[The national Network Genomic Medicine (nNGM) : Model for innovative diagnostics and therapy of lung cancer within a public healthcare system]

Early Metabolic Response by PET Predicts Sensitivity to Next-Line Targeted Therapy in EGFR-Mutated Lung Cancer with Unknown Mechanism of Acquired Resistance

Targeted therapy with epidermal growth factor receptor (EGFR) tyrosine kinase inhibitors (TKIs) has established the precision oncology paradigm in lung cancer. Most patients with EGFR-mutated lung cancer respond but eventually acquire resistance. Methods: Patients exhibiting the EGFR p.T790M resistance biomarker benefit from sequenced targeted therapy with osimertinib. We hypothesized that metabolic response as detected by 18F-FDG PET after short-course osimertinib identifies additional patients susceptible to sequenced therapy. Results: Fourteen patients with EGFR-mutated lung cancer and resistance to first- or second-generation EGFR TKI testing negatively for EGFR p.T790M were enrolled in a phase II study. Five patients (36%) achieved a metabolic 18F-FDG PET response and continued osimertinib. In those, the median duration of treatment was not reached (95% CI, 24 mo to not estimable), median progression-free survival was 18.7 mo (95% CI, 14.6 mo to not estimable), and median overall survival was 41.5 mo. Conclusion: Connecting theranostic osimertinib treatment with early metabolic response assessment by PET enables early identification of patients with unknown mechanisms of TKI resistance who derive dramatic clinical benefit from sequenced osimertinib. This defines a novel paradigm for personalization of targeted therapies in patients with lung cancer dependent on a tractable driver oncogene.

[The collaborative project "Personalized medicine for oncology" (PM4Onco) as part of the Medical Informatics Initiative (MII)]

The collaborative project Personalized Medicine for Oncology (PM4Onco) was launched in 2023 as part of the National Decade against Cancer (NKD) and is executed within the Medical Informatics Initiative (MII). Its aim is to establish a sustainable infrastructure for the integration and use of data from clinical and biomedical research and therefore combines the experience and preliminary work of all four consortia of the MII and the leading oncology centers in Germany. The data provided by PM4Onco will be prepared in a suitable form to support decision making in molecular tumor boards. This concept and infrastructure will be extended to 23 participating partner sites and thus improve access to targeted therapies based on clinical information and analysis of molecular genetic alterations in tumors at different stages of the disease. This will help to improve the treatment and prognosis of tumor diseases.Clinical cancer registries are involved in the project to improve data quality through standardized documentation routines. Clinical experts advise on the expansion of the core datasets for personalized medicine (PM). Information on quality of life and treatment outcomes reported by patients in questionnaires, which is rarely collected outside of clinical trials, will make a significant contribution. Patient representatives are involved from the onset to ensure that the important perspective of patients is taken into account in the decision-making process. PM4Onco thus creates an alliance between the MII, oncological centers of excellence, clinical cancer registries, young scientists, patients, and citizens to strengthen and advance PM in cancer therapy.

[Personalized medicine in oncology]

Due to the considerable technological progress in molecular and genetic diagnostics as well as increasing insights into the molecular pathogenesis of diseases, there has been a fundamental paradigm shift in the past two decades from a "one-size-fits-all approach" to personalized, molecularly informed treatment strategies. Personalized medicine or precision medicine focuses on the genetic, physiological, molecular, and biochemical differences between individuals and considers their effects on the development, prevention, and treatment of diseases. As a pioneer of personalized medicine, the field of oncology is particularly noteworthy, where personalized diagnostics and treatment have led to lasting change in the treatment of cancer patients in recent years. In this article, the significant change towards personalized treatment concepts, especially in the field of personalized oncology, will be discussed and examined in more detail.

Supporting the decision to perform molecular profiling for cancer patients based on routinely collected data through the use of machine learning

BACKGROUND

Personalized medicine offers targeted therapy options for cancer treatment. However, the decision whether to include a patient into next-generation sequencing (NGS) testing is not standardized. This may result in some patients receiving unnecessary testing while others who could benefit from it are not tested. Typically, patients who have exhausted conventional treatment options are of interest for consideration in molecularly targeted therapy. To assist clinicians in decision-making, we developed a decision support tool using routine data from a precision oncology program.

