The revised lung adenocarcinoma classification-an imaging guide

Application of computer-aided detection for NCCN-based follow-up recommendation in subsolid nodules: Effect on inter-observer agreement

RATIONALE AND OBJECTIVES

Computer-aided detection (CAD) of pulmonary nodules reduces the impact of observer variability, improving the reliability and reproducibility of nodule assessments in clinical practice. Therefore, this study aimed to assess the impact of CAD on inter-observer agreement in the follow-up management of subsolid nodules.

MATERIALS AND METHODS

A dataset comprising 60 subsolid nodule cases was constructed based on the National Cancer Center lung cancer screening data. Five observers independently assessed all low-dose computed tomography scans and assigned follow-up management strategies to each case according to the National Comprehensive Cancer Network (NCCN) guidelines, using both manual measurements and CAD assistance. The linearly weighted Cohen's kappa test was used to measure agreement between paired observers. Agreement among multiple observers was evaluated using the Fleiss kappa statistic.

RESULTS

The agreement of the five observers for NCCN follow-up management categorization was moderate when measured manually, with a Fleiss kappa score of 0.437. Utilizing CAD led to a notable enhancement in agreement, achieving a substantial consensus with a Fleiss kappa value of 0.623. After using CAD, the proportion of major and substantial management discrepancies decreased from 27.5% to 15.8% and 4.8% to 1.5%, respectively (p < 0.01). In 23 lung cancer cases presenting as part-solid nodules, CAD significantly elevates the average sensitivity in detecting lung cancer cases presenting as part-solid nodules (overall sensitivity, 82.6% vs. 92.2%; p < 0.05).

CONCLUSION

The application of CAD significantly improves inter-observer agreement in the follow-up management strategy for subsolid nodules. It also demonstrates the potential to reduce substantial management discrepancies and increase detection sensitivity in lung cancer cases presenting as part-solid nodules.

Baseline whole-lung CT features deriving from deep learning and radiomics: prediction of benign and malignant pulmonary ground-glass nodules

Objective

To develop and validate the model for predicting benign and malignant ground-glass nodules (GGNs) based on the whole-lung baseline CT features deriving from deep learning and radiomics.

Methods

This retrospective study included 385 GGNs from 3 hospitals, confirmed by pathology. We used 239 GGNs from Hospital 1 as the training and internal validation set; 115 and 31 GGNs from Hospital 2 and Hospital 3 as the external test sets 1 and 2, respectively. An additional 32 stable GGNs from Hospital 3 with more than five years of follow-up were used as the external test set 3. We evaluated clinical and morphological features of GGNs at baseline chest CT and extracted the whole-lung radiomics features simultaneously. Besides, baseline whole-lung CT image features are further assisted and extracted using the convolutional neural network. We used the back-propagation neural network to construct five prediction models based on different collocations of the features used for training. The area under the receiver operator characteristic curve (AUC) was used to compare the prediction performance among the five models. The Delong test was used to compare the differences in AUC between models pairwise.

Results

The model integrated clinical-morphological features, whole-lung radiomic features, and whole-lung image features (CMRI) performed best among the five models, and achieved the highest AUC in the internal validation set, external test set 1, and external test set 2, which were 0.886 (95% CI: 0.841-0.921), 0.830 (95%CI: 0.749-0.893) and 0.879 (95%CI: 0.712-0.968), respectively. In the above three sets, the differences in AUC between the CMRI model and other models were significant (all P < 0.05). Moreover, the accuracy of the CMRI model in the external test set 3 was 96.88%.

Conclusion

The baseline whole-lung CT features were feasible to predict the benign and malignant of GGNs, which is helpful for more refined management of GGNs.

Invasive mucinous adenocarcinoma misdiagnosed as pneumonia: A case report

Common imaging findings of invasive mucinous adenocarcinoma (IMA) include consolidation of the lung parenchyma, nodules, and ground-glass changes. However, the IMA imaging findings in the present case included diffuse, patchy and blurry density shadows through both lungs. To the best of the authors' knowledge, this image pattern has rarely been reported. The patient provided his consent and authorized the publication of photographs featuring his likeness. The present study reported a patient was diagnosed with IMA via pathologic and genetic analyses. Following antibiotic treatment, the lesions in both sides became larger. Further examinations were completed and IMA was confirmed by biopsy pathohistological examination. Pathological specimens were negative for almost all driver genes mutations, except KRAS. The patients and family refused further treatment, including chemotherapy, radiotherapy and interventional chemotherapy and the patient was discharged from The First Affiliated Hospital of Chengdu Medical College. The present case report emphasized that IMA should be suspected when imaging studies show diffuse lesions throughout both lungs. When a patient does not respond to treatment, clinicians should consider alternative diagnoses.

