Protein Profiling of WERI-RB1 and Etoposide-Resistant WERI-ETOR Reveals New Insights into Topoisomerase Inhibitor Resistance in Retinoblastoma

Kakkassery, Vinodh; Gemoll, Timo; Kraemer, Miriam M.; Sauer, Thorben; Tura, Aysegül; Ranjbar, Mahdy; Grisanti, Salvatore; Joachim, Stephanie C.; Mergler, Stefan; Reinhard, Jacqueline

doi:10.3390/ijms23074058

Cited by 4 publications

(3 citation statements)

References 55 publications

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“…In the past, several groups have explored the chemoresistance mechanisms in RB by developing drug-resistant clones from original RB cell lines and primary cell cultures established from enucleated specimens. 22 – 25 However, only a few gene expression studies have used either tumor or liquid biopsy samples from patients with RB. As EVs reflect the cell of origin at particular physiological and pathological conditions, they offer real-time monitoring of the tumor burden and response to treatment.…”

Section: Discussionmentioning

confidence: 99%

Identifying Treatment Resistance Related Pathways by Analyzing Serum Extracellular Vesicles of Patients With Resistant Versus Regressed Retinoblastoma

Manukonda,

Jakati,

Attem

et al. 2023

Invest. Ophthalmol. Vis. Sci.

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Purpose To identify the genes and pathways responsible for treatment resistance (TR) in retinoblastoma (RB) by analyzing serum small extracellular vesicles (sEVs) of patients with TR active RB (TR-RB) and completely regressed RB (CR-RB). Methods Serum-derived sEVs were characterized by transmission electron microscopy and nanoparticle tracking analysis. sEV transcriptome profiles of two TR-RB and one CR-RB with good response (>20 years tumor free) were compared to their age-matched controls ( n = 3). Gene expression data were analyzed by the R Bioconductor package. The CD9 protein and mRNA expression of CD9, CD63, and CD81 were studied in five RB tumors and two control retinae by immunohistochemistry and quantitative reverse transcription–polymerase chain reaction. Results The isolated serum sEVs were round shaped and within the expected size (30–150 nm), and they had zeta potentials ranging from −10.8 to 15.9 mV. The mean ± SD concentrations of sEVs for two adults and four children were 1.1 × 10 12 ± 0.1 and 5.8 × 10 11 ± 1.7 particles/mL. Based on log2 fold change of ±2 and P < 0.05 criteria, there were 492 dysregulated genes in TR-RB and 184 in CR-RB. KAT2B , VWA1 , CX3CL1 , MLYCD , NR2F2 , USP46-AS1 , miR6724-4 , and LINC01257 genes were specifically dysregulated in TR-RB. Negative regulation of apoptotic signaling, cell growth, and proton transport genes were greater than fivefold expressed only in TR-RB. CD9, CD63, and CD81 mRNA levels were high in RB tumors versus control retina, with increased and variable CD9 immunoreactivity in the invasive areas of the tumor. Conclusions Serum sEVs could serve as a potential liquid biopsy source for understanding TR mechanisms in RB.

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Section: Discussionmentioning

confidence: 99%

Identifying Treatment Resistance Related Pathways by Analyzing Serum Extracellular Vesicles of Patients With Resistant Versus Regressed Retinoblastoma

Manukonda,

Jakati,

Attem

et al. 2023

Invest. Ophthalmol. Vis. Sci.

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“…It has been reported that NGF is involved in the regulation of TRPM8 activation, thereby controlling the influx of Ca 2+ , influencing the sensitivity of RB cells to etoposide [ 103 ]. Additionally, Vinodh Kakkassery et al determined that the pathways “retinoid metabolism and transport” and “sphingolipid de novo biosynthesis” are associated with etoposide resistance [ 104 ]. Furthermore, Jacqueline Reinhard et al demonstrated that components of the extracellular matrix play a crucial role in the development of chemotherapy resistance in RB [ 105 ].…”

Section: Epigenetics Of Rbmentioning

confidence: 99%

Molecular Biological Research on the Pathogenic Mechanism of Retinoblastoma

Ma,

Li,

Sun

et al. 2024

CIMB

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Retinoblastoma (RB) is the most common intraocular malignant tumor in children, primarily attributed to the bi-allelic loss of the RB1 gene in the developing retina. Despite significant progress in understanding the basic pathogenesis of RB, comprehensively unravelling the intricate network of genetics and epigenetics underlying RB tumorigenesis remains a major challenge. Conventional clinical treatment options are limited, and despite the continuous identification of genetic loci associated with cancer pathogenesis, the development of targeted therapies lags behind. This review focuses on the reported genomic and epigenomic alterations in retinoblastoma, summarizing potential therapeutic targets for RB and providing insights for research into targeted therapies.

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“… 80 Similar ML-based genetic analyses have revealed mechanisms for chemotherapy resistance in Rb including pathways related to retinoid metabolism and sphingolipid synthesis. 81 …”

Section: Studies In Rbmentioning

confidence: 99%

Clinical Applications of Machine Learning in the Management of Intraocular Cancers: A Narrative Review

Chandrabhatla

Horgan

Cotton

et al. 2023

Invest. Ophthalmol. Vis. Sci.

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Purpose There is great promise in use of machine learning (ML) for the diagnosis, prognosis, and treatment of various medical conditions in ophthalmology and beyond. Applications of ML for ocular neoplasms are in early development and this review synthesizes the current state of ML in ocular oncology. Methods We queried PubMed and Web of Science and evaluated 804 publications, excluding nonhuman studies. Metrics on ML algorithm performance were collected and the Prediction model study Risk Of Bias ASsessment Tool was used to evaluate bias. We report the results of 63 unique studies. Results Research regarding ML applications to intraocular cancers has leveraged multiple algorithms and data sources. Convolutional neural networks (CNNs) were one of the most commonly used ML algorithms and most work has focused on uveal melanoma and retinoblastoma. The majority of ML models discussed here were developed for diagnosis and prognosis. Algorithms for diagnosis primarily leveraged imaging (e.g., optical coherence tomography) as inputs, whereas those for prognosis leveraged combinations of gene expression, tumor characteristics, and patient demographics. Conclusions ML has the potential to improve the management of intraocular cancers. Published ML models perform well, but were occasionally limited by small sample sizes owing to the low prevalence of intraocular cancers. This could be overcome with synthetic data enhancement and low-shot ML techniques. CNNs can be integrated into existing diagnostic workflows, while non-neural networks perform well in determining prognosis.

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Protein Profiling of WERI-RB1 and Etoposide-Resistant WERI-ETOR Reveals New Insights into Topoisomerase Inhibitor Resistance in Retinoblastoma

Cited by 4 publications

References 55 publications

Identifying Treatment Resistance Related Pathways by Analyzing Serum Extracellular Vesicles of Patients With Resistant Versus Regressed Retinoblastoma

Identifying Treatment Resistance Related Pathways by Analyzing Serum Extracellular Vesicles of Patients With Resistant Versus Regressed Retinoblastoma

Molecular Biological Research on the Pathogenic Mechanism of Retinoblastoma

Clinical Applications of Machine Learning in the Management of Intraocular Cancers: A Narrative Review

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