Genotyping and genomic profiling of non-small-cell lung cancer: implications for current and future therapies

Deep learning analysis to predict EGFR mutation status in lung adenocarcinoma manifesting as pure ground-glass opacity nodules on CT

Background

Epidermal growth factor receptor-tyrosine kinase inhibitors (EGFR-TKIs) showed potency as a non-invasive therapeutic approach in pure ground-glass opacity nodule (pGGN) lung adenocarcinoma. However, optimal methods of extracting information about EGFR mutation from pGGN lung adenocarcinoma images remain uncertain. We aimed to develop, validate, and evaluate the clinical utility of a deep learning model for predicting EGFR mutation status in lung adenocarcinoma manifesting as pGGN on computed tomography (CT).

Methods

We included 185 resected pGGN lung adenocarcinomas in the primary cohort. The patients were divided into training (n = 125), validation (n = 23), and test sets (n = 37). A preoperative CT-based deep learning model with clinical factors as well as clinical and radiomics models was constructed and applied to the test set. We evaluated the clinical utility of the deep learning model by applying it to 83 GGNs that received EGFR-TKI from an independent cohort (clinical validation set), and treatment response was regarded as the reference standard.

Results

The prediction efficiencies of each model were compared in terms of area under the curve (AUC). Among the 185 pGGN lung adenocarcinomas, 122 (65.9%) were EGFR-mutant and 63 (34.1%) were EGFR-wild type. The AUC of the clinical, radiomics, and deep learning with clinical models to predict EGFR mutations were 0.50, 0.64, and 0.85, respectively, for the test set. The AUC of deep learning with the clinical model in the validation set was 0.72.

Conclusions

Deep learning approach of CT images combined with clinical factors can predict EGFR mutations in patients with lung adenocarcinomas manifesting as pGGN, and its clinical utility was demonstrated in a real-world sample.

Computed tomography-based radiomics quantification predicts epidermal growth factor receptor mutation status and efficacy of first-line targeted therapy in lung adenocarcinoma

Background

Biomarkers that predict the efficacy of first-line tyrosine kinase inhibitors (TKIs) are pivotal in epidermal growth factor receptor (EGFR) mutant advanced lung adenocarcinoma. Imaging-based biomarkers have attracted much attention in anticancer therapy. This study aims to use the machine learning method to distinguish EGFR mutation status and further explores the predictive role of EGFR mutation-related radiomics features in response to first-line TKIs.

Methods

We retrospectively analyzed pretreatment CT images and clinical information from a cohort of lung adenocarcinomas. We entered the top-ranked features into a support vector machine (SVM) classifier to establish a radiomics signature that predicted EGFR mutation status. Furthermore, we identified the best response-related features based on EGFR mutant-related features in first-line TKI therapy patients. Then we test and validate the predictive effect of the best response-related features for progression-free survival (PFS).

Results

Six hundred ninety-two patients were enrolled in building radiomics signatures. The 13 top-ranked features were input into an SVM classifier to establish the radiomics signature of the training cohort (n = 514), and the predictive score of the radiomics signature was assessed on an independent validation group with 178 patients and obtained an area under the curve (AUC) of 74.13%, an F1 score of 68.29%, a specificity of 79.55%, an accuracy of 70.79%, and a sensitivity of 62.22%. More importantly, the skewness-Low (≤0.882) or 10th percentile-Low group (≤21.132) had a superior partial response (PR) rate than the skewness-High or 10th percentile-High group (p < 0.01). Higher skewness (hazard ratio (HR) = 1.722, p = 0.001) was also found to be significantly associated with worse PFS.

Conclusions

The radiomics signature can be used to predict EGFR mutation status. Skewness may contribute to the stratification of disease progression in lung cancer patients treated with first-line TKIs.

A systematic review of economic evaluations of tyrosine kinase inhibitors for non-small cell lung cancer (NSCLC)

INTRODUCTION

Although tyrosine kinase inhibitors (TKIs) have improved the efficacy of treatment for non-small cell lung cancer (NSCLC), the accessibility of TKIs is limited due to high costs. Despite the critical role of the cost-effectiveness of TKIs on decision-making, no systematic reviews have compared the cost-effectiveness of comparable TKIs. Therefore, we systemically reviewed the economic evaluation studies on various TKIs for NSCLC.

AREAS COVERED

We searched PubMed and the Cochran Library to identify the published economic evaluation studies of TKIs in NSCLC patients that were published by January 2022. All of the included studies (n = 38) evaluated the cost-effectiveness of epidermal growth factor receptor (EGFR)-TKIs (n = 29) or anaplastic lymphocyte kinase (ALK)-TKIs (n = 9). The cost-effectiveness results were reported as the incremental cost-effectiveness ratio per quality-adjusted life-year, except for three studies.

EXPERT OPINION

We found that the economic evaluation studies of the first and second generation of EGFR-TKIs and ALK-TKIs varied by the country and study settings, such as comparator and input parameters. In 12 studies, osimertinib (EGFR-TKI) was not cost-effective compared to other first/second EGFR-TKIs, regardless of the study settings. More evidence can be provided about cost-effectiveness of the third-generation TKIs in future research.

Deciphering associations between three RNA splicing-related genetic variants and lung cancer risk

Limited efforts have been made in assessing the effect of genome-wide profiling of RNA splicing-related variation on lung cancer risk. In the present study, we first identified RNA splicing-related genetic variants linked to lung cancer in a genome-wide profiling analysis and then conducted a two-stage (discovery and replication) association study in populations of European ancestry. Discovery and validation were conducted sequentially with a total of 29,266 cases and 56,450 controls from both the Transdisciplinary Research in Cancer of the Lung and the International Lung Cancer Consortium as well as the OncoArray database. For those variants identified as significant in the two datasets, we further performed stratified analyses by smoking status and histological type and investigated their effects on gene expression and potential regulatory mechanisms. We identified three genetic variants significantly associated with lung cancer risk: rs329118 in JADE2 (P = 8.80E-09), rs2285521 in GGA2 (P = 4.43E-08), and rs198459 in MYRF (P = 1.60E-06). The combined effects of all three SNPs were more evident in lung squamous cell carcinomas (P = 1.81E-08, P = 6.21E-08, and P = 7.93E-04, respectively) than in lung adenocarcinomas and in ever smokers (P = 9.80E-05, P = 2.70E-04, and P = 2.90E-05, respectively) than in never smokers. Gene expression quantitative trait analysis suggested a role for the SNPs in regulating transcriptional expression of the corresponding target genes. In conclusion, we report that three RNA splicing-related genetic variants contribute to lung cancer susceptibility in European populations. However, additional validation is needed, and specific splicing mechanisms of the target genes underlying the observed associations also warrants further exploration.

A narrative review of deep learning applications in lung cancer research: from screening to prognostication

Background and Objective

Deep learning (DL) algorithms have been developed for various tasks, including lung nodule detection on chest radiographs or lung cancer computed tomography screening, potential candidate selection in lung cancer screening, malignancy prediction for indeterminate pulmonary nodules, lung cancer staging, treatment response prediction, prognostication, and prediction of genetic mutations in lung cancer. Furthermore, these DL algorithms have been applied in various clinical settings in order for them to be generalized in real-world clinical practice. Multiple DL algorithms have been corroborated to be on par with experts or current clinical prediction models for several specific tasks. However, no article has yet comprehensively reviewed DL algorithms dedicated to lung cancer research. This narrative review presents an overview of the literature dealing with DL techniques applied in lung cancer research and briefly summarizes the results according to the DL algorithms’ clinical use cases.

Methods

we performed a narrative review by searching the Embase and OVID-MEDLINE databases for articles published in English from October, 2016 until September, 2021 and reviewing the bibliographies of key references to identify important literature related to DL in lung cancer research. The background, development, results, and clinical implications of each DL algorithm are briefly discussed. Lastly, we end this review article by highlighting future directions in lung cancer research using DL techniques.

Key Content and Findings

DL algorithms have been introduced to show comparable or higher performance than human experts in various clinical settings. Specifically, they have been actively applied to detect lung nodules in chest radiographs or computed tomography (CT) examinations, optimize candidate selection for lung cancer screening (LCS), predict the malignancy of lung nodules, stage lung cancer, and predict treatment response, patients’ prognoses, and genetic mutations in lung cancers.

Conclusions

DL algorithms have corroborated their potential value for various tasks, ranging from lung cancer screening to prognostication of lung cancer patients. Future research is warranted for the clinical application of these algorithms in daily clinical practice and verification of their real-world clinical usefulness.

Predictive Value of High Preoperative Serum Total Protein and Elevated Hematocrit in Patients with Non-Small-Cell Lung Cancer after Radical Resection

BACKGROUND

The relationship between the dynamic alterations of nutritional indexes before and after surgery, and the prognosis of non-small-cell lung cancer (NSCLC) after radical surgery are unclear. Methods: This study enrolled 100 NSCLC patients in stages I-III who received radical surgery. The preoperative and postoperative 6-month levels of nine nutrition-related indicators were assessed in patients. Survival was analyzed using Kaplan-Meier curves as well as Cox regression models.

RESULTS

Patients had better disease-free survival (DFS) with baseline total protein (TP) >76.66 g/L (75% vs. 50%, P = .027), baseline albumin (ALB) >37.7 g/L (60% vs. 26.7%, P = .002), baseline albumin to globulin ratio (AGR) >1.31 (63.5% vs. 40.5%, P = .006), or baseline globulin (GLOB) <31.42 g/L (39.4% vs. 62.7%, P = .037). Moreover, patients with increased hematocrit (HCT) (69.8% vs. 43.9% P = .013) and mean corpuscular volume (MCV) (73.2% vs. 42.4%, P = .014) at the postoperative 6-month examination had superior DFS. Cox proportional hazards regression analyses demonstrated that age >65 years, adenocarcinoma (pathological type), higher baseline TP, and post-surgery elevated HCT independently predicted favorable DFS.

