Pushing the Frontiers of Cancer Research: Highlights from the Frontiers in Cancer Science Conference 2023.

The 15th annual Frontiers in Cancer Science (FCS) conference gathered scientific experts who shared the latest research converging upon several themes of cancer biology. These themes included the dysregulation of metabolism, cell death, and other signaling processes in cancer cells; using patient "omics" datasets and single-cell and spatial approaches to investigate heterogeneity, understand therapy resistance, and identify targets; innovative strategies for inhibiting tumors, including rational drug combinations and improved drug delivery mechanisms; and advances in models that can facilitate screening for cancer vulnerabilities and drug testing. We hope the insights from this meeting will stimulate further progress in the field.

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.

Pazopanib elicits remarkable response in metastatic porocarcinoma: a functional precision medicine approach.

Metastatic porocarcinomas (PCs) are vanishingly rare, highly aggressive skin adnexal tumors with mortality rates exceeding 70%. Their rarity has precluded the understanding of their disease pathogenesis, let alone the conduct of clinical trials to evaluate treatment strategies. There are no effective agents for unresectable PCs. Here, we successfully demonstrate how functional precision medicine was implemented in the clinic for a metastatic PC with no known systemic treatment options. Comprehensive genomic profiling of the tumor specimen did not yield any actionable genomic aberrations. However, ex vivo drug testing predicted pazopanib efficacy, and indeed, administration of pazopanib elicited remarkable clinicoradiological response. Pazopanib and its class of drugs should be evaluated for efficacy in other cases of PC, and the rationale for efficacy should be determined when PC tumor models become available. A functional precision medicine approach could be useful to derive effective treatment options for rare cancers.

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.

NanoBeacon.AI: AI-Enhanced Nanodiamond Biosensor for Automated Sensitivity Prediction to Oxidative Phosphorylation Inhibitors.

Spalt-like transcription factor 4 (SALL4) is an oncofetal protein that has been identified to drive cancer progression in hepatocellular carcinoma (HCC) and hematological malignancies. Furthermore, a high SALL4 expression level is correlated to poor prognosis in these cancers. However, SALL4 lacks well-structured small-molecule binding pockets, making it difficult to design targeted inhibitors. SALL4-induced expression of oxidative phosphorylation (OXPHOS) genes may serve as a therapeutically targetable vulnerability in HCC through OXPHOS inhibition. Because OXPHOS functions through a set of genes with intertumoral heterogeneous expression, identifying therapeutic sensitivity to OXPHOS inhibitors may not rely on a single clear biomarker. Here, we developed a workflow that utilized molecular beacons, nucleic-acid-based, activatable sensors with high specificity to the target mRNA, delivered by nanodiamonds, to establish an artificial intelligence (AI)-assisted platform for rapid evaluation of patient-specific drug sensitivity. Specifically, when the HCC cells were treated with the nanodiamond-medicated OXPHOS biosensor, high sensitivity and specificity of the sensor allowed for improved identification of OXPHOS expression in cells. Assisted by a trained convolutional neural network, drug sensitivity of cells toward an OXPHOS inhibitor, IACS-010759, could be accurately predicted. AI-assisted OXPHOS drug sensitivity assessment could be accomplished within 1 day, enabling rapid and efficient clinical decision support for HCC treatment. The work proposed here serves as a foundation for the patient-based subtype-specific therapeutic research platform and is well suited for precision medicine.

Biomedical applications of nanodiamonds: From drug-delivery to diagnostics.

Advances in nanotechnology have great potential to address many unmet clinical and biomedical needs. Nanodiamonds, as a class of carbon nanoparticles with unique properties, may be useful towards a versatile range of biomedical applications from drug delivery to diagnostics. This review describes how these properties of nanodiamonds facilitate their application in different fields of biomedicine, including delivery of chemotherapy drugs, peptides, proteins, nucleic acids and biosensors. Additionally, clinical potential of nanodiamonds, with studies in both preclinical and clinical stages, is also reviewed here, highlighting the translational potential of nanodiamonds in biomedical research.

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.

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.

Addressing antimicrobial resistance with the IDentif.AI platform: Rapidly optimizing clinically actionable combination therapy regimens against nontuberculous mycobacteria.

