Identify Drug Combinations Research Articles

DDDR-05. SPATIALLY RESOLVED DRUG SENSITIVITY PROFILING IN GLIOBLASTOMA

Abstract BACKGROUND Glioblastoma is the most common malignant primary brain tumor in adults, characterized by poor overall survival and an urgent need for more effective treatment strategies. Macroscopic heterogeneity, as observed through MR imaging, divides the tumor into distinct regions: a tumor-cell dense contrast-enhancing tumor core and a non-contrast-enhancing infiltration zone that appears hyperintense on T2/FLAIR imaging. The infiltration zone contains both infiltrating tumor cells and non-malignant cells from the local brain microenvironment. Although genomic and transcriptomic differences between the tumor core and the infiltration zone have been reported, functional differences, such as differential drug sensitivities, remain largely unknown. METHODS To investigate this, we collected region-specific glioblastoma patient tissue samples from both the contrast-enhancing tumor core and the non-contrast-enhancing but 5-aminolevulinic acid (5-ALA) fluorescent infiltration zone during surgery. Using pharmacoscopy, a microscopy-based single-cell drug screening platform, we assessed drug responses across regions and patients. RESULTS To determine whether drug responses within a single tumor region were more similar compared to responses across different regions we collected multiple tissue samples from each tumor region in seven patients. We identified region-specific drug responses for temozolomide and lomustine, among other drugs, in some of the patients. However, drug sensitivities were not consistent within the same region across different patients. Thus, across patients, the region-specific drug responses were obscured by patient heterogeneity. To address intra- and inter-tumor drug response heterogeneity, we developed a complementarity score based on region-specific drug response data of 60 drugs across 23 paired samples. The complementarity score identified promising drug combinations that demonstrated strong anti-tumor activity across tumor regions and patients. CONCLUSIONS Monotherapy approaches have largely failed in glioblastoma, despite extensive molecular profiling efforts aimed at identifying tumor-specific vulnerabilities. Identifying drug combinations that synergistically target the locoregional heterogeneity of glioblastoma may open up new avenues of personalized therapy.

A Phase Ib Study of Selinexor in Combination with Pembrolizumab in Patients with Metastatic Melanoma.

Immune checkpoint inhibitors (ICIs) have substantially advanced the treatment of patients with malignant melanoma. However, improving therapeutic efficacy requires identifying drug combinations that elicit durable responses without inducing intolerable toxicity. Within that context, selinexor emerges as a possible combination option that has been shown in preclinical studies to enhance the efficacy of ICI therapy. Methods: In this phase 1b study, we investigated selinexor in combination with pembrolizumab in 25 patients with advanced non-uveal melanoma. Patients received selinexor at a dosage of 60 mg taken orally twice weekly, and pembrolizumab intravenously at a dosage of 200 mg every 3 weeks. Results: Despite the high incidence of adverse events (96%), most treatment-related toxicities were manageable with supportive care and dose reductions. The most common adverse events of any grade were nausea (n = 20; 80%), decreased white blood cell count (n = 15; 60%), vomiting (n = 14; 56%), anemia (n = 12; 48%), fatigue (n = 12; 48%), and decreased platelet count (n = 12; 48%). The 10 patients with treatment-naïve evaluable disease had an objective response rate (ORR) of 70% (n = 7, including three patients with complete response), which was significantly higher than that of the 14 patients with prior anti-programmed cell death protein 1 (anti-PD-1) therapy, whose ORR was 7% (n = 1; p = 0.002). Stable disease was observed in two patients (20%) with treatment-naïve disease and seven patients (50%) with prior anti-PD-1 therapy. Conclusion: Selinexor combined with pembrolizumab showed promising antitumor activity in patients with treatment-naïve metastatic melanoma. The toxicity profile of the combination was consistent with that reported for individual agents, with no additional safety concerns.

232 (PB220): Identifying drug combinations targeting Claudin-1 for colorectal cancer

232 (PB220): Identifying drug combinations targeting Claudin-1 for colorectal cancer

Combination Therapy with Vitamin D and Metformin: A Potential Approach to Mitigate Testicular Dysfunction in Type 2 Diabetes Mellitus.

