DETAILED ACTION
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on Jul 22 2026 has been entered.
Applicant’s response, filed Jul 22 2026, has been fully considered. Rejections and/or objections not reiterated from previous Office Actions are hereby withdrawn. The following rejections and/or objections are either reiterated or newly applied. They constitute the complete set presently being applied to the instant application.
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 .
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 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.
Claim Status
Claims 23-34, 36-44, and 46 are pending.
Claims 1-22, 35, and 45 are canceled.
Claims 25-26 are objected to.
Claims 23-34, 36-44, and 46 are rejected.
Priority
This application is a CON of 15/493,722, filed Apr 21 2017 (now USP 11,107,217), which claims priority to PRO 62/325,764, filed Apr 21 2016, and PRO 62/484,034, filed Apr 11 2017.
Accordingly, each of claims 23-34, 36-44, and 46 are afforded the effective filing date of Apr 21 2016.
Claim Objections
Unless otherwise noted, the outstanding objections to the claims are withdrawn in view of the amendments submitted herein.
The claims are objected to because of the following informalities. The instant objection is maintained from the previous Office Actions.
In claims 25-26(a), “when differing levels of neovascularization is” and “when similar levels of neovascularization is” should both be amended to recite “are” instead of “is”.
In claims 25-26(a) should be amended to recite “EGFR-positive mutation status” to maintain consistent claim language.
Claim Rejections- 35 USC § 112
Unless otherwise stated, the outstanding rejections to the claims are withdrawn in view of the amendments submitted herein.
35 USC § 112(b)
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
Claim 38 is rejected under 35 U.S.C. 112(b) as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor, regards as the invention. The instant rejection is maintained from the previous Office Action.
Claim 38 recites “wherein, during said performing operation, imaging data of at least separate first and second regions of interest (ROIs) within the in vivo peritumoral edematous tissue are analyzed and compared to determine the level of spatial heterogeneity or similarity therebetween”. However, Claim 23, from which claim 38 depends, already recites a separate “analyzing” step of “the MRI data corresponding to the separate first and second ROIs to determine a level of spatial heterogeneity or similarity therebetween”. It is therefore not clear if claim 38 intends to further limit the performing operation such that a prior analyzing and comparing step is required in addition to the analyzing step already recited in claim 23, or if claim 38 fails to further limit claim 23 because it intends to recite the same step as is already recited therein. For compact examination, any art reading on comparing ROIs will be considered relevant. The rejection may be overcome by clarifying the scope of the claim.
Response to Applicant Arguments
Applicant has provided no arguments regarding the 35 USC 112(b) rejection of claim 38. It is accordingly maintained.
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 23-24, 26-34, and 36-44 are rejected under 35 U.S.C. 101 because the claimed invention is directed to one or more judicial exceptions without significantly more. Any newly recited portions are necessitated by claim amendment.
MPEP 2106 organizes judicial exception analysis into Steps 1, 2A (Prongs One and Two) and 2B as follows below. MPEP 2106 and the following USPTO website provide further explanation and case law citations: uspto.gov/patent/laws-and-regulations/examination-policy/examination-guidance-and-training-materials.
Framework with which to Evaluate Subject Matter Eligibility:
Step 1: Are the claims directed to a process, machine, manufacture, or composition of matter;
Step 2A, Prong One: Do the claims recite a judicially recognized exception, i.e. a law of nature, a natural phenomenon, or an abstract idea;
Step 2A, Prong Two: If the claims recite a judicial exception under Prong One, then is the judicial exception integrated into a practical application (Prong Two); and
Step 2B: If the claims do not integrate the judicial exception, do the claims provide an inventive concept.
Framework Analysis as Pertains to the Instant Claims:
Step 1
With respect to Step 1: yes, the claims are directed to methods, a non-transitory computer-readable storage medium, and a system, i.e., a process, machine, or manufacture within the above 101 categories [Step 1: YES; See MPEP § 2106.03].
Step 2A, Prong One
With respect to Step 2A, Prong One, the claims recite judicial exceptions in the form of abstract ideas. The MPEP at 2106.04(a)(2) further explains that abstract ideas are defined as:
mathematical concepts (mathematical formulas or equations, mathematical relationships and mathematical calculations);
certain methods of organizing human activity (fundamental economic practices or principles, managing personal behavior or relationships or interactions between people); and/or
mental processes (procedures for observing, evaluating, analyzing/ judging and organizing information).
The claims also recite a law of nature or a natural phenomenon. The MPEP at 2106.04(b) further explains that laws of nature and natural phenomena include naturally occurring principles/relations and nature-based products that are naturally occurring or that do not have markedly different characteristics compared to what occurs in nature.
With respect to the instant claims, under the Step 2A, Prong One evaluation, the claims are found to recite abstract ideas that fall into the grouping of mental processes (in particular procedures for observing, analyzing and organizing information) and mathematical concepts (in particular mathematical relationships and formulas) as well as a law of nature or a natural phenomenon are as follows:
Independent claims 23-24: performing quantitative pattern analysis of magnetic resonance imaging (MRI) data corresponding to MRI of peritumoral edematous tissue of a patient suspected of having a neoplasm associated with an EGFR mutation to determine a level of spatial heterogeneity or similarity within the peritumoral edematous tissue comprising identifying separate, non-overlapping first region of interest (ROI) and a second ROI within the peritumoral edematous tissue;
analyzing the MRI data corresponding to the separate first and second ROIs to determine a level of spatial heterogeneity or similarity therebetween, wherein the MRI data comprises dynamic susceptibility contrast material-enhanced magnetic resonance imaging (DSC-MRI) data, dynamic contrast enhanced (DCE) MRI perfusion image data, T1-weighted (pre- and post- contrast) data, T2-weighted (pre- and post-contrast) data, and/or T2-weighted fluid- attenuated inversion recovery (T2-FLAIR) data; and
assigning EGFR mutation status as one of negative or positive based on the level of spatial heterogeneity or similarity determined during said analyzing step, wherein EGFR - negative mutation status is assigned when differing levels of neovascularization are determined to exist between said first and second ROIs and EGFR -positive status is assigned when similar levels of neovascularization are determined to exist in said first and second ROIs.
