Large Cell Carcinoma Research Articles

“Crosstalk between non-coding RNAs and transcription factor LRF in non-small cell lung cancer”

Epigenetic approaches in direct correlation with assessment of critical genetic mutations in non-small cell lung cancer (NSCLC) are currently very intensive, as the epigenetic components underlying NSCLC development and progression have attained high recognition. In this level of research, established human NSCLC cell lines as well as experimental animals are widely used to detect novel biomarkers and pharmacological targets to treat NSCLC. The epigenetic background holds a great potential for the identification of epi-biomarkers for treatment response however, it is highly complex and requires precise definition as these phenomena are variable between NSCLC subtypes and systems origin.We engaged an in-depth characterization of non-coding (nc)RNAs prevalent in human KRAS-mutant NSCLC cell lines A549 and H460 and mouse KRAS-mutant NSCLC tissue by Next Generation Sequencing (NGS) and quantitative Real Time PCRs (qPCRs). Also, the transcription factor (TF) LRF, a known epigenetic silencer, was examined as a modulator of non-coding RNAs expression. Finally, interacting networks underlying epigenetic variations in NSCLC subtypes were created. Data derived from our study highlights the divergent epigenetic profiles of NSCLC of human and mouse origin, as well as the significant contribution of 12qf1: 109,709,060–109,747,960 mouse chromosomal region to micro-RNA upregulated species. Furthermore, the novel epigenetic miR-148b-3p/lncPVT1/ZBTB7A axis was identified, which differentiates human cell line of lung adenocarcinoma from large cell lung carcinoma, two characteristic NSCLC subtypes.The detailed recording of epigenetic events in NSCLC and combinational studies including networking between ncRNAs and TFs validate the identification of significant epigenetic features, prevailing in NSCLC subtypes and among experimental models. Our results enrich knowledge in the field and empower research on the epigenetic prognostic biomarkers of the disease progression, NSCLC subtypes discrimination and advancement to patient-tailored treatments.

Abstract 3759: First clinical evidence of the tissue nanomechanical signature as a novel diagnostic and prognostic tool in patients with non-small-cell lung cancer

Abstract Introduction: Complex structural remodelling of cells and extracellular matrix during cancer initiation and progression elicit substantial biomechanical alterations, which can be measured by Atomic Force Microscopy (AFM) at the nanoscale level. Clinical and translational evidence indicates that tissue nanomechanical signature (NS) can be a useful tool for rapid cancer diagnosis, and assessment of cancer aggressiveness. We demonstrated that the AFM-based Automated and Reliable Tissue Diagnostics (ARTIDIS) platform represents a valid method for rapid diagnosis, prognosis, and treatment response assessment in patients with breast cancer. Here we aimed to use this platform to examine tumour biopsies from non-small cell lung cancer (NSCLC) patients for the first time. Methods: This is an ongoing, single center study at the Hospital Clinic of Barcelona and University of Barcelona (UHB) Spain, aiming to enroll 75 patients by January 2024 in the first phase, followed by 200 patients in the expansion phase. To date, 54 matching tumor and non-tumor fresh tissue biopsies were taken by experienced pathologists from fresh surgical resection specimens from 27 patients with previously diagnosed NSCLC. Tissue biopsies were measured with the ARTIDIS device in a blinded manner, without knowing the malignancy status of the biopsy. Routine clinico-pathologic parameters have been collected. The multiparametric nanomechanical signature has been analyzed in an automated manner using the proprietary AI-based ARTIDISNET software platform together with available clinico-pathologic parameters. Results: In total, the NSCLC tissue samples included 16 squamous cell carcinoma (SCC), 6 Adenocarcinoma (ADC), 1 large cell carcinoma (LCC) and 4 other histological subtypes. Non-tumor tissues exhibited significantly varied composition, including normal lung parenchyma, bronchial tissues, fibrosis and inflammatory changes. Among the 27 patients, 5 patients developed distant progression within 12 months after ARTIDIS measurements. Results analysis showed that the ARTIDIS NS can reliably differentiate malignant NSCLC lesions from non-tumor lung tissue, with a sensitivity of 0.84, specificity of 0.81, positive predictive value (PPV) of 0.79, negative predictive value (NPV) of 0.86 and accuracy of 0.82. In addition, the ARTIDIS NS can differentiate patients with and without distant progression with a sensitivity of 0.62, specificity of 0.81, PPV of 0.42, NPV of 0.90 and accuracy of 0.77. Conclusions: Here we demonstrated first clinical evidence of the ARTIDIS tissue NS as a novel diagnostic and prognostic tool in patients with NSCLC. This study aims to establish the diagnostic, prognostic, and predictive NS in patients NSCLC. In addition to local expansion at the UHB, study results will be further validated in ARTIDIS clinical program at MD Anderson Cancer Centre, USA. Citation Format: Jordi Alcaraz, Héctor Sanz-Fraile, Elba Marin, Paula Gausa Busquets, Kate Neal, Marselina Arshakyan, Marc Rico, Marc Boada, David Sanchez, Reinier Oropesa Nunez, Gitika Srivastava, Ahmed Jizawi, Sara Nizzero, Tobias Appenzeller, Mariam Gachechiladze, Marko Loparic, Marija Plodinec, Noemi Reguart. First clinical evidence of the tissue nanomechanical signature as a novel diagnostic and prognostic tool in patients with non-small-cell lung cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 3759.

