The diagnosis of non-small cell lung cancer in the molecular era

Deep learning radiomics for the prediction of epidermal growth factor receptor mutation status based on MRI in brain metastasis from lung adenocarcinoma patients

BACKGROUND

Early and accurate identification of epidermal growth factor receptor (EGFR) mutation status in non-small cell lung cancer (NSCLC) patients with brain metastases is critical for guiding targeted therapy. This study aimed to develop a deep learning radiomics model utilizing multi-sequence magnetic resonance imaging (MRI) to differentiate between EGFR mutant type (MT) and wild type (WT).

METHODS

In this retrospective study, 288 NSCLC patients with confirmed brain metastases were enrolled, including 106 with EGFR MT and 182 with EGFR WT. All patients were randomly divided into a training dataset (75%) and a validation dataset (25%). Radiomics and deep learning features were extracted from the brain metastatic lesions using contrast-enhanced T1-weighted (T1CE) and T2-weighted (T2W) MRI images. Features extraction and selection were performed using the least absolute shrinkage and selection operator (LASSO) and ResNet34. The predictive performance of the signatures for EGFR mutation status was assessed using receiver operating characteristic (ROC) curves and area under the curve (AUC) analyses.

RESULTS

No significant differences were found between the training and validation datasets. A four-feature radiomics signature (RS) demonstrated excellent predictive accuracy for EGFR MT, with α-binormal-based and empirical AUCs of 0.931 (95% CI: 0.880-0.940) and 0.926 (95% CI: 0.877-0.933), respectively. Incorporating deep learning signature (DLS) further enhanced the model's performance, achieving α-binormal-based and empirical AUCs of 0.943 (95% CI: 0.921-0.965) and 0.938 (95% CI: 0.914-0.962) in the training dataset. These findings were confirmed in the validation dataset, with AUCs of 0.936 (95% CI: 0.917-0.955) and 0.921 (95% CI: 0.901-0.941), demonstrating robust and consistent predictive performance.

CONCLUSIONS

The multi-sequence MRI-based deep learning radiomics model exhibited high efficacy in predicting EGFR mutation status in NSCLC patients with brain metastases. This approach, which integrates advanced radiological features with deep learning techniques, offers a non-invasive and accurate method for determining EGFR mutation status, potentially guiding personalized treatment decisions in clinical practice.

Development and validation of a nomogram for predicting histologic subtypes of subpleural non-small cell lung cancer using ultrasound parameters and clinical data

Aims

To develop and validate an individualized nomogram for differentiating the histologic subtypes (adenocarcinoma and squamous cell carcinoma) of subpleural non-small cell lung cancer (NSCLC) based on ultrasound parameters and clinical data.

Methods

This study was conducted retrospectively between March 2018 and December 2019. Patients were randomly assigned to a development cohort (DC, n=179) and a validation cohort (VC, n=77). A total of 7 clinical parameters and 16 ultrasound parameters were collected. Least absolute shrinkage and selection operator regression analysis was employed to identify the most significant predictors utilizing a 10-fold cross-validation. The multivariate logistic regression model was applied to investigate the relevant factors. An individualized nomogram was then developed. Receiver operating characteristic (ROC) curve, calibration plot and decision curve analysis (DCA) were applied for model validation in both DC and VC.

Results

Following the final regression analysis, gender, serum carcinoembryonic antigen, lesion size and perfusion defect in contrast-enhanced ultrasound were entered into the nomogram. The model showed moderate predictive ability, with an area under the ROC curve of 0.867 for DC and 0.838 for VC. The calibration curves of the model showed good agreement between actual and predicted probabilities. The ROC and DCA curves demonstrated that the nomogram exhibited a good predictive performance.

Conclusion

We developed a nomogram that can predict the histologic subtypes of subpleural NSCLC. Both internal and external validation revealed optimal discrimination and calibration, indicating that the nomogram may have clinical utility. This model has the potential to assist clinicians in making treatment recommendations.

Habitat-Based Radiomics for Predicting EGFR Mutations in Exon 19 and 21 From Brain Metastasis

RATIONALE AND OBJECTIVES

To explore and externally validate habitat-based radiomics for preoperative prediction of epidermal growth factor receptor (EGFR) mutations in exon 19 and 21 from MRI imaging of non-small cell lung cancer (NSCLC)-originated brain metastasis (BM).

METHODS

A total of 170, 62 and 61 patients from center 1, center 2 and center 3, respectively were included. All patients underwent contrast-enhanced T1-weighted (T1CE) and T2-weighted (T2W) MRI scans. Radiomics features were extracted from the tumor active (TA) and peritumoral edema (PE) regions in each MRI slice. The most important features were selected by the least absolute shrinkage and selection operator regression to develop radiomics signatures based on TA (RS-TA), PE (RS-PE) and their combination (RS-Com). Receiver operating characteristic (ROC) curve analysis was performed to access performance of radiomics models for both internal and external validation cohorts.

RESULTS

10, four and six most predictive features were identified to be strongly associated with the EGFR mutation status, exon 19 and exon 21, respectively. The RSs derived from the PE region outperformed those from the TA region for predicting the EGFR mutation, exon 19 and exon 21. The RS-Coms generated the highest performance in the primary training (AUCs, RS-EGFR-Com vs. RS-exon 19-Com vs. RS-exon 21-Com, 0.955 vs. 0.946 vs. 0.928), internal validation (AUCs, RS-EGFR-Com vs. RS-exon 19-Com vs. RS-exon 21-Com, 0.879 vs. 0.819 vs. 0.882), external validation 1 (AUCs, RS-EGFR-Com vs. RS-exon 19-Com vs. RS-exon 21-Com, 0.830 vs. 0.825 vs. 0.822), and external validation 2 (AUCs, RS-EGFR-Com vs. RS-exon 19-Com vs. RS-exon 21-Com, 0.812 vs. 0.818 vs. 0.800) cohort.

