Probabilistic Cancer Risk Research Articles

Assessment of health risk associated with arsenic exposure from soil, groundwater, polished rice for setting target cleanup level nearby abandoned mines

This study focused on health risk assessment via multi-routes of As exposure to establish a target cleanup level (TCL) in abandoned mines. Soil, ground water, and rice samples were collected near ten abandoned mines in November 2009. The As contaminations measured in all samples were used for determining the probabilistic health risk by Monte-Carlo simulation techniques. The human exposure to As compound was attributed to ground water ingestion. Cancer risk probability (R) via ground water and rice intake exceeded the acceptable risk range of <TEX>$10^{-6}{\sim}10^{-4}$</TEX> in all selected mines. In particular, the MB mine showed the higher R value than other mines. The non-carcinogenic effects, estimated by comparing the average As exposure with corresponding reference dose were determined by hazard quotient (HQ) values, which were less than 1.0 via ground water and rice intake in SD, NS, and MB mines. This implied that the non-carcinogenic toxic effects, due to this exposure pathway had a greater possibility to occur than those in other mines. Besides, hazard index (HI) values, representing overall toxic effects by summed the HQ values were also greater than 1.0 in SD, NS, JA, and IA mines. This revealed that non-carcinogenic toxic effects were generally occurred. The As contaminants in all selected mines exceeded the TCL values for target cancer risk (<TEX>$10^{-6}$</TEX>) through ground water ingestion and rice intake. However, the As level in soil was greater than TCL value for target cancer risk via inadvertent soil ingestion pathway, except for KK mine. In TCL values for target hazard quotient (THQ), the As contaminants in soil did not exceed such TCL value. On the contrary, the As levels in ground water and polished rice in SD, NS, IA, and MB mines were also beyond the TCL values via ground water and rice intake. This study concluded that the health risks through ground water and rice intake were greater than those though soil inadvertent ingestion and dermal contact. In addition, it suggests that the abandoned mines to exceed the risk-based TCL values are carefully necessary to monitor for soil remediation.

Results of a pilot study to improve identification and referral for people at high risk for hereditary colorectal cancer syndromes

Background Five percent of colorectal cancers are attributable to hereditary syndromes for which genetic testing is clinically available. Effective surveillance strategies and riskreducing surgeries lower risk for people diagnosed with inherited colorectal cancer syndromes. However, from a public health standpoint, the existence of effective interventions is futile if not applied broadly and appropriately to the population at risk. Efficient identification of people with hereditary risk remains a challenge, and failure to systematically identify people at high hereditary risk results in preventable morbidity and mortality. The William C. Bernstein M.D. Familial Cancer Registry (Bernstein Registry) is a research-based program that triages participants for hereditary cancer risk. Selfreported family histories are assessed and participants are given tailored information about their hereditary risk factors. People at high risk are given information about the availability and advisability of clinical genetics services. Pilot populations are being enrolled to establish registry processes and determine the feasibility of implementation at a population-based level. This pilot group includes volunteers from a previously established colorectal cancer registry. Materials and methods A 7-item screening questionnaire determines potential eligibility for the Bernstein Registry using elements of early onset, multiple primary and family history of cancers. Potentially eligible participants are contacted by telephone and a four-generation cancer history is generated via guided interview. Pedigrees are analyzed by genetic counselors for hereditary cancer risk probabilities based on published guidelines. Targeted, tailored risk information is provided in writing along with a copy of the family tree. A list of genetic counseling service providers is included for people at high risk.

