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  1. Article: Proteomic Profile Distinguishes New Subpopulations of Breast Cancer Patients with Different Survival Outcomes.

    Tobiasz, Joanna / Polanska, Joanna

    Cancers

    2023  Volume 15, Issue 17

    Abstract: As a highly heterogeneous disease, breast cancer (BRCA) demonstrates a diverse molecular portrait. The well-established molecular classification (PAM50) relies on gene expression profiling. It insufficiently explains the observed clinical and ... ...

    Abstract As a highly heterogeneous disease, breast cancer (BRCA) demonstrates a diverse molecular portrait. The well-established molecular classification (PAM50) relies on gene expression profiling. It insufficiently explains the observed clinical and histopathological diversity of BRCAs. This study aims to demographically and clinically characterize the six BRCA subpopulations (basal, HER2-enriched, and four luminal ones) revealed by their proteomic portraits. GMM-based high variate protein selection combined with PCA/UMAP was used for dimensionality reduction, while the k-means algorithm allowed patient clustering. The statistical analysis (log-rank and Gehan-Wilcoxon tests, hazard ratio HR as the effect size ES) showed significant differences across identified subpopulations in Disease-Specific Survival (
    Language English
    Publishing date 2023-08-24
    Publishing country Switzerland
    Document type Journal Article
    ZDB-ID 2527080-1
    ISSN 2072-6694
    ISSN 2072-6694
    DOI 10.3390/cancers15174230
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  2. Article ; Online: GMM-Based Expanded Feature Space as a Way to Extract Useful Information for Rare Cell Subtypes Identification in Single-Cell Mass Cytometry.

    Suwalska, Aleksandra / Polanska, Joanna

    International journal of molecular sciences

    2023  Volume 24, Issue 18

    Abstract: Cell subtype identification from mass cytometry data presents a persisting challenge, particularly when dealing with millions of cells. Current solutions are consistently under development, however, their accuracy and sensitivity remain limited, ... ...

    Abstract Cell subtype identification from mass cytometry data presents a persisting challenge, particularly when dealing with millions of cells. Current solutions are consistently under development, however, their accuracy and sensitivity remain limited, particularly in rare cell-type detection due to frequent downsampling. Additionally, they often lack the capability to analyze large data sets. To overcome these limitations, a new method was suggested to define an extended feature space. When combined with the robust clustering algorithm for big data, it results in more efficient cell clustering. Each marker's intensity distribution is presented as a mixture of normal distributions (Gaussian Mixture Model, GMM), and the expanded space is created by spanning over all obtained GMM components. The projection of the initial flow cytometry marker domain into the expanded space employs GMM-based membership functions. An evaluation conducted on three established cellular identification algorithms (FlowSOM, ClusterX, and PARC) utilizing the most substantial publicly available annotated dataset by Samusik et al. demonstrated the superior performance of the suggested approach in comparison to the standard. Although our approach identified 20 cell clusters instead of the expected 24, their intra-cluster homogeneity and inter-cluster differences were superior to the 24-cluster FlowSOM-based solution.
    MeSH term(s) Algorithms ; Big Data ; Cluster Analysis ; Flow Cytometry ; Normal Distribution
    Language English
    Publishing date 2023-09-13
    Publishing country Switzerland
    Document type Journal Article
    ZDB-ID 2019364-6
    ISSN 1422-0067 ; 1422-0067 ; 1661-6596
    ISSN (online) 1422-0067
    ISSN 1422-0067 ; 1661-6596
    DOI 10.3390/ijms241814033
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  3. Article ; Online: Can T1-Weighted Magnetic Resonance Imaging Significantly Improve Mini-Mental State Examination-Based Distinguishing Between Mild Cognitive Impairment and Early-Stage Alzheimer's Disease?

    Marcisz, Anna / Polanska, Joanna

    Journal of Alzheimer's disease : JAD

    2023  Volume 92, Issue 3, Page(s) 941–957

    Abstract: Background: Detecting early-stage Alzheimer's disease (AD) is still problematic in clinical practice. This work aimed to find T1-weighted MRI-based markers for AD and mild cognitive impairment (MCI) to improve the screening process.: Objective: Our ... ...