Dependent claims 26-34 and 36-44 recite further steps that limit the judicial exceptions in independent claims 1 and 12 and, as such, also are directed to those abstract ideas. For example, claims 26 and 36 further limit the EGFR mutation; claims 27 and 39 further limit the location of the ROIs; claims 28, 30, and 41 further limit the type of MRI data being analyzed in claims 23-24; claims 29 and 37 further limits the performing step to determining perfusion temporal dynamics between said first and second ROIs is determined via a time-series of MRI data; claims 31-32, 40, and 42 and further limit the performing step to including applying the multi-variance statistical procedure of principal component analysis; claims 33-34 and 43-44 further limit the performing step to including measuring separability between the ROIs using a Bhattacharyya coefficient analysis; claim 35 repeats the conditions of claim 24 for assigning EGFR negative or positive mutation status; and claim 38 further limits performing the quantitative pattern analysis step of claim 23.
The abstract ideas recited in the claims are evaluated under the Broadest Reasonable Interpretation (BRI) and determined to each cover performance either in the mind and/or by mathematical operation because the method only requires a user to manually determine EGFR mutation status based on MRI data. Without further detail as to the methodology involved in “identifying” ROIs, “performing quantitative pattern analysis” or “analyzing” the ROIs, and “assigning” mutation status, under the BRI, one may simply, for example, receive data by hand and use pen and paper to identify regions adjacent to and further away from the tumor, determine the level of heterogeneity or similarity between the regions, and, based on that heterogeneity or similarity, assign EGFR mutation status. Some of these steps and those recited in the dependent claims, such as using a multi-variance statistical approach such as principal component analysis to perform the quantitative pattern analysis or analyzing step, require mathematical techniques as the only supported embodiments, as is disclosed in the specification as published at: [0037; 0065; 0080; 0112].
The claims also recite a natural relationship between the spatial heterogeneity or similarity between regions of peritumoral edematous tissue and the mutation status of EGFR. Therefore, the claims recite a law of nature or a natural phenomenon (see MPEP 2106.04(b)).
Therefore, claims 23-24 and those claims dependent therefrom recite an abstract idea and a law of nature/natural phenomenon [Step 2A, Prong 1: YES; See MPEP § 2106.04].
Step 2A, Prong Two
Because the claims do recite judicial exceptions, direction under Step 2A, Prong Two, provides that the claims must be examined further to determine whether they integrate the judicial exceptions into a practical application (MPEP 2106.04(d)). A claim can be said to integrate a judicial exception into a practical application when it applies, relies on, or uses the judicial exception in a manner that imposes a meaningful limit on the judicial exception. This is performed by analyzing the additional elements of the claim to determine if the judicial exceptions are integrated into a practical application (MPEP 2106.04(d).I.; MPEP 2106.05(a-h)). If the claim contains no additional elements beyond the judicial exceptions, the claim is said to fail to integrate the judicial exceptions into a practical application (MPEP 2106.04(d).III).
Additional elements, Step 2A, Prong Two
With respect to the instant recitations, the claims recite the following additional elements:
The claims include non-abstract computing elements. For example, independent claim 23 includes a non-transitory computer-readable storage medium comprising stored instructions which, when executed by one or more computer processors, cause the one or more computer processors to perform a method; independent claim 24 includes a system comprising at least one processor configured to perform a method.
Considerations under Step 2A, Prong Two
With respect to Step 2A, Prong Two, steps directed to additional non-abstract computing elements do not describe any specific computational steps by which the “computer parts” perform or carry out the judicial exceptions, nor do they provide any details of how specific structures of the computer, such as the computer-readable recording media, are used to implement these functions. The claims state nothing more than a generic computer which performs the functions that constitute the judicial exceptions. Hence, these are mere instructions to apply the judicial exceptions using a computer, and therefore the claim does not integrate that judicial exceptions into a practical application. The courts have weighed in and consistently maintained that when, for example, a memory, display, processor, machine, etc.… are recited so generically (i.e., no details are provided) that they represent no more than mere instructions to apply the judicial exception on a computer, and these limitations may be viewed as nothing more than generally linking the use of the judicial exception to the technological environment of a computer (MPEP 2106.05(f)).
The specification as published discloses that the calculated index contributes to precision medicine in order to provide an improvement at [0080], but does not provide an explanation for how the additional elements provide these improvements. Therefore, the additional elements do not clearly improve the functioning of a computer, or comprise an improvement to any other technical field. Further, the additional elements do not clearly affect a particular treatment; they do not clearly require or set forth a particular machine; they do not clearly effect a transformation of matter; nor do they clearly provide a nonconventional or unconventional step (MPEP2106.04(d)).
Thus, none of the claims recite additional elements which would integrate a judicial exception into a practical application, and the claims are directed to one or more judicial exceptions [Step 2A, Prong 2: NO; See MPEP § 2106.04(d)].
Step 2B (MPEP 2106.05.A i-vi)
According to analysis so far, the additional elements described above do not provide significantly more than the judicial exception. A determination of whether additional elements provide significantly more also rests on whether the additional elements or a combination of elements represents other than what is well-understood, routine, and conventional. Conventionality is a question of fact and may be evidenced as: a citation to an express statement in the specification or to a statement made by an applicant during prosecution that demonstrates a well-understood, routine or conventional nature of the additional element(s); a citation to one or more of the court decisions as discussed in MPEP 2106(d)(II) as noting the well-understood, routine, conventional nature of the additional element(s); a citation to a publication that demonstrates the well-understood, routine, conventional nature of the additional element(s); and/or a statement that the examiner is taking official notice with respect to the well-understood, routine, conventional nature of the additional element(s).
With respect to the instant claims, the prior art review to Lemee et al. (Neuro-Oncology, 2015, 17(10):1322-1332; cited on the Oct 26 2021 IDS) discloses that acquiring MRI data consisting of DCE, T1, T2, and T2-FLAIR data, is a data gathering element that is routine, well-understood and conventional in the art. Said portions of the prior art are, for example, (p. 1322, col. 2, par. 2; p. 1326, col. 1, par. 1; Table 1). As such, the claims simply append well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception (MPEP2106.05(d)). The data gathering steps as recited in the instant claims constitute a general link to a technological environment which is insufficient to constitute an inventive concept which would render the claims significantly more than the judicial exception (MPEP2106.05(g)&(h)).
With respect to claims 23-24 and those claims dependent therefrom, the computer-related elements or the general purpose computer do not rise to the level of significantly more than the judicial exception. The claims state nothing more than a generic computer which performs the functions that constitute the judicial exceptions. Hence, these are mere instructions to apply the judicial exceptions using a computer, which the courts have found to not provide significantly more when recited in a claim with a judicial exception (Alice Corp., 573 U.S. at 225-26, 110 USPQ2d at 1984; see MPEP 2106.05(A)). The specification as published also notes that computer processors and systems, as example, are commercially available or widely used at [0038-0039; 0090]. The additional elements are set forth at such a high level of generality that they can be met by a general purpose computer. Therefore, the computer components constitute no more than a general link to a technological environment, which is insufficient to constitute an inventive concept that would render the claims significantly more than the judicial exceptions (see MPEP 2106.05(b)I-III).