Adding predictive and diagnostic values of pulmonary ground-glass nodules on lung cancer via novel non-invasive tests

Pulmonary ground-glass nodules (GGNs) are highly associated with lung cancer. Extensive studies using thin-section high-resolution CT images have been conducted to analyze characteristics of different types of GGNs in order to evaluate and determine the predictive and diagnostic values of GGNs on lung cancer. Accurate prediction of their malignancy and invasiveness is critical for developing individualized therapies and follow-up strategies for a better clinical outcome. Through reviewing the recent 5-year research on the association between pulmonary GGNs and lung cancer, we focused on the radiologic and pathological characteristics of different types of GGNs, pointed out the risk factors associated with malignancy, discussed recent genetic analysis and biomarker studies (including autoantibodies, cell-free miRNAs, cell-free DNA, and DNA methylation) for developing novel diagnostic tools. Based on current progress in this research area, we summarized a process from screening, diagnosis to follow-up of GGNs.

Combined Radiomic and Visual Assessment for Improved Detection of Lung Adenocarcinoma Invasiveness on Computed Tomography Scans: A Multi-Institutional Study

Objective

The timing and nature of surgical intervention for semisolid abnormalities are dependent upon distinguishing between adenocarcinoma-in-situ (AIS), minimally invasive adenocarcinoma (MIA), and invasive adenocarcinoma (INV). We sought to develop and evaluate a quantitative imaging method to determine invasiveness of small, ground-glass lesions on computed tomography (CT) chest scans.

Methods

The study comprised 268 patients from 4 institutions with resected (<=3 cm) semisolid lesions with confirmed histopathological diagnosis of MIA/AIS or INV. A total of 248 radiomic texture features from within the tumor nodule (intratumoral) and adjacent to the nodule (peritumoral) were extracted from manually annotated lung nodules of chest CT scans. The datasets were randomly divided, with 40% of patients used for training and 60% used for testing the machine classifier (Training D_Train, N=106; Testing, D_Test, N=162).

Results

The top five radiomic stable features included four intratumoral (Laws and Haralick feature families) and one peritumoral feature within 3 to 6 mm of the nodule (CoLlAGe feature family), which successfully differentiated INV from MIA/AIS nodules with an AUC of 0.917 [0.867-0.967] on D_Train and 0.863 [0.79-0.931] on D_Test. The radiomics model successfully differentiated INV from MIA cases (<1 cm AUC: 0.76 [0.53-0.98], 1-2 cm AUC: 0.92 [0.85-0.98], 2-3 cm AUC: 0.95 [0.88-1]). The final integrated model combining the classifier with the radiologists’ score gave the best AUC on D_Test (AUC=0.909, p<0.001).

Conclusions

Addition of advanced image analysis via radiomics to the routine visual assessment of CT scans help better differentiate adenocarcinoma subtypes and can aid in clinical decision making. Further prospective validation in this direction is warranted.

LW1497, an Inhibitor of Malate Dehydrogenase, Suppresses TGF-β1-Induced Epithelial-Mesenchymal Transition in Lung Cancer Cells by Downregulating Slug

LW1497 suppresses the expression of the hypoxia-inducing factor (HIF)-1α inhibiting malate dehydrogenase. Although hypoxia and HIF-1α are known to be important in cancer, LW1497 has not been therapeutically applied to cancer yet. Thus, we investigated the effect of LW1497 on the epithelial-mesenchymal transition (EMT) of lung cancer cells. A549 and H1299 lung cancer cells were induced to undergo via TGF-β1 treatment, resulting in the downregulation of E-cadherin and upregulation of N-cadherin and Vimentin concurrently with increases in the migration and invasion capacities of the cells. These effects of TGF-β1 were suppressed upon co-treatment of the cells with LW1497. An RNA-seq analysis revealed that LW1497 induced differential expression of genes related to hypoxia, RNA splicing, angiogenesis, cell migration, and metastasis in the A549 lung cancer cell lines. We confirmed the differential expression of Slug, an EMT-related transcription factor. Results from Western blotting and RT-PCR confirmed that LW1497 inhibited the expression of EMT markers and Slug. After orthotopically transplanting A549 cancer cells into mice, LW1497 was administered to examine whether the lung cancer progression was inhibited. We observed that LW1497 reduced the area of cancer. In addition, the results from immunohistochemical analyses showed that LW1497 downregulated EMT markers and Slug. In conclusion, LW1497 suppresses cancer progression through the inhibition of EMT by downregulating Slug.

Interobserver variability in Lung CT Screening Reporting and Data System categorisation in subsolid nodule-enriched lung cancer screening CTs

OBJECTIVES

To assess interobserver agreement in Lung CT Screening Reporting and Data System (Lung-RADS) categorisation in subsolid nodule-enriched low-dose screening CTs.