Type 2 diabetes mellitus (T2DM) is a multifactorial disease that cannot be linked to a single pathway, causing the observed heterogeneity among T2DM patients. Despite this level of heterogeneity, T2DM is majorly managed by metformin (MET) monotherapy. However, recent findings have associated long-term metformin intake with progressive oxidative pancreatic β cell damage as the disease progresses. Hence, a significant number of patients treated with MET need an alternate therapy. Hence, identifying drug combinations that can effectively alleviate different diabetes complications would serve as a more promising therapy that can translate into active use. Hence, this study was designed to explore the possible synergistic effect of vitamin D and metformin on T2DM-induced testicular dysfunction. Thirty healthy male Wistar rats (weight: 120-150 g and age: 10 ± 2 weeks) were randomly divided into control, diabetes untreated (HFD+STZ), diabetes + vitamin D (1000 IU/kg), diabetes + metformin (180 mg/kg), and diabetes + vitamin D + metformin. All treatments lasted for 28 days and animals were sacrificed using IP injection of ketamine and xylaxine (40 and 4 mg/kg respectively). Vitamin D improved the ameliorative effect of metformin on T2DM-induced hyperglycemia and lipid dysmetabolism, accompanied by a significant decrease in testicular lactate dehydrogenase and lactate. Also, vitamin D + metformin significantly increased serum luteinizing hormone, follicle-stimulating hormone, testosterone, and testicular 5α reductase activities. Furthermore, vitamin D improved the anti-inflammatory and antioxidant effects of metformin by significantly decreasing T2DM-induced increase in testicular interleukin 1beta, interleukin 6, TNF-α, nitric oxide, and NF-κB and increasing T2DM-induced decrease in interleukin 10, glutathione, superoxide dismutase, catalase, GPx, and Nrf2. Vitamin D enhanced the ameliorative effect of metformin on T2DM-induced testicular dysfunction.

Acquired NF2 mutation confers resistance to TRK inhibition in an ex vivo LMNA::NTRK1-rearranged soft-tissue sarcoma cell model.

Genomic rearrangements of the neurotrophic receptor tyrosine kinase genes (NTRK1, NTRK2, and NTRK3) are the most common mechanism of oncogenic activation for this family of receptors, resulting in sustained cancer cell proliferation. Several targeted therapies have been approved for tumours harbouring NTRK fusions and a new generation of TRK inhibitors has already been developed due to acquired resistance. We established a patient-derived LMNA::NTRK1-rearranged soft-tissue sarcoma cell model ex vivo with an acquired resistance to targeted TRK inhibition. Molecular profiling of the resistant clones revealed an acquired NF2 loss of function mutation that was absent in the parental cell model. Parental cells showed continuous sensitivity to TRK-targeted treatment, whereas the resistant clones were insensitive. Furthermore, resistant clones showed upregulation of the MAPK and mTOR/AKT pathways in the gene expression based on RNA sequencing data and increased sensitivity to MEK and mTOR inhibitor therapy. Drug synergy was seen using trametinib and rapamycin in combination with entrectinib. Medium-throughput drug screening further identified small compounds as potential drug candidates to overcome resistance as monotherapy or in combination with entrectinib. In summary, we developed a comprehensive model of drug resistance in an LMNA::NTRK1-rearranged soft-tissue sarcoma and have broadened the understanding of acquired drug resistance to targeted TRK therapy. Furthermore, we identified drug combinations and small compounds to overcome acquired drug resistance and potentially guide patient care in a functional precision oncology setting. © 2024 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.

