Biology subjects

Popovici, V.

Publications and source records attributed to Popovici, V..

3 recordsLinked to original sources

bioRxiv2025

A Multiresolution Hierarchical Approach to Peak Picking for High Resolution Mass Spectrometry Data Analysis

MotivationAccurate peak detection is a critical first step in high-resolution mass spectrometry (HRMS) data analysis. Most existing tools rely on centroiding and grid-based assumptions, which simplify the raw profile data at the cost of information loss and reduced detection accuracy. ResultsWe present MRH, a novel peak detection method that operates directly on raw HRMS data using a hierarchical, multi-resolution decomposition of the (RT, m/z) space. Peaks are identified within resolution-dependent regions of interest using image-processing techniques, and an ambiguity score is introduced to quantify detection confidence. Benchmarking against widely used methods (apLCMS, centWave, and gridmass) shows that MRH consistently achieves higher F1 scores across concentrations, particularly excelling at low concentrations where competing tools fail. MRH also provides accurate localization of peaks and low ambiguity in detections, highlighting both robustness and precision. AvailabilityA proof-of-concept implementation of MRH and the evaluation dataset are freely available at https://github.com/VojtechBarton/MRH.

bioinformatics↗

bioRxiv2024

A quantitative tumor-wide analysis of morphological heterogeneity of colorectal adenocarcinoma

Morphologic heterogeneity of colorectal adenocarcinoma (CRC) is poorly understood. Previously, we identified morphological patterns associated with CRC molecular subtypes, and showed that these patterns have distinct molecular motifs (Budinska et al., 2023). Here, we evaluated the heterogeneity of these patterns across CRC. Three pathologists evaluated dominant, secondary, and tertiary morphology on four different tissue blocks per tumor in a pilot set of 22 CRCs (n=88). An artificial intelligence (AI) image analysis tool was trained using the pathologist-rated tumors to assess the morphologic heterogeneity on an expanded set of 161 CRCs (644 images). Heterogeneity was expressed as a combination of morphology patterns (morphotypes) across slides and normalized Shannons index (NSI). All pathologists agreed that the majority of tumors had 2-3 different dominant morphotypes, and that the complex tubular (CT) morphotype was the most common. AI analysis confirmed these observations in the full set. CT morphotype combined with all other dominant morphotypes within a tumor. Desmoplastic (DE) morphotype was rarely dominant and rarely combined with other dominant morphotypes. Mucinous (MU) was most often combined with solid/trabecular (TB) and papillary (PP). Most tumors showed medium or high NSI, but without clinical consequence. The proportion of DE morphotype was associated with higher T-stage, N-stage, metastasis, AJCC-stage, and shorter relapse-free survival, and MU morphotype was associated with higher grade, right side, microsatellite instability, and shorter overall survival. In conclusion, we observed high intratumoral morphological heterogeneity of CRC, and that not heterogeneity per se, but the proportion of certain morphotypes showed associations with clinical outcome. This has implications for molecular profiling of CRC.

cancer biology↗

bioRxiv2023

Molecular portraits of colorectal cancer morphological regions

Heterogeneity of colorectal carcinoma (CRC) represents a major hurdle towards personalized medicine. Efforts based on whole tumor profiling demonstrated that the CRC molecular subtypes were associated with specific tumor morphological patterns representing tumor subregions. We hypothesize that whole- tumor molecular descriptors depend on the morphological heterogeneity with significant impact on current molecular predictors. We investigated intra-tumor heterogeneity by morphology-guided transcriptomics to better understand the links between gene expression and tumor morphology represented by six morphological patterns (morphotypes): complex tubular, desmoplastic, mucinous, papillary, serrated, and solid/trabecular. Whole-transcriptome profiling by microarrays of 202 tumor regions (morphotypes, tumor-adjacent normal tissue, supportive stroma, and matched whole tumors) from 111 stage II-IV CRCs identified morphotype-specific gene expression profiles and molecular programs and differences in their cellular buildup. The proportion of cell types (fibroblasts, epithelial and immune cells) and differentiation of epithelial cells were the main drivers of the observed disparities with activation of EMT and TNF- signaling in contrast to MYC and E2F targets signaling, defining major gradients of changes at molecular level. Several gene expression-based (including single-cell) classifiers, prognostic and predictive signatures were examined to study their behavior across morphotypes. Most exhibited important morphotype-dependent variability within same tumor sections, with regional predictions often contradicting the whole-tumor classification. The results show that morphotype-based tumor sampling allows the detection of molecular features that would otherwise be distilled in whole tumor profile, while maintaining histopathology context for their interpretation. This represents a practical approach at improving the reproducibility of expression profiling and, by consequence, of gene-based classifiers.

cancer biology↗