CONCLUSION

The developed habitat-based radiomics model can be used to accurately predict the EGFR mutation subtypes, which may potentially guide personalized treatments for NSCLC patients with BM.

Characterization of immunogenic cell death regulators predicts survival and immunotherapy response in lung adenocarcinoma

Lung adenocarcinoma (LUAD) is highly lethal tumor; understanding immune response is crucial for current effective treatment. Research investigated immunogenic cell death (ICD) impact on LUAD through 75 ICD-related genes which encompass cell damage, endoplasmic reticulum stress, microenvironment, and immunity. Transcriptome data and clinical info were analyzed, revealing two ICD-related clusters: B, an immune osmotic subgroup, had better prognosis, stronger immune signaling, and higher infiltration, while A represented an immune-deficient subgroup. Univariate Cox analysis identified six prognostic genes (AGER, CD69, CD83, CLEC9A, CTLA4, and NT5E), forming a validated risk score model. It was validated across datasets, showing predictive performance. High-risk group had unfavorable prognosis, lower immune infiltration, and higher chemotherapy sensitivity. Conversely, low-risk group had better prognosis, higher immune infiltration, and favorable immunotherapy response. The key gene NT5E was examined via immunohistochemistry, with higher expression linked to poorer prognosis. NT5E was predominantly expressed in B cells, fibroblasts, and endothelial cells, correlated with immune checkpoints. These outcomes suggest that NT5E can serve as a LUAD therapeutic target. The study highlights gene predictive value, offers an efficient tumor assessment tool, guides clinical treatment strategies, and identifies NT5E as therapeutic target for LUAD.

High TLX1 Expression Correlates with Poor Prognosis and Immune Infiltrates in Patients with Lung Adenocarcinoma

BACKGROUND

To develop optimal personalized therapy for lung adenocarcinoma (LUAD), potential biomarkers associated with the prognosis are urgently needed. It is unclear what role T Cell Leukemia Homeobox 1 (TLX1) plays in LUAD.

OBJECTIVE

In this study, TLX1's relationship with LUAD was investigated using TCGA database analysis, bioinformatics analysis, and experimental validation.

METHODS

We examined the expression of TLX1 in pan cancer and LUAD, the relationship between TLX1 expression and clinical features, immune infiltration, its diagnostic and prognostic value, as well as TLX1 related pathways. The analysis included various statistical methods, including the Kaplan-Meier method, Cox regression analysis, GSEA, and immune infiltration analysis. TLX1 expression in LUAD cell lines was validated using qRT-PCR.

RESULT

In LUAD patients, high expression of TLX1 was associated with T stage (P<0.001). High TLX1 expression was associated with worse overall survival (OS) (HR: 1.57; 95% CI: 1.18-2.1; P=0.002). And TLX1 HR: 1.619; 95% CI: 1.012-2.590; P=0.044) was independently correlated with OS in LUAD patients. TLX1 expression was associated with the pathways, including Rho GTPase effectors, DNA repair, TCF dependent signaling in response to WNT, signaling by Nuclear Receptors, signaling by Notch, chromatin-modifying enzymes, ESR-mediated signaling, cellular senescence, and transcriptional regulation by Runx1. TLX1 expression was correlated with aDC, Tcm, and TReg cells. The expression of TLX1 was significantly increased in LUAD cells compared to BEAS-2B cells.

CONCLUSION

An association between high TLX1 expression and poor survival and immune infiltration was found in LUAD patients. There may be a potential role for TLX1 in diagnosis, prognosis, and immunotherapy for LUAD.

Brain-Tumor Interface-Based MRI Radiomics Models to Determine EGFR Mutation, Response to EGFR-TKI and T790M Resistance Mutation in Non-Small Cell Lung Carcinoma Brain Metastasis

BACKGROUND

Preoperative assessment of epidermal growth factor receptor (EGFR) status, response to EGFR-tyrosine kinase inhibitors (TKI) and development of T790M mutation in non-small cell lung carcinoma (NSCLC) patients with brain metastases (BM) is important for clinical decision-making, while previous studies were only based on the whole BM.

PURPOSE

To investigate values of brain-to-tumor interface (BTI) for determining the EGFR mutation, response to EGFR-TKI and T790M mutation.

STUDY TYPE

Retrospective.

POPULATION

Two hundred thirty patients from Hospital 1 (primary cohort) and 80 patients from Hospital 2 (external validation cohort) with BM and histological diagnosis of primary NSCLC, and with known EGFR status (biopsy) and T790M mutation status (gene sequencing).

FIELD STRENGTH/SEQUENCE

Contrast-enhanced T1-weighted (T1CE) and T2-weighted (T2W) fast spin echo sequences at 3.0T MRI.

ASSESSMENT

Treatment response to EGFR-TKI therapy was determined by the Response Evaluation Criteria in Solid Tumors. Radiomics features were extracted from the 4 mm thickness BTI and selected by least shrinkage and selection operator regression. The selected BTI features and volume of peritumoral edema (VPE) were combined to construct models using logistic regression.

