WO2020205204A1 - Méthodes de détection du cancer de la prostate - Google Patents

Méthodes de détection du cancer de la prostate Download PDF

Info

Publication number
WO2020205204A1
WO2020205204A1 PCT/US2020/022555 US2020022555W WO2020205204A1 WO 2020205204 A1 WO2020205204 A1 WO 2020205204A1 US 2020022555 W US2020022555 W US 2020022555W WO 2020205204 A1 WO2020205204 A1 WO 2020205204A1
Authority
WO
WIPO (PCT)
Prior art keywords
prostate cancer
subject
likelihood
likelihood score
prostate
Prior art date
Application number
PCT/US2020/022555
Other languages
English (en)
Inventor
David Okrongly
Original Assignee
Opko Diagnostics, Llc
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Opko Diagnostics, Llc filed Critical Opko Diagnostics, Llc
Publication of WO2020205204A1 publication Critical patent/WO2020205204A1/fr

Links

Classifications

    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
    • G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
    • G01N33/53—Immunoassay; Biospecific binding assay; Materials therefor
    • G01N33/574—Immunoassay; Biospecific binding assay; Materials therefor for cancer
    • G01N33/57407—Specifically defined cancers
    • G01N33/57434—Specifically defined cancers of prostate
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
    • G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
    • G01N33/53—Immunoassay; Biospecific binding assay; Materials therefor
    • G01N33/574—Immunoassay; Biospecific binding assay; Materials therefor for cancer
    • G01N33/57484—Immunoassay; Biospecific binding assay; Materials therefor for cancer involving compounds serving as markers for tumor, cancer, neoplasia, e.g. cellular determinants, receptors, heat shock/stress proteins, A-protein, oligosaccharides, metabolites
    • G01N33/57488—Immunoassay; Biospecific binding assay; Materials therefor for cancer involving compounds serving as markers for tumor, cancer, neoplasia, e.g. cellular determinants, receptors, heat shock/stress proteins, A-protein, oligosaccharides, metabolites involving compounds identifable in body fluids