    Abstract Background: Detecting early-stage Alzheimer's disease (AD) is still problematic in clinical practice. This work aimed to find T1-weighted MRI-based markers for AD and mild cognitive impairment (MCI) to improve the screening process.
    Objective: Our assumption was to build a screening model that would be accessible and easy to use for physicians in their daily clinical routine.
    Methods: The multinomial logistic regression was used to detect status: AD, MCI, and normal control (NC) combined with the Bayesian information criterion for model selection. Several T1-weighted MRI-based radiomic features were considered explanatory variables in the prediction model.
    Results: The best radiomic predictor was the relative brain volume. The proposed method confirmed its quality by achieving a balanced accuracy of 95.18%, AUC of 93.25%, NPV of 97.93%, and PPV of 90.48% for classifying AD versus NC for the European DTI Study on Dementia (EDSD). The comparison of the two models: with the MMSE score only as an independent variable and corrected for the relative brain value and age, shows that the addition of the T1-weighted MRI-based biomarker improves the quality of MCI detection (AUC: 67.04% versus 71.08%) while maintaining quality for AD (AUC: 93.35% versus 93.25%). Additionally, among MCI patients predicted as AD inconsistently with the original diagnosis, 60% from ADNI and 76.47% from EDSD were re-diagnosed as AD within a 48-month follow-up. It shows that our model can detect AD patients a few years earlier than a standard medical diagnosis.
    Conclusion: The created method is non-invasive, inexpensive, clinically accessible, and efficiently supports AD/MCI screening.
    MeSH term(s) Humans ; Alzheimer Disease/diagnostic imaging ; Alzheimer Disease/pathology ; Bayes Theorem ; Brain/diagnostic imaging ; Brain/pathology ; Diffusion Tensor Imaging ; Cognitive Dysfunction/diagnostic imaging ; Cognitive Dysfunction/pathology ; Magnetic Resonance Imaging/methods
    Language English
    Publishing date 2023-02-20
    Publishing country Netherlands
    Document type Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't ; Research Support, U.S. Gov't, Non-P.H.S.
    ZDB-ID 1440127-7
    ISSN 1875-8908 ; 1387-2877
    ISSN (online) 1875-8908
    ISSN 1387-2877
    DOI 10.3233/JAD-220806
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  4. Article ; Online: DiviK: divisive intelligent K-means for hands-free unsupervised clustering in big biological data.

    Mrukwa, Grzegorz / Polanska, Joanna

    BMC bioinformatics

    2022  Volume 23, Issue 1, Page(s) 538

    Abstract: Background: Investigating molecular heterogeneity provides insights into tumour origin and metabolomics. The increasing amount of data gathered makes manual analyses infeasible-therefore, automated unsupervised learning approaches are utilised for ... ...

    Abstract Background: Investigating molecular heterogeneity provides insights into tumour origin and metabolomics. The increasing amount of data gathered makes manual analyses infeasible-therefore, automated unsupervised learning approaches are utilised for discovering tissue heterogeneity. However, automated analyses require experience setting the algorithms' hyperparameters and expert knowledge about the analysed biological processes. Moreover, feature engineering is needed to obtain valuable results because of the numerous features measured.
    Results: We propose DiviK: a scalable stepwise algorithm with local data-driven feature space adaptation for segmenting high-dimensional datasets. The algorithm is compared to the optional solutions (regular k-means, spatial and spectral approaches) combined with different feature engineering techniques (None, PCA, EXIMS, UMAP, Neural Ions). Three quality indices: Dice Index, Rand Index and EXIMS score, focusing on the overall composition of the clustering, coverage of the tumour region and spatial cluster consistency, are used to assess the quality of unsupervised analyses. Algorithms were validated on mass spectrometry imaging (MSI) datasets-2D human cancer tissue samples and 3D mouse kidney images. DiviK algorithm performed the best among the four clustering algorithms compared (overall quality score 1.24, 0.58 and 162 for d(0, 0, 0), d(1, 1, 1) and the sum of ranks, respectively), with spectral clustering being mostly second. Feature engineering techniques impact the overall clustering results less than the algorithms themselves (partial [Formula: see text] effect size: 0.141 versus 0.345, Kendall's concordance index: 0.424 versus 0.138 for d(0, 0, 0)).
    Conclusions: DiviK could be the default choice in the exploration of MSI data. Thanks to its unique, GMM-based local optimisation of the feature space and deglomerative schema, DiviK results do not strongly depend on the feature engineering technique applied and can reveal the hidden structure in a tissue sample. Additionally, DiviK shows high scalability, and it can process at once the big omics data with more than 1.5 mln instances and a few thousand features. Finally, due to its simplicity, DiviK is easily generalisable to an even more flexible framework. Therefore, it is helpful for other -omics data (as single cell spatial transcriptomic) or tabular data in general (including medical images after appropriate embedding). A generic implementation is freely available under Apache 2.0 license at https://github.com/gmrukwa/divik .
    Language English
    Publishing date 2022-12-12
    Publishing country England
    Document type Journal Article
    ZDB-ID 2041484-5
    ISSN 1471-2105 ; 1471-2105
    ISSN (online) 1471-2105
    ISSN 1471-2105
    DOI 10.1186/s12859-022-05093-z
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  5. Article: Quantifying Spatial Heterogeneity of Tumor-Infiltrating Lymphocytes to Predict Survival of Individual Cancer Patients.

