scholarly journals Integrated time course omics analysis distinguishes immediate therapeutic response from acquired resistance

2017 ◽  
Author(s):  
Genevieve Stein-O’Brien ◽  
Luciane T Kagohara ◽  
Sijia Li ◽  
Manjusha Thakar ◽  
Ruchira Ranaweera ◽  
...  
Keyword(s):  
Targeted Therapies ◽  
Time Course ◽  
Therapeutic Response ◽  
Acquired Resistance ◽  
New Drugs ◽  
Integrated Analysis ◽  
Omics Data ◽  
Epigenetic Alterations ◽  
Molecular Changes ◽  
Omics Analysis

AbstractBACKGROUNDTargeted therapies specifically act by blocking the activity of proteins that are encoded by genes critical for tumorigenesis. However, most cancers acquire resistance and long-term disease remission is rarely observed. Understanding the time course of molecular changes responsible for the development of acquired resistance could enable optimization of patients’ treatment options. Clinically, acquired therapeutic resistance can only be studied at a single time point in resistant tumors. To determine the dynamics of these molecular changes, we obtained high throughput omics data weekly during the development of cetuximab resistance in a head and neck cancer in vitro model.RESULTSAn unsupervised algorithm, CoGAPS, was used to quantify the evolving transcriptional and epigenetic changes. Applying a PatternMarker statistic to the results from CoGAPS enabled novel heatmap-based visualization of the dynamics in these time course omics data. We demonstrate that transcriptional changes result from immediate therapeutic response or resistance, whereas epigenetic alterations only occur with resistance. Integrated analysis demonstrates delayed onset of changes in DNA methylation relative to transcription, suggesting that resistance is stabilized epigenetically.CONCLUSIONSGenes with epigenetic alterations associated with resistance that have concordant expression changes are hypothesized to stabilize resistance. These genes include FGFR1, which was associated with EGFR inhibitor resistance previously. Thus, integrated omics analysis distinguishes the timing of molecular drivers of resistance. Our findings provide a relevant towards better understanding of the time course progression of changes resulting in acquired resistance to targeted therapies. This is an important contribution to the development of alternative treatment strategies that would introduce new drugs before the resistant phenotype develops.

scholarly journals Integrated time course omics analysis distinguishes immediate therapeutic response from acquired resistance

2018 ◽  
Vol 10 (1) ◽  
Author(s):  
Genevieve Stein-O’Brien ◽  
Luciane T. Kagohara ◽  
Sijia Li ◽  
Manjusha Thakar ◽  
Ruchira Ranaweera ◽  
...  
Keyword(s):  
Time Course ◽  
Therapeutic Response ◽  
Acquired Resistance ◽  
Omics Analysis

scholarly journals Systematic Review of Biomarkers To Monitor Therapeutic Response in Leishmaniasis

2014 ◽  
Vol 59 (1) ◽  
pp. 1-14 ◽  
Author(s):  
Anke E. Kip ◽  
Manica Balasegaram ◽  
Jos H. Beijnen ◽  
Jan H. M. Schellens ◽  
Peter J. de Vries ◽  
...  
Keyword(s):  
Systematic Review ◽  
Therapeutic Response ◽  
Parasite Burden ◽  
New Drugs ◽  
Future Research ◽  
Kala Azar ◽  
Pharmacodynamic Markers ◽  
Pharmacodynamic Biomarkers ◽  
Antigenic Targets

ABSTRACTRecently, there has been a renewed interest in the development of new drugs for the treatment of leishmaniasis. This has spurred the need for pharmacodynamic markers to monitor and compare therapies specifically for visceral leishmaniasis, in which the primary recrudescence of parasites is a particularly long-term event that remains difficult to predict. We performed a systematic review of studies evaluating biomarkers in human patients with visceral, cutaneous, and post-kala-azar dermal leishmaniasis, which yielded a total of 170 studies in which 53 potential pharmacodynamic biomarkers were identified. In conclusion, the large majority of these biomarkers constituted universal indirect markers of activation and subsequent waning of cellular immunity and therefore lacked specificity. Macrophage-related markers demonstrate favorable sensitivity and times to normalcy, but more evidence is required to establish a link between these markers and clinical outcome. Most promising are the markers directly related to the parasite burden, but future effort should be focused on optimization of molecular or antigenic targets to increase the sensitivity of these markers. In general, future research should focus on the longitudinal evaluation of the pharmacodynamic biomarkers during treatment, with an emphasis on the correlation of studied biomarkers and clinical parameters.