METHODS

A retrospective review of low-dose screening CT reports from 2013 to 2017 using keyword searches for subsolid nodules identified 54 baseline CT scans. With an additional 108 negative screening CT scans, a total of 162 CT scans were categorised according to the Lung-RADS by two fellowship-trained thoracic radiologists in consensus. We randomly selected 20, 20, 10, and 10 scans from categories 1/2, 3, 4A, and 4B CT scans, respectively, to ensure balanced category representation. Five radiologists classified the 60 CT scans into Lung-RADS categories. The frequencies of concordance and minor and major discordance were calculated, with major discordance defined as at least 6 months of management discrepancy. We used Cohen's κ statistics to analyse reader agreement.

RESULTS

An average of 60.3% (181 of 300) of all cases and 45.0% (90 of 200) of positive screens were correctly categorised. The minor and major discordance rates were 12.3% and 27.3% overall and 18.5% and 36.5% in positive screens, respectively. The concordance rate was significantly higher among experienced thoracic radiologists. Overall, the interobserver agreement was moderate (mean κ, 0.45; 95% confidence interval: 0.40-0.51). The proportion of part-solid risk-dominant nodules was significantly higher in cases with low rates of accurate categorisation.

CONCLUSION

This retrospective study observed variable accuracy and moderate interobserver agreement in radiologist categorisation of subsolid nodules in screening CTs. This inconsistency may affect management recommendations for lung cancer screening.

KEY POINTS

• Diagnostic performance for Lung-RADS categorisation is variable among radiologists with fair to moderate interobserver agreement in subsolid nodule-enriched CT scans. • Experienced thoracic radiologists showed more accurate and consistent Lung-RADS categorisation than radiology residents. • The relative abundance of part-solid nodules was a potential factor related to increased disagreement in Lung-RADS categorisation.

Deep learning classification of lung cancer histology using CT images

Tumor histology is an important predictor of therapeutic response and outcomes in lung cancer. Tissue sampling for pathologist review is the most reliable method for histology classification, however, recent advances in deep learning for medical image analysis allude to the utility of radiologic data in further describing disease characteristics and for risk stratification. In this study, we propose a radiomics approach to predicting non-small cell lung cancer (NSCLC) tumor histology from non-invasive standard-of-care computed tomography (CT) data. We trained and validated convolutional neural networks (CNNs) on a dataset comprising 311 early-stage NSCLC patients receiving surgical treatment at Massachusetts General Hospital (MGH), with a focus on the two most common histological types: adenocarcinoma (ADC) and Squamous Cell Carcinoma (SCC). The CNNs were able to predict tumor histology with an AUC of 0.71(p = 0.018). We also found that using machine learning classifiers such as k-nearest neighbors (kNN) and support vector machine (SVM) on CNN-derived quantitative radiomics features yielded comparable discriminative performance, with AUC of up to 0.71 (p = 0.017). Our best performing CNN functioned as a robust probabilistic classifier in heterogeneous test sets, with qualitatively interpretable visual explanations to its predictions. Deep learning based radiomics can identify histological phenotypes in lung cancer. It has the potential to augment existing approaches and serve as a corrective aid for diagnosticians.

18F-FDG PET-based radiomics model for predicting occult lymph node metastasis in clinical N0 solid lung adenocarcinoma

Background

This study aimed to develop a preoperative positron emission tomography (PET)-based radiomics model for predicting occult lymph node metastasis (OLM) in clinical N0 (cN0) solid lung adenocarcinoma.

Methods

The preoperative fluorine-18-fludeoxyglucose (¹⁸F-FDG) PET images of 370 patients with cN0 lung adenocarcinoma confirmed by histopathological examination were retrospectively reviewed. Patients were divided into training and validation sets. Radiomics features and relevant data were extracted from PET images. A nomogram was developed in a training set via univariate and multivariate logistic analyses, and its performance was assessed by concordance-index (C-index), calibration curves, and decision curve analysis (DCA) in the training and validation sets.

Results

The multivariate logistic regression analysis showed that only carcinoembryonic antigen (CEA), metabolic tumor volume (MTV), and the radiomics signature had statistically significant differences between patients with and without OLM (P<0.05). A nomogram was developed based on the logistic analyses, and its C-index was 0.769 in the training set and 0.768 in the validation set. The calibration curve demonstrated good consistency between the nomogram-predicted probability of OLM and the actual rate. The DCA also confirmed the clinical utility of the nomogram.

Conclusions

A PET/computed tomography (CT)-based radiomics model including CEA, MTV, and the radiomics signature was developed and demonstrated adequate predictive accuracy and clinical net benefit in the present study, and was conveniently used to facilitate the individualized preoperative prediction of OLM.

