Name: using rna-sequencing to detect novel splice variants related to drug resistance in in vitro cancer models
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Description: Watch this Scientific Journal Video about Using RNA-sequencing to Detect Novel Splice Variants Related to Drug Resistance in In Vitro Cancer Models at JoVE.com

The authors would like to acknowledge prof. J.J. McGuire, prof. R. Kofler and dr. K. Quint for providing the resistant cell lines used in this work. The study has been founded by grants from Cancer Center Amsterdam (CCA) Foundation (to JC, EG and RS), the KiKa (Children Cancer-free grant for AW) foundation, the Law Offices of Peter G. Angelos Grant from the Mesothelioma Applied Research Foundation (to VEG and EG), Associazione Italiana per la Ricerca sul Cancro (AIRC), Istituto Toscano Tumori (ITT), and Regione Toscana Bando FAS Salute (to EG).

1. Characterization of Drug Resistance Profiles through Cytotoxicity Assays
<ol>
	<li>Leukemic Cell Line Culture
	<ol>
		<li>Maintain the parental T-cell ALL cell line CCRF-CEM (CEM-WT) as well as its drug resistant sublines, including CEM/R30dm, CEM-R5 and CEM-C3, in 25 cm2 in 10 ml RPMI-1640 medium containing 2.3 &#181;M folic acid supplemented with 10% fetal calf serum and 100 units/ml penicillin G and 100 &#181;g/ml streptomycin.</li>
		<li>Culture the cells in a humidified atmosphere at...

<ol>
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Here we describe a novel approach that combines well-established cytotoxicity screening techniques and powerful NGS-based transcriptomic analyses to identify differential splicing events in relation to drug resistance. Spectrophotometric assays are convenient and robust high-throughput methods to assess drug sensitivity in in vitro cancer models and represent the first choice for many laboratories performing cytotoxicity screenings. Troubleshooting as well as possible variations for this method were extensively ...

