Genomic landscape and actionable mutations of brain metastases derived from non–small cell lung cancer: A systematic review

Background Brain metastases derived from non–small cell lung cancer (NSCLC) represent a significant clinical problem. We aim to characterize the genomic landscape of brain metastases derived from NSCLC and assess clinical actionability. Methods We searched Embase, MEDLINE, Web of Science, and BIOSIS from inception to 18/19 May 2022. We extracted information on patient demographics, smoking status, genomic data, matched primary NSCLC, and programmed cell death ligand 1 expression. Results We found 72 included papers and data on 2346 patients. The most frequently mutated genes from our data were EGFR (n = 559), TP53 (n = 331), KRAS (n = 328), CDKN2A (n = 97), and STK11 (n = 72). Common missense mutations included EGFR L858R (n = 80) and KRAS G12C (n = 17). Brain metastases of ever versus never smokers had differing missense mutations in TP53 and EGFR, except for L858R and T790M in EGFR, which were seen in both subgroups. Of the top 10 frequently mutated genes that had primary NSCLC data, we found 37% of the specific mutations assessed to be discordant between the primary NSCLC and brain metastases. Conclusions To our knowledge, this is the first systematic review to describe the genomic landscape of brain metastases derived from NSCLC. These results provide a comprehensive outline of frequently mutated genes and missense mutations that could be clinically actionable. These data also provide evidence of differing genomic landscapes between ever versus never smokers and primary NSCLC compared to the BM. This information could have important consequences for the selection and development of targeted drugs for these patients

A genome-wide gene-environment interaction study of breast cancer risk for women of European ancestry

Background Genome-wide studies of gene–environment interactions (G×E) may identify variants associated with disease risk in conjunction with lifestyle/environmental exposures. We conducted a genome-wide G×E analysis of ~ 7.6 million common variants and seven lifestyle/environmental risk factors for breast cancer risk overall and for estrogen receptor positive (ER +) breast cancer. Methods Analyses were conducted using 72,285 breast cancer cases and 80,354 controls of European ancestry from the Breast Cancer Association Consortium. Gene–environment interactions were evaluated using standard unconditional logistic regression models and likelihood ratio tests for breast cancer risk overall and for ER + breast cancer. Bayesian False Discovery Probability was employed to assess the noteworthiness of each SNP-risk factor pairs. Results Assuming a 1 × 10–5 prior probability of a true association for each SNP-risk factor pairs and a Bayesian False Discovery Probability < 15%, we identified two independent SNP-risk factor pairs: rs80018847(9p13)-LINGO2 and adult height in association with overall breast cancer risk (ORint = 0.94, 95% CI 0.92–0.96), and rs4770552(13q12)-SPATA13 and age at menarche for ER + breast cancer risk (ORint = 0.91, 95% CI 0.88–0.94). Conclusions Overall, the contribution of G×E interactions to the heritability of breast cancer is very small. At the population level, multiplicative G×E interactions do not make an important contribution to risk prediction in breast cancer