The role of monoamine oxidase A in HPV-16 E7-induced epithelial-mesenchymal transition and HIF-1α protein accumulation in non-small cell lung cancer cells

Our previous studies have found that human papillomavirus (HPV)-16 E7 oncoprotein promotes epithelial-mesenchymal transition (EMT) and hypoxia-inducible factor-1α (HIF-1α) protein accumulation in non-small cell lung cancer (NSCLC) cells and monoamine oxidase A (MAOA) is highly expressed in NSCLC tissues. Here, we further explored the role of MAOA in HPV-16 E7-induced EMT and HIF-1α protein accumulation in A549 and NCI-H460 NSCLC cells. Our results showed that HPV-16 E7 enhanced MAOA expression in NSCLC cells. Additionally, MAOA knockout inhibited HPV-16 E7-induced migration, invasion, and EMT, and significantly reduced HPV-16 E7-induced ROS generation and HIF-1α protein accumulation via promoting its degradation. Furthermore, MAOA knockout suppressed HPV-16 E7-induced ERK1/2 activation. In vivo, MAOA knockout inhibited tumor growth, metastasis, and the expression of EMT-related markers and HIF-1α proteins induced by HPV-16 E7 in NCI-H460 NSCLC subcutaneous xenograft and in situ intrapulmonary models of nude mice. Taken together, our findings provide evidence that MAOA plays a key role in EMT and HIF-1α protein accumulation induced by HPV-16 E7 in NSCLC cells, suggesting that MAOA may be a potential therapeutic target for HPV-related NSCLC.

Secondary Resistant Mutations to Small Molecule Inhibitors in Cancer Cells

Secondary resistant mutations in cancer cells arise in response to certain small molecule inhibitors. These mutations inevitably cause recurrence and often progression to a more aggressive form. Resistant mutations may manifest in various forms. For example, some mutations decrease or abrogate the affinity of the drug for the protein. Others restore the function of the enzyme even in the presence of the inhibitor. In some cases, resistance is acquired through activation of a parallel pathway which bypasses the function of the drug targeted pathway. The Catalogue of Somatic Mutations in Cancer (COSMIC) produced a compendium of resistant mutations to small molecule inhibitors reported in the literature. Here, we build on these data and provide a comprehensive review of resistant mutations in cancers. We also discuss mechanistic parallels of resistance.

Lung Cancer Screening, Version 3.2018, NCCN Clinical Practice Guidelines in Oncology

Lung cancer is the leading cause of cancer-related mortality in the United States and worldwide. Early detection of lung cancer is an important opportunity for decreasing mortality. Data support using low-dose computed tomography (LDCT) of the chest to screen select patients who are at high risk for lung cancer. Lung screening is covered under the Affordable Care Act for individuals with high-risk factors. The Centers for Medicare & Medicaid Services (CMS) covers annual screening LDCT for appropriate Medicare beneficiaries at high risk for lung cancer if they also receive counseling and participate in shared decision-making before screening. The complete version of the NCCN Guidelines for Lung Cancer Screening provides recommendations for initial and subsequent LDCT screening and provides more detail about LDCT screening. This manuscript focuses on identifying patients at high risk for lung cancer who are candidates for LDCT of the chest and on evaluating initial screening findings.

Occult mediastinal lymph node metastasis in FDG-PET/CT node-negative lung adenocarcinoma patients: Risk factors and histopathological study

BACKGROUND

The aim of this study was to investigate predictive factors of occult mediastinal lymph node metastasis (MLNM) in preoperative 18 F-fluorodeoxy-glucose PET/CT node-negative lung adenocarcinoma patients.

METHODS

We reviewed the clinical data and PET/CT parameters of 360 consecutive pulmonary adenocarcinoma patients who were scheduled to undergo anatomical pulmonary resection and systemic mediastinal node dissection. The nodal metastasis was pathologically defined and all resected tumors were classified according to the 2011 IASLC/ATS/ERS classification. Univariate and multivariate analysis were conducted to evaluate the associations between clinicopathological variables and MLNM.

RESULTS

Of all 360 patients, 54 (15.0%) had pathological N2 diseases. The serum CEA level, nodule type, hilar nodal SUVmax, tumor SUVmax, size, location and histologic subtype were associated with MLNM significantly on univariate analysis. On multivariate analysis, CEA ≥ 5.0 ng/mL (P < 0.001), solid nodule (P = 0.012), tumor SUVmax ≥ 3.7 (P < 0.027), hilar nodal SUVmax ≥ 2.0 (P < 0.001) and centrally located tumor (P = 0.035) were independent risk factors for MLNM. The area under the ROC curve (AUC) for tumor SUVmax and hilar nodal SUVmax in predicting MLNM was 0.764 and 0.730, respectively, and the combined use of five factors yielded a higher AUC of 0.885.

