Quantitative scoring of differential drug sensitivity for individually optimized anticancer therapies

Protein Profiles Predict Treatment Responses to the PI3K Inhibitor Umbralisib in Patients with Chronic Lymphocytic Leukemia

PURPOSE

The management of chronic lymphocytic leukemia (CLL) has significantly improved with targeted therapies. However, many patients experience a suboptimal response. To optimally select the best therapy, predictive biomarkers are necessary. In this study, we used the phosphoinositide 3-kinase (PI3K) inhibitor umbralisib as a model to (i) understand the impact of targeted treatment on cell signaling and immunophenotypes in responders and nonresponders, (ii) identify molecular features that predict individual treatment responses, and (iii) suggest alternative treatment options for the nonresponders.

EXPERIMENTAL DESIGN

We performed functional phenotyping of CLL cells from patients enrolled in two clinical trials with umbralisib, administered either as a monotherapy (NCT02742090, n = 55) or in combination with the Bruton tyrosine kinase (BTK) inhibitor acalabrutinib (NCT04624633, n = 12).

RESULTS

We found that umbralisib monotherapy led to significant changes in (phospho)protein levels, including AKT (pS473), in responders but not in nonresponders. Furthermore, the proportion of cytotoxic natural killer (NK) cells increased at the end of the study but only in responders, suggesting a role in the antitumor response. To identify molecular predictors of response, we used the baseline levels of 30 (phospho)proteins in the monotherapy cohort as input features for a machine learning model, which achieved significant prediction accuracy in cross-validation and maintained its predictive power in the combination cohort. Drug sensitivity profiling of the CLL cells at baseline suggested that PI3K + Bcl-2 inhibitors are effective in umbralisib nonresponders.

CONCLUSIONS

Functional phenotyping reveals differential cellular responses to umbralisib treatment in responders and nonresponders; predicts treatment response of individual patients with CLL; and suggests alternative treatment options for the nonresponders.

Functional screening identifies kinesin spindle protein inhibitor filanesib as a potential treatment option for hepatoblastoma

Hepatoblastoma is a rare pediatric liver malignancy usually treated with surgery and chemotherapy. To explore new treatment options for hepatoblastoma, drug screening was performed using six cell models established from aggressive hepatoblastoma tumors and healthy pediatric primary hepatocytes. Of the 527 screened compounds, 98 demonstrated cancer-selective activity in at least one hepatoblastoma model. The kinesin spindle protein (KSP) inhibitor filanesib was effective in all models and was further evaluated. Filanesib induced G2/M arrest and apoptosis in hepatoblastoma cells at concentrations tolerable to primary hepatocytes. Prominent nuclear fragmentation was observed in filanesib-treated hepatoblastoma cells. Genes participating in cell cycle regulation were noted to be differentially expressed after filanesib treatment. Filanesib reduced the rate of tumor growth in 4/5 hepatoblastoma mice models. One of these models showed complete growth arrest. Our results suggest that filanesib is a potential candidate for hepatoblastoma treatment and should be investigated in future clinical trials.

Targeted inhibition of WIP1 and histone H3K27 demethylase activity synergistically suppresses neuroblastoma growth

High-risk neuroblastoma frequently exhibits segmental gain of chromosome 17q, including the locus of PPM1D, which encodes the phosphatase WIP1, a regulator of p53 activity, DNA repair, and apoptosis. High expression of PPM1D is correlated to poor prognosis, and genetic or pharmacologic inhibition of WIP1 suppresses neuroblastoma growth. Here, we show that combining drugs that target WIP1 and H3K27 demethylation induces synergistic cytotoxicity in neuroblastoma. We screened 527 different compounds together with inhibitors of WIP1 and identified a strong cytotoxic synergism between the WIP1 inhibitor SL-176 and GSK-J4, a specific inhibitor of the H3K27 demethylase JMJD3. Viability assays in neuroblastoma cell lines and treatment of tumor spheroids confirmed the synergistic effect of combining SL-176 with GSK-J4. Immunoblot experiments demonstrated a marked effect on WIP1 downstream targets and apoptosis markers, while qPCR showed a synergistic upregulation of p53 downstream targets PUMA and p21. RNA sequencing revealed a vast number of differentially expressed genes, suggesting a pervasive effect of this drug combination on transcription, with enrichment of pathways involved in DNA damage response. Finally, this drug combination was confirmed to reduce tumor growth in zebrafish xenograft experiments. In conclusion, the combination of the WIP1 inhibitor SL-176 and the epigenetic modifier GSK-J4 induces synergistic cytotoxicity in neuroblastoma cells by potentiating p53 downstream effects.

High-throughput drug screening identifies SMAC mimetics as enhancers of NK-cell cytotoxicity in chronic myeloid leukemia

ABSTRACT

Natural killer (NK) cells have proven to be safe and effective immunotherapies, associated with favorable treatment responses in chronic myeloid leukemia (CML). Augmenting NK-cell function with oncological drugs could improve NK-cell-based immunotherapies. Here, we used a high-throughput drug screen consisting of >500 small-molecule compounds, to systematically evaluate the effects of oncological drugs on primary NK cells against CML cells. We identified second mitochondrially derived activator of caspases (SMAC) mimetics as potent enhancers of NK-cell cytotoxicity in both cell lines and primary patient samples. In contrast, several drug classes, including glucocorticoids and tyrosine kinase inhibitors such as dasatinib, inhibited NK-cell cytotoxicity. Single-cell RNA sequencing revealed drug-induced transcriptomic changes in both NK and target CML cells. SMAC mimetics upregulated NF-κB target genes in NK cells, potentially contributing to their enhanced cytotoxicity. Inhibitory drugs dexamethasone, dasatinib, and sotrastaurin prevented NK-cell transition to an activated state and suppressed the expression of interferon gamma (IFN-γ) by NK cells, thus preventing IFN-γ-mediated target cell transcriptomic response. In conclusion, we discovered that SMAC mimetics sensitize cancer cells to NK-cell-mediated killing, with potential clinical applications especially in patients with advanced phase CML.

Exome-wide association study reveals 7 functional variants associated with ex-vivo drug response in acute myeloid leukemia patients

Acute myeloid leukemia (AML) is an aggressive blood cancer characterized by poor survival outcomes. Further, due to the extreme molecular heterogeneity of the disease, drug treatment response varies from patient to patient. The variability of drug response can cause unnecessary treatment in more than half of the patients with no or partial therapy responses leading to severe side effects, monetary as well as time loss. Understanding the genetic risk factors underlying the drug response in AML can help with improved prediction of treatment responses and identification of biomarkers in addition to mechanistic insights to monitor treatment response. Here, we report the results of the first Exome-Wide Association Study (EWAS) of ex-vivo drug response performed to date with 175 AML cases and 47 drugs. We used information from 55,423 germline exonic SNPs to perform the analysis. We identified exome-wide significant (p < 9.02 × 10- 7) associations for rs113985677 in CCIN with tamoxifen response, rs115400838 in TRMT5 with idelalisib response, rs11878277 in HDGFL2 with entinostat, and rs2229092 in LTA associated with vorinostat response. Further, using multivariate genome-wide association analysis, we identified the association of rs11556165 in ATRAID, and rs11236938 in TSKU with the combined response of all 47 drugs and 29 nonchemotherapy drugs at the genome-wide significance level (p < 5 × 10- 8). Additionally, a significant association of rs35704242 in NIBAN1 was associated with the combined response for nonchemotherapy medicines (p = 2.51 × 10- 8), and BI.2536, gefitinib, and belinostat were identified as the central traits. Our study represents the first EWAS to date on ex-vivo drug response in AML and reports 7 new associated loci that help to understand the anticancer drug response in AML patients.

Exploiting NRF2-ARE pathway activation in papillary renal cell carcinoma

Papillary renal cell carcinoma (pRCC) is the second most frequent renal cancer subtype but has no indicated targeted treatments. MET inhibition may be a treatment for MET-driven pRCC, but there is a large group of non-MET-driven pRCC without targeted therapy. Activation of NRF2-ARE pathway has been suggested to be involved in pRCC. To study the relevance of the NRF2-ARE pathway, we characterized 60 pRCCs by copy number analysis and Whole Exome Sequencing. Because stabilisation of NRF2 results in enhanced expression of NQO1, a reductase that prevents production of reactive oxygen species, protein expression of NQO1 was analysed by immunohistochemistry (IHC) from tissue microarrays (TMAs) and by enzymatic activity assay. Finally, patient-derived pRCC cells (PDCs) were applied for drug profiling with 18 NRF2-ARE pathway inhibitors. We identified MET mutations in 5%, and mutations in four genes of NRF2-ARE pathway (NFE2L2, KEAP1, CUL3 and BACH1) in 10% of 60 pRCC samples. IHC analysis of TMAs of 638 renal cancers showed the correlation of the expression of NQO1 with poor survival outcome (p < .001) and high tumour grade (p < .001) and stage (p < .001) in pRCC. NQO1 mRNA, protein levels and enzymatic activity were increased in 56% of matched pRCC tissue samples and patient-derived cells (PDCs, n = 9). Drug screening revealed that Brusatol and Convallatoxin are potential novel drugs for pRCC. Inhibition of NRF2 represents a novel therapeutic approach for MET-independent pRCC patients.

