A functional personalised oncology approach against metastatic colorectal cancer in matched patient derived organoids

Globally, colorectal cancer (CRC) is the third most frequently occurring cancer. Progression on to an advanced metastatic malignancy (metCRC) is often indicative of poor prognosis, as the 5-year survival rates of patients decline rapidly. Despite the availability of many systemic therapies for the management of metCRC, the long-term efficacies of these regimens are often hindered by the emergence of treatment resistance due to intratumoral and intertumoral heterogeneity. Furthermore, not all systemic therapies have associated biomarkers that can accurately predict patient responses. Hence, a functional personalised oncology (FPO) approach can enable the identification of patient-specific combinatorial vulnerabilities and synergistic combinations as effective treatment strategies. To this end, we established a panel of CRC patient-derived organoids (PDOs) as clinically relevant biological systems, of which three pairs of matched metCRC PDOs were derived from the primary sites (ptCRC) and metastatic lesions (mCRC). Histological and genomic characterisation of these PDOs demonstrated the preservation of histopathological and genetic features found in the parental tumours. Subsequent application of the phenotypic-analytical drug combination interrogation platform, Quadratic Phenotypic Optimisation Platform, in these pairs of PDOs identified patient-specific drug sensitivity profiles to epigenetic-based combination therapies. Most notably, matched PDOs from one patient exhibited differential sensitivity patterns to the rationally designed drug combinations despite being genetically similar. These findings collectively highlight the limitations of current genomic-driven precision medicine in guiding treatment strategies for metCRC patients. Instead, it suggests that epigenomic profiling and application of FPO could complement the identification of novel combinatorial vulnerabilities to target synchronous ptCRC and mCRC.

Effects of empagliflozin on progression of chronic kidney disease: a prespecified secondary analysis from the empa-kidney trial

BACKGROUND

Sodium-glucose co-transporter-2 (SGLT2) inhibitors reduce progression of chronic kidney disease and the risk of cardiovascular morbidity and mortality in a wide range of patients. However, their effects on kidney disease progression in some patients with chronic kidney disease are unclear because few clinical kidney outcomes occurred among such patients in the completed trials. In particular, some guidelines stratify their level of recommendation about who should be treated with SGLT2 inhibitors based on diabetes status and albuminuria. We aimed to assess the effects of empagliflozin on progression of chronic kidney disease both overall and among specific types of participants in the EMPA-KIDNEY trial.

METHODS

EMPA-KIDNEY, a randomised, controlled, phase 3 trial, was conducted at 241 centres in eight countries (Canada, China, Germany, Italy, Japan, Malaysia, the UK, and the USA), and included individuals aged 18 years or older with an estimated glomerular filtration rate (eGFR) of 20 to less than 45 mL/min per 1·73 m2, or with an eGFR of 45 to less than 90 mL/min per 1·73 m2 with a urinary albumin-to-creatinine ratio (uACR) of 200 mg/g or higher. We explored the effects of 10 mg oral empagliflozin once daily versus placebo on the annualised rate of change in estimated glomerular filtration rate (eGFR slope), a tertiary outcome. We studied the acute slope (from randomisation to 2 months) and chronic slope (from 2 months onwards) separately, using shared parameter models to estimate the latter. Analyses were done in all randomly assigned participants by intention to treat. EMPA-KIDNEY is registered at ClinicalTrials.gov, NCT03594110.

FINDINGS

Between May 15, 2019, and April 16, 2021, 6609 participants were randomly assigned and then followed up for a median of 2·0 years (IQR 1·5-2·4). Prespecified subgroups of eGFR included 2282 (34·5%) participants with an eGFR of less than 30 mL/min per 1·73 m2, 2928 (44·3%) with an eGFR of 30 to less than 45 mL/min per 1·73 m2, and 1399 (21·2%) with an eGFR 45 mL/min per 1·73 m2 or higher. Prespecified subgroups of uACR included 1328 (20·1%) with a uACR of less than 30 mg/g, 1864 (28·2%) with a uACR of 30 to 300 mg/g, and 3417 (51·7%) with a uACR of more than 300 mg/g. Overall, allocation to empagliflozin caused an acute 2·12 mL/min per 1·73 m2 (95% CI 1·83-2·41) reduction in eGFR, equivalent to a 6% (5-6) dip in the first 2 months. After this, it halved the chronic slope from -2·75 to -1·37 mL/min per 1·73 m2 per year (relative difference 50%, 95% CI 42-58). The absolute and relative benefits of empagliflozin on the magnitude of the chronic slope varied significantly depending on diabetes status and baseline levels of eGFR and uACR. In particular, the absolute difference in chronic slopes was lower in patients with lower baseline uACR, but because this group progressed more slowly than those with higher uACR, this translated to a larger relative difference in chronic slopes in this group (86% [36-136] reduction in the chronic slope among those with baseline uACR <30 mg/g compared with a 29% [19-38] reduction for those with baseline uACR ≥2000 mg/g; ptrend<0·0001).

INTERPRETATION

Empagliflozin slowed the rate of progression of chronic kidney disease among all types of participant in the EMPA-KIDNEY trial, including those with little albuminuria. Albuminuria alone should not be used to determine whether to treat with an SGLT2 inhibitor.