Abstract 1874: Common vulnerabilities of stem cells along the Barrett's-dysplasia-adenocarcinoma sequence

Abstract Barrett's esophagus (BE) is an early precursor of esophageal adenocarcinoma and therefore a relevant target of preemptive therapeutics. Using robust stem cell cloning technologies, we have cloned stem cells from patient-matched biopsies of BE, low-grade dysplasia (LGD), high-grade dysplasia (HGD), and esophageal adenocarcinoma (EAC) from multiple patients. Whole exome sequencing of these stem cells reveals the mutational maturation along the sequence of BE, LGD, HGD, and EAC at unprecedented resolution of both copy number and single nucleotide variation. In vitro differentiation and xenografting of these clones recapitulates their original histology and shows that only the EAC stem cells yield robust tumors in immunodeficient mice. Leveraging the intrinsic immortality of these stem cells, we have performed high-throughput screens for small molecules that selectively kill BE stem cells without harming patient-matched normal esophageal stem cells. These screening efforts evolved into synthetic lethal strategies that identified drug combinations that selectively eliminate, in vitro, BE stem cells from 20 of 23 cases. Remarkably, this same synthetic lethal drug combination also eliminated in vitro stem cells of LGD, HGD, and EAC that were not components of the original screens. We have used synthetic chemistry to generate a series of novel molecules based on the identified drug combination that significantly improved the in vitro efficacy to sub-nanomolar levels. These novel molecules directed to the stem cell component of BE, LGD, HGD, and EAC also show potent effects against tumors generated from EAC stem cells in immunodeficient mice. We anticipate that these synthetically lethal drug combinations will have potential utility against precursor lesions to preempt EAC as well as against EAC itself. Citation Format: Wa Xian, Frank McKeon, Christopher Crum, Jaffer Ajani, Peter Davies, Matthew Vincent, William Bachovchin, Hung-Sen Lai, Shuang Pan, Amber Su, Ashley Hoffman, Raul Caballero Montes, Melina Khorrami, Melika Khorrami, Yusuke Yamamoto. Common vulnerabilities of stem cells along the Barrett's-dysplasia-adenocarcinoma sequence [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 1874.

SNSynergy: Similarity network-based machine learning framework for synergy prediction towards new cell lines and new anticancer drug combinations

The computational method has been proven to be a promising means for pre-screening large-scale anticancer drug combinations to support precision oncology applications. Pioneering efforts have been made to develop machine learning technology for predicting drug synergy, but high computational cost for training models as well as great diversity and limited size in screening data escalate the difficulty of prediction. To address this challenge, we propose a simple machine learning framework, namely Similarity Network-based Synergy prediction (SNSynergy), for predicting synergistic effects towards new cell lines and new drug combinations by two locally weighted models CLSN and DCSN. This framework only requires a small amount of auxiliary data, like genomics information of cell lines and the molecular fingerprints or targets of drugs. Based on the assumption that similar cell lines and similar drug combinations have similar synergistic effects, CLSN and DCSN predict synergy scores through capturing individual synergy contributions of nearest cell line and drug combination neighbors, respectively. High correlations between predicted and measured synergy scores on two leading cancer cell line pharmacogenomic screening datasets (the O′Neil dataset and the NCI-ALMANAC dataset) demonstrate the effectiveness and robustness of SNSynergy. Many of the identified drug combinations are consistent with previous studies, or have been explored in clinical settings against the specific cancer type, showing that SNSynergy has the potential to supply cost-saving and effective high-throughput screening for prioritizing the most applicable cell lines and the most promising drug combinations.

CRISPR screening identifies BET and mTOR inhibitor synergy in cholangiocarcinoma through serine glycine one carbon.

Patients with cholangiocarcinoma have poor clinical outcomes due to late diagnoses, poor prognoses, and limited treatment strategies. To identify drug combinations for this disease, we have conducted a genome-wide CRISPR screen anchored on the bromodomain and extraterminal domain (BET) PROTAC degrader ARV825, from which we identified anticancer synergy when combined with genetic ablation of members of the mTOR pathway. This combination effect was validated using multiple pharmacological BET and mTOR inhibitors, accompanied by increased levels of apoptosis and cell cycle arrest. In a xenograft model, combined BET degradation and mTOR inhibition induced tumor regression. Mechanistically, the 2 inhibitor classes converged on H3K27ac-marked epigenetic suppression of the serine glycine one carbon (SGOC) metabolism pathway, including the key enzymes PHGDH and PSAT1. Knockdown of PSAT1 was sufficient to replicate synergy with single-agent inhibition of either BET or mTOR. Our results tie together epigenetic regulation, metabolism, and apoptosis induction as key therapeutic targets for further exploration in this underserved disease.

