The authors would like to acknowledge our colleagues at H3 Biomedicine for their feedback during the manuscript preparation.

1. Assessing GI50 for Compound(s) of Interest
<ol>
	<li>Generate growth curve(s) for cell lines of interest to assess the appropriate seeding density. Plot the cell number at various time points following seeding (days 2, 4, 6 and 8) relative to day 0. This will provide a relative growth rate of the cell line of interest and should be used to assess the initial seeding density such that confluence is not reached in 96-well plates within 7 days.</li>
	<li>Once growth curves have been determined, seed appropriate number of cells in non-transparent, clear bottom 96-well plates in 100 &#181;l of cell media to assess GI50. Seed the number of cells based on the cell line used and determined in 1.1. Typically, seed 3x103 cells for fast-growing cell lines with a doubling-time of ~24 hr, e.g., HCT116.
	<ol>
		<li>Prepare an assay plate (day-1). For the assay plate, seed cells at desired density in 100 &#181;l of culture media in wells shaded in blue (Figure 1). Wells highlighted in white contain only media.</li>
		<li>Prepare a control plate (day-1). For the control plate, seed the same density of cells as in step 1.2.1 in 100 &#181;l of culture media in a separate 96-well plate. This &#39;Day 0&#39; reading will help in interpreting the cytostatic/cytotoxic nature of the compounds being analyzed (Figure 2).</li>
	</ol>
	</li>
	<li>The next day (day 0), read the control plate. Equilibrate luminescent cell viability reagent (cellTiter-Glo substrate mixed with buffer according to manufacturer&#39;s instructions) to RT and mix gently by inverting contents to obtain a homogenous solution. Add 80 &#181;l of reagent to the 100 &#181;l of cell/media mix and shake contents for 30 min&#160;to induce cell lysis.
	<ol>
		<li>Record luminescence with a luminometer set to an exposure of 0.1-1.0 sec and detection wavelength of 560 nm.</li>
	</ol>
	</li>
	<li>Add test compounds (compounds of interest) to assay plates (day 0). Make a 1:4 serial dilution of compounds in DMSO at 200x final concentration for a total of 10 concentrations (9 dilutions containing compound and one DMSO only, compound plate). As an initial starting point, aim for a 200x lowest dose of 0.03 &#181;M and a top dose of 2,000 &#181;M (final volume 200 &#181;l).</li>
	<li>Add serially diluted compounds to medium to make a compound-medium mix at 10x final concentration (final volume 100 &#181;l, intermediate plate). Store the compound plate at -20 &#176;C for use on day 3 of the assay. Add 10 &#181;l of compound-medium mix to cells in triplicates such that the highest dose is 10 &#181;M (e.g., rows B, C and D, day 0) (Figure 1). Incubate assay plates for 3 days at 37 &#176;C. Discard intermediate plate(s) after use.</li>
	<li>On day 3, prepare a 400 &#181;l 1x compound-medium mix using the compound plates prepared in 1.4. Invert assay plates to remove media and pat dry on autoclaved paper towels 2-3x to remove residual media. Add compound/media (100 &#181;l/well) to wells shaded in blue and add 100 &#181;l of media to perimeter wells to prevent evaporation (Figure 1). Re-incubate the assay plates at 37 &#176;C for an additional 3 days.</li>
	<li>On day 6, assess relative number of viable cells by performing a luminescence reading as described in step 1.3.</li>
	<li>Use the day 0 reading to assess the static/toxic nature of compounds. Toxic agents induce apoptosis whereas cytostatic agents induce cell cycle arrest. If compound is toxic (day 6 reading is below day 0 reading), consider choosing GI50, and GI50 x 5 concentrations for resistance assays. However, if the compound induces stasis (equal to or higher than d0 reading), consider GI100 and GI100x5 for resistance assays (Figure 2).</li>
</ol>
2. Setting up Drug Resistance Assays
<ol>
	<li>If working with a cytostatic agent, seed cells at a confluence of 30-40% in 150 mm2 tissue culture dishes (volume= 30 ml) for the resistance assay. If working with a toxic agent, seed at a 70-80% confluence.</li>
	<li>For cell lines which harbor an intact mismatch repair (MMR) mechanism, incubate cells from step 2.1 O/N at 37 &#176;C with the carcinogen N-ethyl-N-nitrosourea (ENU). Treat cells with 30 &#181;l of a stock solution of 50 mg/ml ENU (final concentration 50 &#181;g/ml) to enhance genomic instability. For cell lines that have a defective MMR (Tables 2 and 3), treatment with ENU or other carcinogens may not be necessary.