    Suwalska, Aleksandra / Zientek, Lukasz / Polanska, Joanna / Marczyk, Michal

    Journal of personalized medicine

    2022  Volume 12, Issue 7

    Abstract: Tumor-infiltrating lymphocytes (TILs), identified on HE-stained histopathological images in the cancer area, are indicators of the adaptive immune response against cancers and play a major role in personalized cancer immunotherapy. Recent works indicate ... ...

    Abstract Tumor-infiltrating lymphocytes (TILs), identified on HE-stained histopathological images in the cancer area, are indicators of the adaptive immune response against cancers and play a major role in personalized cancer immunotherapy. Recent works indicate that the spatial organization of TILs may be prognostic of disease-specific survival and recurrence. However, there are a limited number of methods that were proposed and tested in analyses of the spatial structure of TILs. In this work, we evaluated 14 different spatial measures, including the one developed for other omics data, on 10,532 TIL maps from 23 cancer types in terms of reproducibility, uniqueness, and impact on patient survival. For each spatial measure, 16 different scenarios for the definition of prognostic factor were tested. We found no difference in survival prediction when TIL maps were stored as binary images or continuous TIL probability scores. When spatial measures were discretized into a low and high category, a higher correlation with survival was observed. Three measures with the highest cancer prognosis capability were spatial autocorrelation, GLCM M1, and closeness centrality. Most of the tested measures could be further tuned to increase prediction performance.
    Language English
    Publishing date 2022-07-07
    Publishing country Switzerland
    Document type Journal Article
    ZDB-ID 2662248-8
    ISSN 2075-4426
    ISSN 2075-4426
    DOI 10.3390/jpm12071113
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  6. Article: Influence of single-cell RNA sequencing data integration on the performance of differential gene expression analysis.

    Kujawa, Tomasz / Marczyk, Michał / Polanska, Joanna

    Frontiers in genetics

    2022  Volume 13, Page(s) 1009316

    Abstract: Large-scale comprehensive single-cell experiments are often resource-intensive and require the involvement of many laboratories and/or taking measurements at various times. This inevitably leads to batch effects, and systematic variations in the data ... ...

    Abstract Large-scale comprehensive single-cell experiments are often resource-intensive and require the involvement of many laboratories and/or taking measurements at various times. This inevitably leads to batch effects, and systematic variations in the data that might occur due to different technology platforms, reagent lots, or handling personnel. Such technical differences confound biological variations of interest and need to be corrected during the data integration process. Data integration is a challenging task due to the overlapping of biological and technical factors, which makes it difficult to distinguish their individual contribution to the overall observed effect. Moreover, the choice of integration method may impact the downstream analyses, including searching for differentially expressed genes. From the existing data integration methods, we selected only those that return the full expression matrix. We evaluated six methods in terms of their influence on the performance of differential gene expression analysis in two single-cell datasets with the same biological study design that differ only in the way the measurement was done: one dataset manifests strong batch effects due to the measurements of each sample at a different time. Integrated data were visualized using the UMAP method. The evaluation was done both on individual gene level using parametric and non-parametric approaches for finding differentially expressed genes and on gene set level using gene set enrichment analysis. As an evaluation metric, we used two correlation coefficients, Pearson and Spearman, of the obtained test statistics between reference, test, and corrected studies. Visual comparison of UMAP plots highlighted ComBat-seq, limma, and MNN, which reduced batch effects and preserved differences between biological conditions. Most of the tested methods changed the data distribution after integration, which negatively impacts the use of parametric methods for the analysis. Two algorithms, MNN and Scanorama, gave very poor results in terms of differential analysis on gene and gene set levels. Finally, we highlight ComBat-seq as it led to the highest correlation of test statistics between reference and corrected dataset among others. Moreover, it does not distort the original distribution of gene expression data, so it can be used in all types of downstream analyses.
    Language English
    Publishing date 2022-11-01
    Publishing country Switzerland
    Document type Journal Article
    ZDB-ID 2606823-0
    ISSN 1664-8021
    ISSN 1664-8021
    DOI 10.3389/fgene.2022.1009316
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  7. Book ; Online: Isotopic envelope identification by analysis of the spatial distribution of components in MALDI-MSI data

