scholarly journals Network-based machine learning in colorectal and bladder organoid models predicts anti-cancer drug efficacy in patients

2020 ◽  
Vol 11 (1) ◽  
Author(s):  
JungHo Kong ◽  
Heetak Lee ◽  
Donghyo Kim ◽  
Seong Kyu Han ◽  
Doyeon Ha ◽  
...  
Keyword(s):  
Machine Learning ◽  
Cancer Patient ◽  
Cancer Patients ◽  
Three Dimensional ◽  
Predictive Biomarkers ◽  
Drug Efficacy ◽  
Cancer Drug ◽  
Drug Responses ◽  
Anti Cancer ◽  
Culture Models

Abstract Cancer patient classification using predictive biomarkers for anti-cancer drug responses is essential for improving therapeutic outcomes. However, current machine-learning-based predictions of drug response often fail to identify robust translational biomarkers from preclinical models. Here, we present a machine-learning framework to identify robust drug biomarkers by taking advantage of network-based analyses using pharmacogenomic data derived from three-dimensional organoid culture models. The biomarkers identified by our approach accurately predict the drug responses of 114 colorectal cancer patients treated with 5-fluorouracil and 77 bladder cancer patients treated with cisplatin. We further confirm our biomarkers using external transcriptomic datasets of drug-sensitive and -resistant isogenic cancer cell lines. Finally, concordance analysis between the transcriptomic biomarkers and independent somatic mutation-based biomarkers further validate our method. This work presents a method to predict cancer patient drug responses using pharmacogenomic data derived from organoid models by combining the application of gene modules and network-based approaches.

scholarly journals Three-Dimensional (3D) Culture Models in Cancer Investigation, Drug Testing and Immune Response Evaluation

2020 ◽  
Vol 22 (1) ◽  
pp. 150
Author(s):  
Roberto Benelli ◽  
Maria Raffaella Zocchi ◽  
Alessandro Poggi
Keyword(s):  
Drug Testing ◽  
Three Dimensional ◽  
3D Culture ◽  
Cancer Drug ◽  
Response Evaluation ◽  
Preclinical Models ◽  
Safety And Efficacy ◽  
Anti Cancer ◽  
Culture Models ◽  
Definition Of

Preclinical models for the definition of anti-cancer drug safety and efficacy are constantly evolving [...]

scholarly journals PDXGEM: Patient-Derived Tumor Xenograft based Gene Expression Model for Predicting Clinical Response to Anticancer Therapy in Cancer Patients

2019 ◽  
Author(s):  
Youngchul Kim ◽  
Daewon Kim ◽  
Biwei Cao ◽  
Rodrigo Carvajal ◽  
Minjung Kim
Keyword(s):  
Gene Expression ◽  
Cancer Patient ◽  
Cancer Patients ◽  
Cancer Drugs ◽  
Drug Responses ◽  
Drug Activity ◽  
Anti Cancer ◽  
Expression Model ◽  
Gene Expression Model ◽  
Pdx Models

AbstractBackgroundCancer is a highly heterogeneous disease and shows varying responses to anti-cancer drugs. Although several approaches have been attempted to predict the drug responses by analyzing molecular profiling data of tumors from preclinical cancer models or cancer patients, there is still a great need of developing highly accurate prediction models of response to the anti-cancer drugs for clinical applications toward personalized medicine. Here, we present PDXGEM pipeline to build a predictive gene expression model (GEM) for cancer patients’ drug responses on the basis of data on gene expression and drug activity in patient-derived xenograft (PDX) models.ResultsDrug sensitivity biomarkers were identified by an association analysis between gene expression levels and post-treatment tumor volume changes in PDX models. Only biomarkers with concordant co-expression patterns between the PDX and cancer patient tumors were used in random-forest algorithm to build a drug response prediction model, so called PDXGEM. We applied PDXGEM to several cytotoxic chemotherapy as well as targeted therapy agents that are used to treat breast cancer, pancreatic cancer, colorectal cancer, or non-small cell lung cancer. Significantly accurate predictions of PDXGEM for pathological and survival outcomes were observed through extensive validation analyses of multiple independent cancer patient datasets obtained from retrospective observational study and prospective clinical trials.ConclusionOur results demonstrated a strong potential of utilizing molecular profiles and drug activity data of PDX tumors for developing a clinically translatable predictive cancer biomarkers for cancer patients. PDXGEM web application is publicly available athttp://pdxgem.moffitt.org.

scholarly journals In Situ Vitrification of Lung Cancer Organoids on a Microwell Array