Definitions

  • the present disclosure is based, at least in part, on the surprising finding that aggressive prostate cancer can be more accurately detected in subjects having an elevated risk for aggressive prostate cancer using certain biomarkers (e.g., kallikreins), clinical factors, and imaging techniques.
  • biomarkers e.g., kallikreins
  • subjects suspected of having aggressive prostate cancer undergo one or more assessments to determine the likelihood of aggressive prostate cancer.
  • Subjects that have a risk above a relatively low threshold ate recommended for biopsy. Biopsy involves an invasive procedure that is expensive and exposes the subject to surgical risks, including infection, anesthesia complications, bleeding problems, clots, etc.
  • certain disorders that enhance the risk of morbidity and/or mortality associated with biopsy are common in subjects having an elevated risk for aggressive prostate cancer.
  • aspects of the disclosure relate to the recognition that there is a need to reduce unnecessary biopsies in subjects previously identified as having an elevated risk of aggressive prostate cancer.
  • the minimally invasive methods and systems, described herein, are particularly well-suited for the detection of aggressive prostate cancer and the reduction of unnecessary biopsies in subjects previously identified as having an elevated risk of aggressive prostate cancer.
  • a method of evaluating a subject for prostate biopsy may comprise i) subjecting a blood sample of the subject to immunoassays that measure levels of total prostate specific antigen (tPSA), free prostate specific antigen (fPSA), intact prostate specific antigen (iPSA), and human kaliikrein 2 (hK2); ii) determining a first likelihood score for prostate cancer based on the levels of fPSA, (PS A, iPSA, and hK2 and one or more clinical factors; iii) if (he first likelihood score is within a threshold range, obtaining a second likelihood score for prostate cancer based at least in part on an outcome of an imaging technique performed on the subject and determining a likelihood of prostate cancer based on the second likelihood score; and iv) if the first likelihood score is below a lower limit of the threshold range or an upper
  • a method of evaluating a subject for prostate biopsy may comprise i) subjecting a blood sample of the subject to immunoassays that measure levels of total prostate specific antigen (tPSA), free prostate specific antigen (fPSA), intact prostate specific antigen (iPSA), and human kaliikrein 2 (hK2); ii) determining a first likelihood score for prostate cancer based on the levels of fPSA, tPSA, iPSA, and hK2 and one or mere clinical factors; iii) if the first likelihood score is within a threshol d range, obtaining a second 1 ikelihood score of prostate cancer based at least in part on an outcome of an imaging technique performed on the subject and determining a likelihood of prostate cancer based on the first likelihood score and the second likelihood score; and tv) if the first likelihood score is below a lower limit of the threshold range or an upper limit of the threshold range, determining the likelihood of prostate cancer based on the
  • a method of evaluating a subject previously diagnosed with prostate cancer may comprise i) subjecting a blood sample of the subject to immunoassays that measure levels of total prostate specific antigen (tPSA), free prostate specific antigen (fPSA), intact prostate specific antigen (iPSA), and human kaliikrein 2 (hK2); ii) determining a first likelihood score for prostate cancer outcomes based on the levels of fPSA, tPSA, iPSA, and hK2 and one or more clinical factors; iii) obtaining a second likelihood score of prostate cancer based at least in part cm an outcome ofan imaging technique performed on the subject; and iv) determining a likelihood of prostate cancer based on the first likelihood score and the second likelihood score.
  • tPSA total prostate specific antigen
  • fPSA free prostate specific antigen
  • iPSA intact prostate specific antigen
  • hK2 human kaliikrein 2
  • the methods may comprise determining a likelihood of prostate cancer based solely on the second likelihood score.
  • the first likelihood score is the probability of prostate cancer.
  • the second likelihood score is a likelihood ratio of prostate cancer.
  • the first likelihood score is determined using a logistic regression model, wherein the logit of the logistic regression model is determined by weighting the measured levels of tPSA, fPSA, iPSA, and hK2 and one or more clinical factors.
  • the second likelihood score is the positive likelihood ratio for the imaging technique when the outcome is at least equivocal for prostate cancer based on radiology reporting scale.
  • the radiology reporting scale is the Likert and/or Prostate Imaging and Data Reporting System (PIRADS) radiology reporting scale.
  • PIRADS Prostate Imaging and Data Reporting System
  • the imaging technique is performed on the subject subsequent to the subjecting step. In some cases, the imaging technique is performed within 1 month of the subjecting step. In certain embodiments, the imaging technique is performed within 6 mentis of the subjecting step. In some instances, the sensitivity of the imaging technique is greater than or equal to about 55% or greater than or equal to about 85%. In certain cases, the specificity of the imaging technique is greater than or equal to about 30% and less than or equal to about 60%.
  • the subject has had a prior prostate biopsy. In some instances, the subject has been previously diagnosed with non-aggressive prostate cancer. In certain cases, the subject has been previously diagnosed with aggressive prostate cancer. In some embodiments, the subject has not been previously diagnosed with prostate cancer. In some cases, the prostate cancer is prostate cancer that is detectable on prostate biopsy. In certain cases, the prostate cancer is aggressive prostate cancer that is detectable on prostate biopsy, to some embodiments, the prostate cancer has a Gleason score of 3 ⁇ + 4. to certain instances, the prostate cancer has a pathological stage of at least T3b. to some cases, the prostate cancer has a Gleason score of 4 + 3.
  • the lower limit of the threshold range is greater than or equal to about 7.5%.
  • the upper limit of the threshold range is greater than or equal to about 32%. In some cases, the upper limit of the threshold range is greater than or equal to about 44%. In certain cases, the threshold range is greater than or equal to about 7.5% and less than or equal to about 44%. to certain embodiments, the threshold range is greater than or equal to about 7.5% and less than or equal to about 32%. In some instances, the threshold range is greater than or equal to about 32% and less than or equal to about 44%.
  • a method of evaluating a subject having a first likelihood score of greater than about 32% for prostate cancer may comprise (i) obtaining information indicative of an Outcome of an imaging technique; and (ii) determining a first likelihood score of prostate cancer based at least in part on the outcome of the imaging technique, wherein the first likelihood score is determined based on levels of IPSA, tPSA, iPSA, and hK2 in a blood sample of the subject and one or more clinical factors.
  • the imaging technique is magnetic resonance imaging: In some cases, the imaging technique is multi-parametric magnetic resonance imaging (mp-MRl). In some instances, the method further comprises performing a magnetic resonance imaging procedure on the subject
  • a method for determining a likelihood of prostate cancer may comprise (i) receiving, via an input interface, information indicative of a level of total prostate specific antigen (tPSA), free prostate specific antigen (IPSA), intact prostate specific antigen (tPSA), and human kallikrcin 2 (hK2) in a subject and information indicative of one or more clinical factors; (ii) evaluating, using at least one processor, a logistic regression model based on the received information to determine a first likelihood score for prostate cancer, wherein evaluating the logistic regression model comprises determining the first likelihood score based on the information indicative of the level of tPSA, fPSA iPSA, and hK2, and the information indicative of one or more clinical factors; (in) comparing, using the at least One processor, the first likelihood score to a threshold range; and determining the likelihood of prostate cancer based on the first likelihood score when the first likelihood score is outside the threshold range and determining the likelihood of prostate cancer based cm a second likelihood score when the first likelihood score is
  • the threshold range is greater titan or equal to about 7.5% and less than or equal to about 44%. In some instances, tire threshold range is greater than or equal to about 7.5% and less than or equal to about 32%. In some cases, the threshold range is greater titan or equal to about 32% and less than or equal to about 44%.
  • the measured levels of IPSA, tPSA, iPSA, and hK2 and age of the subject are weighted using a logistic regression model.
  • determining the first likelihood score comprises weighting a cubic spline term based on the measured tPSA level, to some cases, determining the first likelihood score comprises weighting a cubic spline term based an the measured fPSA level.
  • one or more clinical factors comprise the subject's age.
  • the one or more clinical factors comprise a factor indicative of the outcome of a digital rectal examination performed on the subject.
  • the one or more clinical factors is selected from the group consisting age of the subject; factor indicative of the outcome of a digital rectal examination performed on the subject; results of prior prostate tissue biopsies performed on the subject to date; PSA density; race of subject; family history of prostate cancer, and combinations thereof.
  • a system for determining a likelihood of prostate cancer may comprise (a) a detector configured to measure a level of total prostate specific antigen (tPSA), free prostate specific antigen (fPSA), intact prostate specific antigen (iPSA), and human kallikrcm 2 (hK2) in a subject; and (b) a computer in electronic communication with the detector, wherein the computer comprises: (i) an input interface configured to receive information indicative of a level of tPSA, fPSA , iPSA, and hK2 in the subject, information indicative of one or more clinical factors, and information indicative of an outcome of an imaging technique; (ii) at least one processor programmed to evaluate a logistic regression model based, at least in part, on the received information to determine a likelihood of prostate cancer, wherein evaluating the logistic regression model comprises determining the first likelihood score based on the information indicative of the level of tPSA, fPSA, iPSA, and h
  • the threshold range is greater than or equal to about 7.5% and less than or equal to about 44%. In some instances, the threshold range is greater than or equal to about 7.5% and less than or equal to about 32%.
  • the measured levels of fPSA, tPSA, iPSA, and hK2 and age of the subject are weighted using a logistic regression model. In some cases, determining the first likelihood score comprises weighting a cubic spline term based on the measured tPSA level. In some instances, determining the first likelihood score comprises weighting a cubic spline term based on the measured fPSA level.
  • the prostate cancer has a Gleason score of 3 + 4. In some instances, the prostate cancer has a pathological stage of at least T3b.
  • the imaging technique is magnetic resonance imaging. In some instances, the imaging technique is multi- parametric magnetic resonance imaging.
  • the one or more clinical factors comprise the subject’s age. In some cases, the one or more clinical factors comprise a factor indicative of the outcome of a digital rectal examination performed on the subject.
  • the one or more clinical factors is selected from the group consisting age of the subject; factor indicative of the outcome of a digital rectal examination performed on the subject; results of prior prostate tissue biopsies performed on the subject to date; PSA density; race of subject; family history of prostate cancer; and combinations thereof.
  • FIG. 1A is flowchart showing a process for determining whether a subject has prostate cancer in accordance with some embodiments of the invention.
  • FIG. 1B is flowchart showing a process for determining whether a subject has prostate cancer in accordance with some embodiments of the invention.
  • FIG. 1C is a schematic illustration of a computer configured for implementing a process for determining whether a subject has prostate cancer in accordance with some embodiments of the invention.
  • FIG. 1D is a schematic of a computer network configured for implementing a process for determining whether a subject has prostate cancer in accordance with some embodiments of the invention.
  • aspects of the disclosure relate to methods and systems for detecting prostate cancer and/or evaluating a subject for a prostate biopsy.
  • a subject may undergo an assessment to determine title likelihood of prostate cancer (e.g., aggressive prostate cancer).
  • one or more immunoassay may be performed on a blood sample of the subject to determine biomarker levels that may be used with certain clinical factors to determine a likelihood score for prostate cancer.
  • a subject having a likelihood score within a threshold range may undergo a second assessment to determine the likelihood of prostate cancer.
  • the subject may undergo an imaging technique to detect prostate cancer. The outcome of the second assessment may be used to determine the likelihood of prostate cancer and/or biopsy recommendation.
  • a subject having a likelihood score outside the threshold range does not undergo a second assessment and/or the likelihood may be determined using the likelihood score (e.g., alone).
  • Methods and systems, described herein, may be particularly well-suited for the minimally invasive and cost- effective detection of prostate cancer in subjects.
  • Prostate biopsy is the gold standard for diagnosis of prostate cancer.
  • prostate biopsy is associated with a significant risk of morbidity in many subjects. Therefore, a subject suspected of having prostate cancer, e.g.. due to abnormal total prostate specific antigen (tPSA) levels and/or digital rectal exam (DRE), may undergo an assessment to more accurately determine the likelihood of aggressive prostate cancer prior subjecting the subject to biopsy.
  • tPSA total prostate specific antigen
  • DRE digital rectal exam
  • the assessment should be minimally invasive and cost-effective to prevent morbidity and allow for wide implementation.
  • certain imaging techniques e.g., MR1 are not a typical assessment technique, because the high expense of the procedure would limit its use.
  • subjects with moderate risk e.g., between about 7.5 % and about 44% probability of aggressive prostate cancer, non-aggressive prostate cancer
  • high risk e.g., greater than about 44% probability of aggressive prostate cancer
  • a method comprises performing one or more assessments on a subject (e.g., subject suspected of having prostate cancer) to detect prostate cancer and/or evaluate the subject for prostate biopsy.
  • the method may comprise performing a first assessment to determine a first likelihood score for prostate cancer (e.g., aggressive prostate cancer).
  • the method may further comprise performing a second assessment to determine a second likelihood score for prostate cancer when the first likelihood score foils within a threshold range (e.g., greater than about 7.5% and less than or equal to about 44%, greater than about 7.5% and less than or equal to about 32%).
  • the likelihood of prostate cancer may be determined based (e.g., solely) on the second likelihood score.
  • the likelihood of prostate cancer may be determined based (e.g., solely) on the first likelihood score and the second likelihood score.
  • the first likelihood score may serve as pre-test odds and the second likelihood score may be a likelihood ratio (e.g., positive likelihood ratio, negative likelihood ratio).
  • the pre-test odds and likelihood ratio may be combined to determine the post-test odds and/or post-test probability.
