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 Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that use the word “means” or “step” but are nonetheless not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph because the claim limitation(s) do not recite(s) sufficient structure, materials, or acts to entirely perform the recited function. Such claim limitation(s) is/are:
“a learning information acquisition unit that acquires…” (claim 1, 12, 15 and 17).
“a learning unit that generates a prediction model…” (claim 1, 12, 15 and 17).
“an input information acquisition unit that acquires input information…” (claim 1, 13, 16 and 18).
“a prediction unit that makes related predictions to the therapy…” (claim 1, 13, 16 and 18).
For an analysis of the structure, material, or acts corresponding to the claimed functions, see rejection under 35 USC § 112(b) infra.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant wishes to provide further explanation or dispute the examiner’s interpretation of the corresponding structure, applicant must identify the corresponding structure with reference to the specification by page and line number, and to the drawing, if any, by reference characters in response to this Office action.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-13 and 15-18 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor, or for pre-AIA the applicant regards as the invention.
This application includes one or more claim limitations that use the word “means” or “step” but are nonetheless not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph because the claim limitation(s) do not recite(s) sufficient structure, materials, or acts to entirely perform the recited function. Such claim limitation(s) is/are:
“a learning information acquisition unit that acquires…” (claim 1, 12, 15 and 17).
“a learning unit that generates a prediction model…” (claim 1, 12, 15 and 17).
“an input information acquisition unit that acquires input information…” (claim 1, 13, 16 and 18).
“a prediction unit that makes related predictions to the therapy…” (claim 1, 13, 16 and 18).
However, the written description fails to disclose the corresponding structure, material, or acts for performing the entire claimed function and to clearly link the structure, material, or acts to the function.
Therefore, the claim is indefinite and is rejected under 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, second paragraph. For the purpose of examination, any computer capable of performing the claimed functions reads on the claims.
Applicant may:
(a) Amend the claim so that the claim limitation will no longer be interpreted as a limitation under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph;
(b) Amend the written description of the specification such that it expressly recites what structure, material, or acts perform the entire claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(c) Amend the written description of the specification such that it clearly links the structure, material, or acts disclosed therein to the function recited in the claim, without introducing any new matter (35 U.S.C. 132(a)).
If applicant is of the opinion that the written description of the specification already implicitly or inherently discloses the corresponding structure, material, or acts and clearly links them to the function so that one of ordinary skill in the art would recognize what structure, material, or acts perform the claimed function, applicant should clarify the record by either:
(a) Amending the written description of the specification such that it expressly recites the corresponding structure, material, or acts for performing the claimed function and clearly links or associates the structure, material, or acts to the claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(b) Stating on the record what the corresponding structure, material, or acts, which are implicitly or inherently set forth in the written description of the specification, perform the claimed function. For more information, see 37 CFR 1.75(d) and MPEP §§ 608.01(o) and 2181.
Claims 2-11 and 15-18 are rejected as they are being directly or indirectly dependent on rejected claim 1, 12 and 13.
Claims 1-12, 14-15 and 17 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claims 1, 12 and 14 recites the limitation "administering the anticancer agent to cells collected from the subject". There is insufficient antecedent basis for this limitation in the claim. The claim recites “unspecified subject” before the subject, however, the subject do not have antecedent basis.
Claims 2-11, 15, and 17 are rejected as they are being directly or indirectly dependent on rejected claim 1, and 12.
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.
Claim(s) 1-11 and 14 is/are rejected under 35 U.S.C. 103 as being unpatentable over Majumder et al. (“Predicting clinical response to anticancer drugs using an ex vivo platform that captures tumour heterogeneity”, Nature Communications volume 6, Article number: 6169 (2015)) in view of Colborn et al. (US 20210249132 A1).
Regarding claim 1.