DOES SIZE MATTER? - A LOOK AT PSA PERFORMANCE IN A VOLUME SPECIFIC MANNER

A chemoprevention (CP) strategy has evolved for conducting efficient clinical trials for prostate cancer (PCa) prevention. It integrates five key components, including agents, biomarkers, cohorts, designs, and endpoints. The rationale for the CP strategy relates to the natural history of prostate cancer. There is a wide array of natural and synthetic agents that hold promise for inhibiting, reversing, or modulating the transition from normal to precancer and from precancer to cancer. These agent classes include antiandrogens, antiestrogens, phytoestrogens, antioxidants, anti-inflammatory (proapoptotic) agents, antiproliferation/antidifferentiation agents, signal transduction modulators of receptor tyrosine kinase and ras farnesylation, antiangiogenesis agents, insulinlike growth factor (IGF)–1, peroxisome proliferator–activator receptor modulators (-γ and -δ), and gene-based interventions. Biomarkers and endpoints are guided by the level of evidence required (eg, phase 1, 2, 3). Two candidate surrogate endpoints (SE) based on histology are high-grade prostatic intraepithelial neoplasia (HGPIN) and computer-assisted image analysis of dysplastic lesions. Phase 1 trials use standard endpoints of safety, pharmacokinetics and limited pharmacodynamics. Phase 2 trials use endpoints of modulation of biomarkers and correlation with histology. Phase 3 trials use endpoints of clinical benefit, such as cancer incidence reduction and quality of life. Validation of a biomarker as a SE involves correlation of the biomarker with clinical benefit. Cohorts (target populations) for phase 2/3 trials include the general population of men over age 50 with a normal prostate-specific antigen (PSA), subjects with a strong family history of PCa, subjects with elevated PSA/negative biopsy, and subjects with HGPIN/negative biopsy. These at-risk populations reflect key individual risk factors (age, race, serum PSA [free/total]; serum IGF-1/IGF binding protein (IGFBP)–3; 1, 25(OH)2 D3; family history of PCa; carriers of PCa susceptibility genes [ELAC2, CYP3A4, SRD5A2, etc.]; and histology such as atypia and HGPIN) that could be combined into a multivariate risk model for PCa. The probability of cancer risk (recurrence) is a key factor that impacts on the clinical trial design (power, sample size, and primary endpoint). Multivariate predictive mathematical models for biochemical recurrence after radical prostatectomy by decreasing sample size and time to clinical outcomes maximize trial efficiency and identify the patients most likely to benefit from secondary prevention. The two large primary prevention trials, Prostate Cancer Prevention Trial/Seleninium and Vitamin E Chemoprevention Trial (PCPT/ SELECT), in low- and average-risk subjects have sample sizes of 18,000 to 32,000, with a treatment duration of 7 years to detect a 25% reduction in biopsy-proven PCa. Subjects with HGPIN have the highest known cancer risk (approximately 50% at 3 years), and thus require a small sample size (n = 450) to detect a 33% reduction in cancer incidence. A schema involving three sequential trials for agent registration is described. In summary, a CP strategy that incorporates well-defined agents, clinical and validated SE, and high-risk cohorts defined by genetic and acquired risk factors in a series of well-designed randomized controlled trials provides an efficient pathway for evaluating and approving new agents for PCa prevention.

Human Health Risk Assessment of Naturally Occurring Radioactive Materials in Produced Water—A Case Study

ABSTRACT Human health effects from naturally occurring radioactive materials (NORM) in produced water are of concern due to their bioavailability and bioaccumulation characteristics in finfish and shellfish species used for human consumption. Being chemically similar to calcium, radium concentrates mostly in bones, shells, and exoskeletons. Previous studies have been based on the whole-body bioaccumulation of radium in fish where the distribution of radium in bone/exoskeleton and the edible parts of fish were not considered separately and thus the predicted risks were relatively high. In this article, the distribution of radium in the non-edible and edible parts of fish and the probability of exposure to a produced water plume have been studied in order to characterize human health risks. A probabilistic hydrodynamic model has been incorporated in this study. Using the concentration distribution approach, the mean cancer risks to humans were predicted in the range of 8.6 × 10−7 to 9.5 × 10−7, which were 2.6 to 2.7 times less than the risks predicted by using the whole body concentrations. The exceedence probability of maximum permissible human health cancer risk of 1 × 10−4 is close to zero. At a risk level of 1 × 10−6, the exceedence probability is 21% whereas in the whole body concentration approach it is between 45 to 49%. In this study, no effect on fish from exposure to NORM components in produced water was found.

Reporting BRCA test results to primary care physicians

PurposeThe main purpose of this study was to determine if comprehension of the cancer risk information presented in a hypothetical report for BRCA1/2 gene analyses was influenced by the format in which the information was presented. A secondary objective was to determine physician characteristics that might influence comprehension of the report. MethodsA survey was conducted in which a case vignette describing a young woman at high risk for carrying a BRCA mutation was presented. Survey participants, all primary care practitioners, were asked to interpret a laboratory report that provided the patient's BRCA1/2 test result and accompanying data about the cumulative risk and incidence rates of breast cancer for BRCA1/2 mutation carriers and the general population. These data were presented in the report in either a tabular or a graphic format. The main outcome was measured by the responses to four questions that addressed the probabilistic cancer risk information. Physician predictor variables included medical specialty, practice setting, years in practice, continuing medical education in genetics, and knowledge of cumulative risk. ResultsKnowledge of cumulative risk was the only physician variable that influenced comprehension of the cancer risk information (OR = 31.9;P < 0.001). After adjusting for this variable, the graphic format tended to perform better than the tabular format in conveying breast cancer risk information (OR = 3.1;P = 0.102). ConclusionsMany physicians may be unprepared to interpret genetic risk information, due to lack of understanding of basic epidemiologic terms used to express the risk of disease.

Prostate cancer chemoprevention: Strategies for designing efficient clinical trials