STATISTICAL TESTS

The performance of each radiomics model was evaluated using the area under the receiver operating characteristic (ROC) curve (AUC).

RESULTS

A total of 7, 3, and 3 features were strongly associated with the EGFR mutation status, response to EGFR-TKI and T790M mutation status, respectively. The developed models combining BTI features and VPE can improve the performance than those based on BTI features alone, generating AUCs of 0.814, 0.730, and 0.774 for determining the EGFR mutation, response to EGFR-TKI and T790M mutation, respectively, in the external validation cohort.

DATA CONCLUSION

BTI features and VPE were associated with the EGFR mutation status, response to EGFR-TKI and T790M mutation status in NSCLC patients with BM.

EVIDENCE LEVEL

3 Technical Efficacy: Stage 2.

Coexistence of Rosai-Dorfman disease and Hodgkin's lymphoma in a patient with cervical lymphadenopathy

A man in his 40s, with no tobacco or alcohol habit, was referred to the otorhinolaryngology department presenting with a 2-month history of enlarged left cervical lymphadenopathy with no other signs or symptoms. The ear, nose and throat examination showed no abnormalities apart from the described lymphadenopathy. An ultrasound scan suggested these nodes to be part of either an inflammatory or a malignant process. Subsequent positron emission tomography-CT proved those lymph nodes to be metabolically active, as well as others within the thorax. Cervicotomy was performed and the histopathological analysis showed dilated sinuses and histiocytes with emperipolesis. Suspecting Rosai-Dorfman disease (RDD), high-dose steroid therapy was started; but given no improvement was observed, a second cervicotomy was performed, with the histopathological diagnosis of the latter of Hodgkin's lymphoma. The present article aims to emphasise the need to exclude haematological disorders whenever RDD histology is observed, given their possible coexistence, and a worse outcome and clinical and histopathological semblance.

[Large cell carcinoma of the lung with null immunophenotype: Case report & brief review]

Large cell carcinoma of the lung with null-immunophenotype (LCC-NI) is a diagnostic entity that is especially uncommon now as it does not have any type of cell differentiation or its own molecular alterations. It presents an exceptional diagnostic challenge; indeed, the diagnosis is only possible with complete surgical excision and adequate immunohistochemical and molecular studies. We report the case of a 69-year-old male, with a history of long-term smoking who presented with pleuritic pain. A tumor in the upper lobe of the right lung was detected and removed by lobectomy. Histopathology revealed a neoplasm with large cell morphology without any specific immunophenotype, molecular or genomic rearrangements through next-generation sequencing (NGS) studies, which was diagnosed as LCC-NI.

miR-338-3p acts as a tumor suppressor in lung squamous cell carcinoma by targeting FGFR2/FRS2

Background

Lung cancer refers to the occurrence of malignant tumors in the lung, and squamous cell carcinoma is one of the most common pathological types of non-small cell lung cancer. Studies have shown that microRNAs (miRNAs) play an important role in the occurrence, development, early diagnosis, and treatment of lung cancer. This study aimed to explore the role and possible mechanism of MicroRNA-338-3p (miR-338-3p) in lung squamous cell carcinoma (LUSC).

Method

In this study, we compared 238 LUSC patients with relatively high miR-338-3p expression levels with 238 miR-338-3p expression levels in The Cancer Genome Atlas (TCGA)-LUSC dataset using first-line gene set enrichment analysis (GSEA). Second, the mRNA expression of miR-338-3p, FGFR2, and fibroblast growth factor receptor substrate 2 (FRS2) in 30 lung cancers and adjacent lung tissues was detected using quantitative real-time polymerase chain reaction (qRT-PCR). Finally, in vitro experiments were conducted, whereby the expression levels of miR-338-3p in lung cancer cells (H1703, SKMES1, H2170, H520) and normal lung epithelial cells (16HBE) were detected using qRT-PCR. miR-338-3p was overexpressed in lung cancer cells (H1703), and the cell proliferation (cell counting kit-8 [CCK8] assay), colony formation, cell apoptosis, cell cycle (BD-FACSVerse assay, Becton Dickinson, Bedford, MA, USA), cell invasion, and migration (Transwell assay, Thermo Fischer Corporation, Waltham, MA, USA) were detected.

Results

We found that the expression of miR-338-3p was significantly reduced in LUSC tissues (p ​< ​0.001) and cancer cell lines (P < 0.01), and miR-338-3p was significantly negatively correlated with the expression of FGFR2 (P < 0.001) and FRS2 (P < 0.01). Furthermore, overexpression of miR-338-3p inhibited proliferation (P < 0.001), migration, and invasion (P < 0.001) of LUSC cell lines and increased apoptosis in the G1 phase (P < 0.001) and cell cycle arrest (P < 0.05).

Conclusions

Our study demonstrates that miR-338-3p inhibits tumor cell proliferation and migration by targeting FGFR2 and FRS2 in LUSC. We believe that miR-338-3p may be a promising target for the treatment of LUSC.

