Suhdir Rai, Jennifer E. Wang, K. Wong, X. Qiu, Geoffrey Liu, D. Reisman
{"title":"Abstract A24: BRM polymorphisms, part of a novel epigenetic mechanism, are predictive of cancer risk and clinic outcome in multiple cancer types","authors":"Suhdir Rai, Jennifer E. Wang, K. Wong, X. Qiu, Geoffrey Liu, D. Reisman","doi":"10.1158/1538-7755.CARISK16-A24","DOIUrl":"https://doi.org/10.1158/1538-7755.CARISK16-A24","url":null,"abstract":"BRM polymorphisms lie with the promoter region of the anticancer gene and SWI/SNF catalytic subunit, Brahma (SMARCA2). These polymorphisms statistically correlate with loss of BRM expression in both cell lines and primary lung tumors and function as part of an epigenetic mechanism which underlies BRM reversible silencing. Specifically, these polymorphisms function as the binding site of at least two transcription factors (MEF2D and GATA3) and two HDACs (HDAC3 and HDAC9). These proteins form a complex which drives the reversible silencing of the BRM protein. As BRM can serve as an anticancer protein, in part because BRM and SWI/SNF is required for the normal function of TP53 and RB as well as multiple DNA repair mechanisms, the silencing of BRM potentiates cancer development in mice. As such, BRM polymorphisms are predictive of cancer risk for lung cancer (n=600), breast cancer (N=300), head/neck cancer (n=400), colon cancer (N=250) and lymphoma (N=300) with an odds ratio ranging from 1.8 to 2.3. As these polymorphisms occur more frequently in African Americans, the odds ratio (cancer risk) in African Americans for lung cancer (n=250) is higher (3.5-4.5) as compared to that observed in Caucasians (2-2.3) (N=600). Similarly, BRM polymorphisms have a higher predictive value in HPV positive head/neck cancer with an odds ratio of 3.2 compared with an odds ratio of 2.0 in HPV positive head/neck cancer. This is in part due to the fact that BRM along with the HPV E2 protein regulates the expression of the transforming HPV proteins E6/E7. Unlike other polymorphisms which impart cancer risk and are fixed (cannot be changed), the fact that BRM silencing is reversible by compounds such as Flavonoids and certain NSAIDs, the cancer risk imparted by these polymorphisms can, in theory, be reversed or nullified by changes in diet, thereby making these polymorphisms unique in this respect. As BRM polymorphisms and BRM expression is also tied to differentiation and cell adhesion, these polymorphisms are also predictive of a worse clinical outcome with a hazard ratio ranging from 3 to 10 in pancreatic, head/neck, lung, colon and esophageal cancers. Thus, BRM polymorphisms represent a novel factor which is predictive of cancer risk and clinical outcome in multiple cancer types. Furthermore, these polymorphisms can potentially explain the observed health disparities (higher lung cancer occurrence despite lower rate of tobacco usage) which occur in African Americans. Citation Format: Suhdir Rai, Jennifer Wang, Kit Man Wong, Xiaoping Qiu, Geoff Liu, David Reisman. BRM polymorphisms, part of a novel epigenetic mechanism, are predictive of cancer risk and clinic outcome in multiple cancer types. [abstract]. In: Proceedings of the AACR Special Conference: Improving Cancer Risk Prediction for Prevention and Early Detection; Nov 16-19, 2016; Orlando, FL. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2017;26(5 Suppl):Abstract nr A24.","PeriodicalId":9487,"journal":{"name":"Cancer Epidemiology and Prevention Biomarkers","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88433752","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
C. Ulrich, J. Böhm, C. Warby, Tengda Lin, Mariam Salou, B. Gigic, D. Scherer, Johanna Nattenmueller, J. Ose, Lin Zielske, P. Schrotz-King, T. Koelsch, E. Siegel, Christopher I. Li, A. Ulrich, H. Glimm, J. Samadder, S. Hursting, H. Kauczor
