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WO2019118443A1 - Biomarqueurs prédictifs d'effets secondaires de radiothérapie - Google Patents

Biomarqueurs prédictifs d'effets secondaires de radiothérapie Download PDF

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Publication number
WO2019118443A1
WO2019118443A1 PCT/US2018/064924 US2018064924W WO2019118443A1 WO 2019118443 A1 WO2019118443 A1 WO 2019118443A1 US 2018064924 W US2018064924 W US 2018064924W WO 2019118443 A1 WO2019118443 A1 WO 2019118443A1
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subject
metabolite profile
radiation therapy
cancer
metabolite
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Inventor
Anatoly Dritschilo
Amrita K. CHEEMA
Scott Grindrod
Xiaogang ZHONG
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Georgetown University
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Georgetown University
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Priority to US17/476,148 priority Critical patent/US20220065863A1/en
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    • 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
    • G01N30/00—Investigating or analysing materials by separation into components using adsorption, absorption or similar phenomena or using ion-exchange, e.g. chromatography or field flow fractionation
    • G01N30/02—Column chromatography
    • G01N30/88—Integrated analysis systems specially adapted therefor, not covered by a single one of the groups G01N30/04 - G01N30/86
    • G01N2030/8809—Integrated analysis systems specially adapted therefor, not covered by a single one of the groups G01N30/04 - G01N30/86 analysis specially adapted for the sample
    • G01N2030/8813—Integrated analysis systems specially adapted therefor, not covered by a single one of the groups G01N30/04 - G01N30/86 analysis specially adapted for the sample biological materials
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N2405/00—Assays, e.g. immunoassays or enzyme assays, involving lipids
    • G01N2405/04—Phospholipids, i.e. phosphoglycerides
    • G01N2405/06—Glycophospholipids, e.g. phosphatidyl inositol
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N2800/00—Detection or diagnosis of diseases
    • G01N2800/52—Predicting or monitoring the response to treatment, e.g. for selection of therapy based on assay results in personalised medicine; Prognosis
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N30/00—Investigating or analysing materials by separation into components using adsorption, absorption or similar phenomena or using ion-exchange, e.g. chromatography or field flow fractionation
    • G01N30/02—Column chromatography
    • G01N30/62—Detectors specially adapted therefor
    • G01N30/72—Mass spectrometers

Definitions

  • the present invention relates to methods of determining if a subject has an increased risk of having an adverse reaction to receiving radiation therapy for cancer, for example prostate cancer.
  • the methods comprise analyzing at least one sample from the subject to determine a value of the subject's metabolite profile, and comparing the value of the subject's metabolite profile with the value obtained from subjects determined to define a normal metabolite profile, to determine if the subject's metabolite profile is altered compared to a normal metabolite profile.
  • a difference in the value of the subject's metabolite profile compared to those defined as having a normal metabolite profile is indicative that the subject has an increased risk of having an adverse reaction to receiving radiation therapy for cancer, for example prostate cancer
  • RT Radiation therapy
  • RT Radiation therapy
  • all cancers can be controlled if sufficient radiation doses can be delivered to tumors; however, in practice, the achievable radiation doses are frequently limited by toxicities that may result following exposure of normal tissues to high radiation doses.
  • Organ-specific tissue injuries following prostate irradiation may include acute toxicities (such as cyctitis and enteritis), late toxicities (such as rectal of bladder bleeding) and broad toxicities such as bone marrow depletion or soft tissue necrosis.
  • IMRT intensity modulated radiation therapy
  • SRS stereotactic radiosurgery
  • HDR high-dose rate
  • PBT particle therapy
  • the present invention relates to methods of determining if a subject has an increased risk of having an adverse reaction to receiving radiation therapy for cancer, for example prostate cancer.
  • the methods comprise analyzing at least one sample from the subject to determine a value of the subject's metabolite profile, and comparing the value of the subject's metabolite profile with the value obtained from subjects determined to define a normal metabolite profile, to determine if the subject's metabolite profile is altered compared to a normal metabolite profile.
  • a difference in the value of the subject's metabolite profile compared to those defined as having a normal metabolite profile is indicative that the subject has an increased risk of having an adverse reaction to receiving radiation therapy for cancer, for example prostate cancer
  • the adverse reaction in tumor recurrence in tumor recurrence.
  • the adverse reaction is rectal toxicity.
  • the adverse reaction is urinary toxicity.
  • FIGURE 1 depicts predictive biomarkers of Recurrence Episodes in Prostate Cancer.
