ErbB3 Phosphorylation as Central Event in Adaptive Resistance to Targeted Therapy in Metastatic Melanoma: Early Detection in CTCs during Therapy and Insights into Regulation by Autocrine Neuregulin

Targeted proteomics-determined multi-biomarker profiles developed classifier for prognosis and immunotherapy responses of advanced cervical cancer

Background

Cervical cancer (CC) poses a global health challenge, with a particularly poor prognosis in cases of recurrence, metastasis, or advanced stages. A single biomarker is inadequate to predict CC prognosis or identify CC patients likely to benefit from immunotherapy, presumably owing to tumor complexity and heterogeneity.

Methods

Using advanced Olink proteomics, we analyzed 92 oncology-related proteins in plasma from CC patients receiving immunotherapy, based upon the comparison of protein expression levels of pre-therapy with those of therapy-Cycle 6 in the partial response (PR) group and progressive disease (PD) group, respectively.

Results

55 proteins were identified to exhibit differential expression trends across pre-therapy and post-therapy in both PR and PD groups. Enriched GO terms and KEGG pathways were associated with vital oncological and immunological processes. A logistic regression model, using 5 proteins (ITGB5, TGF-α, TLR3, WIF-1, and ERBB3) with highest AUC values, demonstrated good predictive performance for prognosis of CC patients undergoing immunotherapy and showed potential across different cancer types. The effectiveness of these proteins in prognosis prediction was further validated using TCGA-CESC datasets. A negative correlation and previously unidentified roles of WIF-1 in CC immunotherapy was also first determined.

Conclusion

Our findings reveal multi-biomarker profiles effectively predicting CC prognosis and identifying patients benefitting most from immunotherapy, especially for those with limited treatment options and traditionally poor prognosis, paving the way for personalized immunotherapeutic treatments and improved clinical strategies.

Upregulated expression of miR-4443 and miR-4488 in drug resistant melanomas promotes migratory and invasive phenotypes through downregulation of intermediate filament nestin

BACKGROUND

BRAF-mutant melanoma patients benefit from the combinatorial treatments with BRAF and MEK inhibitors. However, acquired drug resistance strongly limits the efficacy of these targeted therapies in time. Recently, many findings have underscored the involvement of microRNAs as main drivers of drug resistance. In this context, we previously identified a subset of oncomiRs strongly up-regulated in drug-resistant melanomas. In this work, we shed light on the molecular role of two as yet poorly characterized oncomiRs, miR-4443 and miR-4488.

METHODS

Invasion and migration have been determined by wound healing, transwell migration/invasion assays and Real Time Cell Analysis (RTCA) technology. miR-4488 and miR-4443 have been measured by qRT-PCR. Nestin levels have been tested by western blot, confocal immunofluorescence, immunohistochemical and flow cytometry analyses.

RESULTS

We demonstrate that the two oncomiRs are responsible for the enhanced migratory and invasive phenotypes, that are a hallmark of drug resistant melanoma cells. Moreover, miR-4443 and miR-4488 promote an aberrant cytoskeletal reorganization witnessed by the increased number of stress fibers and cellular protrusions-like cancer cell invadopodia. Mechanistically, we identified the intermediate filament nestin as a molecular target of both oncomiRs. Finally, we have shown that nestin levels are able to predict response to treatments in melanoma patients.

CONCLUSIONS

Altogether these findings have profound translational implications in the attempt i) to develop miRNA-targeting therapies to mitigate the metastatic phenotypes of BRAF-mutant melanomas and ii) to identify novel biomarkers able to guide clinical decisions.