CONCLUSION

Lower baseline TP and decreased postoperative HCT levels are independent predictors of prognosis in NSCLC following radical surgical procedures.

Predicting EGFR mutation, ALK rearrangement, and uncommon EGFR mutation in NSCLC patients by driverless artificial intelligence: a cohort study

Background

Timely identification of epidermal growth factor receptor (EGFR) mutation and anaplastic lymphoma kinase (ALK) rearrangement status in patients with non-small cell lung cancer (NSCLC) is essential for tyrosine kinase inhibitors (TKIs) administration. We aimed to use artificial intelligence (AI) models to predict EGFR mutations and ALK rearrangement status using common demographic features, pathology and serum tumor markers (STMs).

Methods

In this single-center study, demographic features, pathology, EGFR mutation status, ALK rearrangement, and levels of STMs were collected from Wuhan Union Hospital. One retrospective set (N = 1089) was used to train diagnostic performance using one deep learning model and five machine learning models, as well as the stacked ensemble model for predicting EGFR mutations, uncommon EGFR mutations, and ALK rearrangement status. A consecutive testing cohort (n = 1464) was used to validate the predictive models.

Results

The final AI model using the stacked ensemble yielded optimal diagnostic performance with areas under the curve (AUC) of 0.897 and 0.883 for predicting EGFR mutation status and 0.995 and 0.921 for predicting ALK rearrangement in the training and testing cohorts, respectively. Furthermore, an overall accuracy of 0.93 and 0.83 in the training and testing cohorts, respectively, were achieved in distinguishing common and uncommon EGFR mutations, which were key evidence in guiding TKI selection.

Conclusions

In this study, driverless AI based on robust variables could help clinicians identify EGFR mutations and ALK rearrangement status and provide vital guidance in TKI selection for targeted therapy in NSCLC patients.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12931-022-02053-2.

PROPHYLACTIC EFFECT OF NITRIC OXIDE DONORS ON RAT MODELS OF EGFR INHIBITORS-INDUCED CUTANEOUS TOXICITIES

Epidermal growth factor receptor inhibitors (EGFRIs) have been established as first-line standard-of-care therapies for non-small cell lung cancer (NSCLC) but are frequently accompanied by adverse dermatological effects, in particular, acneiform rash. There is no effective clinical intervention, partially because of its poorly understood etiology. Here, we show that inhibition of EGFR initiated keratinocyte HaCaT cell cycle arrest and apoptosis, which fueled a robust secondary inflammatory response. Rats gavaged with EGFRI showed a phenotype similar to that of clinical patients, which was in line with the interrupted functions observed in HaCaT keratinocytes. We found that a nitric oxide (NO) donor, nitroglycerin (GTN), was a feasible treatment alternative for EGFRI-induced rash. Restoration of epidermal extracellular signal-regulated kinase (ERK) and a reduction in STAT3 signaling via GTN treatment rescued the cellular functions that had been damaged in vitro and further ameliorated the rash in rat models. In addition, the efficacy of GTN was superior to that of existing clinical interventions. These data highlighted the importance of epidermal EGFR signaling and led to the identification of a small-molecule NO donor as a mediator that can maintain EGFR pathway functions during anti-EGFR therapies, providing a therapeutic anchor point for adverse EGFRI-induced skin effects.

RS1 gene is a novel prognostic biomarker for lung adenocarcinoma

Background

Although it has a poor prognosis, patients with lung adenocarcinoma (LUAD) have a relatively higher 5‐year survival period. Thus, it is necessary to identify effective prognostic markers to evaluate the effect of early treatment. RS1 gene encodes retinoschisin, a key protein in congenital retinoschisis, while few studies have been reported on the association between RS1 and cancer prognosis.

Methods

We performed bioinformatic analyses based on the data obtained from The Cancer Genome Atlas and Gene Expression Omnibus databases to demonstrate the expression level of RS1 was related to the LUAD prognosis and our findings were verified in‐vitro and clinical samples. Then, we explored the potential mechanism of how RS1 expression influenced the prognosis of LUAD.

Results

Compared with normal tissues, the RS1 expression was significantly lower in tumor tissues. The Multivariate Cox regression model showed that RS1 could be used as an independent prognostic indicator. Furthermore, we found significant differences in immune cell infiltration between RS1 high and low expression groups, and the proteasome pathway was found enriched in RS1 low expression samples.

Conclusion

In conclusion, our study suggests that RS1 is a novel prognostic biomarker for LUAD. Differences in immune cell infiltration and signaling pathways may contribute to the poor prognosis of LUAD caused by low RS1 expression.

Non-Small Cell Lung Cancer, Version 3.2022, NCCN Clinical Practice Guidelines in Oncology

NCCN Clinical Practice Guidelines in Oncology (NCCN Guidelines) for Non-Small Cell Lung Cancer (NSCLC) provide recommended management for patients with NSCLC, including diagnosis, primary treatment, surveillance for relapse, and subsequent treatment. Patients with metastatic lung cancer who are eligible for targeted therapies or immunotherapies are now surviving longer. This selection from the NCCN Guidelines for NSCLC focuses on targeted therapies for patients with metastatic NSCLC and actionable mutations.

Genomic Alteration Spectrum of Non-Small Cell Lung Cancer Patients in East-China Characterized by Tumor Tissue DNA and Cell-Free DNA

Introduction

From an oncologic perspective, genetic detection is becoming a frontline clinical test, used to identify actionable alterations for targeted therapy, monitor molecular clonal tumor evolution, indicate disease progression and prognosis, and predict medication efficacy and resistance. From analysis of both tumor tissue and cell-free DNA from a large cohort of non-small cell lung cancer patients in East-China, we characterized the full spectrum of genomic alterations.

Methods

The study comprised 3000 unpaired samples including 1351 tumor tissue DNA (tDNA) and 1649 cell-free circulating tumor DNA (cfDNA) samples, from which 67 cancer-related genes were sequenced and the genetic alteration profiles were depicted. Integrative molecular analyses identified the frequently mutated genes, uncovered co-occurring somatic alterations, described the distribution of hotspot variants, analyzed the frequency of variant alleles, and notably distinguished actionable, novel variants.

Results

The most commonly affected genes were EGFR, TP53, KRAS, CDKN2A, and PIK3CA in both tDNA and cfDNA samples. EGFR and CTNNB1, PIK3CA and PTEN, ERBB2 and SMO were found to have frequent co-occurring alterations in tDNA samples, while EGFR and SMO, KRAS and PDGFRA, PIK3CA and KDR were in cfDNA samples. A large number of primary druggable variants were identified in tDNA samples, while numerous drug-resistance variants, rare actionable variants, and non-EGFR actionable variants were detected in cfDNA samples. Novel variants were enriched in KDR, KIT, TP53, ABL1, FGFR1 in tDNA samples while the majority of novel variants were distributed in PDGFRA, EGFR, KIT, ROS1, BRCA2 in cfDNA samples. Variant allele frequency in tDNA samples was significantly (P < 0.001) higher than that in cfDNA samples.

Conclusion

The results revealed considerable differences in the alteration characteristics between the two kinds of specimens. To date, this study represents the largest real-world investigation of its kind, derived from the largest number of patients in East-China. It reinforced and expanded the mechanism of molecular analysis of neoplastic genetic profiles.

Utilization and costs of epidermal growth factor receptor mutation testing and targeted therapy in Medicare patients with metastatic lung adenocarcinoma

Background

Guidelines in 2013 and 2014 recommended Epidermal Growth Factor Receptor (EGFR) testing for metastatic lung adenocarcinoma patients as the efficacy of targeted therapies depends on the mutations. However, adherence to these guidelines and the corresponding costs have not been well-studied.

Methods

We identified 2362 patients at least 65 years old newly diagnosed with metastatic lung adenocarcinoma from January 2013 to December 2015 using the SEER-Medicare database. We examined the utilization patterns of EGFR testing and targeted therapies including erlotinib and afatinib. We further examined the costs of both EGFR testing and targeted therapy in terms of Medicare costs and patient out-of-pocket (OOP) costs.

Results

The EGFR testing rate increased from 38% in 2013 to 51% and 49% in 2014 and 2015 respectively. The testing rate was 54% among the 394 patients who received erlotinib, and 52% among the 42 patients who received afatinib. The median Medicare and OOP costs for testing were $1483 and $293. In contrast, the costs for targeted therapy were substantially higher with median 30-day costs at $6114 and $240 for erlotinib and $6239 and $471 for afatinib.

Conclusion

This population-based study suggests that testing guidelines improved the use of EGFR testing, although there was still a large proportion of patients receiving targeted therapy without testing. The costs of targeted therapy were substantially higher than the testing costs, highlighting the need to improve adherence to testing guidelines in order to improve clinical outcomes while reducing the economic burden for both Medicare and patients.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12913-022-07857-y.

