Individual Cell Lines Research Articles

Antibody-drug Conjugate PCMC1D3-Duocarmycin SA as a Novel Therapeutic Entity for Targeted Treatment of Cancers Aberrantly Expressing MET Receptor Tyrosine Kinase.

Aberrant expression of the MET receptor tyrosine kinase is an oncogenic determinant and a drug target for cancer therapy. Currently, antibody-based biotherapeutics targeting MET are under clinical trials. Here, we report the preclinical and therapeutic evaluation of a novel anti-MET antibody- drug conjugate PCMC1D3-duocarmycin SA (PCMC1D3-DCM) for targeted cancer therapy. The monoclonal antibody PCMC1D3 (IgG1a/κ), generated by a hybridoma technique and specific to one of the MET extracellular domains, was selected based on its high specificity to human MET with a binding affinity of 1.60 nM. PCMC1D3 was conjugated to DCM via a cleavable valine-citrulline dipeptide linker to form an antibody-drug conjugate with a drug-to-antibody ratio of 3.6:1. PCMC1D3-DCM in vitro rapidly induced MET internalization with an internalization efficacy ranging from 6.5 to 17.2h dependent on individual cell lines. Studies using different types of cancer cell lines showed that PCMC1D3-DCM disrupted the cell cycle, reduced cell viability, and caused massive cell death within 96h after treatment initiation. The calculated IC50 values for cell viability reduction were 1.5 to 15.3 nM. Results from mouse xenograft tumor models demonstrated that PCMC1D3-DCM in a single dose injection at 10 mg/kg body weight effectively delayed xenograft tumor growth up to two weeks without signs of tumor regrowth. The calculated tumoristatic concentration, a minimal dose required to balance tumor growth and inhibition, was around 2 mg/kg body weight. Taken together, PCMC1D3-DCM was effective in targeting the inhibition of tumor growth in xenograft models. This work provides the basis for the development of humanized PCMC1D3-DCM for MET-targeted cancer therapy in the future.

Revisiting the Resazurin-Based Sensing of Cellular Viability: Widening the Application Horizon.

Since 1991, the NAD(P)H-aided conversion of resazurin to fluorescent resorufin has been widely used to measure viability based on the metabolic activity in mammalian cell culture and primary cells. However, different research groups have used divergent assay protocols, scarcely reporting the systematic optimization of the assay. Here, we perform extensive studies to fine-tune the experimental protocols utilizing resazurin-based viability sensing. Specifically, we focus on (A) optimization of the assay dynamic range in individual cell lines for the correct measurement of cytostatic and cytotoxic properties of the compounds; (B) dependence of the dynamic range on the physical quantity detected (fluorescence intensity versus change of absorbance spectrum); (C) calibration of the assay for the correct interpretation of data measured in hypoxic conditions; and (D) possibilities for combining the resazurin assay with other methods including measurement of necrosis and apoptosis. We also demonstrate the enhanced precision and flexibility of the resazurin-based assay regarding the readout format and kinetic measurement mode as compared to the widely used analogous assay which utilizes tetrazolium dye MTT. The discussed assay optimization guidelines provide useful instructions for the beginners in the field and for the experienced scientists exploring new ways for measurement of cellular viability using resazurin.

Identification of cell type specific ACE2 modifiers by CRISPR screening.

SARS-CoV-2 infection is initiated by binding of the viral spike protein to its receptor, ACE2, on the surface of host cells. ACE2 expression is heterogeneous both in vivo and in immortalized cell lines, but the molecular pathways that govern ACE2 expression remain unclear. We now report high-throughput CRISPR screens for functional modifiers of ACE2 surface abundance. In liver-derived HuH7 cells, we identified 35 genes whose disruption was associated with a change in the surface abundance of ACE2. Enriched among these ACE2 regulators were established transcription factors, epigenetic regulators, and functional networks. We further characterized individual HuH7 cell lines with disruption of SMAD4, EP300, PIAS1, or BAMBI and found these genes to regulate ACE2 at the mRNA level and to influence cellular susceptibility to SARS-CoV-2 infection. Orthogonal screening of lung-derived Calu-3 cells revealed a distinct set of ACE2 modifiers comprised of ACE2, KDM6A, MOGS, GPAA1, and UGP2. Collectively, our findings clarify the host factors involved in SARS-CoV-2 entry, highlight the cell type specificity of ACE2 regulatory networks, and suggest potential targets for therapeutic development.

