Methods and systems for predicting cancer outcome

• US 20060195269A1
• Filed: 05/19/2005
• Published: 08/31/2006
• Est. Priority Date: 02/25/2004
• Status: Active Grant

• Current Assignee: H Lee Moffitt Cancer Center & Research Institute (State University System of Florida), University of South Florida (State University System of Florida)
• Original Assignee: H Lee Moffitt Cancer Center & Research Institute (State University System of Florida)
• Inventors: Bloom, Gregory C., Yeatman, Timothy J., Eschrich, Steven
• Granted Patent: US 10,181,009 B2
• Time in Patent Office: Days
• US Class Current: 702/20
• CPC Class Codes: C12Q 1/6886 for cancer immunoassay for cancer G01N33/574
• CPC Class Codes: C12Q 2600/106 Pharmacogenomics, i.e. genetic variability in individual responses to drugs and drug metabolism
• CPC Class Codes: C12Q 2600/118 Prognosis of disease development
• CPC Class Codes: G16B 25/00 ICT specially adapted for hybridisation; ICT specially adapted for gene or protein expression
• CPC Class Codes: G16B 25/10 Gene or protein expression profiling; Expression-ratio estimation or normalisation
• 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: G16B 40/20 Supervised data analysis

Abstract

The invention provides a molecular marker set that can be used for prognosis of colorectal cancer in a colorectal cancer patient. The invention also provides methods and computer systems for evaluating prognosis of colorectal cancer in a colorectal cancer patient based on the molecular marker set. The invention also provides methods and computer systems for determining chemotherapy for a colorectal cancer patient and for enrolling patients in clinical trials.

Claims

1. A method for determining a prognosis of colorectal cancer in a colorectal cancer patient, comprising classifying said patient as having a good prognosis or a poor prognosis using measurements of a plurality of gene products in a cell sample taken from said patient, said gene products being respectively products of at least 5 of the genes listed in Table 1 or respective functional equivalents thereof, wherein said good prognosis predicts survival of a patient within a predetermined time period from obtaining a tumor sample from said patient by surgery or from diagnosis of colorectal cancer, and said poor prognosis predicts non-survival of a patient within said time period.

2. The method of claim 1, wherein said plurality of gene products are of at least 5 of the genes listed in Table 1.

3. The method of claim 1, further comprising obtaining said marker profile by a method comprising measuring said plurality of gene products in said tumor cell sample.

4. The method of claim 1, wherein said time period is 3 years.

5. The method of claim 1, wherein said time period is 4 years.

6. The method of claim 1, wherein said time period is 5 years.

7. The method of claim 1, wherein said classifying is carried out by a method comprising using a prognosis predictor, wherein said prognosis predictor receives an input comprising said measurements and provides an output comprising data indicating a good prognosis or a poor prognosis.

8. The method of claim 4, wherein said prognosis predictor is trained with training data from a plurality of colorectal cancer patients, wherein said training data comprise for each of said plurality of colorectal cancer patients (a) measurements of said plurality of gene products in a cell sample taken from said patient and (b) information with respect to whether survival for said time period occurred or not.

9. The method of claim 8, wherein said prognosis predictor is an artificial neural network (ANN).

10. The method of claim 9, wherein said ANN is a feed-forward back-propagation neural network with a single hidden layer of 10 units, a learning rate of 0.05, and a momentum of 0.2.

11. The method of claim 1, wherein said prognosis predictor is a support vector machine (SVM).

12. The method of claim 11, wherein said SVM is a SVM having a dot product kernel.

13. The method of claim 11, wherein said SVM is a SVM having a kernel selected from the group consisting of a d-degree dot product kernel and a Gaussian kernel.

14. The method of claim 1, wherein said gene products are products of at least 10 of the genes, respectively, listed in Table 1.

15. The method of claim 14, wherein said gene products are products of at least 20 of the genes, respectively, listed in Table 1.

16. The method of claim 14, wherein said gene products are products of at least 40 of the genes, respectively, corresponding to the markers listed in Table 1.

17. The method of claim 14, wherein said gene products are products of at least 70 of the genes, respectively, corresponding to the markers listed in Table 1.