scholarly journals A New Era of Neuro-Oncology Research Pioneered by Multi-Omics Analysis and Machine Learning

Biomolecules ◽  
2021 ◽  
Vol 11 (4) ◽  
pp. 565
Author(s):  
Satoshi Takahashi ◽  
Masamichi Takahashi ◽  
Shota Tanaka ◽  
Shunsaku Takayanagi ◽  
Hirokazu Takami ◽  
...  
Keyword(s):  
Machine Learning ◽  
Clinical Situation ◽  
Machine Learning Techniques ◽  
Current Status ◽  
Omics Data ◽  
Amount Of Information ◽  
Oncology Research ◽  
Omics Analysis ◽  
Learning Techniques ◽  
Comprehensive Survey

Although the incidence of central nervous system (CNS) cancers is not high, it significantly reduces a patient’s quality of life and results in high mortality rates. A low incidence also means a low number of cases, which in turn means a low amount of information. To compensate, researchers have tried to increase the amount of information available from a single test using high-throughput technologies. This approach, referred to as single-omics analysis, has only been partially successful as one type of data may not be able to appropriately describe all the characteristics of a tumor. It is presently unclear what type of data can describe a particular clinical situation. One way to solve this problem is to use multi-omics data. When using many types of data, a selected data type or a combination of them may effectively resolve a clinical question. Hence, we conducted a comprehensive survey of papers in the field of neuro-oncology that used multi-omics data for analysis and found that most of the papers utilized machine learning techniques. This fact shows that it is useful to utilize machine learning techniques in multi-omics analysis. In this review, we discuss the current status of multi-omics analysis in the field of neuro-oncology and the importance of using machine learning techniques.

Abstract 39: APOBEC3A drives acquired resistance to targeted therapies in non small cell lung cancer

2021 ◽  
Author(s):  
Hideko Isozaki ◽  
Ammal Abbasi ◽  
Naveed Nikpour ◽  
Adam Langenbucher ◽  
Wenjia Su ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Small Cell Lung Cancer ◽  
Cell Lung Cancer ◽  
Targeted Therapies ◽  
Acquired Resistance ◽  
Small Cell ◽  
Small Cell Lung

Ridge estimation of network models from time-course omics data

2018 ◽  
Vol 61 (2) ◽  
pp. 391-405 ◽  
Author(s):  
Viktorian Miok ◽  
Saskia M. Wilting ◽  
Wessel N. van Wieringen
Keyword(s):  
Time Course ◽  
Network Models ◽  
Omics Data ◽  
Ridge Estimation

scholarly journals Unsupervised integration of single-cell multi-omics datasets with disparities in cell-type representation

2021 ◽  
Author(s):  
Pinar Demetci ◽  
Rebecca Santorella ◽  
Bjorn Sandstede ◽  
Ritambhara Singh
Keyword(s):  
Single Cell ◽  
Optimal Transport ◽  
Integrated Analysis ◽  
Omics Data ◽  
Cell Alignment ◽  
Cell Type ◽  
Type Representation ◽  
Cellular Processes ◽  
Self Tuning ◽  
Unsupervised Algorithms

Integrated analysis of multi-omics data allows the study of how different molecular views in the genome interact to regulate cellular processes; however, with a few exceptions, applying multiple sequencing assays on the same single cell is not possible. While recent unsupervised algorithms align single-cell multi-omic datasets, these methods have been primarily benchmarked on co-assay experiments rather than the more common single-cell experiments taken from separately sampled cell populations. Therefore, most existing methods perform subpar alignments on such datasets. Here, we improve our previous work Single Cell alignment using Optimal Transport (SCOT) by using unbalanced optimal transport to handle disproportionate cell-type representation and differing sample sizes across single-cell measurements. We show that our proposed method, SCOTv2, consistently yields quality alignments on five real-world single-cell datasets with varying cell-type proportions and is computationally tractable. Additionally, we extend SCOTv2 to integrate multiple ($M\geq2$) single-cell measurements and present a self-tuning heuristic process to select hyperparameters in the absence of any orthogonal correspondence information.

scholarly journals Oxidative Stress-Related Mechanisms in Melanoma and in the Acquired Resistance to Targeted Therapies