Despite considerable advances in cancer treatment, resistance of malignant cells to chemotherapy, either intrinsic or acquired upon prolonged drug exposure, is the major reason for treatment failure in a wide range of leukemia and solid tumors1.
In order to delineate the mechanisms underlying drug resistance, in vitro cell line models are developed by stepwise selection of cancer cells resistant to chemotherapeutic agents. This procedure mimics the regimes used in the clinical settings and therefore allows in depth investigation of relevant resistance mechanisms. Resistant cells which survive the treatment are then distinguished from parental sensitive cells by using cell viability/cytotoxicity assays2. In vitro drug resistance profiles of primary cells have been shown to be significantly related to clinical response to chemotherapy3.
High-throughput cytotoxicity assays constitute a convenient method to determine drug sensitivity in vitro. Herein, the viability of cells is assessed by for instance the 3-[4,5-dimethylthiazol-2-yl]-2,5-diphenyl tetrazolium bromide - MTT assay4, which is based on metabolic conversion of certain substrates (i.e., tetrazolium salts) into colored products, thereby reflecting the mitochondrial activity of cells. Alternatively, the cellular protein content can be quantified using the sulforhodamine B (SRB) assay5. Here, the number of viable cells is proportional to the optical density (OD) measured at an appropriate wavelength in a spectrophotometer, with no need of extensive and time-consuming cell counting procedures. The growth inhibition induced by a certain chemotherapeutic drug can be calculated based on the OD of the wells in which cells were treated with a test agent and compared with the OD of untreated control cells. A dose-response curve is obtained by plotting drug concentrations versus percentages of viable cells relative to control cells. Finally, drug sensitivity can be reported as the concentration that results in 50% of cell growth inhibition as compared to untreated cells (IC50).
The mechanisms underlying drug resistance include many different abnormalities, such as alterations affecting gene expression of determinants of drug activity and cellular metabolism. These molecular lesions, including mutations, aberrations at a transcriptional and post-transcriptional level as well as disturbed epigenetic regulation often affect genes involved either in drug metabolism or apoptosis6.
Alternative pre-mRNA splicing and its intricate regulation have recently received considerable attention as a novel entity that may dictate drug resistance of cancer cells7. Up to 95% of human genes are alternatively spliced in normal cells by means of this tightly regulated process which produces many different protein isoforms from the same gene. Alternative splicing is often deregulated in cancer and several tumors are characterized by altered splicing of a growing number of genes involved in drug metabolism (i.e., deoxycytidine kinase, folylpolyglutamate synthetase, or Multidrug resistance proteins)6,8. However, comprehensive analysis of splicing profiles of drug resistant cells is painfully lacking. Therefore, it is imperative to develop high throughput methods for alternative splicing analysis. This could help to develop more effective therapeutic approaches.
During the last decade, the rapid development of next generation sequencing (NGS) technologies has enriched biomedical research with new insights into the molecular mechanisms governing regulation of genome expression and their role in various biological processes9. RNA-sequencing (RNA-seq) is a powerful sub-application of NGS in the field of transcriptomics. It allows a genome-wide profiling (both qualitatively and quantitatively) of the expression patterns of thousands of genes simultaneously and is well suited for the characterization of novel coding mRNAs as well as long non-coding RNA, miRNA, siRNA, and other small RNA classes (e.g., snRNA and piRNA)10,11.
RNA-Seq has many advantages over previous technologies for transcriptome characterization (e.g., Sanger sequencing and expression microarrays). It is not based on existing genome annotation, it has a single-nucleotide level of resolution and it has a broader dynamic range for expression level estimation. Briefly, the basic experimental workflow of RNA-seq experiments consists of polyadenylated transcript (mRNA) selection and fragmentation, followed by conversion into cDNA, library construction and finally, massively parallel deep sequencing12,13. Due to rapid drop of sequencing costs over the last few years, RNA-seq is gradually replacing other technologies and significant efforts are being made to improve the library preparation protocols. For instance, it is now possible to retain the strand information of mRNA transcripts by marking the second strand cDNA with deoxyuridine triphosphate (dUTP) and, prior to PCR amplification, digesting the marked strand with uracil-DNA-glycosilase (UDG). This process enhances the accuracy of gene annotation and estimation of the expression levels14,15.
The analysis and interpretation of RNA-seq data require complex and powerful computational software packages and processing within bioinformatic pipelines16,17. First, the raw reads undergo quality control by removing technical and biological artefacts and discarding (trimming) the sequences which do not reach stringent quality requirements. Subsequently the reads for each sample are mapped to a reference genome and indexed into gene-level, exon-level, or transcript-level, in order to determine the abundance of each category. Depending on the application, refined data are then computed through statistical models for the identification of allele-specific expression, alternative splicing, gene fusions and single nucleotide polymorphisms (SNPs)12. Finally, differential analysis on selected level (i.e., gene expression or alternative splicing) can be used to compare samples obtained under different conditions.
Differential splicing analysis describes the differences in splice site usage between two samples. An increasing number of software packages devoted to this purpose are available based on different statistical models, performances and user interface18. Among these, MATS (Multivariate Analysis of Transcript Splicing) emerges as a freely available and precise computational tool based on a Bayesian statistical framework and designed to detect differential splicing events from either single or paired end RNA-seq data. Starting from the aligned (.bam) files, MATS can detect all major types of alternative splicing events (exon skipping, alternative 3&#39; splice site, alternative 5&#39; splice site, mutually exclusive exons and intron retention - also see Figure 1).
First, the software identifies reads which support a certain splice event, for instance exon skipping, and classifies them into two types. &#34;Inclusion reads&#34; (for the canonical splice event) map within the investigated exon and span the junctions between that specific exon and the two upstream and downstream flanking exons. &#34;Skipping reads&#34; (for the alternative splice event) span the junction between the two flanking exons. Subsequently, MATS returns the normalized inclusion level for both the canonical and alternative events and compares values between samples or conditions. Ultimately, it calculates P-value and false discovery rate (FDR) assuming that the difference in the variant ratio of a gene between two conditions exceeds a given user-defined threshold for each splicing event19,34.
Following differential splicing analysis in conjunction with RNA-seq, an extensive experimental validation is warranted in order to identify true-positive gene candidates18. Quantitative reverse transcribed-polymerase chain reaction (qRT-PCR) is the most commonly used and optimal method in validation of candidates obtained from RNA-Seq analysis20. The aim of this paper is to provide a robust methodology to investigate drug resistance-related splicing profiles in solid tumors and hematological malignancies. Our approach utilizes RNA-seq-based transcriptome profiling of selected cell line models of drug resistant cancers in combination with an established qRT-PCR method for the validation of candidate genes implicated in drug resistance.
The human leukemia cell line models used in this study included pediatric T-cell acute lymphoblastic leukemia (T-ALL) cell line CCRF-CEM (CEM-WT), its two glucocorticoid (GC)-resistant subclones CEM-C7H2-R5C3 (CEM-C3) and CEM-C7R5 (CEM-R5)21,22 and the methotrexate (MTX)-resistant subline CEM/R30dm23. Although current therapies based on GCs and MTX establish clinical benefit in about 90% of cases, the emergence of GC-resistance still represents an unsolved problem with an unclear molecular mechanism. To isolate GC-resistant sub-clones, CEM-WT cells were cultured in 1 &#181;M dexamethasone (Dex) for 2 to 3 weeks. MTX-resistant subline CEM/R30dm was developed through repeated short- term (24 hr) exposure of CEM-WT cells to 30 &#181;M MTX as a mimic of clinical protocols. Interestingly, this cell line also displayed cross-resistance to Dex (unpublished results) for which the mechanism is not fully understood.
The solid tumor model investigated in the present study is pancreatic ductal adenocarcinoma, notorious for its extraordinary refractoriness to chemotherapy. To this end, we selected Panc-1 cell line and its gemcitabine-resistant sub-clone Panc-1R obtained by continuous incubation with 1 &#181;M of the drug24. Here we describe an approach to discover novel mechanisms underlying in-vitro drug resistance by combining three protocols: colorimetric cytotoxicity assays to assess drug sensitivity in leukemic cells and cancer cells from solid tumors, RNA-seq-based pipeline to identify novel splice variants related to drug sensitivity/resistance and RT-PCR and qRT-PCR analysis to validate potential candidates.