	<ol>
		<li>For other cell lines, determine MMR-deficiency by NCI criteria using microsatellite instability12, or based on the published characterization using microsatellite instability assays or genomic/epigenomic profiling of MMR genes13-15.</li>
	</ol>
	</li>
	<li>Treat cells with the test compound(s) of interest at the concentration determined in step 1.8. For highly toxic compounds which cause cell death in a typical three-day viability assay10, start with a low dose treatment (i.e., GI50) and incrementally increase the concentration (multiples of GI50) of compound every 2-3 weeks until robust resistance is observed. Replenish media and compound every 3 d.</li>
	<li>Once the selection has been initiated, change media/compound mix every 3-4 days until resistant clones emerge. Resistance by definition emerges when treated cells show greater growth/viability during drug treatment relative to acute treatment of DMSO-treated control cells.</li>
</ol>
3. Isolating Single Cell Clones
<ol>
	<li>Examine culture dish using phase-contrast microscopy (magnification 40x) for viable clusters of cells.</li>
	<li>Mark satisfactory clones at the bottom of the dish with a marking pen. Pick clones that are of average size (larger colonies may originate from multiple cells) and well isolated from other colonies. Pick clones using one of two approaches outlined in step 3.3.</li>
	<li>Approach 1: Using a Pipettor (Figure 3)
	<ol>
		<li>Remove growth media and rinse with 1x PBS to remove any floating cells.</li>
		<li>Use black marks as guide to &#39;picking&#39; clones with pipet tip (attached to pipettor, p200 preferably).</li>
		<li>Transfer clones into 48-well plates with 200 &#956;l fresh media (with half compound concentration to allow optimal recovery of cells).</li>
		<li>Allow cells to recover for 2-3 days before adding 200 &#956;l fresh media/ideal compound concentration.</li>
		<li>Continue to change media/compound every 3-4 days and continue to expand clones.</li>
	</ol>
	</li>
	<li>Approach 2: Using Cloning Discs (Figure 3)
	<ol>
		<li>Mark clones as described in step 3.2.</li>
		<li>Place 3 mm cloning discs in a 10 cm tissue culture dish containing 5 ml of 0.25% trypsin-EDTA for 2 min.</li>
		<li>Aspirate media from dish containing resistant clones and overlay clones with trypsin soaked cloning discs using sterile disposable forceps.</li>
		<li>Leave for 1-2 min, depending on how easily clones lift off the plates, in a 37 &#176;C incubator.</li>
		<li>Pick up cloning discs using sterile forceps and transfer into 48-well plates with 200 &#956;l fresh media and half concentration of compound.</li>
		<li>Pipette up and down gently to dislodge the cells from cloning discs and incubate O/N at 37 &#176;C (leave cloning discs in wells).</li>
		<li>The next morning, remove the cloning discs from 48-well plates and replenish wells with 200 &#956;l fresh media/compound (half ideal compound concentration).</li>
		<li>Three days later, replenish media with the ideal concentration of compound.</li>
		<li>Continue to change the media/compound every 3-4 days and continue to expand clones.</li>
	</ol>
	</li>
</ol>
4. Assessing Degree of Resistance of Isolated Clones
<ol>
	<li>Once 10-20 colonies are expanded, generate GI50 curves as described in step 1 to assess the degree of resistance.</li>
	<li>Always include control populations treated with DMSO during the selection process. It is very likely that the spectrum of resistance is going to be large (some showing partial whereas others showing full resistance). Collect a few clones from each of these classes as the mechanism of resistance may vary between the two groups.</li>
</ol>
5. Next-generation Sequencing
<ol>
	<li>Spin down 2 million cells (control and resistant clones) (500 x g for 5 min) in 15 ml conical vials for both gDNA and RNA collection (therefore 2 x 2 million).</li>
	<li>Wash twice with 1x PBS and freeze pellets in -80 &#176;C until ready for isolation.</li>
	<li>Use a commercial extraction kit to isolate RNA or gDNA according to manufacturer&#39;s protocol.</li>
	<li>Submit samples for next-generation sequencing using vendor&#39;s protocol10.</li>
</ol>
6. Bioinformatics analysis of samples (whole-exome sequencing)
<ol>
	<li>Preprocess sequence data according to a best practice DNA-seq pipeline16.
