Multi-scale complex systems transdisciplinary analysis of response to therapy

Patent metadata

Field	Value
Publication number	US-10867706-B2
Application number	US-201514826049-A
Country	US
Kind code	B2
Filing date	Aug 13, 2015
Priority date	Jul 20, 2010
Publication date	Dec 15, 2020
Grant date	Dec 15, 2020

Abstract

Described herein are methods and systems to measure dynamics of disease progression, including cancer growth and response, at multiple scales by multiple techniques on the same biologic system. Methods and systems according to the invention permit personalized virtual disease models. Moreover, the invention allows for the integration of previously unconnected data points into an in silico disease model, providing for the prediction of disease progression with and without therapeutic intervention.

First claim

What is claimed is:

1. A method of predicting cancer emergence in a first subject in need thereof, said method comprising:
(a) obtaining a sample from the first subject;
(b) obtaining
(i) a molecular-scale measurement from the sample,
(ii) a cellular scale measurement from the sample,
(iii) an organ-scale measurement from the first subject, and
(iv) an organism-scale measurement from the first subject;

(c) providing the measurements obtained from step (b) to a computer comprising a computer executable code for running a state-evolution simulation model of cancer emergence, wherein the measurements are used by the computer executable code as an initial parameter of the state-evolution simulation model, wherein the state-evolution simulation model comprises:
(i) a molecular-scale simulation model of the cancer,
(ii) a cellular-scale simulation model of the cancer,
(iii) a tissue-scale simulation model of the cancer,
iv) an organism-scale simulation model of the cancer, and
(v) instructions for refining the molecular-scale simulation model, the cellular-scale simulation model, the tissue-scale simulation model, and the organism-scale simulation model based upon output from the molecular-scale simulation model, cellular-scale simulation model, the tissue-scale simulation model, and the organism-scale simulation model;

(d) using the computer, running the state-evolution simulation model to produce an output comprising a prediction of cancer state at the molecular-scale, cellular-scale, tissue-scale and organism-scale level;
(e) comparing the output of the model to outputs obtained for results of at least one of each of (i) molecular-scale measurement, (ii) cellular-scale measurement, (iii) organ-scale measurement, and (iv) organism-scale measurement from a second subject of known cancer status;
(f) based upon the state-evolution simulation model output of (d) and the comparison of (e), generating a prediction of emergence of the cancer in the first subject;
(g) selecting a treatment to ameliorate symptoms associated with the prediction of emergence of the cancer in the first subject; and
(h) administering the treatment to the first subject.

2. The method of claim 1 , wherein said cancer emergence is selected from acute myelogenous leukemia (AML) emergence, non-Hodgkins lymphoma (NHL) emergence, and primary follicular lymphoma emergence.

3. The method of claim 1 , wherein said method includes at least one thousand data points or measurements from said first subject.

4. The method of claim 1 , wherein said cellular-scale or molecular-scale measurements are taken from a tumor, and said cancer emergence is modeled with at least one parameter for cell adhesion, tissue invasion, or microenvironmental substrate gradients.

5. The method of claim 4 , wherein said cellular-scale measurements are selected from at least one of epigenetic somatic cell clock data, cell history patterns, or microenvironmental conditions.

6. The method of claim 5 , wherein said microenvironmental conditions are selected from oxygen levels, nutrient levels, and growth factor levels.

7. The method of claim 1 , wherein said organ-scale measurements are selected from at least one of vasculature structure, three-dimensional physical parameters of an organ, blood flow rate, ECM degradation rate, ECM secretion rate, tissue density, drug extravasation rate, spatial distribution of cells within a tumor, statistical distribution of cells within a tumor, tumor size, or response of a cancer to treatment.

8. The method of claim 7 , wherein said cancer for which response to treatment is measured is selected from acute myelogenous leukemia (AML), non-Hodgkins lymphoma (NHL), and primary follicular lymphoma.

9. The method of claim 7 , wherein said organ measured for three-dimensional physical parameters is a lymph node.

10. The method of claim 1 , wherein said organism-scale measurements are selected from at least one of dietary history, individual medical history, family medical history, geographic history, body-mass index, environmental exposure history, educational history, economic history, or behavioral history.

Assignees

• Applied Invent Llc
• Univ Southern California

Inventors

• Hillis William Daniel
• Agus David B

Classifications

• G16B20/20

  Allele or variant detection, e.g. single nucleotide polymorphism [SNP] detection · CPC title

• G16B5/00

  ICT specially adapted for modelling or simulations in systems biology, e.g. gene-regulatory networks, protein interaction networks or metabolic networks · CPC title

• G16B20/00

  ICT specially adapted for functional genomics or proteomics, e.g. genotype-phenotype associations · CPC title

• G16H50/50 Primary

  for simulation or modelling of medical disorders · CPC title

• G16B99/00

  Subject matter not provided for in other groups of this subclass · CPC title