Researchers Submit Patent Application, “Techniques For Generating Predictive Outcomes Relating To Oncological Lines Of Therapy Using Artificial Intelligence”, for Approval (USPTO 20240006080): F. Hoffmann-La Roche Inc.
2024 JAN 18 (NewsRx) -- By a
The patent’s assignee is
News editors obtained the following quote from the background information supplied by the inventors: “Cancer is one of the leading causes of death globally. Cancers can develop at any location within the human body. There are, however, several common locations where cancer can develop. For example, leading cancer types include cancers of the breast, lung, colon, and blood. Regardless of the type, cancer involves the unconstrained division of some of the body’s cells, which can potentially spread to other tissue around the body. In healthy individuals, cell divisions that create new cells are generally balanced with the death of older or damaged cells. In individuals diagnosed with cancer, however, this balance breaks down. Cancer causes the uncontrolled growth of abnormal cells in the body, even when new cells are not needed. The unrestricted growth of the abnormal cells can form a tumor in tissue of the body. In some cases, the abnormal cells can break away from the tumor, travel through the body’s bloodstreams, and attach to tissue in new areas of the body to potentially form new tumors.
“The uncontrolled growth of these abnormal cells is caused by genetic mutations in cellular deoxyribonucleic acid (DNA). Genetic mutations are often caused by inherited genetics. However, mutations can also be triggered by environmental factors. For example, toxic exposure (e.g., exposure to carcinogens, radiation, and tobacco), lifestyle-related factors (e.g., obesity, diet, and alcohol consumption), age, medications, hormones, random chance, and certain infections (e.g., hepatitis, human papilloma virus (HPV), and Epstein-Barr virus) can cause cancer-related genomic mutations in an otherwise healthy individual.
“Oncology, which is the study and treatment of cancerous cells, presents several unique and significant challenges. First, certain cancers can be caused by a complex combination of multiple mutations across different genes. Modern cancer research suggests that the evolution of a cancer pathway in a subject involves complex dependencies and interactions between multiple genetic mutations. A cancer often develops when the protein produced by one mutation interacts with the protein produced by another mutation. For example, in certain blood cancers, subjects fare far worse when the primary mutation JAK2 V617F (the driving mutation) is activated before a secondary mutation, identified as TET2. Conversely, subjects who had the TET2 mutation activate before the JAK2 V617F driving mutation had much better clinical outcomes. Moreover, due to advances in genomic testing, a subject’s specific molecular subsets can be identified and evaluated for selecting specific treatments, given their molecular characteristics. However, with these advances, many challenges have arisen, such as obtaining the correct genotyping of tumor samples. Thus, identifying lines of therapy for treating cancers is uniquely challenging over other diseases because targeting a primary mutation with, for example, genetic replacement therapy can activate or exacerbate the impact of a secondary mutation, which can make the cancer worse. Isolating causes of cancer can, therefore, be significantly challenging.
“Second, oncological lines of therapy often involve levels of toxicity that can be harmful to subjects. For example, depending on subject-specific risk factors, certain chemotherapies and immunosuppressants can create a life-threatening side effect in the subject. The treatment selection for cancer is, therefore, heavily dependent on an individual’s unique progression-free survival. Further, there is a wide and diverse spectrum of side effects in response to lines of therapy. Additionally, treatment selection varies depending on the subject’s subjective risk tolerance. For example, if a group of subjects with the same cancer at the same stage has a three-year survival probability of 15%, subjects in the group would be willing to accept different aggressiveness of treatment, and a portion of the group may be willing to accept aggressive treatment, such as high-dose radiotherapy, whereas a different portion of the group may only be willing to accept less-aggressive treatment, such as combination therapy. Therefore, treatment selection and side-effect assessments are uniquely challenging in the oncological context.
“Third, certain lines of therapy require authorization before being performed. For example, a physician seeking to perform a gene replacement therapy on a subject may need prior authorization if the therapy targets a different mutation than the mutation that is commonly targeted by other therapies. Associations such as the National Comprehensive Cancer Network (NCCN) and the
“US 2020/0370124 discloses systems and methods for predicting the efficacy of a cancer therapy in a subject. The systems and methods disclosed are predicated on the determination that the number, percentage, or ratio of particular types of single nucleotide variations (SNVs) in the nucleic acid of a subject with cancer who responds to therapy is different to that of a subject who does not respond to therapy. SNVs identified in a nucleic acid molecule can be used to determine a plurality of metrics forming a profile whereupon subjects that are likely to respond to cancer therapy typically have a different profile to subjects that are unlikely to respond to cancer therapy. The plurality of metrics are then applied to a computational model where the computational model selected based on specific subject attributes. The computational model determines a therapy indicator, for example, a numerical percentage, based on the plurality of metrics where the therapy indicator is indicative of a predicted responsiveness to cancer therapy.