METHODS

We trained a machine learning model on clinical data to determine whether molecular profiling should be performed for a patient. To validate the model, the model's predictions were compared with decisions made by a molecular tumor board (MTB) using multiple patient case vignettes with their characteristics.

RESULTS

The prediction model included 440 patients with molecular profiling and 13,587 patients without testing. High area under the curve (AUC) scores indicated the importance of engineered features in deciding on molecular profiling. Patient age, physical condition, tumor type, metastases, and previous therapies were the most important features. During the validation MTB experts made the same decision of recommending a patient for molecular profiling only in 10 out of 15 of their previous cases but there was agreement between the experts and the model in 9 out of 15 cases.

CONCLUSION

Based on a historical cohort, our predictive model has the potential to assist clinicians in deciding whether to perform molecular profiling.

Development, testing and validation of a targeted NGS-panel for the detection of actionable mutations in lung cancer (NSCLC) using anchored multiplex PCR technology in a multicentric setting

Lung cancer is a paradigm for a genetically driven tumor. A variety of drugs were developed targeting specific biomarkers requiring testing for tumor genetic alterations in relevant biomarkers. Different next-generation sequencing technologies are available for library generation: 1) anchored multiplex-, 2) amplicon based- and 3) hybrid capture-based-PCR. Anchored multiplex PCR-based sequencing was investigated for routine molecular testing within the national Network Genomic Medicine Lung Cancer (nNGM). Four centers applied the anchored multiplex ArcherDX-Variantplex nNGMv2 panel to re-analyze samples pre-tested during routine diagnostics. Data analyses were performed by each center and compiled centrally according to study design. Pre-defined standards were utilized, and panel sensitivity was determined by dilution experiments. nNGMv2 panel sequencing was successful in 98.9% of the samples (N = 90). With default filter settings, all but two potential MET exon 14 skipping variants were identified at similar allele frequencies. Both MET variants were found with an adapted calling filter. Three additional variants (KEAP1, STK11, TP53) were called that were not identified in pre-testing analyses. Only total DNA amount but not a qPCR-based DNA quality score correlated with average coverage. Analysis was successful with a DNA input as low as 6.25 ng. Anchored multiplex PCR-based sequencing (nNGMv2) and a sophisticated user-friendly Archer-Analysis pipeline is a robust and specific technology to detect tumor genetic mutations for precision medicine of lung cancer patients.

[Advances in the targeted treatment of non-small cell lung cancer]

Non-small cell lung cancer (NSCLC) paradigmatically shows the potential of personalized and therefore precise cancer treatment. For around one third of the patients, predominantly suffering from adenocarcinoma, targetable driver mutations could be characterized in the meantime. Targeted therapies, mostly with kinase inhibitors, achieve impressive advances in the prolongation of overall survival often over many years and excellent quality of life in patients with advanced NSCLC. Targeted treatment is also increasingly evaluated as adjuvant or neoadjuvant treatment in early inoperable stages of NSCLC. An absolute prerequisite for the use of personalized treatment is upfront broad molecular diagnostics before the decision on first line treatment. The limitations of personalized treatment are the so far unavoidable development of resistance mutations and increasing clonal heterogeneity during the course of the treatment. Approaches to further improve treatment results comprise the development of next-generation inhibitors, the combination of targeted substances, also with chemotherapy and the use of new immunoconjugates.

Evaluation of the effectiveness of a nationwide precision medicine program for patients with advanced non-small cell lung cancer in Germany: a historical cohort analysis

Background

The national Network Genomic Medicine (nNGM) Lung Cancer provides comprehensive and high-quality multiplex molecular diagnostics and standardized personalized treatment recommendation for patients with advanced non-small cell lung cancer (aNSCLC) in Germany. The primary aim of this study was to investigate the effectiveness of the nNGM precision medicine program in terms of overall survival (OS) using real-world data (RWD).