	<ol>
		<li>Map all reads to reference human genome GRCh37 using BWA17. Convert uncompressed SAM formatted alignments to compressed BAM format with Samtools18. Sort alignments by coordinate with Samtools. Add read groups using Picard. (For specific BWA, Samtools and Picard commands, see Supplementary Text 1, lines 1-4)</li>
		<li>Mark all duplicates reads using the MarkDuplicates command from the Picard tool set19. Index this file with Samtools. (Supplementary Text 1, lines 5-6)</li>
		<li>To minimize mismatched bases across all reads, realign reads locally at regions harboring small insertions or deletions using an indel realigner20. (Supplementary Text 1, lines 7-8)</li>
		<li>To further improve the accuracy of variant calling, empirically recalibrate base quality scores using a base quality score recalibration tool. The base quality calibration tool should not only correct initial quality score, but also take into account covariation of several features, including read group, machine cycle, base position, and dinucleotide context (previous+current bases). Generate recalibrated BAM with Picard PrintReads. (For specific commands for this step, see Supplementary Text 1, lines 9-10)</li>
		<li>Repeat steps 6.1.1 through 6.1.4 (Supplementary Text 1, lines 1-10) for each sample sequenced.</li>
	</ol>
	</li>
	<li>Identify single nucleotide variation (SNV) in each clone using a paired variant calling tool19, 21-23. For each clone sequenced, run paired variant calling using the parental clone as the matched &#8220;normal&#8221;. (Supplementary Text 1, line 11)</li>
	<li>Filter recurrent, high-quality variants and prepare for annotation with a variant annotation tool24. Deprioritize variants not common to all resistant clones. (See R script in Supplementary Text 2)</li>
	<li>Annotate variants with an annotation tool25, 26. Many variant annotation tools have an accompanying web app allowing data to be uploaded and processed by a remote server.</li>
	<li>Use a functional impact prediction tool27-29 to prioritize those variants predicted of having high functional impact. Like variant annotation, several tools are available for functional impact prediction through a web interface.</li>
</ol>

<ol>
	<li>Sellers, W. R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+blueprint+for+advancing+genetics-based+cancer+therapy.">A blueprint for advancing genetics-based cancer therapy.</a> Cell. 147 (1), 26-31 (2011).</li><li>Housman, G., 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=Drug+resistance+in+cancer:+an+overview.">Drug resistance in cancer: an overview.</a> Cancers (Basel). 6 (3), 1769-1792 (2014).</li><li>Balbas, M. D., 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=Overcoming+mutation-based+resistance+to+antiandrogens+with+rational+drug+design.">Overcoming mutation-based resistance to antiandrogens with rational drug design.</a> Elife. 2, e00499 (2013).</li><li>Joseph, J. D., 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=A+clinically+relevant+androgen+receptor+mutation+confers+resistance+to+second-generation+antiandrogens+enzalutamide+and+ARN-509.">A clinically relevant androgen receptor mutation confers resistance to second-generation antiandrogens enzalutamide and ARN-509.</a> Cancer Discov. 3 (9), 1020-1029 (2013).</li><li>Korpal, M., 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=An+F876L+mutation+in+androgen+receptor+confers+genetic+and+phenotypic+resistance+to+MDV3100+(enzalutamide).">An F876L mutation in androgen receptor confers genetic and phenotypic resistance to MDV3100 (enzalutamide).</a> Cancer Discov. 3 (9), 1030-1043 (2013).</li><li>Lawrence, M. 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=Mutational+heterogeneity+in+cancer+and+the+search+for+new+cancer-associated+genes.">Mutational heterogeneity in cancer and the search for new cancer-associated genes.</a> Nature. 499 (7457), 214-218 (2013).</li><li>Gerlinger, M., 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=Intratumor+heterogeneity+and+branched+evolution+revealed+by+multiregion+sequencing.">Intratumor heterogeneity and branched evolution revealed by multiregion sequencing.</a> N Engl J Med. 366 (10), 883-892 (2012).</li><li>Whittaker, S. R., 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=A+genome-scale+RNA+interference+screen+implicates+NF1+loss+in+resistance+to+RAF+inhibition.">A genome-scale RNA interference screen implicates NF1 loss in resistance to RAF inhibition.</a> Cancer Discov. 3 (3), 350-362 (2013).