  • the second assessment e.g., imaging technique
  • the likelihood of prostate cancer may be determined based (e.g., solely) on the first likelihood score.
  • the first assessment may utilize a blood sample (e.g., whole blood, plasma, serum).
  • a method for detecting prostate Cancer e.g., aggressive prostate cancer
  • a subject for prostate biopsy involves subjecting a blood sample from the subject to immunoassays that measure levels of tPSA and free prostate specific antigen (fPSA) and at least two protein marker levels selected from the group consisting of intact prostate specific antigen (iPSA), human kallikrein 2 (hK2), pre-pro precursor prostate specific antigen (pre-pro PSA, or [-2] proPSA), Microseminoprotein, beta- (MSMB), and macrophage inhibitory cytokine- 1 (MIC-1).
  • iPSA intact prostate specific antigen
  • hK2 human kallikrein 2
  • pre-pro PSA pre-pro precursor prostate specific antigen
  • MSMB Microseminoprotein
  • MIC-1 macrophage inhibitory cytokine- 1
  • the method comprises subjecting a blood sample from the subject to immunoassay(s) that measure levels of tPSA, fPSA, and two protein marker levels selected from the group consisting of iPSA, hK2, pre-pro PSA, MSMB, and MIC-1.
  • the method comprises subjecting a blood sample from the subject to immunoassays that measure levels of tPSA, fPSA, tPSA, and hK2.
  • the method comprises subjecting a blood sample from the subject to immunoassays that measure levels of tPSA, fPSA, and three protein marker levels selected from the group consisting of iPSA, hK2, pre-pro PSA, MSMB, and MIC-1.
  • the method comprises subjecting a Mood sample from the subject to immunoassays that measure levels of tPSA, fPSA, and four protein marker levels selected from the group consisting of iPSA, hK2, pre-pro PSA, MSMB, and MIC-1.
  • the method comprises subjecting a blood sample from the subject to immunoassays that measure levels of tPSA, fPSA, tPSA, hK2, pre-pro PSA, MSMB, and MIC-1.
  • at least some of the protein markers e.g., one, two, three, four or more, tPSA and fPSA
  • two or more (e.g., three or more, four or more, five or more) protein markers are measured in different assays.
  • a predictive model (e.g., a logistic regression model) that incorporates levels of tPSA and fPSA and at least two protein marker levels selected from the group consisting of tPSA, hK2, pre-pro PSA, MSMB, and MIC-1 to determine the likelihood of prostate cancer (e.g., aggressive prostate cancer) in a subject.
  • a predictive model e.g., a logistic regression model
  • the predictive model further comprises information (e.g., non-clinical information) regarding the subject, such as age and/or other clinical factors described herein.
  • the model outputs a likelihood seme, wherein the output is indicative of the likelihood that a subject has prostate cancer (e.g., aggressive prostate cancer).
  • the model outputs a likelihood score between 0% and 100%.
  • a likelihood score of less than about 8% (e.g., less than about 7.5%) may be associated with a low risk of prostate cancer (e.g.. aggressive prostate cancer).
  • the subject may not be recommended for prostate biopsy and/or an intervention.
  • a likelihood score of greater than about 44% may be associated with a high risk of prostate cancer.
  • the subject may be recommended for prostate biopsy and/of more aggressive treatment, such as radical prostatectomy (RP) car radiation.
  • RP radical prostatectomy
  • a subject with a moderate risk of prostate cancer is also recommended for prostate biopsy and/or an intervention (e.g., active surveillance, radical prostatectomy).
  • an intervention e.g., active surveillance, radical prostatectomy.
  • a subject with a moderate risk e.g., a likelihood score of greater than about 7.5% and less than or equal to about 44%) of prostate cancer may be recommended for prostate biopsy and/or less aggressive intervention, such as, e.g., antiandrogen therapy, immunotherapy, local treatment with cryotherapy or high-intensity focal ultrasound.
  • the moderate risk subject may opt for more aggressive treatment.
  • a subject is recommended for a biopsy and/or intervention when the likelihood score is not associated with a low risk for prostate cancer.
  • a subject is recommended for a biopsy and/or intervention when the likelihood score is greater than or equal to about 7.5% (e.g., greater than or equal to about 8%).
  • the methods, described herein may be particularly useful for reducing unnecessary biopsy and/or intervention in subjects having a moderate risk (i.e., an intermediate risk) of prostate cancer based on an assessment.
  • a subject having a likelihood score e.g. , a first likelihood score
  • a threshold range may undergo a second assessment prior to a biopsy and/or intervention based on the first assessment or first likelihood score.
  • a subject having a likelihood score within a threshold range associated with a moderate risk may be subjected to a second assessment.
  • the threshold range (e.g., for comparing with a first likelihood score) is greater than about 7.5% and less than or equal to about 44% (e.g., greater than or equal to about 8% and less than or equal to about 44%).
  • the threshold range is greater than about 7.5% and less than or equal to about 32% (e.g., greater than or equal to about 8% and less than or equal to about 32%).
  • the threshold range is greater than or equal to about 32% and less than or equal to about 44%.
  • the combination of the first and second assessment may have a significantly greater predictive power for moderate risk subjects than either assessment alone or the expected mathematical cumulative benefit. In some instances, the combination may be synergetic.
  • the first assessment may utilize an imaging technique instead of protein markers (e.g., kalltkreins).
  • the sensitivity of the imaging technique is greater than or equal to about 55%, greater than or equal to about 60%. greater than or equal to about 65%, greater than or equal to about 70%, greater than or equal to about 75%, greater than or equal to about 80%, or greater than or equal to about 85%.
  • the specificity of the imaging technique is greater than or equal to about 20% and less than or equal to about 60% (e.g., greater than or equal to about 20 and less than or equal to about 40, greater than or equal to about 30 and less than or equal to about 60).
  • the first assessment may utilize a magnetic resonance imaging (MRI) technique, such as multi-parametric MRI (mpMRI).
  • MRI magnetic resonance imaging
  • mpMRI multi-parametric MRI
  • a MRI e.g., mpMRI
  • the outcome of the MRI may be a likelihood of prostate cancer based on a radiology reporting scale, such as the Likert scale or the Prostate Imaging and Data Reporting System (PIRADS).
  • the radiology reporting scale assigns a number to an image that is indicative of the likelihood that a subject has prostate cancer (e.g., aggressive prostate cancer).
  • the Likert radiology reporting scale is a 5-point scale that is used to designate a prostate as highly unlikely (1), unlikely (2). equivocal (3), likely (4), and high likely (5) to harbor aggressive or otherwise clinically significant prostate cancer.
  • Likert scores of 1 and 2 may be considered negative for prostate cancer (e.g., low risk).
  • Likert scores of 3-5 may be considered positive for prostate cancer (e.g., intermediate or high risk).
  • PIRADS scores of 0-2 may be considered negative for prostate cancer and PIRADS scores of 3-5 may be considered positive for prostate cancer (e.g., intermediate or high risk).
  • a subject with a radiology reporting scale score associated with the absence of detectable prostate cancer is not recommended for prostate biopsy and/or an intervention.
  • the subject may be recommended for prostate biopsy and/or an intervention (e.g., active surveillance, radical prostatectomy) when the radiology reporting scale score is associated with detectable prostate cancer (e.g., aggressive prostate cancer).
  • a radiology reporting scale score of 3 may be associated with a moderate risk of prostate cancer (e.g., aggressive prostate cancer), to some such cast « the subject may be recommended for prostate biopsy and/or less aggressive intervention.
  • the subject may opt for more aggressive treatment.
  • a radiology reporting scale score of 4 or 5 may be associated with a high risk of prostate cancer. In such cases, the subject may be recommended for prostate biopsy and/or more aggressive treatment.
  • the negative prostate cancer scenes may be associated with a negative likelihood ratio.
  • a score on a radiology reporting scale associated with the absence of detectable prostate cancer may have a negative likelihood ratio of less than or equal to about 0.2 (e g., greater than or equal to about 0.1 and less than or equal to about 0.2, greater titan or equal to about 0.12 and less than or equal to about 0.2, greater than or equal to about 0.15 and less than or equal to about 0.2, greater than or equal to about 0.16 and less than or equal to about 0.2, greater than or equal to about 0.15 and less than or equal to about 0.18, about 0.17).
  • a score on a radiology reporting scale associated with the presence of detectable prostate cancer may have a positive likelihood ratio of greater than or equal to about 12 (e.g., greater than or equal to about 1.5, greater than or equal to about 1.2 and less than or equal to about 2.0, greater than or equal to about 1.5 and less than or equal to about 2.0, greater than or equal to about 1.5 and less than or equal to about 1.8, greater than or equal to about 1.5 and less than or equal to about 1.7, about 1.6).
  • a subject having a score on a radiology reporting scale associated with the presence of detectable prostate cancer may undergo a second assessment. For instance, a subject having a PIRADS score of 3, 4, or 5 may be subjected to a second assessment.
  • a subject identified as having a moderate risk of prostate cancer e.g., aggressive prostate cancer
  • a second assessment may change the risk classification of the subject.
  • the subject’s risk assessment may be based solely on the outcome of the second assessment For example, a subject with a first likelihood score associated with a moderate risk and a second likelihood score associated with a low risk may be classified as low risk, to such cases, a biopsy and/or intervention would not be recommended.
  • a subject with a first likelihood score associated with a moderate risk and a second likelihood score associated with a high risk may be classified as high risk, to such cases, a biopsy and/or intervention may be recommended, in some instances, a subject with a first likelihood score associated with a moderate risk and a second likelihood score associated with a moderate risk may be classified as high risk, In such cases, a biopsy and/or intervention may be recommended.
  • the subject’s risk assessment may be based on the outcome of both the first assessment and the second assessment.
  • the first likelihood score may serve as pretest odds and the second likelihood score may be a likelihood ratio.
  • the pre-test odds and likelihood ratio may be combined mathematically (e.g., multiplied) to determine the post-test odds, which may be converted to a percent likelihood of prostate cancer (e.g., aggressive prostate cancer).
  • the first assessment utilize; protein markers and one or more clinical factors, as described above.
  • the second assessment may utilize an imaging technique, as described herein.
  • the first assessment may comprise subjecting a blood sample of the subject to immunoassays that measure levels of tPSA, fPSA, iPSA, and/or hK2 and determining a first likelihood score for prostate cancer (e.g., aggressive prostate cancer) based on the levels of IPSA, tPSA, iPSA, and hK2 and one or more clinical factors (e.g., age, prior negative biopsy, DRE outcome).
  • the first likelihood score may be determined using a logistic regression model, in which the logit of the logistic regression model is determined at least in part by weighting the measured levels of tPSA, fPSA, iPSA, and/or hK2 and one or more clinical factors. If the first likelihood score is within a threshold range, e.g., that is associated with a moderate risk of prostate cancer (e.g., aggressive prostate cancer), an imaging technique may be performed on the subject, e.g., as part of a second assessment, after the first assessment has been performed.
  • the imaging technique may be, for example, mpMRI, as described herein.
  • the image may be used to produce a second likelihood score, e.g., in the form of a positive or negative likelihood ratio.
  • the likelihood of prostate cancer may be determined based on the second likelihood score. In other embodiments, the likelihood of prostate cancer may be determined based on a combination of the first and second likelihood store, if the first likelihood score is below a lower limit of the threshold range (e g., that is associated with a moderate risk of prostate cancer) or an upper limit of the threshold range, the likelihood of prostate cancer may be determined based on the first likelihood score. For example, if the first likelihood score is less than or equal to about 8% (e.g., less than or equal to about 7.5%) or greater than or equal to about 45%, the likelihood of prostate cancer may be determined based on the first likelihood score.
  • a lower limit of the threshold range e.g., that is associated with a moderate risk of prostate cancer
  • the likelihood of prostate cancer may be determined based on the first likelihood score. For example, if the first likelihood score is less than or equal to about 8% (e.g., less than or equal to about 7.5%) or greater than or equal to about 45%, the likelihood of prostate cancer
  • the first assessment utilizes an imaging technique, as described above, in some such cases, the second assessment may utilize protein markers and one or more clinical factors, as described herein.
  • the first assessment may comprise performing an imaging technique on the subject.
  • the imaging technique may be mpMRI, as described herein.
  • the image may be used to produce a first likelihood score, e.g., in the form of a positive or negative likelihood ratio. If the first likelihood score is positive or otherwise indicative of prostate cancer (e.g., aggressive prostate cancer), a second assessment is performed.
  • the second assessment may comprise subjecting a blood sample of!
  • the subject to immunoassays dial measure levels of tPSA, fPSA, iPSA, and/or hK2 and determining a second likelihood score for prostate cancer (e g., aggressive prostate cancer) based on the levels of fPSA, tPSA, iPSA, and hK2 and one or more clinical factors (e g., age, prior negative biopsy, DRE outcome), in some embodiments, the second likelihood score may be determined using a logistic regression model, as described above.
  • the likelihood of prostate cancer (c.g., aggressive prostate cancer) may be determined based on the second likelihood seme or a combination of the first and second likelihood score. If the first likelihood score is negative, the likelihood of prostate cancer may be determined based on the first likelihood score.
  • the second assessment may be performed after the first assessment.
  • the second assessment may be performed one or more hours, one or more days (e.g., two or more days, four or more days), one or more weeks (e.g., two or more weeks, three or more weeks), or one or more months (c.g., two or more months, four or more months, six or more months, nine or more months) after the first assessment
  • the second assessment is performed within 1 months, within 3 months, within 6 months, within 9 months, or within l year of the first assessment
  • the second assessment may be performed concurrently with the first assessment.
  • a likelihood score (e.g., a first likelihood score) may be based on one or more clinical factors.
  • the one or more clinical factors include the subject’s age, factor indicative of a prior negative biopsy, and/or outcome of DRE.
  • the one or more factors may be one or more factors indicative of the race of subject and/or the family history of prostate cancer.