{"title":"Abstract A26: Body fatness and adipose tissue subtypes are associated with circulating biomarkers of inflammation and angiogenesis in colorectal cancer patients: The ColoCare Study","authors":"C. Ulrich, J. Böhm, C. Warby, Tengda Lin, Mariam Salou, B. Gigic, D. Scherer, Johanna Nattenmueller, J. Ose, Lin Zielske, P. Schrotz-King, T. Koelsch, E. Siegel, Christopher I. Li, A. Ulrich, H. Glimm, J. Samadder, S. Hursting, H. Kauczor","doi":"10.1158/1538-7755.CARISK16-A26","DOIUrl":"https://doi.org/10.1158/1538-7755.CARISK16-A26","url":null,"abstract":"Background: Adiposity has been linked to both risk and prognosis of colorectal cancer; however, the impact of different compartments of adipose tissue (visceral vs. subcutaneous) is unclear. In healthy individuals, adiposity is associated with elevated biomarkers of inflammation, which provides a mechanistic link between adiposity and cancer risk. For prognosis, the downstream effects of inflammation on angiogenesis may be central. We investigated associations between adiposity and biomarkers of inflammation, as well as angiogenesis, in colorectal cancer patients enrolled in the ColoCare Study, an international multicenter patient cohort. Methods: We utilized preoperatively obtained serum samples of patients with newly diagnosed colorectal cancer [n=164; (stage I-IV)] from the ColoCare Study in Heidelberg, Germany, with available diagnostic multi-detector-CT images for adipose tissue quantification. Abdominal adipose tissue was assessed by area-based quantification of visceral (VAT), and subcutaneous adipose tissue (SAT), as well as their ratio (VAT/SAT) on levels L3/L4 and L4/L5. Body mass index (BMI) was calculated (kg/m2) and demographic and clinical-surgical data were abstracted from medical records. Circulating CRP, SAA, VEGF-A, VEGF-D, sICAM-1 and sVCAM-1 levels were assessed on the Meso Scale Discoveries platform with V-plex assays at the Huntsman Cancer Institute (average intra-plate CVs 2.9%, inter-plate CVs 7.9%). Partial correlations and regression analyses, adjusting for age, sex and tumor stage were performed. Results: While overall obesity (BMI) was only significantly associated with sVCAM (r=0.23, p=0.006), visceral adiposity (VAT) was associated with both CRP and SAA (r=0.21, p=0.01 and r=0.17, p=0.04, respectively). There was no association between SAT and the measured biomarkers. Most predictive was the ratio of VAT/SAT on level L3/L4, which was associated with CRP (r=0.18, p=0.04), SAA (r=0.24, p=0.006), sICAM-1 (r=0.18, p=0.04), and particularly VEGF-A (r=0.28, p=0.0008). Similar associations were observed for the VAT/SAT ratio on level L4/5. Conclusions: We demonstrated a link between specifically visceral adiposity and biomarkers of inflammation in colorectal cancer patients. In addition, we showed that visceral adiposity also affects circulating VEGF-A levels. This protein has various effects, including the induction of angiogenesis, vasculogenesis and endothelial cell growth, as well as the promotion of cell migration, and the inhibition of apoptosis. Our findings support a mechanistic role of visceral adipose tissue in colorectal cancer risk and potentially prognosis. Citation Format: Cornelia M. Ulrich, Jurgen Bohm, Christy Warby, Tengda Lin, Mariam Salou, Biljana Gigic, Dominique Scherer, Johanna Nattenmueller, Jennifer Ose, Lin Zielske, Petra Schrotz-King, Torsten Kolsch, Erin Siegel, Christopher Li, Alexis Ulrich, Hanno Glimm, Jewel Samadder, Stephen Hursting, Hans-Ulrich Kauczor. Body fatness and adipose tissue subty","PeriodicalId":9487,"journal":{"name":"Cancer Epidemiology and Prevention Biomarkers","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88923859","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Abstract IA11: Life course epidemiology and breast cancer: translating risk into prevention","authors":"M. Sherman","doi":"10.1158/1538-7755.CARISK16-IA11","DOIUrl":"https://doi.org/10.1158/1538-7755.CARISK16-IA11","url":null,"abstract":"Molecular histology may be conceptualized as the microscopic and molecular characteristics of normal tissues that are required for physiologic function. Over the life course, the molecular histology of the breast changes in response to physiological alterations, imparting spatial and temporal heterogeneity within the breasts of individuals and among women. These transitions contribute to the enormous range and imprecisely defined limits of what pathologists consider normal. Appreciation that molecular histology may reflect the cumulative influence of prior exposures linked to breast cancer risk, and may provide information about risk of developing breast cancer in the future, has stimulated interest in this topic. Unlike the study of breast cancer or its precursors, which represents a focal pathophysiologic deviation from normal, molecular histology, if assessable, would represent the state of the entire at-risk organ, based upon examination of a small tissue sample. The adult breast is characterized