DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Objections
Claims 1-5, 8-9, and 12-18 are objected to because of the following informalities:
Claim 1, line 1, the limitation “a determination of the probability” should be changed to “a determination of a probability”;
Claim 1, lines 3-5, the limitation “using a magnetic resonance spectroscopy device to obtain the level of at least one selected biomarker of a first region of the breast of presumably healthy breast tissue, said level of the selected biomarker having a correlation with the presence or probability” should be changed to “using a magnetic resonance spectroscopy device to obtain a level of at least one selected biomarker of a first region of a breast of the subject of presumably healthy breast tissue, said level of the at least one selected biomarker having a correlation with a presence or probability”;
Claim 1, lines 8-10, the limitation “comparing and analyzing the level of the selected biomarker from the first region of presumably healthy breast tissue with a reference level of the selected biomarkers which correlates with the probability that the subject has or will likely develop breast tumor” should be changed to “comparing and analyzing the level of the at least one selected biomarker from the first region of the presumably healthy breast tissue with a reference level of the at least one selected biomarker which correlates with the presence or the probability of the development of the breast tumor”;
Claim 2, line 1, the limitation “wherein the spectral data is obtained” should be changed to “wherein spectral data is obtained”;
Claim 3, line 1, the limitation “wherein the spectral data is obtained” should be changed to “wherein spectral data is obtained”;
Claim 4, line 2, the limitation “measuring the resonances or cross-peaks of the spectral data” should be changed to “measuring resonances or cross-peaks of spectral data”;
Claim 5, line 2, the limitation “mining digital points in the spectral data” should be changed to “mining digital points in spectral data”;
Claim 8, line 1, the limitation “wherein the selected biomarker(s) is/are selected from the group” should be changed to “wherein the at least one selected biomarker is selected from a group”;
Claim 8, line 3, the limitation “methine, , phosphocholine” should be changed to “methine, phosphocholine”;
Claim 9, line 1, the limitation “a determination of the probability” should be changed to “a determination of a probability”;
Claim 9, lines 3-5, the limitation “a magnetic resonance spectroscopy device to obtain the level of at least one selected biomarker of a first region of the breast of presumably healthy breast tissue, said level of the selected biomarker having a correlation with the presence or probability” should be changed to “a magnetic resonance spectroscopy device to obtain a level of at least one selected biomarker of a first region of a breast of the subject of presumably healthy breast tissue, said level of the at least one selected biomarker having a correlation with a presence or probability”;
Claim 9, lines 8-11, the limitation “a processor for comparing and analyzing the level of the selected biomarker from the first region of presumably healthy breast tissue with a reference level of the selected biomarkers which correlates with the probability that the subject has or will likely develop a breast tumor” should be changed to “a processor for comparing and analyzing the level of the at least one selected biomarker from the first region of the presumably healthy breast tissue with a reference level of the at least one selected biomarker which correlates with the presence or the probability of the development of the breast tumor”;
Claim 12, lines 1-2, the limitation “wherein the processor measures the resonances or cross-peaks of the spectral data” should be changed to “wherein the processor measures resonances or cross-peaks of spectral data”;
Claim 13, line 1, the limitation “wherein the processor mines digital points in the spectral data” should be changed to “wherein the processor mines digital points in spectral data”;
Claim 14, line 1, the limitation “wherein the processor mines digital points” should be changed to “wherein the processor mines the digital points”;
Claim 15, line 1, the limitation “wherein the processor mines digital points” should be changed to “wherein the processor mines the digital points”;
Claim 16, line 1, the limitation “wherein the selected biomarker(s) is/are selected from the group” should be changed to “wherein the at least one selected biomarker is selected from a group”;
Claim 16, line 5, the limitation “creatine and choline,.” should be changed to “creatine and choline.”;
Claim 17, lines 1-2, the limitation “A method of enabling a determination of the probability that a subject has or will develop cancer in an organ” should be changed to “A method of enabling a determination of a probability that a subject has or will develop cancer in an organ of the subject”;
Claim 17, lines 3-5, the limitation “using a magnetic resonance spectroscopy device to obtain the level of at least one selected biomarker of a first region of the organ of presumably healthy tissue, said level of the selected biomarker having a correlation with the presence or probability” should be changed to “using a magnetic resonance spectroscopy device to obtain a level of at least one selected biomarker of a first region of the organ of presumably healthy tissue, said level of the at least one selected biomarker having a correlation with a presence or probability”;
Claim 17, lines 8-10, the limitation “comparing and analyzing the level of the selected biomarker from the first region of presumably healthy tissue with a reference level of the selected biomarkers which correlates with the probability that the subject has or will likely develop a tumor” should be changed to “comparing and analyzing the level of the at least one selected biomarker from the first region of the presumably healthy tissue with a reference level of the at least one selected biomarker which correlates with the presence or the probability of the development of the tumor”;
Claim 18, lines 1-2, the limitation “A system for enabling a determination of the probability that a subject has or will develop cancer in an organ” should be changed to “A system for enabling a determination of a probability that a subject has or will develop cancer in an organ of the subject”;
Claim 18, lines 3-5, the limitation “a magnetic resonance spectroscopy device to obtain the level of at least one selected biomarker of a first region of the organ of presumably healthy tissue, said level of the selected biomarker having a correlation with the presence or probability” should be changed to “a magnetic resonance spectroscopy device to obtain a level of at least one selected biomarker of a first region of the organ of presumably healthy tissue, said level of the at least one selected biomarker having a correlation with a presence or probability”; and
Claim 18, lines 8-10, the limitation “a processor for comparing and analyzing the level of the selected biomarker from the first region of presumably healthy tissue with a reference level of the selected biomarkers which correlates with the probability that the subject has or will likely develop a tumor” should be changed to “a processor for comparing and analyzing the level of the at least one selected biomarker from the first region of the presumably healthy tissue with a reference level of the at least one selected biomarker which correlates with the presence or the probability of the development of the tumor”.