  • Non-limiting examples of clinical factors include age, PSA level, digital rectal examination (DRE) status, prostate volume, PSA density, total tumor length in all prostate biopsy cores, number of tumor containing cores at prostate biopsy, percentage of tumor-containing cares found at prostate biopsy, maximum percentage of cancer in any prostate biopsy core, total tumor length of Gleason grade 4, percentage of Gleason grade 4 tumor, racial group;, single or multiple germline genetic variations and mutations (e.g., AFC, ATM, BAP1, BARD1, BMPR.1A, BRCA1, BRCA2, BRIP1, CDH1.
  • DRE digital rectal examination
  • the one or more factors do not include factors derived from a clinical procedure (e.g., biopsy, DRE).
  • the one or more clinical factors may include the subject’s age and factor indicative of a prior negative biopsy.
  • the one or more factors is not be selected from the group consisting of number of prostate tissue biopsies performed on the subject to date; results of prior prostate tissue biopsies performed on the subject to date; occurrence of any negative biopsy since an initial diagnosis of non- aggressive prostate cancer, occurrence of any negative biopsy within one-year prior to obtaining the blood sample; total number of biopsies since an initial diagnosis of non-aggressive prostate cancer; prostate volume on prior biopsy; number of positive cores on prior biopsy; percent positive cores on prior biopsy; cross-sectional area of cancer in biopsy core sections; maximum cross-sectional area of cancer in any biopsy core sections; PSA density; maximum percent of positive cores from any prior biopsy; maximum number of positive cores from any prior biopsy, and combinations thereof.
  • the one or more factors include factors derived from a clinical procedure (e.g., biopsy, DRE).
  • the one or more factors may be selected from the group consisting of number of prostate tissue biopsies performed on the subject to date, results of prior prostate tissue biopsies performed on the subject to date; occurrence of any negative biopsy since an initial diagnosis of non-aggressive prostate cancer; occurrence of any negative biopsy within one-year prior to obtaining the blood sample; total number of biopsies since an initial diagnosis of non- aggressive prostate cancer, prostate volume on prior biopsy; number of positive cores on prior biopsy; percent positive cores on prior biopsy; cross- sectional area of cancer in biopsy core sections;
  • a subject may not have been (or has not been) previously diagnosed with prostate cancer of any grade.
  • the subject may not have had (or has not had) a prior biopsy.
  • the subject may have had (or has had) one or more negative biopsy.
  • a subject may have been (or has been) previously diagnosed with non-aggressive prostate cancer.
  • the subject may have had (dr has had) one or mote prior biopsies.
  • the previous biopsies may not have (or has not) contained detectable aggressive postate cancer.
  • the subject may be on and/or eligible for active surveillance according to NCCN and/or AUA guidelines. For example, the subject may fall within the very low risk, tow risk, or favorable intermediate risk groups provided in the table below.
  • a method of evaluating a subject previously diagnosed with prostate cancer comprises subjecting a blood sample of the subject to immunoassays that measure levels of total prostate specific antigen (IPSA), free prostate specific antigen (fPSA ), intact prostate specific antigen (iPSA), and human kallikrein 2 (hK2).
  • IPSA total prostate specific antigen
  • fPSA free prostate specific antigen
  • iPSA intact prostate specific antigen
  • hK2 human kallikrein 2
  • the method may further comprise determining a first likelihood score for prostate cancer outcomes based on the levels of fPSA, tPS A, iPS A, and hK2 and one or more clinical factors and obtaining a second likelihood score of prostate cancer based at least in part on an outcome of an imaging technique performed on the subject.
  • the method may also comprise determining a likelihood of prostate cancer based on the first likelihood score and the second likelihood score.
  • Methods are provided herein for determining whether a subject is a candidate for prostate biopsy. Such methods may involve a physician or health care provider obtaining a blood sample from a subject and determining the likelihood that the prostate tissue sample obtained through prostate biopsy and/or RP contains prostate cancer (e.g., aggressive prostate cancer), at least in part, on the methods described herein. Blood samples utilized in the method may be processed locally (e.g., within the same health care facility or business that the subject is being evaluated) or may be sent out to an external or third-party laboratory or facility for processing and analysis.
  • prostate cancer e.g., aggressive prostate cancer
  • a method for detecting prostate cancer in a subject and/or evaluating a subject for prostate biopsy involves subjecting a blood sample from the subject to immunoassays that measure levels of tPSA and (PSA and at least two protein marker levels selected from the group consisting of iPSA, hK2, pre-pro PSA, and MIC- 1.
  • the method comprises subjecting a blood sample from the subject to immunoassays that measure levels of tPSA, fPSA, and two protein marker levels selected from the group consisting of iPSA, hK2, pre-pro PSA, MSMB, and MIC- 1.
  • the method comprises subjecting a blood sample from the subject to immunoassays that measure levels oftPSA, fPSA, iPSA, and hK2.
  • the method comprises subjecting a blood sample from the subject to immunoassays that measure levels of tPSA, fPSA, and three protein marker levels selected from the group consisting of iPSA, hK2, pre-pro PSA, and MIC-1.
  • the method comprises subjecting a blood sample from the subject to immunoassays that measure levels of tPSA, IPSA, and four protein marker levels selected from the group consisting of iPSA, hK2, pre-pro PSA, and MIC-1.
  • the method comprises subjecting a blood sample from the subject to immunoassays that measure levels of tPSA, fPSA, iPSA. hK2, pre-pro PSA, MSMB, and MIC-1.
  • at least some of the protein makers e.g., one, two, three, four or more, tPSA and IPSA are measured in a single assay.
  • two or more (e.g., three or more, four or more, fi ve or more) protein biomarkers are measured in different assays.
  • a predictive model (rig., a logistic regression model) is provided that incorporates levels of tPSA and fPSA and at least two protein marker levels selected from the group consisting of iPSA, hK2, pre-pro PSA, and MIC-1 to determine the likelihood of having prostate cancer.
  • a predictive model e.g., a logistic regression model
  • the predictive model further comprises information regarding the subject, such as age, prostate volume, and/or total tumor length.
  • Any suitable sample that may contain prostate cancer cells can be analyzed by the assay methods described herein.
  • Any suitable sample that may contain markers of prostate cancer cells e.g., one or more kallikrein markers
  • the methods described herein may involve providing a sample obtained from a subject
  • the methods described herein may involve procuring a sample from a subject.
  • the sample to be analyzed by the assay methods is a biological sample.
  • a“biological sample” refers to a composition that comprises tissue, (e.g., whole blood, blood plasma, serum, or protein, from a subject).
  • a biological sample e.g., blood sample
  • a biological sample may include both an initial unprocessed sample taken from a subject as well as subsequently processed, e. g., partially purified or preserved forms of a sample taken from a subject
  • Exemplary samples include Wood (including whole blood, blood plasma, or scrum), tears, or mucus.
  • the sample is a body fluid sample such as a serum or blood plasma sample.
  • the sample is a whole blood sample.
  • multiple biological samples may be collected from a subject, over tins; or at particular time intervals. These multiple samples may be used, for example, to assess disease progression over time or to evaluate the efficacy of a treatment
  • a biological sample can be obtained from a subject using any suitable means known in the art.
  • the sample is obtained from the subject by removing the sample (e.g., a prostate tissue sample) from the subject, fa some embodiments, the sample is obtained from the subject by a surgical procedure (e.g., radical prostatectomy), fa some embodiments, the sample is obtained from the subject by a biopsy (eg. , a prostate biopsy).
  • a prostate biopsy include, but are not limited to, a transrectal ultrasound (TRUS)-guided systematic biopsy of the prostate, a transurethral biopsy, a transperineal prostate biopsy, and a MRl-guided prostate biopsy.
  • TRUS transrectal ultrasound
  • more than one sample is obtained from the same patient (e.g., a blood sample and a prostate biopsy sample), fa some embodiments, the blood sample and prostate biopsy sample are obtained on the same day. fa some embodiments, the prostate biopsy sample is obtained before the blood sample is obtained, fa some embodiments, the blood sample is obtained before the prostale biopsy sample is obtained, fa certain embodiments, mote than one blood sample is obtained. In some embodiments, a first blood sample may be obtained before the prostate biopsy sample, and a second blood sample may be obtained after the prostate biopsy sample. In some embodiments, a blood sample is obtained within about 1 , 2, 3, 4, 5, 6, 7, or 8 days of a prostate biopsy sample.
  • a blood sample is obtained within about 1, 2, 3, 4, 5, 6, 7, or 8 weeks of a prostate biopsy sample. In certain embodiments, a blood sample is obtained within about 1 , 2, 3, 4, 5, 6, 7, 8, 9, 10. 1 1. or 12 months of a biopsy sample, fa some embodiments, a blood sample is obtained from a subject about every 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, or 12 months; or every 1, 2, 3.4, 5, 6, or 7 years.
  • a blood sample is obtained from a subject at least once within 3-9 months, at least once within 4-10 months, at least once within 5-11 months, or at least once within 6-12 months, fa certain embodiments, a blood sample is (Attained from a subject at least once per year for 2, 3, 4, 5, 6, 7, 8, 9, or 10 years.
  • a biological sample may be analyzed for multiple protein marker levels (e.g., levels of four or more of tPSA, fPSA, iPSA, hK2, pre-pro PSA, MIC-1).
  • a biological sample may be analyzed for multiple kallikrein marker levels (eg. , levels of two or more of tPSA, fPSA, iPSA, and hK2).
  • multiple kallikrein marker levels arc determined in parallel in the same assay (e.g., in a multiplex assay).
  • such antigen levels are determined in separate assays.
  • antigen levels are determined from the same original blood draw (e.g., a venous blood draw) from a subject. In some embodiments, antigen levels are determined from different blood draws. In some embodiments, antigen levels are determined using blood preparations from the same or different blood draws, fa some embodiments, one or more antigen levels are determined using a blood preparation and one or more other antigens arc determined using a different type of Mood preparation, e.g., scrum or whole blood.
  • Blood plasma is a pale-yellow liquid component of blood. In some embodiments, blood plasma may be prepared by spinning a tube of blood containing an anticoagulant (e.. Heparin, EDTA, etc.) in a centrifuge until blood pells and debris move to the bottom of the tube, after which the blood plasma may be poured or drawn off.
  • an anticoagulant e.. Heparin, EDTA, etc.
  • the levels of multiple protein markers can be used for various clinical purposes, for example, identifying a subject as likely to have prostate cancer (e.g., aggressive prostate cancer), identifying subjects suitable for a particular treatment (e.g., radical prostatectomy), and/or predicting likelihood of aggressive prostate cancer. Accordingly, described herein are diagnostic and prognostic methods for prostate cancer, for example, aggressive prostate cancer, based on the levels of multiple protein markers (e.g., kallikreins).
  • the terms“subject” or“patient” may be used interchangeably and refer to a subject who needs the analysis as described herein.
  • the subject is a human or a non-human mammal, in some embodiments, the subject is suspected of or is at risk for prostate cancer. In some embodiments, the subject has prostate cancer. In some embodiments, the subject is suspected of or is at risk for high-grade prostate cancer. Examples of prostate cancer include, without limitation, acinar adenocarcinoma, ductal adenocarcinoma, transitional cell (or urothelial ) prostate cancer, squamous cell prostate cancer, and small cell prostate cancer.
  • the subject is a human patient having one or more symptoms of a prostate cancer.
  • the subject may have problems urinating, blood in the urine or semen, erectile dysfunction, pain, weakness or numbness, joss of bladder or bowel control, or a combination thereof
  • the subject has a symptom of prostate cancer, has a history of a symptom of prostate cancer, or has a history of low-grade prostate cancer, in some embodiments, the subject has more than one symptom of prostate cancer or has a history of more titan one symptoms of prostate cancer.
  • the subject has no symptoms of prostate cancer, has no history of a symptom of prostate cancer, or has no history of prostate cancer.
  • the subject is at risk for having an upgrade in prostate cancer.
  • a subject may exhibit one or more symptoms associated with the prostate cancer.
  • such a subject may have one or more risk factors for prostate cancer, for example, an environmental factor associated with prostate cancer (e.g., geographic location), a family history of prostate cancer, or a genetic predisposition to developing prostate cancer,
  • the subject who needs the analysis described herein may be a patient having prostate cancer or suspected of having prostate cancer.
  • a subject may currently be having a relapse, or may have suffered from the disease in the past (e.g., may be currently relapse-free), or may have low-grade prostate cancer.
  • the subject is a human patient who may be on a treatment (i.e., the subject may be receiving treatment) for the disease including, for example, a treatment involving chemotherapy or radiation therapy. In other instances, such a human patient may be free of such a treatment.
  • Levels of prostate specific antigens can be assessed by any appropriate method.
  • antibodies or antigen-binding fragments are provided that are suited for use in immunoassays. Such immunoassays may be competitive or non-competitive immunoassays in either a direct or indirect format.
  • an immunoassay that may be used in accordance with the methods described herein include, but are not limited to, an Enzyme Linked Immunoassay (ELISA), a radioimmunoassay (RIA), a sandwich assay (immunometric assay), a Sangia assay (silver amplified NeoGold immunoassay), a flow cytometry assay, a western blot assay, an immunoprecipitation assay, an immunohistochemistry assay, an immune-microscopy assay, a lateral flow immuno-chromatographic assay, and a proteomics array.
  • Antigens, antibodies, and/or antigen-binding fragments can be immobilized, e.g., by binding to solid supports (e.g., carriers, membrane, columns, proteomics array, etc.).
  • solid supports e.g., carriers, membrane, columns, proteomics array, etc.
  • solid support materials include, but are not limited to: glass, polystyrene, polyvinyl chloride,
  • One or more than one solid support material may be used in the solid supports, and may contain at least one solid material listed above.
  • the nature of the solid support can be either fixed or suspended in a solution (e.g., beads, porous material, or a membrane).
  • labeled antibodies or antigen binding fragments may be used as tracers to detect antigen bound antibody complexes.
  • types of labels which can be used to generate tracers include, but are not limited to: enzymes, radioisotopes, colloidal metals, fluorescent compounds (including time-resolved fluorescence), magnetic, chemiluminescent compounds, electrochemiluminescent compounds, and bioluminescent compounds.
  • Radiolabeled antibodies are prepared in known ways by coupling a radioactive isotope such as 153 Eu, 3 H, 32 P, 35 S, 59 Fe, or 125 I, which can then be detected by gamma counter, scintillation counter, and/or by autoradiography.