Nidogen-1, a Player in KMT2A-Rearranged Pediatric Acute Myeloid Leukemia

Despite advances in outcome, one third of children with acute myeloid leukemia (AML) relapse, and less than half will achieve long-term survival. Relapse in AML has been shown to be driven in part by leukemic stem cells (LSCs), highlighting the unmet medical need to better characterize and target this therapy-resistant cell population. Micro-array profiling of pediatric AML subpopulations (LSCs and leukemic myeloblasts) and their healthy counterparts revealed nidogen-1 (NID1) as expressed in both leukemic subpopulations while absent in the hematopoietic stem cell. Using the TARGET dataset including pediatric AML patients (n = 1025), NID1 expression showed a correlation with worse event-free survival and KMT2A rearrangements. Drug response profiling of a NID1 knockdown model demonstrated differential sensitivity to HSP90 inhibition. RNA sequencing and gene set enrichment analysis between NID1high and NID1low phenotypes showed involvement of NID1 in mitochondrial metabolic pathways known to be enriched in LSCs. Altogether, this study highlights NID1 as a novel oncogene associated with worse EFS and metabolic LSC phenotype in AML. NID1 could serve as a biomarker and aid in further mapping LSCs to establish therapeutic strategies tackling the high relapse rates in pediatric AML.

The first-in-Africa ex vivo drug sensitivity testing platform identifies novel drug combinations for South African leukaemia patient cohort

In South Africa, leukemia remains a major health concern, posing significant challenges in treatment due to its varied subtypes. There is an unmet need for a testing pipeline that can identify drug effects on patient samples in an ex-vivo setting. Using the pilot study with South African patient samples, this paper reports the development of a drug-sensitivity testing pipeline for studying the drug effects in leukemia patient-derived cells. Forty-one (41) patients with Acute myeloid leukemia (AML) (n = 7), Chronic myelogenous leukemia (CML) (n = 30), and Chronic lymphocytic leukemia (CLL) (n = 4) were recruited for this study. Thirty (30) FDA-approved drugs were utilized for single drug sensitivity screening (DSS) on leukemia patient-derived cells with drug concentrations (1-1000 nM). The single DSS showed a distinct sensitivity pattern with different profiles among patients of the same subtype, confirming the need for precision therapy. This study observed irinotecan, used in solid tumour treatment, demonstrated efficacy in PBMCs in many patient samples compared to conventional leukemia drugs such as nilotinib. For drug combination studies, ten clinically relevant drugs were selected and tested based on the results of single drug sensitivity tests. This pilot study marks a crucial stride towards revolutionizing leukemia treatment in South Africa through an innovative ex vivo drug sensitivity testing platform. This pioneering initiative forms the basis for tailored and effective treatment options holding promise for more personalized treatment. Further exploration and validation of these findings could significantly contribute to cancer precision medicine efforts in South Africa.

The specification game: rethinking the evaluation of drug response prediction for precision oncology

Precision oncology plays a pivotal role in contemporary healthcare, aiming to optimize treatments for each patient based on their unique characteristics. This objective has spurred the emergence of various cancer cell line drug response datasets, driven by the need to facilitate pre-clinical studies by exploring the impact of multi-omics data on drug response. Despite the proliferation of machine learning models for Drug Response Prediction (DRP), their validation remains critical to reliably assess their usefulness for drug discovery, precision oncology and their actual ability to generalize over the immense space of cancer cells and chemical compounds. Scientific contribution In this paper we show that the commonly used evaluation strategies for DRP methods can be easily fooled by commonly occurring dataset biases, and they are therefore not able to truly measure the ability of DRP methods to generalize over drugs and cell lines ("specification gaming"). This problem hinders the development of reliable DRP methods and their application to experimental pipelines. Here we propose a new validation protocol composed by three Aggregation Strategies (Global, Fixed-Drug, and Fixed-Cell Line) integrating them with three of the most commonly used train-test evaluation settings, to ensure a truly realistic assessment of the prediction performance. We also scrutinize the challenges associated with using IC50 as a prediction label, showing how its close correlation with the drug concentration ranges worsens the risk of misleading performance assessment, and we indicate an additional reason to replace it with the Area Under the Dose-Response Curve instead.

Exploring high-throughput drug sensitivity testing in neuroblastoma cell lines and patient-derived tumor organoids in the era of precision medicine

INTRODUCTION

Despite druggable events to be present in 80 % of neuroblastomapatients within the Princess Máxima Center precision medicine program 'iTHER', clinical uptake of treatment recommendations has been low, and the clinical impact for individual patients remains hard to predict. This stresses the need for a method integrating genomics and transcriptomics with functional approaches into therapeutic decision making.

METHODS

We aimed to launch an online repository integrating genomics and transcriptomics with high-throughput drug screening (HTS) of nineteen commonly used neuroblastoma cell lines and fifteen neuroblastoma patient-derived organoids (NBL-PDOs). Cell lines, NBL-PDOs and their parental tumors were characterized utilizing (lc)WGS, WES and RNAseq. Cells were exposed to ∼200 compounds. Results were transferred to the R2 visualization platform.

RESULTS

A powerful reference set of cell lines is available, reflecting distinct known pharmacologic vulnerabilities. HTS identified additional therapeutic vulnerabilities, such as a striking correlation between a positive mesenchymal signature and sensitivity to BCL2-inhibitor venetoclax. Finally, we explored personalized drug sensitivities within iTHER, demonstrating HTS can support genomic and transcriptomic results, thereby strengthening the rationale for clinical uptake.

CONCLUSION

We established a dynamic publicly available dataset with detailed genomic, transcriptomic, and pharmacological annotation of classical neuroblastoma cell lines as well as novel sharable NBL-PDOs, representing the heterogeneous landscape of neuroblastoma. We anticipate that in vitro drug screening will be complementary to genomic-guided precision medicine by supporting clinical decision making, thereby improving prognosis for all neuroblastoma patients in the future.

A multiplex single-cell RNA-Seq pharmacotranscriptomics pipeline for drug discovery

The gene-regulatory dynamics governing drug responses in cancer are yet to be fully understood. Here, we report a pipeline capable of producing high-throughput pharmacotranscriptomic profiling through live-cell barcoding using antibody-oligonucleotide conjugates. This pipeline combines drug screening with 96-plex single-cell RNA sequencing. We show the potential of this approach by exploring the heterogeneous transcriptional landscape of primary high-grade serous ovarian cancer (HGSOC) cells after treatment with 45 drugs, with 13 distinct classes of mechanisms of action. A subset of phosphatidylinositol 3-OH kinase (PI3K), protein kinase B (AKT) and mammalian target of rapamycin (mTOR) inhibitors induced the activation of receptor tyrosine kinases, such as the epithelial growth factor receptor (EGFR), and this was mediated by the upregulation of caveolin 1 (CAV1). This drug resistance feedback loop could be mitigated by the synergistic action of agents targeting PI3K-AKT-mTOR and EGFR for HGSOC with CAV1 and EGFR expression. Using this workflow could enable the personalized testing of patient-derived tumor samples at single-cell resolution.

Evaluating feature extraction in ovarian cancer cell line co-cultures using deep neural networks

Single-cell image analysis is crucial for studying drug effects on cellular morphology and phenotypic changes. Most studies focus on single cell types, overlooking the complexity of cellular interactions. Here, we establish an analysis pipeline to extract phenotypic features of cancer cells cultured with fibroblasts. Using high-content imaging, we analyze an oncology drug library across five cancer and fibroblast cell line co-culture combinations, generating 61,440 images and ∼170 million single-cell objects. Traditional phenotyping with CellProfiler achieves an average enrichment score of 62.6% for mechanisms of action, while pre-trained neural networks (EfficientNetB0 and MobileNetV2) reach 61.0% and 62.0%, respectively. Variability in enrichment scores may reflect the use of multiple drug concentrations since not all induce significant morphological changes, as well as the cellular and genetic context of the treatment. Our study highlights nuanced drug-induced phenotypic variations and underscores the morphological heterogeneity of ovarian cancer cell lines and their response to complex co-culture environments.

