5706 results for "canc*"

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• Multi Omics of glioma in the the Chinese Glioma Genome Atlas (CGGA) project

  Study EGAS00001004729

• Genetic ancestry contributes to somatic mutations in lung cancers from admixed Latin American populations

  Inherited lung cancer risk, particularly in non-smokers, is poorly understood. Genomic and ancestry analysis of 1,153 lung cancers from Latin America revealed striking associations between Native American ancestry and their somatic landscape, including tumor mutational burden (TMB), and specific driver mutations in EGFR, KRAS, and STK11. A local Native American ancestry risk score predicted EGFR and KRAS mutation frequency more strongly than global ancestry, suggesting that germline genetics (rather than environmental exposure) underlie these disparities.

  Study EGAS00001004752

• Rucaparib in patients presenting a metastatic breast cancer with Homologous Recombination Deficiency, without germline BRCA1/2 mutation, pronostic value of high LOH genomic score and predictive value of HRDetect (microarray)

  Breast cancer may present genomic alterations leading to homologous recombination deficiency. PARP inhibitors have proved their efficacy in patients with HER2-negative metastatic breast cancer (mBC) harboring germline (g) BRCA1/2 mutations. We conducted the phase 2 RUBY trial to assess the efficacy of rucaparib in HER2-negative mBC with high genomic loss of heterozygosity (LOH) score or somatic, without gBRCA1/2 mutation. 220 of 711 patients with mBC screened for LOH presented high LOH score which was associated with a higher likelihood of death (HR = 1.39, 95% CI: 1.11-1.75, p = 0.005). The primary objective was not reached with a clinical benefit rate (objective response or SD>16 weeks) of 13.5%. Two LOH-high patients, without somatic BRCA1/2 mutation, presented a complete and durable response (14 and 32 months). HRDetect tended to be associated with response to rucaparib, whithout reaching statistical significance (median HRDetect responders versus non responders: 0.465 versus, 0.040, p = 0.2135). Our data suggests that a small subset of patients with high LOH score could derive benefit from PARP inhibitors.

  Study EGAS00001004755

• Rucaparib in patients presenting a metastatic breast cancer with Homologous Recombination Deficiency, without germline BRCA1/2 mutation, pronostic value of high LOH genomic score and predictive value of HRDetect (WGS)

  Study EGAS00001004756

• Immunological hallmarks for clinical response to BCG in bladder cancer

  Intravesical Bacillus Calmette-Guerin (BCG) is an effective immunotherapy for non-muscle invasive bladder cancer (NMIBC). However, recurrence and progression remain frequent warranting deeper insights into its mechanism. We herein comprehensively profiled blood and tissues obtained from NMIBC patients before, during and after BCG treatment using cytometry by time-of-flight (CyTOF) and RNA sequencing to identify the key immune subsets crucial for anti-tumour activity.

  Study EGAS00001004764

• Transcriptional profiling of ovarian tumours and cell lines

  Purpose: Endogenous retroviruses (ERVs) play a role in a variety of biological processes, including embryogenesis and cancer. DNA methyltransferase inhibitors (DNMTi)-induced ERV expression triggers interferon responses in ovarian cancer cells via the viral sensing machinery. Baseline expression of ERVs also occurs in cancer cells, though this process is poorly understood and previously unexplored in epithelial ovarian cancer (EOC). Here, the prognostic and immunomodulatory consequences of baseline ERV expression was assessed in EOC. Experimental Design: ERV expression was assessed using EOC transcriptional data from TCGA and from an independent cohort (HH), as well as from untreated or DNMTi-treated EOC cell lines. LASSO logistic regression defined an ERV expression score to predict patient prognosis. Immunohistochemistry (IHC) was conducted on the HH cohort. Combination of DNMTi treatment with γδ T cells was tested in vitro, using EOC cell lines and patient-derived tumor cells. Results: ERV expression was found to define clinically relevant subsets of EOC patients. An ERV prognostic score was successfully generated in TCGA and validated in the independent cohort. In EOC patients from this cohort, a high ERV score was associated with better survival (log-rank p=0.0009) and correlated with infiltration of CD8+PD1+ T cells (r=0.46, p=0.0001). In vitro, baseline ERV expression dictates the level of ERV induction in response to DNMTi. Manipulation of ERV expression by DNMTi resulted in improved EOC cell killing by cytotoxic immune cells. Conclusions: These findings uncover the potential for baseline ERV expression to robustly inform EOC patient prognosis, influence tumor immune infiltration and affect anti-tumor immunity.

  Study EGAS00001004814

• Integrated Genomic and Transcriptomic Analysis Reveals Unique Characteristics of Hepatic Metastases and Pro-metastatic Role of Complement C1q in Pancreatic Ductal Adenocarcinoma

  Study EGAS00001004821

• AGECAN - Interespecies conservation of brain specific DNA methylation in aging and cancer

  Study EGAS00001004851

• Bulk-RNA Sequencing of high-grade pancreatic and non-pancreatic Neuroendocrine Neoplasms

  Therapeutic decisions in oncology depend on a precise pathological classification of individual neoplasms. Recent years have seen an intensification of research activities aimed at the extraction of clinically relevant information from patient-derived 'omics' data based on Machine-Learning models. However, a comprehensive training of Machine-Learning models requires sufficiently large numbers of training samples, which are usually not available for rare cancer types. The problem is worsened when individual tissues segregate into different cancer subtypes, as their discrimination would require even more training samples. Methods: Here, we report on a new data-augmentation technique to support the training of Machine-Learning models on ‘omics’ data from pancreatic neuroendocrine neoplasms (panNEN). PanNENs display all properties described above: Only about 2-3% of all pancreatic neoplasms are neuroendocrine and they fall into different subtypes with distinctly different prognosis, which makes the precise classification of such samples both difficult and important for therapy decisions. The approach reconstructs a given transcriptome based on healthy pancreatic cell type signatures and creates a Machine-Learning model that integrates the observed reconstruction error and predicted cell type proportions as features. Results: A benchmark of the deconvolution model predictions with the ground-truth found that the model could efficiently predict the sample grading, disease-related patient survival time, and differentiate between different subtypes in four panNEN and one mixed panNEN and non-pancreatic NENs dataset. We compared the predictive performance of the deconvolution-trained model to that of a model trained directly on the transcriptomic data, under inclusion of the Ki-67 classification gold standard biomarker, and found the performances to be comparable. Conclusion: Our approach serves as a data-augmentation technique to facilitate the training of Machine Learning models for rare cancer types via utilization of data from healthy origin. Additionally, the deconvolution results were clinically interpretable and further research can render the deconvolution approach an effective complementary asset for the clinical classification of neoplasms.

  Study EGAS00001004861

• UROMOL 2020 - SNP data

  The molecular landscape in non-muscle-invasive bladder cancer (NMIBC) is characterized by large biological heterogeneity with variable clinical outcomes. Here, we performed a large integrative multi-omics analysis of patients diagnosed with NMIBC (n=834). Transcriptomic analysis identifies four classes (1, 2a, 2b and 3) reflecting tumor biology and disease aggressiveness. Both transcriptome-based subtyping and the level of chromosomal instability provide independent prognostic value beyond established prognostic clinicopathological parameters.

  Study EGAS00001004862