Lung Squamous Cell Carcinoma Research Articles

Abstract B045: The impact of smoking status on the genomic landscape of lung squamous cell carcinoma

Abstract Introduction: Molecular diagnostics have become a standard of care in determining the appropriate treatment paradigm for patients with advanced non-small cell lung cancer (NSCLC). Current guidelines indicate that histology is the most important criteria in deciding on conducting molecular testing. In lung adenocarcinoma, guidelines recommend that all patients with advanced disease undergo comprehensive molecular testing. However, the current National Comprehensive Cancer Guidelines (NCCN) suggest only consideration of molecular testing in lung squamous cell carcinoma (SCC). Despite this recommendation, numerous genomic alterations (GAs) have been observed in lung SCC including amplifications, fusions, and mutations. The list of druggable genetic alterations has been growing, now including ROS1 rearrangements, RET rearrangements, and MET alterations with associated pharmacologic therapies. This suggests a role for broader comprehensive molecular profiling in NSCLC. We sought to assess the impact of smoking status on the proportion of actionable GAs in a cohort of lung SCC patients. Methods: We retrospectively reviewed liquid biopsy reports from Guardant 360 between October 2020 and July 2023 that were obtained in the context of routine clinical care at a single institution, the City of Hope Comprehensive Cancer Center. We then obtained social history from clinical documentation. Patients were categorized based on smoking status with the following categories: non-smoker, remote smoking history (quit >20 years prior), or smoking history (ongoing or quit within the last 20 year). Results: 56 patients were included in the final analysis. 98% (n = 55) patients had a documented smoking history, with 24% (n=13) classified as non-smokers. In total, 96% (n = 54) patients had detectable alterations of variable significance Of the 13 patients classified as non-smokers, 62% (n=8) patients had an actionable GA detected by liquid biopsy which has a current FDA approved agent. Of targetable GAs in the non-smoking cohort, the following alterations were observed: EGFR exon 19 deletion (44% , n=4), MET exon 14 skipping mutation (22%, n=2), EGFR G719S (11%, n=1), EGFR E114K (11% n=1). Of the 8 patients with targetable alterations detected, responses by RECIST included 4 partial response, 2 stable disease, and 2 progressive disease. The objective response rate (ORR) for this group was 55.6% and the clinical benefit rate (CBR) was 77.8%. Discussion: These data help illustrate the importance of genomic testing in lung SCC, particularly in patients with a non- smoking status. Given that 62% of non-smokers harbored a mutation treated with an FDA- approved agent, up-front genomic testing is of paramount importance. For smokers, numerous alterations were also detected which have been shown to result in decreased efficacy of immunotherapy regimens. This analysis helps highlight the potential value of liquid biopsy testing in lung SCC patients with both non-smoking and smoking histories. Citation Format: Adam Rock, Sahil Garg, Ravi Salgia, Victoria Villaflor, Ramya Muddasani, Erminia Massarelli, Colby Jenkins. The impact of smoking status on the genomic landscape of lung squamous cell carcinoma [abstract]. In: Proceedings of the AACR Special Conference: Liquid Biopsy: From Discovery to Clinical Implementation; 2024 Nov 13-16; San Diego, CA. Philadelphia (PA): AACR; Clin Cancer Res 2024;30(21_Suppl):Abstract nr B045.

Abstract 4136539: Management of Malignant SVC Syndrome and Acute SVC Thrombosis with Complex Endovascular Intervention

Superior vena cava (SVC) syndrome occurs when there is SVC occlusion or obstruction. SVC obstruction can cause an increase in venous return pressure up to 40 mm Hg, producing edema of the face, neck and chest wall. Blood flow can be diverted to the right atrium through collateral venous networks in cases of gradual SVC compression. The most common cause of SVC syndrome is malignancy, as seen in 70% of cases. Traditionally, treatment is with radiation therapy but advances have now allowed for an endovascular approach. We present a case of malignant SVC syndrome with a complex percutaneous treatment approach that provided palliation with near immediate symptom improvement. A 64 year-old-female with history of cerebrovascular accident on Eliquis, right upper lobe squamous cell carcinoma of the lung in remission, and a mediastinal nodule under surveillance presented with facial and bilateral upper extremity edema. CT revealed bilateral pulmonary embolism and a large SVC thrombus causing impaired venous return. Invasive angiogram confirmed a 95% thrombotic tubular stenosis for which she underwent aspiration thrombectomy with alpha VAC F-18, then angioplasty with an Armada (Abbott) 10 x 40 mm compliant balloon and stenting with the Express (Boston Scientific) 10 x 37 mm stent. There was 0% residual stenosis and immediate clinical improvement. Oncological workup confirmed the right mediastinal mass to be a recurrence of squamous cell carcinoma. SVC syndrome occurred due to mediastinal mass compression and a near occlusive SVC thrombus. Aspiration thrombectomy was effective in treating the SVC thrombosis and was performed alongside pulmonary embolism thrombectomy. The Armada balloon is intended for lesion dilation in the peripheral arteries, but was safely used here for venous dilation. The Express stent is intended for palliation of malignant biliary neoplasms but has versatility and was utilized here for malignant neoplasm surrounding the SVC. Peripheral endovascular stenting is an advantageous treatment option for malignant SVC syndrome given the challenges due to recurrence from mass effect. This provides additional support to maintain vessel patency and is a safe palliation option for improvement in quality of life.

