cancers, benign thyroid nodules, and normal thyroid; and (4) Zhang teaches m/z 281.248 was present only in normal/benign thyroid, and teaches m/z 723.480 was present in thyroid tissue classified as non-oncocytc encompassing FTC malignancy.
Digitally imaging FNA sample MS data on glass slide, identifying the pixels by histopathology, and producing optimized statistical classifiers from FNA MS data:
It would have been prima facie obvious to one of ordinary skill in the art at the time the invention was filed for Takats to digitally image their FNA biopsy sample MS data on a glass slide, identify histopathology of areas of the sample, and utilize image pixels and statistical analysis to generate a classification of the biopsy. One would have been motivated to, and have a reasonable expectation of success to, because: (1) Takats suggests using MS spectra data of their FNA samples to generate statistical classifiers in order to classify biopsies as cancer, and discloses numerous known statistical analysis methods incorporating cancerous and non-cancerous samples MS data for generating andoptimizing classification; (2) all of Zhang, Guo, Xu, and Wojakowska teach the need to classify thyroid cancers, teach applying known statistical analysis methods to the MS data to produce classifiers, and demonstrate MS lipid and metabolite profile data successfully classify thyroid cancers; (3) Jarmusch demonstrates successfully digitally imaging FNA sample on a glass slide by DESI-MS, identifying the pixels by histopathology, and producing an optimized statistical classifier from the tissue and FNA MS data to distinguish cancer from normal tissue; (4) Zhang and Jarmusch specifically suggests that nondestructive DESI-MS can be adapted for fine needle aspiration biopsy analysis, the gold-standard method for preoperative diagnosis of thyroid lesions; (5) Zhang demonstrates successful DESI-MS digital image analysis for spatial distribution of lipids and metabolites in tissue biopsy samples utilizing pixel measurements on glass slides and commercially available software analysis, histopathologically identifying cells on the slide sample, and employing commercially available statistical software to correlate MS pixel data (presence and levels of lipids/metabolites) with thyroid cancer, benign thyroid, or normal thyroid); and (6) Guo demonstrates successfully digitally imaging lipid levels of thyroid tissue samples on glass slides at various pixels, using commercially available software, and Guo teaches applying known statistical methods to the MS data and optimizing specificity to identify diagnostic biomarkers significantly correlated to the presence or absence of thyroid cancer.
Building statistical classifier from reference library of MS profiles derived from thyroid FNA and/or tissue biopsy samples:
It would have been prima facie obvious to one of ordinary skill in the art at the time the invention was filed to use MS data from either or both FNA and thyroid tissue reference samples in building the statistical classifier of Takats. One would have been motivated to and have a reasonable expectation of success to because: (1) Takats teaches using tissue biopsy MS data to build their reference library for training their statistical classifiers; (2) Takats teaches tissue biopsy can be obtained by FNA; (3) Jarmusch demonstrates successfully building a statistical classifier from a library of MS profiles derived from tissue and FNA samples, demonstrating that FNA biopsies provided molecular information that was similar to that of tissue sections, and demonstrating the classifier successfully distinguished normal from malignant biopsies; and (4) Zhang, Guo, and Xu demonstrate successfully using thyroid tissue biopsy MS data as reference data in their statistical classifiers. It is well within the level of the ordinary skilled artisan to use either or both thyroid FNA and tissue biopsy sample MS data to train their classification algorithm, and one of ordinary skill in the art would have a reasonable expectation of success to use either or both thyroid tissue FNA and biopsy samples training samples, given the samples are taken from the same thyroid tissue, and the cited prior art teach known and established methods for collecting MS data from reference FNA or tissue biopsies, and training classifier algorithms with the reference MS data.
Conducting negative ion mode mass spectrometry in a range of mz/ 100-1,500:
It would have been prima facie obvious to one of ordinary skill in the art at the time the invention was filed to collect mass spectrometry data of over a mass-to charge (m/z) range from 100 to 1,000 in negative ion mode in the method of Takats. One would have been motivated to and have a reasonable expectation of success to because: (1) Takats teaches using negative ion mass spectrometry to measure lipids and metabolites, and demonstrates successfully collecting mass spectrometry (DESI-MS) data of lipids over a mass-to charge (m/z) range from 150 to 1000 in negative ion mode in a wide variety of biological samples; (2) Jarmusch demonstrates successfully producing a diagnostic lipid mass spectrometry profile in the negative ion range of m/z 700-1000; and (3) Zhang demonstrates successfully assaying thyroid tissue samples by DESI-MS in negative ion mode from m/z 100-1,500 successfully producing profiles with various m/z values representing the lipids and metabolites, including PS, PI, PE, PA, FA, DG, and CL. Given the cited references teach and demonstrate successfully collecting mass spectrometry data of lipids and metabolites over a mass-to charge (m/z) range from 150-1,000 and m/z 100-1,500 in negative ion mode for the same purpose of creating a molecular signature (fingerprint), one of ordinary skill in the art could have pursued collecting mass spectrometry data of lipids and metabolites in the overlapping range of m/z 100-1,500 in the method of Takats with a reasonable expectation of success.