  • Panel A ROC curve for an eight-metabolite panel for classification of patients with recurrence episodes.
  • Panel B Biomarker panel (8 metabolites) predictive of recurrence.
  • FIGURE 2 depicts box and whisker plots of plasma 8 metabolite panel results for normal and recurrence groups in the prostate cancer cohort.
  • FIGURE 3 depicts plasma metabolite index (PMI) plot representation demonstrating group stratification.
  • the PMI results are based on the logistic regression model are illustrated as a boxplot that distinguishes between prostate cancer patients who developed recurrence and those that remained cancer free post SBRT.
  • Solid black horizontal lines represent the mean value, while the whiskers denote the spread within a group.
  • Orange and light blue dots represent PC patients who received hormone therapy or not, respectively.
  • the higher index values (left vertical axis) are associated with an increased risk of recurrence in the prostate cancer cohort.
  • FIGURE 4 depicts predictive biomarkers of rectal toxicity episodes, post SBRT in the prostate cancer cohort.
  • Panel A ROC curve for a six metabolite panel for classification of patients with radiation proctitis.
  • Panel B Biomarker panel (6 metabolites) predictive of rectal toxicity.
  • FIGURE 5 depicts box and whisker plots of plasma six-metabolite panel results for normal and radiation proctitis groups in the prostate cancer cohort.
  • FIGURE 6 depicts plasma metabolite index (PMI) plot representation demonstrating group stratification.
  • the PMI results are based on the logistic regression model that are illustrated as a boxplot that distinguishes between prostate cancer patients who developed rectal toxicity and those that remained normal, post SBRT. Solid black horizontal lines represent the mean value, while the whiskers denote the spread within a group. Orange and light blue dots represent PC patients who received hormone therapy or not, respectively.
  • the higher index values (left vertical axis) are associated with an increased risk of radiation proctitis (RP) in the prostate cancer cohort.
  • FIGURE 7 depicts predictive biomarkers of urinary toxicity episodes, post SBRT in the prostate cancer cohort.
  • Panel A ROC curve for a nine metabolite panel for classification of patients with urinary toxicity episodes.
  • Panel B Biomarker panel (9 metabolites) predictive of urinary toxicity.
  • FIGURE 8 depicts box and whisker plots of nine-plasma metabolite panel results for normal and group experiencing urinary toxicity in the prostate cancer cohort.
  • FIGURE 9 depicts plasma metabolite index (PMI) plot representation demonstrating group stratification.
  • the PMI results are based on the logistic regression model are illustrated as a boxplot that distinguishes between prostate cancer patients who developed urinary toxicity and those that remained normal post SBRT.
  • Solid black horizontal lines represent the mean value, while the whiskers denote the spread within a group.
  • Orange and light blue dots represent PC patients who received hormone therapy or not, respectively.
  • the higher index values (left vertical axis) are associated with an increased risk of urinary toxicity in the prostate cancer cohort.
  • the present invention relates to methods of determining if a subject has an increased risk of having an adverse reaction to receiving radiation therapy for cancer, for example prostate cancer.
  • the methods comprise analyzing at least one sample from the subject to determine a value of the subject's metabolite profile, and comparing the value of the subject's metabolite profile with the value obtained from subjects determined to define a normal metabolite profile, to determine if the subject's metabolite profile is altered compared to a normal metabolite profile.
  • a difference in the value of the subject's metabolite profile compared to those defined as having a normal metabolite profile is indicative that the subject has an increased risk of having an adverse reaction to receiving radiation therapy for cancer, for example prostate cancer.
  • the adverse reaction in tumor recurrence.
  • the adverse reaction is rectal toxicity.
  • the adverse reaction is urinary toxicity.
  • the term subject or "test subject” indicates a mammal, in particular a human or non-human primate.
  • cancer is understood in the art and is used to mean such things as an abnormal growth, such as hyperplasia, neoplasia or a tumor, to name a few. According to the methods of the present invention, the subject having the cancer would be a candidate for radiation therapy. Thus, the cancer as used herein does not include those cancers for which radiation therapy is not an option.
  • the cancer is prostate cancer.
  • prostate cancer is well-understood in the art and means a cancer in the prostate gland.
  • the "prostate cancer” can be a pre-cancerous lesion with a low Gleason score, including lesions having a Grade 1, Grade 2, Grade 3, Grade 4 or Grade 5 Gleason score.
  • prostate cancer includes Stage 1, Stage 2, Stage 3, Stage 4 or even recurrent prostate cancer.
