scholarly journals Optical Metabolic Imaging of Heterogeneous Drug Response in Pancreatic Cancer Patient Organoids

2019 ◽  
Author(s):  
Joe T Sharick ◽  
Christine M Walsh ◽  
Carley M Sprackling ◽  
Cheri A Pasch ◽  
Alexander A Parikh ◽  
...  
Keyword(s):  
Pancreatic Cancer ◽  
Treatment Response ◽  
Cancer Patients ◽  
High Throughput ◽  
Drug Response ◽  
Specific Treatment ◽  
Dynamic Responses ◽  
Cellular Heterogeneity ◽  
Metabolic Imaging ◽  
Patient Specific

New tools are needed to match pancreatic cancer patients with effective treatments. Patient-derived organoids offer a high-throughput platform to personalize treatments and discover novel therapies. Currently, methods to evaluate drug response in organoids are limited because they cannot be completed in a clinically relevant time frame, only evaluate response at one time point, and most importantly, overlook cellular heterogeneity. In this study, non-invasive optical metabolic imaging (OMI) of cellular heterogeneity in organoids was evaluated as a predictor of clinical treatment response. Organoids were generated from fresh patient tissue samples acquired during surgery and treated with the same drugs as the patient's prescribed adjuvant treatment. OMI measurements of heterogeneity in response to this treatment were compared to later patient response, specifically to the time to recurrence following surgery. OMI was sensitive to patient-specific treatment response in as little as 24 hours. OMI distinguished subpopulations of cells with divergent and dynamic responses to treatment in living organoids without the use of labels or dyes. OMI of organoids agreed with long-term therapeutic response in patients. With these capabilities, OMI could serve as a sensitive high-throughput tool to identify optimal therapies for individual pancreatic cancer patients, and to develop new effective therapies that address cellular heterogeneity in pancreatic cancer.

scholarly journals 2343

2017 ◽  
Vol 1 (S1) ◽  
pp. 62-62
Author(s):  
Jose Ignacio Varillas ◽  
Jinling Zhang ◽  
Weian Sheng ◽  
Kangfu Chen ◽  
Isis Barnes ◽  
...  
Keyword(s):  
Pancreatic Cancer ◽  
Clinical Outcome ◽  
Treatment Response ◽  
Cancer Patients ◽  
Disease Stage ◽  
Metastatic Pancreatic Cancer ◽  
Patient Specific ◽  
Blood Samples ◽  
Mesenchymal Transition ◽  
Anticancer Treatment