Majumder teaches an evaluation device for evaluating an anticancer effect, the evaluation device (see abstract, “The tumor ecosystem and algorithm, together termed the CANScript technology, can emerge as a powerful platform for enabling personalized medicine.”, also see page 9, under discussion, “Here we have demonstrated the development of a novel technology platform that integrates a comprehensive explant culture with a machine learning algorithm to better predict chemotherapy outcomes.”) comprising:
a learning information acquisition unit that acquires: learning data that includes state information, which is information about cancer in an unspecified subject and indicates at least a cancer state in the unspecified subject (See page 3, Fig, 1, "Clinical information", Tumor stage", also see corresponding figure 1 caption, "The first module involved collecting tumor core or surgical biopsy with tumor staging and pathology information besides clinical/treatment history”, also see page 9, section CANScript as a tool to predict treatment outcome in patients, "The functional read-outs from these CANScripts, quantified in terms of viability, histopathology, proliferation and apoptosis, together with the observed clinical response in the matched patients, classified as progressive disease/non-response (NR), partial response (PR) or complete response (CR) based on PERCIST guidelines (Fig. 7a), were then used as the training set for a novel machine learning algorithm.”);
and training data that is information about an effect of an anticancer agent obtained by administering the anticancer agent to cells collected from the subject, a learning unit that generates a prediction model for making predictions related to therapy using the anticancer agent by causing a learning model to perform supervised learning for a corresponding relationship between the learning data and the training data acquired by the learning information acquisition unit (see page 3, figure 1, “Functional outcome of treatment in terms of cell viability, pathological and morphological analysis, cell proliferation and cell death was quantified… these quantitative scores from the explants were aggregated using a machine learning algorithm to assign a final score, which helped rank the outcomes as CR,PR or NR. The learning algorithm was trained on data from 109 patients. In the final module, these predictions were tested against clinical outcomes from 55 new patients to validate the approach. D1, D2, D3 and D4 indicate different drug regimens.” also see page 9, section CANScript as a tool to predict treatment outcome in patients, “together with the observed clinical response in the matched patients, classified as progressive disease/non-response (NR), partial response (PR) or complete response (CR) based on PERCIST guidelines (Fig. 7a), were then used as the training set for a novel machine learning algorithm. In this algorithm, as the first step, we classified patients as simply responders or non-responders, with a focus on ensuring high sensitivity (true positive rate). This was formulated by maximizing the partial area under the receiver operating characteristic (ROC) curve (partial area under the curve (AUC)) up to an acceptable false positive range (Fig. 7b). To this end, PR and CR were grouped together into a responder (R) category and a linear prediction model was learned using SVMpAUC”);
see pages 3-4, model trained is trained and reused for validation on new patients);
an input information acquisition unit that acquires input information that is information about cancer in a subject serving as a prediction target (see page 3, figure 1, clinical treatments, biopsy plan etc. are provided as input to the prediction also see page 9, section CANScript as a tool to predict treatment outcome in patients, “The model achieved 96.77% sensitivity on the training set (Fig. 7c). We then tested the learned algorithm on a new test group of 55 patients, consisting of 42 HNSCC and 13 CRC patients treated with the same drugs as above, where the model achieved 91.67% specificity and 100% sensitivity (Fig. 7d).”);
and a prediction unit that makes related predictions to the therapy using the anticancer agent with the input information and the prediction model (see page 3, figure 1, “In module three, these quantitative scores from the explants were aggregated using a machine learning algorithm to assign a final score, which helped rank the outcomes as CR,PR or NR. The learning algorithm was trained on data from 109 patients. In the final module, these predictions were tested against clinical outcomes from 55 new patients to validate the approach. D1, D2, D3 and D4 indicate different drug regimens.”, also see page 9, section CANScript as a tool to predict treatment outcome in patients, “The model achieved 96.77% sensitivity on the training set (Fig. 7c). We then tested the learned algorithm on a new test group of 55 patients, consisting of 42 HNSCC and 13 CRC patients treated with the same drugs as above, where the model achieved 91.67% specificity and 100% sensitivity (Fig. 7d).”), wherein the learning information acquisition unit acquires the information about the anticancer effect obtained by administering the anticancer agent to a three-dimensional cell structure including cancer cells collected from the unspecified subject and cells constituting a stroma as the training data (see page 2, “We rationalized that to predict the clinical outcome of chemotherapy with high accuracy, it is therefore important to conserve this clinical ‘global’ heterogeneity with high fidelity in terms of cancer and stromal cells, tumor microenvironment and architecture…To create a clinically relevant predictive model, here we engineered an ex vivo tumor ecosystem, where thin tumor sections with conserved cellular and microenvironmental heterogeneity and architecture were cultured in tissue culture wells coated with grade-matched tumor matrix support in the presence of autologous serum (AS) containing endogenous ligands”, under results, “Indeed, three-dimensional (3D) matrix support is emerging as a critical factor that dynamically determines the fate of tumors in terms of integrity, survival, metastasis and response to chemotherapy.”, also see page 4, “Reconstructing a tumour ecosystem. As the final step towards constructing the CANScript tumour ecosystem, both conditions (that is, TMP and AS) were contextually integrated in the explant system.”, also pages 6-7, “matrix degrading enzyme matrix metallopeptidase 9 (MMP-9) and cancer stem cell markers like CD44 and ALDH1 observed in the parent HNSCC tumours were also preserved in the CANScript tumour ecosystem (Supplementary Fig. 7a–c). It is important to note that unlike common synthetic organotypic inserts, the CANScript platform… exhibited enhanced preservation of native tumour morphology and proliferation status (Supplementary Fig. 7d)…”).
