Patent Application
20140051183
Subject of the present invention is a method of assessing the susceptibility of a subject to acquire cancer and/or assessing the risk of cancer mortality for a subject, who has not had clinically manifest cancer and/or does not have clinically manifest cancer at the time when applying this method, comprising the steps:
Subject of the present invention is a method for prevention of cancer and/or a method for prevention monitoring (which means monitoring success of preventive measures) for a subject, who has not had clinically manifest cancer and/or does not have clinically manifest cancer at the time when applying this method, comprising the steps:
The methods of the present invention are especially valuable in the context of assessing the susceptibility of a subject to acquire cancer and are, thus, especially preferred.
Cancer is by definition a genetic disease, initiated by the activation of oncogenes and/or inactivation of suppressor genes giving rise to the typical cancer cell phenotype. Over the last decades, however, it has become evident that the progression from malignant transformation into manifest tumor disease is highly dependent on non-malignant cells of the host, perhaps most importantly through the recruitment of blood vessels, i.e. the “angiogenic switch”. Imbalance in angiogenesis has been suggested to be of importance for development of arterial hypertension through reduced formation of arterioles and capillaries, thereby increasing the total peripheral vascular resistance. In support of this hypothesis, hypertension is a well known side effect of anti-angiogenic treatment of cancer patients. At the epidemiological level, however, hypertension has been associated with a slightly increased risk of cancer, although the cause of this relationship is unknown. Interestingly, the blood pressure sensitive and vasoactive hormones adrenomedullin, atrial natriuretic peptide and vasopressin, have been shown to have regulatory effects on angiogenesis and cancer cells in experimental models. Here, we hypothesize that host levels of adrenomedullin, atrial natriuretic peptide and/or vasopressin may have a predictive role for cancer. To address this issue, we measured stable fragments of the precursors of these hormones [midregional proadrenomedullin (MR-pro-ADM), midregional proatrial natriuretic peptide (MR-pro-ANP) and C-terminal pre-pro-Vasopressin (copeptin)] in fasting plasma in a large Swedish prospective cohort study and related baseline levels of these three biomarkers to cancer incidence during 15 years of follow-up.
Thus, subject of the present invention is a method of assessing the susceptibility of a subject to acquire cancer and/or assessing the risk of cancer mortality for a subject, who has not had clinically manifest cancer and/or does not have clinically manifest cancer at the time when applying this method, comprising the steps:
After having assessed the susceptibility and/or risk of cancer mortality the subjects may be stratified depending on the need of preventive measures.
Subject of the present invention is a method for prevention of cancer and/or prevention monitoring for a subject, who has not had clinically manifest cancer and/or does not have clinically manifest cancer at the time when applying this method, comprising the steps:
Subject of the present invention is a method of assessing the susceptibility of a subject to acquire cancer and/or assessing the risk of cancer mortality for a subject comprising the steps:
In a special embodiment of the above method of assessing the susceptibility of a subject to acquire cancer and/or assessing the risk of cancer mortality for a subject said subject has not yet being diagnosed as having cancer and/or does not have cancer.
A subject not yet being diagnosed as having cancer is a subject with no prior cancer. Prior or present cancer may be confirmed by histopathology (biopsy or tissue examined after operation). This means a person which have not had cancer or does not have cancer is a person without diagnosed and/or confirmed cancer preferably by histopathology (biopsy or tissue examined after operation).
The methods of the present invention are especially preferred if the subject is a male subject. According to data collected the methods of the present invention are especially suited for a Caucasian subject, more preferred Caucasian European subject, most preferred Caucasian Northern European subject. As above stated the methods of the present invention are especially valuable in the context of assessing the susceptibility of a subject to acquire cancer and are, thus, especially preferred.
In the above method according to the invention a subject may be stratified either as having a(n enhanced) susceptibility or as haven't a(n enhanced) susceptibility to acquire cancer, either as having a(n enhanced) risk of cancer mortality or as haven't. Alternatively to such a yes/no stratification of the subjects according to risk and/or susceptibility the stratification according to the methods of the invention may lead to more than two risk/susceptibility groups, preferably more than three risk/group with escalating risks and/or susceptibility. Certain thresholds for the biomarkers and/or mathematical combinations of biomarkers, other laboratory and clinical parameters may be defined in correlation to a certain risk/susceptibility.