Abstract 5434: A novel combination therapy for lung squamous cell carcinoma

Abstract Lung cancer is the leading cause of cancer deaths in the US. Non-small cell lung cancer (NSCLC) accounts for ~80% of lung cancer cases and is further classified into adenocarcinoma (LUAD), squamous cell carcinoma (LUSC), and large cell carcinoma. New approaches targeting pathways in NSCLC have emerged, resulting in encouraging new treatments that benefit LUAD patients, but very few advances have been made in the treatment options for LUSC which accounts for ~30% of lung cancer cases. NSCLCs, including LUSCs, contain a sub-population of cells that exhibit properties of cancer stem cells (CSCs) and are responsible for tumor initiation, maintenance, therapeutic resistance, and relapse. Therefore, CSCs must be effectively targeted to elicit long lasting therapeutic results in LUSC patients. Our studies have shown that: 1) PKCι is an oncogene and prognostic marker in LUSC; 2) copy number gains (CNGs) of the PKCι gene (PRKCI) drives overexpression of PKCι in 90% of LUSCs; 3) PKCι phosphorylates SOX2, and controls SOX2-mediated transcriptional activation of Hedgehog Acyl Transferase, resulting in Hedgehog (Hh) pathway activation that drives a CSC phenotype in LUSC cells; and 4) the FDA approved compound Auranofin (ANF) is a potent inhibitor of oncogenic PKCι. In this study, we assessed the effect of ANF in combination with the Hh pathway inhibitor, LDE225, on LUSC CSCs and tumor growth. Cell viability, sphere formation and soft agar assays were used to assess the effects of ANF and LDE225 alone or in combination on the growth of LUSC CSCs with PRKCI CNGs or LUAD and LUSC CSCs without PRKCI CNG to determine if PRKCI CNG can be used to predict response to ANF and LDE225 and test the selectivity of response for LUSC. The effects of ANF and LDE225 on the levels of PKCι-Hh signaling components was assessed by qPCR and western blotting. Finally, the efficacy of ANF and LDE225 in combination was evaluated in human primary LUSC patient-derived xenograft (PDX) models that harbor PRKCI CNGs and the ability of PKCι-Hh signaling intermediates to serve as biomarkers of PKCι-Hh pathway inhibition was validated in vivo. We observed a dose-dependent decrease in cell proliferation, clonal expansion and transformed growth in CSCs treated with ANF and LDE225 alone. ANF and LDE225 combined treatment of LUSC CSCs with PRKCI CNGs (but not LUAD or LUSC CSCs without PRKCI CNG) synergistically inhibited CSC phenotypes based on results of combination index analysis. Furthermore, treatment of LUSC PDX models with ANF and LDE225 in combination had a synergistic effect on tumor growth inhibition. Lastly, ANF and LDE225 treatment led to inhibition of PKCι-Hh signaling intermediates demonstrating the on-target effects of treatment. Our data indicate that a PKCι-Hh vertical blockade strategy of combined ANF and LDE225 effectively targets LUSC CSCs and blocks tumor growth in vivo and provides compelling rationale to develop this novel combination strategy further in clinical trials. Citation Format: Dania Alqasrawi, Ryan Argo, Christine Ratliff Rang, Prita Pandya, Skyeler Klinge, Jennifer Lindemann, Nyla Searl, Alan Fields, Verline Justilien. A novel combination therapy for lung squamous cell carcinoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 5434.

Application of Several Transfer Learning Approach for Early Classification of Lung Cancer


 INTRODUCTION: Lung cancer, a fatal disease characterized by abnormal cell growth, ranks as the second most lethal worldwide, as observed in recent research conducted in India and other regions. Early detection is crucial for effective treatment, and manual differentiation of nodule types in CT images poses challenges for radiologists.
 OBJECTIVES: To enhance accuracy and efficiency, deep learning algorithms are proposed for early lung cancer detection. Transfer learning-based computer recognition algorithms have shown promise in providing radiologists with additional insights.
 METHODS: The dataset used in this study comprises 1000 CT scan images representing lung large cell carcinoma, lung adenocarcinoma, lung squamous cell carcinoma, and normal lung cases. A preprocessing phase, including picture rescaling and modification, is applied to the input CT scan images of the lungs, followed by the utilization of a specific transfer learning model to develop a lung cancer detection system.
 RESULTS: The performance of various transfer learning strategies is evaluated using measures such as accuracy, precision, recall, specificity, area under the curve, and F1-score.
 CONCLUSION: Comparative analysis indicates that VGG16 outperforms other models in accurately categorizing different types of lung cancer.

