A risk prediction tool for individuals with a family history of breast, ovarian, or pancreatic cancer: BRCAPANCPRO

Author(s):  
Amanda L. Blackford ◽  
Erica J. Childs ◽  
Nancy Porter ◽  
Gloria M. Petersen ◽  
Kari G. Rabe ◽  
...  
2021 ◽  
Author(s):  
Amanda L. Blackford ◽  
Erica J. Childs ◽  
Nancy Porter ◽  
Gloria Petersen ◽  
Kari Rabe ◽  
...  

2021 ◽  
Vol 39 (15_suppl) ◽  
pp. e16240-e16240
Author(s):  
Viola Barucca ◽  
Andrea Petricca Mancuso ◽  
Salvatore De Marco ◽  
Daniela Iacono ◽  
Carmelilia De Bernardo ◽  
...  

e16240 Background: Germline pathogenetic mutations in BRCA1/2 genes are described in pancreatic cancer patients (PCP) in about 5–9% of cases. The purpose of this study was to determine their relevance in an unselected consecutive cohort of PCP describing family and clinical history. Methods: Patients (pts) were recruited at a single cancer center from September 2019 to October 2020. Participants provided blood for DNA analysis; cancer family history and treatment records were reviewed; DNA was analyzed by Next Generation Sequencing and multiplex ligation-dependent probe amplification for germline variants in BRCA1/2 Results: 69 pts were included, 61 (88,4%) with locally advanced and metastatic pancreatic cancer received first line chemotherapy and 38 (62%) were full eligible for BRCA analysis; 8 out of 69 pts were BRCA screened even if in adjuvant setting, 10 patients are still under evaluation. Out of the 38 first line screened PCP germline BRCA mutations were found in 9 (19%): 4 pts (8,7%) with pathogenetic BRCA-2 variants (subgroup 1 – S1) and 5 pts (10,8%) with variants of unknown significances (VUSs), i.e. c.5339T>C and c.5096G>A in BRCA1 (subgroup 2 – S2). Samples from 29 pts were established as BRCA wild-type (subgroup 3 – S3). Pathogenetic BRCA-2 variants were observed in 2 male and 2 female (median age, 61.5 years, range 48-69), 3 out 4 without family history of breast, ovarian and pancreatic cancer, one patient (pt) had ovarian cancer family history. All pts had a negative personal history of others cancers. All S1 pts received FOLFIRINOX regimen achieving one complete response, 2 partials responses and 1 disease progression with RECIST criteria. The S2 included 2 male and 3 female (median age, 61 years, range 45-70) 2 with family history of pancreatic cancer, no pt had personal history of others cancers; 2 pts had stable disease and 3 disease progression receiving platinum-based regimen (4 pts) and gemcitabine/nabpaclitaxel (1 pt), respectively. Platinum responders were observed only in the well known pathogenetic BRCA-2 variants group with twice a median progression-free survival (PFS, months -ms-) as compared to the one observed in VUSs group. (>6 C.I. 95% 2- >12 ms; vs 3 ms, 95% C.I. 3-12 ms). S3 included 9 male and 20 female, (median age, 66 years, range 42-78); 5 pts had family history of pancreatic or breast cancer, 5 pts had a personal history of other cancers (breast and thyroid). In this group,16 pts received a platinum based regimen and 12 pts have been treated without platinum based regimen. Conclusions: Our results suggest that: 1) BRCA pathogenetic mutations rate (8,7%) is in line with literature data and seems not to be related with family or personal history, and to be associated with a better outcome; 2) No BRCA mutations were detected in patients over 70 years. 3) VUSs subgroup do not seem to benefit from platinum-regimen.


2019 ◽  
Vol 19 (1) ◽  
Author(s):  
Johanna Tolksdorf ◽  
Michael W. Kattan ◽  
Stephen A. Boorjian ◽  
Stephen J. Freedland ◽  
Karim Saba ◽  
...  