Majumder teaches using and reusing prediction models but do not specifically disclose a storage, therefore, Majumder do not specifically teach a storage unit that stores the prediction model generated by the learning unit.
Colborn teaches a storage unit that stores the prediction model generated by the learning unit (see ¶ 38-39, “he system to perform the methods and other functionality described herein may include one or more computer systems including one or more processors, memory and/or storage devices, and communication interfaces to provide a cancer evolver, an artificial-intelligence engine, a database, and a user interface.”, also see ¶ 16, “storing the results in a database”, also ¶ 147, ¶ 149, “The features and parameters describing the Predictive Algorithm 12 are then stored in the Database 5 for future use.”).
Both Majumder and Colborn pertain to the problem of cancer treatments, thus being analogous. It would have been obvious to one skilled in the art before the effective filing date of the claimed invention to combine Majumder and Colborn to teach the above limitations. The motivation for doing so would be “for predicting and optimizing the outcome of treatment protocols in individual cancer patients including a software training phase and a utilization phase, together including collecting data relating to the characteristics of a patient, determining the treatment and cancer history of the patient, using a mathematical model to infer model parameters associated with the treatments applied to the patient, repeating these steps for multiple patients, employing machine-learning algorithms to search for mathematical relationships between patient characteristics and the model parameters, collecting patient parameters for a new patient, using these data and the machine-learning algorithms to predict model parameters that would apply to the new patient, and using the mathematical model and the model parameters to predict the number of cancer cells versus time and their resistance status in the new patient, along with other aspects of the patient's treatment outcome, under a new proposed treatment plan.” (see Colborn abstract).
Regarding claim 3.
Majumder and Colborn teaches the evaluation device according to claim 1,
Majumder further teaches wherein the cells constituting the stroma further include vascular endothelial cells (see page 3, “As shown in Fig. 3b a number of growth factors (represented by EGF, hepatocyte growth factor (HGF), vascular endothelial growth factor (VEGF) and macrophage colony-stimulating factor (MCSF)) were found to be within clinically detectable ranges in patient sera.”).
Regarding claim 4.
