Approximately 20% of lung adenocarcinoma (LUAD) is negative for the lineage-specific oncogene Thyroid transcription factor 1 (TTF-1) and exhibits worse clinical outcome with a low frequency of actionable genomic alterations. To identify molecular features associated with TTF-1–negative LUAD, we compared the transcriptomic and proteomic profiles of LUAD cell lines. SRGN , a chondroitin sulfate proteoglycan Serglycin, was identified as a markedly overexpressed gene in TTF-1–negative LUAD. We therefore investigated the roles and regulation of SRGN in TTF-1–negative LUAD. Proteomic and metabolomic analyses of 41 LUAD cell lines were done using mass spectrometry. The function of SRGN was investigated in 3 TTF-1–negative and 4 TTF-1–positive LUAD cell lines and in a syngeneic mouse model (n = 5 to 8 mice per group). Expression of SRGN was evaluated in 94 and 105 surgically resected LUAD tumor specimens using immunohistochemistry. All statistical tests were 2-sided. SRGN was markedly overexpressed at mRNA and protein levels in TTF-1–negative LUAD cell lines (P < .001 for both mRNA and protein levels). Expression of SRGN in LUAD tumor tissue was associated with poor outcome (hazard ratio = 4.22, 95% confidence interval = 1.12 to 15.86, likelihood ratio test, P = .03), and with higher expression of Programmed cell death 1 ligand 1 (PD-L1) in tumor cells and higher infiltration of Programmed cell death protein 1–positive lymphocytes. SRGN regulated expression of PD-L1 as well as proinflammatory cytokines, including Interleukin-6, Interleukin-8, and C-X-C motif chemokine 1 in LUAD cell lines; increased migratory and invasive properties of LUAD cells and fibroblasts; and enhanced angiogenesis. SRGN was induced by DNA demethylation resulting from Nicotinamide N-methyltransferase–mediated impairment of methionine metabolism. Our findings suggest that SRGN plays a pivotal role in tumor–stromal interaction and reprogramming into an aggressive and immunosuppressive tumor microenvironment in TTF-1–negative LUAD.

Background: Osimertinib—the third-generation epidermal growth factor receptor (EGFR)-tyrosine kinase inhibitor (TKI)—has been widely used as a first-line treatment for patients with metastatic EGFR-mutant non-small cell lung cancer (NSCLC). Osimertinib demonstrated central nervous system activity in patients with brain metastasis; however, its efficacy against other distant metastatic organs, including bone and liver, remains unclear. Therefore, we retrospectively analyzed the clinical efficacy of osimertinib in these patients in comparison to other EGFR-TKIs. Methods: Clinical data of patients with advanced NSCLC receiving gefitinib/erlotinib (n = 183), afatinib (n = 55), or osimertinib (n = 150) at five medical institutions were retrospectively assessed for progression-free survival (PFS), overall survival (OS), and best overall response rate (ORR). Results: In univariate and multivariate analyses, most distant metastases, including the brain and bone, were unrelated to the therapeutic efficacy of osimertinib, although liver metastasis and L858R mutation were independently associated with shorter PFS. PFS and OS in patients with liver metastases were significantly shorter than those in patients without liver metastases (PFS: 7.4 vs. 19.7 months, OS: 12.1 months vs. not reached, respectively). Osimertinib provided significantly longer PFS in patients with brain or bone metastasis and exon 19 deletion than the other EGFR-TKIs. The PFS of patients with liver metastases was not significantly different among the three EGFR-TKI groups. Furthermore, the ORR of osimertinib in patients with liver metastases was significantly attenuated, and the effectiveness was similar to 1^st- or 2^nd -generation EGFR-TKIs. Conclusion: Osimertinib provided better clinical benefits than 1^st- and 2^nd-generation EGFR-TKIs for patients with EGFR-mutant NSCLC, particularly those with brain or bone metastases and exon 19 deletion; however, its efficacy against liver metastasis was remarkably attenuated. New therapeutic developments for patients with EGFR-mutant NSCLC with liver metastases are needed.