Taken alone, the additional elements do not amount to significantly more than the above-identified judicial exception(s). Even when viewed as a combination, the additional elements fail to transform the exception into a patent-eligible application of that exception. Thus, the claims as a whole do not amount to significantly more than the exception itself [Step 2B: NO; See MPEP § 2106.05].
Therefore, instant claims 23-24 and 26-45 are not drawn to eligible subject matter as they are directed to one or more judicial exceptions without significantly more. For additional guidance, applicant is directed generally to the MPEP § 2106.
Response to Applicant Arguments
At p. 9, Applicant submits that claims contain limitations and language regarding steps which cannot practically be performed in the human mind and which are required to be executed on a processor. Further, each of the claims recite that the steps are performed on a process. Thus, Applicant submits that Step 2A Prong A is not met and that this is sufficient to establish subject matter eligibility for the claimed subject matter.
It is respectfully submitted that this is not persuasive. The arguments are not persuasive because they do not present any factual arguments pertaining to the rejection. Applicant has offered no reasoning that or specific arguments why a human mind cannot perform the steps of “performing quantitative pattern analysis”, “analyzing the MRI data”, or “assigning EGFR mutation status”, which are identified as judicial exceptions in the claims. As set forth in the above rejection, it is considered that one could use pen and paper to identify regions adjacent to and further away from the tumor, determine the level of heterogeneity or similarity between the regions, and, based on that heterogeneity or similarity, assign EGFR mutation status.
Further, as the claims are considered to recite an abstract idea, the performance of those abstract ideas on a computer or a processor does not does not negate the mental nature of said limitations. If a claim recites a limitation that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper, the limitation falls within the mental processes grouping, and the claim recites an abstract idea. See, e.g., Benson, 409 U.S. at 67, 65, 175 USPQ at 674-75, 674 (noting that the claimed "conversion of [binary-coded decimal] numerals to pure binary numerals can be done mentally," i.e., "as a person would do it by head and hand."); Synopsys, 839 F.3d at 1139, 120 USPQ2d at 1474 (holding that claims to the mental process of "translating a functional description of a logic circuit into a hardware component description of the logic circuit" are directed to an abstract idea, because the claims "read on an individual performing the claimed steps mentally or with pencil and paper"). The courts do not distinguish between mental processes that are performed entirely in the human mind and mental processes that require a human to use a physical aid (e.g., pen and paper or a slide rule) to perform the claim limitation (See MPEP 2104(a)(2), III: e.g., Benson, 409 U.S. at 67, 65, 175 USPQ at 674-75, 674 (noting that the claimed "conversion of [binary-coded decimal] numerals to pure binary numerals can be done mentally," i.e., "as a person would do it by head and hand."); Synopsys, Inc. v. Mentor Graphics Corp., 839 F.3d 1138, 1139, 120 USPQ2d 1473, 1474 (Fed. Cir. 2016) (holding that claims to a mental process of "translating a functional description of a logic circuit into a hardware component description of the logic circuit" are directed to an abstract idea, because the claims "read on an individual performing the claimed steps mentally or with pencil and paper")). Nor do the courts distinguish between claims that recite mental processes performed by humans and claims that recite mental processes performed on a computer. As the Federal Circuit has explained, "[c]ourts have examined claims that required the use of a computer and still found that the underlying, patent-ineligible invention could be performed via pen and paper or in a person’s mind." Versata Dev. Group v. SAP Am., Inc., 793 F.3d 1306, 1335, 115 USPQ2d 1681, 1702 (Fed. Cir. 2015). See also Intellectual Ventures I LLC v. Symantec Corp., 838 F.3d 1307, 1318, 120 USPQ2d 1353, 1360 (Fed. Cir. 2016) (‘‘[W]ith the exception of generic computer-implemented steps, there is nothing in the claims themselves that foreclose them from being performed by a human, mentally or with pen and paper.’’); Mortgage Grader, Inc. v. First Choice Loan Servs. Inc., 811 F.3d 1314, 1324, 117 USPQ2d 1693, 1699 (Fed. Cir. 2016) (holding that computer-implemented method for "anonymous loan shopping" was an abstract idea because it could be "performed by humans without a computer").
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.
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.
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.
1A. Claims 23-34 and 36-44 are rejected under 35 U.S.C. 103 as being unpatentable over Bakas et al. (Neuro-Oncology, Nov 2015, 17; previously cited) in view of Hu et al. (US 2017/0103525; previously cited). The instant rejection is maintained from the previous Office Action and any newly recited portions are necessitated by claim amendment.
Bakas teaches: Glioblastoma (GBM) is the most common and aggressive adult brain tumor, with 14 months average survival. Epidermal growth factor receptor variant III (EGFRvIII) mutation is an important factor in driving tumor progression and defining prognosis in GBM patients, hence evidence of its presence can affect treatment decisions. The aim of this study is to identify quantitative imaging signatures of EGFRvIII. We used preoperative multi-parametric (T1-Gad, T2-FLAIR, Dynamic-Susceptibility-Contrast) magnetic resonance imaging data from a retrospective cohort of 64 patients (42 EGFRvIII-negative) with de novo GBM. We hypothesized that EGFRvIII-positive tumors, the more aggressive subtype, have a uniformly dense distribution of tumor cells throughout the peritumoral edematous region, as opposed to EGFRvIII-negative tumors, where tumor cell burden decreases farther from the tumor. To assess this peritumoral heterogeneity, we defined one region of interest (ROI) adjacent to the tumor and another at the farthest from the tumor but still within the edematous tissue. Perfusion temporal dynamics of each ROI were summarized via principal component analysis. The Bhattacharyya coefficient was used as a measure of separability (range [0,1]) between the dynamics of the two ROIs, for each patient. Values close to 0 indicate similar perfusion dynamics between the ROIs, which is consistent with uniformly and aggressively infiltrating tumors. Conversely, values close to 1 indicate substantial difference between the two ROIs, which would be consistent with less infiltrative tumors. The distributions of these separability measurements between EGFRvIII-negative and EGFRvIII-positive patients were very highly separable, with median values of 0.48 (Interquartile range: 0.251-0.647) and 0.209 (Interquartile range: 0.064-0.309), respectively. A two-tailed paired t-test confirmed the statistical significance of the results (p-value = 0.00007). These results suggest that discrimination of the EGFRvIII mutation status, which is critical for personalized treatment decisions and response evaluation, can be achieved based solely on assessing the peritumoral heterogeneity on in vivo imaging data.