Appropriate correction is required.
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 1-18 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: The claims are directed to a process/method or a machine/system, and therefore satisfy step 1 of the subject matter eligibility test.
Step 2A, Prong 1: The claims recite the following limitations that are directed to judicial exceptions (abstract ideas): “obtain the level of at least one selected biomarker of a first region of the breast of presumably healthy breast tissue, said level of the selected biomarker having a correlation with the presence or probability of development of a breast tumor in a second region of the breast different from that of the first region of the presumably healthy breast tissue” in claim 1 and similarly in claims 9 and 17-18; “comparing and analyzing the level of the selected biomarker from the first region of presumably healthy breast tissue with a reference level of the selected biomarkers which correlates with the probability that the subject has or will likely develop breast tumor in the second region of the breast” in claim 1 and similarly in claims 9 and 17-18; “wherein the step of comparing and analyzing comprises measuring the resonances or cross-peaks of the spectral data” in claim 4 and similarly in claim 12; and “wherein the step of comparing and analyzing comprises data mining digital points in the spectral data” in claim 5 and similarly in claim 13; etc., which recite either mathematical concepts and/or mental processes that can be performed in the human mind or with the aid of pen and paper.
Step 2A, Prong 2: This judicial exception is not integrated into a practical application because the generically recited computer elements do not add a meaningful limitation to the abstract idea (i.e., the mental processes and/or mathematical concepts) as the generically recited computer elements only amount to simply implementing the abstract idea on the machine. Additional elements include the “magnetic resonance spectroscopy device” in claims 1, 9-11, and 17-18 and the “processor” in claims 9, 12-15, and 18, and other elements/components capable of performing the mere data gathering steps as claimed (i.e., “using a magnetic resonance spectroscopy device to obtain [data]” in claim 1 and similarly in claims 9 and 17-18; “wherein the spectral data is obtained using one-dimensional (1D) magnetic resonance spectroscopy (MRS)” in claim 2 and similarly claim 10; “wherein the spectral data is obtained using two-dimensional (2D) magnetic resonance spectroscopy (MRS)” in claim 3 and similarly claim 11; “wherein the data mining is obtained from one-dimensional (1D) spectral data” in claim 6 and similarly claim 14; “wherein the data mining is obtained from two-dimensional (2D) spectral data” in claim 7 and similarly claim 15; and “wherein the selected biomarker(s) is/are selected from the group comprising…” in claim 8 and similarly claim 16), etc., which are components recited at a high level of generality that merely links the judicial exception to a particular technological environment and/or a computer as a tool to perform the abstract idea.
Step 2B: For similar reasons set forth above, the additional limitations also do not provide an inventive concept that would be substantially more than the judicial exception. Adding insignificant extra-solutionary activity to the judicial exception, e.g., the mere data gathering steps of the claims in conjunction with an abstract idea, does not qualify as “significantly more” when recited in a claim with a judicial exception.
Conclusion: Claims 1-18 are not patent-eligible.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 17-18 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Righi et al. (NPL: “Discrimination of Healthy and Neoplastic Human Colon Tissues by ex Vivo HR-MAS NMR Spectroscopy and Chemometric Analyses”, J. Proteome Res. 2009, vol. 8, no. 4, p. 1859-1869, doi: 10.1021/pr801094b; a copy of which is herein provided by the Examiner, hereinafter Righi).