18. The method of claim 1, wherein said gene products are products of the 53 genes, respectively, listed in Table 2.

19. The method of claim 1, wherein said gene products are products of the 7 genes, respectively, listed in Table 3.

20. The method of claim 1, wherein said gene products are products of the 43 genes, respectively, listed in Table 5.

21. The method of claim 1, wherein said gene products are products of the genes, respectively, identified by a * in Table 5.

22. The method of claim 18, 19 or 20, wherein said time period is 3 years, and wherein each of said gene products is a gene transcript.

24. The method of claim 1, wherein each of said gene products is a gene transcript.

25. The method of claim 24, wherein measurement of each said gene transcript is obtained by a method comprising contacting a positionally-addressable microarray with nucleic acids from said cell sample or nucleic acids derived therefrom under hybridization conditions, and detecting the amount of hybridization that occurs, said microarray comprising one or more polynucleotide probes complementary to a hybridizable sequence of each said gene transcript.

26. The method of claim 25, wherein said microarray is selected from the group consisting of cDNA microarray, ink-jet synthesized microarray, and oligonucleotide microarray.

27. The method of claim 1, wherein each of said plurality of gene products is a protein.

28. The method of claim 1, wherein said patient has an increased level of a gene product of osteopontin or neuregulin 2 isoform 4 relative to the average level of said gene product in patients without colorectal cancer.

29. A method for evaluating whether a colorectal cancer patient should be treated with chemotherapy, comprising (a) classifying said patient as having a good prognosis or a poor prognosis using a method as claimed in any one of claims 1, 2, 3, 7, 15 and 21;

and (b) determining that said patient'"'"'s predicted survival time favors treatment of the patient with chemotherapy if said patient is classified as having a poor prognosis.

30. The method of claim 29, further comprising additionally staging said patient using Duke staging.

31. A method for enrolling colorectal cancer patients for a clinical trial of a chemotherapeutic agent for colorectal cancer, comprising (a) classifying each patient as having a good prognosis or a poor prognosis using a method as claimed in any one of claims 1, 2, 3, 7, 15 and 21;

and (b) assigning each patient having a good prognosis to one patient group and each patient having a poor prognosis to another patient group, at least one of said patient group being enrolled in said clinical trial.

47. A computer system comprising

a processor, and

a memory coupled to said processor and encoding one or more programs, wherein said one or more programs cause the processor to carry out the method of any one of claims 1, 32, 35, and 43.

48. A computer program product for use in conjunction with a computer having a processor and a memory connected to the processor, said computer program product comprising a computer readable storage medium having a computer program mechanism encoded thereon, wherein said computer program mechanism may be loaded into the memory of said computer and cause said computer to carry out the method of any one of claims 1, 32, 35, and 43.

54. The method of claim 1, wherein said plurality of gene products comprises gene products of osteopontin and neuregulin 2 isoform 4, respectively.

23. The method of clam 22, wherein a support vector machine or neural network is used in said classifying step.

32. A method for identifying a set of genes for prognosis of colorectal cancer, comprising:

(a) determining for each of a plurality of genes a metric of correlation between abundance level of a gene product of said gene and survival outcome in a plurality of colorectal cancer patients having known outcomes at a predetermined time after obtaining tumor samples;

(b) selecting one or more genes based on said metric of correlation.

33. The method of claim 32, wherein said step (a) is carried out by a method comprising

(a1) dividing said plurality of colorectal cancer patients into a first group consisting of one or more patients who are living at said predetermined time and a second group consisting of one or more patients who are not living at said predetermined time;

and

(a2) determining a difference in abundance levels between said first group and said second group, wherein said difference represents said metric of correlation.

34. The method of claim 33, wherein said difference for each of said plurality of genes is a relative difference of a significance analysis of microarray (SAM) of expression levels of said gene between said first group and said second group.

35. The method of claim 32, wherein said predetermined time is 3 years.

36. A method for identifying a set of genes for prognosis of colorectal cancer, comprising:

(a) generating a subset of patients by leaving out one or more patients in a plurality of patients having known outcomes at a predetermined time after obtaining tumor samples;

(b) determining for each of a plurality of genes a metric of correlation between abundance level of said gene and survival outcome in said subset of colorectal cancer patients having known outcomes at a predetermined time after obtaining tumor samples;

(c) selecting one or more genes based on said metric of correlation;

(d) repeating steps (a)-(c) for a plurality of iterations, each with a different subset of patients by leaving out one or more patients in said plurality, wherein said one or more patients are different from any previous iteration; and

(e) selecting one or more genes that are selected in at least a predetermined percentage of all iterations.