  • antibodies and antigen-binding fragments may alternatively be labeled with enzymes such as yeast alcohol dehydrogenase, horseradish peroxidase, alkaline phosphatase, and the like, then developed and detected spectrophotometrically or visually.
  • Suitable fluorescent labels include fluorescein, isothiocyanate, fluorescamine, rhodamine, and the like, or complexes (chelates) of lanthanides salts (such as europium, terbium, samarium or dysprosium) with appropriate ligands to improve fluorescence.
  • Suitable chemiluminescent labels may include luminol, imidazole, oxalate ester, luciferin, and others.
  • Suitable electrochemiluminescent labels may include (short for
  • An immunoassay may comprise contacting the sample, e.g., a blood plasma sample, containing an antigen with an antibody, or antigen-binding fragment (e.g., F(ab), F(ab) 2 ), under conditions enabling the formation of binding complexes between antibody or antigen-binding fragment and antigen.
  • a plasma sample is contacted with an antibody or antigen-binding fragment under conditions suitable for binding of the antibody or antigen-binding fragment to a target antigen, if the antigen is present in the sample. This may be performed in a suitable reaction chamber, such as a tube, plate well, microchannel, membrane bath, cell culture dish, microscope slide, and/or other chamber.
  • an antibody or antigen-binding fragment is immobilized on a solid support.
  • the solid support i.., beads
  • the solid support i.., beads
  • the solid support can be further captured onto the surface of an electrode for obtaining an electrochemiluminescent signal.
  • an antibody or antigen binding fragment that binds to an antigen in a sample may be referred to as a capture antibody.
  • the capture antibody comprises a tag (e.g., a biotin label) that facilitates its immobilization to a solid support by an interaction involving tit* tag (e,g., a biotin-streptavidin interaction in which the streptavidin is immobilized to a solid support).
  • the solid support is tire surface of a reaction chamber.
  • tire solid support is of a polymeric membrane (e.g., nitrocellulose strip.
  • tire solid support is suspension of beads (e.g., plain beads or beads with a magnetic core).
  • the solid support is a biological structure (e.g., bacterial cell surface).
  • Other exemplary solid supports are disclosed herein and will be apparent to one of ordinary skill in the art
  • tire antibody or antigen-binding fragment is immobilized on (be solid support prior to contact with the antigen. In other embodiments, immobilization of tire antibody or antigen-binding fragment is performed after formation of binding Complexes between the antibody or antigen binding fragment and the antigen. In still other embodiments, an antigen is hnmobilized on a solid support prior to formation of binding complexes between the antigen and tire antibody or antigen-binding fragment.
  • a tracer may be added to tire reaction chamber to detect immobilized binding complexes. In some embodiments, the tracer comprises a dctectably labeled secondary antibody directed against the antigen. In some embodiments, the tracer comprises a delectably labeled secondary antibody directed against the capture antibody. In some embodiments, the primary antibody or antigen-binding fragment is itself dctectably labeled.
  • immunoassay methods disclosed herein comprise immobilizing antibodies or antigen-binding fragments to a solid support: applying a sample (e.g., a plasma sample) to the solid support under conditions that permit binding of antigen to tire antibodies or antigenbinding fragments, if present in the sample; removing the excess sample from the solid support; applying a tracer (e.g., delectably labeled antibodies or antigen-binding fragments) under conditions that permit binding of the tracer to the antigen-bound immobilized antibodies or antigen-binding fragments; washing the solid support and assaying for the presence of the tracer.
  • a sample e.g., a plasma sample
  • a tracer e.g., delectably labeled antibodies or antigen-binding fragments
  • the antibody or antigen-binding fragment is immobilized on the solid support after contact with the antigen in a reaction chamber. In some embodiments, the antibody or antigen-binding fragment is immobilized on the solid support prior to contact with the antigen in a reaction chamber. In either case, a tracer may be added to the reaction chamber to detect immobilized binding complexes. In some embodiments, a tracer comprises a delectably labeled secondary antibody directed against the antigen. In some embodiments, the tracer comprises a delectably labeled secondary antibody directed against the primary antibody or antigen-binding fragment.
  • the detectable label may be, for example, a radioisotope, a fluorophore, a luminescent molecule, an enzyme, a biotin-moiety, an epitope tag, ora dye molecule.
  • a radioisotope for example, a radioisotope, a fluorophore, a luminescent molecule, an enzyme, a biotin-moiety, an epitope tag, ora dye molecule.
  • a tracer antibody is contacted with a capture antibody in a buffer having a pH in a range of 6.5 to less than 7.75 such that the tracer binds to the capture antibody-antigen complex.
  • a tracer antibody is contacted with a capture antigen-binding fragment in a buffer having a pH in a range of 6.5 to less than 7.75 such that the tracer binds to the capture antigen-binding fragment- antigen complex.
  • the buffer pH is about 6.5, 6.6, 6.7. 6.8, 6.9, 7.0, 7.1, 7.2, 7.3, 7.4, 7.5, or 7.6.
  • capture antibodies may be swapped with tracer antibodies.
  • an immunoassay that measures the level of IPSA involves contacting fPSA present in the plasma blood sample with a capture antibody specific for fPSA or tPSA under conditions in which the first capture antibody binds to fPSA or tPSA, thereby producing a capture- antibody-PSA complex; and detecting the capture-antibody-PSA complex using a tracer specific for fPSA or tPSA.
  • the immunoassay comprises at least one capture antibody specific for fPSA or at least one tracer specific for fPSA.
  • the immunoassay comprises at least one capture antibody specific for fPSA and at least one tracer specific for fPSA.
  • the capture antibody may be a H117 antibody.
  • the tracer comprises a 5A10 antibody or fragment thereof (e.g., a F(ab) fragment).
  • an immunoassay that measures the level of iPSA involves contacting tPSA present in the plasma blood sample with a capture antibody specific for free PSA (which includes iPSA and nicked PSA) or free PSA, under conditions in which the second capture antibody binds at least to iPSA, thereby producing a capture-antibody-PSA complex and detecting the capture- antibody-PSA complex using a second tracer.
  • the tracer comprises a 4D4 antibody.
  • the immunoassay comprises at least one capture antibody specific for intact PSA or at least one tracer specific for intact PSA.
  • the immunoassay comprises at least one capture antibody specific for intact PSA and at least one tracer specific for intact PSA.
  • the capture antibody is a 5A10 antibody or fragment thereof (e.g., a F(ab) fragment).
  • an immunoassay that measures the level of tPSA involves contacting tPSA present in the plasma blood sample with a capture antibody specific for tPSA under conditions in which the third capture antibody binds to tPSA, thereby producing a capture-antibody-tPSA complex; and detecting the capture-antibody-tPSA complex using a third tracer.
  • the selectivity of the capture and third tracer antibodies result in an equimolar detection of free PSA and PSA complcxcd with alpha 1-antichymotrypsin (PSA-ACT). Equimolar detection means that the molar recovery of free PSA is within 5%, 10%, 20% or 30% of the molar recovery of PSA-ACT.
  • the tracer comprises a H50 antibody.
  • the capture antibody is a H117 antibody.
  • an immunoassay that measures the level of hK2 involves contacting PSA in the plasma blood sample with blocking antibodies specific for PSA; contacting hK2 present in the plasma blood sample with a fourth capture antibody specific for hK2 under conditions in which the fourth capture antibody binds to hK2, thereby producing a capture-antibody-hK2 complex; and detecting the capture-antibody-hK2 complex using a fourth tracer.
  • the fourth capture antibody and the fourth tracer may also be capable of binding to PSA.
  • the fourth capture antibody is capable of binding PSA and the fourth tracer is capable of binding to PSA In some embodiments, the fourth capture antibody is incapable of binding PSA and the fourth tracer is capable of binding to PSA. In some embodiments, the fourth capture antibody is capable of binding PSA and the fourth tracer is incapable of binding to PSA. to some
  • the tracer comprises a 7GI antibody.
  • the capture antibody is a 6H10 F(ab) 2 .
  • the blocking antibodies comprise a 5H7 antibody, a 5H6 antibody, and a 2E9 antibody .
  • Table 1 lists examples of antibodies and antigen-binding fragments that may be used in the methods disclosed herein and their corresponding epitopes.
  • any of the immunoassay methods disclosed herein may be performed or implemented using a fluidic device (e.g., a microfluidic device or a cassette) and/or a fluidic sample analyzer (e.g., a microfluidic sample analyzer).
  • a fluidic device e.g., a microfluidic device
  • kallikrein markers e.g., levels of tPSA, IPSA, iPSA, and/or hK2
  • a system may include a fluidic sample analyzer (e.g., a microfluidic sample analyzer) which, for example, may be configured to analyze a sample provided in a device (e.g., a cassette) having one or more fluidic channels (e.g., mtcrofluidic channels) for containing and/or directing flow of a sample that comprises immunoassay components (e.g., antigen-antibody complexes, tracers, etc;),
  • an analyzer comprises an optical system including one or more light sources and/or one or more detectors configured for measuring levels of antigen-antibody complexes and/or tracers present in one or more fluidic channels (e.g., microfluidic channels).
  • systems may include a processor or computer programmed to evaluate a predictive model (e.g., a logistic regression model) in electronic communication with a fluidic device (e.g., a microfluidic device) and/or a fluidic sample analyzer (e.g., a microfluidic sample analyzer) or other device for determining a probability of an event associated with prostate cancer based on levels of markers (e.g., levels of tPSA, fPSA, iPSA, and/or hK2).
  • a predictive model e.g., a logistic regression model
  • a fluidic device e.g., a microfluidic device
  • a fluidic sample analyzer e.g., a microfluidic sample analyzer
  • a system includes a fluidic sample analyzer (e.g., a microfluidic sample analyzer) comprising a housing and an opening in the housing configured to receive a device (e.g., a cassette) having al least one fluidic channel (e.g., a microfluidic channel), wherein the housing includes a component configured to interface with a mating component on the device to detect the device within the housing.
  • the system also includes a pressure-control system positioned within the housing, the pressure-control system configured to pressurize the at least one fluidic channel (e.g., a microfluidic channel) in the device to move the sample through the at least one fluidic channel (e.g., a microfluidic channel).
  • the system further includes an optical system positioned within the housing, the optical system including at least one light source and at least one detector spaced apart from the light source.
  • the light source is configured to pass light through the device when the device is inserted into the sample analyzer and the detector is positioned opposite the light source to detea the amount of light that passes through the cassette.
  • detection components and methods may be used for detecting radioisotopes, colloidal metals, fluorescent compounds (including time-resolved fluorescence), magnetic, chemiluminescent compounds, electrochemiluminescent compounds, and bioluminescent compounds.
  • the system may include a user interface associated with the housing for inputting at least one clinical factor (e.g., the age of a person).
  • the system may include a processor in electronic communication with the fluidic sample analyzer (e.g., a microfluidic sample analyzer), the processor programmed to evaluate a logistic regression model as described herein in combination with information indicative of levels of one or more protein (e.g., kallikrein) markers selected from: tPSA and fPSA and at least two selected from the group consisting of iPSA, pre-pro PSA, MlC-1, and hK2 in a blood sample of a subject previously diagnosed as having a low-grade score prostate cancer.
  • protein e.g., kallikrein
  • aspects of the disclosure provide computer implemented methods for determining the likelihood that a prostate tissue sample obtained from the subject would contain prostate cancer (e.g., aggressive prostate cancer).
  • prostate cancer e.g., aggressive prostate cancer
  • Such methods may involve receiving, via an input interface, information indicative of the level of protein markers (e.g., tPSA, fPSA, iPSA, and/or hK2) present in a sample (e.g., a blood sample) of a subject and receiving, via an input interface, information regarding one or more clinical factors, such as information relating to the subject's age.
  • the methods further involve evaluating, using at least one processor, a suitable predicti ve model (e.g.. a logistic regression model) based, at least in part, on the received information to determine a likelihood of a prostate cancer.
  • a suitable predicti ve model e.g.. a logistic regression model
  • the predictive model may generate the likelihood of prostate cancer based, at least in part, on measured levels of tPSA, fPSA, iPSA, and/or hK2 and patient information, such as information relating to the subject's age.
  • the method farther involves comparing, using the at least one processor, the likelihood to a threshold.
  • the likelihood of prostate cancer may be determined based on the predictive model when the likelihood outside a threshold.
  • the method may further involve receiving, via an input interface, information regarding the outcome of an imaging technique (e.g., radiology reporting scale scores, likelihood ratio, image).
  • the likelihood of prostate cancer may be determined based on the information regarding the outcome of the imaging technique (e.g.. solely) and/or a combination of the predictive model and the information regarding the outcome of the imaging technique.
  • FIG. 1A shows a flowchart of an exemplary process 100 in accordance with some embodiments of the disclosure.
  • one or more values representing patient data or clinical factors e.g., age, prior negative biopsy, outcome of DRE
  • at least one processor for processing using one or more of the techniques described herein.
  • one or mote values representing biomarker data e.g., blood marker data
  • protein markers e.g., tPSA, fPSA, iPSA, and/or hK2
  • the values may be received in any suitable way including, but not limited to, through a local input interface such as a keyboard, touch screen, microphone, or other input device, from a network-connected interface that receives the value(s) from a device located remote from the processor(s), or directly from one or more detectors that measure the blood marker value(s) (e.g.. in an implementation where the processors) arc integrated with a measurement device that includes the one or more detectors).
  • a local input interface such as a keyboard, touch screen, microphone, or other input device
  • a network-connected interface that receives the value(s) from a device located remote from the processor(s), or directly from one or more detectors that measure the blood marker value(s) (e.g. in an implementation where the processors) arc integrated with a measurement device that includes the one or more detectors).
  • the biomarker data is obtained at least in part by performing one or more
  • step 103 In response to receiving one or more clinical factors data value(s) and biomarker values, the process proceeds to step 103, where at least one predictive model a logistic regression model) is evaluated to determine a first likelihood score (e.g,, a likelihood of prostate cancer), wherein the likelihood score is based, at least in part on (e.g., based solely on), the received one or more clinical factors data value(s) and received biomarker values (e.g., blood marker values).