Bcl-2 family inhibitors sensitize human cancer models to therapy

BH3 mimetics, targeting the Bcl-2 family anti-apoptotic proteins, represent a promising therapeutic opportunity in cancers. ABT-199, the first specific Bcl-2 inhibitor, was approved by FDA for the treatment of several hematological malignancies. We have recently discovered IS21, a novel pan BH3 mimetic with preclinical antitumor activity in several tumor types. Here, we evaluated the efficacy of IS21 and other BH3 mimetics, both as single agents and combined with the currently used antineoplastic agents in T-cell acute lymphoblastic leukemia, ovarian cancer, and melanoma. IS21 was found to be active in T-cell acute lymphoblastic leukemia, melanoma, lung, pancreatic, and ovarian cancer cell lines. Bcl-xL and Mcl-1 protein levels predicted IS21 sensitivity in melanoma and ovarian cancer, respectively. Exploring IS21 mechanism of action, we found that IS21 activity depends on the presence of BAX and BAK proteins: complexes between Bcl-2 and Bcl-xL proteins and their main binding partners were reduced after IS21 treatment. In combination experiments, BH3 mimetics sensitized leukemia cells to chemotherapy, ovarian cancer cells and melanoma models to PARP and MAPK inhibitors, respectively. We showed that this enhancing effect was related to the potentiation of the apoptotic pathway, both in hematologic and solid tumors. In conclusion, our data suggest the use of inhibitors of anti-apoptotic proteins as a therapeutic strategy to enhance the efficacy of anticancer treatment.

miR579-3p is an inhibitory modulator of neointimal hyperplasia and transcription factors c-MYB and KLF4

Neointimal hyperplasia (IH) is a common vascular pathology that typically manifests in in-stent restenosis and bypass vein graft failure. Smooth muscle cell (SMC) phenotypic switching is central to IH, both regulated by some microRNAs, yet the role of miR579-3p, a scarcely studied microRNA, is not known. Unbiased bioinformatic analysis suggested that miR579-3p was repressed in human primary SMCs treated with different pro-IH cytokines. Moreover, miR579-3p was software-predicted to target both c-MYB and KLF4 - two master transcription factors known to promote SMC phenotypic switching. Interestingly, treating injured rat carotid arteries via local infusion of miR579-3p-expressing lentivirus reduced IH 14 days after injury. In cultured human SMCs, transfection with miR579-3p inhibited SMC phenotypic switching, as indicated by decreased proliferation/migration and increased SMC contractile proteins. miR579-3p transfection downregulated c-MYB and KLF4, and luciferase assays indicated miR579-3p's targeting of the 3'UTRs of the c-MYB and KLF4 mRNAs. In vivo, immunohistochemistry showed that treatment of injured rat arteries with the miR579-3p lentivirus reduced c-MYB and KLF4 and increased SMC contractile proteins. Thus, this study identifies miR579-3p as a previously unrecognized small-RNA inhibitor of IH and SMC phenotypic switch involving its targeting of c-MYB and KLF4. Further studies on miR579-3p may provide an opportunity for translation to develop IH-mitigating new therapeutics.

DEML: Drug Synergy and Interaction Prediction Using Ensemble-Based Multi-Task Learning

Synergistic drug combinations have demonstrated effective therapeutic effects in cancer treatment. Deep learning methods accelerate identification of novel drug combinations by reducing the search space. However, potential adverse drug-drug interactions (DDIs), which may increase the risks for combination therapy, cannot be detected by existing computational synergy prediction methods. We propose DEML, an ensemble-based multi-task neural network, for the simultaneous optimization of five synergy regression prediction tasks, synergy classification, and DDI classification tasks. DEML uses chemical and transcriptomics information as inputs. DEML adapts the novel hybrid ensemble layer structure to construct higher order representation using different perspectives. The task-specific fusion layer of DEML joins representations for each task using a gating mechanism. For the Loewe synergy prediction task, DEML overperforms the state-of-the-art synergy prediction method with an improvement of 7.8% and 13.2% for the root mean squared error and the R2 correlation coefficient. Owing to soft parameter sharing and ensemble learning, DEML alleviates the multi-task learning 'seesaw effect' problem and shows no performance loss on other tasks. DEML has a superior ability to predict drug pairs with high confidence and less adverse DDIs. DEML provides a promising way to guideline novel combination therapy strategies for cancer treatment.

