# US 12,112,839 B2 · Data based cancer research and treatment systems and methods > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 12,112,839 B2 - Document ID: US12112839B2 - Status: Granted - Kind: B2 - Application number: 18/188,443 - Filed: 2023-03-22 - Published: 2023-07-13 - Granted: 2024-10-08 - Earliest claimed: 2019-09-19 - Assignees: Tempus AI, Inc. - Applicants: Tempus AI, Inc. - Inventors: Christopher Shane Colley; Isaiah Simpson; Brian Reuter; Robert Tell; Hailey Lefkofsky; Hunter Lane; Kevin White; Nike Beaubier; Stephen Bush; Aly Khan; Denise Lau; Kaanan Shah; Eric Lefkofsky - Web page: https://patentgrep.com/patent/US12112839B2 ## CPC - G16H 10/40: for data related to laboratory analysis, e.g. patient specimen analysis - G16H 10/60: for patient-specific data, e.g. for electronic patient records - G16B 20/00: ICT specially adapted for functional genomics or proteomics, e.g. genotype-phenotype associations - G16B 30/00: ICT specially adapted for sequence analysis involving nucleotides or amino acids - G16B 40/00: ICT specially adapted for biostatistics; ICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding - G16B 40/20: Supervised data analysis - G16H 15/00: ICT specially adapted for medical reports, e.g. generation or transmission thereof - G16H 20/10: relating to drugs or medications, e.g. for ensuring correct administration to patients - G16H 20/40: relating to mechanical, radiation or invasive therapies, e.g. surgery, laser therapy, dialysis or acupuncture - G16H 50/20: for computer-aided diagnosis, e.g. based on medical expert systems - G16H 50/30: for calculating health indices; for individual health risk assessment - G16H 50/50: for simulation or modelling of medical disorders - G16H 50/70: for mining of medical data, e.g. analysing previous cases of other patients - G16B 25/00: ICT specially adapted for hybridisation; ICT specially adapted for gene or protein expression - G16H 30/40: for processing medical images, e.g. editing - G16H: HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA - G16B: BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY ## Abstract A method and system comprising storing a set of user application programs each requiring an application specific subset of data to perform application processes and generate a respective genomic variant characterization for each of a plurality of patients with cancerous cells and receiving cancer treatment. The method including, obtaining clinical records data including cancer related information, generating genomic sequencing data for the patient's cancerous cells and normal cells, shaping at least a subset of the genomic sequencing data to generate system structured data. Storing the system structured data in a first database, selecting the application specific data from the first database, storing the application specific data in a second database for application program interfacing, receiving the respective genomic variant characterization from the user application program, and storing the genomic variant characterization from the user application program in a third database. ## Claims ### Claim 1 A method for conducting genomic sequencing, the method comprising the steps of: storing a set of user application programs wherein each of the programs requires an application specific subset of data to perform application processes and generates a respective genomic variant characterization; and for each of a plurality of subjects that have cancerous cells and that receive cancer treatment: a. obtaining clinical records data in original forms where the clinical records data includes cancer state information, treatment types and treatment efficacy information; b. for each subject, using a next generation genomic sequencer to generate genomic sequencing data for the subject's cancerous cells and normal cells; c. shaping at least a subset of the genomic sequencing data to generate system structured data; d. storing the system structured data in a first database; and e. for each user application program; i. selecting the application specific subset of data from the first database; ii. storing the application specific subset of data in a structure optimized for application program interfacing in a second database; and iii. receiving the respective genomic variant characterization from the user application program for each subject of the respective plurality of subjects; and iv. storing the respective genomic variant characterization received from the user application program for each subject of the respective plurality of subjects in a third database. ### Claim 2 (depends on 1) The method of claim 1, wherein generating a respective genomic variant characterization comprises a model and training the model comprises fine tuning to improve the performance of the model, wherein the model is a machine learning algorithm or neural network. ### Claim 3 (depends on 1) The method of claim 1 further including the step of storing a plurality of micro-service programs where each micro-service program includes a data consume definition, a data product to generate definition and a data shaping process that converts consumed data to a data product, the step of shaping including running a sequence of micro-service programs to retrieve data, shape the retrieved data into data products and publish the data products back to the first database as structured data. ### Claim 4 (depends on 1) The method of claim 1, wherein the sequencing is conducted using a plurality of genes from a cell free DNA targeted panel. ### Claim 5 (depends on 1) The method of claim 1, wherein a first user application program of the set of user application programs comprises prediction of a subject having a genomic variant characterization based at least in part on a presence of a biomarker. ### Claim 6 (depends on 5) The method of claim 5, wherein the biomarker is selected from one or more of genomic alteration, microsatellite instability, and tumor-mutational burden. ### Claim 7 (depends on 1) The method of claim 1, wherein a second user application program of the set of user application programs comprises prediction of a subject being at a high-risk of a metastasis to an organ. ### Claim 8 (depends on 1) The method of claim 