FUNDING

Boehringer Ingelheim and Eli Lilly.

Impact of primary kidney disease on the effects of empagliflozin in patients with chronic kidney disease: secondary analyses of the EMPA-KIDNEY trial

BACKGROUND

The EMPA-KIDNEY trial showed that empagliflozin reduced the risk of the primary composite outcome of kidney disease progression or cardiovascular death in patients with chronic kidney disease mainly through slowing progression. We aimed to assess how effects of empagliflozin might differ by primary kidney disease across its broad population.

METHODS

EMPA-KIDNEY, a randomised, controlled, phase 3 trial, was conducted at 241 centres in eight countries (Canada, China, Germany, Italy, Japan, Malaysia, the UK, and the USA). Patients were eligible if their estimated glomerular filtration rate (eGFR) was 20 to less than 45 mL/min per 1·73 m2, or 45 to less than 90 mL/min per 1·73 m2 with a urinary albumin-to-creatinine ratio (uACR) of 200 mg/g or higher at screening. They were randomly assigned (1:1) to 10 mg oral empagliflozin once daily or matching placebo. Effects on kidney disease progression (defined as a sustained ≥40% eGFR decline from randomisation, end-stage kidney disease, a sustained eGFR below 10 mL/min per 1·73 m2, or death from kidney failure) were assessed using prespecified Cox models, and eGFR slope analyses used shared parameter models. Subgroup comparisons were performed by including relevant interaction terms in models. EMPA-KIDNEY is registered with ClinicalTrials.gov, NCT03594110.

FINDINGS

Between May 15, 2019, and April 16, 2021, 6609 participants were randomly assigned and followed up for a median of 2·0 years (IQR 1·5-2·4). Prespecified subgroupings by primary kidney disease included 2057 (31·1%) participants with diabetic kidney disease, 1669 (25·3%) with glomerular disease, 1445 (21·9%) with hypertensive or renovascular disease, and 1438 (21·8%) with other or unknown causes. Kidney disease progression occurred in 384 (11·6%) of 3304 patients in the empagliflozin group and 504 (15·2%) of 3305 patients in the placebo group (hazard ratio 0·71 [95% CI 0·62-0·81]), with no evidence that the relative effect size varied significantly by primary kidney disease (pheterogeneity=0·62). The between-group difference in chronic eGFR slopes (ie, from 2 months to final follow-up) was 1·37 mL/min per 1·73 m2 per year (95% CI 1·16-1·59), representing a 50% (42-58) reduction in the rate of chronic eGFR decline. This relative effect of empagliflozin on chronic eGFR slope was similar in analyses by different primary kidney diseases, including in explorations by type of glomerular disease and diabetes (p values for heterogeneity all >0·1).

INTERPRETATION

In a broad range of patients with chronic kidney disease at risk of progression, including a wide range of non-diabetic causes of chronic kidney disease, empagliflozin reduced risk of kidney disease progression. Relative effect sizes were broadly similar irrespective of the cause of primary kidney disease, suggesting that SGLT2 inhibitors should be part of a standard of care to minimise risk of kidney failure in chronic kidney disease.

FUNDING

Boehringer Ingelheim, Eli Lilly, and UK Medical Research Council.

Red Blood Cell-Derived Extracellular Vesicles Display Endogenous Antiviral Effects and Enhance the Efficacy of Antiviral Oligonucleotide Therapy

The COVID-19 pandemic has resulted in a large number of fatalities and, at present, lacks a readily available curative treatment for patients. Here, we demonstrate that unmodified red blood cell-derived extracellular vesicles (RBCEVs) can inhibit SARS-CoV-2 infection in a phosphatidylserine (PS) dependent manner. Using T cell immunoglobulin mucin domain-1 (TIM-1) as an example, we demonstrate that PS receptors on cells can significantly increase the adsorption and infection of authentic and pseudotyped SARS-CoV-2 viruses. RBCEVs competitively inhibit this interaction and block TIM-1-mediated viral entry into cells. We further extend the therapeutic efficacy of this antiviral treatment by loading antisense oligonucleotides (ASOs) designed to target conserved regions of key SARS-CoV-2 genes into RBCEVs. We establish that ASO-loaded RBCEVs are efficiently taken up by cells in vitro and in vivo to suppress SARS-CoV-2 replication. Our findings indicate that this RBCEV-based SARS-CoV-2 therapeutic displays promise as a potential treatment capable of inhibiting SARS-CoV-2 entry and replication.

