Individual Cell Lines Research Articles

Data-Independent Acquisition of HLA Class I Peptidomes on the Q Exactive Mass Spectrometer Platform.

The characterization of peptides presented by human leukocyte antigen (HLA) class I molecules is crucial for understanding immune processes, biomarker discovery, and the development of novel immunotherapies or vaccines. Mass spectrometry allows the direct identification of thousands of HLA-bound peptides from cell lines, blood, or tissue. In recent years, data-independent acquisition (DIA) mass spectrometry methods have evolved, promising to increase reproducibility and sensitivity over classical data-dependent acquisition (DDA) workflows. Here, we describe a DIA setup on the Q Exactive mass spectrometer, optimized regarding the unique properties of HLA class I peptides. The methodology enables sensitive and highly reproducible characterization of HLA peptidomes from individual cell lines. From up to 16 DDA analyses of 100 million human cells, more than 10000 peptides could be confidently identified, serving as basis for the generation of spectral libraries. This knowledge enabled the subsequent interrogation of DIA data, leading to the identification of peptide sets with >90% overlap between replicate samples, a prerequisite for the comparative study of closely related specimens. Furthermore, >3000 peptides could be identified from just one million cells after DIA analysis using a library generated from 300 million cells. The reduction in sample quantity and the high reproducibility of DIA-based HLA peptidome analysis should facilitate personalized medicine applications.

Abstract A26: Prediction of synergistic anticancer drug combinations by integrating chemical and genetic screens

Abstract Identification of effective drug combinations inhibiting multiple target genes that are essential for cancer cell survival is seen as an effective strategy to overcome drug resistance. Cellular pathways are often redundant, and network rewiring in the face of an assault promotes the emergence of resistant cells that contribute to clinical relapse. However, the number of possible drug-target combinations to investigate by conducting experiments is exponentially large and impractical, therefore warranting the need for systematic approaches to prioritize the most effective drug combinations. Here, we aimed to integrate large-scale drug screening, drug-target affinity knowledge and public RNAi screening datasets on a huge compendium of cancer cell lines to predict synergistic drug combinations. In a case study in an individual breast cancer cell line, MDA-MB-231, we identified drugs selectively active in this cell line compared to the background distribution. We find that target genes of active drugs and other genes involved in related cellular processes were also differentially essential in RNAi screening data. Further, we observe that the drug combinations of these selectively active drugs were synergistic. We propose a systematic approach for integrating chemical screening, drug target and cellular processes knowledge, and functional genetic screens to optimize drug combination testing efforts. Citation Format: Alok Jaiswal, Prson Gautam, Jing Tang, Krister Wennerberg, Tero Aittokallio. Prediction of synergistic anticancer drug combinations by integrating chemical and genetic screens [abstract]. In: Proceedings of the AACR Precision Medicine Series: Opportunities and Challenges of Exploiting Synthetic Lethality in Cancer; Jan 4-7, 2017; San Diego, CA. Philadelphia (PA): AACR; Mol Cancer Ther 2017;16(10 Suppl):Abstract nr A26.

Analysis of VSV pseudotype virus infection mediated by rubella virus envelope proteins

Rubella virus (RV) generally causes a systemic infection in humans. Viral cell tropism is a key determinant of viral pathogenesis, but the tropism of RV is currently poorly understood. We analyzed various human cell lines and determined that RV only establishes an infection efficiently in particular non-immune cell lines. To establish an infection the host cells must be susceptible and permissible. To assess the susceptibility of individual cell lines, we generated a pseudotype vesicular stomatitis virus bearing RV envelope proteins (VSV-RV/CE2E1). VSV-RV/CE2E1 entered cells in an RV envelope protein-dependent manner, and thus the infection was neutralized completely by an RV-specific antibody. The infection was Ca2+-dependent and inhibited by endosomal acidification inhibitors, further confirming the dependency on RV envelope proteins for the VSV-RV/CE2E1 infection. Human non-immune cell lines were mostly susceptible to VSV-RV/CE2E1, while immune cell lines were much less susceptible than non-immune cell lines. However, susceptibility of immune cells to VSV-RV/CE2E1 was increased upon stimulation of these cells. Our data therefore suggest that immune cells are generally less susceptible to RV infection than non-immune cells, but the susceptibility of immune cells is enhanced upon stimulation.