Majumder and Colborn teaches the evaluation device according to claim 1,
Majumder further teaches wherein the learning information acquisition unit acquires the state information indicating the cancer state in the unspecified subject, omics information of the cells collected from the subject (see page 7, “Mutational and translocation spectrum obtained from the whole-exome sequence analysis (Agilent 44Mb, 50 coverage) of HNSCC patient tumours and also their corresponding xenograft tumour (passage no.2) tissues.”, also page 7, “global transcriptome pattern showed a good association between P0 and matched HTXs (Fig. 5c,d)”, also see ¶ 5, “Activation levels of major RTKs by RPPA profiling of patient tumours (n¼5).Q”), drug information about the anticancer agent (see page 3, figure 1, “D1, D2, D3 and D4 indicate different drug regimens”, also see page 10, figure 7, “Performance of learned NR/R model on the training set. Confusion matrix displays the number of patients with various actual and predicted responses to TPF for HNSCC and cetuximab + FOLFIRI for CRC in the training set (n=109).”), and administration performance information about the anticancer agent administered to the three-dimensional cell structure as the learning data (see page 2, figure 1, “The first module involved collecting tumour core or surgical biopsy with tumour staging and pathology information besides clinical/treatment history. In the second module, tumour biopsy was rapidly processed into thin explants. Tumour biopsies were also used to generate either in vivo implants in mice, or processed for isolation and analysis of tumour matrix, which was used to develop the TMPcocktail. The explants were cultured in tumour- and grade-matched TMP and AS and incubated with selected drug regimens. While multiple drug regimens can be used, the one used by the oncologist for the patient was always included in the tumour explant culture. Functional outcome of treatment in terms of cell viability, pathological and morphological analysis, cell proliferation and cell death was quantified. In module three, these quantitative scores from the explants were aggregated using a machine learning algorithm to assign a final score, which helped rank the outcomes as CR,PR or NR.”),
and acquires drug effectiveness information that is a result of determining whether or not the anticancer agent administered to the three-dimensional cell structure is effective as the training data (see page 9, “together with the observed clinical response in the matched patients, classified as progressive disease/non-response (NR), partial response (PR) or complete response (CR) based on PERCIST guidelines (Fig. 7a), were then used as the training set for a novel machine learning algorithm. In this algorithm, as the first step, we classified patients as simply responders or non-responders, with a focus on ensuring high sensitivity (true positive rate).”), and wherein the learning unit generates a prediction model for predicting an effect of the anticancer agent acting on cancer cells of a cancer patient on the basis of the state information in the cancer patient and the omics information of the cells collected from the cancer patient (see page 3 figure 1, “In the final module, these predictions were tested against clinical outcomes from 55 new patients to validate the approach. D1, D2, D3 and D4 indicate different drug regimens.”, also see 9, “CANScript as a tool to predict treatment outcome in patients. The concordance in outcome between HTX in vivo and corresponding CANScript studies suggested the possibility of using the latter for predicting the treatment outcome in patients. The CANScript explants were generated from biopsies of CRC and HNSCC tumours from 109 patients and were incubated with the same drug combination as that administered to the patient…together with the observed clinical response in the matched patients, classified as progressive disease/non-response (NR), partial response (PR) or complete response (CR) based on PERCIST guidelines (Fig. 7a), were then used as the training set for a novel machine learning algorithm. In this algorithm, as the first step, we classified patients as simply responders or non-responders, with a focus on ensuring high sensitivity (true positive rate)… we anticipate that the CANScript platform can emerge as a powerful strategy for predicting chemotherapy outcomes.”).
Regarding claim 5.
Majumder and Colborn teaches the evaluation device according to claim 4,
Majumder further teaches wherein the input information acquisition unit acquires subject information including the state information about the subject serving as the prediction target and the omics information of the cells collected from the subject as the input information, and wherein the prediction unit predicts the effect of the anticancer agent acting on cancer cells of the subject (see figure 1, page 3, “The first module involved collecting tumour core or surgical biopsy with tumour staging and pathology information besides clinical/treatment history. In the second module, tumour biopsy was rapidly processed into thin explants. Tumour biopsies were also used to generate either in vivo implants in mice, or processed for isolation and analysis of tumour matrix, which was used to develop the TMP cocktail. The explants were cultured in tumour- and grade-matched TMP and AS and incubated with selected drug regimens. While multiple drug regimens can be used, the one used by the oncologist for the patient was always included in the tumour explant culture… In module three, these quantitative scores from the explants were aggregated using a machine learning algorithm to assign a final score, which helped rank the outcomes as CR,PR or NR. The learning algorithm was trained on data from 109 patients.”, also see page 7.).
Regarding claim 6.
Majumder and Colborn teaches the evaluation device according to claim 4,
Majumder further teaches wherein the input information acquisition unit acquires a target drug information about an anticancer agent serving as a prediction target as the input information, and wherein the prediction unit predicts an effect of the anticancer agent designated in the target drug information acting on the cancer cells (see figure 1, page 3, “The first module involved collecting tumour core or surgical biopsy with tumour staging and pathology information besides clinical/treatment history. In the second module, tumour biopsy was rapidly processed into thin explants. Tumour biopsies were also used to generate either in vivo implants in mice, or processed for isolation and analysis of tumour matrix, which was used to develop the TMP cocktail. The explants were cultured in tumour- and grade-matched TMP and AS and incubated with selected drug regimens. While multiple drug regimens can be used, the one used by the oncologist for the patient was always included in the tumour explant culture… In module three, these quantitative scores from the explants were aggregated using a machine learning algorithm to assign a final score, which helped rank the outcomes as CR,PR or NR. The learning algorithm was trained on data from 109 patients.”).
