Overall Survival with Osimertinib in Resected EGFR-Mutated NSCLC.

BACKGROUND: Among patients with resected, epidermal growth factor receptor (EGFR)-mutated, stage IB to IIIA non-small-cell lung cancer (NSCLC), adjuvant osimertinib therapy, with or without previous adjuvant chemotherapy, resulted in significantly longer disease-free survival than placebo in the ADAURA trial. We report the results of the planned final analysis of overall survival. METHODS: In this phase 3, double-blind trial, we randomly assigned eligible patients in a 1:1 ratio to receive osimertinib (80 mg once daily) or placebo until disease recurrence was observed, the trial regimen was completed (3 years), or a discontinuation criterion was met. The primary end point was investigator-assessed disease-free survival among patients with stage II to IIIA disease. Secondary end points included disease-free survival among patients with stage IB to IIIA disease, overall survival, and safety. RESULTS: Of 682 patients who underwent randomization, 339 received osimertinib and 343 received placebo. Among patients with stage II to IIIA disease, the 5-year overall survival was 85% in the osimertinib group and 73% in the placebo group (overall hazard ratio for death, 0.49; 95.03% confidence interval [CI], 0.33 to 0.73; P<0.001). In the overall population (patients with stage IB to IIIA disease), the 5-year overall survival was 88% in the osimertinib group and 78% in the placebo group (overall hazard ratio for death, 0.49; 95.03% CI, 0.34 to 0.70; P<0.001). One new serious adverse event, pneumonia related to coronavirus disease 2019, was reported after the previously published data-cutoff date (the event was not considered by the investigator to be related to the trial regimen, and the patient fully recovered). Adjuvant osimertinib had a safety profile consistent with that in the primary analysis. CONCLUSIONS: Adjuvant osimertinib provided a significant overall survival benefit among patients with completely resected, EGFR-mutated, stage IB to IIIA NSCLC. (Funded by AstraZeneca; ADAURA ClinicalTrials.gov number, NCT02511106.).

Artificial Intelligence-Powered Prediction of ALK Gene Rearrangement in Patients With Non-Small-Cell Lung Cancer.

PURPOSE: Several studies reported the possibility of predicting genetic abnormalities in non-small-cell lung cancer by deep learning (DL). However, there are no data of predicting ALK gene rearrangement (ALKr) using DL. We evaluated the ALKr predictability using the DL platform. MATERIALS AND METHODS: We selected 66 ALKr-positive cases and 142 ALKr-negative cases, which were diagnosed by ALKr immunohistochemical staining in our institution from January 2009 to March 2019. We generated virtual slide of 300 slides (150 ALKr-positive slides and 150 ALKr-negative slides) using NanoZoomer. HALO-AI was used to analyze the whole-slide imaging data, and the DenseNet network was used to build the learning model. Of the 300 slides, we randomly assigned 172 slides to the training cohort and 128 slides to the test cohort to ensure no duplication of cases. In four resolutions (16.0/4.0/1.0/0.25 µm/pix), ALKr prediction models were built in the training cohort and ALKr prediction performance was evaluated in the test cohort. We evaluated the diagnostic probability of ALKr by receiver operating characteristic analysis in each ALKr probability threshold (50%, 60%, 70%, 80%, 90%, and 95%). We expected the area under the curve to be 0.64-0.85 in the model of a previous study. Furthermore, in the test cohort data, an expert pathologist also evaluated the presence of ALKr by hematoxylin and eosin staining on whole-slide imaging. RESULTS: The maximum area under the curve was 0.73 (50% threshold: 95% CI, 0.65 to 0.82) in the resolution of 1.0 µm/pix. In this resolution, with an ALKr probability of 50% threshold, the sensitivity and specificity were 73% and 73%, respectively. The expert pathologist's sensitivity and specificity in the same test cohort were 13% and 94%. CONCLUSION: The ALKr prediction by DL was feasible. Further study should be addressed to improve accuracy of ALKr prediction.

Systematic comparison of ranking aggregation methods for gene lists in experimental results.