Abstract Background Online clinical risk prediction tools built on data from multiple cohorts are increasingly being utilized for contemporary doctor-patient decision-making and validation. This report outlines a comprehensive data science strategy for building such tools with application to the Prostate Biopsy Collaborative Group prostate cancer risk prediction tool. Methods We created models for high-grade prostate cancer risk using six established risk factors. The data comprised 8492 prostate biopsies collected from ten institutions, 2 in Europe and 8 across North America. We calculated area under the receiver operating characteristic curve (AUC) for discrimination, the Hosmer-Lemeshow test statistic (HLS) for calibration and the clinical net benefit at risk threshold 15%. We implemented several internal cross-validation schemes to assess the influence of modeling method and individual cohort on validation performance. Results High-grade disease prevalence ranged from 18% in Zurich (1863 biopsies) to 39% in UT Health San Antonio (899 biopsies). Visualization revealed outliers in terms of risk factors, including San Juan VA (51% abnormal digital rectal exam), Durham VA (63% African American), and Zurich (2.8% family history). Exclusion of any cohort did not significantly affect the AUC or HLS, nor did the choice of prediction model (pooled, random-effects, meta-analysis). Excluding the lowest-prevalence Zurich cohort from training sets did not statistically significantly change the validation metrics for any of the individual cohorts, except for Sunnybrook, where the effect on the AUC was minimal. Therefore the final multivariable logistic model was built by pooling the data from all cohorts using logistic regression. Higher prostate-specific antigen and age, abnormal digital rectal exam, African ancestry and a family history of prostate cancer increased risk of high-grade prostate cancer, while a history of a prior negative prostate biopsy decreased risk (all p-values < 0.004). Conclusions We have outlined a multi-cohort model-building internal validation strategy for developing globally accessible and scalable risk prediction tools.


2016 ◽  
Vol 26 (4) ◽  
pp. 806-813 ◽  
Author(s):  
Aimee L. Lucas ◽  
Adam Tarlecki ◽  
Kellie Van Beck ◽  
Casey Lipton ◽  
Arindam RoyChoudhury ◽  
...  

2021 ◽  
Vol 19 (1) ◽  
Author(s):  
Kodai Abe ◽  
Arisa Ueki ◽  
Yusaku Urakawa ◽  
Minoru Kitago ◽  
Tomoko Yoshihama ◽  
...  

Abstract Background Family history is one of the risk factors for pancreatic cancer. It is suggested that patients with pancreatic cancer who have a familial history harbor germline pathogenic variants of BRCA1 and/or BRCA2 (BRCA1/2), PALB2, or ATM. Recently, some germline variants of familial pancreatic cancers (FPCs), including PALB2, have been detected. Several countries, including Japan, perform screening workups and genetic analysis for pancreatic cancers. We have been carrying out active surveillance for FPC through epidemiological surveys, imaging analyses, and genetic analysis. Case presentation Here, we present the case of a female patient harboring pathogenic variants of PALB2 and NBN, with a family history of multiple pancreatic cancer in her younger brother, her aunt, and her father. Moreover, her father harbored a PALB2 pathogenic variant and her daughter harbored the same NBN pathogenic variant. Given the PALB2 and NBN variants, we designed surveillance strategies for the pancreas, breast, and ovary. Conclusions Further studies are required to develop strategies for managing FPCs to facilitate prompt diagnosis before their progression.


2021 ◽  
pp. canprevres.0161.2021
Author(s):  
Bryson W Katona ◽  
Jessica M Long ◽  
Nuzhat A Ahmad ◽  
Sara Attalla ◽  
Angela R Bradbury ◽  
...  

Diagnostics ◽  
2019 ◽  
Vol 9 (4) ◽  
pp. 169
Author(s):  
Matsubayashi ◽  
Kiyozumi ◽  
Ishiwatari ◽  
Uesaka ◽  
Kikuyama ◽  
...  