Methods

A historical nationwide cohort analysis of patients with aNSCLC and initial diagnosis between 04/2019 and 06/2020 was conducted to compare treatment and OS of patients with and without nNGM-participation. Patients participating within the nNGM (nNGM group) were selected based on a prospective nNGM database. The electronic health records (EHR) of the prospective nNGM database were case-specifically linked to claims data (AOK, German health insurance). The control group was selected from claims data of patients receiving usual care without nNGM-participation (non-nNGM group). The minimum follow-up period was six months.

Findings

Overall, n = 509 patients in the nNGM group and n = 7213 patients in the non-nNGM group met the inclusion criteria. Patients participating in the nNGM had a significantly improved OS compared to the non-nNGM group (median OS: 10.5 months vs. 8.7 months, p = 0.008, HR = 0.84, 95% CI: 0.74-0.95). The 1-year survival rates were 46.8% (nNGM) and 41.3% (non-nNGM). The use of approved tyrosine kinase inhibitors (TKI) in the first-line setting was significantly higher in the nNGM group than in the non-nNGM group (nNGM: 8.4% (43/509) vs. non-nNGM: 5.1% (366/7213), p = 0.001). Overall, patients receiving first-line TKI treatment had significantly higher 1-year OS rates than patients treated with PD-1/PD-L1 inhibitors and/or chemotherapy (67.2% vs. 40.2%, p < 0.001).

Interpretation

This is the first study to demonstrate a significant survival benefit and higher utilization of targeted therapies for aNSCLC patients participating within nNGM. Our data indicate that precision medicine programs can enhance collaborative personalized lung cancer care and promote the implementation of treatment innovations and the latest scientific knowledge into clinical routine care.

Funding

The study was funded by the AOK Federal Association Germany.

Extending cBioPortal for Therapy Recommendation Documentation in Molecular Tumor Boards: Development and Usability Study

BACKGROUND

In molecular tumor boards (MTBs), patients with rare or advanced cancers are discussed by a multidisciplinary team of health care professionals. Software support for MTBs is lacking; in particular, tools for preparing and documenting MTB therapy recommendations need to be developed.

OBJECTIVE

We aimed to implement an extension to cBioPortal to provide a tool for the documentation of therapy recommendations from MTB sessions in a secure and standardized manner. The developed extension should be embedded in the patient view of cBioPortal to enable easy documentation during MTB sessions. The resulting architecture for storing therapy recommendations should be integrable into various hospital information systems.

METHODS

On the basis of a requirements analysis and technology analysis for authentication techniques, a prototype was developed and iteratively refined through a user-centered development process. In conclusion, the tool was evaluated via a usability evaluation, including interviews, structured questionnaires, and the System Usability Scale.

RESULTS

The patient view of cBioPortal was extended with a new tab that enables users to document MTB sessions and therapy recommendations. The role-based access control was expanded to allow for a finer distinction among the rights to view, edit, and delete data. The usability evaluation showed overall good usability and a System Usability Scale score of 83.57.

CONCLUSIONS

This study demonstrates how cBioPortal can be extended to not only visualize MTB patient data but also be used as a documentation platform for therapy recommendations.

An overview and a roadmap for artificial intelligence in hematology and oncology

BACKGROUND

Artificial intelligence (AI) is influencing our society on many levels and has broad implications for the future practice of hematology and oncology. However, for many medical professionals and researchers, it often remains unclear what AI can and cannot do, and what are promising areas for a sensible application of AI in hematology and oncology. Finally, the limits and perils of using AI in oncology are not obvious to many healthcare professionals.

METHODS

In this article, we provide an expert-based consensus statement by the joint Working Group on "Artificial Intelligence in Hematology and Oncology" by the German Society of Hematology and Oncology (DGHO), the German Association for Medical Informatics, Biometry and Epidemiology (GMDS), and the Special Interest Group Digital Health of the German Informatics Society (GI). We provide a conceptual framework for AI in hematology and oncology.

RESULTS

First, we propose a technological definition, which we deliberately set in a narrow frame to mainly include the technical developments of the last ten years. Second, we present a taxonomy of clinically relevant AI systems, structured according to the type of clinical data they are used to analyze. Third, we show an overview of potential applications, including clinical, research, and educational environments with a focus on hematology and oncology.