MCRANet: MTSL-based connectivity region attention network for PD-L1 status segmentation in H&E stained images

The quantitative analysis of Programmed death-ligand 1 (PD-L1) via Immunohistochemical (IHC) plays a crucial role in guiding immunotherapy. However, IHC faces challenges, including high costs, time consumption and result variability. Conversely, Hematoxylin-Eosin (H&E) staining offers cost-effectiveness, speed, and stable results. Nonetheless, H&E staining, which solely visualizes cellular morphological features, lacks clinical applicability in detecting biomarker expressions like PD-L1. Substituting H&E staining for IHC in determining PD-L1 status is a clinically significant and challenging task. Motivated by above observations, we propose a Multi-Task supervised learning (MTSL)-based connectivity region attention network (MCRANet) for PD-L1 status segmentation in H&E stained images. To reduce interference from non-tumor areas, the MTSL-based region attention is proposed to enhances the network's capability to distinguish between tumor and non-tumor regions. Consequently, this augmentation further improves the network's segmentation efficacy for PD-L1 positive and negative regions. Furthermore, the PD-L1 expression regions demonstrate interconnection throughout the tissue section. Leveraging this topological prior knowledge, we integrate a connectivity modeling module (CM module) within the MTSL-based region attention module (MRA module) to enhance the precision of MTSL-based region attention localization. This integration further improves the structural similarity between the segmentation results and the ground truth. Extensive visual and quantitative results demonstrate that our supervised-learning-guided MRA module produces more interpretable attention and the introduced CM module provides accurate positional attention to the MRA module. Compared to other state-of-the-art networks, MCRANet exhibits superior segmentation performance with a dice similarity coefficient (DSC) of 79.6 % on the lung squamous cell carcinoma (LUSC) PD-L1 status dataset.

Expression of the lncRNA TPT1-AS1 in lung squamous cell carcinoma and its prognostic value.

Lung squamous cell carcinoma is typically associated with a poor prognosis, highlighting the need for a reliable prognostic marker. Given that the long noncoding RNA TPT1-AS1 has demonstrated aberrant expression in numerous cancers, the prognostic significance of TPT1-AS1 in LUSC was examined. The present study included 115 patients diagnosed with LUSC. The expression levels of TPT1-AS1 and miR-4726-5p in tissues and cells were assessed using RT-qPCR, while the correlation between TPT1-AS1 expression and clinicopathological features was analyzed through the chi-square test. Binding sites between TPT1-AS1 and miR-4726-5p were predicted using a database and confirmed via a dual-luciferase reporter assay. The Pearson correlation coefficients were calculated to determine the relationship between TPT1-AS1 and miR-4726-5p in tumor tissues. The impacts of TPT1-AS1 and miR-4726-5p on cells were evaluated through CCK-8 and Transwell assays. TPT1-AS1 expression was found to be reduced, while miR-4726-5p expression was upregulated in LUSC tissues. Patients exhibiting low TPT1-AS1 expression experienced shorter overall survival. Moreover, TPT1-AS1 was identified as an independent prognostic factor. A dual luciferase reporter assay validated that TPT1-AS1-WT targeted and bound to miR-4726-5p. Additionally, an inverse correlation was observed between miR-4726-5p and TPT1-AS1 in tumors. Cell experiments indicated that the overexpression of TPT1-AS1 led to a decrease in miR-4726-5p expression, consequently inhibiting cell proliferation, migration, and invasion. TPT1-AS1 targets miR-4726-5p, and overexpression of TPT1-AS1 has the potential to serve as a prognostic marker for LUSC and can significantly influence LUSC progression.

Gastroesophageal reflux disease and risk of incident lung cancer: A large prospective cohort study in UK Biobank.