Ex vivo venetoclax sensitivity predicts clinical response in acute myeloid leukemia in the prospective VenEx trial

ABSTRACT

The B-cell lymphoma 2 inhibitor venetoclax has shown promise for treating acute myeloid leukemia (AML). However, identifying patients likely to respond remains a challenge, especially for those with relapsed/refractory (R/R) disease. We evaluated the utility of ex vivo venetoclax sensitivity testing to predict treatment responses to venetoclax-azacitidine in a prospective, multicenter, phase 2 trial. The trial recruited 104 participants with previously untreated (n = 48), R/R (n = 39), or previously treated secondary AML (sAML) (n = 17). The primary end point was complete remission or complete remission with incomplete hematologic recovery (CR/CRi) rate in ex vivo sensitive trial participants during the first 3 therapy cycles. The key secondary end points included the correlations between ex vivo drug sensitivity, responses, and survival. Venetoclax sensitivity was successfully assessed in 102 of 104 participants, with results available within a median of 3 days from sampling. In previously untreated AML, ex vivo sensitivity corresponded to an 85% (34/40) CR/CRi rate, with a median overall survival (OS) of 28.7 months, compared with 5.5 months for ex vivo resistant patients (P = .002). For R/R/sAML, ex vivo sensitivity resulted in a 62% CR/CRi rate (21/34) and median OS of 9.7 vs 3.3 months for ex vivo resistant patients (P < .001). In univariate and multivariate analysis, ex vivo venetoclax sensitivity was the strongest predictor for a favorable treatment response and survival. This trial demonstrates the feasibility of integrating ex vivo drug testing into clinical practice to identify patients with AML, particularly in the R/R setting, who benefit from venetoclax. This trial was registered at www.clinicaltrials.gov as #NCT04267081.

Synergistic combination of orally available safe-in-man pleconaril, AG7404, and mindeudesivir inhibits enterovirus infections in human cell and organoid cultures

Enteroviruses can infect various human organs, causing diseases such as meningitis, the common cold, hand-foot-and-mouth disease, myocarditis, pancreatitis, hepatitis, poliomyelitis, sepsis, and type 1 diabetes. Currently, there are no approved treatments for enterovirus infections. In this study, we identified a synergistic combination of orally available, safe-in-man pleconaril, AG7404, and mindeudesivir, that at non-toxic concentrations effectively inhibited enterovirus replication in human cell and organoid cultures. Importantly, the cocktail did not alter glucose and insulin levels in the culture medium of pancreatic β-cells and preserved the contraction rhythm of infected heart organoids. These findings highlight a promising drug cocktail for further preclinical studies and clinical trials targeting a broad range of enterovirus-mediated diseases.

The Functional Transcriptomic Landscape Informs Therapeutic Strategies in Multiple Myeloma

Several therapeutic agents have been approved for treating multiple myeloma, a cancer of bone marrow-resident plasma cells. Predictive biomarkers for drug response could help guide clinical strategies to optimize outcomes. In this study, we present an integrated functional genomic analysis of tumor samples from patients multiple myeloma that were assessed for their ex vivo drug sensitivity to 37 drugs, clinical variables, cytogenetics, mutational profiles, and transcriptomes. This analysis revealed a multiple myeloma transcriptomic topology that generates "footprints" in association with ex vivo drug sensitivity that have both predictive and mechanistic applications. Validation of the transcriptomic footprints for the anti-CD38 mAb daratumumab (DARA) and the nuclear export inhibitor selinexor (SELI) demonstrated that these footprints can accurately classify clinical responses. The analysis further revealed that DARA and SELI have anticorrelated mechanisms of resistance, and treatment with a SELI-based regimen immediately after a DARA-containing regimen was associated with improved survival in three independent clinical trials, supporting an evolutionary-based strategy involving sequential therapy. These findings suggest that this unique repository and computational framework can be leveraged to inform underlying biology and to identify therapeutic strategies to improve treatment of multiple myeloma. Significance: Functional genomic analysis of primary multiple myeloma samples elucidated predictive biomarkers for drugs and molecular pathways mediating therapeutic response, which revealed a rationale for sequential therapy to maximize patient outcomes.

Mitochondrial-targeting strategies with homoharringtonine: A novel approach for chemoresistant rectal cancer

5-Fluorouracil (5-FU) resistance poses a significant challenge in the treatment of rectal cancer, driving the need for novel therapeutic strategies. In this study, we established 5-FU-resistant rectal cancer cell lines (SW837-r, SNU-C1-r) and identified homoharringtonine (HHT) as a potent and selective anticancer agent through high-throughput drug screening of 291 compounds. HHT displayed the highest selective drug sensitivity score (sDSS), showing strong activity against resistant cells while sparing normal rectal epithelial cells. Further investigations revealed that 5-FU-resistant cells exhibited enhanced mitochondrial biogenesis and function compared to normal cells, and HHT exerted its cytotoxic effects by targeting mitochondrial respiration. HHT significantly reduced oxygen consumption rate (OCR), mitochondrial complex I activity, and ATP production in resistant cells, with mitochondrial respiration-deficient ρ0 cells showing reduced sensitivity to HHT. In vivo, HHT alone reduced tumor growth by 60 % in the resistant xenograft model. In the sensitive xenograft model, combination therapy with 5-FU achieved an 85 % reduction in tumor volume compared to controls, with tumors in the combination group displaying minimal regrowth. These results demonstrate that HHT effectively targets mitochondrial function in resistant rectal cancer cells and, in combination with 5-FU, offers synergistic antitumor effects and prolonged tumor suppression. The favorable safety profile of HHT further highlights its potential as a promising therapeutic agent for overcoming 5-FU resistance in rectal cancer.

Patient-derived acellular ascites fluid affects drug responses in ovarian cancer cell lines through the activation of key signalling pathways

Malignant ascites is commonly produced in advanced epithelial ovarian cancer (EOC) and serves as unique microenvironment for tumour cells. Acellular ascites fluid (AAF) is rich in signalling molecules and has been proposed to play a role in the induction of chemoresistance. Through in vitro testing of drug sensitivity and by assessing intracellular phosphorylation status in response to mono- and combination treatment of five EOC cell lines after incubation with AAFs derived from 20 different patients, we investigated the chemoresistance-inducing potential of ascites. We show that the addition of AAFs to the culture media of EOC cell lines has the potential to induce resistance to standard-of-care drugs (SCDs). We also show that AAFs induce time- and concentration-dependent activation of downstream signalling to signal transducer and activator of transcription 3 (STAT3), and concomitantly altered phosphorylation of mitogen-activated protein kinase kinase (MEK), phosphoinositide 3-kinase (PI3K)-protein kinase B (AKT) and nuclear factor NF-kappa-B (NFκB). Antibodies targeting the interleukin-6 receptor (IL6R) effectively blocked phosphorylation of STAT3 and STAT1. Treatments with SCDs were effective in reducing cell viability in only a third of 30 clinically relevant conditions examined, defined as combinations of drugs, different cell lines and AAFs. Combinations of SCDs and novel therapeutics such as trametinib, fludarabine or rapamycin were superior in another third. Notably, we could nominate effective treatment combinations in almost all conditions except in 4 out of 30 conditions, in which trametinib or fludarabine showed higher efficacy alone. Taken together, our study underscores the importance of the molecular characterisation of individual patients' AAFs and the impact on treatment resistance as providing clinically meaningful information for future precision treatment approaches in EOC.

Molecular and functional profiling of primary normal ovarian cells defines insights into cancer development and drug responses

Patients with ovarian cancer, especially the high-grade serous ovarian cancer (HGSOC) subtype, face poor prognosis due to late diagnosis and treatment resistance. Owing to the high heterogeneity of HGSOC, identifying the origin of the disease and optimal treatments is difficult. Here, we characterized two primary immortalized human ovarian cell lines, human ovarian surface epithelium (HOSE)1C and HOSE2C, comparing their molecular profiling with representative HGSOC cells. We identified molecular features associated with normal and malignant phenotype of ovarian cells by applying single-cell transcriptomics, high-content image-based cell painting, and high-throughput drug testing. Our findings reveal distinct transcriptomic and morphological profiles for the two HOSEs, with a stromal phenotype. Moreover, their responses to the tumor microenvironment differ, exemplified by STAT1 and GREM1 upregulation in HOSE1C and HOSE2C, respectively. We identified selective activation of ERK/MEK targeted inhibitors in cancer cells compared to HOSEs. This study offers insights into the normal and malignant ovarian cells, shedding light on cancer development and drug responses.