by well-developed terminal duct lobular units (TDLUs), which comprise the functional unit of milk production and represent the source of nearly all breast cancer precursors. Physiological changes in human breasts are likely driven by paracrine mechanisms, suggesting that tissue context and cellular topography are critical elements in physiology and pathophysiology. The breast undergoes profound changes with completion of childbearing and aging. Age-related TDLU involution may be conceptualized as a protective mechanism that lowers breast cancer risk following completion of childbearing, and in this context, delayed or incomplete involution is a breast cancer risk factor. With aging, the percent of the breast comprised of fibroglandular tissue declines, which is associated with a reduction in mammographic density, a strong breast cancer risk factor. Mammographic density is also imperfectly correlated with epithelial content in the breast. Women with benign breast disease whose surrounding normal breast tissue does not undergo age-appropriate TDLU involution are at increased risk of developing breast cancer, and both breast density and TDLU involution are independent markers of breast cancer risk. However, an important challenge is to understand the markers and mechanisms that underlie breast involution with aging, including both the epithelial and non-epithelial components, and to learn why both density and TDLU content decline with aging as breast cancer incidence rises. The thesis of this presentation is that understanding the amount of epithelium at-risk, the insults it sustains and the mechanisms that lead to its elimination, persistence or expansion may provide a window into the development of integrative biomarkers of risk that can translate into improved screening and prevention. However, progress towards this goal is quite early. Citation Format: Mark E. Sherman. Life course epidemiology and breast cancer: translating risk into prevention. [ab","PeriodicalId":9487,"journal":{"name":"Cancer Epidemiology and Prevention Biomarkers","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84312908","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
A. Cust, A. Smit, D. Espinoza, Keogh Louise, P. Butow, K. Dunlop, J. Kirk, A. Newson
{"title":"Abstract B15: Communicating information about personalised genomic risk of melanoma to family, friends, and health professionals","authors":"A. Cust, A. Smit, D. Espinoza, Keogh Louise, P. Butow, K. Dunlop, J. Kirk, A. Newson","doi":"10.1158/1538-7755.CARISK16-B15","DOIUrl":"https://doi.org/10.1158/1538-7755.CARISK16-B15","url":null,"abstract":"Background: It is anticipated that cancer risk prediction tools, including those with genomic risk information, will increasingly be used to communicate personalised cancer risk to the public. Receiving information on personal genomic risk of cancer might encourage conversations about cancer prevention and early detection with family, friends and health professionals, but few studies have examined this. Aims: To explore participant communication about personal genomic risk of melanoma to family, friends and health professionals, using a mixed-methods approach, and to examine results according to participants9 genomic risk category (low, average, high). Methods: We conducted a study examining the impact of giving information on personalised genomic risk of melanoma to the public. Participants (n=101) received a personalised booklet presenting their melanoma genomic risk based on variants in 21 genes, together with telephone-based genetic counselling and generic educational materials. They completed a questionnaire 3-months after receiving their personal genomic risk of melanoma. To further contextualise these data, we conducted semi-structured qualitative interviews with 30 participants. Results: Participants9 communication with health professionals according to melanoma genomic risk category was 41% for high-risk, 16% for average-risk and 13% for low-risk (P=0.02). Communication with family was 83% for high-risk, 65% for average-risk, 79% for low-risk participants (P=0.19); and communication with friends was 55% for high-risk, 43% for average-risk, 54% for low-risk participants (P=0.49). Preliminary thematic analysis found that preventive behaviours and early detection were raised by participants in discussions with family and doctors. Reasons for not communicating