Name: establishing a competing risk regression nomogram model for survival data
Uploaded: 2020-10-23T13:00:00
Description: Watch this Scientific Journal Video about Establishing a Competing Risk Regression Nomogram Model for Survival Data at JoVE.com

The study was supported by grants from the general program of Zhejiang Province Natural Science Foundation (grant number LY19H160020) and key program of the Jinhua Municipal Science &#38; Technology Bureau (grant number 2016-3-005, 2018-3-001d and 2019-3-013).

The research protocol was approved by the Ethics Committee of Jinhua Hospital, Zhejiang University School of Medicine. For this experiment, the cases were obtained from the Surveillance, Epidemiology, and End Results (SEER) database. SEER is an open-access database that includes demographic, incidence and survival data from 18 population-based cancer registries. We registered on the SEER website and signed a letter of assurance to acquire the research data (12296-Nov2018).
1. Data source
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S., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Nomogram+predicting+long-term+survival+after+d2+gastrectomy+for+gastric+cancer.">Nomogram predicting long-term survival after d2 gastrectomy for gastric cancer.</a> Journal of Clinical Oncology. 30 (31), 3834-3840 (2012).</li><li>Karakiewicz, P. I., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Multi-institutional+validation+of+a+new+renal+cancer-specific+survival+nomogram.">Multi-institutional validation of a new renal cancer-specific survival nomogram.</a> Journal of Clinical Oncology. 25 (11), 1316-1322 (2007).</li><li>Liang, W., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Development+and+validation+of+a+nomogram+for+predicting+survival+in+patients+with+resected+non-small-cell+lung+cancer.">Development and validation of a nomogram for predicting survival in patients with resected non-small-cell lung cancer.</a> Journal of Clinical Oncology. 33 (8), 861-869 (2015).</li><li>Valentini, V., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Nomograms+for+predicting+local+recurrence,+distant+metastases,+and+overall+survival+for+patients+with+locally+advanced+rectal+cancer+on+the+basis+of+European+randomized+clinical+trials.">Nomograms for predicting local recurrence, distant metastases, and overall survival for patients with locally advanced rectal cancer on the basis of European randomized clinical trials.</a> Journal of Clinical Oncology. 29 (23), 3163-3172 (2011).</li><li>Iasonos, A., Schrag, D., Raj, G. V., Panageas, K. S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=How+to+build+and+interpret+a+nomogram+for+cancer+prognosis.">How to build and interpret a nomogram for cancer prognosis.</a> Journal of Clinical Oncology. 26 (8), 1364-1370 (2008).</li><li>Chisholm, J. C., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Prognostic+factors+after+relapse+in+nonmetastatic+rhabdomyosarcoma:+a+nomogram+to+better+define+patients+who+can+be+salvaged+with+further+therapy.">Prognostic factors after relapse in nonmetastatic rhabdomyosarcoma: a nomogram to better define patients who can be salvaged with further therapy.</a> Journal of Clinical Oncology. 29 (10), 1319-1325 (2011).</li><li>Brockman, J. A., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Nomogram+Predicting+Prostate+Cancer-specific+Mortality+for+Men+with+Biochemical+Recurrence+After+Radical+Prostatectomy.">Nomogram Predicting Prostate Cancer-specific Mortality for Men with Biochemical Recurrence After Radical Prostatectomy.</a> European Urology. 67 (6), 1160-1167 (2015).</li><li>Zhou, H., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Nomogram+to+Predict+Cause-Specific+Mortality+in+Patients+With+Surgically+Resected+Stage+I+Non-Small-Cell+Lung+Cancer:+A+Competing+Risk+Analysis.">Nomogram to Predict Cause-Specific Mortality in Patients With Surgically Resected Stage I Non-Small-Cell Lung Cancer: A Competing Risk Analysis.</a> Clinical Lung Cancer. 19 (2), 195-203 (2018).</li><li>Fu, J., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=De-escalating+chemotherapy+for+stage+II+colon+cancer.">De-escalating chemotherapy for stage II colon cancer.</a> Therapeutic Advances in Gastroenterology. 12, 1756284819867553 (2019).</li><li>Chen, D., Li, J., Chong, J. 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R package version 2.2-7 Available from: <a target="_blank" href="https://CRAN.R-project.org/package=cmprsk">https://CRAN.R-project.org/package=cmprsk</a> (2014)</li><li>Scrucca, L., Santucci, A., Aversa, F. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Regression+modeling+of+competing+risk+using+R:+an+in+depth+guide+for+clinicians.">Regression modeling of competing risk using R: an in depth guide for clinicians.</a> Bone Marrow Transplantation. 45 (9), 1388-1395 (2010).</li><li>Zhang, Z., Geskus, R. B., Kattan, M. W., Zhang, H., Liu, T. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Nomogram+for+survival+analysis+in+the+presence+of+competing+risks.">Nomogram for survival analysis in the presence of competing risks.</a> Annals in Translational Medicine. 5 (20), 403 (2017).</li><li>Geskus, R. B. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Cause-specific+cumulative+incidence+estimation+and+the+fine+and+gray+model+under+both+left+truncation+and+right+censoring.">Cause-specific cumulative incidence estimation and the fine and gray model under both left truncation and right censoring.</a> Biometrics. 67 (1), 39-49 (2011).</li><li>Fu, J., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Young-onset+breast+cancer:+a+poor+prognosis+only+exists+in+low-risk+patients.">Young-onset breast cancer: a poor prognosis only exists in low-risk patients.</a> Journal of Cancer. 10 (14), 3124-3132 (2019).</li><li>de Glas, N. 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The overall goal of the current study was to establish a specific competing-risk nomogram that could describe real-world diseases and to develop a convenient individual assessment model for clinicians to approach treatment decisions. Here, we provide a step-by-step tutorial for establishing nomograms based on the Cox regression model and competing risk regression model and further performing subgroup analysis. Zhang et al.18 introduced an approach to create a competing-risk nomogram, but the main ...

