Investigating New Drug Options for Temozolomide Resistant IDH1 Mutant Glioma

Gliomas, a prevalent form of malignant brain tumors in adults, often exhibit mutations in the isocitrate dehydrogenase 1 (IDH1) gene. Temozolomide (TMZ) is a commonly used chemotherapy drug for treating gliomas; however, the development of drug resistance poses a significant challenge to its effectiveness. My study aimed to investigate new drug options for IDH1 mutant gliomas and was divided into two main parts. The first part focused on reversing TMZ resistance and identifying synergistic drugs, while the second part sought alternative treatments for IDH1 mutant TMZ-resistant gliomas. To achieve the objectives of the first part, patient-derived glioma tumorspheres (PDTs) harboring IDH1 mutations were utilized. Vehicle and TMZ treated tumor models were subjected to transcriptional, metabolic, and epigenetic analyses. Transcriptome analysis revealed the upregulation of the p53 signaling pathway and its associated transcription factor, TP53. Notably, combining the p53 activator RITA with TMZ demonstrated strong synergy in certain PDTs. Metabolome analysis uncovered that glycolytic inhibition with the glucose analog 2-DG (2-Deoxy-D-glucose) or combining Mildronate, L-carnitine biosynthesis inhibitor, with TMZ treatment showed efficacy in specific PDTs. Additionally, employing epigenetic approaches using decitabine (DAC) in combination with TMZ revealed robust synergistic effects in select PDTs. These findings underscore the significance of genetic and metabolic heterogeneity among cells in gliomas. In the pursuit of alternative drugs, a high-throughput miniaturized screening identified more than 20 potential candidate drugs, among which the YAP inhibitor Verteporfin (VP) emerged as a promising option. VP exhibited anti-tumor activity in IDH1 mutant PDTs independent of the YAP1 protein. It downregulated the nucleocytoplasmic transport pathway, with NUP107 identified as an upstream regulator associated with VP response. In conclusion, this study elucidated the intricate interplay of signaling pathways and their impact on drug sensitivity in diverse glioma cell populations. It emphasized the need to consider the complexities inherent to gliomas when devising effective therapeutic strategies. The findings provide valuable insights into the development of alternative treatments and strategies to overcome TMZ resistance in IDH1 mutant gliomas

Anticancer drug synergy prediction in understudied tissues using transfer learning

ocaa212Objective: Drug combination screening has advantages in identifying cancer treatment options with higher efficacy without degradation in terms of safety. A key challenge is that the accumulated number of observations in in-vitro drug responses varies greatly among different cancer types, where some tissues are more understudied than the others. Thus, we aim to develop a drug synergy prediction model for understudied tissues as a way of overcoming data scarcity problems. Materials and Methods: We collected a comprehensive set of genetic, molecular, phenotypic features for cancer cell lines. We developed a drug synergy prediction model based on multitask deep neural networks to integrate multimodal input and multiple output. We also utilized transfer learning from data-rich tissues to data-poor tissues. Results: We showed improved accuracy in predicting synergy in both data-rich tissues and understudied tissues. In data-rich tissue, the prediction model accuracy was 0.9577 AUROC for binarized classification task and 174.3 mean squared error for regression task. We observed that an adequate transfer learning strategy significantly increases accuracy in the understudied tissues. Conclusions: Our synergy prediction model can be used to rank synergistic drug combinations in understudied tissues and thus help to prioritize future in-vitro experiments. Code is available at https://github.com/yejinjkim/synergy-transfer.Peer reviewe