5. Claim 112 remains rejected under 35 U.S.C. 103 as being unpatentable over WO 2016/142691, Takats et al, published September 2016; Jarmusch (2016). Ambient ionization-mass spectrometry: Advances toward intrasurgical cancer detection (Doctoral dissertation, Purdue University); Zhang et al (Cancer Research, 2016, 76:6588-6597) and Zhang Supplementary Figures and Tables; Guo et al (Anal. Bioannal. Chem., 2014, 406:4357-4370); Xu et al (Journal of Proteome Research, 2015, 14:3315-3321); and Wojakowska et al (Int. J. Endocrinol., 2015, Vol. 2015; Article ID 258763, internet pages 1-13); as applied to claims , 30-33, 35, 38, 40-44, 113-117 above, and further in view of Sans et al (Cancer Research, 2017, 77:2903-2913).
Takats, Jarmusch, Zhang, Guo, Xu, and Wojakowska (the combined references) teach a method for identifying whether a thyroid nodule in a subject is benign or malignant comprising:
obtaining a FNA biopsy sample from a thyroid nodule;
performing mass spectrometry (DESI-MS) on the FNA biopsy to generate mass spectrometry data;
using a statistical classifier to detect whether the thyroid nodule is benign or malignant based in the mass spectrometry data, wherein the statistical classifier comprises a database of molecule signatures of lipids and metabolites, and the molecular signatures are based on reference profiles obtained by mass spectrometry;
identifying the thyroid nodule as benign when m/z 281.248 is present in the mass spectrometry data, or identifying the thyroid nodule as malignant FTC when m/z 723.480 is present in the mass spectrometry data.
The combined references do not teach generating the statistical classifier using the least absolute shrinkage and selection operator (LASSO) method.
Sans teaches collecting mass spectrometry data using DESI-MS in either negative or positive ion mode from m/z 100-1,500 from normal and cancerous ovarian tissue samples and generating the statistical classifier using the least absolute shrinkage and selection operator (LASSO) method (Materials and Methods, p. 2904, col. 1-2):
Statistical analysis
MS data corresponding to the areas of interest were extracted from the ion images using MSiReader software. The m/z range was discretized by performing hierarchical clustering and cutting the resulting dendrogram at distance 0.05. Peaks appearing in more than 10% of the pixels were kept for analysis. For two-class classification (normal vs. HGSC, and HGSC vs. BOT), logistic regression was performed with Lasso regularization using the “glmnet” package (26) in the R language. Regularization parameters were determined by 3-fold cross-validation (CV) analysis. The data were randomly divided in a training and validation set of samples, 50–50 per patient basis. For three-class classification (normal vs. BOT vs. HGSC), a customized training approach was employed as previously described (27).
Sans teaches (Results p. 2904, col.2):
“Characteristic metabolic profiles for HGSC, serous BOT, and normal ovary samples were observed in both polarities and presented a remarkable diversity of metabolic species. In the negative ion mode, small metabolites, saturated and unsaturated fatty acids, sphingolipids (SP), and several classes of glycerophospholipids (GP) such as ceramides (Cer), cardiolipins (CL), glycerophosphoethanolamines (PE), glycerophosphoglycerols (PG), glycerophosphoserines (PS), and glycerophosphoinositols (PI) were observed (Fig. 1A)”
Sans produced lipid and metabolite profiles characteristic of cancer or non-cancerous tissue samples and conclude the distinct molecular compositions associated with normal ovarian tissues, and borderline and high-grade tumors strongly suggest lipid and metabolite species as biomarkers for cancer diagnosis and aggressiveness. (Results, p. 2904, col. 2 to p. 2906, col. 2).
