Biology subjects

Punnen, S.

Publications and source records attributed to Punnen, S..

3 recordsLinked to original sources

bioRxiv2024

Targeting Endoplasmic Reticulum Stress and Nitroso-Redox Imbalance in Neuroendocrine Prostate Cancer: The Therapeutic Role of Nitric Oxide

Neuroendocrine prostate cancer (NEPC) is an aggressive and therapy-resistant subtype of prostate cancer. Current standard-of-care treatment for NEPC involves chemotherapies, which largely exert their cytotoxic effects by forming DNA crosslinks, disrupting DNA replication and transcription in NEPC cells. However, these therapies are often met with resistance, partly due to increased endoplasmic reticulum (ER) stress, which facilitates cancer cell survival and adaptive mechanisms. Despite its critical role, the molecular landscape underlying ER stress in NEPC remains inadequately understood. Here we showed that ER stress is intimately linked to the metabolic reprogramming of NEPC cells, a process that supports their transition from adenocarcinoma to a neuroendocrine phenotype. We identified MYCN as a key driver of this process, promoting unfolded protein response (UPR) elements that enhance ER stress by increasing the efflux of calcium ions through the ER which later is absorbed by the mitochondria and assist in increasing the overall glycolytic stress, thereby adding to the extended survival and metastatic potential of an NEPC cell. Our previous studies highlighted the importance of S-nitrosylation as a protein modification that is dysregulated in high-grade PCa. In this context, structural analysis of MYCN revealed potential S-nitrosylation sites at the positions Cys4, 186, and 464, respectively. However, similar to the castration-resistant stage, this modification is hindered in NEPC due to impaired nitric oxide (NO) production from dysregulated endothelial nitric oxide synthases (eNOS). We found that exogenous NO supplementation S-nitrosylates MYCN, reducing its binding to protein molecules which are essential to assist with increasing ER stress in NEPC cells. Exogenous supplementation of NO reduced the overall tumor burden in the mice harboring orthotopic NEPC cells and reduced the metastasis to the brain and liver. In conclusion, the findings from this study enrich our understanding of the mechanisms driving the ER stress responses in NEPC phenotype and how NO supplementation could pave the way as potential therapeutics for this challenging cancer. HIGHLIGHTSO_LIEndoplasmic reticulum (ER) stress is intricately linked to metabolic reprogramming, which supports the transition from prostate adenocarcinoma to neuroendocrine prostate cancer (NEPC). C_LIO_LIMYCN increases the ER stress in NEPC cells and is correlated with increased nitroso-redox imbalance. C_LIO_LIStructural analysis reveals potential S-nitrosylation sites on MYCN. Exogenous nitric oxide (NO) supplementation induces S-nitrosylation, disrupting MYCNs role in enhancing ER stress. C_LIO_LINO supplementation reduced tumor burden and metastasis in NEPC-bearing mice, highlighting its potential as a therapeutic avenue for NEPC. C_LIO_LIExogenous NO supplementation inhibits ER stress by targeting unfolded protein response (UPR) elements and decreasing calcium ion efflux, inhibiting the glycolytic stress in NEPC. C_LI Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=191 SRC="FIGDIR/small/624202v1_ufig1.gif" ALT="Figure 1"> View larger version (57K): org.highwire.dtl.DTLVardef@1fb292forg.highwire.dtl.DTLVardef@4d00f7org.highwire.dtl.DTLVardef@17a7f43org.highwire.dtl.DTLVardef@1393286_HPS_FORMAT_FIGEXP M_FIG C_FIG

cancer biology↗

bioRxiv2024

Synthetic Histology Images for Training AI Models: A Novel Approach to Improve Prostate Cancer Diagnosis

Prostate cancer (PCa) poses significant challenges for timely diagnosis and prognosis, leading to high mortality rates and increased disease risk and treatment costs. Recent advancements in machine learning and digital imagery offer promising potential for developing automated and objective assessment pipelines that can reduce human capital and resource costs. However, the reliance of AI models on large amounts of clinical data for training presents a significant challenge, as this data is often biased, lacking diversity, and not readily available. Here we aim to address this limitation by employing customized generative adversarial network (GAN) models to produce high-quality synthetic images of different PCa grades (radical prostatectomy (RP)) and needle biopsies, which were customized to account for the granularity associated with each Gleason grade. The generated images were subjected to multiple rounds of benchmarking, quantifications and quality control assessment before being used to train an AI model (EfficientNet) for grading digital histology images of adenocarcinoma specimens (RP sections) and needle biopsies obtained from the PANDA challenge repository. Validation was performed using the AI model trained with synthetic data to grade digital histology from the cancer genome atlas (TCGA) (RP sections) and needle biopsy data from Radboud University Medical Center and Karolinska Institute. Results demonstrated that the AI model trained with a combination of image patches derived from original and enhanced synthetic images outperformed the model trained with original digital histology images. Together, this study demonstrates the potential of customized GAN models to generate a large cohort of synthetic data that can train AI models to effectively grade PCa specimens. This approach could potentially eliminate the need for extensive clinical data for training any AI model in the domain of digital imagery, leading to cost and time-effective diagnosis and prognosis.

cancer biology↗

bioRxiv2022

Generative Adversarial Networks Can Create High Quality Artificial Prostate Cancer Magnetic Resonance Images

PurposeRecent integration of open-source data to machine learning models, especially in the medical field, has opened new doors to study disease progression and/or regression. However, the limitation of using medical data for machine learning approaches is the specificity of data to a particular medical condition. In this context, most recent technologies like generative adversarial networks (GAN) could be used to generate high quality synthetic data that preserves the clinical variability. Materials and MethodsIn this study, we used 139 T2-weighted prostate magnetic resonant images (MRI) from various sources as training data for Single Natural Image GAN (SinGAN), to make a generative model. A deep learning semantic segmentation pipeline trained the model to segment the prostate boundary on 2D MRI slices. Synthetic images with a high-level segmentation boundary of the prostate were filtered and used in the quality control assessment by participating scientists with varying degree of experience (more than 10 years, 1 year, or no experience) to work with MRI images. ResultsThe most experienced participating group correctly identified conventional vs synthetic images with 67% accuracy, the group with 1 year of experience correctly identified the images with 58% accuracy, and group with no prior experience reached 50% accuracy. Nearly half (47%) of the synthetic images were mistakenly evaluated as conventional images. Interestingly, a blinded quality assessment by a board-certified radiologist to differentiate conventional and synthetic images was not significantly different in context of the mean quality of synthetic and conventional images. ConclusionsThis study shows promise that high quality synthetic images from MRI can be generated using GAN. Such an AI model may contribute significantly to various clinical applications which involves supervised machine learning approaches.

bioinformatics↗