Bakas is therefore considered to teach claims 23-34 and 36-44 except for assigning EGFR mutation status based on differing or similar levels of neovascularization.
However, the prior art to Hu discloses a system and method for characterizing tissues of a subject using multi-parametric magnetic resonance (MR) imaging (abstract). Hu teaches that EGFR amplification showed highly significant correlations with LBP texture on rCBV maps as well as three separate features on T2W imaging: two texture-based features (on DOST and GLCM) and standard deviation (SD) of raw T2W signal [0120]. Hu teaches that rCBV texture captures the contributions and signal patterns of the individual voxels within that ROI, giving insight to microvascular distribution and heterogeneity, and that that texture of rCBV correlates with EGFR status [0126]. Hu teaches the link between EGFR and T2W signal patterns of tumoral/vasogenic edema has been suggested previously [0126]. Further, Hu teaches that microvessel morphology on perfusion MRI (“pMRI”) may help identify some of the key biophysical characteristics associated with tumor pathogenesis [0006] and performing Dynamic Susceptibility-weighted Contrast-enhanced (“DSC”) Perfusion MRI (“pMRI”) [0056-0058]. The instant specification discloses that “Dynamic Susceptibility Contrast (DSC)-MRI” refers to an MRI capable of assessing cerebral microvasculature” [0040], and that “ the term “neovascularization” refers to the formation of functional microvascular networks with red blood cell perfusion” [0034]. Therefore, it is considered that the method of Hu fairly teaches assigning EGFR status based on differences in neovascularization as instantly claimed, as they measure microvessel morphology differences to assign EGFR status.
Regarding claims 23-45 in view of the rejection over Bakas, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine, in the course of routine experimentation and with a reasonable expectation of success, the methods of Bakas and Hu because each disclose methods for determining spatial heterogeneity around glioblastomas based on EGFR status. The motivation would been to examine vascularization, as taught by Hu [0126].
1B. Claim 46 is rejected under 35 U.S.C. 103 as being unpatentable over Bakas et al. (Neuro-Oncology, Nov 2015, 17; previously cited) in view of Hu et al. (US 2017/0103525; previously cited) and Johnson et al. (Science Translational Medicine, 20157(275), pp.275ra22-275ra22, p. 1-14; newly cited). The instant rejection is newly stated and is necessitated by claim amendment.
Bakas teaches: Glioblastoma (GBM) is the most common and aggressive adult brain tumor, with 14 months average survival. Epidermal growth factor receptor variant III (EGFRvIII) mutation is an important factor in driving tumor progression and defining prognosis in GBM patients, hence evidence of its presence can affect treatment decisions. The aim of this study is to identify quantitative imaging signatures of EGFRvIII. We used preoperative multi-parametric (T1-Gad, T2-FLAIR, Dynamic-Susceptibility-Contrast) magnetic resonance imaging data from a retrospective cohort of 64 patients (42 EGFRvIII-negative) with de novo GBM. We hypothesized that EGFRvIII-positive tumors, the more aggressive subtype, have a uniformly dense distribution of tumor cells throughout the peritumoral edematous region, as opposed to EGFRvIII-negative tumors, where tumor cell burden decreases farther from the tumor. To assess this peritumoral heterogeneity, we defined one region of interest (ROI) adjacent to the tumor and another at the farthest from the tumor but still within the edematous tissue. Perfusion temporal dynamics of each ROI were summarized via principal component analysis. The Bhattacharyya coefficient was used as a measure of separability (range [0,1]) between the dynamics of the two ROIs, for each patient. Values close to 0 indicate similar perfusion dynamics between the ROIs, which is consistent with uniformly and aggressively infiltrating tumors. Conversely, values close to 1 indicate substantial difference between the two ROIs, which would be consistent with less infiltrative tumors. The distributions of these separability measurements between EGFRvIII-negative and EGFRvIII-positive patients were very highly separable, with median values of 0.48 (Interquartile range: 0.251-0.647) and 0.209 (Interquartile range: 0.064-0.309), respectively. A two-tailed paired t-test confirmed the statistical significance of the results (p-value = 0.00007). These results suggest that discrimination of the EGFRvIII mutation status, which is critical for personalized treatment decisions and response evaluation, can be achieved based solely on assessing the peritumoral heterogeneity on in vivo imaging data.
Bakas is therefore considered to teach claims 46 except for assigning EGFR mutation status based on differing or similar levels of neovascularization and an EGFR-targeting therapy that is a chimeric antibody receptor (CAR) T-cell therapy comprising an anti-EGFR targeted component.
However, the prior art to Hu discloses a system and method for characterizing tissues of a subject using multi-parametric magnetic resonance (MR) imaging (abstract). Hu teaches that EGFR amplification showed highly significant correlations with LBP texture on rCBV maps as well as three separate features on T2W imaging: two texture-based features (on DOST and GLCM) and standard deviation (SD) of raw T2W signal [0120]. Hu teaches that rCBV texture captures the contributions and signal patterns of the individual voxels within that ROI, giving insight to microvascular distribution and heterogeneity, and that that texture of rCBV correlates with EGFR status [0126]. Hu teaches the link between EGFR and T2W signal patterns of tumoral/vasogenic edema has been suggested previously [0126]. Further, Hu teaches that microvessel morphology on perfusion MRI (“pMRI”) may help identify some of the key biophysical characteristics associated with tumor pathogenesis [0006] and performing Dynamic Susceptibility-weighted Contrast-enhanced (“DSC”) Perfusion MRI (“pMRI”) [0056-0058]. The instant specification discloses that “Dynamic Susceptibility Contrast (DSC)-MRI” refers to an MRI capable of assessing cerebral microvasculature” [0040], and that “ the term “neovascularization” refers to the formation of functional microvascular networks with red blood cell perfusion” [0034]. Therefore, it is considered that the method of Hu fairly teaches assigning EGFR status based on differences in neovascularization as instantly claimed, as they measure microvessel morphology differences to assign EGFR status.
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine, in the course of routine experimentation and with a reasonable expectation of success, the methods of Bakas and Hu because each disclose methods for determining spatial heterogeneity around glioblastomas based on EGFR status. The motivation would been to examine vascularization, as taught by Hu [0126].