Some pathogenic mechanisms suggest a potential relationship between gastroesophageal reflux disease (GERD) and respiratory diseases. However, evidence regarding the association between GERD and lung cancer is mixed. We aim to explore this relationship based on data from the large-scale UK Biobank study. We performed a cross-sectional and prospective cohort study in 501,569 (45.58% male) individuals included in the UK Biobank at baseline (2006-2010). The Cox proportional hazards model and logistic regression models were used to assess the relationship between GERD and lung cancer, small cell lung cancer (SCLC), lung squamous cell carcinoma (LUSC), and lung adenocarcinoma (LUAD). During a mean follow-up of 11.54 years, 3,863 (0.84%) incident lung cancer cases were identified. In the cross-sectional analysis using logistic models, significant associations were found between GERD and prevalent lung cancer cases (odds ratio [OR] = 1.87, 95% confidence interval [95% CI]: 1.45-2.38) and subtypes of lung cancer, with an OR (95% CI) of 3.19 (1.47-6.79) for SCLC, 2.08 (1.33-3.21) for LUSC, 1.85 (1.15-2.91) for LUAD. In the follow-up analysis using Cox models, GERD was associated with an increased risk of lung cancer (hazard ratio [HR] = 1.24, 95%CI: 1.14-1.34). Similar associations were also observed between GERD and SCLC (HR = 1.39, 95% CI: 1.09-1.78), LUSC (HR = 1.40, 95% CI: 1.18-1.65), and LUAD (HR = 1.17, 95% CI: 1.02-1.33). The risk of lung cancer resulting from GERD was mainly elevated in former smokers (HR = 1.38, 95% CI: 1.23-1.54) and current smokers (HR = 1.18, 95% CI: 1.04-1.34), but not in never-smokers (HR = 0.89, 95% CI: 0.70-1.14). No significant association was observed in former smokers who had quit smoking for at least 25 years. We found that GERD was positively associated with an increased risk of lung cancer, especially among smokers. Awareness of this association may be beneficial for prevention and treatment strategies of both diseases.

A causal association between chemokines and the risk of lung cancer: a univariate and multivariate mendelian randomization study

BackgroundObservational studies and experimental evidence have shown that chemokines play important roles in lung cancer development, progression, and treatment. However, few studies have examined the causal association between them.MethodsSummary data of chemokines and lung cancer were obtained from genome-wide association studies. Mendelian randomization (MR) analyses were performed by five methods, Inverse variance weighted (IVW), Weighted median estimation, MR-Egger, Simple mode and Weighted, with IVW as the primary analysis method. Sensitivity analysis was used to assess the reliability of the MR results. Multivariate Mendelian randomization studies were used to infer whether causality was influenced by potential mediators. The expression levels of CCL21 were analyzed by quantitative real-time PCR.ResultsWe found that CCL21 was negatively associated with lung adenocarcinoma risk. CCL25 was positively associated with lung squamous cell carcinoma risk. CCL5 was negatively associated with small cell lung cancer risk. CCL21, CCL24, CCL27, and CCL28 was positively associated with small cell lung cancer risk. After multivariate Mendelian randomization adjustment for smoking behavior, it was found that the effect of CCL25 on lung squamous cell cancer disappeared, and the effect of CCL21 on small cell lung cancer was quite opposite to the univariate. The receiver operating characteristic curve indicated that chemokines had high accuracy in the diagnosis of lung cancer. CCL21 expression levels showed large differences in lung adenocarcinoma cells.ConclusionThese results highlighted the causal effects of chemokines on lung cancer and suggested a mediating role of smoking behavior in the association between chemokines and lung cancer.

Unveiling the distinctive variations in multi-omics triggered by TP53 mutation in lung cancer subtypes: An insight from interaction among intratumoral microbiota, tumor microenvironment, and pathology

The TP53 mutation is one of the most common gene mutations in non-small cell lung cancer (NSCLC) and plays a significant role in the occurrence, development, and prognosis of both lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC). Recent studies have also suggested the predictive value of TP53 mutations in the response to immunotherapy for NSCLC. It is known that intratumoral microbiota, tumor immune microenvironment (TIME) and histology are associated with the roles of TP53 mutation in NSCLC. However, the intrinsic associations among these three factors and their underlying interaction with TP53 mutation are not well understood. Additionally, the potential of predicting TP53 mutations using deep learning methods has not yet been fully evaluated. In this paper, we comprehensively evaluated the tissue microbiome, host gene expression characteristics, and histopathological slides of 992 NSCLC patients obtained from the cancer genome atlas (TCGA) and validated the findings using multi-omics data of 332 NSCLC patients from the Clinical Proteomic Tumor Analysis Consortium (CPTAC). Compared to LUSC, LUAD exhibited more substantial differences between patients with and without TP53 mutation in all three aspects. In LUAD, our results imply underlying links between the tissue microbiome and immune cell components in the TIME, and show that the abundance of immune cells is reflected in histology slides. Furthermore, we propose a novel multimodal deep learning model that focuses on histopathology images, which achieves an area under the curve (AUC) of 0.84 in LUAD. In summary, TP53 mutation of LUAD resulted more significant changes in intratumoral microbiota, TIME and histology than that of LUSC. And histopathology images can be used to predict TP53 mutation in LUAD with reasonable accuracy.

