1,425 research outputs found

    Clinical actionability of comprehensive genomic profiling for management of rare or refractory cancers

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    Background. The frequency with which targeted tumor sequencing results will lead to implemented change in care is unclear. Prospective assessment of the feasibility and limitations of using genomic sequencing is critically important. Methods. A prospective clinical study was conducted on 100 patients with diverse-histology, rare, or poor-prognosis cancers to evaluate the clinical actionability of a Clinical Laboratory Improvement Amendments (CLIA)-certified, comprehensive genomic profiling assay (FoundationOne), using formalin-fixed, paraffin-embedded tumors. The primary objectives were to assess utility, feasibility, and limitations of genomic sequencing for genomically guided therapy or other clinical purpose in the setting of a multidisciplinary molecular tumor board. Results. Of the tumors from the 92 patients with sufficient tissue, 88 (96%) had at least one genomic alteration (average 3.6, range 0–10). Commonly altered pathways included p53 (46%), RAS/RAF/MAPK (rat sarcoma; rapidly accelerated fibrosarcoma; mitogen-activated protein kinase) (45%), receptor tyrosine kinases/ligand (44%), PI3K/AKT/mTOR (phosphatidylinositol-4,5-bisphosphate 3-kinase; protein kinase B; mammalian target of rapamycin) (35%), transcription factors/regulators (31%), and cell cycle regulators (30%). Many low frequency but potentially actionable alterations were identified in diverse histologies. Use of comprehensive profiling led to implementable clinical action in 35% of tumors with genomic alterations, including genomically guided therapy, diagnostic modification, and trigger for germline genetic testing. Conclusion. Use of targeted next-generation sequencing in the setting of an institutional molecular tumor board led to implementable clinical action in more than one third of patients with rare and poor-prognosis cancers. Major barriers to implementation of genomically guided therapy were clinical status of the patient and drug access. Early and serial sequencing in the clinical course and expanded access to genomically guided early-phase clinical trials and targeted agents may increase actionability. Implications for Practice: Identification of key factors that facilitate use of genomic tumor testing results and implementation of genomically guided therapy may lead to enhanced benefit for patients with rare or difficult to treat cancers. Clinical use of a targeted next-generation sequencing assay in the setting of an institutional molecular tumor board led to implementable clinical action in over one third of patients with rare and poor prognosis cancers. The major barriers to implementation of genomically guided therapy were clinical status of the patient and drug access both on trial and off label. Approaches to increase actionability include early and serial sequencing in the clinical course and expanded access to genomically guided early phase clinical trials and targeted agents

    Combinatorial Blood Platelets-Derived circRNA and mRNA Signature for Early-Stage Lung Cancer Detection

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    The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/ijms24054881/s1.Despite the diversity of liquid biopsy transcriptomic repertoire, numerous studies often exploit only a single RNA type signature for diagnostic biomarker potential. This frequently results in insufficient sensitivity and specificity necessary to reach diagnostic utility. Combinatorial biomarker approaches may offer a more reliable diagnosis. Here, we investigated the synergistic contributions of circRNA and mRNA signatures derived from blood platelets as biomarkers for lung cancer detection. We developed a comprehensive bioinformatics pipeline permitting an analysis of platelet- circRNA and mRNA derived from non-cancer individuals and lung cancer patients. An optimal selected signature is then used to generate the predictive classification model using machine learning algorithm. Using an individual signature of 21 circRNA and 28 mRNA, the predictive models reached an area under the curve (AUC) of 0.88 and 0.81, respectively. Importantly, combinatorial analysis including both types of RNAs resulted in an 8-target signature (6 mRNA and 2 circRNA), enhancing the differentiation of lung cancer from controls (AUC of 0.92). Additionally, we identified five biomarkers potentially specific for early-stage detection of lung cancer. Our proof-of-concept study presents the first multi-analyte-based approach for the analysis of platelets-derived biomarkers, providing a potential combinatorial diagnostic signature for lung cancer detection.European Union’s Horizon 2020 research and innovation program under the Marie Skłodowska-Curie 765492

    Graph Theoretic and Pearson Correlation-Based Discovery of Network Biomarkers for Cancer

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    Two graph theoretic concepts—clique and bipartite graphs—are explored to identify the network biomarkers for cancer at the gene network level. The rationale is that a group of genes work together by forming a cluster or a clique-like structures to initiate a cancer. After initiation, the disease signal goes to the next group of genes related to the second stage of a cancer, which can be represented as a bipartite graph. In other words, bipartite graphs represent the cross-talk among the genes between two disease stages. To prove this hypothesis, gene expression values for three cancers— breast invasive carcinoma (BRCA), colorectal adenocarcinoma (COAD) and glioblastoma multiforme (GBM)—are used for analysis. First, a co-expression gene network is generated with highly correlated gene pairs with a Pearson correlation coefficient ≥ 0.9. Second, clique structures of all sizes are isolated from the co-expression network. Then combining these cliques, three different biomarker modules are developed—maximal clique-like modules, 2-clique-1-bipartite modules, and 3-clique-2-bipartite modules. The list of biomarker genes discovered from these network modules are validated as the essential genes for causing a cancer in terms of network properties and survival analysis. This list of biomarker genes will help biologists to design wet lab experiments for further elucidating the complex mechanism of cancer

    Novel anti-endothelial therapeutic strategies in malignant melanoma : the metronomic approach.