The prior art to Johnson discloses the development and characterization of anti-EGFR variant III chimeric antigen receptor T cells for glioblastoma (title; abstract; entire document is relevant).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine, in the course of routine experimentation and with a reasonable expectation of success, the methods of Bakas in view of Hu and Johnson because each reference discloses methods for examining EGFR mutations. As anti-EGFR variant III chimeric antigen receptor T cells for glioblastoma are a known EGFR-targeting therapy, as taught by Johnson, it would have been obvious to use such a therapy to treat an EGFR positive patient with such a therapy.
2A. Claims 23-24, 27-33, and 37-43 are rejected under 35 U.S.C. 103 as being unpatentable over Hu et al. (US 2017/0103525; previously cited) in view of Akbari et al. (Radiology, 2014, 273(2):502-510; cited on the Oct 26 2021 IDS). The instant rejection is maintained from the previous Office Action and any newly recited portions are necessitated by claim amendment.
The prior art to Hu discloses a system and method for characterizing tissues of a subject using multi-parametric magnetic resonance (MR) imaging (abstract). Hu teaches the instant features as follows.
Claim 23 discloses a non-transitory computer-readable storage medium comprising stored instructions which, when executed by one or more computer processors, cause the one or more computer processors to perform operations of the method. Claim 24 discloses a system for in vivo detection of epidermal growth factor receptor (EGFR) mutation status within peritumoral edematous tissue of a patient suspected of having a neoplasm associated with an EGFR mutation, comprising: at least one processor configured to perform steps of the method.
Hu teaches a computer-implemented method, a computer-readable medium, and a processor programmed for characterizing tissues of a subject using multi-parametric imaging [0009; 0011; 0043].
The steps of claims 23-24 comprise:
performing quantitative pattern analysis of magnetic resonance imaging (MRI) data corresponding to MRI of peritumoral edematous tissue of a patient suspected of having a neoplasm associated with an epidermal growth factor receptor (EGFR) mutation to determine a level of spatial heterogeneity or similarity within the peritumoral edematous tissue comprising identifying separate, non- overlapping first region of interest (ROI) and a second ROI within the peritumoral edematous tissue, wherein the MRI data comprises dynamic susceptibility contrast material-enhanced magnetic resonance imaging (DSC-MRI) data, dynamic contrast enhanced (DCE) MRI perfusion image data, T1-weighted (pre- and post- contrast) data, T2-weighted (pre- and post-contrast) data, and/or T2-weighted fluid- attenuated inversion recovery (T2-FLAIR) data;
Hu teaches selecting at least one region of interest (ROI) in the MR images acquired from a subject, performing a texture analysis on corresponding ROIs, and applying a classification scheme (abstract; [0009-0012; 0029-0040]). Hu teaches that texture features used in texture analysis generally capture different aspects of the spatial distribution of intensities to provide a quantitative indication of image texture (i.e., performing quantitative pattern analysis to determine a level of spatial heterogeneity or similarity) [0036]. Hu teaches examining images taken from the surrounding non-enhancing parenchyma, or Brain Around Tumor (BAT) which is indistinguishable from non-tumor vasogenic edema, in patients with Glioblastoma [0005; 0031; 0054]. Hu is therefore considered to fairly teach examining in vivo peritumoral edematous tissue as instantly claimed. Hu teaches that the set of multi-parametric MR images may include T1-Weighted (“T1W”) images, T2-Weighted (“T2W”) images, diffusion tensor images (“DTI”), perfusion MR images, dynamic susceptibility-weighted contrast-enhanced (“DSC”) perfusion MR images, and other image types [0032]. Hu also teaches T2W/FLAIR MR images [0087].
Hu does not teach separate, non-overlapping first and second ROI’s.
analyzing the MRI data corresponding to the separate first and second ROIs to determine a level of spatial heterogeneity or similarity therebetween; and
Hu does not teach this limitation.
assigning EGFR mutation status as one of negative or positive based on the level of spatial heterogeneity or similarity determined during said analyzing step, wherein EGFR - negative mutation status is assigned when differing levels of neovascularization is determined to exist between said first and second ROIs and EGFR -positive status is assigned when similar levels of neovascularization is determined to exist in said first and second ROIs.
Hu teaches applying a classification scheme to generate a report which indicate regions or locations of high and/or low tumor content, or with specific genetic profiles (i.e., assigning mutation status) [0040]. Hu teaches that in imaging-genetic correlative studies of MR images, EGFR amplification (i.e., EGFR mutation status) showed highly significant correlations with LBP texture on rCBV maps and T2W imaging from the MR images of BAT tissue and edema (Tables 5-6; [0120]). Hu teaches that EGFR amplification showed highly significant correlations with LBP texture on rCBV maps as well as three separate features on T2W imaging: two texture-based features (on DOST and GLCM) and standard deviation (SD) of raw T2W signal [0120]. Hu teaches that rCBV texture captures the contributions and signal patterns of the individual voxels within that ROI, giving insight to microvascular distribution and heterogeneity, and that that texture of rCBV correlates with EGFR status [0126]. Hu teaches the link between EGFR and T2W signal patterns of tumoral/vasogenic edema has been suggested previously [0126]. Further, Hu teaches that microvessel morphology on perfusion MRI (“pMRI”) may help identify some of the key biophysical characteristics associated with tumor pathogenesis [0006] and performing Dynamic Susceptibility-weighted Contrast-enhanced (“DSC”) Perfusion MRI (“pMRI”) [0056-0058]. The instant specification discloses that “Dynamic Susceptibility Contrast (DSC)-MRI” refers to an MRI capable of assessing cerebral microvasculature” [0040], and that “ the term “neovascularization” refers to the formation of functional microvascular networks with red blood cell perfusion” [0034]. Therefore, it is considered that the method of Hu fairly teaches assigning EGFR status based on differences in neovascularization as instantly claimed, as they measure microvessel morphology differences to assign EGFR status.
Hu does not teach separate, non-overlapping first and second ROI’s.
However, the prior art to Akbari discloses an analysis of dynamic susceptibility contrast material–enhanced magnetic resonance (MR) images to uncover unique tissue characteristics in patients with glioblastoma (abstract). Akbari teaches obtaining dynamic susceptibility contrast-enhanced MR imaging data and creating a map of heterogeneity within the peritumoral region to determine a heterogeneity score (abstract; p. 504, col. 1, p.ar 2 through p. 506, col. 2, par. 2; entire document is relevant). Akbari teaches defining several regions of interest, including two near-far ROIs within the edema and/or peritumoral region (p. 504, col. 3, par. 1). Akbari teaches training a model using the near-far ROIs to generate heterogeneity scores (i.e., determine a level of spatial heterogeneity or similarity between separate first and second ROIs) (p. 505, col. 3, par. 2 through p. 506, col. 2, par. 2).
