Identification of low-dose multidrug combinations for sunitinib-naive and pre-treated renal cell carcinoma

Machine learning-driven prognostic analysis of cuproptosis and disulfidptosis-related lncRNAs in clear cell renal cell carcinoma: a step towards precision oncology

Cuproptosis and disulfidptosis, recently discovered mechanisms of cell death, have demonstrated that differential expression of key genes and long non-coding RNAs (lncRNAs) profoundly influences tumor development and affects their drug sensitivity. Clear cell renal cell carcinoma (ccRCC), the most common subtype of kidney cancer, presently lacks research utilizing cuproptosis and disulfidptosis-related lncRNAs (CDRLRs) as prognostic markers. In this study, we analyzed RNA-seq data, clinical information, and mutation data from The Cancer Genome Atlas (TCGA) on ccRCC and cross-referenced it with known cuproptosis and disulfidptosis-related genes (CDRGs). Using the LASSO machine learning algorithm, we identified four CDRLRs-ACVR2B-AS1, AC095055.1, AL161782.1, and MANEA-DT-that are strongly associated with prognosis and used them to construct a prognostic risk model. To verify the model's reliability and validate these four CDRLRs as significant prognostic factors, we performed dataset grouping validation, followed by RT-qPCR and external database validation for differential expression and prognosis of CDRLRs in ccRCC. Gene function and pathway analysis were conducted using Gene Ontology (GO) and Gene Set Enrichment Analysis (GSEA) for high- and low-risk groups. Additionally, we have analyzed the tumor mutation burden (TMB) and the immune microenvironment (TME), employing the oncoPredict and Immunophenoscore (IPS) algorithms to assess the sensitivity of diverse risk categories to targeted therapeutics and immunosuppressants. Our predominant objective is to refine prognostic predictions for patients with ccRCC and inform treatment decisions by conducting an exhaustive study on cuproptosis and disulfidptosis.

Mechanisms of tyrosine kinase inhibitor resistance in renal cell carcinoma

Renal cell carcinoma (RCC), the most prevalent type of kidney cancer, is a significant cause of cancer morbidity and mortality worldwide. Antiangiogenic tyrosine kinase inhibitors (TKIs), in combination with immune checkpoint inhibitors (ICIs), are among the first-line treatment options for patients with advanced RCC. These therapies target the vascular endothelial growth factor receptor (VEGFR) tyrosine kinase pathway and other kinases crucial to cancer proliferation, survival, and metastasis. TKIs have yielded substantial improvements in progression-free survival (PFS) and overall survival (OS) for patients with advanced RCC. However, nearly all patients eventually progress on these drugs as resistance develops. This review provides an overview of TKI resistance in RCC and explores different mechanisms of resistance, including upregulation of alternative proangiogenic pathways, epithelial-mesenchymal transition (EMT), decreased intracellular drug concentrations due to efflux pumps and lysosomal sequestration, alterations in the tumor microenvironment including bone marrow-derived cells (BMDCs) and tumor-associated fibroblasts (TAFs), and genetic factors such as single nucleotide polymorphisms (SNPs). A comprehensive understanding of these mechanisms opens the door to the development of innovative therapeutic approaches that can effectively overcome TKI resistance, thereby improving outcomes for patients with advanced RCC.

Advanced in vitro models for renal cell carcinoma therapy design

Renal cell carcinoma (RCC) and its principal subtype, clear cell RCC, are the most diagnosed kidney cancer. Despite substantial improvement over the last decades, current pharmacological intervention still fails to achieve long-term therapeutic success. RCC is characterized by a high intra- and inter-tumoral heterogeneity and is heavily influenced by the crosstalk of the cells composing the tumor microenvironment, such as cancer-associated fibroblasts, endothelial cells and immune cells. Moreover, multiple physicochemical properties such as pH, interstitial pressure or oxygenation may also play an important role. These elements are often poorly recapitulated in in vitro models used for drug development. This inadequate recapitulation of the tumor is partially responsible for the current lack of an effective and curative treatment. Therefore, there are needs for more complex in vitro or ex vivo drug screening models. In this review, we discuss the current state-of-the-art of RCC models and suggest strategies for their further development.

Modulation of lncRNA links endothelial glycocalyx to vascular dysfunction of tyrosine kinase inhibitor

AIMS

Novel cancer therapies leading to increased survivorship of cancer patients have been negated by a concomitant rise in cancer therapies-related cardiovascular toxicities. Sunitinib, a first line multi-receptor tyrosine kinase inhibitor, has been reported to cause vascular dysfunction although the initiating mechanisms contributing to this side effect remain unknown. Long non-coding RNAs (lncRNAs) are emerging regulators of biological processes in endothelial cells (ECs); however, their roles in cancer therapies-related vascular toxicities remain underexplored.