Background: Current standard of care (SOC) regimens against nontuberculous mycobacteria (NTM) usually result in unsatisfactory therapeutic responses, primarily due to multi-drug resistance and antibiotic susceptibility-guided therapies. In the midst of rising incidences in NTM infections, strategies to develop NTM-specific treatments have been explored and validated. Methods: To provide an alternative approach to address NTM-specific treatment, IDentif.AI was harnessed to rapidly optimize and design effective combination therapy regimens against Mycobacterium abscessus (M. abscessus), the highly resistant and rapid growth species of NTM. IDentif.AI interrogated the drug interaction space from a pool of 6 antibiotics, and pinpointed multiple clinically actionable drug combinations. IDentif.AI-pinpointed actionable combinations were experimentally validated and their interactions were assessed using Bliss independence model and diagonal measurement of n-way drug interactions. Results: Notably, IDentfi.AI-designed 3- and 4-drug combinations demonstrated greater %Inhibition efficacy than the SOC regimens. The platform also pinpointed two unique drug interactions (Levofloxacin (LVX)/Rifabutin (RFB) and LVX/Meropenem (MEM)) that may serve as the backbone of potential 3- and 4-drug combinations like LVX/MEM/RFB, which exhibited 58.33±4.99 %Inhibition efficacy against M. abscessus. Further analysis of LVX/RFB via Bliss independence model pointed to dose-dependent synergistic interactions in clinically actionable concentrations. Conclusions: IDentif.AI-designed combinations may provide alternative regimen options to current SOC combinations that are often administered with Amikacin, which has been known to induce ototoxicity in patients. Furthermore, IDentif.AI pinpointed 2-drug interactions may also serve as the backbone for the development of other effective 3- and 4-drug combination therapies. The findings in this study suggest that this platform may contribute to NTM-specific drug development.

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.

The IDentif.AI-x pandemic readiness platform: Rapid prioritization of optimized COVID-19 combination therapy regimens.

IDentif.AI-x, a clinically actionable artificial intelligence platform, was used to rapidly pinpoint and prioritize optimal combination therapies against COVID-19 by pairing a prospective, experimental validation of multi-drug efficacy on a SARS-CoV-2 live virus and Vero E6 assay with a quadratic optimization workflow. A starting pool of 12 candidate drugs developed in collaboration with a community of infectious disease clinicians was first narrowed down to a six-drug pool and then interrogated in 50 combination regimens at three dosing levels per drug, representing 729 possible combinations. IDentif.AI-x revealed EIDD-1931 to be a strong candidate upon which multiple drug combinations can be derived, and pinpointed a number of clinically actionable drug interactions, which were further reconfirmed in SARS-CoV-2 variants B.1.351 (Beta) and B.1.617.2 (Delta). IDentif.AI-x prioritized promising drug combinations for clinical translation and can be immediately adjusted and re-executed with a new pool of promising therapies in an actionable path towards rapidly optimizing combination therapy following pandemic emergence.

Hepatocellular carcinoma organoid co-cultures mimic angiocrine crosstalk to generate inflammatory tumor microenvironment.

Hepatocellular carcinoma (HCC) is the sixth most common cancer and the second leading cause of cancer worldwide. Despite approvals of several therapeutics to treat advanced HCC in the past few years, the impact of anti-angiogenic treatment on HCC patient overall survival remains limited. This suggests there may be alternative, perfusion-independent roles of endothelial cells that support tumor progression. Thus, we leveraged a well-defined hydrogel system to establish co-culture models to mimic and characterize the angiocrine crosstalk between HCC and endothelial cells in vitro. Co-cultures of HCC cell lines or patient-derived xenograft organoids with endothelial cells exhibited the upregulation of MCP-1, IL-8 and CXCL16, suggesting that the HCC-endothelial interactions established in our models recapitulate known angiocrine signaling. Additionally, by subjecting co-cultures and mono-cultures to RNA sequencing, transcriptomic analysis revealed an upregulation in the expression of genes associated with tumor necrosis factor (TNF) signaling, such as that of chemokines, suggesting that endothelial cells induce HCC cells to generate an inflammatory microenvironment by recruiting immune cells. Finally, HCC-endothelial angiocrine crosstalk in the co-culture models polarized macrophages towards a pro-inflammatory and pro-angiogenic phenotype, paralleling a tumor-associated macrophage subset previously reported in HCC. Together, these findings suggest that these HCC-endothelial co-culture models may serve as important models to understand and target the interplay between angiogenesis and the immune milieu.