Regarding claims 23-24, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine, in the course of routine experimentation and with a reasonable expectation of success, the methods of Hu and Akbari because both references disclose methods for analyzing spatial heterogeneity in peritumoral samples of glioblastomas. The motivation to compare the near and far ROIs as taught by Akbari using the method of Hu would have been to examine levels of infiltration of the tumor, as taught by Akbari (p. 504, col. 3, par. 1).
Regarding claims 27 and 39, Hu in view of Akbari teaches claims 23-24 as described above. Claims 27 and 39 add that said first ROI within the in vivo peritumoral edematous tissue corresponds to a region of tissue adjacent to an enhancing part of a tumor, and wherein said second ROI corresponds to a separate region of tissue within the in vivo peritumoral edematous tissue located at a location spaced farthest from the enhancing part of the tumor along a periphery of the in vivo peritumoral edematous tissue.
Hu does not teach this limitation.
However, Akbari teaches determining two ROIs within the edema and/or peritumoral region, one immediately adjacent to the enhancing tumor (near) and the other at the distal edema boundary (far) (p. 504, col. 2, par. 1).
Regarding claim 28, Hu in view of Akbari teaches claims 24 and 27 as described above. Claims 28 adds that said first ROI is defined on a contrast-enhanced T1-weighted (T1-CE) MRI during said identifying, and wherein said second ROI is defined on a T2-weighted fluid-attenuated inversion recovery (T2-FLAIR) MRI during said identifying step.
Hu teaches that the set of multi-parametric MR images may include T1-Weighted (“T1W”) images, T2-Weighted (“T2W”) images, diffusion tensor images (“DTI”), perfusion MR images, dynamic susceptibility-weighted contrast-enhanced (“DSC”) perfusion MR images, and other image types [0032]. Hu also teaches T2W/FLAIR MR images [0087]. Hu does not teach two separate ROIs.
However, Akbari teaches determining two ROIs within the edema and/or peritumoral region, one immediately adjacent to the enhancing tumor (near) and the other at the distal edema boundary (far) (p. 504, col. 2, par. 1).
Regarding claim 28, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify, in the course of routine experimentation and with a reasonable expectation of success, the method of Hu in view of Akbari to analyze different types of MRI images between the ROIs because there is a motivation provided by Hu to performing a texture analysis on corresponding ROIs (abstract; [0009-0012; 0029-0040]), and there are a finite number of MRI imaging types to choose from. Therefore, a person of ordinary skill in the art has good reason to pursue the known options within their technical grasps. If this leads to anticipated success, it is likely the product not of innovation but of ordinary skill and common sense.
Regarding claim 29, Hu in view of Akbari teaches claim 24 as described above. The method of claim 29 adds that during said analyzing step, heterogeneity or similarity of perfusion temporal dynamics between said first and second ROIs is determined via a time-series of MRI data.
Hu teaches that the MR images can include a perfusion image (claim 2; [0032; 0044; 0056] and using those images for analysis [0073; 0109], but does not teach determining perfusion temporal dynamics as instantly claimed.
However, the prior art to Akbari discloses an analysis of dynamic susceptibility contrast material–enhanced magnetic resonance (MR) images to uncover unique tissue characteristics in patients with glioblastoma (abstract). Akbari teaches determining temporal dynamics of blood perfusion from MR imaging (p. 504, col. 2, par. 2 through p. 505, col. 3, par. 1).
Regarding claim 30 and 41, Hu in view of Akbari teaches claims 23-24 and 40 as described above. Claims 30 and 41 further add that the MRI data is dynamic susceptibility contrast material-enhanced magnetic resonance imaging (DSC- MRI).
Hu teaches that the MR images may be dynamic susceptibility-weighted contrast-enhanced (“DSC”) perfusion MR images [0032; 0044; 0058; 0109].
Regarding claims 31-32, 40, and 42, Hu in view of Akbari teaches claims 23-24 as described above. Claims 31 and 40 add that said performing step includes applying a multi-variance statistical procedure to the MRI data to determine perfusion temporal dynamics of the first and second ROIs. Claims 32 and 42 add that said multi-variance statistical procedure is Principal Component Analysis (PCA).
Hu teaches using a Principal Component Analysis to reduce the features generated using the texture algorithm [0037; 0047; 0065-0066].
Regarding claims 33 and 43, Hu in view of Akbari teaches claims 23-24 and 40 as described above. Claims 33 and 43 further add that said analyzing operation includes measuring separability between the perfusion temporal dynamics determined for the first and second ROIs, and wherein said assigning operation includes an assignment of EGFR-positive mutation status when the separability is low and indicates that the perfusion temporal dynamics between the first and second ROIs are similar and an assignment of EGFR- negative mutation status when the separability is high and indicates that perfusion temporal dynamics between the first and second ROIs are heterogeneous.
Hu teaches that EGFR amplification showed highly significant correlations with LBP texture on rCBV maps as well as three separate features on T2W imaging: two texture-based features (on DOST and GLCM) and standard deviation (SD) of raw T2W signal [0120]. Hu teaches that rCBV texture captures the contributions and signal patterns of the individual voxels within that ROI, giving insight to microvascular distribution and heterogeneity, and that that texture of rCBV correlates with EGFR status [0126], which is interpreted to mean that high texture in rCBV indicates EGFR-positive status as instantly claimed.
Regarding claim 37, Hu in view of Akbari teaches claim 23 as described above. The method of claim 37 adds that during said performing step, spatial heterogeneity or similarity of perfusion temporal dynamics is determined within the in vivo peritumoral edematous tissue.
Hu teaches that the MR images can include a perfusion image (claim 2; [0032; 0044; 0056] and using those images for analysis [0073; 0109], but does not teach determining perfusion temporal dynamics as instantly claimed.
However, the prior art to Akbari discloses an analysis of dynamic susceptibility contrast material–enhanced magnetic resonance (MR) images to uncover unique tissue characteristics in patients with glioblastoma (abstract). Akbari teaches determining temporal dynamics of blood perfusion from MR imaging (p. 504, col. 2, par. 2 through p. 505, col. 3, par. 1).