<table border="0" cellpadding="0" cellspacing="0" width="732">
	<tbody>
		<tr height="21">
			<td height="21" style="height:21px;width:252px;">Sulforhodamine B</td>
			<td style="width:195px;">Sigma-Aldrich</td>
			<td style="width:119px;">230162</td>
			<td style="width:167px;"></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:252px;">Trichloroacetic acid</td>
			<td style="width:195px;">Sigma-Aldrich</td>
			<td style="width:119px;">251399</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:252px;">CCRF-CEM</td>
			<td style="width:195px;">ATCC, Manassas, VA, USA</td>
			<td style="width:119px;">ATCC CCL-119</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:252px;">Panc-1</td>
			<td style="width:195px;">ATCC, Manassas, VA, USA</td>
			<td style="width:119px;">ATCC CRL-1469</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:252px;">DMEM high glucose</td>
			<td style="width:195px;">Lonza, Basel, Switzerland</td>
			<td style="width:119px;">12-604F</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:252px;">RPMI-1640&#160;</td>
			<td style="width:195px;">Gibco, Carlsbad, CA, USA</td>
			<td style="width:119px;">11875093</td>
			<td></td>
		</tr>
		<tr height="42">
			<td height="42" style="height:42px;width:252px;">Fetal bovine and calf serum</td>
			<td style="width:195px;">Greiner Bio-One, Frickenhausen, Germany</td>
			<td style="width:119px;">758093</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;">penicillin G streptomycin sulphate</td>
			<td style="width:195px;">Gibco, Carlsbad, CA, USA</td>
			<td style="width:119px;">15140122</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:252px;">Tris(hydroxymethyl)-aminomethane</td>
			<td style="width:195px;">Sigma Aldrich</td>
			<td style="width:119px;">252859</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:252px;">MTT formazan</td>
			<td style="width:195px;">Sigma Aldrich</td>
			<td style="width:119px;">M2003</td>
			<td></td>
		</tr>
		<tr height="42">
			<td height="42" style="height:42px;width:252px;">Anthos-Elisa-reader 2001</td>
			<td style="width:195px;">Labtec, Heerhugowaard, Netherlands</td>
			<td></td>
			<td>UV-Vis 96-well plate spectrophotometer</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:252px;">Greiner CELLSTAR 96 well plates</td>
			<td style="width:195px;">Greiner/Sigma</td>
			<td>M0812-100EA</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:252px;">Trypsin/EDTA Solution 100 ml</td>
			<td style="width:195px;">Lonza, Basel, Switzerland</td>
			<td>CC-5012</td>
			<td></td>
		</tr>
		<tr height="42">
			<td height="42" style="height:42px;">CELLSTAR Cell Culture Flasks 25 cm2</td>
			<td style="width:195px;">Greiner Bio-One, Frickenhausen, Germany</td>
			<td>82051-074</td>
			<td></td>
		</tr>
		<tr height="42">
			<td height="42" style="height:42px;">CELLSTAR Cell Culture Flasks 75 cm3</td>
			<td style="width:195px;">Greiner Bio-One, Frickenhausen, Germany</td>
			<td>82050-856</td>
			<td></td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">Phosphate Buffered Saline (NaCl 0.9%)</td>
			<td>B.Braun Melsungen AG, Germany</td>
			<td>362 3140</td>
			<td></td>
		</tr>
	</tbody>
</table>

using rna-sequencing to detect novel splice variants related to drug resistance in in vitro cancer models