CONCLUSION

Increased primary tumor and hilar lymph node SUVmax, solid nodule, centrally located tumor and increased CEA level predicted the increased risk of mediastinal lymph node metastasis. Combined use of these factors improved the diagnostic capacity for predicting N2 disease preoperatively. Invasive mediastinal staging should be considered for patients with these risk factors, even those with a negative mediastinum on PET/CT.

Exploration of methylation-driven genes for monitoring and prognosis of patients with lung adenocarcinoma

Background

As one of the most common malignant tumors in humans, lung cancer has experienced a gradual increase in morbidity and mortality. This study examined prognosis-related methylation-driven genes specific to lung adenocarcinoma (LUAD) to provide a basis for prognosis prediction and personalized targeted therapy for LUAD patients.

Methods

The methylation and survival time data from LUAD patients in the TCGA database were downloaded. The MethylMix algorithm was used to identify the differential methylation status of LUAD and adjacent tissues based on the β-mixture model to obtain disease-related methylation-driven genes. A COX regression model was then used to screen for LUAD prognosis-related methylation-driven genes, and a linear risk model based on five methylation-driven gene expression profiles was constructed. A methylation and gene expression combined survival analysis was performed to further explore the prognostic value of 5 genes independently.

Results

There were 118 differentially expressed methylation-driven genes in the LUAD tissues and adjacent tissues. Five of the genes, CCDC181, PLAU, S1PR1, ELF3, and KLHDC9, were used to construct a prognostic risk model. Overall, the survival time was significantly lower in the high-risk group compared with that in the low-risk group (P < 0.05). In addition, the methylation and gene expression combined survival analysis found that the combined expression levels of the genes CCDC181, PLAU, and S1PR1 as well as KLHDC9 alone can be used as independent prognostic markers or drug targets.

Conclusion

Our findings provide an important bioinformatic basis and relevant theoretical basis for guiding subsequent LUAD early diagnosis and prognosis assessments.

Electronic supplementary material

The online version of this article (10.1186/s12935-018-0691-z) contains supplementary material, which is available to authorized users.

A Decision Analysis of Follow-up and Treatment Algorithms for Nonsolid Pulmonary Nodules

Purpose To evaluate management strategies and treatment options for patients with ground-glass nodules (GGNs) by using decision-analysis models. Materials and Methods A simulation was developed for 1 000 000 hypothetical patients with GGNs undergoing follow-up per the Lung Imaging Reporting and Data System (Lung-RADS) recommendations. The initial age range was 55-75 years (mean, 64 years). Nodules could grow and develop solid components over time. Clinically significant malignancy rates were calibrated to data from the National Lung Screening Trial. Annual versus 3-year-interval follow-up of Lung-RADS category 2 nodules was compared, and different treatment strategies were tested (stereotactic body radiation therapy, surgery, and no therapy). Results Overall, 2.3% (22 584 of 1 000 000) of nodules were clinically significant malignancies; 6.3% (62 559 of 1 000 000) of nodules were treated. Only 30% (18 668 of 62 559) of Lung-RADS category 4B or 4X nodules were clinically significant malignancies. The risk of clinically significant malignancy for persistent nonsolid nodules after baseline was higher than Lung-RADS estimates for categories 2 and 3 (3% vs <1% and 1%-2%, respectively). Overall survival (OS) at 10 years was 72% (527 827 of 737 306; 95% confidence interval [CI]: 71%, 72%) with annual follow-up and 71% (526 507 of 737 306; 95% CI: 71%, 72%) with 3-year-interval follow-up (P < .01). At 10 years, OS among patients whose nodules progressed to Lung-RADS category 4B or 4X was 80% after radiation therapy (49 945 of 62 559; 95% CI: 80%, 80%), 79% after surgery (49 139 of 62 559; 95% CI: 78%, 79%), and 74% after no therapy (46 512 of 62 559; 95% CI: 74%, 75%) (P < .01). Conclusion Simulation modeling suggests that the follow-up interval for evaluating ground-glass nodules can be increased from 1 year to 3 years with minimal change in outcomes. Stereotactic body radiation therapy demonstrated the best outcomes compared with lobectomy and with no therapy for nonsolid nodules. © RSNA, 2018 Online supplemental material is available for this article.