Índice para predecir el riesgo de cáncer de pulmón

El cáncer de pulmón es la consecuencia de un crecimiento incontrolado y anormal de las células del pulmón, su incidencia y mortalidad es elevada en todo el mundo. El objetivo de este trabajo fue diseñar un índice para predecir el riesgo de desarrollar el cáncer de pulmón. Se realizó un estudio de casos y testigos desde el 1ro de enero de 2018 hasta 30 de junio de 2020, en pacientes diagnosticados con cáncer de pulmón en el hospital general universitario “Carlos Manuel de Céspedes” de Bayamo provincia de Granma. Los factores de mayor valor patogénico fueron índice tabáquico (OR = 5,21; IC = 2,57 a 10,55; p = 0,000) la fibrosis pulmonar (OR = 4,06; IC = 1,61 a 10,23; p = 0,000) y el antecedente familiar de cáncer (OR = 3,30; IC = 1,50 a 7,06; p = 0,000) todos de forma independiente. El índice clasificó correctamente al 78 % de los pacientes, con una sensibilidad (70,0 %) y la especificidad (86,0 %). Un área bajo la curva ROC de 0,802 (IC 95 % = 0,706 a 0,818; p = 0,000) indica que el índice discrimina mejor que el azar el riesgo de desarrollar el cáncer de pulmón de forma significativa. También la prueba de Hosmer y Lemeshow indica buena calibración del índice (p 0,489). El índice diseñado, a partir de los factores de riesgo independientes, permite predecir, el riesgo de desarrollar el cáncer de pulmón con adecuada validez. Palabras claves: cáncer de pulmón, factores de riesgo, índice

Molecular Pathology of Lung Cancer

This overview of the molecular pathology of lung cancer includes a review of the most salient molecular alterations of the genome, transcriptome, and the epigenome. The insights provided by the growing use of next-generation sequencing (NGS) in lung cancer will be discussed, and interrelated concepts such as intertumor heterogeneity, intratumor heterogeneity, tumor mutational burden, and the advent of liquid biopsy will be explored. Moreover, this work describes how the evolving field of molecular pathology refines the understanding of different histologic phenotypes of non-small-cell lung cancer (NSCLC) and the underlying biology of small-cell lung cancer. This review will provide an appreciation for how ongoing scientific findings and technologic advances in molecular pathology are crucial for development of biomarkers, therapeutic agents, clinical trials, and ultimately improved patient care.

Gut Microbiota-Derived PGF2α Fights against Radiation-Induced Lung Toxicity through the MAPK/NF-κB Pathway

Radiation pneumonia is a common and intractable side effect associated with radiotherapy for chest cancer and involves oxidative stress damage and inflammation, prematurely halting the remedy and reducing the life quality of patients. However, the therapeutic options for the complication have yielded disappointing results in clinical application. Here, we report an effective avenue for fighting against radiation pneumonia. Faecal microbiota transplantation (FMT) reduced radiation pneumonia, scavenged oxidative stress and improved lung function in mouse models. Local chest irradiation shifted the gut bacterial taxonomic proportions, which were preserved by FMT. The level of gut microbiota-derived PGF2α decreased following irradiation but increased after FMT. Experimental mice with PGF2α replenishment, via an oral route, exhibited accumulated PGF2α in faecal pellets, peripheral blood and lung tissues, resulting in the attenuation of inflammatory status of the lung and amelioration of lung respiratory function following local chest irradiation. PGF2α activated the FP/MAPK/NF-κB axis to promote cell proliferation and inhibit apoptosis with radiation challenge; silencing MAPK attenuated the protective effect of PGF2α on radiation-challenged lung cells. Together, our findings pave the way for the clinical treatment of radiotherapy-associated complications and underpin PGF2α as a gut microbiota-produced metabolite.

Radiomics for Predicting Lung Cancer Outcomes Following Radiotherapy: A Systematic Review

Lung cancer's radiomic phenotype may potentially inform clinical decision-making with respect to radical radiotherapy. At present there are no validated biomarkers available for the individualisation of radical radiotherapy in lung cancer and the mortality rate of this disease remains the highest of all other solid tumours. MEDLINE was searched using the terms 'radiomics' and 'lung cancer' according to the Preferred Reporting Items for Systematic Reviews and Met-Analyses (PRISMA) guidance. Radiomics studies were defined as those manuscripts describing the extraction and analysis of at least 10 quantifiable imaging features. Only those studies assessing disease control, survival or toxicity outcomes for patients with lung cancer following radical radiotherapy ± chemotherapy were included. Study titles and abstracts were reviewed by two independent reviewers. The Radiomics Quality Score was applied to the full text of included papers. Of 244 returned results, 44 studies met the eligibility criteria for inclusion. End points frequently reported were local (17%), regional (17%) and distant control (31%), overall survival (79%) and pulmonary toxicity (4%). Imaging features strongly associated with clinical outcomes include texture features belonging to the subclasses Gray level run length matrix, Gray level co-occurrence matrix and kurtosis. The median cohort size for model development was 100 (15-645); in the 11 studies with external validation in a separate independent population, the median cohort size was 84 (21-295). The median number of imaging features extracted was 184 (10-6538). The median Radiomics Quality Score was 11% (0-47). Patient-reported outcomes were not incorporated within any studies identified. No studies externally validated a radiomics signature in a registered prospective study. Imaging-derived indices attained through radiomic analyses could equip thoracic oncologists with biomarkers for treatment response, patterns of failure, normal tissue toxicity and survival in lung cancer. Based on routine scans, their non-invasive nature and cost-effectiveness are major advantages over conventional pathological assessment. Improved tools are required for the appraisal of radiomics studies, as significant barriers to clinical implementation remain, such as standardisation of input scan data, quality of reporting and external validation of signatures in randomised, interventional clinical trials.