OBJECTIVES/SPECIFIC AIMS: The goal of this research is to use circulating tumor cells (CTC) enumeration and characterization to monitor anticancer treatment response. Emerging evidence strongly suggests the implications that epithelial-to-mesenchymal transition may have in cancer metastasis. Consequently, we hope to elucidate the significance of mesenchymal and stem-like CTCs in the peripheral blood of metastatic pancreatic cancer patients by analyzing the prevalence and frequency trends of CD133+ CTCs, as they relate to clinical events. We also hope to determine if there is a correlation between EpCAM+ CTCs and CD133+ CTCs numbers with tumor size, disease stage, and patient clinical outcome. METHODS/STUDY POPULATION: Blood samples of patients with metastatic pancreatic cancer (stage IV) were obtained from the University of Florida Health Cancer Center after informed consent through an IRB-approved protocol. CTC capture, characterization, and enumeration was performed on the blood of these cancer patients during their anticancer treatment. Patients had blood drawn for this purpose at time points aligned with clinical phlebotomy (every 2 weeks). CTC capture was performed by introducing treated patient blood samples into antibody-functionalized microdevices. The PDMS devices were functionalized by immobilizing either anti-EpCAM or anti-CD133, through an avidin-biotin complex. After capture, cells were fixated and permeabilized with 4% paraformaldehyde and 0.2% Triton X-100, respectively. Three-color immunocytochemistry (anti-cytokeratin-FITC, anti-CD45-PE, and DAPI) was performed to identify CTCs from nonspecifically captured blood cells. To be counted as a CTC, based on the FDA-approved technical definition, a cell with the appropriate cell size and morphology must be nucleated (DAPI+), express cytokeratin (CK+), and lack the leukocytic CD45 marker (CD45−). RESULTS/ANTICIPATED RESULTS: We tested the clinical utility of the device for monitoring the response of patients with advanced pancreatic cancer to a chemotherapy treatment consisting of anticancer drugs including 5-fluorouracil, leucovorin, oxaliplatin, and dasatinib. We have detected EpCAM+ CTCs in 47/47 (100%) and CD133+ CTCs in 41/47 (87.2%) of blood samples, coming from a cohort of 16 patients. We studied the correlation between the CTC numbers and the clinical result of patients in the study. We found that the size and changes in the size of the primary tumor (confirmed by CT scans) correlated with the frequency and increase/decrease trends in the number of CTCs detected. We expect to find some relationship between the number of detected CD133+ CTCs, or rather stem-like CTCs, and the clinical outcome of patients (eg, disease progression leading to withdrawal from study). DISCUSSION/SIGNIFICANCE OF IMPACT: Enumeration of patient CTCs and stem-like CTCs at different stages of a patient’s treatment may correlate with disease stage and prognosis, and prove useful in monitoring early recurrence, patient-specific treatment response, and newly acquired resistances; all of which would aid in providing guidance for the next step in clinical intervention. This type of liquid biopsy technology has great potential to make an impact in the future of personalized medicine and point-of-care diagnostics, as well as become a sturdy tool for translational research.

scholarly journals Modeling glioblastoma invasion using human brain organoids and single-cell transcriptomics

2019 ◽  
Author(s):  
Teresa G Krieger ◽  
Stephan M Tirier ◽  
Jeongbin Park ◽  
Tanja Eisemann ◽  
Heike Peterziel ◽  
...  
Keyword(s):  
Single Cell ◽  
Specific Treatment ◽  
Cellular Heterogeneity ◽  
Patient Specific ◽  
Before And After ◽  
Invasive Capacity ◽  
Transcriptional Changes ◽  
Potential Interactions

AbstractGlioblastoma multiforme (GBM) are devastating neoplasms with high invasive capacity. GBM has been difficult to study in vitro. Therapeutic progress is also limited by cellular heterogeneity within and between tumors. To address these challenges, we present an experimental model using human cerebral organoids as a scaffold for patient-derived glioblastoma cell invasion. By tissue clearing and confocal microscopy, we show that tumor cells within organoids extend a network of long microtubes, recapitulating the in vivo behavior of GBM. Single-cell RNA-seq of GBM cells before and after co-culture with organoid cells reveals transcriptional changes implicated in the invasion process that are coherent across patient samples, indicating that GBM cells reactively upregulate genes required for their dispersion. Functional therapeutic targets are identified by an in silico receptor-ligand pairing screen detecting potential interactions between GBM and organoid cells. Taken together, our model has proven useful for studying GBM invasion and transcriptional heterogeneity in vitro, with applications for both pharmacological screens and patient-specific treatment selection at a time scale amenable to clinical practice.

scholarly journals Modeling glioblastoma invasion using human brain organoids and single-cell transcriptomics

2020 ◽  
Vol 22 (8) ◽  
pp. 1138-1149 ◽  
Author(s):  
Teresa G Krieger ◽  
Stephan M Tirier ◽  
Jeongbin Park ◽  
Katharina Jechow ◽  
Tanja Eisemann ◽  
...  
Keyword(s):  
Single Cell ◽  
Specific Treatment ◽  
Therapy Resistance ◽  
Cellular Heterogeneity ◽  
Patient Specific ◽  
Before And After ◽  
Invasive Capacity ◽  
Transcriptional Changes

