DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Status
Claims 1-12 are currently pending and examined on the merits.
Priority
The instant application is a 371 of PCT/EP2021/064062 filed on 5/26/2021 and claims foreign priority under U.S.C. 119 to Application EP20177361.1 filed on 5/29/2020, in Europe. At this point in examination, the effective filing date of claims 1-12 is 5/29/2020.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 9/23/2025 is in compliance with the provisions of 37 CFR 1.97. A signed copy of the corresponding 1449 form has been included with this Office Action.
Specification
There are hyperlinks in pg. 9, para. 3, line 2 and pg. 10, para. 3, line 14 of the instant specification. The disclosure is objected to because it contains an embedded hyperlink and/or other form of browser-executable code. Applicant is required to delete the embedded hyperlink and/or other form of browser-executable code; references to websites should be limited to the top-level domain name without any prefix such as http:// or other browser-executable code. See MPEP § 608.01.
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.
Claim 12 is rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claim(s) does/do not fall within at least one of the four categories of patent eligible subject matter. Claim 12 is drawn to a computer program. The computer program product/computer readable media is not limited to a physical embodiment and may read on carrier waves and other nonstatutory media. See, e.g., In re Nuiten, Docket no. 2006-1371 (Fed. Cir. Sept. 20, 2007)(slip. op. at 18)(“A transitory, propagating signal like Nuijten's is not a process, machine, manufacture, or composition of matter.' … Thus, such a signal cannot be patentable subject matter.”).
Claims 1-12 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claims recite: (a) mathematical concepts, (e.g., mathematical relationships, formulas or equations, mathematical calculations); and (b) mental processes, i.e., concepts performed in the human mind, (e.g., observation, evaluation, judgement, opinion).
Eligibility Step 1: Claims 1-9 are directed to a method (process) of determining a risk of mortality of a cancer patient. Claim 10 is directed to a method (process) of assessing an anti-cancer therapy. Claim 11 is directed to a method (process) of selecting cancer patients for treatment with an anti-cancer therapy. Therefore, these claims are encompassed by the categories of statutory subject matter, and thus satisfy the subject matter eligibility requirements under Step 1.
[Step 1: YES]
Eligibility Step 2A: First, it is determined in Prong One whether a claim recites a judicial exception, and if so, then it is determined in Prong Two whether the recited judicial exception is integrated into a practical application of that exception.
Eligibility Step 2A, Prong One: In determining whether a claim is directed to a judicial exception, examination is performed that analyzes whether the claim recites a judicial exception, i.e., whether a law of nature, natural phenomenon, or abstract idea is set forth described in the claim.
Claims 1 and 5-11 recite the following steps which fall within the mental processes and/or mathematical concepts groups of abstract ideas, as noted below.
Independent claim 1 further recites:
using a mathematical model of mortality risk to determine a risk of mortality of the cancer patient based on the received patient data (i.e., mental processes, mathematical concepts).
Dependent claim 5 further recites:
wherein the determination of the mortality risk comprises calculating a numerical score representing the mortality risk (i.e., mental processes, mathematical concepts).
Dependent claim 6 further recites:
wherein the determination of the mortality risk further comprises comparing the calculated score to one or more predetermined threshold values, or to calculated scores for other cancer patients (i.e., mental processes).
Dependent claim 7 further recites:
wherein the model is formed by determining the weightings from the training data (i.e., mental processes).
Dependent claim 8 further recites:
wherein the model is formed by performing multivariable Cox regression analysis on the training data for a plurality of subjects, preferably at least 1000 subjects (i.e., mental processes, mathematical concepts).
Dependent claim 9 further recites:
wherein: the model is formed by: assigning a respective weighting,
w
i
, to each of the model parameters (i.e., mental processes);
wherein: the model is formed by: determining a respective mean,
m
i
, of values of each model parameter over the training data (i.e., mental processes, mathematical concepts);
wherein: the determination of the mortality risk comprises calculating a numerical score according to the following formula:
s
c
o
r
e
=
∑
i
w
i
(
m
i
j
-
m
i
)
where
w
i
is the weighting of the i-th model parameter,
m
i
is the mean of the i-th model parameter, and
m
i
j
is the value of the i-th model parameter for a j-th cancer patient for whom the score is to be calculated (i.e., mental processes, mathematical concepts).
Independent claim 10 further recites:
determining a risk of mortality of a patient at plural different times while the patient is receiving the anti-cancer therapy by performing the method of claim 1 at each of the plural times (i.e., mental processes);
analysing the resulting determined risks to determine an efficacy of the anti-cancer therapy (i.e., mental processes).
Independent claim 11 further recites:
determining a risk of mortality of a candidate patient using the method of claim 1 (i.e., mental processes);
using the determining risk to decide whether to select each candidate patient (i.e., mental processes).
The abstract ideas recited in the claims are evaluated under the broadest reasonable interpretation (BRI) of the claim limitations when read in light of and consistent with the specification. As noted in the foregoing section, the claims are determined to contain limitations that can practically be performed in the human mind with the aid of a pencil and paper, and therefore recite judicial exceptions from the mental process grouping of abstract ideas. Additionally, the recited limitations that are identified as judicial exceptions from the mathematical concepts grouping of abstract ideas are abstract ideas irrespective of whether or not the limitations are practical to perform in the human mind.
Therefore, claims 1 and 5-11 recite an abstract idea.
[Step 2A, Prong One: YES]
Eligibility Step 2A, Prong Two: In determining whether a claim is directed to a judicial exception, further examination is performed that analyzes if the claim recites additional elements that, when examined as a whole, integrates the judicial exception(s) into a practical application (MPEP 2106.04(d)). A claim that integrates a judicial exception into a practical application will apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception. The claimed additional elements are analyzed to determine if the abstract idea is integrated into a practical application (MPEP 2106.04(d)(I); MPEP 2106.05(a-h)). If the claim contains no additional elements beyond the abstract idea, the claim fails to integrate the abstract idea into a practical application (MPEP 2106.04(d)(III)).
The judicial exceptions identified in Eligibility Step 2A, Prong One are not integrated into a practical application because of the reasons noted below.
Claim 1 recites the additional non-abstract elements of data gathering:
receiving patient data representing information about a cancer patient (claim 1);
outputting the determined risk of mortality, wherein: the mathematical model models a relationship determined from training data between the risk of mortality and values of at least the following model parameters:
(i) age;
(ii) gender;
(iii) haemoglobin or haematocrit level in blood;
(iv) urea nitrogen level in serum or plasma;
(v) alkaline phosphatase enzymatic activity level in serum or plasma;
(vi) protein level in serum or plasma;
(vii) level of albumin in serum or plasma;
(viii) chloride or sodium level in serum or plasma;
(ix) ratio of eosinophils to leukocytes in blood;
(x) lactate dehydrogenase enzymatic activity level in serum or plasma;
(xi) heart rate;
(xii) systolic blood pressure;
(xiii) Eastern cooperative oncology group (ECOG) performance status;
(xiv) ratio of neutrophils to lymphocytes in blood;
(xv) ratio of aspartate aminotransferase enzymatic activity level in serum or plasma to alanine aminotransferase enzymatic activity level in serum or plasma; and
(xvi) TNM classification of tumor stage (claim 1).