</li><li>Wacker, S. A., Houghtaling, B. R., Elemento, O., Kapoor, T. M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Using+transcriptome+sequencing+to+identify+mechanisms+of+drug+action+and+resistance.">Using transcriptome sequencing to identify mechanisms of drug action and resistance.</a> Nat Chem Biol. 8 (3), 235-237 (2012).</li><li>Adams, D. 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=NAMPT+Is+the+Cellular+Target+of+STF-31-Like+Small-Molecule+Probes.">NAMPT Is the Cellular Target of STF-31-Like Small-Molecule Probes.</a> ACS Chem Biol. 9 (10), 2247-2254 (2014).</li><li>Bhang, H. E., 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=Studying+clonal+dynamics+in+response+to+cancer+therapy+using+high-complexity+barcoding.">Studying clonal dynamics in response to cancer therapy using high-complexity barcoding.</a> Nat Med. 21 (5), 440-448 (2015).</li><li>Boland, C. R., 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=A+National+Cancer+Institute+Workshop+on+Microsatellite+Instability+for+cancer+detection+and+familial+predisposition:+development+of+international+criteria+for+the+determination+of+microsatellite+instability+in+colorectal+cancer.">A National Cancer Institute Workshop on Microsatellite Instability for cancer detection and familial predisposition: development of international criteria for the determination of microsatellite instability in colorectal cancer.</a> Cancer Res. 58 (22), 5248-5257 (1998).</li><li>Heinen, C. D., Richardson, D., White, R., Groden, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Microsatellite+instability+in+colorectal+adenocarcinoma+cell+lines+that+have+full-length+adenomatous+polyposis+coli+protein.">Microsatellite instability in colorectal adenocarcinoma cell lines that have full-length adenomatous polyposis coli protein.</a> Cancer Res. 55 (21), 4797-4799 (1995).</li><li>Yamada, N. A., Castro, A., Farber, R. A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Variation+in+the+extent+of+microsatellite+instability+in+human+cell+lines+with+defects+in+different+mismatch+repair+genes.">Variation in the extent of microsatellite instability in human cell lines with defects in different mismatch repair genes.</a> Mutagenesis. 18 (3), 277-282 (2003).</li><li>Ahmed, D., 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=Epigenetic+and+genetic+features+of+24+colon+cancer+cell+lines.">Epigenetic and genetic features of 24 colon cancer cell lines.</a> Oncogenesis. 2, e71 (2013).</li><li>. GATK Available from: <a target="_blank" href="https://www.broadinstitute.org/gatk/guide">https://www.broadinstitute.org/gatk/guide</a> (2015)</li><li>Li, H., Durbin, R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Fast+and+accurate+long-read+alignment+with+Burrows-Wheeler+transform.">Fast and accurate long-read alignment with Burrows-Wheeler transform.</a> Bioinformatics. 26 (5), 589-595 (2010).</li><li>Li, 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=The+Sequence+Alignment/Map+format+and+SAMtools.">The Sequence Alignment/Map format and SAMtools.</a> Bioinformatics. 25 (16), 2078-2079 (2009).</li><li>McKenna, 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=The+Genome+Analysis+Toolkit:+a+MapReduce+framework+for+analyzing+next-generation+DNA+sequencing+data.">The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data.</a> Genome Res. 20 (9), 1297-1303 (2010).</li><li>Cibulskis, K., 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=Sensitive+detection+of+somatic+point+mutations+in+impure+and+heterogeneous+cancer+samples.">Sensitive detection of somatic point mutations in impure and heterogeneous cancer samples.</a> Nat Biotechnol. 31 (3), 213-219 (2013).</li><li>Larson, D. E., 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=SomaticSniper:+identification+of+somatic+point+mutations+in+whole+genome+sequencing+data.">SomaticSniper: identification of somatic point mutations in whole genome sequencing data.</a> Bioinformatics. 28 (3), 311-317 (2012).</li><li>Koboldt, D. 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=VarScan+2:+somatic+mutation+and+copy+number+alteration+discovery+in+cancer+by+exome+sequencing.">VarScan 2: somatic mutation and copy number alteration discovery in cancer by exome sequencing.</a> Genome Res. 22 (3), 568-576 (2012).</li><li>. Oncotator Available from: <a target="_blank" href="https://www.broadinstitute.org/oncotator">https://www.broadinstitute.org/oncotator</a> (2015)</li><li>Wang, K., Li, M., Hakonarson, H. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=ANNOVAR:+functional+annotation+of+genetic+variants+from+high-throughput+sequencing+data.">ANNOVAR: functional annotation of genetic variants from high-throughput sequencing data.</a> Nucleic Acids Res. 38 (16), e164 (2010).