CONCLUSION

Thus, this article provides a point of reference for hematologists and oncologists, and at the same time sets forth a framework for the further development and clinical deployment of AI in hematology and oncology in the future.

Real-world data for precision cancer medicine-A European perspective

Leveraging real-world data (RWD) for drug access is necessary to overcome a key challenge of modern precision oncology: tackling numerous low-prevalence oncogenic mutations across cancers. Withholding a potentially active medication in patients with rare mutations for the sake of control chemotherapy or "best" supportive care is neither practicable nor ethically justifiable anymore, particularly as RWD could meanwhile be used instead, according to scientific principles outlined by the US Food and Drug Administration, European Medicines Agency and other stakeholders. However, practical implementation varies, with occasionally opposite recommendations based on the same evidence in different countries. In the face of growing need for precision drugs, more transparency of evaluation, a priori availability of guidance for the academia and industry, as well as a harmonized framework for health technology assessment across the European Union (EU) are imperative. These could in turn trigger infrastructural changes in national and pan-European registries, cancer management guidelines (e.g., frequency of routine radiologic restaging, inclusion of patient-reported outcomes), and the health data space, to ensure conformity with declared standards and facilitate extraction of RWD sets (including patient-level data) suitable for approval and pricing with minimal effort. For an EU-wide unification of precision cancer medicine, collective negotiation of drug supply contracts and funding solidarity would additionally be required to handle the financial burden. According to experience from pivotal European programs, off-label use could potentially also be harmonized across EU-states to accelerate availability of novel drugs, streamline collection of valuable RWD, and mitigate related costs through wider partnerships with pharmaceutical companies.

[Cytopathology and molecular diagnostics of non-small cell lung cancer (NSCLC)]

Cytological specimens from endobronchial aspirates and pleural effusions are frequently used materials in the diagnostics of non-small cell lung cancer (NSCLC). In the same way as histological samples from endobronchial and transbronchial biopsy material or computed tomography (CT)-guided needle biopsies, cytological specimens are eminently suitable for molecular and immunohistological biomarker diagnostics of NSCLC, provided optimal techniques and clear diagnostic algorithms are employed. This article presents the typical processing techniques and a scheme for biomarker analytics and discusses an optimal approach for comprehensive diagnostics of NSCLC. When cytological specimens are processed and used in this way, the analytics are equivalent to those from histopathological specimens. For a detailed and advanced description of cytological and molecular techniques on cytological specimens the reader is referred to our own review articles.

Use of algorithms for identifying patients in a German claims database: learnings from a lung cancer case

BACKGROUND

The analysis of statutory health insurance (SHI) data is a little-used approach for understanding treatment and care as well as resource use of lung cancer (LC) patients in Germany. The aims of this observational, retrospective, longitudinal analysis of structured data were to analyze the healthcare situation of LC patients in Germany based on routine data from SHI funds, to develop an algorithm that sheds light on LC types (non-small cell / NSCLC vs. small cell / SCLC), and to gain new knowledge to improve needs-based care.

METHODS

Anonymized billing data of approximately four million people with SHI were analyzed regarding ICD-10 (German modification), documented medical interventions based on the outpatient SHI Uniform Assessment Standard Tariff (EBM) or the inpatient Operations and Procedure Code (OPS), and the dispensing of prescription drugs to outpatients (ATC classification). The study included patients who were members of 64 SHI funds between Jan-1st, 2015 and Dec-31st, 2016 and who received the initial diagnosis of LC in 2015 and 2016.

RESULTS

The analysis shows that neither the cancer type nor the cancer stage can be unambiguously described by the ICD-10 coding. Furthermore, an assignment based on the prescribed medication provides only limited information: many of the drugs are either approved for both LC types or are used off-label, making it difficult to assign them to a specific LC type. Overall, 25% of the LC patients were unambiguously identifiable as NSCLC vs SCLC based on the ICD-10 code, the drug therapy, and the billing data.

CONCLUSIONS

The current coding system appears to be of limited suitability for drawing conclusions about LC and therefore the SHI patient population. This makes it difficult to analyze the healthcare data with the aim of gathering new knowledge to improve needs-based care. The approach chosen for this study did not allow for development of a LC differentiation algorithm based on the available healthcare data. However, a better overview of patient specific needs could make it possible to modify the range of services provided by the SHI funds. From this perspective, it makes sense, in a first step, to refine the ICD-10 system to facilitate NSCLC vs. SCLC classification.