Data gathering steps are not an abstract idea, they are extra-solution activity, as they collect the data needed to carry out the JE. The data gathering does not impose any meaningful limitation on the JE, or how the JE is performed. The additional limitation (data gathering) must have more than a nominal or insignificant relationship to the identified judicial exception. (MPEP 2106.04/.05, citing Intellectual Ventures LLC v. Symantee Corp, McRO, TLI communications, OIP Techs. Inc. v. Amason.com Inc., Electric Power Group LLC v. Alstrom S.A.). Dependent claims 2-4 recite information further limiting the additional elements indicated above.
Claim 12 recites the additional non-abstract element (EIA) of a general-purpose computer system or parts thereof:
a computer program (claim 12).
The EIA do not provide any details of how specific structures of the computer elements are used to implement the JE. The claims require nothing more than a general-purpose computer to perform the functions that constitute the judicial exceptions. The computer elements of the claims do not provide improvements to the functioning of the computer itself (as in DDR Holdings, LLC v. Hotels.com LP); they do not provide improvements to any other technology or technical field (as in Diamond v. Diehr); nor do they utilize a particular machine (as in Eibel Process Co. v. Minn. & Ont. Paper Co.). Hence, these are mere instructions to apply the JE using a computer, and therefore the claim does not recite integrate that JE into a practical application.
Thus, the additionally recited elements merely invoke a computer as a tool, and/or amount to insignificant extra-solution data gathering activity, and as such, when all limitations in claims 1-12 have been considered as a whole, the claims are deemed to not recite any additional elements that would integrate a judicial exception into a practical application. Claims 1 and 12 contain additional elements that would not integrate a judicial exception into a practical application and are further probed for inventive concept in Step 2B.
[Step 2A, Prong Two: NO]
Eligibility Step 2B: Because the claims recite an abstract idea, and do not integrate that abstract idea into a practical application, the claims are probed for a specific inventive concept. The judicial exception alone cannot provide that inventive concept or practical application (MPEP 2106.05). Identifying whether the additional elements beyond the abstract idea amount to such an inventive concept requires considering the additional elements individually and in combination to determine if they amount to significantly more than the judicial exception (MPEP 2106.05A i-vi).
The claims do not include any additional elements that are sufficient to amount to significantly more than the judicial exception(s) because of the reasons noted below.
With respect to claim 1: The limitations identified above as non-abstract elements (EIA) related to data gathering do not rise to the level of significantly more than the judicial exception. Activities such as data gathering do not improve the functioning of a computer, or comprise an improvement to any other technical field. The limitations do not require or set forth a particular machine, they do not affect a transformation of matter, nor do they provide an unconventional step (citing McRO and Trading Technologies Int’l v. IBG). Data gathering steps constitute a general link to a technological environment. Simply appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception are insufficient to provide significantly more (as discussed in Alice Corp.,).
With respect to claim 12: The limitations identified above as non-abstract elements (EIA) related to general-purpose computer systems do not rise to the level of significantly more than the judicial exception. These elements do not improve the functioning of the computer itself, or comprise an improvement to any other technical field (Trading Technologies Int’l v. IBG, TLI Communications). They do not require or set forth a particular machine (Ultramercial v. Hulu, LLC., Alice Corp. Pty. Ltd v. CLS Bank Int’l), they do not affect a transformation of matter, nor do they provide an unconventional step. Simply appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception are insufficient to provide significantly more (as discussed in Alice Corp., CyberSource v. Retail Decisions, Parker v. Flook, Versata Development Group v. SAP America).
[Step 2B: NO]
Therefore, claims 1-12 are patent ineligible under 35 U.S.C. § 101.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1, 5, 8, and 12 are rejected under 35 U.S.C. 103 as being unpatentable over Yu et al. [US20200126636A1], in view of Yoon et al. (Head & Neck, 2020, 42(8), 1699-1712), Kvale et al. (Cancer Epidemiology, 2010, 35(1), 30-36), as provided in the IDS filed 9/23/2025, Reid et al. (PLOS One, 2017, 12(4), 1-31), Harrop et al. [US20160195554A1], and Lee et al. (Clinical Genitourinary Cancer, 2016, 15(3), e379-e385).
With respect to claim 1:
Yu et al. discloses training data involving a set of cancer patients that underwent cancer treatments, which associates results from laboratory tests conducted on the cancer patients and tumor types of the cancer patients with whether the individuals from the set of cancer patients died within a threshold number of weeks from initiation of the cancer treatments (pg. 2, col. 2, para. [0027], lines 2-10; receiving patient data representing information about a cancer patient). Yu et al. discloses multiple clinicopathological markers comprising age, gender, hematocrit (%), hemoglobin (g/L), protein (g/L), albumin (g/L), chloride (mmol/L), sodium (mmol/L), eosinophils/leukocytes (%), lactate dehydrogenase (U/L), and neutrophils/lymphocytes (%) (pg. 1, col. 2, para. [0008]; (i) age, (ii) gender, (iii) haemoglobin or haematocrit level in blood, (vi) protein level in serum or plasma, (vii) level of albumin in serum or plasma, (viii) chloride or sodium level in serum or plasma, (ix) ratio of eosinophils to leukocytes in blood, (x) lactate dehydrogenase enzymatic activity level in serum or plasma, (xiv) ratio of neutrophils to lymphocytes in blood). Also, further discloses performing a blood draw using standard techniques to assess a patient’s clinicopathological markers such as alkaline phosphatase (pg. 9, col. 1, para. [0126]; (v) alkaline phosphatase enzymatic activity level in serum or plasma). Yu et al. discloses that several studies relied upon subjective and investigator-dependent parameters such as performance status (i.e., Eastern Cooperative Oncology Group (“ECOG”), Karnfosky index) (pg. 10, col. 2, para. [0146], lines 10-13; (xiii) Eastern cooperative oncology group (ECOG) performance status).
Yu et al. does not disclose using a mathematical model of mortality risk to determine a risk of mortality of the cancer patient based on the received patient data.
However, Yoon et al. discloses calculating a mortality risk score for cancer patients by summing expression values of selected miRNAs and covariates weighted by regression coefficients obtained from multivariate Cox regression analyses (pg. 1702, col. 2, para. 3, lines 3-9). Also, further discloses the prognostic model as consisting of miRNAs, TNM stage, and histologic grade (pg. 1706, col. 2, para. 2, lines 1-11). This teaches a mathematical model for calculating mortality risk of cancer patients based on patient data.
Yu et al. and Yoon et al. do not disclose outputting the determined risk of mortality, wherein: the mathematical model models a relationship determined from training data between the risk of mortality and values of at least the following model parameters.
However, Kvale et al. discloses determining whether a baseline history of cancer was independently associated with all-cause mortality in a propensity-matched population of community-dwelling older adults, and identifying factors associated with mortality among cancer survivors in this population (pg. 30, col. 2, para. 2, lines 1-5). Also, further discloses using bivariate and multivariable Cox regression models to determine predictors of mortality in 827 participants with history of cancer (pg. 33, col. 1, para. 2, lines 1-3). This teaches that patients with history of cancer were used as training data for modeling a relationship between the predictors of mortality and the risk of mortality, as seen in Figure 3 (pg. 34, Fig. 3).
