Small cell lung cancer (SCLC; accounting for approximately 13%–15% of all lung cancers) is an exceptionally lethal malignancy characterized by rapid doubling time and high propensity to metastasize. In contrast to the increasingly personalized therapies in other types of lung cancer, SCLC is still regarded as a homogeneous disease and the prognosis of SCLC patients remains poor. Recently, however, substantial progress has been made in our understanding of SCLC biology. Advances in genomics and development of new preclinical models have facilitated insights into the intratumoral heterogeneity and specific genetic alterations of this disease. This worldwide resurgence of studies on SCLC has ultimately led to the development of novel subtype-specific classifications primarily based on the neuroendocrine features and distinct molecular profiles of SCLC. Importantly, these biologically distinct subtypes might define unique therapeutic vulnerabilities. Herein, we summarize the current knowledge on the molecular profiles of SCLC subtypes with a focus on their potential clinical implications.

KRAS mutations are the most frequent gain-of-function alterations in patients with lung adenocarcinoma (LADC) in the Western world. Although they have been identified decades ago, prior efforts to target KRAS signaling with single-agent therapeutic approaches such as farnesyl transferase inhibitors, prenylation inhibition, impairment of KRAS downstream signaling, and synthetic lethality screens have been unsuccessful. Moreover, the role of KRAS oncogene in LADC is still not fully understood, and its prognostic and predictive impact with regards to the standard of care therapy remains controversial. Of note, KRAS-related studies that included general non-small cell lung cancer (NSCLC) population instead of LADC patients should be very carefully evaluated. Recently, however, comprehensive genomic profiling and wide-spectrum analysis of other co-occurring genetic alterations have identified unique therapeutic vulnerabilities. Novel targeted agents such as the covalent KRAS G12C inhibitors or the recently proposed combinatory approaches are some examples which may allow a tailored treatment for LADC patients harboring KRAS mutations. This review summarizes the current knowledge about the therapeutic approaches of KRAS-mutated LADC and provides an update on the most recent advances in KRAS-targeted anti-cancer strategies, with a focus on potential clinical implications.

This study aims to characterize tumor-infiltrating macrophages (TAMs), myeloid-derived suppressor cells (MDSC), and the related molecular milieu regulating anti-tumor immunity in limited-stage neuroendocrine (NE)-high and NE-low small cell lung cancer. Primary tumors and matched lymph node (LN) metastases of 32 resected, early-stage SCLC patients were analyzed by immunohistochemistry (IHC) with antibodies against pan-macrophage marker CD68, M2-macrophage marker CD163, and MDSC marker CD33. Area-adjusted cell counting on TMAs showed that TAMs are the most abundant cell type in the TME, and their number in tumor nests exceeds the number of CD3 + T-cells (64% vs. 38% in NE-low and 71% vs. 18% in NE-high). Furthermore, the ratio of CD163-expressing M2-polarized TAMs in tumor nests was significantly higher in NE-low vs. NE-high tumors (70% vs. 31%). TAM density shows a strong positive correlation with CD45 and CD3 in tumor nests, but not in the stroma. fGSEA analysis on a targeted RNAseq oncological panel of 2560 genes showed that NE-high tumors exhibited increased enrichment in pathways related to cell proliferation, whereas in NE-low tumors, immune response pathways were significantly upregulated. Interestingly, we identified a subset of NE-high tumors representing an immune-oasis phenotype, but with a different gene expression profile compared to NE-low tumors. In contrast, we found that a limited subgroup of NE-low tumors is immune-deserted and express distinct cellular pathways from NE-high tumors. Furthermore, we identified potential molecular targets based on our expression data in NE-low and immune-oasis tumor subsets, including CD70, ANXA1, ITGB6, TP63, IFI27, YBX3 and CXCR2.