MAGICPL: A Generic Process Description Language for Distributed Pseudonymization Scenarios

OBJECTIVES

 Pseudonymization is an important aspect of projects dealing with sensitive patient data. Most projects build their own specialized, hard-coded, solutions. However, these overlap in many aspects of their functionality. As any re-implementation binds resources, we would like to propose a solution that facilitates and encourages the reuse of existing components.

METHODS

 We analyzed already-established data protection concepts to gain an insight into their common features and the ways in which their components were linked together. We found that we could represent these pseudonymization processes with a simple descriptive language, which we have called MAGICPL, plus a relatively small set of components. We designed MAGICPL as an XML-based language, to make it human-readable and accessible to nonprogrammers. Additionally, a prototype implementation of the components was written in Java. MAGICPL makes it possible to reference the components using their class names, making it easy to extend or exchange the component set. Furthermore, there is a simple HTTP application programming interface (API) that runs the tasks and allows other systems to communicate with the pseudonymization process.

RESULTS

 MAGICPL has been used in at least three projects, including the re-implementation of the pseudonymization process of the German Cancer Consortium, clinical data flows in a large-scale translational research network (National Network Genomic Medicine), and for our own institute's pseudonymization service.

CONCLUSIONS

 Putting our solution into productive use at both our own institute and at our partner sites facilitated a reduction in the time and effort required to build pseudonymization pipelines in medical research.

Brigatinib versus other second-generation ALK inhibitors as initial treatment of anaplastic lymphoma kinase positive non-small cell lung cancer with deep phenotyping: study protocol of the ABP trial

BACKGROUND

Availability of potent anaplastic lymphoma kinase (ALK) tyrosine kinase inhibitors (TKI) has pushed the median survival of ALK⁺ non-smallcell lung cancer (NSCLC) patients to over five years. In particular, second-generation ALK TKI have demonstrated superiority compared to the first-generation compound crizotinib and are meanwhile standard first-line treatment. However, clinical courses of individual patients vary widely, with secondary development of drug resistance and intracranial progression remaining important problems. While these limitations highlight the need for better disease monitoring and additional therapeutic tools, molecular tumor features are increasingly recognized as crucial determinants of clinical outcome. This trial aims to optimize management of ALK⁺ NSCLC by analyzing the efficacy of second-generation ALK inhibitors in conjunction with deep longitudinal phenotyping across two treatment lines.

METHODS/DESIGN

In this exploratory prospective phase II clinical trial, newly diagnosed ALK⁺ NSCLC patients will be randomized into two treatment arms, stratified by presence of brain metastases and ECOG performance status: brigatinib (experimental arm) vs. any other approved second-generation ALK TKI. Tumor tissue and blood samples will be collected for biomarker analysis at the beginning and throughout the study period to investigate baseline molecular tumor properties and analyze the development of acquired drug resistance. In addition, participating investigators and patients will have the possibility of fast-track molecular tumor and ctDNA profiling at the time of disease progression using state-of-the-art next-generation sequencing (NGS), in order to support decisions regarding next-line therapy.

DISCUSSION

Besides supporting therapeutic decisions for enrolled patients, the ABP trial primarily aims to deepen the understanding of the underlying biology and facilitate development of a framework for individualized management of ALK⁺ NSCLC according to molecular features. Patients with low molecular risk and the perspective of a "chronic disease" will be distinguished from "high-risk" cases, molecular properties of which will be utilized to elaborate improved methods of non-invasive monitoring and novel preclinical models in order to advance therapeutic strategies.

TRIAL REGISTRATION

Clinicaltrials.gov , NCT04318938. Registered March 182,020, https://www.clinicaltrials.gov/ct2/show/NCT04318938 Eudra-CT, 2019-001828-36. Registered September 302,019, https://www.clinicaltrialsregister.eu/ctr-search/search?query=2019-001828-36.