Yu et al., Yoon et al., and Kvale et al. do not disclose (iv) urea nitrogen level in serum or plasma.
However, Reid et al. discloses serum urea/blood urea nitrogen in Table 3. Prognostic biomarkers subdivided by evidence based medicine modified GRADE criteria (pg. 21, Table 3). This teaches urea nitrogen level in serum or plasma.
Yu et al., Yoon et al., Kvale et al., and Reid et al. do not disclose the following model parameters:
(xi) heart rate;
(xii) systolic blood pressure;
(xvi) TNM classification of tumor stage.
However, Harrop et al. discloses collecting vital signs including systolic and diastolic blood pressures (mmHg) and heart rate (bpm) (pg. 40, col. 1, para. [0713], lines 1-3; (xi) heart rate, (xii) systolic blood pressure). Also, further discloses cancers being staged using the TNM Classification of Malignant Tumors (TNM) (pg. 18, col. 2, para. [0265], lines 10-18; (xvi) TNM classification of tumor stage).
Yu et al., Yoon et al., Kvale et al., Reid et al., and Harrop et al. do not disclose (xv) ratio of aspartate aminotransferase enzymatic activity level in serum or plasma to alanine aminotransferase enzymatic activity level in serum or plasma.
However, Lee et al. discloses a Kaplan-Meier analysis of progression-free, overall, and cancer-specific survival based on the AST/ALT ratio among 583 patients after surgical treatment for upper tract urothelial cancer (pg. e382, Figure 1). This teaches a ratio of aspartate aminotransferase (AST) enzymatic activity level in serum or plasma to alanine aminotransferase (ALT) enzymatic activity level in serum or plasma.
It would have been prima facie obvious to one of ordinary skill in the art to combine the model parameters disclosed by Yu et al. with determining a risk of mortality disclosed by Yoon et al., the mathematical model disclosed by Kvale et al., and other model parameters (i.e., urea nitrogen level, heart rate, etc.) disclosed by Reid et al., Harrop et al., and Lee et al. One would be motivated to combine model parameters with a mathematical model to determine a risk of mortality because the 5-plex prognostic marker panel disclosed by Yoon et al. demonstrated significantly greater prognostic power with an AUC of 0.83 when combining large tumor size and depth of tumor invasion, and histologic grading (pg. 1710, col. 2, para. 1, lines 7-10). This means that a mathematical model incorporating this determination of mortality risk will be highly reliable and precise. Kvale et al. discloses that their study is the first report of an association between a history of cancer and mortality that was well balanced in 45 measured baseline covariates (pg. 34, col. 1, para. 1, lines 5-9). This means that the mathematical model will be well balanced when modeling the relationship between mortality and multiple model parameters. Reid et al. discloses that elevated serum urea was demonstrated as a significant predictor of survival in three studies by multivariate analysis (pg. 24, para. 1, lines 1-2). This means that urea nitrogen level is a significant model parameter for the mathematical model in determining mortality risk. Harrop et al. discloses identifying an early marker of efficacy for the cancer vaccine MVA-5T4, where an antibody response specific for 5T4 is associated with enhanced survival (pg. 14, col. 2, para. [0215], lines 4-10). This means incorporating model parameters from this method into the mathematical model will allow for more effective determination of mortality risk. Lee et al. discloses that the AST/ALT ratio is an easily accessible and relatively inexpensive, yet clinically valuable prognostic factor (pg. e382, col. 1, para. 1, lines 2-4). This means that the ratio of aspartate aminotransferase to alanine aminotransferase enzymatic activity levels is an accessible and less costly model parameter. There is a likelihood of success, since these methods deal with treatment, prognosis, or biomarker analysis in cancer patients and are well known in the field of clinical sciences.
With respect to claim 5:
Yu et al., Kvale et al., Reid et al., Harrop et al., and Lee et al. do not disclose wherein the determination of the mortality risk comprises calculating a numerical score representing the mortality risk.
However, Yoon et al. discloses calculating a mortality risk score for cancer patients by summing expression values of selected miRNAs and covariates weighted by regression coefficients obtained from multivariate Cox regression analyses (pg. 1702, col. 2, para. 3, lines 3-9). This teaches determining mortality risk by calculating a score representing the mortality risk.
With respect to claim 8:
Yu et al., Yoon et al., Reid et al., Harrop et al., and Lee et al. do not disclose wherein the model is formed by performing multivariable Cox regression analysis on the training data for a plurality of subjects, preferably at least 1000 subjects.
However, Kvale et al. discloses determining whether a baseline history of cancer was independently associated with all-cause mortality in a propensity-matched population of community-dwelling older adults, and identifying factors associated with mortality among cancer survivors in this population (pg. 30, col. 2, para. 2, lines 1-5). Also, further discloses using bivariate and multivariable Cox regression models to determine predictors of mortality in 827 participants with history of cancer (pg. 33, col. 1, para. 2, lines 1-3). This teaches performing multivariable Cox regression analysis on patients with history of cancer as training data to model the relationship between the resulting predictors of mortality and the risk of mortality, as seen in Figure 3 (pg. 34, Fig. 3).
With respect to claim 12:
Yoon et al., Kvale et al., Reid et al., Harrop et al., and Lee et al. do not disclose a computer program comprising instructions which, when the program is executed by a computer, cause the computer to carry out the method of claim 1.
However, Yu et al. discloses a computing system that includes one or more processors in communication with the computer memory and configured to execute program instructions (pg. 2, col. 2, para. [0027], lines 10-12). This teaches a computer program configured to execute program instructions.
Yu et al. does not disclose the method of claim 1.
However, Yu et al., Yoon et al., Kvale et al., Reid et al., Harrop et al., and Lee et al. disclose the components of the method of claim 1.
Yu et al. discloses training data involving a set of cancer patients that underwent cancer treatments, which associates results from laboratory tests conducted on the cancer patients and tumor types of the cancer patients with whether the individuals from the set of cancer patients died within a threshold number of weeks from initiation of the cancer treatments (pg. 2, col. 2, para. [0027], lines 2-10; receiving patient data representing information about a cancer patient). Yu et al. discloses multiple clinicopathological markers comprising age, gender, hematocrit (%), hemoglobin (g/L), protein (g/L), albumin (g/L), chloride (mmol/L), sodium (mmol/L), eosinophils/leukocytes (%), lactate dehydrogenase (U/L), and neutrophils/lymphocytes (%) (pg. 1, col. 2, para. [0008]; (i) age, (ii) gender, (iii) haemoglobin or haematocrit level in blood, (vi) protein level in serum or plasma, (vii) level of albumin in serum or plasma, (viii) chloride or sodium level in serum or plasma, (ix) ratio of eosinophils to leukocytes in blood, (x) lactate dehydrogenase enzymatic activity level in serum or plasma, (xiv) ratio of neutrophils to lymphocytes in blood). Also, further discloses performing a blood draw using standard techniques to assess a patient’s clinicopathological markers such as alkaline phosphatase (pg. 9, col. 1, para. [0126]; (v) alkaline phosphatase enzymatic activity level in serum or plasma). Yu et al. discloses that several studies relied upon subjective and investigator-dependent parameters such as performance status (i.e., Eastern Cooperative Oncology Group (“ECOG”), Karnfosky index) (pg. 10, col. 2, para. [0146], lines 10-13; (xiii) Eastern cooperative oncology group (ECOG) performance status).