METHODS AND RESULTS

We performed lncRNA expression profiling to identify potential lncRNAs that are dysregulated in human-induced pluripotent stem cell-derived ECs (iPSC-ECs) treated with sunitinib. We show that the lncRNA hyaluronan synthase 2 antisense 1 (HAS2-AS1) is significantly diminished in sunitinib-treated iPSC-ECs. Sunitinib was found to down-regulate HAS2-AS1 by an epigenetic mechanism involving hypermethylation. Depletion of HAS2-AS1 recapitulated sunitinib-induced detrimental effects on iPSC-ECs, whereas CRISPR-mediated activation of HAS2-AS1 reversed sunitinib-induced dysfunction. We confirmed that HAS2-AS1 stabilizes the expression of its sense gene HAS2 via an RNA/mRNA heteroduplex formation. Knockdown of HAS2-AS1 led to reduced synthesis of hyaluronic acid (HA) and up-regulation of ADAMTS5, an enzyme involved in extracellular matrix degradation, resulting in disruption of the endothelial glycocalyx which is critical for ECs. In vivo, sunitinib-treated mice showed reduced coronary flow reserve, accompanied by a reduction in Has2os and degradation of the endothelial glycocalyx. Finally, we identified that treatment with high molecular-weight HA can prevent the deleterious effects of sunitinib both in vitro and in vivo by preserving the endothelial glycocalyx.

CONCLUSIONS

Our findings highlight the importance of lncRNA-mediated regulation of the endothelial glycocalyx as an important determinant of sunitinib-induced vascular toxicity and reveal potential novel therapeutic avenues to attenuate sunitinib-induced vascular dysfunction.

Platform combining statistical modeling and patient-derived organoids to facilitate personalized treatment of colorectal carcinoma

BACKGROUND

We propose a new approach for designing personalized treatment for colorectal cancer (CRC) patients, by combining ex vivo organoid efficacy testing with mathematical modeling of the results.

METHODS

The validated phenotypic approach called Therapeutically Guided Multidrug Optimization (TGMO) was used to identify four low-dose synergistic optimized drug combinations (ODC) in 3D human CRC models of cells that are either sensitive or resistant to first-line CRC chemotherapy (FOLFOXIRI). Our findings were obtained using second order linear regression and adaptive lasso.

RESULTS

The activity of all ODCs was validated on patient-derived organoids (PDO) from cases with either primary or metastatic CRC. The CRC material was molecularly characterized using whole-exome sequencing and RNAseq. In PDO from patients with liver metastases (stage IV) identified as CMS4/CRIS-A, our ODCs consisting of regorafenib [1 mM], vemurafenib [11 mM], palbociclib [1 mM] and lapatinib [0.5 mM] inhibited cell viability up to 88%, which significantly outperforms FOLFOXIRI administered at clinical doses. Furthermore, we identified patient-specific TGMO-based ODCs that outperform the efficacy of the current chemotherapy standard of care, FOLFOXIRI.

CONCLUSIONS

Our approach allows the optimization of patient-tailored synergistic multi-drug combinations within a clinically relevant timeframe.

Network-informed discovery of multidrug combinations for ERα+/HER2-/PI3Kα-mutant breast cancer

Breast cancer is a persistent threat to women worldwide. A large proportion of breast cancers are dependent on the estrogen receptor α (ERα) for tumor progression. Therefore, targeting ERα with antagonists, such as tamoxifen, or estrogen deprivation by aromatase inhibitors remain standard therapies for ERα + breast cancer. The clinical benefits of monotherapy are often counterbalanced by off-target toxicity and development of resistance. Combinations of more than two drugs might be of great therapeutic value to prevent resistance, and to reduce doses, and hence, decrease toxicity. We mined data from the literature and public repositories to construct a network of potential drug targets for synergistic multidrug combinations. With 9 drugs, we performed a phenotypic combinatorial screen with ERα + breast cancer cell lines. We identified two optimized low-dose combinations of 3 and 4 drugs of high therapeutic relevance to the frequent ERα + /HER2-/PI3Kα-mutant subtype of breast cancer. The 3-drug combination targets ERα in combination with PI3Kα and cyclin-dependent kinase inhibitor 1 (p21). In addition, the 4-drug combination contains an inhibitor for poly (ADP-ribose) polymerase 1 (PARP1), which showed benefits in long-term treatments. Moreover, we validated the efficacy of the combinations in tamoxifen-resistant cell lines, patient-derived organoids, and xenograft experiments. Thus, we propose multidrug combinations that have the potential to overcome the standard issues of current monotherapies.

