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
<ol>
	<li>Obtain cases from the databases as well as permission (if any) to use the cases from the registries. 
	NOTE: The cohort data are uploaded in Supplementary File 1. Readers who already have survival data with competing risks can skip this section.</li>
</ol>
2. Installing and loading packages and importing data
NOTE: Perform the following procedures based on R software (version 3.5.3) using the packages rms15 and cmprsk16 (http://www.r-project.org/).
<ol>
	<li>Install rms and cmprsk R packages. 
	&#62;install.packages(&#34;rms&#34;) 
	&#62;install.packages(&#34;cmprsk&#34;)</li>
	<li>Load the R packages. 
	&#62;library(&#34;rms&#34;) 
	&#62;library(&#34;cmprsk&#34;)</li>
	<li>Import the cohort data. 
	&#62;Dataset&#60;-read.csv(&#34;&#8230;/Cohort Data.csv&#34;) # cohort data is the example</li>
</ol>
3. Nomogram based on the Cox Proportional Hazards Regression model
<ol>
	<li>Establish the Cox Proportional Hazards Regression model. 
	NOTE: The independent variables (X) include categorical variables (dummy variables, such as race) and continuous variables (such as age). The factors significant in the univariable analysis will be selected for the use in multivariable analysis.
	<ol>
		<li>Fit the Cox proportional hazards model to the data. Establish the Cox proportional hard regression model using the function cph. The simplified format in R is shown below: 
		&#62; f0 &#60;- cph(Surv(Survivalmonths, status) ~ factor1+ factor2+&#8230;, 
		x=T, y=T, surv=T, data=Dataset) 
		NOTE: Death was set as the status in the example code.</li>
	</ol>
	</li>
	<li>Develop a Cox Regression Nomogram using the commands detailed below. 
	&#62; nom &#60;- nomogram(f0, fun=list(function(x) surv(24, x)&#8230;), funlabel=c(&#8220;2-year predicted survival rate&#8221;&#8230;), maxscale=100, fun.at) 
	&#62; plot (nom) 
	NOTE: Take the 2-year predicted survival rate as an example.</li>
</ol>
4. Nomogram based on the Competing Risk Regression Model
<ol>
	<li>Establish the Competing Risk Regression Model.
	<ol>
		<li>Fit the competing risk regression model. Readers could include the factors that they consider important, this step could be skipped. In the example, the factors significant in the univariable analysis are included. 
		NOTE: The censoring variable is coded as 1 for the event of interest and as 2 for the competing risk event. To facilitate the analysis, Scrucca et al.17 provide an R function factor2ind(), which creates a matrix of indicator variables from a factor.</li>
		<li>For categorical variables, carefully code them numerically when including them in the competing model. That is, for a categorical variable made of J levels, create J-1 dummy variables or indicator variables.</li>
		<li>To establish a competing risk regression model, first place prognostic variables into a matrix. Use the function cbind() to concatenate the variables by columns and fit them into the competing regression model. 
		&#62;x &#60;-cbind(factor2ind(factor1, &#34;1&#34;), factor2ind(factor2, &#34;1&#34;)&#8230;) 
		&#62; mod&#60;- crr (Survivalmonths, fstatus, failcode=1 or 2, cov1=x)</li>
	</ol>
	</li>
	<li>Plot the competing nomogram 
	NOTE: The beta value (&#946; value) is the regression coefficient of a variate (X) in the formula of the Cox proportional hazards regression. The X.score (comprehensive effect of the dependent variable) and X.real (at special timepoints, for example, 60 months, to predict the cumulative incidence function) are calculated from the Cox regression model and then, a nomogram is established.
	<ol>