Chemical Interrogation of Nuclear Size Identifies Compounds with Cancer Cell Line-Specific Effects on Migration and Invasion.

Background: Lower survival rates for many cancer types correlate with changes in nuclear size/scaling in a tumor-type/tissue-specific manner. Hypothesizing that such changes might confer an advantage to tumor cells, we aimed at the identification of commercially available compounds to guide further mechanistic studies. We therefore screened for Food and Drug Administration (FDA)/European Medicines Agency (EMA)-approved compounds that reverse the direction of characteristic tumor nuclear size changes in PC3, HCT116, and H1299 cell lines reflecting, respectively, prostate adenocarcinoma, colonic adenocarcinoma, and small-cell squamous lung cancer. Results: We found distinct, largely nonoverlapping sets of compounds that rectify nuclear size changes for each tumor cell line. Several classes of compounds including, e.g., serotonin uptake inhibitors, cyclo-oxygenase inhibitors, β-adrenergic receptor agonists, and Na+/K+ ATPase inhibitors, displayed coherent nuclear size phenotypes focused on a particular cell line or across cell lines and treatment conditions. Several compounds from classes far afield from current chemotherapy regimens were also identified. Seven nuclear size-rectifying compounds selected for further investigation all inhibited cell migration and/or invasion. Conclusions: Our study provides (a) proof of concept that nuclear size might be a valuable target to reduce cell migration/invasion in cancer treatment and (b) the most thorough collection of tool compounds to date reversing nuclear size changes specific to individual cancer-type cell lines. Although these compounds still need to be tested in primary cancer cells, the cell line-specific nuclear size and migration/invasion responses to particular drug classes suggest that cancer type-specific nuclear size rectifiers may help reduce metastatic spread.

Pharmacokinetics of PEGylated Gold Nanoparticles: In Vitro-In Vivo Correlation.

Data suitable for assembling a physiologically-based pharmacokinetic (PBPK) model for nanoparticles (NPs) remain relatively scarce. Therefore, there is a trend in extrapolating the results of in vitro and in silico studies to in vivo nanoparticle hazard and risk assessment. To evaluate the reliability of such approach, a pharmacokinetic study was performed using the same polyethylene glycol-coated gold nanoparticles (PEG-AuNPs) in vitro and in vivo. As in vitro models, human cell lines TH1, A549, Hep G2, and 16HBE were employed. The in vivo PEG-AuNP biodistribution was assessed in rats. The internalization and exclusion of PEG-AuNPs in vitro were modeled as first-order rate processes with the partition coefficient describing the equilibrium distribution. The pharmacokinetic parameters were obtained by fitting the model to the in vitro data and subsequently used for PBPK simulation in vivo. Notable differences were observed in the internalized amount of Au in individual cell lines compared to the corresponding tissues in vivo, with the highest found for renal TH1 cells and kidneys. The main reason for these discrepancies is the absence of natural barriers in the in vitro conditions. Therefore, caution should be exercised when extrapolating in vitro data to predict the in vivo NP burden and response to exposure.

Pan-Cancer Prediction of Cell-Line Drug Sensitivity Using Network-Based Methods.

The development of reliable predictive models for individual cancer cell lines to identify an optimal cancer drug is a crucial step to accelerate personalized medicine, but vast differences in cancer cell lines and drug characteristics make it quite challenging to develop predictive models that result in high predictive power and explain the similarity of cell lines or drugs. Our study proposes a novel network-based methodology that breaks the problem into smaller, more interpretable problems to improve the predictive power of anti-cancer drug responses in cell lines. For the drug-sensitivity study, we used the GDSC database for 915 cell lines and 200 drugs. The theory of optimal mass transport was first used to separately cluster cell lines and drugs, using gene-expression profiles and extensive cheminformatic drug features, represented in a form of data networks. To predict cell-line specific drug responses, random forest regression modeling was separately performed for each cell-line drug cluster pair. Post-modeling biological analysis was further performed to identify potential biological correlates associated with drug responses. The network-based clustering method resulted in 30 distinct cell-line drug cluster pairs. Predictive modeling on each cell-line-drug cluster outperformed alternative computational methods in predicting drug responses. We found that among the four drugs top-ranked with respect to prediction performance, three targeted the PI3K/mTOR signaling pathway. Predictive modeling on clustered subsets of cell lines and drugs improved the prediction accuracy of cell-line specific drug responses. Post-modeling analysis identified plausible biological processes associated with drug responses.

