scholarly journals Patient-derived organoid (PDO) platforms to facilitate clinical decision making

2021 ◽  
Vol 19 (1) ◽  
Author(s):  
Lisa Liu ◽  
Lei Yu ◽  
Zhichao Li ◽  
Wujiao Li ◽  
WeiRen Huang
Keyword(s):  
Decision Making ◽  
Cell Lines ◽  
Drug Screening ◽  
Clinical Decision Making ◽  
Therapy Response ◽  
Clinical Decision ◽  
Chip Technology ◽  
Vitro Model ◽  
Therapeutic Decision Making

AbstractBased on recent advances in organoid research as well as the need to find more accurate models for drug screening in cancer research, patient-derived organoids have emerged as an effective in vitro model system to study cancer. Showing numerous advantages over 2D cell lines, 3D cell lines, and primary cell culture, organoids have been applied in drug screening to demonstrate the correlation between genetic mutations and sensitivity to targeted therapy. Organoids have also been used in co-clinical trials to compare drug responses in organoids to clinical responses in the corresponding patients. Numerous studies have reported the successful use of organoids to predict therapy response in cancer patients. Recently, organoids have been adopted to predict treatment response to radiotherapy and immunotherapy. The development of high throughput drug screening and organoids-on-a-chip technology can advance the use of patient-derived organoids in clinical practice and facilitate therapeutic decision-making.

THE VALUE OF IN VITRO DIAGNOSTIC TESTING IN THE PROVISION OF MEDICAL CARE TO ELDERLY PATIENTS WITH CARDIOVASCULAR DISEASES

2020 ◽  
Vol 65 (3) ◽  
pp. 191-196
Author(s):  
A. S. Pushkin ◽  
O. V. Lyang ◽  
T. A. Ahmedov ◽  
S. A. Rukavishnikova
Keyword(s):  
Decision Making ◽  
Medical Care ◽  
Laboratory Tests ◽  
Clinical Decision Making ◽  
Diagnostic Testing ◽  
Clinical Decision ◽  
Initial Examination ◽  
Attending Physicians ◽  
The Impact

In vitro diagnostics are used at all stages of patient care. The aim of this study was to assess the impact of laboratory examination on clinical decision-making in providing medical care to patients with a cardiovascular profile. We also took into account the level of financing for the laboratory industry in the Russian Federation. We divided our study on three sequential steps: literature review, survey of clinicians and test-survey of clinicians. The share of costs for the laboratory tests in 2017 amounted to about 8% of the total funding for Russian health care. About 80% (70; 90) of the visits of the attending physicians are associated with the appointment of laboratory tests. Among patients who were prescribed any laboratory test - in 62.1% (95% CI 16.9-24.9) cases, the results of these tests influenced clinical decision making related to the initiation, modification or termination of any treatment. All visits of clinicians were divided by purpose: tests were prescribed in almost 100% (90; 100) cases during the initial examination, in 40% (20; 60) cases during repeated visits, and in 40% (15; 40) cases when patients were examined before discharge. In more than half of cases (57,4%; n=31), doctors correctly assumed about the about the share of financing of the laboratory industry. The majority of respondents considered the amount of expenses adequate and recommended to maintain the current level in the future. According to attending physicians, new laboratory markers should demonstrate additional information about clinical relevance to improve patient outcomes. Thus, in current economic realities, future laboratory tests should be financially maximally available and at the same time be clinically highly effective auxiliary instruments. It creates new challenges in finding laboratory biomarkers and putting them into clinical practice.

scholarly journals Empowering Clinical Decision Making in Oligometastatic Colorectal Cancer: The Potential Role of Drug Screening of Patient-Derived Organoids

2021 ◽  
pp. 1192-1199
Author(s):  
Gianluca Mauri ◽  
Erika Durinikova ◽  
Alessio Amatu ◽  
Federica Tosi ◽  
Andrea Cassingena ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Decision Making ◽  
Drug Screening ◽  
Potential Role ◽  
Clinical Decision Making ◽  
Clinical Decision

scholarly journals Feasibility study of in vitro drug sensitivity assay of advanced non-small cell lung adenocarcinomas

