KRAS-mutant non-small cell lung cancer (NSCLC) therapy based on tepotinib and omeprazole combination.

BACKGROUND: KRAS-mutant non-small cell lung cancer (NSCLC) shows a relatively low response rate to chemotherapy, immunotherapy and KRAS-G12C selective inhibitors, leading to short median progression-free survival, and overall survival. The MET receptor tyrosine kinase (c-MET), the cognate receptor of hepatocyte growth factor (HGF), was reported to be overexpressed in KRAS-mutant lung cancer cells leading to tumor-growth in anchorage-independent conditions. METHODS: Cell viability assay and synergy analysis were carried out in native, sotorasib and trametinib-resistant KRAS-mutant NSCLC cell lines. Colony formation assays and Western blot analysis were also performed. RNA isolation from tumors of KRAS-mutant NSCLC patients was performed and KRAS and MET mRNA expression was determined by real-time RT-qPCR. In vivo studies were conducted in NSCLC (NCI-H358) cell-derived tumor xenograft model. RESULTS: Our research has shown promising activity of omeprazole, a V-ATPase-driven proton pump inhibitor with potential anti-cancer properties, in combination with the MET inhibitor tepotinib in KRAS-mutant G12C and non-G12C NSCLC cell lines, as well as in G12C inhibitor (AMG510, sotorasib) and MEK inhibitor (trametinib)-resistant cell lines. Moreover, in a xenograft mouse model, combination of omeprazole plus tepotinib caused tumor growth regression. We observed that the combination of these two drugs downregulates phosphorylation of the glycolytic enzyme enolase 1 (ENO1) and the low-density lipoprotein receptor-related protein (LRP) 5/6 in the H358 KRAS G12C cell line, but not in the H358 sotorasib resistant, indicating that the effect of the combination could be independent of ENO1. In addition, we examined the probability of recurrence-free survival and overall survival in 40 early lung adenocarcinoma patients with KRAS G12C mutation stratified by KRAS and MET mRNA levels. Significant differences were observed in recurrence-free survival according to high levels of KRAS mRNA expression. Hazard ratio (HR) of recurrence-free survival was 7.291 (p = 0.014) for high levels of KRAS mRNA expression and 3.742 (p = 0.052) for high MET mRNA expression. CONCLUSIONS: We posit that the combination of the V-ATPase inhibitor omeprazole plus tepotinib warrants further assessment in KRAS-mutant G12C and non G12C cell lines, including those resistant to the covalent KRAS G12C inhibitors.

Combinatorial Blood Platelets-Derived circRNA and mRNA Signature for Early-Stage Lung Cancer Detection.

Despite the diversity of liquid biopsy transcriptomic repertoire, numerous studies often exploit only a single RNA type signature for diagnostic biomarker potential. This frequently results in insufficient sensitivity and specificity necessary to reach diagnostic utility. Combinatorial biomarker approaches may offer a more reliable diagnosis. Here, we investigated the synergistic contributions of circRNA and mRNA signatures derived from blood platelets as biomarkers for lung cancer detection. We developed a comprehensive bioinformatics pipeline permitting an analysis of platelet-circRNA and mRNA derived from non-cancer individuals and lung cancer patients. An optimal selected signature is then used to generate the predictive classification model using machine learning algorithm. Using an individual signature of 21 circRNA and 28 mRNA, the predictive models reached an area under the curve (AUC) of 0.88 and 0.81, respectively. Importantly, combinatorial analysis including both types of RNAs resulted in an 8-target signature (6 mRNA and 2 circRNA), enhancing the differentiation of lung cancer from controls (AUC of 0.92). Additionally, we identified five biomarkers potentially specific for early-stage detection of lung cancer. Our proof-of-concept study presents the first multi-analyte-based approach for the analysis of platelets-derived biomarkers, providing a potential combinatorial diagnostic signature for lung cancer detection.

Deterministic reprogramming and signaling activation following targeted therapy in non-small cell lung cancer driven by mutations or oncogenic fusions.