Background: Although lung adenocarcinoma (LADC) with sensitizing mutations of the epidermal growth factor receptor (EGFR) is highly sensitive to EGFR tyrosine kinase inhibitors (EGFR-TKIs), in most cases disease progression inevitably occurs. Our aim was to investigate the predictive and prognostic significance of adjusted tumoral EGFR variant allele frequency (EGFR-aVAF) in the above setting. Methods: Eighty-nine Caucasian advanced-stage LADC patients with known exon-specific EGFR mutations undergoing EGFR-TKI treatment were included. The correlations of EGFR-aVAF with clinicopathological variables including progression-free and overall survival (PFS and OS, respectively) were retrospectively analyzed. Results: Of 89 EGFR-mutant LADC patients, 46 (51.7%) had exon 19 deletion, while 41 (46.1%) and 2 (2.2%) patients had exon 21- and exon 18-point mutations, respectively. Tumoral EGFR-aVAF was significantly higher in patients harboring EGFR exon 19 mutations than in those with exon 21-mutant tumors (P<0.001). Notably, patients with EGFR exon 19 mutant tumors demonstrated significantly improved PFS (P=0.003) and OS (P=0.02) compared to patients with exon 21 mutations. Irrespective of specific exon mutations, a statistically significant positive linear correlation was found between EGFR-aVAF of tumoral tissue and PFS (r=0.319; P=0.002). High (≥70%) EGFR-aVAF was an independent predictor of longer PFS [vs. low (<70%) EGFR-aVAF; median PFSs were 52 vs. 26 weeks, respectively; P<0.001]. Additionally, patients with high EGFR-aVAF also had significantly improved OS than those with low EGFR-aVAF (P=0.011). Conclusions: Our study suggests that high (≥70%) EGFR-aVAF of tumoral tissue predicts benefit from EGFR-TKI treatment in advanced LADC and, moreover, that exon 19 EGFR mutation is associated with high EGFR-aVAF and improved survival outcomes.

Purpose: Acquired chemoresistance is a frequent event in small cell lung cancer (SCLC), one of the deadliest human malignancies. Histone deacetylase inhibitors (HDACi) have been shown to synergize with different chemotherapeutic agents including cisplatin. Accordingly, we aimed to investigate the dual targeting of HDAC inhibition and chemotherapy in SCLC. Experimental Design: The efficacy of HDACi and chemotherapy in SCLC was investigated both in vitro and in vivo. Synergistic drug interactions were calculated based on the HSA model (Combenefit software). Results from the proteomic analysis were confirmed via ICP-MS, cell-cycle analysis, and comet assays. Results: Single entinostat- or chemotherapy significantly reduced cell viability in human neuroendocrine SCLC cells. The combination of entinostat with either cisplatin, carboplatin, irinotecan, epirubicin, or etoposide led to strong synergy in a subset of resistant SCLC cells. Combination treatment with entinostat and cisplatin significantly decreased tumor growth in vivo. Proteomic analysis comparing the groups of SCLC cell lines with synergistic and additive response patterns indicated alterations in cell-cycle regulation and DNA damage repair. Cell-cycle analysis revealed that cells exhibiting synergistic drug responses displayed a shift from G1 to S-phase compared with cells showing additive features upon dual treatment. Comet assays demonstrated more DNA damage and decreased base excision repair in SCLC cells more responsive to combination therapy. Conclusions: In this study, we decipher the molecular processes behind synergistic interactions between chemotherapy and HDAC inhibition. Moreover, we report novel mechanisms to overcome drug resistance in SCLC, which may be relevant to increasing therapeutic success.

This study aims to combine computed tomography (CT)-based texture analysis (QTA) and a microbiome-based biomarker signature to predict the overall survival (OS) of immune checkpoint inhibitor (ICI)-treated non-small cell lung cancer (NSCLC) patients by analyzing their CT scans (n = 129) and fecal microbiome (n = 58). One hundred and five continuous CT parameters were obtained, where principal component analysis (PCA) identified seven major components that explained 80% of the data variation. Shotgun metagenomics (MG) and ITS analysis were performed to reveal the abundance of bacterial and fungal species. The relative abundance of Bacteroides dorei and Parabacteroides distasonis was associated with long OS (>6 mo), whereas the bacteria Clostridium perfringens and Enterococcus faecium and the fungal taxa Cortinarius davemallochii, Helotiales, Chaetosphaeriales, and Tremellomycetes were associated with short OS (≤6 mo). Hymenoscyphus immutabilis and Clavulinopsis fusiformis were more abundant in patients with high (≥50%) PD-L1-expressing tumors, whereas Thelephoraceae and Lachnospiraceae bacterium were enriched in patients with ICI-related toxicities. An artificial intelligence (AI) approach based on extreme gradient boosting evaluated the associations between the outcomes and various clinicopathological parameters. AI identified MG signatures for patients with a favorable ICI response and high PD-L1 expression, with 84% and 79% accuracy, respectively. The combination of QTA parameters and MG had a positive predictive value of 90% for both therapeutic response and OS. According to our hypothesis, the QTA parameters and gut microbiome signatures can predict OS, the response to therapy, the PD-L1 expression, and toxicity in NSCLC patients treated with ICI, and a machine learning approach can combine these variables to create a reliable predictive model, as we suggest in this research.