Lung cancer histopathology image classification using transfer learning with convolution neural network model.

Lung cancer (LC) is a harmful malignant tumor and potentially lethal illness. Therefore, early detection of LC is an urgent need, and dependent on the type of histology and the type of disease. The use of deep learning algorithms (DL) is required to analyse the histopathology images of LC and make treatment decisions accordingly. This study aimed to apply pretrained EfficientNetB7 model to facilitate the process of classifying LC histopathology images as primary malignancy categories (adenocarcinoma, squamous cell carcinoma and large cell carcinoma) for early treatment of LC patients. Also, aims to analyse the performance of the proposed model using the accuracy measure. The dataset of 15000 histopathology images of lung cancer were examined. EfficientNetB7, a special type of convolution neural network (CNN), pretrained with ImageNet for transfer learning were trained on this dataset. Accuracy metric was used for the evaluation of the proposed model The feature extraction was performed by applying transfer learning using EfficientNetB7 as pretrained model. The proposed model achieved 99.77% accuracy, while previous studies model achieved over 90 to 99% accuracy. The employment of CNN based EfficientNetB7 model for the classification of LC based on histopathology images can speed up the diagnosis of LC and reduce the burden on pathologists for the early treatment of patients.

The Clinical Value of Pre-Diagnostic Thrombocytosis for the Detection of Lung Cancer in Primary Care.

Thrombocytosis is a risk marker for lung cancer in primary care. We investigated whether thrombocytosis presents pre-diagnostically for all the histological subtypes of lung cancer and its association with the stage at diagnosis. A matched cohort study used English electronic primary care data linked to the national cancer registry. Patients diagnosed with lung cancer aged ≥40 years with no prior history of malignancy were matched by age, sex, and general practice to five controls without lung cancer. Multivariable logistic regression models quantified the incidence of pre-diagnostic thrombocytosis and advanced-stage diagnoses, adjusting for COPD diagnosis, smoking status, and anti-platelet drug prescriptions. A total of 9504 cases were matched to 45,647 controls, consisting of 3260 (34%) adenocarcinomas (ADC), 2020 (21%) squamous cell carcinomas (SCC), 70 (<1%) large-cell carcinomas (LCC), and 1089 (12%) small-cell lung cancers (SCLC). The patients with lung cancer were 8.9 (95% CI 8.0-9.9) times more likely to exhibit pre-diagnostic thrombocytosis than the controls. The odds ratios were highest for the comparison between SCC and ADC (1.8, 95% CI 1.5-2.1). Thrombocytosis is associated with advanced-stage ADC and SCC but presented equally for early- and advanced-stage SCLC. Pre-diagnostic thrombocytosis may aid in the detection of all the histological subtypes in primary care.

Myriad Presentation of Adenocarcinoma Lung

Lung adenocarcinoma is the leading cause of cancer related deaths worldwide. The causes for adenocarcinoma include smoking, air pollution, family history, occupational exposure, silica, asbestos, diesel fumes, and heavy metals. Smoking increases lung cancer risk by 5- to 10 fold with a clear dose–response relationship and environmental tobacco smoke among non-smokers increases lung cancer risk by about 20 %.[1] Lung cancers can be classified into two types based on histology as small cell carcinoma and non-small cell carcinoma. Adenocarcinoma falls under non-small cell carcinoma. Non-small cell carcinoma is further classified into adenocarcinoma, squamous cell carcinoma, and large cell carcinoma. Polyserositis is a condition where there is an inflammation of serous membranes with effusion. Most common sites are pleura and pericardium presenting with symptoms like chest pain, and shortness of breath.[2] The causes include idiopathic, infectious disease like tuberculosis, autoimmune conditions like SLE, and neoplasm. Neoplasm is the most common cause (nearly one-third of cases). Autoimmune disease like Adult-onset Still's disease presents with cardiac and pulmonary involvement pleural effusion, pericarditis and serositis.[3] A possible manifestation of systemic lupus erythematosus is extensive serous involvement like pericardial effusion and pleural effusion.[4] In this case report, we discuss the presentation of adenocarcinoma and pericardial effusion with liver metastasis in a 74-year-old female.