PRIMARY CELL CULTURE OF AEDES ALBOPICTUS MIDGUT CELLS: A PROSPECTIVE MODEL FOR IN VITRO STUDY OF ARBOVIRUSES

Objective: Midgut cells play a key role in the propagation of mosquito borne Arboviruses. The existing mosquito cell lines for studying viral pathogenesis are derived either from larvae or from eggs since there is no cell line available from the mosquito midgut. Therefore, to delineate the in situ viral interaction which naturally occurs within the mosquito midgut and represent cellular pathogenesis in human beings, the present work was aimed to develop a primary cell line from the midgut cells of Aedes albopictus.Methods: The midgut cells of A. albopictus were collected, cultured and incubated at 28°C to study the growth after every 24 hrs for 7 days.Result: The primary cell culture showed an increasing growth pattern of columnar cells up to 48 hrs followed by decrease in cell population afterward. However, the number of stem cells increased significantly throughout the study period, and their population outnumbered the columnar cells after 72 hrs. There was no significant change of goblet cells and regenerating cells which were scanty in number throughout the experiment.Conclusion: The present method will help to develop the individual cell lines from mosquito midgut and study the host pathogen interaction in arboviral diseases in future.

RNA-seq reveals distinctive RNA profiles of small extracellular vesicles from different human liver cancer cell lines.

Liver cancer (LC) is one of the most common cancers and represents the third highest cause of cancer-related deaths worldwide. Extracellular vesicle (EVs) cargoes, which are selectively enriched in RNA, offer great promise for the diagnosis, prognosis and treatment of LC. Our study analyzed the RNA cargoes of EVs derived from 4 liver-cancer cell lines: HuH7, Hep3B, HepG2 (hepato-cellular carcinoma) and HuH6 (hepatoblastoma), generating two different sets of sequencing libraries for each. One library was size-selected for small RNAs and the other targeted the whole transcriptome. Here are reported genome wide data of the expression level of coding and non-coding transcripts, microRNAs, isomiRs and snoRNAs providing the first comprehensive overview of the extracellular-vesicle RNA cargo released from LC cell lines. The EV-RNA expression profiles of the four liver cancer cell lines share a similar background, but cell-specific features clearly emerge showing the marked heterogeneity of the EV-cargo among the individual cell lines, evident both for the coding and non-coding RNA species.

Multi-omics of 34 colorectal cancer cell lines - a resource for biomedical studies

BackgroundColorectal cancer (CRC) cell lines are widely used pre-clinical model systems. Comprehensive insights into their molecular characteristics may improve model selection for biomedical studies.MethodsWe have performed DNA, RNA and protein profiling of 34 cell lines, including (i) targeted deep sequencing (n = 612 genes) to detect single nucleotide variants and insertions/deletions; (ii) high resolution DNA copy number profiling; (iii) gene expression profiling at exon resolution; (iv) small RNA expression profiling by deep sequencing; and (v) protein expression analysis (n = 297 proteins) by reverse phase protein microarrays.ResultsThe cell lines were stratified according to the key molecular subtypes of CRC and data were integrated at two or more levels by computational analyses. We confirm that the frequencies and patterns of DNA aberrations are associated with genomic instability phenotypes and that the cell lines recapitulate the genomic profiles of primary carcinomas. Intrinsic expression subgroups are distinct from genomic subtypes, but consistent at the gene-, microRNA- and protein-level and dominated by two distinct clusters; colon-like cell lines characterized by expression of gastro-intestinal differentiation markers and undifferentiated cell lines showing upregulation of epithelial-mesenchymal transition and TGFβ signatures. This sample split was concordant with the gene expression-based consensus molecular subtypes of primary tumors. Approximately ¼ of the genes had consistent regulation at the DNA copy number and gene expression level, while expression of gene-protein pairs in general was strongly correlated. Consistent high-level DNA copy number amplification and outlier gene- and protein- expression was found for several oncogenes in individual cell lines, including MYC and ERBB2.ConclusionsThis study expands the view of CRC cell lines as accurate molecular models of primary carcinomas, and we present integrated multi-level molecular data of 34 widely used cell lines in easily accessible formats, providing a resource for preclinical studies in CRC.

