The development of next-generation sequencing (NGS) has provided a unique opportunity for accurate identification of genetic predispositions to certain cancers. Understanding the phenotypic consequences of genomic variations has led to improvements in clinical diagnoses, treatments, and prognoses, and provided additional insights into disease etiology and potential therapeutic targets. Beyond cancer, it has been hypothesized that hereditary cancer genes may also be associated with other conditions. To better understand the range of conditions associated with hereditary cancer genes, Dr Chenjie Zeng and colleagues conducted a phenome-wide association study (PheWAS) that analyzed phenotypic data derived from health records of 3 cohorts (2 US based, 1 UK based) that included over 214,000 participants.
The analysis uncovered 19 new associations that were not previously documented in the Online Mendelian Inheritance in Man database, an encyclopedic platform that provides comprehensive curation of existing knowledge on gene-phenotype associations to help inform clinical decision-making. Of these, there were 7 associations with malignant tumors: CHEK2 with leukemia and plasma cell neoplasms, ATM with gastric and pancreatic cancers, MUTYH (biallelic) with kidney cancer, MSH6 with bladder cancer, and APC with benign liver/intrahepatic bile duct tumors. Ten genes were associated with nonneoplastic diseases, including inflammatory-related disorders. Among participants without a history of cancer, statistically significant associations were identified between BRCA1/2 and ovarian cyst, PTEN and chronic gastritis, and MEN1 and acute pancreatitis.
In an accompanying editorial, Dr Kailin Yang and Dr Jacob Scott state that an integrated system of data collection, storage, analysis, and modeling will build a critical foundation for computational medicine and have a sustained impact on management of cancer and other diseases. They suggest that integrating genomic data generated from NGS and clinical data from electronic health records (EHRs) may reveal critical novel information with the potential to improve strategies for cancer prevention. As EHR systems evolve, incorporating gene-phenotype associations into artificial intelligence, gene learning, and other data science tools may lead to greater success in cancer prevention and reduced mortality rates from malignant neoplasms in clinical practice.
This analysis highlights potential benefits of EHR data in genomic medicine, and future studies are expected to help validate the observed associations and enhance applicability of the findings. The authors propose that the sensitivity of detection may be increased by correlating International Classification of Diseases, Tenth Revision codes with other findings from clinical examinations, laboratory tests, and medical imaging embedded within the medical records. It should be noted that the analysis included a relatively small sample size of populations of non-European descent. Therefore, future research should aim to include more diverse populations to increase the generalizability of the findings and facilitate precision medicine for all patients. Researchers are encouraged to consider options to increase enrollment from members of ethnic minority groups in future analyses. Involving patients from diverse ethnic backgrounds and applying the findings to low- and middle-income countries has the potential to expand the impact of the PheWAS on a global scale.
Recognizing and understanding the full clinical spectrum of hereditary cancer syndromes can facilitate early cancer detection and better management. This study supports application of a lower threshold for pursuing genetic testing and counseling, especially in patients with more than 1 benign condition that may indicate a higher chance of an associated variant. For example, the association and progression between benign phenotypes such as ovarian cyst and pancreatitis and the corresponding malignant neoplasms helps clarify the predictive value when observing a pertinent benign condition early in life and enables the clinician to fine-tune measures for cancer prevention.