Claudin1 decrease induced by 1,25-dihydroxy-vitamin D3 potentiates gefitinib resistance therapy through inhibiting AKT activation-mediated cancer stem-like properties in NSCLC cells

Claudins, the integral tight junction proteins that regulate paracellular permeability and cell polarity, are frequently dysregulated in cancer; however, their roles in regulating EGFR tyrosine kinase inhibitors (EGFR-TKIs) resistance in non-small cell lung cancer (NSCLC) are unknown. To this end, we performed GEO dataset analysis and identified that claudin1 was a critical regulator of EGFR-TKI resistance in NSCLC cells. We also found that claudin1, which was highly induced by continuous gefitinib treatment, was significantly upregulated in EGFR-TKI-resistant NSCLC cells. By knocking down claudin1 in cell lines and xenograft models, we established that gefitinib resistance was decreased. Moreover, claudin1 knockdown suppressed the expression levels of pluripotency markers (Oct4, Nanog, Sox2, CD133, and ALDH1A1). Claudin1 loss inhibited phosphorylated AKT (p-AKT) expression and reduced cancer cell stemness by suppressing AKT activation. Furthermore, SKL2001, a β-catenin agonist, upregulated the expression levels of claudin1, p-AKT, and pluripotency markers, and 1,25-dihydroxy-vitamin D3 (1,25(OH)₂D₃) reduced claudin1 expression, AKT activation, and cancer cell stemness by inhibiting β-catenin, and suppressed claudin1/AKT pathway mediated cancer stem-like properties and gefitinib resistance. Collectively, inhibition of claudin1-mediated cancer stem-like properties by 1,25(OH)₂D₃ may decrease gefitinib resistance through the AKT pathway, which may be a promising therapeutic strategy for inhibiting gefitinib resistance in EGFR-mutant lung adenocarcinoma.

Advances in pathogenesis and therapeutic strategies for osteoporosis

Osteoporosis, is the most common bone disorder worldwide characterized by low bone mineral density, leaving affected bones vulnerable to fracture. Bone homeostasis depends on the precise balance between bone resorption by osteoclasts and bone matrix formation by mesenchymal lineage osteoblasts, and involves a series of complex and highly regulated steps. Bone homeostasis will be disrupted when the speed of bone resorption is faster than bone formation. Based on various regulatory mechanisms of bone homeostasis, a series of drugs targeting osteoporosis have emerged in clinical practice, including bisphosphonates, selective estrogen receptor modulators, calcitonin, molecular-targeted drugs and so on. However, many drugs have major adverse effects or are unsuitable for long-term use. Therefore, it is very urgent to find more effective therapeutic drugs based on the new pathogenesis of osteoporosis. In this review, we summarize novel mechanisms involved in the pathological process of osteoporosis, including the roles of gut microbiome, autophagy, iron balance and cellular senescence. Based on the above pathological mechanism, we found promising drugs for osteoporosis treatment, such as: probiotics, alpha-ketoglutarate, senolytics and hydrogen sulfide. This new finding may provide an important basis for elucidating the complex pathological mechanisms of osteoporosis and provide promising drugs for clinical osteoporosis treatment.

Next Generation of Cancer Drug Repurposing: Therapeutic Combination of Aspirin and Oseltamivir Phosphate Potentiates Gemcitabine to Disable Key Survival Pathways Critical for Pancreatic Cancer Progression

Resistance to chemotherapeutics and high metastatic rates contribute to the abysmal survival rate in patients with pancreatic cancer. An alternate approach for treating human pancreatic cancer involves repurposing the anti-inflammatory drug, aspirin (ASA), with oseltamivir phosphate (OP) in combination with the standard chemotherapeutic agent, gemcitabine (GEM). The question is whether treatment with ASA and OP can sensitize cancer cells to the cytotoxicity induced by GEM and limit the development of chemoresistance. To assess the key survival pathways critical for pancreatic cancer progression, we used the AlamarBlue cytotoxicity assay to determine the cell viability and combination index for the drug combinations, flow cytometric analysis of annexin V apoptosis assay to detect apoptotic and necrotic cells, fluorometric QCM™ chemotaxis migration assay to assess cellular migration, fluorometric extracellular matrix (ECM) cell adhesion array kit to assess the expression of the ECM proteins, scratch wound assay using the 96-well WoundMaker™, and the methylcellulose clonogenic assay to assess clonogenic potential. The combination of ASA and OP with GEM significantly upended MiaPaCa-2 and PANC-1 pancreatic cancer cell viability, clonogenic potential, expression of critical extracellular matrix proteins, migration, and promoted apoptosis. ASA in combination with OP significantly improves the effectiveness of GEM in the treatment of pancreatic cancer and disables key survival pathways critical to disease progression.

Machine learning and bioinformatics analysis revealed classification and potential treatment strategy in stage 3-4 NSCLC patients

BACKGROUND

Precision medicine has increased the accuracy of cancer diagnosis and treatment, especially in the era of cancer immunotherapy. Despite recent advances in cancer immunotherapy, the overall survival rate of advanced NSCLC patients remains low. A better classification in advanced NSCLC is important for developing more effective treatments.

METHOD

The calculation of abundances of tumor-infiltrating immune cells (TIICs) was conducted using Cell-type Identification By Estimating Relative Subsets Of RNA Transcripts (CIBERSORT), xCell (xCELL), Tumor IMmune Estimation Resource (TIMER), Estimate the Proportion of Immune and Cancer cells (EPIC), and Microenvironment Cell Populations-counter (MCP-counter). K-means clustering was used to classify patients, and four machine learning methods (SVM, Randomforest, Adaboost, Xgboost) were used to build the classifiers. Multi-omics datasets (including transcriptomics, DNA methylation, copy number alterations, miRNA profile) and ICI immunotherapy treatment cohorts were obtained from various databases. The drug sensitivity data were derived from PRISM and CTRP databases.

RESULTS

In this study, patients with stage 3-4 NSCLC were divided into three clusters according to the abundance of TIICs, and we established classifiers to distinguish these clusters based on different machine learning algorithms (including SVM, RF, Xgboost, and Adaboost). Patients in cluster-2 were found to have a survival advantage and might have a favorable response to immunotherapy. We then constructed an immune-related Poor Prognosis Signature which could successfully predict the advanced NSCLC patient survival, and through epigenetic analysis, we found 3 key molecules (HSPA8, CREB1, RAP1A) which might serve as potential therapeutic targets in cluster-1. In the end, after screening of drug sensitivity data derived from CTRP and PRISM databases, we identified several compounds which might serve as medication for different clusters.

CONCLUSIONS

Our study has not only depicted the landscape of different clusters of stage 3-4 NSCLC but presented a treatment strategy for patients with advanced NSCLC.

Long Non-Coding RNA AL513318.2 as ceRNA Binding to hsa-miR-26a-5p Upregulates SLC6A8 Expression and Predicts Poor Prognosis in Non-Small Lung Cancer

Background

Studies have demonstrated that the regulatory role of competitive endogenous RNA (ceRNA) networks is closely related to tumorigenesis, which provides new targets for tumor therapy. In this study, the focus was to explore the ceRNA networks that regulate SLC6A8 expression and their prognosis in non-small cell lung cancer (NSCLC).

Methods

Firstly, the Cancer Genome Atlas (TCGA) data combined with immunohistochemical staining was used to compare SLC6A8 expression in NSCLC tissues and normal tissues. Thereafter, samples from the immunohistochemical staining of NSCLC were integrated with clinical follow-up data for prognostic analysis. The Starbase database was employed to search for SLC6A8-targeted miRNAs and lncRNAs, and survival analysis was performed using clinical data from TCGA to obtain SLC6A8 expression and prognosis-related ceRNA networks. Finally, the prognostic and therapeutic prospects of SLC6A8 in NSCLC were further analyzed from methylation sites and the immune microenvironment.

Results

The study results revealed that SLC6A8 was significantly overexpressed in NSCLC tissues compared to normal tissues, and clinical follow-up data showed that the overexpression group was associated with poor prognosis. In addition, the Starbase data combined with TCGA clinical data analysis demonstrated that the AL513318.2/hsa-miR-26a-5p/SLC6A8 network regulates SLC6A8 overexpression in NSCLC and is associated with poor prognosis. Methylation analysis revealed that 11 methylation sites were closely associated with the prognosis of NSCLC. In addition, the immune prognostic risk model showed that the high-risk group was associated with a poorer prognosis than the low-risk group, despite showing a better immunotherapy outcome.

Conclusion

In summary, the AL513318.2/hsa-miR-26a-5p/SLC6A8 network upregulates SLC6A8 expression in NSCLC and is associated with poor prognosis. Therefore it may be a prognostic biomarker of NSCLC and a potential therapeutic target.

Liquid Biopsy Testing for the Management of Patient with Non-Small Cell Lung Cancer Carrying a Rare Exon-20 EGFR Insertion

Increasing evidence suggests that liquid biopsy might play a relevant role in the management of metastatic non-small cell lung cancer (NSCLC) patients. Here, we show how the Molecular Tumor Board (MTB) in our cancer center employed liquid biopsy to support therapeutic decisions in a patient with NSCLC carrying a rare EGFR mutation. A 44-year-old woman, never-smoker with an EGFR, ALK, and ROS1-negative lung adenocarcinoma and multiple brain metastases received systemic therapy and surgery before being referred to our Institute. The MTB suggested NGS testing of tumor biopsy that revealed a rare exon-20 EGFR insertion (p.His773dup; c.2315_2316insCCA) and EGFR amplification. The MTB recommended treatment with erlotinib and follow-up with liquid biopsy, by using both cell-free DNA (cfDNA) and circulating tumor cells (CTCs). An increase of EGFR mutation levels in cfDNA revealed resistance to treatment about 6 months before clinical progression. Extremely low levels of EGFR p.T790M were detected at progression. Based on preclinical data suggesting activity of osimertinib against EGFR exon-20 insertions, the MTB recommended treatment with brain and bone radiotherapy and osimertinib. A dramatic reduction of EGFR mutation levels in the cfDNA was observed after 4 weeks of treatment. The PET scan demonstrated a metabolic partial remission that was maintained for 9 months. This case supports the evidence that liquid biopsy can aid in the management of metastatic NSCLC. It also suggests that treatment with osimertinib might be a therapeutic option in patients with EGFR exon-20 insertions when a clinical trial is not available.

