OA: Pathogenic Mutations in Cancer-Predisposing Genes: A Survey of 300 Patients....

open access: Pathogenic Mutations in Cancer-Predisposing Genes: A Survey of 300 Patients with Whole-Genome Sequencing and Lifetime Electronic Health Records

 Abstract

Background

It is unclear whether and how whole-genome sequencing (WGS) data can be used to implement genomic medicine. Our objective is to retrospectively evaluate whether WGS can facilitate improving prevention and care for patients with susceptibility to cancer syndromes.

Methods and Findings

We analyzed genetic mutations in 60 autosomal dominant cancer-predisposition genes in 300 deceased patients with WGS data and nearly complete long-term (over 30 years) medical records. To infer biological insights from massive amounts of WGS data and comprehensive clinical data in a short period of time, we developed an in-house analysis pipeline within the SeqHBase software framework to quickly identify pathogenic or likely pathogenic variants. The clinical data of the patients who carried pathogenic and/or likely pathogenic variants were further reviewed to assess their clinical conditions using their lifetime EHRs. Among the 300 participants, 5 (1.7%) carried pathogenic or likely pathogenic variants in 5 cancer-predisposing genes: one in APC, BRCA1, BRCA2, NF1, and TP53 each. When assessing the clinical data, each of the 5 patients had one or more different types of cancers, fully consistent with their genetic profiles. Among these 5 patients, 2 died due to cancer while the others had multiple disorders later in their lifetimes; however, they may have benefited from early diagnosis and treatment for healthier lives, had the patients had genetic testing in their earlier lifetimes.

Conclusions

We demonstrated a case study where the discovery of pathogenic or likely pathogenic germline mutations from population-wide WGS correlates with clinical outcome. The use of WGS may have clinical impacts to improve healthcare delivery.


The 5 patients, who carried “pathogenic” or “likely pathogenic” variants, were expected to express autosomal dominant cancer-predisposing syndromes based on their genetic profiles. We reviewed the 5 patients’ lifetime EHRs and found the following results: (i) One male patient who carried a stop-gain mutation (rs72953290) in APC had prostate cancer, many colon polyps, and colorectal cancer. In addition, he had family history of colon cancer. (ii) One female carried a frameshift deletion (rs80357735) in BRCA1 that is predicted to result in a significantly increased risk for breast and ovarian cancer. In fact, this patient had no known family history of breast cancer and did not receive yearly breast exams or mammograms. She was diagnosed with breast and ovarian cancer in her early-50s and died 6 years later. (iii) One male patient, who carried a frameshift insertion (rs80359499) in BRCA2, had prostate cancer in his early-70’s and died a few years later. (iv) One male patient, who carried a stop-gain mutation (rs866445127) in NF1, had multiple skin (both basal cell and squamous) cancers. (v) One female patient, who carried a missense mutation (rs587782596) in TP53, had pancreatic cancer. Additionally her child died from melanoma in his 30’s. The summary information is presented in Table 2. Namely, each of the 5 patients had one or more different types of cancers, demonstrating consistency with their genetic profiles.

Even though there are no family members in the 300 deceased patients, it is conceivable that family history recorded in EHRs can help disease prevention. As mentioned in the 2^th patient who carried the cancer-predisposing mutation in BRCA1, she died from breast and ovarian cancer in her later 50’s. This case implied that it could be important to have aggressive screening and prophylactic surgery for patients with BRCA1 mutations. In addition, it is very likely that more aggressive surveillance or preventative measures could have extended the lives of those patients if genetic testing had been done in their earlier ages. Therefore, combining WGS and EHRs could potentially improve personalized healthcare.