Abstract Background Glioblastoma (GBM) consists of devastating neoplasms with high invasive capacity, which have been difficult to study in vitro in a human-derived model system. Therapeutic progress is also limited by cellular heterogeneity within and between tumors, among other factors such as therapy resistance. To address these challenges, we present an experimental model using human cerebral organoids as a scaffold for patient-derived GBM cell invasion. Methods This study combined tissue clearing and confocal microscopy with single-cell RNA sequencing of GBM cells before and after co-culture with organoid cells. Results We show that tumor cells within organoids extend a network of long microtubes, recapitulating the in vivo behavior of GBM. Transcriptional changes implicated in the invasion process are coherent across patient samples, indicating that GBM cells reactively upregulate genes required for their dispersion. Potential interactions between GBM and organoid cells identified by an in silico receptor–ligand pairing screen suggest functional therapeutic targets. Conclusions Taken together, our model has proven useful for studying GBM invasion and transcriptional heterogeneity in vitro, with applications for both pharmacological screens and patient-specific treatment selection on a time scale amenable to clinical practice.

Multi-analyte liquid biopsies based treatment in advanced refractory cancers.

2020 ◽  
Vol 38 (15_suppl) ◽  
pp. e15623-e15623
Author(s):  
Sewanti Limaye ◽  
Darshana Patil ◽  
Dadasaheb B Akolkar ◽  
Timothy Crook ◽  
Anantbhushan Ranade ◽  
...  
Keyword(s):  
Treatment Response ◽  
Liquid Biopsy ◽  
Tumor Tissue ◽  
Single Gene ◽  
Objective Response ◽  
Specific Treatment ◽  
Patient Specific ◽  
Solid Organ ◽  
Liquid Biopsies ◽  
Blood Draw

e15623 Background: Tumor tissue profiling following invasive biopsies is presently the standard approach for indication-based therapy management in solid organ cancers. However, challenges in biopsy are traditionally described due to proximity to vital organs, or patients’ co-morbidities or unwillingness for an invasive procedure. Liquid biopsies for evaluation of cancers are also largely restricted to single gene testing for selection of targeted therapy agents. We developed a comprehensive liquid biopsy based multi-analyte (molecular and functional) investigation of the cancer (eLBx: Encyclopedic Liquid Biopsy) for selection and management of individualised treatments in a cohort of advanced refractory cancers. Methods: We obtained 20 mL blood from 65 patients with solid organ cancers where the disease had progressed following failure of at least two lines of systemic therapies and where biopsy to obtain tumor tissue for molecular profiling of tumor was unviable. Cell free tumor DNA (ctDNA) was interrogated for mutations, while exosomal mRNA was profiled for gene expression. Viable circulating tumor associated cells (C-TACs) were tested in vitro for chemoresistance and used to determine expression of cell surface signalling receptors by immunocytochemistry (ICC). The findings were integrated to generate patient-specific treatment regimens. In patients who received treatment, response was determined radiologically. Results: Fifty-one patients received eLBx-guided personalized treatments with combinations of cytotoxic, targeted and endocrine agents. No two patients received the same treatment regimen. Forty-three patients were evaluable for treatment response per protocol among whom Partial Response (PR) was observed in 14 patients yielding an Objective Response Rate (ORR) of 32.6%. Additionally, 23 patients showed Stable Disease thus yielding an overall Disease Control rate of 86.1%. Median Progression Free Survival (PFS) was 108 days. There were no Grade IV therapy related Adverse Events or therapy related deaths. Conclusions: The ability to make informed treatment choices from a convenient blood draw implies a reduced dependence on invasive biopsies for disease management. We demonstrate successful management of advanced refractory solid tumor malignancies using an integrational non-invasive multi-analyte liquid biopsy approach. Clinical trial information: CTRI/2019/02/017548.