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    Treatment strategies for advanced malignancy remain limited in their success, despite major advances in the understanding of cancer aetiology and molecular biology. The incidence of many cancers, including melanoma, continues to rise, with a huge demand for therapies even if treatment goals are purely cytostatic. One particular therapeutic strategy is the metronomic (continuous and low) dosing of conventional chemotherapy. There is evidence to suggest that tumour vasculature is the main target of this dosing schedule resulting in an overall ‘non specific’ anti-angiogenic effect. It is now being studied in clinical trials alone and in combination with specific antiangiogenic agents.This thesis had two main aims: firstly to investigate the additive or synergistic antiendothelial effects of a number of conventional cytotoxic agents (Temozolomide, Paclitaxel, Vinorelbine, Etoposide, Carboplatin) in vitro given in a metronomic schedule in combination with a specific anti-angiogenic compound (Sorafenib) and a non-specific sompound (Combretastatin). The anti-proliferative, cytotoxic activities of the metronomic combinatorial schedules were assessed on microvascular endothelial cells and cancer cells using an MTT proliferation assay. Results confirmed significant (

    Molecular Portraits of Cancer Evolution and Ecology

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    Research on the molecular lesions that drive cancers holds the translational promise of unmasking distinct disease subtypes in otherwise pathologically identical patients. Yet clinical adoption is hindered by the reproducibility crisis for cancer biomarkers. In this thesis, a novel metric uncovered transcriptional diversity within individual non-small cell lung cancers, driven by chromosomal instability. Existing prognostic biomarkers were confounded by tumour sampling bias, arising from this diversity, in ~50% of patients assessed. An atlas of consistently expressed genes was derived to address this diagnostic challenge, yielding a clonal biomarker robust to sampling bias. This diagnostic based on cancer evolutionary principles maintained prognostic value in a metaanalysis of >900 patients, and over known risk factors in stage I disease, motivating further development as a clinical assay. Next, in situ RNA profiles of immune, fibroblast and endothelial cell subsets were generated from cancerous and adjacent non-malignant lung tissue. The phenotypic adaptation of stromal cells in the tumour microenvironment undermined the performance of existing molecular signatures for cell-type enumeration. Transcriptome-wide analysis delineated ~10% of genes displaying cell-type-specific expression, paving the way for high-fidelity signatures for the accurate digital dissection of tumour ecology. Lastly, the impact of branching, Darwinian evolution on the detection of epistatic interactions was evaluated in a pan-cancer analysis. The clonal status of driver genes was associated with the proportion of significant epistatic findings in 44-78% of the cancer-types assessed. Integrating the clonal architecture of tumours in future analyses could help decipher evolutionary dependencies. This work provides pragmatic solutions for refining molecular portraits of cancer in the light of their evolutionary and ecological features, moving the needle for precision cancer diagnostics

    Combinatorial Drug Therapy for Cancer in the Post-Genomic Era.

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    Over the past decade, whole genome sequencing and other 'omics' technologies have defined pathogenic driver mutations to which tumor cells are addicted. Such addictions, synthetic lethalities and other tumor vulnerabilities have yielded novel targets for a new generation of cancer drugs to treat discrete, genetically defined patient subgroups. This personalized cancer medicine strategy could eventually replace the conventional one-size-fits-all cytotoxic chemotherapy approach. However, the extraordinary intratumor genetic heterogeneity in cancers revealed by deep sequencing explains why de novo and acquired resistance arise with molecularly targeted drugs and cytotoxic chemotherapy, limiting their utility. One solution to the enduring challenge of polygenic cancer drug resistance is rational combinatorial targeted therapy

    Combinatorial BCL2 family expression in acute myeloid leukemia stem cells predicts clinical response to azacitidine/venetoclax

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    UNLABELLED: The BCL2 inhibitor venetoclax (VEN) in combination with azacitidine (5-AZA) is currently transforming acute myeloid leukemia (AML) therapy. However, there is a lack of clinically relevant biomarkers that predict response to 5-AZA/VEN. Here, we integrated transcriptomic, proteomic, functional, and clinical data to identify predictors of 5-AZA/VEN response. Although cultured monocytic AML cells displayed upfront resistance, monocytic differentiation was not clinically predictive in our patient cohort. We identified leukemic stem cells (LSC) as primary targets of 5-AZA/VEN whose elimination determined the therapy outcome. LSCs of 5-AZA/VEN-refractory patients displayed perturbed apoptotic dependencies. We developed and validated a flow cytometry-based Mediators of apoptosis combinatorial score (MAC-Score) linking the ratio of protein expression of BCL2, BCL-xL, and MCL1 in LSCs. MAC scoring predicts initial response with a positive predictive value of more than 97% associated with increased event-free survival. In summary, combinatorial levels of BCL2 family members in AML-LSCs are a key denominator of response, and MAC scoring reliably predicts patient response to 5-AZA/VEN. SIGNIFICANCE: Venetoclax/azacitidine treatment has become an alternative to standard chemotherapy for patients with AML. However, prediction of response to treatment is hampered by the lack of clinically useful biomarkers. Here, we present easy-to-implement MAC scoring in LSCs as a novel strategy to predict treatment response and facilitate clinical decision-making. This article is highlighted in the In This Issue feature, p. 1275

    Artificial Intelligence in Oncology Drug Discovery and Development

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    There exists a profound conflict at the heart of oncology drug development. The efficiency of the drug development process is falling, leading to higher costs per approved drug, at the same time personalised medicine is limiting the target market of each new medicine. Even as the global economic burden of cancer increases, the current paradigm in drug development is unsustainable. In this book, we discuss the development of techniques in machine learning for improving the efficiency of oncology drug development and delivering cost-effective precision treatment. We consider how to structure data for drug repurposing and target identification, how to improve clinical trials and how patients may view artificial intelligence
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