Biology subjects

Castellano, E.

Publications and source records attributed to Castellano, E..

4 recordsLinked to original sources

bioRxiv2025

RAS-PI3K Pathway in CAFs Shapes Physicochemical Properties of Tumor ECM and restrains Tumor progression

Cancer-associated fibroblasts display extensive functional plasticity, yet the signaling mechanisms that stabilize distinct CAF states remain poorly understood. Here, we identify stromal RAS-PI3K signaling as a key regulator of myofibroblastic CAF identity and matrix competence in lung cancer. Genetic disruption of the RAS-PI3K interaction in fibroblasts preserved upstream responsiveness to activating cues but impaired cytoskeletal remodeling, contractile execution and acquisition of a matrix-remodeling CAF program. As a result, RAS-PI3K-deficient CAFs generated extracellular matrices with defective collagen and fibronectin organization, reduced stiffness, altered microstructural properties and broad compositional remodeling of the matrisome, including changes in ECM glycoproteins and glycosylation-associated enzymes. These altered matrices failed to efficiently provide directional, adhesive and proliferative cues to tumor cells, attenuated EMT-associated programs and increased sensitivity to cisplatin plus pemetrexed. In vivo, fibroblast-specific RAS-PI3K disruption reduced tumor burden, decreased tumor cell proliferation, prolonged survival and improved chemotherapy response. Loss of RAS-PI3K signaling did not simply suppress CAF activation, but redirected CAFs toward a STAT3/NF-{kappa}B-linked immunomodulatory state associated with IL6, CXCL12 and PDGFR induction. Functionally, this altered stromal state promoted iNOS-associated macrophage features and was accompanied in vivo by reduced macrophage accumulation and increased CD8 T cell infiltration. These findings establish RAS-PI3K signaling as a stromal state-control mechanism that couples myofibroblastic matrix competence to tumor-supportive microenvironmental function while restraining alternative immunomodulatory CAF programs.

cancer biology↗

bioRxiv2024

Pharmacological SOS1 inhibitor BI-3406 demonstrates in vivo anti-tumor activity comparable to SOS1 genetic ablation in KRAS mutant tumors

Resistance to KRASmut inhibitors frequently arises, warranting further searches for anti-RAS cancer therapies. We evaluated the tolerability and efficacy of SOS1 pharmacological inhibition in comparison to genetic ablation in different KRAS-dependent tumor settings. Contrary to the rapid lethality caused by SOS1 genetic ablation in SOS2KO mice, SOS1 pharmacological inhibition by its specific inhibitor BI-3406 did not significantly affect animal weight/viability nor cause noteworthy systemic toxicity. In BI-3406-treated KRASmut MEFs, we observed significantly reduced RAS-GTP levels and RAS downstream signaling, as well as decreased tumor burden and slower disease progression resulting from tumor-intrinsic and extrinsic therapeutic drug effects. In vivo analyses of KRASG12D allografts in immunocompromised mice and KRASG12D-driven lung adenocarcinomas in immunocompetent mice showed that systemic BI-3406 treatment impaired tumor growth and downmodulated components of the tumor microenvironment comparably to the KRASG12D inhibitor MRTX1133. Markedly stronger synergistic antitumor effects were observed upon concomitant BI-3406+MRTX113 treatment, confirming SOS1 as an actionable therapy target in RAS-dependent cancers.

cancer biology↗

bioRxiv2024

Fibre tracing in biomedical images: An objective comparison between seven algorithms

Obtaining the traces and the characteristics of elongated structures is an important task in computer vision pipelines. In biomedical applications, the analysis of traces of vasculature, nerves or fibres of the extracellular matrix can help characterise processes like angiogenesis or the effect of a certain treatment. This paper presents an objective comparison of six existing methodologies (Edge detection, CT Fire, Scale Space, Twombli, U-Net and Graph Based) and one new approach called Trace Ridges to trace biomedical images with fibre-like structures. Trace Ridges is a fully automatic and fast algorithm that combines a series of image-processing algorithms including filtering, watershed transform and edge detection to obtain an accurate delineation of the fibre-like structures in a rapid time. To compare the algorithms, four biomedical data sets with very different characteristics were selected. Ground truth was obtained by manual delineation of the fibre-like structures. Three pre-processing filtering options were used as a first step: no filtering, Gaussian low-pass and DnCnn, a deep-learning filtering. Three distance error metrics (total, average and maximum distance from the obtained traces to the ground truth) and processing time were calculated. It was observed that no single algorithm outperformed the others in all metrics. For the total distance error, which was considered the most significative, Trace Ridges ranked first, followed by Graph Based, U-Net, Twombli, Scale Space, CT Fire and Edge Detection. In terms of speed, Trace Ridges ranked second only slightly slower than Edge Detection. Code is freely available at github.com/youssefarafat/Trace Ridges.

cancer biology↗

bioRxiv2020

CyTOF analysis identifies unusual immune cells in urine of BCG-treated bladder cancer patients

High grade non-muscle-invasive bladder tumours are treated with transurethral resection followed by recurrent intravesical instillations of Bacillus Calmette Guerin (BCG). Although bladder cancer patients respond well to BCG, important questions remain unanswered, including how to identify at early stages non-responder patients and patients at risk to abandon the treatment. Here, we analysed the cells released into the urine of bladder cancer patients longitudinally 3-7 days after BCG instillations. Mass cytometry (CyTOF) analyses revealed that most cells were granulocytes and monocytes rather than effector lymphocytes, and most expressed activation markers. A novel population of CD15+CD66b+CD14+ CD16+ cells was very abundant in several samples and expression of these markers was confirmed using flow cytometry and qPCR. Samples of patients with a stronger inflammatory response contained more cells in urine; however, this was not due to haematuria, as the proportions of the cell populations observed were different from blood. We provide the proof-of-concept for a new approach to analyse samples that may help classify patients and identify those at risk of BCG infection and other unwanted BCG-related events.

immunology↗