5624 results for "canc*"

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• Clonal fitness inferred from timeseries modeling of single cell cancer genomes

  Progress in defining genomic fitness landscapes in cancer by copy number alterations (CNA) has been impeded by lack of single cell and timeseries sampling. We generated 42,000 single cell whole genomes (scWGS) from breast epithelium and primary triple negative breast cancer (TNBC) patient-derived xenografts (PDX) collected during multi-year time series. Using a Wright-Fisher population genetics model, we inferred reproducible CNA-defined clonal fitness dynamics induced by TP53 mutation and cisplatin chemotherapy, with accurate forecasting of experimentally enforced clonal competition dynamics. Drug treatment in three long-term serially passaged TNBC PDX resulted in cisplatin resistant clones that had shown low fitness in the untreated setting. By contrast high fitness clones from treatment naive controls were eradicated reflecting an inversion of the fitness landscape. Upon drug release selective pressure dynamics were reversed indicating a fitness cost of treatment resistance. Taken together, our findings reveal clonal fitness dynamics linked to CNA and therapeutic resistance in polyclonal tumours.

  Study EGAS00001004448

• Whole genome sequencing and whole exome sequencing of DIPG tumors and matched normal tissue

  Diffuse Intrinsic Pontine Glioma (DIPG) is a fatal brain cancer that arises in the brainstem of children with no effective treatment. To understand what drives DIPGs we integrated whole-genome-sequencing with methylation, expression and copy-number profiling.

  Study EGAS00001000575

• BMP4 and temozolomide synergize in the majority of patient derived glioblastoma cultures

  The poor prognosis of patients with glioblastoma is partly due to resistance to standard treatment, which combines chemoradiation using temozolomide (TMZ). The concept of cancer stem cells provided new insight into tumor resistance and management, and isolated Glioblastoma stem-like cells (GSCs) which contribute to this therapy resistance. Bone morphogenetic protein 4 (BMP4) is a differentiation factor that stimulates astroglial differentiation of such GSCs, thereby reducing their self-renewal. Exposing GSCs to BMP4 could therefore sensitize them to chemotherapy with TMZ, but previous studies with various treatment paradigms have produced contradictory results in this matter. Therefore, we re-defined the optimal treatment paradigm for the combination of TMZ and BMP4 and subsequently re-assessed the inter-tumoral variability in response to co-treatment with TMZ+BMP4 in vitro. First, the simultaneous treatment was found more effective than sequential therapy. Second, after application of our optimized treatment protocol, 70% of a total of 20 patient-derived glioblastoma cultures showed synergy when the treatments were combined. The synergistic effect was a result of increased cellular apoptosis and not of decreased proliferation. Comparative RNA-sequencing suggested that combination treatment results in decreased MAPK signaling. We conclude that addition of BMP4 to standard therapy with TMZ could enlarge the group of patients with good therapeutic response.

  Study EGAS00001007095

• Immunogenomic landscape of hematological malignancies

  We integrated over 8,000 transcriptomes and over 2,000 samples with multilevel genomic data of hematological cancers to investigate how immunological features are linked to cancer subtypes, genetic and epigenetic alterations, and patient survival. For more detailed follow-up analyses in acute myeloid leukemia (AML), single-cell RNA sequencing was performed on 8 AML bone marrow samples at diagnosis to profile the phenotypes of blasts and infiltrating immune cells in different disease subtypes.

  Study EGAS00001004444

• Patient-tailored design for selective co-inhibition of leukemic cell subpopulations

  The extensive primary and secondary drug resistance in many cancer types requires rational approaches to design personalized and selective combinatorial therapies that do not only show synergistic effect in overall cancer cell killing but also result in minimal toxic side effects on non-malignant cells. To address the combinatorial explosion in the number of relevant combinations, we implemented a machine learning approach that prioritizes patient-customized drug combinations with a desired synergy-efficacy-toxicity balance by combining single-cell RNA-sequencing with ex vivo single-agent testing in scarce patient-derived primary cells. When applied to two diagnostic and two refractory AML patient cases, each with a different genetic background, our integrated approach accurately predicted patient-specific combinations that were shown to result not only in synergistic cancer cell co-inhibition but were also capable of targeting specific AML cell subpopulations that emerge in differing stages of disease pathogenesis or treatment regimens. Our data-driven approach provides an unbiased means for systematic identification of personalized combinatorial regimens that selectively co-inhibit leukemic cells while avoiding inhibition of non-malignant cells, and highlight the relevance of considering cell heterogeneity for personalized cancer therapy.