Regarding claims 17-18, Righi discloses a method (and a corresponding system comprising a magnetic resonance spectroscopy device and a processor) of enabling a determination of the probability that a subject has or will develop cancer in an organ (see, e.g., Page 1859, Abstract, lines 1-11, “The metabolic profile of human healthy and neoplastic colorectal tissues was obtained using ex vivo High-Resolution Magic Angle Spinning (HR-MAS) NMR spectroscopy. Principal Components Analysis (PCA) and Partial Least Squares Discriminant Analysis (PLS-DA) were applied to NMR data in order to highlight the biochemical differences between healthy and neoplastic colorectal tissues. The synergic combination of ex vivo HR-MAS NMR spectroscopy with Multivariate Data Analysis enables discrimination between healthy and tumoral colorectal tissues and identification of the increase of taurine, acetate, lactate, and lipids, and the decrease of polyols and sugars as tumoral characteristics. Moreover, it was found that macroscopically/histologically normal colorectal tissues, collected at least 15 cm from the adenocarcinoma, are characterized by a metabolic pattern quite similar to that typical of tumoral lesions. It was shown that ex vivo HR-MAS NMR spectroscopy, performed on intact specimens, may be of great potentiality in the clinical evaluation of human neoplastic colorectal tissues”), comprising:
using a magnetic resonance spectroscopy device to obtain the level of at least one selected biomarker of a first region of the organ of presumably healthy tissue (see, e.g., Page 1859, Abstract, lines 1-11, “The metabolic profile of human healthy and neoplastic colorectal tissues was obtained using ex vivo High-Resolution Magic Angle Spinning (HR-MAS) NMR spectroscopy. Principal Components Analysis (PCA) and Partial Least Squares Discriminant Analysis (PLS-DA) were applied to NMR data in order to highlight the biochemical differences between healthy and neoplastic colorectal tissues. The synergic combination of ex vivo HR-MAS NMR spectroscopy with Multivariate Data Analysis enables discrimination between healthy and tumoral colorectal tissues and identification of the increase of taurine, acetate, lactate, and lipids, and the decrease of polyols and sugars as tumoral characteristics. Moreover, it was found that macroscopically/histologically normal colorectal tissues, collected at least 15 cm from the adenocarcinoma, are characterized by a metabolic pattern quite similar to that typical of tumoral lesions. It was shown that ex vivo HR-MAS NMR spectroscopy, performed on intact specimens, may be of great potentiality in the clinical evaluation of human neoplastic colorectal tissues”, and Page 1859, col. 2, lines 1-11, “In vivo Nuclear Magnetic Resonance spectroscopy (in vivo NMR) has been widely applied in biomedical research, with particular attention to molecular evaluations regarding diagnosis, treatment, and prognosis of a wide variety of human neoplasms. […] The authors reported that the most commonly detected metabolites were choline and lipid”), said level of the selected biomarker having a correlation with the presence or probability of development of a tumor in a second region of the organ different from that of the first region of the presumably healthy tissue (see, e.g., Page 1862, col. 2, lines 19-39, “The spectra of the corresponding macroscopically/histologically normal colon specimens, collected during surgery at least 15 cm from the adenocarcinoma of the same patients, are reported in Figure 3 (right). The metabolic profiles are quite different also for this group of spectra, even though the samples are all classified as normal by histological analysis. The comparison with the corresponding neoplastic tissues hardly reveals any systematic difference. […] A certain variability is also observed within the class of healthy samples (not shown). We can conclude that all three classes of colorectal tissues here studied (neoplastic, histologically normal, and healthy) are characterized by a certain degree of metabolic heterogeneity, also within the same subclass of tumors. A statistical multivariate analysis of ex vivo HR-MAS NMR data may thus be helpful to find metabolic markers of the healthy and neoplastic state of colorectal tissues, and to classify the samples”, and Page 1867, col. 2, lines 11-25, “Lipids are present both in normal and neoplastic colon tissues, their amount being greater in the latter. The increase in lipid content is not so important as observed in human gastric adenocarcinoma, where the presence of a high amount of lipids can be used as the most effective discriminating marker between normal and adenocarcinoma tissues. In conclusion, despite the high degree of metabolic heterogeneity highlighted by ex vivo HR-MAS NMR spectroscopy, the combination of NMR data with multivariate analysis shows significant differences in the metabolic profile of healthy tissues with respect to neoplastic and macroscopically normal ones. We were able to confirm that high levels of Tau, Ac, Lac and lipids, and low levels of ChoCC, Cr, Glu plus Gln, polyols (Myo and Scy), and Glc are molecular characteristics of the colon adenocarcinoma tissues”); and