Yu et al. does not disclose using a mathematical model of mortality risk to determine a risk of mortality of the cancer patient based on the received patient data.
However, Yoon et al. discloses calculating a mortality risk score for cancer patients by summing expression values of selected miRNAs and covariates weighted by regression coefficients obtained from multivariate Cox regression analyses (pg. 1702, col. 2, para. 3, lines 3-9). Also, further discloses the prognostic model as consisting of miRNAs, TNM stage, and histologic grade (pg. 1706, col. 2, para. 2, lines 1-11). This teaches a mathematical model for calculating mortality risk of cancer patients based on patient data.
Yu et al. and Yoon et al. do not disclose outputting the determined risk of mortality, wherein: the mathematical model models a relationship determined from training data between the risk of mortality and values of at least the following model parameters.
However, Kvale et al. discloses determining whether a baseline history of cancer was independently associated with all-cause mortality in a propensity-matched population of community-dwelling older adults, and identifying factors associated with mortality among cancer survivors in this population (pg. 30, col. 2, para. 2, lines 1-5). Also, further discloses using bivariate and multivariable Cox regression models to determine predictors of mortality in 827 participants with history of cancer (pg. 33, col. 1, para. 2, lines 1-3). This teaches that patients with history of cancer were used as training data for modeling a relationship between the predictors of mortality and the risk of mortality, as seen in Figure 3 (pg. 34, Fig. 3).
Yu et al., Yoon et al., and Kvale et al. do not disclose (iv) urea nitrogen level in serum or plasma.
However, Reid et al. discloses serum urea/blood urea nitrogen in Table 3. Prognostic biomarkers subdivided by evidence based medicine modified GRADE criteria (pg. 21, Table 3). This teaches urea nitrogen level in serum or plasma.
Yu et al., Yoon et al., Kvale et al., and Reid et al. do not disclose the following model parameters:
(xi) heart rate;
(xii) systolic blood pressure;
(xvi) TNM classification of tumor stage.
However, Harrop et al. discloses collecting vital signs including systolic and diastolic blood pressures (mmHg) and heart rate (bpm) (pg. 40, col. 1, para. [0713], lines 1-3; (xi) heart rate, (xii) systolic blood pressure). Also, further discloses cancers being staged using the TNM Classification of Malignant Tumors (TNM) (pg. 18, col. 2, para. [0265], lines 10-18; (xvi) TNM classification of tumor stage).
Yu et al., Yoon et al., Kvale et al., Reid et al., and Harrop et al. do not disclose (xv) ratio of aspartate aminotransferase enzymatic activity level in serum or plasma to alanine aminotransferase enzymatic activity level in serum or plasma.
However, Lee et al. discloses a Kaplan-Meier analysis of progression-free, overall, and cancer-specific survival based on the AST/ALT ratio among 583 patients after surgical treatment for upper tract urothelial cancer (pg. e382, Figure 1). This teaches a ratio of aspartate aminotransferase (AST) enzymatic activity level in serum or plasma to alanine aminotransferase (ALT) enzymatic activity level in serum or plasma.
Claims 2-4 are rejected under 35 U.S.C. 103 as being unpatentable over Yu et al. [US20200126636A1], Yoon et al. (Head & Neck, 2020, 42(8), 1699-1712), Kvale et al. (Cancer Epidemiology, 2010, 35(1), 30-36), Reid et al. (PLOS One, 2017, 12(4), 1-31), Harrop et al. [US20160195554A1], and Lee et al. (Clinical Genitourinary Cancer, 2016, 15(3), e379-e385) as applied to claims 1, 5, 8, and 12 above, in view of Vold et al. (BMC Pulmonary Medicine, 2015, 15(9), 1-12).
Yu et al., Yoon et al., Kvale et al., Reid et al., Harrop et al., and Lee et al. are applied to claims 1, 5, 8, and 12 above.
With respect to claim 2:
Yu et al., Yoon et al., Reid et al., Harrop et al., and Lee et al. do not disclose wherein the mathematical model models the relationship determined from training data between the risk of mortality and values of the model parameters (i)-(xvi) and at least the following parameters.
However, Kvale et al. discloses determining whether a baseline history of cancer was independently associated with all-cause mortality in a propensity-matched population of community-dwelling older adults, and identifying factors associated with mortality among cancer survivors in this population (pg. 30, col. 2, para. 2, lines 1-5). Also, further discloses using bivariate and multivariable Cox regression models to determine predictors of mortality in 827 participants with history of cancer (pg. 33, col. 1, para. 2, lines 1-3). This teaches that patients with history of cancer were used as training data for modeling a relationship between the predictors of mortality and the risk of mortality, as seen in Figure 3 (pg. 34, Fig. 3).
Yoon et al., Kvale et al., Reid et al., Harrop et al., and Lee et al. do not disclose the following model parameters:
(xvii) smoking history;
(xviii) number of metastatic sites;
(xix) platelet level in blood;
(xx) calcium level in serum or plasma;
(xxi) glucose level in blood;
(xxii) ratio of lymphocytes to leukocytes in blood;
(xxiii) level of bilirubin in serum or plasma;
(xxiv) level of monocytes in blood;
(xxvi) body mass index.
However, Yu et al. discloses smoking status (pg. 17, col. 1, para. [0212], lines 6-9; (xvii) smoking history). Also, further discloses number of metastatic sites (pg. 11, col. 1, para. [0148], lines 1-7; pg. 14, col. 1, para. [0181], Table 2; (xviii) number of metastatic sites). Yu et al. discloses multiple clinicopathological markers comprising platelets (
10
3
/μL), calcium (mmol/L), glucose (mmol/L), lymphocytes/leukocytes (%), bilirubin (μmol/L), and monocytes (
10
3
/μL) (pg. 1, col. 2, para. [0008]; (xix) platelet level in blood, (xx) calcium level in serum or plasma, (xxi) glucose level in blood, (xxii) ratio of lymphocytes to leukocytes in blood, (xxiii) level of bilirubin in serum or plasma, (xxiv) level of monocytes in blood). Yu et al. also discloses body mass index (pg. 6, col. 2, para. [0086], lines 8-9; (xxvi) body mass index).
Yu et al., Yoon et al., Kvale et al., Reid et al., Harrop et al., and Lee et al. do not disclose (xxv) level of oxygen saturation in arterial blood.
However, Vold et al. discloses recording Sp
O
2
values, which is arterial oxygen saturation (pg. 5, col. 1, para. 1, line 1, Table 1). This teaches level of oxygen saturation in arterial blood.