Regarding claim 7.
Majumder and Colborn teaches the evaluation device according to claim 4,
Majumder further teaches wherein the input information acquisition unit acquires target drug information about an anticancer agent serving as a prediction target as the input information, and wherein the prediction unit predicts an effect of the anticancer agent designated in the target drug information acting on the cancer cells for each cancer state in a patient (see figure 1, page 3, “The first module involved collecting tumour core or surgical biopsy with tumour staging and pathology information besides clinical/treatment history. In the second module, tumour biopsy was rapidly processed into thin explants. Tumour biopsies were also used to generate either in vivo implants in mice, or processed for isolation and analysis of tumour matrix, which was used to develop the TMP cocktail. The explants were cultured in tumour- and grade-matched TMP and AS and incubated with selected drug regimens. While multiple drug regimens can be used, the one used by the oncologist for the patient was always included in the tumour explant culture… In module three, these quantitative scores from the explants were aggregated using a machine learning algorithm to assign a final score, which helped rank the outcomes as CR,PR or NR. The learning algorithm was trained on data from 109 patients.”).
Regarding claim 8.
Majumder and Colborn teaches the evaluation device according to claim 4,
Majumder further teaches wherein the state information includes information indicating a type of cancer in the subject, wherein the input information acquisition unit acquires target drug information about an anticancer agent serving as a prediction target as the input information, and wherein the prediction unit predicts an effect of the anticancer agent designated in the target drug information acting on the cancer cells for each type of cancer (see page 2, “The integration of the tumour ecosystems with a novel machine learning algorithm formed the CANScript platform, which reliably predicted the therapeutic efficacy of targeted and cytotoxic drugs in patients with head and neck squamous cell carcinoma (HNSCC) and colorectal cancer (CRC). The robustness of this platform in predicting clinical response could potentially be useful for personalizing cancer treatment.”, also see page 4, figure 2, also page 5, “primary tumour was observed only in the case of the CANScript platform that integrated both TMP and AS, while supplementing the explant cultures with either AS or TMP(þEGF) alone resulted in distinct transcriptomic signatures (Fig. 4f,g)”, also see page 7, figure 5, “The plot shows six distinct clusters comprising of four pairs of colon carcinoma and two pairs of HNSCC samples.”).
Regarding claim 9.
Majumder and Colborn teaches the evaluation device according to claim 4,
Majumder further teaches wherein the administration performance information includes information about a combination of a plurality of anticancer agents administered to the three-dimensional cell structure, wherein the drug effectiveness information includes a result of determining an anticancer effect in the combination of the plurality of anticancer agents administered to the three-dimensional cell structure, wherein the input information acquisition unit acquires target drug information corresponding to the combination of the plurality of anticancer agents serving as a prediction target as the input information, and wherein the prediction unit predicts an effect of the combination of the anticancer agents designated in the target drug information acting on the cancer cells of the subject (see page 3, figure 1, “these quantitative scores from the explants were aggregated using a machine learning algorithm to assign a final score, which helped rank the outcomes as CR,PR or NR. The learning algorithm was trained on data from 109 patients. In the final module, these predictions were tested against clinical outcomes from 55 new patients to validate the approach. D1, D2, D3 and D4 indicate different drug regimens.”).
Regarding claim 10.
Majumder and Colborn teaches the evaluation device according to claim 4,
Colborn further teaches wherein the drug effectiveness information includes a result of determining whether or not the cancer cells of the subject have acquired resistance to a predetermined anticancer agent, wherein the input information acquisition unit acquires subject information including the state information about the subject serving as the prediction target and the omics information of the cells collected from the subject as the input information, and wherein the prediction unit predicts a degree to which the cancer cells of the subject acquire the resistance to the predetermined anticancer agent (see ¶ 94, “In this Step (h), the same or a similar mathematical model is used as was used in Step (c) and the model parameters predicted in Step (g) is used to predict the number of cancer cells versus time and their resistance status in the new patient (and hence the patient's treatment outcome) under any proposed treatment for which parameters have been determined in Step (g). The resistance status of a cancer cell may be sensitive, partially resistant, or fully resistant to each proposed treatment.”, also see ¶ 105, “a classification ML technique is used, and the cancer is modeled as including two subpopulations of cells subject to exponential growth and decay, one sensitive to treatment and another resistant to treatment. The results of several ML algorithms are pooled to generate a probability distribution of the time to progression for patients of each class. This is further described as follows.”).