Acquired Hemophilia A after Tislelizumab Treatment in a Patient with Right Lung Squamous Cell Carcinoma.

Acquired Hemophilia A after Tislelizumab Treatment in a Patient with Right Lung Squamous Cell Carcinoma.

Complex shape markers can detect alterations in the spatial distribution of cell nuclei in human lung squamous cell carcinoma: a useful tool for automatic analysis?

Lung cancer is the leading cause of cancer-related death. The use of computational methods to quantify changes that are not perceptible to the human eye is increasing in digital pathology imaging and has quickly improved detection rates at a low cost. Therefore, the present study aims to use complex computational shape markers as tools for automated analysis of the spatial distribution of cells in microscopy images of squamous cell lung carcinoma (SqCC). Photomicrographs from pathology glass slides in the LC25000 dataset were used in this study. Compared with those of the control, the fractal dimension (28%) and lacunarity (41%) of the cell nuclei changed in SqCC. The multifractal analysis revealed a significant difference in parameters Dq, α, and f(α) for all values of q (-10 to + 10), with a greater increase for more positive q values. The values at q + 10 increased by 34% for Dq, 36% for α, and 53% for f(α) in the SqCC images. The circularity, area, and perimeter also changed in the SqCC images. However, the parameters of aspect ratio, roundness, and solidity did not significantly differ between SqCC and benign tissue. The complex shape markers with the greatest changes in this study were the f(α) values for multifractality (53%) and lacunarity (41%). In conclusion, automated quantification of the spatial distribution of cell nuclei can be a fast, low-cost tool for evaluating the microscopic characteristics of SqCC; therefore, complex shape markers could be useful tools for software and artificial intelligence to detect lung carcinoma.

Liquid-Liquid Phase Separation in the Prognosis of Lung Adenocarcinoma: An Integrated Analysis.

Lung adenocarcinoma (LUAD) is a highly lethal malignancy. Liquid-Liquid Phase Separation (LLPS) plays a crucial role in targeted therapies for lung cancer and in the progression of lung squamous cell carcinoma. However, the role of LLPS in the progression and prognosis of LUAD remains insufficiently explored. This study employed a multi-step approach to identify LLPS prognosis-related genes in LUAD. First, differential analysis, univariate Cox regression analysis, Random Survival For-est (RSF) method, and Least Absolute Shrinkage and Selection Operator (LASSO) Cox regres-sion analysis were utilized to identify five LLPS prognosis-related genes. Subsequently, LASSO Cox regression was performed to establish a prognostic score termed the LLPS-related progno-sis score (LPRS). Comprehensive analyses were then conducted based on the LPRS, including survival analysis, clinical feature analysis, functional enrichment analysis, and tumor microenvi-ronment assessment. The LPRS was integrated with additional clinicopathological factors to de-velop a prognostic nomogram for LUAD patients. Immunohistochemical validation was per-formed on clinical tissue samples to further validate the findings. Finally, the relationship be-tween KRT6A, one of the identified genes, and epidermal growth factor receptor (EGFR) muta-tions was investigated. The LPRS was established using five LLPS-related genes: IGF2BP1, KRT6A, LDHA, PKP2, and PLK1. Higher LPRS was closely associated with poor survival outcomes, gender, progression-free survival (PFS), and advanced TNM stage. Furthermore, LPRS emerged as an independent prognostic factor for LUAD. A nomogram integrating LPRS, TNM stage, and age demonstrated remarkable predictive accuracy for prognosis among patients with LUAD. LLPS likely influences LUAD prognosis through the activity of IGF2BP1, KRT6A, LDHA, PKP2, and PLK1. KRT6A exhibits significant upregulation in LUAD, particularly in patients with EGFR mutations. This study introduces a novel LPRS model that demonstrates high accuracy in pre-dicting the clinical prognosis of LUAD. Moreover, the findings suggest that KRT6A may play a critical role in the LLPS-mediated malignant progression of LUAD.

