RESULTS: We present an automated gene prediction pipeline, Seqping that uses self-training HMM models and transcriptomic data. The pipeline processes the genome and transcriptome sequences of the target species using GlimmerHMM, SNAP, and AUGUSTUS pipelines, followed by MAKER2 program to combine predictions from the three tools in association with the transcriptomic evidence. Seqping generates species-specific HMMs that are able to offer unbiased gene predictions. The pipeline was evaluated using the Oryza sativa and Arabidopsis thaliana genomes. Benchmarking Universal Single-Copy Orthologs (BUSCO) analysis showed that the pipeline was able to identify at least 95% of BUSCO's plantae dataset. Our evaluation shows that Seqping was able to generate better gene predictions compared to three HMM-based programs (MAKER2, GlimmerHMM and AUGUSTUS) using their respective available HMMs. Seqping had the highest accuracy in rice (0.5648 for CDS, 0.4468 for exon, and 0.6695 nucleotide structure) and A. thaliana (0.5808 for CDS, 0.5955 for exon, and 0.8839 nucleotide structure).
CONCLUSIONS: Seqping provides researchers a seamless pipeline to train species-specific HMMs and predict genes in newly sequenced or less-studied genomes. We conclude that the Seqping pipeline predictions are more accurate than gene predictions using the other three approaches with the default or available HMMs.
METHODS: In this phase 3, international, randomized trial, we assigned in a 1:1 ratio patients with advanced NSCLC with EGFR exon 20 insertions who had not received previous systemic therapy to receive intravenous amivantamab plus chemotherapy (amivantamab-chemotherapy) or chemotherapy alone. The primary outcome was progression-free survival according to blinded independent central review. Patients in the chemotherapy group who had disease progression were allowed to cross over to receive amivantamab monotherapy.
RESULTS: A total of 308 patients underwent randomization (153 to receive amivantamab-chemotherapy and 155 to receive chemotherapy alone). Progression-free survival was significantly longer in the amivantamab-chemotherapy group than in the chemotherapy group (median, 11.4 months and 6.7 months, respectively; hazard ratio for disease progression or death, 0.40; 95% confidence interval [CI], 0.30 to 0.53; P<0.001). At 18 months, progression-free survival was reported in 31% of the patients in the amivantamab-chemotherapy group and in 3% in the chemotherapy group; a complete or partial response at data cutoff was reported in 73% and 47%, respectively (rate ratio, 1.50; 95% CI, 1.32 to 1.68; P<0.001). In the interim overall survival analysis (33% maturity), the hazard ratio for death for amivantamab-chemotherapy as compared with chemotherapy was 0.67 (95% CI, 0.42 to 1.09; P = 0.11). The predominant adverse events associated with amivantamab-chemotherapy were reversible hematologic and EGFR-related toxic effects; 7% of patients discontinued amivantamab owing to adverse reactions.
CONCLUSIONS: The use of amivantamab-chemotherapy resulted in superior efficacy as compared with chemotherapy alone as first-line treatment of patients with advanced NSCLC with EGFR exon 20 insertions. (Funded by Janssen Research and Development; PAPILLON ClinicalTrials.gov number, NCT04538664.).
MATERIALS AND METHODS: The OncoCarta(™) panel v1.0 assay was used to characterize oncogenic mutations. In addition, exons 4-11 of the TP53 gene were sequenced. Statistical analyses were conducted to identify associations between mutations and selected clinico-pathological characteristics and risk habits.
RESULTS: Oncogenic mutations were detected in PIK3CA (5.7%) and HRAS (2.4%). Mutations in TP53 were observed in 27.7% (31/112) of the OSCC specimens. Oncogenic mutations were found more frequently in non-smokers (p = 0.049) and TP53 truncating mutations were more common in patients with no risk habits (p = 0.019). Patients with mutations had worse overall survival compared to those with absence of mutations; and patients who harbored DNA binding domain (DBD) and L2/L3/LSH mutations showed a worse survival probability compared to those patients with wild type TP53. The majority of the oncogenic and TP53 mutations were G:C > A:T and A:T > G:C base transitions, regardless of the different risk habits.
CONCLUSION: Hotspot oncogenic mutations which are frequently present in common solid tumors are exceedingly rare in OSCC. Despite differences in risk habit exposure, the mutation frequency of PIK3CA and HRAS in Asian OSCC were similar to that reported in OSCC among Caucasians, whereas TP53 mutations rates were significantly lower. The lack of actionable hotspot mutations argue strongly for the need to comprehensively characterize gene mutations associated with OSCC for the development of new diagnostic and therapeutic tools.