Cytology–Based Specimen Triage for Epidermal Growth Factor Receptor Mutation Testing of Malignant Pleural Effusions in Non–Small Cell Lung Cancer

Introduction

Malignant pleural effusions are common in non–small cell lung cancer (NSCLC). Molecular testing is among the most critical steps in the management of patients with advanced NSCLC. However, the optimal approach for epidermal growth factor receptor (EGFR) mutation testing in such effusion samples remains unclear.

Methods

We prospectively collected effusion samples from patients with EGFR–mutant NSCLC. Following sample centrifugation, genomic DNA and cell–free DNA were respectively extracted from the sediment and supernatants. EGFR mutation was detected through a real–time PCR assay.

Results

A total of 108 effusions from 78 patients were examined, with 12 and 96 obtained before and after EGFR tyrosine kinase inhibitor treatment, respectively. Carcinoma cells or atypical cells were identified in 73 effusions (67.6%). EGFR mutations were detected in 86 (79.6%) sediment and 84 (77.8%) supernatant samples. Among the effusions with positive cytological findings, the EGFR mutation detection rates were 95.9% (70/73) and 86.3% (63/73) in the sediment and supernatants, respectively. Among the effusions with negative cytological findings, the corresponding detection rates were 45.7% (16/35) and 60% (21/35), respectively. Current clinical practice is to arrange EGFR mutation testing only for sediment from cytologically positive effusions. Through the proposed cytology–based specimen triage, wherein sediment and supernatants with positive and negative cytological findings, respectively, are tested, the detection rate was increased from 64.8% (70/108) to 84.3% (91/108). At half of the cost, this strategy provided a detection rate only slightly lower than the rate in a combined test of all the sediment and supernatants (87.0%, 94/108).

Conclusions

The separate extraction of DNA from sediment and supernatants obtained from centrifuged effusion samples can improve the overall EGFR mutation detection rate. The present cytology–based specimen triage is an efficient strategy for EGFR mutation testing in patients with EGFR–mutant NSCLC.

HER3 activation contributes toward the emergence of ALK inhibitor-tolerant cells in ALK-rearranged lung cancer with mesenchymal features

Anaplastic lymphoma kinase-tyrosine kinase inhibitors (ALK-TKIs) have shown dramatic efficacy in patients with ALK-rearranged lung cancer; however, complete response in these patients is rare. Here, we investigated the molecular mechanisms underlying the emergence and maintenance of drug-tolerant cells in ALK-rearranged lung cancer. Cell based-assays demonstrated that HER3 activation and mesenchymal-to-epithelial transition, mediated through ZEB1 proteins, help maintain cell survival and induce the emergence of ALK-TKI-tolerant cells. Compared with ALK-TKIs alone, cotreatment with pan-HER inhibitor afatinib and ALK-TKIs prevented tumor regrowth, leading to the eradication of tumors in ALK-rearranged tumors with mesenchymal features. Moreover, pre-treatment vimentin expression in clinical specimens obtained from patients with ALK-rearranged lung cancer was associated with poor ALK-TKI treatment outcomes. These results demonstrated that HER3 activation plays a pivotal role in the emergence of ALK-TKI-tolerant cells. Furthermore, the inhibition of HER3 signals combined with ALK-TKIs dramatically improves treatment outcomes for ALK-rearranged lung cancer with mesenchymal features.

Preclinical assessment of combination therapy of EGFR tyrosine kinase inhibitors in a highly heterogeneous tumor model

The development of tyrosine kinase inhibitors (TKIs) has improved the treatment of non-small cell lung cancer (NSCLC) with epidermal growth factor receptor (EGFR) mutations. The current research priority is to provide viable treatments for patients who have drug-resistant EGFR mutations. We evaluated the drug sensitivity of various EGFR mutants to monotherapies and combination therapies of EGFR-TKIs. In vitro, the transforming potential and drug sensitivity of 357 EGFR variants were assessed. In vivo, we tested the sensitivity of EGFR variants to different regimens of EGFR-TKIs by examining changes in the proportion of each variant within the tumor. Out of 357 variants thoroughly examined for transforming activities, 144 (40.3%) and 282 (79.0%) transformed 3T3 and Ba/F3 cells, respectively. Among the latter variants, 50 (17.7%) were found to be resistant or only partly resistant to osimertinib or afatinib. Four of 25 afatinib-resistant variants (16%) were sensitive to osimertinib, whereas 25 of 46 osimertinib-resistant variants (54.3%) were sensitive to afatinib. Despite the lack of a synergistic impact, TKI combination treatment effectively reduced in vivo the heterogeneous tumors composed of 3T3 cells with different EGFR variants. Regimens starting with afatinib and subsequently switched to osimertinib suppressed tumor development more efficiently than the opposite combination. Combination EGFR-TKI treatment may decrease tumor growth and prevent the development of resistant variants. This work created an experimental model of a heterogeneous tumor to find the best combination therapy regimen and proposes a basic notion of EGFR-TKI combination therapy to enhance the prognosis of NSCLC patients.

Microdissection Methods Utilizing Single-Cell Subtype Analysis and the Impact on Precision Medicine

Improving the utilization of tumor tissue from diagnostic biopsies is an unmet medical need. This is especially relevant today in the rapidly evolving precision oncology field where tumor genotyping is often essential for the indication of many advanced and targeted therapies. National Comprehensive Cancer Network (NCCN) guidelines now mandate molecular testing for clinically actionable targets in certain malignancies. Utilizing advanced stage lung cancer as an example, an improved genotyping approach for solid tumors is possible. The strategy involves optimization of the microdissection process and analysis of a large number of identical target cells from formalin-fixed paraffin-embedded (FFPE) specimens sharing similar characteristics, in other words, single-cell subtype analysis. The shared characteristics can include immunostaining status, cell phenotype, and/or spatial location within a histological section. Synergy between microdissection and droplet digital PCR (ddPCR) enhances the molecular analysis. We demonstrate here a methodology that illustrates genotyping of a solid tumor from a small tissue biopsy sample in a time- and cost-efficient manner, using immunostain targeting as an example.

AIR-Net: A novel multi-task learning method with auxiliary image reconstruction for predicting EGFR mutation status on CT images of NSCLC patients

Automated and accurate EGFR mutation status prediction using computed tomography (CT) imagery is of great value for tailoring optimal treatments to non-small cell lung cancer (NSCLC) patients. However, existing deep learning based methods usually adopt a single task learning strategy to design and train EGFR mutation status prediction models with limited training data, which may be insufficient to learn distinguishable representations for promoting prediction performance. In this paper, a novel multi-task learning method named AIR-Net is proposed to precisely predict EGFR mutation status on CT images. First, an auxiliary image reconstruction task is effectively integrated with EGFR mutation status prediction, aiming at providing extra supervision at the training phase. Particularly, we adequately employ multi-level information in a shared encoder to generate more comprehensive representations of tumors. Second, a powerful feature consistency loss is further introduced to constrain semantic consistency of original and reconstructed images, which contributes to enhanced image reconstruction and offers more effective regularization to AIR-Net during training. Performance analysis of AIR-Net indicates that auxiliary image reconstruction plays an essential role in identifying EGFR mutation status. Furthermore, extensive experimental results demonstrate that our method achieves favorable performance against other competitive prediction methods. All the results executed in this study suggest that the effectiveness and superiority of AIR-Net in precisely predicting EGFR mutation status of NSCLC.

Pyroptosis-Related Gene Signature Is a Novel Prognostic Biomarker for Sarcoma Patients

Sarcoma is a rare and an extremely aggressive form of cancer that originates from mesenchymal cells. Pyroptosis exerts a dual effect on tumours by inhibiting tumour cell proliferation while creating a microenvironment suitable for tumour cell development and proliferation. However, the significance of pyroptosis-related gene (PRG) expression in sarcoma has not yet been evaluated. Here, we conduct a retrospective analysis to examine PRG expression in 256 sarcoma samples from The Cancer Genome Atlas database. We identified the PRGs that had a significant correlation with overall patient survival in sarcoma by performing a univariate Cox regression analysis. Subsequently, we conducted a LASSO regression analysis and created a risk model for a six-PRG signature. As indicated from the Kaplan–Meier analysis, this signature revealed a significant difference between high- and low-risk sarcoma patients. A receiver operating characteristic curve analysis confirmed that this signature could predict overall patient survival in sarcoma patients with high sensitivity and specificity. Gene ontology annotation and Kyoto Encyclopaedia of Genes and Genomes pathway enrichment analyses revealed that five independent PRGs were closely associated with increased immune activity. Moreover, we also deciphered that increased number of immune cells infiltrated the tumour microenvironment in sarcoma. In brief, the PRG signature can effectively act as novel prognostic biomarker for sarcoma patients and is associated with the tumour immune microenvironment.

Mechanisms of gefitinib-induced QT prolongation

Gefitinib, a tyrosine kinase inhibitor, was the first targeted therapy for non-small cell lung cancer (NSCLC). Gefitinib could block human Ether-à-go-go-Related Gene (hERG) channel, an important target in drug-induced long QT syndrome. However, it is unclear whether gefitinib could induce QT interval prolongation. Here, whole-cell patch-clamp technique was used for evaluating the effect of gefitinib on rapidly-activating delayed rectifier K⁺ current (I_Kr), slowly-activating delayed rectifier K⁺ current (I_Ks), transient outward potassium current (I_to), inward rectifier K⁺ current (I_K1) and on action potentials in guinea pig ventricular myocytes. The Langendorff heart perfusion technique was used to determine drug effect on the ECG. Gefitinib depressed I_Kr by binding to open and closed hERG channels in a concentration-dependent way (IC_50: 1.91 μM). The inhibitory effect of gefitinib on wildtype hERG channels was reduced at the hERG mutants Y652A, S636A, F656V and S631A (IC_50: 8.51, 13.97, 18.86, 32.99 μM), indicating that gefitinib is a pore inhibitor of hERG channels. In addition, gefitinib accelerated hERG channel inactivation and decreased channel steady-state inactivation. Gefitinib also decreased I_Ks with IC₅₀ of 23.8 μM. Moreover, gefitinib increased action potential duration (APD) in guinea pig ventricular myocytes and the corrected QT interval (QTc) in isolated perfused guinea pig hearts in a concentration-dependent way (1-30 μM). These findings indicate that gefitinib could prolong QTc interval by potently blocking hERG channel, modulating kinetic properties of hERG channel. Partial block of KCNQ1/KCNE1 could also contribute to delayed repolarization and prolonged QT interval. Thus, caution should be taken when gefitinib is used for NSCLC treatment.