genomic risk included: concern about causing worry and not feeling a need to share the information. Conclusions: Genomic risk information prompted conversations about melanoma risk and prevention, most frequently with family. When stratified by genomic risk, comparable numbers of participants discussed their genomic risk with family and friends, but communication with health professionals was more frequent among participants in a high-risk category. Citation Format: Anne E. Cust, Amelia K. Smit, David Espinoza, Keogh Louise, Phyllis N. Butow, Kate Dunlop, Judy Kirk, Ainsley J. Newson. Communicating information about personalised genomic risk of melanoma to family, friends, and health professionals. [abstract]. In: Proceedings of the AACR Special Conference: Improving Cancer Risk Prediction for Prevention and Early Detection; Nov 16-19, 2016; Orlando, FL. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2017;26(5 Suppl):Abstract nr B15.","PeriodicalId":9487,"journal":{"name":"Cancer Epidemiology and Prevention Biomarkers","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85325318","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Aung Ko Win, M. Jenkins, J. Dowty, A. Antoniou, Andrew Lee, Yingye Zheng, N. Lindor, P. Newcomb, J. Hopper, R. MacInnis
{"title":"Abstract PR10: Development of a comprehensive colorectal cancer risk prediction tool (CRiPT) incorporating known and unknown major genes and polygenes","authors":"Aung Ko Win, M. Jenkins, J. Dowty, A. Antoniou, Andrew Lee, Yingye Zheng, N. Lindor, P. Newcomb, J. Hopper, R. MacInnis","doi":"10.1158/1538-7755.CARISK16-PR10","DOIUrl":"https://doi.org/10.1158/1538-7755.CARISK16-PR10","url":null,"abstract":"Aim: We aimed to develop a comprehensive Colorectal cancer Risk Prediction Tool (CRiPT). To achieve this, it is necessary to incorporate germline mutations in the DNA mismatch repair genes and MUTYH to account for a proportion of the familial aggregation of colorectal cancer. Population prevalence of these mutations and the genetic and environmental causes of the remaining familial aggregation, however, are not known. Methods: We studied the families of 5,744 colorectal cancer cases (probands) recruited from population cancer registries in the USA, Canada and Australia, and screened probands for mutations in the mismatch repair genes MLH1 , MSH2 , MSH6 , and PMS2 , and MUTYH . We fitted modified segregation analysis models to the cancer history of first-degree relatives, conditional on the age at diagnosis of the proband, using the software MENDEL. We determined the genetic model that best explained the familial aggregation of colorectal cancer by estimating the prevalence of mutations in the known susceptibility genes, the prevalence of and hazard ratio for unmeasured high-risk gene mutations, and the variance of the unmeasured polygenic component, using a χ2 goodness-of-fit test. Results: The best fitting model was a mixed dominant model with the polygenic standard deviation varying by age. Under that model, we estimated 1 in 279 of the population carry mutations in the mismatch repair genes ( MLH = 1 in 1946, MSH2 = 1 in 2841, MSH6 = 1 in 758, PMS2 = 1 in 714), 1 in 45 carry mutations in MUTYH , and 1 in 504 carry mutations in unknown major gene(s) which are associated with on average a 31-fold increased risk of colorectal cancer. The estimated variance of the polygenic component decreased from 1.8 for age Conclusion: CRiPT is a comprehensive prediction model that incorporates both known and unknown major genes and polygenes. CRiPT can provide the probabilities of having a mutation in a DNA mismatch repair gene or MUTYH as well as estimate future risk (e.g., 5-year risk) of developing colorectal cancer. This model is similar to the Breast and Ovarian Analysis of Disease Incidence and Carrier Estimation Algorithm (BOADICEA) that calculates for women the probabilities of carrying a BRCA1 or BRCA2 mutation and their future risk of developing breast and ovarian cancer based on their family history. Further work will include measured environmental factors and genetic variants to CRiPT, and it will be useful for genetic counselling and targeted colorectal cancer screening in clinical practices. This abstract is also being presented as Poster B04. Citation Format: Aung Ko Win, Mark A. Jenkins, James G. Dowty, Antonis C. Antoniou, Andrew Lee, Yingye Zheng, Noralane M. Lindor, Polly A. Newcomb, John L. Hopper, Robert J. MacInnis. Development of a comprehensive colorectal cancer risk prediction tool (CRiPT) incorporating known and unknown major genes and polygenes. [abstract]. In: Proceedings of the AACR Special Conference: Improving Cancer Risk Predict","PeriodicalId":9487,"journal":{"name":"Cancer Epidemiology and Prevention Biomarkers","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85634453","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