  • the term means "increased risk” is used to mean that the test subject has an increased chance of developing or acquiring an adverse reaction to radiation therapy for cancer, for example prostate cancer, compared to a normal individual.
  • the increased risk may be relative or absolute and may be expressed qualitatively or quantitatively.
  • an increased risk may be expressed as simply determining the subject's metabolite profile and placing the patient in an
  • “increased risk” category based upon previous population or individual studies. Alternatively, a numerical expression of the subject's increased risk may be determined based upon the metabolite profile. As used herein, examples of expressions of an increased risk include but are not limited to, odds, probability, odds ratio, p-values, attributable risk, metabolite index score, relative frequency, positive predictive value, negative predictive value, and relative risk.
  • the attributable risk can also be used to express an increased risk.
  • the AR describes the proportion of individuals in a population exhibiting an adverse reaction to radiation therapy for cancer due to a specific member of the metabolite risk profile. AR may also be important in quantifying the role of individual components (specific member) in reaction etiology and in terms of the public health impact of the individual marker.
  • the public health relevance of the AR measurement lies in estimating the proportion of cases of an adverse reaction to radiation therapy for cancer in the population that could be prevented if the profile or individual component were absent.
  • the increased risk of a patient can be determined from p-values that are derived from association studies. Specifically, associations with specific profiles can be performed using regression analysis by regressing the metabolite profile with an adverse reaction to radiation therapy for cancer. In addition, the regression may or may not be corrected or adjusted for one or more factors.
  • the factors for which the analyses may be adjusted include, but are not limited to age, sex, weight, ethnicity, geographic location, fasting state, general health of the subject, alcohol or drug consumption, caffeine or nicotine intake and circadian rhythms, and the subject's Prostate Specific Antigen (PSA) status to name a few.
  • PSA Prostate Specific Antigen
  • Increased risk can also be determined from p-values that are derived using logistic regression.
  • Binomial (or binary) logistic regression is a form of regression which is used when the dependent is a dichotomy and the independents are of any type.
  • Logistic regression can be used to predict a dependent variable on the basis of continuous and/or categorical independents and to determine the percent of variance in the dependent variable explained by the independents; to rank the relative importance of independents; to assess interaction effects; and to understand the impact of covariate control variables.
  • Logistic regression applies maximum likelihood estimation after transforming the dependent into a "logit" variable (the natural log of the odds of the dependent occurring or not). In this way, logistic regression estimates the probability of a certain event occurring.
  • SAS statistical analysis software
  • a general purpose package similar to Stata and SPSS
  • Ready-to-use procedures handle a wide range of statistical analyses, including but not limited to, analysis of variance, regression, categorical data analysis, multivariate analysis, survival analysis, psychometric analysis, cluster analysis, and nonparametric analysis.
  • select embodiments of the present invention comprise the use of a computer comprising a processor and the computer is configured or programmed to generate one or more metabolite profiles and/or to determine statistical risk.
  • the methods may also comprise displaying the one or more profiles and/or risk profiles on a screen that is communicatively connected to the computer.
  • two different computers can be used: one computer configured or programmed to generate one or more metabolite profiles and a second computer configured or programmed to determine statistical risk. Each of these separate computers can be communicatively linked to its own display or to the same display.
  • the phrase "metabolite profile” means the combination of a subject's metabolites found in the peripheral blood or portions thereof, such as but not limited to plasma or serum.
  • the metabolite profile is a collection of measurements, such as but not limited to a quantity or concentration, for individual metabolites taken from a test sample of the subject.
  • test samples or sources of components for the metabolite profile include, but are not limited to, biological fluids, which can be tested by the methods of the present invention described herein, and include but are not limited to whole blood, such as but not limited to peripheral blood, serum, plasma,
  • Test samples to be assayed also include but are not limited to tissue specimens including normal and abnormal tissue.
  • levels of the individual lipid components of the metabolite profile are assessed using mass spectrometry in conjunction with ultra-performance liquid chromatography (UPLC), high-performance liquid chromatography (HPLC), gas chromatography (GC), gas chromatography/mass spectroscopy (GC/MS), and UPLC to name a few.
  • UPLC ultra-performance liquid chromatography
  • HPLC high-performance liquid chromatography
  • GC gas chromatography
  • GC/MS gas chromatography/mass spectroscopy
  • UPLC ultra-performance liquid chromatography
  • Other methods of assessing levels of the individual components include biological methods, such as but not limited to ELISA assays.