“Thus, there is a need to improve personalized selection of lines of therapy for subjects diagnosed with cancer, personalized assessments of side effects, and verification that lines of therapy comply with existing guidelines, so as to improve treatment efficacy for individual subjects diagnosed with cancer.”
As a supplement to the background information on this patent application, NewsRx correspondents also obtained the inventors’ summary information for this patent application: “In some embodiments, a computer-implemented method is provided for predicting subject-specific outcomes of oncological lines of therapy. The method can include identifying a particular subject having been diagnosed with a type of cancer and retrieving a genomic data set corresponding to the particular subject. A line of therapy can be proposed to be performed on the particular subject. The genomic data set can include a mutational profile, which can include the molecular characteristics of a subject’s tumor, such the molecular pattern, a mutation order (e.g., indicating a series of multiple genetic mutations that mutated at different times), and so on. The computer-implemented method can also include identifying a set of other subjects having been diagnosed with the same type of cancer as the subject. Each other subject may have undergone the line of therapy and may be associated with a treatment outcome. The computer-implemented method can also include retrieving another genomic data set for each other subject of the set of other subjects. The other genomic data set can include another mutation profile. The computer-implemented method can include inputting, for each other subject of the set of other subjects, the mutational profile of the particular subject and the other mutational profile of the other subject into a trained similarity model. The trained similarity model may have been trained to generate a similarity weight representing a predicted degree to which the mutational profile of the particular subject is similar to the other mutational profile of the other subject. The computer-implemented method can include determining, based on the similarity weights outputted by the trained similarity model, a predicted treatment outcome of performing the line of therapy on the particular subject. Upon determining that at least one of the similarity weights outputted by the similarity model is within a threshold, the computer-implemented method can include identifying one of the other subjects based on the determination and assigning the treatment outcome of the identified other subject as the predicted treatment outcome for the particular subject. Upon determining that none of the similarity weights outputted by the similarity model is within the threshold, then the computer-implemented method can include identifying another set of subjects having been diagnosed with a different type of cancer than the particular subject to search for a mutational profile that is similar to the mutational profile of the particular subject.
“In some embodiments, a system is provided that includes one or more data processors and a non-transitory, computer-readable storage medium containing instructions which, when executed on the one or more data processors, cause the one or more data processors to perform part or all of one or more methods disclosed herein.
“In some embodiments, a computer-program product is provided that is tangibly embodied in a non-transitory, machine-readable storage medium and that includes instructions configured to cause one or more processors to perform part or all of one or more methods disclosed herein.
“Some embodiments of the present disclosure include a system including one or more processors. In some embodiments, the system includes a non-transitory, computer-readable storage medium containing instructions which, when executed on the one or more processors, cause the one or more processors to perform part or all of one or more methods and/or part or all of one or more processes disclosed herein. Some embodiments of the present disclosure include a computer-program product tangibly embodied in a non-transitory, machine-readable storage medium, including instructions configured to cause one or more processors to perform part or all of one or more methods and/or part or all of one or more processes disclosed herein.
“The terms and expressions which have been employed are used as terms of description and not of limitation, and there is no intention in the use of such terms and expressions of excluding any equivalents of the features shown and described or portions thereof, but it is recognized that various modifications are possible within the scope of the invention claimed. Thus, it should be understood that although the present invention as claimed has been specifically disclosed by embodiments and optional features, modification and variation of the concepts herein disclosed may be resorted to by those skilled in the art, and that such modifications and variations are considered to be within the scope of this invention as defined by the appended claims.
“In the appended figures, similar components and/or features can have the same reference label. Further, various components of the same type can be distinguished by a dash following the reference label and by a second label that distinguishes among the similar components. If only the first reference label is used in the specification, the description is applicable to any one of the similar components having the same first reference label irrespective of the second reference label.”
The claims supplied by the inventors are:
“1. A computer-implemented method for predicting subject-specific outcomes of oncological lines of therapy, the method comprising: identifying a particular subject having been diagnosed with a type of cancer, wherein a line of therapy is proposed to be performed on the particular subject; retrieving a genomic data set corresponding to the particular subject, the genomic data set including a mutational profile indicating one or more molecular characteristics of the particular subject; identifying a set of other subjects having been diagnosed with the same type of cancer as the subject, and each other subject having undergone the line of therapy and being associated with a treatment outcome; retrieving another genomic data set for each other subject of the set of other subjects, the other genomic data set including another mutational profile; inputting, for each other subject of the set of other subjects, the mutational profile of the particular subject and the other mutational profile of the other subject into a trained similarity model, the trained similarity model having been trained to generate a similarity weight representing a predicted degree to which the mutational profile of the particular subject is similar to the other mutational profile of the other subject; determining, based on the similarity weights outputted by the trained similarity model, a predicted treatment outcome of performing the line of therapy on the particular subject, wherein: upon determining that at least one of the similarity weights outputted by the similarity model is within a threshold, identifying one of the other subjects based on the determination and assigning the treatment outcome of the identified other subject as the predicted treatment outcome for the particular subject; and/or upon determining that none of the similarity weights outputted by the similarity model are within the threshold, identifying another set of subjects having been diagnosed with a different type of cancer than the particular subject to search for a mutational profile that is similar to the mutational profile of the particular subject.
