Biology subjects

Nenutil, R.

Publications and source records attributed to Nenutil, R..

4 recordsLinked to original sources

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↗

bioRxiv2022

Shedding Light on the Black Box of a Neural Network Used to Detect Prostate Cancer in Whole Slide Images by Occlusion-Based Explainability

Diagnostic histopathology is facing increasing demands due to aging populations and expanding healthcare programs. Semi-automated diagnostic systems employing deep learning methods are one approach to alleviate this pressure, with promising results for many routine diagnostic procedures. However, one major issue with deep learning approaches is their lack of interpretability--after adequate training they perform their assigned tasks admirably, but do not explain how they reach their conclusions. Knowledge of how a given method performs its task with high sensitivity and specificity would be advantageous to understand the key features responsible for diagnosis, and should in turn allow fine-tuning of deep learning approaches. This paper presents a deep learning-based system for carcinoma detection in whole slide images of prostate core biopsies, achieving state-of-the-art performance; 100% area under curve and sensitivity of 0.978 for 8 detected false positives on average per slide. Furthermore, we investigated various methods to extract the key features used by the neural network for classification. Of these, the technique called occlusion, adapted to whole slide images, analyzes the sensitivity of the detection system to changes in the input images. This technique produces heatmaps indicating which parts of the image have the strongest impact on the systems output that a histopathologist can examine to identify the networks reasoning behind a given classification. Reassuringly, the heatmaps identified several prevailing histomorphological features characterizing carcinoma, e.g. single-layered epithelium, presence of small lumina, and hyperchromatic nuclei with halos. A convincing finding was the recognition of their mimickers in non-neoplastic tissue. The results show that the neural network approach to recognize prostatic cancer is similar to that taken by a human pathologist at medium optical resolution. The use of explain-ability heatmaps provides added value for automated digital pathology to analyze and fine-tune deep learning systems, and improves trust in computer-based decisions.

pathology↗

bioRxiv2019

Breast cancer classification based on proteotypes obtained by SWATH mass spectrometry

Accurate breast cancer classification is vital for patient management decisions, and better tumour classification is expected to enable more precise and eventually personalized treatment to improve patient outcomes. Here, we present a novel quantitative proteotyping approach based on SWATH mass spectrometry and establish key proteins for breast tumour classification derived from proteotype data. The study was based on 96 tissue samples representing five breast cancer subtypes according to conventional classification. Correlation of SWATH proteotype patterns indicated groups that largely recapitulate these subtypes. However, the proteotype-based classification also revealed varying degrees of heterogeneity within the conventional subtypes, with triple negative tumours being the most heterogeneous. Proteins that contributed most strongly to the proteotype-based classification include INPP4B, CDK1, and ERBB2, which are associated with oestrogen receptor status, tumour grade, and HER2 status, respectively. While these three key proteins exhibited high levels of correlation between protein and transcript levels (R>0.67), general correlation did not exceed R=0.29, indicating the value of protein-level measurements of biomarkers and disease-regulated genes. Overall, our data shows how large-scale protein-level measurements by next-generation proteomics can lead to improved patient stratification for precision medicine.

cancer biology↗