    Glodek, Anna / Polańska, Joanna / Gawin, Marta

    2023  

    Abstract: One of the significant steps in the process leading to the identification of proteins is mass spectrometry, which allows for obtaining information about the structure of proteins. Removing isotope peaks from the mass spectrum is vital and it is done in a ...

    Abstract One of the significant steps in the process leading to the identification of proteins is mass spectrometry, which allows for obtaining information about the structure of proteins. Removing isotope peaks from the mass spectrum is vital and it is done in a process called deisotoping. There are different algorithms for deisotoping, but they have their limitations, they are dedicated to different methods of mass spectrometry. Data from experiments performed with the MALDI-ToF technique are characterized by high dimensionality. This paper presents a method for identifying isotope envelopes in MALDI-ToF molecular imaging data based on the Mamdani-Assilan fuzzy system and spatial maps of the molecular distribution of peaks included in the isotopic envelope. Several image texture measures were used to evaluate spatial molecular distribution maps. The algorithm was tested on eight datasets obtained from the MALDI-ToF experiment on samples from the National Institute of Oncology in Gliwice from patients with cancer of the head and neck region. The data were subjected to pre-processing and feature extraction. The results were collected and compared with three existing deisotoping algorithms. The analysis of the obtained results showed that the method for identifying isotopic envelopes proposed in this paper enables the detection of overlapping envelopes by using the approach oriented to study peak pairs. Moreover, the proposed algorithm enables the analysis of large data sets.
    Keywords Statistics - Machine Learning ; Computer Science - Machine Learning ; Quantitative Biology - Quantitative Methods
    Subject code 006
    Publishing date 2023-02-12
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  8. Article: Glycemic control in children with type 1 diabetes treated with the advanced hybrid closed loop system 2-year prospective, observational, two-center study.

    Seget, Sebastian / Chobot, Agata / Tarasiewicz, Mateusz / Bielawska, Anna / Rusak, Ewa / Ochab, Agnieszka / Polanska, Joanna / Jarosz-Chobot, Przemysława

    Frontiers in endocrinology

    2024  Volume 15, Page(s) 1332418

    Abstract: Background and aims: MiniMed 780G is the first Advanced Hybrid Closed Loop (AHCL) system in Poland, approved in the EU in 2020. To date, observations of glycemic control up to 12 months have been published. This study aimed to analyze glycemic control ... ...

    Abstract Background and aims: MiniMed 780G is the first Advanced Hybrid Closed Loop (AHCL) system in Poland, approved in the EU in 2020. To date, observations of glycemic control up to 12 months have been published. This study aimed to analyze glycemic control and anthropometric parameters in children and adolescents with type 1 diabetes (T1D) after two years of using the AHCL system.
    Materials and methods: We prospectively collected anthropometric data, pump, and continuous glucose records of fifty T1D children (9.9 ± 2.4 years, 24 (48%) boys, T1D for 3.9 ± 2.56 years) using an AHCL system. We compared the two-week AHCL records obtained after AHCL enrollment with data 6, 12, and 24 months after starting AHCL.
    Results: Time in range (70-180 mg/dl) and BMI z-score did not change during the 2 years of observation (p>0.05). The percentage of autocorrection in total daily insulin increased significantly (p<0.005).
    Conclusion: Glycemic control in the investigated group of children with T1D treated with the AHCL system for 2 years remained stable. Children in this group maintained weight and optimal metabolic control, most likely due to autocorrection boluses.
    MeSH term(s) Adolescent ; Male ; Child ; Humans ; Female ; Diabetes Mellitus, Type 1/drug therapy ; Glycemic Control ; Prospective Studies ; Anthropometry ; Body Fluids
    Language English
    Publishing date 2024-02-08
    Publishing country Switzerland
    Document type Observational Study ; Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 2592084-4
    ISSN 1664-2392
    ISSN 1664-2392
    DOI 10.3389/fendo.2024.1332418
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  9. Article ; Online: Analysis of the Applicability of microRNAs in Peripheral Blood Leukocytes as Biomarkers of Sensitivity and Exposure to Fractionated Radiotherapy towards Breast Cancer.