LW-MorphCNN: a lightweight morphological attention-based subtype classification network for lung cancer

Abstract Lung cancer is generally considered one of the most deadly cancers globally. If it can be identified early and diagnosed correctly, the survival probability of patients can be significantly improved. In this process, histopathological examination is a commonly used method for diagnosing and detecting lung cancer. It is crucial to accurately identify lung cancer subtypes from histopathological images, as this helps doctors formulate effective treatment plans. However, the visual inspection in histopathological diagnosis requires a large amount of time and also depends on the subjective perception of clinicians. Therefore, this paper proposes a lightweight lung cancer subtype classification network based on morphological attention (LW-MorphCNN), which is used to automatically classify the histopathological images of benign lung tumors, ADC (adenocarcinoma), and SCC (squamous cell carcinoma) provided in the public dataset LC25000 (Lung and Colon). This paper takes histopathological images as input and conducts a comparative analysis with classic networks such as VGG16, VGG19, DenseNet121, and ResNet50, as well as existing classification methods proposed in the same work. The network proposed in this paper is superior to other networks in terms of parameter quantity and performance, with an accuracy rate and F1 - score reaching 99.47% and 99.44% respectively. Clinicians can install the provided LW-MorphCNN in the hospital to confirm the diagnosis results.

IGF2BP3/CTCF Axis-Dependent NT5DC2 Promotes M2 Macrophage Polarization to Enhance the Malignant Progression of Lung Squamous Cell Carcinomas.

Lung squamous cell carcinoma (LUSC) is a type of lung cancer that develops in the squamous cells. It is known to be promoted by the activation of various signaling pathways and the dysregulation of key regulatory molecules. One such molecule, 5'-nucleotidase domain containing 2 (NT5DC2), has been identified as a critical regulator in various cancers including lung cancer. However, there are no data regarding its role in LUSC. The mRNA expression of insulin-like growth factor 2 mRNA-binding protein 3 (IGF2BP3), CCCTC-binding factor (CTCF), and NT5DC2 was analyzed using quantitative real-time polymerase chain reaction (qRT-PCR), whereas their protein expression was assessed using a western blotting assay. Cell proliferation was determined using a cell counting kit-8 (CCK-8) assay. Cell apoptosis, CD11b expression, and CD206 expression were analyzed using flow cytometry. Tube formation was assessed through a tube formation assay. Glucose consumption, lactate production, and ATP levels were measured using colorimetric methods. The effect of NT5DC2 on the malignant progression of LUSC cells was analyzed using a xenograft mouse model assay. The levels of transforming growth factor-beta 1 (TGF-β1) and interleukin-10 (IL-10) were detected using enzyme-linked immunosorbent assays. The associations among IGF2BP3, CTCF and NT5DC2 were identified using dual-luciferase reporter assay, RNA immunoprecipitation assay and m6A RNA immunoprecipitation assay. The expression of NT5DC2 was found to be upregulated in LUSC tissues and cells when compared with normal lung tissues and normal human bronchial epithelial cells. Silencing of NT5DC2 inhibited LUSC cell proliferation, tube formation, glycolysis, M2 macrophage polarization, and tumor formation while inducing cell apoptosis. In addition, CTCF was found to transcriptionally activate NT5DC2 in LUSC cells. IGF2BP3 stabilized the mRNA expression of CTCF through m6A methylation. Further, overexpression of CTCF or NT5DC2 attenuated the effects of IGF2BP3 silencing in both NCI-520 and SK-MES-1 cells. The IGF2BP3/CTCF axis-dependent NT5DC2 promotes M2 macrophage polarization, thereby enhancing the malignant progression of LUSC. This study was the first to reveal the role of NT5DC2 in LUSC and the underlying mechanism. The result suggests that targeting the IGF2BP3/CTCF/NT5DC2 axis may have clinical significance in the treatment of LUSC.

Pyroptosis-related gene GSDMC indicates poor prognosis and promotes tumor progression by activating the AKT/mTOR pathway in lung squamous cell carcinoma.

Lung squamous cell carcinoma (LUSC) is one of the most common malignant tumors of the respiratory. Pyroptosis plays an essential role in cancer, but there is limited research investigating pyroptosis in LUSC. In this study, pyroptosis-related genes were observed to have extensive multiomics alterations in LUSC through analysis of the TCGA database. Utilizing machine learning for selection and verifying expression levels, GSDMC was chosen as the critical gene for further experiments. Our research found that GSDMC is overexpressed in LUSC tissues and cells, and is associated with poor prognosis. Knockdown of GSDMC in LUSC inhibits cell proliferation, invasion, metastasis, chemotherapeutic sensitivity, and reduced tumor formation in nude mice, accompanied by downregulation of proliferative and EMT-related protein expression. However, these effects were counteracted in cells where GSDMC is overexpressed. Mechanistically, the oncogenic role of GSDMC is primarily achieved through the activation of the AKT/mTOR pathway, and this effect can be significantly reversed by rapamycin. Finally, SMAD4's interaction with the promoter region of GSDMC results in the suppression of GSDMC expression. In summary, our study through bioinformatics and experimental approaches not only proves that SMAD4 regulates the protumorigenic role of GSDMC through transcriptional targeting, but also indicates the possibility of developing the SMAD4/GSDMC/AKT/mTOR signaling axis as a potential biomarker and treatment target for LUSC.

