Gene Expression Profiling as a Potential Tool for Precision Oncology in Non-Small Cell Lung Cancer

Abstract

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.

Integrative pharmacogenomics revealed three subtypes with different immune landscapes and specific therapeutic responses in lung adenocarcinoma

“…Therefore, it is urgent to develop a novel classification strategy that accurately identify the patients responding to drugs. In contrast to most previous reports studying precision medicine approaches from the cancer hallmark perspective [36] , we integrated pharmacogenomics data of LUAD cell lines and proposed the pharmacogenomics-based system. We integrated CMap and pRRophetic analysis to identify subtype-specific sensitive drugs: ICBs, doxorubicin, tipifarnib, AZ628, and AZD6244 were for S-Ⅰ; Cisplatin, camptothecin, roscovitine, and A.443654 were for S-Ⅱ; Docetaxel, paclitaxel, vinorelbine, and BIBW2992 were for S-III.…”

The Intriguing Thyroid Hormones–Lung Cancer Association as Exemplification of the Thyroid Hormones–Cancer Association: Three Decades of Evolving Research

“…It could also lead to identification of patient-specific and/or cancer-specific factors that determine the effect of TH on cancer outcome. The transcriptional profiling—a promising tool to stratify cancer patients and guide anti-cancer treatment—may facilitate the comprehensive characterization of the expression level of the TH-related pathways and genes involved in lung cancer progression [ 129 ].…”

Transcriptional profiling of molecular pathways allows for the definition of robust lung squamous cell carcinoma molecular subtypes with specific vulnerabilities

“…Previous transcriptome-based classifications used individual gene expression measures, which are prone to multiple sources of variability. 2 Detailed methodology, gene expression datasets and schematic view of the bioinformatics framework are shown in Table S1 and Figures S1 and S2, respectively. Five SCC subtypes were identified based on the combined transcriptional behaviour of the 50 pathways (Figure 1A,B): SCC1 (9.9% of patients), SCC2 (23.9%), SCC3 (25.8%), SCC4 (31.0%) and SCC5 (9.4%).…”