Drug resistance remains a major problem in the treatment of cancer for both hematological malignancies and solid tumors. Intrinsic or acquired resistance can be caused by a range of mechanisms, including increased drug elimination, decreased drug uptake, drug inactivation and alterations of drug targets. Recent data showed that other than by well-known genetic (mutation, amplification) and epigenetic (DNA hypermethylation, histone post-translational modification) modifications, drug resistance mechanisms might also be regulated by splicing aberrations. This is a rapidly growing field of investigation that deserves future attention in order to plan more effective therapeutic approaches. The protocol described in this paper is aimed at investigating the impact of aberrant splicing on drug resistance in solid tumors and hematological malignancies. To this goal, we analyzed the transcriptomic profiles of several in vitro models through RNA-seq and established a qRT-PCR based method to validate candidate genes. In particular, we evaluated the differential splicing of DDX5 and PKM transcripts. The aberrant splicing detected by the computational tool MATS was validated in leukemic cells, showing that different DDX5 splice variants are expressed in the parental vs. resistant cells. In these cells, we also observed a higher PKM2/PKM1 ratio, which was not detected in the Panc-1 gemcitabine-resistant counterpart compared to parental Panc-1 cells, suggesting a different mechanism of drug-resistance induced by gemcitabine exposure.

The cytotoxicity assays described in the protocol provide a reliable and robust method to assess the resistance of cancer cells to chemotherapeutic agents in vitro. By means of the MTT assay, sensitivity to Dex was determined in four T-ALL cell lines, including Dex-sensitive parental CEM-WT cells, and three Dex-resistant sublines: CEM/R30dm, CEM-R5 and CEM-C3. Two different concentration ranges had to be used due to the large difference in sensitivity between CEM-WT (2 &#181;M - ...

Watch this Scientific Journal Video about Using RNA-sequencing to Detect Novel Splice Variants Related to Drug Resistance in In Vitro Cancer Models at JoVE.com

Using RNA-sequencing to Detect Novel Splice Variants Related to Drug Resistance in In Vitro Cancer Models

Here we describe a protocol aimed at investigating the impact of aberrant splicing on drug resistance in solid tumors and hematological malignancies. To this goal, we analyzed the transcriptomic profiles of parental and resistant in vitro models through RNA-seq and established a qRT-PCR based method to validate candidate genes.

Using RNA-sequencing to Detect Novel Splice Variants Related to Drug Resistance in In Vitro Cancer Models

Drug resistance remains a major problem in the treatment of cancer for both hematological malignancies and solid tumors. Intrinsic or acquired resistance ...