A. Lophatananon, K. Alajmi, Emma Thorpe, J. Hughes, J. Blodgett, B. Fisher, Simon Rogers, Erika Waters, K. Muir
{"title":"Abstract PR14: Development of a cancer risk prediction tool for use in the Risk Estimation For Lifestyle Enhancement Combined Trial (REFLECT)","authors":"A. Lophatananon, K. Alajmi, Emma Thorpe, J. Hughes, J. Blodgett, B. Fisher, Simon Rogers, Erika Waters, K. Muir","doi":"10.1158/1538-7755.CARISK16-PR14","DOIUrl":"https://doi.org/10.1158/1538-7755.CARISK16-PR14","url":null,"abstract":"Exposure to modifiable lifestyle and environmental risk factors accounts for approximately 40% of all cancers in the UK. Therefore, primary prevention is of growing importance and an effective and engaging strategy that encourages long-term adoption of healthy lifestyle behaviours is required. Several multivariable risk prediction models have been developed to assess an individual9s risk of developing specific cancers. Such models can be used in a variety of settings for prevention, screening, and guiding investigation and treatment. Models aimed at predicting future disease risk that contains modifiable factors may be of particular use for targeting health promotion activities at an individual level. We have therefore developed a UK version of the well-established U.S. derived “YourDiseaseRisk” model which allow users to quantify their individual risk of developing individual cancers relative to the population average risk. The UK-Manchester version of “YourDiseaseRisk” computes 10 year cancer risk for 11 cancer types utilising UK figures for prevalence of risk factors and cancer incidence. The model can be used to estimate cancer risk for use in community settings. Using a variety of qualitative and quantitative methods we have assessed the impact of the REFLECT risk model on public understanding of cancer risk factors and UK NHS Cancer Screening programs. We have also explored public opinion and perceptions regarding the provision of information on of genetic susceptibility to aid in further personalising cancer risk information. This abstract is also being presented as PosterB10. Citation Format: Artitaya Lophatananon, Kawthar Alajmi, Emma Thorpe, John Hughes, Joanna Blodgett, Bernadette Fisher, Simon Rogers, Erika K. Waters, Kenneth R. Muir. Development of a cancer risk prediction tool for use in the Risk Estimation For Lifestyle Enhancement Combined Trial (REFLECT). [abstract]. In: Proceedings of the AACR Special Conference: Improving Cancer Risk Prediction for Prevention and Early Detection; Nov 16-19, 2016; Orlando, FL. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2017;26(5 Suppl):Abstract nr PR14.","PeriodicalId":9487,"journal":{"name":"Cancer Epidemiology and Prevention Biomarkers","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81770488","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Abstract B23: Detection of susceptibility to childhood Acute Lymphoblastic Leukemia (ALL)","authors":"C. Tebbi","doi":"10.1158/1538-7755.CARISK16-B23","DOIUrl":"https://doi.org/10.1158/1538-7755.CARISK16-B23","url":null,"abstract":"Currently, there are no known methods to predict of susceptibility to, and means for prevent Acute Lymphoblastic Leukemia (ALL). We have evaluated and patented a group of proteins dubbed Protein X from a certain strain of Aspergillus Flavus (AF) and developed methods for screening and identifying totally asymptomatic patients in remission of ALL, including long term survivors of this disease, distinguishing them from normal controls. Subject to institutional approved consents/assents, 15-20 ml of blood was obtained from 40 cases of ALL in children and young adults, including long term survivors of ALL. Controls were normal individuals, sickle cell patients undergoing partial exchange transfusion and patients with solid tumors. Mononuclear leukocytes (MNL) of ALL patients in remission and controls were separated using Ficoll Paque Plus (GE Healthcare). Epstein