Instead of stratification into certain risk groups risk stratification may be accomplished by a continuous scaling.
After having stratified a subject as having a(n enhanced) susceptibility to acquire cancer or having a(n enhanced) risk of cancer mortality several measures may be taken in order to prevent and/or delay the clinical manifestation of cancer.
These measures may encompass but are not limited to the following measures:
Intensification and/or enhancing the frequency of diagnostic measures, preventive medication, change of exercise activities, change of lifestyle, change of alimentation.
Once a person has been stratified as being a subject having a(n enhanced) susceptibility to acquire cancer or having a(n enhanced) risk of cancer mortality the method of assessing the susceptibility of said subject to acquire cancer and/or assessing the risk of cancer mortality for said subject may be conducted again and/or several times in order to monitor the prevention progress.
The group of subjects not having had clinically manifest cancer and/or not having clinically manifest cancer may encompass subjects with pre-forms of cancer which are however not a clinically manifest cancer. A subject not having had clinically manifest cancer is a subject that had no prior cancer defined by clinical and histological diagnosis; a subject not having clinically manifest cancer is a subject wherein cancer is excluded according to the baseline examination via physical examination and questionnaire according to the Examples. In any case said subject has not yet being diagnosed as having cancer and/or does not have cancer.
Subject of the present invention is also a method for therapy stratification and/or monitoring of success of preventive measures for a subject who has not clinically manifest cancer, comprising the steps:
In a special embodiment of the above method of the present invention said subject has not yet being diagnosed as having cancer and does not have cancer.
In an analysis shown in Examples the markers that are used according to the methods of the present invention, namely pro-ANP or fragments thereof, and/or pro-Vasopressin or fragments thereof and/or pro-ADM or fragments thereof, any of said fragments having at least a lengths of 12 amino acids predict incident cancer both, when all incident cancers are considered occurring within the next 15 years after biomarker examination, and, importantly, also, when the first four years of the 15 years follow up period are omitted from the analysis. If the methods of the present invention would be usable only for subjects having pre-forms of cancer, then it would have been expected, that prediction of incident cancer would not work for predicting cancers in years 5-15 of the follow up period. Therefore, it is clearly demonstrated that the methods of the present invention are equally predictive for subjects who might have certain pre-forms of cancer, but also for subjects not having pre-forms of cancer. In a preferred embodiment said subject is male.
In another preferred embodiment of the method according to the invention said method is used in a screening method for subjects, preferably subjects of the male population.
Said fragments are any fragments derivable from the prohormones of Adrenomedullin, Vasopressin and ANP (proAdrenomedullin, pro-Vasopressin, pro-ANP). Known fragments of pro-ADM include PAMP, MR-pro-ADM, ADM, CT-pro-ADM (Adrenotensin). Known fragments of pro-Vasopressin include Vasopressin (AVP, antidiuretic hormone, ADH), Neurophysin II, copeptin. Known fragments of pro-ANP include ANP, NT-pro-ANP, MR-pro-ANP.
The sequences of the pre-pro-hormones are as follows:
Additional information on the sequences:
pre-pro-ADM: http://www.uniprot.org/uniprot/P35318
pre-pro-Vasopressin: http://www.uniprot.org/uniprot/P01185
pre-pro-ANP: http://www.uniprot.org/uniprot/P01160
In an especially preferred embodiment the level of MR-pro-ADM with SEQ ID NO:4 is determined.
In another especially preferred embodiment the level of copeptin with SEQ ID NO:5 is determined. In another especially preferred embodiment the level of MR-pro-ANP with SEQ ID NO:6 is determined.
Combination of markers may give additional information. Thus, it is also a preferred embodiment according to the methods of the present invention to use the levels of pro-ANP or fragments thereof and pro-Vasopressin or fragments thereof, especially preferred is the use of MR-pro-ANP and Copeptin for the methods of the present invention.
It is also preferred embodiment according to the methods of the present invention to use the level of pro-ADM and fragments thereof and pro-ANP or fragments thereof, especially preferred is the use of MR-pro-ANP and MR-pro-ADM as a combination of marker.
In Cox proportional hazards models it was analyzed whether two markers would give independent and significant predictive information on the development of incident cancer, and thus a combination of the two would give more information than either one alone. The analysis was exemplarily done for all men.