  • a first likelihood score e.g, a likelihood of prostate cancer
  • step 104 After determining a first likelihood score (e.g., likelihood of prostate cancer), the process proceeds to step 104, where the likelihood score is compared to a threshold (e.g., threshold range). If the likelihood score is within the threshold, the process proceeds to step 105B.
  • a threshold e.g., threshold range.
  • imaging data are received by at least one processor for processing using one or more of the techniques described herein.
  • the data may be received in any suitable way including, but not limited to, through a local input interface such as a keyboard, touch screen, microphone, or other input device, from a network-connected interface that receives the data from a device located remote from the processors), or directly from one or more computers store and/or analyze the image from the imaging technique (euchg., in an implementation where the processor(s) are integrated with an imaging device).
  • the process proceeds by determining a second likelihood score based on the imaging data.
  • the process may then proceed by determining the likelihood of prostate cancer, using the imaging data (e.g., alone) and/or a combination of the imaging data and the first likelihood score produced in step 103.
  • the process further proceeds by outputting or communicating to a user (e.g., a physician, a Healthcare provider, and/or a patient) the likelihood to guide further diagnostic procedure and/or treatment decisions.
  • a user e.g., a physician, a Healthcare provider, and/or a patient
  • step 105A If the first likelihood score determined at step 104 is outside of the threshold, the process proceeds to step 105A, whore the probability is outputted or communicated to a user (e.g., a physician, a healthcare provider, and/or a patient) to guide further diagnostic procedure and/or treatment decisions.
  • a user e.g., a physician, a healthcare provider, and/or a patient
  • a first assessment may produce a first likelihood score.
  • the first l ikelihood score may be compared to threshold range.
  • the threshold range may be greater than about 7.5% and less than or equal to about 44%.
  • file subject when the first likelihood score is below the lower limit of the threshold range, file subject is not recommended for biopsy as shown in FIG. 1B.
  • file subject when the first likelihood score is above the upper limit of file threshold range, file subject may be recommended for biopsy.
  • the subject may undergo a second assessment.
  • the second assessment may be mpMRI as shown in FIG. 1B.
  • the outcome of the MR1 may be a radiology reporting scale score, such as a PIRADS score, as illustrated in FIG. 1B.
  • a radiology reporting scale score such as a PIRADS score
  • the subject when the outcome of the MRI is indicative of prostate cancer (e.g., PIRADS score 3-5), the subject may be recommended for biopsy. In some embodiments, when the outcome of the MRI is indicative of the absence of prostate cancer (e.g., PIRADS score 0-2), the subject is not be recommended for biopsy.
  • Such methods may involve receiving, via an input interface ⁇ information indicative of the level of protein markers (e.g., tPSA, fPSA, iPSA, and/or hK2) present in a sample (e.g., a blood sample) of a subject and receiving, via an input interface, one or more clinical factors, such as information relating to the subject's age, prostate volume, and/or total tumor length.
  • the methods further involve evaluating, using at least one processor, a suitable predictive model (e.g., a logistic regression model) based, at least in part, on the received information to determine a likelihood of aggressive prostate cancer.
  • a suitable predictive model e.g., a logistic regression model
  • the predictive model may generate the likelihood of aggressive prostate cancer based, at least in part, on measured levels of tPSA, IPS A, iPSA, and/or hK2 and one or more clinical factors information, such as information relating to the subject’s age, prior negative biopsy, and/or outcome of DRE.
  • the method further involves comparing, using the at least one processor, the likelihood (e,g-, a first likelihood score) to a threshold.
  • the likelihood of prostate cancer may be determined based on the predictive model when the likelihood is outside a threshold.
  • the method further involves receiving, via an input interface, information regarding the outcome of an imaging techniques (e.g., radiology reporting scale scores, likelihood ratio, image).
  • the likelihood of prostate cancer may be determined based on the information regarding the outcome of the imaging technique (e.g., solely) and/or a combination of the predictive model and the information regarding the outcome of the imaging technique.
  • the likelihood may be outputted or communicated in any suitable way.
  • the likelihood may be outputted or communicated by displaying a numeric value representing the likelihood on a display screen of a device.
  • the likelihood may be outputted or communicated using one or more lights or other visual indicators on a device.
  • the likelihood may be provided or communicated using audio output, tactile output, visual output, or some combination of one or more of audio, tactile, and visual output.
  • outputting or communicating the likelihood comprises sending information to a network-connected device to inform a user (e.g., a doctor, a healthcare provider, and/or a patient) about the determined likelihood.
  • the likelihood may be determined by one or more processors located at a remote site, and an indication of the likelihood may be sent to an electronic device of a user (e.g., a physician, a healthcare provider, or a patient) using one or more networks, in response to determining the likelihood at the remote site.
  • the electronic device that provides output to a user in accordance with the techniques described herein may be any suitable device including, but not limited to, a laptop, desktop, or tablet computer, a smartphone, a pager, a personal digital assistant, and an electronic display.
  • the first likelihood score (referred to as probability in the equation below) for the event associated with prostate cancer is determined in accordance with equation (1), reproduced below:
  • logit (L) is determined using any of a plurality of logistic regression models.
  • logistic regression models Non- limiting examples of different types of logistic regression models that may be used in accordance with the techniques described herein include:
  • linear splines arc included only for tPSA and IPSA to reduce the number of variables and simplify the model.
  • priorbx is a binary valu e indicate of whether a subject had a prior biopsy to detect prostate cancer. A value of 1 indicates that a prior biopsy occurred and a value of 0 indicates that the prior biopsy did not occur.
  • cubic splines are included for each term.
  • a cubic spline with four knots is described, it should be appreciated, however, that a cubic spline using any suitable «umber of knots including, but not limited to, five knots, six knots, seven knots, and eight knots, may alternatively be used.
  • the splines may be determined using the following equations: (10)
  • knot1 and knot4 are external knots for the cubic spline
  • knot2 and knot3 are internal knots for the cubic spline.
  • the external knots may be set as the minimum and maximum levels of tPSA, fPSA, iPSA, or hK2 in a population.
  • An internal knot e.g., knot2
  • Another internal knot e.g., knot3
  • knot3 may be set as the 66.6 percentile value of tPSA, fiPSA, iPSA, or hK2 levels in a population.
  • the internal knots are specified within the range of between about 2 to about 8 and between about 3 to about 6 for tPSA, between about 0.25 to about 2 and between about 0.5 to about 1.5 for fPSA, between about 0.2 to about 0.5 and between about 0,4 to about 0,8 for tPSA, and between about 0.02 to about 0.04 and between about 0.04 to about 0.08 for hK2.
  • values of 3.92 and 5.61 are used for the internal knots for tPSA
  • values of 0.82 and 1.21 are used for the internal knots for IPSA
  • values of 0.3 and 0.51 are used for the internal knots of iPSA
  • values of 0.036 and 0.056 are used for the internal knots of hK2.
  • one or more Mental knots for tPSA may independently be in the range of between about 3 to about 5, between about 3 to about 6, between about 2.5 to about 6, between about 2.5 tp about 6.5, between about 5 to about 8, between about 5.5 to about 8, between about 5 to about 9, between about 5 to about 10, between about 1 to about 5, between about 1 to about 4, and between about 1 to about 3. Other ranges are also possible;
  • one or more internal knots for fPSA may independently be in the range of between about 0.1 to about 1.0, between about 0.1 to about 1.2, between about 0.3 to about 0.8, between about 0.4 to about 0.9, between about 0.5 to about 1.2, between about 0.7 to about 1.4, between about 0.7 to about 0.9, between about 1.1 to about 1.6, between about 1.1 to about 1.2, and between about 1.1 to about 2. Other ranges are also possible.
  • one or more internal knots for iPSA may independently be in the range of between about 0.05 to about 0.5, between about 0.1 to about 0.5, between about 0.2 to about 0.5, between about 0.1 to about 0.8, between about 0.2 to about 0.8, between about 0.4 to about 0.8, between about 0.4 to about 1.0, between about 0,3 to about 0.6, between about 0.5 to about 1.0, and between about 0.6 to about 0.8. Other ranges are also possible.
  • one or more internal knots for hK2 may independently be in the range of between about 0.01 to about 0.03, between about 0.01 to about 0.04, between about 0.01 to about 0.05, between about 0.02 to about 0.05, between about 0.02 to about 0.06, between about 0.03 to about 0.05, between about 0.4 to about 0.07, between about 0.04 to about 1.0, between about 0.5 to about 1.0, and between about 0.6 to about 1.0. Other ranges are also possible.
  • cubic splines incorporating any suitable number of internal knots may be used, and the example of a cubic spline including two internal knots is provided merely for illustration and not limitation.
  • the knots may be placed within one or more of the ranges discussed above, or in some other suitable range.
  • the knots may be specified such that the length of the segments ofthe spline between each of the pairs of neighboring knots is essentially equal.
  • the model selected may depend on the whether or not a threshold level of tPSA is detected in sample, lit some embodiments, if the level of tPSA is above a threshold in a sample, then the predictive model is as follows: (13)
  • the range of values of the weighting coefficients in this model is as set forth in Table 1 below. Coefficients suitable for determining the probability that a prostate tissue biopsy will have a cancer of any grade are shown in the second and third columns; whereas coefficients suitable for determining the probability that a prostate tissue biopsy will have a cancer of high grade are shown in the fourth and fifth columns.
  • Table I Weighting Coefficients to be used when level of tPSA is greater than Threshold
  • the predictive model is as follows:
  • the range of values of the weighting coefficients in this model is as set forth in Table 2 below. Coefficients suitable for determining the probability that a prostate tissue biopsy will have a cancer of any grade are shown in the second and third columns; whereas coefficients suitable for determining the probability that a prostate tissue biopsy will have a cancer of high grade are shown in the fourth and fifth columns.
  • Table 2 Weighting Coefficients to be used when level of (PSA is less (than or equal to a threshold
  • the spline terms of sp1 (tPSA), sp2(tPSA), sp 1(fPSA), and sp2(iPSA) in the model above may be determined according to the cubic spline formula presented above under model #7 above (Equations (10 and 1 1)).
  • the values of internal knots 2 and 3 and external knots 1 and 4 are within the ranges set forth in Table 3 below for tPSA and fPSA.
  • FIG. 1C An illustrative implementation of a computer system 106 on which some or all of the techniques and/or user interactions described herein may be implemented is shown in FIG. 1C.
  • the computer system 106 may include one or more processors 107 and one or more computer-readable non-transitory storage media (e.g., memory 108 and one or more non-volatile storage media 110).
  • the processors) 107 may control writing data to and reading data from the memory 108 and the nonvolatile storage device 110 in any suitable manner, as the aspects of the present invention described herein are not limited in this respect
  • the processors) 107 may execute one or more instructions, such as program modules, stored in one or more computer-readable storage media (e.g., the memory 108), which may serve as non-transitory computer-readable storage media storing instructions for execution by the processor 107.
  • program modules include routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular abstract data types.
  • Embodiments may also be implemented in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network.
  • program modules may be located in both local and remote computer storage media including memory storage devices.
  • Data inputs and program commands may be receivcd by the computer 106 through a input interface 109.
  • the input interface 109 may comprise a keyboard, touchscreen, USB port, CD drive, DVD drive, or other input interface.
  • Computer 106 may operate in a networked environment using logical connections to one or more remote computers.
  • the one or more remote computers may include a personal computer, a server, a router, a network PC, a peer device or other common network node, and typically include many or all of the elements described above relative to the computer 106.
  • Logical connections between computer 106 and the one or more remote computers may include, but are not limited to, a local area network (LAN) and a wide area network (WAN), but may also include other networks.
  • LAN local area network
  • WAN wide area network
  • Such networks may be based on any suitable technology and may operate according to any suitable protocol and may include wireless networks, wired networks or fiber optic networks.
  • Such networking environments are commonplace in offices, enterprise-wide computer networks, intranets and the Internet
  • the computer 106 When used in a LAN networking environment, the computer 106 may be connected to the LAN through a network interface or adapter. When used in a WAN networking environment, the computer 106 typically includes a modem or other means for establishing communications over the WAN, such as the Internet In a networked environment program modules, or portions thereof, may be stored in the remote memory storage device.
  • Various inputs described herein for assessing a risk of prostate cancer and/or determining a prostate gland volume may be received by computer 106 via a network (e.g., a LAN, a WAN, or some other network) from one or more remote computers 0r devices that stores data associated with the inputs.
  • a network e.g., a LAN, a WAN, or some other network
  • One or more of the remote computers/dcviccs may perform analysis on remotely-stored data prior to sending analysis results as the input data to computer 106.
  • the remotely stored data may be sent to computer 106 as it was stored remotely
  • inputs may be received directly by a user of computer 106 using any of a number of input interfaces (e.g., input interface 109) that may be incorporated as components of computer 106.
  • input interfaces e.g., input interface 109
  • outputs described herein may be provided visually on an output device (e.g., a display) connected directly to computer 106 or the outputs) may be provided to a remotely-located output device connected to computer 106 via one or more wired or wireless networks, as embodiments of the invention are not limited in this respect
  • Outputs described herein may additionally or alternatively be provided other than using visual presentation.
  • computer 186 or a remote computer to which an output is provided may include one or more output interfaces including, but not limited to speakers, and vibratory output interfaces, for providing an indication of the output. Any of these outputs may be used to communicate the results of any of the herein described methods to one or more users (e.g., a physician, a healthcare provider, and/or a patient).