It would have been prima facie obvious to one of ordinary skill in the art to combine the method of determining a risk of mortality disclosed by Yu et al., Yoon et al., Kvale et al., Reid et al., Harrop et al., and Lee et al. with the level of arterial oxygen saturation disclosed by Vold et al. One would be motivated to combine determining a risk of mortality with oxygen saturation levels in arterial blood because Vold et al. discloses that the frequency of death due to pulmonary diseases increased by decreasing Sp
O
2
: 104 out of 4563 (2.3%) participants with baseline Sp
O
2
> 96%, 45 out of 537 (8.4%) with Sp
O
2
93-95%, and 12 out of 53 (22.6%) with Sp
O
2
≤
92% (pg. 5, col. 1, para. 3, lines 7-11). This means that the risk of mortality is significantly associated with oxygen saturation levels in arterial blood. There is a likelihood of success, since all methods deal with treatment, prognosis, or biomarker analysis in cancer patients and are well known in the field of clinical sciences.
With respect to claim 3:
Yu et al., Yoon et al., Reid et al., Harrop et al., Lee et al., and Vold et al. do not disclose wherein the mathematical model models the relationship determined from training data between the risk of mortality and values of the model parameters (i)-(xxvi) and at least the following parameter.
However, Kvale et al. discloses determining whether a baseline history of cancer was independently associated with all-cause mortality in a propensity-matched population of community-dwelling older adults, and identifying factors associated with mortality among cancer survivors in this population (pg. 30, col. 2, para. 2, lines 1-5). Also, further discloses using bivariate and multivariable Cox regression models to determine predictors of mortality in 827 participants with history of cancer (pg. 33, col. 1, para. 2, lines 1-3). This teaches that patients with history of cancer were used as training data for modeling a relationship between the predictors of mortality and the risk of mortality, as seen in Figure 3 (pg. 34, Fig. 3).
Yoon et al., Kvale et al., Reid et al., Harrop et al., Lee et al., and Vold et al. do not disclose (xxvii) alanine aminotransferase enzymatic activity level in serum or plasma.
However, Yu et al. discloses multiple clinicopathological markers comprising alanine amino transferase (U/L) (pg. 1, col. 2, para. [0008]). This teaches alanine aminotransferase enzymatic activity level in serum or plasma.
With respect to claim 4:
Yu et al., Yoon et al., Reid et al., Harrop et al., Lee et al., and Vold et al. do not disclose wherein the mathematical model models the relationship determined from training data between the risk of mortality and values of the model parameters (i)-(xxvii) and at least the following parameters.
However, Kvale et al. discloses determining whether a baseline history of cancer was independently associated with all-cause mortality in a propensity-matched population of community-dwelling older adults, and identifying factors associated with mortality among cancer survivors in this population (pg. 30, col. 2, para. 2, lines 1-5). Also, further discloses using bivariate and multivariable Cox regression models to determine predictors of mortality in 827 participants with history of cancer (pg. 33, col. 1, para. 2, lines 1-3). This teaches that patients with history of cancer were used as training data for modeling a relationship between the predictors of mortality and the risk of mortality, as seen in Figure 3 (pg. 34, Fig. 3).
Yoon et al., Kvale et al., Reid et al., Harrop et al., Lee et al., and Vold et al. do not disclose (xxviii) level of eosinophils in blood.
However, Yu et al. discloses multiple clinicopathological markers comprising eosinophils (
10
3
/μL) (pg. 1, col. 2, para. [0008]). This teaches a level of eosinophils in blood.
Yu et al., Yoon et al., Kvale et al., Reid et al., Lee et al., and Vold et al. do not disclose (xxix) diastolic blood pressure.
However, Harrop et al. discloses collecting vital signs including systolic and diastolic blood pressures (mmHg) and heart rate (bpm) (pg. 40, col. 1, para. [0713], lines 1-3). This teaches diastolic blood pressure.
Claim 6 is rejected under 35 U.S.C. 103 as being unpatentable over Yu et al. [US20200126636A1], Yoon et al. (Head & Neck, 2020, 42(8), 1699-1712), Kvale et al. (Cancer Epidemiology, 2010, 35(1), 30-36), Reid et al. (PLOS One, 2017, 12(4), 1-31), Harrop et al. [US20160195554A1], and Lee et al. (Clinical Genitourinary Cancer, 2016, 15(3), e379-e385) as applied to claims 1, 5, 8, and 12 above, in view of Kemp [US20180197632A1].
Yu et al., Yoon et al., Kvale et al., Reid et al., Harrop et al., and Lee et al. are applied to claims 1, 5, 8, and 12 above.
With respect to claim 6:
Yu et al., Yoon et al., Kvale et al., Reid et al., Harrop et al., and Lee et al. do not disclose wherein the determination of the mortality risk further comprises comparing the calculated score to one or more predetermined threshold values, or to calculated scores for other cancer patients.
However, Kemp discloses comparing generated mortality risk values for each patient to a threshold value (pg. 19, col. 1, para. [0114], lines 8-13). This teaches comparing a calculated score to a threshold value.
It would have been prima facie obvious to one of ordinary skill in the art to modify the method of determining a risk of mortality disclosed by Yu et al., Yoon et al., Kvale et al., Reid et al., Harrop et al., and Lee et al. to incorporate comparing mortality risk to a threshold value disclosed by Kemp. One would be motivated to modify the method to incorporate this comparison because empirical patient information (EPI) systems can be updated in real time as new patient information is being collected through electronic medical records, which increases the overall accuracy and precision of the scoring systems and methods disclosed by Kemp (pg. 18, col. 1, para. [0108], lines 29-35). This means the method of determining mortality risk incorporating the comparison between mortality risk and threshold values will be highly accurate and precise. There is a likelihood of success, since methods dealing with treatment, prognosis, or biomarker analysis in cancer patients and scoring mortality risks in patients are well known techniques in the field of clinical sciences.
Claims 7 and 9 are rejected under 35 U.S.C. 103 as being unpatentable over Yu et al. [US20200126636A1], Yoon et al. (Head & Neck, 2020, 42(8), 1699-1712), Kvale et al. (Cancer Epidemiology, 2010, 35(1), 30-36), Reid et al. (PLOS One, 2017, 12(4), 1-31), Harrop et al. [US20160195554A1], and Lee et al. (Clinical Genitourinary Cancer, 2016, 15(3), e379-e385) as applied to claims 1, 5, 8, and 12 above, in view of Royston et al. (BMC Medical Research Methodology, 2013, 13(33), 1-15).
Yu et al., Yoon et al., Kvale et al., Reid et al., Harrop et al., and Lee et al. are applied to claims 1, 5, 8, and 12 above.
With respect to claim 7:
Yu et al., Yoon et al., Kvale et al., Reid et al., Harrop et al., and Lee et al. do not disclose wherein the model comprises a weighted sum of deviations in the patient data from mean values of the model parameters in the training data, and the model is formed by determining the weightings from the training data.
