Update on familial pancreatic cancer.

Approximately 5% to 10% of patients with pancreatic cancer have one or more first-degree relatives with this disease. A subset of these individuals have a hereditary form of pancreatic cancer designated by association with such hereditary disorders as familial adenomatous polyposis, hereditary nonpolyposis colorectal cancer, hereditary pancreatitis, or familial atypical multiple mole melanoma (FAMMM) syndrome. A subset of those FAMMM kindred with the CDKN2A (p16) germline mutation that expresses both pancreatic cancer and malignant melanoma may constitute a new hereditary pancreatic cancer-prone syndrome.

Hereditary pancreatic cancer.

Hereditary pancreatic cancer (PC) appears to be exceedingly heterogeneous, as evidenced by its association with a variety of integrally associated diverse cancers and/or differing mendelian inherited cancer syndromes, which include the Lynch syndrome II variant of hereditary nonpolyposis colorectal cancer, hereditary breast-ovarian cancer syndrome in families with the BRCA2 mutation, hereditary pancreatitis, Peutz-Jeghers polyposis and the familial atypical multiple-mole melanoma syndrome in families with the CDKN2A (p16) germline mutation. Because of this heterogeneity, we provide a conservative estimate that about 5% (1,460) of PC cases in the US annually are hereditary. Although this number is relatively small, members of hereditary PC families serve as excellent models for studying the etiology, natural history, biomarkers, pathogenesis, potential carcinogenic exposures and their perturbation of underlying genetic events, and treatment of PC. These individuals would benefit greatly from method(s) capable of detecting cancer at an early stage, and such knowledge would also be useful for improving the diagnosis of the much more common 'sporadic' form of PC.

Automated detection of hereditary syndromes using data mining.

Computer-based data mining methodology applied to family history clinical data can algorithmically create highly accurate, clinically oriented hereditary disease pattern recognizers. For the example of hereditary colon cancer, the data mining's selection of relevant factors to assess for hereditary colon cancer was statistically significant (P < 0.05). All final recognizer-formulated patterns of hereditary colon cancer were independently confirmed by a clinical expert. Applied to previously analyzed family histories, the recognizer identified the definitive hereditary histories, correctly responded negatively to the putative hereditary histories, and correctly responded negatively to empirically elevated colon cancer risk situations. This capability facilitates patient selection for DNA studies in search of gene mutations. When genetic mutations are included as parameters in a patient database for a genetic disease, the process yields an expert system which characterizes variations in clinical disease presentations in terms of genetic mutations. Such information can greatly improve the efficiency of gene testing.