MOTIVATION: A common experimental output in biomedical science is a list of genes implicated in a given biological process or disease. The gene lists resulting from a group of studies answering the same, or similar, questions can be combined by ranking aggregation methods to find a consensus or a more reliable answer. Evaluating a ranking aggregation method on a specific type of data before using it is required to support the reliability since the property of a dataset can influence the performance of an algorithm. Such evaluation on gene lists is usually based on a simulated database because of the lack of a known truth for real data. However, simulated datasets tend to be too small compared to experimental data and neglect key features, including heterogeneity of quality, relevance and the inclusion of unranked lists. RESULTS: In this study, a group of existing methods and their variations that are suitable for meta-analysis of gene lists are compared using simulated and real data. Simulated data were used to explore the performance of the aggregation methods as a function of emulating the common scenarios of real genomic data, with various heterogeneity of quality, noise level and a mix of unranked and ranked data using 20 000 possible entities. In addition to the evaluation with simulated data, a comparison using real genomic data on the SARS-CoV-2 virus, cancer (non-small cell lung cancer) and bacteria (macrophage apoptosis) was performed. We summarize the results of our evaluation in a simple flowchart to select a ranking aggregation method, and in an automated implementation using the meta-analysis by information content algorithm to infer heterogeneity of data quality across input datasets. AVAILABILITY AND IMPLEMENTATION: The code for simulated data generation and running edited version of algorithms: https://github.com/baillielab/comparison_of_RA_methods. Code to perform an optimal selection of methods based on the results of this review, using the MAIC algorithm to infer the characteristics of an input dataset, can be downloaded here: https://github.com/baillielab/maic. An online service for running MAIC: https://baillielab.net/maic. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Optimising tissue acquisition and the molecular testing pathway for patients with non-small cell lung cancer: A UK expert consensus statement.

Targeted therapy against actionable variants has revolutionised the treatment landscape for non-small cell lung cancer (NSCLC). Approximately half of NSCLC adenocarcinomas have an actionable variant, making molecular testing a critical component of the diagnostic process to personalise therapeutic options, optimise clinical outcomes and minimise toxicity. Recently, genomic testing in England has undergone major changes with the introduction of Genomic Laboratory Hubs, designed to consolidate and enhance existing laboratory provision and deliver genomic testing as outlined in the National Genomic Test Directory. Similar changes are ongoing in Scotland, Wales and Northern Ireland. However, multiple challenges exist with current tissue acquisition procedures and the molecular testing pathway in the UK, including quantity and quality of available tissue, adequacy rates, test availability among genomic laboratories, turnaround times, multidisciplinary team communication, and limited guidance and standardisation. The COVID-19 pandemic has added an extra layer of complexity. Herein, we summarise best practice recommendations, based on expert opinion, to overcome existing challenges in the UK. The least invasive biopsy technique should be undertaken with the aim of acquiring the greatest quality and quantity of tissue. Use of sedation should be considered to improve patient experience. Rapid on-site evaluation may also be useful to help guide adequate sampling, and liquid biopsy may be beneficial in some instances. Sample processing should be appropriate to facilitate biomarker testing, in particular, next-generation sequencing for comprehensive genomic information. Steps to optimise tissue utilisation and turnaround times, such as planning of tissue usage, limiting immunohistochemistry, tumour enrichment, and reflex testing at diagnosis, should be implemented. Guidelines for tissue acquisition and sample processing may help to improve sample adequacy to perform downstream testing. Communication among genomic laboratories will help to standardise test availability across England and local auditing could identify further areas for optimisation, including ways to improve turnaround times and adequacy rates.

Molecular Beacon for Detection miRNA-21 as a Biomarker of Lung Cancer.

Lung cancer (LC) is the leading cause of cancer-related death worldwide. Although the diagnosis and treatment of non-small cell lung cancer (NSCLC), which accounts for approximately 80% of LC cases, have greatly improved in the past decade, there is still an urgent need to find more sensitive and specific screening methods. Recently, new molecular biomarkers are emerging as potential non-invasive diagnostic agents to screen NSCLC, including multiple microRNAs (miRNAs) that show an unusual expression profile. Moreover, peripheral blood mononuclear cells' (PBMCs) miRNA profile could be linked with NSCLC and used for diagnosis. We developed a molecular beacon (MB)-based miRNA detection strategy for NSCLC. Following PBMCs isolation and screening of the expression profile of a panel of miRNA by RT-qPCR, we designed a MB targeting of up-regulated miR-21-5p. This MB 21-5p was characterized by FRET-melting, CD, NMR and native PAGE, allowing the optimization of an in-situ approach involving miR-21-5p detection in PBMCs via MB. Data show the developed MB approach potential for miR-21-5p detection in PBMCs from clinical samples towards NSCLC.