Abstract 5452: Antitumor activity of miR-34a in peritoneal mesothelioma relies on c-MET and AXL inhibition: Persistent activation of ERK and AKT signaling as a possible cytoprotective mechanism

Abstract The value of microRNAs (miRNAs) as novel targets for cancer therapy is now widely recognized. However, no information is currently available on the expression/functional role of miRNAs in diffuse malignant peritoneal mesothelioma (DMPM), a rapidly lethal disease, poorly responsive to conventional treatments, for which the development of new therapeutic strategies is urgently needed. Here, we evaluated the expression and biological effects of miR-34a -one of the most widely deregulated miRNAs in cancer and for which a lipid-formulated mimic is already clinically available- in a large cohort of DMPM clinical samples and a unique collection of in house-developed DMPM preclinical models, with the aim to assess the potential of a miR-34a-based approach for disease treatment. miR-34a was found to be significantly down-regulated in forty-five DMPM clinical specimens and five established cell lines compared to normal peritoneum. miR-34a reconstitution inhibited cell proliferation in a time-dependent manner, though to a different extent and with a different kinetics across individual cell lines. Such a variable antiproliferative effect was paralleled by a different kinetics of apoptosis induction. In addition, miR-34a ectopic expression significantly declined DMPM cell invasion ability and impaired the secretion of angiogenesis-related factors. Phenocopy experiments showed that miR-34a oncosuppressive activities mainly rely on c-MET and AXL down-regulation and the consequent interference with the activation of downstream signaling. Interestingly, a persistent activation of ERK1/2 and AKT in two miR-34a-reconstituted cell lines was found to counteract the antiproliferative and proapoptotic effects of miRNA, yet not affecting its anti-invasive activity. Most importantly, when three subcutaneous/orthotopic DMPM xenograft models were used to examine the impact of miR-34a on tumorigenicity, a significant reduction in tumor take and growth was consistently observed. Our preclinical data, showing impressive inhibitory effects induced by miR-34a on DMPM cell proliferation, invasion and growth in immunodeficient mice, strongly suggest the potential clinical utility of a miR-34a-replacement therapy for the treatment of such a still incurable disease. On the other hand, we provide the first evidence of a potential cytoprotective/resistance mechanism that may arise towards miRNA-based therapies through the persistent activation of receptor tyrosine kinase downstream signaling. Citation Format: Rihan El Bezawy, Michelandrea De Cesare, Marzia Pennati, Marcello Deraco, Paolo Gandellini, Valentina Zuco, Nadia Zaffaroni. Antitumor activity of miR-34a in peritoneal mesothelioma relies on c-MET and AXL inhibition: Persistent activation of ERK and AKT signaling as a possible cytoprotective mechanism [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 5452. doi:10.1158/1538-7445.AM2017-5452

Abstract 5559: Using cancer dependency data to discover tumor suppressive and oncogenic functional modules

Abstract Efforts to define protein complexes and their functional networks are critical for systems-level understanding of the pathways involved in human cancer. Current methods to catalog human protein complexes via physical interaction are often unable to resolve functional differences between complex members or infer relationships governed by sub-stoichiometric interactions. While functional wiring maps in yeast have been generated by measuring epistatic interactions between pairs of genes, efforts to scale this concept in individual human cell lines have been met with challenges and have only been able to characterize limited numbers of genes at a time. We have developed a scalable approach that can measure functional similarity without the constraints of pairwise genetic interaction experiments. Using data from genome-wide RNAi and CRISPR dropout screens performed in hundreds of cancer cell lines, we leveraged the heterogeneity of gene dependencies across cancer types to measure functional similarity between thousands of genes at once, which in turn allowed us to recreate known inter- and intra-complex functional relationships and to uncover tumor suppressive and oncogenic functional modules in cancer-relevant pathways such as proteolysis, metabolism and transcription. Applying these approaches to the mammalian SWI/SNF (BAF) chromatin remodeling complex, which is mutated in over 20% of human cancer, revealed three functional modules that arose separately during metazoan evolution, one of which is entirely novel and uncharacterized. We then performed biochemical experiments that fully support three specialized complex configurations, each with distinct size, subunit composition, and function. These data reorganize the BAF complex into previously unrecognized modules that better explain mutational burden in human cancer. Notably, we observe that that all known BAF-driven, highly penetrant rare cancers and neurodevelopmental disorders involve disruption within a single functional module we defined, underscoring the value of evaluating disease genomics through the lens of functional modularity. Citation Format: Joshua Pan, Robin M. Meyers, Brittany C. Michel, Ann E. Sizemore, Francisca Vazquez, Barbara A. Weir, William C. Hahn, Aviad Tsherniak, Cigall Kadoch. Using cancer dependency data to discover tumor suppressive and oncogenic functional modules [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 5559. doi:10.1158/1538-7445.AM2017-5559