In vitro modeling of the complex retinal condition age-related macular degeneration

Aim: To model a complex retinal disease such as age-related macular degeneration (AMD) in vitro, we aimed to combine genetic and environmental risk factors in a retinal pigment epithelium (RPE) cell culture model generated via induced pluripotent stem cells (iPSCs) from subjects with an extremely high and an extremely low genetic disease risk. As an external stimulus, we chose defined oxidative stress conditions. Methods: Patients were genotyped for known AMD-associated genetic variants and their individual genetic risk score (GRS) was calculated defining individual iPSC-RPE cell lines which reflect the extreme ends of the genetic risk for AMD. Sodium iodate (NaIO3, SI) was used to induce oxidative stress and cellular responses were followed by analyzing nuclear factor erythroid 2-related factor 2 (NRF2) pathway activation by mRNA and protein expression. Results: We present a collection of eight iPSC-RPE cell lines, with four each harboring an extreme low or an extreme high GRS for AMD. RPE identity was verified structurally and functionally. We found that 24 and 72 h of SI treatment induced a significant upregulation of NRF2 response genes HMOX1 and NQO1, without showing cytotoxic effects or negatively influencing RPE cell integrity. High- vs. low-risk cell lines revealed similar first line defenses in oxidative stress response mediated through the NRF2 pathway. Conclusion: Delineating the NRF2-mediated oxidative stress response was sought in iPSC-RPE cell lines with maximally divergent genetic AMD risk profiles. Under the specific stress conditions chosen, our data indicate that genetic predisposition to AMD may not exert a major influence on the NRF2 signaling pathway.

Comparative characterization and analysis of telomere length in stem cells derived from deciduous and permanent teeth

Understanding the influence of age on growth kinetics and telomere length in dental stem cells is essential for the successful development of cell therapies. Hence, the present study compared the basic cellular and phenotypical characteristics of stem cells from human exfoliated deciduous teeth (SHEDs) and dental pulp stem cells (DPSCs) of permanent teeth and their telomere lengths using quantitative real-time polymerase chain reaction. The study is an in vitro original research article. Primary cultures of SHED and DPSCs (n = 6 each) were successfully established in vitro, and the parameters analyzed were the morphology, viability, proliferation rate, population doubling time (PDT), phenotypic markers expression, and the relative telomere lengths. Data were analyzed by analysis of variance and P < 0.05 was considered statistically significant. SHED and DPSCs exhibited a small spindle-shaped fibroblast-like morphology with >90% viability. The proliferation assay showed that the cells had a typical growth pattern. The PDT values of SHED and DPSCs were 29.03 ± 9.71 h and 32.05 ± 9.76 h, respectively. Both cells were positive for surface markers CD29, CD44, and CD90. However, they were negative for CD45 and human leukocyte antigen DR. Although the differences in relative telomere lengths between the individual cell lines of SHED and DPSCs were observed, no significant (P > 0.05) variations were found for the mean T/S ratios of both the cells. SHED and DPSCs displayed similar morphology, proliferation rates, and phenotypic features. The relative telomere lengths were slightly shorter in DPSCs than SHED, but the values were not significantly different. Thus, SHED and DPSCs can be considered as recognized sources for regenerative applications in dentistry.

Measuring Early Germ-Layer Specification Bias in Human Pluripotent Stem Cells.

Human pluripotent stem cells have a wide variety of potential applications, ranging from clinical translation to in vitro disease modeling. However, there is significant variation in the potential of individual cell lines to differentiate towards each of the three germ layers as a result of (epi)genetic background, culture conditions, and other factors. We describe here in detail a methodology to evaluate this bias using short directed differentiation towards neuroectoderm, mesendoderm, and definitive endoderm in combination with quantification by RT-qPCR and immunofluorescent stains.

Combination treatment optimization using a pan-cancer pathway model.