comparing and analyzing the level of the selected biomarker from the first region of presumably healthy tissue with a reference level of the selected biomarkers which correlates with the probability that the subject has or will likely develop a tumor in the second region of the organ (see, e.g., Page 1859, Abstract, lines 2-4, “Principal Components Analysis (PCA) and Partial Least Squares Discriminant Analysis (PLS-DA) were applied to NMR data in order to highlight the biochemical differences between healthy and neoplastic colorectal tissues”, and Page 1860, col. 1, lines 18-28, “Here, we report a study on ex vivo HR-MAS NMR of healthy and neoplastic colorectal tissues in combination with multi-variate methods, PCA and PLS-DA. Monodimensional and bidimensional NMR experiments were used to obtain the metabolic profile and identify the metabolites characterizing the colorectal tissues. PCA was carried out as explorative analysis in order to obtain an overview on the whole data set without forcing any model, and to extract relevant information. PLS-DA was used to build a classification model able to separate the classes of healthy and neoplastic human colorectal tissues based on their ex vivo HR-MAS NMR spectra”, and Page 1862, col. 2, lines 19-39, “The spectra of the corresponding macroscopically/histologically normal colon specimens, collected during surgery at least 15 cm from the adenocarcinoma of the same patients, are reported in Figure 3 (right). The metabolic profiles are quite different also for this group of spectra, even though the samples are all classified as normal by histological analysis. The comparison with the corresponding neoplastic tissues hardly reveals any systematic difference. […] A certain variability is also observed within the class of healthy samples (not shown). We can conclude that all three classes of colorectal tissues here studied (neoplastic, histologically normal, and healthy) are characterized by a certain degree of metabolic heterogeneity, also within the same subclass of tumors. A statistical multivariate analysis of ex vivo HR-MAS NMR data may thus be helpful to find metabolic markers of the healthy and neoplastic state of colorectal tissues, and to classify the samples”, and Page 1867, col. 2, lines 11-25, “Lipids are present both in normal and neoplastic colon tissues, their amount being greater in the latter. The increase in lipid content is not so important as observed in human gastric adenocarcinoma, where the presence of a high amount of lipids can be used as the most effective discriminating marker between normal and adenocarcinoma tissues. In conclusion, despite the high degree of metabolic heterogeneity highlighted by ex vivo HR-MAS NMR spectroscopy, the combination of NMR data with multivariate analysis shows significant differences in the metabolic profile of healthy tissues with respect to neoplastic and macroscopically normal ones. We were able to confirm that high levels of Tau, Ac, Lac and lipids, and low levels of ChoCC, Cr, Glu plus Gln, polyols (Myo and Scy), and Glc are molecular characteristics of the colon adenocarcinoma tissues”).
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-16 are rejected under 35 U.S.C. 103 as being unpatentable over Righi et al. (NPL: “Discrimination of Healthy and Neoplastic Human Colon Tissues by ex Vivo HR-MAS NMR Spectroscopy and Chemometric Analyses”, J. Proteome Res. 2009, vol. 8, no. 4, p. 1859-1869, doi: 10.1021/pr801094b; a copy of which is herein provided by the Examiner, hereinafter Righi) in view of Bittner (US 2004/0254444 A1, hereinafter Bittner).
Regarding claims 1 and 9, Righi discloses a method (and a corresponding system comprising a magnetic resonance spectroscopy device and a processor) of enabling a determination of the probability that a subject has or will develop cancer in an organ (see, e.g., Page 1859, Abstract, lines 1-11, “The metabolic profile of human healthy and neoplastic colorectal tissues was obtained using ex vivo High-Resolution Magic Angle Spinning (HR-MAS) NMR spectroscopy. Principal Components Analysis (PCA) and Partial Least Squares Discriminant Analysis (PLS-DA) were applied to NMR data in order to highlight the biochemical differences between healthy and neoplastic colorectal tissues. The synergic combination of ex vivo HR-MAS NMR spectroscopy with Multivariate Data Analysis enables discrimination between healthy and tumoral colorectal tissues and identification of the increase of taurine, acetate, lactate, and lipids, and the decrease of polyols and sugars as tumoral characteristics. Moreover, it was found that macroscopically/histologically normal colorectal tissues, collected at least 15 cm from the adenocarcinoma, are characterized by a metabolic pattern quite similar to that typical of tumoral lesions. It was shown that ex vivo HR-MAS NMR spectroscopy, performed on intact specimens, may be of great potentiality in the clinical evaluation of human neoplastic colorectal tissues”), comprising:
using a magnetic resonance spectroscopy device to obtain the level of at least one selected biomarker of a first region of the organ of presumably healthy tissue (see, e.g., Page 1859, Abstract, lines 1-11, “The metabolic profile of human healthy and neoplastic colorectal tissues was obtained using ex vivo High-Resolution Magic Angle Spinning (HR-MAS) NMR spectroscopy. Principal Components Analysis (PCA) and Partial Least Squares Discriminant Analysis (PLS-DA) were applied to NMR data in order to highlight the biochemical differences between healthy and neoplastic colorectal tissues. The synergic combination of ex vivo HR-MAS NMR spectroscopy with Multivariate Data Analysis enables discrimination between healthy and tumoral colorectal tissues and identification of the increase of taurine, acetate, lactate, and lipids, and the decrease of polyols and sugars as tumoral characteristics. Moreover, it was found that macroscopically/histologically normal colorectal tissues, collected at least 15 cm from the adenocarcinoma, are characterized by a metabolic pattern quite similar to that typical of tumoral lesions. It was shown that ex vivo HR-MAS NMR spectroscopy, performed on intact specimens, may be of great potentiality in the clinical evaluation of human neoplastic colorectal tissues”, and Page 1859, col. 2, lines 1-11, “In vivo Nuclear Magnetic Resonance spectroscopy (in vivo NMR) has been widely