Subregional radiomics analysis for the detection of the EGFR mutation on thoracic spinal metastases from lung cancer

The present study intended to use radiomic analysis of spinal metastasis subregions to detect epidermal growth factor receptor (EGFR) mutation. In total, 94 patients with thoracic spinal metastasis originated from primary lung adenocarcinoma (2017-2020) were studied. All patients underwent T1-weighted (T1W) and T2 fat-suppressed (T2FS) MRI scans. The spinal metastases (tumor region) were subdivided into phenotypically consistent subregions based on patient- and population-level clustering: Three subregions, S1, S2 and S3, and the total tumor region. Radiomics features were extracted from each subregion and from the whole tumor region as well. Least shrinkage and selection operator (LASSO) regression were used for feature selection and radiomics signature definition. Detection performance of S3 was better than all other regions using T1W (AUCs, S1 versus S2 versus S3 versus whole tumor, 0.720 versus 0.764 versus 0.786 versus 0.758) and T2FS (AUCs, S1 versus S2 versus S3 versus whole tumor, 0.791 versus 0.708 versus 0.838 versus 0.797) MRI. The multi-regional radiomics signature derived from the joint of inner subregion S3 from T1W and T2FS MRI achieved the best detection capabilities with AUCs of 0.879 (ACC = 0.774, SEN = 0.838, SPE = 0.840) and 0.777 (ACC = 0.688, SEN = 0.947, SPE = 0.615) in the training and test sets, respectively. Our study revealed that MRI-based radiomic analysis of spinal metastasis subregions has the potential to detect the EGFR mutation in patients with primary lung adenocarcinoma.

Influencing Factors and Prognostic Value of 18F-FDG PET/CT Metabolic and Volumetric Parameters in Non-Small Cell Lung Cancer

Objective

This study aims to explore factors influencing metabolic and volumetric parameters of [¹⁸F]fluorodeoxyglucose positron emission tomography/computed tomography (¹⁸F-FDG PET/CT) imaging in non-small cell lung cancer (NSCLC) and the predictive value for prognosis of NSCLC.

Methods

Retrospective analysis was performed on 133 NSCLC patients who received ¹⁸F-FDG PET/CT imaging. After ¹⁸F-FDG injection at 3.7 MBq/kg, 1 h early imaging and 2 h delayed imaging were performed. The metabolic and volumetric parameters such as SUVmax, SUVpeak, SULmax, SULpeak, MTV and TLG were measured. The tumor markers including CFYRA21-1, NSE, SCC-ag and the immunohistochemical biomarkers including Ki-67, P53 and CK-7 were examined. All patients were followed up for 24 months, and the 1-year and 2-year overall survival rate (OS) were recorded.

Results

There were significant differences in metabolic and volumetric parameters (SUVmax, SUVpeak, SULmax, SULpeak and TLG) between adenocarcinoma and squamous cell carcinoma of NSCLC. SUVmax, SUVpeak, SULmax, SULpeak, MTV and TLG were correlated with tumor marker NSE and TNM stage. MTV and TLG were related to CYFRA21-1, and only MTV was associated with SCC-ag. SUVpeak and SULmax were related to P53. In addition, early SULpeak and delayed MTV were significant prognostic factors of 1-year OS, while early SUVpeak, delayed TLG and delayed MTV were predictive factors of 2-year OS in NSCLC.

Conclusion

The metabolic and volumetric parameters of ¹⁸F-FDG PET/CT were related to a variety of factors such as NSE, CFYRA21-1, SCC-ag, P53 and TNM stage, and have a predictive value in prognosis of NSCLC.

A subregion-based positron emission tomography/computed tomography (PET/CT) radiomics model for the classification of non-small cell lung cancer histopathological subtypes

Background

This study classifies lung adenocarcinoma (ADC) and squamous cell carcinoma (SCC) using subregion-based radiomics features extracted from positron emission tomography/computed tomography (PET/CT) images.

Methods

In this study, the standard ¹⁸F-fluorodeoxyglucose (FDG) PET/CT images of 150 patients with lung ADC and 100 patients with SCC were retrospectively collected from the PET Center of the First Affiliated Hospital, College of Medicine, Zhejiang University. First, the 3D feature vector of each tumor voxel (whose basis is PET value, CT value, and CT local dominant orientation) was extracted. Using K-means individual clustering and population clustering, each tumor was divided into 4 subregions that reflect intratumoral regional heterogeneity. Next, based on each subregion, 385 radiomics features were extracted. Clinical features including age, gender, and smoking history were included. Thus, there were a total of 1,543 features extracted from PET/CT images and clinical reports. Statistical tests were then used to eliminate irrelevant and redundant features, and the recursive feature elimination (RFE) algorithm was used to select the best feature subset to classify SCC and ADC. Finally, 7 types of classifiers were tested to achieve the optimized model for the classification: support vector machine (SVM) with linear kernel, SVM with radial basis function kernel (SVM-RBF), random forest, logistic regression, Gaussian process classifier, linear discriminant analysis, and the AdaBoost classifier. Furthermore, 5-fold cross-validation was applied to obtain the sensitivity, specificity, accuracy, and area under the curve (AUC) for performance evaluation.

Results

Our model exhibited the best performance with the subregion radiomics features and SVM-RBF classifier, with a 5-fold cross-validation sensitivity, specificity, accuracy, and AUC of 0.8538, 0.8758, 0.8623, and 0.9155, respectively. The interquartile range feature from subregion 2 of CT and the gender feature from the clinical reports are the 2 optimized features that achieved the highest comprehensive score.