The design of efficient combination therapies is a difficult key challenge in the treatment of complex diseases such as cancers. The large heterogeneity of cancers and the large number of available drugs renders exhaustive in vivo or even in vitro investigation of possible treatments impractical. In recent years, sophisticated mechanistic, ordinary differential equation-based pathways models that can predict treatment responses at a molecular level have been developed. However, surprisingly little effort has been put into leveraging these models to find novel therapies. In this paper we use for the first time, to our knowledge, a large-scale state-of-the-art pan-cancer signaling pathway model to identify candidates for novel combination therapies to treat individual cancer cell lines from various tissues (e.g., minimizing proliferation while keeping dosage low to avoid adverse side effects) and populations of heterogeneous cancer cell lines (e.g., minimizing the maximum or average proliferation across the cell lines while keeping dosage low). We also show how our method can be used to optimize the drug combinations used in sequential treatment plans—that is, optimized sequences of potentially different drug combinations—providing additional benefits. In order to solve the treatment optimization problems, we combine the Covariance Matrix Adaptation Evolution Strategy (CMA-ES) algorithm with a significantly more scalable sampling scheme for truncated Gaussian distributions, based on a Hamiltonian Monte-Carlo method. These optimization techniques are independent of the signaling pathway model, and can thus be adapted to find treatment candidates for other complex diseases than cancers as well, as long as a suitable predictive model is available.

Systematic Studies of the Circadian Clock Genes Impact on Temperature Compensation and Cell Proliferation Using CRISPR Tools.

Simple SummaryOne of the major characteristics of the circadian clock is temperature compensation, and previous studies suggested a single clock gene may determine the temperature compensation. In this study, we report the first full collection of clock gene knockout cell lines using CRISPR/Cas9 tools. Our full collections indicate that the temperature compensation is a complex gene regulation system instead of being regulated by any single gene. Besides, we systematically compared the proliferation rates and circadian periods using our full collections, and we found that the cell growth rate is not dependent on the circadian period. Therefore, complex interaction between clock genes and their protein products may underlie the mechanism of temperature compensation, which needs further investigations.Mammalian circadian genes are capable of producing a self-sustained, autonomous oscillation whose period is around 24 h. One of the major characteristics of the circadian clock is temperature compensation. However, the mechanism underlying temperature compensation remains elusive. Previous studies indicate that a single clock gene may determine the temperature compensation in several model organisms. In order to understand the influence of each individual clock gene on the temperature compensation, twenty-three well-known mammalian clock genes plus Timeless and Myc genes were knocked out individually, using a powerful gene-editing tool, CRISPR/Cas9. First, Bmal1, Cry1, and Cry2 were knocked out as examples to verify that deleting genes by CRISPR is effective and precise. Cell lines targeting twenty-two genes were successfully edited in mouse fibroblast NIH3T3 cells, and off-target analysis indicated these genes were correctly knocked out. Through measuring the luciferase reporters, the circadian periods of each cell line were recorded under two different temperatures, 32.5 °C and 37 °C. The temperature compensation coefficient Q10 was subsequently calculated for each cell line. Estimations of the Q10 of these cell lines showed that none of the individual cell lines can adversely affect the temperature compensation. Cells with a longer period at lower temperature tend to have a shorter period at higher temperature, while cells with a shorter period at lower temperature tend to be longer at higher temperature. Thus, the temperature compensation is a fundamental property to keep cellular homeostasis. We further conclude that the temperature compensation is a complex gene regulation system instead of being regulated by any single gene. We also estimated the proliferation rates of these cell lines. After systematically comparing the proliferation rates and circadian periods, we found that the cell growth rate is not dependent on the circadian period.

CSIG-13. A DYNAMIC CAUSAL MODEL OF GLIOBLASTOMA STEM CELL SIGNALING PREDICTS EFFECTS OF KINASE INHIBITORS

Abstract Glioblastomas and glioblastoma stem cells are heterogeneous with respect to mutations, gene expression, and response to drugs. To make predictive responses of individual GBM stem cell lines to kinase inhibitors, we have constructed a causal model of glioblastoma stem cell signaling. The core model was built starting from pathways identified from TCGA mutation data with the addition of the Jak/STAT, Hedgehog, and Notch pathways. Elements and relations between them were validated and extended using the PCNet interaction database and the INDRA database which includes machine read extractions from the biomedical literature. The result is a high confidence executable model consisting of 209 elements (proteins, genes, RNAs) and 370 regulatory logic rules between the elements. Stochastic simulations of the model provide dynamic (quantile) changes in time and responses to perturbations. The output simulates activity of individual nodes as well as cell cycle progression, apoptosis, and differentiation. To simulate the responses of individual cell lines to kinase inhibitors, the model was initialized using DNA sequencing data, RNA-seq, and reverse phase protein array (RPPA) data from each cell line. Comparing the results of the simulations to the drug responses of 11 different kinase targets in 3 cell lines, the model was 88% accurate in predicting effects on growth and survival. The model was further tested by comparing the effects of Mek inhibition of each of the cell lines in the model to the results observed in the RPPA data which overlap by 127 elements. In this case, there was less than 65% concordance between the model and the data for individual nodes. Discrepancies between the model predictions and the data are being investigated to determine whether the model logic or extent needs to be revised to improve the model. This modeling approach is a step toward developing algorithms for personalized therapeutics for GBM.