1, wherein a third user application program of the set of user application programs comprises prediction of a subject being qualified for a clinical trial or treatment. ### Claim 9 (depends on 1) The method of claim 1, wherein a fourth user application program of the set of user application programs comprises prediction of a subject having the genomic variant characterization within a time period. ### Claim 10 (depends on 1) The method of claim 1, wherein a fifth user application program of the set of user application programs comprises prediction of a subject having an oncological event, and wherein the oncological event comprises one or more of: a diagnosis of an oncological disease state; a response to a therapy; a suitability for a therapy; a suitability for a clinical trial; a progression free survival; a progression of cancer; a metastasis of cancer; and an origin of a metastasized tumor. ### Claim 11 (depends on 1) The method of claim 1 wherein each cancer state includes a plurality of factors, the method further including the steps of using a processor to automatically perform the steps of analyzing subject genomic sequencing data that is associated with subjects having at least a common subset of cancer state factors to identify treatments of genomically similar subjects that experience treatment efficacies above a threshold level. ### Claim 12 (depends on 1) The method of claim 1 wherein each cancer state includes a plurality of factors, the method further including the steps of using a processor to automatically identify, for specific cancer types, highly efficacious cancer treatments and, for each highly efficacious cancer treatment, identify at least one genomic sequencing data subset that is different for subjects that experienced treatment efficacy above a first threshold level when compared to subjects that experienced treatment efficacy below a second threshold level. ### Claim 13 (depends on 1) The method of claim 1 wherein the sequencing is performed on a particular cancer type. ### Claim 14 (depends on 1) The method of claim 1, further comprising providing the respective genomic variant characterization stored in the third database as one or more of: an electronic message; an alert within an electronic health record system; or a status report. ### Claim 15 (depends on 1) The method of claim 1, further including the step of providing identification of an immunotherapy associated with clinical response based on the provided genomic variant characterization, wherein the identification of an immunotherapy is one of CAR-T therapy, antibody therapy, cytokine therapy, adoptive t-cell therapy, anti-CD47 therapy, anti-GD2 therapy, immune checkpoint inhibitor and neoantigen therapy. ### Claim 16 (depends on 1) The method of claim 1, wherein generating a respective genomic variant characterization comprises detecting a fusion event. ### Claim 17 (depends on 1) The method of claim 1, wherein the cancer cells are from a tumor tissue and the normal cells from a blood specimen. ### Claim 18 (depends on 1) The method of claim 1, wherein the genomic sequencing data generated from the subject's cancerous cells are generated from cell free DNA of the cancerous cells. ### Claim 19 A system for conducting genomic sequencing, comprising: a computer including a processing device, the processing device configured to: store a set of user application programs wherein each of the programs requires an application specific subset of data to perform application processes and generates a respective genomic variant characterization; and for each of a plurality of subjects that have cancerous cells and that receive cancer treatment: a. obtain clinical records data in original forms where the clinical records data includes cancer state information, treatment types and treatment efficacy information; b. for each subject, use a next generation genomic sequencer to generate genomic sequencing data for the subject's cancerous cells and normal cells; c. shape at least a subset of the genomic sequencing data to generate system structured data; d. store the system structured data in a first database; and e. for each user application program; i. select the application specific subset of data from the first database; ii. store the application specific subset of data in a structure optimized for application program interfacing in a second database; iii. receive the respective genomic variant characterization from the user application program for each subject of the respective plurality of subjects; and iv. store the respective genomic variant characterization received from the user application program for each subject of the respective plurality of subjects in a third database. ### Claim 20 A non-transitory computer-readable storage medium having stored thereon program code instructions that, when executed by a processor, cause the processor to: store a set of user application programs wherein each of the programs requires an application specific subset of data to perform application processes and generates a respective genomic variant characterization; and for each of a plurality of subjects that have cancerous cells and that receive cancer treatment: a. obtain clinical records data in original forms where the clinical records data includes cancer state information, treatment types and treatment efficacy information; b. for each subject, use a next generation genomic sequencer to generate genomic sequencing data for the subject's cancerous cells and normal cells; c. shape at least a subset of the genomic sequencing data to generate system structured data; d. store the system structured data in a first database; and e. for each user application program; i. select the application specific subset of data from the first database; ii. store the application specific subset of data in a structure optimized for application program interfacing in a second database; iii. receive the respective genomic variant characterization from the user application program for each subject of the respective plurality of subjects; and iv. store the respective genomic variant characterization received from the user application program for each subject of the respective plurality of subjects in a third database.