Predicting Prostate Cancer Recurrence Using a Prognostic Model that Combines Immunohistochemical Staining and Gene Expression Profiling

20190252075

16/237392

December 31, 2018

An analysis and display system generates and displays a score indicative of whether cancer will recur in a patient. In a learning phase, a phenomic feature of tumor tissue is measured. A corresponding phenomic feature is defined. The phenomic feature may be measured through image analysis of digital images taken of tissue slices stained with IHC-based stains. A genomic feature of the tissue is also measured. This may entail obtaining a probe count indicative of a degree of expression of a particular gene. A bivariate feature is calculated using both the phenomic and genomic information. A network including the bivariate feature is displayed. In a diagnostic phase, raw phenomic and genomic data is obtained from a tissue sample taken from the patient. From the data, a score for the bivariate feature, and scores for the other features, are calculated. The score is a function of the underlying feature scores.

1. A method involving a system for generating a score, wherein the score is indicative of whether a cancer patient will have a recurrence of cancer, the method comprising: (a) measuring a phenomic feature based on biomarker positive objects detected by the system in a digital image of a first tissue slice, wherein the first tissue slice was stained with at least one protein-specific immunohistochemical (IHC) biomarker; (b) as a result of the measuring of (a) generating a univariate phenomic feature score value; (c) measuring a genomic feature based on detecting objects marked with at least one gene-specific probe biomarker detected by the system in tissue of a second tissue slice, wherein the first tissue slice and the second tissue slice are both taken from the same tissue sample taken from the cancer patient; (d) as a result of the measuring of (c) generating a univariate genomic feature score value; (e) based at least in part on the measuring of (a) and the measuring of (c) generating a bivariate feature score value, wherein the bivariate feature score value indicates a strength of a relationship between the phenomic feature and the genomic feature; and (f) determining the score by evaluating a function, wherein the function is a function of at least the bivariate feature score value, wherein (a) through (f) are performed by the system.

2. The method of claim 1, wherein the function of (f) is also a function of the univariate phenomic feature score value generated in (b), and is also a function of the univariate genomic feature score value generated in (d).

3. The method of claim 1, wherein the function of (f) is a majority voting function, wherein the univariate phenomic feature score value generated in (b) is a first vote, wherein the univariate genomic feature score value generated in (d) is a second vote, and wherein the bivariate feature score value generated in (e) is a third vote.

4. The method of claim 1, wherein the gene-specific probe biomarker of (c) is a probe that attaches to a particular sequence of nucleotides of an mRNA strand.

5. The method of claim 1, wherein the score determined in (f) is indicative of whether measurable prostate-specific antigen (PSA) is present in the cancer patient's blood.

6. The method of claim 1, wherein the system stores a cut-point value for a bivariate feature, and wherein the generating of the bivariate feature score value in (e) involves: (e1) determining a bivariate feature value for the bivariate feature, wherein the bivariate feature value is determined based at least in part on raw phenomic measurement data obtained in (a) and on raw genomic measurement data obtained in (c); and (e2) comparing the bivariate feature value determined in (e1) to the cut-point value, wherein the cut-point value was stored in the system prior to the measuring of (a) and prior to the measuring of (c).

7. The method of claim 1, wherein the measuring of (c) involves a counter device that outputs a count value, wherein the count value is indicative of a number of gene-specific probes counted, and wherein the counter device is a part of the system.

8. The method of claim 1, wherein the system comprises a display, the method further comprising: (g) displaying a rendering of a network on the display of the system, wherein the network includes a first node representing a phenomic feature, a second node representing a genomic feature, and edge that extends between the first node and the second node, wherein the edge represents the bivariate feature.

9. A method comprising: (a) receiving raw phenomic feature measurement data onto a system; (b) defining a phenomic feature based at least in part on the raw phenomic feature measurement data received in (a), wherein the phenomic feature includes a list of ranked values; (c) receiving raw genomic feature measurement data onto the system; (d) defining a genomic feature based at least in part on the raw genomic feature measurement data received in (c), wherein the genomic feature includes a list of ranked values; (e) defining a bivariate feature by combining ranked values of the list of ranked values of the phenomic feature with ranked values of the list of ranked values of the genomic feature thereby generating a list of ranked values for the bivariate feature, wherein the defining of the bivariate feature in (e) further involves determining and storing a cut-point value for the bivariate feature; (f) receiving a phenomic feature measurement data value onto the system, wherein the phenomic feature measurement data value is data obtained by analyzing a digital image of a first portion of tissue of a tissue sample, wherein the tissue sample is from a cancer patient; (g) receiving a genomic feature measurement data value onto the system, wherein the genomic feature measurement data value is data obtained by analyzing a second portion of the tissue of the tissue sample; (h) calculating a first score for the bivariate feature based at least in part on the phenomic feature measurement data value received in (f), the genomic feature measurement data value received in (g), and the cut-point value of (e), wherein the receiving of (f) and the receiving of (g) and the calculating in (h) are all performed after the defining of the bivariate feature in (e); and (i) determining a second score by evaluating a function, wherein the function is a function of the first score calculated in (h), wherein (a) through (i) are performed by the system, and wherein the second score is indicative of whether the cancer patient will have a recurrence of cancer.

10. The method of claim 9, wherein the system also calculates a third score for a phenomic feature, and wherein the system also calculates a fourth score for a genomic feature, and wherein the function in (i) is also a function of the third score and a function of the fourth score.

11. The method of claim 9, wherein the receiving of the raw genomic feature measurement data in (c) is a receiving of a digital file onto the system, wherein the digital file includes a digital count value.

12. The method of claim 9, wherein the receiving of the genomic feature measurement data value in (g) is a count of a number of gene-specific probes.

13. The method of claim 9, wherein the genomic feature measurement data value received in (g) is indicative of a degree of expression of a gene in the second portion of the tissue of the tissue sample.

14. A method of generating a network of prognostic features for cancer recurrence of a cancer patient, comprising: (a) measuring an immunohistochemical-based (IHC-based) feature of a tissue sample of a tumor of the cancer patient and computing the IHC-based feature; (b) measuring a gene expression feature of the tissue sample and computing the gene expression feature; (c) computing a bivariate feature, wherein the bivariate feature provides significant prognostic information on the cancer recurrence; and (d) displaying the network on a computer display, wherein a first node of the network represents the IHC-base feature computed in (a), wherein a second node of the network represents the gene expression feature computed in (b), and wherein an edge that extends between the first and second nodes represents the bivariate feature computed in (c).

15. The method of claim 14, wherein the size of a node of the displayed network indicates a prognostic value of a feature represented by the node, wherein some nodes of the network as displayed on the computer display are larger than other nodes of the network as displayed on the computer display.

16. The method of claim 14, wherein the width of an edge of the displayed network indicates a prognostic value of a bivariate feature represented by the edge, wherein some edges of the network as displayed on the computer display are wider than other edges of the network as displayed on the computer display.

17. The method of claim 14, wherein the bivariate feature computed in (c) is computed using a fuzzy logic combination of two features including the operators “and” and “not”.

18. The method of claim 14, wherein the bivariate feature that is computed in (c) includes a prognostic value.

19. The method of claim 14, wherein the bivariate feature that is computed in (a) includes a cut-point value.

20. The method of claim 14, wherein the display is a part of a system, and wherein (a) through (d) are performed by the system.

16; 12; 16; 16

edspap.20190252075