From clinical specimens to human cancer preclinical models-a journey the NCI-cell line database-25 years later

AI integrations with lung cancer screening: Considerations in developing AI in a public health setting

Lung cancer screening implementation has led to expanded imaging of the chest in older, tobacco-exposed populations. Growing numbers of screening cases are also found to have CT-detectable emphysema or elevated levels of coronary calcium, indicating the presence of coronary artery disease. Early interventions based on these additional findings, especially with coronary calcium, are emerging and follow established protocols. Given the pace of diagnostic innovation and the potential public health impact, it is timely to review issues in developing useful chest CT screening infrastructure as chest CT screening will soon involve millions of participants worldwide. Lung cancer screening succeeds because it detects curable, early primary lung cancer by characterizing and measuring changes in non-calcified, lung nodules in the size-range from 3mm to 15 mm in diameter. Therefore, close attention to imaging methodology is essential to lung screening success and similar image quality issues are required for reliable quantitative characterization of early emphysema and coronary artery disease. Today's emergence of advanced image analysis using artificial intelligence (AI) is disrupting many aspects of medical imaging including chest CT screening. Given these emerging technological and volume trends, a major concern is how to balance the diverse needs of parties committed to building AI tools for precise, reproducible, and economical chest CT screening, while addressing the public health needs of screening participants receiving this service. A new consortium, the Alliance for Global Implementation of Lung and Cardiac Early Disease Detection and Treatment (AGILEDxRx) is committed to facilitate broad, equitable implementation of multi-disciplinary, high quality chest CT screening using advanced computational tools at accessible cost.

Tumor Cells Modulate Macrophage Phenotype in a Novel In Vitro Co-Culture Model of the NSCLC Tumor Microenvironment

INTRODUCTION

Macrophage phenotype in the tumor microenvironment correlates with prognosis in NSCLC. Immunosuppressive macrophages promote tumor progression, whereas proinflammatory macrophages may drive an antitumor immune response. How individual NSCLCs affect macrophage phenotype is a major knowledge gap.

METHODS

To systematically study the impact of lung cancer cells on macrophage phenotypes, we developed an in vitro co-culture model that consisted of molecularly and clinically annotated patient-derived NSCLC lines, human cancer-associated fibroblasts, and murine macrophages. Induced macrophage phenotype was studied through quantitative real-time polymerase chain reaction and validated in vivo using NSCLC xenografts through quantitative immunohistochemistry and clinically with The Cancer Genome Atlas (TCGA)-"matched" patient tumors.

RESULTS

A total of 72 NSCLC cell lines were studied. The most frequent highly induced macrophage-related gene was Arginase-1, reflecting an immunosuppressive M2-like phenotype. This was independent of multiple clinicopathologic factors, which also did not affect M2:M1 ratios in matched TCGA samples. In vivo, xenograft tumors established from high Arginase-1-inducing lines (Arghi) had a significantly elevated density of Arg1+ macrophages. Matched TCGA clinical samples to Arghi NSCLC lines had a significantly higher ratio of M2:M1 macrophages (p = 0.0361).

CONCLUSIONS

In our in vitro co-culture model, a large panel of patient-derived NSCLC lines most frequently induced high-expression Arginase-1 in co-cultured mouse macrophages, independent of major clinicopathologic and oncogenotype-related factors. Arghi cluster-matched TCGA tumors contained a higher ratio of M2:M1 macrophages. Thus, this in vitro model reproducibly characterizes how individual NSCLC modulates macrophage phenotype, correlates with macrophage polarization in clinical samples, and can serve as an accessible platform for further investigation of macrophage-specific therapeutic strategies.

Lung Cancer Computational Biology and Resources

Comprehensive clinical, pathological, and molecular data, when appropriately integrated with advanced computational approaches, are transforming the way we characterize and study lung cancer. Clinically, cancer registry and publicly available historical clinical trial data enable retrospective analyses to examine how socioeconomic factors, patient demographics, and cancer characteristics affect treatment and outcome. Pathologically, digital pathology and artificial intelligence are revolutionizing histopathological image analyses, not only with improved efficiency and accuracy, but also by extracting additional information for prognostication and tumor microenvironment characterization. Genetically and molecularly, individual patient tumors and preclinical models of lung cancer are profiled by various high-throughput platforms to characterize the molecular properties and functional liabilities. The resulting multi-omics data sets and their interrogation facilitate both basic research mechanistic studies and translation of the findings into the clinic. In this review, we provide a list of resources and tools potentially valuable for lung cancer basic and translational research. Importantly, we point out pitfalls and caveats when performing computational analyses of these data sets and provide a vision of future computational biology developments that will aid lung cancer translational research.