Assessing the inter-observer variability of Computer-Aided Nodule Assessment and Risk Yield (CANARY) to characterize lung adenocarcinomas

Lung adenocarcinoma (ADC), the most common lung cancer type, is recognized increasingly as a disease spectrum. To guide individualized patient care, a non-invasive means of distinguishing indolent from aggressive ADC subtypes is needed urgently. Computer-Aided Nodule Assessment and Risk Yield (CANARY) is a novel computed tomography (CT) tool that characterizes early ADCs by detecting nine distinct CT voxel classes, representing a spectrum of lepidic to invasive growth, within an ADC. CANARY characterization has been shown to correlate with ADC histology and patient outcomes. This study evaluated the inter-observer variability of CANARY analysis. Three novice observers segmented and analyzed independently 95 biopsy-confirmed lung ADCs from Vanderbilt University Medical Center/Nashville Veterans Administration Tennessee Valley Healthcare system (VUMC/TVHS) and the Mayo Clinic (Mayo). Inter-observer variability was measured using intra-class correlation coefficient (ICC). The average ICC for all CANARY classes was 0.828 (95% CI 0.76, 0.895) for the VUMC/TVHS cohort, and 0.852 (95% CI 0.804, 0.901) for the Mayo cohort. The most invasive voxel classes had the highest ICC values. To determine whether nodule size influenced inter-observer variability, an additional cohort of 49 sub-centimeter nodules from Mayo were also segmented by three observers, with similar ICC results. Our study demonstrates that CANARY ADC classification between novice CANARY users has an acceptably low degree of variability, and supports the further development of CANARY for clinical application.

YdjC chitooligosaccharide deacetylase homolog induces keratin reorganization in lung cancer cells: involvement of interaction between YDJC and CDC16

Lung cancer is a fatal disease with a high mortality rate. The perinuclear reorganization of keratin 8 (K8) is an important biochemical phenomenon reflecting changes in the physical properties of metastatic cancer. However, there is not much of information about the regulatory molecules involved in phosphorylation and perinuclear reorganization of K8.

In this study, we investigated the role and molecular mechanisms of YdjC chitooligosaccha- ride deacetylase homolog (YDJC) in sphingosylphosphorylcholine (SPC)-induced phosphorylation and reorganization of K8, and migration and invasion (SPC-induced events). SPC induced expression of YDJC in a dose- and time-dependent manner. Gene silencing of YDJC suppressed SPC-induced events. YDJC overexpression induced the SPC-induced events. YDJC deacetylase dominant negative mutant (YDJCD13A) did not induce SPC-induced events. YDJC siRNA reduced ERK activation and overexpression of YDJC induced ERK activation. The siRNA of ERK1 or ERK2 suppressed YDJC-induced phosphorylation and reorganization of K8, and migration and invasion. Co-immunoprecipitation revealed that YDJC binds to CDC16. Interestingly, CDC16 siRNA induced SPC-induced events. Overexpression of CDC16 blocked SPC-induced events. KMPLOT analysis based on public microarray data revealed the poor prognosis of lung cancer patients with high expression of YDJC compared with patients with low expression of YDJC.

The collective results indicate that YDJC is involved in SPC-induced events in A549 lung cancer cells by interacting with CDC16. YDJC overexpression might be involved in the progression of lung cancer. These results also suggest that suppression of YDJC or boosting of CDC16 interaction with YDJC might be a novel way to prevent progression of lung cancer.

Clinicopathological Characteristics and Mutations Driving Development of Early Lung Adenocarcinoma: Tumor Initiation and Progression

Lung cancer is the leading cause of cancer-related deaths worldwide, with lung adenocarcinoma representing the most common lung cancer subtype. Among all lung adenocarcinomas, the most prevalent subset develops via tumorigenesis and progression from atypical adenomatous hyperplasia (AAH) to adenocarcinoma in situ (AIS), to minimally invasive adenocarcinoma (MIA), to overt invasive adenocarcinoma with a lepidic pattern. This stepwise development is supported by the clinicopathological and molecular characteristics of these tumors. In the 2015 World Health Organization classification, AAH and AIS are both defined as preinvasive lesions, whereas MIA is identified as an early invasive adenocarcinoma that is not expected to recur if removed completely. Recent studies have examined the molecular features of lung adenocarcinoma tumorigenesis and progression. EGFR-mutated adenocarcinoma frequently develops via the multistep progression. Oncogene-induced senescence appears to decrease the frequency of the multistep progression in KRAS- or BRAF-mutated adenocarcinoma, whose tumor evolution may be associated with epigenetic alterations and kinase-inactive mutations. This review summarizes the current knowledge of tumorigenesis and tumor progression in early lung adenocarcinoma, with special focus on its clinicopathological characteristics and their associations with driver mutations (EGFR, KRAS, and BRAF) as well as on its molecular pathogenesis and progression.

Association of Omics Features with Histopathology Patterns in Lung Adenocarcinoma

Adenocarcinoma accounts for more than 40% of lung malignancy, and microscopic pathology evaluation is indispensable for its diagnosis. However, how histopathology findings relate to molecular abnormalities remains largely unknown. Here, we obtained H&E-stained whole-slide histopathology images, pathology reports, RNA sequencing, and proteomics data of 538 lung adenocarcinoma patients from The Cancer Genome Atlas and used these to identify molecular pathways associated with histopathology patterns. We report cell-cycle regulation and nucleotide binding pathways underpinning tumor cell dedifferentiation, and we predicted histology grade using transcriptomics and proteomics signatures (area under curve >0.80). We built an integrative histopathology-transcriptomics model to generate better prognostic predictions for stage I patients (p = 0.0182 ± 0.0021) compared with gene expression or histopathology studies alone, and the results were replicated in an independent cohort (p = 0.0220 ± 0.0070). These results motivate the integration of histopathology and omics data to investigate molecular mechanisms of pathology findings and enhance clinical prognostic prediction.