INTRODUCTION: Targeted therapy is used to treat lung adenocarcinoma caused by epidermal growth factor receptor (EGFR) mutations in the tyrosine kinase domain and rare subtypes (<5%) of non-small cell lung cancer. These subtypes include fusion oncoproteins like anaplastic lymphoma kinase (ALK), ROS1, rearranged during transfection (RET), and other receptor tyrosine kinases (RTKs). The use of diverse selective oral inhibitors, including those targeting rat sarcoma viral oncogene homolog (KRAS) mutations, has significantly improved clinical responses, extending progression-free and overall survival. AREAS COVERED: Resistance remains a critical issue in lung adenocarcinoma, notably in EGFR mutant, echinoderm microtubule associated protein-like 4 (EML4)-ALK fusion, and KRAS mutant tumors, often associated with epithelial-to-mesenchymal transition (EMT). EXPERT OPINION: Despite advancements in next generation EGFR inhibitors and EML4-ALK therapies with enhanced brain penetrance and identifying resistance mutations, overcoming resistance has not been abated. Various strategies are being explored to overcome this issue to achieve prolonged cancer remission and delay resistance. Targeting yes-associated protein (YAP) and the mechanisms associated with YAP activation through Hippo-dependent or independent pathways, is desirable. Additionally, the exploration of liquid-liquid phase separation in fusion oncoproteins forming condensates in the cytoplasm for oncogenic signaling is a promising field for the development of new treatments.

HMGB1 Expression Levels Correlate with Response to Immunotherapy in Non-Small Cell Lung Cancer.

Purpose: High-mobility group box 1 protein (HMGB1) is subject to exportin 1 (XPO1)-dependent nuclear export, and it is involved in functions implicated in resistance to immunotherapy. We investigated whether HMGB1 mRNA expression was associated with response to immune checkpoint inhibitors (ICI) in non-small cell lung cancer (NSCLC). Patients and Methods: RNA was isolated from pretreatment biopsies of patients with advanced NSCLC treated with ICI. Gene expression analysis of several genes, including HMGB1, was conducted using the NanoString Counter analysis system (PanCancer Immune Profiling Panel). Western blotting analysis and cell viability assays in EGFR and KRAS mutant cell lines were carried out. Evaluation of the antitumoral effect of ICI in combination with XPO1 blocker (selinexor) and trametinib was determined in a murine Lewis lung carcinoma model. Results: HMGB1 mRNA levels in NSCLC patients treated with ICI correlated with progression-free survival (PFS) (median PFS 9.0 versus 18.0 months, P=0.008, hazard ratio=0.30 in high versus low HMGB1). After TNF-α stimulation, HMGB1 accumulates in the cytoplasm of PC9 cells, but this accumulation can be prevented by using selinexor or antiretroviral drugs. Erlotinib or osimertinib with selinexor in EGFR-mutant cells and trametinib plus selinexor in KRAS mutant abolish tumor cell proliferation. Selinexor with a PD-1 inhibitor with or without trametinib abrogates the tumor growth in the murine Lewis lung cancer model. Conclusion: An in-depth exploration of the functions of HMGB1 mRNA and protein is expected to uncover new potential targets and provide a basis for treating metastatic NSCLC in combination with ICI.

Brief Report: circRUNX1 as Potential Biomarker for Cancer Recurrence in EGFR Mutation-Positive Surgically Resected NSCLC.