Drug Repurposing to Identify a Synergistic High-Order Drug Combination to Treat Sunitinib-Resistant Renal Cell Carcinoma

Simple Summary

In this study, drug combination screening was used to design a multidrug combination consisting of repurposed drugs to treat sunitinib-resistant clear cell renal cell carcinoma. In the frame of this project, the multidrug combination has been optimized and validated and an insight into the mechanism of action is given. The multidrug combinations significantly altered the transcription of genes related to apoptosis and metabolic pathways. Further analysis of the metabolism revealed strong upregulation of the presence of sphingolipids after multidrug combination treatment. Final evaluation for translation of the multidrug combination in ex vivo organoid-like cultures demonstrated significant anti-cancer efficacy.

Abstract

Repurposed drugs have been evaluated for the management of clear cell renal cell carcinoma (ccRCC), but only a few have influenced the overall survival of patients with advanced disease. To combine repurposed non-oncology with oncological drugs, we applied our validated phenotypic method, which consisted of a reduced experimental part and data modeling. A synergistic optimized multidrug combination (ODC) was identified to significantly reduce the energy levels in cancer remaining inactive in non-cancerous cells. The ODC consisted of Rapta-C, erlotinib, metformin and parthenolide and low doses. Molecular and functional analysis of ODC revealed a loss of adhesiveness and induction of apoptosis. Gene-expression network analysis displayed significant alterations in the cellular metabolism, confirmed by LC-MS based metabolomic analysis, highlighting significant changes in the lipid classes. We used heterotypic in vitro 3D co-cultures and ex vivo organoids to validate the activity of the ODC, maintaining an efficacy of over 70%. Our results show that repurposed drugs can be combined to target cancer cells selectively with prominent activity. The strong impact on cell adherence and metabolism indicates a favorable mechanism of action of the ODC to treat ccRCC.

Cytoplasmic Localization Isoform of Cyclin Y Enhanced the Metastatic Ability of Lung Cancer via Regulating Tropomyosin 4

Cyclin Y (CCNY) is a novel cyclin and highly conserved in metazoan species. Previous studies from our and other laboratory indicate that CCNY play a crucial role in tumor progression. There are two CCNY isoform which has different subcellular distributions, with cytoplasmic isoform (CCNYc) and membrane distribution isoform (CCNYm). However, the expression and function of CCNY isoforms is still unclear. We firstly found CCNYc was expressed in natural lung cancer tissue and cells through the subcellular distribution. Co-IP and immunofluorescence showed that both CCNYm and CCNYc could interact with PFTK1. Further studies illustrated that CCNYc but not CCNYm enhanced cell migration and invasion activity both in vivo and vitro. The function of CCNYc could be inhibited by suppression of PFTK1 expression. In addition, our data indicated that tropomyosin 4 (TPM4), a kind of actin-binding proteins, was down-regulated by suppression of CCNY. F-actin assembly could be controlled by CCNYc as well as PFTK1 and TPM4. As a result, CCNY was mainly expressed in lung cancer. CCNYc could promote cell motility and invasion. It indicated that CCNYc/PFTK1 complex could promote cell metastasis by regulating the formation of F-actin via TPM4.

Molecular and Functional Analysis of Sunitinib-Resistance Induction in Human Renal Cell Carcinoma Cells

Resistance in clear cell renal cell carcinoma (ccRCC) against sunitinib is a multifaceted process encompassing numerous molecular aberrations. This induces clinical complications, reducing the treatment success. Understanding these aberrations helps us to select an adapted treatment strategy that surpasses resistance mechanisms, reverting the treatment insensitivity. In this regard, we investigated the dominant mechanisms of resistance to sunitinib and validated an optimized multidrug combination to overcome this resistance. Human ccRCC cells were exposed to single or chronic treatment with sunitinib to obtain three resistant clones. Upon manifestation of sunitinib resistance, morphometric changes in the cells were observed. At the molecular level, the production of cell membrane and extracellular matrix components, chemotaxis, and cell cycle progression were dysregulated. Molecules enforcing the cell cycle progression, i.e., cyclin A, B1, and E, were upregulated. Mass spectrometry analysis revealed the intra- and extracellular presence of N-desethyl sunitinib, the active metabolite. Lysosomal sequestration of sunitinib was confirmed. After treatment with a synergistic optimized drug combination, the cell metabolic activity in Caki-1-sunitinib-resistant cells and 3D heterotypic co-cultures was reduced by >80%, remaining inactive in non-cancerous cells. These results demonstrate geno- and phenotypic changes in response to sunitinib treatment upon resistance induction. Mimicking resistance in the laboratory served as a platform to study drug responses.