  • the assessment of the levels of the individual components of the metabolite profile can be expressed as absolute or relative values and may or may not be expressed in relation to another component, a standard an internal standard or another molecule of compound known to be in the sample. If the levels are assessed as relative to a standard or internal standard, the standard may be added to the test sample prior to, during or after sample processing.
  • a sample is taken from the subject.
  • the sample may or may not processed prior assaying levels of the components of the metabolite profile.
  • whole blood may be taken from an individual and the blood sample may be processed, e.g., centrifuged, to isolate plasma or serum from the blood.
  • the sample may or may not be stored, e.g., frozen, prior to processing or analysis.
  • the term "adverse reaction” as it relates to radiation therapy for cancer is cancer recurrence after receiving the therapy, rectal toxicity or urinary toxicity.
  • the recurrence of cancer for example prostate cancer
  • a subject is "cured" of cancer, for example prostate cancer, at least initially, if the subject is in remission and remains cancer free for at least 5 years from the time of the prescribed therapy to remove or destroy the diseased tissue.
  • the rectal toxicity should be attributable to the subject receiving radiation therapy for prostate cancer. As is well-understood, rectal toxicity can occur as a result of other factors besides receiving radiation.
  • the methods of the present invention include determining if a subject has or has a risk of developing rectal toxicity from a source other than receiving radiation therapy for cancer, for example prostate cancer. If the subject is free of rectal toxicity prior to receiving radiation therapy for cancer, the methods of the present invention can be performed on the subject that is a candidate for receiving radiation therapy for cancer.
  • the urinary toxicity should be attributable to the subject receiving radiation therapy for cancer. As is well-understood, urinary toxicity can occur as a result of other factors besides receiving radiation.
  • the methods of the present invention include determining if a subject has or has a risk of developing urinary toxicity from a source other than receiving radiation therapy for cancer. If the subject does not have urinary toxicity prior to receiving radiation therapy for cancer, the methods of the present invention can be performed on the subject that is a candidate for receiving radiation therapy for cancer, for example prostate cancer.
  • individual components of the metabolite profile include but are not limited to phosphatidylcholine acyl-alkyl C40:l (PC ae C40:l), phosphatidylcholine acyl-alkyl C40:6 (PC ae C4Q:6), phosphatidylcholine acyl-alkyl C42:l (PC ae C42:l), Arginine, Adenosine, phosphatidylcholine diacyl C26:0 (PC aa C26:0), phosphatidylcholine acyl-alkyl C36:2 (PC ae C36:2) and lysophosphatidylcholine acyl C26:l (LysoPC a C26:l).
  • Metabolite C species e.g., C3, denote acyicarnitines (ACs).
  • PC Phosphocholine
  • metabolites display combined numbers of carbon atoms for their two acyl groups (snl and sn2 positions), e.g., C38, whereas the combined number of double bonds (unsaturation) is displayed after the colon, e.g., C38:6.
  • Acyl group linkages to choline backbone for PCs feature ester (a) or ether (e) linkage, e.g., PC ae C42:l.
  • the individual levels of each of the metabolites are lower than those compared to normal levels.
  • one, two, three, four, five, six, seven or eight of the levels of each of the metabolites are lower than normal levels while others, if any, are higher than normal levels.
  • the individual levels of each of the metabolites are higher than those compared to normal levels.
  • one, two, three, four, five, six, seven or eight of the levels of each of the metabolites are higher than normal levels while others, if any, are higher than normal levels.
  • the levels of depletion or augmentation of the metabolites compared to normal levels can vary when treating a subject having cancer or determining the risk of cancer recurrence after receiving radiation therapy for cancer, for example prostate cancer.
  • the levels of any one or more of the metabolites is at least 1.05, 1.1, 1.2, 1.3, 1.4, 1.5, 1.6, 1.7, 1.8, 1.9, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20 lower than normal levels.
  • the number of "times" the levels of a metabolite is lower or higher over normal can be a relative or absolute number of times.
  • the levels of the metabolites may be normalized to a standard and these normalized levels can then be compared to one another to determine if a metabolite is lower or higher.
  • the metabolite profile comprises at least two, three, four, five, six, seven or all eight of the metabolites listed above. If two metabolites are used in generating the metabolite profile, any combination of the two listed above can be used. If three metabolites are used in generating the metabolite profile, any combination of three of the metabolites listed above can be used. If four metabolites are used in generating the metabolite profile, any combination of four of the metabolites listed above can be used.
