The primary diagnosis of thyroid tumors based on histopathological patterns can be ambiguous in some cases, so proper classification of thyroid diseases might be improved if molecular biomarkers support cytological and histological assessment. In this work, tissue microarrays representative for major types of thyroid malignancies—papillary thyroid cancer (classical and follicular variant), follicular thyroid cancer, anaplastic thyroid cancer, and medullary thyroid cancer—and benign thyroid follicular adenoma and normal thyroid were analyzed by mass spectrometry imaging (MSI), and then different computation approaches were implemented to test the suitability of the registered profiles of tryptic peptides for tumor classification. Molecular similarity among all seven types of thyroid specimens was estimated, and multicomponent classifiers were built for sample classification using individual MSI spectra that corresponded to small clusters of cells. Moreover, MSI components showing the most significant differences in abundance between the compared types of tissues detected and their putative identity were established by annotation with fragments of proteins identified by liquid chromatography-tandem mass spectrometry in corresponding tissue lysates. In general, high accuracy of sample classification was associated with low inter-tissue similarity index and a high number of components with significant differences in abundance between the tissues. Particularly, high molecular similarity was noted between three types of tumors with follicular morphology (adenoma, follicular cancer, and follicular variant of papillary cancer), whose differentiation represented the major classification problem in our dataset. However, low level of the intra-tissue heterogeneity increased the accuracy of classification despite high inter-tissue similarity (which was exemplified by normal thyroid and benign adenoma). We compared classifiers based on all detected MSI components (n = 1536) and the subset of the most abundant components (n = 147). Despite relatively higher contribution of components with significantly different abundance and lower overall inter-tissue similarity in the latter case, the precision of classification was generally higher using all MSI components. Moreover, the classification model based on individual spectra (a single-pixel approach) outperformed the model based on mean spectra of tissue cores. Our result confirmed the high feasibility of MSI-based approaches to multi-class detection of cancer types and proved the good performance of sample classification based on individual spectra (molecular image pixels) that overcame problems related to small amounts of heterogeneous material, which limit the applicability of classical proteomics.

FGFR signalling is one of the most prominent pathways involved in cell growth and development as well as cancer progression. FGFR1 amplification occurs in approximately 20% of all squamous cell lung carcinomas (SCC), a predominant subtype of non-small cell lung carcinoma (NSCLC), indicating FGFR as a potential target for the new anti-cancer treatment. However, acquired resistance to this type of therapies remains a serious clinical challenge. Here, we investigated the NSCLC cell lines response and potential mechanism of acquired resistance to novel selective FGFR inhibitor CPL304110. We found that despite significant genomic differences between CPL304110-sensitive cell lines, their resistant variants were characterised by upregulated p38 expression/phosphorylation, as well as enhanced expression of genes involved in MAPK signalling. We revealed that p38 inhibition restored sensitivity to CPL304110 in these cells. Moreover, the overexpression of this kinase in parental cells led to impaired response to FGFR inhibition, thus confirming that p38 MAPK is a driver of resistance to a novel FGFR inhibitor. Taken together, our results provide an insight into the potential direction for NSCLC targeted therapy.

In our previous microarray study we identified two subgroups of high-grade serous ovarian cancers with distinct gene expression and survival. Among differentially expressed genes was an Integrin beta-like 1 (ITGBL1), coding for a poorly characterized protein comprised of ten EGF-like repeats. Here, we have analyzed the influence of ITGBL1 on the phenotype of ovarian cancer (OC) cells. We analyzed expression of four putative ITGBL1 mRNA isoforms in five OC cell lines. OAW42 and SKOV3, having the lowest level of any ITGBL1 mRNA, were chosen to produce ITGBL1-overexpressing variants. In these cells, abundant ITGBL1 mRNA expression could be detected by RT-PCR. Immunodetection was successful only in the culture media, suggesting that ITGBL1 is efficiently secreted. We found that ITGBL1 overexpression affected cellular adhesion, migration and invasiveness, while it had no effect on proliferation rate and the cell cycle. ITGBL1-overexpressing cells were significantly more resistant to cisplatin and paclitaxel, major drugs used in OC treatment. Global gene expression analysis revealed that signaling pathways affected by ITGBL1 overexpression were mostly those related to extracellular matrix organization and function, integrin signaling, focal adhesion, cellular communication and motility; these results were consistent with the findings of our functional studies. Overall, our results indicate that higher expression of ITGBL1 in OC is associated with features that may worsen clinical course of the disease.