The overall goal of this protocol is to investigate the association of aberrant splicing with drug resistance profiles in in vitro models of solid tumors and hematological malignancies. This method can help answer key question in the field of molecular pharmacology and chemotherapy resistance such as which are the mechanism underlying drug resistance on post-transcriptional level? The main advantage of this technique is that it combines well established cytotoxicity screening protocol with powerful next generation sequencing based transcriptomic analysis.This technique could lead to a better understanding of the molecular mechanisms underlying chemotherapy failure and it will improve the early detection of drug resistance. This method can provide insight into genome-wide determinants of drug resistance in cell line models. But it can also be applied to other systems such as primary patient samples and xenograft.Demonstrating this procedure will be Johan Fomirno, a technician from our laboratory at the Cancer Center Amsterdam. To perform the MTT assay for leukemic cells, prepare 96-well flat bottom experimental plate. Dedicate wells to drug concentrations to control cells and to control medium without cells.Prepare a drug dilution range of dexamethasone or dex. Add 30 microliters from each dex dilution into an appropriate well of the 96-well plate. Make sure to include each concentration in triplicate.Harvest exponentially growing leukemic cells and re-suspend them at their optimal seeding concentration. Add 120 microliters of the cell suspension to each well containing either the drug solution or the growth medium, and incubate at 37 degrees Celsius with 5%carbon dioxide for 72 hours. After the incubation step, add 15 microliters of MTT reagent to each well by using a repetitive pipette.Shake the plate for five minutes with a plate shaker up to a maximum of 900 shakes per minute. Place the plates back at 37 degrees Celsius with 5%carbon dioxide in a cell culture incubator and incubate for another four to six hours. Following incubation, make sure to thoroughly re-suspend the formazan crystals as the absorbance values depend on proper solubilization of the compound.Add 150 microliters of the acidified isopropanol to each well and mix well with a multichannel pipette to thoroughly re-suspend all the formazan crystals. Using a microplate reader, determine the optical density or OD at 540 and 720 nanometers to ensure an accurate measurement by correcting for background OD.To perform SRB assay for pancreatic carcinoma cells, prepare a 96-well flat bottom experimental plate. Seed pancreatic carcinoma cells growing in exponential phase in triplicate in a 96-well flat bottom plates at the appropriate density in 100 microliters of medium by using a multichannel pipette.Add 100 microliters of medium to medium-only wells and incubate overnight at 37 degrees Celsius with 5%carbon dioxide to ensure proper adhesion of the cells to the plate. Add 100 microliters from each dilution into an appropriate well of the 96-well plate by using a multichannel pipette. Make sure to have each concentration in triplicate.Incubate at 37 degrees Celsius with 5%carbon dioxide for 72 hours. Next, add 25 microliters of cold TCA solution to the wells by using a multichannel pipette. Incubate the plates for at least 60 minutes at four degrees Celsius to precipitate and fix the proteins at the bottom of the wells.Empty the plate by removing the medium and dry briefly on a tissue. Wash five times with tap water before emptying the plate and drying at room temperature. Add 50 microliters of SRB solution per well by using a repetitive pipette and stain for 15 minutes at room temperature.Then, empty the plate by removing the SRB stain. After washing the plate four times with 1%acetic acid, empty the plate and let it dry at room temperature. Add 150 microliters of Tris solution per well by using a multichannel pipette and to mix for three minutes on a plate shaker up to a maximum of 900 shakes per minute.Read the optical density at 540 nanometers. Spin down the samples at 300 times g for three minutes. After removing the supernatant, extract total mRNA using commercially available silica membrane spin columns.Determine the concentration and purity of total RNA by using UV-Vis spectrophotometer. To prepare the sequencing library, use two micrograms of total mRNA per sample. Follow mRNA library preparation protocol according to manufacturer's instructions.After pooling the libraries in a single sample up to a final concentration of 10 nanomoles per liter using a bioanalyzer, use a high throughput sequencing system with single read 100 base pair mode. Design all primers as described in the text protocol. For first strand cDNA synthesis, set up reverse transcription of one microgram of the isolated RNA to cDNA by using 200 units per microliter of Moloney murine leukemia virus reverse transcriptase in its reaction buffer diluted one to five with sterile water.To perform the RT-PCR reaction, add one microliter of cDNA to the PCR reaction mix and place the tubes in the thermocycler. Run the program as described in the text protocol. Following PCR, load the samples into a 1%agarose gel and run the gel in TBE buffer at 100 volts for approximately 30 minutes in an electrophoresis system.To avoid cross contamination, use filter tips while performing the qRT-PCR assay. To perform qRT-PCR, prepare cyanine green PCR reaction mix for each specific splice variant to be detected as well as for the housekeeping gene. Load the master mix on a white 96-well plate and prepare duplicates for each sample.Add five microliters of cDNA diluted 10 times in water to each mix before placing the plate in the thermocycler. Growth inhibition curves as determined by MTT assay for leukemic cells and SRB assay for pancreatic carcinoma cells are depicted here. Curves for dexamethasone resistant and gemcitabine resistant cells are shifted to the right indicating growth inhibition as compared to the parental sensitive cells.The concentration and quality of cDNA libraries for all cell lines is determined with a bioanalyzer DNA chip. Electropherograms show fluorescence peaks at approximately 300 base pairs which are indicative of good quality for the subsequent RNA sequencing procedure. Short reads of the gene candidate DDX5 can be visualized as Sashimi plots where connecting lines represent splice junctions.DDX5 transcript shows differential usage of exon 12 and CEM wild-type and CEM/R30dm cells as compared to glucocorticoid-resistant cell lines, CEM-C3 and CEM-R5. RNA sequencing results are validated by performing RT-PCR on selected genes for which primer pairs and yield to exons located upstream and downstream of the alternatively spliced exon. Differential splicing of the DDX5 gene is shown here.Quantification of differentially-spliced transcripts through qRT-PCR assay using isoform-specific reverse primers confirms the specific down regulation of the DDX5 delta exon 12 splice variant and a specific glucocorticoid-resistant sub-lines. Once mastered, this technique can be done in three to four weeks. While attempting this procedure, it's important to remember to keep RNA samples on ice in order to avoid degradation.Following this procedure, other methods like ectopic overexpression of novel splice variant can be performed. This will answer additional questions like functional impact of alternate splicing profile on drug resistance. This technique can pave the way for researches in the field of molecular pharmacology to explore genome-wide determinants of drug resistance.