Oncosuppressive miRNAs loaded in lipid nanoparticles potentiate targeted therapies in BRAF-mutant melanoma by inhibiting core escape pathways of resistance

BRAF-mutated melanoma relapsing after targeted therapies is an aggressive disease with unmet clinical need. Hence the need to identify novel combination therapies able to overcome drug resistance. miRNAs have emerged as orchestrators of non-genetic mechanisms adopted by melanoma cells to challenge therapies. In this context we previously identified a subset of oncosuppressor miRNAs downregulated in drug-resistant melanomas. Here we demonstrate that lipid nanoparticles co-encapsulating two of them, miR-199-5p and miR-204-5p, inhibit tumor growth both in vitro and in vivo in combination with target therapy and block the development of drug resistance. Mechanistically they act by directly reducing melanoma cell growth and also indirectly by hampering the recruitment and reprogramming of pro-tumoral macrophages. Molecularly, we demonstrate that the effects on macrophages are mediated by the dysregulation of a newly identified miR-204-5p-miR-199b-5p/CCL5 axis. Finally, we unveiled that M2 macrophages programs are molecular signatures of resistance and predict response to therapy in patients. Overall, these findings have strong translational implications to propose new combination therapies making use of RNA therapeutics for metastatic melanoma patients.

MiR-323a regulates ErbB3/EGFR and blocks gefitinib resistance acquisition in colorectal cancer

The rapid onset of resistance to epidermal growth factor receptor tyrosine kinase inhibitor (EGFR-TKI) limits its clinical utility in colorectal cancer (CRC) patients, and pan-erb-b2 receptor tyrosine kinase (ErbB) treatment strategy may be the alternative solution. The aim of this study was to develop a possible microRNA multi-ErbB treatment strategy to overcome EGFR-TKI resistance. We detect the receptor tyrosine kinase activity in gefitinib-resistant colorectal cancer cells, ErbB3/EGFR is significantly activated and provides a potential multi-ErbB treatment target. MiR-323a-3p, a tumor suppressor, could target both ErbB3 and EGFR directly. Apoptosis is the miR-323a-3p inducing main biological process by functional enrichment analysis, and The EGFR and ErbB signaling are the miR-323a-3p inducing main pathway by KEGG analysis. MiR-323a-3p promotes CRC cells apoptosis by targeting ErbB3-phosphoinositide 3‐kinases (PI3K)/PKB protein kinase (Akt)/glycogen synthase kinase 3 beta (GSK3β)/EGFR-extracellular regulated MAP kinase (Erk1/2) signaling directly. And miR-323a-3p, as a multi-ErbBs inhibitor, increase gefitinib sensitivity of the primary cell culture from combination miR-323a-3p and gefitinib treated subcutaneous tumors. MiR-323a-3p reverses ErbB3/EGFR signaling activation in gefitinib-resistant CRC cell lines and blocks acquired gefitinib resistance.

A disease network-based deep learning approach for characterizing melanoma

Multiple types of genomic variations are present in cutaneous melanoma and some of the genomic features may have an impact on the prognosis of the disease. The access to genomics data via public repositories such as The Cancer Genome Atlas (TCGA) allows for a better understanding of melanoma at the molecular level, therefore making characterization of substantial heterogeneity in melanoma patients possible. Here, we proposed an approach that integrates genomics data, a disease network, and a deep learning model to classify melanoma patients for prognosis, assess the impact of genomic features on the classification and provide interpretation to the impactful features. We integrated genomics data into a melanoma network and applied an autoencoder model to identify subgroups in TCGA melanoma patients. The model utilizes communities identified in the network to effectively reduce the dimensionality of genomics data into a patient score profile. Based on the score profile, we identified three patient subtypes that show different survival times. Furthermore, we quantified and ranked the impact of genomic features on the patient score profile using a machine-learning technique. Follow-up analysis of the top-ranking features provided us with the biological interpretation of them at both pathway and molecular levels, such as their mutation and interactome profiles in melanoma and their involvement in pathways associated with signaling transduction, immune system and cell cycle. Taken together, we demonstrated the ability of the approach to identify disease subgroups using a deep learning model that captures the most relevant information of genomics data in the melanoma network.