Micromachines ◽  
2021 ◽  
Vol 12 (6) ◽  
pp. 624
Author(s):  
Qiang Liu ◽  
Tian Zhao ◽  
Xianning Wang ◽  
Zhongyao Chen ◽  
Yawei Hu ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Drug Sensitivity ◽  
Simple Procedure ◽  
Three Dimensional ◽  
Cancer Drug ◽  
Microwell Array ◽  
Sensitivity Tests ◽  
Anti Cancer

Three-dimensional cultured patient-derived cancer organoids (PDOs) represent a powerful tool for anti-cancer drug development due to their similarity to the in vivo tumor tissues. However, the culture and manipulation of PDOs is more difficult than 2D cultured cell lines due to the presence of the culture matrix and the 3D feature of the organoids. In our other study, we established a method for lung cancer organoid (LCO)-based drug sensitivity tests on the superhydrophobic microwell array chip (SMAR-chip). Here, we describe a novel in situ cryopreservation technology on the SMAR-chip to preserve the viability of the organoids for future drug sensitivity tests. We compared two cryopreservation approaches (slow freezing and vitrification) and demonstrated that vitrification performed better at preserving the viability of LCOs. Next, we developed a simple procedure for in situ cryopreservation and thawing of the LCOs on the SMAR-chip. We proved that the on-chip cryopreserved organoids can be recovered successfully and, more importantly, showing similar responses to anti-cancer drugs as the unfrozen controls. This in situ vitrification technology eliminated the harvesting and centrifugation steps in conventional cryopreservation, making the whole freeze–thaw process easier to perform and the preserved LCOs ready to be used for the subsequent drug sensitivity test.

Prevascularized, Co-Culture Model for Breast Cancer Drug Development

2012 ◽  
Author(s):  
Lauren Marshall ◽  
Isabel Löwstedt ◽  
Paul Gatenholm ◽  
Joel Berry
Keyword(s):  
Breast Cancer ◽  
Drug Development ◽  
Three Dimensional ◽  
Human Tumor ◽  
3D Models ◽  
Cancer Drug ◽  
Cancer Therapies ◽  
Anti Cancer

The objective of this study was to create 3D engineered tissue models to accelerate identification of safe and efficacious breast cancer drug therapies. It is expected that this platform will dramatically reduce the time and costs associated with development and regulatory approval of anti-cancer therapies, currently a multi-billion dollar endeavor [1]. Existing two-dimensional (2D) in vitro and in vivo animal studies required for identification of effective cancer therapies account for much of the high costs of anti-cancer medications and health insurance premiums borne by patients, many of whom cannot afford it. An emerging paradigm in pharmaceutical drug development is the use of three-dimensional (3D) cell/biomaterial models that will accurately screen novel therapeutic compounds, repurpose existing compounds and terminate ineffective ones. In particular, identification of effective chemotherapies for breast cancer are anticipated to occur more quickly in 3D in vitro models than 2D in vitro environments and in vivo animal models, neither of which accurately mimic natural human tumor environments [2]. Moreover, these 3D models can be multi-cellular and designed with extracellular matrix (ECM) function and mechanical properties similar to that of natural in vivo cancer environments [3].

Predictive biomarkers for personalised anti-cancer drug use: Discovery to clinical implementation

2010 ◽  
Vol 46 (5) ◽  
pp. 869-879 ◽  
Author(s):  
Nayef A. Alymani ◽  
Murray D. Smith ◽  
David J. Williams ◽  
Russell D. Petty
Keyword(s):  
Drug Use ◽  
Predictive Biomarkers ◽  
Cancer Drug ◽  
Clinical Implementation ◽  
Anti Cancer

scholarly journals Beyond 3D culture models of cancer

2015 ◽  
Vol 7 (283) ◽  
pp. 283ps9-283ps9 ◽  
Author(s):  
Kandice Tanner ◽  
Michael M. Gottesman
Keyword(s):  
In Vitro Culture ◽  
Three Dimensional ◽  
3D Culture ◽  
Drug Efficacy ◽  
Spatiotemporal Evolution ◽  
Culture Models ◽  
Dynamic Interplay

The mechanisms underlying the spatiotemporal evolution of tumor ecosystems present a challenge in evaluating drug efficacy. In this Perspective, we address the use of three-dimensional in vitro culture models to delineate the dynamic interplay between the tumor and the host microenvironment in an effort to attain realistic platforms for assessing pharmaceutical efficacy in patients.

scholarly journals 3D Culture as a Clinically Relevant Model for Personalized Medicine

