Browsing by Subject "disease progression"

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  • Xu, Haifeng; Lien, Tonje; Bergholtz, Helga; Fleischer, Thomas; Djerroudi, Lounes; Vincent-Salomon, Anne; Sorlie, Therese; Aittokallio, Tero (2021)
    Ductal carcinoma in situ (DCIS) is a preinvasive form of breast cancer with a highly variable potential of becoming invasive and affecting mortality of the patients. Due to the lack of accurate markers of disease progression, many women with detected DCIS are currently overtreated. To distinguish those DCIS cases who are likely to require therapy from those who should be left untreated, there is a need for robust and predictive biomarkers extracted from molecular or genetic profiles. We developed a supervised machine learning approach that implements multi-omics feature selection and model regularization for the identification of biomarker combinations that could be used to distinguish low-risk DCIS lesions from those with a higher likelihood of progression. To investigate the genetic heterogeneity of disease progression, we applied this approach to 40 pure DCIS and 259 invasive breast cancer (IBC) samples profiled with genome-wide transcriptomics, DNA methylation, and DNA copy number variation. Feature selection using the multi-omics Lasso-regularized algorithm identified both known genes involved in breast cancer development, as well as novel markers for early detection. Even though the gene expression-based model features led to the highest classification accuracy alone, methylation data provided a complementary source of features and improved especially the sensitivity of correctly classifying DCIS cases. We also identified a number of repeatedly misclassified DCIS cases when using either the expression or methylation markers. A small panel of 10 gene markers was able to distinguish DCIS and IBC cases with high accuracy in nested cross-validation (AU-ROC = 0.99). The marker panel was not specific to any of the established breast cancer subtypes, suggesting that the 10-gene signature may provide a subtype-agnostic and cost-effective approach for breast cancer detection and patient stratification. We further confirmed high accuracy of the 10-gene signature in an external validation cohort (AU-ROC = 0.95), profiled using distinct transcriptomic assay, hence demonstrating robustness of the risk signature.
  • Nikoskinen, Tuuli; Schmidt, Eeva-Kaisa; Strbian, Daniel; Kiuru-Enari, Sari; Atula, Sari (Helsingfors universitet, 2015)
    Background: Finnish type of hereditary gelsolin amyloidosis (FGA) is one of the most common diseases of Finnish disease heritage. Existing FGA knowledge is based only on smaller patient series, so our aim was to elucidate the natural course of the disease in a comprehensive sample of patients and to build up a national FGA patient registry. Methods: An inquiry about the known and suspected signs of FGA, sent to the members of Finnish Amyloidosis Association, telephone contacts and hospital records were utilised to create the registry. Results: A total of 227 patients were entered to the database. The first symptom was ophthalmological for 167 patients (73.6%) at the mean age of 39 years. Corneal lattice dystrophy (CLD) was reported at the mean age of 43 years. Impaired vision, polyneuropathy, facial nerve paresis and cutis laxa appeared on average between 52 and 57 years. Carpaltunnel syndrome (CTS) was reported by 86 patients (37.9%). Nine patients (4.0%) had a pacemaker and 12 (6.1%) had cardiomyopathy. Conclusions: The first symptom was ophthalmological in most cases. Except for CLD, no prominent difference in the age of appearance was found between the major symptoms. CTS, cardiac pacemakers and cardiomyopathy were remarkably more common compared to the general population.