Conclusions

Our proposed model showed that SCC and ADC could be classified successfully using PET/CT images, which could be a promising tool to assist radiologists or medical physicists during diagnosis. The subregion-based method illustrated that non-small cell lung cancer (NSCLC) depicts intratumoral regional heterogeneity on both CT and PET images. By defining these heterogeneities through a subregion-based method, the diagnostic performance was improved. The 3D feature vector (whose basis is PET value, CT value, and CT local dominant orientation) showed superiority in reflecting NSCLC intratumoral regional heterogeneity.

Consistency of recommendations for the diagnosis and treatment of non-small cell lung cancer: a systematic review

BACKGROUND

To systematically assess the consistency of recommendations regarding diagnosis and treatment of non-small cell lung cancer (NSCLC) in clinical practice guidelines (CPGs).

METHODS

We systematically searched relevant literature databases and websites to identify CPGs related to NSCLC. We extracted the general characteristics of the included guidelines and their recommendations and descriptively compared and analyzed the consistency of recommendations across the guidelines.

RESULTS

A total of 28 NSCLC guidelines were retrieved. The recommendations covered mainly diagnosis and treatment. The recommendations in the guidelines differed substantially in various topics, such as the application of positron emission tomography (PET) and the classification of stage III. Fourteen guidelines divided stage III into two types: operable and inoperable; and the remaining 14 guidelines into three sub-stages IIIA, IIIB and IIIC. Recommendations regarding the treatment in stage III were relatively inconsistent. In driver gene (EGFR, ALK, ROS1) positive patients, targeted therapy was the most common recommendation for first-line treatment, but recommendations regarding second-line treatment varied according to the site of the mutation. In driver gene negative patients, immunotherapy was the most frequently recommended option as both first- and second-line treatment, followed by chemotherapy.

DISCUSSION

A number of countries are devoting themselves to develop NSCLC guidelines and the process of updating guidelines is accelerating, yet recommendations between guidelines are not consistent. We adopted a systematic review method to systematically search and analyze the NSCLC guidelines worldwide. We objectively reviewed the differences in recommendations for NSCLC diagnosis and treatment between the guidelines. Inconsistency of recommendations across guidelines can result from multiple potential reasons. Such as, the guidelines developed time, different countries and regions and many more. Poor consistency across CPGs can confuse the guideline users, and we therefore advocate paying more attention to examining the controversies and updating guidelines timely to improve the consistency among CPGs. Our study had also several limitations, we limited the search to CPGs published in Chinese or English, the interpretation of recommendations is inherently subjective, we did not evaluate the details of the clinical content of the CPG recommendations. Our research presents the current status of NSCLC guidelines worldwide and give the opportunity to pay more attention to the existing gaps. Further investigations should determine the reasons for inconsistency, the implications for recommendation development, and the role of synthesis across recommendations for optimal guidance of clinical care treatment. With the continuous revision and update of the guidelines, we are confident that future guidelines will be formulated with higher quality to form clear, definite and consistent recommendations for NSCLC diagnosis and treatment.

Comprehensive and Computable Molecular Diagnostic Panel (C2Dx) From Small Volume Specimens for Precision Oncology: Molecular Subtyping of Non-Small Cell Lung Cancer From Fine Needle Aspirates

The Comprehensive, Computable NanoString Diagnostic gene panel (C2Dx) is a promising solution to address the need for a molecular pathological research and diagnostic tool for precision oncology utilizing small volume tumor specimens. We translate subtyping-related gene expression patterns of Non-Small Cell Lung Cancer (NSCLC) derived from public transcriptomic data which establish a highly robust and accurate subtyping system. The C2Dx demonstrates supreme performance on the NanoString platform using microgram-level FNA samples and has excellent portability to frozen tissues and RNA-Seq transcriptomic data. This workflow shows great potential for research and the clinical practice of cancer molecular diagnosis.

Targeted Therapy and Checkpoint Immunotherapy in Lung Cancer

Lung cancer is the leading cause of cancer mortality. It is classified into different histologic subtypes, including adenocarcinoma, squamous carcinoma, and large cell carcinoma (commonly referred as non-small cell lung cancer) and small cell lung cancer. Comprehensive molecular characterization of lung cancer has expanded our understanding of the cellular origins and molecular pathways affected in each of these subtypes. Many of these genetic alterations represent potential therapeutic targets for which drugs are constantly under development. This article discusses the molecular characteristics of the main lung cancer subtypes and discusses the current guidelines and novel targeted therapies, including checkpoint immunotherapy.

AHNAK2 Is Associated with Poor Prognosis and Cell Migration in Lung Adenocarcinoma

BACKGROUND

Lung adenocarcinoma (LUAD), as the main subtype of lung cancer, is one of the common causes of cancer-related deaths worldwide. The AHNAK family is correlated with cell structure and migration, cardiac calcium channel signaling, and tumor metastasis. Previous studies showed AHNAK2 could promote tumor progression and cell migration in melanoma and renal clear cell carcinoma. However, the role of AHNAK2 in LUAD remains unknown.

METHODS

We examined the levels of AHNAK2 in pathological specimens and the database of Clinical Proteomic Tumor Analysis Consortium-Lung adenocarcinoma (CPTAC-LUAD), The Cancer Genome Atlas-Lung Adenocarcinoma (TCGA-LUAD), Gene Expression Omnibus dataset (GSE72094, GSE26939), and The Genotype-Tissue Expression (GTEx) of lung tissue samples. Univariate Cox regression, multivariate Cox regression, and Kaplan-Meier survival analysis were performed to reveal the relationship between AHNAK2 and prognosis. A nomogram was constructed to predict 2- or 3-year overall survival and validated via calibration curves, receiver operating characteristic (ROC) analysis, and decision curve analysis (DCA). Furthermore, Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis were used to explore the functional role of AHNAK2 in lung adenocarcinoma. Finally, by transfecting siRNA, we examined the regulatory effect of AHNAK2 on cell migration.