Effect of radioiodine therapy (131I) on blood cells of differentiated thyroid carcinoma patients

Thyroid cancer accounts for about 1% of all types of cancers, with about 80% of cases being differentiated and 20% undifferentiated. Our study was conducted on 209 Differentiated Thyroid Carcinoma (DTC) patients treated at the Department of Nuclear Medicine of Nghe An Cancer Hospital with 91.39% of patients having papillary thyroid carcinoma, with the mean age of 47.91 ± 12.08. The geographic distribution of these patients was determined to be 68.42% coastal plains. Patients living in mountainous areas accounted for only 6.22%. For our experiment, treatment for patients including having the tumor removed and thyroid tissue destroyed using 131I. Patients were then prescribed with alternative hormones varying at different doses depending on the degree and stage of cancer. Among 209 patients, 8 patients were treated with 30mCi dose(3.83%); 196 patients treated with the dose of 100–150 mCi (93.78%) and 5 patients treated 200–250 mCi (2.39%). After 3–5 days of treatment, we observed significant changes in blood count with p&lt;0.05. Initially, erythrocytes and hemoglobin count decreased slightly for 30–150 mCi dose patients but with 200–250 mCi dose patients, those indexes fell below the reference level. The platelet index decreased significantly after 3–5 days of treatment (p&lt;0.05). Total leukocytes, lymphocytes and mono count decreased sharply after treatment (p&lt;0.05) but neutrophils changed very little. After 3–6 months of treatment, the blood cell formula gradually stabilized, the recovery of red blood cells took place faster than the white blood cells and platelets. The recovery of blood cells of patients treated with doses of 200–500 mCi took place more slowly (p&lt;0.05). Thus, the use of 131I in the treatment of DTC is a common and effective method, but they may also be at risk of impairing blood cells, especially in high-dose 131I patients. However, this change is still within the allowable limits and after 6 months of treatment, the blood cell counts tend to return to their original values. The speed of recovery depends on the individual blood cell line and the amount of treatment. This research result can be used as a reference for deciding on 131I dosage and supportive measures, interventions in treatment, helping patients avoiding the decrease of blood cells and increase the effectiveness of the treatment process.

Lytic effects of water on cancer cells: Implications for post-operative irrigation.

Intraoperative tumour spillage can be concerning during cancer excisions, given it can lead to tumour-cell re-implantation and local recurrence. Examples include bladder tumour recurrences post-transurethral resection, or peritoneal spillage during laparotomy/laparoscopy for bowel and ovarian cancers. One approach to reducing implantation is mechanical wash out of free-floating tumour cells. Irrigation with water may have additional effectiveness compared to iso-osmotic irrigants (e.g. saline) by causing osmotic cytolysis, but this is not well-characterised. This in vitro study aimed to ascertain the time-course of osmotic effects of water on various cancer cell lines to provide guidance for clinical usage. Assays were conducted on six cancer cell lines (bladder [HT1197, HT1376], colon [KM12, LIM2405], kidney [SKRC52], and ovarian [COV434]). Cells were exposed to water or 0.9% saline and cell counts were performed using a haemocytometer at 10, 20, 40, 60, 120 and 180 min. Cell viability was determined using Trypan Blue exclusion. In all cell lines, exposure to water led to 100% cell lysis within a median time of 40 min (range 10-180 min), while exposure to saline led to a gradual decline in cell viability (median 50.2%, range 6.7%-100.0%) over 3h, and did not result in complete cell lysis. An increase in osmotic gradient equivalent to a concentration of 5% NaCl was sufficient to impede the effects of water-mediated cell lysis. Our studies suggest that water has a rapid osmolytic effect on cancer cells. The required exposure time to reach 0% cell viability varied between individual cell lines.