  • computer 106 is illustrated in FIG. 1C as being a single device, in some embodiments, computer 106 may comprise a plurality of devices communicatively coupled to perform some or all of the functionality described herein, and computer 106 is only one illustrative implementation of a computer that may be used in accordance with embodiments of the invention.
  • computer 106 may be integrated into and/or in electronic communication with a system.
  • computer 106 may be included in a networked environment, where information about one or more blood markers, used to determine a probability of prostate cancer, is seed from ah external source to computer 186 for analysts using one or more of the techniques described herein.
  • networked environment 111 in accordance with some embodiments of the invention is shown in FIG. 1 D,
  • computer 106 is connected to an assay system 112 via network 114.
  • network 114 may be any suitable type of wired or wireless network, and may include one or more local area networks (LANs) or wide area networks (WANs), such as the Internet.
  • LANs local area networks
  • WANs wide area networks
  • the calculation methods, steps, simulations, algorithms, systems, and system elements described herein may be implemented using a computer system, such as the various embodiments of computer systems described below.
  • the methods, steps, systems, and system elements described herein are not limited in their implementation to any specific computer system described herein, as many other different machines may be used.
  • the computer system may include a processor, for example, a commercially available processor such as one of the series x86, Celeron and Pentium processors, available from Intel, similar devices from AMD and Cyrix, the 680X0 series microprocessors available from Motorola, the PowerPC microprocessor from IBM, and ARM processors. Many other processors are available, and the computer system is not limited to a particular processor.
  • a processor typically executes a program called an operating system, of which Windows 7, Windows 8, UNIX, Linux, DOS, VMS, MacOS and OSX, and iOS are examples, which controls the execution of other computer programs and provides scheduling, debugging, input/output control, accounting, compilation, storage assignment, data management and memory management, communication control and related services.
  • the processor and operating system together define a computer platform for which application programs in high-level programming languages are written.
  • the computer system is not limited to a particular computer platform.
  • the computer system may include a memory system, which typically includes a computer readable and wrileable non-volatile recording medium, of which a magnetic disk, optical disk, a flash memory, and tape are examples.
  • a recording medium may be removable, for example, a floppy disk, read/write CD or memory stick, or may be permanent (such as, for example, a hard drive).
  • Such a recording medium stores signals, typically in binary form (i.e., a form interpreted as a sequence of one and zeros).
  • a disk e.g., magnetic or optical
  • Such signals may define a software program, e.g., an application program, to be executed by the microprocessor, or information to be processed by the application program.
  • the memory system of the computer system also may include an integrated circuit memory element, which typically is a volatile, random access memory such as a dynamic random access memory (DRAM) or static memory (SRAM).
  • DRAM dynamic random access memory
  • SRAM static memory
  • the processor causes programs and data to be read from the non-volatile recording medium into the integrated circuit memory e lement, which typically allows for foster access to the program instructions and data by the processor titan does the non-volatile recording medium.
  • the processor generally manipulates the data within the integrated circuit memory element in accordance with the program instructions and then copies the manipulated data to the non-volatile recording medium after processing is completed.
  • a variety of mechanisms are known for managing data movement between the non-volatile recording medium and the integrated circuit memory element, and the computer system that implements the methods, steps, systems and system elements described above is not limited thereto.
  • the computer system is not limited to a particular memory system.
  • At least part of such a memory system described above may be used to store one or more data structures (e.g., look-up tables) or equations described above.
  • at least part of the non- volatile recording medium may store at least part of a database that includes one or more of such data structures.
  • a database may be any of a variety of types of databases, for example, a file system including one or more flat-file data structures where data is organized into data units separated by delimmiters. a relational database where data is organized into data units stored in tabled, an object- oriented database where data is organized into data units stored as objects, another type of database, or any combination thereof.
  • the computer system may include a video and audio data I/O subsystem.
  • An audio portion of the subsystem may include an analog-to-digital (A/D) converter, which receives analog audio information and converts it to digital information.
  • the digital information may be compressed using known compression systems for storage on the hard disk to use at another time.
  • a typical video portion of the I/O subsystem may include a video image compressor/decompressor of which many are known in the art. Such compressor/decompressors convert analog video information into compressed digital information, and vice-versa.
  • the compressed digital information may be stored on hard disk for use at a later time.
  • the computer system may include one or more output devices.
  • Example output derices include a cathode ray tube (CRT) display, liquid crystal displays (LCD) and other video output devices, printers, communication devices such as a modem or network interface, storage devices such as disk or tape, and audio output devices such as a speaker.
  • CTR cathode ray tube
  • LCD liquid crystal displays
  • audio output devices such as a speaker.
  • the computer system also may include one or more input devices.
  • Example input devices include a keyboard, keypad, track ball, mouse, pen and tablet, communication devices such as described above, and data input devices such as audio and video capture devices and sensors.
  • the computer system is not limited to the particular input or output devices described herein.
  • any type of computer system may be used to implement various embodiments described herein. Aspects of the disclosure may be implemented in software, hardware or firmware, or any combination thereof.
  • the computer system may include specially programmed, special purpose hardware, for example, an application-specific integrated circuit (ASIC).
  • ASIC application-specific integrated circuit
  • Such special-purpose hardware may be configured to implement one or more of the methods, steps, simulations, algorithms, systems, and system elements described above as part of the computer system described above or as an independent component
  • the computer system and components thereof may be programmable using any of a variety of one or more suitable computer programming languages.
  • Such languages may include procedural programming languages, for example, C, Pascal, Fortran and BASIC, object-oriented languages, for example, C++, Java, Eiffel, and other languages, such as a scripting language or even an assembly language.
  • the methods, steps, simulations, algorithms, systems, and system elements may be implemented using any of a variety of suitable programming languages, including procedural programming languages, object-oriented programming languages, other languages and combinations thereof, which may be executed by such a computer system. Such methods, steps, simulations, algorithms, systems, and system elements can be implemented as separate modules of a computer program, or can be implemented individually as separate computer programs. Such modules and programs can be executed on separate computers.
  • Such methods, steps, simulations, algorithms, systems, and system elements may be implemented as a computer program product tangibly embodied as computer-readable signals on a computer-readable medium, for example, a non-volatile recording medium, an integrated circuit memory element, or a combination thereof.
  • a computer program product may comprise computer-readable signals tangibly embodied on the computer-readable medium that define instructions, for example, as part of one or more programs, that, as a result of being executed by a computer, instruct the computer to perform the method, step, simulation, algorithm, system, or system element.
  • a subject at ride for prostate cancer may be treated with any appropriate therapeutic agent or any combination of appropriate therapies.
  • provided methods may include selecting a treatment for a subject based on the output of the described method, e.g., determining a likelihood of prostate cancer.
  • provided methods may include selecting a treatment for a subject based on the output of the described method determining a likelihood of aggressive prostate cancer.
  • the method comprises one or both of selecting or administering a therapeutic agent, e.g., a chemotherapy, a radiation therapy, a surgical therapy, a cryotherapy, a hormone therapy, and/or an immunotherapy, for administration to the subject based on the output of the assay, e.g., determining a likelihood of prostate cancer.
  • a therapeutic agent e.g., a chemotherapy, a radiation therapy, a surgical therapy, a cryotherapy, a hormone therapy, and/'or an immunotherapy, for administration to the subject based on the output of the assay determining a likelihood of aggressive prostate cancer.
  • the therapeutic agent is administered one or more times to the subject.
  • the therapeutic agent e.g., chemotherapy, radiation therapy, surgical therapy, cryotherapy, hormone therapy, and/or immunotherapy
  • Combination therapy e.g., chemotherapy and radiation therapy
  • the first therapy may be administered before or after the administration of the other therapy.
  • the first therapy and another therapy e.g., a therapeutic agent
  • the first agent and the other therapy may also be administered at greater temporal intervals.
  • a chemotherapeutic agent is administered to a subject
  • the chemotherapeutic agents include, but are not limited to, Docetaxel (Taxotere), Cabazitaxel (Jevtana), Mitoxantrone (Novantrone), and Estramustine (Emcyt).
  • a radiation therapy is administered to a subject.
  • radiation therapy include, but are not limited to, ionizing radiation, gamma-radiation, neutron beam radiotherapy, electron beam radiotherapy, proton therapy, brachytherapy, systemic radioactive isotopes, and radiosensitizers.
  • a surgical therapy is administered to a subject.
  • a surgical therapy include, but are not limited to, radical prostatectomy, radical retropubic prostatectomy, radical perineal prostatectomy, laparoscopic radical prostatectomy, and robotic-assisted laparoscopic radical prostatectomy.
  • a hormone therapy is administered to a subject
  • a hormone therapy include, but are not limited to, orchiectomy, luteinizing hormone-releasing hormone (LHRH) agonists (e.g., Leuprolide, Goscrelin, Triptorelin, and Histrelin), LHRH antagonists (e.g., Degarelix), CYP17 inhibitors (e.g., Abiratcrone), and anti-androgens (e.g., Flutamide, Bicalutamide, Nilutamide, Enzalutamide, Estrogen, and Keloconazole).
  • LHRH luteinizing hormone-releasing hormone
  • LHRH antagonists e.g., Degarelix
  • CYP17 inhibitors e.g., Abiratcrone
  • anti-androgens e.g., Flutamide, Bicalutamide, Nilutamide, Enzalutamide, Estrogen, and Keloconazole.
  • a cryotherapy is administered to a subject.
  • an immunotherapy is administered to a subject.
  • the immunotherapy is sipul eucel- T.
  • an anti-metastasis therapy is administered to a subject. Examples of an anti-metastasis therapy include, but are not limited to, bisphosphonates (e.g., Zoledronic acid), Denosumab, and corticosteroids (e.g. , prednisone and dexamethasone).
  • Example 1 Kallikrein markers based test in combination with mpMRI reduces prostate biopsy more than either test alone.
  • This example describes a multi-institutional study that evaluated the combination of an assessment based on kailikrein markers and patient information (clinical factors), as described below, and an assessment based on mpMRI. The combination resulted in a greater reduction in biopsies than either test alone.
  • the method was evaluated using either test alone, versus both tests in sequence.
  • Single Test patients were stratified with tow risk results avoiding a Bx and intermediate or high risk getting a Bx.
  • Combination Test high-risk patients by the first test would receive a Bx and low risk would not.
  • the second test would be used for determining if a Bx would occur.
  • the final cohort included 407 men, of which 114 were found to have a HG cancer.
  • Combination strategies yielded higher specificities, leading to larger biopsy reductions in the range of 38-41%, while 11 -15 men had an undetected HG.
  • Sensitivity and NPV did not appreciably differ between strategies as shown in Table 4. Similar results were found in the category of men with a PSA of 2-10 and 3-10 ng/mL.
  • the kallikrein marker-based test was performed as follows.
  • the kallikrein marker based test is an assay based on a panel of four kallikrein markers that included total prostate specific antigen (tPSA), free PSA (fPSA). intact PSA (iPSA), and human Kallikrein 2 (hK2) linked to patient specific information via a multivariate algorithm.
  • This algorithm returns two calibrated probabilities: one for the risk of cancer of any grade and another for the risk of high grade cancer (Gleason 7 or greater) prior to biopsy.
  • the four kallikrein markers have been studied individually and in various combinations for prostate cancer detection applications.
  • a logistic regression algorithm incorporating the blood plasma levels of these four markers as well as patient-specific information such as age, existence of prior negative prostate biopsy(-ies), and optionally result from a digital rectal exam (DRE), demonstrated a higher positive predictive value for prostate cancer than (he PSA test alone.
  • Levels (e.g., in ng/inL) of tPSA, fPSA, iPSA, and hK2 present in human plasma samples were determined using the AutoDELFIA automatic immunoassay system. The average amount of each marker was calculated flam the duplicate tests for each marker and used in a predictive model to determine a risk score for a given human plasma sample as presented in Example 2.
  • tPSA and IPSA may also be determined using an Elecsys immunoassay analyzer (Roche Diagnostics).
  • Each run used at least one set of three plates- one plate for f/tPSA, one plate for iPSA and one plate for hK2.
  • a complete run at full capacity involved two sets of these three plates. The whole procedure involved approximately 3 to 5 hours from the initiation to obtaining the test results depending on the number of plates being run.
  • a formula for a predictive model for calculating risk of cancer on biopsy was established through the calibration study and is presented below, where the variables related to DRE may be optional. As noted, a different formula is used depending on the total PSA levels. Moreover, different weighting coefficients are used depending on whether the model is being used to determine the probability of a biopsy containing a delectable cancer of any grade versus a detectable cancer of high grade (c.g., Gleason score of 7.0 or greater). Weighting coefficients arc within the ranges specified in Tables 1 and 2 herein. The variables of the formulae arc described in Table 4.
  • Sp[var] 1 and sp[var]2 are computed for total and free PSA using the formulae above.
  • the spline term for total PSA was calculated using knot values within the ranges specified in Table 3.