In another preferred embodiment according to the methods of the present invention the level of pro-ANP or fragments thereof, copeptin or fragments thereof and pro-ADM or fragments thereof is determined. It is especially preferred that the level MR-pro-ADM with SEQ ID NO:4, copeptin with SEQ ID NO:5 and MR-pro-ANP with SEQ ID NO:6 thereof is determined and used in the methods according to the present invention.
Further, marker may also be included in the methods of the present invention as Connective Tissue-Activating Peptide III published as a novel blood marker for early lung cancer detection (Yee et al., Connective Tissue-Activating Peptide III: A Novel Blood Biomarker for Early Lung Cancer Detection, J. Clin Onco 2009, Vol. 27:17, 2786-2792). Further marker may be neutrophil activating protein-2 (NAP-2) and haptoglobin as well as Procalcitonin and fragments thereof (EP 09011073.5) and C-Reactive Protein levels. (Zhang et al., C-Reachtive Protein Levels Are Not Associated with Increased Risk for Colorectal Cancer in Women, Annals Int. Med 2005, 142:6, 425-432). At least one of these markers may be added to the methods according to the present invention or more than of these markers selected from the group comprising Connective Tissue-Activating Peptide III, neutrophil activating protein-2, haptoglobin, Procalcitonin and fragments thereof and C-Reactive Protein.
In order to improve the accuracy of the methods of the present invention further marker/risk factors may be included into said methods. Said marker may be selected from the group comprising smoking, cancer heredity, pro-BNP or fragments thereof, Cystatin C or fragments thereof, age.
In a preferred embodiment of the invention said subject(s) are younger than 60 years. In another preferred embodiment the screening method is performed on subjects being younger than 60 years.
The inclusion of the risk factor age into the methods of the present invention is especially preferred.
In the following a specific example is given wherein certain parameters are used in order to calculate an individual's risk to get cancer. A person skilled in the art will understand that the methods of the present invention are not limited to the below specific calculation. Variations of this calculation may be performed by a person skilled in the art. E.g. a marker/risk factor may be added or omitted in comparison to the below calculation example.
Formula:
Based on the Cox proportional hazards model a formula can be derived, which can be used to calculate an individual's risk to get cancer. Such procedure has been used analogously in the past to develop the Framingham Risk Score for the calculation of an individual's risk to suffer from future cardiovascular events. Particular formulas may differ depending on which and how many variables have been analyzed in Cox models, and which follow-up period is considered.
Principally, the formula reads:
Individual risk to get the endpoint of interest in a certain period [%]=1−S0̂exp (xb). In this formula S0 represents the proportion of all individuals in a population, which does not get the endpoint of interest in this certain period, or more precisely: S0(T) is the survival function at T years for a subject for whom each predictor variable is equal to the average value of that variable for the entire set of subjects in the study. In this formula xb represents the sum of weighted β-coefficient of the variables from the corresponding Cox proportional hazards model, or more precisely: Xb is the sum of all risk factors minus the average of that variable in the population weighted by the regression coefficients from a Cox proportional hazards regression model.
In an example, here the formula to calculate an individual's risk to get cancer over a period of 16 years after assessment of variables has been developed for all males in the study based on Cox proportional hazards model 1.
The following variables were introduced in the model:
smoke=current smoker (1/0)
her_cancer=cancer heredity: One or more first degree relative had cancer (1/0)
LNNTBNP=NT-BNP [pg/mL] (log-transformed)
CYSTC=Cystatin C [mg/L] (log-transformed)
LNCOPEPTIN=Copeptin [pmol/L] (log-transformed)
MRADM=MR-pro-ADM [nmol/L]
LNMRANP=MR-pro-ANP [pmol/L] (log-transformed)
The β-coefficient of these variables from the Cox proportional hazards model were (Betas are given for 1 unit increase for continuous variables and for the condition present in dichotomous variables):
b_smoke=0.15546
b_her_cancer=0.09054
b_LNNTBNP=0.00965
b_CYSTC=−0.02697
b_AGE=0.07018
b_LNCOPEPTIN=0.17058
b_MRADM=0.96756
b_LNMRANP=−0.39251
xb was:
xb=0.155457*(CURRENT_SMOKER−0.274887)+0.090539*(HER_CANCER—0−0.444005)+0.009652*(LNNtBNP−3.860854)−0.026975*(cystC−0.795469)+0.070179*(AGE−57.530925)+0.170579*(LNCOPEPTIN−1.881057)+0.967559*(MRADM−0.451704)−0.392512*(LNMRANP−4.127731)
The quantitative contribution of each variable to the result is directly reflected by the absolute size of the coefficient with which the variable is multiplied with.