Deciphering the Therapeutic Potential of Novel Pentyloxyamide-Based Class I, IIb HDAC Inhibitors against Therapy-Resistant Leukemia

Histone deacetylase inhibitors (HDACi) are established anticancer drugs, especially in hematological cancers. This study aimed to design, synthesize, and evaluate a set of HDACi featuring a pentyloxyamide connecting unit linker region and substituted phenylthiazole cap groups. A structural optimization program yielded HDACi with nanomolar inhibitory activity against histone deacetylase class I/IIb enzymes. The novel inhibitors (4d and 4m) showed superior antileukemic activity compared to several approved HDACi. Furthermore, 4d and 4m displayed synergistic activity when combined with chemotherapeutics, decitabine, and clofarabine. In vitro pharmacokinetic studies showed the most promising profile for 4d with intermediate microsomal stability, excellent plasma stability, and concentration-independent plasma protein binding. Additionally, 4d demonstrated comparable in vivo pharmacokinetics to vorinostat. When administered in vivo, 4d effectively inhibited the proliferation of leukemia cells without causing toxicity. Furthermore, the binding modes of 4d and 4m to the catalytic domain 2 of HDAC6 from Danio rerio were determined by X-ray crystallography.

The ROCK-1/2 inhibitor RKI-1447 blocks N-MYC, promotes cell death, and emerges as a synergistic partner for BET inhibitors in neuroblastoma

High-risk neuroblastoma has a poor prognosis despite intensive treatment, highlighting the need for new therapeutic strategies. Genetic alterations in activators and inactivators of Rho GTPase have been identified in neuroblastoma suggested to activate Rho/Rho-kinase (ROCK) signaling. ROCK has also been implicated in therapy resistance. Therefore, we have explored the efficacy of the dual ROCK inhibitor RKI-1447 in neuroblastoma, emphasizing combination strategies. Treatment with RKI-1447 resulted in decreased growth, increased cell death, and inhibition of N-MYC in vitro and in vivo. A combination screen revealed enhanced effects between RKI-1447 and BET inhibitors. Synergistic effects from RKI-1447 and the BET inhibitor, ABBV-075, were confirmed in various neuroblastoma models, including zebrafish. Interestingly, ABBV-075 increased phosphorylation of both myosin light chain 2 and cofilin, downstream effectors of ROCK, increases that were blocked by adding RKI-1447. The combination treatment also augmented an inhibitory effect on C-MYC and, less pronounced, N-MYC protein expression. BET inhibitors have shown preclinical efficacy against neuroblastoma, but acquired resistance has limited their therapeutic benefit. We reveal that the combination of ROCK and BET inhibitors offers a promising treatment approach that can potentially mitigate resistance to BET inhibitors and reduce toxicity.

High-throughput screening identified pacritinib as a promising therapeutic approach to overcome lenvatinib resistance in hepatocellular carcinoma by targeting IRAK1

Lenvatinib resistance presents a significant challenge in the clinical management of advanced hepatocellular carcinoma (HCC). To address this issue, we established lenvatinib resistant HCC cells and performed high-throughput screening using FDA-approved anti-cancer drug library. Through quantitative selective drug sensitivity scoring (sDSS), pacritinib, a well-known JAK inhibitor, emerged as the top candidate, displaying the highest sDSS score among 219 compounds. We further demonstrated that pacritinib reduced the viability of a panel of HCC cell lines in a dose-dependent manner, while exhibiting minimal effects on normal liver cells. Interestingly, pacritinib, but not other JAK inhibitors such as ruxolitinib, upadacitinib, or filgotinib, acted synergistically with lenvatinib in HCC cells. In lenvatinib-resistant HCC cells, pacritinib significantly decreased proliferation and induced apoptosis. Rescue studies using IL-1 receptor-associated kinase 1 (IRAK1) overexpression and knockdown revealed that pacritinib's effects are mediated through IRAK1, with minimal impact on normal liver cells. In a xenograft model of lenvatinib-resistant HCC, oral administration of pacritinib significantly reduced tumor size without affecting body weight. Immunohistochemical analysis of tumor sections revealed that pacritinib reduced Ki67 staining and phosphorylated IRAK1. Our findings suggest that pacritinib may be a promising therapeutic option for the treatment of advanced HCC, particularly in patients who have developed resistance to lenvatinib.

Pan-Cancer Drug Sensitivity Prediction from Gene Expression using Deep Learning

Cancer is a group of complex diseases, with tumor heterogeneity, durable drug efficacy, emerging resistance, and host toxicity presenting major challenges to the development of effective cancer therapeutics. While traditionally used methods have remained limited in their capacity to overcome these challenges in cancer drug development, efforts have been made in recent years toward applying "big data" to cancer research and precision oncology. By curating, standardizing, and integrating data from various databases, we developed deep learning architectures that use perturbation and baseline transcriptional signatures to predict efficacious small molecule compounds and genetic dependencies in cancer. A series of internal validations followed by prospective validation in prostate cancer cell lines were performed to ensure consistent performance and model applicability. We report SensitivitySeq, a novel bioinformatics tool for prioritizing small molecule compounds and gene dependencies in silico to drive the development of targeted therapies for cancer. To the best of our knowledge, this is the first supervised deep learning approach, validated in vitro, to predict drug sensitivity using baseline cancer cell line gene expression alongside cell line-independent perturbation-response consensus signatures.

Recent advances in and applications of ex vivo drug sensitivity analysis for blood cancers

Blood cancers, including leukemia, multiple myeloma, and lymphoma, pose significant challenges owing to their heterogeneous nature and the limitations of traditional treatments. Precision medicine has emerged as a transformative approach that offers tailored therapeutic strategies based on individual patient profiles. Ex vivo drug sensitivity analysis is central to this advancement, which enables testing of patient-derived cancer cells against a panel of therapeutic agents to predict clinical responses. This review provides a comprehensive overview of the latest advancements in ex vivo drug sensitivity analyses and their application in blood cancers. We discuss the development of more comprehensive drug response metrics and the evaluation of drug combinations to identify synergistic interactions. Additionally, we present evaluation of the advanced therapeutics such as antibody-drug conjugates using ex vivo assays. This review describes the critical role of ex vivo drug sensitivity analyses in advancing precision medicine by examining technological innovations and clinical applications. Ultimately, these innovations are paving the way for more effective and individualized treatments, improving patient outcomes, and establishing new standards for the management of blood cancers.

Integrative Drug Screening and Multiomic Characterization of Patient-derived Bladder Cancer Organoids Reveal Novel Molecular Correlates of Gemcitabine Response

BACKGROUND AND OBJECTIVE

Predicting response to therapy for each patient's tumor is critical to improving long-term outcomes for muscle-invasive bladder cancer. This study aims to establish ex vivo bladder cancer patient-derived organoid (PDO) models that are representative of patients' tumors and determine the potential efficacy of standard of care and curated experimental therapies.

METHODS

Tumor material was collected prospectively from consented bladder cancer patients to generate short-term PDO models, which were screened against a panel of clinically relevant drugs in ex vivo three-dimensional culture. Multiomic profiling was utilized to validate the PDO models, establish the molecular characteristics of each tumor, and identify potential biomarkers of drug response. Gene expression (GEX) patterns between paired primary tissue and PDO samples were assessed using Spearman's rank correlation coefficients. Molecular correlates of therapy response were identified using Pearson correlation coefficients and Kruskal-Wallis tests with Dunn's post hoc pairwise comparison testing.

KEY FINDINGS AND LIMITATIONS

A total of 106 tumors were collected from 97 patients, with 65 samples yielding sufficient material for complete multiomic molecular characterization and PDO screening with six to 32 drugs/combinations. Short-term PDOs faithfully represent the tumor molecular characteristics, maintain diverse cell types, and avoid shifts in GEX-based subtyping that accompany long-term PDO cultures. Utilizing an integrative approach, novel correlations between ex vivo drug responses and genomic alterations, GEX, and protein expression were identified, including a multiomic signature of gemcitabine response. The positive predictive value of ex vivo drug responses and the novel multiomic gemcitabine response signature need to be validated in future studies.

CONCLUSIONS AND CLINICAL IMPLICATIONS

Short-term PDO cultures retain the molecular characteristics of tumor tissue and avoid shifts in expression-based subtyping that have plagued long-term cultures. Integration of multiomic profiling and ex vivo drug screening data identifies potential predictive biomarkers, including a novel signature of gemcitabine response.

PATIENT SUMMARY

Better models are needed to predict patient response to therapy in bladder cancer. We developed a platform that uses short-term culture to best mimic each patient's tumor and assess potential sensitivity to therapeutics.