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Research

JoVE Journal

Cancer Research

Utilizing Functional Genomics Screening to Identify Potentially Novel Drug Targets in Cancer Cell Spheroid Cultures

The identification of functional driver events in cancer is central to furthering our understanding of cancer biology and indispensable for the discovery of the next generation of novel drug targets. It is becoming apparent that more complex models of cancer are required to fully appreciate the contributing factors that drive tumorigenesis in vivo and increase the efficacy of novel therapies that make the transition from pre-clinical models to clinical trials.
Here we present a methodology for generating uniform and reproducible tumor spheroids that can be subjected to siRNA functional screening. These spheroids display many characteristics that are found in solid tumors that are not present in traditional two-dimension culture. We show that several commonly used breast cancer cell lines are amenable to this protocol. Furthermore, we provide proof-of-principle data utilizing the breast cancer cell line BT474, confirming their dependency on amplification of the epidermal growth factor receptor HER2 and mutation of phosphatidylinositol-4,5-biphosphate 3-kinase (PIK3CA) when grown as tumor spheroids. Finally, we are able to further investigate and confirm the spatial impact of these dependencies using immunohistochemistry.

Identifying novel drug targets that transition from pre-clinical testing to human trials is a scientific priority. To that end, here we describe a functional genomics approach for examining the impact of gene depletion on cancer cell line spheroids, which more appropriately model human cancers in vivo.

The identification of functional driver events in cancer is central to furthering our understanding of cancer biology and indispensable for the discovery ...

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 ...

Generation of Prostate Cancer Cell Models of Resistance to the Anti-mitotic Agent Docetaxel

Microtubule targeting agents (MTAs) are a mainstay in the treatment of a wide range of tumors. However, acquired resistance to chemotherapeutic drugs is a common mechanism of disease progression and a prognostic-determinant feature of malignant tumors. In prostate cancer (PC), resistance to MTAs such as the taxane Docetaxel dictates treatment failure as well as progression towards lethal stages of disease that are defined by a poor prognosis and high mortality rates. Though studied for decades, the array of mechanisms contributing to acquired resistance are not completely understood, and thus pose a significant limitation to the development of new therapeutic strategies that could benefit patients in these advanced stages of disease. In this protocol, we describe the generation of Docetaxel-resistant prostate cancer cell lines that mimic lethal features of late-stage prostate cancer, and therefore can be used to study the mechanisms by which acquired chemoresistance arises. Despite potential limitations intrinsic to a cell based model, such as the loss of resistance properties over time, the Docetaxel-resistant cell lines produced by this method have been successfully used in recent studies and offer&#160;the opportunity to advance our molecular understanding of acquired chemoresistance in lethal prostate cancer.

Resistance to cancer therapies contributes to disease progression and death. Determining the mechanistic underpinnings of resistance is crucial for improving therapeutic response. This manuscript details the protocol to generate taxane-resistant cell models of prostate cancer (PC) to help dissecting the pathways involved in progression to Docetaxel resistance in PC patients.

Microtubule targeting agents (MTAs) are a mainstay in the treatment of a wide range of tumors. However, acquired resistance to chemotherapeutic drugs is a ...

Establishment of Gastric Cancer Patient-derived Xenograft Models and Primary Cell Lines

The use of preclinical models to advance our understanding of tumor biology and investigate the efficacy of therapeutic agents is key to cancer research. Although there are many established gastric cancer cell lines and many conventional transgenic mouse models for preclinical research, the disadvantages of these in vitro and in vivo models limit their applications. Because the characteristics of these models have changed in culture, they no longer model tumor heterogeneity, and their responses have not been able to predict responses in humans. Thus, alternative models that better represent tumor heterogeneity are being developed. Patient-derived xenograft (PDX) models preserve the histologic appearance of cancer cells, retain intratumoral heterogeneity, and better reflect the relevant human components of the tumor microenvironment. However, it usually takes 4-8 months to develop a PDX model, which is longer than the expected survival of many gastric patients. For this reason, establishing primary cancer cell lines may be an effective complementary method for drug response studies. The current protocol describes methods to establish PDX models and primary cancer cell lines from surgical gastric cancer samples. These methods provide a useful tool for drug development and cancer biology research.

The current protocol describes methods to establish patient-derived xenograft (PDX) models and primary cancer cell lines from surgical gastric cancer samples. The methods provide a useful tool for drug development and cancer biology research.

The use of preclinical models to advance our understanding of tumor biology and investigate the efficacy of therapeutic agents is key to cancer research. ...