Abstract 434: Characterizing the role of serine metabolism in pediatric sarcomas

Abstract Sarcomas represent a diverse group of malignancies with unique molecular and pathological characteristics. In order to improve sarcoma treatment, a better understanding of the alterations associated with specific sarcoma subtypes is critically important. Renewed interest in the altered metabolic properties of cancer cells has led to an exploration of targeting metabolic dependencies as a novel therapeutic strategy. Metabolism of the amino acid serine is frequently altered in cancer, supporting a number of critical biological processes, including protein, lipid, and nucleotide synthesis, and redox balance. The first, rate-limiting step in the serine synthesis pathway is catalyzed by the enzyme 3-phosphoglycerate dehydrogenase (PHGDH), which is overexpressed in several cancers. Previous work has shown that PHGDH loss or inhibition is selectively toxic to cancer cells with high PHGDH expression or increased flux through the serine synthesis pathway. In this study, we have characterized the dependency of pediatric sarcomas on serine metabolism by examining expression of PHGDH in Ewing sarcoma and rhabdomyosarcoma cell lines, and evaluating the effects of PHGDH inhibition and serine deprivation on cellular proliferation and bioenergetic properties. We show that PHGDH is highly expressed in pediatric sarcoma cell lines, and that PHGDH knockdown resulted in decreased proliferation, especially under conditions of serine limitation. Moreover, pharmacological inhibition of PHGDH resulted in a dose-dependent decrease in proliferation and mitochondrial bioenergetic function. Furthermore, individual sarcoma cell lines were differentially sensitive to serine deprivation, indicating that some sarcoma cells may depend on extracellular serine in addition to de novo serine synthesis. Our findings suggest that the dependency of pediatric sarcomas on serine metabolism should be further investigated in order to identify vulnerabilities that could be targeted for potential therapeutic benefit. Citation Format: Sameer Issaq, Ria Kidner, Jason Rohde, Matthew Boxer, Lee Helman. Characterizing the role of serine metabolism in pediatric sarcomas [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 434. doi:10.1158/1538-7445.AM2017-434

Abstract 2586: CellMinerCDB: Enabling cross-database exploration of molecular pharmacology data and response determinant discovery in cancer cell lines

Abstract Cancer cell line panels are widely used for evaluating drug response across diverse tissue types. A growing set of molecular profiling data complements measurements of chemosensitivity, providing novel avenues for response determinant discovery and clinical translation. Accessing and inter-relating data from different sources is essential for evaluating such determinants, but remains challenging. To enable wider access to cell line pharmacogenomic data, we have developed CellMinerCDB (CellMiner Cross-Database, discover.nci.nih.gov/cellminercdb), a web application integrating data from several widely studied cancer cell line panels, including the NCI-60 (NIH), GDSC (Sanger/MGH), and CCLE/CTRP (Broad). All together, our database spans over 1300 distinct cell lines, 400 clinically relevant cancer drugs, 20,000 experimental compounds, and molecular profiling data, such as gene/protein expression, DNA copy, methylation, and mutational status. Cell line and tested drug overlaps allow cross-database validation of genomic and drug data, and CellMinerCDB simplifies this by transparently matching differently named entities between sources. Data exploration can be additionally restricted to particular tissue types, with individual cell lines annotated to the OncoTree ontology for consistent treatment across sources. A range of analysis tools support interactive data exploration, from 2D plots of drug response and molecular profiling features to exhaustive correlation analyses and multivariate predictive models. We illustrate the power and utility of CellMinerCDB with examples of response determinant discovery and predictive modeling for Top1 and PARP inhibitors. Beginning with established individual determinants, such as SLFN11 mRNA expression, we show how both unbiased and biological knowledge network-based feature selection methods enable iterative refinement of a multivariate genomic signature of drug response. For Top 1 inhibitors, additional predictive features include expression of chromatin remodeling factors and genes modulating apoptosis capacity, while complementary PARP inhibitor response determinants include PARP1 and drug efflux pump expression. Pathway and process-based gene annotations allow biological interpretation of response predictive features. CellMinerCDB also includes ongoing algorithmic work to improve the construction of multivariate predictive models using constraints from biological networks. These approaches bridge a limiting gap in existing methods, which either ignore biological knowledge altogether or are limited to exploration within known pathways and processes. Citation Format: Vinodh N. Rajapakse, Augustin Luna, Chris Sander, William C. Reinhold, Yves Pommier. CellMinerCDB: Enabling cross-database exploration of molecular pharmacology data and response determinant discovery in cancer cell lines [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 2586. doi:10.1158/1538-7445.AM2017-2586

The Effects of Selected Sesquiterpenes from Myrica rubra Essential Oil on the Efficacy of Doxorubicin in Sensitive and Resistant Cancer Cell Lines.