Introduction: As recently evidenced by the ADAURA trial, most patients with stages IB to IIIA of resected EGFR-mutant lung adenocarcinoma benefit from osimertinib as adjuvant therapy. Nevertheless, predictive markers of response and recurrence are still an unmet need for more than 10% of these patients. Some circular RNAs (circRNAs) have been reported to play a role in tumor growth and proliferation. In this project, we studied circRNA expression levels in formalin-fixed, paraffin-embedded lung tumor samples to explore their biomarker potential and develop a machine learning (ML)-based signature that could predict the benefit of adjuvant EGFR tyrosine kinase inhibitors in patients with EGFR-mutant NSCLC. Methods: Patients with surgically resected EGFR mutant-positive, stages I to IIIB NSCLC were recruited from February 2007 to December 2015. Formalin-fixed, paraffin-embedded tumor samples were retrospectively collected from those patients with a follow-up period of more than or equal to 36 months (N = 76). Clinicopathologic features were annotated. Total RNA was purified and quantified prior nCounter processing with our circRNA custom panel. Data analysis and ML were performed taking into consideration circRNA expression levels and recurrence-free survival (RFS). RFS was defined from the day of surgery to the day when recurrence was detected radiologically or the death owing to any cause. Results: Of the 76 patients with EGFR mutation-positive NSCLC included in the study, 34 relapsed within 3 years after resection. The median age of the relapsing cohort was 71.5 (range: 49-89) years. Most patients were nonsmokers (n = 21; 61.8%) and of female sex (n = 21; 61.8%). Most cases (n = 17; 50%) presented an exon 21 mutation, whereas 15 and four patients had an exon 19 and exon 18 mutation, respectively. Differential expression analysis revealed that circRUNX1, along with circFUT8 and circAASDH, was up-regulated in relapsing patients (p < 0.05 and >2 fold-change). A ML-based circRNA signature predictive of recurrence in patients with EGFR mutation-positive NSCLC, comprising circRUNX1, was developed. Our final model including selected 6-circRNA signature with random forest algorithm was able to classify relapsing patients with an accuracy of 83% and an area under the receiver operating characteristic curve of 0.91.RFS was significantly shorter not only for the subgroup of patients with high versus low circRUNX1 expression but also for the group classified as recurrent by the ML circRNA signature when compared with those classified as nonrecurrent. Conclusions: Our findings suggest that circRUNX1 and the presented ML-developed signature could be novel tools to predict the benefit of adjuvant EGFR tyrosine kinase inhibitors with regard to RFS in patients with EGFR-mutant NSCLC. The training and validation phases of our ML signature will be conducted including bigger independent cohorts.

Digital multiplexed analysis of circular RNAs in FFPE and fresh non-small cell lung cancer specimens.

Although many studies highlight the implication of circular RNAs (circRNAs) in carcinogenesis and tumor progression, their potential as cancer biomarkers has not yet been fully explored in the clinic due to the limitations of current quantification methods. Here, we report the use of the nCounter platform as a valid technology for the analysis of circRNA expression patterns in non-small cell lung cancer (NSCLC) specimens. Under this context, our custom-made circRNA panel was able to detect circRNA expression both in NSCLC cells and formalin-fixed paraffin-embedded (FFPE) tissues. CircFUT8 was overexpressed in NSCLC, contrasting with circEPB41L2, circBNC2, and circSOX13 downregulation even at the early stages of the disease. Machine learning (ML) approaches from different paradigms allowed discrimination of NSCLC from nontumor controls (NTCs) with an 8-circRNA signature. An additional 4-circRNA signature was able to classify early-stage NSCLC samples from NTC, reaching a maximum area under the ROC curve (AUC) of 0.981. Our results not only present two circRNA signatures with diagnosis potential but also introduce nCounter processing following ML as a feasible protocol for the study and development of circRNA signatures for NSCLC.

Multiplex Analysis of CircRNAs from Plasma Extracellular Vesicle-Enriched Samples for the Detection of Early-Stage Non-Small Cell Lung Cancer.

BACKGROUND: The analysis of liquid biopsies brings new opportunities in the precision oncology field. Under this context, extracellular vesicle circular RNAs (EV-circRNAs) have gained interest as biomarkers for lung cancer (LC) detection. However, standardized and robust protocols need to be developed to boost their potential in the clinical setting. Although nCounter has been used for the analysis of other liquid biopsy substrates and biomarkers, it has never been employed for EV-circRNA analysis of LC patients. METHODS: EVs were isolated from early-stage LC patients (n = 36) and controls (n = 30). Different volumes of plasma, together with different number of pre-amplification cycles, were tested to reach the best nCounter outcome. Differential expression analysis of circRNAs was performed, along with the testing of different machine learning (ML) methods for the development of a prognostic signature for LC. RESULTS: A combination of 500 µL of plasma input with 10 cycles of pre-amplification was selected for the rest of the study. Eight circRNAs were found upregulated in LC. Further ML analysis selected a 10-circRNA signature able to discriminate LC from controls with AUC ROC of 0.86. CONCLUSIONS: This study validates the use of the nCounter platform for multiplexed EV-circRNA expression studies in LC patient samples, allowing the development of prognostic signatures.