Activation of the NRF2 pathway through gain-of-function mutations or loss-of-function of its suppressor KEAP1 is a frequent finding in lung cancer. NRF2 activation has been reported to alter the tumor microenvironment. Here, we demonstrated that NRF2 alters tryptophan metabolism through the kynurenine pathway that is associated with a tumor-promoting, immune suppressed microenvironment. Specifically, proteomic profiles of 47 lung adenocarcinoma (LUAD) cell lines (11 KEAP1 mutant and 36 KEAP1 wild-type) revealed the tryptophan-kynurenine enzyme kynureninase (KYNU) as a top overexpressed protein associated with activated NRF2. The siRNA-mediated knockdown of NFE2L2, the gene encoding for NRF2, or activation of the NRF2 pathway through siRNA-mediated knockdown of KEAP1 or via chemical induction with the NRF2-activator CDDO-Me confirmed that NRF2 is a regulator of KYNU expression in LUAD. Metabolomic analyses confirmed KYNU to be enzymatically functional. Analysis of multiple independent gene expression datasets of LUAD, as well as a LUAD tumor microarray demonstrated that elevated KYNU was associated with immunosuppression, including potent induction of T-regulatory cells, increased levels of PD1 and PD-L1, and resulted in poorer survival. Our findings indicate a novel mechanism of NRF2 tumoral immunosuppression through upregulation of KYNU.

Comprehensive analysis of omics data, such as genome, transcriptome, proteome, metabolome, and interactome, is a crucial technique for elucidating the complex mechanism of cancer onset and progression. Recently, a variety of new findings have been reported based on multi-omics analysis in combination with various clinical information. However, integrated analysis of multi-omics data is extremely labor intensive, making the development of new analysis technology indispensable. Artificial intelligence (AI), which has been under development in recent years, is quickly becoming an effective approach to reduce the labor involved in analyzing large amounts of complex data and to obtain valuable information that is often overlooked in manual analysis and experiments. The use of AI, such as machine learning approaches and deep learning systems, allows for the efficient analysis of massive omics data combined with accurate clinical information and can lead to comprehensive predictive models that will be desirable for further developing individual treatment strategies of immunotherapy and molecular target therapy. Here, we aim to review the potential of AI in the integrated analysis of omics data and clinical information with a special focus on recent advances in the discovery of new biomarkers and the future direction of personalized medicine in non-small lung cancer.

Using a combination of mass-spectrometry and aptamer array-based proteomics, we characterized the protein features of circulating extracellular vesicles (EVs) in the context of lung (LUAD) and pancreatic ductal (PDAC) adenocarcinomas. We profiled EVs isolated from conditioned media of LUAD and PDAC cell lines to identify EV-associated protein cargoes released by these cancer cell types. Analysis of the resulting data identified LUAD and PDAC specific and pan-adenocarcinoma EV protein signatures. Bioinformatic analyses confirmed enrichment of proteins annotated to vesicle-associated processes and intracellular compartments, as well as representation of cancer hallmark functions and processes. Analysis of upstream regulator networks indicated significant enrichment of TP53, MYC, TGFB1 and KRAS-driven network effectors (p = 1.69 × 10⁻⁷⁷–2.93 × 10⁻⁴⁹) manifest in the adenocarcinoma sEV protein cargoes. We extended these findings by profiling the proteome of EVs isolated from lung (N = 15) and pancreatic ductal (N = 6) adenocarcinoma patient plasmas obtained at time of diagnosis, along with EVs derived from matched healthy controls (N = 21). Exploration of these proteomic data revealed abundant protein features in the plasma EVs with capacity to distinguish LUAD and PDAC cases from controls, including features yielding higher performance in the plasma EV isolates relative to unfractionated plasmas.