Multiplexed combinatorial drug screening using droplet-based microfluidics

The therapy of most cancers has greatly benefited from the use of targeted drugs. However, their effects are often short-lived since many tumors develop resistance against these drugs. Resistance of tumor cells against drugs can be adaptive or acquired and is often caused by genetic or non-genetic heterogeneity between tumor cells. A potential solution to overcome drug resistance is the use of drug combinations addressing multiple targets at once. Finding potent drug combinations against heterogeneous tumors is challenging. One reason is the high number of possible combinations. Another reason is the possibility of inter-patient heterogeneity in drug responses, making patient tailored treatments necessary. These require screens on patient material, which would drastically benefit from miniaturization, as it is the case in droplet-based microfluidics. However, drug screens in droplets against primary tumor cells have so far only been performed at a modest chemical complexity (55 treatment conditions) and with low content readouts. In this thesis we aimed at developing a droplet-based microfluidic workflow that allows the generation of high numbers of drug combinations in picolitre-sized droplets and their multiplexed analysis. To this end, we have established a pipeline to produce up to 420 drug combinations in droplets. We were able to significantly increase the number of possible combinations by building a microfluidic setup that comprises valve and micro-titer plate based injection of drugs into microfluidic devices for droplet generation Furthermore, we integrated a DNA-based barcoding approach to encode each treatment condition, enabling their multiplexed analyses since all droplets can be stored and processed together, which highly increases the throughput. With the established approach we can perform barcoding of each cells’ transcriptome according to the drugs it was exposed to in the droplet. Thereby, the effects of drug combinations on gene expression can be studied in a highly multiplexed way using RNA-Sequencing. We applied the developed approach to run combinatorial drug screens in droplets and analysed the effects of in total 630 drug combinations on gene expression in K562 cells. The low number of cells needed (max. 2 million cells) for such screens, could enable their application directly on tumor biopsies, thus paving the way for personalized therapy approaches. Since the established workflow is compatible with single cell readouts, we also envision its application to analyse drug resistances in heterogeneous tumor samples on the single cell level

Transcriptomic data integration for precision medicine in leukemia

This thesis is comprised of three studies demonstrating the application of different statistical and bioinformatic approaches to address distinct challenges of implementing precision medicine strategies for hematological malignancies. The approaches focus on the analysis of next-generation sequencing data, including both genomic and transcriptomics, to deconvolute disease biology and underlying mechanisms of drug sensitivities and resistance. The outcomes of the studies have clinical implications for advancing current diagnosis and treatment paradigms in patients with hematological diseases. Study I, RNA sequencing has not been widely adopted in a clinical diagnostic setting due to continuous development and lack of standardization. Here, the aim was to evaluate the efficiency of two different RNA-seq library preparation protocols applied to cells collected from acute myeloid leukemia (AML) and acute lymphoblastic leukemia (ALL) patients. The poly-A-tailed mRNA selection (PA) and ribo- depletion (RD) based RNA-seq library preparation protocols were compared and evaluated for detection of gene fusions, variant calling and gene expression profiling. Overall, both protocols produced broadly consistent results and similar outcomes. However, the PA protocol was more efficient in quantifying expression of leukemia marker genes and drug targets. It also provided higher sensitivity and specificity for expression-based classification of leukemia. In contrast, the RD protocol was more suitable for gene fusion detection and captured a greater number of transcripts. Importantly, high technical variations were observed in samples from two leukemia patient cases suggesting further development of strategies for transcriptomic quantification and data analysis. Study II, the BCL-2 inhibitor venetoclax is an approved and effective agent in combination with hypomethylating agents or low dose cytarabine for AML patients, unfit for intensive induction chemotherapy. However, a limited number of patients responding to venetoclax and development of resistance to the treatment presents a challenge for using the drug to benefit the majority of the AML patients. The aim was to investigate genomic and transcriptomic biomarkers for venetoclax sensitivity and enable identification of the patients who are most responsive to venetoclax treatment. We found that venetoclax sensitive samples are enriched with WT1 and IDH1/IDH2 mutations. Intriguingly, HOX family genes, including HOXB9, HOXA5, HOXB3, HOXB4, were found to be significantly overexpressed in venetoclax sensitive patients. Thus, these HOX-cluster genes expression biomarkers can be explored in a clinical trial setting to stratify AML patients responding to venetoclax based therapies. Study III, venetoclax treatment does not benefit all AML patients that demands identifying biomarkers to exclude the patients from venetoclax based therapies. The aim was to investigate transcriptomic biomarkers for ex vivo venetoclax resistance in AML patients. The correlation of ex vivo venetoclax response with gene expression profiles using a machine learning approach revealed significant overexpression of S100 family genes, S100A8 and S100A9. Moreover, high expression ofS100A9was found to be associated with birabresib (BET inhibitor) sensitivity. The overexpression of S100A8 and S100A9 could potentially be used to detect and monitor venetoclax resistance. The combination of BCL-2 and BET inhibitors may sensitize AML cells to venetoclax upon BET inhibition and block leukemic cell survival.In this thesis, the aim was to utilize gene expression information for advanced precision medicine outcomes in patients with hematological malignancies. In the study, I, the contemporary mainstream library preparation protocols, Ribo-depletion and PolyA enrichment used for RNA sequencing, were compared in order to select the protocol that suffices the goal of the experiment, especially in patients with acute leukemias. In study II, we applied bioinformatics approaches to identify IDH1/2 mutation and HOX family gene expression correlated with ex vivo sensitivity to BCL-2 inhibitor venetoclax in acute myeloid leukemia (AML) patients. In study III, statistical and machine learning methods were implemented to identify S100A8/A9 gene expression biomarkers for ex vivo resistance to venetoclax in AML patients. In summary, this thesis addresses the challenges of utilizing gene expression information to stratify patients based on biomarkers to promote precision medicine practice in hematological malignancies

