NEW YORK – Members of the international Breast Cancer Association Consortium have characterized breast tumor features corresponding to rare pathogenic germline variants in several known breast cancer risk genes.
"These results may inform guidelines for eligibility for gene panel sequencing and breast cancer surveillance in the general population," senior author Nasim Mavaddat, a researcher with the University of Cambridge Centre for Cancer Genetic Epidemiology, said in an email. She added that the results "will help improve cancer risk prediction models, and will be included in 'CanRisk,' a cancer risk prediction model already in use by genetic counsellors and other clinicians."
For a paper published in JAMA Oncology on Thursday, the researchers searched for pathogenic germline variants in nine breast cancer risk genes using panel sequence data for tens of thousands of breast cancer cases and controls. They then explored ties between specific susceptibility genes and breast tumor features ranging from tumor size, stage, and grade to estrogen/progesterone receptor and ERBB2/HER2 status.
"Tumors arising in women harbouring pathogenic variants in the nine breast cancer genes … were found to differ substantially in their tumor pathology profiles, although there were some similarities that could be traced to known biological functions of the genes," Mavaddat said, noting that the genes "contributed disproportionately to more aggressive breast cancer, particularly among younger women."
These analyses relied on gene panel sequence data for 42,680 breast cancer patients of European or East Asian ancestry enrolled in more than three dozen studies, along with 46,387 unaffected individuals, to search for protein-truncating or likely pathogenic germline variants in BRCA1, BRCA2, PALB2, CHEK2, ATM, BARD1, TP53, RAD51C, and RAD51D.
The team's findings suggested that breast cancer pathology and other clinical features vary between individuals depending on the cancer susceptibility variants they carry in their germline, hinting that an individual's breast cancer subtype and other tumor features may eventually provide clues that can help inform the classification of variants of uncertain significance in breast cancer risk genes.
Pathogenic germline variants in the RAD51C, RAD51D, and BARD1 genes were associated with triple-negative breast cancers lacking enhanced estrogen receptor, progesterone receptor, or ERBB2/HER2 activity, for example, while patients with triple-negative breast cancer were less likely to carry CHEK2 risk variants than patients with other breast cancer subtypes.
Although BRCA1 variants corresponded to enhanced breast cancer risk across the board, those variants carried a somewhat higher risk of triple-negative breast cancer, the authors reported. Similarly, protein-truncating variants in BRCA2 and PALB2 were linked to all breast cancer subtypes, but carried a slightly higher risk of triple-negative breast cancer and hormone receptor-positive/ERBB2-negative breast cancer.
High-grade hormone receptor-positive and ERBB2-negative tumors also turned up more frequently in individuals with pathogenic germline variants in the ATM gene, while hormone receptor-negative/ERBB2-positive tumors were more common in CHEK2 mutation carriers and TP53 variants were associated with ERBB-positive breast cancers with or without hormone receptor positivity.
Other tumor features corresponded to germline risk as well, the team noted. In particular, tumor size and stage were typically more advanced for breast cancer cases carrying protein-truncating variants in genes such as BRCA2, CHEK2, and PALB2, and those individuals were more likely to have cancer-affected lymph nodes.
More broadly, the researchers found that the presence of pathogenic germline risk variants in general coincided with higher-grade tumors at the time of diagnosis, though that risk appeared to diminish as individuals got older.
"Knowing the age and tumor subtype distributions associated with individual [breast cancer] genes can potentially aid guidelines for gene panel testing, risk prediction, and variant classification," the authors reported, "and guide targeted screening strategies."