Browse
Submit Data
Databases
API
Help

Dataset Information

31 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Molecular Landscape and Therapeutic Strategies in Cholangiocarcinoma: An Integrated Translational Approach towards Precision Medicine.

ABSTRACT: Cholangiocarcinomas (CCAs) are heterogeneous biliary tract malignancies with dismal prognosis, mainly due to tumor aggressiveness, late diagnosis, and poor response to current therapeutic options. High-throughput technologies have been used as a fundamental tool in unveiling CCA molecular landscape, and several molecular classifications have been proposed, leading to various targeted therapy trials. In this review, we aim to analyze the critical issues concerning the status of precision medicine in CCA, discussing molecular signatures and clusters, related to both anatomical classification and different etiopathogenesis, and the latest therapeutic strategies. Furthermore, we propose an integrated approach comprising the CCA molecular mechanism, pathobiology, clinical and histological findings, and treatment perspectives for the ultimate purpose of improving the methods of patient allocations in clinical trials and the response to personalized therapies.

SUBMITTER: Casadio M

PROVIDER: S-EPMC8199244 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Similar Datasets

Precision medicine in cholangiocarcinoma.

Project description:Cholangiocarcinoma is one of the epithelial cancers with the poorest prognosis and the narrowest therapeutic choice in humans. Compared with other cancer types, cholangiocarcinoma has been often neglected by oncology and liver research studies, thereby leaving many issues unsolved. Apart from the early and marked aggressiveness, one of the main reasons of the still unsatisfying clinical management of cholangiocarcinoma is its wide tumor heterogeneity needing more than other diseases a 'precision medicine' approach. In this regard, in the last few years there has been an awakening of interest aimed at dissecting the complex molecular and genomic profile of cholangiocarcinoma. Thus, a range of molecular players have been recently identified as putative mechanistic determinants of cholangiocarcinoma invasiveness, encompassing tyrosine kinase receptors, metabolic enzymes, transcription factors, small GTPases, ubiquitin ligases, and chromatin-remodelling proteins, whose aberrant expression may derive from stochastic mutations as well as from pro-oncogenic paracrine signals released by the stromal microenvironment, which is particularly exuberant in cholangiocarcinoma. Herein, we sought to overview the most relevant observations unravelling the genomic landscape of cholangiocarcinoma, and the prognostic and predictive biomarkers that consequently have been emerging. Then, we will discuss innovative treatment approaches derived from conventional chemotherapy, targeted therapies, antiangiogenic therapies and immunotherapy, and how they are opening new avenues towards a precision medicine in cholangiocarcinoma.

| S-EPMC6087799 | biostudies-literature

Towards precision medicine: Omics approach for COVID-19.

Project description:The coronavirus disease 2019 (COVID-19) pandemic had a devastating impact on human society. Beginning with genome surveillance of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the development of omics technologies brought a clearer understanding of the complex SARS-CoV-2 and COVID-19. Here, we reviewed how omics, including genomics, proteomics, single-cell multi-omics, and clinical phenomics, play roles in answering biological and clinical questions about COVID-19. Large-scale sequencing and advanced analysis methods facilitate COVID-19 discovery from virus evolution and severity risk prediction to potential treatment identification. Omics would indicate precise and globalized prevention and medicine for the COVID-19 pandemic under the utilization of big data capability and phenotypes refinement. Furthermore, decoding the evolution rule of SARS-CoV-2 by deep learning models is promising to forecast new variants and achieve more precise data to predict future pandemics and prevent them on time.

| S-EPMC9846903 | biostudies-literature

Immune targets for therapeutic development in depression: towards precision medicine.

Project description:Over the past two decades, compelling evidence has emerged indicating that immune mechanisms can contribute to the pathogenesis of major depressive disorder (MDD) and that drugs with primary immune targets can improve depressive symptoms. Patients with MDD are heterogeneous with respect to symptoms, treatment responses and biological correlates. Defining a narrower patient group based on biology could increase the treatment response rates in certain subgroups: a major advance in clinical psychiatry. For example, patients with MDD and elevated pro-inflammatory biomarkers are less likely to respond to conventional antidepressant drugs, but novel immune-based therapeutics could potentially address their unmet clinical needs. This article outlines a framework for developing drugs targeting a novel patient subtype within MDD and reviews the current state of neuroimmune drug development for mood disorders. We discuss evidence for a causal role of immune mechanisms in the pathogenesis of depression, together with targets under investigation in randomized controlled trials, biomarker evidence elucidating the link to neural mechanisms, biological and phenotypic patient selection strategies, and the unmet clinical need among patients with MDD.

| S-EPMC8763135 | biostudies-literature

Cushing's disease: towards precision medicine.

Project description:The pathogenesis of Cushing's disease is poorly understood; two recent reports identifying somatic mutations in USP8 in pituitary corticotroph tumors provide exciting advances in this field. These mutations alter EGFR trafficking and signaling, raising the prospect that EGFR inhibitors may move the treatment of this disease into the era of precision medicine.

| S-EPMC4456621 | biostudies-other

Systems approach for congruence and selection of cancer models towards precision medicine.