Review of clinical and emerging biomarkers for early diagnosis and treatment management of pancreatic cancer: towards personalised medicine

2021 ◽  
pp. 1-13
Author(s):  
Ernest Osei ◽  
Christabel Oghinan ◽  
Akua Asare ◽  
Hillary Ho ◽  
Solomon Manful
Keyword(s):  
Pancreatic Cancer ◽  
High Risk ◽  
Early Detection ◽  
Early Diagnosis ◽  
Specific Treatment ◽  
High Specificity ◽  
Patient Specific ◽  
Patient Response ◽  
Clinical Biomarkers ◽  
Specificity And Sensitivity

Abstract Background: Pancreatic cancer is the 12th most commonly diagnosed cancer and the 3rd leading cause of cancer mortality and accounts for approximately 2·7% of all newly diagnosed cancer cases and 6·4% of all cancer mortalities in Canada. It has a very poor survival rate mainly due to the difficulty of detecting the disease at an early stage. Consequently, in the advancement of disease management towards the concept of precision medicine that takes individual patient variabilities into account, several investigators have focused on the identification of effective clinical biomarkers with high specificity and sensitivity, capable of early diagnosis of symptomatic patients and early detection of the disease in asymptomatic individuals at high risk for developing pancreatic cancer. Materials and methods: We searched several databases from August to December 2020 for relevant studies published in English between 2000 and 2020 and reporting on biomarkers for the management of pancreatic cancer. In this narrative review paper, we describe 13 clinical and emerging biomarkers for pancreatic cancers used in screening for early detection and diagnosis, to identify patients’ risk for metastatic disease and subsequent relapse, to monitor patient response to specific treatment and to provide clinicians the possibility of prospectively identifying groups of patients who will benefit from a particular treatment. Conclusions: Current and emerging biomarkers for pancreatic cancer with high specificity and sensitivity has the potential to account for individual patient variabilities, for early detection of disease before the onset of metastasis to improve treatment outcome and patients’ survival, help screen high-risk populations, predict prognosis, provide accurate information of patient response to specific treatment and improve patients monitoring during treatment. Thus, the future holds promise for the use of effective clinical biomarkers or a panel of biomarkers for personalised patient-specific targeted medicine for pancreatic cancer.

Prognostic and predictive potential of omics studies from blood in pancreatic cancer patients treated with concurrent radiotherapy, gemcitabine, and cetuximab.

2015 ◽  
Vol 33 (3_suppl) ◽  
pp. 326-326
Author(s):  
Peter E Huber ◽  
Carmen Timke ◽  
Nils H Nicolay ◽  
Ramon Lopez
Keyword(s):  
Gene Expression ◽  
Pancreatic Cancer ◽  
Clinical Outcome ◽  
Cancer Patients ◽  
Whole Blood ◽  
Side Effect ◽  
Locally Advanced ◽  
Patient Specific ◽  
Blood Samples ◽  
Blood Transcriptomics

326 Background: Pancreatic cancer is a cancer with dismal prognosis. Multimodal therapy approaches integrating targeted drugs into radiochemotherapy regimens may be promising concepts for patients with locally advanced disease. It would be desirable to better understand patterns of therapy response, clinical outcome and side effect spectra by performing gene expression and protein analyses from blood samples before, during and after actual treatments. Methods: At defined time points before, during and after neoadjuvant triple therapy consisting of IMRT +/-gemcitabine chemotherapy +/-cetuximab EGFR antibodies, blood samples were collected from 21 patients with advanced pancreatic cancer. Whole blood transcriptomics was performed using Agilent’s human genome wide microarray platform. Quantitative serum protein analysis was performed using the ‘FastQuant’ (sandwich antibody) system on 20 selected proteins with a focus on cytokines and angiogenesis related proteins. Cluster analyses were performed and array data were correlated to local control, survival, CA19-9 response, and side effects e.g. acne grading and hematotoxicity. Results: Whole blood transcriptomics was feasible and showed statistically significant prognostic and predictive value for therapy specific and patient specific expression signatures. RNA expression signatures obtained before and during treatment course appeared to have significant statistical power to predict CA19-9 response, clinical outcome and side effect parameters. Similarly, the serum proteomic time course correlated with clinical outcome. Moreover, cetuximab increased e.g. PDGF, MCP-1, Rantes, Il-6, Angiopoietin, serum and downregulated TIMP-1, Il-8, and Angiogenin levels while radiochemotherapy had primarily the respective opposite effects. Conclusions: Whole blood analyses provides a promising tool to monitor pancreatic cancer patients undergoing radiotherapy+gemcitabine+cetuximab, and the protein and gene expression “signatures” from blood may be prognostic and predictive for clinical endpoints.