Deep learning-based classification of breast cancer cells using transmembrane receptor dynamics.

MotivationMotions of transmembrane receptors on cancer cell surfaces can reveal biophysical features of the cancer cells, thus providing a method for characterizing cancer cell phenotypes. While conventional analysis of receptor motions in the cell membrane mostly relies on the mean-squared displacement plots, much information is lost when producing these plots from the trajectories. Here we employ deep learning to classify breast cancer cell types based on the trajectories of epidermal growth factor receptor (EGFR). Our model is an artificial neural network trained on the EGFR motions acquired from six breast cancer cell lines of varying invasiveness and receptor status: MCF7 (hormone receptor positive), BT474 (HER2-positive), SKBR3 (HER2-positive), MDA-MB-468 (triple negative, TN), MDA-MB-231 (TN) and BT549 (TN).ResultsThe model successfully classified the trajectories within individual cell lines with 83% accuracy and predicted receptor status with 85% accuracy. To further validate the method, epithelial–mesenchymal transition (EMT) was induced in benign MCF10A cells, noninvasive MCF7 cancer cells and highly invasive MDA-MB-231 cancer cells, and EGFR trajectories from these cells were tested. As expected, after EMT induction, both MCF10A and MCF7 cells showed higher rates of classification as TN cells, but not the MDA-MB-231 cells. Whereas deep learning-based cancer cell classifications are primarily based on the optical transmission images of cell morphology and the fluorescence images of cell organelles or cytoskeletal structures, here we demonstrated an alternative way to classify cancer cells using a dynamic, biophysical feature that is readily accessible.Availability and implementationA python implementation of deep learning-based classification can be found at https://github.com/soonwoohong/Deep-learning-for-EGFR-trajectory-classification.Supplementary information Supplementary data are available at Bioinformatics online.

Investigation of REFINED CNN ensemble learning for anti-cancer drug sensitivity prediction.

MotivationAnti-cancer drug sensitivity prediction using deep learning models for individual cell line is a significant challenge in personalized medicine. Recently developed REFINED (REpresentation of Features as Images with NEighborhood Dependencies) CNN (Convolutional Neural Network)-based models have shown promising results in improving drug sensitivity prediction. The primary idea behind REFINED-CNN is representing high dimensional vectors as compact images with spatial correlations that can benefit from CNN architectures. However, the mapping from a high dimensional vector to a compact 2D image depends on the a priori choice of the distance metric and projection scheme with limited empirical procedures guiding these choices.ResultsIn this article, we consider an ensemble of REFINED-CNN built under different choices of distance metrics and/or projection schemes that can improve upon a single projection based REFINED-CNN model. Results, illustrated using NCI60 and NCI-ALMANAC databases, demonstrate that the ensemble approaches can provide significant improvement in prediction performance as compared to individual models. We also develop the theoretical framework for combining different distance metrics to arrive at a single 2D mapping. Results demonstrated that distance-averaged REFINED-CNN produced comparable performance as obtained from stacking REFINED-CNN ensemble but with significantly lower computational cost.Availability and implementationThe source code, scripts, and data used in the paper have been deposited in GitHub (https://github.com/omidbazgirTTU/IntegratedREFINED).Supplementary information Supplementary data are available at Bioinformatics online.

NGMA-3. Use of multi-omics data to initialize and validate a causal model of glioblastoma stem cell signaling

Glioblastomas and glioblastoma stem cells are heterogeneous with respect to mutations, gene expression, and response to drugs. To make predictive responses of individual GBM stem cell lines to drugs, we have constructed a causal model of glioblastoma stem cell signaling. The core model was built starting from pathways identified from TCGA mutation data with the addition of the Jak/STAT, Hedgehog, and Notch pathways. Elements and relations between them were validated and extended using the PCNet interaction database and the INDRA database which includes machine read extractions from the biomedical literature. The result is a high confidence executable model consisting of 209 element and 370 rules of interaction between the elements. Stochastic simulations of the model provide dynamic (quantile) changes in time and responses to perturbations. The output provides activity of individual nodes as well as a cellular output state of cell cycle progression, apoptosis, or differentiation. To simulate the responses of individual cell lines to kinase inhibitors, the model was initialized using DNA sequencing data, RNA-seq, and reverse phase protein array (RPPA) data from each cell line. Comparing the results of the simulations to the drug responses of 11 different kinase targets, the model was 88% accurate in predicting effects on growth and survival. The model was further tested by comparing the effects of Mek inhibition of each of the cell lines in model to the results observed in the RPPA data which overlap by 127 elements. In this case, there was 62% concordance between the model and data when binned into quintiles. Discrepancies between the model predictions and the data are being investigated to determine whether the model logic or extent needs to be revised to improve the model. This modeling approach is a step toward developing algorithms for personalized therapeutics for GBM based on multi-omics data.