Distinct molecular profiles and shared drug vulnerabilities in pancreatic metastases of renal cell carcinoma

Clear-cell renal cell carcinoma (ccRCC) is the most common origin of pancreatic metastases (PM). Distinct genomic aberrations, favorable prognosis, and clinical observations on high angiogenesis, and succeeding tyrosine kinase inhibitor (TKI) sensitivity have been reported in PM-ccRCC. However, no functional or single-cell studies have been conducted thus far. We recruited five PM-ccRCC patients and investigated the genomic, single-cell transcriptomic, and drug sensitivity profiles of their patient-derived cells (PDCs). The PM depicted both expected and novel genomic alterations. Further, the transcriptomics differed from both primary and metastatic ccRCC, with upregulations of the PI3K/mTOR and - supporting the clinical observations - angiogenesis pathways. Data integration at pathway level showed that transcriptomics explained drug sensitivities the best. Accordingly, PM-ccRCC PDCs shared sensitivity to many PI3K/mTOR inhibitors. Altogether, we show distinct genomic and transcriptomic signatures in PM-ccRCC, highlight the superiority of transcriptomics in interpreting drug sensitivities, and encourage the use of TKIs and PI3K/mTOR inhibitors in PM-ccRCC.

Venetoclax triggers sublethal apoptotic signaling in venetoclax-resistant acute myeloid leukemia cells and induces vulnerability to PARP inhibition and azacitidine

Venetoclax plus azacitidine treatment is clinically beneficial for elderly and unfit acute myeloid leukemia (AML) patients. However, the treatment is rarely curative, and relapse due to resistant disease eventually emerges. Since no current clinically feasible treatments are known to be effective at the state of acquired venetoclax resistance, this is becoming a major challenge in AML treatment. Studying venetoclax-resistant AML cell lines, we observed that venetoclax induced sublethal apoptotic signaling and DNA damage even though cell survival and growth were unaffected. This effect could be due to venetoclax inducing a sublethal degree of mitochondrial outer membrane permeabilization. Based on these results, we hypothesized that the sublethal apoptotic signaling induced by venetoclax could constitute a vulnerability in venetoclax-resistant AML cells. This was supported by screens with a broad collection of drugs, where we observed a synergistic effect between venetoclax and PARP inhibition in venetoclax-resistant cells. Additionally, the venetoclax-PARP inhibitor combination prevented the acquisition of venetoclax resistance in treatment naïve AML cell lines. Furthermore, the addition of azacitidine to the venetoclax-PARP inhibitor combination enhanced venetoclax induced DNA damage and exhibited exceptional sensitivity and long-term responses in the venetoclax-resistant AML cell lines and samples from AML patients that had clinically relapsed under venetoclax-azacitidine therapy. In conclusion, we mechanistically identify a new vulnerability in acquired venetoclax-resistant AML cells and identify PARP inhibition as a potential therapeutic approach to overcome acquired venetoclax resistance in AML.

Optimizing drug combinations for T-PLL: restoring DNA damage and P53-mediated apoptotic responses

ABSTRACT

T-prolymphocytic leukemia (T-PLL) is a mature T-cell neoplasm associated with marked chemotherapy resistance and continued poor clinical outcomes. Current treatments, that is, the CD52-antibody alemtuzumab, offer transient responses, with relapses being almost inevitable without consolidating allogeneic transplantation. Recent more detailed concepts of T-PLL's pathobiology fostered the identification of actionable vulnerabilities: (1) altered epigenetics, (2) defective DNA damage responses, (3) aberrant cell-cycle regulation, and (4) deregulated prosurvival pathways, including T-cell receptor and JAK/STAT signaling. To further develop related preclinical therapeutic concepts, we studied inhibitors of histone deacetylases ([H]DACs), B-cell lymphoma 2 (BCL2), cyclin-dependent kinase (CDK), mouse double minute 2 (MDM2), and classical cytostatics, using (1) single-agent and combinatorial compound testing in 20 well-characterized and molecularly profiled primary T-PLL (validated by additional 42 cases) and (2) 2 independent murine models (syngeneic transplants and patient-derived xenografts). Overall, the most efficient/selective single agents and combinations (in vitro and in mice) included cladribine, romidepsin ([H]DAC), venetoclax (BCL2), and/or idasanutlin (MDM2). Cladribine sensitivity correlated with expression of its target RRM2. T-PLL cells revealed low overall apoptotic priming with heterogeneous dependencies on BCL2 proteins. In additional 38 T-cell leukemia/lymphoma lines, TP53 mutations were associated with resistance toward MDM2 inhibitors. P53 of T-PLL cells, predominantly in wild-type configuration, was amenable to MDM2 inhibition, which increased its MDM2-unbound fraction. This facilitated P53 activation and downstream signals (including enhanced accessibility of target-gene chromatin regions), in particular synergy with insults by cladribine. Our data emphasize the therapeutic potential of pharmacologic strategies to reinstate P53-mediated apoptotic responses. The identified efficacies and their synergies provide an informative background on compound and patient selection for trial designs in T-PLL.

Knowledge graphs facilitate prediction of drug response for acute myeloid leukemia

Acute myeloid leukemia (AML) is a highly aggressive and heterogeneous disease, underscoring the need for improved therapeutic options and methods to optimally predict responses. With the wealth of available data resources, including clinical features, multiomics analysis, and ex vivo drug screening from AML patients, development of drug response prediction models has become feasible. Knowledge graphs (KGs) embed the relationships between different entities or features, allowing for explanation of a wide breadth of drug sensitivity and resistance mechanisms. We designed AML drug response prediction models guided by KGs. Our models included engineered features, relative gene expression between marker genes for each drug and regulators (e.g., transcription factors). We identified relative gene expression of FGD4-MIR4519, NPC2-GATA2, and BCL2-NFKB2 as predictive features for venetoclax ex vivo drug response. The KG-guided models provided high accuracy in independent test sets, overcame potential platform batch effects, and provided candidate drug sensitivity biomarkers for further validation.

High-throughput neural stem cell-based drug screening identifies S6K1 inhibition as a selective vulnerability in sonic hedgehog-medulloblastoma

BACKGROUND

Medulloblastoma (MB) is one of the most common malignant brain tumors in children. Current treatments have increased overall survival but can lead to devastating side effects and late complications in survivors, emphasizing the need for new, improved targeted therapies that specifically eliminate tumor cells while sparing the normally developing brain.

METHODS

Here, we used a sonic hedgehog (SHH)-MB model based on a patient-derived neuroepithelial stem cell system for an unbiased high-throughput screen with a library of 172 compounds with known targets. Compounds were evaluated in both healthy neural stem cells (NSCs) and tumor cells derived from the same patient. Based on the difference of cell viability and drug sensitivity score between normal cells and tumor cells, hit compounds were selected and further validated in vitro and in vivo.

RESULTS

We identified PF4708671 (S6K1 inhibitor) as a potential agent that selectively targets SHH-driven MB tumor cells while sparing NSCs and differentiated neurons. Subsequent validation studies confirmed that PF4708671 inhibited the growth of SHH-MB tumor cells both in vitro and in vivo, and that knockdown of S6K1 resulted in reduced tumor formation.

CONCLUSIONS

Overall, our results suggest that inhibition of S6K1 specifically affects tumor growth, whereas it has less effect on non-tumor cells. Our data also show that the NES cell platform can be used to identify potentially effective new therapies and targets for SHH-MB.

In-vitro assays for immuno-oncology drug efficacy assessment and screening for personalized cancer therapy: scopes and challenges

INTRODUCTION

Immunotherapies have revolutionized cancer treatment, but often fail to produce desirable therapeutic outcomes in all patients. Due to the inter-patient heterogeneity and complexity of the tumor microenvironment, personalized treatment approaches are gaining demand. Researchers have long been using a range of in-vitro assays including 2D models, organoid co-cultures, and cancer-on-a-chip platforms for cancer drug screening. A comparative analysis of these assays with their suitability, high-throughput capacity, and clinical translatability is required for optimal translational use.

AREAS COVERED

The review summarized in-vitro platforms with their comparative advantages and limitations including construction strategies, and translational potential for immuno-oncology drug efficacy assessment. We also discussed end-point analysis strategies so that researchers can contextualize their usefulness and optimally design experiments for personalized immunotherapy efficacy prediction.

EXPERT OPINION

Researchers developed several in-vitro platforms that can provide information on personalized immunotherapy efficacy from different angles. Image-based assays are undoubtedly more suitable to gather a wide range of information including cellular morphology and phenotypical behaviors but need significant improvement to overcome issues including background noise, sample preparation difficulty, and long duration of experiment. More studies and clinical trials are needed to resolve these issues and validate the assays before they can be used in real-life scenarios.