applied in biomedical research, with particular attention to molecular evaluations regarding diagnosis, treatment, and prognosis of a wide variety of human neoplasms. […] The authors reported that the most commonly detected metabolites were choline and lipid”), said level of the selected biomarker having a correlation with the presence or probability of development of a cancerous tumor in a second region of the organ different from that of the first region of the presumably healthy tissue (see, e.g., Page 1862, col. 2, lines 19-39, “The spectra of the corresponding macroscopically/histologically normal colon specimens, collected during surgery at least 15 cm from the adenocarcinoma of the same patients, are reported in Figure 3 (right). The metabolic profiles are quite different also for this group of spectra, even though the samples are all classified as normal by histological analysis. The comparison with the corresponding neoplastic tissues hardly reveals any systematic difference. […] A certain variability is also observed within the class of healthy samples (not shown). We can conclude that all three classes of colorectal tissues here studied (neoplastic, histologically normal, and healthy) are characterized by a certain degree of metabolic heterogeneity, also within the same subclass of tumors. A statistical multivariate analysis of ex vivo HR-MAS NMR data may thus be helpful to find metabolic markers of the healthy and neoplastic state of colorectal tissues, and to classify the samples”, and Page 1867, col. 2, lines 11-25, “Lipids are present both in normal and neoplastic colon tissues, their amount being greater in the latter. The increase in lipid content is not so important as observed in human gastric adenocarcinoma, where the presence of a high amount of lipids can be used as the most effective discriminating marker between normal and adenocarcinoma tissues. In conclusion, despite the high degree of metabolic heterogeneity highlighted by ex vivo HR-MAS NMR spectroscopy, the combination of NMR data with multivariate analysis shows significant differences in the metabolic profile of healthy tissues with respect to neoplastic and macroscopically normal ones. We were able to confirm that high levels of Tau, Ac, Lac and lipids, and low levels of ChoCC, Cr, Glu plus Gln, polyols (Myo and Scy), and Glc are molecular characteristics of the colon adenocarcinoma tissues”); and
comparing and analyzing the level of the selected biomarker from the first region of presumably healthy tissue with a reference level of the selected biomarkers which correlates with the probability that the subject has or will likely develop the cancerous tumor in the second region of the organ (see, e.g., Page 1859, Abstract, lines 2-4, “Principal Components Analysis (PCA) and Partial Least Squares Discriminant Analysis (PLS-DA) were applied to NMR data in order to highlight the biochemical differences between healthy and neoplastic colorectal tissues”, and Page 1860, col. 1, lines 18-28, “Here, we report a study on ex vivo HR-MAS NMR of healthy and neoplastic colorectal tissues in combination with multi-variate methods, PCA and PLS-DA. Monodimensional and bidimensional NMR experiments were used to obtain the metabolic profile and identify the metabolites characterizing the colorectal tissues. PCA was carried out as explorative analysis in order to obtain an overview on the whole data set without forcing any model, and to extract relevant information. PLS-DA was used to build a classification model able to separate the classes of healthy and neoplastic human colorectal tissues based on their ex vivo HR-MAS NMR spectra”, and Page 1862, col. 2, lines 19-39, “The spectra of the corresponding macroscopically/histologically normal colon specimens, collected during surgery at least 15 cm from the adenocarcinoma of the same patients, are reported in Figure 3 (right). The metabolic profiles are quite different also for this group of spectra, even though the samples are all classified as normal by histological analysis. The comparison with the corresponding neoplastic tissues hardly reveals any systematic difference. […] A certain variability is also observed within the class of healthy samples (not shown). We can conclude that all three classes of colorectal tissues here studied (neoplastic, histologically normal, and healthy) are characterized by a certain degree of metabolic heterogeneity, also within the same subclass of tumors. A statistical multivariate analysis of ex vivo HR-MAS NMR data may thus be helpful to find metabolic markers of the healthy and neoplastic state of colorectal tissues, and to classify the samples”, and Page 1867, col. 2, lines 11-25, “Lipids are present both in normal and neoplastic colon tissues, their amount being greater in the latter. The increase in lipid content is not so important as observed in human gastric adenocarcinoma, where the presence of a high amount of lipids can be used as the most effective discriminating marker between normal and adenocarcinoma tissues. In conclusion, despite the high degree of metabolic heterogeneity highlighted by ex vivo HR-MAS NMR spectroscopy, the combination of NMR data with multivariate analysis shows significant differences in the metabolic profile of healthy tissues with respect to neoplastic and macroscopically normal ones. We were able to confirm that high levels of Tau, Ac, Lac and lipids, and low levels of ChoCC, Cr, Glu plus Gln, polyols (Myo and Scy), and Glc are molecular characteristics of the colon adenocarcinoma tissues”).
Righi does not specifically disclose enabling a determination of the probability that a subject has or will specifically develop breast cancer (and wherein the first and second regions of the organ/tissue are specifically regions of breast tissue).