  Study EGAS00001004614

• BIOKEY: A single-cell catalogue of the dynamic changes underlying Checkpoint Immunotherapy response in Early Breast Cancer

  Checkpoint immunotherapy combined with neoadjuvant chemotherapy improves complete pathologic response in a subset of breast cancer patients. Here, we applied single-cell profiling to tumor biopsies collected before and during anti-PD1 therapy. One-third of tumors exhibited proliferative T-cells expanding along CD8+ or CD4+ lineages, which were either characterized by increased cytotoxicity and exhaustion or improved T-helper function, respectively. Lineage tracing in non-expanding tumors revealed at which point in the lineage T-cells were impaired, while gene expression modeling along these lineages revealed novel genes and underlying transcription factors involved in T-cell expansion. Interestingly, different dendritic and myeloid cell phenotypes could either stimulate or inhibit expanding T-cells, while cell-to-cell communication revealed an integrated immune context highly predictive of T-cell expansion, consisting of immune-stimulatory/-inhibitory interactions between cancer and various immune cell types. Our data yield unprecedented insights into the dynamic changes underlying checkpoint immunotherapy response in breast cancer.

  Study EGAS00001004809

• Single-cell RNA-seq data from metastatic ovarian cancer for quality control study

  Quality control is a crucial preliminary step in any single-cell RNAseq experiment, where hard thresholds are commonly used. In order to develop a methodology for a more precise cell filtering in the early steps of a scRNAseq data analysis, we collected tissue samples before chemotherapy from 4 patients.

  Study EGAS00001005066

• High-resolution lung adenocarcinoma expression subtypes identify tumors with dependencies on MET, CDK4, CDK6, and PD-L1

  Lung adenocarcinoma is one of the most common cancer types with various treatment modalities. However, better biomarkers to predict therapeutic response are still needed to improve precision medicine. We utilized a consensus hierarchical clustering approach on 509 LUAD cases from TCGA to identify five robust LUAD expression subtypes. We then integrated genomic (patient and cell line) and proteomic data to help define biomarkers of response to targeted therapies and immunotherapies. This approach defined subtypes with unique proteogenomic and dependency profiles.

  Study EGAS00001006461

• Tissue DNA, WBC DNA and cfDNA (deep-)sequencing of mCRC patients treated with doublet chemotherapy and anti-EGFR in the CAIRO5 study

  This study assessed whether ctDNA based treatment response monitoring could be performed in a tumor tissue-independent manner by combining ultra-deep targeted sequencing analyses of cfDNA with patient-matched white blood cell (WBC) derived DNA. In total, 183 cfDNA and 49 WBC samples, along with 28 tissue samples, from 52 metastatic colorectal cancer patients participating in the prospective phase III CAIRO5 clinical trial (NCT02162563) were analyzed using the PGDx elio plasma resolve assay.

  Study EGAS00001006695

• Interactions between the tumor and the systemic response of breast cancer patients

  Breast cancer (BC) research has largely focused on the molecular properties of the tumor proper. However, to understand how cancer progresses and ultimately modulates patient outcome, we also require an understanding of the molecular changes in the patient systemic response (SR). Toward this end, we generated and analyzed RNA profiles from tumor and matched blood samples in 173 BC patients. We designed a system (MIxT) to explore and link tightly co-expressed gene sets (modules) across matched tissues. The gene composition of modules, and their expression, largely vary across tissues. Distinct patterns of expression in the SR are predominantly detected in highly immunogenic BC subtypes. We also find that the expression pattern of a module in one tissue is correlated to the expression pattern of a module in the other tissue in a fashion that depends on subtype. For example, systemic immunosuppression is detected in basalL patients in a fashion that is proportional to the level of expression of several tumor modules highlighting immune evasion mechanisms active in these particular cancers.

  Study EGAS00001001804