5732 results for "canc*"

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• Long-read and short-read isoform sequencing in breast cancer

  The goal of this project was to perform long-read RNA sequencing (LR-seq, PacBio) in combination with short-read RNA-seq for systematic characterization of the isoform diversity in primary breast tumor samples. We sequenced the full-length transcriptomes of 26 breast tumors and 4 normal breast samples.

  Study EGAS00001004819

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

  Study EGAS00001004821

• Immune trajectory of response and adverse effect in immunotherapy-treated hepatocellular carcinoma

  Whilst anti-PD1 immune checkpoint blockade (ICB) has revolutionised treatment for many cancers, its response rates in hepatocellular carcinoma (HCC) remain modest, coupled with common incidences of immune-related adverse events (irAEs). To identify biomarkers and decipher the mechanisms for response and irAEs, we performed multi-dimensional immunoprofiling on peripheral blood and tissue collected before and during anti-PD1 ICB treatment from HCC patients. Single-cell analyses enabled us to identify peripheral biomarkers, in particular, CD11c+ antigen-presenting cells with varying degrees of HLA-DR expression and CD8 effector memory (EM) T cells with CXCR3 tissue-homing capability that are associated with both response and irAEs. We also dissected the mechanisms behind how these immune subsets interface between response and toxicity, showing their intersecting yet diverging trajectories leading to these distinct clinical fates. This study offers new insights on the mechanisms and potentially novel strategies to enhance response and ameliorate irAEs in cancer immunotherapy.

  Study EGAS00001004843

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

  Study EGAS00001004851

• Comprehensive genomic and transcriptomic analysis of three synchronous primary tumours and a recurrence from a head and neck cancer patient

  Synchronous primary malignancies occur in a small proportion of head and neck squamous cell carcinoma patients. Here, we analysed three synchronous primaries and a recurrence from one patient, by comparing the genomic and transcriptomic profiles among the tumour samples and determining the recurrence origin. We found remarkable levels of heterogeneity among the primary tumours, and through the patterns of shared mutations, we traced the origin of the recurrence. Interestingly, the patient carried germline variants which might have predisposed him to carcinogenesis, together with a history of alcohol and tobacco consumption. Mutational signature analysis confirmed the impact of alcohol exposure, with Signature 16 present in all tumour samples. Characterisation of immune cell infiltration highlighted an immunosuppressive environment in all samples, which exceeded the potential activity of T cells. Studies such as the one described here have important clinical value and contribute to personalised treatment decisions for patients with synchronous primaries and matched recurrences.

  Study EGAS00001004857

• 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

• Genetic makeup of agnospheres

  Agnospheres are cultures of cancer stem cells isolated from tissue biopsies from patients with Cancer of Unknown Primary (CUPs). The latter is a lethal malignancy (median survival <1 year), representing approximately the 3% of all cancers, and characterized by metastatic dissemination in the absence of a primary tumor recognizable by a standardized protocol including thorough body imaging and histopathological analysis. Agnospheres are long-term propagated in culture and, after subcutaneous injection into immunocompromised mice, they display high tumorigenicity and quickly reproduce the clinical presentation of the original tumor, featuring a multi-organ metastatic pattern and poorly differentiated histology. The goal of the project is to analyse the mutational profile of agnospheres in detail, as to verify its correspondance with that of the original tumors. Genetic information will be exploited to investigate the genetic drivers of the CUP phenotype, in particular the hypermetastatic ability and the differentiative block.

  Study EGAS00001004868

• Multiregional sequencing of IDH-WT glioblastoma reveals high genetic heterogeneity and a dynamic evolutionary history

  Glioblastoma is the most common and aggressive primary brain malignancy in adults. In addi-tion to extensive inter-patient heterogeneity, glioblastoma shows intra-tumor extensive cellular and molecular heterogeneity, both spatially and temporally. This heterogeneity is one of the main reasons for the poor prognosis and overall survival. Moreover, it raises the important question of whether the molecular characterization of a single bioptic sample, as performed in standard di-agnostics, actually represents the entire lesion. In this study, we sequenced the whole-exome of nine spatially different cancer regions of three primary glioblastomas. We characterized their mutational profiles and copy number alterations, with implications for our understanding of tu-mor biology in relation to clonal architecture and evolutionary dynamics, as well as therapeuti-cally relevant alterations.

  Study EGAS00001005128

• Molecular evolution and clinical trajectories of prostate cancer identifies novel markers for risk stratification - additional data

  Study EGAS00001004884