The time to event survival analysis is quite common in clinical studies. Survival data measure the time span from the start time until the occurrence of the event of interest, but the occurrence of the event of interest is often precluded by another event. If more than one type of end point is present, they are called competing risks end points. In this case, the standard hazard analysis (i.e., Cox proportional cause-specific hazards model) often does not work well because individuals experiencing another type of event are censored. Individuals who experience a competing event often remain in the risk set, as the competing risks are usually not independent. Therefore, Fine and Gray1 studied the regression model estimation for the sub distribution of a competing risk. In a competing risk setting, three different types of events can be discriminated.
One measures overall survival (OS) by demonstrating a direct clinical benefit from new treatment methods for a disease. OS measures the survival time from time of origin (i.e., time of diagnosis or treatment) to the time of death due to any cause and generally evaluates the absolute risk of death, thereby failing to differentiate the causes of death (e.g., cancer-specific death (CSD) or non-cancer-specific death (non-CSD))2. OS is, therefore, considered as the most important endpoint. The events of interest are often cancer related, while the non-cancer-specific events, which include heart disease, traffic accidents or other unrelated causes, are considered competing events. Malignant patients with a favorable prognosis, who are expected to survive longer, are often at a greater risk of non-CSD. That is, the OS will be diluted by other causes of death and fail to correctly interpret the real effectiveness of clinical treatment. Therefore, OS may not be the optimal measure for accessing the outcomes of disease3. Such biases could be corrected by the competing risk regression model.
There are two main methods for competing risk data: cause-specific hazard models (Cox models) and subdistribution hazard models (competing models). In the following protocol, we present two methods to generate nomograms based on the cause-specific hazard model and the subdistribution hazard model. The cause-specific hazard model can be made to fit in the Cox proportional hazards model, which treats subjects who experience the competing event as censored at the time that the competing event occurred. In the subdistribution hazard model that was introduced by Fine and Gray1 in 1999, three different types of events can be discriminated, and individuals who experience a competing event remain at the risk set forever.
A nomogram is a mathematical representation of the relationship between three or more variables4. Medical nomograms consider biological and clinical event as variables (e.g., tumor grade and patient age) and generate probabilities of a clinical event (e.g., cancer recurrence or death) that is graphically depicted as a statistical prognostic model for a given individual. Generally, a nomogram is formulated based on the results of the Cox proportional hazards model5,6,7,8,9,10.
However, when competing risks are present, a nomogram based on the Cox model might fail to perform well. Though several previous studies11,12,13,14 have applied the competing risk nomogram to estimate the probability of CSD, few studies have described how to establish the nomogram based on a competing risk regression model, and there is no existing package available to accomplish this. Therefore, the method presented below will provide a step-by-step protocol to establish a specific competing-risk nomogram based on a competing risk regression model as well as a risk score estimation to aid clinicians in treatment decision-making.