Barr virus (EBV) was obtained commercially. Positive and negative controls for Protein X were aflatoxin and Mycocladus Corymbifera (MC). Avian leukosis virus (ALV) was used as control for EBV. MNL were co-incubated with Protein X ± EBV ± irradiation, for periods of 1-72 hours. Controls were treated identically with appropriate substitutions. Test and control MNLs were examined for genetic markers, NF-κB and cell surface markers (CSM) including CD10/CD19, CD34/CD19, and CD34/CD117. Changes were expressed as percentage of control. Using ELISA, plasmas were tested for antibodies against Protein X ± EBV time experiments reveled 72 hours was optimum for achieving results. Upon 72 hours exposure of MNL from ALL to Protein X ± EBV, cells from ALL patents in remission developed cell surface phenotypes typical of ALL. This was not seen in controls. Addition of EBV ± radiation to Protein X, enhanced these effects in MNL of ALL and not controls. Changes were statistically significant and clearly separated ALL from controls. Evaluation of NF-κB revealed enhancement in ALL and not controls. Aflatoxin indiscriminately induced cell surface marker changes in both, normal and ALL, while ALV and supernatant of MC had no effect. ELISA, using Protein X ± EBV, distinguished ALL from controls. Gene array and biomarkers confirmed transformation to leukemic cell markers upon exposure to Protein X in cells from ALL patients but not controls. These studies reveal, in vitro, upon exposure to Protein X, unlike normal controls, MNL from ALL patients in remission develop cell surface phenotypes and genetic markers typical of ALL. These techniques have potential for screening for ALL and may have implications for etiology of ALL and its prevention. Citation Format: Cameron K. Tebbi. Detection of susceptibility to childhood Acute Lymphoblastic Leukemia (ALL). [abstract]. In: Proceedings of the AACR Special Conference: Improving Cancer Risk Prediction for Prevention and Early Detection; Nov 16-19, 2016; Orlando, FL. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2017;26(5 Suppl):Abstract nr B23.","PeriodicalId":9487,"journal":{"name":"Cancer Epidemiology and Prevention Biomarkers","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77791563","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
L. Yaghjyan, R. Tamimi, K. Bertrand, C. Scott, M. Jensen, S. Pankratz, K. Brandt, D. Visscher, A. Norman, Fergus Cough, J. Shepherd, B. Fan, Yunni-Yi Chen, Lin Ma, Andrew H. Beck, S. Cummings, K. Kerlikowske, C. Vachon
{"title":"Abstract B27: Interaction of mammographic breast density with menopausal status and postmenopausal hormone use in relation to the risk of aggressive breast cancer subtypes","authors":"L. Yaghjyan, R. Tamimi, K. Bertrand, C. Scott, M. Jensen, S. Pankratz, K. Brandt, D. Visscher, A. Norman, Fergus Cough, J. Shepherd, B. Fan, Yunni-Yi Chen, Lin Ma, Andrew H. Beck, S. Cummings, K. Kerlikowske, C. Vachon","doi":"10.1158/1538-7755.carisk16-b27","DOIUrl":"https://doi.org/10.1158/1538-7755.carisk16-b27","url":null,"abstract":"Purpose: The evidence on associations of mammographic breast density with breast cancer risk by combination of tumor aggressiveness features is limited. We examined associations of breast density phenotypes with risk of aggressive breast tumor features by menopausal status, and current postmenopausal hormone therapy. Methods: This study included 2,635 invasive breast cancer cases and 4,059 controls from participants of four nested case-control studies within four established cohorts: the Mayo Mammography Health Study, the Nurses Health Study, Nurses Health Study II, and San Francisco Mammography Registry. Percent breast density, absolute dense and non-dense areas were assessed from digitized film-screen mammograms using a computer-assisted threshold technique and standardized across studies. We used polytomous logistic regression to quantify the associations of breast density measures with risk of breast tumor aggressiveness (defined as presence of 2 or more of the following tumor characteristics: size ≥2cm, grade 2 or 3, or positive nodes), stratified by menopausal status and current hormone therapy (i.e., premenopausal, postmenopausal/estrogen therapy, postmenopausal/combined therapy, and postmenopausal/no hormones). We also evaluated differences in the strength of associations across categories. In a secondary analysis, we examined these