Novel involvement of RhebL1 in sphingosylphosphorylcholine-induced keratin phosphorylation and reorganization: Binding to and activation of AKT1

Sphingosylphosphorylcholine induces keratin phosphorylation and reorganization, and increases viscoelasticity of metastatic cancer cells such as PANC-1 cells. However, the mechanism involved in sphingosylphosphorylcholine-induced keratin phosphorylation and reorganization is largely unknown. Sphingosylphosphorylcholine dose- and time-dependently induces the expression of RhebL1. The involvement of RhebL1 in sphingosylphosphorylcholine-induced events including keratin 8 (K8) phosphorylation, reorganization, migration and invasion was examined. Gene silencing of RhebL1 suppressed the sphingosylphosphorylcholine-induced events and overexpression of RhebL1 enhanced those events even without sphingosylphosphorylcholine treatment. We examined whether the G protein function of RhebL1 induces K8 phosphorylation using constitutively active RhebL1Q64L and dominant negative RhebL1D60K. G protein activity of RhebL1 is involved in sphingosylphosphorylcholine-induced K8 phosphorylation. We found that RhebL1 binds and activates AKT1. G protein activity of RhebL1 is involved in the binding and activation of AKT1. MK2206 (AKT inhibitor) and gene silencing of AKT1 inhibited the sphingosylphosphorylcholine-induced events, whereas overexpression of activated-AKT1 induced K8 phosphorylation, reorganization, migration and invasion even without sphingosylphosphorylcholine treatment.

The collective results indicate that RhebL1 is involved in sphingosylphosphorylcholine-induced events in A549 lung cancer cells via binding to AKT1 leading to activation of it. These results suggest that suppression of RhebL1 or inhibition of RhebL1′s binding to AKT1 might be a novel way that prevents changes in the physical properties of metastatic cancer cells.

Associations between radiologist-defined semantic and automatically computed radiomic features in non-small cell lung cancer

Tumor phenotypes captured in computed tomography (CT) images can be described qualitatively and quantitatively using radiologist-defined "semantic" and computer-derived "radiomic" features, respectively. While both types of features have shown to be promising predictors of prognosis, the association between these groups of features remains unclear. We investigated the associations between semantic and radiomic features in CT images of 258 non-small cell lung adenocarcinomas. The tumor imaging phenotypes were described using 9 qualitative semantic features that were scored by radiologists, and 57 quantitative radiomic features that were automatically calculated using mathematical algorithms. Of the 9 semantic features, 3 were rated on a binary scale (cavitation, air bronchogram, and calcification) and 6 were rated on a categorical scale (texture, border definition, contour, lobulation, spiculation, and concavity). 32-41 radiomic features were associated with the binary semantic features (AUC = 0.56-0.76). The relationship between all radiomic features and the categorical semantic features ranged from weak to moderate (|Spearmen's correlation| = 0.002-0.65). There are associations between semantic and radiomic features, however the associations were not strong despite being significant. Our results indicate that radiomic features may capture distinct tumor phenotypes that fail to be perceived by naked eye that semantic features do not describe and vice versa.

Early Lung Adenocarcinoma in Mice: Micro-Computed Tomography Manifestations and Correlation with Pathology

Lung cancer is the most common fatal malignancy for both men and women and adenocarcinoma is the most common histologic type. Early diagnosis of lung cancer can significantly improve the survival rate of patients. This study aimed to investigate the micro-computed tomography (micro-CT) manifestations of early lung adenocarcinoma (LAC) in mice and to provide a new perspective for early clinical diagnosis. Early LAC models in 10 mice were established by subcutaneously injecting 1-methyl-3-nitro-1-nitrosoguanidine (MNNG) solution. Micro-CT scan and multiple planar reconstruction (MPR) were used for mouse lungs. Micro-CT features of early LAC, especially the relationships between tumor and bronchus, were analyzed and correlated with pathology. Micro-CT findings of early LAC were divided into three types: non-solid (n = 8, 6%), partly solid (n = 85, 64%) and totally solid (n = 39, 30%). Tumor-bronchus relationships, which could be observed in 110 of 132(83%) LAC, were classified into four patterns: type I (n = 16, 15%), bronchus was truncated at the margin of the tumor; type II (n = 33, 30%), bronchus penetrated into the tumor with tapered narrowing and interruption; type III (n = 38, 35%), bronchus penetrated into the tumor with a patent and intact lumen; type IV (n = 99, 90%), bronchus ran at the border of the tumor with an intact or compressed lumen. Micro-CT manifestations of early LAC correlated well with pathological findings. Micro-CT can clearly demonstrate the features of mouse early LAC and bronchus-tumor relationships, and can also provide a new tool and perspective for the study of early LAC.