Association between Airport Ultrafine Particles and Lung Cancer Risk: The Multiethnic Cohort Study.

Ultrafine particles (UFP) are unregulated air pollutants abundant in aviation exhaust. Emerging evidence suggests that UFPs may impact lung health due to their high surface area-to-mass ratio and deep penetration into airways. This study aimed to assess long-term exposure to airport-related UFPs and lung cancer incidence in a multiethnic population in Los Angeles County. Within the California Multiethnic Cohort, we examined the association between long-term exposure to airport-related UFPs and lung cancer incidence. Multivariable Cox proportional hazards regression models were used to estimate the effect of UFP exposure on lung cancer incidence. Subgroup analyses by demographics, histology and smoking status were conducted. Airport-related UFP exposure was not associated with lung cancer risk [per one IGR HR, 1.01; 95% confidence interval (CI), 0.97-1.05] overall and across race/ethnicity. A suggestive positive association was observed between a one IQR increase in UFP exposure and lung squamous cell carcinoma (SCC) risk (HR, 1.08; 95% CI, 1.00-1.17) with a Phet for histology = 0.05. Positive associations were observed in 5-year lag analysis for SCC (HR, 1.12; 95% CI, CI, 1.02-1.22) and large cell carcinoma risk (HR, 1.23; 95% CI, 1.01-1.49) with a Phet for histology = 0.01. This large prospective cohort analysis suggests a potential association between airport-related UFP exposure and specific lung histologies. The findings align with research indicating that UFPs found in aviation exhaust may induce inflammatory and oxidative injury leading to SCC. These results highlight the potential role of airport-related UFP exposure in the development of lung SCC.

SMARCA4 deficiency and mutations are frequent in large cell lung carcinoma and are prognostically significant

SMARCA4 mutation has emerged as a marker of poor prognosis in lung cancer and has potential predictive value in cancer treatment, but recommendations for which patients require its investigation are lacking. We comprehensively studied SMARCA4 alterations and the clinicopathological significance in a large cohort of immunohistochemically-subtyped non-small cell lung cancer (NSCLC). A total of 1416 patients was studied for the presence of SMARCA4 deficiency by immunohistochemistry (IHC). Thereafter, comprehensive sequencing of tumours was performed for 397 of these patients to study the mutational spectrum of SWI/SNF and SMARCA4 aberrations. IHC evidence of SMARCA4 deficiency was found in 2.9% of NSCLC. Of the sequenced tumours, 38.3% showed aberration in SWI/SNF complex, and 9.3% had SMARCA4 mutations. Strikingly, SMARCA4 aberrations were much more prevalent in large cell carcinoma (LCC) than other histological tumour subtypes. SMARCA4-deficient and SMARCA4-mutated tumours accounted for 40.5% and 51.4% of all LCC, respectively. Multivariable analyses confirmed SMARCA4 mutation was an independent prognostic factor in lung cancer. The immunophenotype of a subset of these tumours frequently showed TTF1 negativity and HepPAR1 positivity. SMARCA4 mutation or its deficiency was associated with positive smoking history and poor prognosis. It also demonstrated mutual exclusion with EGFR mutation. Taken together, the high incidence of SMARCA4 aberrations in LCC may indicate its diagnostic and prognostic value. Our study established the necessity of SMARCA4 IHC in the identification of SMARCA4-aberrant tumours, and this may be of particular importance in LCC and tumours without known driver events.

Characterization of two transcriptomic subtypes of marker-null large cell carcinoma of the lung suggests different origin and potential new therapeutic perspectives

Pulmonary large cell carcinoma (LCC) is an undifferentiated neoplasm lacking morphological, histochemical, and immunohistochemical features of small cell lung cancer, adenocarcinoma (ADC), or squamous cell carcinoma (SCC). The available molecular information on this rare disease is limited. This study aimed to provide an integrated molecular overview of 16 cases evaluating the mutational asset of 409 genes and the transcriptomic profiles of 20,815 genes. Our data showed that TP53 was the most frequently inactivated gene (15/16; 93.7%) followed by RB1 (5/16; 31.3%) and KEAP1 (4/16; 25%), while CRKL and MYB genes were each amplified in 4/16 (25%) cases and MYC in 3/16 (18.8%) cases; transcriptomic analysis identified two molecular subtypes including a Pure-LCC and an adenocarcinoma like-LCC (ADLike-LCC) characterized by different activated pathways and cell of origin. In the Pure-LCC group, POU2F3 and FOXI1 were distinctive overexpressed markers. A tuft cell-like profile and the enrichment of a replication stress signature, particularly involving ATR, was related to this profile. Differently, the ADLike-LCC were characterized by an alveolar-cell transcriptomic profile and association with AIM2 inflammasome complex signature. In conclusion, our study split the histological marker-null LCC into two different transcriptomic entities, with POU2F3, FOXI1, and AIM2 genes as differential expression markers that might be probed by immunohistochemistry for the differential diagnosis between Pure-LCC and ADLike-LCC. Finally, the identification of several signatures linked to replication stress in Pure-LCC and inflammasome complex in ADLike-LCC could be useful for designing new potential therapeutic approaches for these subtypes.

