Multidisciplinary and interprofessional collaboration as a necessity to drive modern oncology research trials in the era of precision oncology

2021 ◽  
Author(s):  
Jian Tan ◽  
Ian S Boon
Keyword(s):  
Interprofessional Collaboration ◽  
Precision Oncology ◽  
Oncology Research

Practical Aspects of Implementing and Applying Health Care Cloud Computing Services and Informatics to Cancer Clinical Trial Data

2021 ◽  
pp. 826-832
Author(s):  
Jay G. Ronquillo ◽  
William T. Lester
Keyword(s):  
Health Care ◽  
Clinical Trial ◽  
Cancer Research ◽  
Cloud Computing ◽  
Clinical Trials ◽  
Laboratory Tests ◽  
Cancer Clinical Trial ◽  
Cancer Clinical Trials ◽  
Precision Oncology ◽  
Oncology Research

PURPOSE Cloud computing has led to dramatic growth in the volume, variety, and velocity of cancer data. However, cloud platforms and services present new challenges for cancer research, particularly in understanding the practical tradeoffs between cloud performance, cost, and complexity. The goal of this study was to describe the practical challenges when using a cloud-based service to improve the cancer clinical trial matching process. METHODS We collected information for all interventional cancer clinical trials from ClinicalTrials.gov and used the Google Cloud Healthcare Natural Language Application Programming Interface (API) to analyze clinical trial Title and Eligibility Criteria text. An informatics pipeline leveraging interoperability standards summarized the distribution of cancer clinical trials, genes, laboratory tests, and medications extracted from cloud-based entity analysis. RESULTS There were a total of 38,851 cancer-related clinical trials found in this study, with the distribution of cancer categories extracted from Title text significantly different than in ClinicalTrials.gov ( P < .001). Cloud-based entity analysis of clinical trial criteria identified a total of 949 genes, 1,782 laboratory tests, 2,086 medications, and 4,902 National Cancer Institute Thesaurus terms, with estimated detection accuracies ranging from 12.8% to 89.9%. A total of 77,702 API calls processed an estimated 167,179 text records, which took a total of 1,979 processing-minutes (33.0 processing-hours), or approximately 1.5 seconds per API call. CONCLUSION Current general-purpose cloud health care tools—like the Google service in this study—should not be used for automated clinical trial matching unless they can perform effective extraction and classification of the clinical, genetic, and medication concepts central to precision oncology research. A strong understanding of the practical aspects of cloud computing will help researchers effectively navigate the vast data ecosystems in cancer research.

scholarly journals Recent Advances in Liquid Biopsy in Precision Oncology Research

2019 ◽  
Vol 42 (3) ◽  
pp. 337-342 ◽  
Author(s):  
Yoshiharu Sato ◽  
Ryo Matoba ◽  
Kikuya Kato
Keyword(s):  
Liquid Biopsy ◽  
Precision Oncology ◽  
Oncology Research ◽  
Recent Advances

scholarly journals The shortest path is not the one you know: application of biological network resources in precision oncology research

Mutagenesis ◽  
2015 ◽  
Vol 30 (2) ◽  
pp. 191-204 ◽  
Author(s):  
I. Kuperstein ◽  
L. Grieco ◽  
D. P. A. Cohen ◽  
D. Thieffry ◽  
A. Zinovyev ◽  
...  
Keyword(s):  
Shortest Path ◽  
Biological Network ◽  
Precision Oncology ◽  
Network Resources ◽  
Oncology Research ◽  
The One

scholarly journals High-Content Analysis-Based Sensitivity Prediction and Novel Therapeutics Screening for c-Met-Addicted Glioblastoma

Cancers ◽  
2021 ◽  
Vol 13 (3) ◽  
pp. 372
Author(s):  
Jeong-Woo Oh ◽  
Yun Jeong Oh ◽  
Suji Han ◽  
Nam-Gu Her ◽  
Do-Hyun Nam
Keyword(s):  
Content Analysis ◽  
Large Scale ◽  
Drug Sensitivity ◽  
Drug Repurposing ◽  
Genetic Alterations ◽  
High Content Screening ◽  
Precision Oncology ◽  
Novel Therapeutics ◽  
Oncology Research ◽  
High Content Analysis