Histone-lysine N-methyltransferase EHMT2 (G9a) inhibition mitigates tumorigenicity in Myc-driven liver cancer

Hepatocellular carcinoma (HCC) is the third deadliest and sixth most common cancer in the world. Histone-lysine N-methyltransferase EHMT2 (also known as G9a) is a histone methyltransferase frequently overexpressed in many cancer types, including HCC. We showed that Myc-driven liver tumours have a unique H3K9 methylation pattern with corresponding G9a overexpression. This phenomenon of increased G9a was further observed in our c-Myc-positive HCC patient-derived xenografts. More importantly, we showed that HCC patients with higher c-Myc and G9a expression levels portend a poorer survival with lower median survival months. We demonstrated that c-Myc interacts with G9a in HCC and cooperates to regulate c-Myc-dependent gene repression. In addition, G9a stabilises c-Myc to promote cancer development, contributing to the growth and invasive capacity in HCC. Furthermore, combination therapy between G9a and synthetic-lethal target of c-Myc, CDK9, demonstrates strong efficacy in patient-derived avatars of Myc-driven HCC. Our work suggests that targeting G9a could prove to be a potential therapeutic avenue for Myc-driven liver cancer. This will increase our understanding of the underlying epigenetic mechanisms of aggressive tumour initiation and lead to improved therapeutic and diagnostic options for Myc-driven hepatic tumours.

IDentif.AI-Omicron: Harnessing an AI-Derived and Disease-Agnostic Platform to Pinpoint Combinatorial Therapies for Clinically Actionable Anti-SARS-CoV-2 Intervention

Nanomedicine-based and unmodified drug interventions to address COVID-19 have evolved over the course of the pandemic as more information is gleaned and virus variants continue to emerge. For example, some early therapies (e.g., antibodies) have experienced markedly decreased efficacy. Due to a growing concern of future drug resistant variants, current drug development strategies are seeking to find effective drug combinations. In this study, we used IDentif.AI, an artificial intelligence-derived platform, to investigate the drug-drug and drug-dose interaction space of six promising experimental or currently deployed therapies at various concentrations: EIDD-1931, YH-53, nirmatrelvir, AT-511, favipiravir, and auranofin. The drugs were tested in vitro against a live B.1.1.529 (Omicron) virus first in monotherapy and then in 50 strategic combinations designed to interrogate the interaction space of 729 possible combinations. Key findings and interactions were then further explored and validated in an additional experimental round using an expanded concentration range. Overall, we found that few of the tested drugs showed moderate efficacy as monotherapies in the actionable concentration range, but combinatorial drug testing revealed significant dose-dependent drug-drug interactions, specifically between EIDD-1931 and YH-53, as well as nirmatrelvir and YH-53. Checkerboard validation analysis confirmed these synergistic interactions and also identified an interaction between EIDD-1931 and favipiravir in an expanded range. Based on the platform nature of IDentif.AI, these findings may support further explorations of the dose-dependent drug interactions between different drug classes in further pre-clinical and clinical trials as possible combinatorial therapies consisting of unmodified and nanomedicine-enabled drugs, to combat current and future COVID-19 strains and other emerging pathogens.

Splice-switch oligonucleotide-based combinatorial platform prioritizes synthetic lethal targets CHK1 and BRD4 against MYC-driven hepatocellular carcinoma

Deregulation of MYC is among the most frequent oncogenic drivers in hepatocellular carcinoma (HCC). Unfortunately, the clinical success of MYC-targeted therapies is limited. Synthetic lethality offers an alternative therapeutic strategy by leveraging on vulnerabilities in tumors with MYC deregulation. While several synthetic lethal targets of MYC have been identified in HCC, the need to prioritize targets with the greatest therapeutic potential has been unmet. Here, we demonstrate that by pairing splice-switch oligonucleotide (SSO) technologies with our phenotypic-analytical hybrid multidrug interrogation platform, quadratic phenotypic optimization platform (QPOP), we can disrupt the functional expression of these targets in specific combinatorial tests to rapidly determine target-target interactions and rank synthetic lethality targets. Our SSO-QPOP analyses revealed that simultaneous attenuation of CHK1 and BRD4 function is an effective combination specific in MYC-deregulated HCC, successfully suppressing HCC progression in vitro. Pharmacological inhibitors of CHK1 and BRD4 further demonstrated its translational value by exhibiting synergistic interactions in patient-derived xenograft organoid models of HCC harboring high levels of MYC deregulation. Collectively, our work demonstrates the capacity of SSO-QPOP as a target prioritization tool in the drug development pipeline, as well as the therapeutic potential of CHK1 and BRD4 in MYC-driven HCC.

Rational drug combination design in patient-derived avatars reveals effective inhibition of hepatocellular carcinoma with proteasome and CDK inhibitors

Background

Hepatocellular carcinoma (HCC) remains difficult to treat due to limited effective treatment options. While the proteasome inhibitor bortezomib has shown promising preclinical activity in HCC, clinical trials of bortezomib showed no advantage over the standard-of-care treatment sorafenib, highlighting the need for more clinically relevant therapeutic strategies. Here, we propose that rational drug combination design and validation in patient-derived HCC avatar models such as patient-derived xenografts (PDXs) and organoids can improve proteasome inhibitor-based therapeutic efficacy and clinical potential.

Methods

HCC PDXs and the corresponding PDX-derived organoids (PDXOs) were generated from primary patient samples for drug screening and efficacy studies. To identify effective proteasome inhibitor-based drug combinations, we applied a hybrid experimental-computational approach, Quadratic Phenotypic Optimization Platform (QPOP) on a pool of nine drugs comprising proteasome inhibitors, kinase inhibitors and chemotherapy agents. QPOP utilizes small experimental drug response datasets to accurately identify globally optimal drug combinations.