S0 (the proportion of all individuals in a population, which did not get cancer in the 16 year follow-up period) was 0.80.
The 16 year risk for males can be calculated as 1-0.80 exp(?β(X−mean(X)).
The Malmö Diet and Cancer (MDC) study is a population-based, prospective epidemiologic cohort of 28449 men (born between 1923-1945) and women (born between 1923-1950) from the city of Malmö in southern Sweden who underwent baseline examinations between 1991 and 1996. From this cohort, 6103 persons were randomly selected 1991-1994 to participate in the MDC Cardiovascular Cohort (MDC-CC), which was designed to investigate the epidemiology of carotid artery disease. Fasting plasma samples were available in a total of 5543 subjects in the MDC-CC and 336 subjects had cancer prior to the baseline examination (84 males and 252 females). Subjects in the MDC-CC who had fasting plasma available, were free from prior or prevalent cancer and had data on the complete set of covariates included in model 2 (see statistical methods) were included in the dataset analyzed in the current study (4061 individuals; 1768 males and 2293 females). Prevalent, clinically manifest cancer at baseline examination was excluded by physical examination and questionnaire.
According to the studies, as outlined herein, all subjects with no prior cancer are persons which were not reported to the Swedish Cancer Register. The Swedish Cancer Register was started in 1958 and it covers 99% of all tumors nation-wide. As 98% have histopathological (=morphological) evidence, the register is of good quality.
Subclinical cancer: All participants underwent a careful medical history interview and standard blood samples including hemoglobin and blood cell counts, lipids and glucose. The medical history interview, which included cancer, was performed by a nurse. Abnormal blood sample results or symptoms resulted in consultation of physician at the clinic and that physician then decided whether to go for further evaluation, consult a specialist etc. Thus, the setting would be a nurse based screening with consultation of physician when indicated based on abnormal blood tests or symptoms.
Blood pressure was measured using a mercury-column sphygmomanometer after 10 minutes of rest in the supine position. Data on smoking, cancer heredity and use of antihypertensive and antidiabetic medications was ascertained from a questionnaire. Cancer heredity was defined as having at least one first degree relative diagnosed with cancer. Current smoking was defined as any cigarette smoking within the past year. Diabetes mellitus was defined as having a fasting whole blood glucose of >6.0 mmol/L, self-reported physician diagnosis of diabetes or use of antidiabetic medications. Body mass index (BMI) was defined as the weight in kilograms divided by the square of the height in meters. Myocardial infarction prior to the baseline exam was defined and retrieved as described previously.
In fasted EDTA plasma specimens were frozen immediately after collection at the MDC-CC baseline exam, we measured MR-pro-ANP, MR-pro-ADM and copeptin using immunoluminometric sandwich assays as described previously (BRAHMS, AG, Germany) (Morgenthaler et al., Measurement of Midregional Proadrenomedullin in Plasma with an Immunoluminometric Assay, Clinical Chemistry 51:10, 1823-1829 (2005)). N-terminal pro-B-type natriuretic peptide (N-BNP) was determined using the Dimension RxL automated N-BNP method (Siemens Diagnostics, Nuremberg, Germany) and cystatin C was measured using a particle-enhanced immuno-nephelometric assay (N Latex Cystatin C, Dade Behring, Deerfield, Ill.).
We measured fasting high-density lipoprotein cholesterol (HDL), insulin and triglycerides according to standard procedures at the Department of Clinical Chemistry, University Hospital Malmö and low-density lipoprotein cholesterol (LDL) was calculated according to Friedewald's formula.
All participants gave written informed consent and the study was approved by the Ethical Committee at Lund University, Lund, Sweden.
Cancer events were defined and subdivided according to the European Prospective Investigation on Cancer and Nutrition (EPIC) definition with the exception that cervix uteri cancer in situ was not regarded as a cancer event. Information on cancer events (both prevalent and incident events) was retrieved up until Dec. 31, 2007 by record linkage with the Swedish Cancer Register (SCR) using a unique 10-digit civil registration number. The SCR was set up in 1958 and all malignant tumors are to be reported. Tumor site was registered according to ICD-7 and the ICD version used at diagnosis. Histopathological type was coded according to the C24 classification (REF). Approximately 99% of all tumors diagnosed at Swedish Hospitals are registered in the SCR and 98% are morphologically verified (REF).