Natural product based anticancer drug combination discovery assisted by deep learning and network analysis

Drug combination therapies have shown effective performance in treating cancer through increased efficacy and circumvention of drug resistance through synergy. Two avenues can be used to discover drug combinations: a novel approach that utilizes natural products compared with the textbook approach of utilizing existing chemotherapy drug combinations. Many natural products achieve efficacy due to synergistic interactions between the active ingredients. Therefore, the pharmacophore relationships in herbal compounds which synergize can potentially be applied to chemotherapy drugs to drive combination discovery. Machine learning approaches have been developed to identify drug combinations, especially deep neural networks (DNN), which have achieved state-of-the-art performance in many drug discovery tasks. Here, a drug protein interaction (DPI) prediction DNN, DeepDPI, was developed to employ DPI drug representations and achieved state-of-the-art performance using the DrugBank database. Two DNNs were also developed to predict novel drug combinations: DeepNPD, which predicts combinations in natural products, and DeepCombo, which predicts synergy in chemotherapy drugs, using the HERB and DrugCombDB databases respectively. An ensemble architecture enhanced with a novel similarity based weight adjustment (SBWA) approach was used and both models accurately predicted drug combinations for both known and unknown drugs. Lastly, a screening was conducted using each model where DeepNPD predicted combinations where drugs had similar targets, while DeepCombo predicted combinations where one agent potentiated the other, with both models’ predicted combinations investigated through a network-based analysis and identifying as a synergistic combinations in literature. DeepNPD notably identified Thioguanine and Hydroxyurea and DeepCombo discovered Vinblastine and Dasatinib as hits for potential new anticancer drug combinations. DeepNPD illustrates how natural products are a novel path where new drug combinations can be discovered.

CRISPR-Cas System Is an Effective Tool for Identifying Drug Combinations That Provide Synergistic Therapeutic Potential in Cancers.

Despite numerous efforts, the therapeutic advancement for neuroblastoma and other cancer treatments is still ongoing due to multiple challenges, such as the increasing prevalence of cancers and therapy resistance development in tumors. To overcome such obstacles, drug combinations are one of the promising applications. However, identifying and implementing effective drug combinations are critical for achieving favorable treatment outcomes. Given the enormous possibilities of combinations, a rational approach is required to predict the impact of drug combinations. Thus, CRISPR-Cas-based and other approaches, such as high-throughput pharmacological and genetic screening approaches, have been used to identify possible drug combinations. In particular, the CRISPR-Cas system (Clustered Regularly Interspaced Short Palindromic Repeats) is a powerful tool that enables us to efficiently identify possible drug combinations that can improve treatment outcomes by reducing the total search space. In this review, we discuss the rational approaches to identifying, examining, and predicting drug combinations and their impact.

Dynamic BH3 profiling identifies pro-apoptotic drug combinations for the treatment of malignant pleural mesothelioma

Malignant pleural mesothelioma (MPM) has relatively ineffective first/second-line therapy for advanced disease and only 18% five-year survival for early disease. Drug-induced mitochondrial priming measured by dynamic BH3 profiling identifies efficacious drugs in multiple disease settings. We use high throughput dynamic BH3 profiling (HTDBP) to identify drug combinations that prime primary MPM cells derived from patient tumors, which also prime patient derived xenograft (PDX) models. A navitoclax (BCL-xL/BCL-2/BCL-w antagonist) and AZD8055 (mTORC1/2 inhibitor) combination demonstrates efficacy in vivo in an MPM PDX model, validating HTDBP as an approach to identify efficacious drug combinations. Mechanistic investigation reveals AZD8055 treatment decreases MCL-1 protein levels, increases BIM protein levels, and increases MPM mitochondrial dependence on BCL-xL, which is exploited by navitoclax. Navitoclax treatment increases dependency on MCL-1 and increases BIM protein levels. These findings demonstrate that HTDBP can be used as a functional precision medicine tool to rationally construct combination drug regimens in MPM and other cancers.

KGANSynergy: knowledge graph attention network for drug synergy prediction.