Advancing NSCLC pathological subtype prediction with interpretable machine learning: a comprehensive radiomics-based approach.

This research aims to develop and assess the performance of interpretable machine learning models for diagnosing three histological subtypes of non-small cell lung cancer (NSCLC) utilizing CT imaging data. A retrospective cohort of 317 patients diagnosed with NSCLC was included in the study. These individuals were randomly segregated into two groups: a training set comprising 222 patients and a validation set with 95 patients, adhering to a 7:3 ratio. A comprehensive extraction yielded 1,834 radiomic features. For feature selection, statistical methodologies such as the Mann-Whitney U test, Spearman's rank correlation, and one-way logistic regression were employed. To address data imbalance, the Synthetic Minority Over-sampling Technique (SMOTE) was utilized. The study designed three distinct models to predict adenocarcinoma (ADC), squamous cell carcinoma (SCC), and large cell carcinoma (LCC). Six different classifiers, namely Logistic Regression, Support Vector Machine, Decision Tree, Random Forest, eXtreme Gradient Boosting (XGB), and LightGBM, were deployed for model training. Model performance was gauged through accuracy metrics and the area under the receiver operating characteristic (ROC) curves (AUC). To interpret the diagnostic process, the Shapley Additive Explanations (SHAP) approach was applied. For the ADC, SCC, and LCC groups, 9, 12, and 8 key radiomic features were selected, respectively. In terms of model performance, the XGB model demonstrated superior performance in predicting SCC and LCC, with AUC values of 0.789 and 0.848, respectively. For ADC prediction, the Random Forest model excelled, showcasing an AUC of 0.748. The constructed machine learning models, leveraging CT imaging, exhibited robust predictive capabilities for SCC, LCC, and ADC subtypes of NSCLC. These interpretable models serve as substantial support for clinical decision-making processes.

The prognostic significance of PD-1 and its ligands in non-small cell lung cancer.

In this study, we aimed to investigate the prognostic value of programmed cell death protein 1 (PD-1), programmed cell death ligand 1 (PD-L1), and programmed cell death ligand 2 (PD-L2) expressions on immune and cancer cells in terms of survival in patients with lung adenocarcinoma, squamous cell carcinoma, and large cell carcinoma. Between January 2000 and December 2012, a total of 191 patients (172 males, 19 females; mean age: 60.3±8.4 years; range, 38 to 78 years) who were diagnosed with non-small cell lung cancer and underwent anatomic resection and mediastinal lymph node dissection were retrospectively analyzed. The patients were evaluated in three groups including lung squamous cell carcinoma (n=61), adenocarcinoma (n=66), and large-cell carcinoma (n=64). The survival rates of all three groups were compared in terms of immunohistochemical expression levels of PD-1, PD-L1, and PD-L2. The mean follow-up was 71.8±47.9 months. In all histological subtypes, PD-1 expressions on tumor and immune cells were observed in 33% (61/191) and in 53.1% (102/191) of the patients, respectively. Higher expression levels of PD-L1 and PD-L2 at any intensity on tumor and immune cells were defined only in lung adenocarcinomas, and PD-L1 and PD-L2 values were detected in 36.4% (22/64) of these patients. The PD-L1 expressions on tumor and immune cells were observed in 41.7% (10/24) and 25% (6/24) of the patients, respectively. The PD-L2 expressions on tumor and immune cells were detected in 16.7% (4/24) and 8.4% (2/24) of the patients, respectively. Univariate and multivariate analyses revealed that PD-1 expression in tumor cells was an independent prognostic factor in all histological subtypes. Our study results suggest that PD-1 expression is a poor prognostic factor for overall survival in patients with completely resected adenocarcinoma, squamous cell carcinoma, and large cell carcinoma.

The assessment of Dickkopf-1 and Dickkopf-2 protein concentration in different subtypes of non-small cell lung cancer subtypes.