Clinical Categorization Algorithm (CLICAL) and Machine Learning Approach (SRF-CLICAL) to Predict Clinical Benefit to Immunotherapy in Metastatic Melanoma Patients: Real-World Evidence from the Istituto Nazionale Tumori IRCCS Fondazione Pascale, Napoli, Italy

The real-life application of immune checkpoint inhibitors (ICIs) may yield different outcomes compared to the benefit presented in clinical trials. For this reason, there is a need to define the group of patients that may benefit from treatment. We retrospectively investigated 578 metastatic melanoma patients treated with ICIs at the Istituto Nazionale Tumori IRCCS Fondazione "G. Pascale" of Napoli, Italy (INT-NA). To compare patients' clinical variables (i.e., age, lactate dehydrogenase (LDH), neutrophil-lymphocyte ratio (NLR), eosinophil, BRAF status, previous treatment) and their predictive and prognostic power in a comprehensive, non-hierarchical manner, a clinical categorization algorithm (CLICAL) was defined and validated by the application of a machine learning algorithm-survival random forest (SRF-CLICAL). The comprehensive analysis of the clinical parameters by log risk-based algorithms resulted in predictive signatures that could identify groups of patients with great benefit or not, regardless of the ICI received. From a real-life retrospective analysis of metastatic melanoma patients, we generated and validated an algorithm based on machine learning that could assist with the clinical decision of whether or not to apply ICI therapy by defining five signatures of predictability with 95% accuracy.

The Role of Notch, Hedgehog, and Wnt Signaling Pathways in the Resistance of Tumors to Anticancer Therapies

Resistance to therapy is the major hurdle in the current cancer management. Cancer cells often rewire their cellular process to alternate mechanisms to resist the deleterious effect mounted by different therapeutic approaches. The major signaling pathways involved in the developmental process, such as Notch, Hedgehog, and Wnt, play a vital role in development, tumorigenesis, and also in the resistance to the various anticancer therapies. Understanding how cancer utilizes these developmental pathways in acquiring the resistance to the multi-therapeutic approach cancer can give rise to a new insight of the anti-therapy resistance mechanisms, which can be explored for the development of a novel therapeutic approach. We present a brief overview of Notch, Hedgehog, and Wnt signaling pathways in cancer and its role in providing resistance to various cancer treatment modalities such as chemotherapy, radiotherapy, molecular targeted therapy, and immunotherapy. Understanding the importance of these molecular networks will provide a rational basis for novel and safer combined anticancer therapeutic approaches for the improvement of cancer treatment by overcoming drug resistance.

The Promise of Liquid Biopsy to Predict Response to Immunotherapy in Metastatic Melanoma

Metastatic melanoma is the deadliest form of skin cancer whose incidence has been rising dramatically over the last few decades. Nowadays, the most successful approach in treating advanced melanoma is immunotherapy which encompasses the use of immune checkpoint blockers able to unleash the immune system's activity against tumor cells. Immunotherapy has dramatically changed clinical practice by contributing to increasing long term overall survival. Despite these striking therapeutic effects, the clinical benefits are strongly mitigated by innate or acquired resistance. In this context, it is of utmost importance to develop methods capable of predicting patient response to immunotherapy. To this purpose, one major step forward may be provided by measuring non-invasive biomarkers in human fluids, namely Liquid Biopsies (LBs). Several LB approaches have been developed over the last few years thanks to technological breakthroughs that have allowed to evaluate circulating components also when they are present in low abundance. The elements of this so-called "circulome" mostly encompass: tumor DNA, tumor and immune cells, soluble factors and non-coding RNAs. Here, we review the current knowledge of these molecules as predictors of response to immunotherapy in metastatic melanoma and predict that LB will soon enter into routine practice in order to guide clinical decisions for cancer immunotherapy.

