Brain metastasis (BM) genetically diverges from the primary tumor in non-small-cell lung cancer (NSCLC).Hence, accurately capturing clinically relevant alterations is pivotal for the delivery of targeted therapies. Circulating tumor DNA (ctDNA) sequencing has emerged as a promising liquid biopsy in the biomarkerbased clinical management of recurrent and extracranial metastatic NSCLC. However, absence of simultaneous sequencing data from brain metastatic sites prevents the de nitive evaluation of the e cacy of ctDNA representing genetic pro les in BM. Here, we performed parallel genomic comparisons between matched BM and primary tumor DNA, plasma ctDNA, and cerebrospinal uid (CSF) ctDNA. The results indicated that CSF ctDNA had a greater ability than plasma ctDNA in comprehensively representing the mutational landscape of BM, with CSF ctDNA detecting all BM mutations in 83.33% of patients while plasma ctDNA was only 27.78%. Mutant allele frequency (MAF) in CSF ctDNA was highly correlated with the tumor size of BM (r = 0.95), and the mean MAF in CSF ctDNA was more abundant than that in plasma ctDNA (38.05% vs. 4.57%). MAF and tumor mutational burden (TMB) in CSF ctDNA were strongly associated with those in BM (r = 0.96 and 0.97, respectively). Of note, CSF ctDNA had signi cantly higher concordance with BM than plasma ctDNA (99.33% vs. 67.44%), facilitating the identi cation of clinically relevant mutations. Moreover, we found that plasma ctDNA has stronger pro ling performance with a concordance of 93.01% in multiple brain metastases, equivalent to CSF ctDNA. Collectively, our study indicates that CSF ctDNA is superior to plasma ctDNA in accurately represent the pro ling of single BM, plasma ctDNA could be an alternative liquid biopsy material to be applied in multiple brain metastatic NSCLC.

Homozygous deletion (HD) of CDKN2A and CDKN2B (CDKN2A/BHD) is the most frequent copy‐number variation (CNV) in lung adenocarcinoma (LUAD). CDKN2A/BHD has been associated with poor outcomes in LUAD; however, the mechanisms of its prognostic effect remain unknown. We analyzed genome, transcriptome, and clinical data from 517 patients with LUAD from the Cancer Genome Atlas (TCGA) and from 788 primary LUAD tumor and matched control samples from the MSK‐IMPACT clinical cohort. CDKN2A/BHD was present in 19.1% of the TCGA‐LUAD cohort and in 5.7% of the MSK‐IMPACT cohort. CDKN2A/BHD patients had shorter disease‐free survival and overall survival compared with CDKN2A/BWT individuals in both cohorts. Differences in clinical features did not influence the outcomes in the CDKN2A/BHD population. Mutation analyses showed that overall tumor mutational burden and mutations in classical drivers such as EGFR and RB1 were not associated with CDKN2A/BHD. In contrast, homozygous deletion of type I interferons (IFN‐IHD) frequently co‐occurred with CDKN2A/BHD. CDKN2A/B and IFN‐I are co‐located in the same p21.3 region of chromosome 9. The co‐occurrence of CDKN2A/BHD and IFN‐IHD was not related to whole‐genome doubling, chromosome instability, or aneuploidy. Patients with co‐occurring CDKN2A/BHD and IFN‐IHD had shorter disease‐free survival and overall survival compared with CDKN2A/BWT patients. CDKN2A/BHDIFN‐IHD had downregulated several key immune response pathways, suggesting that poor prognosis in CDKN2A/BHD LUAD could potentially be attributed to an immunosuppressive tumor microenvironment as a result of IFN‐I depletion.

Duplex sequencing technology has been widely used in the detection of low-frequency mutations in circulating tumor deoxyribonucleic acid (DNA), but how to determine the sequencing depth and other experimental parameters to ensure the stable detection of low-frequency mutations is still an urgent problem to be solved. The mutation detection rules of duplex sequencing constrain not only the number of mutated templates but also the number of mutation-supportive reads corresponding to each forward and reverse strand of the mutated templates. To tackle this problem, we proposed a Depth Estimation model for stable detection of Low-Frequency MUTations in duplex sequencing (DELFMUT), which models the identity correspondence and quantitative relationships between templates and reads using the zero-truncated negative binomial distribution without considering the sequences composed of bases. The results of DELFMUT were verified by real duplex sequencing data. In the case of known mutation frequency and mutation detection rule, DELFMUT can recommend the combinations of DNA input and sequencing depth to guarantee the stable detection of mutations, and it has a great application value in guiding the experimental parameter setting of duplex sequencing technology.