Lung cancer is one of the most prevalent cancers worldwide. Dickkopf-1 (DKK-1) and -2 (DKK-2) are important proteins for the regulated Wnt signalling pathway. Alternations in the Wnt pathway are associated with tumour progression. The aim of the study was to analyse the concentration of DKK-1 and DKK-2 in tumour and matched non-tumour (NT) samples of 65 patients with non-small cell lung cancer (NSCLC), including 3 subtypes: adenocarcinoma (AC), squamous cell carcinoma (SCC), and large cell carcinoma (LCC). The protein concentration was measured by enzyme-linked immunosorbent assay (ELISA) in homogenates. The difference between the level of DKK-1 in tumour and NT specimens was not significant for the whole NSCLC group and SCC and LCC subtype, while in AC samples they were significantly higher (p = 0.028). The highest concentration of DKK-1 was found in the advanced NSCLC samples, with the T4 parameter as well as stage III. Significantly decreased DKK-2 concentrations were detected in all NSCLC subtypes (p < 0.05). Moreover, the DKK-2 level was higher in non-smokers than in smokers. The results indicate that concentrations of DKKs were different in relation to subtypes as well as clinical and socio-demographic parameters. The concentration of DKKs could be associated with the progression of NSCLC. We suggest that DKK-1 could play an oncogenic role in AC, while DKK-2 could be a tumour suppressor in all NSCLC subtypes. Dickkopf-1 and DKK-2 proteins could have differential roles in the Wnt signalling pathway, which is important in many cellular processes, such as proliferation and apoptosis.

A comprehensive review of clinical outcomes and safety profile of durvalumab in non-small cell lung cancer

Abstract Non-small cell lung cancer (NSCLC), encompassing adenocarcinomas, squamous cell carcinomas, and large cell carcinomas, is a prevalent and varied lung cancer type. It holds a significant share of lung malignancies in India and the United States. The advancement of local NSCLC is significantly influenced by smoking, and the recommended therapeutic approach for these cases is chemoradiotherapy. However, the prognosis remains challenging, necessitating innovative treatments. Durvalumab, an immune checkpoint inhibitor, shows promise against unresectable stage 3 NSCLC. Blocking programmed death ligand-1 interactions amplify immune responses against tumors. Durvalumab has exhibited efficacy in trials and improved survival when combined with chemotherapy for extensive-stage small-cell lung cancer. Adverse effects include cough, rash, hypothyroidism, and abdominal pain. Immunotherapies like durvalumab bring hope for better outcomes, but continuous research is crucial for refining treatments and enhancing patient survival.

Exploring the Mechanism of Ferroptosis Induction by Sappanone A in Cancer: Insights into the Mitochondrial Dysfunction Mediated by NRF2/xCT/GPX4 Axis.

Non-small cell lung cancer (NSCLC), a major subtype of lung cancer, encompasses squamous cell carcinoma, adenocarcinoma, and large cell carcinoma. Compared to small cell lung cancer, NSCLC cells grow and divide more slowly, and their metastasis occurs at a later stage. Currently, chemotherapy is the primary treatment for this disease. Sappanone A (SA) is a flavonoid compound extracted from the plant Caesalpinia sappan, known for its antitumor, redox-regulating, and anti-inflammatory properties. Recent studies have investigated the interaction of SA with mitochondrial pathways in regulating cell death through the Nrf-2/GPX-4/xCT axis. This study specifically explores the mechanism by which SA affects mitochondrial morphology and structure through the regulation of mitophagy and mitochondrial biogenesis in tumor cells. The study primarily utilizes second-generation transcriptomic sequencing data and molecular docking techniques to elucidate the role of SA in regulating programmed cell death in tumor cells. The omics results indicate that SA treatment significantly targets genes involved in oxidative phosphorylation, mitophagy, mitochondrial dynamics, and oxidative stress. Further findings confirmed that the Nrf-2/GPX4/xCT pathway serves as a crucial target of SA in the treatment of NSCLC. Knockdown of Nrf-2 (si-Nrf-2) and Nrf-2 overexpression (ad-Nrf-2) were shown to modulate the therapeutic efficacy of SA to varying degrees. Additionally, modifications to the GPX4/xCT genes significantly affected the regulatory effects of SA on mitochondrial autophagy, biogenesis, and energy metabolism. These regulatory mechanisms may be mediated through the caspase pathway and ferroptosis-related signaling. Molecular biology experiments have demonstrated that SA intervention further inhibits the phosphorylation of FUNDC1 at Tyr18 and downregulates TOM20 expression. SA treatment was found to reduce the expression of PGC1α, Nrf-1, and Tfam, resulting in a decrease in mitochondrial respiration and energy metabolism. Overexpression of Nrf-2 was shown to counteract the regulatory effects of SA on mitophagy and mitochondrial biogenesis. Confocal microscopy experiments further revealed that SA treatment increases mitochondrial fragmentation, subsequently inducing mitochondrial pathway-mediated programmed cell death. However, genetic modification of the Nrf-2/GPX4/xCT pathway significantly altered the regulatory effects of SA on tumor cells. In conclusion, SA has been identified as a promising therapeutic agent for NSCLC. The mitochondrial pathway-mediated apoptosis and ferroptosis may represent key mechanisms in regulating tumor cell death. Targeting the Nrf-2/GPX-4/xCT axis offers a novel therapeutic approach for maintaining mitochondrial homeostasis within the cellular microenvironment.