Synergistic drug interactions of the histone deacetylase inhibitor givinostat (ITF2357) in CRLF2-rearranged pediatric B-cell precursor acute lymphoblastic leukemia identified by high-throughput drug screening

Combining multiple drugs broadens the window of therapeutic opportunities and is crucial for diseases that are currently lacking fully curative treatments. A powerful emerging tool for selecting effective drugs and combinations is the high-throughput drug screening (HTP). The histone deacetylase inhibitor (HDACi) givinostat (ITF2357) has been shown to act effectively against CRLF2-rearranged pediatric B-cell precursor acute lymphoblastic leukemia (BCP-ALL), a subtype characterized by poor outcome and enriched in children with Down Syndrome, very fragile patients with a high susceptibility to treatment-related toxicity. The aim of this study is to investigate possible synergies with givinostat for these difficult-to-treat patients by performing HTP screening with a library of 174 drugs, either approved or in preclinical studies. By applying this approach to the CRLF2-r MHH-CALL-4 cell line, we identified 19 compounds with higher sensitivity in combination with givinostat compared to the single treatments. Next, the synergy between givinostat and the promising candidates was further validated in CRLF2r cell lines with a broad matrix of concentrations. The combinations with trametinib (MEKi) or venetoclax (BCL2i) were found to be the most effective and with the greatest synergy across three metrics (ZIP, HAS, Bliss). Their efficacy was confirmed in primary blasts treated ex vivo at concentration ranges with a safe profile on healthy cells. Finally, we described givinostat-induced modifications in gene expression of MAPK and BCL-2 family members, supporting the observed synergistic interactions. Overall, our study represents a model of drug repurposing strategy using HTP screening for identifying synergistic, efficient, and safe drug combinations.

Oncogenic and immunological targets for matched therapy of pediatric blood cancer patients: Dutch iTHER study experience

Over the past 10 years, institutional and national molecular tumor boards have been implemented for relapsed or refractory pediatric cancer to prioritize targeted drugs for individualized treatment based on actionable oncogenic lesions, including the Dutch iTHER platform. Hematological malignancies form a minority in precision medicine studies. Here, we report on 56 iTHER leukemia/lymphoma patients for which we considered cell surface markers and oncogenic aberrations as actionable events, supplemented with ex vivo drug sensitivity for six patients. Prior to iTHER registration, 34% of the patients had received allogeneic hematopoietic cell transplantation (HCT) and 18% CAR-T therapy. For 51 patients (91%), a sample with sufficient tumor percentage (≥20%) required for comprehensive diagnostic testing was obtained. Up to 10 oncogenic actionable events were prioritized in 49/51 patients, and immunotherapy targets were identified in all profiled patients. Targeted treatment(s) based on the iTHER advice was given to 24 of 51 patients (47%), including immunotherapy in 17 patients, a targeted drug matching an oncogenic aberration in 12 patients, and a drug based on ex vivo drug sensitivity in one patient, resulting in objective responses and a bridge to HCT in the majority of the patients. In conclusion, comprehensive profiling of relapsed/refractory hematological malignancies showed multiple oncogenic and immunotherapy targets for a precision medicine approach, which requires multidisciplinary expertise to prioritize the best treatment options for this rare, heavily pretreated pediatric population.

An Improved PDE6D Inhibitor Combines with Sildenafil To Inhibit KRAS Mutant Cancer Cell Growth

The trafficking chaperone PDE6D (or PDEδ) was proposed as a surrogate target for K-Ras, leading to the development of a series of inhibitors that block its prenyl binding pocket. These inhibitors suffered from low solubility and suspected off-target effects, preventing their clinical development. Here, we developed a highly soluble, low nanomolar PDE6D inhibitor (PDE6Di), Deltaflexin3, which has the lowest off-target activity as compared to three prominent reference compounds. Deltaflexin3 reduces Ras signaling and selectively decreases the growth of KRAS mutant and PDE6D-dependent cancer cells. We further show that PKG2-mediated phosphorylation of Ser181 lowers K-Ras binding to PDE6D. Thus, Deltaflexin3 combines with the approved PKG2 activator Sildenafil to more potently inhibit PDE6D/K-Ras binding, cancer cell proliferation, and microtumor growth. As observed previously, inhibition of Ras trafficking, signaling, and cancer cell proliferation remained overall modest. Our results suggest reevaluating PDE6D as a K-Ras surrogate target in cancer.

Defining the KRAS- and ERK-dependent transcriptome in KRAS-mutant cancers

How the KRAS oncogene drives cancer growth remains poorly understood. Therefore, we established a systemwide portrait of KRAS- and extracellular signal-regulated kinase (ERK)-dependent gene transcription in KRAS-mutant cancer to delineate the molecular mechanisms of growth and of inhibitor resistance. Unexpectedly, our KRAS-dependent gene signature diverges substantially from the frequently cited Hallmark KRAS signaling gene signature, is driven predominantly through the ERK mitogen-activated protein kinase (MAPK) cascade, and accurately reflects KRAS- and ERK-regulated gene transcription in KRAS-mutant cancer patients. Integration with our ERK-regulated phospho- and total proteome highlights ERK deregulation of the anaphase promoting complex/cyclosome (APC/C) and other components of the cell cycle machinery as key processes that drive pancreatic ductal adenocarcinoma (PDAC) growth. Our findings elucidate mechanistically the critical role of ERK in driving KRAS-mutant tumor growth and in resistance to KRAS-ERK MAPK targeted therapies.

Integrated drug profiling and CRISPR screening identify BCR::ABL1-independent vulnerabilities in chronic myeloid leukemia

BCR::ABL1-independent pathways contribute to primary resistance to tyrosine kinase inhibitor (TKI) treatment in chronic myeloid leukemia (CML) and play a role in leukemic stem cell persistence. Here, we perform ex vivo drug screening of CML CD34+ leukemic stem/progenitor cells using 100 single drugs and TKI-drug combinations and identify sensitivities to Wee1, MDM2, and BCL2 inhibitors. These agents effectively inhibit primitive CD34+CD38- CML cells and demonstrate potent synergies when combined with TKIs. Flow-cytometry-based drug screening identifies mepacrine to induce differentiation of CD34+CD38- cells. We employ genome-wide CRISPR-Cas9 screening for six drugs, and mediator complex, apoptosis, and erythroid-lineage-related genes are identified as key resistance hits for TKIs, whereas the Wee1 inhibitor AZD1775 and mepacrine exhibit distinct resistance profiles. KCTD5, a consistent TKI-resistance-conferring gene, is found to mediate TKI-induced BCR::ABL1 ubiquitination. In summary, we delineate potential mechanisms for primary TKI resistance and non-BCR::ABL1-targeting drugs, offering insights for optimizing CML treatment.

Overcoming limitations in current measures of drug response may enable AI-driven precision oncology

Machine learning (ML) models of drug sensitivity prediction are becoming increasingly popular in precision oncology. Here, we identify a fundamental limitation in standard measures of drug sensitivity that hinders the development of personalized prediction models - they focus on absolute effects but do not capture relative differences between cancer subtypes. Our work suggests that using z-scored drug response measures mitigates these limitations and leads to meaningful predictions, opening the door for sophisticated ML precision oncology models.

Improving the power of drug toxicity measurements by quantitative nuclei imaging

Imaging-based anticancer drug screens are becoming more prevalent due to development of automated fluorescent microscopes and imaging stations, as well as rapid advancements in image processing software. Automated cell imaging provides many benefits such as their ability to provide high-content data, modularity, dynamics recording and the fact that imaging is the most direct way to access cell viability and cell proliferation. However, currently most publicly available large-scale anticancer drugs screens, such as GDSC, CTRP and NCI-60, provide cell viability data measured by assays based on colorimetric or luminometric measurements of NADH or ATP levels. Although such datasets provide valuable data, it is unclear how well drug toxicity measurements can be integrated with imaging data. Here we explored the relations between drug toxicity data obtained by XTT assay, two quantitative nuclei imaging methods and trypan blue dye exclusion assay using a set of four cancer cell lines with different morphologies and 30 drugs with different mechanisms of action. We show that imaging-based approaches provide high accuracy and the differences between results obtained by different methods highly depend on drug mechanism of action. Selecting AUC metrics over IC50 or comparing data where significantly drugs reduced cell numbers noticeably improves consistency between methods. Using automated cell segmentation protocols we analyzed mitochondria activity in more than 11 thousand drug-treated cells and showed that XTT assay produces unreliable data for CDK4/6, Aurora A, VEGFR and PARP inhibitors due induced cell size growth and increase in individual mitochondria activity. We also explored several benefits of image-based analysis such as ability to monitor cell number dynamics, dissect changes in total and individual mitochondria activity from cell proliferation, and ability to identify chromatin remodeling drugs. Finally, we provide a web tool that allows comparing results obtained by different methods.