Predictive molecular pathology of lung cancer in Germany with focus on gene fusion testing: Methods and quality assurance

Predictive molecular testing has become an important part of the diagnosis of any patient with lung cancer. Using reliable methods to ensure timely and accurate results is inevitable for guiding treatment decisions. In the past few years, parallel sequencing has been established for mutation testing, and its use is currently broadened for the detection of other genetic alterations, such as gene fusion and copy number variations. In addition, conventional methods such as immunohistochemistry and in situ hybridization are still being used, either for formalin-fixed, paraffin-embedded tissue or for cytological specimens. For the development and broad implementation of such complex technologies, interdisciplinary and regional networks are needed. The Network Genomic Medicine (NGM) has served as a model of centralized testing and decentralized treatment of patients and incorporates all German comprehensive cancer centers. Internal quality control, laboratory accreditation, and participation in external quality assessment is mandatory for the delivery of reliable results. Here, we provide a summary of current technologies used to identify patients who have lung cancer with gene fusions, briefly describe the structures of NGM and the national NGM (nNGM), and provide recommendations for quality assurance.

Influence of Biopsy Technique on Molecular Genetic Tumor Characterization in Non-Small Cell Lung Cancer-The Prospective, Randomized, Single-Blinded, Multicenter PROFILER Study Protocol

The detection of molecular alterations is crucial for the individualized treatment of advanced non-small cell lung cancer (NSCLC). Missing targetable alterations may have a major impact on patient's progression free and overall survival. Although laboratory testing for molecular alterations has continued to improve; little is known about how biopsy technique affects the detection rate of different mutations. In the retrospective study detection rate of epidermal growth factor (EGFR) mutations in tissue extracted by bronchoscopic cryobiopsy (CB was significantly higher compared to other standard biopsy techniques. This prospective, randomized, multicenter, single blinded study evaluates the accuracy of molecular genetic characterization of NSCLC for different cell sampling techniques. Key inclusion criteria are suspected lung cancer or the suspected relapse of known NSCLC that is bronchoscopically visible. Patients will be randomized, either to have a CB or a bronchoscopic forceps biopsy (FB). If indicated, a transbronchial needle aspiration (TBNA) of suspect lymph nodes will be performed. Blood liquid biopsy will be taken before tissue biopsy. The primary endpoint is the detection rate of molecular genetic alterations in NSCLC, using CB and FB. Secondary endpoints are differences in the combined detection of molecular genetic alterations between FB and CB, TBNA and liquid biopsy. This trial plans to recruit 540 patients, with 178 evaluable patients per study cohort. A histopathological and molecular genetic evaluation will be performed by the affiliated pathology departments of the national network for genomic medicine in lung cancer (nNGM), Germany. We will compare the diagnostic value of solid tumor tissue, lymph node cells and liquid biopsy for the molecular genetic characterization of NSCLC. This reflects a real world clinical setting, with potential direct impact on both treatment and survival.

Co-occurrence of targetable mutations in Non-small cell lung cancer (NSCLC) patients harboring MAP2K1 mutations

BACKGROUND

MAP2K1 mutations are rare in non-small cell lung cancer (NSCLC) and considered to be mutually exclusive from known driver mutations. Activation of the MEK1-cascade is considered pivotal in resistance to targeted therapy approaches, and MAP2K1 K57 N mutation could be linked to resistance in preclinical models. We set out this study to detect MAP2K1 mutations and potentially targetable co-mutations using a molecular multiplex approach.

METHODS

Between 2012 and 2018, we routinely analyzed 14.512 NSCLC patients with two next-generation sequencing (NGS) panels. In a subset of patients, fluorescence in-situ hybridization was performed to detect rearrangements or amplifications. We assessed clinical parameters and co-occurring mutations and compared treatment outcomes of different forms of systemic therapy.

RESULTS

We identified 66 (0.5%) patients with MAP2K1 mutations. Both adenocarcinoma (n = 62) and squamous cell carcinoma (n = 4) histology. The presence of the mutations was linked to smoking, and transversions were more common than transitions. K57 N was the most frequent MAP2K1 mutation (n = 25). Additional mutations were found in 57 patients (86.4%). Mutations of TP53 were detected in 33 patients, followed by KEAP1 mutations in 28.1%. 24 patients (36.4%) had either MAP2K1-only or a co-occurring aberration considered targetable, including EGFR mutations, a BRAF V600E mutation and ROS1 rearrangements. Outcome analyses revealed a trend toward benefit from pemetrexed treatment.

