SYSTEMS AND METHODS FOR IDENTIFYING CANCER TREATMENTS FROM NORMALIZED BIOMARKER SCORES

• US 20180358128A1
• Filed: 06/12/2018
• Published: 12/13/2018
• Est. Priority Date: 06/13/2017
• Status: Active Grant

• Current Assignee: Bostongene Corporation
• Original Assignee: Bostongene Corporation
• Inventors: Bagaev, Alexander, Frenkel, Feliks, Ataullakhanov, Ravshan
• Granted Patent: US 10,340,030 B2
• Time in Patent Office: Days
• CPC Class Codes: C12Q 1/6886 for cancer immunoassay for cancer G01N33/574
• CPC Class Codes: C12Q 2600/156 Polymorphic or mutational markers
• CPC Class Codes: C12Q 2600/158 Expression markers
• CPC Class Codes: G06F 16/285 Clustering or classification
• CPC Class Codes: G06F 17/18 for evaluating statistical data , e.g. average values, frequency distributions, probability functions, regression analysis forecasting specially adapted for a specific administrative, business or logistic context G06Q10/04
• CPC Class Codes: G16B 20/00 ICT specially adapted for functional genomics or proteomics, e.g. genotype-phenotype associations
• CPC Class Codes: G16B 25/10 Gene or protein expression profiling; Expression-ratio estimation or normalisation
• CPC Class Codes: G16B 30/00 ICT specially adapted for sequence analysis involving nucleotides or amino acids
• CPC Class Codes: 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
• CPC Class Codes: G16B 40/20 Supervised data analysis
• CPC Class Codes: G16B 40/30 Unsupervised data analysis
• CPC Class Codes: G16B 45/00 ICT specially adapted for bioinformatics-related data visualisation, e.g. displaying of maps or networks
• CPC Class Codes: G16B 5/00 ICT specially adapted for modelling or simulations in systems biology, e.g. gene-regulatory networks, protein interaction networks or metabolic networks
• CPC Class Codes: G16B 5/20 Probabilistic models
• CPC Class Codes: G16B 50/00 ICT programming tools or database systems specially adapted for bioinformatics
• CPC Class Codes: G16B 50/30 Data warehousing; Computing architectures
• CPC Class Codes: G16H 10/20 for electronic clinical trials or questionnaires
• CPC Class Codes: G16H 20/00 ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance
• CPC Class Codes: G16H 20/10 relating to drugs or medications, e.g. for ensuring correct administration to patients
• CPC Class Codes: G16H 20/40 relating to mechanical, radiation or invasive therapies, e.g. surgery, laser therapy, dialysis or acupuncture
• CPC Class Codes: G16H 50/20 : for computer-aided diagnosis, e.g. based on medical expert systems
• CPC Class Codes: G16H 50/30 : for calculating health indices; for individual health risk assessment
• CPC Class Codes: G16H 50/50 : for simulation or modelling of medical disorders
• CPC Class Codes: G16H 50/70 : for mining of medical data, e.g. analysing previous cases of other patients
• CPC Class Codes: G16H 70/20 : relating to practices or guidelines

Abstract

Techniques for generating therapy biomarker scores and visualizing same. The techniques include determining, using a patient'"'"'s sequence data and distributions of biomarker values across one or more reference populations, a first set of normalized scores for a first set of biomarkers associated with a first therapy, and a second set of normalized scores for a second set of biomarkers associated with a second therapy, generating a graphical user interface (GUI) including a first portion associated with the first therapy and having at least one visual characteristic determined based on a normalized score of the respective biomarker in the first set of normalized scores; and a second portion associated with a second therapy and having at least one visual characteristic determined based on a normalized score of the respective biomarker in the second set of normalized scores; and displaying the generated GUI.

Claims

1. A system, comprising:

at least one computer hardware processor;

at least one database that stores biomarker information; and

at least one non-transitory computer-readable storage medium storing processor-executable instructions that, when executed by the at least one computer hardware processor, cause the at least one computer hardware processor to perform;

obtaining sequencing data about at least one biological sample of a subject;

accessing, in the at least one database, biomarker information indicating a distribution of values for each biomarker in at least a reference subset of a plurality of biomarkers across a respective group of people, each of the plurality of biomarkers being associated with at least one therapy in a plurality of therapies;

determining, using the sequencing data and the biomarker information;

a first set of normalized scores for a first set of biomarkers associated with a first therapy in the plurality of therapies; and

a second set of normalized scores for a second set of biomarkers associated with a second therapy in the plurality of therapies, wherein the first set of biomarkers is different from the second set of biomarkers;

providing the first set of normalized scores as input to a statistical model to obtain a first therapy score for the first therapy;

providing the second set of normalized scores as input to the statistical model to obtain a second therapy score for the second therapy;

generating a graphical user interface (GUI), wherein the GUI comprises;

a first portion associated with the first therapy in the plurality of therapies, the first portion including a first plurality of GUI elements, each of the first plurality of GUI elements being associated with a respective biomarker in the plurality of biomarkers and having at least one visual characteristic determined based on a normalized score of the respective biomarker in the first set of normalized scores; and

a second portion associated with the second therapy in the plurality of therapies, the second portion including a second plurality of GUI elements different from the first plurality of GUI elements, each of the second plurality of GUI elements being associated with a respective biomarker in the plurality of biomarkers and having at least one visual characteristic determined based on a normalized score of the respective biomarker in the second set of normalized scores; and

displaying the generated GUI.