A phenotypic screening approach to target p60AmotL2-expressing invasive cancer cells

BACKGROUND

Tumor cells have the ability to invade and form small clusters that protrude into adjacent tissues, a phenomenon that is frequently observed at the periphery of a tumor as it expands into healthy tissues. The presence of these clusters is linked to poor prognosis and has proven challenging to treat using conventional therapies. We previously reported that p60AmotL2 expression is localized to invasive colon and breast cancer cells. In vitro, p60AmotL2 promotes epithelial cell invasion by negatively impacting E-cadherin/AmotL2-related mechanotransduction.

METHODS

Using epithelial cells transfected with inducible p60AmotL2, we employed a phenotypic drug screening approach to find compounds that specifically target invasive cells. The phenotypic screen was performed by treating cells for 72 h with a library of compounds with known antitumor activities in a dose-dependent manner. After assessing cell viability using CellTiter-Glo, drug sensitivity scores for each compound were calculated. Candidate hit compounds with a higher drug sensitivity score for p60AmotL2-expressing cells were then validated on lung and colon cell models, both in 2D and in 3D, and on colon cancer patient-derived organoids. Nascent RNA sequencing was performed after BET inhibition to analyse BET-dependent pathways in p60AmotL2-expressing cells.

RESULTS

We identified 60 compounds that selectively targeted p60AmotL2-expressing cells. Intriguingly, these compounds were classified into two major categories: Epidermal Growth Factor Receptor (EGFR) inhibitors and Bromodomain and Extra-Terminal motif (BET) inhibitors. The latter consistently demonstrated antitumor activity in human cancer cell models, as well as in organoids derived from colon cancer patients. BET inhibition led to a shift towards the upregulation of pro-apoptotic pathways specifically in p60AmotL2-expressing cells.

CONCLUSIONS

BET inhibitors specifically target p60AmotL2-expressing invasive cancer cells, likely by exploiting differences in chromatin accessibility, leading to cell death. Additionally, our findings support the use of this phenotypic strategy to discover novel compounds that can exploit vulnerabilities and specifically target invasive cancer cells.

Monosomy 7/del(7q) cause sensitivity to inhibitors of nicotinamide phosphoribosyltransferase in acute myeloid leukemia

ABSTRACT

Monosomy 7 and del(7q) (-7/-7q) are frequent chromosomal abnormalities detected in up to 10% of patients with acute myeloid leukemia (AML). Despite unfavorable treatment outcomes, no approved targeted therapies exist for patients with -7/-7q. Therefore, we aimed to identify novel vulnerabilities. Through an analysis of data from ex vivo drug screens of 114 primary AML samples, we discovered that -7/-7q AML cells are highly sensitive to the inhibition of nicotinamide phosphoribosyltransferase (NAMPT). NAMPT is the rate-limiting enzyme in the nicotinamide adenine dinucleotide salvage pathway. Mechanistically, the NAMPT gene is located at 7q22.3, and deletion of 1 copy due to -7/-7q results in NAMPT haploinsufficiency, leading to reduced expression and a therapeutically targetable vulnerability to the inhibition of NAMPT. Our results show that in -7/-7q AML, differentiated CD34+CD38+ myeloblasts are more sensitive to the inhibition of NAMPT than less differentiated CD34+CD38- myeloblasts. Furthermore, the combination of the BCL2 inhibitor venetoclax and the NAMPT inhibitor KPT-9274 resulted in the death of significantly more leukemic blasts in AML samples with -7/-7q than the NAMPT inhibitor alone. In conclusion, our findings demonstrate that AML with -7/-7q is highly sensitive to NAMPT inhibition, suggesting that NAMPT inhibitors have the potential to be an effective targeted therapy for patients with monosomy 7 or del(7q).

The combination of pleconaril, rupintrivir, and remdesivir efficiently inhibits enterovirus infections in vitro, delaying the development of drug-resistant virus variants

Enteroviruses are a significant global health concern, causing a spectrum of diseases from the common cold to more severe conditions like hand-foot-and-mouth disease, meningitis, myocarditis, pancreatitis, and poliomyelitis. Current treatment options for these infections are limited, underscoring the urgent need for effective therapeutic strategies. To find better treatment option we analyzed toxicity and efficacy of 12 known broad-spectrum anti-enterovirals both individually and in combinations against different enteroviruses in vitro. We identified several novel, synergistic two-drug and three-drug combinations that demonstrated significant inhibition of enterovirus infections in vitro. Specifically, the triple-drug combination of pleconaril, rupintrivir, and remdesivir exhibited remarkable efficacy against echovirus (EV) 1, EV6, EV11, and coxsackievirus (CV) B5, in human lung epithelial A549 cells. This combination surpassed the effectiveness of single-agent or dual-drug treatments, as evidenced by its ability to protect A549 cells from EV1-induced cytotoxicity across seven passages. Additionally, this triple-drug cocktail showed potent antiviral activity against EV-A71 in human intestinal organoids. Thus, our findings highlight the therapeutic potential of the pleconaril-rupintrivir-remdesivir combination as a broad-spectrum treatment option against a range of enterovirus infections. The study also paves the way towards development of strategic antiviral drug combinations with virus family coverage and high-resistance barriers.

Characterisation of new in vitro models and identification of potentially active drugs in angiosarcoma

Angiosarcoma is a rare soft tissue sarcoma originating from endothelial cells. Given that current treatments for advanced disease have shown limited efficacy, alternative therapies need to be identified. In rare diseases, patient-derived cell models are crucial for screening anti-tumour activity. In this study, cell line models were characterised in 2D and 3D cultures. The cell lines' growth, migration and invasion capabilities were explored, confirming them as useful tools for preclinical angiosarcoma studies. By screening a drug library, we identified potentially effective compounds: 8-amino adenosine impacted cell growth and inhibited migration and invasion at considerably low concentrations as a single agent. No synergistic effect was detected when combining with paclitaxel, gemcitabine or doxorubicin. These results suggest that this compound could be a potentially useful drug in the treatment of AGS.

Feasibility of functional precision medicine for guiding treatment of relapsed or refractory pediatric cancers

Children with rare, relapsed or refractory cancers often face limited treatment options, and few predictive biomarkers are available that can enable personalized treatment recommendations. The implementation of functional precision medicine (FPM), which combines genomic profiling with drug sensitivity testing (DST) of patient-derived tumor cells, has potential to identify treatment options when standard-of-care is exhausted. The goal of this prospective observational study was to generate FPM data for pediatric patients with relapsed or refractory cancer. The primary objective was to determine the feasibility of returning FPM-based treatment recommendations in real time to the FPM tumor board (FPMTB) within a clinically actionable timeframe (<4 weeks). The secondary objective was to assess clinical outcomes from patients enrolled in the study. Twenty-five patients with relapsed or refractory solid and hematological cancers were enrolled; 21 patients underwent DST and 20 also completed genomic profiling. Median turnaround times for DST and genomics were within 10 days and 27 days, respectively. Treatment recommendations were made for 19 patients (76%), of whom 14 received therapeutic interventions. Six patients received subsequent FPM-guided treatments. Among these patients, five (83%) experienced a greater than 1.3-fold improvement in progression-free survival associated with their FPM-guided therapy relative to their previous therapy, and demonstrated a significant increase in progression-free survival and objective response rate compared to those of eight non-guided patients. The findings from our proof-of-principle study illustrate the potential for FPM to positively impact clinical care for pediatric and adolescent patients with relapsed or refractory cancers and warrant further validation in large prospective studies. ClinicalTrials.gov registration: NCT03860376 .

High-content analysis identified synergistic drug interactions between INK128, an mTOR inhibitor, and HDAC inhibitors in a non-small cell lung cancer cell line

BACKGROUND

The development of drug resistance is a major cause of cancer therapy failures. To inhibit drug resistance, multiple drugs are often treated together as a combinatorial therapy. In particular, synergistic drug combinations, which kill cancer cells at a lower concentration, guarantee a better prognosis and fewer side effects in cancer patients. Many studies have sought out synergistic combinations by small-scale function-based targeted growth assays or large-scale nontargeted growth assays, but their discoveries are always challenging due to technical problems such as a large number of possible test combinations.

METHODS

To address this issue, we carried out a medium-scale optical drug synergy screening in a non-small cell lung cancer cell line and further investigated individual drug interactions in combination drug responses by high-content image analysis. Optical high-content analysis of cellular responses has recently attracted much interest in the field of drug discovery, functional genomics, and toxicology. Here, we adopted a similar approach to study combinatorial drug responses.