The motivation utilized in the combination of claim 1, super, applies equally as well to claim 10.
Regarding claim 11.
Majumder and Colborn teaches the evaluation device according to claim 4,
Colborn further teaches wherein the drug effectiveness information includes a result of determining whether or not the cancer cells of the subject have acquired resistance to a predetermined first anticancer agent, wherein the administration performance information includes information indicating whether or not a second anticancer agent different from the first anticancer agent administered to the cancer cells of the subject has been administered after the cancer cells of the subject acquired resistance to the predetermined first anticancer agent, wherein the input information acquisition unit acquires subject information including the state information about the subject serving as the prediction target and the omics information of cells collected from the subject as the input information, and wherein the prediction unit predicts an effect of the second anticancer agent acting on the cancer cells of the subject after the cancer cells of the subject acquired the resistance to the first anticancer agent (see ¶ 94, “In this Step (h), the same or a similar mathematical model is used as was used in Step (c) and the model parameters predicted in Step (g) is used to predict the number of cancer cells versus time and their resistance status in the new patient (and hence the patient's treatment outcome) under any proposed treatment for which parameters have been determined in Step (g). The resistance status of a cancer cell may be sensitive, partially resistant, or fully resistant to each proposed treatment.”).
The motivation utilized in the combination of claim 1, super, applies equally as well to claim 11.
Claim 14 recites a method to perform the device recited in claim 1. Therefore the rejection of claim 1 above applies equally here.
Claim(s) 2 is/are rejected under 35 U.S.C. 103 as being unpatentable over Majumder et al. (“Predicting clinical response to anticancer drugs using an ex vivo platform that captures tumour heterogeneity”, Nature Communications volume 6, Article number: 6169 (2015)) in view of Colborn et al. (US 20210249132 A1) in further view of Alcaraz et al. (“Stromal markers of activated tumor associated fibroblasts predict poor survival and are associated with necrosis in non-small cell lung cancer”).
Regarding claim 2.
Majumder and Colborn teaches the evaluation device according to claim 1,
Majumder further teaches see page 4, figures 2, primary tumour characteristics in explants include stroma, also see page 9, “A key attribute of the CANScript platform is its ability to capture the intratumoral heterogeneity to a greater degree than achieved by biomarker-based selection of cancer cells. Cancer stem cells, stromal cells such as intra and peritumoral immune cells, and vascular components can further add to the heterogeneity”, however, do not specifically teach wherein the cells constituting the stroma include fibroblast cells.
Alcaraz teaches wherein the cells constituting the stroma include fibroblast cells (see page 152, “it is increasingly acknowledged the prominent role of the stiff desmoplastic tumor stroma that surrounds carcinoma cells in the progression of lung cancer and other solid tumors [4]. This desmoplastic stroma is rich in activated fibroblasts (referred to as cancer- or tumor associated fibroblasts (TAFs)), infiltrated immune cells and other less frequent cell types, in the background of an abundant deposition of fibrillar collagens and other fibrotic extracellular matrix components [4,5]. Of note, TAFs are largely responsible for the aberrant stromal deposition of fibrillar collagens within the tumor stroma [6], and are receiving increasing interest as a therapeutic target [7], as illustrated by the recent approval of the antiangiogenic and antifibrotic drug nintedanib to treat lung ADC patients in combination with docetaxel”).
Majumder, Colborn and Alcaraz pertain to the problem of cancer treatments, thus being analogous. It would have been obvious to one skilled in the art before the effective filing date of the claimed invention to combine Majumder, Colborn and Alcaraz to teach the above limitations. The motivation for doing so would be “This desmoplastic stroma is rich in activated fibroblasts (referred to as cancer- or tumor associated fibroblasts (TAFs)), infiltrated immune cells and other less frequent cell types, in the background of an abundant deposition of fibrillar collagens and other fibrotic extracellular matrix components [4,5]. Of note, TAFs are largely responsible for the aberrant stromal deposition of fibrillar collagens within the tumor stroma [6], and are receiving increasing interest as a therapeutic target [7], as illustrated by the recent approval of the antiangiogenic and antifibrotic drug nintedanib to treat lung ADC patients in combination with docetaxel.” (see Alcaraz page 152).