However, in the same field of endeavor of MRI spectroscopy, Bittner discloses enabling a determination of the probability that a subject has or will develop breast cancer, and wherein the first and second regions of the organ/tissue are regions of breast tissue (see, e.g., Abstract, “Methods for screening normal risk, asymptomatic individuals for breast cancer are provided, which may enable early diagnosis of breast cancer. The methods may involve the use of a contrast agent while screening normal risk, asymptomatic individuals using MRI technology. The methods may involve use of MRI spectroscopy while screening individuals using MRI technology. Also provided are methods of detecting breast cancer in normal risk, asymptomatic individuals, which include screening such individuals using MRI technology”, and Para. [0036], “MRI spectroscopy can non-invasively obtain physiologic images and spectra, based on the relative concentrations of cellular chemicals and metabolites. MRI spectroscopy can provide physiologic information about the relative concentrations of metabolites such as, citrate, creatine, and choline within the breast tissue by measuring the specific resonances for the metabolites from small volumes of tissue throughout the breast. The amount of individual resonance present for each metabolite is related to the concentration of these metabolites and changes in these concentrations can be used to identify breast cancer from normal breast tissue and from non-cancerous changes of the breast tissue. For example, breast cancer will demonstrate significantly higher choline levels and significantly lower citrate levels as compared to normal breast tissues and benign changes in the breast”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the method and the system of Righi by including enabling a determination of the probability that a subject has or will specifically develop breast cancer (and wherein the first and second regions of the organ/tissue are specifically regions of breast tissue), as disclosed by Bittner. One of ordinary skill in the art would have been motivated to make this modification in order to improve the ability to detect early cancers (specifically breast cancers) and improve the ability to differentiate between breast cancer and normal breast tissue, as recognized by Bittner (see, e.g., Abstract, and Para. [0035-0039]).
Regarding claims 2 and 10, Righi modified by Bittner discloses the method of claim 1 and the system of claim 9, respectively, as set forth above. Righi further discloses wherein the spectral data is obtained using one-dimensional (1D) magnetic resonance spectroscopy (MRS) (see, e.g., Page 1860, col. 1, lines 18-23, “Here, we report a study on ex vivo HR-MAS NMR of healthy and neoplastic colorectal tissues in combination with multi-variate methods, PCA and PLS-DA. Monodimensional and bidimensional NMR experiments were used to obtain the metabolic profile and identify the metabolites characterizing the colorectal tissues”, and Page 1860, col. 2, lines 12-28, “Nuclear Magnetic Resonance Spectroscopy. […] 13C HR-MAS NMR spectra were recorded with a Bruker Avance400 spectrometer operating at 400.13 and 100.61 MHz, respectively. […] Samples were spun at 4000 Hz and three different types of monodimensional (1D) proton spectra were acquired”, and Page 1862, col. 1, lines 5-8, “The assignment of metabolites is based on the analysis of 1D 1H, and selected 2D (COSY, TOCSY and HSQC […]) NMR spectra. The obtained data are compared with those from the literature”).
Regarding claims 3 and 11, Righi modified by Bittner discloses the method of claim 1 and the system of claim 9, respectively, as set forth above. Righi further discloses wherein the spectral data is obtained using two-dimensional (2D) magnetic resonance spectroscopy (MRS) (see, e.g., Page 1860, col. 1, lines 18-23, “Here, we report a study on ex vivo HR-MAS NMR of healthy and neoplastic colorectal tissues in combination with multi-variate methods, PCA and PLS-DA. Monodimensional and bidimensional NMR experiments were used to obtain the metabolic profile and identify the metabolites characterizing the colorectal tissues”, and Page 1860, col. 2, lines 12 and 40-50 to Page 1861, col. 1, lines 1-2, “Nuclear Magnetic Resonance Spectroscopy. […] Two-dimensional (2D) 1H,1H-COrrelation SpectroscopY (COSY) spectra were acquired using a standard pulse sequence (cosygpprqf) […] Two-dimensional 1H,1H-TOtal Correlation SpectroscopY (TOCSY) spectra were acquired using a standard pulse sequence (mlevph-pr) […] Two-dimensional 1H,13C-Heteronuclear Single Quantum Coherence (HSQC) spectra were acquired using a standard echo-antiecho pulse sequence (hsqcedetgp)”, and Page 1862, col. 1, lines 5-8, “The assignment of metabolites is based on the analysis of 1D 1H, and selected 2D (COSY, TOCSY and HSQC […]) NMR spectra. The obtained data are compared with those from the literature”).