Sans explain applying the LASSO method to their DESI-MS data to generate classifiers for malignant or benign samples (Figure 4; p. 2906, col. 2 to p. 2907, col. 2):
Statistical prediction and molecular diagnosis of HGSC
DESI-MS imaging of tissue samples results in a large amount of molecular and spatial information (hundreds of molecular ions/hundreds of data points/sample) and thus calls for refined statistical evaluation to define what changes in molecular expression are significantly different between phenotypes and to build robust statistical classifiers. The Lasso method was performed on a random training set of samples to yield a model with parsimonious sets of m/z values for discriminating between the classes. A mathematical weight for each mass spectral feature was calculated by the Lasso depending on the importance that the feature had in characterizing a certain class. The predictive accuracy of the model with the selected features was evaluated using an independent validation set, and presented as agreement (%) with pathologic results. To classify HGSC pixels in comparison with normal tissue, MS data were extracted from tumor-concentrated regions or stromal areas within the selected tissues slides. First, we built a classifier for HGSC using a training subset of samples (8 normal, 23 HGSC). Three-fold CV was performed on a pixelby-pixel basis using a total of 20,082 pixels evaluated in the negative ion mode, resulting in an overall agreement of 97.1%. The statistical model was then applied to the validation set of samples (7 normal, 25 HGSC), which resulted in an overall agreement of 96.5% for 18,671 pixels (Fig. 4A). The area under the receiver operating characteristic curve values (AUC ¼ 0.98 for CV; AUC ¼ 0.97 for validation set) demonstrate the high performance for normal versus HGSC discrimination. Analysis per patient allowed correct classification of 100% of the patients in CV, whereas 1 HG sample was misclassified as normal out of the total 25 validation set samples (Supplementary Table S2). A subset of 25 m/z values selected by the Lasso as most significant contributors to the model were tentatively identified as small metabolites, saturated and polyunsaturated fatty acids, and GPs (Supplementary Table S3). The positive ion mode data were also analyzed by the Lasso to predict HGSC. Following the same strategy, overall agreements of 96.7% (AUC ¼ 0.96) and 95.5% (AUC ¼ 0.95) for CV and validation sets were achieved, respectively (Fig. 4B). The Lasso selected 21 m/z values characteristic for the model, the majority of which were identified as PCs, CEs, and TGs (Supplementary Table S3). These results demonstrate DESI-MS and Lasso's capabilities of diagnosing the most aggressive form of serous ovarian cancers, which is relevant due to the high occurrence and poor prognosis of HGSC compared with other subtypes (4).
Sans explains applying the generated classifier to predict benign versus malignant samples (“Statistical prediction of cancer aggressiveness for HGSC and BOT tissue” p. 2907, col. 2 to 2909, col. 1; and “Statistical prediction of intratumor heterogeneity” p. 2910 col. 1).
Sans cites Zhang et al (cited above) as reference #31, teaching that cardiolipins (CL) are known biomarkers for cancer, wherein CL was increased in ovarian high grade serous carcinoma (HGSC) compared to normal tissue (p. 2909, col. 2 and p. 2911, col. 1).
Sans concludes (p. 2911, col. 2):
“The classification models generated by the Lasso were successful in interpreting the large data sets, identifying molecular predictors of each tissue type as well as providing robust statistical classifiers. HGSC was classified with high accuracy in comparison with healthy stromal ovarian tissues, for both negative and positive ion mode data (96.4% overall agreement).”
“Importantly, we also investigated predictive markers of tumor aggressiveness by directly comparing borderline and aggressive serous tumors using a two-class molecular model. Due to the contrasting biological pathways involved in BOT (which can develop to LGSC) and HGSC, both serous ovarian cancers were anticipated to entail distinct molecular features (4). The two-class classification models DESI-MS imaging data presented an overall accuracy of 93.0% in predicting HGSC and BOT, which demonstrates the clinical value of this technique in differentiating tumors with distinct invasive and aggressive behaviors.”
It would have been prima facie obvious to one of ordinary skill in the art at the time the invention was filed to generate the statistical classifier using the LASSO method in the method of the combined references. One would have been motivated to and have a reasonable expectation of success to because: (1) the combined references teach and demonstrate generating classifiers from DESI-MS data to distinguish cancer from benign samples; and (2) Sans teaches the classification models generated by the Lasso from DESI-MS data were successful in interpreting the large data sets, identifying molecular predictors of each tissue type as well as providing robust statistical classifiers. Given the need to generate classifiers from DESI-MS data in order to distinguish malignant and benign tissues taught by the combined references and Sans, and given the known success of generating a statistical classifier using the LASSO method with DESI-MS data, and successfully applying the classifier to detect malignant and benign tissues, as demonstrated by Sans, one of skill in the art could have pursued generating the statistical classifier using the LASSO method of Sans in the method of the combined references with a reasonable expectation of success.
Response to Relevant Arguments
6. Applicants state the cited references do not teach or render obvious the amended claim limitations and new claims
7. Applicant’s arguments and the amended claim limitations have been addressed in the rejection above adding the Jarmusch reference and the Zhang Supplemental Figures and Tables data demonstrating identification of benign or FTC malignant thyroid based on specific m/z values claimed.
8. All other rejections under 35 USC 112(b) recited in the Office Action mailed May 29, 2025 are hereby withdrawn in view of amendments.
9. Conclusion: No claim is allowed.
10. Any inquiry concerning this communication or earlier communications from the examiner should be directed to LAURA B GODDARD whose telephone number is (571)272-8788. The examiner can normally be reached Mon-Fri, 7am-3:30pm.
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/Laura B Goddard/Primary Examiner, Art Unit 1642