Multi-scale complex systems transdisciplinary analysis of response to therapy

摘要

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.

主权项

1. A method of predicting progression of a cancer in a subject in need thereof, comprising:
(a) obtaining from the subject a sample comprising molecular scale information and cellular scale information; (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 subject, and(iv) an organism-scale measurement from the 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 progression, wherein the measurements are used by the computer executable code as an initial parameter of the state-evolution simulation model, wherein the state-evolution 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, cellular scale simulation model, tissue-scale simulation model, and the organism-scale simulation model based upon output from the molecular-scale simulation model, cellular scale simulation model, tissue-scale simulation model, and 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, cellular, 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 progression status; (f) based upon the state-evolution simulation model output of (d) and the comparison of (e), generating a prediction of progression of the cancer in the subject; (g) selecting a treatment regimen to ameliorate symptoms associated with the prediction of progression of the cancer in the subject at risk of cancer progression; (h) administering the treatment to the subject; (i) monitoring the progression of the cancer in the subject at risk of cancer progression undergoing the treatment regimen; (j) revising the state evolution model if the progression of the cancer in the subject at risk of cancer progression differs from the prediction of progression of the cancer in the subject of (f); and (k) repeating steps (a) and (j) so as to iteratively improve predictive accuracy of the state evolution model.