(1) Background: Recent advances in precision oncology research rely on indicating specific genetic alterations associated with treatment sensitivity. Developing ex vivo systems to identify cancer patients who will respond to a specific drug remains important. (2) Methods: cells from 12 patients with glioblastoma were isolated, cultured, and subjected to high-content screening. Multi-parameter analyses assessed the c-Met level, cell viability, apoptosis, cell motility, and migration. A drug repurposing screen and large-scale drug sensitivity screening data across 59 cancer cell lines and patient-derived cells were obtained from 125 glioblastoma samples. (3) Results: High-content analysis of patient-derived cells provided robust and accurate drug responses to c-Met-targeted agents. Only the cells of one glioblastoma patient (PDC6) showed elevated c-Met level and high susceptibility to the c-Met inhibitors. Multi-parameter image analysis also reflected a decreased c-Met expression and reduced cell growth and motility by a c-Met-targeting antibody. In addition, a drug repurposing screen identified Abemaciclib as a distinct CDK4/6 inhibitor with a potent c-Met-inhibitory function. Consistent with this, we present large-scale drug sensitivity screening data showing that the Abemaciclib response correlates with the response to c-Met inhibitors. (4) Conclusions: Our study provides a new insight into high-content screening platforms supporting drug sensitivity prediction and novel therapeutics screening.

A novel approach to quantitatively synthesize subgroup findings from multiple trials.

2020 ◽  
Vol 38 (15_suppl) ◽  
pp. e14081-e14081
Author(s):  
Devon J Boyne ◽  
Paul Arora ◽  
Darren Brenner
Keyword(s):  
Standard Error ◽  
Treatment Effects ◽  
Meta Analysis ◽  
Specific Treatment ◽  
Precision Oncology ◽  
Oncology Research ◽  
Novel Approach ◽  
Number Of Patients ◽  
Multiple Trials ◽  
The Difference

e14081 Background: The identification of characteristics that stratify patients according to the likelihood of treatment response is a key goal of precision oncology research. Within randomized trials, effect modification is typically examined through subgroup analyses or the inclusion of an interaction term(s) in a multivariable regression model. To date, systematic reviews of such findings have relied upon qualitative summaries or subgroup-specific meta-analyses. Such approaches are problematic because they do not quantify the magnitude or the degree of uncertainty in the difference in the treatment effects between subgroups. To address this gap, we propose a novel approach that can be used to quantitatively pool subgroup findings from multiple trials. Methods: Our procedure is focused on the estimation of the pooled difference between the subgroup-specific treatment effects within a two-stage meta-analysis. In the first stage, the magnitude and standard error of the difference between the subgroup-specific treatment effects is estimated for each study. When studying relative effect measures, this difference can be estimated on the log-scale, e.g., study-specific difference = log(RRsubgroup A) – log(RRsubgroup B). The standard error of this difference can then be estimated using subgroup-specific 95% CIs. A meta-analysis of the study-specific differences is then conducted. When the differences are estimated on the log-scale, the pooled quantity can be re-expressed as the ratio of the subgroup specific ORs, RRs, or HRs whereby a ratio equal to 1.00 indicates that the subgroup-specific estimates are equal in magnitude. Results: Case-studies and empirical simulations highlighting the application of our approach will be presented. Potential extensions of this methodology will also be discussed. Such extensions include the use of dose-response models to address subgroup categories of a continuous variable and the inclusion of three or more treatments within a network meta-analysis. Conclusions: This method may help to better identify and quantify the degree of heterogeneity of subgroup differences across trials, particularly in settings where the number of patients within each trial is limited.

scholarly journals OncoTree: A Cancer Classification System for Precision Oncology