establishing a competing risk regression nomogram model for survival data

The Kaplan&#8211;Meier method and Cox proportional hazards regression model are the most common analyses in the survival framework. These are relatively easy to apply and interpret and can be depicted visually. However, when competing events (e.g., cardiovascular and cerebrovascular accidents, treatment-related deaths, traffic accidents) are present, the standard survival methods should be applied with caution, and real-world data cannot be correctly interpreted. It may be desirable to distinguish different kinds of events that may lead to the failure and treat them differently in the analysis. Here, the methods focus on using the competing regression model to identify significant prognostic factors or risk factors when competing events are present. Additionally, nomograms based on a proportional hazard regression model and a competing regression model are established to help clinicians make individual assessments and risk stratifications in order to explain the impact of controversial factors on prognosis.

Survival characteristics of the example cohort 
In the example cohort, a total of 8,550 eligible patients were included in the analysis and the median follow-up time was 88 months (range, 1 to 95 months). A total of 679 (7.94%) patients were younger than 40 years old and 7,871 (92.06%) patients were older than 40. At the end of the trial, 7,483 (87.52%) patients were still alive, 662 (7.74%) died because of breast cancer, and 405 (4.74%) patients died because of other causes (competing risks).
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Watch this Scientific Journal Video about Establishing a Competing Risk Regression Nomogram Model for Survival Data at JoVE.com

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Presented here is a protocol to build nomograms based on the Cox proportional hazards regression model and competing risk regression model. The competing method is a more rational method to apply when competing events are present in the survival analysis.

The Kaplan&#8211;Meier method and Cox proportional hazards regression model are the most common analyses in the survival framework. These are relatively ...

This is the protocol to deal with the normal grants based on the competing risk regression model which is a more rational method to apply when the competing events such as the traffic accidents are passed in the survival analysis. Individuals who experienced our competing events often are reminding the risks that, as the competing risks are usually not independent. The competing normal grant is more appropriate to evaluate the probability of testing events for individuals in analysis.After installing the RMS and competing risk R packages, load them and import the cohort data To establish the Cox proportional hazards regression model, fit the model to the data using the CPH function then develop a Cox regression nomogram, taking the two year predicted survival rate as an example. Use the meta package in R to calculate the risk score and draw a forest plot. After installing and loading the R packages, obtain the group risk score, or GRS and divide the cohort into three subgroups.Then draw the forest plot, obtaining the hazard ratio lower competence interval, and upper confidence interval with the CRR function. To establish the competing risk regression model, start by placing prognostic variables into a matrix. Use the Cbind function to concatenate the variables by columns and fit them into the model.Then use the nomogram function to construct Cox Nome. Get the baseline cumulative incidents function, or CIF, and replace the X beta and X point of the competing risk regression model. Replace the total X point in X reel, then calculate the X score and plot the nomogram.The equation for the X score and X rail relationship can be calculated according to the intrinsic attribution of the competing model. CIF zero means baseline CIF, which is calculated by the predict CRR function. In the example cohort a total of 8, 550 eligible patients were included in the analysis and the median followup time was 88 months.The cumulative incidences of tumor death and no tumor death and competing events were by the Kaplan-Meier method, and the competing risk regression function, respectively. The sum of the cumulative incidences of tumor death and no tumor death, calculated with the Kaplan-Meier method, was higher than the sum of the estimates of all causes of death, which was equal to the cumulative incidents of cancer specific death when a competing method was used. A nomogram was constructed using the Cox proportional regression model based on significant factors, which included marital status, race, histological type, differentiated grade, T-classification and N-classification.A nomogram was also constructed using the competing risk regression model. Based on the risk score, the cohort was classified into three subgroups:low risk, medium risk and high risk. The forest plot was used to clearly present the interaction between the group risk score and the specific factor.When considering age, only the low risk group showed a worse prognosis for younger women, indicating that young age may act as a protective factor of prognosis in medium and high-risk groups. When attempting this protocol it is important to fully understand the different survive models in the 10 to event analysis and the tools are properly mastered for individualized guidance. The model performance is evaluated in terms of the discrimination and the caliber ration performance.Following this procedure, the calibration curve and they're the same gaps can be performance to validate the efficiency of the competing normal grant.