RESULTS

By examining all possible combinations of 12 drug compounds in 6 different drug classes, such as mTOR inhibitors, HDAC inhibitors, HSP90 inhibitors, MT inhibitors, DNA inhibitors, and proteasome inhibitors, we successfully identified synergism between INK128, an mTOR inhibitor, and HDAC inhibitors, which has also been reported elsewhere. Our high-content analysis further showed that HDAC inhibitors, HSP90 inhibitors, and proteasome inhibitors played a dominant role in combinatorial drug responses when they were mixed with MT inhibitors, DNA inhibitors, or mTOR inhibitors, suggesting that recessive drugs could be less prioritized as components of multidrug cocktails.

CONCLUSIONS

In conclusion, our optical drug screening platform efficiently identified synergistic drug combinations in a non-small cell lung cancer cell line, and our high-content analysis further revealed how individual drugs in the drug mix interact with each other to generate combinatorial drug response.

Designing patient-oriented combination therapies for acute myeloid leukemia based on efficacy/toxicity integration and bipartite network modeling

Acute myeloid leukemia (AML), a heterogeneous and aggressive blood cancer, does not respond well to single-drug therapy. A combination of drugs is required to effectively treat this disease. Computational models are critical for combination therapy discovery due to the tens of thousands of two-drug combinations, even with approved drugs. While predicting synergistic drugs is the focus of current methods, few consider drug efficacy and potential toxicity, which are crucial for treatment success. To find effective new drug candidates, we constructed a bipartite network using patient-derived tumor samples and drugs. The network is based on drug-response screening and summarizes all treatment response heterogeneity as drug response weights. This bipartite network is then projected onto the drug part, resulting in the drug similarity network. Distinct drug clusters were identified using community detection methods, each targeting different biological processes and pathways as revealed by enrichment and pathway analysis of the drugs' protein targets. Four drugs with the highest efficacy and lowest toxicity from each cluster were selected and tested for drug sensitivity using cell viability assays on various samples. Results show that ruxolitinib-ulixertinib and sapanisertib-LY3009120 are the most effective combinations with the least toxicity and the best synergistic effect on blast cells. These findings lay the foundation for personalized and successful AML therapies, ultimately leading to the development of drug combinations that can be used alongside standard first-line AML treatment.

Liprin-α1 contributes to oncogenic MAPK signaling by counteracting ERK activity

PTPRF interacting protein alpha 1 (PPFIA1) encodes for liprin-α1, a member of the leukocyte common antigen-related protein tyrosine phosphatase (LAR-RPTPs)-interacting protein family. Liprin-α1 localizes to adhesive and invasive structures in the periphery of cancer cells, where it modulates migration and invasion in head and neck squamous cell carcinoma (HNSCC) and breast cancer. To study the possible role of liprin-α1 in anticancer drug responses, we screened a library of oncology compounds in cell lines with high endogenous PPFIA1 expression. The compounds with the highest differential responses between high PPFIA1-expressing and silenced cells across cell lines were inhibitors targeting mitogen-activated protein kinase kinase (MEK)/extracellular signal-regulated kinases (ERK) signaling. KRAS proto-oncogene, GTPase (KRAS)-mutated MDA-MB-231 cells were more resistant to trametinib upon PPFIA1 knockdown compared with control cells. In contrast, liprin-α1-depleted HNSCC cells with low RAS activity showed a context-dependent response to MEK/ERK inhibitors. Importantly, we showed that liprin-α1 depletion leads to increased p-ERK1/2 levels in all our studied cell lines independent of KRAS mutational status, suggesting a role of liprin-α1 in the regulation of MAPK oncogenic signaling. Furthermore, liprin-α1 depletion led to more pronounced redistribution of RAS proteins to the cell membrane. Our data suggest that liprin-α1 is an important contributor to oncogenic RAS/MAPK signaling, and the status of liprin-α1 may assist in predicting drug responses in cancer cells in a context-dependent manner.

Simple but powerful interactive data analysis in R with R/LinekdCharts

In research involving data-rich assays, exploratory data analysis is a crucial step. Typically, this involves jumping back and forth between visualizations that provide overview of the whole data and others that dive into details. For example, it might be helpful to have one chart showing a summary statistic for all samples, while a second chart provides details for points selected in the first chart. We present R/LinkedCharts, a framework that renders this task radically simple, requiring very few lines of code to obtain complex and general visualization, which later can be polished to provide interactive data access of publication quality.

HTSplotter: An end-to-end data processing, analysis and visualisation tool for chemical and genetic in vitro perturbation screening

In biomedical research, high-throughput screening is often applied as it comes with automatization, higher-efficiency, and more and faster results. High-throughput screening experiments encompass drug, drug combination, genetic perturbagen or a combination of genetic and chemical perturbagen screens. These experiments are conducted in real-time assays over time or in an endpoint assay. The data analysis consists of data cleaning and structuring, as well as further data processing and visualisation, which, due to the amount of data, can easily become laborious, time-consuming and error-prone. Therefore, several tools have been developed to aid researchers in this process, but these typically focus on specific experimental set-ups and are unable to process data of several time points and genetic-chemical perturbagen screens. To meet these needs, we developed HTSplotter, a web tool and Python module that performs automatic data analysis and visualization of visualization of eitherendpoint or real-time assays from different high-throughput screening experiments: drug, drug combination, genetic perturbagen and genetic-chemical perturbagen screens. HTSplotter implements an algorithm based on conditional statements to identify experiment types and controls. After appropriate data normalization, including growth rate normalization, HTSplotter executes downstream analyses such as dose-response relationship and drug synergism assessment by the Bliss independence (BI), Zero Interaction Potency (ZIP) and Highest Single Agent (HSA) methods. All results are exported as a text file and plots are saved in a PDF file. The main advantage of HTSplotter over other available tools is the automatic analysis of genetic-chemical perturbagen screens and real-time assays where growth rate and perturbagen effect results are plotted over time. In conclusion, HTSplotter allows for the automatic end-to-end data processing, analysis and visualisation of various high-throughput in vitro cell culture screens, offering major improvements in terms of versatility, efficiency and time over existing tools.

Robust scoring of selective drug responses for patient-tailored therapy selection

Most patients with advanced malignancies are treated with severely toxic, first-line chemotherapies. Personalized treatment strategies have led to improved patient outcomes and could replace one-size-fits-all therapies, yet they need to be tailored by testing of a range of targeted drugs in primary patient cells. Most functional precision medicine studies use simple drug-response metrics, which cannot quantify the selective effects of drugs (i.e., the differential responses of cancer cells and normal cells). We developed a computational method for selective drug-sensitivity scoring (DSS), which enables normalization of the individual patient's responses against normal cell responses. The selective response scoring uses the inhibition of noncancerous cells as a proxy for potential drug toxicity, which can in turn be used to identify effective and safer treatment options. Here, we explain how to apply the selective DSS calculation for guiding precision medicine in patients with leukemia treated across three cancer centers in Europe and the USA; the generic methods are also widely applicable to other malignancies that are amenable to drug testing. The open-source and extendable R-codes provide a robust means to tailor personalized treatment strategies on the basis of increasingly available ex vivo drug-testing data from patients in real-world and clinical trial settings. We also make available drug-response profiles to 527 anticancer compounds tested in 10 healthy bone marrow samples as reference data for selective scoring and de-prioritization of drugs that show broadly toxic effects. The procedure takes <60 min and requires basic skills in R.

Drug combinations screening using a Bayesian ranking approach based on dose-response models

Drug combinations have been of increasing interest in recent years for the treatment of complex diseases such as cancer, as they could reduce the risk of drug resistance. Moreover, in oncology, combining drugs may allow tackling tumor heterogeneity. Identifying potent combinations can be an arduous task since exploring the full dose-response matrix of candidate combinations over a large number of drugs is costly and sometimes unfeasible, as the quantity of available biological material is limited and may vary across patients. Our objective was to develop a rank-based screening approach for drug combinations in the setting of limited biological resources. A hierarchical Bayesian 4-parameter log-logistic (4PLL) model was used to estimate dose-response curves of dose-candidate combinations based on a parsimonious experimental design. We computed various activity ranking metrics, such as the area under the dose-response curve and Bliss synergy score, and we used the posterior distributions of ranks and the surface under the cumulative ranking curve to obtain a comprehensive final ranking of combinations. Based on simulations, our proposed method achieved good operating characteristics to identifying the most promising treatments in various scenarios with limited sample sizes and interpatient variability. We illustrate the proposed approach on real data from a combination screening experiment in acute myeloid leukemia.