Characterization of Renal Cell Carcinoma Heterotypic 3D Co-Cultures with Immune Cell Subsets

Two-dimensional cell culture-based platforms are easy and reproducible, however, they do not resemble the heterotypic cell-cell interactions or the complex tumor microenvironment. These parameters influence the treatment response and the cancer cell fate. Platforms to study the efficacy of anti-cancer treatments and their impact on the tumor microenvironment are currently being developed. In this study, we established robust, reproducible, and easy-to-use short-term spheroid cultures to mimic clear cell renal cell carcinoma (ccRCC). These 3D co-cultures included human endothelial cells, fibroblasts, immune cell subsets, and ccRCC cell lines, both parental and sunitinib-resistant. During spheroid formation, cells induce the production and secretion of the extracellular matrix. We monitored immune cell infiltration, surface protein expression, and the response to a treatment showing that the immune cells infiltrated the spheroid co-cultures within 6 h. Treatment with an optimized drug combination or the small molecule-based targeted drug sunitinib increased immune cell infiltration significantly. Assessing the therapeutic potential of this drug combination in this platform, we revealed that the expression of PD-L1 increased in 3D co-cultures. The cost- and time-effective establishment of our 3D co-culture model and its application as a pre-clinical drug screening platform can facilitate the treatment validation and clinical translation.

FGFR-TKI resistance in cancer: current status and perspectives

Fibroblast growth factor receptors (FGFRs) play key roles in promoting the proliferation, differentiation, and migration of cancer cell. Inactivation of FGFRs by tyrosine kinase inhibitors (TKI) has achieved great success in tumor-targeted therapy. However, resistance to FGFR-TKI has become a concern. Here, we review the mechanisms of FGFR-TKI resistance in cancer, including gatekeeper mutations, alternative signaling pathway activation, lysosome-mediated TKI sequestration, and gene fusion. In addition, we summarize strategies to overcome resistance, including developing covalent inhibitors, developing dual-target inhibitors, adopting combination therapy, and targeting lysosomes, which will facilitate the transition to precision medicine and individualized treatment.

Optimized Combination of HDACI and TKI Efficiently Inhibits Metabolic Activity in Renal Cell Carcinoma and Overcomes Sunitinib Resistance

Simple Summary

To ameliorate the situation for kidney cancer patients and to broaden the application of available drugs, we initiated this research to enhance the anti-cancer activity through combination treatment. There is an unmet need for innovative treatment strategies and optimized drug combinations haven proven to be an adequate solution. We identified a four-drug combination of two histone deacetylate and two tyrosine kinase inhibitors that is effective in sunitinib-naïve and -resistant human renal cell carcinoma cells. Through our research, we demonstrated the superior anti-cancer activity of an optimized drug combination in comparison to single drugs, while maintaining a good safety/selectivity profile. We anticipate that the development and use of well-established drug combinations will be enforced offering personalized and more diverse treatment options in clinical conditions.

Abstract

Clear cell renal cell carcinoma (ccRCC) is characterized by high histone deacetylase (HDAC) activity triggering both cell motility and the development of metastasis. Therefore, there is an unmet need to establish innovative strategies to advance the use of HDAC inhibitors (HDACIs). We selected a set of tyrosine kinase inhibitors (TKIs) and HDACIs to test them in combination, using the validated therapeutically guided multidrug optimization (TGMO) technique based on experimental testing and in silico data modeling. We determined a synergistic low-dose three-drug combination decreasing the cell metabolic activity in metastatic ccRCC cells, Caki-1, by over 80%. This drug combination induced apoptosis and showed anti-angiogenic activity, both in original Caki-1 and in sunitinib-resistant Caki-1 cells. Through phosphoproteomic analysis, we revealed additional targets to improve the translation of this combination in 3-D (co-)culture systems. Cell–cell and cell–environment interactions increased, reverting the invasive and metastatic phenotype of Caki-1 cells. Our data suggest that our optimized low-dose drug combination is highly effective in complex in vitro settings and promotes the activity of HDACIs.

Integrating Phenotypic Search and Phosphoproteomic Profiling of Active Kinases for Optimization of Drug Mixtures for RCC Treatment

Combined application of multiple therapeutic agents presents the possibility of enhanced efficacy and reduced development of resistance. Definition of the most appropriate combination for any given disease phenotype is challenged by the vast number of theoretically possible combinations of drugs and doses, making extensive empirical testing a virtually impossible task. We have used the streamlined-feedback system control (s-FSC) technique, a phenotypic approach, which converges to optimized drug combinations (ODC) within a few experimental steps. Phosphoproteomics analysis coupled to kinase activity analysis using the novel INKA (integrative inferred kinase activity) pipeline was performed to evaluate ODC mechanisms in a panel of renal cell carcinoma (RCC) cell lines. We identified different ODC with up to 95% effectivity for each RCC cell line, with low doses (ED_5-25) of individual drugs. Global phosphoproteomics analysis demonstrated inhibition of relevant kinases, and targeting remaining active kinases with additional compounds improved efficacy. In addition, we identified a common RCC ODC, based on kinase activity data, to be effective in all RCC cell lines under study. Combining s-FSC with a phosphoproteomic profiling approach provides valuable insight in targetable kinase activity and allows for the identification of superior drug combinations for the treatment of RCC.