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 12-13 and 15-18 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Majumder et al. (“Predicting clinical response to anticancer drugs using an ex vivo platform that captures tumour heterogeneity”, Nature Communications volume 6, Article number: 6169 (2015)).
Regarding claim 12.
Majumder teaches a learning device comprising: a learning information acquisition unit that acquires: learning data that is information about cancer in an unspecified subject (See page 3, Fig, 1, "Clinical information", Tumor stage", also see corresponding figure 1 caption, "The first module involved collecting tumor core or surgical biopsy with tumor staging and pathology information besides clinical/treatment history”, also see page 9, section CANScript as a tool to predict treatment outcome in patients, "The functional read-outs from these CANScripts, quantified in terms of viability, histopathology, proliferation and apoptosis, together with the observed clinical response in the matched patients, classified as progressive disease/non-response (NR), partial response (PR) or complete response (CR) based on PERCIST guidelines (Fig. 7a), were then used as the training set for a novel machine learning algorithm.”);
and training data that is information about an effect of an anticancer agent obtained by administering the anticancer agent to cells collected from the subject, and
a learning unit that generates a prediction model for making predictions related to therapy using the anticancer agent by causing a learning model to perform supervised learning for a corresponding relationship between the learning data and the training data acquired by the learning information acquisition unit (see page 3, figure 1, “Functional outcome of treatment in terms of cell viability, pathological and morphological analysis, cell proliferation and cell death was quantified… these quantitative scores from the explants were aggregated using a machine learning algorithm to assign a final score, which helped rank the outcomes as CR,PR or NR. The learning algorithm was trained on data from 109 patients. In the final module, these predictions were tested against clinical outcomes from 55 new patients to validate the approach. D1, D2, D3 and D4 indicate different drug regimens.” also see page 9, section CANScript as a tool to predict treatment outcome in patients, “together with the observed clinical response in the matched patients, classified as progressive disease/non-response (NR), partial response (PR) or complete response (CR) based on PERCIST guidelines (Fig. 7a), were then used as the training set for a novel machine learning algorithm. In this algorithm, as the first step, we classified patients as simply responders or non-responders, with a focus on ensuring high sensitivity (true positive rate). This was formulated by maximizing the partial area under the receiver operating characteristic (ROC) curve (partial area under the curve (AUC)) up to an acceptable false positive range (Fig. 7b). To this end, PR and CR were grouped together into a responder (R) category and a linear prediction model was learned using SVMpAUC”).
Regarding claim 13.
Majumder teaches a prediction device comprising: an input information acquisition unit that acquires input information that is information about cancer in a subject serving as a prediction target (see page 3, figure 1, clinical treatments, biopsy plan etc. are provided as input to the prediction also see page 9, section CANScript as a tool to predict treatment outcome in patients, “The model achieved 96.77% sensitivity on the training set (Fig. 7c). We then tested the learned algorithm on a new test group of 55 patients, consisting of 42 HNSCC and 13 CRC patients treated with the same drugs as above, where the model achieved 91.67% specificity and 100% sensitivity (Fig. 7d).”);
and a prediction unit that makes related predictions to therapy using an anticancer agent with the input information and a prediction model (see page 3, figure 1, “In module three, these quantitative scores from the explants were aggregated using a machine learning algorithm to assign a final score, which helped rank the outcomes as CR,PR or NR. The learning algorithm was trained on data from 109 patients. In the final module, these predictions were tested against clinical outcomes from 55 new patients to validate the approach. D1, D2, D3 and D4 indicate different drug regimens.”, also see page 9, section CANScript as a tool to predict treatment outcome in patients, “The model achieved 96.77% sensitivity on the training set (Fig. 7c). We then tested the learned algorithm on a new test group of 55 patients, consisting of 42 HNSCC and 13 CRC patients treated with the same drugs as above, where the model achieved 91.67% specificity and 100% sensitivity (Fig. 7d).”), wherein the prediction model is a model for making the prediction related to the therapy using the anticancer agent generated by causing a learning model to perform supervised learning for a corresponding relationship between learning data that is information about cancer