Information on total mortality and cancer mortality during follow-up was retrieved by linking the unique 10-digit civil registration number with the Swedish National Cause of Death Register (SNCDR) with ICD10 codes C00-D48 as the main cause of death (or corresponding codes in previous ICD versions) defining cancer mortality.
Comparisons in clinical characteristics between sexes were performed with t-test or Mann-Whitney test depending on normality for continuous variables and with chi-square test for dichotomous variables. We analyzed time to first event in relation to baseline biomarker levels using Cox proportional hazards models with baseline age as time scale variable. Apart from this age adjustment the three biomarkers alone and in combination were always entered into the model together with current smoking, cancer heredity, cystatin C (as marker of glomerular filtration rate) and N-BNP (as marker of sub-clinical heart failure) (model 1 covariates) unless otherwise specified in the text. The motif for adjustment for cystatin C was that the three biomarkers all are small molecules mainly cleared from plasma by glomerular filtration. The reason for entering N-BNP into the model, as a sensitive marker for subclinical heart failure and left ventricular dysfunction, was to test and adjust for any potential relationship between heart failure and cancer as all of the three biomarkers have previously been found to be elevated in heart failure.
Biomarkers with skewed distributions (MR-pro-ANP, copeptin and N-BNP) were logarithmically transformed before analysis, and the relationship between biomarker levels and incident cancer, cancer mortality and total mortality is expressed as hazard ratio per 1 standard deviation increase in the respective biomarker and in quartile analyses as hazard ratio for each quartile with the lowest quartile defined as the referent (hazard ratio 1.0) and as hazard ratio per quartile increase to obtain the P-value for trend over quartiles.
To get a summed effect estimate of the relationship between the three biomarkers and incident cancer, cancer mortality and total mortality we summed the Z-scores for the three biomarkers weighted for their respective β-coefficient from the corresponding Cox proportional hazards model (MR-pro-ADM, MR-pro-ANP and copeptin entered simultaneously together with model 1 covariates), and the weighted sum of the Z-scores was referred to as “biomarker score”.
All Cox proportional hazards models which were significant after model 1 adjustment, were further adjusted in for model 2 covariates (apart from in analyses of subtypes of cancer) which included all model 1 covariates together with BMI, systolic and diastolic blood pressure, antihypertensive treatment, myocardial infarction prior to baseline, diabetes mellitus, LDL, HDL and fasting insulin.
In the Cox proportional hazards models for analyses of subtypes of cancer, the sample sizes differ from the total sample sizes and varies between the different cancer subtype analyses as subjects with other subtypes of incident cancer than the one specifically analyzed were excluded from the “control group” and the numbers of events for each subtype differs from the over-all distribution of first events (Table 2) as a first cancer subtype event was allowed to be preceded by another subtype of cancer without being censored.
In all Cox proportional hazards models, subjects were censored at the time of event, death, emigration from Sweden or at the end of follow-up. The proportionality of hazards assumption was confirmed using Schoenfeld's global test.
Crude Kaplan-Meier curves of cumulative incidence (beginning at baseline) were created for comparison of the biomarker score quartiles in analysis of incident cancer.
All analyses were performed using Stata software version 8.0 (Stata Corp) and throughout a two-sided P-value <0.05 was considered statistically significant.
The characteristics of the study population without cancer prior to and at the baseline exam are shown in Table 1. Baseline plasma concentration of N-BNP, MR-pro-ANP and MR-pro-ADM was significantly higher whereas copeptin was lower in females as compared to males. During the follow-up period [median (inter-quartile range) 14.6 (13.6-15.2) years in males and 14.8 (14.1-15.6) years in females], 366 first cancer events occurred in males and 368 in females. The complete spectrum of various subtypes of incident cancers events is shown in Table 2. In males, the most common forms of incident cancer were prostate cancer (40.4%), colorectal cancer (10.4%), pulmonary and tracheal cancer (8.5%) and urinary tract cancer (7.7%), whereas in females breast cancer (37.5%), colorectal cancer (12.0%), pulmonary and tracheal cancer (7.1%), urinary tract cancer (3.8%), corpus uteri cancer (5.2%), cervix uteri cancer (3.5%) and ovary cancer (3.8%) predominated.