Abstract 1341: Inhibiting the nuclear exporter XPO1 and the antiapoptotic factor BCL2 is synergistic in XPO1 and SF3B1 mutant hematologic malignancies

Abstract Mutations in the nuclear exporter XPO1 and the spliceosomal protein SF3B1 are bona fide cancer drivers and occur across a spectrum of hematologic cancers, specifically in chronic lymphocytic leukemia (CLL), myelodysplastic syndromes (MDS) and acute myeloid leukemia (AML). Selinexor is a potent, XPO1 inhibitor that was recently approved in diffuse large B-cell lymphoma and is being tested in MDS and AML. The BCL2 inhibitor, venetoclax, is approved for use in CLL and has also shown to be effective in myeloid malignancies. XPO1 inhibition exerts its antineoplastic effects by blocking key tumorigenic pathways, such as NFκB, and decreasing the anti-apoptotic protein MCL1. We therefore hypothesized that combining selinexor and venetoclax would have potential synergy and could be used to target hematologic malignancies bearing XPO1 and SF3B1 mutations. We first determined whether XPO1 and SF3B1 mutant cell lines showed differential response to either selinexor or venetoclax monotherapy. Five lymphoma cell lines; 3 diffuse large B-cell lymphoma (SU-DHL-6, SU-DHL-16, FARAGE) and 2 classical Hodgkin lymphoma (L428 and SUP-HD1), were used based on the presence or absence of XPO1 mutations. SUDHL-16 and SUP-HD1 are heterozygous for the XPO1 E571K hotspot mutation while SUDHL-6, FARAGE and SUP-HD1 are wildtype. These cell lines were used to assess sensitivity to Selinexor or Venetoclax using the CellTiter Glo assay. The XPO1 mutant cells showed increased sensitivity to selinexor (IC50 = 16-35nM vs 41-231nM). Additionally, the XPO1 mutant cell lines showed increased sensitivity to single-agent venetoclax (XPO1 mutant IC50 = 2-13nM vs 5-2853nM). SF3B1 K666N mutant K562 and Nalm-6 cells also showed increased sensitivity to selinexor compared to wildtype cells (IC50 =116-190nM vs 212-1405nM), as well as to venetoclax as compared to wildtype (IC50 =149nM-1.28 μM vs 1.26-1.4μM). Next, we tested the synergy of the combination of these drugs in the XPO1 and SF3B1 mutant cell lines. Increasing concentrations of the individual drugs were applied to each individual cell line in a 6x6 matrix and the Bliss Independence model was used to calculate synergy of the combination. As hypothesized, the combination of Selinexor and venetoclax showed synergy in both the XPO1 and SF3B1 mutant cell lines (synergy scores 4.5-16.5). Western blots of treated wildtype versus mutant cells showed decreased levels of p65 and MCL1 with selinexor (but not venetoclax) that was enhanced with combination therapy, specifically in mutant cell lines. In conclusion, inhibiting the nuclear exporter XPO1 and the anti-apoptotic factor BCL2 is synergistic in both XPO1 and SF3B1 mutant cells. This combination is highly promising as an all oral regimen for hematologic malignancies bearing these mutations. Further, preclinical testing in in vivo using XPO1 and SF3B1 mutant and wildtype mice is ongoing. Citation Format: Tulasigeri M. Totiger, Sana Chaudhry, Jumana Afaghani, Justin Taylor. Inhibiting the nuclear exporter XPO1 and the antiapoptotic factor BCL2 is synergistic in XPO1 and SF3B1 mutant hematologic malignancies [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 1341.