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Cancer Research

Establishing Dual Resistance to EGFR-TKI and MET-TKI in Lung Adenocarcinoma Cells In Vitro with a 2-step Dose-escalation Procedure

Drug resistance is a major challenge in cancer therapy. The generation of resistant sublines in vitro is necessary for discovering novel mechanisms to overcome this challenge. Here, a 2-step dose-escalation method for establishing dual-resistance to an epidermal growth factor receptor (EGFR)-tyrosine kinase inhibitor (TKI), gefitinib, and a MET-TKI, PHA665752, is described. This method is based on simple stepwise dose-escalation of inhibitors for inducing acquired resistance in cell lines. The alternate method for generating resistant sublines involves exposing the cells to high concentrations of the inhibitor in one step. The stepwise dose-escalation method has a higher possibility of successfully inducing acquired resistance than this method. Activating EGFR mutations are biomarkers of a response to treatment with EGFR-TKI, which is an applied first-line treatment for non-small cell lung cancers (NSCLC) that harbor these mutations. However, despite reports of effective responses, the use of EGFR-TKI is limited because tumors inevitably acquire resistance. The major mechanisms behind EGFR-TKI resistance include a secondary mutation at the gatekeeper site, T790M in exon 20 of EGFR, and a bypass signal of MET. Thus, a potential solution for this issue would be a combination of EGFR-TKI and MET-TKI. This combined treatment has been shown to be effective in an in vitro study model. Acquired gefitinib-resistance was established through MET-amplification by stepwise dose-escalation of gefitinib for 12 months, and a cell line named PC-9MET1000 was generated in a previous study. To further investigate the mechanisms of acquired MET-TKI and EGFR-TKI resistance, a MET-TKI, PHA665752, was administered to these cells with stepwise dose-escalation in the presence of gefitinib for 12 months. This protocol has also been successfully applied for a number of combination therapies to establish acquired resistance to other inhibitor molecules.

Establishing Dual Resistance to EGFR-TKI and MET-TKI in Lung Adenocarcinoma Cells In Vitro with a 2-step Dose-escalation Procedure

An in vitro method for establishing dual resistance to an EGFR-TKI and a MET-TKI in cancer cells is described. This method is useful for developing treatments for patients with EGFR-mutations, who exhibit disease progression despite EGFR-TKI treatment with MET-amplification. It can also be modified for inhibitors targeting other molecules.

Drug resistance is a major challenge in cancer therapy. The generation of resistant sublines in vitro is necessary for discovering novel mechanisms to ...

Morphology-Based Distinction Between Healthy and Pathological Cells Utilizing Fourier Transforms and Self-Organizing Maps

The appearance and the movements of immune cells are driven by their environment. As a reaction to a pathogen invasion, the immune cells are recruited to the site of inflammation and are activated to prevent a further spreading of the invasion. This is also reflected by changes in the behavior and the morphological appearance of the immune cells. In cancerous tissue, similar morphokinetic changes have been observed in the behavior of microglial cells: intra-tumoral microglia have less complex 3-dimensional shapes, having less-branched cellular processes, and move more rapidly than those in healthy tissue. The examination of such morphokinetic properties requires complex 3D microscopy techniques, which can be extremely challenging when executed longitudinally. Therefore, the recording of a static 3D shape of a cell is much simpler, because this does not require intravital measurements and can be performed on excised tissue as well. However, it is essential to possess analysis tools that allow the fast and precise description of the 3D shapes and allows the diagnostic classification of healthy and pathogenic tissue samples based solely on static, shape-related information. Here, we present a toolkit that analyzes the discrete Fourier components of the outline of a set of 2D projections of the 3D cell surfaces via Self-Organizing Maps. The application of artificial intelligence methods allows our framework to learn about various cell shapes as it is applied to more and more tissue samples, whilst the workflow remains simple.