Clinical translation of patient-derived tumour organoids- bottlenecks and strategies.

Multiple three-dimensional (3D) tumour organoid models assisted by multi-omics and Artificial Intelligence (AI) have contributed greatly to preclinical drug development and precision medicine. The intrinsic ability to maintain genetic and phenotypic heterogeneity of tumours allows for the reconciliation of shortcomings in traditional cancer models. While their utility in preclinical studies have been well established, little progress has been made in translational research and clinical trials. In this review, we identify the major bottlenecks preventing patient-derived tumour organoids (PDTOs) from being used in clinical setting. Unsuitable methods of tissue acquisition, disparities in establishment rates and a lengthy timeline are the limiting factors for use of PDTOs in clinical application. Potential strategies to overcome this include liquid biopsies via circulating tumour cells (CTCs), an automated organoid platform and optical metabolic imaging (OMI). These proposed solutions accelerate and optimize the workflow of a clinical organoid drug screening. As such, PDTOs have the potential for potential applications in clinical oncology to improve patient outcomes. If remarkable progress is made, cancer patients can finally benefit from this revolutionary technology.

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.

A chemical biology approach reveals a dependency of glioblastoma on biotin distribution.

Glioblastoma (GBM) is a uniformly lethal disease driven by glioma stem cells (GSCs). Here, we use a chemical biology approach to unveil previously unknown GBM dependencies. By studying sulconazole (SN) with anti-GSC properties, we find that SN disrupts biotin distribution to the carboxylases and histones. Transcriptomic and metabolomic analyses of SN-treated GSCs reveal metabolic alterations that are characteristic of biotin-deficient cells, including intracellular cholesterol depletion, impairment of oxidative phosphorylation, and energetic crisis. Furthermore, SN treatment reduces histone biotinylation, histone acetylation, and expression of superenhancer-associated GSC critical genes, which are also observed when biotin distribution is genetically disrupted by holocarboxylase synthetase (HLCS) depletion. HLCS silencing impaired GSC tumorigenicity in an orthotopic xenograft brain tumor model. In GBM, high HLCS expression robustly indicates a poor prognosis. Thus, the dependency of GBM on biotin distribution suggests that the rational cotargeting of biotin-dependent metabolism and epigenetic pathways may be explored for GSC eradication.

Improving the therapeutic ratio of radiotherapy against radioresistant cancers: Leveraging on novel artificial intelligence-based approaches for drug combination discovery.

Despite numerous advances in cancer radiotherapy, tumor radioresistance remain one of the major challenges limiting treatment efficacy of radiotherapy. Conventional strategies to overcome radioresistance involve understanding the underpinning molecular mechanisms, and subsequently using combinatorial treatment strategies involving radiation and targeted drug combinations against these radioresistant tumors. These strategies exploit and target the molecular fingerprint and vulnerability of the radioresistant clones to achieve improved efficacy in tumor eradication. However, conventional drug-screening approaches for the discovery of new drug combinations have been proven to be inefficient, limited and laborious. With the increasing availability of computational resources in recent years, novel approaches such as Quadratic Phenotypic Optimization Platform (QPOP), CURATE.AI and Drug Combination and Prediction and Testing (DCPT) platform have emerged to aid in drug combination discovery and the longitudinally optimized modulation of combination therapy dosing. These platforms could overcome the limitations of conventional screening approaches, thereby facilitating the discovery of more optimal drug combinations to improve the therapeutic ratio of combinatorial treatment. The use of better and more accurate models and methods with rapid turnover can thus facilitate a rapid translation in the clinic, hence, resulting in a better patient outcome. Here, we reviewed the clinical observations, molecular mechanisms and proposed treatment strategies for tumor radioresistance and discussed how novel approaches may be applied to enhance drug combination discovery, with the aim to further improve the therapeutic ratio and treatment efficacy of radiotherapy against radioresistant cancers.

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.