However, Royston et al. discloses constructing a prognostic index by taking the linear predictor from a Cox model, which is a weighted sum of the variables in the model, where the weights are the regression coefficients (pg. 3, col. 2, para. 4, lines 1-4). Also, further discloses centering the prognostic index on ‘average risk’ by subtracting the mean of -1.32 for all individuals in the validation dataset (pg. 5, col. 1, para. 3, lines 1-5). Royston et al. discloses developing a multivariable Cox model for the Rotterdam (derivation) data, where it is reasonable to consider this model as consisting of its regression coefficients and their covariance matrix (pg. 3, col. 2, para. 1, lines 1-4; pg. 3, col. 2, para. 2, lines 3-5). This teaches a weighted sum of deviations of the variables in the validation data from a mean value in the derivation data, and weightings are determined from the derivation data.
With respect to claim 9:
Yu et al., Kvale et al., Reid et al., Harrop et al., Lee et al., and Royston et al. do not disclose wherein: the model is formed by: assigning a respective weighting,
w
i
, to each of the model parameters.
However, Yoon et al. discloses a prognostic model weighting five covariates by relative contribution (pg. 1706, col. 2, para. 2, lines 1-11). This teaches assigning a weighting to each model parameter.
Yoon et al., Kvale et al., Reid et al., Harrop et al., Lee et al., and Royston et al. do not disclose wherein: the model is formed by: determining a respective mean,
m
i
, of values of each model parameter over the training data.
However, Yu et al. discloses means of values of model parameters such as age, body mass index, and haemoglobin (g/L) (pg. 14, col. 1, para. [0181], Table 2). This teaches determining a mean of values of each model parameter over training data.
Kvale et al., Reid et al., Harrop et al., and Lee et al. do not disclose wherein: the determination of the mortality risk comprises calculating a numerical score according to the following formula:
s
c
o
r
e
=
∑
i
w
i
(
m
i
j
-
m
i
)
where
w
i
is the weighting of the i-th model parameter,
m
i
is the mean of the i-th model parameter, and
m
i
j
is the value of the i-th model parameter for a j-th cancer patient for whom the score is to be calculated.
However, Yoon et al. discloses a prognostic model weighting five covariates by relative contribution (pg. 1706, col. 2, para. 2, lines 1-11). This teaches the weighting of the i-th model parameter.
Yu et al. discloses means of values of model parameters such as age, body mass index, and haemoglobin (g/L) (pg. 14, col. 1, para. [0181], Table 2). This teaches the mean of the i-th model parameter.
Royston et al. discloses constructing a prognostic index by taking the linear predictor from a Cox model, which is a weighted sum of the variables in the model, where the weights are the regression coefficients (pg. 3, col. 2, para. 4, lines 1-4). Also, further discloses centering the prognostic index on ‘average risk’ by subtracting the mean of -1.32 for all individuals in the validation dataset (pg. 5, col. 1, para. 3, lines 1-5). This teaches a weighted sum of deviations of the variables in the validation data from a mean value, where the variables are the values of the i-th model parameter for a j-th cancer patient for whom the score is to be calculated.
It would have been prima facie obvious to one of ordinary skill in the art to modify the method of determining a risk of mortality disclosed by Yu et al., Yoon et al., Kvale et al., Reid et al., Harrop et al., and Lee et al. to incorporate the scoring formula disclosed by Royston et al. One would be motivated to incorporate the scoring formula into the method of determining mortality risk because the evaluation of the Rotterdam model disclosed by Royston et al. came out reasonably satisfactorily, or validated quite well (pg. 13, col. 1, para. 3). This means the scoring formula will be reliable and consistent in the method of determining mortality risk. There is a likelihood of success, since all methods deal with treatment, prognosis, or biomarker analysis in cancer patients and are well known in the field of clinical sciences.
Claim 10 is rejected under 35 U.S.C. 103 as being unpatentable over Etzioni et al. (Urologic Oncology: Seminars and Original Investigations, 2016, 33(3), 122-127), in view of Yu et al. [US20200126636A1], Yoon et al. (Head & Neck, 2020, 42(8), 1699-1712), Kvale et al. (Cancer Epidemiology, 2010, 35(1), 30-36), Reid et al. (PLOS One, 2017, 12(4), 1-31), Harrop et al. [US20160195554A1], and Lee et al. (Clinical Genitourinary Cancer, 2016, 15(3), e379-e385).
With respect to claim 10:
Regarding the recited method of assessing an anti-cancer therapy, comprising determining a risk of mortality of a patient at plural different times while the patient is receiving the anti-cancer therapy by performing the method of claim 1 at each of the plural times, Etzioni et al. discloses determining overall survival of patients in control and treatment groups in 2 month intervals (pg. 123, Fig. 1, lines 1-3). This teaches determining mortality risk of patients at plural different times while patients are receiving treatment.
Regarding the recited analysing the resulting determined risks to determine an efficacy of the anti-cancer therapy, Etzioni et al. discloses comparing overall survival between Panel A and Panel B groups, noting that treatment is equally efficacious from a relative hazards perspective and the two number needed to treats (NNTs) over the same period are still different because the baseline survival is different in both panels (pg. 124, col. 1, para. 1, lines 4-7). This teaches analyzing the determined risks in the plots to determine efficacy of the anti-cancer treatment.
Etzioni et al. does not disclose the method of claim 1, or any of the components recited in the method of claim 1.
However, Yu et al., Yoon et al., Kvale et al., Reid et al., Harrop et al., and Lee et al. disclose the components of the method of claim 1.
Yu et al. discloses training data involving a set of cancer patients that underwent cancer treatments, which associates results from laboratory tests conducted on the cancer patients and tumor types of the cancer patients with whether the individuals from the set of cancer patients died within a threshold number of weeks from initiation of the cancer treatments (pg. 2, col. 2, para. [0027], lines 2-10; receiving patient data representing information about a cancer patient). Yu et al. discloses multiple clinicopathological markers comprising age, gender, hematocrit (%), hemoglobin (g/L), protein (g/L), albumin (g/L), chloride (mmol/L), sodium (mmol/L), eosinophils/leukocytes (%), lactate dehydrogenase (U/L), and neutrophils/lymphocytes (%) (pg. 1, col. 2, para. [0008]; (i) age, (ii) gender, (iii) haemoglobin or haematocrit level in blood, (vi) protein level in serum or plasma, (vii) level of albumin in serum or plasma, (viii) chloride or sodium level in serum or plasma, (ix) ratio of eosinophils to leukocytes in blood, (x) lactate dehydrogenase enzymatic activity level in serum or plasma, (xiv) ratio of neutrophils to lymphocytes in blood). Also, further discloses performing a blood draw using standard techniques to assess a patient’s clinicopathological markers such as alkaline phosphatase (pg. 9, col. 1, para. [0126]; (v) alkaline phosphatase enzymatic activity level in serum or plasma). Yu et al. discloses that several studies relied upon subjective and investigator-dependent parameters such as performance status (i.e., Eastern Cooperative Oncology Group (“ECOG”), Karnfosky index) (pg. 10, col. 2, para. [0146], lines 10-13; (xiii) Eastern cooperative oncology group (ECOG) performance status).
Yu et al. does not disclose using a mathematical model of mortality risk to determine a risk of mortality of the cancer patient based on the received patient data.