Cross-Linking Ligation and Sequencing of Hybrids (qCLASH) Reveals an Unpredicted miRNA Targetome in Melanoma Cells

MicroRNAs are key post-transcriptional gene regulators often displaying aberrant expression patterns in cancer. As microRNAs are promising disease-associated biomarkers and modulators of responsiveness to anti-cancer therapies, a solid understanding of their targetome is crucial. Despite enormous research efforts, the success rates of available tools to reliably predict microRNAs (miRNA)-target interactions remains limited. To investigate the disease-associated miRNA targetome, we have applied modified cross-linking ligation and sequencing of hybrids (qCLASH) to BRAF-mutant melanoma cells. The resulting RNA-RNA hybrid molecules provide a comprehensive and unbiased snapshot of direct miRNA-target interactions. The regulatory effects on selected miRNA target genes in predicted vs. non-predicted binding regions was validated by miRNA mimic experiments. Most miRNA-target interactions deviate from the central dogma of miRNA targeting up to 60% interactions occur via non-canonical seed pairing with a strong contribution of the 3' miRNA sequence, and over 50% display a clear bias towards the coding sequence of mRNAs. miRNAs targeting the coding sequence can directly reduce gene expression (miR-34a/CD68), while the majority of non-canonical miRNA interactions appear to have roles beyond target gene suppression (miR-100/AXL). Additionally, non-mRNA targets of miRNAs (lncRNAs) whose interactions mainly occur via non-canonical binding were identified in melanoma. This first application of CLASH sequencing to cancer cells identified over 8 K distinct miRNA-target interactions in melanoma cells. Our data highlight the importance non-canonical interactions, revealing further layers of complexity of post-transcriptional gene regulation in melanoma, thus expanding the pool of miRNA-target interactions, which have so far been omitted in the cancer field.

Cancer Stem Cells and the Slow Cycling Phenotype: How to Cut the Gordian Knot Driving Resistance to Therapy in Melanoma

Simple Summary

Cancer stem cells play a central role in the development of cancer and are poorly sensitive to standard chemotherapy and radiotherapy. Furthermore, they are also responsible for the onset of drug resistance. This also occurs in malignant melanoma, the deadliest form of skin cancer. Hence, cancer stem cells eradication is one of the main challenges for medical oncology. Here, we conducted a bioinformatics approach aimed to identify the main circuits and proteins underpinning cancer stem cell fitness in melanoma. Several lessons emerged from our work and may help to conceptualize future therapeutic approaches to prolong the efficacy of current therapies.

Abstract

Cancer stem cells (CSCs) have historically been defined as slow cycling elements that are able to differentiate into mature cells but without dedifferentiation in the opposite direction. Thanks to advances in genomic and non-genomic technologies, the CSC theory has more recently been reconsidered in a dynamic manner according to a “phenotype switching” plastic model. Transcriptional reprogramming rewires this plasticity and enables heterogeneous tumors to influence cancer progression and to adapt themselves to drug exposure by selecting a subpopulation of slow cycling cells, similar in nature to the originally defined CSCs. This model has been conceptualized for malignant melanoma tailored to explain resistance to target therapies. Here, we conducted a bioinformatics analysis of available data directed to the identification of the molecular pathways sustaining slow cycling melanoma stem cells. Using this approach, we identified a signature of 25 genes that were assigned to four major clusters, namely (1) kinases and metabolic changes, (2) melanoma-associated proteins, (3) Hippo pathway and (4) slow cycling/CSCs factors. Furthermore, we show how a protein−protein interaction network may be the main driver of these melanoma cell subpopulations. Finally, mining The Cancer Genome Atlas (TCGA) data we evaluated the expression levels of this signature in the four melanoma mutational subtypes. The concomitant alteration of these genes correlates with the worst overall survival (OS) for melanoma patients harboring BRAF-mutations. All together these results underscore the potentiality to target this signature to selectively kill CSCs and to achieve disease control in melanoma.

Drug tolerance to target therapy in melanoma revealed at single cell level: What next?