in an unspecified subject and training data that is information about an effect of the anticancer agent obtained by administering the anticancer agent to cells collected from the subject (see page 3, figure 1, “Functional outcome of treatment in terms of cell viability, pathological and morphological analysis, cell proliferation and cell death was quantified… these quantitative scores from the explants were aggregated using a machine learning algorithm to assign a final score, which helped rank the outcomes as CR,PR or NR. The learning algorithm was trained on data from 109 patients. In the final module, these predictions were tested against clinical outcomes from 55 new patients to validate the approach. D1, D2, D3 and D4 indicate different drug regimens.” also see page 9, section CANScript as a tool to predict treatment outcome in patients, “together with the observed clinical response in the matched patients, classified as progressive disease/non-response (NR), partial response (PR) or complete response (CR) based on PERCIST guidelines (Fig. 7a), were then used as the training set for a novel machine learning algorithm. In this algorithm, as the first step, we classified patients as simply responders or non-responders, with a focus on ensuring high sensitivity (true positive rate). This was formulated by maximizing the partial area under the receiver operating characteristic (ROC) curve (partial area under the curve (AUC)) up to an acceptable false positive range (Fig. 7b). To this end, PR and CR were grouped together into a responder (R) category and a linear prediction model was learned using SVMpAUC”, see page 2, “We rationalized that to predict the clinical outcome of chemotherapy with high accuracy, it is therefore important to conserve this clinical ‘global’ heterogeneity with high fidelity in terms of cancer and stromal cells, tumor microenvironment and architecture…To create a clinically relevant predictive model, here we engineered an ex vivo tumor ecosystem, where thin tumor sections with conserved cellular and microenvironmental heterogeneity and architecture were cultured in tissue culture wells coated with grade-matched tumor matrix support in the presence of autologous serum (AS) containing endogenous ligands”, under results, “Indeed, three-dimensional (3D) matrix support is emerging as a critical factor that dynamically determines the fate of tumors in terms of integrity, survival, metastasis and response to chemotherapy.”, also see page 4, “Reconstructing a tumour ecosystem. As the final step towards constructing the CANScript tumour ecosystem, both conditions (that is, TMP and AS) were contextually integrated in the explant system.”, also pages 6-7, “matrix degrading enzyme matrix metallopeptidase 9 (MMP-9) and cancer stem cell markers like CD44 and ALDH1 observed in the parent HNSCC tumours were also preserved in the CANScript tumour ecosystem (Supplementary Fig. 7a–c). It is important to note that unlike common synthetic organotypic inserts, the CANScript platform… exhibited enhanced preservation of native tumour morphology and proliferation status (Supplementary Fig. 7d)…”).
Claim 15 recites a program to perform the device recited in claim 12. Therefore the rejection of claim 12 above applies equally here.
Claim 16 recites a program to perform the device recited in claim 13. Therefore the rejection of claim 13 above applies equally here.
Claim 17 recites a non-transitory computer-readable storage medium to perform the device recited in claim 12. Therefore the rejection of claim 12 above applies equally here.
Claim 18 recites a non-transitory computer-readable storage medium to perform the device recited in claim 13. Therefore the rejection of claim 13 above applies equally here.
Related arts:
YABUUCHI et al. (US 20210257067 A1) teaches risk score is calculated such that a user having a short elapsed time until development has a larger value than a user having a long elapsed time until development. Then, a prediction model is generated by inputting the training data to a learning machine and causing the learning machine to learn such that the output becomes the correct answer data.
Beck et al. (US 10650929 B1) teaches annotated pathology images associated with a first group of patients in a clinical trial. Each of the annotated pathology images is associated with survival data for a respective patient. Each of the annotated pathology images includes an annotation describing a tissue characteristic category for a portion of the image. Values for one or more features are extracted from each of the annotated pathology images. A model is trained based on the survival data and the extracted values for the features. The trained model is stored on a storage device.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to IMAD M KASSIM whose telephone number is (571)272-2958. The examiner can normally be reached 10:30AM-5:30PM, M-F (E.S.T.).
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Michael J. Huntley can be reached at (303) 297 - 4307. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/IMAD KASSIM/Primary Examiner, Art Unit 2129