However, Yoon et al. discloses calculating a mortality risk score for cancer patients by summing expression values of selected miRNAs and covariates weighted by regression coefficients obtained from multivariate Cox regression analyses (pg. 1702, col. 2, para. 3, lines 3-9). Also, further discloses the prognostic model as consisting of miRNAs, TNM stage, and histologic grade (pg. 1706, col. 2, para. 2, lines 1-11). This teaches a mathematical model for calculating mortality risk of cancer patients based on patient data.
Yu et al. and Yoon et al. do not disclose outputting the determined risk of mortality, wherein: the mathematical model models a relationship determined from training data between the risk of mortality and values of at least the following model parameters.
However, Kvale et al. discloses determining whether a baseline history of cancer was independently associated with all-cause mortality in a propensity-matched population of community-dwelling older adults, and identifying factors associated with mortality among cancer survivors in this population (pg. 30, col. 2, para. 2, lines 1-5). Also, further discloses using bivariate and multivariable Cox regression models to determine predictors of mortality in 827 participants with history of cancer (pg. 33, col. 1, para. 2, lines 1-3). This teaches that patients with history of cancer were used as training data for modeling a relationship between the predictors of mortality and the risk of mortality, as seen in Figure 3 (pg. 34, Fig. 3).
Yu et al., Yoon et al., and Kvale et al. do not disclose (iv) urea nitrogen level in serum or plasma.
However, Reid et al. discloses serum urea/blood urea nitrogen in Table 3. Prognostic biomarkers subdivided by evidence based medicine modified GRADE criteria (pg. 21, Table 3). This teaches urea nitrogen level in serum or plasma.
Yu et al., Yoon et al., Kvale et al., and Reid et al. do not disclose the following model parameters:
(xi) heart rate;
(xii) systolic blood pressure;
(xvi) TNM classification of tumor stage.
However, Harrop et al. discloses collecting vital signs including systolic and diastolic blood pressures (mmHg) and heart rate (bpm) (pg. 40, col. 1, para. [0713], lines 1-3; (xi) heart rate, (xii) systolic blood pressure). Also, further discloses cancers being staged using the TNM Classification of Malignant Tumors (TNM) (pg. 18, col. 2, para. [0265], lines 10-18; (xvi) TNM classification of tumor stage).
Yu et al., Yoon et al., Kvale et al., Reid et al., and Harrop et al. do not disclose (xv) ratio of aspartate aminotransferase enzymatic activity level in serum or plasma to alanine aminotransferase enzymatic activity level in serum or plasma.
However, Lee et al. discloses a Kaplan-Meier analysis of progression-free, overall, and cancer-specific survival based on the AST/ALT ratio among 583 patients after surgical treatment for upper tract urothelial cancer (pg. e382, Figure 1). This teaches a ratio of aspartate aminotransferase (AST) enzymatic activity level in serum or plasma to alanine aminotransferase (ALT) enzymatic activity level in serum or plasma.
It would have been prima facie obvious to one of ordinary skill in the art to combine the method of assessing an anti-cancer therapy disclosed by Etzioni et al. with determining a risk of mortality disclosed by Yoon et al., the mathematical model disclosed by Kvale et al., and model parameters disclosed by Yu et al., Reid et al., Harrop et al., and Lee et al. One would be motivated to combine assessing anti-cancer therapy with model parameters and a mathematical model to determine a risk of mortality because the 5-plex prognostic marker panel disclosed by Yoon et al. demonstrated significantly greater prognostic power with an AUC of 0.83 when combining large tumor size and depth of tumor invasion, and histologic grading (pg. 1710, col. 2, para. 1, lines 7-10). This means that assessing anti-cancer therapy with a mathematical model incorporating this determination of mortality risk will be highly reliable and precise. Kvale et al. discloses that their study is the first report of an association between a history of cancer and mortality that was well balanced in 45 measured baseline covariates (pg. 34, col. 1, para. 1, lines 5-9). This means that the mathematical model used in assessing anti-cancer therapy will be well balanced when modeling the relationship between mortality and multiple model parameters. Reid et al. discloses that elevated serum urea was demonstrated as a significant predictor of survival in three studies by multivariate analysis (pg. 24, para. 1, lines 1-2). This means that urea nitrogen level is a significant model parameter in determining mortality risk for assessing anti-cancer therapy. Harrop et al. discloses identifying an early marker of efficacy for the cancer vaccine MVA-5T4, where an antibody response specific for 5T4 is associated with enhanced survival (pg. 14, col. 2, para. [0215], lines 4-10). This means incorporating model parameters from this method into the mathematical model will allow for more effective determination of mortality risk in assessing anti-cancer therapy. Lee et al. discloses that the AST/ALT ratio is an easily accessible and relatively inexpensive, yet clinically valuable prognostic factor (pg. e382, col. 1, para. 1, lines 2-4). This means that the ratio of aspartate aminotransferase to alanine aminotransferase enzymatic activity levels is an accessible and less costly model parameter. Yu et al. discloses that the F1 prognostic model using clinicopathological markers such as albumin and lactate dehydrogenase predicted patients with high risk of early mortality in the EAGLE trial, making it a powerful predictor of overall survival (OS) (pg. 18, col. 2, para. [0223], lines 1-5; pg. 18, col. 2, para. [0224], lines 1-5). This means model parameters taught by Yu et al. are strong predictors useful for determining risk of mortality in assessing anti-cancer therapy. There is a likelihood of success, since these methods deal with treatment, prognosis, or biomarker analysis in cancer patients and are well known in the field of clinical sciences.
Claim 11 is rejected under 35 U.S.C. 103 as being unpatentable over Jones et al. (European Journal of Cancer, 2018, 103, 176-183), in view of Yu et al. [US20200126636A1], Yoon et al. (Head & Neck, 2020, 42(8), 1699-1712), Kvale et al. (Cancer Epidemiology, 2010, 35(1), 30-36), Reid et al. (PLOS One, 2017, 12(4), 1-31), Harrop et al. [US20160195554A1], and Lee et al. (Clinical Genitourinary Cancer, 2016, 15(3), e379-e385).
With respect to claim 11:
Regarding the recited method of selecting cancer patients for treatment with an anti-cancer therapy, comprising determining a risk of mortality of a candidate patient using the method of claim 1 and using the determining risk to decide whether to select each candidate patient, Jones et al. discloses using the 30-day mortality risk in conjunction with their study’s median survival estimates to assist the patient selection process (pg. 182, col. 2, para. 2, lines 7-13). Also, further discloses determining factors associated with calculated 30-day mortality risks in order to improve patient selection for chemotherapy (pg. 177, col. 1, para. 1, lines 10-19). This teaches determining mortality risks of patients and using the determined risks to decide whether to select a patient.
Jones et al. does not disclose determining a risk of mortality of a candidate patient using the method of claim 1, or any of the components recited in the method of claim 1.
However, Yu et al., Yoon et al., Kvale et al., Reid et al., Harrop et al., and Lee et al. disclose the components of the method of claim 1.