Combination therapy is widely used to treat complex diseases, particularly in patients who respond poorly to monotherapy. For example, compared with the use of a single drug, drug combinations can reduce drug resistance and improve the efficacy of cancer treatment. Thus, it is vital for researchers and society to help develop effective combination therapies through clinical trials. However, high-throughput synergistic drug combination screening remains challenging and expensive in the large combinational space, where an array of compounds are used. To solve this problem, various computational approaches have been proposed to effectively identify drug combinations by utilizing drug-related biomedical information. In this study, considering the implications of various types of neighbor information of drug entities, we propose a novel end-to-end Knowledge Graph Attention Network to predict drug synergy (KGANSynergy), which utilizes neighbor information of known drugs/cell lines effectively. KGANSynergy uses knowledge graph (KG) hierarchical propagation to find multi-source neighbor nodes for drugs and cell lines. The knowledge graph attention network is designed to distinguish the importance of neighbors in a KG through a multi-attention mechanism and then aggregate the entity's neighbor node information to enrich the entity. Finally, the learned drug and cell line embeddings can be utilized to predict the synergy of drug combinations. Experiments demonstrated that our method outperformed several other competing methods, indicating that our method is effective in identifying drug combinations.

Abstract LB182: Targeting arginine methylation for improved therapy in triple negative breast cancer

Abstract Since the discovery of DNA repair deficiency as risk factors for breast cancer, our knowledge of their function in maintaining human genomic integrity and a normal biological function has greatly advanced, ultimately culminating in development of PARP inhibitors that can specifically target patients who harbor these mutations. However, to date PARP inhibitors have demonstrated only modest clinical success as monotherapy in triple-negative breast cancer. In addition, conventional therapies are often coupled with severe toxicity. Therefore, there is a dire need to develop more effective combination therapies. Although there are many existing tools to predict possible actionable targets, it remains challenging to effectively and systematically identify drug combinations for better breast cancer therapeutics. This is because most tumors carry multiple genetic alterations that cannot be suppressed with mono-therapies; and diverse confounding factors such as intra-tumor and inter-tumor heterogeneity exist across patient populations. Our preliminary proteomics study has revealed a new protein mark called arginine methylation as a novel target for breast cancer. We found that inhibition of arginine methylation can sensitize cancer cells for PARP inhibition treatment. We further assessed and validated this actionable and synergistic drug combination in patient-derived xenograft models. Together, our novel target combinations discovered here may serve as important biomarkers to help develop more effective combination therapy. Furthermore, our results help uncover patient-specific signaling mechanisms, a critical step towards precision medicine in breast cancer therapy, which is a benefit and potential long-term outcome. Citation Format: Yang Li, Daniel J. McGrail, Sharad Awasthi, S. Stephen Yi, Mark T. Bedford, Nidhi Sahni. Targeting arginine methylation for improved therapy in triple negative breast cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 2 (Clinical Trials and Late-Breaking Research); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(8_Suppl):Abstract nr LB182.

Abstract LB169: Machine learning-based approach for glioblastoma drug repurposing on real-world patient data

Abstract Glioblastoma (GBM) is the most aggressive and deadly type of brain cancer. The poor survival rate of GBM is often attributed to the low therapeutic efficiency of current front-line therapy, treatment resistance, and high recurrence rate. Sizeable clinical data of cancer patients preserve a great potential for drug discovery and drug repurposing research, enabling comprehensive screening of treatment effects among frequently-prescribed medications. To efficiently characterize potent drug candidates and develop novel treatment strategies for GBM, we designed a machine-learning-based pipeline that predicts repurposable drug combinations from large-scale patient claims data. Our pipeline first identified pseudo-randomized drug-user and non-user cohorts from heterogeneous clinical records and balanced the confounding effects using the Inverse Probability of Treatment Weighting (IPTW) method. Next, our model computed the averaged treatment effect on the treated (ATT) values to determine the treatment effects of our candidates. Our methodology identified several promising drug combinations that have been found to be effective in inhibiting GBM in cell line experiments. This research was conducted by integrating computational and experimental approaches to identify drug combinations, providing a promising strategy for the development of new treatments for GBM. Citation Format: Ko-Hong Lin, Yejin Kim, Dung-Fang Lee, Xiaoqian Jiang. Machine learning-based approach for glioblastoma drug repurposing on real-world patient data [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 2 (Clinical Trials and Late-Breaking Research); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(8_Suppl):Abstract nr LB169.