Results

Preliminary drug screening highlighted the increased susceptibility of HCC PDXOs towards proteasome inhibitors. Through QPOP, the combination of second-generation proteasome inhibitor ixazomib (Ixa) and CDK inhibitor dinaciclib (Dina) was identified to be effective against HCC. In vitro and in vivo studies demonstrated the synergistic pro-apoptotic and anti-proliferative activity of Ixa + Dina against HCC PDXs and PDXOs. Furthermore, Ixa + Dina outperformed sorafenib in mitigating tumor formation in mice. Mechanistically, increased activation of JNK signaling mediates the combined anti-tumor effects of Ixa + Dina in HCC tumor cells.

Conclusions

Rational drug combination design in patient-derived avatars highlights the therapeutic potential of proteasome and CDK inhibitors and represents a feasible approach towards developing more clinically relevant treatment strategies for HCC.

Enhanced penetrative siRNA delivery by a nanodiamond drug delivery platform against hepatocellular carcinoma 3D models

Small interfering RNA (siRNA) can cause specific gene silencing and is considered promising for treating a variety of cancers, including hepatocellular carcinoma (HCC). However, siRNA has many undesirable physicochemical properties that limit its application. Additionally, conventional methods for delivering siRNA are limited in their ability to penetrate solid tumors. In this study, nanodiamonds (NDs) were evaluated as a nanoparticle drug delivery platform for improved siRNA delivery into tumor cells. Our results demonstrated that ND-siRNA complexes could effectively be formed through electrostatic interactions. The ND-siRNA complexes allowed for efficient cellular uptake and endosomal escape that protects siRNA from degradation. Moreover, ND delivery of siRNA was more effective at penetrating tumor spheroids compared to liposomal formulations. This enhanced penetration capacity makes NDs ideal vehicles to deliver siRNA against solid tumor masses as efficient gene knockdown and decreased tumor cell proliferation were observed in tumor spheroids. Evaluation of ND-siRNA complexes within the context of a 3D cancer disease model demonstrates the potential of NDs as a promising gene delivery platform against solid tumors, such as HCC.

Targeting RNA Editing of Antizyme Inhibitor 1: a Potential Oligonucleotide-Based Antisense Therapy for Cancer

Dysregulated adenosine-to-inosine (A-to-I) RNA editing is implicated in various cancers. However, no available RNA editing inhibitors have so far been developed to inhibit cancer-associated RNA editing events. Here, we decipher the RNA secondary structure of antizyme inhibitor 1 (AZIN1), one of the best-studied A-to-I editing targets in cancer, by locating its editing site complementary sequence (ECS) at the 3′ end of exon 12. Chemically modified antisense oligonucleotides (ASOs) that target the editing region of AZIN1 caused a substantial exon 11 skipping, whereas ECS-targeting ASOs effectively abolished AZIN1 editing without affecting splicing and translation. We demonstrate that complete 2′-O-methyl (2′-O-Me) sugar ring modification in combination with partial phosphorothioate (PS) backbone modification may be an optimal chemistry for editing inhibition. ASO3.2, which targets the ECS, specifically inhibits cancer cell viability in vitro and tumor incidence and growth in xenograft models. Our results demonstrate that this AZIN1-targeting, ASO-based therapeutics may be applicable to a wide range of tumor types.

IDentif.AI: Rapidly optimizing combination therapy design against severe Acute Respiratory Syndrome Coronavirus 2 (SARS-Cov-2) with digital drug development

The emergence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) led to multiple drug repurposing clinical trials that have yielded largely uncertain outcomes. To overcome this challenge, we used IDentif.AI, a platform that pairs experimental validation with artificial intelligence (AI) and digital drug development to rapidly pinpoint unpredictable drug interactions and optimize infectious disease combination therapy design with clinically relevant dosages. IDentif.AI was paired with a 12-drug candidate therapy set representing over 530,000 drug combinations against the SARS-CoV-2 live virus collected from a patient sample. IDentif.AI pinpointed the optimal combination as remdesivir, ritonavir, and lopinavir, which was experimentally validated to mediate a 6.5-fold enhanced efficacy over remdesivir alone. Additionally, it showed hydroxychloroquine and azithromycin to be relatively ineffective. The study was completed within 2 weeks, with a three-order of magnitude reduction in the number of tests needed. IDentif.AI independently mirrored clinical trial outcomes to date without any data from these trials. The robustness of this digital drug development approach paired with in vitro experimentation and AI-driven optimization suggests that IDentif.AI may be clinically actionable toward current and future outbreaks.

Abstract 3929: Patient-derived pancreatic cancer organoids as in-vitro cancer models for personalizing therapy

Pancreatic ductal adenocarcinoma (PDAC) is the most aggressive solid tumor. Unfortunately, patients with this disease have not benefited from the advances in precision medicine seen in other malignancies. The lack of actionable mutations as well as predictive biomarkers has hampered progress in this area. Combination therapy remains the cornerstone of systemic treatment, with FOLFIRINOX being the most effective therapeutic regimen in PDAC. However, it is clear that only subset of patients are responsive, and the toxicities of this 4-drug combination are intolerable to many patients particularly in the adjuvant setting. There is a clear need for more personalized rational drug combinations that are optimized to maximize efficacy while minimizing toxicity.