Yu et al. discloses training data involving a set of cancer patients that underwent cancer treatments, which associates results from laboratory tests conducted on the cancer patients and tumor types of the cancer patients with whether the individuals from the set of cancer patients died within a threshold number of weeks from initiation of the cancer treatments (pg. 2, col. 2, para. [0027], lines 2-10; receiving patient data representing information about a cancer patient). Yu et al. discloses multiple clinicopathological markers comprising age, gender, hematocrit (%), hemoglobin (g/L), protein (g/L), albumin (g/L), chloride (mmol/L), sodium (mmol/L), eosinophils/leukocytes (%), lactate dehydrogenase (U/L), and neutrophils/lymphocytes (%) (pg. 1, col. 2, para. [0008]; (i) age, (ii) gender, (iii) haemoglobin or haematocrit level in blood, (vi) protein level in serum or plasma, (vii) level of albumin in serum or plasma, (viii) chloride or sodium level in serum or plasma, (ix) ratio of eosinophils to leukocytes in blood, (x) lactate dehydrogenase enzymatic activity level in serum or plasma, (xiv) ratio of neutrophils to lymphocytes in blood). Also, further discloses performing a blood draw using standard techniques to assess a patient’s clinicopathological markers such as alkaline phosphatase (pg. 9, col. 1, para. [0126]; (v) alkaline phosphatase enzymatic activity level in serum or plasma). Yu et al. discloses that several studies relied upon subjective and investigator-dependent parameters such as performance status (i.e., Eastern Cooperative Oncology Group (“ECOG”), Karnfosky index) (pg. 10, col. 2, para. [0146], lines 10-13; (xiii) Eastern cooperative oncology group (ECOG) performance status).
Yu et al. does not disclose using a mathematical model of mortality risk to determine a risk of mortality of the cancer patient based on the received patient data.
However, Yoon et al. discloses calculating a mortality risk score for cancer patients by summing expression values of selected miRNAs and covariates weighted by regression coefficients obtained from multivariate Cox regression analyses (pg. 1702, col. 2, para. 3, lines 3-9). Also, further discloses the prognostic model as consisting of miRNAs, TNM stage, and histologic grade (pg. 1706, col. 2, para. 2, lines 1-11). This teaches a mathematical model for calculating mortality risk of cancer patients based on patient data.
Yu et al. and Yoon et al. do not disclose outputting the determined risk of mortality, wherein: the mathematical model models a relationship determined from training data between the risk of mortality and values of at least the following model parameters.
However, Kvale et al. discloses determining whether a baseline history of cancer was independently associated with all-cause mortality in a propensity-matched population of community-dwelling older adults, and identifying factors associated with mortality among cancer survivors in this population (pg. 30, col. 2, para. 2, lines 1-5). Also, further discloses using bivariate and multivariable Cox regression models to determine predictors of mortality in 827 participants with history of cancer (pg. 33, col. 1, para. 2, lines 1-3). This teaches that patients with history of cancer were used as training data for modeling a relationship between the predictors of mortality and the risk of mortality, as seen in Figure 3 (pg. 34, Fig. 3).
Yu et al., Yoon et al., and Kvale et al. do not disclose (iv) urea nitrogen level in serum or plasma.
However, Reid et al. discloses serum urea/blood urea nitrogen in Table 3. Prognostic biomarkers subdivided by evidence based medicine modified GRADE criteria (pg. 21, Table 3). This teaches urea nitrogen level in serum or plasma.
Yu et al., Yoon et al., Kvale et al., and Reid et al. do not disclose the following model parameters:
(xi) heart rate;
(xii) systolic blood pressure;
(xvi) TNM classification of tumor stage.
However, Harrop et al. discloses collecting vital signs including systolic and diastolic blood pressures (mmHg) and heart rate (bpm) (pg. 40, col. 1, para. [0713], lines 1-3; (xi) heart rate, (xii) systolic blood pressure). Also, further discloses cancers being staged using the TNM Classification of Malignant Tumors (TNM) (pg. 18, col. 2, para. [0265], lines 10-18; (xvi) TNM classification of tumor stage).
Yu et al., Yoon et al., Kvale et al., Reid et al., and Harrop et al. do not disclose (xv) ratio of aspartate aminotransferase enzymatic activity level in serum or plasma to alanine aminotransferase enzymatic activity level in serum or plasma.
However, Lee et al. discloses a Kaplan-Meier analysis of progression-free, overall, and cancer-specific survival based on the AST/ALT ratio among 583 patients after surgical treatment for upper tract urothelial cancer (pg. e382, Figure 1). This teaches a ratio of aspartate aminotransferase (AST) enzymatic activity level in serum or plasma to alanine aminotransferase (ALT) enzymatic activity level in serum or plasma.
It would have been prima facie obvious to one of ordinary skill in the art to combine the method of selecting cancer patients for treatment disclosed by Jones et al. with determining a risk of mortality disclosed by Yoon et al., the mathematical model disclosed by Kvale et al., and model parameters disclosed by Yu et al., Reid et al., Harrop et al., and Lee et al. One would be motivated to combine patient selection with model parameters and a mathematical model to determine a risk of mortality because the 5-plex prognostic marker panel disclosed by Yoon et al. demonstrated significantly greater prognostic power with an AUC of 0.83 when combining large tumor size and depth of tumor invasion, and histologic grading (pg. 1710, col. 2, para. 1, lines 7-10). This means that selecting cancer patients with a mathematical model incorporating this determination of mortality risk will be highly reliable and precise. Kvale et al. discloses that their study is the first report of an association between a history of cancer and mortality that was well balanced in 45 measured baseline covariates (pg. 34, col. 1, para. 1, lines 5-9). This means that the mathematical model used in selecting cancer patients will be well balanced when modeling the relationship between mortality and multiple model parameters. Reid et al. discloses that elevated serum urea was demonstrated as a significant predictor of survival in three studies by multivariate analysis (pg. 24, para. 1, lines 1-2). This means that urea nitrogen level is a significant model parameter in determining mortality risk for selecting cancer patients. Harrop et al. discloses identifying an early marker of efficacy for the cancer vaccine MVA-5T4, where an antibody response specific for 5T4 is associated with enhanced survival (pg. 14, col. 2, para. [0215], lines 4-10). This means incorporating model parameters from this method into the mathematical model will allow for more effective determination of mortality risk in selecting cancer patients. Lee et al. discloses that the AST/ALT ratio is an easily accessible and relatively inexpensive, yet clinically valuable prognostic factor (pg. e382, col. 1, para. 1, lines 2-4). This means that the ratio of aspartate aminotransferase to alanine aminotransferase enzymatic activity levels is an accessible and less costly model parameter. Yu et al. discloses that the F1 prognostic model using clinicopathological markers such as albumin and lactate dehydrogenase predicted patients with high risk of early mortality in the EAGLE trial, making it a powerful predictor of overall survival (OS) (pg. 18, col. 2, para. [0223], lines 1-5; pg. 18, col. 2, para. [0224], lines 1-5). This means model parameters taught by Yu et al. are strong predictors useful for determining risk of mortality in selecting cancer patients. There is a likelihood of success, since these methods deal with treatment, prognosis, or biomarker analysis in cancer patients and are well known in the field of clinical sciences.
Conclusion
No claims are allowed.
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/J.N.L./Examiner, Art Unit 1686
/LARRY D RIGGS II/Supervisory Patent Examiner, Art Unit 1686
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