“2. The computer-implemented method for predicting subject-specific outcomes of oncological lines of therapy, as recited in claim 1, further comprising: retrieving yet another mutational profile for each other subject of the other set of other subjects, each other subject of the other set having a different type of cancer than the particular subject; inputting, for each other subject of the other set of other subjects, the mutational profile of the particular subject and the other mutational profile of the other subject of the other set into the trained similarity model; determining, based on the similarity weights outputted by the trained similarity model, that at least one of the similarity weights outputted by the similarity model is within the threshold; and identifying one of the other subjects of the other set based on the determination and assigning the treatment outcome of the identified other subject of the other set as the predicted treatment outcome for the particular subject; and/or wherein the mutational profile includes a mutational profile associated with the particular subject, wherein the mutation order represents a series of multiple genetic mutations that mutated at different times.
“3. The computer-implemented method for predicting subject-specific outcomes of oncological lines of therapy, as recited in claim 1, further comprising: performing a clustering operation on a set of other subject records, the clustering operation being based on one or more outcomes of the line of therapy and forming one or more clusters.
“4. The computer-implemented method for predicting subject-specific outcomes of oncological lines of therapy, as recited in claim 1, wherein the similarity model is trained using a training data set, wherein the training data set includes pairs of mutational profiles labeled as being similar or not similar.
“5. The computer-implemented method for predicting subject-specific outcomes of oncological lines of therapy, as recited in claim 1, wherein the predicted treatment outcome includes one or more subject-specific side effects or a progression-free survival specific to characteristics of the particular subject.
“6. The computer-implemented method for predicting subject-specific outcomes of oncological lines of therapy, as recited in claim 1, wherein contextual information associated with the particular subject includes the genomic profile associated with the subject.
“7. The computer-implemented method for predicting subject-specific outcomes of oncological lines of therapy, as recited in claim 1, further comprising: generating the contextual information associated with the particular subject by: querying a genomic profile data store for the genomic profile associated with the particular subject; querying a radiological images data store for one or more radiological images associated with the particular subject; querying a medical research data store for content data relating to at least one feature attributed to particular the subject; querying a clinical information data store for clinical information associated with the particular subject; querying a claims data store for one or more health insurance claims submitted by or on behalf of the particular subject; and/or querying a subject-provided input data store for subject data provided by the particular subject, wherein the subject data is in one or more data formats.
“8. The computer-implemented method for predicting subject-specific outcomes of oncological lines of therapy, as recited in claim 1, wherein the treatment outcome includes one or more subject-specific side effects, which are outputted at a computing device of the subject using a chatbot.
“9. The computer-implemented method for predicting subject-specific outcomes of oncological lines of therapy, as recited in claim 1, wherein the subject record includes data identified in an electronic medical record corresponding to the subject.
“10. The computer-implemented method for predicting subject-specific outcomes of oncological lines of therapy, as recited in claim 1, wherein the type of cancer with which the subject is diagnosed includes at least one or more of breast cancer, lung cancer, colon cancer, or hematological cancer.
“11. The computer-implemented method for predicting subject-specific outcomes of oncological lines of therapy, as recited in claim 1, wherein a knowledge graph is accessible using a cloud-based oncological application configured to provide predictive functionality relating to clinical decision-making.
“12. The computer-implemented method for predicting subject-specific outcomes of oncological lines of therapy, as recited in claim 1, further comprising: detecting data leakage associated with the reasoning module, the data leakage exposing a feature of the set of features included in the subject record or exposing an item of the contextual information associated with the subject; and in response to detecting data leakage associated with the reasoning module, executing a data-leakage prevention protocol that prevents or blocks exposure of the feature of the set of features included in the subject record.
“13. The computer-implemented method for predicting subject-specific outcomes of oncological lines of therapy, as recited in claim 1, further comprising: generating, using a feature-selection model, a reduced-dimensionality subject record characterizing the subject, the reduced-dimensionality subject record removing one or more features from the set of features included in the subject record, the one or more features being characterized as noise.”
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