We have established a protocol for the generation of PDAC organoids from endoscopic ultrasound biopsies for personalizing systemic therapy based on the principle of phenotype driven oncology. Organoids generated are analyzed for KRAS and p53 mutations to confirm their tumor origin. By applying a phenotypic optimization platform (QPOP) on these organoids, we are able to identify optimized drug combinations from a pool of both FDA-approved and preclinical drugs. As expected, FOLFIRINOX did not always rank high by treatment efficacy suggesting that alternative treatment may be more beneficial for these patients. In addition, we identified that the efficacy of FOLFIRINOX was driven by two-drug synergistic combinations which varied between patients. This can potentially advise modifications to this regimen when toxicity arises. From a discovery perspective, our platform has the capability of deriving novel drug combinations. The combination of gemcitabine and Abraxane with a WEE1-inhibitor was found to be synergistic in selected PDAC patients. Furthermore, patients with similar drug response profiles can be studied together to identify predictive markers of sensitivity.

To demonstrate that this platform can indeed be applied to prospectively recommend treatment strategies for patients with progressive disease on first-line chemotherapy, we are establishing a clinical trial to personalize second-line therapy, with the endpoint of overall response rate. Overall, by combining patient-derived organoids with rational optimized drug combinations, we aim to personalize therapy for patients with PDAC to improve overall survival.

Citation Format: Claire Alexandra Zhen Chew, Li Min Tay, Lissa Hooi, Edward Kai-Hua Chow, Glenn K. Bonney, Shing Leng Chan. Patient-derived pancreatic cancer organoids as in-vitro cancer models for personalizing therapy [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 3929.

Abstract 1788: A high-throughput chemical genetic screen reveals SALL4-induced metabolic vulnerabilities in cancer

Transcription factors are important drivers of cancer but the development of therapeutics against these factors has had limited success. We developed a stringent high-throughput chemical genetic screening platform to identify compounds that target oncogenic transcription factor SALL4 dependency in liver cancer. The platform comprises SALL4 low- and high-expressing endogenous and engineered isogenic liver cancer cell lines. Unexpectedly, from screening 21,575 natural product extracts, the top hits were four oxidative phosphorylation inhibitors that selectively reduced SALL4-dependent cell viability. The ATP synthase inhibitor oligomycin suppressed SALL4-expressing cancer in lung and liver cancer cell culture models, and in patient-derived xenograft models of liver cancer. Oligomycin also synergized with sorafenib, the standard-of-care targeted therapy in liver cancer, to effectively suppress SALL4-driven tumorigenesis in vivo. When aberrantly expressed in cancer, SALL4 binds ~50% of mitochondrial genes, including many oxidative phosphorylation genes, to predominantly upregulate their expression. SALL4 upregulation also alters the levels of oxidative phosphorylation-related metabolites and functionally increases oxidative phosphorylation activity. Application of our endogenous/isogenic transcription factor-screening platform revealed a therapeutically actionable oxidative phosphorylation vulnerability in SALL4-expressing cancers.

Citation Format: Justin L. Tan, Feng Li, Joanna Z. Yeo, Kol Jia Yong, Mahmoud A. Bassal, Guo Hao Ng, May Yin Lee, Chung Yan Leong, Hong Kee Tan, Chan-Shuo Wu, Bee Hui Liu, Hon Man Chan, Zi Hui Tan, Yun Shen Chan, Siyu Wang, Zhi Han Lim, Tan Boon Toh, Lissa Hooi, Kia Ngee Low, Siming Ma, Nikki R. Kong, Alicia J. Stein, Yue Wu, Matan T. Thangavelu, Atsushi Suzuki, Giridharan Periyasamy, John M. Asara, Yock Young Dan, Glenn K. Bonney, Edward K. Chow, Guo-Dong Lu, Huck Hui Ng, Yoganathan Kanagasundaram, Siew Bee Ng, Wai Leong Tam, Li Chai, Daniel G. Tenen. A high-throughput chemical genetic screen reveals SALL4-induced metabolic vulnerabilities in cancer [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 1788.

Targeted Inhibition of Purine Metabolism Is Effective in Suppressing Hepatocellular Carcinoma Progression