Drug resistance strongly impairs the efficacy of virtually every kind of anticancer therapy. This phenomenon is commonly fueled by intrinsic or acquired mechanisms. In this mini-review, focusing on BRAF-mutated melanoma as prototypical example, we analyze how recent studies that make use of single cell analysis identify the involvement of distinct transcriptional trajectories as the common thread at the basis of drug tolerance. The identification of these transcriptional trajectories provide a mechanistic basis for the development of both intrinsic and acquired drug resistance. These studies also suggest that hitting these transcriptional trajectories through personalized adaptive treatments can delay or abrogate the onset of drug resistance.

Mechanisms of Acquired BRAF Inhibitor Resistance in Melanoma: A Systematic Review

This systematic review investigated the literature on acquired v-raf murine sarcoma viral oncogene homolog B1 (BRAF) inhibitor resistance in patients with melanoma. We searched MEDLINE for articles on BRAF inhibitor resistance in patients with melanoma published since January 2010 in the following areas: (1) genetic basis of resistance; (2) epigenetic and transcriptomic mechanisms; (3) influence of the immune system on resistance development; and (4) combination therapy to overcome resistance. Common resistance mutations in melanoma are BRAF splice variants, BRAF amplification, neuroblastoma RAS viral oncogene homolog (NRAS) mutations and mitogen-activated protein kinase kinase 1/2 (MEK1/2) mutations. Genetic and epigenetic changes reactivate previously blocked mitogen-activated protein kinase (MAPK) pathways, activate alternative signaling pathways, and cause epithelial-to-mesenchymal transition. Once BRAF inhibitor resistance develops, the tumor microenvironment reverts to a low immunogenic state secondary to the induction of programmed cell death ligand-1. Combining a BRAF inhibitor with a MEK inhibitor delays resistance development and increases duration of response. Multiple other combinations based on known mechanisms of resistance are being investigated. BRAF inhibitor-resistant cells develop a range of 'escape routes', so multiple different treatment targets will probably be required to overcome resistance. In the future, it may be possible to personalize combination therapy towards the specific resistance pathway in individual patients.

Reverse transcriptase inhibition potentiates target therapy in BRAF-mutant melanomas: effects on cell proliferation, apoptosis, DNA-damage, ROS induction and mitochondrial membrane depolarization

Abstract

Target therapies based on BRAF and MEK inhibitors (MAPKi) have changed the therapeutic landscape for metastatic melanoma patients bearing mutations in the BRAF kinase. However, the emergence of drug resistance imposes the necessity to conceive novel therapeutic strategies capable to achieve a more durable disease control. In the last years, retrotransposons laying in human genome have been shown to undergo activation during tumorigenesis, where they contribute to genomic instability. Their activation can be efficiently controlled with reverse transcriptase inhibitors (RTIs) frequently used in the treatment of AIDS. These drugs have demonstrated anti-proliferative effects in several cancer models, including also metastatic melanoma. However, to our knowledge no previous study investigated the capability of RTIs to mitigate drug resistance to target therapy in BRAF-mutant melanomas. In this short report we show that the non-nucleoside RTI, SPV122 in combination with MAPKi strongly inhibits BRAF-mutant melanoma cell growth, induces apoptosis, and delays the emergence of resistance to target therapy in vitro. Mechanistically, this combination strongly induces DNA double-strand breaks, mitochondrial membrane depolarization and increased ROS levels. Our results shed further light on the molecular activity of RTI in melanoma and pave the way to their use as a novel therapeutic option to improve the efficacy of target therapy.

Discoidin Domain Receptors in Melanoma: Potential Therapeutic Targets to Overcome MAPK Inhibitor Resistance

Melanoma is a highly malignant skin cancer with high propensity to metastasize and develop drug resistance, making it a difficult cancer to treat. Current therapies targeting BRAF (V600) mutations are initially effective, but eventually tumors overcome drug sensitivity and reoccur. This process is accomplished in part by reactivating alternate signaling networks that reinstate melanoma proliferative and survival capacity, mostly through reprogramming of receptor tyrosine kinase (RTK) signaling. Evidence indicates that the discoidin domain receptors (DDRs), a set of RTKs that signal in response to collagen, are part of the kinome network that confer drug resistance. We previously reported that DDR1 is expressed in melanomas, where it can promote tumor malignancy in mouse models of melanoma, and thus, DDR1 could be a promising target to overcome drug resistance. In this review, we summarize the current knowledge on DDRs in melanoma and their implication for therapy, with emphasis in resistance to MAPK inhibitors.

