PancPRO is the first risk prediction model for familial pancreatic cancer and provides mutation carrier probability and absolute risk for a specified age interval. Our validation indicated that PancPRO provided accurate risk assessment, discriminating between individuals with and without incident pancreatic cancers. Although we have previously reported risk ratios of incident pancreatic cancer as a function of the number of affected family members,30,33
PancPRO can further discriminate between individuals at higher and lower risk with the same number of affected family members. PancPRO does this by using full pedigree data (information on affected and unaffected family members) and age of family members combined with knowledge of the genetic transmission of pancreatic cancer. However, because of our validation timeframe, we present single-year risk estimates. These small annual risks can translate into a substantial risk of developing a fatal cancer over an individual’s lifetime.
A number of factors, including gene carrier status, environmental exposures, and chance, contribute to the development of disease and the age at which disease develops. Therefore, prognostic models and carrier models have less inherent variability than disease onset prediction models. Yet PancPRO performs comparably to prognostic34
and carrier models,25,35
as well as the most successful disease onset prediction models.36
The inclusion of complete family history data in PancPRO may explain the strength of this model. Although the NFPTR represents a select study population, our validation used prospective data; therefore, the factors that motivate families to enter the registry should not impact the results of our validation. Furthermore, NFPTR participants often learn of the registry while seeking information on hereditary pancreatic cancer; therefore, they are likely similar to families seeking clinical risk assessment.
Our validation confirms that this is a well-formed model likely to have a positive impact on clinical practice.37
However, our ability to assess calibration, especially in subgroups, is limited by the rarity of pancreatic cancer, such that only 26 incident patient cases were observed in this large high-risk cohort. It is worth noting that the caveats that apply to the use of risk prediction models in general38
are also relevant here. When a model is used for selecting a subset of individuals or families by setting a threshold on risk, there is a possibility of both false negatives and false positives. The appropriate thresholds for clinical interventions should consider explicitly their associated risks and benefits, which may change with an individual’s personal values and circumstances. Although risk models can efficiently and objectively summarize relevant information for patients, decision making should take place in concert with a health professional.39
PancPRO estimates do not include patient-specific SEs. Although technically possible,22
risk prediction models rarely provide patient-specific SEs because a common difficulty within the risk prediction field is properly communicating these additional uncertainties to patients and health professionals.
Although clinical genetic testing for pancreatic cancer is currently limited, genetic counseling can still be of value.40
PancPRO can form the basis for cancer risk counseling and can guide the design of screening trials for early pancreatic cancer detection in asymptomatic individuals.14,15
For example, Canto et al12,13
successfully used endoscopic ultrasound in the screening of asymptomatic individuals with a family history of pancreatic cancer. In fact, our model offered the greatest discrimination among individuals aged 65 years and younger at baseline, the group most likely to undergo and benefit from early detection screening. Because PancPRO provides a quantitative assessment of risk, it can contribute to defining the high-risk population that would benefit most from investigational screening techniques.
PancPRO estimates risk using data on pancreatic cancer only. The validation data set did not exclude families with known genetic syndromes, nor did the segregation analysis from which the model estimates were derived. Aside from a handful of the validation families, clinical genetic testing for BRCA2 or p16 mutations was not performed, and only two of the 961 families presented with a family history or symptoms indicative of familial atypical multiple mole melanoma or Peutz-Jeghers syndrome. Collection of a complete family history, such as the data needed for the model, may alert clinicians to the presence of other cancer syndromes requiring additional investigation.
A natural next step in expanding PancPRO is to separately model the effects of mutations in BRCA2 or other known genes. Currently, precise estimates of the penetrance of pancreatic cancer in BRCA2 mutation carriers are not available, partly because most BRCA2 studies select for individuals who developed breast and/or ovarian cancer at an early age. Conversely, the NFPTR may under-represent BRCA2 families in which there is an excess of early-onset breast cancer because these families may already be included as part of high-risk breast cancer studies. Because the penetrance estimate derived in the segregation model is an approximation based on the combined penetrance of all pancreatic cancer susceptibility loci, we anticipate improvements in model accuracy as we identify and incorporate the direct effects of these loci. Similarly, risk factors, such as cigarette exposure, history of pancreatitis, and diabetes mellitus, may be included in PancPRO once estimates of risk are available for both gene carriers and nongene carriers (ie, does smoking cause a two- to three-fold increase in pancreatic cancer risk in gene carriers as it does in sporadic patients, or is the genetic effect so powerful that smoking has a much more limited role). Currently, PancPRO models a major gene responsible for strong familial clustering to distinguish between likely carriers and noncarriers of this major gene as a first but very important step in risk assessment.
In summary, we developed PancPRO, the first risk prediction model for pancreatic cancer, and successfully validated it using prospective (incident) pancreatic cancer data from one of the largest registries of familial pancreatic cancer. Our study highlights how detailed family history improves risk prediction. We hope PancPRO will be a useful tool to identify high-risk individuals for ongoing and future early detection trials.