Antioxidants ◽  
2021 ◽  
Vol 10 (12) ◽  
pp. 1942
Author(s):  
Stefania Pizzimenti ◽  
Simone Ribero ◽  
Marie Angele Cucci ◽  
Margherita Grattarola ◽  
Chiara Monge ◽  
...  
Keyword(s):  
Oxidative Stress ◽  
Targeted Therapies ◽  
Current Knowledge ◽  
Mapk Pathway ◽  
Acquired Resistance ◽  
Genetic Alterations ◽  
Advanced Melanoma ◽  
Braf Mutations ◽  
Downstream Signaling ◽  
Activating Mutations

Melanoma is a highly aggressive cancer with the poorest prognosis, representing the deadliest form of skin cancer. Activating mutations in BRAF are the most frequent genetic alterations, present in approximately 50% of all melanoma cases. The use of specific inhibitors towards mutant BRAF variants and MEK, a downstream signaling target of BRAF in the MAPK pathway, has significantly improved progression-free and overall survival in advanced melanoma patients carrying BRAF mutations. Nevertheless, despite these improvements, resistance still develops within the first year of therapy in around 50% of patients, which is a significant problem in managing BRAF-mutated advanced melanoma. Understanding these mechanisms is one of the mainstreams of the research on BRAFi/MEKi acquired resistance. Both genetic and epigenetic mechanisms have been described. Moreover, in recent years, oxidative stress has emerged as another major force involved in all the phases of melanoma development, from initiation to progression until the onsets of the metastatic phenotype and chemoresistance, and has thus become a target for therapy. In the present review, we discuss the current knowledge on oxidative stress and its signaling in melanoma, as well as the oxidative stress-related mechanisms in the acquired resistance to targeted therapies.

Therapeutic Strategies in Diseases of the Digestive Tract - 2015 and Beyond Targeted Therapies in Colon Cancer Today and Tomorrow

2016 ◽  
Vol 34 (5) ◽  
pp. 574-579 ◽  
Author(s):  
Michael Pohl ◽  
Wolff Schmiegel
Keyword(s):  
Targeted Therapies ◽  
Immune Checkpoint ◽  
Predictive Biomarkers ◽  
Therapeutic Strategies ◽  
New Drugs ◽  
Diagnostic Tools ◽  
Molecular Heterogeneity ◽  
Cancer Type ◽  
Significant Progress ◽  
Ras Genes

Background: Colorectal cancer (CRC) is the third most common cancer type in Western countries. Significant progress has been made in the last decade in the therapy of metastatic CRC (mCRC) with a median overall survival (OS) of patients exceeding 30 months. The integration of biologic targeted therapies and anti-epidermal growth factor receptor (EGFR) monoclonal antibodies (MABs) in the treatment of patients with genomically selected all-RAS wild-type mCRC leads to a significant progress in advanced incurable disease state. After the introduction of the anti-VEGF MAB bevacizumab, the FDA approved with ramucirumab the second antiangiogenic MAB for the mCRC treatment. Further new drugs are on the horizon and new diagnostic tools will be introduced soon. Key Messages: Molecular heterogeneity of mCRC has been recognized as pivotal in the evolution of clonal populations during anti-EGFR therapies. Mutations in RAS genes predict a lack of response to anti-EGFR MABs. Mutations in the mitogen-activated protein kinase-phosphoinositide 3-kinase pathways like BRAF or PIK3CA mutations or HER2/ERBB2 or MET amplifications bypass EGFR signaling and also may confer resistance to anti-EGFR MABs. HER2/ERBB2 amplification is a further driver of resistance to anti-EGFR MABs in mCRC. The phase II study of HER2 Amplification for Colo-Rectal Cancer Enhanced Stratification (HERACLES) discovers that a dual HER2-targeted therapy may be an option for HER2-amplified mCRC. The mismatch repair deficiency predicts responsiveness to an immune checkpoint blockade with the anti-PD-1 immune checkpoint inhibitor pembrolizumab. Conclusions: The understanding of primary (de novo) and secondary (acquired) resistance to anti-EGFR therapies, new targeted therapies, immuno-oncology and about predictive biomarkers in mCRC is guiding the development of rational therapeutic strategies. Combinations of targeted therapies are necessary to effectively treat drug-resistant cancers. Liquid biopsy is an upcoming new tool in the primary diagnosis and follow-up analysis of mutations in circulating tumor DNA.

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