Comparative Effectiveness of Atezolizumab, Nivolumab, and Docetaxel in Patients With Previously Treated Non-Small Cell Lung Cancer

IMPORTANCE

Evidence regarding real-world effectiveness of therapies for patients with advanced non-small cell lung cancer (NSCLC) whose tumors are resistant to platinum-based chemotherapy is lacking.

OBJECTIVE

To compare the effectiveness of the immune checkpoint inhibitors atezolizumab (programmed cell death ligand 1 inhibitor) and nivolumab (programmed cell death 1 inhibitor) and the chemotherapy drug docetaxel in patients with advanced NSCLC resistant to platinum-based chemotherapy.

DESIGN, SETTING, AND PARTICIPANTS

This comparative effectiveness study compared patients aged 18 years or older with advanced NSCLC who initiated atezolizumab, docetaxel, or nivolumab and who had previously been exposed to platinum-based chemotherapy using nationally representative real-world data from more than 280 US cancer clinics. Patients were followed-up from May 2011 to March 2020. Data analysis was performed between April and June 2021. Comparisons of interest were between atezolizumab vs docetaxel and atezolizumab vs nivolumab.

EXPOSURES

Initiation of atezolizumab, nivolumab, or docetaxel monotherapy.

MAIN OUTCOME AND MEASURES

The main outcome was overall survival (OS).

RESULTS

A total of 3336 patients (mean [SD] age, 67.1 [9.49] years; 1820 [54.6%] men and 1516 [45.4%] women) were assessed in the main analysis, including 206 patients receiving atezolizumab, 500 receiving docetaxel, and 2630 receiving nivolumab. Patients receiving atezolizumab were older than those treated with docetaxel (mean age [SD], 68.3 [9.4] years vs 65.6 [9.5] years), and were more likely to have been treated in an academic setting (39 patients [18.9%]) than those receiving docetaxel (49 patients [9.8%]) and nivolumab (128 patients [4.9%]). After adjustment for baseline characteristics, atezolizumab was associated with a significantly longer OS compared with docetaxel (adjusted hazard ratio [aHR], 0.79; 95% CI, 0.64-0.97). No significant difference in OS was observed between atezolizumab and nivolumab (aHR, 1.07; 95% CI, 0.89-1.28). These findings were consistent across all patient subgroups tested, and robust to plausible deviations from random missingness for Eastern Cooperative Oncology Group performance status in real-world data (eg, the tipping point for loss of a significantly beneficial effect for atezolizumab vs docetaxel was achieved if patients in the docetaxel group missing baseline Eastern Cooperative Oncology Group performance status had a mean performance status of 1.43 higher than expected).

CONCLUSIONS AND RELEVANCE

In this comparative effectiveness study, atezolizumab was superior to docetaxel and matched nivolumab in prolonging OS in a real-world cohort of patients with advanced NSCLC who previously received platinum-based chemotherapy.

Simultaneous determination of dacomitinib and its major metabolite, O-desmethyl dacomitinib in human plasma by LC-MS/MS and its application to clinical testing in patients with non-small cell lung cancer

Dacomitinib, an irreversible pan-ErbB tyrosine kinase inhibitor targeting the human epidermal growth factor receptor, is used for the treatment of metastatic non-small cell lung cancer. To facilitate the investigations on its metabolism and other relevant studies, based on high-performance liquid chromatography-tandem mass spectrometry (HPLC-MS/MS), a rapid and sensitive bioanalytical technique was established and fully validated for simultaneous quantification of dacomitinib and its main metabolite in human plasma. The plasma samples were treated with acetonitrile containing 0.1% formic acid and the liquid supernatant was collected, dried and dissolved in methanol-water-formic acid (200:800:1, v/v) before injection. The chromatographic separation was performed on an ACE Excel C₁₈ column (2.1 mm × 50.0 mm, i.d., 5 μm) by gradient elution with a mixture of buffer (5 mM ammonium acetate in 0.1% formic acid) and acetonitrile, serving as the mobile phase, with an overall run time of 4 min. Dacomitinib, O-desmethyl dacomitinib and IS were subsequently detected on an AB QTRAP 5500 mass spectrometer in positive ion and multiple reaction monitoring modes at the precursor-to-product transitions of m/z 470.4 → 385.0, m/z 456.0 → 370.9 and m/z 480.2 → 385.1, respectively. The accuracy and precision of determinations were guaranteed within the concentration ranges of 0.25-100 ng/mL for dacomitinib and 0.20-80 ng/mL for O-desmethyl dacomitinib. The intra- and inter-assay accuracy ranged from 92.00% to 104.50% and the intra- and inter-assay precision was less than 8.20% for each analyte. The method was validated and the relevant parameters, including selectivity, interference among analytes and internal standard, carry-over effect, dilution integrity, extraction recovery, matrix effect, and stability, all satisfied the requirements formulated by the US Food and Drug Administration and the European Medicines Agency. The clinical applicability of the fully-validated method was evaluated in medicated samples from patients on dacomitinib.

Predictive role of neutropenia under crizotinib treatment in ALK-rearranged nonsmall cell lung cancer patients: A single-institution retrospective analysis

Background

Anaplastic lymphoma kinase (ALK)-rearranged nonsmall cell lung cancer (NSCLC) represents a molecular subgroup with high sensitivity to ALK inhibitors. Tyrosine kinase inhibitor crizotinib, an anticancer drug acting as an ALK inhibitor, has shown remarkable response in ALK-positive NSCLC. The aim of our study is to explore the adverse events (AEs) of patients on crizotinib therapy and analyze the predictability of AEs for better survival or response on NSCLC patients.

Methods

The medical records of our ALK-positive metastatic NSCLC patients who applied between years 2013 and 2018 had been reviewed retrospectively. ALK positivity of all patients had been detected by fluorescence in situ hybridization and no other driver mutations were present. Patient demographics, performance status, smoking history, previous treatments, metastatic sites, and AEs were recorded for further analyses.

Results

Thirty-six ALK-positive metastatic NSCLC patients were included in the study. Median follow-up was 30.1 months. Median progression-free survival (PFS) for patients who developed hepatic, cardiac, or endocrine toxicities was similar when compared to patients who did not develop. Although there was a numeric median PFS difference between patients who did develop visual disorders (18.4 months) and did not develop visual disorders (15.5 month), this was not regarded as statistically significant. However, median PFS of the patients who developed neutropenia upon crizotinib treatment (31.9 months) was found to be more favorable than the patients with normal neutrophil counts (12.8 months) (P = 0.026).

Conclusion

Neutropenia under crizotinib treatment was found to be associated with improved PFS suggesting that neutropenia might be an important determinant in treatment and survival strategies.

Bevacizumab biosimilar LY01008 compared with bevacizumab (Avastin) as first-line treatment for Chinese patients with unresectable, metastatic, or recurrent non-squamous non-small-cell lung cancer: A multicenter, randomized, double-blinded, phase III trial

BACKGROUND

Previous studies have demonstrated the preclinical pharmacological and toxicological consistency, and clinical pharmacokinetic equivalence of bevacizumab biosimilar LY01008 with reference bevacizumab (Avastin). This randomized controlled trial aimed to compare the efficacy and safety of LY01008 with Avastin in first-line treatment of Chinese patients with advanced or recurrent non-squamous non-small cell lung cancer (NSCLC).

METHODS

Stage IIIB-IV NSCLC patients with evaluable lesions, good physical status, and adequate organ functions from 67 centers across China were randomized in a ratio of 1:1 to receive LY01008 or Avastin 15 mg/kg intravenously in combination with paclitaxel/carboplatin (combined treatment) for 4-6 cycles, followed by maintenance monotherapy with LY01008 until disease progression, intolerable toxicity, or death. The primary endpoint was objective response rate (ORR) in accordance with Response Evaluation Criteria in Solid Tumors (RECIST) version 1.1 confirmed by independent radiological review committees (IRRC). Secondary endpoints included disease control rate (DCR), duration of response (DoR), progression-free survival (PFS), overall survival (OS), and safety. This study was registered in ClinicalTrials.gov (NCT03533127).

RESULTS

Between December 15th , 2017, and May 15th , 2019, a total of 649 patients were randomized to the LY01008 (n = 324) or Avastin (n = 325) group. As of September 25th , 2019 for primary endpoint analysis, 589 patients received ORR evaluation, with a median number of combined treatment cycles of 5 (range 1-6) and median duration of treatment of 3.0 (range 0.0-5.1) months. ORR of response-evaluable patients in the LY01008 and Avastin groups were 48.5% and 53.0%, respectively. The stratified ORR ratio was 0.91 (90% CI 0.80-1.04, within the prespecified equivalence margin of 0.75-1.33). Up to May 15th , 2020, with a median follow-up of 13.6 (range 0.8-28.4) months, no notable differences in DCR, median DoR, median PFS, median OS, and 1-year OS rate were observed between the LY01008 and Avastin groups. There were no clinically meaningful differences in safety and immunogenicity across treatment groups.

CONCLUSIONS

LY01008 demonstrated similarity to Avastin in terms of efficacy and safety in Chinese patients with advanced or recurrent non-squamous NSCLC. LY01008 combined with paclitaxel/carboplatin is expected to become a new treatment option for unresectable, metastatic, or recurrent non-squamous NSCLC patients in the first-line setting.