associations while excluding cases with mammogram date within 2 years of diagnosis. Results: Positive associations of percent density and dense area and inverse associations of non-dense area with breast cancer risk were stronger in aggressive vs. non-aggressive tumors (OR=2.62, 95%CI 2.08-3.31 vs. OR=1.94, 95%CI 1.62-2.33 for percent density≥51% vs. 11-25%, p-heterogeneity=0.001; OR=1.89, 95%CI 1.54-2.31 vs. OR=1.65, 95%CI 1.41-1.93 for dense area 4th vs. 2nd quartile, p-heterogeneity=0.015; OR=0.56, 95%CI 0.44-0.72 vs. OR=0.71, 95%CI 0.59-0.86 for non-dense area 4th vs 2nd quartile, p-heterogeneity=0.007, respectively). These patterns were similar across all menopausal and hormone therapy groups (P-interactions=0.62, 0.76, and 0.23, for percent density, dense area and non-dense area, respectively). Excluding cases diagnosed within 2 years of mammography resulted in similar findings. Conclusion: Mammographic density phenotypes were more strongly associated with aggressive cancer (having two or more of the following: size ≥2cm, grade 2 or 3, or positive nodes) vs. non-aggressive types of breast cancer across categories of menopause and hormone therapy types. Citation Format: Lusine Yaghjyan, Rulla Tamimi, Kimberly Bertrand, Christopher G. Scott, Matthew R. Jensen, Shane Pankratz, Kathleen Brandt, Daniel Visscher, Aaron Norman, Fergus Cough, John Shepherd, Bo Fan, Yunn-Yi Chen, Lin Ma, Andrew H. Beck, Steven R. Cummings, Karla Kerlikowske, Celine Vachon. Interaction of mammographic breast density with menopausal status and postmenopausal hormone use in relation to the risk of aggressive breast cancer subtypes. ","PeriodicalId":9487,"journal":{"name":"Cancer Epidemiology and Prevention Biomarkers","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74276456","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Yingye Zheng, Xinwei Hua, Aung Ko Win, M. Jenkins, R. MacInnis, P. Newcomb
{"title":"Abstract PR05: Does a comprehensive family history of colorectal cancer improve risk prediction?","authors":"Yingye Zheng, Xinwei Hua, Aung Ko Win, M. Jenkins, R. MacInnis, P. Newcomb","doi":"10.1158/1538-7755.CARISK16-PR05","DOIUrl":"https://doi.org/10.1158/1538-7755.CARISK16-PR05","url":null,"abstract":"Background: Family history of colorectal cancer (CRC) is a strong and well-established risk factor for CRC. To date, however, family history (FH) of the disease is generally only broadly categorized (usually as present or absent) in most risk prediction models (Freedman et al. 2009). These approaches fail to fully utilize information on family history and may lead to suboptimal predictive performance of CRC risk. We investigated the utility of a CRC risk model that incorporates a comprehensive family history of CRC as well as information on known genetic and environmental risk factors and personal characteristics. Methods: We used data from the Colon Cancer Family Registry (CCFR), a large, international consortium of six study centers. Prediction models were developed based on incident invasive CRC cases (N = 4445) and population-based controls (N = 3967) that were recruited from three study sites (Seattle, USA; Ontario, Canada; and Melbourne, Australia). A familial risk profile (FRP) score, a probability index of absolute risk for lifetime CRC was estimated based on family structure, age of onset for affected relatives and the polygenic effect of MLH1, MSH2, MSH6, PMS2 and MUTYH using modified segregation analysis, an approach adapted from Antoniou et al (2002)). Two sets of gender-specific logistic regression models were built: (I) the FRP models, which included FRP and other known risk factors (e.g., BMI, consumption of red meat, calcium and NSAID use duration, smoking amount (pack-years), a history of polyps, and history of FOBT, sigmoidoscopy, colonoscopy, fruit intake, and use of hormone replacement therapy for female); and (II) binary FH models, which replaced FRP with a binary indicator (yes/no) for any self-reported first-degree family member with CRC. 5-year absolute risks were calculated based on the estimated odds ratios (OR), country-, sex- and age-specific CRC incidence rate and mortality due to causes other than CRC. Model validation was conducted with unaffected relatives (N=12,120) and population-based controls (N=1,899) from five study sites based on the follow-up information on incident CRC and death status. The primary endpoint was CRC