2. The system of claim 1, wherein the plurality of biomarkers includes a first biomarker, and wherein determining a normalized score for each biomarker in at least the subject subset of the plurality of biomarkers comprises:

determining a first normalized score for the first biomarker using the distribution of values for the first biomarker.

3. The system of claim 2, wherein determining the first normalized score comprises:

determining an un-normalized score for the first biomarker using the sequencing data;

determining a Z-score based on the first distribution of values for the first biomarker; and

determining a normalized score for the first biomarker based on the un-normalized score and the Z-score.

4. The system of claim 1, wherein determining therapy scores for the plurality of therapies comprises determining a first therapy score for a first therapy in the plurality of therapies as a sum of two or more scores in the set of normalized biomarker scores for the subject.

5. The system of claim 1, wherein determining therapy scores for the plurality of therapies comprises determining a first therapy score for a first therapy in the plurality of therapies at least in part by:

determining weights for two or more scores in the set of normalized biomarker scores for the subject; and

determining the first therapy score as a sum of the two or more scores, summands of the sum being weighted by the determined weights.

6. The system of claim 5, wherein determining the weights comprises determining the weights using a machine learning technique.

7. The system of claim 5, wherein determining the weights comprises determining the weights using a generalized linear model.

8. The system of claim 5, wherein determining the weights comprises determining the weights using a logistic regression model.

9. The system of claim 1, wherein the plurality of therapies comprises a first therapy and a second therapy different from the first therapy, and wherein determining therapy scores for the plurality of therapies comprises:

determining a first therapy score for the first therapy using a first subset of the set of normalized biomarker scores for the subject; and

determining a second therapy score for the second therapy using a second subset of the set of normalized biomarker scores for the subject,

wherein the second subset is different from the first subset.

10. The system of claim 1, wherein the processor-executable instructions further cause the at least one processor to perform:

recommending at least one of the plurality of therapies for the subject based on the determined therapy scores.

11. The system of claim 1, wherein the plurality of therapies comprise at least two therapies selected from the group consisting of:

an anti-PD1 therapy, an anti-CTLA4 therapy, an IL-2 therapy, an IFN alpha therapy, an anti-cancer vaccine therapy, an anti-angiogenic therapy, and an anti-CD20 therapy.

12. The system of claim 11, wherein determining the normalized biomarker scores for the subject comprises determining a normalized score for each of at least three biomarkers selected from the group of biomarkers associated with anti-PD1 therapy in Table 2.

13. The system of claim 11, wherein determining the normalized biomarker scores for the subject comprises determining a normalized score for each of at least three biomarkers selected from the group of biomarkers associated with anti-CTLA4 therapy in Table 2.

14. The system of claim 11, wherein determining the normalized biomarker scores for the subject comprises determining a normalized score for each of at least three biomarkers selected from the group of biomarkers associated with IL-2 therapy in Table 2.

15. The system of claim 11, wherein determining the normalized biomarker scores for the subject comprises determining a normalized score for each of at least three biomarkers selected from the group of biomarkers associated with IFN alpha therapy in Table 2.

16. The system of claim 11, wherein determining the normalized biomarker scores for the subject comprises determining a normalized score for each of at least three biomarkers selected from the group of biomarkers associated with anti-cancer vaccine therapy in Table 2.

17. The system of claim 11, wherein determining the normalized biomarker scores for the subject comprises determining a normalized score for each of at least three biomarkers selected from the group of biomarkers associated with anti-angiogenic therapy in Table 2.

18. The system of claim 11, wherein determining the normalized biomarker scores for the subject comprises determining a normalized score for each of at least three biomarkers selected from the group of biomarkers associated with anti-CD20 therapy in Table 2.

19. The system of claim 1, wherein the at least one visual characteristic comprises color of a GUI element and/or size of the GUI element.

20. The system of claim 1, wherein the processor-executable instructions further cause the at least one processor to perform:

in response to receiving, via the GUI, a user selection of the first therapy, presenting, via the GUI, information about at least one biomarker with which at least one of the first plurality of GUI elements is associated.

21. The system of claim 1, wherein the first therapy is associated with a first therapy score and the second therapy is associated with a second therapy score, and wherein the first portion and the second portion are positioned, relative to one another in the GUI, based on relative magnitude of the first therapy score and the second therapy score.