Targeted Therapies for Melanoma

The incidence of cutaneous malignant melanoma is increasing worldwide. Despite available modern therapeutical options, long-term survival of patients in advanced stages of the disease remains rather limited until now. Detailed insights into etiopathogenesis and mechanisms of tumour progression enable physicians to manipulate distinct molecular structures and pathways therapeutically and so treat the tumour. Unfortunately, the acquisition of therapeutic resistance frequently terminates these therapeutical interventions. The presented special issue is focusing on the research and therapeutic experience of leading scientists, and it summarises the state of the art of targeted therapy of melanoma and suggests the new perspectives of the treatment of disease.

Asiaticoside Antagonizes Proliferation and Chemotherapeutic Drug Resistance in Hepatocellular Carcinoma (HCC) Cells

Background

Hepatocellular carcinoma (HCC) is the fifth most prevalent malignant tumor in China after lung cancer, gastric cancer, esophageal cancer, and breast cancer, and has a high mortality rate. Though there are a series of therapeutic strategies is now available for HCC in clinical practice, the 5-year survival rate after surgery is still low. In addition, multi-drug resistance (MDR) is one of the most important factors responsible for the low survival rate and poor therapy response in HCC. Hence, novel treatment strategies and molecules for HCC need to be developed.

Material/Methods

We assessed the effect of asiaticoside, a natural product derived from Centella asiatica (L.) Urban, on HCC cell proliferation and drug resistance.

Results

Our data indicated that asiaticoside significantly inhibited the proliferation of HCC cell lines QGY-7703 and Bel-7402 in a dose- and time-dependent manner. Moreover, asiaticoside significantly induced apoptosis in QGY-7703 and Bel-7402 cells. Treatment with asiaticoside also caused G1 cell cycle arrest in QGY-7703 and Bel-7402 cells. Western blot assay results indicated that the mechanism underlying the effects of asiaticoside involves inhibiting the activity of the PI3K/Akt and MAPK/ERK pathways. Furthermore, asiaticoside significantly antagonized P-gp-mediated MDR in HCC cells.

Conclusions

Our results suggest that asiaticoside has the potential to be applied in the treatment of HCC patients, but further evidence is needed to confirm our results, particularly in vivo efficacy.

In Vitro Biophysical and Biological Characterization of Lipid Nanoparticles Co-Encapsulating Oncosuppressors miR-199b-5p and miR-204-5p as Potentiators of Target Therapy in Metastatic Melanoma

Uncontrolled MAPK signaling is the main oncogenic driver in metastatic melanomas bearing mutations in BRAF kinase. These tumors are currently treated with the combination of BRAF/MEK inhibitors (MAPKi), but this therapy is plagued by drug resistance. In this context we recently discovered that several microRNAs are involved in the development of drug resistance. In particular miR-204-5p and miR-199b-5p were found to function as antagonists of resistance because their enforced overexpression is able to inhibit melanoma cell growth in vitro either alone or in combination with MAPKi. However, the use of miRNAs in therapy is hampered by their rapid degradation in serum and biological fluids, as well as by the poor intracellular uptake. Here, we developed lipid nanoparticles (LNPs) encapsulating miR-204-5p, miR-199b-5p individually or in combination. We obtained LNPs with mean diameters < 200 nm and high miRNA encapsulation efficiency. These formulations were tested in vitro on several melanoma cell lines sensitive to MAPKi or rendered drug resistant. Our results show that LNPs encapsulating combinations of the two oncosuppressor miRNAs are highly efficient in impairing melanoma cell proliferation and viability, affect key signaling pathways involved in melanoma cell survival, and potentiate the efficacy of drugs inhibiting BRAF and MEK. These results warrant further assessment of the anti-tumor efficacy of oncosuppressor miRNAs encapsulating LNPs in in vivo tumor models.