Publications by authors named "Edgar Cardenas De La Hoz"

This study focuses on refining growth-rate-based drug response metrics for patient-derived tumor organoid screening using brightfield live-cell imaging. Traditional metrics like Normalized Growth Rate Inhibition (GR) and Normalized Drug Response (NDR) have been used to assess organoid responses to anticancer treatments but face limitations in accurately quantifying cytostatic and cytotoxic effects across varying growth rates. Here, we introduce the Normalized Organoid Growth Rate (NOGR) metric, specifically developed for brightfield imaging-based assays.

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Purpose: Inadequate perfusion is the most common cause of partial flap loss in tissue transfer for post-mastectomy breast reconstruction. The current state-of-the-art uses computed tomography angiography (CTA) to locate the best perforators. Unfortunately, these techniques are expensive and time-consuming and not performed during surgery.

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Background: This study explores the repurposing of Auranofin (AF), an anti-rheumatic drug, for treating non-small cell lung cancer (NSCLC) adenocarcinoma and pancreatic ductal adenocarcinoma (PDAC). Drug repurposing in oncology offers a cost-effective and time-efficient approach to developing new cancer therapies. Our research focuses on evaluating AF's selective cytotoxicity against cancer cells, identifying RNAseq-based biomarkers to predict AF response, and finding the most effective co-therapeutic agents for combination with AF.

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The global shortage of corneal endothelial graft tissue necessitates the exploration of alternative therapeutic strategies. Rho-associated protein kinase inhibitors (ROCKi), recognized for their regenerative potential in cardiology, oncology, and neurology, have shown promise in corneal endothelial regeneration. This study investigates the repurposing potential of additional ROCKi compounds.

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Background: It remains challenging to obtain positive outcomes with chimeric antigen receptor (CAR)-engineered cell therapies in solid malignancies, like colorectal cancer (CRC) and pancreatic ductal adenocarcinoma (PDAC). A major obstacle is the lack of targetable surface antigens that are not shared by healthy tissues. CD70 emerges as interesting target, due to its stringent expression pattern in healthy tissue and its apparent role in tumor progression in a considerable amount of malignancies.

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Pancreatic ductal adenocarcinoma (PDAC) is one of the most lethal diseases, characterized by a treatment-resistant and invasive nature. In line with these inherent aggressive characteristics, only a subset of patients shows a clinical response to the standard of care therapies, thereby highlighting the need for a more personalized treatment approach. In this study, we comprehensively unraveled the intra-patient response heterogeneity and intrinsic aggressive nature of PDAC on bulk and single-organoid resolution.

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Patient-derived tumor organoids (PDTOs) hold great promise for preclinical and translational research and predicting the patient therapy response from ex vivo drug screenings. However, current adenosine triphosphate (ATP)-based drug screening assays do not capture the complexity of a drug response (cytostatic or cytotoxic) and intratumor heterogeneity that has been shown to be retained in PDTOs due to a bulk readout. Live-cell imaging is a powerful tool to overcome this issue and visualize drug responses more in-depth.

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Background: Patient-derived organoids are invaluable for fundamental and translational cancer research and holds great promise for personalized medicine. However, the shortage of available analysis methods, which are often single-time point, severely impede the potential and routine use of organoids for basic research, clinical practise, and pharmaceutical and industrial applications.

Methods: Here, we developed a high-throughput compatible and automated live-cell image analysis software that allows for kinetic monitoring of organoids, named Organoid Brightfield Identification-based Therapy Screening (OrBITS), by combining computer vision with a convolutional network machine learning approach.

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Melanoma remains a deadly cancer despite significant advances in immune checkpoint blockade and targeted therapies. The incidence of melanoma is also growing worldwide, which highlights the need for novel treatment options and strategic combination of therapies. Here, we investigate non-thermal plasma (NTP), an ionized gas, as a promising, therapeutic option.

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Non-thermal plasma (NTP) therapy has been emerging as a promising cancer treatment strategy, and recently, its ability to locally induce immunogenic cancer cell death is being unraveled. We hypothesized that the chemical species produced by NTP reduce immunosuppressive surface proteins and checkpoints that are overexpressed on cancerous cells. Here, 3D in vitro tumor models, an in vivo mouse model, and molecular dynamics simulations are used to investigate the effect of NTP on CD47, a key innate immune checkpoint.

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One of the most important and error-prone tasks in biological image analysis is the segmentation of touching or overlapping cells. Particularly for optical microscopy, including transmitted light and confocal fluorescence microscopy, there is often no consistent discriminative information to separate cells that touch or overlap. It is desired to partition touching foreground pixels into cells using the binary threshold image information only, and optionally incorporating gradient information.

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Cancer cell migration is essential for metastasis, during which cancer cells move through the tumor and reach the blood vessels. , cancer cells are exposed to contact guidance and chemotactic cues. Depending on the strength of such cues, cells will migrate in a random or directed manner.

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Neural stem cell activity in the ventricular-subventricular zone (V-SVZ) decreases with aging, thought to occur by a unidirectional decline. However, by analyzing the V-SVZ transcriptome of male mice at 2, 6, 18, and 22 months, we found that most of the genes that change significantly over time show a reversal of trend, with a maximum or minimum expression at 18 months. In vivo, MASH1 progenitor cells decreased in number and proliferation between 2 and 18 months but increased between 18 and 22 months.

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Neural stem and progenitor cells (NPCs) generate processes that extend from the cell body in a dynamic manner. The NPC nucleus migrates along these processes with patterns believed to be tightly coupled to mechanisms of cell cycle regulation and cell fate determination. Here, we describe a new segmentation and tracking approach that allows NPC processes and nuclei to be reliably tracked across multiple rounds of cell division in phase-contrast microscopy images.

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