Identification of B cell subsets based on antigen receptor sequences using deep learning.

B cell receptors (BCRs) denote antigen specificity, while corresponding cell subsets indicate B cell functionality. Since each B cell uniquely encodes this combination, physical isolation and subsequent processing of individual B cells become indispensable to identify both attributes. However, this approach accompanies high costs and inevitable information loss, hindering high-throughput investigation of B cell populations. Here, we present BCR-SORT, a deep learning model that predicts cell subsets from their corresponding BCR sequences by leveraging B cell activation and maturation signatures encoded within BCR sequences. Subsequently, BCR-SORT is demonstrated to improve reconstruction of BCR phylogenetic trees, and reproduce results consistent with those verified using physical isolation-based methods or prior knowledge. Notably, when applied to BCR sequences from COVID-19 vaccine recipients, it revealed inter-individual heterogeneity of evolutionary trajectories towards Omicron-binding memory B cells. Overall, BCR-SORT offers great potential to improve our understanding of B cell responses.

Identification of genomic mutations associated with clinical outcomes of induction chemotherapy in patients with head and neck squamous cell carcinoma.

PURPOSE: We performed deep sequencing of target genes in head and neck squamous cell carcinoma (HNSCC) tumors to identify somatic mutations that are associated with induction chemotherapy (IC) response. METHODS: Patients who were diagnosed with HNSCC were retrospectively identified. Patients who were treated with IC were divided into two groups: good responders and poor responders by tumor response and progression-free survival. Targeted gene sequencing for 2404 somatic mutations of 44 genes was performed on HNSCC tissues. Mutations with total coverage of <500 were excluded, and the cutoff for altered allele frequency was >10 %. RESULTS: Of the 71 patients, 45 were treated upfront with IC. Mean total coverage was 1941 per locus, and 42.2 % of tumors had TP53 mutations. Thirty-three mutations in TP53, NOTCH3, FGFR2, FGFR3, ATM, EGFR, MET, PTEN, FBXW7, SYNE1, and SUFU were frequently altered in poor responders. Among the patients who were treated with IC, those with unfavorable genomic profiles had significantly poorer overall survival than those without unfavorable genomic profiles (hazard ratio 6.45, 95 % confidence interval 2.07-20.10, P < 0.001). CONCLUSIONS: Comprehensive analysis of mutation frequencies identified unfavorable genomic profiles, and the patients without unfavorable genomic profiles can obtain clinical benefits from IC in patients with HNSCC.

Comparison of targeted next-generation sequencing with conventional sequencing for predicting the responsiveness to epidermal growth factor receptor-tyrosine kinase inhibitor (EGFR-TKI) therapy in never-smokers with lung adenocarcinoma.

PURPOSE: To investigate the clinical utility of targeted next-generation sequencing (NGS) for predicting the responsiveness to epidermal growth factor receptor (EGFR)-tyrosine kinase inhibitor (TKI) therapy, we compared the efficacy with conventional sequencing in never-smokers with lung adenocarcinoma (NSLAs). PATIENTS AND METHODS: We obtained DNA from 48 NSLAs who received gefitinib or erlotinib for their recurrent disease after surgery. Sanger sequencing and peptide nucleic acid clamp polymerase chain reaction (PCR) were used to analyze EGFR, KRAS, BRAF, and PIK3CA mutations. We analyzed ALK, RET, and ROS1 rearrangements by fluorescent in situ hybridization or reverse transcriptase-PCR and quantitative real-time PCR. After molecular screening, Ion Torrent NGS was performed in 31 cases harboring only EGFR exon 19 deletions (19DEL), an L858R mutation, or none of the above mutations. RESULTS: The 31 samples were divided into four groups: (1) responders to EGFR-TKIs with only 19DEL or L858R (n=15); (2) primary resistance to EGFR-TKI with only 19DEL or L858R (n=4); (3) primary resistance to EGFR-TKI without any mutations (n=8); (4) responders to EGFR-TKI without any mutations (n=4). With NGS, all conventionally detected mutations were confirmed except for one L858R in group 2. Additional uncovered predictive mutations with NGS included one PIK3CA E542K in group 2, two KRAS (G12V and G12D), one PIK3CA E542K, one concomitant PIK3CA and EGFR L858R in group 3, and one EGFR 19DEL in group 4. CONCLUSIONS: Targeted NGS provided a more accurate and clinically useful molecular classification of NSLAs. It may improve the efficacy of EGFR-TKI therapy in lung cancer.