diagnosis within 5-year after baseline. We used calibration plots to compare the predicted 5-year absolute risks with the observed cumulative incidence rates. Receiver Operating Characteristic (ROC) curve analyses were conducted and areas under the ROC curve (AUC) were used to assess the discriminatory capacity for separating subjects with and without a CRC diagnosis within 5 years, accounting for censoring and competing risk. Results: The ORs (95% confidence interval [CI]) using the FRP per 10% increase were 1.16 (1.11-1.20) for males, and 1.09 (1.06-1.12) for females in the FRP models, while the ORs for the binary FH model were 2.32 (1.88- 2.85) for men and 1.70 (1.38-2.09) for women. The FRP models provided slightly better calibration, with average predicted risks falling within the 95% CIs o","PeriodicalId":9487,"journal":{"name":"Cancer Epidemiology and Prevention Biomarkers","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74194869","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Abstract IA23: Reducing cancer risk by enabling women to breastfeed","authors":"A. Stuebe","doi":"10.1158/1538-7755.CARISK16-IA23","DOIUrl":"https://doi.org/10.1158/1538-7755.CARISK16-IA23","url":null,"abstract":"In reproductive physiology, lactation follows pregnancy. In traditional populations, children continue to breastfeed for 3 to 4 years, suggesting that sustained lactation is the biological norm. However, cultural norms are markedly different; while breastfeeding rates in the US have risen dramatically over the past 40 years, just 22.3% of US mothers are able meet consensus medical recommendations for 6 months of exclusive breastfeeding, and only 30.7% continue to breastfeeding through one year. Evidence continues to accrue that this disruption of normal physiology is associated with adverse health outcomes for mothers and children, including higher maternal rates of breast and ovarian cancer and higher childhood rates of acute lymphocytic leukemia. These data suggest that enabling more women to breastfeed may be an effective cancer prevention strategy. In this session, we will review evidence supporting a protective association between lactation and cancer risk for mothers and children. We will further explore evidence-based strategies to assist women in initiating and sustaining breastfeeding. A recent simulation study found that enabling 90% of women to breastfeed optimally after each birth, defined as 6 months of exclusive breastfeeding and continued breastfeeding for 1 year, would lower population rates of maternal breast cancer and childhood acute lymphocytic leukemia (ALL). In this MCMC simulation, authors considered the impact of a change in breastfeeding rates from current to optimal conditions for a cohort of women born in a single year and followed from age 15 to 70. Under steady state conditions, these results approximate the annual impact of optimal breastfeeding across the population. The authors found that enabling optimal breastfeeding would prevent 185 cases of ALL [95% CI 49 to 309] and 5,023 cases of breast cancer [3,965 to 6,021], as well as 42 breast cancer deaths [22 to 62]. Evidence-based public health strategies to increase breastfeeding rates have been promulgated by the U.S. Surgeon General in the 2011 Call to Action to Support Breastfeeding. These strategies span various socioecological factors that influence whether a woman decides to breastfeed, and whether she is able to sustain breastfeeding in the setting of social and practical constraints. Targeted efforts are further needed to address substantial racial and ethnic disparities in breastfeeding rates, particularly given evidence that never having breastfed is associated with an increased risk of triple-negative breast cancer among black women. Promising strategies include incorporating peer and profession support into prenatal and postpartum care, implementing the WHO Ten Steps, a set of evidence-based maternity care practices, enacting paid parental leave, and ensuring that child care providers enable families to continue breastfeeding. Disruption of breastfeeding is associated with adverse population health outcomes for mothers and children, including breast canc","PeriodicalId":9487,"journal":{"name":"Cancer Epidemiology and Prevention Biomarkers","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83525566","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}