Regarding claim 38, Hu in view of Akbari teaches claim 23 as described above. The method of claim 38 adds that during said performing operation, imaging data of at least separate first and second regions of interest (ROIs) within the in vivo peritumoral edematous tissue are analyzed and compared to determine the level of spatial heterogeneity or similarity therebetween, which Akbari is considered to teach as described above (p. 504, col. 3, par. 1) (p. 505, col. 3, par. 2 through p. 506, col. 2, par. 2).
2B. Claims 26 and 36 are rejected under 35 U.S.C. 103 as being unpatentable over Hu in view of Akbari, as applied to claims 23-24 above, and in further view of Tykocinski et al. (Neuro-Oncology, 2012, 14(5):613-623; cited on the Oct 26 2021 IDS). The instant rejection is newly stated and is based upon further consideration of the claims.
Regarding claims 26 and 36, Hu in view of Akbari teaches claims 23-24 as described above. Claims 26 and 36 further add that the mutation in the patient is EGFR variant III (vIII), a point mutation at EGFR A289V, a point mutation at EGFR variant G598, and/or a point mutation at EGFR variant R108, with reference to the residue numbering of SEQ ID NO:1.
Hu teaches detecting EGFR amplification [0120], but does not teach the specific mutations in the patient.
However, the prior art to Tykocinski discloses an assessment of the accuracy of magnetic resonance perfusion-weighted imaging (MR-PWI) in discriminating the EGFRvIII-expressing glioblastoma subtype in patients identified as having either wild-type or EGFRvIII mutations, finding that maximum relative tumor blood volume determined from the MRI had high discrimination accuracy for EGFRvIII samples (abstract; entire document is relevant).
Regarding claims 26 and 36, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine, in the course of routine experimentation and with a reasonable expectation of success, the methods of Hu in view of Akbari and Tykocinski because each reference disclose MRI methods analyzing glioblastomas in relation to EGFR status. As Tykocinski has shown that MRI data can be used to identify EGFRvIII-positive samples, it would have been obvious to one of ordinary skill in the art to use the method of Hu in view of Akbari on patients with EGFRvIII glioblastoma rather than only on patients with amplified EGFRvIII, especially as the instant specification as published discloses that EGFR version III causes constitutive signaling in the absence of ligand binding [0006], which would have a similar effect to EGFR amplification as taught by Hu, with the expectation that the method would be similarly successful.
2C. Claims 34 and 44 are rejected under 35 U.S.C. 103 as being unpatentable over Hu in view of Akbari, as applied to claims 23-24, 33, 40, and 43 above, and in further view of Sumana et al. (IJESR, 2013, 4(1):1350-1354; previously cited). The instant rejection is maintained from the previous Office Action and any newly recited portions are necessitated by claim amendment.
Regarding claims 34 and 44, Hu in view of Akbari teaches claims 23-24, 33, 40, and 43 as described above. Claims 34 and 44 further adds that separability is measured via Bhattacharyya coefficient analysis.
Hu does not teach this limitation.
However, the prior art to Sumana discloses a new region merging based interactive image segmentation method (abstract) which employs the well-known goodness-of-fit statistical metric Bhattacharyya coefficient to define a similarity measure between two regions (p. 1351, par. 4).
Regarding claims 34 and 44, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine, in the course of routine experimentation and with a reasonable expectation of success, the methods of Hu in view of Akbari with Sumana because each reference discloses methods for analyzing MRI data. The motivation to use the Bhattacharyya coefficient as taught by Sumana would have been to use a well-known metric define a similarity measure between two regions, as taught by Sumana (p. 1351, par. 4).
2D. Claims 25 and 46 are rejected under 35 U.S.C. 103 as being unpatentable over Hu et al. (US 2017/0103525; previously cited) in view of Akbari et al. (Radiology, 2014, 273(2):502-510; cited on the Oct 26 2021 IDS), Tykocinski et al. (Neuro-Oncology, 2012, 14(5):613-623; cited on the Oct 26 2021 IDS), and Johnson et al. (Science Translational Medicine, 20157(275), pp.275ra22-275ra22, p. 1-14; newly cited). The instant rejection is newly stated and is based upon further consideration of the claims.
Claims 25 and 46 disclose methods for targeted treatment of a patient having a neoplasm associated with an epidermal growth factor receptor (EGFR) mutation, the method comprising:
(a) non-invasively detecting EGFR variant III positive mutation status within peritumoral edematous tissue of a the patient using a computer-implemented method comprising executing on a processor: performing quantitative pattern analysis of magnetic resonance imaging (MRI) data corresponding to MRI of an in vivo peritumoral edematous tissue to determine a level of spatial heterogeneity or similarity with the in vivo peritumoral edematous tissue; assigning EGFR variant III mutation status as one of negative or positive based on the level of spatial heterogeneity or similarity determined during said performing step, wherein EGFR -negative mutation status is assigned when differing levels of neovascularization is determined to exist between a first region of interest (ROI) and a second ROI, and EGFR -positive status is assigned when similar levels of neovascularization is determined to exist in said first and second ROIs; and
(b) treating the EGFR positive patient with a EGFR-targeting therapy.
Claim 46 further adds that the EGFR-targeting therapy is a chimeric antibody receptor (CAR) T-cell therapy comprising an anti-EGFR targeted component.
Hu teaches selecting at least one region of interest (ROI) in the MR images acquired from a subject, performing a texture analysis on corresponding ROIs, and applying a classification scheme (abstract; [0009-0012; 0029-0040]). Hu teaches that texture features used in texture analysis generally capture different aspects of the spatial distribution of intensities to provide a quantitative indication of image texture (i.e., performing quantitative pattern analysis to determine a level of spatial heterogeneity or similarity) [0036]. Hu teaches examining images taken from the surrounding non-enhancing parenchyma, or Brain Around Tumor (BAT) which is indistinguishable from non-tumor vasogenic edema, in patients with Glioblastoma [0005; 0031; 0054]. Hu is therefore considered to fairly teach examining in vivo peritumoral edematous tissue as instantly claimed. Hu teaches that EGFR amplification showed highly significant correlations with LBP texture on rCBV maps as well as three separate features on T2W imaging: two texture-based features (on DOST and GLCM) and standard deviation (SD) of raw T2W signal [0120]. Hu teaches that rCBV texture captures the contributions and signal patterns of the individual voxels within that ROI, giving insight to microvascular distribution and heterogeneity, and that that texture of rCBV correlates with EGFR status [0126]. Hu teaches the link between EGFR and T2W signal patterns of tumoral/vasogenic edema has been suggested previously [0126]. Therefore, it is considered that the method of Hu fairly teaches assigning EGFR status based on differences in neovascularization as instantly claimed.