Regarding claims 4 and 12, Righi modified by Bittner discloses the method of claim 1 and the system of claim 9, respectively, as set forth above. Righi further discloses wherein the step of comparing and analyzing comprises measuring the resonances or cross-peaks of the spectral data (see, e.g., Page 1859, Abstract, lines 2-4, “Principal Components Analysis (PCA) and Partial Least Squares Discriminant Analysis (PLS-DA) were applied to NMR data in order to highlight the biochemical differences between healthy and neoplastic colorectal tissues”, and Page 1860, col. 1, lines 18-28, “Here, we report a study on ex vivo HR-MAS NMR of healthy and neoplastic colorectal tissues in combination with multi-variate methods, PCA and PLS-DA. Monodimensional and bidimensional NMR experiments were used to obtain the metabolic profile and identify the metabolites characterizing the colorectal tissues. PCA was carried out as explorative analysis in order to obtain an overview on the whole data set without forcing any model, and to extract relevant information. PLS-DA was used to build a classification model able to separate the classes of healthy and neoplastic human colorectal tissues based on their ex vivo HR-MAS NMR spectra”, and Page 1862, col. 2, lines 19-39, “The spectra of the corresponding macroscopically/histologically normal colon specimens, collected during surgery at least 15 cm from the adenocarcinoma of the same patients, are reported in Figure 3 (right). The metabolic profiles are quite different also for this group of spectra, even though the samples are all classified as normal by histological analysis. The comparison with the corresponding neoplastic tissues hardly reveals any systematic difference. […] A certain variability is also observed within the class of healthy samples (not shown). We can conclude that all three classes of colorectal tissues here studied (neoplastic, histologically normal, and healthy) are characterized by a certain degree of metabolic heterogeneity, also within the same subclass of tumors. A statistical multivariate analysis of ex vivo HR-MAS NMR data may thus be helpful to find metabolic markers of the healthy and neoplastic state of colorectal tissues, and to classify the samples”, and Page 1867, col. 2, lines 11-25, “Lipids are present both in normal and neoplastic colon tissues, their amount being greater in the latter. The increase in lipid content is not so important as observed in human gastric adenocarcinoma, where the presence of a high amount of lipids can be used as the most effective discriminating marker between normal and adenocarcinoma tissues. In conclusion, despite the high degree of metabolic heterogeneity highlighted by ex vivo HR-MAS NMR spectroscopy, the combination of NMR data with multivariate analysis shows significant differences in the metabolic profile of healthy tissues with respect to neoplastic and macroscopically normal ones. We were able to confirm that high levels of Tau, Ac, Lac and lipids, and low levels of ChoCC, Cr, Glu plus Gln, polyols (Myo and Scy), and Glc are molecular characteristics of the colon adenocarcinoma tissues”, and Table 2 on Page 1863 with footnote description).
Regarding claims 5 and 13, Righi modified by Bittner discloses the method of claim 1 and the system of claim 9, respectively, as set forth above. Righi further discloses wherein the step of comparing and analyzing comprises data mining digital points in the spectral data (see, e.g., Page 1859, Abstract, lines 2-4, “Principal Components Analysis (PCA) and Partial Least Squares Discriminant Analysis (PLS-DA) were applied to NMR data in order to highlight the biochemical differences between healthy and neoplastic colorectal tissues”, and Page 1860, col. 1, lines 18-28, “Here, we report a study on ex vivo HR-MAS NMR of healthy and neoplastic colorectal tissues in combination with multi-variate methods, PCA and PLS-DA. Monodimensional and bidimensional NMR experiments were used to obtain the metabolic profile and identify the metabolites characterizing the colorectal tissues. PCA was carried out as explorative analysis in order to obtain an overview on the whole data set without forcing any model, and to extract relevant information. PLS-DA was used to build a classification model able to separate the classes of healthy and neoplastic human colorectal tissues based on their ex vivo HR-MAS NMR spectra”, and Page 1862, col. 2, lines 19-39, “The spectra of the corresponding macroscopically/histologically normal colon specimens, collected during surgery at least 15 cm from the adenocarcinoma of the same patients, are reported in Figure 3 (right). The metabolic profiles are quite different also for this group of spectra, even though the samples are all classified as normal by histological analysis. The comparison with the corresponding neoplastic tissues hardly reveals any systematic difference. […] A certain variability is also observed within the class of healthy samples (not shown). We can conclude that all three classes of colorectal tissues here studied (neoplastic, histologically normal, and healthy) are characterized by a certain degree of metabolic heterogeneity, also within the same subclass of tumors. A statistical multivariate analysis of ex vivo HR-MAS NMR data may thus be helpful to find metabolic markers of the healthy and neoplastic state of colorectal tissues, and to classify the samples”, and Page 1867, col. 2, lines 11-25, “Lipids are present both in normal and neoplastic colon tissues, their amount being greater in the latter. The increase in lipid content is not so important as observed in human gastric adenocarcinoma, where the presence of a high amount of lipids can be used as the most effective discriminating marker between normal and adenocarcinoma tissues. In conclusion, despite the high degree of metabolic heterogeneity highlighted by ex vivo HR-MAS NMR spectroscopy, the combination of NMR data with multivariate analysis shows significant differences in the metabolic profile of healthy tissues with respect to neoplastic and macroscopically normal ones. We were able to confirm that high levels of Tau, Ac, Lac and lipids, and low levels of ChoCC, Cr, Glu plus Gln, polyols (Myo and Scy), and Glc are molecular characteristics of the colon adenocarcinoma tissues”).