In all analyses, the proportionality of hazards assumption was met. In males, there was an independent relationship between MR-pro-ANP and copeptin, respectively, and incident cancer and a borderline significant relationship between MR-ADM and incident cancer (Table 4). As shown in analyses of quartiles, the relationship with incident cancer seemed to be graded over the distribution of MR-pro-ANP, copeptin and MR-ADM (Table 4). In contrast, N-BNP had no relationship with incident cancer with a hazard ratio (95% confidence interval) per standard deviation increase in N-BNP of 1.01 (0.90-1.13; P=0.931). When MR-pro-ANP, copeptin and MR-ADM were entered simultaneously in the model together model 1 covariates and backward elimination with a retention P-value of <0.10 was applied, the three biomarkers were all retained and significantly related to future cancer development with per standard deviation increase in biomarker level hazard ratio of 0.83 (0.75-0.93; P=0.001) for MR-pro-ANP, 1.14 (1.01-1.29; P=0.028) for copeptin and 1.12 (1.00-1.24; P=0.042) for MR-pro-ADM. To get a summed effect estimate of the relationship between the three biomarkers and incident cancer, the biomarker score (which had a negative component for MR-pro-ANP but positive for copeptin and MR-pro-ADM) was entered in the Cox proportional hazards model with model 1 covariates. The per standard deviation increase of the biomarker score hazard ratio for incident cancer was highly significant and the top versus bottom quartile of the biomarker score identified a near two-fold difference in risk of future cancer (Table 4 and
To test if the relationship between the biomarkers and cancer susceptibility in males was driven by a specific subtype of cancer, we performed sub-analyses of the main cancer forms. None of the individual biomarkers were significantly related to any of the main cancer subtypes in males (prostate, colorectal, urinary tract and pulmonary/tracheal cancer) (Table 7) suggesting that the biomarkers are related to general cancer susceptibility. In line with this, the point estimates of the per standard deviation hazard ratios for the cancer subtype specific biomarker scores were all above 1.0 (between 1.13-1.25) and that of the most common form of male cancer, i.e. prostate cancer, was significant (P=0.041) (Table 7). In females, as in the analyses of relationship between biomarkers and general cancer susceptibility, we found no evidence of association with the main subtypes of cancer in females (Table 3).
Biomarker Relationships with Incident Cancer in Younger and Older Subsets of the Population
Screening and any potential prevention of cancer is likely to be most meaningful in subjects with relatively long remaining life time. In addition, cardiovascular conditions affecting the levels of the studied biomarkers, such as heart failure and hypertension, as well as use of antihypertensive medications, increase steeply with age and may obscure any relationship between the biomarkers and cancer. We therefore studied the three biomarkers in relation to incident cancer separately in subjects below the median of age (range 45.9-57.9 years for males and 45.9-57.7 years for females) (“young males” and “young females”) and in subjects above the median age (range 57.9-68.0 years for males and 57.7-67.9 years for females) (“old males” and “old females”).
In young males (n=884 subjects who developed n=133 first incident cancer events during follow-up), the hazard ratios per standard deviation biomarker increase were significant for all three biomarkers and markedly higher than in the male population as a whole (Table 4). The hazard ratio of the biomarker score was near double as high as among all males. In quartile analyses, the risk of cancer was shown to increase gradually with the biomarker score and the difference in cancer risk between the top versus bottom quartile was more than 3-fold. Additional adjustment in model 2 did not change these results (not shown). In older men (n=884 subjects who developed n=233 first incident cancer events during follow-up) we found no significant associations between the biomarkers and incident cancer (Table 4). Thus, each of the three studied biomarkers, and in particular the combination of the three, strongly and independently predicts cancer in the younger half of our male study population, a relationship that seems to drive the association seen in the entire group of males. Similar to the results of analyses of biomarker associations with the main subtypes of cancer in all males, the corresponding analyses in men below the median age did not reveal any specific subtype of cancer driving the biomarker association observed with general cancer susceptibility. However, as expected, the point estimates of the hazard ratios per standard deviation increase in biomarker scores were higher (ranging between 1.27-2.57) and the corresponding P-values for all of the four main subtypes of cancer were <0.10 (ranging between 0.031-0.093) (Table 7).