EGFR DNA Methylation Correlates With EGFR Expression, Immune Cell Infiltration, and Overall Survival in Lung Adenocarcinoma

Background

The epidermal growth factor receptor (EGFR) is a primary target of molecular targeted therapy for lung adenocarcinoma (LUAD). The mechanisms that lead to epigenetic abnormalities of EGFR in LUAD are still unclear. The purpose of our study was to evaluate the abnormal methylation of EGFR CpG sites as potential biomarkers for LUAD.

Methods

To assess the differentially methylation CpG sites of EGFR in LUAD, we used an integrative study of Illumina HumanMethylation450K and RNA-seq data from The Cancer Genome Atlas (TCGA). We evaluated and compared EGFR multiple-omics data to explore the role of CpG sites located in EGFR promoter regions and gene body regions and the association with transcripts, protein expression levels, mutations, and somatic copy number variation. We calculated the correlation coefficients between CpG sites of EGFR and immune infiltration fraction (by MCPcounter and ESTIMATE) and immune-related pathways in LUAD. Finally, we validated the differential methylation of clinically and prognostically relevant CpG sites using quantitative methylation-specific PCR (qMSP).

Results

We found that the methylation level of many EGFR CpGs in the promoter region was negatively correlated with the transcription level, protein expression, and SCNV, while the methylation at the gene body region was positively correlated with these features. The methylation level of EGFR CpGs in the promoter region was positively correlated with the level of immune infiltration and IFN-γ signature, while the opposite was found for methylation of the gene body region. The qMSP results showed that cg02316066 had a high methylation level, while cg02166842 had a low methylation level in LUAD. There was a high degree of co-methylation between cg02316066 and cg03046247.

Conclusion

Our data indicate that EGFR is an epigenetic regulator in LUAD acting through DNA methylation. Our research provides a theoretical basis for the further detection of EGFR DNA methylation as a predictive biomarker for LUAD survival and immunotherapy.

Toward personalized treatment approaches for non-small-cell lung cancer

Worldwide, lung cancer is the most common cause of cancer-related deaths. Molecular targeted therapies and immunotherapies for non-small-cell lung cancer (NSCLC) have improved outcomes markedly over the past two decades. However, the vast majority of advanced NSCLCs become resistant to current treatments and eventually progress. In this Perspective, we discuss some of the recent breakthrough therapies developed for NSCLC, focusing on immunotherapies and targeted therapies. We highlight our current understanding of mechanisms of resistance and the importance of incorporating genomic analyses into clinical studies to decipher these further. We underscore the future role of neoadjuvant and maintenance combination therapy approaches to potentially cure early disease. A major challenge to successful development of rational combination therapies will be the application of robust predictive biomarkers for clear-cut patient stratification, and we provide our views on clinical research areas that could influence how NSCLC will be managed over the coming decade.

Clinical cancer genomic profiling

Technological innovation and rapid reduction in sequencing costs have enabled the genomic profiling of hundreds of cancer-associated genes as a component of routine cancer care. Tumour genomic profiling can refine cancer subtype classification, identify which patients are most likely to benefit from systemic therapies and screen for germline variants that influence heritable cancer risk. Here, we discuss ongoing efforts to enhance the clinical utility of tumour genomic profiling by integrating tumour and germline analyses, characterizing allelic context and identifying mutational signatures that influence therapy response. We also discuss the potential clinical utility of more comprehensive whole-genome and whole-transcriptome sequencing and ultra-sensitive cell-free DNA profiling platforms, which allow for minimally invasive, serial analyses of tumour-derived DNA in blood.

Integrative Multi-Omics Approaches in Cancer Research: From Biological Networks to Clinical Subtypes

Multi-omics approaches are novel frameworks that integrate multiple omics datasets generated from the same patients to better understand the molecular and clinical features of cancers. A wide range of emerging omics and multi-view clustering algorithms now provide unprecedented opportunities to further classify cancers into subtypes, improve the survival prediction and therapeutic outcome of these subtypes, and understand key pathophysiological processes through different molecular layers. In this review, we overview the concept and rationale of multi-omics approaches in cancer research. We also introduce recent advances in the development of multi-omics algorithms and integration methods for multiple-layered datasets from cancer patients. Finally, we summarize the latest findings from large-scale multi-omics studies of various cancers and their implications for patient subtyping and drug development.

The Pyroptosis-Related Signature Predicts Prognosis and Indicates Immune Microenvironment Infiltration in Gastric Cancer

Gastric cancer (GC) is one of the leading causes of cancer-related deaths and shows high levels of heterogeneity. The development of a specific prognostic model is important if we are to improve treatment strategies. Pyroptosis can arise in response to H. pylori, a primary carcinogen, and also in response to chemotherapy drugs. However, the prognostic evaluation of GC to pyroptosis is insufficient. Consensus clustering by pyroptosis-related regulators was used to classify 618 patients with GC from four GEO cohorts. Following Cox regression with differentially expressed genes, our prognosis model (PS-score) was built by LASSO-Cox analysis. The TCGA-STAD cohort was used as the validation set. ESTIMATE, CIBERSORTx, and EPIC were used to investigate the tumor microenvironment (TME). Immunotherapy cohorts by blocking PD1/PD-L1 were used to investigate the treatment response. The subtyping of GC based on pyroptosis-related regulators was able to classify patients according to different clinical traits and TME. The difference between the two subtypes identified in this study was used to develop a prognosis model which we named “PS-score.” The PS-score could predict the prognosis of patients with GC and his/her overall survival time. A low PS-score implies greater inflammatory cell infiltration and better response of immunotherapy by PD1/PD-L1 blockers. Our findings provide a foundation for future research targeting pyroptosis and its immune microenvironment to improve prognosis and responses to immunotherapy.

Potentiation of calcium-activated chloride secretion and barrier dysfunction may underlie EGF receptor tyrosine kinase inhibitor-induced diarrhea

Epidermal growth factor receptor tyrosine kinase inhibitors (EGFr TKIs) are first‐line therapies for various cancers, and cause dose‐limiting severe diarrhea in many patients. We hypothesized that diarrhea caused by EGFr TKIs might reflect actions on epithelial transport, barrier function, or both, which we tested using cell cultures including murine and human enteroid‐derived monolayers (EDMs), analyzed using electrophysiological and other relevant methods. EGFr TKIs (such as afatinib, erlotinib, and osimertinib) reversed the acute inhibitory effect of EGF on chloride secretion induced by carbachol (CCh) across T84 human colonic epithelial cells, which correlated with the diarrhea‐inducing effect of each agent clinically. EGFr TKIs also reduced transepithelial electrical resistance (TEER), whereas co‐treatment with CCh delayed the decrease in TEER compared with that of cells co‐treated with EGF. Furthermore, afatinib and erlotinib prevented EGF‐ or CCh‐induced EGFr phosphorylation. EGFr TKIs also suppressed phosphorylation of extracellular signal‐regulated kinase (Erk)1/2 in response to EGF, whereas they had weaker effects on CCh‐induced Erk1/2 phosphorylation. In human EDMs, EGF potentiated ion transport induced by CCh, whereas afatinib reversed this effect. The ability of EGFr TKIs to reverse the effects of EGF on calcium‐dependent chloride secretion could contribute to the diarrheal side effects of these agents, and their disruption of epithelial barrier dysfunction is likely also pathophysiologically significant. CCh‐activated Erk1/2 phosphorylation was relatively insensitive to EGFr TKIs and delayed the deleterious effects of EGFr TKIs on barrier function. These findings confirm and extend those of other authors, and may be relevant to designing strategies to overcome the diarrheal side effects of EGFr TKIs.

Value of radiomics model based on multi-parametric magnetic resonance imaging in predicting epidermal growth factor receptor mutation status in patients with lung adenocarcinoma

Background

The epidermal growth factor receptor (EGFR) is an important therapeutic target for patients with non-small-cell lung cancer (NSCLC). Radiomics and radiogenomics have emerged as attractive research topics aiming to extract mineable high-dimensional features from medical images and show potential to correlate with the gene mutation. Herein, we aim to develop a magnetic resonance imaging (MRI)-based radiomics model for pretreatment prediction of the EGFR status in patients with lung adenocarcinoma.

Methods

A total of 92 patients with pathologically confirmed lung adenocarcinoma were retrospectively enrolled in this study. EGFR genotype was analyzed by sequence testing. All patients were randomized into training and test group in a 7:3 ratio using the R software. Radiomics features were extracted from T2 weighted imaging (T2WI), diffusion-weighted imaging (DWI), and apparent diffusion coefficient (ADC); radiomics signatures were built using the least absolute shrinkage and selection operator (LASSO) and logistic regression. Preoperative clinical factors and image features associated with EGFR were also evaluated. A nomogram including sex, smoking status, and radiomics signatures was constructed. A total of five radiomics models were built, and the area under the curve (AUC) was used to evaluate their performance of EGFR mutation prediction.

Results

Among the three single-sequence models, the ADC model showed the best prediction performance. The AUCs of the ADC, DWI, T2WI prediction model in the test cohort were 0.805 (95% CI: 0.610 to 1.000), 0.722 (95% CI: 0.519 to 0.924), and 0.655 (95% CI: 0.438 to 0.872), respectively. Compared with the single-sequence model, the multi-sequence prediction model showed better performed [AUC_test =0.838 (95% CI: 0.685 to 0.992)]. The AUC of the nomogram in the training group was 0.925 (95% CI: 0.855 to 0.994) and 0.727 (95% CI: 0.531 to 0.924) in the test group, respectively.

Conclusions

The radiomics model based on MRI might have the potential to predict EGFR mutation in patients with lung adenocarcinoma. The multi-sequence model had better performance than other models.