Two alternative cell line models for the study of multiorganic metastasis and immunotherapy in Lung Squamous Cell Carcinoma

There is a paucity of adequate mouse models and cell lines available to study lung squamous cell carcinoma (LUSC). We have generated and characterized two models of phenotypically different transplantable LUSC cell lines (UN-SCC679 and UN-SCC680) derived from an N-nitroso-tris-chloroethylurea (NTCU) chemically-induced mouse model in A/J mice. Furthermore, we genetically characterized and compared both LUSC cell lines by performing whole exome and RNA sequencing. These experiments revealed similar genetic and transcriptomic patterns that may correspond to the classical LUSC human subtype. In addition, we compared the immune landscape generated by both tumor cells lines in vivo and assessed their response to immune checkpoint inhibition. The differences between the two cell lines are a good model for the remarkable heterogeneity of human squamous cell carcinoma. Study of the metastatic potential of these models revealed that both cell lines represent the human LUSC organotropism to the brain, bones, liver and adrenal glands. In summary, we have generated a very valuable cell line tools for LUSC research that recapitulates the complexity of the human disease.

Ancestry matters: Building inclusivity into preclinical study design

Human cell lines (CLs) are key assets for biomedicine but lack ancestral diversity. Here, we explore why genetic diversity among cell-based models is essential for making preclinical research more equitable and widely translatable. We lay out practical actions that can be taken to improve inclusivity in study design.

SCLC-CellMiner: A Resource for Small Cell Lung Cancer Cell Line Genomics and Pharmacology Based on Genomic Signatures

CellMiner-SCLC (https://discover.nci.nih.gov/SclcCellMinerCDB/) integrates drug sensitivity and genomic data, including high-resolution methylome and transcriptome from 118 patient-derived small cell lung cancer (SCLC) cell lines, providing a resource for research into this "recalcitrant cancer." We demonstrate the reproducibility and stability of data from multiple sources and validate the SCLC consensus nomenclature on the basis of expression of master transcription factors NEUROD1, ASCL1, POU2F3, and YAP1. Our analyses reveal transcription networks linking SCLC subtypes with MYC and its paralogs and the NOTCH and HIPPO pathways. SCLC subsets express specific surface markers, providing potential opportunities for antibody-based targeted therapies. YAP1-driven SCLCs are notable for differential expression of the NOTCH pathway, epithelial-mesenchymal transition (EMT), and antigen-presenting machinery (APM) genes and sensitivity to mTOR and AKT inhibitors. These analyses provide insights into SCLC biology and a framework for future investigations into subtype-specific SCLC vulnerabilities.

From clinical specimens to human cancer preclinical models-a journey the NCI-cell line database-25 years later

The intramural the National Cancer Institute (NCI) and more recently the University of Texas Southwestern Medical Center with many different collaborators comprised a complex, multi-disciplinary team that collaborated to generated large, comprehensively annotated, cell-line related research resources which includes associated clinical, and molecular characterization data. This material has been shared in an anonymized fashion to accelerate progress in overcoming lung cancer, the leading cause of cancer death across the world. However, this cell line collection also includes a range of other cancers derived from patient-donated specimens that have been remarkably valuable for other types of cancer and disease research. A comprehensive analysis conducted by the NCI Center for Research Strategy of the 278 cell lines reported in the original Journal of Cellular Biochemistry Supplement, documents that these cell lines and related products have since been used in more than 14 000 grants, and 33 207 published scientific reports. This has resulted in over 1.2 million citations using at least one cell line. Many publications involve the use of more than one cell line, to understand the value of the resource collectively rather than individually; this method has resulted in 2.9 million citations. In addition, these cell lines have been linked to 422 clinical trials and cited by 4700 patents through publications. For lung cancer alone, the cell lines have been used in the research cited in the development of over 70 National Comprehensive Cancer Network clinical guidelines. Finally, it must be underscored again, that patient altruism enabled the availability of this invaluable research resource.