β-caryophyllene oxide (CAO), α-humulene (HUM), trans-nerolidol (NER) and valencene (VAL) are constituents of the essential oil of Myrica rubra (MEO), which has significant antiproliferative effect in various cancer cell lines. In the present study, we compared the antiproliferative effect of these sesquiterpenes alone and in combination with the cytostatic drug doxorubicin (DOX) in cancer cell lines with different sensitivity to DOX. Two ovarian cancer cell lines (sensitive A2780 and partly resistant SKOV3) and two lymphoblast cancer cell lines (sensitive CCRF/CEM and completely resistant CEM/ADR) were used. The observed effects varied among sesquiterpenes and also differed in individual cell lines, with only VAL being effective in all the cell lines. A strong synergism of DOX with NER was found in the A2780 cells, while DOX acted synergistically with HUM and CAO in the SKOV3 cells. In the CCRF/CEM cells, a synergism of DOX with CAO and NER was observed. In resistant CEM/ADR cells, sesquiterpenes did not increase DOX efficacy, although they significantly increased accumulation of DOX (up to 10-times) and rhodamine-123 (substrate of efflux transporter ABCB1) within cancer cells. In conclusion, the tested sesquiterpenes were able to improve DOX efficacy in the sensitive and partly resistant cancer cells, but not in cells completely resistant to DOX.

An Efficient and Flexible Cell Aggregation Method for 3D Spheroid Production.

Monolayer cell culture does not adequately model the in vivo behavior of tissues, which involves complex cell-cell and cell-matrix interactions. Three-dimensional (3D) cell culture techniques are a recent innovation developed to address the shortcomings of adherent cell culture. While several techniques for generating tissue analogues in vitro have been developed, these methods are frequently complex, expensive to establish, require specialized equipment, and are generally limited by compatibility with only certain cell types. Here, we describe a rapid and flexible protocol for aggregating cells into multicellular 3D spheroids of consistent size that is compatible with growth of a variety of tumor and normal cell lines. We utilize varying concentrations of serum and methyl cellulose (MC) to promote anchorage-independent spheroid generation and prevent the formation of cell monolayers in a highly reproducible manner. Optimal conditions for individual cell lines can be achieved by adjusting MC or serum concentrations in the spheroid formation medium. The 3D spheroids generated can be collected for use in a wide range of applications, including cell signaling or gene expression studies, candidate drug screening, or in the study of cellular processes such as tumor cell invasion and migration. The protocol is also readily adapted to generate clonal spheroids from single cells, and can be adapted to assess anchorage-independent growth and anoikis-resistance. Overall, our protocol provides an easily modifiable method for generating and utilizing 3D cell spheroids in order to recapitulate the 3D microenvironment of tissues and model the in vivo growth of normal and tumor cells.