In females we did not detect any biomarker relationship with cancer whether the younger (n=1147 subjects who developed n=150 first incident cancer events during follow-up) or older (n=1146 subjects who developed n=218 first incident cancer events during follow-up) females were studied (Table 6).
In order to test if the relationship between the three biomarkers and incident cancer in males is linked to mechanisms preceding cancer development or whether it is actually driven by subclinical and not yet diagnosed cancer, we excluded all cancer events diagnosed within the first four years of follow-up in analyses of all males (67 of 366 cases excluded) and young males (20 of 133 cases excluded). After these exclusions the results were similar to the results obtained without excluding early events with hazard ratio per standard deviation increase of 0.82 (0.71-0.95; P=0.009) for MR-pro-ANP, 1.16 (1.02-1.33; P=0.023) for copeptin, 1.13 (0.99-1.29; P=0.081) for MR-pro-ADM and 1.28 (1.13-1.44; P<0.001) for the biomarker score in all males. In young males the corresponding hazard ratios per standard deviation were 0.71 (0.58-0.86; P=0.001) for MR-pro-ANP, 1.24 (1.01-1.54; P=0.037) for copeptin, 1.19 (0.99-1.44; P=0.069) for MR-pro-ADM and 1.49 (1.24-1.79; P<0.001) for the biomarker score. Thus, our results indicate that decreased levels of MR-pro-ANP and increased levels of copeptin and MR-pro-ADM precede cancer development rather than being markers of already existing but non-diagnosed cancer.
Next, we tested whether the biomarkers are related to risk of cancer mortality (Table 8). Among 1768 males, 107 subjects died in cancer during follow-up. Whereas MR-pro-ANP and copeptin were not significantly related cancer mortality, one standard deviation increase of MR-pro-ADM was associated with a 1.25 fold increased risk of cancer mortality and males in the top quartile versus the bottom quartile of MR-pro-ADM had an almost doubled risk of cancer mortality (Table 8). The biomarker score was also related to risk of cancer mortality in males, although that association was mainly accounted for by MR-pro-ADM (Table 8). After model 2 adjustment the relationship between MR-pro-ADM and cancer mortality in males was borderline significant (P=0.085) whereas the biomarker score remained significant (P=0.031).
In males, a total of 259 deaths (regardless of cause) occurred during follow-up and after model 2 adjustment, one standard deviation increase of MR-pro-ADM level significantly predicted total mortality with hazard ratio of 1.16 (1.01-1.34; P=0.035) whereas MR-pro-ANP [1.04 (0.89-1.21); P=0.655], copeptin [1.02 (0.89-1.16); P=0.790)] and the biomarker score [1.06 (0.93-1.21; P=0.404)] did not.
In the younger half of the male population, 33 subjects died in cancer during follow-up. One standard deviation increase of MR-pro-ADM was associated with a 1.55 fold increased risk of cancer mortality and the risk of dying in cancer was 2.5 fold higher in the top as compared to bottom quartile of MR-pro-ADM and 4-fold higher in the top as compared to bottom quartile of the biomarker score (Table 8) whereas MR-pro-ANP and copeptin were not individually significantly related to cancer mortality (Table 8). After model 2 adjustment MR-pro-ADM (P=0.003) and the biomarker score (P=0.002) remained significantly related to cancer mortality.
Among young males a total of 66 deaths (regardless of cause) occurred during follow up and after model 2 adjustment, one standard deviation increase of MR-pro-ADM level significantly predicted total mortality with a hazard ratio of 1.35 (1.04-1.75) (P=0.023) whereas MR-pro-ANP [1.04 (0.78-1.38); P=0.792], copeptin [1.05 (0.81-1.37); P=0.702] and the biomarker score [1.17 (0.91-1.52); P=0.227] did not.
In the older half of the male population and among females (all females, young females and old females), there was no relationship between biomarker levels and cancer mortality except for in quartile analyses of MR-pro-ADM (Table 8 and Table 5) and there was no significant relationship with total mortality in any of these populations (not shown).
| Number | Date | Country | Kind |
|---|---|---|---|
| 10 166 536.2 | Jun 2010 | EP | regional |
| Filing Document | Filing Date | Country | Kind | 371c Date |
|---|---|---|---|---|
| PCT/EP11/03019 | 6/17/2011 | WO | 00 | 10/15/2013 |