  • any combination of five of the metabolites listed above can be used. If six metabolites are used in generating the metabolite profile, any combination of six of the metabolites listed above can be used. If seven metabolites are used in generating the metabolite profile, any combination of seven of the metabolites listed above can be used. Of course, all eight metabolites can be used in generating the metabolite profile to treat a subject having cancer or to determine risk of cancer recurrence after receiving radiation therapy for cancer.
  • individual components of the metabolite profile include but are not limited to phosphatidylcholine acyl-alkyl C36:l (PC ae C36:l), phosphatidylcholine acyl-alkyl C42:G (PC ae C42:0), sphingomyelin C2Q:2 (SM C20:2), O-Acetyl-L-Carnitine, 2-Aminoadipic acid and the ratio of
  • the individual levels of each of the metabolites are lower than those compared to normal levels. In another embodiment, one, two, three, four, five or six of the levels of each of the metabolites are lower than normal levels while others, if any, are higher than normal levels. In another embodiment, the individual levels of each of the metabolites are higher than those compared to normal levels. In another embodiment, one, two, three, four, five or six of the levels of each of the metabolites are higher than normal levels while others, if any, are higher than normal levels.
  • the levels of depletion or augmentation of the metabolites compared to normal levels can vary when treating a subject having cancer or determining the risk of developing rectal toxicity after receiving radiation therapy for cancer.
  • the levels of any one or more of the metabolites is at least 1.05, 1.1, 1.2, 1.3, 1.4, 1.5, 1.6, 1.7, 1.8, 1.9, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18,
  • the number of "times" the levels of a metabolite is lower or higher over normal can be a relative or absolute number of times.
  • the levels of the metabolites may be normalized to a standard and these normalized levels can then be compared to one another to determine if a metabolite is lower or higher.
  • the metabolite profile comprises at least two, three, four, five or all six of the metabolites listed above. If two metabolites are used in generating the metabolite profile, any combination of the two listed above can be used. If three metabolites are used in generating the metabolite profile, any combination of three of the metabolites listed above can be used. If four metabolites are used in generating the metabolite profile, any combination of four of the metabolites listed above can be used. If five metabolites are used in generating the metabolite profile, any combination of five of the metabolites listed above can be used. Of course, all six metabolites can be used in generating the metabolite profile to treat a subject having cancer or to determine risk of developing rectal toxicity after receiving radiation therapy for cancer.
  • individual components of the metabolite profile include but are not limited to lysophosphatidylcholine acyl C20:4 (LysoPC a C20:4), phosphatidylcholine diacyi C34:2 (PC aa C34:2), phosphatidylcholine acyl-alkyl C4G:5 (PC ae C4Q:5), phosphatidylcholine diacyl C36:l (PC aa C36:l), phosphatidylcholine diacyl C40:5 (PC aa C4G:5), phosphatidylcholine acyl-alkyl C40:3 (PC ae C40:3), lysophosphatidylcholine acyl C18:2 (LysoPC a C18:2), lysophosphatidylcholine acyl
  • the individual levels of each of the metabolites are lower than those compared to normal levels. In another embodiment, one, two, three, four, five, six, seven, eight or nine of the levels of each of the metabolites are lower than normal levels while others, if any, are higher than normal levels. In another embodiment, the individual levels of each of the metabolites are higher than those compared to normal levels. In another embodiment, one, two, three, four, five, six, seven, eight or nine of the levels of each of the metabolites are higher than normal levels while others, if any, are higher than normal levels.
  • the levels of depletion or augmentation of the metabolites compared to normal levels can vary when treating a subject having cancer or determining the risk of developing urinary toxicity after receiving radiation therapy for cancer.
  • the levels of any one or more of the metabolites is at least 1.05, 1.1, 1.2, 1.3, 1.4, 1.5, 1.6, 1.7, 1.8, 1.9, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20 lower than normal levels.
  • the number of "times" the levels of a metabolite is lower or higher over normal can be a relative or absolute number of times.
  • the levels of the metabolites may be normalized to a standard and these normalized levels can then be compared to one another to determine if a metabolite is lower or higher.
  • the metabolite profile when treating a subject having cancer or determining the risk of developing urinary toxicity after receiving radiation therapy for cancer, comprises at least two, three, four, five, six, seven, eight or all nine of the metabolites listed above. If two metabolites are used in generating the metabolite profile, any combination of the two listed above can be used. If three metabolites are used in generating the metabolite profile, any combination of three of the metabolites listed above can be used. If four metabolites are used in generating the metabolite profile, any combination of four of the metabolites listed above can be used.