Landscapes

  • Health & Medical Sciences (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Immunology (AREA)
  • Engineering & Computer Science (AREA)
  • Chemical & Material Sciences (AREA)
  • Urology & Nephrology (AREA)
  • Biomedical Technology (AREA)
  • Molecular Biology (AREA)
  • Hematology (AREA)
  • Cell Biology (AREA)
  • Microbiology (AREA)
  • Biotechnology (AREA)
  • Oncology (AREA)
  • Hospice & Palliative Care (AREA)
  • Food Science & Technology (AREA)
  • Medicinal Chemistry (AREA)
  • Physics & Mathematics (AREA)
  • Analytical Chemistry (AREA)
  • Biochemistry (AREA)
  • General Health & Medical Sciences (AREA)
  • General Physics & Mathematics (AREA)
  • Pathology (AREA)
  • Investigating Or Analysing Biological Materials (AREA)

Abstract

Certains aspects de l'invention concernent des méthodes et des systèmes pour détecter un cancer de la prostate et/ou évaluer un sujet pour une biopsie de la prostate. Dans certains modes de réalisation, un sujet peut subir une évaluation pour déterminer la probabilité de cancer de la prostate. Par exemple, un ou plusieurs dosages immunologiques peuvent être effectués sur un échantillon de sang du sujet pour déterminer des taux de biomarqueurs qui peuvent être utilisés avec certains facteurs cliniques pour déterminer un score de probabilité pour un cancer agressif de la prostate. Un sujet ayant un score de probabilité dans une plage de seuil peut subir une seconde évaluation pour déterminer la probabilité de cancer de la prostate. Par exemple, le sujet peut subir une technique d'imagerie pour détecter un cancer de la prostate. Le résultat de la seconde évaluation peut être utilisé pour déterminer la probabilité d'un cancer agressif de la prostate et/ou une recommandation de biopsie. Les méthodes et les systèmes selon l'invention peuvent être particulièrement bien adaptés à la détection invasive au minimum et rentable du cancer de la prostate chez des sujets.
PCT/US2020/022555 2019-04-03 2020-03-13 Méthodes de détection du cancer de la prostate WO2020205204A1 (fr)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
US201962828935P 2019-04-03 2019-04-03
US62/828,935 2019-04-03

Publications (1)

Publication Number Publication Date
WO2020205204A1 true WO2020205204A1 (fr) 2020-10-08

Family

ID=70277472

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/US2020/022555 WO2020205204A1 (fr) 2019-04-03 2020-03-13 Méthodes de détection du cancer de la prostate

Country Status (1)

Country Link
WO (1) WO2020205204A1 (fr)

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20200395123A1 (en) * 2019-06-16 2020-12-17 International Business Machines Corporation Systems and methods for predicting likelihood of malignancy in a target tissue

Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20130273643A1 (en) 2012-03-05 2013-10-17 Arctic Partners Oy Methods and apparatuses for predicting risk of prostate cancer and prostate gland volume
US8765062B2 (en) 2010-04-16 2014-07-01 Opko Diagnostics, Llc Systems and devices for analysis of samples
WO2015137870A1 (fr) * 2014-03-11 2015-09-17 Phadia Ab Procédé de détection de cancer à tumeur solide
US20160025732A1 (en) 2014-03-28 2016-01-28 Opko Diagnostics, Llc Compositions and methods related to diagnosis of prostate cancer
US20170089904A1 (en) * 2014-03-28 2017-03-30 Opko Diagnostics, Llc Compositions and methods for active surveillance of prostate cancer
US20190072555A1 (en) * 2017-08-14 2019-03-07 Opko Diagnostics, Llc Multiplex assays for evaluating prostate cancer status

Patent Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US8765062B2 (en) 2010-04-16 2014-07-01 Opko Diagnostics, Llc Systems and devices for analysis of samples
US20130273643A1 (en) 2012-03-05 2013-10-17 Arctic Partners Oy Methods and apparatuses for predicting risk of prostate cancer and prostate gland volume
WO2015137870A1 (fr) * 2014-03-11 2015-09-17 Phadia Ab Procédé de détection de cancer à tumeur solide
US20160025732A1 (en) 2014-03-28 2016-01-28 Opko Diagnostics, Llc Compositions and methods related to diagnosis of prostate cancer
US20170089904A1 (en) * 2014-03-28 2017-03-30 Opko Diagnostics, Llc Compositions and methods for active surveillance of prostate cancer
US20190072555A1 (en) * 2017-08-14 2019-03-07 Opko Diagnostics, Llc Multiplex assays for evaluating prostate cancer status

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20200395123A1 (en) * 2019-06-16 2020-12-17 International Business Machines Corporation Systems and methods for predicting likelihood of malignancy in a target tissue

Similar Documents

Publication Publication Date Title
JP7256781B2 (ja) 前立腺がんの診断に関連する組成物および方法
AU2016254127B2 (en) Compositions and methods for active surveillance of prostate cancer
Passerini et al. The pitfalls of CA19-9: routine testing and comparison of two automated immunoassays in a reference oncology center
US20170089904A1 (en) Compositions and methods for active surveillance of prostate cancer
Isariyawongse et al. Significant discrepancies between diagnostic and pathologic Gleason sums in prostate cancer: the predictive role of age and prostate-specific antigen
US20210208146A1 (en) Methods for detecting prostate cancer pathology associated with adverse outcomes
WO2020205204A1 (fr) Méthodes de détection du cancer de la prostate
Wieczorek et al. Inosine monophosphate dehydrogenase 2 as a marker of aggressive and advanced prostate cancer
Wang et al. Predictive model containing PI-RADS v2 score for postoperative seminal vesicle invasion among prostate cancer patients
Ren et al. Development and validation of a predictive model for diagnosing prostate cancer after transperineal prostate biopsy
Yu et al. Establishment and validation of a nomogram model for predicting postoperative recurrence-free survival in stage IA3 lung adenocarcinoma: a retrospective cohort study
Moul et al. NADiA® ProsVue™ PSA Slope Is an Independent Prognostic Marker for Identifying Men at Reduced Risk for Clinical Recurrence of Prostate Cancer after Radical Prostatectomy
Yang et al. Development and validation of peritoneal metastasis in gastric cancer based on simplified clinicopathological features and serum tumour markers
Jansson Family History and Prognosis of Prostate Cancer
EA041331B1 (ru) Композиции и методы, имеющие отношение к диагностике рака предстательной железы

Legal Events

Date Code Title Description
121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 20718441

Country of ref document: EP

Kind code of ref document: A1

NENP Non-entry into the national phase

Ref country code: DE

122 Ep: pct application non-entry in european phase

Ref document number: 20718441

Country of ref document: EP

Kind code of ref document: A1