23. The system of claim 1, wherein the sequencing data is one or more of:

DNA sequencing data, RNA sequencing data, or proteome sequencing data.

24. The system of claim 1, wherein each of the therapies are selected from immunotherapy and targeted therapy.

22. The system of 1, wherein each of the plurality of biomarkers is selected from the group consisting of:

a genetic biomarker, a cellular biomarker, a saccharide biomarker, a lipid biomarker, a heterocyclic biomarker, an elementary compound biomarker, an imaging biomarker, an anthropological biomarker, a personal habit biomarker, a disease-state biomarker, and an expression biomarker.

25. At least one non-transitory computer-readable storage medium storing processor-executable instructions that, when executed by the at least one computer hardware processor, cause the at least one computer hardware processor to perform:

obtaining sequencing data about at least one biological sample of a subject;

accessing, in at least one database, biomarker information indicating a distribution of values for each biomarker in at least a reference subset of a plurality of biomarkers across a respective group of people, each of the plurality of biomarkers being associated with at least one therapy in a plurality of therapies;

determining, using the sequencing data and the biomarker information;

a first set of normalized scores for a first set of biomarkers associated with a first therapy in the plurality of therapies; and

a second set of normalized scores for a second set of biomarkers associated with a second therapy in the plurality of therapies, wherein the first set of biomarkers is different from the second set of biomarkers;

providing the first set of normalized scores as input to a statistical model to obtain a first therapy score for the first therapy;

providing the second set of normalized scores as input to the statistical model to obtain a second therapy score for the second therapy;

generating a graphical user interface (GUI), wherein the GUI comprises;

a first portion associated with the first therapy in the plurality of therapies, the first portion including a first plurality of GUI elements, each of the first plurality of GUI elements being associated with a respective biomarker in the plurality of biomarkers and having at least one visual characteristic determined based on a normalized score of the respective biomarker in the first set of normalized scores; and

a second portion associated with the second therapy in the plurality of therapies, the second portion including a second plurality of GUI elements different from the first plurality of GUI elements, each of the second plurality of GUI elements being associated with a respective biomarker in the plurality of biomarkers and having at least one visual characteristic determined based on a normalized score of the respective biomarker in the second set of normalized scores; and

displaying the generated GUI.

26. The at least one non-transitory computer-readable storage medium of claim 25, wherein the plurality of biomarkers includes a first biomarker, and wherein determining a normalized score for each biomarker in at least the subject subset of the plurality of biomarkers comprises:

determining a first normalized score for the first biomarker using the distribution of values for the first biomarker.

27. The at least one non-transitory computer-readable storage medium of claim 26, wherein determining the first normalized score comprises:

determining an un-normalized score for the first biomarker using the sequencing data;

determining a Z-score based on the first distribution of values for the first biomarker; and

determining a normalized score for the first biomarker based on the un-normalized score and the Z-score.

28. A method, comprising:

using the at least one computer hardware processor to perform;

obtaining sequencing data about at least one biological sample of a subject;

accessing, in at least one database, biomarker information indicating a distribution of values for each biomarker in at least a reference subset of a plurality of biomarkers across a respective group of people, each of the plurality of biomarkers being associated with at least one therapy in a plurality of therapies;

determining, using the sequencing data and the biomarker information;

a first set of normalized scores for a first set of biomarkers associated with a first therapy in the plurality of therapies; and

a second set of normalized scores for a second set of biomarkers associated with a second therapy in the plurality of therapies, wherein the first set of biomarkers is different from the second set of biomarkers;

providing the first set of normalized scores as input to a statistical model to obtain a first therapy score for the first therapy;

providing the second set of normalized scores as input to the statistical model to obtain a second therapy score for the second therapy;

generating a graphical user interface (GUI), wherein the GUI comprises;

a first portion associated with the first therapy in the plurality of therapies, the first portion including a first plurality of GUI elements, each of the first plurality of GUI elements being associated with a respective biomarker in the plurality of biomarkers and having at least one visual characteristic determined based on a normalized score of the respective biomarker in the first set of normalized scores; and

a second portion associated with the second therapy in the plurality of therapies, the second portion including a second plurality of GUI elements different from the first plurality of GUI elements, each of the second plurality of GUI elements being associated with a respective biomarker in the plurality of biomarkers and having at least one visual characteristic determined based on a normalized score of the respective biomarker in the second set of normalized scores; and

displaying the generated GUI.

29. The method of claim 28, wherein the plurality of biomarkers includes a first biomarker, and wherein determining a normalized score for each biomarker in at least the subject subset of the plurality of biomarkers comprises:

determining a first normalized score for the first biomarker using the distribution of values for the first biomarker.

30. The method of claim 29, wherein determining the first normalized score comprises:

determining an un-normalized score for the first biomarker using the sequencing data;

determining a Z-score based on the first distribution of values for the first biomarker; and

determining a normalized score for the first biomarker based on the un-normalized score and the Z-score.