Analysis of extracellular vesicle mRNA derived from plasma using the nCounter platform.

Extracellular vesicles (EVs) are double-layered phospholipid membrane vesicles that are released by most cells and can mediate intercellular communication through their RNA cargo. In this study, we tested if the NanoString nCounter platform can be used for the analysis of EV-mRNA. We developed and optimized a methodology for EV enrichment, EV-RNA extraction and nCounter analysis. Then, we demonstrated the validity of our workflow by analyzing EV-RNA profiles from the plasma of 19 cancer patients and 10 controls and developing a gene signature to differentiate cancer versus control samples. TRI reagent outperformed automated RNA extraction and, although lower plasma input is feasible, 500 µL provided highest total counts and number of transcripts detected. A 10-cycle pre-amplification followed by DNase treatment yielded reproducible mRNA target detection. However, appropriate probe design to prevent genomic DNA binding is preferred. A gene signature, created using a bioinformatic algorithm, was able to distinguish between control and cancer EV-mRNA profiles with an area under the ROC curve of 0.99. Hence, the nCounter platform can be used to detect mRNA targets and develop gene signatures from plasma-derived EVs.

Biological therapies in lung cancer treatment: using our immune system as an ally to defeat the malignancy.

INTRODUCTION: Biological therapies, with immunotherapy leading the field, have arisen as one of the quickest expanding areas of research for cancer treatment in the last few years. The clear benefits for patients are undeniable, satisfying the long-awaited necessity of a target-specific therapy. However, its full potential remains still unexploited due to a lack of response in a majority of patients and pending reliable biomarkers. AREAS COVERED: This review provides a summarizing view of the current biological therapies for lung cancer, focusing on immunotherapy - including immune checkpoint inhibitors, adoptive cell therapy and vaccines available in clinical/pre-clinical settings or currently in development. A thorough analysis of the technical and functional differences among all therapies is provided, along with a critical discussion of prospective treatments and potential biomarkers. EXPERT OPINION: The use of immunotherapy in the treatment of cancer has provided clear benefits for patients. Still, exploitation of the full potential of immune checkpoint inhibitors alone or in combination, or adoptive cell therapies is hampered by, amongst other reasons, the lack of reliable biomarkers and possible adverse immune effects. We postulate that the development of liquid biopsy-based diagnostics will help to overcome these limitations in the near future.

Osimertinib and pterostilbene in EGFR-mutation-positive non-small cell lung cancer (NSCLC).

Monotherapy with epidermal growth factor receptor (EGFR) tyrosine kinase inhibitors (TKIs) still leads to incomplete responses in most EGFR-mutation positive non-small cell lung cancer (NSCLC) patients, often due to acquired resistance through activation of parallel compensatory pathways. We have previously shown that co-targeting EGFR, signal transducer and activator of transcription 3 (STAT3), and Src-yes-associated protein 1 (YAP1) was highly synergistic in vitro and in vivo. In the present study, we treated EGFR-mutation positive cell lines with the combination of osimertinib plus a natural compound, pterostilbene, which has been reported to abrogate Src and STAT3 activation. Methods: Cell viability assays and immunoblotting were performed to reveal the mechanisms of action of pterostilbene, osimertinib and pterostilbene plus osimertinib in five EGFR-mutation positive NSCLC and one triple negative breast cancer (TNBC) cell lines. Results: Osimertinib plus pterostilbene yielded synergistic effects in all EGFR-mutation positive NSCLC cell lines investigated. Surprisingly, pterostilbene alone did not inhibit, nor downregulate Src phosphorylation in the EGFR-mutation positive NSCLC cell lines or the TNBC cell line, MDA-MB-231. However, the double combination of osimertinib plus pterostilbene reversed the osimertinib-induced STAT3, YAP1, and CUB domain-containing protein-1 (CDCP1) phosphorylation and slightly suppressed Src phosphorylation in PC9 and H1975 cells. Conclusion: The results of this study indicate that pterostilbene may be used to abrogate the activated resistance pathways of single osimertinib treatment in EGFR-mutation positive NSCLC. Future studies should focus on in vivo translation and confirmation of these results.