Co-mutation of TP53 and TTN is Correlated with the Efficacy of Immunotherapy in Lung Squamous Cell Carcinoma.

Immunotherapy has been a promising treatment in advanced lung cancer. However, only a few patients could benefit from it. Herein, we aimed to explore mutationrelated predictive biomarkers in lung squamous cell carcinoma (LUSC), which could help develop clinical immunotherapy strategies and screen beneficial populations. Co-occurrence and mutually exclusive analysis was conducted on the TCGA-LUSC cohort. Correlations between the gene mutation status and tumor mutation burden (TMB) levels, and neo-antigen levels were analyzed by Wilcoxon test. Kaplan-Meier method was employed to analyze the progression-free survival (PFS) of lung cancer patients with immunotherapy. Gene set enrichment analysis (GSEA) was used to investigate the functional changes affected by TP53mut/TTNmut. The immune cell infiltration landscape in co-mutation subgroups was analyzed using CIBERSORT. 1) TP53, TTN, CSMD3, MUC16, RYR2, LRP1B, USH2A, SYNE1, ZFHX4, FAM135B, KMT2D, and NAV3 were frequently mutated in LUSC patients. 2) TMB levels in highly mutated groups were higher than that in wild type groups. 3) There were higher neoantigen levels in mutation group compared to the wild-type group, and LUSC patients in mutation group had longer PFS. 4) TP53mut/TTNmut co-mutation group exhibited higher TMB levels and better response to immunotherapy. 5) A host of immune-related signaling pathways was inhibited in TP53mut/TTNmut subgroup. 6) There were more T follicular helper cells and NK cells were in TP53mut/TTNmut subgroup than in the WT subgroup. The LUSC patients with TP53 and TTN co-mutation had higher TMB levels and better response to immunotherapy. The TP53 and TTN co-mutation is a promising novel biomarker to assist LUSC immunotherapy evaluation.

Lymph node metastasis prediction from in-situ lung squamous cell carcinoma histopathology images using deep learning.

Lung squamous cell carcinoma (LUSC), a subtype of non-small cell lung cancer, represents a significant portion of lung cancer cases with distinct histologic patterns impacting prognosis and treatment. The current pathological assessment methods face limitations such as inter-observer variability, necessitating more reliable techniques. This study seeks to predict lymph node metastasis in LUSC using deep learning models applied to histopathology images of primary tumors, offering a more accurate and objective method for diagnosis and prognosis.Whole slide images (WSIs) from the Outdo-LUSC and TCGA-LUSC cohorts were used to train and validate deep-learning models. Multi-instance learning was applied, with patch-level predictions aggregated into WSI-level outcomes. The study employed the ResNet-18 network, transfer learning, and rigorous data preprocessing. To represent WSI features, innovative techniques like patch likelihood histogram (PLH) and bag of words (BoW) were used, followed by training of machine learning classifiers, including the ExtraTrees algorithm.The diagnostic model for lymph node metastasis showed strong performance, particularly using the ExtraTrees algorithm, as demonstrated by receiver operating characteristic (ROC) curves and Grad-CAM visualizations. The signature generated by the ExtraTrees algorithm, named LN_ISLUSCH (lymph node status-related in-situ lung squamous cell carcinoma histopathology), achieved an area under the curve (AUC) of 0.941 (95% CI: 0.926-0.955) in the training set and 0.788 (95% CI: 0.748-0.827) in the test set. Kaplan–Meier analyses confirmed that the LN_ISLUSCH model was a significant prognostic factor (p = 0.02).This study underscores the potential of artificial intelligence in enhancing diagnostic precision in pathology. The LN_ISLUSCH model stands out as a promising tool for predicting lymph node metastasis and prognosis in LUSC. Future studies should focus on larger and more diverse cohorts and explore the integration of additional omics data to further refine predictive accuracy and clinical utility.