37. The method of claim 36, wherein said step (c) is carried out by a method comprising

(c1) ranking said plurality of genes according to said metric;

and

(c2) selecting a given number of genes that ranked with the highest correlation.

38. The method of claim 37, wherein said given number is 50 and said predetermined percentage is about 75%.

39. The method of claim 38, wherein said difference for each of said plurality of genes is an absolute t-value of a t-test of expression levels of said gene between said first group and said second group.

40. The method of claim 39, wherein said selecting is carried out by a method comprising selecting a gene if the p-value of said gene corresponds to a predetermined significance level of p-value less than 0.05.

41. The method of claim 40, wherein said predetermined time is 3 years.

42. A method of identifying genes that discriminate between colorectal cancer patients that have a poor prognosis and colorectal patients that have a good prognosis comprising analyzing survival data and RNA levels of colorectal cancer patients using SAM, clustering analysis, or a neural network to select genes whose RNA levels correlate with a selected survival time.

43. A method for constructing a prognosis predictor for prognosis of colorectal cancer, comprising:

(a) generating a subset of patients by leaving out one or more patients in a plurality of patients having known outcomes at a predetermined time after obtaining tumor samples;

(b) determining for each of a plurality of genes a metric of correlation between expression level of said gene and survival outcome in a plurality of colorectal cancer patients having known outcomes at a predetermined time after obtaining tumor samples from a plurality of colorectal cancer patients having known outcomes at a predetermined time after obtaining tumor samples;

(c) selecting one or more genes based on said metric of correlation;

(d) training a prognosis predictor, wherein said prognosis predictor receives an input comprising a marker profile comprising expression levels of said one or more genes selected in step (c) and provides an output comprising data indicating a good prognosis or a poor prognosis, with training data from said subset of patients, wherein said training data comprise for each of said subset of patients a marker profile comprising measurements of said one or more genes in a tumor cell sample taken from said patient and prognosis information;

(e) determining a prognosis for at least one of said one or more patients who are left out in step (a);

(f) repeating steps (a)-(e) for a plurality of iterations, each with a different subset of patients by leaving out one or more patients in said plurality, wherein said one or more patients are different from any previous iteration;

(g) selecting one or more genes that are selected in at least a predetermined percentage of all iterations; and

(h) training a prognosis predictor, wherein said prognosis predictor receives an input comprising a marker profile comprising expression levels of said one or more genes selected in step (g) and provides an output comprising data indicating a good prognosis or a poor prognosis, with training data from said subset of patients, wherein said training data comprise for each of said plurality of patients a marker profile comprising measurements of said one or more genes in a tumor cell sample taken from said patient and prognosis information.

44. The method of claim 43, wherein said step (c) is carried out by a method comprising

(c1) ranking said plurality of genes according to said metric;

and

(c2) selecting a given number of genes that ranked with the highest correlation.

45. The method of claim 44, wherein said prognosis predictor is an artificial neural network (ANN).

46. The method of claim 43, wherein said step (b) is carried out by a method comprising

(b1) dividing said plurality of colorectal cancer patients into a first group consisting of one or more patients who are living at said predetermined time and a second group consisting of one or more patients who are not living at said predetermined time;

and

(b2) determining a difference in expression levels between said first group and said second group, wherein said difference represents said metric of correlation.

49. A microarray comprising for each of a plurality of genes, said genes being at least 5 of the genes listed in Table 1, one or more polynucleotide probes complementary and hybridizable to a sequence in said gene, wherein polynucleotide probes complementary and hybridizable to said genes constitute at least 50% of the probes on said microarray.

50. The microarray of claim 49, wherein said plurality of genes is at least 10 of the genes listed in Table 1.

51. The microarray of claim 49, wherein said plurality of genes is at least 20 of the genes listed in Table 1.

52. The microarray of claim 49, wherein said plurality of genes comprises the 43 genes listed in Table 5.

53. A kit comprising the microarray of claim 49 in a sealed container.