Process for discriminating between biological states based on hidden patterns from biological data

• US 6,925,389 B2
• Filed: 07/18/2001
• Issued: 08/02/2005
• Est. Priority Date: 07/18/2000
• Status: Expired due to Term

• Current Assignee: Aspira Women's Health, Inc., The United States of America, as represented by the Secretary, Department of Health and Human Services
• Original Assignee: Correlogic Systems, Inc. (Aspira Women's Health, Inc.), United States Department Of Health And Human Services (Government of the United States of America)
• Inventors: Liotta, Lance A., Petricoin, Emanuel F. III, Levine, Peter J., Hitt, Ben A.
• Primary Examiner(s): Zeman, Mary K.
• Application Number:
  US09/906,661
• Publication Number:
  US 20030004402A1
• Time in Patent Office: 1,476 Days
• Field of Search: 702 19- 32, 703/11, 707/3, 707/102, 435/6, 435/7.1
• US Class Current: 702/19
• CPC Class Codes: G16B 25/00 ICT specially adapted for hybridisation; ICT specially adapted for gene or protein expression
• 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 40/10 Signal processing, e.g. from mass spectrometry [MS] or from PCR
• CPC Class Codes: G16H 10/40 for data related to laboratory analysis, e.g. patient specimen analysis
• CPC Class Codes: G16H 50/70 for mining of medical data, e.g. analysing previous cases of other patients
• CPC Class Codes: Y10S 707/99933 Query processing, i.e. searching
• CPC Class Codes: Y10S 707/99943 Generating database or data structure, e.g. via user interface

Abstract

The invention describes a process for determining a biological state through the discovery and analysis of hidden or non-obvious, discriminatory biological data patterns. The biological data can be from health data, clinical data, or from a biological sample, (e.g., a biological sample from a human, e.g., serum, blood, saliva, plasma, nipple aspirants, synovial fluids, cerebrospinal fluids, sweat, urine, fecal matter, tears, bronchial lavage, swabbings, needle aspirantas, semen, vaginal fluids, pre-ejaculate.), etc. which is analyzed to determine the biological state of the donor. The biological state can be a pathologic diagnosis, toxicity state, efficacy of a drug, prognosis of a disease, etc. Specifically, the invention concerns processes that discover hidden discriminatory biological data patterns (e.g., patterns of protein expression in a serum sample that classify the biological state of an organ) that describe biological states.

Claims

1. A method of determining whether a biological sample taken from a subject indicates that the subject has a disease by analyzing a data stream that is obtained by performing an analysis of the biological sample, comprising:

abstracting the data stream to produce a sample vector that characterizes the data stream in a predetermined vector space containing a diagnostic cluster, the diagnostic cluster being a disease cluster, the disease cluster corresponding to the presence of the disease;

determining whether the sample vector rests within the disease cluster; and

if the sample vector rests within the diseased cluster, identifying the biological sample as indicating that the subject has the disease, and displaying the result.

2. The method of claim 1, wherein the data stream is data describing an expression of molecules in the biological sample.

3. The method of claim 2, wherein the molecules are proteins.

4. The method of claim 2, wherein the molecules are selected from the group consisting of proteins, peptides, phospholipids, DNA, and RNA.

5. The method of claim 2, wherein the biological sample is serum.

6. The method of claim 2, wherein the biological sample is selected from the group consisting of serum, blood, saliva, plasma, nipple aspirants, synovial fluids, cerebrospinal fluids, sweat, urine, fecal matter, tears, bronchial lavage, swabbings, needle aspirants, semen, vaginal fluids, and pre-ejaculate.

7. The method of claim 2, wherein the biological sample is selected from the group consisting of tissue culture supernatants, lyophilized tissue cultures, and viral cultures.

8. The method of claim 1, wherein the disease is one in which the patterns of expression of inherent molecules in the diseased state are different from those in the non-diseased state.

9. The method of claim 1, wherein the disease is a cancer.

10. The method of claim 9, wherein the cancer is breast cancer.

11. The method of claim 9, wherein the cancer is a carcinoma.

12. The method of claim 9, wherein the cancer is selected from the group consisting of carcinomas, melanomas, lymphomas, sarcomas, blastomas, leukemias, myelomas, and neural tumors.

13. The method of claim 9, wherein the cancer is a carcinoma, said carcinoma being a prostatic carcinoma.

14. The method of claim 9, wherein the cancer is a carcinoma, said carcinoma being an ovarian carcinoma.

15. The method of claim 1, wherein the disease is selected from the group consisting of auto-immune diseases, Alzheimer'"'"'s disease and arthritis.