Synthesize heterogeneous biological knowledge via representation learning for Alzheimer’s disease drug repurposing

Developing drugs for treating Alzheimer's disease has been extremely challenging and costly due to limited knowledge of underlying mechanisms and therapeutic targets. To address the challenge in AD drug development, we developed a multi-task deep learning pipeline that learns biological interactions and AD risk genes, then utilizes multi-level evidence on drug efficacy to identify repurposable drug candidates. Using the embedding derived from the model, we ranked drug candidates based on evidence from post-treatment transcriptomic patterns, efficacy in preclinical models, population-based treatment effects, and clinical trials. We mechanistically validated the top-ranked candidates in neuronal cells, identifying drug combinations with efficacy in reducing oxidative stress and safety in maintaining neuronal viability and morphology. Our neuronal response experiments confirmed several biologically efficacious drug combinations. This pipeline showed that harmonizing heterogeneous and complementary data/knowledge, including human interactome, transcriptome patterns, experimental efficacy, and real-world patient data shed light on the drug development of complex diseases.

Abstract A072: Strategies to improve KRAS G12D inhibitor (MRTX1133) therapy in pancreatic ductal adenocarcinoma

Abstract Background: More than 90% of pancreatic ductal adenocarcinomas (PDAC) harbor oncogenic KRAS, with KRAS G12D being the predominant mutation. Recently, a novel KRAS G12D inhibitor, MRTX-1133, has been developed, potentially revolutionizing PDAC treatment. However, clinical studies on KRAS G12C inhibitors suggest that there may be intrinsic resistance to KRAS inhibitors (KRASi) and acquired resistance can develop rapidly in patients. Moreover, our preliminary data suggest that acquired resistance mechanisms to chemotherapy can also confer partial resistance to KRASi. We hypothesize that effective killing of tumor cells with KRASi will require a rationally designed combinatorial treatment. Methods: We tested chemotherapy and targeted pathway inhibitors which can inhibit signaling upstream and downstream of KRAS to identify drugs that synergize with MRTX-1133. We treated resected human PDAC and mouse tumors from Kras-G12D; P53-R172H; PDX-Cre (KPC) prepared as organotypic slice cultures to test our identified drug combinations. We orthotopically inoculated KPC PDAC cells and treated mice with vehicle, single-agent drugs (MRTX1133 or Afatinib), or combination therapy. We established MRTX-1133-resistant (MRTXR) models in human SUIT2 cells, human organoids and mouse KPC cell lines to study the mechanisms of KRASi resistance. We performed RNA-seq and Reverse Phase Protein Array in these resistance models to identify pathways differentially activated during resistance development. Results: We found that irreversible ErbB inhibitors, Afatinib and Neratinib, had the greatest synergy with MRTX-1133 in parental and in MRTXR cells. We found that combination therapy with MRTX-1133 and Afatinib decreased the number of proliferating cells compared to the control or single-agent treatment in the human and mouse slice cultures. Combination therapy with MRTX1133 and Afatinib significantly reduced tumor growth in mice compared to the vehicle or single-agent controls. We also found that resistant cells demonstrated increased EGFR and HER2 phosphorylation compared to parental cells and that the MRTX-1133 and Afatinib drug combo synergistically decreased EGFR and HER2 phosphorylation. We identified and mapped the differentially expressed pathways and genes involved in KRASi resistance using RNA-seq and RPPA analyses. Conclusion: We identified a likely pathway of resistance to novel KRAS G12D inhibitors, and we demonstrate that this resistance can be effectively targeted using existing therapeutics targeting the ErbB pathway. Citation Format: Kevin Christian M. Gulay, Edgar Esparza, Xinlian Zhang, Evangeline S. Mose, Jonathan Weitz, Minya Pu, Karen Messer, Andrew M. Lowy, Herve Tiriac. Strategies to improve KRAS G12D inhibitor (MRTX1133) therapy in pancreatic ductal adenocarcinoma [abstract]. In: Proceedings of the AACR Special Conference on Pancreatic Cancer; 2022 Sep 13-16; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2022;82(22 Suppl):Abstract nr A072.