Hu does not specifically teach detecting EGFR variant III positive mutation status.
However, the prior art to Tykocinski discloses an assessment of the accuracy of magnetic resonance perfusion-weighted imaging (MR-PWI) in discriminating the EGFRvIII-expressing glioblastoma subtype in patients identified as having either wild-type or EGFRvIII mutations, finding that maximum relative tumor blood volume determined from the MRI had high discrimination accuracy for EGFRvIII samples (abstract; entire document is relevant).
Regarding claims 25 and 46, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine, in the course of routine experimentation and with a reasonable expectation of success, the methods of Hu in and Tykocinski because each reference discloses MRI methods analyzing glioblastomas in relation to EGFR status. As Tykocinski has shown that MRI data can be used to identify EGFRvIII-positive samples, it would have been obvious to one of ordinary skill in the art to use the method of Hu in view of Akbari on patients with EGFRvIII glioblastoma rather than only on patients with amplified EGFRvIII, especially as the instant specification as published discloses that EGFR version III causes constitutive signaling in the absence of ligand binding [0006], which would have a similar effect to EGFR amplification as taught by Hu, with the expectation that the method would be similarly successful.
Hu teaches targeted therapies for different genes [0099], but not for EGFR.
However, the prior art to Johnson discloses the development and characterization of anti-EGFR variant III chimeric antigen receptor T cells for glioblastoma (title; abstract; entire document is relevant).
Regarding claims 25 and 46, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine, in the course of routine experimentation and with a reasonable expectation of success, the methods of Hu in view of Tykocinski and Johnson because each reference discloses methods concerning EGFR mutation status. It would have been obvious to one of ordinary skill in the art to use a chimeric antibody receptor (CAR) T-cell therapy comprising an anti-EGFR targeted component for treatment, as taught by Johnson, rather than a general therapy as taught by Hu, because one of ordinary skill in the art would have been able to carry out such a substitution, and the results were reasonably predictable. The substitution of a chimeric antibody receptor (CAR) T-cell therapy comprising an anti-EGFR targeted component for a general therapy is no more than the simple substitution of one known element for another.
Hu does not teach separate, non-overlapping first and second ROI’s.
However, the prior art to Akbari discloses an analysis of dynamic susceptibility contrast material–enhanced magnetic resonance (MR) images to uncover unique tissue characteristics in patients with glioblastoma (abstract). Akbari teaches obtaining dynamic susceptibility contrast-enhanced MR imaging data and creating a map of heterogeneity within the peritumoral region to determine a heterogeneity score (abstract; p. 504, col. 1, p.ar 2 through p. 506, col. 2, par. 2; entire document is relevant). Akbari teaches defining several regions of interest, including two near-far ROIs within the edema and/or peritumoral region (p. 504, col. 3, par. 1). Akbari teaches training a model using the near-far ROIs to generate heterogeneity scores (i.e., determine a level of spatial heterogeneity or similarity between separate first and second ROIs) (p. 505, col. 3, par. 2 through p. 506, col. 2, par. 2).
Regarding claims 25 and 46, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine, in the course of routine experimentation and with a reasonable expectation of success, the methods of Hu in view of Tykocinski and Johnson with Akbari because both references disclose methods for analyzing spatial heterogeneity in peritumoral samples of glioblastomas. The motivation to compare the near and far ROIs as taught by Akbari using the method of Hu would have been to examine levels of infiltration of the tumor, as taught by Akbari (p. 504, col. 3, par. 1).
Response to Applicant Arguments
At p. 9-10, Applicant submits that neither Bakas nor Hu teach or suggest the means for determining EGFR mutation status recited in the claims. Applicant submits that Hu does teach or suggest imaging data of at least separate first and second regions of interest (ROIs) within in vivo peritumoral edematous tissue to determine the level of spatial heterogeneity or similarity therebetween, or the EGFR mutations or variants.
It is respectfully submitted that this is not persuasive. As set forth in the above rejection, Bakas is considered to teach the limitations that Applicant submits are not taught by Hu. Hu is not relied upon for these limitations in the above rejection over Bakas in view of Hu. Further, Bakas clearly teaches examining EGFRvIII-positive tumors. Therefore, Bakas in view of Hu is considered to teach claims 23-34 and 36-44 and all the limitations of claim 46 except for the CAR T-cell therapy comprising an anti-EGFR targeted component, for which Johnson is relied upon.
At p. 10-11, regarding rejection 2A, Applicant submits that the rejection is believed to be rendered moot by the language in claims 23 and 24 reciting specific MRI data. Applicant submits that the combination of Hu and Akbari does not teach separate first and second regions of interest (ROIs) within in vivo peritumoral edematous tissue to determine the level of spatial heterogeneity or similarity therebetween for detecting specific EGFR mutations or variants or for targeted EGFR therapies.
It is respectfully submitted that this is not persuasive. As described in the above rejection, Hu clearly teaches the different types of claimed data at least at [0032] and [0087].
It is further noted that claims 23-24, 27-33, and 37-43, which are rejected over Hu in view of Akbari, do not recite specific EGFR mutations or variants or for targeted EGFR therapies. Therefore, Applicant’s arguments are not commensurate with the scope of the claims.
Finally, even though Hu does not teach examining separate first and second regions of interest (ROIs) within in vivo peritumoral edematous tissue to determine the level of spatial heterogeneity or similarity therebetween, Akbari does, as described in the above rejection. Even though Akbari does not teach identifying EGFR status from MRI data, Hu does. It is considered that it would be obvious of one of ordinary skill in the art to combine the methods to arrive at the claimed invention for the reasons set forth the in the above rejection.
At p. 11-12, regarding rejection 2B, Applicant’s arguments are moot in view of the newly cited rejection.
At p. 12-13, regarding rejection 2C, Applicant reiterates their arguments regarding the amendment of the specific MRI data into claims 23-24 and the supposed deficiencies of Hu and Akbari. Applicant further adds that Sumana does teach or suggest methods for identifying EGFR status, or addressing issues with imaging associated with peritumoral edematous tissue in a subject.
It is respectfully submitted that this is not persuasive. Sumana is not relied upon for teaching the features of the claims that are considered to be taught by Hu in view of Akbari, but for teaching measuring separability via Bhattacharyya coefficient analysis in MRI analysis is a known process. As described in the above rejection, it is considered that it would have been obvious to one of ordinary skill in the art to apply such an analysis to any MRI data.
At p. 13-14, regarding rejection 2D, Applicant’s arguments are moot in view of the newly cited rejection.
Conclusion
No claims are allowed.
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