Most breast cancers arise from luminal epithelial cells and 25–30% of these tumours overexpress the ErbB-2 receptor. Herein, a non-transformed, immortalized cell system was used to investigate the effects of ErbB-2 overexpression in luminal epithelial cells. The phenotypic consequence of ErbB-2 overexpression is a shortening of the G1 phase of the cell cycle and early S phase entry, which leads to hyperproliferation. We show that this effect was mediated through the up-regulation of cdk6 and cyclins D1 and E, and enhanced degradation and relocalization of p27Kip1. These changes were effected predominantly through enhanced MAPK signalling, resulting in cdk2 hyperactivation. PI3K signalling also participated in cell cycle progression, since PI3K and MAPK coordinately regulated changes in cyclin D1 and cdk6 expression. Cdk4 activity was not required for cell cycle progression in these cells, and was constitutively inhibited through its association with p16INK4A. MAPK-dependent induction of p21Cip1 was also necessary for G1 phase progression, although its degradation by the proteasome was required for S phase entry. These data provide new insights into the complex molecular mechanisms underlying mitogenic cell cycle control in luminal epithelial cells, the cell type relevant to primary breast cancer, and show how ErbB-2 overexpression subverts this normal control.

Background: Ovarian cancer is characterized by high mortality rates, primarily due to diagnosis at late stages. Current biomarkers, such as CA125, have demonstrated limited efficacy for early detection. While high-dimensional proteomics offers a more comprehensive view of systemic biology, the analysis of such data, where the number of features far exceeds the number of samples, presents a significant computational challenge. Methods: This study utilized a nested case–control cohort of longitudinal pre-diagnostic serum samples from the UK Collaborative Trial of Ovarian Cancer Screening (UKCTOCS) profiled for eight candidate ovarian cancer biomarkers (CA125, HE4, PEBP4, CHI3L1, FSTL1, AGR2, SLPI, DNAH17) and 92 additional cancer-associated proteins from the Olink Oncology II panel. We employed a Synolitic Graph Neural Network framework that transforms high-dimensional multi-protein data into sample-specific, interconnected graphs using a synolitic network approach. These graphs, which encode the relational patterns between proteins, were then used to train Graph Neural Network (GNN) models for classification. Performance of the network approach was evaluated together with conventional machine learning approaches via 5-fold cross-validation on samples collected within one year of diagnosis and a separate holdout set of samples collected one to two years prior to diagnosis. Results: In samples collected within one year of ovarian cancer diagnosis, conventional machine learning models—including XGBoost, random forests, and logistic regression—achieved the highest discriminative performance, with XGBoost reaching an ROC-AUC of 92%. Graph Convolutional Networks (GCNs) achieved moderate performance in this interval (ROC-AUC ~71%), with balanced sensitivity and specificity comparable to mid-performing conventional models. In the 1–2 year early-detection window, conventional model performance declined sharply (XGBoost ROC-AUC 46%), whereas the GCN maintained robust discriminative ability (ROC-AUC ~74%) with relatively balanced sensitivity and specificity. These findings indicate that while conventional approaches excel at detecting late pre-diagnostic signals, GNNs are more stable and effective at capturing subtle early molecular changes. Conclusions: The synolitic GNN framework demonstrates robust performance in early pre-diagnostic detection of ovarian cancer, maintaining accuracy where conventional methods decline. These results highlight the potential of network-informed machine learning to identify subtle proteomic patterns and pathway-level dysregulation prior to clinical diagnosis. This proof-of-concept study supports further development of GNN approaches for early ovarian cancer detection and warrants validation in larger, independent cohorts.

<p>Patient characteristics of UoL serum sample cohorts.</p>

Differentially expressed transcripts (mRNA and microRNA) between disease-associated fibroblasts and normal activated (NA) or normal fibroblasts (NF).

<p>Patient characteristics of samples included on TMAs histologically stained for TNC.</p>

Detection of thrombospondin 1 (TSP-1) by MRM in resectable PDAC cases versus advanced PDAC cases

Supplementary Material and Methods

Supplementary Figure 2A from The Role of S100P in the Invasion of Pancreatic Cancer Cells Is Mediated through Cytoskeletal Changes and Regulation of Cathepsin D

Background Earlier detection of pancreatic ductal adenocarcinoma (PDAC) is key to improving patient outcomes, as it is mostly detected at advanced stages which are associated with poor survival. Developing non-invasive blood tests for early detection would be an important breakthrough. Methods The primary objective of the work presented here is to use a dataset that is prospectively collected, to quantify a set of cancer-associated proteins and construct multi-marker models with the capacity to predict PDAC years before diagnosis. The data used is part of a nested case-control study within the UK Collaborative Trial of Ovarian Cancer Screening and is comprised of 218 samples, collected from a total of 143 post-menopausal women who were diagnosed with pancreatic cancer within 70 months after sample collection, and 249 matched non-cancer controls. We develop a stacked ensemble modelling technique to achieve robustness in predictions and, therefore, improve performance in newly collected datasets. Results Here we show that with ensemble learning we can predict PDAC status with an AUC of 0.91 (95% CI 0.75–1.0), sensitivity of 92% (95% CI 0.54–1.0) at 90% specificity, up to 1 year prior to diagnosis, and at an AUC of 0.85 (95% CI 0.74–0.93) up to 2 years prior to diagnosis (sensitivity of 61%, 95% CI 0.17–0.83, at 90% specificity). Conclusions The ensemble modelling strategy explored here outperforms considerably biomarker combinations cited in the literature. Further developments in the selection of classifiers balancing performance and heterogeneity should further enhance the predictive capacity of the method.

Comparison of TSP-1 tissue expression in PDAC patients with clinicopathological parameters.

Peptides and their fragment ions used in MRM assays for TSP-1.

<p>Top upstream regulators significantly enriched in PDAC vs PAT and PAT vs CP, as determined using IPA.</p>

<p>Patient characteristics of samples used for discovery and independent validation.</p>

Supplementary Tables 1A-1C from The Role of S100P in the Invasion of Pancreatic Cancer Cells Is Mediated through Cytoskeletal Changes and Regulation of Cathepsin D

PDF file - 206K, Table S1 Numbers of test positive cases and controls using CA19-9 37 U/mL and CA125 30 U/mL cut-offs Figure S1 Scatter plots showing distribution of CA19-9, CA125, CEACAM1 and REG3A levels against time to diagnosis for discovery set. Zero represents the point of clinical diagnosis. Figure S2 Examples of CA19-9 and CA125 levels in individual cases with serial/longitudinal samples. Figure S3 Box and whisker plots showing serum levels of CA19-9 and CA125 for case control validation samples grouped into different time to diagnosis groups. Whisker limits represent the 5th and 95th percentiles, the box limits represent interquartile range, the horizontal line the median and the cross the mean. Case and control groups were compared using the Mann-Whitney test; significant P values (<0.05) are shown above the plots.

Patient Characteristics of iTRAQ sample pools.

Supplementary Figure 1 from The Role of S100P in the Invasion of Pancreatic Cancer Cells Is Mediated through Cytoskeletal Changes and Regulation of Cathepsin D

Representative data from the T5600 iTRAQ serum 8-plex experiment. iTRAQ labels were used as follows: 113: 0-6m cases, 114: 0-6m controls, 115: 6-12m cases, 116: 6-12m controls, 117: PDAC non obstructed, 118: PDAC obstructed, 119: CP, 121: HC (PBRU) (all samples relative to 121 - HC).

Fibroblast markers and RT-PCR validation for mRNA and microRNA arrays.