A family history of pancreatic cancer (PC) is a risk factor of PC, and risk levels increase as affected families grow in number and/or develop PC at younger ages. Familial pancreatic cancer (FPC) is defined as a client having at least two PC cases in a first degree relatives. In the narrow sense, FPC does not include some inherited cancer syndromes that are known to increase the risks of PC, such as Peutz–Jeghers syndrome (PJS), hereditary pancreatitis (HP), hereditary breast ovarian cancer syndrome (HBOC), and so on. FPC accounts for 5%–10% of total PC diagnoses and is marked by several features in genetic, epidemiological, and clinicopathological findings that are similar to or distinct from conventional PC. Recent advances in genetic medicine have led to an increased ability to identify germline variants of cancer-associated genes. To date, high-risk individuals (HRIs) in many developed countries, including FPC kindreds and inherited cancer syndromes, are screened clinically to detect and treat early-stage PC. This article highlights the concept of FPC and the most recent data on its detection.


2006 ◽  
Vol 3 (10) ◽  
pp. 586-591 ◽  
Author(s):  
Rajesh N Keswani ◽  
Amy Noffsinger ◽  
Irving Waxman

2016 ◽  
Vol 6 (1) ◽  
Author(s):  
Xifeng Wu ◽  
Chi Pang Wen ◽  
Yuanqing Ye ◽  
MinKwang Tsai ◽  
Christopher Wen ◽  
...  

Abstract The objective of this study was to develop markedly improved risk prediction models for lung cancer using a prospective cohort of 395,875 participants in Taiwan. Discriminatory accuracy was measured by generation of receiver operator curves and estimation of area under the curve (AUC). In multivariate Cox regression analysis, age, gender, smoking pack-years, family history of lung cancer, personal cancer history, BMI, lung function test, and serum biomarkers such as carcinoembryonic antigen (CEA), bilirubin, alpha fetoprotein (AFP), and c-reactive protein (CRP) were identified and included in an integrative risk prediction model. The AUC in overall population was 0.851 (95% CI = 0.840–0.862), with never smokers 0.806 (95% CI = 0.790–0.819), light smokers 0.847 (95% CI = 0.824–0.871), and heavy smokers 0.732 (95% CI = 0.708–0.752). By integrating risk factors such as family history of lung cancer, CEA and AFP for light smokers, and lung function test (Maximum Mid-Expiratory Flow, MMEF25–75%), AFP and CEA for never smokers, light and never smokers with cancer risks as high as those within heavy smokers could be identified. The risk model for heavy smokers can allow us to stratify heavy smokers into subgroups with distinct risks, which, if applied to low-dose computed tomography (LDCT) screening, may greatly reduce false positives.


2007 ◽  
Vol 25 (11) ◽  
pp. 1417-1422 ◽  
Author(s):  
Wenyi Wang ◽  
Sining Chen ◽  
Kieran A. Brune ◽  
Ralph H. Hruban ◽  
Giovanni Parmigiani ◽  
...  

Purpose The rapid fatality of pancreatic cancer is, in large part, the result of an advanced stage of diagnosis for the majority of patients. Identification of individuals at high risk of developing pancreatic cancer is a first step towards the early detection of this disease. Individuals who may harbor a major pancreatic cancer susceptibility gene are one such high-risk group. The goal of this study was to develop and validate PancPRO, a Mendelian model for pancreatic cancer risk prediction in individuals with familial pancreatic cancer, to identify high-risk individuals. Methods PancPRO was built by extending the Bayesian modeling framework developed for BRCAPRO, trained using published data, and validated using independent prospective data on 961 families enrolled onto the National Familial Pancreas Tumor Registry, including 26 individuals who developed incident pancreatic cancer during follow-up. Results We developed a risk prediction model, PancPRO, and free software for the estimation of pancreatic cancer susceptibility gene carrier probabilities and absolute pancreatic cancer risk. Model validation demonstrated an observed to predicted pancreatic cancer ratio of 0.83 (95% CI, 0.52 to 1.20) and high discriminatory ability, with an area under the receiver operating characteristic curve of 0.75 (95% CI, 0.68 to 0.81) for PancPRO. Conclusion PancPRO is the first risk prediction model for pancreatic cancer. When we validated our model using the largest registry of familial pancreatic cancer, our model provided accurate risk assessment. Our findings highlight the importance of detailed family history for clinical cancer risk assessment and demonstrate that accurate genetic risk assessment is possible even when the causative genes are not known.


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