CONCLUSION

Our analysis shows that MAP2K1-mutated NSCLC patients might frequently present with potentially targetable aberrations. Their role in providing resistance in these subtypes and the possible therapeutic opportunities justify further analyses of this rare NSCLC subgroup.

Optimizing the Analytical Value of Oncology-Related Data Based on an In-Memory Analysis Layer: Development and Assessment of the Munich Online Comprehensive Cancer Analysis Platform

Background

Many comprehensive cancer centers incorporate tumor documentation software supplying structured information from the associated centers’ oncology patients for internal and external audit purposes. However, much of the documentation data included in these systems often remain unused and unknown by most of the clinicians at the sites.

Objective

To improve access to such data for analytical purposes, a prerollout of an analysis layer based on the business intelligence software QlikView was implemented. This software allows for the real-time analysis and inspection of oncology-related data. The system is meant to increase access to the data while simultaneously providing tools for user-friendly real-time analytics.

Methods

The system combines in-memory capabilities (based on QlikView software) with innovative techniques that compress the complexity of the data, consequently improving its readability as well as its accessibility for designated end users. Aside from the technical and conceptual components, the software’s implementation necessitated a complex system of permission and governance.

Results

A continuously running system including daily updates with a user-friendly Web interface and real-time usage was established. This paper introduces its main components and major design ideas. A commented video summarizing and presenting the work can be found within the Multimedia Appendix.

Conclusions

The system has been well-received by a focus group of physicians within an initial prerollout. Aside from improving data transparency, the system’s main benefits are its quality and process control capabilities, knowledge discovery, and hypothesis generation. Limitations such as run time, governance, or misinterpretation of data are considered.

STRIP2, a member of the striatin-interacting phosphatase and kinase complex, is implicated in lung adenocarcinoma cell growth and migration

Lung adenocarcinoma (LUAD) accounts for ~40% of lung cancer cases, and the 5-year relative survival rate is no more than 1%. Dysregulation of components of striatin-interacting phosphatase and kinase (STRIPAK) complexes is associated with various diseases, including cancer. Striatin-interacting protein 2 (STRIP2), also called Fam40b, has been reported to regulate tumor cell growth and migration. Here, we investigated the role of STRIP2 in LUAD growth, migration and the underlying mechanisms. Analysis of data from The Cancer Genome Atlas database revealed that STRIP2 is highly expressed and predicted poor outcomes in patients with LUAD. Moreover, quantitative RT-PCR (qRT-PCR) analysis revealed that the mRNA expression of STRIP2 is greater in all tested LUAD cells than in a normal lung cell line. To investigate the function of STRIP2, we overexpressed STRIP2 in SPC-A1 cells and depleted STRIP2 in Calu-3 cells. Cell proliferation was evaluated by Cell Counting Kit-8 and colony-forming assays, and Transwell assay was employed to test cell invasion and migration. Our results indicate that STRIP2 depletion suppressed cell proliferation, invasion and migration in Calu-3 cells, and overexpression of STRIP2 had the opposite effects in SPC-A1 cells. Moreover, we discovered that STRIP2 depletion reduced the protein levels of p-Akt and phosphorylated-mammalian target of rapamycin (p-mTOR) in Calu-3 cells, whereas STRIP2 overexpression increased levels of these proteins in SPC-A1 cells. Furthermore, we found that silencing of STRIP2 clearly enhanced protein levels of E-cadherin and reduced levels of N-cadherin, Vimentin and matrix metalloproteinase-9 in Calu-3 cells, whereas overexpression of STRIP2 had the opposite effect in SPC-A1 cells. Our data indicate that STRIP2 promotes the proliferation and motility of LUAD cells, and this may be mediated through the regulation of the Akt/mTOR pathway and epithelial-mesenchymal transition. These results may facilitate the development of therapeutic strategies to treat LUAD.