Computer systems for treating diseases
The present invention relates to computer-implemented methods and system for analysing a biomarker which cycles in a subject. In some other aspects, the present invention relates to analysing a biomarker which at least initially increases or decreases in amount in a subject following a treatment for a disease. In further aspects, the present invention relates to computer-implemented methods and systems for determining a preferred time to administer a therapy to treat a disease in a subject. The present invention also relates to computer program product to implement the methods. Further, the present invention relates to methods of determining the timing of treating a disease in a subject in which the immune system is cycling.
1. A non-transitory computer-readable information storage medium having stored thereon instructions, that if executed by one or more processors, perform a method comprising:
i) obtaining from a first memory measurements of an immune system marker in samples from a subject having a disease characterized by cycling immune system markers;
ii) obtaining from a second memory a model of cycling of the immune system marker;
iii) fitting the measurements to the model;
iv) obtaining a best-fit curve of the measurements to the model to estimate a periodicity of the cycling of the immune system marker;
v) projecting the obtained best-fit curve into the future; and
vi) outputting a preferred time in the future, based on the projected best-fit curve, to administer a therapy to treat the disease.
2. The computer-readable information storage medium of claim 1 , wherein the best-fit curve is a fit to a periodic model of the cycling of the immune system marker, and obtaining the best-fit curve further comprises fitting the periodic model to the measurements multiple times using different initial conditions for each fit.
3. The computer-readable information storage medium of claim 1 , wherein the best-fit curve is a fit to a harmonic model of the cycling of the immune system marker, and obtaining the best-fit curve further comprises fitting the harmonic model to the measurements multiple times using different initial conditions for each fit.
4. The computer-readable information storage medium of claim 1 , wherein the method further comprises generating a confidence measure by comparing a similarity in a fit result produced by each fitting.
5. The computer-readable information storage medium of claim 1 , wherein obtaining the best-fit curve further comprises imposing a box constraint on at least some parameters of the model to guide optimization to biologically realistic regions.
6. The computer-readable information storage medium of claim 1 , wherein the model has a heavy tail to allow for noisy nature of the measurements.
7. The computer-readable information storage medium of claim 1 , wherein the method further comprises determining a variance-stabilizing transformation of the measurements prior to determining the periodicity based on the transformed measurements.
8. The computer-readable information storage medium of claim 7 , wherein the variance-stabilizing transformation of measurements is logarithm of the measurements.
9. The computer-readable information storage medium of claim 1 , wherein the measurements are for two or more different immune system markers which are cycling in the subject.
10. The computer-readable information storage medium of claim 1 , wherein the disease is cancer, an infection, a degenerative disease, an autoimmune disease or transplant rejection.
11. The computer-readable information storage medium of claim 10 , wherein the disease is cancer.
12. The computer-readable information storage medium of claim 1 , wherein the immune system marker is an acute phase inflammatory marker.
13. The computer-readable information storage medium of claim 12 , wherein the acute phase inflammatory marker is selected from one or more of c-reactive protein, serum amyloid A, serum amyloid P component, complement proteins such as C2, C3, C4, C5, C9, B, C1 inhibitor and C4 binding protein, fibrinogen, von Willebrand factor, α1-antitrypsin, α1-antichymotrypsin, α2-antiplasmin, heparin cofactor II, plasminogen activator inhibitor I, haptoglobin, haemopexin, ceruloplasmin, manganese superoxide dismutase, α1-acid glycoprotein, haeme oxygenase, mannose-binding protein, leukocyte protein I, lipoporotein (a), lipopolysaccharide-binding protein, an interleukin (IL) or receptors thereof.
14. The computer-readable information storage medium of claim 13 , wherein the interleukin is one or more of IL-1, IL-2, IL-6 and IL-10.
15. The computer-readable information storage medium of claim 1 , wherein the immune system marker is a tumour marker.
16. The computer-readable information storage medium of claim 15 , wherein the tumour marker is AFP, CA 15-3, CA 19-9, CA 125, calcitonin, catecholamines, CEA, hCG/beta hCG, 5HIAA, PSA, sertonin or thyroglobulin.
17. A computer system for determining a preferred time to administer a therapy to treat a disease characterized by cycling immune system markers, the computer system comprising: a first memory for storing measurements of an immune system marker in samples from a subject having a disease characterized by cycling immune system markers; a second memory for storing a model of cycling of the immune system marker; and one or more processors to:
i) fit the measurements to the model;
ii) obtain a best-fit curve of the measurements to the model to estimate a periodicity of the cycling of the immune system marker;
iii) project the obtained best-fit curve into the future; and
iv) output a preferred time in the future, based on the projected best-fit curve, to administer a therapy to treat the disease.
18. The computer system of claim 17 , wherein the best-fit curve is a fit to a periodic model of the cycling of the immune system marker.
19. The computer system of claim 17 , wherein the best-fit curve is a fit to a harmonic model of the cycling of the immune system marker.
20. The computer system of claim 17 , wherein the model has a heavy tail to allow for noisy nature of the measurements.
21. The computer system of claim 17 , wherein the measurements are for two or more different immune system markers which are cycling in the subject.
22. The computer system of claim 17 , wherein the disease is cancer, an infection, a degenerative disease, an autoimmune disease or transplant rejection.
23. The computer system of claim 22 , wherein the disease is cancer.
24. The computer system of claim 17 , wherein the immune system marker is an acute phase inflammatory marker.