Biology subjects

Albuquerque, M.

Publications and source records attributed to Albuquerque, M..

3 recordsLinked to original sources

bioRxiv2026

Distinct Progression Paths Underlie the Diversity of Pancreatic Papillary Cystic Neoplasms

Surgical decisions for intraductal papillary mucinous neoplasms (IPMNs), precursors of pancreatic cancer, depend on distinguishing low- from high-grade dysplasia, a distinction that molecular markers could refine. We microdissected 433 tumor regions from 135 patients and integrated transcriptomics, deep-learning histology, and KRAS/GNAS RNA-called variants. RNA programs and morphological motifs separated lesions by phenotype, intestinal versus others, rather than by dysplasia grade. Along gastric IPMN trajectories, low-grade lesions recapitulated the pit-chief polarity of normal gastric glands, whereas high-grade lesions were indistinguishable from pancreatobiliary lesions in morphology, RNA programs, and driver mutations. Intestinal IPMNs arose through two routes: a canonical GNAS-associated pathway and a KRAS-associated shift from low-grade gastric features, with lesions sharing driver mutations retaining common programs across phenotypes. Resolving these phenotypic trajectories clarified distinct progression paths and enabled phenotype-specific prediction of high-grade dysplasia in mucus aspirated from 94 resected lesions. These findings support a promising fluid-based assessment of IPMN status.

cancer biology↗

bioRxiv2024

Self-supervised learning to predict intrahepatic cholangiocarcinoma transcriptomic classes on routine histology

ObjectiveThe transcriptomic classification of intrahepatic cholangiocarcinomas (iCCA) has been recently refined from two to five classes, associated with pathological features, targetable genetic alterations and survival. Despite its prognostic and therapeutic value, the classification is not routinely used in the clinic because of technical limitations, including insufficient tissue material or the cost of molecular analyses. Here, we assessed a self-supervised learning (SSL) model for predicting iCCA transcriptomic classes on whole-slide digital histological images (WSIs) DesignTranscriptomic classes defined from RNAseq data were available for all samples. The SSL method, called Giga-SSL, was used to train our model on a discovery set of 766 biopsy slides (n=137 cases) and surgical samples (n=109 cases) from 246 patients in a five-fold cross-validation scheme. The model was validated in The Cancer Genome Atlas (TCGA) (n= 29) and a French external validation set (n=32). ResultsOur model showed good to very good performance in predicting the four most frequent transcriptomic class in the discovery set (area under the curve [AUC]: 0.63-0.84), especially for the hepatic stem-like class (37% of cases, AUC 0.84). The model performed equally well in predicting these four transcriptomic classes in the two validation sets, with AUCs ranging from 0.76 to 0.80 in the TCGA set and 0.62 to 0.92 in the French external set. ConclusionWe developed and validated an SSL-based model for predicting iCCA transcriptomic classes on routine histological slides of biopsy and surgical samples, which may impact iCCA management by predicting prognosis and guiding the treatment strategy.

cancer biology↗

bioRxiv2023

Ship-to-Shore Training for Active Deep-Sea Capacity Development

Sailing on scientific expeditions as an early career researcher (ECR) offers the beneficial opportunity to gain field experience and training. However, the number of available berths to achieve the scientific goals of an expedition limits the number of onboard participants. Telepresence and remote learning can be utilised to increase the number of active participants, broadening the reach of capacity development. The 2021 iMirabilis2 expedition on board the Spanish Research Vessel Sarmiento de Gamboa used telepresence to virtually involve ECRs from several countries in deep-sea science. One year post-expedition, a survey of onshore participants was conducted to assess and quantify the effectiveness of the peer-to-peer ECR ship-to-shore scheme. During the expedition, live, interactive training via WhatsApp and Zoom was utilised by onshore ECRs more than traditional static, uni-directional methods of blog posts and pre-recorded videos. All respondents either agreed or strongly agreed that the scheme provided an inclusive and accessible platform to share deep-sea science. These results suggest similar schemes could be used to supplement shorter duration at-sea-training, used prior to a seagoing experience to better prepare ECRs, or to allow members of the science community unable to join an expedition in person to actively participate remotely, increasing inclusivity.

scientific communication and education↗