FTO facilitates cancer metastasis by modifying the m6A level of FAP to induce integrin/FAK signaling in non-small cell lung cancer.

BACKGROUND: Emerging evidence suggests the critical roles of N6-methyladenosine (m6A) RNA modification in tumorigenesis and tumor progression. However, the role of m6A in non-small cell lung cancer (NSCLC) is still unclear. This study aimed to explore the role of the m6A demethylase fat mass and obesity-associated protein (FTO) in the tumor metastasis of NSCLC. METHODS: A human m6A epitranscriptomic microarray analysis was used to identify downstream targets of FTO. Quantitative real-time PCR (qRT‒PCR) and western blotting were employed to evaluate the expression levels of FTO and FAP in NSCLC cell lines and tissues. Gain-of-function and loss-of-function assays were conducted in vivo and in vitro to assess the effects of FTO and FAP on NSCLC metastasis. M6A-RNA immunoprecipitation (MeRIP), RNA immunoprecipitation (RIP), luciferase reporter assays, and RNA stability assays were used to explore the mechanism of FTO action. Co-immunoprecipitation (co-IP) assays were used to determine the mechanism of FAP in NSCLC metastasis. RESULTS: FTO was upregulated and predicted poor prognosis in patients with NSCLC. FTO promoted cell migration and invasion in NSCLC, and the FAK inhibitor defactinib (VS6063) suppressed NSCLC metastasis induced by overexpression of FTO. Mechanistically, FTO facilitated NSCLC metastasis by modifying the m6A level of FAP in a YTHDF2-dependent manner. Moreover, FTO-mediated metastasis formation depended on the interactions between FAP and integrin family members, which further activated the FAK signaling. CONCLUSION: Our current findings provided valuable insights into the role of FTO-mediated m6A demethylation modification in NSCLC metastasis. FTO was identified as a contributor to NSCLC metastasis through the activation of the FAP/integrin/FAK signaling, which may be a potential therapeutic target for NSCLC. Video Abstract.

Emerging evidence suggests the crucial roles of N⁶-methyladenosine (m⁶A) RNA modification in tumorigenesis and progression. Nonetheless, the role of m⁶A in NSCLC remains unclear. The purpose of this study was to investigate the role of m⁶A demethylase fat mass and obesity-associated protein (FTO) in the tumor metastasis of non-small cell lung cancer (NSCLC). Results illustrated that FTO was upregulated and predicted poor prognosis in NSCLC patients. FTO promoted cell migration and invasion in NSCLC, and the FAK inhibitor defactinib (VS6063) suppressed NSCLC metastasis induced by overexpression of FTO. Mechanistically, FTO facilitated NSCLC metastasis by modifying the m⁶A level of FAP in a YTHDF2-dependent manner. Moreover, FTO-mediated metastasis formation depended on the interactions between FAP and integrin family members, which further activated the FAK signaling. Our current findings provided valuable insights into the role of FTO-mediated m⁶A demethylation modification in NSCLC metastasis. FTO was identified as a contributor to NSCLC metastasis through the activation of the FAP/integrin/FAK signaling, which may be a potential therapeutic target for NSCLC.

The role of targeted therapy and immune therapy in the management of non-small cell lung cancer brain metastases.

Brain metastases are a significant source of morbidity and mortality in patients with non-small cell lung cancer. Historically, surgery and radiation therapy have been essential to maintaining disease control within the central nervous system due to poorly penetrant conventional chemotherapy. With the advent of targeted therapy against actionable driver mutations, there is potential to control limited and asymptomatic intracranial disease and delay local therapy until progression. In this review paper, intracranial response rates and clinical outcomes to biological and immune therapies are summarized from the literature and appraised to assist clinical decision making and identify areas for further research. Future clinical trials ought to prioritize patient-centered quality of life and neurocognitive measures as major outcomes and specifically stratify patients based on mutational marker status, disease burden, and symptom acuity.

3D deep learning versus the current methods for predicting tumor invasiveness of lung adenocarcinoma based on high-resolution computed tomography images.

Background: Different pathological subtypes of lung adenocarcinoma lead to different treatment decisions and prognoses, and it is clinically important to distinguish invasive lung adenocarcinoma from preinvasive adenocarcinoma (adenocarcinoma in situ and minimally invasive adenocarcinoma). This study aims to investigate the performance of the deep learning approach based on high-resolution computed tomography (HRCT) images in the classification of tumor invasiveness and compare it with the performances of currently available approaches. Methods: In this study, we used a deep learning approach based on 3D conventional networks to automatically predict the invasiveness of pulmonary nodules. A total of 901 early-stage non-small cell lung cancer patients who underwent surgical treatment at Shanghai Chest Hospital between November 2015 and March 2017 were retrospectively included and randomly assigned to a training set (n=814) or testing set 1 (n=87). We subsequently included 116 patients who underwent surgical treatment and intraoperative frozen section between April 2019 and January 2020 to form testing set 2. We compared the performance of our deep learning approach in predicting tumor invasiveness with that of intraoperative frozen section analysis and human experts (radiologists and surgeons). Results: The deep learning approach yielded an area under the receiver operating characteristic curve (AUC) of 0.946 for distinguishing preinvasive adenocarcinoma from invasive lung adenocarcinoma in the testing set 1, which is significantly higher than the AUCs of human experts (P<0.05). In testing set 2, the deep learning approach distinguished invasive adenocarcinoma from preinvasive adenocarcinoma with an AUC of 0.862, which is higher than that of frozen section analysis (0.755, P=0.043), senior thoracic surgeons (0.720, P=0.006), radiologists (0.766, P>0.05) and junior thoracic surgeons (0.768, P>0.05). Conclusions: We developed a deep learning model that achieved comparable performance to intraoperative frozen section analysis in determining tumor invasiveness. The proposed method may contribute to clinical decisions related to the extent of surgical resection.