Tumor‐specific metabolic rewiring, acquired to confer a proliferative and survival advantage over nontransformed cells, represents a renewed focus in cancer therapy development. Hepatocellular carcinoma (HCC), a malignancy that has hitherto been resistant to compounds targeting oncogenic signaling pathways, represents a candidate cancer to investigate the efficacy of selectively antagonizing such adaptive metabolic reprogramming. To this end, we sought to characterize metabolic changes in HCC necessary for tumorigenesis. We analyzed gene expression profiles in three independent large‐scale patient cohorts who had HCC. We identified a commonly deregulated purine metabolic signature in tumors with the extent of purine biosynthetic enzyme up‐regulation correlated with tumor grade and a predictor of clinical outcome. The functional significance of enhanced purine metabolism as a hallmark in human HCC was then validated using a combination of HCC cell lines, patient‐derived xenograft (PDX) organoids, and mouse models. Targeted ablation of purine biosynthesis by knockdown of the rate‐limiting enzyme inosine‐5′‐monophosphate dehydrogenase (IMPDH) or using the drug mycophenolate mofetil (MMF) reduced HCC proliferation in vitro and decreased the tumor burden in vivo. In comparing the sensitivities of PDX tumor organoids to MMF therapy, we found that HCC tumors defined by high levels of IMPDH and guanosine nucleosides were most susceptible to treatment. Mechanistically, a phosphoinositide 3‐kinase (PI3K)–E2F transcription factor 1 (E2F1) axis coordinated purine biosynthetic enzyme expression, deregulation of which altered the activity of mitogen‐activated protein kinase/RAS signaling. Simultaneously abolishing PI3K signaling and IMPDH activity with clinically approved inhibitors resulted in greatest efficacy in reducing tumor growth in a PDX mouse model. Conclusion: Enhanced purine metabolic activity regulated by PI3K pathway‐dependent activation of E2F1 promotes HCC carcinogenesis, suggesting the potential for targeting purine metabolic reprogramming as a precision therapeutic strategy for patients with HCC.

A Flexi-PEGDA Upconversion Implant for Wireless Brain Photodynamic Therapy

Near-infrared (NIR) activatable upconversion nanoparticles (UCNPs) enable wireless-based phototherapies by converting deep-tissue-penetrating NIR to visible light. UCNPs are therefore ideal as wireless transducers for photodynamic therapy (PDT) of deep-sited tumors. However, the retention of unsequestered UCNPs in tissue with minimal options for removal limits their clinical translation. To address this shortcoming, biocompatible UCNPs implants are developed to deliver upconversion photonic properties in a flexible, optical guide design. To enhance its translatability, the UCNPs implant is constructed with an FDA-approved poly(ethylene glycol) diacrylate (PEGDA) core clad with fluorinated ethylene propylene (FEP). The emission spectrum of the UCNPs implant can be tuned to overlap with the absorption spectra of the clinically relevant photosensitizer, 5-aminolevulinic acid (5-ALA). The UCNPs implant can wirelessly transmit upconverted visible light till 8 cm in length and in a bendable manner even when implanted underneath the skin or scalp. With this system, it is demonstrated that NIR-based chronic PDT is achievable in an untethered and noninvasive manner in a mouse xenograft glioblastoma multiforme (GBM) model. It is postulated that such encapsulated UCNPs implants represent a translational shift for wireless deep-tissue phototherapy by enabling sequestration of UCNPs without compromising wireless deep-tissue light delivery.

Frequent upregulation of G9a promotes RelB-dependent proliferation and survival in multiple myeloma

Background

Multiple myeloma is an incurable hematological malignancy characterized by a heterogeneous genetic and epigenetic landscape. Although a number of genetic aberrations associated with myeloma pathogenesis, progression and prognosis have been well characterized, the role of many epigenetic aberrations in multiple myeloma remain elusive. G9a, a histone methyltransferase, has been found to promote disease progression, proliferation and metastasis via diverse mechanisms in several cancers. A role for G9a in multiple myeloma, however, has not been previously explored.

Methods

Expression levels of G9a/EHMT2 of multiple myeloma cell lines and control cells Peripheral Blood Mononuclear Cells (PBMCs) were analyzed. Correlation of G9a expression and overall survival of multiple myeloma patients were analyzed using patient sample database. To further study the function of G9a in multiple myeloma, G9a depleted multiple myeloma cells were built by lentiviral transduction, of which proliferation, colony formation assays as well as tumorigenesis were measured. RNA-seq of G9a depleted multiple myeloma with controls were performed to explore the downstream mechanism of G9a regulation in multiple myeloma.

Results

G9a is upregulated in a range of multiple myeloma cell lines. G9a expression portends poorer survival outcomes in a cohort of multiple myeloma patients. Depletion of G9a inhibited proliferation and tumorigenesis in multiple myeloma. RelB was significantly downregulated by G9a depletion or small molecule inhibition of G9a/GLP inhibitor UNC0642, inducing transcription of proapoptotic genes Bim and BMF. Rescuing RelB eliminated the inhibition in proliferation and tumorigenesis by G9a depletion.

Conclusions

In this study, we demonstrated that G9a is upregulated in most multiple myeloma cell lines. Furthermore, G9a loss-of-function analysis provided evidence that G9a contributes to multiple myeloma cell survival and proliferation. This study found that G9a interacts with NF-κB pathway as a key regulator of RelB in multiple myeloma and regulates RelB-dependent multiple myeloma survival. G9a therefore is a promising therapeutic target for multiple myeloma.