A chemoprevention (CP) strategy has evolved for conducting efficient clinical trials for prostate cancer (PCa) prevention. It integrates five key components, including agents, biomarkers, cohorts, designs, and endpoints. The rationale for the CP strategy relates to the natural history of prostate cancer. There is a wide array of natural and synthetic agents that hold promise for inhibiting, reversing, or modulating the transition from normal to precancer and from precancer to cancer. These agent classes include antiandrogens, antiestrogens, phytoestrogens, antioxidants, anti-inflammatory (proapoptotic) agents, antiproliferation/antidifferentiation agents, signal transduction modulators of receptor tyrosine kinase and ras farnesylation, antiangiogenesis agents, insulinlike growth factor (IGF)–1, peroxisome proliferator–activator receptor modulators (-γ and -δ), and gene-based interventions. Biomarkers and endpoints are guided by the level of evidence required (eg, phase 1, 2, 3). Two candidate surrogate endpoints (SE) based on histology are high-grade prostatic intraepithelial neoplasia (HGPIN) and computer-assisted image analysis of dysplastic lesions. Phase 1 trials use standard endpoints of safety, pharmacokinetics and limited pharmacodynamics. Phase 2 trials use endpoints of modulation of biomarkers and correlation with histology. Phase 3 trials use endpoints of clinical benefit, such as cancer incidence reduction and quality of life. Validation of a biomarker as a SE involves correlation of the biomarker with clinical benefit. Cohorts (target populations) for phase 2/3 trials include the general population of men over age 50 with a normal prostate-specific antigen (PSA), subjects with a strong family history of PCa, subjects with elevated PSA/negative biopsy, and subjects with HGPIN/negative biopsy. These at-risk populations reflect key individual risk factors (age, race, serum PSA [free/total]; serum IGF-1/IGF binding protein (IGFBP)–3; 1, 25(OH) 2 D3; family history of PCa; carriers of PCa susceptibility genes [ ELAC2, CYP3A4, SRD5A2, etc.]; and histology such as atypia and HGPIN) that could be combined into a multivariate risk model for PCa. The probability of cancer risk (recurrence) is a key factor that impacts on the clinical trial design (power, sample size, and primary endpoint). Multivariate predictive mathematical models for biochemical recurrence after radical prostatectomy by decreasing sample size and time to clinical outcomes maximize trial efficiency and identify the patients most likely to benefit from secondary prevention. The two large primary prevention trials, Prostate Cancer Prevention Trial/Seleninium and Vitamin E Chemoprevention Trial (PCPT/ SELECT), in low- and average-risk subjects have sample sizes of 18,000 to 32,000, with a treatment duration of 7 years to detect a 25% reduction in biopsy-proven PCa. Subjects with HGPIN have the highest known cancer risk (approximately 50% at 3 years), and thus require a small sample size (n = 450) to detect a 33% reduction in cancer incidence. A schema involving three sequential trials for agent registration is described. In summary, a CP strategy that incorporates well-defined agents, clinical and validated SE, and high-risk cohorts defined by genetic and acquired risk factors in a series of well-designed randomized controlled trials provides an efficient pathway for evaluating and approving new agents for PCa prevention.

Multimedia Risk‐Based Soil Cleanup at a Gasoline‐Contaminated Site Using Vapor Extraction

AbstractAt a utility service center, gasoline from an underground storage tank had leaked into subsurface vadose zone soils for several years. To remediate the site, a soil vapor extraction (SVE) system was installed and operated. At the completion of the SVE operation, gasoline‐containing residues in several confirmation soil borings exceeded agency‐mandated cleanup levels. Rather than continue with SVE, a risk‐based approach was developed to evaluate what levels of gasoline‐containing residues could be left in the soil and still protect human health. The risk‐based approach consisted of simulating the fate of chemical residues through the vadose zone and then into both the ground water and atmosphere. Receptor point concentrations were predicted, and health risks were assessed. The risk assessment concluded that ingestion of contaminated ground water and inhalation of air while showering were the largest potential contributors to risk, and that risks associated with inhalation of vapor‐containing ambient air are small. However, all predicted risks are below the acceptable risk levels of 10−6 individual cancer risk probability and 1.0 hazard index. Therefore, the lead agency accepted the recommendation that the site requires no further remediation. The service center continues normal operations today.

The need for new methods to backcalculate soil cleanup targets in interval and probabilistic cancer risk assessments

Abstract Ordinary algebra may be used to backcalculate health‐based cleanup targets in deterministic risk assessments, but it does not work in interval or probabilistic risk assessments. Equations with interval or random variables do not follow the rules of ordinary algebra. This paper explains the need for more sophisticated methods to backcalculate soil cleanup targets when using interval or random variables.

Age at menarche, probability of ovulation and breast cancer risk.

An analysis is undertaken of the frequency of ovulation in 17 groups of women aged 15 to 19 who had been the subjects of other studies. A urine specimen of at least 8 h accumulation had been provided on the 20th or 21st day of a menstrual cycle by 681 women. Analysis is restricted to 431 specimens which had been collected between 11 and 3 days prior to the onset of the subsequent menstrual period. A pregnanediol concentration of less than 1 mg per litre in such a specimen was taken as evidence that the cycle was anovular. The probability of a cycle being anovular was inversely and significantly related to the number of years since menarche, and, with years since menarche held constant, was positively but not significantly associated with age at menarche. This observation indicates that women with early menarche do not have a longer duration of exposure to anovular cycles than do those whose menarche is delayed, and that variation in the duration of exposure to post-menarcheal anovular cycles does not explain the association of breast cancer risk with early age at menarche. The 17 groups of women were classified into four categories according to ethnic origin and breast cancer incidence in the populations from which they derived. Anovular cycles were not more common in the high-risk groups; indeed, the two centers in the lowest risk category had the highest proportion of anovular cycles.