Hypoalbuminaemia as a Prognostic Biomarker of First-Line Treatment Resistance in Metastatic Non-small Cell Lung Cancer.

Introduction: Despite significant advances in systemic anticancer therapy (SACT) for non-small cell lung cancer (NSCLC), many patients still fail to respond to treatment or develop treatment resistance. Albumin, a biomarker of systemic inflammation and malnutrition, predicts survival in many cancers. We evaluated the prognostic significance of albumin in patients receiving first-line targeted therapy or immunotherapy-based SACT for metastatic NSCLC. Methods: All patients treated with first-line targeted therapy or immunotherapy-based SACT for metastatic NSCLC at a regional Scottish cancer centre were identified. Serum albumin at pre-treatment, after 12-weeks of treatment, and at the time of progressive disease were recorded. The relationship between albumin (≥ 35g/L v <35g/L) and overall survival (OS) was examined. Results: Data were available for 389 patients of both targeted therapy cohort (n = 159) and immunotherapy-based therapy cohort (n = 230). Pre-treatment albumin was predictive of OS in each cohort at HR1.82 (95%CI 1.23-2.7) (p =0.003) and HR2.55 (95%CI 1.78-3.65) (p < 0.001), respectively. Pre-treatment albumin <35 g/L was associated with a significantly higher relative risk of death within 12 weeks in each cohort at RR9.58 (95%CI 2.20-41.72, p = 0.003) and RR3.60 (95%CI 1.74-6.57, p < 0.001), respectively. The 12-week albumin was predictive of OS in each cohort at HR1.88 (95%CI 1.86-4.46) (p < 0.001) and HR2.67 (95%CI 1.74-4.08) (p < 0.001), respectively. 46 out of 133 (35%) evaluable patients treated with targeted therapy and 43 out of 169 (25%) treated with immunotherapy-based therapy crossed over albumin prognostic groups between pre-treatment and 12-week. The prognostic value of 12-week albumin was independent of pre-treatment albumin status. A majority of patients had albumin <35g/L at the time of progressive disease when it was also predictive of survival following progressive disease at HR2.48 (95%CI 1.61-3.82) (p < 0.001) and HR2.87 (95%CI 1.91-4.31) (p < 0.001) respectively). Conclusions: Albumin is a reliable prognostic factor in patients with metastatic NSCLC, predicting survival independent of the class of drug treatment at various time points during the patient journey. Tracking albumin concentrations during systemic therapy may indicate disease activity or treatment response over time.

Selective versus systematic lymph node dissection (other than sampling) for clinical N2-negative non-small cell lung cancer: a meta-analysis of observational studies.

BACKGROUND: The proper extent of lymph node dissection is still controversial. Hence, we compared the clinical efficacy between two strategies of lymph node dissection [selective lymph node dissection (SLND) and systematic lymph node dissection (LND)] for clinical N2-negative non-small cell lung cancer (NSCLC) patients. METHODS: After searching five databases, six cohort studies were eligible for this meta-analysis and the primary endpoint was overall survival (OS). In order to provide a comprehensive perspective, we estimated some perioperative outcomes as well. Either fixed effect or random effects model were properly selected to evaluate the data according to the heterogeneity of included studies. RESULTS: A total of 7,333 patients with clinical N2-negative NSCLC patients were analyzed for OS. The pooled results demonstrated that LND did not improve survival in OS [hazard ratio (HR) =1.05, 95% confidence interval (CI): 0.82-1.34, P=0.69] compared with SLND. In accordance with OS, there is no significant difference in DFS between LND and SLND (HR =0.98, 95% CI: 0.78-1.23, P=0.87). Moreover, SLND could significantly reduce the operative time [mean difference (MD) =-21.45, 95% CI: -29.53 to -13.36, P<0.001] and blood loss (MD =-28.88, 95% CI: -44.38 to -13.39, P<0.001). Both postoperative morbidity and recurrence showed no significant between two groups. CONCLUSIONS: SLND is an alternative to LND for clinical N2-negative NSCLC patients, which may even provide clinical benefits. However, more randomized controlled trials (RCTs) are expected to determine whether SLND is valid and practical to become a standard procedure of surgical treatment for early-stage NSCLC patients.

Predictive markers for anti-PD-1/PD-L1 therapy in non-small cell lung cancer-where are we?

The selection of patients for immunotherapy remains challenging given the lack of highly specific and highly sensitive biomarkers. Nevertheless, it is essential that testing laboratories are able to fulfil licencing criteria by providing the tests which have been validated as providing useful predictive information. Programmed cell death protein 1 (PD-1) expression assessment is now established in routine practice, although the situation regarding the selection of a particular assay remains complex, and testing protocols are likely to change in future. It is probable that programmed death-ligand 1 (PD-L1) expression assessment will be supplemented in the near future by tumour mutation burden (TMB), but this will require novel solutions to allow testing to be completed using small tissue samples and within narrow timeframes. While DNA mismatch repair (MMR) testing and CD8 T-cell density may also have a role to play in predicting immunotherapy response, their roles are not well-defined at present. Above all, the main challenge facing laboratories will be to perform this multitude of tests alongside the molecular markers already established in clinical practice [e.g., epidermal growth factor receptor (EGFR) mutation, anaplastic lymphoma kinase (ALK) translocation, ROS proto-oncogene 1 (ROS1) translocation]; the challenge for pathologists and clinicians will be to develop algorithms which will integrate the complex set of results from these tests and provide clinically-useful management regimens.