Here, we provide a workflow that allows the identification of healthy and pathological cells based on their 3-dimensional shape. We describe the process of using 2D projection outlines based on the 3D surfaces to train a Self-Organizing Map that will provide objective clustering of the investigated cell populations.

The appearance and the movements of immune cells are driven by their environment. As a reaction to a pathogen invasion, the immune cells are recruited to ...

Studying Triple Negative Breast Cancer Using Orthotopic Breast Cancer Model

Triple-negative breast cancer (TNBC) is an aggressive breast cancer subtype with limited therapeutic options. When compared to patients with less aggressive breast tumors, the 5-year survival rate of TNBC patients is 77% due to their characteristic drug-resistant phenotype and metastatic burden. Toward this end, murine models have been established aimed at identifying novel therapeutic strategies limiting TNBC tumor growth and metastatic spread. This work describes a practical guide for the TNBC orthotopic model where MDA-MB-231 breast cancer cells suspended in a basement membrane matrix are implanted in the fourth mammary fat pad, which closely mimics the cancer cell behavior in humans. Measurement of tumors by caliper, lung metastasis assessment via in vivo and ex vivo imaging, and molecular detection are discussed. This model provides an excellent platform to study therapeutic efficacy and is especially suitable for the study of the interaction between the primary tumor and distal metastatic sites.

This work presets an advanced protocol to accurately assess tumor loading by detection of green fluorescent protein and bioluminescence signals as well as the integration of quantitative molecular detection technique.

Triple-negative breast cancer (TNBC) is an aggressive breast cancer subtype with limited therapeutic options. When compared to patients with less ...

A Data Integration Workflow to Identify Drug Combinations Targeting Synthetic Lethal Interactions

A synthetic lethal interaction between two genes is given when knock-out of either one of the two genes does not affect cell viability but knock-out of both synthetic lethal interactors leads to loss of cell viability or cell death. The best studied synthetic lethal interaction is between BRCA1/2 and PARP1, with PARP1 inhibitors being used in clinical practice to treat patients with BRCA1/2 mutated tumors. Large genetic screens in model organisms but also in haploid human cell lines have led to the identification of numerous additional synthetic lethal interaction pairs, all being potential targets of interest in the development of novel tumor therapies. One approach is to therapeutically target genes with a synthetic lethal interactor that is mutated or significantly downregulated in the tumor of interest. A second approach is to formulate drug combinations addressing synthetic lethal interactions. In this article, we outline a data integration workflow to evaluate and identify drug combinations targeting synthetic lethal interactions. We make use of available datasets on synthetic lethal interaction pairs, homology mapping resources, drug-target links from dedicated databases, as well as information on drugs being investigated in clinical trials in the disease area of interest. We further highlight key findings of two recent studies of our group on drug combination assessment in the context of ovarian and breast cancer.

Large genetic screens in model organisms have led to the identification of negative genetic interactions. Here, we describe a data integration workflow using data from genetic screens in model organisms to delineate drug combinations targeting synthetic lethal interactions in cancer.

A synthetic lethal interaction between two genes is given when knock-out of either one of the two genes does not affect cell viability but knock-out of ...

A 3D Spheroid Model for Glioblastoma

Two-dimensional (2D) cell cultures do not mimic in vivo tumor growth satisfactorily. Therefore, three-dimensional (3D) culture spheroid models were developed. These models may be particularly important in the field of neuro-oncology. Indeed, brain tumors have the tendency to invade the healthy brain environment. We describe herein an ideal 3D glioblastoma spheroid-based assay that we developed to study tumor invasion. We provide all technical details and analytical tools to successfully perform this assay.

Here, we describe an easy-to-use invasion assay for glioblastoma. This assay is suitable for glioblastoma stem-like cells. A Fiji macro for easy quantification of invasion, migration, and proliferation is also described.

Two-dimensional (2D) cell cultures do not mimic in vivo tumor growth satisfactorily. Therefore, three-dimensional (3D) culture spheroid models were ...