Interstitial lung disease pattern turned out to be a predominantly lepidic lung adenocarcinoma

We report a case of a 46-year-old woman without any medical history who presented to our Respiratory Department with exertional dyspnoea for the last 6 weeks associated with non-productive cough. Chest radiography showed bilateral diffuse interstitial opacity. Bronchoalveolar lavage and transbronchial biopsies performed during flexible bronchoscopy as a step in the diagnostic workup of idiopathic interstitial pneumonia showed cells of pulmonary adenocarcinoma.

Targeted therapies for the treatment of non-small-cell lung cancer: Monoclonal antibodies and biological inhibitors

The usual treatments for patients with non-small-cell lung cancer (NSCLC), such as advanced lung adenocarcinoma, are unspecific and aggressive, and include lung resection, radiotherapy and chemotherapy. Recently, treatment with monoclonal antibodies and biological inhibitors has emerged as an effective alternative, generating effective results with few side effects. In recent years, several clinical trials using monoclonal antibodies presented potential benefits to NSCLC, and 4 of them are already approved for the treatment of NSCLC, such as cetuximab, bevacizumab, nivolumab and pembrolizumab. Also, biological inhibitors are attractive tolls for biological applications. Among the approved inhibitors are crizotinib, erlotinib, afatinib and gefitinib, and side effects are usually mild to intense. Nevertheless, biological molecule treatments are under development, and several new monoclonal antibodies and biological inhibitors are in trial to treat NSCLC. Also under trial study are as follows: anti-epidermal growth factor receptor (EGFR) antibodies (nimotuzumab and ficlatuzumab), anti-IGF 1 receptor (IGF-1R) monoclonal antibody (figitumumab), anti-NR-LU-10 monoclonal antibody (nofetumomab) as well as antibodies directly affecting the cytotoxic T-lymphocyte-associated antigen 4 (CTLA-4) molecule (ipilimumab and tremelimumab), to receptor activator of nuclear factor-kappa B ligand (RANKL) (denosumab) or to polymerase enzyme (veliparib and olaparib). Among new inhibitors under investigation are poly-ADP ribose polymerase (PARP) inhibitors (veliparib and olaparib) and phosphatidylinositol 3-kinase (PI3K) inhibitor (buparlisib). However, the success of immunotherapies still requires extensive research and additional controlled trials to evaluate the long-term benefits and side effects.

Lung Adenocarcinoma: Predictive Value of KRAS Mutation Status in Assessing Local Recurrence in Patients Undergoing Image-guided Ablation

Purpose To establish the relationship between KRAS mutation status and local recurrence after image-guided ablation of lung adenocarcinoma. Materials and Methods This study consisted of a HIPAA-compliant institutional review board-approved retrospective review of 56 primary lung adenocarcinomas in 54 patients (24 men, 30 women; median age, 72 years; range, 54-87 years) treated with percutaneous image-guided ablation and with available genetic mutational analysis. KRAS mutation status and additional clinical and technical variables-Eastern Cooperative Oncology Group (ECOG) status, smoking history, stage at diagnosis, status (new primary or not), history of radiation, history of surgery, prior systemic treatment, modality of ablation, size of nodule, ablation margin, and presence of ground-glass appearance-were recorded and evaluated in relation to time to local recurrence, which was calculated from the time of ablation to the first radiographic evidence of recurrence. Predictors of outcome were identified by using a proportional hazards model for both univariate and multivariate analysis, with death as a competing risk. Results Technical success was 100%. Of the 56 ablated tumors, 37 (66%) were wild type for KRAS and 19 (34%) were KRAS mutants. The 1-year and 3-year cumulative incidences of recurrence were 20% and 35% for wild-type KRAS compared with 40% and 63% for KRAS mutant tumors. KRAS mutation status was a significant predictor of local recurrence at both univariate (P = .05; subdistribution hazard ratio [sHR], 2.32) and multivariate (P = .006; sHR, 3.75) analysis. At multivariate analysis, size (P = .026; sHR, 2.54) and ECOG status (P = .012; sHR, 2.23) were also independent significant predictors, whereas minimum margin (P = .066) was not. Conclusion The results of this study show that there is a relationship between KRAS mutation status and local recurrence after image-guided ablation of lung adenocarcinoma. Specifically, KRAS mutation status of the ablated lesion is a significant predictor of time to local recurrence, independent of size and margin. ^© RSNA, 2016.