Pancreatic cancer: Assessment of neoadjuvant chemotherapy outcome based on radiomics of pretreatment computed tomography.

2019 ◽  
Vol 37 (15_suppl) ◽  
pp. e15767-e15767
Author(s):  
Xiang Li ◽  
Tianyu Tang ◽  
Xueli Bai ◽  
Tingbo Liang
Keyword(s):  
Computed Tomography ◽  
Pancreatic Cancer ◽  
Regression Analysis ◽  
Neoadjuvant Chemotherapy ◽  
Treatment Response ◽  
Cancer Patients ◽  
Tumor Size ◽  
Cox Regression ◽  
Cox Regression Analysis ◽  
Histopathologic Response

e15767 Background: The objective response rate to neoadjuvant chemotherapy (NAC) was limited to around 35% in pancreatic cancer and as more as 30% patients show no benefit to NAC. In this instance, predicting the response to NAC may play an important role in individual treatment for pancreatic cancer patients. We aim to evaluate contrast enhanced-computed tomography (CE-CT) features in predicting treatment response and survival after neoadjuvant chemotherapy (NAC) for patients with borderline resectable and locally advanced pancreatic cancer. Methods: Sixty-one pancreatic cancer patients receiving NAC were enrolled and underwent abdominal CE-CT before treatment. All patients were divided into groups according to the changes of tumor size after treatment. 396 radiomics features were extracted from three-dimensional ROIs (region of interest) based on pretreatment CE-CT images of each patient. The optimal features were selected and three supervised machine learning classifiers were developed. Finally, univariate and multivariate analyses were performed to evaluate the capability of the selected features in predicting histopathologic response and outcomes. Results: Nine, seven and five radiomics features were selected as optimal features for three experiments respectively. Two features, Haralick Entropy and Histogram Entropy, were found consistent in experiments and were both higher in patients with tumor enlargement. Moreover, lower Histogram Entropy was significantly associated with a better histopathologic response (p = 0.008) and smaller tumor size (p = 0.041) in patients with tumor resection. In univariate Cox regression analysis, lower Histogram Entropy (P = 0.006) and lower Haralick Entropy (P = 0.001) predicted a better prognosis. Meanwhile, lower Haralick Entropy (p = 0.048) was independent predictor for longer survival time in multivariate Cox regression analysis. Conclusions: Radiomics features are strongly correlated with NAC treatment response and prognosis in pancreatic cancer, suggesting the great potential of imaging radiomics to help tailoring the treatment into the era of personalized medicine

SLCO1B1 Polymorphism Is a Drug Response Predictive Marker for Advanced Pancreatic Cancer Patients Treated With Gemcitabine, S-1, or Gemcitabine Plus S-1

Pancreas ◽  
2018 ◽  
Vol 47 (5) ◽  
pp. 637-642 ◽  
Author(s):  
Yasunori Sato ◽  
Hideki Ueno ◽  
Tatsuya Ioka ◽  
Shinichi Ohkawa ◽  
Masafumi Ikeda ◽  
...  
Keyword(s):  
Pancreatic Cancer ◽  
Cancer Patients ◽  
Drug Response ◽  
Advanced Pancreatic Cancer ◽  
Predictive Marker
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