2021 ◽  
pp. 221-230
Author(s):  
Ritika Kundra ◽  
Hongxin Zhang ◽  
Robert Sheridan ◽  
Sahussapont Joseph Sirintrapun ◽  
Avery Wang ◽  
...  
Keyword(s):  
Open Source ◽  
Classification System ◽  
Information Exchange ◽  
Cancer Genomics ◽  
Cancer Classification ◽  
Cancer Center ◽  
Molecular Testing ◽  
Precision Oncology ◽  
Snomed Ct ◽  
Oncology Research

PURPOSE Cancer classification is foundational for patient care and oncology research. Systems such as International Classification of Diseases for Oncology (ICD-O), Systematized Nomenclature of Medicine Clinical Terms (SNOMED-CT), and National Cancer Institute Thesaurus (NCIt) provide large sets of cancer classification terminologies but they lack a dynamic modernized cancer classification platform that addresses the fast-evolving needs in clinical reporting of genomic sequencing results and associated oncology research. METHODS To meet these needs, we have developed OncoTree, an open-source cancer classification system. It is maintained by a cross-institutional committee of oncologists, pathologists, scientists, and engineers, accessible via an open-source Web user interface and an application programming interface. RESULTS OncoTree currently includes 868 tumor types across 32 organ sites. OncoTree has been adopted as the tumor classification system for American Association for Cancer Research (AACR) Project Genomics Evidence Neoplasia Information Exchange (GENIE), a large genomic and clinical data-sharing consortium, and for clinical molecular testing efforts at Memorial Sloan Kettering Cancer Center and Dana-Farber Cancer Institute. It is also used by precision oncology tools such as OncoKB and cBioPortal for Cancer Genomics. CONCLUSION OncoTree is a dynamic and flexible community-driven cancer classification platform encompassing rare and common cancers that provides clinically relevant and appropriately granular cancer classification for clinical decision support systems and oncology research.

Spotlight on the Clinician in the Life Participation Approach to Aphasia: Balancing Relationship-Centered Care and Professionalism

2020 ◽  
Vol 5 (2) ◽  
pp. 414-424
Author(s):  
Rochelle Cohen-Schneider ◽  
Melodie T. Chan ◽  
Denise M. McCall ◽  
Allison M. Tedesco ◽  
Ann P. Abramson
Keyword(s):  
Social Workers ◽  
Therapeutic Relationship ◽  
Interprofessional Collaboration ◽  
Opportunity To Learn ◽  
Subjective Experiences ◽  
Speech Language Pathologists ◽  
Self Disclosure ◽  
Centered Care ◽  
High Standards ◽  
Therapeutic Encounter

Background Speech-language pathologists make clinical decisions informed by evidence-based theory and “beliefs, values and emotional experiences” ( Hinckley, 2005 , p. 265). These subjective processes, while not extensively studied, underlie the workings of the therapeutic relationship and contribute to treatment outcomes. While speech-language pathologists do not routinely pay attention to subjective experiences of the therapeutic encounter, social workers do. Thus, the field of social work makes an invaluable contribution to the knowledge and skills of speech-language pathologists. Purpose This clinical focus article focuses on the clinician's contribution to the therapeutic relationship by surfacing elements of the underlying subjective processes. Method Vignettes were gathered from clinicians in two community aphasia programs informed by the principles of the Life Participation Approach to Aphasia. Results and Discussion By reflecting on and sharing aspects of clinical encounters, clinicians reveal subjective processing occurring beneath the surface. The vignettes shed light on the following clinical behaviors: listening to the client's “whole self,” having considerations around self-disclosure, dealing with biases, recognizing and surfacing clients' identities, and fostering hope. Speech-language pathologists are given little instruction on the importance of the therapeutic relationship, how to conceptualize this relationship, and how to balance this relationship with professionalism. Interprofessional collaboration with social workers provides a rich opportunity to learn ways to form and utilize the benefits of a strong therapeutic relationship while maintaining high standards of ethical behavior. Conclusion This clinical focus article provides speech-language pathologists with the “nuts and bolts” for considering elements of the therapeutic relationship. This is an area that is gaining traction in the field of speech-language pathology and warrants further investigation.

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