		<li>Use the function nomogram to construct Cox nom (as listed in step 3.2).</li>
		<li>Replace X.beta and X.point as well as total.points, X.real, and X.score of the competing risk regression model.
		<ol>
			<li>Get the baseline cif, that is cif(min). See Supplementary file 2 for details. 
			&#62; x0=x 
			&#62; x0 &#60;- as.matrix(x0) 
			&#62; lhat &#60;- matrix(0, nrow = length(mod$uftime), ncol = nrow(x0)) 
			&#62; for (j in 1:nrow(x0)) lhat[, j] &#60;- cumsum(exp(sum(x0[j, ] * mod$coef)) * mod$bfitj) 
			&#62; lhat &#60;- cbind(mod$uftime, 1 - exp(-lhat)) 
			&#62; suv&#60;-as.data.frame(lhat) 
			&#62; colnames(suv)&#60;- c(&#34;time&#34;) 
			&#62; line24&#60;-which(suv$time==&#34;24&#34;) 
			&#62; cif.min24&#60;-suv[line24,which.min(suv[line24,])]</li>
			<li>Replace the X.beta and X.point. 
			&#62; lmaxbeta&#60;-which.max(abs(mod$coef)) 
			&#62; maxbeta&#60;-abs(mod$coef[lmaxbeta]) 
			&#62; race0&#60;-0 
			&#62; names(race0)&#60;-&#34;race:1&#34; 
			&#62; race.beta&#60;-c(race0,mod$coef[c(&#34;race:2&#34;,&#34;race:3&#34;)]) 
			&#62; race.beta.min&#60;-race.beta[which.min(race.beta)] 
			&#62; race.beta1&#60;-race.beta-race.beta.min 
			&#62; race.scale&#60;-(race.beta1/maxbeta*100) # how the scale is calculated 
			&#62; nom$Race$Xbeta&#60;-race.beta1 
			&#62; nom$Race$points&#60;-race.scale 
			NOTE: Take race as an example.</li>
			<li>Replace the total X.point and X.real. 
			&#62; nom$total.points$x&#60;-c(0,50,100, &#8230;) 
			&#62; real.2y&#60;-c(0.01,0.1,0.2,&#8230;) 
			NOTE: Replacements are according to the minimax value.</li>
			<li>Calculate the X.score and plot the nomogram. 
			&#62; score.2y&#60;-log(log((1-real.2y),(1-cif.min24)))/(maxbeta/100) 
			&#62; nom$`2-year survival`$x&#60;-score.2y 
			&#62; nom$`2-year survival`$x.real&#60;-real.2y 
			&#62; nom$`2-year survival`$fat&#60;-as.character(real.2y) 
			&#62; plot(nom) 
			NOTE: X.score=log(log((1-X.real),(1-cif0)))/(maxbeta/100). The equations for the X.score and X.real relationship can be calculated according to the intrinsic attribution of the competing model(crr). Cif0 means baseline cif, which will be calculated by the predict.crr function.</li>
		</ol>
		</li>
	</ol>
	</li>
</ol>
5. Subgroup analysis based on the Group Risk Score (GRS)
<ol>
	<li>Calculate the risk score (RS) 
	NOTE: Calculate the risk score for each patient by totalling the points of every variable. Cut-off values are used to classify the cohort. Taking 3 subgroups as an example, use the package meta to draw a forest plot.
	<ol>
		<li>Install and load the R packages 
		&#62; install.packages(&#34;meta&#34;) 
		&#62; library(&#34;meta&#34;)</li>
		<li>Obtain the GRS and divide the cohort into 3 subgroups. 
		&#62; d1&#60;-Dataset 
		&#62; d1$X&#60;-nom$X$points 
		&#62; #For example, d1$race[d1$race==1]&#60;-nom$race$point[1] 
		&#62; d1$RS&#60;-d1$race + d1$marry + d1$histology + d1$grademodify + d1$Tclassification + d1$Nclassification 
		&#62; d1$GRS&#60;- cut(d1$RS, quantile(d1$RS, seq(0, 1,1/3)), include.lowest = TRUE, labels = 1:3)</li>
		<li>Draw the forest plot. Get the HR, LCI and UCI via the function crr. 
		&#62; subgroup&#60;-crr(ftime, fstatus, cov1, failcode=1) 
		&#62; HR&#60;- summary(subgroup)$conf.int[1] 
		&#62; LCI&#60;- summary(subgroup)$conf.int[3] 
		&#62; UCI&#60;- summary(subgroup)$conf.int[4] 
		&#62; LABxx&#60;-c(&#34;Low Risk&#34;, &#34;Median Risk&#34;, &#34;High Risk&#34;) 
		&#62; xx&#60;-metagen(log(HR), lower = log(LCI), upper = log(UCI), studlab = LABxx, sm = &#34;HR&#34;) 
		&#62; forest(xx, col.square = &#34;black&#34;, hetstat =TRUE, leftcols = &#34;studlab&#34;)</li>
	</ol>
	</li>
</ol>