Designing Novel Compounds for the Treatment and Management of RET-Positive Non-Small Cell Lung Cancer-Fragment Based Drug Design Strategy.

Rearranged during transfection (RET) is an oncogenic driver receptor that is overexpressed in several cancer types, including non-small cell lung cancer. To date, only multiple kinase inhibitors are widely used to treat RET-positive cancer patients. These inhibitors exhibit high toxicity, less efficacy, and specificity against RET. The development of drug-resistant mutations in RET protein further deteriorates this situation. Hence, in the present study, we aimed to design novel drug-like compounds using a fragment-based drug designing strategy to overcome these issues. About 18 known inhibitors from diverse chemical classes were fragmented and bred to form novel compounds against RET proteins. The inhibitory activity of the resultant 115 hybrid molecules was evaluated using molecular docking and RF-Score analysis. The binding free energy and chemical reactivity of the compounds were computed using MM-GBSA and density functional theory analysis, respectively. The results from our study revealed that the developed hybrid molecules except for LF21 and LF27 showed higher reactivity and stability than Pralsetinib. Ultimately, the process resulted in three hybrid molecules namely LF1, LF2, and LF88 having potent inhibitory activity against RET proteins. The scrutinized molecules were then subjected to molecular dynamics simulation for 200 ns and MM-PBSA analysis to eliminate a false positive design. The results from our analysis hypothesized that the designed compounds exhibited significant inhibitory activity against multiple RET variants. Thus, these could be considered as potential leads for further experimental studies.

Spectacular Progress in Lung Cancer Care.

Against the difficult and trying backdrop of the pandemic, cancer investigators persisted, and for patients with lung cancer, that persistence paid off in spectacular ways. With several new FDA approved treatments, as well as 2 new targetable mutations in non-small cell lung cancer (NSCLC), 2020 was a banner year in the overall lung cancer space. ONCOLOGY® recently sat down with Jennifer W. Carlisle, MD, of Emory University's Winship Cancer Institute, to discuss the many advances made during the last year for patients with lung cancer along with her hopes for further significant milestones in the year to come.

Editorial: Global Regulatory Initiatives Deliver Accelerated Approval of the First Bispecific Therapeutic Monoclonal Antibody for Advanced Non-Small Cell Lung Cancer (NSCLC).

The coronavirus disease 2019 (COVID-19) pandemic has affected the number of completed clinical trials, particularly in oncology. Between 80-85% of all lung cancers are non-small cell lung cancer (NSCLC), and of these, between 2-3% have an EGFR exon 20 insertion, which is associated with increased cell proliferation, metastasis, and a lack of response to chemotherapy and epidermal growth factor receptor (EGFR) inhibitors. Until this year, there were no available targeted therapies for advanced NSCLC with this genetic subtype. However, in May 2021, the US Food and Drug Administration (FDA) granted accelerated approval for amivantamab-vmjw (Rybrevant®), a bispecific monoclonal antibody, targeting activating and resistant EGFR and MET mutations and amplifications. This FDA approval was for adult patients with locally advanced metastatic NSCLC, with disease progression on or following platinum-based chemotherapy. The FDA also approved the Guardant360® companion diagnostic, a next-generation sequencing platform for circulating tumor DNA (ctDNA), which is a liquid biopsy assay. In 2019, Project Orbis was launched by the FDA Oncology Center of Excellence as a global collaborative review program to facilitate rapid global access for patients to innovative cancer therapies. This Editorial aims to highlight how global regulatory initiatives from the FDA have delivered accelerated approval of the first bispecific therapeutic monoclonal antibody, amivantamab-vmjw (Rybrevant®), and a companion diagnostic for patients with advanced NSCLC with an EGFR exon 20 insertion.