A pan-cancer survey of cell line tumor similarity by feature-weighted molecular profiles

Nanotechnology-assisted intracellular delivery of antibody as a precision therapy approach for KRAS-driven tumors

The Kirsten Rat Sarcoma Virus (KRAS) oncoprotein, one of the most prevalent mutations in cancer, has been deemed undruggable for decades. The hypothesis of this work was that delivering anti-KRAS monoclonal antibody (mAb) at the intracellular level could effectively target the KRAS oncoprotein. To reach this goal, we designed and developed tLyP1-targeted palmitoyl hyaluronate (HAC16)-based nanoassemblies (HANAs) adapted for the association of bevacizumab as a model mAb. Selected candidates with adequate physicochemical properties (below 150 nm, neutral surface charge), and high drug loading capacity (>10%, w/w) were adapted to entrap the antiKRASG12V mAb. The resulting antiKRASG12V-loaded HANAs exhibited a bilayer composed of HAC16 polymer and phosphatidylcholine (PC) enclosing a hydrophilic core, as evidenced by cryogenic-transmission electron microscopy (cryo-TEM) and X-ray photoelectron spectroscopy (XPS). Selected prototypes were found to efficiently engage the target KRASG12V and, inhibit proliferation and colony formation in KRASG12V-mutated lung cancer cell lines. In vivo, a selected formulation exhibited a tumor growth reduction in a pancreatic tumor-bearing mouse model. In brief, this study offers evidence of the potential to use nanotechnology for developing anti-KRAS precision therapy and provides a rational framework for advancing mAb intracellular delivery against intracellular targets.

Stem cell modeling of nervous system tumors

Nervous system tumors, particularly brain tumors, represent the most common tumors in children and one of the most lethal tumors in adults. Despite decades of research, there are few effective therapies for these cancers. Although human nervous system tumor cells and genetically engineered mouse models have served as excellent platforms for drug discovery and preclinical testing, they have limitations with respect to accurately recapitulating important aspects of the pathobiology of spontaneously arising human tumors. For this reason, attention has turned to the deployment of human stem cell engineering involving human embryonic or induced pluripotent stem cells, in which genetic alterations associated with nervous system cancers can be introduced. These stem cells can be used to create self-assembling three-dimensional cerebral organoids that preserve key features of the developing human brain. Moreover, stem cell-engineered lines are amenable to xenotransplantation into mice as a platform to investigate the tumor cell of origin, discover cancer evolutionary trajectories and identify therapeutic vulnerabilities. In this article, we review the current state of human stem cell models of nervous system tumors, discuss their advantages and disadvantages, and provide consensus recommendations for future research.

In vitro to clinical translation of combinatorial effects of doxorubicin and dexrazoxane in breast cancer: a mechanism-based pharmacokinetic/pharmacodynamic modeling approach

Dexrazoxane (DEX) is the only drug clinically approved to treat Doxorubicin-induced cardiotoxicity (DIC), however its impact on the anticancer efficacy of DOX is not extensively studied. In this manuscript, a proof-of-concept in vitro study is carried out to quantitatively characterize the anticancer effects of DOX and DEX and determine their nature of drug-drug interactions in cancer cells by combining experimental data with modeling approaches. First, we determined the static concentration-response of DOX and DEX in breast cancer cell lines, JIMT-1 and MDA-MB-468. With a three-dimensional (3D) response surface analysis using a competitive interaction model, we characterized their interaction to be modestly synergistic in MDA-MB-468 or modestly antagonistic in JIMT-1 cells. Second, a cellular-level, pharmacodynamic (PD) model was developed to capture the time-course effects of the two drugs which determined additive and antagonistic interactions for DOX and DEX in MDA-MB-468 and JIMT-1, respectively. Finally, we performed in vitro to in vivo translation by utilizing DOX and DEX clinical dosing regimen that was previously identified to be maximally cardioprotective, to drive tumor cell PD models. The resulting simulations showed that a 10:1 DEX:DOX dose ratio over three cycles of Q3W regimen of DOX results in comparable efficacy based on MDA-MB-468 (additive effect) estimates and lower efficacy based on JIMT-1 (antagonistic effect) estimates for DOX + DEX combination as compared to DOX alone. Thus, our developed cell-based PD models can be used to simulate different scenarios and better design preclinical in vivo studies to further optimize DOX and DEX combinations.

Systematic transcriptional analysis of human cell lines for gene expression landscape and tumor representation

Cell lines are valuable resources as model for human biology and translational medicine. It is thus important to explore the concordance between the expression in various cell lines vis-à-vis human native and disease tissues. In this study, we investigate the expression of all human protein-coding genes in more than 1,000 human cell lines representing 27 cancer types by a genome-wide transcriptomics analysis. The cell line gene expression is compared with the corresponding profiles in various tissues, organs, single-cell types and cancers. Here, we present the expression for each cell line and give guidance for the most appropriate cell line for a given experimental study. In addition, we explore the cancer-related pathway and cytokine activity of the cell lines to aid human biology studies and drug development projects. All data are presented in an open access cell line section of the Human Protein Atlas to facilitate the exploration of all human protein-coding genes across these cell lines.