RESULTS

The expression of AHNAK2 was upregulated in tumor samples and correlated with poor prognosis in LUAD patients. Nomogram with AHNAK2 and clinical parameters showed a good prediction in overall survival (OS), especially the 2-year OS. In addition, functional analyses and wound healing assay suggested that AHNAK2 might be involved in the regulation of migration in LUAD.

CONCLUSION

In summary, our study showed that AHNAK2 might be a novel biomarker in LUAD and revealed the potential mechanism of AHNAK2 in LUAD progression which could provide new insights for target therapy.

Relevance Function of Linc-ROR in the Pathogenesis of Cancer

Long non-coding RNAs (lncRNAs) are the key components of non-coding RNAs (ncRNAs) with a length of 200 nucleotides. They are transcribed from the so-called “dark matter” of the genome. Increasing evidence have shown that lncRNAs play an important role in the pathophysiology of human diseases, particularly in the development and progression of tumors. Linc-ROR, as a new intergenic non-protein coding RNA, has been considered to be a pivotal regulatory factor that affects the occurrence and development of human tumors, including breast cancer (BC), colorectal cancer (CRC), pancreatic cancer (PC), hepatocellular carcinoma (HCC), and so on. Dysregulation of Linc-ROR has been closely related to advanced clinicopathological factors predicting a poor prognosis. Because linc-ROR can regulate cell proliferation, apoptosis, migration, and invasion, it can thus be used as a potential biomarker for patients with tumors and has potential clinical significance as a therapeutic target. This article reviewed the role of linc-ROR in the development of tumors, its related molecular mechanisms, and clinical values.

Characterization of TNNC1 as a Novel Tumor Suppressor of Lung Adenocarcinoma

In this study, we describe a novel function of TNNC1 (Troponin C1, Slow Skeletal and Cardiac Type), a component of actin-bound troponin, as a tumor suppressor of lung adenocarcinoma (LUAD). First, the expression of TNNC1 was strongly down-regulated in cancer tissues compared to matched normal lung tissues, and down-regulation of TNNC1 was shown to be strongly correlated with increased mortality among LUAD patients. Interestingly, TNNC1 expression was enhanced by suppression of KRAS, and ectopic expression of TNNC1 in turn inhibited KRASG12D-mediated anchorage independent growth of NIH3T3 cells. Consistently, activation of KRAS pathway in LUAD patients was shown to be strongly correlated with down-regulation of TNNC1. In addition, ectopic expression of TNNC1 inhibited colony formation of multiple LUAD cell lines and induced DNA damage, cell cycle arrest and ultimately apoptosis. We further examined potential correlations between expression levels of TNNC1 and various clinical parameters and found that low-level expression is significantly associated with invasiveness of the tumor. Indeed, RNA interference-mediated down-regulation of TNNC1 led to significant enhancement of invasiveness in vitro. Collectively, our data indicate that TNNC1 has a novel function as a tumor suppressor and is targeted for down-regulation by KRAS pathway during the carcinogenesis of LUAD.

Identification Six Metabolic Genes as Potential Biomarkers for Lung Adenocarcinoma

Metabolic genes have been reported to act as crucial roles in tumor progression. Lung adenocarcinoma (LUAD) is one of the most common cancers worldwide. This study aimed to predict the potential mechanism and novel markers of metabolic signature in LUAD. The gene expression profiles and the clinical parameters were obtained from The Cancer Genome Atlas-Lung adenocarcinoma (TCGA-LUAD) and Gene Expression Omnibus data set (GSE72094). A total of 105 differentially expressed metabolic genes of intersect expression in TCGA-LUAD and GSE72094 were screened by R language. Univariate Cox regression model found 18 survival-related genes and then the least absolute shrinkage and selection operator model was successfully constructed. Six significant genes prognostic model was validated though independent prognosis analysis. The model revealed high values for prognostic biomarkers by time-dependent receiver operating characteristic (ROC) analysis, risk score, Heatmap, and nomogram. In addition, Gene Set Enrichment Analysis showed that multiplex metabolism pathways correlated with LUAD. Furthermore, we found the six genes aberrantly expressed in LUAD samples. Our study showed that metabolism pathways play important roles in LUAD progression. The six metabolic genes could predict potential prognostic and diagnostic biomarkers in LUAD.

Implementation of Novel Molecular Biomarkers for Non-small Cell Lung Cancer in the Netherlands: How to Deal With Increasing Complexity

The diagnostic landscape of non-small cell lung cancer (NSCLC) is changing rapidly with the availability of novel treatments. Despite high-level healthcare in the Netherlands, not all patients with NSCLC are tested with the currently relevant predictive tumor markers that are necessary for optimal decision-making for today's available targeted or immunotherapy. An expert workshop on the molecular diagnosis of NSCLC involving pulmonary oncologists, clinical chemists, pathologists, and clinical scientists in molecular pathology was held in the Netherlands on December 10, 2018. The aims of the workshop were to facilitate cross-disciplinary discussions regarding standards of practice, and address recent developments and associated challenges that impact future practice. This paper presents a summary of the discussions and consensus opinions of the workshop participants on the initial challenges of harmonization of the detection and clinical use of predictive markers of NSCLC. A key theme identified was the need for broader and active participation of all stakeholders involved in molecular diagnostic services for NSCLC, including healthcare professionals across all disciplines, the hospitals and clinics involved in service delivery, healthcare insurers, and industry groups involved in diagnostic and treatment innovations. Such collaboration is essential to integrate different technologies into molecular diagnostics practice, to increase nationwide patient access to novel technologies, and to ensure consensus-preferred biomarkers are tested.