Histone Deacetylase Inhibition Restores Therapy Response in TP53mutated Acute Lymphoblastic Leukemia to Chemotherapy

Background: Although TP53 is the most frequently mutated gene in cancer, in Acute Lymphoblastic leukemia (ALL), mutations or deletions affecting TP53 are rare, with an incidence of only 3% at diagnosis. However, at relapse TP53 aberrations are found in about 12% of the patients and predict a very poor outcome. As a consequence, relapsed TP53 deleted ALL is now classified as 'very high risk, together with t(1;19) and t(17;19) ALL. Since p53-mediated apoptosis is an endpoint for many (cytotoxic) drugs, loss of p53 function induces therapy resistance not only to classical chemotherapeutics, but also newly introduced immunotherapies such as bi-specific T cell engagers such as Blinatumomab or CAR-T cell therapies. Therefore, there is an urgent clinical need for more effective strategies to treat TP53 aberrant relapsed ALL. Methods: Using CRISPR/Cas9, we introduced frameshift mutations in the TP53 gene to generate isogenic wildtype and knockout models in the TP53WT B-cell precursor ALL (BCP-ALL) cell lines Nalm6 and RCH-ACV. After confirming the presence of TP53 deletions, we treated these isogenic models with a panel of treatment protocol drugs, demonstrating that TP53 loss negatively affects response to therapy to most contemporary drugs used in the treatment of relapsed ALL. To identify drug combinations that may reverse TP53 mediated therapy resistance, we assessed the viability of these isogenic models in a drug screen containing 198 (pre)clinical drugs. The most interesting targets were subsequently combined with the previously tested treatment protocol drugs to determine potential synergies. Effects on cell viability were determined by amine exposure and MTT assay and confirmed by measuring PARP cleavage. Additionally, positive drug interactions were validated in vivo using immunocompromised mice engrafted with RCH-ACV p53KO cells expressing a luciferase reporter gene. Results: We observed that loss of p53 protects BCP-ALL cell lines Nalm6 and RCHACV from drug-induced apoptosis, leading to increased resistance to all drugs used currently in the treatment of relapsed ALL, except for Asparaginase. In a subsequent drug screen, we found that the histone deacetylase (HDAC) 1/2 inhibitor romidepsin shows strong single agent activity at single nanomolar concentrations. Moreover, romidepsin was found to strongly synergize with contemporary ALL treatment drugs in TP53KO and TP53WT cells (Figure 1A). This effect was strongest in TP53KO cells, effectively reversing therapy resistance induced by TP53 loss. Gene expression profiling suggest that romidepsin may lead to reactivation of TP53 target genes in response to cellular stress, even in the absence of functional p53. Finally, preliminary mouse experiments using TP53 deleted RCH-ACV cells showed that romidepsin improves the response to cytarabine in TP53 aberrant leukemia in vivo (Figure 1B) without signs of added toxicity. A mouse trial testing a potential synergy between romidepsin and cytarabine using TP53 deleted patient derived xenografts (PDX) is currently underway. Conclusions: Loss of p53 protects BCP-ALL cells from currently used (chemo)therapy strategies. Romidepsin can restore response to therapy in TP53 deleted ALL, presumably by reactivation of p53 target genes in response to cellular stress. Therefore, romidepsin may be used to improve therapy response in relapsed TP53 deleted ALL. Since romidepsin already has FDA approval for use in other hematological malignancies, our findings may be rapidly translated into clinical practice. Figure 1View largeDownload PPTFigure 1View largeDownload PPT Close modal

Synergistic combination therapy delivered via layer-by-layer nanoparticles induces solid tumor regression of ovarian cancer.