In Vitro Assay to Study Tumor-macrophage Interaction

Tumor associated macrophages (TAMs) account for a large percentage of cells in the tumor mass for different types of cancers. Glioblastoma (GBM), a malignant brain tumor with no cure, has up to a half the tumor mass TAMs. TAMs can be pro-tumoral or anti-tumoral, depending on the activation of specific genes in the cells. Genetic mutations in the tumors, through regulating cytokine expression, can affect recruitment of TAMs to the tumor microenvironment. Here, we describe a quantitative cell-based assay to assess macrophage recruitment by the conditioned medium from the tumor cells. This assay uses the human macrophage cell line MV-4-11 to study macrophage attraction by the conditioned medium from glioblastoma, allowing for high reproducibility and low variability. Data generated with this assay can contribute to a better understanding of the interaction between the tumor and the tumor microenvironment. Similar assay can be used to assess interaction between the tumor cells and other immune cells, including T cells and natural killer (NK) cells.

This article represents a useful in vitro assay to evaluate the capability of conditioned medium from tumor cells to attract macrophages.

Tumor associated macrophages (TAMs) account for a large percentage of cells in the tumor mass for different types of cancers. Glioblastoma (GBM), a ...

In Vitro Evaluation of Oncogenic Transformation in Human Mammary Epithelial Cells

Tumorigenesis is a multi-step process in which cells acquire capabilities&#160;that allow their growth, survival, and dissemination under hostile conditions. Different tests seek to identify and quantify these hallmarks of cancerous cells; however, they often focus on a single aspect of cellular transformation and, in fact, multiple tests are required for their proper characterization. The purpose of this work is to provide researchers with a set of tools to assess cellular transformation in vitro from a broad perspective, thereby making it possible to draw sound conclusions.
A sustained proliferative signaling activation is the major feature of tumoral tissues and can be easily monitored under in vitro conditions by calculating the number of population doublings achieved over time. Besides, the growth of cells in 3D cultures allows their interaction with surrounding cells, resembling what occurs in vivo. This enables the evaluation of cellular aggregation and, together with immunofluorescent labeling of distinctive cellular markers, to obtain information on another relevant feature of tumoral transformation: the loss of proper organization. Another remarkable characteristic of transformed cells is their capacity to grow without attachment to other cells and to the extracellular matrix, which can be evaluated with the anchorage assay.
Detailed experimental procedures to evaluate cell growth rate, to perform immunofluorescent labeling of cell lineage markers in 3D cultures, and to test anchorage-independent cell growth in soft agar are provided. These methodologies are optimized for Breast Primary Epithelial Cells (BPEC) due to its relevance in breast cancer; however, procedures can be applied to other cell types after some adjustments.

This protocol provides experimental in vitro tools to evaluate the transformation of human mammary cells. Detailed steps to follow-up cell proliferation rate, anchorage-independent growth capacity, and distribution of cell lineages in 3D cultures with basement membrane matrix are described.

Tumorigenesis is a multi-step process in which cells acquire capabilities&#160;that allow their growth, survival, and dissemination under hostile ...

Patient-Derived Tumor Explants As a "Live" Preclinical Platform for Predicting Drug Resistance in Patients

An understanding of drug resistance and the development of novel strategies to sensitize highly resistant cancers rely on the availability of suitable preclinical models that can accurately predict patient responses. One of the disadvantages of existing preclinical models is the inability to contextually preserve the human tumor microenvironment (TME) and accurately represent intratumoral heterogeneity, thus limiting the clinical translation of data. By contrast, by representing the culture of live fragments of human tumors, the patient-derived explant (PDE) platform allows drug responses to be examined in a three-dimensional (3D) context that mirrors the pathological and architectural features of the original tumors as closely as possible. Previous reports with PDEs have documented the ability of the platform to distinguish chemosensitive from chemoresistant tumors, and it has been shown that this segregation is predictive of patient responses to the same chemotherapies. Simultaneously, PDEs allow the opportunity to interrogate molecular, genetic, and histological features of tumors that predict drug responses, thereby identifying biomarkers for patient stratification as well as novel interventional approaches to sensitize resistant tumors. This paper reports PDE methodology in detail, from collection of patient samples through to endpoint analysis. It provides a detailed description of explant derivation and culture methods, highlighting bespoke conditions for particular tumors, where appropriate. For endpoint analysis, there is a focus on multiplexed immunofluorescence and multispectral imaging for the spatial profiling of key biomarkers within both tumoral and stromal regions. By combining these methods, it is possible to generate quantitative and qualitative drug response data that can be related to various clinicopathological parameters and thus potentially be used for biomarker identification.