Project IDentif.AI: Harnessing Artificial Intelligence to Rapidly Optimize Combination Therapy Development for Infectious Disease Intervention

In 2019/2020, the emergence of coronavirus disease 2019 (COVID‐19) resulted in rapid increases in infection rates as well as patient mortality. Treatment options addressing COVID‐19 included drug repurposing, investigational therapies such as remdesivir, and vaccine development. Combination therapy based on drug repurposing is among the most widely pursued of these efforts. Multi‐drug regimens are traditionally designed by selecting drugs based on their mechanism of action. This is followed by dose‐finding to achieve drug synergy. This approach is widely‐used for drug development and repurposing. Realizing synergistic combinations, however, is a substantially different outcome compared to globally optimizing combination therapy, which realizes the best possible treatment outcome by a set of candidate therapies and doses toward a disease indication. To address this challenge, the results of Project IDentif.AI (Identifying Infectious Disease Combination Therapy with Artificial Intelligence) are reported. An AI‐based platform is used to interrogate a massive 12 drug/dose parameter space, rapidly identifying actionable combination therapies that optimally inhibit A549 lung cell infection by vesicular stomatitis virus within three days of project start. Importantly, a sevenfold difference in efficacy is observed between the top‐ranked combination being optimally and sub‐optimally dosed, demonstrating the critical importance of ideal drug and dose identification. This platform is disease indication and disease mechanism‐agnostic, and potentially applicable to the systematic N‐of‐1 and population‐wide design of highly efficacious and tolerable clinical regimens. This work also discusses key factors ranging from healthcare economics to global health policy that may serve to drive the broader deployment of this platform to address COVID‐19 and future pandemics.

Nanodiamond-Mediated Delivery of a G9a Inhibitor for Hepatocellular Carcinoma Therapy

Hepatocellular carcinoma (HCC) is the most common primary liver cancer with high mortality but limited therapeutic options. Epigenetic regulations including DNA methylation and histone modification control gene expressions and play a crucial role during tumorigenesis. G9a, also known as EHMT2 (euchromatic histone-lysine N-methyltransferase 2), is a histone methyltransferase predominantly responsible for dimethylation of histone H3 lysine 9 (H3K9). G9a has been shown to play a key role in promoting tumor progression. Recent studies have identified that G9a is a critical mediator of HCC pathogenesis. UNC0646 is a G9a inhibitor that has shown potent in vitro efficacy. However, due to its water insolubility, the in vivo efficacy of UNC0646 is not satisfactory. In this study, nanodiamonds (NDs) were utilized as a drug delivery platform to improve in vivo delivery of this small-molecule inhibitor. Our results showed that ND-UNC0646 complexes could be rapidly synthesized by physical adsorption, meanwhile possessing favorable drug delivery properties and was able to improve the dispersibility of UNC0646 in water, therefore making it amenable for intravenous administration. The release profile of UNC0646 from ND-UNC0646 was demonstrated to be pH-responsive. Moreover, ND-UNC0646 maintained the biological functionality of UNC0646, with higher efficacy in reducing H3K9 methylation as well as enhanced invasion suppressive effects. Most importantly, increased in vivo efficacy was demonstrated using an orthotopic HCC mouse model, which paves the way of translating this small-molecule inhibitor toward HCC treatment. Our work demonstrates the potential of NDs in the clinical application for HCC treatment.

New High-Throughput Screening Identifies Compounds That Reduce Viability Specifically in Liver Cancer Cells That Express High Levels of SALL4 by Inhibiting Oxidative Phosphorylation

BACKGROUND & AIMS

Some oncogenes encode transcription factors, but few drugs have been successfully developed to block their activity specifically in cancer cells. The transcription factor SALL4 is aberrantly expressed in solid tumor and leukemia cells. We developed a screen to identify compounds that reduce the viability of liver cancer cells that express high levels of SALL4, and we investigated their mechanisms.

METHODS

We developed a stringent high-throughput screening platform comprising unmodified SNU-387 and SNU-398 liver cancer cell lines and SNU-387 cell lines engineered to express low and high levels of SALL4. We screened 1597 pharmacologically active small molecules and 21,575 natural product extracts from plant, bacteria, and fungal sources for those that selectively reduce the viability of cells with high levels of SALL4 (SALL4hi cells). We compared gene expression patterns of SALL4hi cells vs SALL4-knockdown cells using RNA sequencing and real-time polymerase chain reaction analyses. Xenograft tumors were grown in NOD/SCID gamma mice from SALL4hi SNU-398 or HCC26.1 cells or from SALL4lo patient-derived xenograft (PDX) cells; mice were given injections of identified compounds or sorafenib, and the effects on tumor growth were measured.

RESULTS

Our screening identified 1 small molecule (PI-103) and 4 natural compound analogues (oligomycin, efrapeptin, antimycin, and leucinostatin) that selectively reduced viability of SALL4hi cells. We performed validation studies, and 4 of these compounds were found to inhibit oxidative phosphorylation. The adenosine triphosphate (ATP) synthase inhibitor oligomycin reduced the viability of SALL4hi hepatocellular carcinoma and non-small-cell lung cancer cell lines with minimal effects on SALL4lo cells. Oligomycin also reduced the growth of xenograft tumors grown from SALL4hi SNU-398 or HCC26.1 cells to a greater extent than sorafenib, but oligomycin had little effect on tumors grown from SALL4lo PDX cells. Oligomycin was not toxic to mice. Analyses of chromatin immunoprecipitation sequencing data showed that SALL4 binds approximately 50% of mitochondrial genes, including many oxidative phosphorylation genes, to activate their transcription. In comparing SALL4hi and SALL4-knockdown cells, we found SALL4 to increase oxidative phosphorylation, oxygen consumption rate, mitochondrial membrane potential, and use of oxidative phosphorylation-related metabolites to generate ATP.