The majority of patients with high grade serous ovarian cancer (HGSOC) develop recurrent disease and chemotherapy resistance. To identify drug combinations that would be effective in treatment of chemotherapy resistant disease, we examined the efficacy of drug combinations that target the three antiapoptotic proteins most commonly expressed in HGSOC-BCL2, BCL-XL, and MCL1. Co-inhibition of BCL2 and BCL-XL (ABT-263) with inhibition of MCL1 (S63845) induces potent synergistic cytotoxicity in multiple HGSOC models. Since this drug combination is predicted to be toxic to patients due to the known clinical morbidities of each drug, we developed layer-by-layer nanoparticles (LbL NPs) that co-encapsulate these inhibitors in order to target HGSOC tumor cells and reduce systemic toxicities. We show that the LbL NPs can be designed to have high association with specific ovarian tumor cell types targeted in these studies, thus enabling a more selective uptake when delivered via intraperitoneal injection. Treatment with these LbL NPs displayed better potency than free drugs in vitro and resulted in near-complete elimination of solid tumor metastases of ovarian cancer xenografts. Thus, these results support the exploration of LbL NPs as a strategy to deliver potent drug combinations to recurrent HGSOC. While these findings are described for co-encapsulation of a BCL2/XL and a MCL1 inhibitor, the modular nature of LbL assembly provides flexibility in the range of therapies that can be incorporated, making LbL NPs an adaptable vehicle for delivery of additional combinations of pathway inhibitors and other oncology drugs.

Genome-wide CRISPR screens identify combinations of candidate latency reversing agents for targeting the latent HIV-1 reservoir.

Reversing HIV-1 latency promotes killing of infected cells and is essential for cure strategies; however, no single latency reversing agent (LRA) or LRA combination have been shown to reduce HIV-1 latent reservoir size in persons living with HIV-1 (PLWH). Here, we describe an approach to systematically identify LRA combinations to reactivate latent HIV-1 using genome-wide CRISPR screens. Screens on cells treated with suboptimal concentrations of an LRA can identify host genes whose knockout enhances viral gene expression. Therefore, inhibitors of these genes should synergize with the LRA. We tested this approach using AZD5582, an activator of the noncanonical nuclear factor κB (ncNF-κB) pathway, as an LRA and identified histone deacetylase 2 (HDAC2) and bromodomain-containing protein 2 (BRD2), part of the bromodomain and extra-terminal motif (BET) protein family targeted by BET inhibitors, as potential targets. Using CD4+ T cells from PLWH, we confirmed synergy between AZD5582 and several HDAC inhibitors and between AZD5582 and the BET inhibitor, JQ1. A reciprocal screen using suboptimal concentrations of an HDAC inhibitor as an LRA identified BRD2 and ncNF-κB regulators, especially BIRC2, as synergistic candidates for use in combination with HDAC inhibition. Moreover, we identified and validated additional synergistic drug candidates in latency cell line cells and primary lymphocytes isolated from PLWH. Specifically, the knockout of genes encoding CYLD or YPEL5 displayed synergy with existing LRAs in inducing HIV mRNAs. Our study provides insights into the roles of host factors in HIV-1 reactivation and validates a system for identifying drug combinations for HIV-1 latency reversal.

Traject3d allows label-free identification of distinct co-occurring phenotypes within 3D culture by live imaging

Single cell profiling by genetic, proteomic and imaging methods has expanded the ability to identify programmes regulating distinct cell states. The 3-dimensional (3D) culture of cells or tissue fragments provides a system to study how such states contribute to multicellular morphogenesis. Whether cells plated into 3D cultures give rise to a singular phenotype or whether multiple biologically distinct phenotypes arise in parallel is largely unknown due to a lack of tools to detect such heterogeneity. Here we develop Traject3d (Trajectory identification in 3D), a method for identifying heterogeneous states in 3D culture and how these give rise to distinct phenotypes over time, from label-free multi-day time-lapse imaging. We use this to characterise the temporal landscape of morphological states of cancer cell lines, varying in metastatic potential and drug resistance, and use this information to identify drug combinations that inhibit such heterogeneity. Traject3d is therefore an important companion to other single-cell technologies by facilitating real-time identification via live imaging of how distinct states can lead to alternate phenotypes that occur in parallel in 3D culture.