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	<li>Fine, J. P., Gray, R. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+proportional+hazards+model+for+the+subdistribution+of+a+competing+risk.">A proportional hazards model for the subdistribution of a competing risk.</a> Journal of the American Statistical Association. 94 (446), 496-509 (1999).</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=Real-world+impact+of+non-breast+cancer-specific+death+on+overall+survival+in+resectable+breast+cancer.">Real-world impact of non-breast cancer-specific death on overall survival in resectable breast cancer.</a> Cancer. 123 (13), 2432-2443 (2017).</li><li>Kim, H. T. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Cumulative+incidence+in+competing+risks+data+and+competing+risks+regression+analysis.">Cumulative incidence in competing risks data and competing risks regression analysis.</a> Clinical Cancer Research. 13, 559-565 (2007).</li><li>Balachandran, V. P., Gonen, M., Smith, J. J., DeMatteo, R. P. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Nomograms+in+oncology:+more+than+meets+the+eye.">Nomograms in oncology: more than meets the eye.</a> Lancet Oncology. 16 (4), 173-180 (2015).</li><li>Han, D. 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. 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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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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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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 ...

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10.0K 조회수.  Second Affiliated Hospital of Zhejiang University School of Medicine. 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.

비디오: Establishing a Competing Risk Regression Nomogram Model for Survival Data

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.

폐 선 암 세포에서 생체 외에서 2 단계 복용량 단계적 확대 절차와 EGFR TKI와 만난 TKI 듀얼 저항 설정

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

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암 연구학

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 ...

Evaluation of Colorectal Cancer Risk and Prevalence by Stool DNA Integrity Detection

Nowadays, stool DNA can be isolated and analyzed by several methods. The long fragments of DNA in stool can be detected by a qPCR assay, which provides a reliable probability of the presence of pre-neoplastic or neoplastic colorectal lesions. This method, called fluorescence long DNA (FL-DNA), is a fast, non-invasive procedure that is an improvement upon the primary prevention system. This method is based on evaluation of fecal DNA integrity by quantitative amplification of specific targets of genomic DNA. In particular, the evaluation of DNA fragments longer than 200 bp allows for detection of patients with colorectal lesions with very high specificity. However, this system and all currently available stool DNA tests present some general issues that need to be addressed (e.g., the frequency at which tests should be carried out and optimal number of stool samples collected at each timepoint for each individual). However, the main advantage of FL-DNA is the possibility to use it in association with a test currently used in the CRC screening program, known as the immunochemical-based fecal occult blood test (iFOBT). Indeed, both tests can be performed on the same sample, reducing costs and achieving a better prediction of the eventual presence of colorectal lesions.

The presented diagnostic FL-DNA kit is a time-saving and user-friendly method to determine the reliable probability of the presence of colorectal cancer lesions.

대변 DNA 무결성 검출에 의한 대장암 위험 및 유병률 평가

Nowadays, stool DNA can be isolated and analyzed by several methods. The long fragments of DNA in stool can be detected by a qPCR assay, which provides a ...

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.

정형외 유방암 모델을 사용하여 삼중 음성 유방암 을 연구

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.

합성 치명적인 상호 작용을 대상으로 하는 약물 조합을 식별 하기 위해 데이터 통합 워크플로우

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.

교모세포종을 위한 3D 스페로이드 모형

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.

암 환자의 혈장 샘플에서 세포없는 DNA 의 검출

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.

단일 핵 RNA-seq 및 ATAC-seq를 위한 신선한 냉동 뇌 종양에서 핵 절연

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 ...

Biobank for Translational Medicine: Standard Operating Procedures for Optimal Sample Management

Biobanks are key research infrastructures aimed at the collection, storage, processing, and sharing of high-quality human biological samples and associated data for research, diagnosis, and personalized medicine. The Biobank for Translational and Digital Medicine Unit at the European Institute of Oncology (IEO) is a landmark in this field. Biobanks collaborate with clinical divisions, internal and external research groups, and industry, supporting patients' treatment and scientific progress, including innovative diagnostics, biomarker discovery, and clinical trial design. Given the central role of biobanks in modern research, biobanking standard operating procedures (SOPs) should be extremely precise. SOPs and controls by certified specialists ensure the highest quality of samples for the implementation of science-based, diagnostic, prognostic, and therapeutic personalized strategies. However, despite numerous efforts to standardize and harmonize biobanks, these protocols, which follow a strict set of rules, quality controls, and guidelines based on ethical and legal principles, are not easily accessible. This paper presents the biobank standard operating procedures of a large cancer center.

Biobanks are crucial resources for biomedical research and the Biobank for Translational and Digital Medicine Unit at the European Institute of Oncology is a model in this field. Here, we provide a detailed description of biobanks' standard operating procedures for the management of different types of human biological samples.

Establishing a Competing Risk Regression Nomogram Model for Survival Data

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