Patient-Derived Tumor Explants As a &quot;Live&quot; Preclinical Platform for Predicting Drug Resistance in Patients

This paper describes methods for the generation, drug treatment, and analysis of patient-derived explants for assessing tumor drug responses in a live, patient-relevant, preclinical model system.

An understanding of drug resistance and the development of novel strategies to sensitize highly resistant cancers rely on the availability of suitable ...

Pooled shRNA Library Screening to Identify Factors that Modulate a Drug Resistance Phenotype

Understanding clinically relevant driver mechanisms of acquired chemo-resistance is crucial for elucidating ways to circumvent resistance and improve survival in patients with acute myeloid leukemia (AML). A small fraction of leukemic cells that survive chemotherapy have a poised epigenetic state to tolerate chemotherapeutic insult. Further exposure to chemotherapy allows these drug persister cells to attain a fixed epigenetic state, which leads to altered gene expression, resulting in the proliferation of these drug-resistant populations and eventually relapse or refractory disease. Therefore, identifying epigenetic modulations that necessitate the survival of drug-resistant leukemic cells is critical. We detail a protocol to identify epigenetic modulators that mediate resistance to the nucleoside analog cytarabine (AraC) using pooled shRNA library screening in an acquired cytarabine-resistant AML cell line. The library consists of 5,485 shRNA constructs targeting 407 human epigenetic factors, which allows high-throughput epigenetic factor screening.

High-throughput RNA interference (RNAi) screening using a pool of lentiviral shRNAs can be a tool to detect therapeutically relevant synthetic lethal targets in malignancies. We provide a pooled shRNA screening approach to investigate the epigenetic effectors in acute myeloid leukemia (AML).

Understanding clinically relevant driver mechanisms of acquired chemo-resistance is crucial for elucidating ways to circumvent resistance and improve ...

Quantifying Telomere Length Using Non-Radioactive Chemiluminescence Detection

Optimization of Performance Parameters of the TAGGG Telomere Length Assay

Telomeres are repetitive sequences which are present at chromosomal ends; their shortening is a characteristic feature of human somatic cells. Shortening occurs due to a problem with end replication and the absence of the telomerase enzyme, which is responsible for maintaining telomere length. Interestingly, telomeres also shorten in response to various internal physiological processes, like oxidative stress and inflammation, which may be impacted due to extracellular agents like pollutants, infectious agents, nutrients, or radiation. Thus, telomere length serves as an excellent biomarker of aging and various physiological health parameters. The TAGGG telomere length assay kit is used to quantify average telomere lengths using the telomere restriction fragment (TRF) assay and is highly reproducible. However, it is an expensive method, and because of this, it is not employed routinely for large sample numbers. Here, we describe a detailed protocol for an optimized and cost-effective measurement of telomere length using Southern blots or TRF analysis and non-radioactive chemiluminescence-based detection.

Author Spotlight: Optimization of Performance Parameters of the TAGGG Telomere Length Assay  

Here, we describe in detail the protocol for quantifying telomere length using non-radioactive chemiluminescent detection, with a focus on the optimization of various performance parameters of the TAGGG telomere length assay kit, such as buffer quantities and probe concentrations.

Telomeres are repetitive sequences which are present at chromosomal ends; their shortening is a characteristic feature of human somatic cells. Shortening ...

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Ovarian Cancer Patient-Derived Organoid Models for Pre-Clinical Drug Testing

Ovarian cancer is a fatal gynecologic cancer and the fifth leading cause of cancer death among women in the United States. Developing new drug treatments is crucial to advancing healthcare and improving patient outcomes. Organoids are in-vitro three-dimensional multicellular miniature organs. Patient-derived organoid (PDO) models of ovarian cancer may be optimal for drug screening because they more accurately recapitulate tissues of interest than two-dimensional cell culture models and are inexpensive compared to patient-derived xenografts. In addition, ovarian cancer PDOs mimic the variable tumor microenvironment and genetic background typically observed in ovarian cancer. Here, a method is described that can be used to test conventional and novel drugs on PDOs derived from ovarian cancer tissue and ascites. A luminescence-based adenosine triphosphate (ATP) assay is used to measure viability, growth rate, and drug sensitivity. Drug screens in PDOs can be completed in 7-10 days, depending on the rate of organoid formation and drug treatments.

We present a protocol that can be used to conduct therapeutic drug testing with patient-derived ovarian cancer organoids.

Ovarian cancer is a fatal gynecologic cancer and the fifth leading cause of cancer death among women in the United States. Developing new drug treatments ...