Biology subjects

Dolcetti, L.

Publications and source records attributed to Dolcetti, L..

3 recordsLinked to original sources

bioRxiv2023

Chemotherapeutic regulation of the ROS/MondoA-dependent TXNIP/GDF15 axis; and derivation of a new organoid metric as a predictive biomarker

Chemotherapy, the standard of care treatment for cancer patients with advanced disease, has been increasingly recognised to activate host immune responses to produce durable outcomes. Here, in colorectal adenocarcinoma (CRC) we identify chemotherapy-induced Thioredoxin Interacting Protein (TXNIP), a MondoA-dependent tumor suppressor gene, as a negative regulator of Growth/Differentiation Factor 15 (GDF15). GDF15 is a negative prognostic factor in CRC and promotes the differentiation of regulatory T cells (Tregs), through CD48 ligation. Intriguingly, multiple models including patient-derived tumor organoids demonstrate that loss of TXNIP/GDF15 axis functionality is associated with advanced disease or chemotherapeutic resistance, with transcriptomic or proteomic GDF15/TXNIP ratios showing potential as a prognostic biomarker. These findings illustrate a potentially common pathway where chemotherapy-induced epithelial stress drives local immune remodelling for patient benefit, with disruption of this pathway seen in refractory or advanced cases.

cancer biology↗

bioRxiv2023

CytoPb: Segmentation-free and threshold-free analysis of highly multiplexed images of biological tissue

We present a novel method and code, CytoPb, for the analysis of multiplexed images that is segmentation-free and threshold-free and produces results that correlate well with existing methods but is faster to run, needs minimal training and removes subjectivity associated with the validation of single-cell segmentation and thresholding. In three data sets cell abundance measures correlate with previous methods (Pearsons coef. >0.75). Neighbourhood analysis and cell-cell proximity are possible.

immunology↗

bioRxiv2021

RUNIMC: An R-based package for imaging mass cytometry data analysis and pipeline validation

We propose a novel pipeline for the analysis of imaging mass cytometry data, comparing an unbiased approach, representing the actual gold standard, with a novel biased method. We made use of both synthetic/ controlled datasets as well as two datasets obtained from FFPE sections of follicular lymphoma, and head and neck patients, stained with a 14 and 29-markers panels respectively. The novel pipeline, denominated RUNIMC, has been completely developed in R and contained in a single package. The novelty resides in the ease with which multi-class random forest classifier can be used to classify image features, making the pathologists and expert classification pivotal, and the use of a random forest regression approach that permits a better detection of cell boundaries, and alleviates the necessity of relying on a perfect nuclear staining.

cancer biology↗