# US 10,480,033 B2 · MiRNA-based predictive models for diagnosis and prognosis of prostate cancer

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- Number: US 10,480,033 B2
- Document ID: US10480033B2
- Status: Granted
- Kind: B2
- Application number: 15/543,721
- Filed: 2016-01-14
- Published: 2018-01-04
- Granted: 2019-11-19
- Earliest claimed: 2015-01-14
- Assignees: Ohio State Innovation Foundation; University of Freiburg
- Applicants: Ohio State Innovation Foundation; University of Freiburg
- Inventors: Arnab Chakravarti; Erica McKenzie Bell; Simon Kirste; Anca-Ligia Grosu
- Web page: https://patentgrep.com/patent/US10480033B2

## CPC

- C12Q 1/6886: for cancer (immunoassay for cancer G01N33/575)
- G16B 25/00: ICT specially adapted for hybridisation; ICT specially adapted for gene or protein expression
- G16B 25/10: Gene or protein expression profiling; Expression-ratio estimation or normalisation
- 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
- G16B 40/20: Supervised data analysis
- C12Q 2600/106: Pharmacogenomics, i.e. genetic variability in individual responses to drugs and drug metabolism
- C12Q 2600/118: Prognosis of disease development
- C12Q 2600/158: Expression markers
- C12Q 2600/178: miRNA, siRNA or ncRNA
- C12Q: MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS (immunoassay G01N33/53); COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
- G16B: BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY

## Abstract

The lack of clear predictors of prostate cancer progression leads to subjective decision-making regarding courses of treatment. The identification of new biomarkers that are predictive of recurrence after radical prostatectomy would advance the field of prostate cancer treatment. Disclosed are miRNAs that can be used as molecular biomarkers to detect or predict the progression of prostate cancer and to adjust a treatment plan accordingly. Furthermore, kits are included for the detection of these miRNAs.

## Claims

### Claim 1

A method for treating a subject based on a high biochemical recurrence risk score following post-prostatectomy salvage radiation therapy, comprising
a) determining a miRNA expression profile from a sample from the subject of at least 1 miRNA selected from the group consisting of hsa-miR-628-3p, hsa-miR-1193, hsa-miR-601, hsa-miR-4516, hsa-miR-320e, hsa-miR-508-3p, hsa-miR-598, hsa-miR-626, hsa-miR-563; and
b) calculating a-miRNA-based recurrence risk score from the miRNA expression profile; wherein a high recurrence risk score when compared to Gleason score and lymph node status alone, wherein a high recurrence risk score is based on a regression value of about 0.5 or greater, is an indication of biochemical recurrence within 1 to 5 years; and
c) treating the subject with a high recurrence risk score with adjuvant therapy, radiation therapy, palliative care, or a combination thereof.

### Claim 2 (depends on 1)

The method of claim 1, wherein the at least 1 miRNA comprises miR-4516, miR-601, or a combination thereof.

### Claim 3 (depends on 1)

The method of claim 1, wherein the adjuvant therapy comprises chemotherapy, hormone therapy, biologic therapy, radiation therapy, or a combination thereof.

### Claim 4 (depends on 1)

The method of claim 1, wherein the salvage radiation therapy comprises external beam radiation therapy or internal radiation therapy.

### Claim 5 (depends on 1)

The method of claim 1, wherein the sample is a tumor or a bodily fluid.

### Claim 6 (depends on 1)

The method of claim 1, further comprising grading a tumor according to a Gleason score, and using the Gleason score as a covariate in calculating the recurrence risk score.

### Claim 7 (depends on 1)

The method of claim 1, further comprising determining one or more properties of a tumor including the subject's age, PSA levels, lymph node classification (pN), pathologic tumor classification (pT), resection status, D'Amico classification, and/or Stephenson nomograms and using the one or more properties as covariates in calculating the recurrence risk score.