LncRNA SNHG15: A new budding star in human cancers

OBJECTIVES

Long non-coding RNAs (lncRNAs) represent an important group of non-coding RNAs (ncRNAs) with more than 200 nucleotides in length that are transcribed from the so-called genomic "dark matter." Mounting evidence has shown that lncRNAs have manifested a paramount function in the pathophysiology of human diseases, especially in the pathogenesis and progression of cancers. Despite the exponential growth in lncRNA publications, our understanding of regulatory mechanism of lncRNAs is still limited, and a lot of controversies remain in the current lncRNA knowledge.The purpose of this article is to explore the clinical significance and molecular mechanism of SNHG15 in tumors.

MATERIALS & METHODS

We have systematically searched the Pubmed, Web of Science, Embase and Cochrane databases. We provide an overview of current evidence concerning the functional role, mechanistic models and clinical utilities of SNHG15 in human cancers in this review.

RESULTS

Small nucleolar RNA host gene 15 (SNHG15), a novel lncRNA, is identified as a key regulator in tumorigenesis and progression of various human cancers, including colorectal cancer (CRC), gastric cancer (GC), pancreatic cancer (PC) and hepatocellular carcinoma (HCC). Dysregulation of SNHG15 has been revealed to be dramatically correlated with advanced clinicopathological factors and predicts poor prognosis, suggesting its potential clinical value as a promising biomarker and therapeutic target for cancer patients.

CONCLUSIONS

LncRNA SNHG15 may serve as a prospective and novel biomarker for molecular diagnosis and therapeutics in patients with cancer.

Single Cell Mass Cytometry of Non-Small Cell Lung Cancer Cells Reveals Complexity of In vivo And Three-Dimensional Models over the Petri-dish

Single cell genomics and proteomics with the combination of innovative three-dimensional (3D) cell culture techniques can open new avenues toward the understanding of intra-tumor heterogeneity. Here, we characterize lung cancer markers using single cell mass cytometry to compare different in vitro cell culturing methods: two-dimensional (2D), carrier-free, or bead-based 3D culturing with in vivo xenografts. Proliferation, viability, and cell cycle phase distribution has been investigated. Gene expression analysis enabled the selection of markers that were overexpressed: TMEM45A, SLC16A3, CD66, SLC2A1, CA9, CD24, or repressed: EGFR either in vivo or in long-term 3D cultures. Additionally, TRA-1-60, pan-keratins, CD326, Galectin-3, and CD274, markers with known clinical significance have been investigated at single cell resolution. The described twelve markers convincingly highlighted a unique pattern reflecting intra-tumor heterogeneity of 3D samples and in vivo A549 lung cancer cells. In 3D systems CA9, CD24, and EGFR showed higher expression than in vivo. Multidimensional single cell proteome profiling revealed that 3D cultures represent a transition from 2D to in vivo conditions by intermediate marker expression of TRA-1-60, TMEM45A, pan-keratin, CD326, MCT4, Gal-3, CD66, GLUT1, and CD274. Therefore, 3D cultures of NSCLC cells bearing more putative cancer targets should be used in drug screening as the preferred technique rather than the Petri-dish.

EGFR-AS1/HIF2A regulates the expression of FOXP3 to impact the cancer stemness of smoking-related non-small cell lung cancer

Background:

Early data showed that FOXP3 could induce epithelial-mesenchymal transition by stimulating the Wnt/β-catenin signaling pathway in non-small cell lung cancer (NSCLC). However, how the expression of FOXP3 is regulated in NSCLC remains unknown. We thus explored the impacts of the long noncoding RNA EGFR antisense RNA 1 (EGFR-AS1) and hypoxia-inducible factor-2A (HIF2A) on FOXP3 expression and the cancer stemness of NSCLC.

Methods:

Lung tissues samples from 87 patients with NSCLC and two NSCLC cell lines were used in this study. The regulation of FOXP3 and lung cancer cell stemness by EGFR-AS1 and HIF2A was determined at molecular levels in NSCLC tissue samples and cultured cells in the presence/absence of the smoking carcinogen, 4-(N-methyl-N-nitrosamino)-1-(3-pyridyl)-1-butanone (NNK) (also known as nicotine-derived nitrosamine ketone). The results were confirmed in tumor xenograft models.

Results:

We found that NNK decreased the expression of EGFR-AS1 in the long term, but increased the expression of HIF2A and FOXP3 to stimulate lung cancer cell stemness. EGFR-AS1 significantly inhibited FOXP3 expression and NSCLC cell stemness, whereas HIF2A obviously promoted both. The enhancement of lung cancer stemness by FOXP3 was, at least partially, via stimulating Notch1, as the inhibition of Notch1 could markedly diminish the effect of FOXP3.

Conclusions:

FOXP3, the expression of which is under the fine control of EGFR-AS1, is a critical molecule that promotes NSCLC cancer cell stemness through stimulating the Notch1 pathway.