2020 ◽  
Vol 7 (1) ◽  
pp. e000505
Author(s):  
Emoke Papp ◽  
Anita Steib ◽  
Elhusseiny MM Abdelwahab ◽  
Judit Meggyes-Rapp ◽  
Laszlo Jakab ◽  
...  
Keyword(s):  
Clinical Decision Making ◽  
Therapy Response ◽  
Clinical Decision ◽  
Response To Therapy ◽  
Solid Tumours ◽  
Chemosensitivity Test ◽  
Pleural Effusions ◽  
In Vitro Chemosensitivity ◽  
Lung Adenocarcinomas

Background Despite improved screening techniques, diagnosis of lung cancer is often late and its prognosis is poor. In the present study, in vitro chemosensitivity of solid tumours and pleural effusions of lung adenocarcinomas were analysed and compared with clinical drug response.Methods Tumour cells were isolated from resected solid tumours or pleural effusions, and cryopreserved. Three-dimensional (3D) tissue aggregate cultures were set up when the oncoteam reached therapy decision for individual patients. The aggregates were then treated with the selected drug or drug combination and in vitro chemosensitivity was tested individually measuring ATP levels. The clinical response to therapy was assessed by standard clinical evaluation over an 18 months period.Results Based on the data, the in vitro chemosensitivity test results correlate well with clinical treatment response.Conclusions Such tests if implemented into the clinical decision making process might allow the selection of an even more individualised chemotherapy protocol which could lead to better therapy response.

scholarly journals Diagnosis and therapeutic decision-making for the neutropenic patient

Hematology ◽  
2021 ◽  
Vol 2021 (1) ◽  
pp. 492-503
Author(s):  
James A. Connelly ◽  
Kelly Walkovich
Keyword(s):  
Decision Making ◽  
Clinical Decision Making ◽  
Neutropenic Patient ◽  
Peripheral Blood Smear ◽  
Clinical Decision ◽  
Medical Decision ◽  
Diagnostic Process ◽  
Mature Neutrophil ◽  
Neutropenic Patients ◽  
Therapeutic Decision Making

Abstract Determining the cause of a low neutrophil count in a pediatric or adult patient is essential for the hematologist's clinical decision-making. Fundamental to this diagnostic process is establishing the presence or lack of a mature neutrophil storage pool, as absence places the patient at higher risk for infection and the need for supportive care measures. Many diagnostic tests, eg, a peripheral blood smear and bone marrow biopsy, remain important tools, but greater understanding of the diversity of neutropenic disorders has added new emphasis on evaluating for immune disorders and genetic testing. In this article, a structure is provided to assess patients based on the mechanism of neutropenia and to prioritize testing based on patient age and hypothesized pathophysiology. Common medical quandaries including fever management, need for growth factor support, risk of malignant transformation, and curative options in congenital neutropenia are reviewed to guide medical decision-making in neutropenic patients.

scholarly journals Single-Cell Proteomics Identifies Leukemia Landscape Associated with Clinical Outcomes in R/R AML Treated with MDM2i (Milademetan) and FLT3i (Quizartinib): Putative Role of CD68 and Diversity Index

Blood ◽  
2021 ◽  
Vol 138 (Supplement 1) ◽  
pp. 3443-3443
Author(s):  
Muharrem Muftuoglu ◽  
Li Li ◽  
Mahesh Basyal ◽  
Shaoheng Liang ◽  
Carissa J. Ball ◽  
...  
Keyword(s):  
Decision Making ◽  
Single Cell ◽  
Research Funding ◽  
Clinical Decision Making ◽  
Therapy Response ◽  
Clinical Decision ◽  
Response To Therapy ◽  
Leukemia Cells ◽  
Npm1 Mutation ◽  
Pre Treatment