Understanding the impact of radiotherapy fractionation on overall survival in a large head and neck squamous cell carcinoma dataset: a comprehensive approach combining mechanistic and machine learning models.

Treating head and neck squamous cell carcinomas (HNSCC), especially human papillomavirus negative (HPV-) and locally advanced cases, remains difficult. Our previous analyses of radiotherapy-only HNSCC clinical trials data using mechanistically-motivated models of tumor repopulation and killing by radiotherapy predicted that hyperfractionation with twice-daily fractions, or hypofractionation involving increased doses/fraction and reduced treatment durations, both improve tumor control and reduce late normal tissue toxicity, compared with standard protocols using 35×2 Gy. Here we further investigated the validity of these conclusions by analyzing a large modern dataset on 3,346 HNSCC radiotherapy patients from the University Health Network in Toronto, Canada, where 42.5% of patients were also treated with chemotherapy. We used a two-step approach that combines mechanistic modeling concepts with state-of-the-art machine learning, beginning with Random Survival Forests (RSF) for an exploratory analysis and followed by Causal Survival Forests (CSF) for a focused causal analysis. The mechanistic concept of biologically effective dose (BED) was implemented for the standard dose-independent (DI) tumor repopulation model, our alternative dose-dependent (DD) repopulation model, and a simple model with no repopulation (BEDsimp). These BED variants were included in the RSF model, along with age, stage, HPV status and other relevant variables, to predict patient overall survival (OS) and cause-specific mortality (deaths from the index cancer, other cancers or other causes). Model interpretation using Shapley Additive Explanations (SHAP) values and correlation matrices showed that high values of BEDDD or BEDDI, but not BEDsimp, were associated with decreased patient mortality. Targeted causal inference analyses were then performed using CSF to estimate the causal effect of each BED variant on OS. They revealed that high BEDDD (>61.8 Gy) or BEDDI (>57.6 Gy), but not BEDsimp, increased patient restricted mean survival time (RMST) by 0.5-1.0 years and increased survival probability (SP) by 5-15% several years after treatment. In addition to population-level averages, CSF generated individual-level causal effect estimates for each patient, facilitating personalized medicine. These findings are generally consistent with those of our previous mechanistic modeling, implying the potential benefits of altered radiotherapy fractionation schemes (e.g. 25×2.4 Gy, 20×2.75 Gy, 18×3.0 Gy) which increase BEDDD and BEDDI and counteract tumor repopulation more effectively than standard fractionation. Such regimens may represent potentially useful hypofractionated options for treating HNSCC.

A histopathological review of gynaecological malignancies in Katsina state North-Western Nigeria.

Gynaecological cancers, which affect the female genital tract, constitute a significant public health problem, especially in developing countries. Some of these malignancies have known aetiology and premalignant stages making them preventable. Understanding the burden of gynaecological malignancies in our environment will provide baseline information and help form strategies for their control. To describe the histological subtypes of gynaecological cancers, their frequency and age distribution trends in Katsina State over the 10-year study period. This was a 10-year retrospective cross-sectional multicenter study of all histologically diagnosed gynaecological cancer cases seen from 1st January 2012 to 31st December 2021 at Federal Teaching Hospital Katsina, General Hospital Katsina and General Amadi Rimi Specialist Hospital Katsina. Data for this study were extracted from departmental record registers of the pathology laboratories of the corresponding hospitals whose laboratories provide pathology services to the State. Cancer distribution over the years was sorted based on the primary site of diagnosis, histological diagnosis and age. Data were analysed using Statistical Package for Social Science version 28 and results were presented in tables and charts. Two thousand three hundred and fifty-nine cancers were seen over the 10-year study period. Of these cases, 58.4% (n = 1,378) were females. Gynaecological malignancies accounted for 18.7% (441/2,359) of all cancers and 32.0% (441/1,378) of all female cancers. The highest frequency of gynaecological cancers was seen in women who were within the age groups of 40-49 and 50-59, and the lowest was seen in women who were ≥90 years old. The mean age was 48.9 ± 14.9 years. The most common site of gynaecological malignancies was the cervix uteri (n = 262, 59.4%) followed by the ovary (n = 106, 24.0%). Other sites in descending order were corpus uteri (n = 29, 6.6%), vulva (n = 9, 2.0%) and vagina (n = 2, 0.5%). The most common histo-morphologic subtypes were large-cell keratinizing squamous cell carcinoma in the cervix, large-cell non-keratinizing squamous cell carcinoma in the cervix and cystadenocarcinoma in the ovary. Choriocarcinoma was found in 33 cases (7.5%). This study demonstrated the various histotypes of gynaecological malignancies and their trends in Katsina state. The leading cancer was found to be cervical cancer which is mainly preventable. It is hoped that data from this study will provide a basis for making and implementing policies and strategies to lessen the problems of gynaecological malignancies through regular screening programs, especially for cervical cancer and accepting human papilloma virus (HPV) vaccination take-up.

