Recent technological advances and the application of high-throughput mutation and transcriptome analyses have improved our understanding of cancer diseases, including non-small cell lung cancer. For instance, genomic profiling has allowed the identification of mutational events which can be treated with specific agents. However, detection of DNA alterations does not fully recapitulate the complexity of the disease and it does not allow selection of patients that benefit from chemo- or immunotherapy. In this context, transcriptional profiling has emerged as a promising tool for patient stratification and treatment guidance. For instance, transcriptional profiling has proven to be especially useful in the context of acquired resistance to targeted therapies and patients lacking targetable genomic alterations. Moreover, the comprehensive characterization of the expression level of the different pathways and genes involved in tumor progression is likely to better predict clinical benefit from different treatments than single biomarkers such as PD-L1 or tumor mutational burden in the case of immunotherapy. However, intrinsic technical and analytical limitations have hindered the use of these expression signatures in the clinical setting. In this review, we will focus on the data reported on molecular classification of non-small cell lung cancer and discuss the potential of transcriptional profiling as a predictor of survival and as a patient stratification tool to further personalize treatments.

Classifying germline variants in hereditary cancer genes remains challenging and requires integrating diverse lines of evidence. BoostDM is a computational method originally developed to identify somatic cancer driver mutations by detecting signals of positive selection. Given the functional overlap between somatic and germline pathogenic variants in cancer genes, we evaluated the utility of BoostDM for interpreting germline variants in hereditary cancer genes. We assessed BoostDM’s performance across six genes with dual roles in sporadic and hereditary cancer ( ATM , BRCA1 , BRCA2 , CDH1 , PTEN , TP53 ), using gene-specific BoostDM models. A total of 1275 germline single nucleotide variants with expert-reviewed pathogenic and benign classifications were included. BoostDM scores were compared to those from AlphaMissense and REVEL, two leading pathogenicity predictors for missense variants. BoostDM correctly classified 74.5% of pathogenic/likely pathogenic and 98.6% of non-synonymous benign/likely benign variants overall. It performed particularly well for non-synonymous, non-missense variants (92.3% sensitivity). For missense variants, BoostDM correctly identified 46% of pathogenic and 95.5% of benign variants. While BoostDM did not outperform AlphaMissense or REVEL, it demonstrated high specificity (99.5%) and positive predictive value (PPV = 98%) for missense variants with high scores ( > 0.5). Gene-specific performance varied, with TP53 showing the most robust results. In conclusion, BoostDM predictions are not a replacement for ACMG/AMP-guided germline variant classification, especially for missense changes. However, its high specificity and PPV suggest that high BoostDM scores can provide supportive evidence of pathogenicity, prompting further clinical and functional investigation.

PURPOSE: Somatic next-generation sequencing (NGS) panels are widely used in precision oncology to detect clinically actionable genomic alterations. However, interpreting diverse DNA and RNA alterations remains challenging because of the complexity of tumor-only data and the limitations of current pipelines, which are often proprietary, noncustomizable, or lack visual reports to support clinical interpretation. We present ClinBioNGS, an open-source, panel-agnostic bioinformatics pipeline for the comprehensive analysis of somatic NGS cancer panels in both clinical and translational settings. MATERIALS AND METHODS: ClinBioNGS is a modular, fully containerized workflow implemented in Nextflow. It supports integrated analysis of DNA and RNA data, including multicaller small variant detection, copy number alteration (CNA) profiling, gene fusion and splice variant identification, and evaluation of complex genomic biomarkers such as tumor mutational burden and microsatellite instability. Variants are annotated and prioritized using established clinical frameworks. The results are compiled in a self-contained interactive HTML report with dynamic tables and informative visualizations to facilitate clinical interpretation. Validation included SEQC2 reference data sets across six commercial panels, and benchmarking was performed on 2,024 clinical tumor samples analyzed with three commercial platforms. RESULTS: ClinBioNGS achieved high accuracy in SEQC2 validation, with precision (0.987-1.000), recall (0.920-0.997), and F1 scores (0.956-0.999) across diverse panels. In a clinical benchmarking with real-world data, the pipeline demonstrated high concordance with commercial solutions for small variants (97%), CNAs (89%), and RNA alterations (94%), while also identifying additional high-confidence alterations missed by vendor pipelines. CONCLUSION: ClinBioNGS provides a robust, flexible, and transparent solution for standardized analysis of somatic NGS cancer panels. It supports reproducible, clinically oriented interpretation of genomic data and is freely available for noncommercial research-use only at GitHub.