Data based cancer research and treatment systems and methods

Patent metadata

Field	Value
Publication number	US-11640859-B2
Application number	US-201916771451-A
Country	US
Kind code	B2
Filing date	Oct 17, 2019
Priority date	Oct 17, 2018
Publication date	May 2, 2023
Grant date	May 2, 2023

Abstract

A method and system for conducting genomic sequencing, the method comprising storing a set of user application programs wherein each of the programs requires an application specific subset of data, for each of a plurality of patients that have cancerous cells and that receive cancer treatment, obtaining clinical records data in original forms including cancer state information, treatment types and treatment efficacy information, storing the clinical records data in a semi-structured first database, for each patient, using a genomic sequencer to generate genomic sequencing data for the patient's cancerous cells and normal cells, storing the sequencing data in the first database, 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, storing the system structured data in a second database, for each user application program, selecting the application specific subset of data from the second database and storing the application specific subset of data in a structure optimized for application program interfacing in a third database.

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;
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;
(c) for each patient, using a next generation genomic sequencer to generate genomic sequencing data for 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.

2. 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 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 2 wherein at least one of the micro-services is a variant annotation service.

11. 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.

12. The method of claim 11 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.

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

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

15. The method of claim 11 further including a set of visualization tools and associated interfaces useable by a system user to analyze the second database data.

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

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

18. 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.

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 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.

20. 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.

21. The method of claim 1 wherein the sequencing data includes DNA sequencing data.

22. The method of claim 1 wherein the sequencing data includes only DNA sequencing data.

23. The method of claim 1 wherein the sequencing data includes only RNA sequencing data.

24. The method of claim 1 wherein the sequencing is conducted using a tumor gene panel.

25. The method of claim 24 , wherein the sequencing is conducted using a plurality of genes from the tumor gene panel.

26. The method of claim 24 , wherein the sequencing is conducted using at least one gene from the cell-free gene panel.

27. The method of claim 24 further including detecting a fusion event.

28. The method of claim 27 wherein the fusion event is a TMPRSS-ERG fusion.

29. The method of claim 1 wherein the sequencing is conducted using a whole-exome gene panel.

30. The method of claim 29 wherein the sequencing is conducted using at least one gene from the whole-exome gene panel.

31. The method of claim 1 wherein sequencing is done on the KRAS gene of the patient's cancerous cells and normal cells.

32. The method of claim 1 wherein sequencing is done on the PIK3CA gene of the patient's cancerous cells and normal cells.

33. The method of claim 1 wherein sequencing is done on the CDKN2A gene of the patient's cancerous cells and normal cells.

34. The method of claim 1 wherein sequencing is done on the PTEN gene of the patient's cancerous cells and normal cells.

35. The method of claim 1 wherein sequencing is done on the ARID1A gene of the patient's cancerous cells and normal cells.

36. The method of claim 1 wherein sequencing is done on the APC gene of the patient's cancerous cells and normal cells.

37. The method of claim 1 wherein sequencing is done on

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

• G16H50/70 Primary

  for mining of medical data, e.g. analysing previous cases of other patients · CPC title

• G16H10/60

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

• G16B50/20

  Heterogeneous data integration · CPC title

• G16H20/00

  ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance · CPC title

• G16B50/30

  Data warehousing; Computing architectures · CPC title