Abstract IA05: Regulation of Ras proteins and their effectors

Abstract Ras proteins regulate multiple phenotypes, including proliferation, contact inhibition, cell motility, metabolism, and genome integrity. This range of phenotypes may relate to the number of different effector pathways that Ras activates. The best validated of these is the Raf/MAPK pathway. Ras proteins can activate PI 3-kinase pathways directly, though this seems to vary between tissue types. Ras proteins bind and activate RalGDS, but this pathway is less well understood. In addition to these three major pathways, Ras proteins in their GTP state interact directly with several other potential effectors. Analysis of the contributions that these pathways make towards cancer phenotypes using genetic approaches or RNA interference has been complicated by redundancies within each effector pathway. For example, there are three Raf isoforms, 3 PI 3-kinase isoforms, etc. This explains why screens for single genes that Ras depends on have not identified classical Ras effectors. Furthermore, each pathway may have several relevant phenotypes, such as effects on cell cycle progression, survival, and metabolic stress. Therefore, screens that depend on one phenotype, such as proliferation or survival, might miss other important functions. In addition to these technical complications, it is important to recognize differences in signaling between different tumor types. For example, in lung adenocarcinoma, the RTK/RAS/MAPK pathway can be activated by mutations in RTKs, activation of SOS, loss of NF1, mutation in KRAS, or activation of BRAF, with similar clinical outcomes. In pancreatic cancer, only mutation in KRAS appears capable of driving malignancy. In lung adenocarcinoma and pancreatic cancer, KRAS mutations are likely initiating events, in colorectal cancer they are definitely not, and exist in the context of un-regulated beta-catenin signaling. To address these issues we analyzed the importance of each canonical effector pathway, plus 37 other potential effectors, in 100 cell lines derived from lung, pancreatic and GI tissues, measuring five different parameters on a single cell basis. We used combinations of highly potent and well characterized siRNAs that enabled us to knock down all the paralogs within an effector node at once. For example, we were able to knock down ARAF, BRAF and RAF1, together, in the “RAF node”. We took advantage of the variable nature of siRNA knockdown to generate dose-response curves for each node, measuring effects on five parameters on each cell. This analysis revealed the expected dependence on tissue of origin, with pancreatic cells being more dependent on Ras signaling than lung adenocarcinoma. Unexpectedly, we found that most KRAS mutant tumor cell lines fell into two major classes: those that depend strongly on KRAS itself, and the Raf/MAPK pathway, and those that had lost dependence on KRAS, but were strongly dependent on RSK p90 S6 kinase. These cells were not dependent on PI 3-kinase genes themselves, but rather on PDK1, loss of LKB1 and other aspects of downstream PI 3-kinase signaling. We also identified a small number of cells that depended strongly on RalGDS. Each of these subsets had remarkably different properties, in terms of energy metabolism, EMT status, and cell-cell interaction. We conclude that oncogenic Ras proteins can indeed activate several downstream pathways, but in individual cell lines, one effector pathway predominates. Citation Format: Tina Yuan, Frank McCormick. Regulation of Ras proteins and their effectors. [abstract]. In: Proceedings of the AACR Special Conference on Translational Control of Cancer: A New Frontier in Cancer Biology and Therapy; 2016 Oct 27-30; San Francisco, CA. Philadelphia (PA): AACR; Cancer Res 2017;77(6 Suppl):Abstract nr IA05.

Organoid technologies meet genome engineering.

Three-dimensional (3D) stem cell differentiation cultures recently emerged as a novel model system for investigating human embryonic development and disease progression in vitro, complementing existing animal and two-dimensional (2D) cell culture models. Organoids, the 3D self-organizing structures derived from pluripotent or somatic stem cells, can recapitulate many aspects of structural organization and functionality of their in vivo organ counterparts, thus holding great promise for biomedical research and translational applications. Importantly, faithful recapitulation of disease and development processes relies on the ability to modify the genomic contents in organoid cells. The revolutionary genome engineering technologies, CRISPR/Cas9 in particular, enable investigators to generate various reporter cell lines for prompt validation of specific cell lineages as well as to introduce disease-associated mutations for disease modeling. In this review, we provide historical overviews, and discuss technical considerations, and potential future applications of genome engineering in 3D organoid models.

Cell-Specific Computational Modeling of the PIM Pathway in Acute Myeloid Leukemia.

Personalized therapy is a major goal of modern oncology, as patient responses vary greatly even within a histologically defined cancer subtype. This is especially true in acute myeloid leukemia (AML), which exhibits striking heterogeneity in molecular segmentation. When calibrated to cell-specific data, executable network models can reveal subtle differences in signaling that help explain differences in drug response. Furthermore, they can suggest drug combinations to increase efficacy and combat acquired resistance. Here, we experimentally tested dynamic proteomic changes and phenotypic responses in diverse AML cell lines treated with pan-PIM kinase inhibitor and fms-related tyrosine kinase 3 (FLT3) inhibitor as single agents and in combination. We constructed cell-specific executable models of the signaling axis, connecting genetic aberrations in FLT3, tyrosine kinase 2 (TYK2), platelet-derived growth factor receptor alpha (PDGFRA), and fibroblast growth factor receptor 1 (FGFR1) to cell proliferation and apoptosis via the PIM and PI3K kinases. The models capture key differences in signaling that later enabled them to accurately predict the unique proteomic changes and phenotypic responses of each cell line. Furthermore, using cell-specific models, we tailored combination therapies to individual cell lines and successfully validated their efficacy experimentally. Specifically, we showed that cells mildly responsive to PIM inhibition exhibited increased sensitivity in combination with PIK3CA inhibition. We also used the model to infer the origin of PIM resistance engineered through prolonged drug treatment of MOLM16 cell lines and successfully validated experimentally our prediction that this resistance can be overcome with AKT1/2 inhibition. Cancer Res; 77(4); 827-38. ©2016 AACR.