Abstract Distinct mutations could differentially regulate cellular programs and alter the proteomic landscape in AML. Sequential acquisition of various mutations not only leads to clonal diversification but also alters the leukemia proteomic landscape through activation of mutation-specific gene programs. Characterization of AML proteomic profiles and diversity could be utilized as a measure of genetic imprint on leukemia proteome to inform clinical decision-making. We reasoned that diverse leukemia-specific proteomic profile could be indicative of the presence of multiple mutations activating numerous pathways, thus leading to a heterogenous clonal composition and more likely to therapy resistance. We aimed to test this hypothesis by assessing the proteomic profiles of FLT3-ITD AML patients treated with MDM2i (Milademetan) plus FLT3i (Quizartinib) (NCT03552029) and interrogate the association between proteomic landscape and therapy response. We assessed single-cell proteomic profiles of 35 sequentially collected samples for six selected patients treated with MDM2i+FLT3i using CyTOF, enabling us to assess expression of 51-parameters across leukemia compartments and identify leukemic clones with distinct proteomic profiles. Three patients achieved CRi while three patients did not respond. This allowed us to start interrogating proteomic signatures for their ability to predict response to therapy. We performed single-cell analysis and interrogated the phenotypic profiles of leukemia compartments to assess leukemia hierarchies, defined by spatial organization of leukemic subpopulations, and whether mutations in AML were associated with unique phenotypes. Notably, we found that NPM1-mutant (Mt) leukemia cells lacked CD34 expression, expressed high levels of CD99 and had patchy c-kit expression. Despite lacking a canonical marker, CD34, high-dimensional analysis positioned NPM1-Mt leukemia cells spatially in close proximity to CD34+ leukemia cells (NPM1 WT), indicating that NPM1 WT and Mt leukemia cells are closely related. CD34+ expressing cells most likely serve as the founding clone and acquisition of NPM1 mutation led to emergence of CD34- leukemia clones. As expected, all three patients who achieved CRi were NPM1-Mt and NPM1-Mt leukemia cells in CRi patients expressed CD68. Importantly, we also observed that CD68+ leukemia cells were eradicated in a NR patient where only a fraction of leukemia cells expressed CD68. This suggests that NPM1 mutations could activate unique cellular programs and induce distinct differentiation states (CD68), which could sensitize leukemia cells to MDM2+FLT3 inhibition. Altogether, NPM1 mutation status and CD68 expression level were associated with therapy response. Next, we mapped the response kinetics and quantified survived leukemia cells across multiple timepoints. Strikingly, MDM2i+FLT3i almost completely eliminated circulating blasts in responders (R) by day 8 while leukemia blasts persisted in NR (median blast %: 0.11 in R vs 19.8 in NR). This indicates that assessment of therapy response as early as day 8 could provide insights into the overall response and identify patients who will fail to achieve CR. Importantly, patients with reduced leukemia blasts at day 8 were also leukemia-free in BM at the end of cycle 1. Lastly, we sought to investigate the association between proteomic landscape diversity and therapy response, and quantified the number of leukemia subpopulations by unsupervised clustering. The median number of subpopulations detected in R vs NR at baseline were 3 and 9, respectively. We also utilized the inverse Simpson index to quantify the proteomic diversity of leukemia compartments and to further investigate the association between proteomic diversity and therapy outcome in an unbiased manner. The median diversity indices in R vs NR were 64 vs 212, revealing that patients with CR had restricted pre-treatment proteomic diversity. These findings suggest that a pre-treatment diverse phenotypic landscape could portend poor therapeutic outcome. Altogether, single-cell proteomic analysis identified correlates associated with overall clinical response in AML patients treated with MDM2i+FLT3i. Further validation is needed in a larger cohort of patients. Such approaches could be utilized in clinical-trial settings to predict therapy response with targeted agents and inform clinical decision-making. Disclosures Lesegretain: Daiichi-Sankyo Inc.: Current Employment. Daver: Amgen: Consultancy, Research Funding; Glycomimetics: Research Funding; Trovagene: Consultancy, Research Funding; Hanmi: Research Funding; Genentech: Consultancy, Research Funding; Trillium: Consultancy, Research Funding; Novimmune: Research Funding; ImmunoGen: Consultancy, Research Funding; Abbvie: Consultancy, Research Funding; FATE Therapeutics: Research Funding; Astellas: Consultancy, Research Funding; Sevier: Consultancy, Research Funding; Gilead Sciences, Inc.: Consultancy, Research Funding; Pfizer: Consultancy, Research Funding; Bristol Myers Squibb: Consultancy, Research Funding; Daiichi Sankyo: Consultancy, Research Funding; Novartis: Consultancy; Jazz Pharmaceuticals: Consultancy, Other: Data Monitoring Committee member; Dava Oncology (Arog): Consultancy; Celgene: Consultancy; Syndax: Consultancy; Shattuck Labs: Consultancy; Agios: Consultancy; Kite Pharmaceuticals: Consultancy; SOBI: Consultancy; STAR Therapeutics: Consultancy; Karyopharm: Research Funding; Newave: Research Funding. Andreeff: AstraZeneca: Research Funding; Glycomimetics: Consultancy; Reata, Aptose, Eutropics, SentiBio; Chimerix, Oncolyze: Current holder of individual stocks in a privately-held company; Breast Cancer Research Foundation: Research Funding; Aptose: Consultancy; ONO Pharmaceuticals: Research Funding; Oxford Biomedica UK: Research Funding; Medicxi: Consultancy; Syndax: Consultancy; Karyopharm: Research Funding; Novartis, Cancer UK; Leukemia & Lymphoma Society (LLS), German Research Council; NCI-RDCRN (Rare Disease Clin Network), CLL Foundation; Novartis: Membership on an entity's Board of Directors or advisory committees; Daiichi-Sankyo: Consultancy, Research Funding; Senti-Bio: Consultancy; Amgen: Research Funding.