Conventional NK cells and tissue-resident ILC1s join forces to control liver metastasis

The liver is a major metastatic target organ, and little is known about the role of immunity in controlling hepatic metastases. Here, we discovered that the concerted and nonredundant action of two innate lymphocyte subpopulations, conventional natural killer cells (cNKs) and tissue-resident type I innate lymphoid cells (trILC1s), is essential for antimetastatic defense. Using different preclinical models for liver metastasis, we found that trILC1 controls metastatic seeding, whereas cNKs restrain outgrowth. Whereas the killing capacity of trILC1s was not affected by the metastatic microenvironment, the phenotype and function of cNK cells were affected in a cancer type-specific fashion. Thus, individual cancer cell lines orchestrate the emergence of unique cNK subsets, which respond differently to tumor-derived factors. Our findings will contribute to the development of therapies for liver metastasis involving hepatic innate cells.

Identification and Validation of Molecular Markers of Tumor Cell Resistance to Natural Killer Cells through CRISPR-Based Screens and Large-Scale Phenotypic Screens of Pooled Tumor Cell Lines

Natural killer (NK) cellsexhibit potent activity in pre-clinical models of diverse hematologic malignancies and solid tumors. The in vivo anti-tumor effects of NK cells are not confounded by graft vs. host disease, providing a key advantage over strategies exploiting T cell reactivity. Indeed, infusion of high numbers of NK cells, either autologous or allogeneic, after their ex vivo expansion and activation, has been feasible and safe in clinical studies. Because of these considerations, NK cells had been viewed as a promising therapeutic approach for a disease like multiple myeloma (MM), which remains incurable, even after initial responses to novel immunotherapies, including anti-CD38 antibodies; combinations of thalidomide derivatives with immune checkpoint blockade; and even CAR-T cells. However, our prior studies and more recent unpublished work indicate that tumor cells can exhibit decreased response to NK due to the protective effect of nonmalignant mesenchymal stromal cells and depending on the genetic background of the tumor cells. To our knowledge, there have been limited, if any, reports of systematic screens to quantify NK cell cytotoxicity against large panels of molecularly characterized tumor cell lines. Moreover, while there have been earlier RNAi-based studies, to the best of our knowledge there have not been any genome-wide CRISPR-based screens for candidate NK cell resistance genes. To address this void, we sought to identify and validate mechanisms regulating tumor cell response to NK cells, by performing orthogonal screens in 2 systems: (i) Using similar approaches as in other screens conducted by our lab in other models, we performed a genome-wide CRISPR gene editing screen (GECKO library) in the NK cell-sensitive colorectal Ca line HCT15 and identified candidate genes for which knockout confers resistance to the NK cell line KHYG1. (ii) We studied the cytotoxicity of NK cells (primary NK cells or KHYG1; at different time points and effector-to-tumor [E2T] ratios) against a pool of DNA-barcoded 568 lineage diverse solid tumor lines from the Cancer Cell Line Encyclopedia (CCLE), using the PRISM multiplexed cell viability screening technology. This PRISM screen expanded on a previous screen across 100 cancer cell lines and yielded consistent results supporting the reproducibility of the method. We correlated NK cell cytotoxicity with the CCLE genomic feature data set (mRNA expression, copy number variation, and gene mutational statues) for each of the 568 cell lines. The screens provided candidate “hits” which were concordant in both studies and included recognizable regulators of NK cell biology (e.g. loss-of-function of death receptors such as FAS or gain-of-function of HLA-E correlating with NK cell resistance); as well as other plausible, but previously under-appreciated candidates, which will be considered for further studies in this project. We did not observe significant tumor type-specific differences in NK cell responses in this screen, suggesting that candidate markers identified in these studies may be relevant across lineages, including MM and blood cancers (even though these lines were not included so far in our PRISM or CRISPR screens). Additional ongoing “multi-omic” analyses seek to further examine the links between the phenotypic responses to NK cells with the molecular features of these cell lines (e.g. integrating mutations, DNA methylation, proteomic [RPPA] etc.). Our study highlights the feasibility of combining CRISPR/Cas9-based functional genomic screens (on genome-wide and sub-genome scale) on individual cell lines with large scale phenotypic screens on pooled barcoded cell lines to identify candidate biomarkers of tumor cell sensitivity vs. resistance to NK cells. Notably our PRISM screen represents, to our knowledge, the largest effort to-date to correlate the molecular annotation of tumor cells with their response to uniform treatment, pre-clinical or clinical, to a cell-based immunotherapy. DisclosuresMitsiades:Novartis: Research Funding; Janssen/Johnson & Johnson: Research Funding; Ono: Research Funding; TEVA: Research Funding; Abbvie: Research Funding; Takeda: Other: Employment of family member.