Abstract P6-02-07: Metabolomic analysis by nuclear magnetic resonance spectroscopy discriminates hormone receptor positive/HER2 negative breast cancer cell lines resistant to palbociclib

Abstract Introduction: Clinical trials of palbociclib in combination with endocrine therapy have recently shown unprecedented activity for the treatment of hormone receptor positive/HER2 negative (HR+/HER2neg) advanced breast cancer. However, de novo and acquired resistance to palbociclib limit its clinical utility. Moreover, combining palbociclib with endocrine therapy increases toxicity and costs of the treatment. Identifying patients more likely to benefit from this compound and understanding the mechanisms of resistance to palbociclib is critical. In this study we investigated whether metabolomic profiles of breast cancer cell lines with acquired resistance to palbociclib (PDR) differ from their sensitive counterpart (PDS). In addition we sought to identify metabolic biomarkers of sensitivity to palbociclib by analyzing a breast cancer cell line unable to acquire resistance to palbociclib. Material and methods: We have established in our lab three PDR HR+/HER2neg breast cancer models (MCF7L, T47D and ZR75-1) by chronically exposing cells to escalating doses of palbociclib. PDR derivatives show IC50 values 6 to 30 times higher than their PDS counterparts. One additional model, CAMA-1, was unable to develop resistance. Whole-cell lysates and conditioned cell culture media from five replicates of each of the PDS and PDR models and from CAMA-1 were analyzed by nuclear magnetic resonance (NMR). Principal component analysis (PCA) was used as first exploratory analysis and as dimension reduction technique. Canonical Analysis (CA) was used to discriminate different groups. Differentially expressed metabolites between PDR and PDS models and between CAMA-1 and PDS cells were analyzed. Results: Unsupervised PCA analyses of H NMR spectra, in which no information about PDS and PDR was inserted in the statistical model, correctly identified individual cell lines on both whole-cell lysates and conditioned media. However this analysis did not discriminate PDS from PDR within each model. Using a supervised approach, in which the statistical model was trained to discriminate between PDS and PDR, these groups were categorized with accuracy of 80% using whole-cell lysates and of 65% using conditioned media, using a cross-validation analysis by repeatedly testing the model on blind samples. CAMA-1 was correctly identified as a PDS model; however it showed a distinct metabolic profile compared to other PDS models. Over 30 metabolites were identified as differentially expressed between PDS and PDR models in lysates and conditioned media, but only glycerophosphocholine levels in conditioned media remained significantly higher in PDR compared to PDS models after correction for multiple testing. Conclusions: In this study we show that analysis of metabolic profile of cells lysates discriminates PDR from PDS cell lines with a high accuracy. Analysis of metabolic pathways implicated in resistance/sensitivity to palbociclib is ongoing and might help identifying new targets to overcome resistance. Additionally, metabolites associated with palbociclib resistance may be potentially tested in clinical samples as biomarkers for patients stratification. Further studies are warranted. Citation Format: Bonechi M, Guarducci C, Meoni G, Tenori L, Biagioni C, Schiff R, Osborne CK, Luchinat C, Di Leo A, Malorni L, Migliaccio I. Metabolomic analysis by nuclear magnetic resonance spectroscopy discriminates hormone receptor positive/HER2 negative breast cancer cell lines resistant to palbociclib [abstract]. In: Proceedings of the 2016 San Antonio Breast Cancer Symposium; 2016 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2017;77(4 Suppl):Abstract nr P6-02-07.

Differential cytotoxic and inflammatory potency of amorphous silicon dioxide nanoparticles of similar size in multiple cell lines

The likelihood of environmental and health impacts of silicon dioxide nanoparticles (SiNPs) has risen, due to their increased use in products and applications. The biological potency of a set of similarly-sized amorphous SiNPs was investigated in a variety of cells to examine the influence of physico-chemical and biological factors on their toxicity. Cellular LDH and ATP, BrdU incorporation, resazurin reduction and cytokine release were measured in human epithelial A549, human THP-1 and mouse J774A.1 macrophage cells exposed for 24 h to suspensions of 5–15, 10–20 and 12 nm SiNPs and reference particles. The SiNPs were characterized in dry state and in suspension to determine their physico-chemical properties. The dose-response data were simplified into particle potency estimates to facilitate the comparison of multiple endpoints of biological effects in cells. Mouse macrophages were the most sensitive to SiNP exposures. Cytotoxicity of the individual cell lines was correlated while the cytokine responses differed, supported by cell type-specific differences in inflammation-associated pathways. SiNP (12 nm), the most cytotoxic and inflammogenic nanoparticle had the highest surface acidity, dry-state agglomerate size, the lowest trace metal and organics content, the smallest surface area and agglomerate size in suspension. Particle surface acidity appeared to be the most significant determinant of the overall biological activity of this set of nanoparticles. Combined with the nanoparticle characterization, integration of the biological potency estimates enabled a comprehensive determination of the cellular reactivity of the SiNPs. The approach shows promise as a useful tool for first-tier screening of SiNP toxicity.