Subtype specific metabolic vulnerabilities in pancreatic cancer

Pancreatic cancer has historically been characterised by its poor prognosis, with very little increase in 5-year expectancy relative to other, comparable cancertypes. This clinical observation is largely due to existing difficulties in identifying therapeutics effective in managing metastasised disease, a task compounded by the heterogeneity associated with pancreatic cancers. Concentrated efforts have been made in recent times to mitigate this issue, with the emergence of a range of subtyping strategies allowing for the stratification of patients. This categorisation of patients into workable groups thus serves to limit the degree of heterogeneity found within subgroups, with hypothetical, and otherwise unobservable, vulnerabilities shared between subtypes. This thesis aims to explore these potential therapeutically exploitable vulnerabilities by describing the extensive characterisation of pancreatic cancer subtypes via a diverse collection of patient derived cell-lines. This characterisation was achieved by profiling of the transcriptome via RNA-seq analyses, the proteome via mass-spectrometric approaches, and activation status of metabolic processes associated with oncogenesis in pancreatic cancer via functional assays. This work therefore facilitates the identification of vulnerabilities by utilising the profiles of subtypes generated in this manner and devising therapeutic strategies effective in treating the disease by interrogating dysregulated pathways. Within PDCLs, two pancreatic cancer subtypes were first identified which aligned with those described in patients: the squamous and classical subtypes. Preliminary profiling efforts highlighted a dysregulation of genes involved in metabolism across these subtypes in vitro, with active glycolysis associated with the aggressive squamous subtype and fatty acid biosynthesis and metabolism upregulated in the classical subtype. Further proteomic characterisation then validated this observation, providing further evidence for the existence of distinct metabolic profiles associated with these subgroups. Follow-up experimentation which focused on metabolic outputs then generated metabolic profiles for each subtype, with in vitro phenotypes reflecting findings at the transcriptome and proteome level and demonstrating enhanced glycolysis and fatty acid oxidation in the squamous and classical subtypes respectively. Subsequent attempts to target arms within those subtype-associated metabolic pathways yielded mixed results. Inhibiting glycolysis via targeting of ALDOA successfully mediated a selective response in cell-lines associated with the squamous subtype, while classical cell-lines required a combination therapy to suppress metabolic flexibility to induce sensitivity to inhibition of fatty acid synthesis via targeting of FASN. An adjacent and complementary arm of research involved collaborative highthroughput drug repurposing screens to identify additional targets for follow-up. This involved an initial screen of ~600 compounds in 8 PDCLs. Results generated as part of this screening approach highlighted the potency of statins in effecting a significant response selectively in squamous cell-lines. Research probing the mechanism by which statins induce this selective inhibition suggested that differences in degradation of the statin target HMGCR and cholesterol homeostasis may confer resistance to cell-lines classified as squamous, with findings demonstrating the potential of dietary components found in commonly ingested foodstuffs to mitigate the effects of statins in the otherwise sensitive, squamous subtype. This thesis therefore identified a range of therapeutic strategies effective in mediating sensitivity in in vitro pancreatic subtypes, with mechanisms of actions determined for each strategy. Results have demonstrated that pancreatic cancer cells exhibit differential sensitivities to metabolic inhibition, with subtype classification found to act as a predictor of sensitivity. As these in vitro subtypes recapitulate stratifications described in patients, these therapeutic strategies are of clinical relevance in the treatment of pancreatic cancer