CONCLUSIONS

In a screening for compounds that reduce the viability of cells that express high levels of the transcription factor SALL4, we identified inhibitors of oxidative phosphorylation, which slowed the growth of xenograft tumors from SALL4hi cells in mice. SALL4 activates the transcription of genes that regulate oxidative phosphorylation to increase oxygen consumption, mitochondrial membrane potential, and ATP generation in cancer cells. Inhibitors of oxidative phosphorylation might be used for the treatment of liver tumors with high levels of SALL4.

Dual-Targeting Dual-Action Platinum(IV) Platform for Enhanced Anticancer Activity and Reduced Nephrotoxicity

A novel and highly efficient dual-targeting platform was designed to ensure targeted in vivo delivery of dual-action Pt^IV prodrugs. The dual targeting was established by liposomal encapsulation of Pt^IV complexes, thereby utilizing the enhanced permeability and retention (EPR) effect as the first stage of targeting to attain a high accumulation of the drug-loaded liposomes in the tumor. After the release of the Pt^IV prodrug inside cancer cells, a second stage of targeting directed a portion of the Pt^IV prodrugs to the mitochondria. Upon intracellular reduction, these Pt^IV prodrugs released two bioactive molecules, acting both on the mitochondrial and on the nuclear DNA. Our Pt^IV system showed excellent activity in vitro and in vivo, characterized by a cytotoxicity in a low micromolar range and complete tumor remission, respectively. Notably, marked in vivo activity was accompanied by reduced kidney toxicity, highlighting the unique therapeutic potential of our novel dual-targeting dual-action platform.

Abstract 5818: Globally optimizing therapeutic combinations against bortezomib-resistant multiple myeloma using a quantitative parabolic optimization platform

Multiple myeloma is an incurable hematological malignancy that relies on drug combinations as first and secondary lines of treatment. The inclusion of proteasome inhibitors, such as bortezomib, into these drug combination regimens has improved median survival. Resistance to bortezomib, however, is a common occurrence that ultimately contributes to treatment failure. Thus, there remains a need to identify improved drug combinations that may serve as later lines of treatment for improved treatment against bortezomib-resistant multiple myeloma. We have developed the quantitative parabolic optimization platform (QPOP) to optimize drug combinations against bortezomib-resistant multiple myeloma. By mapping phenotypic output data to parabolic response surfaces, QPOP is able to deterministically optimize drug combinations as well as drug dosages. By continuously optimizing in multiple systems of interest, from in vitro to in vivo, drug combinations can be globally optimized for greater efficacy in increasingly complex biological systems. While QPOP does not rely on molecular mechanism modeling or prediction, identified optimal drug combinations can reverse DNA hypermethylation and silencing of tumor suppressors that occurs following acquired bortezomib-resistance in multiple myeloma. Furthermore, this drug combination is broadly effective across a range of primary multiple myeloma patient samples. Beyond bortezomib-resistant multiple myeloma, global optimization of drug combinations by QPOP can serve to improve drug combination design across a range of other cancers and diseases through a continuous optimization process across the entire drug development pipeline.

Citation Format: Masturah Rashid, Tan Boon Toh, Lissa Hooi, Aleidy Silva, Yanzhou Zhang, Neerja Karnani, Sudhakar Jha, Chih-Ming Ho, Wee Joo Chng, Dean Ho, Edward Kai-Hua Chow. Globally optimizing therapeutic combinations against bortezomib-resistant multiple myeloma using a quantitative parabolic optimization platform [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 5818.

Generation of matched patient-derived xenograft in vitro-in vivo models using 3D macroporous hydrogels for the study of liver cancer

Hepatocellular carcinoma (HCC) is the third leading cause of cancer death worldwide, often manifesting at the advanced stage when cure is no longer possible. The discrepancy between preclinical findings and clinical outcome in HCC is well-recognized. So far, sorafenib is the only targeted therapy approved as first-line therapy for patients with advanced HCC. There is an urgent need for improved preclinical models for the development of HCC-targeted therapies. Patient-derived xenograft (PDX) tumor models have been shown to closely recapitulate human tumor biology and predict patient drug response. However, the use of PDX models is currently limited by high costs and low throughput. In this study, we engineered in vitro conditions conducive for the culture of HCC-PDX organoids derived from a panel of 14 different HCC-PDX lines through the use of a three-dimensional macroporous cellulosic sponge system. To validate the in vitro HCC-PDX models, both in vivo and in vitro HCC-PDX models were subjected to whole exome sequencing and RNA-sequencing. Correlative studies indicate strong concordance in genomic and transcriptomic profiles as well as intra-tumoral heterogeneity between each matched in vitro-in vivo HCC-PDX pairs. Furthermore, we demonstrate the feasibility of using these in vitro HCC-PDX models for drug testing, paving the way for more efficient preclinical studies in HCC drug development.