Project description:Cancer models are instrumental as a substitute for human studies and to expedite basic, translational, and clinical cancer research. For a given cancer type, a wide selection of models, such as cell lines, patient-derived xenografts, organoids and genetically modified murine models, are often available to researchers. However, how to quantify their congruence to human tumors and to select the most appropriate cancer model is a largely unsolved issue. Here, we present Congruence Analysis and Selection of CAncer Models (CASCAM), a statistical and machine learning framework for authenticating and selecting the most representative cancer models in a pathway-specific manner using transcriptomic data. CASCAM provides harmonization between human tumor and cancer model omics data, systematic congruence quantification, and pathway-based topological visualization to determine the most appropriate cancer model selection. The systems approach is presented using invasive lobular breast carcinoma (ILC) subtype and suggesting CAMA1 followed by UACC3133 as the most representative cell lines for ILC research. Two additional case studies for triple negative breast cancer (TNBC) and patient-derived xenograft/organoid (PDX/PDO) are further investigated. CASCAM is generalizable to any cancer subtype and will authenticate cancer models for faithful non-human preclinical research towards precision medicine.

| S-EPMC10805322 | biostudies-literature

Translational research identifies a metabolism pathway involved in first-episode of schizophrenia: Towards precision medicine.

Project description: Not available

| S-EPMC6710904 | biostudies-literature

Comprehensive genomic landscape and precision therapeutic approach in biliary tract cancers.

Project description:Biliary tract cancers have dismal prognoses even when cytotoxic chemotherapy is administered. There is an unmet need to develop precision treatment approaches using comprehensive genomic profiling. A total of 121 patients with biliary tract cancers were analyzed for circulating-tumor DNA (ctDNA) and/or tissue-based tumor DNA (tissue-DNA) using clinical-grade next-generation sequencing: 71 patients (59%) had ctDNA; 90 (74%), tissue-DNA; and 40 (33%), both. Efficacy of targeted therapeutic approaches was assessed based upon ctDNA and tissue-DNA. At least one characterized alteration was detected in 76% of patients (54/71) for ctDNA [median, 2 (range, 0-9)] and 100% (90/90) for tissue-DNA [median, 4 (range, 1-9)]. Most common alterations occurred in TP53 (38%), KRAS (28%), and PIK3CA (14%) for ctDNA vs TP53 (44%), CDKN2A/B (33%) and KRAS (29%) for tissue-DNA. In 40 patients who had both ctDNA and tissue-DNA sequencing, overall concordance was higher between ctDNA and metastatic site tissue-DNA than between ctDNA and primary tumor DNA (78% vs 65% for TP53, 100% vs 74% for KRAS and 100% vs 87% for PIK3CA [But not statistical significance]). Among 80 patients who received systemic treatment, the molecularly matched therapeutic regimens based on genomic profiling showed a significantly longer progression-free survival (hazard ratio [95%confidence interval], 0.60 [0.37-0.99]. P = .047 [multivariate]) and higher disease control rate (61% vs 35%, P = .04) than unmatched regimens. Evaluation of ctDNA and tissue-DNA is feasible in biliary tract cancers.

| S-EPMC7739197 | biostudies-literature

Towards precision medicine based on a continuous deep learning optimization and ensemble approach.

Project description:We developed a continuous learning system (CLS) based on deep learning and optimization and ensemble approach, and conducted a retrospective data simulated prospective study using ultrasound images of breast masses for precise diagnoses. We extracted 629 breast masses and 2235 images from 561 cases in the institution to train the model in six stages to diagnose benign and malignant tumors, pathological types, and diseases. We randomly selected 180 out of 3098 cases from two external institutions. The CLS was tested with seven independent datasets and compared with 21 physicians, and the system's diagnostic ability exceeded 20 physicians by training stage six. The optimal integrated method we developed is expected accurately diagnose breast masses. This method can also be extended to the intelligent diagnosis of masses in other organs. Overall, our findings have potential value in further promoting the application of AI diagnosis in precision medicine.

| S-EPMC9898519 | biostudies-literature

Towards precision medicine in thyroid cancer.

Project description: Not available

| S-EPMC7607071 | biostudies-literature

Targeting tumor microenvironment for cholangiocarcinoma: Opportunities for precision medicine.

Project description:Systemic treatments (e.g., chemotherapy and targeted therapies) have limited efficacy for patients with locally advanced - unresectable - and metastatic cholangiocarcinoma (CCA), with an overall survival of less than a year. Tumor microenvironment (TME) represents the ecosystem surrounding the tumor which comprises immune cells, fibroblasts, endothelial cells, and a wide range of soluble factors. CCA TME is characterized by an abundant desmoplastic stroma, exhibits a high heterogeneity and it plays a central role in cancer onset and progression. There is growing evidence suggesting that it is possible to target TME in association with other treatment modalities, such as cytotoxic chemotherapy or targeted therapies, paving the way to possible combination strategies with a synergistic effect. Herein, we describe the components of CCA TME - such as cancer-associated fibroblasts and other cells of pivotal importance - with their most relevant interactions, focusing on the preclinical rationale for the development of effective anticancer treatments.

| S-EPMC9396390 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data