  • any combination of five of the metabolites listed above can be used. If six metabolites are used in generating the metabolite profile, any combination of six of the metabolites listed above can be used. If seven metabolites are used in generating the metabolite profile, any combination of seven of the metabolites listed above can be used. If eight metabolites are used in generating the metabolite profile, any combination of eight of the metabolites listed above can be used. Of course, all nine metabolites can be used in generating the metabolite profile to treat a subject having cancer or to determine risk of developing urinary toxicity after receiving radiation therapy for cancer.
  • Tables 1 through 3 below list exemplary analysis of the metabolites used for each specific adverse reaction to receiving radiation therapy for cancer, for example prostate cancer.
  • a "mean fold change" of one (1) indicates no change while values less than one indicate a negative change in the diagnostic group as compared to the normal control (NC).
  • values greater than one indicate a positive change in the diagnostic group compared to NC.
  • Techniques to assay levels of individual components of any non-lipid component of the metabolite profile from test samples are well known to the skilled technician, and the invention is not limited by the means by which the components are assessed. In one embodiment, levels of the individual components of the non-lipid portion of the profile are assessed using quantitative arrays, PCR, Northern Blot analysis, Western Blot analysis, mass spectroscopy, high-performance liquid
  • metabolite levels include biological methods, such as but not limited to ELISA assays.
  • determining levels of, for example, a fragment of protein being analyzed may be sufficient to conclude or assess that an individual component of the metabolite profile, including the lipid and non-lipid portions of the metabolite profile, being analyzed is increased or decreased.
  • arrays or blots are used to determine metabolite levels, the presence/absence/strength of a detectable signal may be sufficient to assess levels of metabolites.
  • the subject's metabolite profile is compared to the profile that is deemed to be a normal metabolite profile.
  • an individual or group of individuals may be first assessed for the lack of any observable or noticeable adverse reactions to radiation therapy for cancer, for example prostate cancer.
  • the metabolite profile of the individual or group of individuals can then be determined to establish a "normal metabolite profile.”
  • a normal metabolite profile can be ascertained from the same subject when the subject is deemed to not have cancer, for example prostate cancer, and is displaying no signs (clinical or otherwise) of cancer.
  • a "normal" metabolite profile is assessed in the same subject from whom the sample is taken prior to the onset of measureable, perceivable or diagnosed sign of cancer. That is, the term "normal” with respect to a metabolite profile can be used to mean the subject's baseline metabolite profile prior to the onset of cancer or receiving radiation therapy for cancer. The metabolite profile can then be reassessed periodically and compared to the subject's baseline metabolite profile.
  • a normal metabolite profile is assessed in a sample from a different subject or patient (from the subject being analyzed) and this different subject does not have or is not suspected of having cancer or showed no observable adverse reaction to receiving radiation therapy for cancer.
  • the normal metabolite profile is assessed in a population of healthy individuals, the constituents of which do not have cancer or showed no observable adverse reaction to receiving radiation therapy for cancer.
  • the subject's metabolite profile can be compared to a normal metabolite profile generated from a single normal sample or a metabolite profile generated from more than one normal sample.
  • measurements of the individual components e.g., concentration, ratio, log ratios etc.
  • concentration, ratio, log ratios etc. of the normal metabolite profile can fall within a range of values, and values that do not fall within this "normal range” are said to be outside the normal range.
  • These measurements may or may not be converted to a value, number, factor or score as compared to measurements in the "normal range.”
  • a measurement for a specific metabolite that is below the normal range may be assigned a value or -1, -2, -3, etc., depending on the scoring system devised.
  • the "metabolite profile value" can be a single value, number, factor or score given as an overall collective value to the individual molecular components of the profile, or to the categorical components, i.e., a phosphatidylcholine portion, a biogenic amine portion and/or an amino acid portion.
  • the metabolite value may simply be the overall score of each individual or categorical value.
  • the metabolite profile value could be a useful single number or score, the actual value or magnitude of which could be an indication of the actual risk of developing an adverse reaction to radiation therapy for cancer, e.g., the "more negative” the value, the greater the risk of developing an adverse reaction to radiation therapy for cancer.
  • the "metabolite profile value" can be a series of values, numbers, factors or scores given to the individual components of the overall profile.
  • the "metabolite profile value” may be a combination of values, numbers, factors or scores given to individual components of the profile as well as values, numbers, factors or scores collectively given to a group of components, such as a phosphatidylcholine portion, an acylcarnitine portion, a biogenic amine portion and/or an amino acid portion.
  • the metabolite profile value may comprise or consist of individual values, number, factors or scores for specific component as well as values, numbers, factors or scores for a group on components.