Multi-omics technologies applied to tuberculosis drug discovery

Multi-omics strategies are indispensable tools in the search for new anti-tuberculosis drugs. Omics methodologies, where the ensemble of a class of biological molecules are measured and evaluated together, enable drug discovery programs to answer two fundamental questions. Firstly, in a discovery biology approach, to find new targets in druggable pathways for target-based investigation, advancing from target to lead compound. Secondly, in a discovery chemistry approach, to identify the mode of action of lead compounds derived from high-throughput screens, progressing from compound to target. The advantage of multi-omics methodologies in both of these settings is that omics approaches are unsupervised and unbiased to a priori hypotheses, making omics useful tools to confirm drug action, reveal new insights into compound activity, and discover new avenues for inquiry. This review summarizes the application of Mycobacterium tuberculosis omics technologies to the early stages of tuberculosis antimicrobial drug discovery

CRISPRi-based radiation modifier screen identifies long non-coding RNA therapeutic targets in glioma.

BackgroundLong non-coding RNAs (lncRNAs) exhibit highly cell type-specific expression and function, making this class of transcript attractive for targeted cancer therapy. However, the vast majority of lncRNAs have not been tested as potential therapeutic targets, particularly in the context of currently used cancer treatments. Malignant glioma is rapidly fatal, and ionizing radiation is part of the current standard-of-care used to slow tumor growth in both adult and pediatric patients.ResultsWe use CRISPR interference (CRISPRi) to screen 5689 lncRNA loci in human glioblastoma (GBM) cells, identifying 467 hits that modify cell growth in the presence of clinically relevant doses of fractionated radiation. Thirty-three of these lncRNA hits sensitize cells to radiation, and based on their expression in adult and pediatric gliomas, nine of these hits are prioritized as lncRNA Glioma Radiation Sensitizers (lncGRS). Knockdown of lncGRS-1, a primate-conserved, nuclear-enriched lncRNA, inhibits the growth and proliferation of primary adult and pediatric glioma cells, but not the viability of normal brain cells. Using human brain organoids comprised of mature neural cell types as a three-dimensional tissue substrate to model the invasive growth of glioma, we find that antisense oligonucleotides targeting lncGRS-1 selectively decrease tumor growth and sensitize glioma cells to radiation therapy.ConclusionsThese studies identify lncGRS-1 as a glioma-specific therapeutic target and establish a generalizable approach to rapidly identify novel therapeutic targets in the vast non-coding genome to enhance radiation therapy

Proteostasis regulators modulate proteasomal activity and gene expression to attenuate multiple phenotypes in Fabry disease

The lysosomal storage disorder Fabry disease is characterized by a deficiency of the lysosomal enzyme \u3b1-Galactosidase A. The observation that missense variants in the encoding GLA gene often lead to structural destabilization, endoplasmic reticulum retention and proteasomal degradation of the misfolded, but otherwise catalytically functional enzyme has resulted in the exploration of alternative therapeutic approaches. In this context, we have investigated proteostasis regulators (PRs) for their potential to increase cellular enzyme activity, and to reduce the disease-specific accumulation of the biomarker globotriaosylsphingosine in patient-derived cell culture. The PRs also acted synergistically with the clinically approved 1-deoxygalactonojirimycine, demonstrating the potential of combination treatment in a therapeutic application. Extensive characterization of the effective PRs revealed inhibition of the proteasome and elevation of GLA gene expression as paramount effects. Further analysis of transcriptional patterns of the PRs exposed a variety of genes involved in proteostasis as potential modulators. We propose that addressing proteostasis is an effective approach to discover new therapeutic targets for diseases involving folding and trafficking-deficient protein mutants