The data from independent gene expression sources may be integrated for the purpose of molecular diagnostics of cancer. So far, multiple approaches were described. Here, we investigated the impacts of different data fusion strategies on classification accuracy and feature selection stability, which allow the costs of diagnostic tests to be reduced. We used molecular features (gene expression) combined with a feature extracted from the independent clinical data describing a patient’s sample. We considered the dependencies between selected features in two data fusion strategies (early fusion and late fusion) compared to classification models based on molecular features only. We compared the best accuracy classification models in terms of the number of features, which is connected to the potential cost reduction of the diagnostic classifier. We show that for thyroid cancer, the extracted clinical feature is correlated with (but not redundant to) the molecular data. The usage of data fusion allows a model to be obtained with similar or even higher classification quality (with a statistically significant accuracy improvement, a p-value below 0.05) and with a reduction in molecular dimensionality of the feature space from 15 to 3–8 (depending on the feature selection method). Both strategies give comparable quality results, but the early fusion method provides better feature selection stability.

Background: Renin-angiotensin system inhibitors (RASIs) are widely used in the treatment of hypertension. However, their impact on the outcome of the combined treatment of rectal cancer is poorly understood. The aim of this study was to assess the effect of RASIs on the survival of rectal cancer patients with associated hypertension after neoadjuvant treatment and radical resection. Methods: Between 2008 and 2016, 242 radical (R0) rectal resections for cancer were performed after neoadjuvant treatment in patients with associated hypertension. At the time of treatment, 158 patients were on RASIs, including 35 angiotensin-receptor antagonists (ARB) users and 123 angiotensin-converting enzyme inhibitors (ACEI) users. Eighty-four patients were on drugs other than RASIs (non-RASI users). The survival analysis was performed using the Kaplan–Meier estimator with the log-rank test and the Cox proportional hazards model. Results: The log-rank test showed a significantly worse overall survival (OS) in the group of ACEI users compared to ARB users (p = 0.009) and non-RASI users (p = 0.013). Disease-free survival (DFS) was better in the group of ARB users compared to ACEI users. However, the difference was not statistically significant (p = 0.064). The Multivariate Cox analysis showed a significant beneficial effect of ARBs on OS (HR: 0.326, 95% CI: 0.147–0.724, p = 0.006) and ARBs on DFS (HR: 0.339, 95% CI: 0.135–0.850, p = 0.021) compared to ACEIs. Other factors affecting OS included age (HR: 1.044, 95% CI: 1.016–1.073, p = 0.002), regional lymph node metastasis (ypN +) (HR: 2.157, 95% CI: 1.395–3.334, p = 0.001) and perineural invasion (PNI) (HR: 3.864, 95% CI: 1.799–8.301, p = 0.001). Additional factors affecting DFS included ypN + (HR: 2.310, 95% CI: 1.374–3.883, p = 0.002) and PNI (HR: 4.351, 95% CI: 1.584–11.954, p = 0.004). Conclusions: The use of ARBs instead of ACEIs may improve the outcome of the combined therapy for rectal cancer patients with associated hypertension.

Since the very beginning of the COVID-19 pandemic, control policies and restrictions have been the hope for containing the rapid spread of the virus. However, the psychological and economic toll they take on society entails the necessity to develop an optimal control strategy. Assessment of the effectiveness of these interventions aided with mathematical modelling remains a non-trivial issue in terms of numerical conditioning due to the high number of parameters to estimate from a highly noisy dataset and significant correlations between policy timings. We propose a solution to the problem of parameter non-estimability utilizing data from a set of European countries. Treating a subset of parameters as common for all countries and the rest as country-specific, we construct a set of individualized models incorporating 13 different pandemic control measures, and estimate their parameters without prior assumptions. We demonstrate high predictive abilities of these models on an independent validation set and rank the policies by their effectiveness in reducing transmission rates. We show that raising awareness through information campaigns, providing income support, closing schools and workplaces, cancelling public events, and maintaining an open testing policy have the highest potential to mitigate the pandemic.