  • individual values from the metabolites can be used to develop a single score, such as a "combined metabolite index,” which may utilize weighted scores from the individual component values reduced to a diagnostic number value.
  • the combined metabolite index may also be generated using non-weighted scores from the individual component values.
  • the threshold value would be or could be set by the combined metabolite index from one or more normal subjects.
  • the value of the metabolite profile can be the collection of data from the individual measurements and need not be converted to a scoring system, such that the "metabolite profile value" is a collection of the individual measurements of the individual components of the profile.
  • a subject is diagnosed of having an increased risk of an adverse reaction to radiation therapy for cancer if the subject's nine, eight, seven, six, five, four, three, two or even one of the metabolites herein are at abnormal levels.
  • the attending health care provider may subsequently prescribe or institute a treatment program or prescribe a different treatment for cancer.
  • the present invention also provides for methods of screening individuals as candidates for treatment of an adverse reaction to radiation therapy for cancer.
  • the attending healthcare worker may begin treatment, based on the subject's metabolite profile, before there are perceivable, noticeable or measurable signs of an adverse reaction to radiation therapy for cancer in the individual.
  • the invention provides methods of treating a subject having cancer, for example prostate cancer.
  • the treatment methods include obtaining a subject's metabolite or composite profile as defined herein and prescribing a treatment regimen to the subject if the metabolite and/or composite profile indicate that the subject is at risk of suffering from an adverse reaction to radiation therapy for cancer, for example prostate cancer.
  • Suitable radiation therapies for prostate cancer are well-known, and the methods disclosed and described herein are not dependent on the specific type of radiation therapy for cancer.
  • the objective of this study was to employ a high through put metabolomics approach for delineating a biomarker panel predictive of radiation induced adverse effects in patients treated for prostate cancer. Such biomarkers aid in early detection of tissue toxicity in cancer patients, so that intervention can be initiated early in patients at risk.
  • SBRT stereotactic body radiation therapy
  • CTV clinical target volume
  • the prescribed doses of 35-36.25 Gy are delivered in five fractions of 7-7.25 Gy over 2 weeks.
  • Symptom management medications were prescribed based on the treating physician's clinical judgment and urinary symptoms were managed with alpha-adrenergic antagonists and bothersome bowel symptoms were managed with anti-diarrheal medication (loperamide).
  • Stable isotope labeled multiple reaction monitoring mass spectrometry (SID-MRM-MS) was used for quantitation of 350 metabolites. Metabolite extraction was performed using 25 pL of plasma sample was mixed with 175 pL of 40% acetonitrile in 25% methanol and 35% water containing internal standards [stable isotope labeled). The samples were incubated on ice for 10 minutes and centrifuged at 14,000 rpm at 4°C for 20 minutes. The supernatant was transferred to a fresh tube and used for UPLC-QQQ -MS analysis.
  • Each plasma sample (2 pL) was injected onto a reverse-phase CSH C18 1.7pM 2.1 x 100 mm column using an Acquity UPLC online with a triple quadrupole MS (Xevo TQ-S, Waters Corporation, USA) G2-QTOF system operating in the MRM mode.
  • the m/z values of the measured metabolites were normalized with log transformation that stabilizes variance, followed by quantile normalization to achieve uniform empirical distribution of intensities (measure of metabolite abundance) across samples.
  • the m/z values of the measured metabolites from tissue samples were normalized with log transformation that stabilized the variance, followed by quantile normalization to make the empirical distribution of intensities the same across samples.
  • the m/z values of the measured metabolites from plasma samples were normalized with log transformation that stabilized the variance, followed by quantile normalization to make the empirical distribution of intensities the same across samples.
  • Feature selection was performed using a ROC regularized learning technique, which uses the least absolute shrinkage and selection operator (LASSO) penalty.
  • the regularization path was obtained over a grid of values for the tuning parameter lambda through 10-fold cross-validation. Then the optimal value of the tuning parameter lambda, obtained by the cross-validation procedure was used to fit the model. Finally, all the features with non-zero coefficients were retained as the candidate biomarker panel. This technique is known to reduce overfitting and variance in classification.
  • the classification performance of the biomarker panel was assessed using the area under the ROC (receiver operating characteristic) curve (AUC).
  • AUC receiver operating characteristic
  • the ROC curve can be understood as a plot of the probability of classifying correctly positive samples against the rate of incorrectly classifying true negative samples. Therefore, the AUC measure of an ROC plot is a measure of predictive accuracy. Due to the perfect separation for the classification, the panel was also evaluated using the hidden logistic regression model with the maximum estimated likelihood (MEL) estimator, and the AUC scores were similar.