Probing the metabolic phenotype of breast cancer cells by multiple tracer stable isotope resolved metabolomics

Breast cancers vary by their origin and specific set of genetic lesions, which gives rise to distinct phenotypes and differential response to targeted and untargeted chemotherapies. To explore the functional differences of different breast cell types, we performed Stable Isotope Resolved Metabolomics (SIRM) studies of one primary breast (HMEC) and three breast cancer cells (MCF-7, MDAMB-231, and ZR75-1) having distinct genotypes and growth characteristics, using 13C6-glucose, 13C-1+2-glucose, 13C5,15N2-Gln, 13C3-glycerol, and 13C8-octanoate as tracers. These tracers were designed to probe the central energy producing and anabolic pathways (glycolysis, pentose phosphate pathway, Krebs Cycle, glutaminolysis, nucleotide synthesis and lipid turnover). We found that glycolysis was not associated with the rate of breast cancer cell proliferation, glutaminolysis did not support lipid synthesis in primary breast or breast cancer cells, but was a major contributor to pyrimidine ring synthesis in all cell types; anaplerotic pyruvate carboxylation was activated in breast cancer versus primary cells. We also found that glucose metabolism in individual breast cancer cell lines differed between in vitro cultures and tumor xenografts, but not the metabolic distinctions between cell lines, which may reflect the influence of tumor architecture/microenvironment.

The NCI-60 Methylome and Its Integration into CellMiner.

A unique resource for systems pharmacology and genomic studies is the NCI-60 cancer cell line panel, which provides data for the largest publicly available library of compounds with cytotoxic activity (∼21,000 compounds), including 108 FDA-approved and 70 clinical trial drugs as well as genomic data, including whole-exome sequencing, gene and miRNA transcripts, DNA copy number, and protein levels. Here, we provide the first readily usable genome-wide DNA methylation database for the NCI-60, including 485,577 probes from the Infinium HumanMethylation450k BeadChip array, which yielded DNA methylation signatures for 17,559 genes integrated into our open access CellMiner version 2.0 (https://discover.nci.nih.gov/cellminer). Among new insights, transcript versus DNA methylation correlations revealed the epithelial/mesenchymal gene functional category as being influenced most heavily by methylation. DNA methylation and copy number integration with transcript levels yielded an assessment of their relative influence for 15,798 genes, including tumor suppressor, mitochondrial, and mismatch repair genes. Four forms of molecular data were combined, providing rationale for microsatellite instability for 8 of the 9 cell lines in which it occurred. Individual cell line analyses showed global methylome patterns with overall methylation levels ranging from 17% to 84%. A six-gene model, including PARP1, EP300, KDM5C, SMARCB1, and UHRF1 matched this pattern. In addition, promoter methylation of two translationally relevant genes, Schlafen 11 (SLFN11) and methylguanine methyltransferase (MGMT), served as indicators of therapeutic resistance or susceptibility, respectively. Overall, our database provides a resource of pharmacologic data that can reinforce known therapeutic strategies and identify novel drugs and drug targets across multiple cancer types. Cancer Res; 77(3); 601-12. ©2016 AACR.

A local effect model-based interpolation framework for experimental nanoparticle radiosensitisation data

A local effect model (LEM)-based framework capable of interpolating nanoparticle-enhanced photon-irradiated clonogenic cell survival fraction measurements as a function of nanoparticle concentration was developed and experimentally benchmarked for gold nanoparticle (AuNP)-doped bovine aortic endothelial cells (BAECs) under superficial kilovoltage X-ray irradiation. For three different superficial kilovoltage X-ray spectra, the BAEC survival fraction response was predicted for two different AuNP concentrations and compared to experimental data. The ability of the developed framework to predict the cell survival fraction trends is analysed and discussed. This developed framework is intended to fill in the existing gaps of individual cell line response as a function of NP concentration under photon irradiation and assist the scientific community in planning future pre-clinical trials of high Z nanoparticle-enhanced photon radiotherapy.