2017 ◽  
Vol 22 (3) ◽  
pp. 245-253 ◽  
Author(s):  
Eliza Li Shan Fong ◽  
Tan Boon Toh ◽  
Hanry Yu ◽  
Edward Kai-Hua Chow
Keyword(s):  
Cancer Research ◽  
Drug Development ◽  
Cancer Patients ◽  
Cancer Progression ◽  
Three Dimensional ◽  
3D Culture ◽  
Research Field ◽  
Cancer Drug ◽  
Specific Patient ◽  
Culture Systems

Advances in understanding many of the fundamental mechanisms of cancer progression have led to the development of molecular targeted therapies. While molecular targeted therapeutics continue to improve the outcome for cancer patients, tumor heterogeneity among patients, as well as intratumoral heterogeneity, limits the efficacy of these drugs to specific patient subtypes, as well as contributes to relapse. Thus, there is a need for a more personalized approach toward drug development and diagnosis that takes into account the diversity of cancer patients, as well as the complex milieu of tumor cells within a single patient. Three-dimensional (3D) culture systems paired with patient-derived xenografts or patient-derived organoids may provide a more clinically relevant system to address issues presented by personalized or precision medical approaches. In this review, we cover the current methods available for applying 3D culture systems toward personalized cancer research and drug development, as well as key challenges that must be addressed in order to fully realize the potential of 3D patient-derived culture systems for cancer drug development. Greater implementation of 3D patient-derived culture systems in the cancer research field should accelerate the development of truly personalized medical therapies for cancer patients.

scholarly journals Development of a High-Throughput Three-Dimensional Invasion Assay for Anti-Cancer Drug Discovery

PLoS ONE ◽  
2013 ◽  
Vol 8 (12) ◽  
pp. e82811 ◽  
Author(s):  
Nikki A. Evensen ◽  
Jian Li ◽  
Jie Yang ◽  
Xiaojun Yu ◽  
Nicole S. Sampson ◽  
...  
Keyword(s):  
Drug Discovery ◽  
High Throughput ◽  
Three Dimensional ◽  
Cancer Drug ◽  
Invasion Assay ◽  
Cancer Drug Discovery ◽  
Anti Cancer

scholarly journals Anti-cancer Therapy Leads to Increased Cardiovascular Susceptibility to COVID-19

2021 ◽  
Vol 8 ◽  
Author(s):  
Caroline Lozahic ◽  
Helen Maddock ◽  
Hardip Sandhu
Keyword(s):  
Cancer Therapy ◽  
Cancer Patients ◽  
Respiratory Disease ◽  
Myocardial Injury ◽  
Cardiac Injury ◽  
Cardiovascular Complications ◽  
World Health ◽  
Cancer Drug ◽  
Health Crisis ◽  
Anti Cancer

Anti-cancer treatment regimens can lead to both acute- and long-term myocardial injury due to off-target effects. Besides, cancer patients and survivors are severely immunocompromised due to the harsh effect of anti-cancer therapy targeting the bone marrow cells. Cancer patients and survivors can therefore be potentially extremely clinically vulnerable and at risk from infectious diseases. The recent global outbreak of the novel coronavirus severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and its infection called coronavirus disease 2019 (COVID-19) has rapidly become a worldwide health emergency, and on March 11, 2020, COVID-19 was declared a global pandemic by the World Health Organization (WHO). A high fatality rate has been reported in COVID-19 patients suffering from underlying cardiovascular diseases. This highlights the critical and crucial aspect of monitoring cancer patients and survivors for potential cardiovascular complications during this unprecedented health crisis involving the progressive worldwide spread of COVID-19. COVID-19 is primarily a respiratory disease; however, COVID-19 has shown cardiac injury symptoms similar to the cardiotoxicity associated with anti-cancer therapy, including arrhythmia, myocardial injury and infarction, and heart failure. Due to the significant prevalence of micro- and macro-emboli and damaged vessels, clinicians worldwide have begun to consider whether COVID-19 may in fact be as much a vascular disease as a respiratory disease. However, the underlying mechanisms and pathways facilitating the COVID-19-induced cardiac injury in cancer and non-cancer patients remain unclear. Investigations into whether COVID-19 cardiac injury and anti-cancer drug-induced cardiac injury in cancer patients and survivors might synergistically increase the cardiovascular complications and comorbidity risk through a “two-hit” model are needed. Identification of cardiac injury mechanisms and pathways associated with COVID-19 development overlapping with anti-cancer therapy could help clinicians to allow a more optimized prognosis and treatment of cancer survivors suffering from COVID-19. The following review will focus on summarizing the harmful cardiovascular risk of COVID-19 in cancer patients and survivors treated with an anti-cancer drug. This review will improve the knowledge of COVID-19 impact in the field of cardio-oncology and potentially improve the outcome of patients.

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