Immune-related genes and gene sets for predicting the response to anti-programmed death 1 therapy in patients with primary or metastatic non-small cell lung cancer

Although antibodies targeting the immune checkpoint protein programmed death-1 (PD-1) exert therapeutic effects in patients with primary or metastatic non-small cell lung cancer (NSCLC), the majority of patients exhibit partial or complete resistance to anti-PD1 treatment. Thus, the aim of the present study was to identify reliable biomarkers for predicting the response to anti-PD-1 therapy. The present study analyzed tumor specimens isolated from 24 patients (13 with primary and 11 with metastatic NSCLC) prior to treatment with approved PD1-targeting antibodies. The expression profile of 395 immune-related genes was examined using RNA immune-oncology panel sequencing. The results demonstrated that six immune-related differently expressed genes (DEGs), including HLA-F-AS1, NCF1, RORC, DMBT1, KLRF1 and IL-18, and five DEGs, including HLA-A, HLA-DPA1, TNFSF18, IFI6 and PTK7, may be used as single biomarkers for predicting the efficacy of anti-PD-1 treatment in patients with primary and with metastatic NSCLC, respectively. In addition, two DEG sets comprising either six (HLA-F-AS1, NCF1, RORC, DMBT1, KLRF and IL-18) or two (HLA-A and TNFSF18) DEGs as potential combination biomarkers for predicting the efficacy of anti-PD-1 therapy in patients with NSCLC. Patients with a calculated expression level of the DEG sets >6.501 (primary NSCLC) or >6.741 (metastatic NSCLC) may benefit from the anti-PD-1 therapy. Overall, these findings provided a basis for the identification of additional biomarkers for predicting the response to anti-PD-1 treatment.

Gene Expression Levels of the Prolyl Hydroxylase Domain Proteins PHD1 and PHD2 but Not PHD3 Are Decreased in Primary Tumours and Correlate with Poor Prognosis of Patients with Surgically Resected Non-Small-Cell Lung Cancer

Simple Summary

Hypoxia correlates with poor prognosis in several cancer types, including lung cancer. Prolyl hydroxylase domain proteins (PHDs) belong to an evolutionarily conserved superfamily of dioxygenases that play a role in cell oxygen sensing and homeostasis. In this study, we evaluated PHD1, PHD2 and PHD3 mRNA expression in 60 NSCLC tumours and compared it to that in normal lungs and evaluated the prognostic significance of these differences for distinguishing the survival of NSCLC patients treated with radical surgery. Our results showed that the mRNA expression PHD1 and PHD2 in NSCLC primary tumours was decreased, which correlated with larger tumour size and poor prognosis of patients. PHD1 also showed borderline independent prognostic value in multivariate analysis. In contrast, we found no associations between PHD3 expression and any of the observed parameters. Our results suggest that loss of PHD1 and PHD2 expression is associated with the development and progression of NSCLC, whereas PHD1 could be further assessed as a prognostic marker in NSCLC.

Abstract

Background: Hypoxia correlates with poor prognosis in several cancer types, including lung cancer. Prolyl hydroxylase domain proteins (PHDs) play a role in cell oxygen sensing, negatively regulating the hypoxia-inducible factor (HIF) pathway. Our study aim was to evaluate PHD1, PHD2 and PHD3 mRNA expression levels in primary tumours and normal lungs of non-small-cell lung cancer (NSCLC) patients and to correlate it with selected regulators of HIF signalling, with clinicopathological characteristics and overall survival (OS). Methods: Tumour tissue samples were obtained from 60 patients with surgically resected NSCLC who were treated with radical surgery. In 22 out of 60 cases, matching morphologically normal lung tissue was obtained. PHD1, PHD2 and PHD3 mRNA expressions were measured using RT-qPCR. Results: The PHD1 and PHD2 mRNA levels in primary tumours were significantly decreased compared to those in normal lungs (both p < 0.0001). PHD1 and PHD2 expression in tumours was positively correlated (r_s = 0.82; p < 0.0001) and correlated well with HIF pathway downstream genes HIF1A, PKM2 and PDK1. Decreased PHD1 and PHD2 were associated with larger tumour size, higher tumour stage (PHD1 only) and squamous cell carcinoma. Patients with low PHD1 and patients with low PHD2 expression had shorter OS than patients with high PHD1 (p = 0.02) and PHD2 expression (p = 0.01). PHD1 showed borderline independent prognostic values in multivariate analysis (p = 0.06). In contrast, we found no associations between PHD3 expression and any of the observed parameters. Conclusions: Our results show that reduced expression of PHD1 and PHD2 is associated with the development and progression of NSCLC. PHD1 could be further assessed as a prognostic marker in NSCLC.

Actionable driver DNA variants and fusion genes can be detected in archived cytological specimens with the Oncomine Dx Target Test Multi-CDx system in lung cancer

BACKGROUND

Molecular testing is critical for identifying actionable variants in lung cancer for precision medicine. When tumor tissue samples are unavailable, archived cytological specimens (ACSs) can be used. The authors examined whether oncogenic variants could be accurately detected in ACSs versus paired formalin-fixed, paraffin-embedded (FFPE) tumor tissues with in vitro diagnostic tests.

METHODS

The authors collected 18 ACSs and 15 FFPE tissues from 15 patients with lung cancer and investigated genomic profiles with the Oncomine Dx Target Test Multi-CDx system, which is an integrated next-generation sequencing platform that comprehensively examines 4 companion diagnostic target genes (epidermal growth factor receptor [EGFR]; B-Raf proto-oncogene, serine/threonine kinase [BRAF]; anaplastic lymphoma kinase [ALK]; and ROS proto-oncogene 1, receptor tyrosine kinase [ROS1]). They compared the quantity and quality of extracted nucleic acids, the sequencing quality control (QC), and the detected variants between ACSs and FFPE tissues.

RESULTS

The total amount of DNA and RNA obtained from 1 slide was higher in FFPE tissues than ACSs. The RNA integrity number was higher in ACSs. There were no differences in sequencing QC between ACSs and FFPE tissues. A total of 21 variants, including EGFR mutations and ALK and ROS1 fusion genes, were detected in both ACSs and FFPE tissues with 100% concordance.

CONCLUSIONS

ACSs can be a feasible alternative with which to identify actionable mutations and fusion genes via the Oncomine Dx Target Test Multi-CDx system.

A meta-analysis of Watson for Oncology in clinical application

Using the method of meta-analysis to systematically evaluate the consistency of treatment schemes between Watson for Oncology (WFO) and Multidisciplinary Team (MDT), and to provide references for the practical application of artificial intelligence clinical decision-support system in cancer treatment. We systematically searched articles about the clinical applications of Watson for Oncology in the databases and conducted meta-analysis using RevMan 5.3 software. A total of 9 studies were identified, including 2463 patients. When the MDT is consistent with WFO at the ‘Recommended’ or the ‘For consideration’ level, the overall concordance rate is 81.52%. Among them, breast cancer was the highest and gastric cancer was the lowest. The concordance rate in stage I–III cancer is higher than that in stage IV, but the result of lung cancer is opposite (P < 0.05).Similar results were obtained when MDT was only consistent with WFO at the "recommended" level. Moreover, the consistency of estrogen and progesterone receptor negative breast cancer patients, colorectal cancer patients under 70 years old or ECOG 0, and small cell lung cancer patients is higher than that of estrogen and progesterone positive breast cancer patients, colorectal cancer patients over 70 years old or ECOG 1–2, and non-small cell lung cancer patients, with statistical significance (P < 0.05). Treatment recommendations made by WFO and MDT were highly concordant for cancer cases examined, but this system still needs further improvement. Owing to relatively small sample size of the included studies, more well-designed, and large sample size studies are still needed.

Long-term response with low-dose of apatinib combined with S-1 in pretreated patient with advanced squamous cell lung cancer: A case report

RATIONALE

Squamous cell lung cancer is one of the major pathological types in patients with non-small cell lung cancer. Since treatment with angiogenic agents and target drugs in patients with advanced squamous cell lung cancer is not promising, there are limited strategies to improve the outcome in such patients. Herein, we report a pretreated patient with advanced squamous cell lung cancer, who received low-dose of apatinib combined with S-1 as salvage treatment, with good long-term response.

PATIENT CONCERNS

The patient complained of dry cough for one month without any relief by medication. Otherwise, she denied any other medical or family history.

DIAGNOSIS

According to the chest computed tomography, and pathologic findings from biopsy for lesion in lung, the patient was diagnosed with lung squamous cell lung cancer with enlargement of bilateral supraclavicular lymph nodes suggesting metastasis, staged as IIIb.

INTERVENTIONS

The patient received gemcitabine plus cisplatin as first line treatment, and gemcitabine as maintenance therapy. After progression, she received vinorelbine as second line treatment. After progression again, she received low-dose apatinib combined with S-1 as third line treatment.

OUTCOMES

With the follow-up period from October 21, 2014, to April 6, 2019, there were 15 months, 9 months, and 24 months of progression-free survival time for first line (including maintenance therapy), second line, and third line treatment, respectively. The only adverse event was neutropenia at grade 2 (CTC AE) occurring during the maintenance treatment.

LESSONS

This case indicated that low-dose apatinib combined with S-1 might be effective and safe in selected pretreated patients with advanced squamous cell lung cancer. It might be worthy to conduct further researches to investigate the efficacy and safety of the combination therapy in such patients.