Detection and Classification of Lung Nodule in CT Scan Images Using CNN

The Identification of lung malignant cells at premature stage remain as remarkable research area for researchers. Lung nodules are account as a normal reason for death in people all over the world. Identification of lung nodules at the initial stage can build the pace of endurance of patients. In this article computed tomography (CT) scan images as input to classify lung malignant cells of non-small cell lung cancer and categories according to subtypes of cancer by using the image processing technique with a convolutional neural network (CNN). The segmentation of the CT scan is performed to simplify the representation for meaningful and easier analysis. The feature extraction technique is used which is independent from the size and rotation of the image. These extracted features will use as an input to convolutional neural network (CNN), it pass through different layer of neural network at the end output layer classifies the affected and non-affected areas of the lung nodule with respect to subtypes (adenocarcinoma, squamous cell carcinoma, and large cell carcinoma). The deep learning algorithms VGG 16 show accuracy 85.01% while another deep learning algorithms VGG 19 show best accuracy 98.74% to correctly classify the nodules and subtypes.

Prognostic evaluation of relapse based on squamous cell carcinoma antigen, CXCR2, and CD44V6 blood levels in patients with Stage I–II squamous cell lung cancer

Lung cancer, the leading cause of cancer-related mortality, predominantly exists as non-small cell lung cancer, accounting for approximately 85% of cases and comprising adenocarcinoma, squamous cell carcinoma (SCC), and large cell carcinoma as the three most prevalent histological subtypes. This study focused on investigating pre- and post-operative concentrations of SCC antigen in blood serum, as well as the percentage of CXCR2-containing lymphocytes and CD44v6-containing monocytes in blood cell populations among patients with Stages I&amp;ndash;II squamous cell lung cancer (SCLC) within 1 year after tumor resection. The primary objective was to assess their potential for predicting relapse. The study cohort comprised 57 patients (32 men and 25 women) with newly diagnosed squamous cell lung cancer (21 at stage I and 36 at stage II). Following tumor resection, categorized as R0 in terms of surgical intervention, all parameters were examined before surgery and at 3 weeks, 3 months, and 6 months postoperatively. Analysis revealed that the probability of relapse could be accurately predicted, ranging from 68.4% to 89.5%, based on differences in SCC antigen concentration, the percentage of lymphocytes with CXCR2, and monocytes with the CD44v6 receptor during various post-operative intervals. Subsequent regression analysis and the formulation of a combined model incorporating the above-mentioned parameters led to an enhanced predictive value for tumor recurrence, reaching 96.5% accuracy (with specificity at 95.6% and sensitivity at 100%). These results indicate the potential utility of the combined model as an additional marker for predicting postoperative relapse in patients with Stage I&amp;ndash;II SCLC.

Current status and prospect of PET-related imaging radiomics in lung cancer.

Lung cancer is highly aggressive, which has a high mortality rate. Major types encompass lung adenocarcinoma, lung squamous cell carcinoma, lung adenosquamous carcinoma, small cell carcinoma, and large cell carcinoma. Lung adenocarcinoma and lung squamous cell carcinoma together account for more than 80% of cases. Diverse subtypes demand distinct treatment approaches. The application of precision medicine necessitates prompt and accurate evaluation of treatment effectiveness, contributing to the improvement of treatment strategies and outcomes. Medical imaging is crucial in the diagnosis and management of lung cancer, with techniques such as fluoroscopy, computed radiography (CR), digital radiography (DR), computed tomography (CT), magnetic resonance imaging (MRI), positron emission tomography (PET)/CT, and PET/MRI being essential tools. The surge of radiomics in recent times offers fresh promise for cancer diagnosis and treatment. In particular, PET/CT and PET/MRI radiomics, extensively studied in lung cancer research, have made advancements in diagnosing the disease, evaluating metastasis, predicting molecular subtypes, and forecasting patient prognosis. While conventional imaging methods continue to play a primary role in diagnosis and assessment, PET/CT and PET/MRI radiomics simultaneously provide detailed morphological and functional information. This has significant clinical potential value, offering advantages for lung cancer diagnosis and treatment. Hence, this manuscript provides a review of the latest developments in PET-related radiomics for lung cancer.