    Marczyk, Michal / Polańska, Joanna / Wojcik, Andrzej / Lundholm, Lovisa

    International journal of molecular sciences

    2021  Volume 22, Issue 16

    Abstract: Biomarkers for predicting individual response to radiation and for dose verification are needed to improve radiotherapy. A biomarker should optimally show signal fidelity, meaning that its level is stable and proportional to the absorbed dose. miRNA ... ...

    Abstract Biomarkers for predicting individual response to radiation and for dose verification are needed to improve radiotherapy. A biomarker should optimally show signal fidelity, meaning that its level is stable and proportional to the absorbed dose. miRNA levels in human blood serum were suggested as promising biomarkers. The aim of the present investigation was to test the miRNA biomarker in leukocytes of breast cancer patients undergoing external beam radiotherapy. Leukocytes were isolated from blood samples collected prior to exposure (control); on the day when a total dose of 2 Gy, 10 Gy, or 20 Gy was reached; and one month after therapy ended (46-50 Gy in total). RNA sequencing was performed and univariate analysis was used to analyse the effect of the radiation dose on the expression of single miRNAs. To check if combinations of miRNAs can predict absorbed dose, a multinomial logistic regression model was built using a training set from eight patients (representing 40 samples) and a validation set with samples from the remaining eight patients (15 samples). Finally, Broadside, an explorative interaction mining tool, was used to extract sets of interacting miRNAs. The most prominently increased miRNA was miR-744-5p, followed by miR-4461, miR-34a-5p, miR-6513-5p, miR-1246, and miR-454-3p. Decreased miRNAs were miR-3065-3p, miR-103a-2-5p, miR-30b-3p, and miR-5690. Generally, most miRNAs showed a relatively strong inter-individual variability and different temporal patterns over the course of radiotherapy. In conclusion, miR-744-5p shows promise as a stable miRNA marker, but most tested miRNAs displayed individual signal variability which, at least in this setting, may exclude them as sensitive biomarkers of radiation response.
    MeSH term(s) Aged ; Biomarkers, Tumor/blood ; Biomarkers, Tumor/genetics ; Breast Neoplasms/blood ; Breast Neoplasms/genetics ; Breast Neoplasms/radiotherapy ; Dose Fractionation, Radiation ; Female ; Gene Expression Profiling/methods ; Gene Expression Regulation, Neoplastic/radiation effects ; Humans ; MicroRNAs/genetics ; Middle Aged ; Sequence Analysis, RNA ; Treatment Outcome ; Up-Regulation
    Chemical Substances Biomarkers, Tumor ; MIRN744 microRNA, human ; MicroRNAs
    Language English
    Publishing date 2021-08-13
    Publishing country Switzerland
    Document type Journal Article
    ZDB-ID 2019364-6
    ISSN 1422-0067 ; 1422-0067 ; 1661-6596
    ISSN (online) 1422-0067
    ISSN 1422-0067 ; 1661-6596
    DOI 10.3390/ijms22168705
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  10. Article: Epigenetic signature of ionizing radiation in therapy-related AML patients.

    O'Brien, Gráinne / Cecotka, Agnieszka / Manola, Kalliopi N / Pagoni, Maria N / Polanska, Joanna / Badie, Christophe

    Heliyon

    2023  Volume 10, Issue 1, Page(s) e23244

    Abstract: Therapy-related acute myeloid leukaemia (t-AML) is a late side effect of previous chemotherapy (ct-AML) and/or radiotherapy (rt-AML) or immunosuppressive treatment. t-AMLs, which account for ∼10-20 % of all AML cases, are extremely aggressive and have a ... ...

    Abstract Therapy-related acute myeloid leukaemia (t-AML) is a late side effect of previous chemotherapy (ct-AML) and/or radiotherapy (rt-AML) or immunosuppressive treatment. t-AMLs, which account for ∼10-20 % of all AML cases, are extremely aggressive and have a poor prognosis compared to
    Language English
    Publishing date 2023-12-02
    Publishing country England
    Document type Journal Article
    ZDB-ID 2835763-2
    ISSN 2405-8440
    ISSN 2405-8440
    DOI 10.1016/j.heliyon.2023.e23244
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