Comprehensive Pan-cancer analysis of CD73: explore its association with prognosis and tumor immune microenvironment

BackgroundCD73 is adenosine generation molecule, which is involved in the immune regulation. However, the roles of CD73 in the tumor microenvironment remain unknown. MethodsCD73 expression levels in pan-cancers were analyzed, based on The Cancer Genome Atlas (TCGA), Genotype-Tissue Expression (GTEx) and Human Protein ALTAS (HPA) databases. Kaplan-Meier (KM) plotter was used to analyze the prognostic values of CD73. Immune scores in pan-cancers was evaluated by ESTIMATE. TIMER2.0 and UCSCXena were used to explore the correlation between CD73 and immune infiltration/immune checkpoints/tumor mutation burden (TMB)/microsatellite instability (MSI). CD73 expression correlated genes in tumor tissues were screened by using LinkedOmics tool. ResultsFirstly, CD73 was significant increased in following cancer types: esophageal carcinoma (ESCA), glioblastoma multiforme (GBM), head and neck squamous cell carcinoma (HNSC), brain lower grade glioma (LGG), lung adenocarcinoma (LUAD), pancreatic adenocarcinoma (PAAD) and stomach adenocarcinoma (STAD). Secondly, high CD73 expression was associated with poor overall survival in patients with breast invasive carcinoma (BRCA), CESC, HNSC, liver hepatocellular carcinoma (LIHC), LUAD, lung squamous cell carcinoma (LUSC), PAAD and STAD. However, in KIRC and UCEC, patients high CD73 expression showed a favorable prognosis. Thirdly, the immune scores of the high CD73 expression in bladder urothelial carcinoma (BLCA), BRCA, kidney chromophobe (KICH), LUAD, LUSC, ovarian serous cystadenocarcinoma (OV), pheochromocytoma and paraganglioma (PCPG), prostate adenocarcinoma (PRAD), and skin cutaneous melanoma (SKCM) were significant higher than that of patients with low CD73 expression. Furthermore, we observed a positive correlation between CD73 and the multiple immune cells infiltration, including CD4+ memory T cells, CD8+ T cells, Treg cells, myeloid DC and macrophage, particularly in BRCA and LUAD. There was no strong correlation between CD73 and TMB/MSI. In LUAD, CD73 expression correlated genes were mainly enrichment in positive regulation of cell proliferation, cell adhesion, positive regulation of kinase activity, cellular response to LPS. However, in UCEC, CD73 correlated genes were mainly associated with fatty acid omega-oxidation, protein localization to endoplasmic reticulum exit site, endoplasmic reticulum to Golgi vesicle-mediated transport. ConclusionCD73 could be used to predict the prognosis of patients with several cancers. The potential functional mechanism of CD73 involved in cancer progress may not only dependent on its immunomodulatory effects.

Evaluation of Cyclin D1 protein and its association with the clinicopathological characteristics and prognosis of lung cancer: A retrospective study from Southern Kerala, India.

Cyclin D1 is a protein that can enhance the proliferation of cancer cells and has been detected in various malignancies, including lung cancer. However, routine examinations for Cyclin D1 in lung cancer cases have not been conducted in Kerala. This study sought to evaluate the links between cyclin D1 expression, clinicopathological characteristics, and 2-year survival rates in lung cancer. This retrospective cohort study used medical records and paraffin blocks of lung cancer patients at the Regional Cancer Centre in Kerala, India, between 2015 and 2018. The data were collected from 61 subjects, comprising of lung adenocarcinoma (18%), lung squamous cell carcinoma (27.9%), non-small-cell lung carcinoma (18%), poorly differentiated carcinoma (19.7%), and negative for malignant cells (16.4%). Data analysis was conducted using SPSS. The study revealed that 31.10% of the lung cancer patients exhibited overexpression of cyclin D1. A significant correlation was observed between cyclin D1 expression and histopathological results (P = 0.002), indicating that the level of cyclin D1 might be linked to specific histopathological subtypes of lung cancer. Despite this significant finding, cyclin D1 expression did not show any association with the clinical stage of the cancer or other clinical characteristics of the patients. Furthermore, when examining the 2-year survival rates of the patients, the study found no significant difference between those who had overexpression of cyclin D1 and those who did not (P = 0.145). Cyclin D1 expression was associated with histology type of lung cancer with no significant association to prognosis.

Landscape of targeted therapies for lung squamous cell carcinoma.

Lung cancer, a common type of malignant neoplasm, has seen significant advancements in the treatment of lung adenocarcinoma (LUAD). However, the management of lung squamous cell carcinoma (LSCC) continues to pose challenges. Traditional treatment methods for LSCC encompass surgical resection, chemotherapy, and radiotherapy. The introduction of targeted therapy and immunotherapy has greatly benefited LSCC patients, but issues such as limited immune response rates and adverse reactions persist. Therefore, gaining a deeper comprehension of the underlying mechanisms holds immense importance. This review provides an in-depth overview of classical signaling pathways and therapeutic targets, including the PI3K signaling pathway, CDK4/6 pathway, FGFR1 pathway and EGFR pathway. Additionally, we delve into alternative signaling pathways and potential targets that could offer new therapeutic avenues for LSCC. Lastly, we summarize the latest advancements in targeted therapy combined with immune checkpoint blockade (ICB) therapy for LSCC and discuss the prospects and challenges in this field.