Detection of Cell-Free DNA in Blood Plasma Samples of Cancer Patients

Identifying mutations in tumors of cancer patients is a very important step in disease management. These mutations serve as biomarkers for tumor diagnosis as well as for the treatment selection and its response in cancer patients. The current gold standard method for detecting tumor mutations involves a genetic test of tumor DNA by means of tumor biopsies. However, this invasive method is difficult to be performed repeatedly as a follow-up test of the tumor mutational repertoire. Liquid biopsy is a new and emerging technique for detecting tumor mutations as an easy-to-use and non-invasive biopsy approach.
Cancer cells multiply rapidly. In parallel, numerous cancer cells undergo apoptosis. Debris from these cells are released into a patient&#8217;s circulatory system, together with finely fragmented DNA pieces, called cell-free DNA (cfDNA) fragments, which carry tumor DNA mutations. Therefore, for identifying cfDNA based biomarkers using liquid biopsy technique, blood samples are collected from the cancer patients, followed by the separation of plasma and buffy coat. Next, plasma is processed for the isolation of cfDNA, and the respective buffy coat is processed for the isolation of a patient&#39;s genomic DNA. Both nucleic acid samples are then checked for their quantity and quality; and analyzed for mutations using next-generation sequencing (NGS) techniques.
In this manuscript, we present a detailed protocol for liquid biopsy, including blood collection, plasma, and buffy coat separation, cfDNA and germline DNA extraction, quantification of cfDNA or germline DNA, and cfDNA fragment enrichment analysis.

In this paper we present a detailed protocol for non-invasive liquid biopsy technique, including blood collection, plasma and buffy coat separation, cfDNA and germline DNA extraction, quantification of cfDNA or germline DNA, and cfDNA fragment enrichment analysis.

Identifying mutations in tumors of cancer patients is a very important step in disease management. These mutations serve as biomarkers for tumor diagnosis ...

Nuclei Isolation from Fresh Frozen Brain Tumors for Single-Nucleus RNA-seq and ATAC-seq

Adult diffuse gliomas exhibit inter- and intra-tumor heterogeneity. Until recently, the majority of large-scale molecular profiling efforts have focused on bulk approaches that led to the molecular classification of brain tumors. Over the last five years, single cell sequencing approaches have highlighted several important features of gliomas. The majority of these studies have utilized fresh brain tumor specimens to isolate single cells using flow cytometry or antibody-based separation methods. Moving forward, the use of fresh-frozen tissue samples from biobanks will provide greater flexibility to single cell applications. Furthermore, as the single-cell field advances, the next challenge will be to generate multi-omics data from either a single cell or the same sample preparation to better unravel tumor complexity. Therefore, simple and flexible protocols that allow data generation for various methods such as single-nucleus RNA sequencing (snRNA-seq) and single nucleus Assay for Transposase-Accessible Chromatin with high-throughput sequencing (snATAC-seq) will be important for the field.
Recent advances in the single cell field coupled with accessible microfluidic instruments such as the 10x genomics platform have facilitated single cell applications in research laboratories. To study brain tumor heterogeneity, we developed an enhanced protocol for the isolation of single nuclei from fresh frozen gliomas. This protocol merges existing single cell protocols and combines a homogenization step followed by filtration and buffer mediated gradient centrifugation. The resulting samples are pure single nuclei suspensions that can be used to generate single nucleus gene expression and chromatin accessibility data from the same nuclei preparation.

Intra-tumoral heterogeneity is an inherent feature of tumors, including gliomas. We developed a simple and efficient protocol that utilizes a combination of buffers and gradient centrifugation to isolate single nuclei from fresh frozen glioma tissues for single nucleus RNA and ATAC sequencing studies.

Adult diffuse gliomas exhibit inter- and intra-tumor heterogeneity. Until recently, the majority of large-scale molecular profiling efforts have focused ...

An R-Based Landscape Validation of a Competing Risk Model

The Cox proportional hazard model is widely applied for survival analyses in clinical settings, but it is not able to cope with multiple survival outcomes. Different from the traditional Cox proportional hazard model, competing risk models consider the presence of competing events and their combination with a nomogram, a graphical calculating device, which is a useful tool for clinicians to conduct a precise prognostic prediction. In this study, we report a method for establishing the competing risk nomogram, that is, the evaluation of its discrimination (i.e., concordance index and area under the curve) and calibration (i.e., calibration curves) abilities, as well as the net benefit (i.e., decision curve analysis). In addition, internal validation using bootstrap resamples of the original dataset and external validation using an external dataset of the established competing risk nomogram were also performed to demonstrate its extrapolation ability. The competing risk nomogram should serve as a useful tool for clinicians to predict prognosis with the consideration of competing risks.

