Compositions and methods for active surveillance of prostate cancer

Patent metadata

Field	Value
Publication number	US-12326453-B2
Application number	US-201615143345-A
Country	US
Kind code	B2
Filing date	Apr 29, 2016
Priority date	Mar 28, 2014
Publication date	Jun 10, 2025
Grant date	Jun 10, 2025

Abstract

Aspects of the disclosure relate to improved methods and systems for active surveillance of subject having non-aggressive prostate cancer.

First claim

What is claimed is:

1. A method, comprising:
i) selecting a subject having a total prostate specific antigen (tPSA) level of less than or equal to 25 ng/ml comprising obtaining a blood sample from the subject, removing blood plasma from the blood sample, and subjecting the blood plasma to an immunoassay that measures a level of tPSA in the blood plasma;
ii) subjecting the blood plasma from the selected subject to one or more immunoassays that measure levels of kallikrein markers, wherein the kallikrein markers comprise free prostate specific antigen (fPSA), intact prostate specific antigen (iPSA), and human kallikrein 2 (hK2);
iii) determining the probability that a prostate tissue biopsy obtained from the subject would contain detectable aggressive prostate cancer using a logistic regression model with aggressive prostate cancer as the outcome, wherein the logistic regression model is:
Xβ=β 0 +β 1 age+β 2 tpsa+β 3 sptpsa1+β 4 sptpsa2+β 5 fpsa+β 6 spfpsa1+β 7 spfpsa2+β 8 ipsa+β 9 hK2+β 10 dre neg +β 11 dre pos +β 12 priorbx;

wherein β 0 -β 12 are weighting coefficients, age is the age of the subject when the blood is obtained, tpsa is concentration of tPSA in ng/mL, fpsa is concentration of fPSA in ng/mL, ipsa is concentration of iPSA in ng/mL, hK2 is concentration of hK2 in ng/ml, sptpsa1 is a first spline term for tPSA, sptpsa2 is a second spline term for tPSA, spfpsa1 is a first spline term for fPSA, spfpsa2 is a second spline term for fPSA, dre neg is a value of 1 if a digital rectal examination has been confirmed as negative or a value of 0 otherwise, dre pos is a value of 1 if a digital rectal examination has been confirmed as positive or a value of 0 otherwise, and priorbx is 0 if no prior negative biopsy and 1 if the subject had a prior negative biopsy;
iv) comparing the probability from step iii) to a probability threshold level of 12.5%, wherein when the probability from step iii) is below the probability threshold level of 12.5%, the subject undergoes active surveillance without obtaining a prostate tissue biopsy from the subject, wherein the active surveillance comprises repeating steps i)-iii) at least once within an interval of 6 months to 1 year to calculate a repeated probability; and
v) obtaining a prostate tissue biopsy from the subject when the probability of step iii) or the repeated probability of step iv) is above the probability threshold level of 12.5%; and
vi) identifying the subject as having aggressive prostate cancer based on the obtained prostate tissue biopsy of step v) indicating the presence of aggressive prostate cancer and the probability of step iii) or the repeated probability of step iv) being above the probability threshold level of 12.5%, wherein the aggressive prostate cancer is associated with a Gleason score of 7 or more; and
vii) treating the identified subject of step vi), wherein treatment comprises prostatectomy or radiotherapy.

2. The method of claim 1 further comprising repeating steps i)-iii) at least twice in step iv), wherein the interval between each set of steps i)-iii) is in a range of 6 months to 1 year.

3. The method of claim 1 further comprising repeating steps i)-iii) at least once per year for up to five years before step v).

Assignees

• Opko Diagnostics Llc

Inventors

• Dong Yan
• Vickers Andrew J
• Sjoberg Daniel
• Scardino Peter T
• Lilja Hans

Classifications

• G01N33/57555 Primary

  of the prostate · CPC title

• G06N7/01

  Probabilistic graphical models, e.g. probabilistic networks · CPC title

• G16B20/00

  ICT specially adapted for functional genomics or proteomics, e.g. genotype-phenotype associations · CPC title

• G16H50/20

  for computer-aided diagnosis, e.g. based on medical expert systems · CPC title

• G16B40/00

  ICT specially adapted for biostatistics; ICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding · CPC title