scholarly journals Deep learning in cancer pathology: a new generation of clinical biomarkers

2020 ◽  
Author(s):  
Amelie Echle ◽  
Niklas Timon Rindtorff ◽  
Titus Josef Brinker ◽  
Tom Luedde ◽  
Alexander Thomas Pearson ◽  
...  
Keyword(s):  
Decision Making ◽  
Image Analysis ◽  
Deep Learning ◽  
Clinical Decision Making ◽  
External Validation ◽  
Therapy Response ◽  
Clinical Decision ◽  
Tumour Tissue ◽  
Molecular Features ◽  
Clinical Biomarkers

AbstractClinical workflows in oncology rely on predictive and prognostic molecular biomarkers. However, the growing number of these complex biomarkers tends to increase the cost and time for decision-making in routine daily oncology practice; furthermore, biomarkers often require tumour tissue on top of routine diagnostic material. Nevertheless, routinely available tumour tissue contains an abundance of clinically relevant information that is currently not fully exploited. Advances in deep learning (DL), an artificial intelligence (AI) technology, have enabled the extraction of previously hidden information directly from routine histology images of cancer, providing potentially clinically useful information. Here, we outline emerging concepts of how DL can extract biomarkers directly from histology images and summarise studies of basic and advanced image analysis for cancer histology. Basic image analysis tasks include detection, grading and subtyping of tumour tissue in histology images; they are aimed at automating pathology workflows and consequently do not immediately translate into clinical decisions. Exceeding such basic approaches, DL has also been used for advanced image analysis tasks, which have the potential of directly affecting clinical decision-making processes. These advanced approaches include inference of molecular features, prediction of survival and end-to-end prediction of therapy response. Predictions made by such DL systems could simplify and enrich clinical decision-making, but require rigorous external validation in clinical settings.

scholarly journals Genomic Characterization and Therapeutic Targeting of HPV Undetected Cervical Carcinomas

Cancers ◽  
2021 ◽  
Vol 13 (18) ◽  
pp. 4551
Author(s):  
Fiona J. Ruiz ◽  
Aishwarya Sundaresan ◽  
Jin Zhang ◽  
Chandra S. Pedamallu ◽  
Mari K. Halle ◽  
...  
Keyword(s):  
Cell Lines ◽  
Cancer Cell ◽  
Cell Cycle Progression ◽  
Clinical Decision Making ◽  
Predictive Biomarker ◽  
Clinical Decision ◽  
Cancer Cell Lines ◽  
Survival Outcomes ◽  
Alternative Treatment Strategy

Cervical cancer tumors with undetectable HPV (HPVU) have been underappreciated in clinical decision making. In this study, two independent CC datasets were used to characterize the largest cohort of HPVU tumors to date (HPVU = 35, HPV+ = 430). Genomic and transcriptome tumor profiles and patient survival outcomes were compared between HPV+ and HPVU tumors. In vitro analyses were done to determine efficacy of the selective CDK4/6 inhibitor palbociclib on HPVU cancer cell lines. Patients with HPVU CC tumors had worse progression-free and overall survival outcomes compared to HPV+ patients. TP53, ARID1A, PTEN, ARID5B, CTNNB1, CTCF, and CCND1 were identified as significantly mutated genes (SMGs) enriched in HPVU tumors, with converging functional roles in cell cycle progression. In vitro HPVU, but not HPV+, cancer cell lines with wild type RB1 were sensitive to palbociclib monotherapy. These results indicate that HPVU status can be translated into the clinic as a predictive biomarker of poor patient response to standard of care treatments. We suggest primary cervix tumors be routinely tested for HPV prior to treatment to identify patients who will benefit from more aggressive precision-driven therapy. Our results identify palbociclib as a lead candidate as an alternative treatment strategy for HPVU CC patients.

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