Data based cancer research and treatment systems and methods

Patent metadata

Field	Value
Publication number	US-11705226-B2
Application number	US-201916657804-A
Country	US
Kind code	B2
Filing date	Oct 18, 2019
Priority date	Sep 19, 2019
Publication date	Jul 18, 2023
Grant date	Jul 18, 2023

Abstract

A method and system for storing user application programs and micro-service programs, for each of multiple patients that have cancerous cells and receive treatment, includes obtaining clinical records data in original forms, storing it in a semi-structured first database, generating sequencing data for the patient's cancerous and normal cells using a next generation genomic sequencer, storing the sequencing data in the first database, shaping at least some of the first database data to generate system structured data optimized for searching and including clinical record data, storing the structured data in a second database, for each user application program, selecting an application-specific subset of data from the second database and storing it in a structure optimized for application program interfacing in a third database, wherein an orchestration manager operatively connected to one or more micro-service programs receives status messages and initiates a respective micro-service program when program prerequisites are satisfied.

First claim

The invention claimed is:

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 generate user output;
storing a plurality of micro-service programs;
for each of a plurality of patients 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. storing the clinical records data in a semi-structured first database, the storing including identifying one or more of a document type or source of the clinical records, the storing further including extracting data from the clinical records using a machine learning algorithm (MLA), deep learning algorithm (DLA), or neural network (NN), wherein the MLA, DLA, or NN is selected from among a plurality of MLAs, DLAs, and/or NNs based upon the identified one or more of the document type or source of the clinical records;
c. for each patient, using a next generation genomic sequencer to generate DNA and whole-transcriptome RNA genomic sequencing data from the patient's cancerous cells and normal cells;
d. storing the sequencing data in the first database;
e. shaping at least a subset of the first database data to generate system structured data including clinical record data and sequencing data wherein the system structured data is optimized for searching;
f. storing the system structured data in a second database;
g. for each user application program:
i. selecting the application specific subset of data from the second database; and
ii. storing the application specific subset of data in a structure optimized for application program interfacing in a third database; and

wherein an orchestration manager is operatively connected to one or more micro-service programs to receive status messages from micro-service programs and initiate a respective micro-service program when prerequisites of that respective micro-service program are satisfied.

2. The method of claim 1 wherein 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 on data in the first database to retrieve data, shape the retrieved data into data products and publish the data products back to the second database as structured data.

3. The method of claim 2 further including storing a new data alert in an alert list in response to a new clinical record or a new micro-service data product being stored in the second database.

4. The method of claim 3 further including each micro-service program monitoring the alert list and determining if stored data is to be consumed by that micro-service program independent of all other micro-service programs.

5. The method of claim 4 wherein at least a subset of the micro-service programs operate sequentially to condition data.

6. The method of claim 4 wherein at least a subset of the micro-service programs specify the same data to consume definition.

7. The method of claim 3 wherein the step of shaping includes at least one manual step to be performed by a system user and wherein the system adds a data shaping activity to a user's work queue in response to at least one of the alerts being added to the alert list.

8. The method of claim 2 wherein the first database includes both unstructured original clinical data records and semi-structured data generated by the micro-service programs.

9. The method of claim 2 wherein each micro-service program operates automatically and independently when data that meets the data to consume definition is stored to the first database.

10. The method of claim 1 wherein the application programs include operational programs and wherein at least a subset of the operational programs comprise a physician suite of programs useable to consider cancer state treatment options.

11. The method of claim 10 wherein at least a subset of the operational programs comprise a suite of data shaping programs usable by a system user to shape data stored in the first database.

12. The method of claim 11 wherein the data shaping programs are for use by a radiologist.

13. The method of claim 11 wherein the data shaping programs are for use by a pathologist.

14. The method of claim 10 further including a set of visualization tools and associated interfaces useable by a system user to analyze the second database data.

15. The method of claim 1 wherein the third database includes a subset of the second database data.

16. The method of claim 15 wherein the third database includes data derived from the second database data.

17. The method of claim 1 further including the steps of presenting a user interface to a system user that includes data that indicates how genomic sequencing data affects different treatment efficacies.

18. 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 patient genomic sequencing data that is associated with patients having at least a common subset of cancer state factors to identify treatments of genomically similar patients that experience treatment efficacies above a threshold level.

19. 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 patients that experienced treatment efficacy above a first threshold level when compared to patients that experienced treatment efficacy below a second threshold level.

20. The method of claim 1 , wherein one or more micro-service programs calculate a likelihood of microsatellite instability when both a tumor genomic sequencing result and a normal genomic sequencing result are available for consumption.

21. The method of claim 1 , wherein one or more micro-service programs calculate an expression level of PD-L1 when an RNA expression of a patient's specimen is available for consumption.

22. The method of claim 1 , wherein one or more micro-service programs generate predictions on a biomarker status of a patient's specimen when an image of an H&E slide and genomic sequencing results are available for consumption.

23. The method of claim 1 , wherein one or more micro-service programs generate predictions on a depression biomarker status of a patient's specimen using a depression sequencing pipeline.

24. The method of claim 1 , wherein one or more micro-service programs identifies variations between two or more RNA expression data sets and generates a correction factor to normalize the two or more data sets into a normalized data set.

25. The method of claim 1 , wherein one or more micro-service programs links abstracted medical information from patient records with structured entities and stores the abstracted medical information in a structured format based at least in part on the linked structured entity.

Assignees

• Tempus Labs
• Tempus Labs Inc

Inventors

• Colley Shane
• Simpson Isaiah
• Reuter Brian
• Tell Robert
• Lefkofsky Hailey
• Lane Hunter
• White Kevin
• Beaubier Nike
• Bush Stephen
• Khan Aly
• Lau Denise
• Shah Kaanan
• Lefkofsky Eric

Classifications

• G16H10/60 Primary

  for patient-specific data, e.g. for electronic patient records · CPC title

• G16H15/00

  ICT specially adapted for medical reports, e.g. generation or transmission thereof · CPC title

• G16H20/40

  relating to mechanical, radiation or invasive therapies, e.g. surgery, laser therapy, dialysis or acupuncture · CPC title

• G16H50/20

  for computer-aided diagnosis, e.g. based on medical expert systems · CPC title

• G16H50/30

  for calculating health indices; for individual health risk assessment · CPC title