16. The method of claim 1, wherein the disease is glomerulonephritis.

17. The method of claim 1, wherein the disease is any infectious disease.

18. The method of claim 1, wherein the data stream is formed by any high throughput data generation method.

19. The method of claim 1, wherein the data stream is based on data associated with a time of flight mass spectrum.

20. The method of claim 19, wherein the time of flight mass spectrum is generated by surface-enhanced laser desorption time-of-flight mass spectroscopy.

21. The method of claim 19, wherein the time of flight mass spectrum is generated by matrix assisted laser desorption ionization time of flight.

22. The method of claim 1, wherein the data stream is based on data associated with a spectrum.

23. The method of claim 1, wherein the data stream is based on data associated with a mass spectrum.

24. The method of claim 1, wherein the diagnostic cluster is a first diagnostic cluster, the vector space contains a second diagnostic cluster, the second diagnostic cluster is a healthy cluster, the healthy cluster corresponding to an absence of the disease, further comprising:

determining whether the sample vector rests within the healthy cluster; and

if the sample vector rests within the healthy cluster, identifying the biological sample as indicating that the subject does not have the disease, and displaying the result.

25. A method of determining whether a biological sample taken from a subject indicates that the subject does not have a disease by analyzing a data stream that is obtained by performing an analysis of the biological sample, comprising:

abstracting the data stream to produce a sample vector that characterizes the data stream in a predetermined vector space containing a diagnostic cluster, the diagnostic cluster being a healthy cluster, the healthy cluster corresponding to the absence of the disease;

determining whether the sample vector rests within the healthy cluster; and

if the sample vector rests within the healthy cluster, identifying the biological sample as indicating that the subject does not have the disease, and displaying the result.

26. The method of claim 25, wherein the data stream is data describing an expression of molecules in the biological sample.

27. The method of claim 26, wherein the molecules are proteins.

28. The method of claim 26, wherein the molecules are selected from the group consisting of proteins, peptides, phospholipids, DNA, and RNA.

29. The method of claim 26, wherein the biological sample is serum.

30. The method of claim 26, wherein the biological sample is selected from the group consisting of serum, blood, saliva, plasma, nipple aspirants, synovial fluids, cerebrospinal fluids, sweat, urine, fecal matter, tears, bronchial lavage, swabbings, needle aspirants, semen, vaginal fluids, and pre-ejaculate.

31. The method of claim 26, wherein the biological sample is selected from the group consisting of tissue culture supernatants, lyophilized tissue cultures, and viral cultures.

32. The method of claim 25, wherein the disease is one in which the patterns of expression of inherent molecules in the diseased state are different from those in the non-diseased state.

33. The method of claim 25, wherein the disease is a cancer.

34. The method o claim 33, wherein the cancer is breast cancer.

35. The method of claim 33, wherein the cancer is a carcinoma.

36. The method of claim 33, wherein the cancer is selected from the group consisting of carcinomas, melanomas, lymphomas, sarcomas, blastomas, leukemias, myelomas, and neural tumors.

37. The method of claim 33, wherein the cancer is a carcinoma, said carcinoma being a prostatic carcinoma.

38. The method of claim 33, wherein the cancer is a carcinoma, said carcinoma being an ovarian carcinoma.

39. The method of claim 25, wherein the disease is selected from the group consisting of auto-immune diseases, Alzheimer'"'"'s di ease and arthritis.

40. The method of claim 25, wherein the disease is glomerulonephritis.

41. The method of claim 25, wherein the disease is any infectious disease.

42. The method of claim 25, wherein the data stream is formed by any high throughput data generation method.

43. The method of claim 25, wherein the data stream is based on data associated with a time of flight mass spectrum.

44. The method of claim 43, wherein the time of flight mass spectrum is generated by surface-enhanced laser desorption time-of-flight mass spectroscopy.

45. The method of claim 43, wherein the time of flight mass spectrum is generated by matrix assisted laser desorption ionization time of flight.

46. The method of claim 25, wherein the data stream is based on data associated with a spectrum.

47. The method of claim 25, wherein the data stream is based on data associated with a mass spectrum.

48. The method of claim 25, wherein the diagnostic cluster is a first diagnostic cluster, the vector space contains a second diagnostic cluster, the second diagnostic cluster is a disease cluster, the disease cluster corresponding to a presence of the disease, further comprising:

determining whether the sample vector rests within the disease cluster; and

if the sample vector rests within the disease cluster, identifying the biological sample as indicating that the subject has the disease, and displaying the result.