Molecular Biology of Pediatric and Adult Ovarian Germ Cell Tumors: A Review

Ovarian germ cell tumors (OGCTs) are rare in adults; indeed, they occur predominantly in children, adolescents, and young adults, and they account for approximately 11% of cancer diagnoses in these groups. Because OGCTs are rare tumors, our current understanding of them is sparse; this is because few studies have investigated the molecular basis of pediatric and adult cancers. Here, we review the etiopathogenesis of OGCTs in children and adults, and we address the molecular landscape of these tumors, including integrated genomic analysis, microRNAs, DNA methylation, the molecular implications of treatment resistance, and the development of in vitro and in vivo models. An elucidation of potential molecular alterations may provide a novel field for understanding the pathogenesis, tumorigenesis, diagnostic markers, and genetic peculiarity of the rarity and complexity of OGCTs.

Genomic and Transcriptomic Analyses of Malignant Pleural Mesothelioma (MPM) Samples Reveal Crucial Insights for Preclinical Testing

Cell lines are extensively used to study cancer biology. However, the use of highly passaged commercial cell lines has to be questioned, as they do not closely resemble the originating tumor. To understand the reliability of preclinical models for Malignant pleural mesothelioma (MPM) studies, we have performed whole transcriptome and whole exome analyses of fresh frozen MPM tumors and compared them to cell lines generated from these tumors, as well as commercial cell lines and a preclinical MPM mouse model. Patient-derived cell lines were generated from digested fresh tumors and whole exome sequencing was performed on DNA isolated from formalin-fixed, paraffin-embedded (FFPE) tumor samples, corresponding patient-derived cell lines, and normal tissue. RNA sequencing libraries were prepared from 10 fresh frozen tumor samples, the 10 corresponding patient-derived cell lines, and 7 commercial cell lines. Our results identified alterations in tumor suppressor genes such as FBXW7, CDKN2A, CDKN2B, and MTAP, all known to drive MPM tumorigenesis. Patient-derived cell lines correlate to a high degree with their originating tumor. Gene expressions involved in multiple pathways such as EMT, apoptosis, myogenesis, and angiogenesis are upregulated in tumor samples when compared to patient-derived cell lines; however, they are downregulated in commercial cell lines compared to patient-derived cell lines, indicating significant differences between the two model systems. Our results show that the genome and transcriptome of tumors correlate to a higher degree with patient-derived cell lines rather than commercial cell lines. These results are of major relevance for the scientific community in regard to using cell lines as an appropriate model, resembling the pathway of interest to avoid misleading results for clinical applications.

Computational estimation of quality and clinical relevance of cancer cell lines

Immortal cancer cell lines (CCLs) are the most widely used system for investigating cancer biology and for the preclinical development of oncology therapies. Pharmacogenomic and genome‐wide editing screenings have facilitated the discovery of clinically relevant gene–drug interactions and novel therapeutic targets via large panels of extensively characterised CCLs. However, tailoring pharmacological strategies in a precision medicine context requires bridging the existing gaps between tumours and in vitro models. Indeed, intrinsic limitations of CCLs such as misidentification, the absence of tumour microenvironment and genetic drift have highlighted the need to identify the most faithful CCLs for each primary tumour while addressing their heterogeneity, with the development of new models where necessary. Here, we discuss the most significant limitations of CCLs in representing patient features, and we review computational methods aiming at systematically evaluating the suitability of CCLs as tumour proxies and identifying the best patient representative in vitro models. Additionally, we provide an overview of the applications of these methods to more complex models and discuss future machine‐learning‐based directions that could resolve some of the arising discrepancies.

Power in Numbers: Harnessing Combinatorial and Integrated Screens to Advance Nanomedicine

Nanocarriers have significant potential to advance personalized medicine through targeted drug delivery. However, to date, efforts to improve nanoparticle accumulation at target disease sites have largely failed to translate clinically, stemming from an incomplete understanding of nano-bio interactions. While progress has been made to evaluate the effects of specific physical and chemical nanoparticle properties on trafficking and uptake, there is much to be gained from controlling these properties singularly and in combination to determine their interactions with different cell types. We and others have recently begun leveraging library-based nanoparticle screens to study structure-function relationships of lipid- and polymer-based drug delivery systems to guide nanoparticle design. These combinatorial screening efforts are showing promise in leading to the successful identification of critical characteristics that yield improved and specific accumulation at target sites. However, there is a crucial need to equally consider the influence of biological complexity on nanoparticle delivery, particularly in the context of clinical translation. For example, tissue and cellular heterogeneity presents an additional dimension to nanoparticle trafficking, uptake, and accumulation; applying imaging and screening tools as well as bioinformatics may further expand our understanding of how nanoparticles engage with cells and tissues. Given recent advances in the fields of omics and machine learning, there is substantial promise to revolutionize nanocarrier development through the use of integrated screens, harnessing the combinatorial parameter space afforded both by nanoparticle libraries and clinically annotated biological data sets in combination with high throughput in vivo studies.