Regarding claims 6 and 14, Righi modified by Bittner discloses the method of claim 5 and the system of claim 13, respectively, as set forth above. Righi further discloses wherein the data mining is obtained from one-dimensional (1D) spectral data (see, e.g., Page 1860, col. 1, lines 18-23, “Here, we report a study on ex vivo HR-MAS NMR of healthy and neoplastic colorectal tissues in combination with multi-variate methods, PCA and PLS-DA. Monodimensional and bidimensional NMR experiments were used to obtain the metabolic profile and identify the metabolites characterizing the colorectal tissues”, and Page 1860, col. 2, lines 12-28, “Nuclear Magnetic Resonance Spectroscopy. […] 13C HR-MAS NMR spectra were recorded with a Bruker Avance400 spectrometer operating at 400.13 and 100.61 MHz, respectively. […] Samples were spun at 4000 Hz and three different types of monodimensional (1D) proton spectra were acquired”, and Page 1862, col. 1, lines 5-8, “The assignment of metabolites is based on the analysis of 1D 1H, and selected 2D (COSY, TOCSY and HSQC […]) NMR spectra. The obtained data are compared with those from the literature”).
Regarding claims 7 and 15, Righi modified by Bittner discloses the method of claim 5 and the system of claim 13, respectively, as set forth above. Righi further discloses wherein the data mining is obtained from two-dimensional (2D) spectral data (see, e.g., Page 1860, col. 1, lines 18-23, “Here, we report a study on ex vivo HR-MAS NMR of healthy and neoplastic colorectal tissues in combination with multi-variate methods, PCA and PLS-DA. Monodimensional and bidimensional NMR experiments were used to obtain the metabolic profile and identify the metabolites characterizing the colorectal tissues”, and Page 1860, col. 2, lines 12 and 40-50 to Page 1861, col. 1, lines 1-2, “Nuclear Magnetic Resonance Spectroscopy. […] Two-dimensional (2D) 1H,1H-COrrelation SpectroscopY (COSY) spectra were acquired using a standard pulse sequence (cosygpprqf) […] Two-dimensional 1H,1H-TOtal Correlation SpectroscopY (TOCSY) spectra were acquired using a standard pulse sequence (mlevph-pr) […] Two-dimensional 1H,13C-Heteronuclear Single Quantum Coherence (HSQC) spectra were acquired using a standard echo-antiecho pulse sequence (hsqcedetgp)”, and Page 1862, col. 1, lines 5-8, “The assignment of metabolites is based on the analysis of 1D 1H, and selected 2D (COSY, TOCSY and HSQC […]) NMR spectra. The obtained data are compared with those from the literature”).
Regarding claims 8 and 16, Righi modified by Bittner discloses the method of claim 1 and the system of claim 9, respectively, as set forth above. Righi further discloses wherein the selected biomarker(s) is/are selected from the group comprising specific parts of lipid or triglyceride, cholesterol, cholesterol ester, methylene protons β to COO, methine, phosphocholine, glucose, glycine, myo-inositol, glycerol, glutamine, scyllo-inositol, histamine, methyl-malonic acid (MMA), MMA1, histidine, taurine, creatine, creatine and choline (see, e.g., Page 1859, Abstract, lines 1-7, “The metabolic profile of human healthy and neoplastic colorectal tissues was obtained using ex vivo High-Resolution Magic Angle Spinning (HR-MAS) NMR spectroscopy. […] The synergic combination of ex vivo HR-MAS NMR spectroscopy with Multivariate Data Analysis enables discrimination between healthy and tumoral colorectal tissues and identification of the increase of taurine, acetate, lactate, and lipids, and the decrease of polyols and sugars as tumoral characteristics”, and Page 1860, col. 1, lines 18-23, “Here, we report a study on ex vivo HR-MAS NMR of healthy and neoplastic colorectal tissues in combination with multi-variate methods, PCA and PLS-DA. Monodimensional and bidimensional NMR experiments were used to obtain the metabolic profile and identify the metabolites characterizing the colorectal tissues” , and Page 1867, col. 2, lines 11-25, “Lipids are present both in normal and neoplastic colon tissues, their amount being greater in the latter. The increase in lipid content is not so important as observed in human gastric adenocarcinoma, where the presence of a high amount of lipids can be used as the most effective discriminating marker between normal and adenocarcinoma tissues. In conclusion, despite the high degree of metabolic heterogeneity highlighted by ex vivo HR-MAS NMR spectroscopy, the combination of NMR data with multivariate analysis shows significant differences in the metabolic profile of healthy tissues with respect to neoplastic and macroscopically normal ones. We were able to confirm that high levels of Tau, Ac, Lac and lipids, and low levels of ChoCC, Cr, Glu plus Gln, polyols (Myo and Scy), and Glc are molecular characteristics of the colon adenocarcinoma tissues”, and Table 2 on Page 1863).
Conclusion
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/T.D./Examiner, Art Unit 3798
/PASCAL M BUI PHO/Supervisory Patent Examiner, Art Unit 3798