Targeted gene panels identify a high frequency of pathogenic germline variants in patients diagnosed with a hematological malignancy and at least one other independent cancer.
Deepak SinghalChristopher N HahnSimone K FeursteinLi Yan A WeeLuke MomaMonika M KutynaRakchha ChhetriLeila EshraghiAndreas W SchreiberJinghua FengPaul P-S WangMilena BabicWendy T ParkerSong GaoSarah MooreSoma DasDavid ThomasSwetansu PattnaikAnna L BrownRichard J D'AndreaNicola K PoplawskiDaniel ThomasHamish S ScottLucy A GodleyDevendra K HiwasePublished in: Leukemia (2021)
The majority of studies assessing the contribution of pathogenic germline variants (PGVs) to cancer predisposition have focused on patients with single cancers. We analyzed 45 known cancer predisposition genes (CPGs) in germline samples of 202 patients with hematological malignancies (HMs) plus one or more other independent cancer managed at major tertiary medical centers on two different continents. This included 120 patients with therapy-related myeloid neoplasms (t-MNs), where the HM occurred after cytotoxic treatment for a first malignancy, and 82 patients with multiple cancers in which the HM was not preceded by cytotoxic therapy (MC-HM). Using American College of Medical Genetics/Association for Molecular Pathology variant classification guidelines, 13% of patients had PGVs, most frequently identified in CHEK2 (17% of PGVs), BRCA1 (13%), DDX41 (13%), and TP53 (7%). The frequency of PGVs in MC-HM was higher than in t-MN, although not statistically significant (18 vs. 9%; p = 0.085). The frequency of PGVs in lymphoid and myeloid HM patients was similar (19 vs. 17.5%; p > 0.9). Critically, patients with PGVs in BRCA1, BRCA2 or TP53 did not satisfy current clinical phenotypic criteria for germline testing. Our data suggest that a personal history of multiple cancers, one being a HM, should trigger screening for PGVs.
Keyphrases
- end stage renal disease
- papillary thyroid
- high frequency
- ejection fraction
- newly diagnosed
- chronic kidney disease
- healthcare
- peritoneal dialysis
- squamous cell
- machine learning
- dna repair
- genome wide
- acute myeloid leukemia
- patient reported outcomes
- immune response
- gene expression
- deep learning
- stem cells
- transcription factor
- drug delivery
- electronic health record
- ionic liquid
- smoking cessation
- artificial intelligence