Deep CNN Model Using CT Radiomics Feature Mapping Recognizes EGFR Gene Mutation Status of Lung Adenocarcinoma

To recognize the epidermal growth factor receptor (EGFR) gene mutation status in lung adenocarcinoma (LADC) has become a prerequisite of deciding whether EGFR-tyrosine kinase inhibitor (EGFR-TKI) medicine can be used. Polymerase chain reaction assay or gene sequencing is for measuring EGFR status, however, the tissue samples by surgery or biopsy are required. We propose to develop deep learning models to recognize EGFR status by using radiomics features extracted from non-invasive CT images. Preoperative CT images, EGFR mutation status and clinical data have been collected in a cohort of 709 patients (the primary cohort) and an independent cohort of 205 patients. After 1,037 CT-based radiomics features are extracted from each lesion region, 784 discriminative features are selected for analysis and construct a feature mapping. One Squeeze-and-Excitation (SE) Convolutional Neural Network (SE-CNN) has been designed and trained to recognize EGFR status from the radiomics feature mapping. SE-CNN model is trained and validated by using 638 patients from the primary cohort, tested by using the rest 71 patients (the internal test cohort), and further tested by using the independent 205 patients (the external test cohort). Furthermore, SE-CNN model is compared with machine learning (ML) models using radiomics features, clinical features, and both features. EGFR(-) patients show the smaller age, higher odds of female, larger lesion volumes, and lower odds of subtype of acinar predominant adenocarcinoma (APA), compared with EGFR(+). The most discriminative features are for texture (614, 78.3%) and the features of first order of intensity (158, 20.1%) and the shape features (12, 1.5%) follow. SE-CNN model can recognize EGFR mutation status with an AUC of 0.910 and 0.841 for the internal and external test cohorts, respectively. It outperforms the CNN model without SE, the fine-tuned VGG16 and VGG19, three ML models, and the state-of-art models. Utilizing radiomics feature mapping extracted from non-invasive CT images, SE-CNN can precisely recognize EGFR mutation status of LADC patients. The proposed method combining radiomics features and deep leaning is superior to ML methods and can be expanded to other medical applications. The proposed SE-CNN model may help make decision on usage of EGFR-TKI medicine.

Non-Small Cell Carcinoma-Not Otherwise Specified on Cytology Specimens in Patients with Solitary Pulmonary Lesion: Primary Lung Cancer or Metastatic Cancer?

Context:

Subtyping of solitary pulmonary lesion (SPL) in small amount of cytology specimen using a limited panel of immunohistochemistry (IHC) markers is very important to the correct choice of treatment. This study was performed to categorize non-small cell carcinoma-not otherwise specified (NSCC-NOS) on cytology in patients with SPL, especially with regard to the incidence of metastatic cancer.

Materials and Methods:

We reviewed 91 cases, in which a precise morphology-based, lineage-specific IHC-aided subtyping was not possible, that qualified as NSCC-NOS on cytology. A stepwise clinical approach and IHC of organ-specific markers was performed on each cell block (CB) to exclude metastasis from extrapulmonary malignancies.

Results:

Of the 91 evaluated cases, 65 (71.4%) were diagnosed as non-small cell lung carcinoma (NSCLC)-NOS, 24 (26.4%) were metastatic cancer, and the remaining 2 (2.2%) had undetermined diagnoses. The most frequent primary tumor site was the colorectum (41.7%), followed by breast (20.8%), kidney (8.3%), and then stomach, duodenum, liver, pancreas, gallbladder, prostate, and skin (4.2% each, 1 of 24). Moreover, we found that 7 of the 24 patients with metastatic cancer had a history of extrapulmonary malignancy that was unknown at the time of cytology-based diagnosis.

Conclusions:

These results underscored the need for accurate and stepwise clinical correlation to rule out the possibility of pulmonary metastasis from other sites and appropriate but judicious IHC (i.e., CDX2) on CB for SPL to increase refinement of the cytology diagnosis of NSCC-NOS.

Checkpoint Blockade in Lung Cancer With Driver Mutation: Choose the Road Wisely

Immune checkpoint blockade with PD-(L)1 antibodies has revolutionized the treatment of advanced non-small cell lung cancer (NSCLC). Similarly, the identification and targeting of oncogene drivers in metastatic NSCLC has dramatically improved patient outcomes with an expanding list of potentially actionable alterations and targeted therapies. Many of these molecular aberrations are more common in patients with little or no smoking history and adenocarcinoma histology. Certain molecular subsets of NSCLC, though gaining greatly from targeted therapy approaches, may derive less benefit from immune checkpoint blockade. The optimal identification, targeting, and sequencing of targeted therapies, immunotherapy, and chemotherapy are essential to continue to improve patient outcomes in advanced NSCLC. Herein, we review the role of immunotherapy in locally advanced and metastatic disease for patients with actionable driver alterations. Never-smoking patients have a high probability of having lung cancer that harbors one of these molecular aberrations that can be matched to a tyrosine kinase inhibitor with greatly improved clinical outcomes. Some of these patients with driver mutations may derive less benefit from immune checkpoint inhibitor approaches (either alone or combined with chemotherapy), especially compared with smoking-associated NSCLC. Given that PD-1 blockade alone or with platinum-based chemotherapy is the de facto first-line therapy (depending on level of PD-L1 expression) for nontargetable metastatic NSCLC, we also review treatment in never-smoking patients for whom molecular testing results are pending and the likelihood of identifying a driver mutation is high.

Early serum tumor marker levels after fourteen days of tyrosine kinase inhibitor targeted therapy predicts outcomes in patients with advanced lung adenocarcinoma

Objective

Image evaluation strategy for lung cancer patients has difficulty obtaining the appropriate quantity of diffuse lung nodules and bone metastases. The study was to demonstrate whether early variations in the levels of serum 4-tumor markers (4-TMs)(carcinoembryonic antigen [CEA], cancer antigen [CA]125, CA19-9, and CA15-3) after TKI targeted therapy were associated with treatment response in patients with lung adenocarcinoma.

Methods

Patients with stage IIIB-IV lung adenocarcinoma taking epidermal growth factor receptor (EGFR) TKIs or anaplastic lymphoma kinase (ALK) inhibitors were enrolled prospectively from June 2012 to February 2015. According to the variations of the percentage of change in 4-TM levels (4-TMpc), we divided patients into ascending (increases in 4-TMpc over the 7th- 14th day) and descending (decreases in 4-TMpc over the 7th- 14th day) groups.

Results

184 patients were enrolled, and 89% had at least one of the pre-treatment evaluable TMs and were further analyzed. An excellent response to the TKI targeted therapy was accurately predicted in the descending group, as determined using receiver operating characteristic curve analysis (an area under the curve, 0.83). Multivariate Cox hazards model analyses demonstrated that the type of 4-TMpc and mutation status were the strongest predictors of progression-free survival (PFS)(descending versus ascending, hazard ratios [HR] 0.30, 95% confidence interval [CI], 0.19–0.47; sensitive mutation versus wide type, HR 0.30, 95% CI, 0.19–0.48).

Conclusions

Type of 4-TMpc 14 days after TKI targeted therapy is associated with an image response and PFS, without regarding mutation status, in patients with advanced lung adenocarcinoma.

Magnetic particle targeting for diagnosis and therapy of lung cancers

Over the past decade, the growing interest in targeted lung cancer therapy has guided researchers toward the cutting edge of controlled drug delivery, particularly magnetic particle targeting. Targeting of tissues by magnetic particles has tackled several limitations of traditional drug delivery methods for both cancer detection (e.g., using magnetic resonance imaging) and therapy. Delivery of magnetic particles offers the key advantage of high efficiency in the local deposition of drugs in the target tissue with the least harmful effect on other healthy tissues. This review first overviews clinical aspects of lung morphology and pathogenesis as well as clinical features of lung cancer. It is followed by reviewing the advances in using magnetic particles for diagnosis and therapy of lung cancers: (i) a combination of magnetic particle targeting with MRI imaging for diagnosis and screening of lung cancers, (ii) magnetic drug targeting (MDT) through either intravenous injection and pulmonary delivery for lung cancer therapy, and (iii) computational simulations that models new and effective approaches for magnetic particle drug delivery to the lung, all supporting improved lung cancer treatment. The review further discusses future opportunities to improve the clinical performance of MDT for diagnosis and treatment of lung cancer and highlights clinical therapy application of the MDT as a new horizon to cure with minimal side effects a wide variety of lung diseases and possibly other acute respiratory syndromes (COVID-19, MERS, and SARS).

Comprehensive genomic profiling for non-small-cell lung cancer: health and budget impact

Background

Single-gene tests and hotspot panels targeting specific subsets of biomarkers constitute the Canadian genomic testing landscape for non-small-cell lung cancer (nsclc). However, newer testing options such as comprehensive genomic profiling (cgp) offer improved detection rates and identification of multiple classes of genomic alterations in a single assay, minimizing tissue requirements and turnaround time. The objective of the present analysis was to assess the health and budget impacts of adopting cgp testing for nsclc in Canada.

Methods

This study assessed the impact of funding the cgp tests FoundationOne CDx and FoundationOne Liquid (Foundation Medicine, Cambridge, MA, U.S.A.) over a 3-year time horizon using a Canadian societal perspective for Ontario. Conventional testing strategies were summarized into two reference scenarios: a series of single-gene tests only, and reflex single-gene testing followed by a hotspot panel for negative results. Four adoption scenarios for cgp testing were considered: replacing all single-gene and hotspot panel testing, replacing hotspot panel testing only, use after negative single-gene and hotspot testing, and use of FoundationOne Liquid in individuals with insufficient tissue for conventional testing.

Results

When cgp testing was assumed to replace all conventional testing with 50% uptake, the budget impact per person per year ranged from $0.71 to $0.87, depending on the reference scenario, with a 3-year gain of 680.9 life-years and 3831 working days over the full cohort.

Conclusions

Given the present testing landscape for patients with nsclc in Canada, listing cgp testing could optimize the selection of appropriately targeted treatments, and thus add life-years and productivity for this population, with a minimal budget impact.