The present protocol describes codes in R for evaluating the discrimination and calibration abilities of a competing risk model, as well as codes for the internal and external validation of it.

The Cox proportional hazard model is widely applied for survival analyses in clinical settings, but it is not able to cope with multiple survival ...

Experimental Melanoma Immunotherapy Model Using Tumor Vaccination with a Hematopoietic Cytokine

Fms-like tyrosine kinase 3 ligand (Flt3L) is a hematopoietic cytokine that promotes the survival and differentiation of dendritic cells (DCs). It has been used in tumor vaccines to activate innate immunity and enhance antitumor responses. This protocol demonstrates a therapeutic model using cell-based tumor vaccine consisting of Flt3L-expressing B16-F10 melanoma cells along with phenotypic and functional analysis of immune cells in the tumor microenvironment (TME). Procedures for cultured tumor cell preparation, tumor implantation, cell irradiation, tumor size measurement, intratumoral immune cell isolation, and flow cytometry analysis are described. The overall goal of this protocol is to provide a preclinical solid tumor immunotherapy model, and a research platform to study the relationship between tumor cells and infiltrating immune cells. The immunotherapy protocol described here can be combined with other therapeutic modalities, such as immune checkpoint blockade (anti-CTLA-4, anti-PD-1, anti-PD-L1 antibodies) or chemotherapy in order to improve the cancer therapeutic effect of melanoma.

The protocol presents a cancer immunotherapy model using cell-based tumor vaccination with Flt3L-expressing B16-F10 melanoma. This protocol demonstrates the procedures, including preparation of cultured tumor cells, tumor implantation, cell irradiation, measurement of tumor growth, isolation of intratumoral immune cells, and flow cytometry analysis.

Fms-like tyrosine kinase 3 ligand (Flt3L) is a hematopoietic cytokine that promotes the survival and differentiation of dendritic cells (DCs). It has been ...

Intracranial Cannula Implantation for Serial Locoregional Chimeric Antigen Receptor (CAR) T Cell Infusions in Mice

Pediatric CNS tumors are responsible for the majority of cancer-related deaths in children and have poor prognoses, despite advancements in chemotherapy and radiotherapy. As many tumors lack efficacious treatments, there is a crucial need to develop more promising therapeutic options, such as immunotherapies; the use of chimeric antigen receptor (CAR) T cell therapy&#160;directed against CNS tumors is of particular interest. Cell surface targets such as B7-H3, IL13RA2, and the disialoganglioside GD2 are highly expressed on the surface of several pediatric and adult CNS tumors,&#160;raising the opportunity to use CAR T cell therapy against these and other surface targets. To evaluate the repeated locoregional delivery of CAR T cells in preclinical murine models, an indwelling catheter system that recapitulates indwelling catheters currently being used in human clinical trials was established. Unlike stereotactic delivery, the indwelling catheter system allows for repeated dosing without the use of multiple surgeries. This protocol describes the intratumoral placement of a fixed guide cannula that has been used to successfully test serial CAR T cell infusions in orthotopic murine models of pediatric brain tumors. Following orthotopic injection and engraftment of the tumor cells in mice, intratumoral placement of a fixed guide cannula is completed on a stereotactic apparatus and secured with screws and acrylic resin. Treatment cannulas are then inserted through the fixed guide cannula for repeated CAR T cell delivery. Stereotactic placement of the guide cannula can be adjusted to deliver CAR T cells directly into the lateral ventricle or other locations in the brain. This platform offers a reliable mechanism for the preclinical testing of repeated intracranial infusions of CAR T cells and other novel therapeutics for these devastating pediatric tumors.

Intracranial Cannula Implantation for Serial Locoregional Chimeric Antigen Receptor CAR T Cell Infusions in Mice

Central nervous system (CNS) tumors are the leading cause of cancer-related death in children, and locoregional immune-based therapies are increasingly being tested for patients in clinical trials. This protocol describes methods for locoregional cannula implantation in mice for the preclinical evaluation of immunotherapeutic infusions targeting CNS tumors.

Pediatric CNS tumors are responsible for the majority of cancer-related deaths in children and have poor prognoses, despite advancements in chemotherapy ...