The diagnosis value of dual-energy computed tomography (DECT) multi-parameter imaging in lung adenocarcinoma and squamous cell carcinoma.

Lung cancer continues to pose a serious risk to human health. With a high mortality rate, non-small cell lung cancer (NSCLC) is the major type of lung cancer, making up to 85% of all cases of lung cancer. Lung adenocarcinoma (AC), and lung squamous cell carcinoma (SC) are the two primary types of NSCLC. Determining the pathological type of NSCLC is important in establishing the most effective treatment method. Dual-energy computed tomography (DECT) multi-parameter imaging is an imaging technology that provides accurate and reliable disease diagnosis, and its uses are utilized for the combined diagnostic efficacy of AC and SC. The purpose of this study was to investigate the diagnostic value of spectral parameters of DECT in efficacy to AC and SC, and their combined diagnostic efficacy was also analyzed. We conducted a retrospective analysis of clinical and imaging data for 36 patients diagnosed with SC and 35 patients with AC. These patients underwent preoperative DECT chest scans, encompassing both arterial and venous phases, at our hospital from December 2020 to April 2022. The tumor diameter, water concentration (WC), iodine concentration (IC), normalized iodine concentration (NIC), Z effective (Zeff), and slope of the curve (K) in lesions were evaluated during two scanning phases in the two separate pathological types of lung cancers. The differences in parameters between these two types of lung cancers were statistically analyzed. In addition, receiver operating characteristic (ROC) curves were performed for these parameters to distinguish between SC and AC. In a univariate analysis involving 71 lung cancer patients, the results from Zeff, IC, NIC, and K from the AC's arterial and venous phase images were more elevated than those from the SC (P < 0.05). In contrast, the WC results were lower than those from SC (P < 0.05). The area under the ROC curve (AUC) for multi-parameter joint prediction typing was 0.831, with a corresponding sensitivity of 63.9% and specificity of 94.3%. It is possible to distinguish between central SC and AC using the spectrum characteristics of DECT-enhanced scanning (Zeff, IC, NIC, K, WC, and tumor diameter). Diagnostic effectiveness can be greatly improved when multiple variables are included.

Automatic lung cancer subtyping using rapid on-site evaluation slides and serum biological markers

BackgroundRapid on-site evaluation (ROSE) plays an important role during transbronchial sampling, providing an intraoperative cytopathologic evaluation. However, the shortage of cytopathologists limits its wide application. This study aims to develop a deep learning model to automatically analyze ROSE cytological images.MethodsThe hierarchical multi-label lung cancer subtyping (HMLCS) model that combines whole slide images of ROSE slides and serum biological markers was proposed to discriminate between benign and malignant lesions and recognize different subtypes of lung cancer. A dataset of 811 ROSE slides and paired serum biological markers was retrospectively collected between July 2019 and November 2020, and randomly divided to train, validate, and test the HMLCS model. The area under the curve (AUC) and accuracy were calculated to assess the performance of the model, and Cohen’s kappa (κ) was calculated to measure the agreement between the model and the annotation. The HMLCS model was also compared with professional staff.ResultsThe HMLCS model achieved AUC values of 0.9540 (95% confidence interval [CI]: 0.9257–0.9823) in malignant/benign classification, 0.9126 (95% CI: 0.8756–0.9365) in malignancy subtyping (non-small cell lung cancer [NSCLC], small cell lung cancer [SCLC], or other malignancies), and 0.9297 (95% CI: 0.9026–0.9603) in NSCLC subtyping (lung adenocarcinoma [LUAD], lung squamous cell carcinoma [LUSC], or NSCLC not otherwise specified [NSCLC-NOS]), respectively. In total, the model achieved an AUC of 0.8721 (95% CI: 0.7714–0.9258) and an accuracy of 0.7184 in the six-class classification task (benign, LUAD, LUSC, NSCLC-NOS, SCLC, or other malignancies). In addition, the model demonstrated a κ value of 0.6183 with the annotation, which was comparable to cytopathologists and superior to trained bronchoscopists and technicians.ConclusionThe HMLCS model showed promising performance in the multiclassification of lung lesions or intrathoracic lymphadenopathy, with potential application to provide real-time feedback regarding preliminary diagnoses of specimens during transbronchial sampling procedures.Clinical trial numberNot applicable.