  • MEL maximum estimated likelihood
  • the individual markers were also analyzed, and the AUC score was estimated for the regression with each marker, all of them showing high discriminative value for distinguishing "high” vs "low” and “recur” vs "low” patient group, to rule out correlation with the patients' hormone therapy status.
  • the cohort of SBRT treated patients is shown in Figure 1. Of 105 patients, 10 developed biochemical recurrences with an average time of 18 months. To develop a predictive panel of recurrence, the pre-radiation plasma metabolite profiles were compared to a sub-set of patients who remained cancer free during this time. The panel was adjusted for age, PSA levels and Gleason's grade.
  • a sub-set analyses was performed in patients not receiving hormone therapy to rule out the influence of hormone therapy on the marker panel, in high risk and recurrence patient categories.
  • the eight-member PMI helps identify the degree to which individuals are 'at-risk' of an outcome (recurrence, rectal or bladder late effects).
  • the natural log of odds in the model was transformed to 0-100 index value using a linear mapping.
  • a predictive biomarker panels of adverse outcomes of radiation therapy was also developed.
  • a six metabolite biomarker panel yielded an AUC of 98.3% ( Figure 4, Panels A and B).
  • the relative abundance of biomarkers in the two comparative groups is illustrated in Figure 5 while the six-member PMI (6PMI) ( Figure 6), helped stratify patients who later developed rectal toxicity from those who did not develop radiation induced adverse symptoms, underscoring the power of this technology.

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Abstract

La présente invention concerne des méthodes permettant de déterminer si un sujet présente un risque accru de présenter une réaction indésirable à la réception d'une radiothérapie pour le cancer de la prostate. Les procédés comprennent l'analyse d'au moins un échantillon du sujet afin de déterminer une valeur du profil de métabolite du sujet, et la comparaison de la valeur du profil de métabolite du sujet avec la valeur obtenue à partir de sujets déterminés afin de définir un profil de métabolite normal, afin de déterminer si le profil de métabolite du sujet est modifié par rapport à un profil de métabolite normal. Une différence de la valeur du profil métabolique du sujet par rapport à celles définies comme présentant un profil de métabolite normal indique que le sujet présente un risque accru de présenter une réaction indésirable à la réception d'une radiothérapie pour le cancer de la prostate.
PCT/US2018/064924 2017-12-11 2018-12-11 Biomarqueurs prédictifs d'effets secondaires de radiothérapie Ceased WO2019118443A1 (fr)

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Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2002068960A1 (fr) * 2001-02-07 2002-09-06 Baylor College Of Medicine Technique de determination de pronostic post therapeutique d'un cancer de la prostate
US20080305962A1 (en) * 2005-07-29 2008-12-11 Ralph Markus Wirtz Methods and Kits for the Prediction of Therapeutic Success, Recurrence Free and Overall Survival in Cancer Therapies
US20120040383A1 (en) * 2010-08-12 2012-02-16 Wei Jia Methods and Kits Relating To Metabolite Biomarkers For Colorectal Cancer
US20120326025A1 (en) * 2010-01-29 2012-12-27 IMG Institut fur Medizinische Genomforschung Planu ngsgesellschaft Diagnosing prostate cancer relapse
US20150241406A1 (en) * 2012-10-02 2015-08-27 Charité Universitätsmedizin Berlin Means and Methods for Diagnosing Recurrence of Prostate Cancer After Prostatectomy

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2002068960A1 (fr) * 2001-02-07 2002-09-06 Baylor College Of Medicine Technique de determination de pronostic post therapeutique d'un cancer de la prostate
US20080305962A1 (en) * 2005-07-29 2008-12-11 Ralph Markus Wirtz Methods and Kits for the Prediction of Therapeutic Success, Recurrence Free and Overall Survival in Cancer Therapies
US20120326025A1 (en) * 2010-01-29 2012-12-27 IMG Institut fur Medizinische Genomforschung Planu ngsgesellschaft Diagnosing prostate cancer relapse
US20120040383A1 (en) * 2010-08-12 2012-02-16 Wei Jia Methods and Kits Relating To Metabolite Biomarkers For Colorectal Cancer
US20150241406A1 (en) * 2012-10-02 2015-08-27 Charité Universitätsmedizin Berlin Means and Methods for Diagnosing Recurrence of Prostate Cancer After Prostatectomy

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