</li><li>Cingolani, P., 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=A+program+for+annotating+and+predicting+the+effects+of+single+nucleotide+polymorphisms,+SnpEff:+SNPs+in+the+genome+of+Drosophila+melanogaster+strain+w1118;+iso-2;+iso-3.">A program for annotating and predicting the effects of single nucleotide polymorphisms, SnpEff: SNPs in the genome of Drosophila melanogaster strain w1118; iso-2; iso-3.</a> Fly. 6 (2), 80-92 (2012).</li><li>Reva, B., Antipin, Y., Sander, C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Predicting+the+functional+impact+of+protein+mutations:+application+to+cancer+genomics.">Predicting the functional impact of protein mutations: application to cancer genomics.</a> Nucleic Acids Res. 39 (17), (2011).</li><li>Ng, P. C., Henikoff, S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=SIFT:+Predicting+amino+acid+changes+that+affect+protein+function.">SIFT: Predicting amino acid changes that affect protein function.</a> Nucleic Acids Res. 31 (13), 3812-3814 (2003).</li><li>Adzhubei, I., Jordan, D. M., Sunyaev, S. R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Predicting+functional+effect+of+human+missense+mutations+using+PolyPhen-2.">Predicting functional effect of human missense mutations using PolyPhen-2.</a> Curr Protoc Hum Genet. Chapter 7, Unit 7.20 (2013).</li><li>Barretina, 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=The+Cancer+Cell+Line+Encyclopedia+enables+predictive+modelling+of+anticancer+drug+sensitivity.">The Cancer Cell Line Encyclopedia enables predictive modelling of anticancer drug sensitivity.</a> Nature. 483 (7391), 603-607 (2012).</li><li>Abaan, O. D., 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=The+exomes+of+the+NCI-60+panel:+a+genomic+resource+for+cancer+biology+and+systems+pharmacology.">The exomes of the NCI-60 panel: a genomic resource for cancer biology and systems pharmacology.</a> Cancer Res. 73 (14), 4372-4382 (2013).</li><li>Forbes, 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=Cosmic+2005.">Cosmic 2005.</a> Br J Cancer. 94 (2), 318-322 (2006).</li><li>Shah, S. P., 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=The+clonal+and+mutational+evolution+spectrum+of+primary+triple-negative+breast+cancers.">The clonal and mutational evolution spectrum of primary triple-negative breast cancers.</a> Nature. 486 (7403), 395-399 (2012).</li><li>Behjati, 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=Recurrent+PTPRB+and+PLCG1+mutations+in+angiosarcoma.">Recurrent PTPRB and PLCG1 mutations in angiosarcoma.</a> Nat Genet. 46 (4), 376-379 (2014).</li><li>de las Alas, M. M., Aebi, S., Fink, D., Howell, S. B., Los, G. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Loss+of+DNA+mismatch+repair:+effects+on+the+rate+of+mutation+to+drug+resistance.">Loss of DNA mismatch repair: effects on the rate of mutation to drug resistance.</a> J Natl Cancer Inst. 89 (20), 1537-1541 (1997).</li><li>Kotani, 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=A+novel+mutation+in+the+miR-128b+gene+reduces+miRNA+processing+and+leads+to+glucocorticoid+resistance+of+MLL-AF4+acute+lymphocytic+leukemia+cells.">A novel mutation in the miR-128b gene reduces miRNA processing and leads to glucocorticoid resistance of MLL-AF4 acute lymphocytic leukemia cells.</a> Cell Cycle. 9 (6), 1037-1042 (2010).</li><li>Straussman, R., 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=Tumour+micro-environment+elicits+innate+resistance+to+RAF+inhibitors+through+HGF+secretion.">Tumour micro-environment elicits innate resistance to RAF inhibitors through HGF secretion.</a> Nature. 487 (7408), 500-504 (2012).</li><li>Yang, X., Sexauer, A., Levis, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Bone+marrow+stroma-mediated+resistance+to+FLT3+inhibitors+in+FLT3-ITD+AML+is+mediated+by+persistent+activation+of+extracellular+regulated+kinase.">Bone marrow stroma-mediated resistance to FLT3 inhibitors in FLT3-ITD AML is mediated by persistent activation of extracellular regulated kinase.</a> Br J Haematol. 164 (1), 61-72 (2014).</li><li>Arora, V. K., 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=Glucocorticoid+receptor+confers+resistance+to+antiandrogens+by+bypassing+androgen+receptor+blockade.">Glucocorticoid receptor confers resistance to antiandrogens by bypassing androgen receptor blockade.</a> Cell. 155 (6), 1309-1322 (2013).</li><li>Schenone, M., Dancik, V., Wagner, B. K., Clemons, P. A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Target+identification+and+mechanism+of+action+in+chemical+biology+and+drug+discovery.">Target identification and mechanism of action in chemical biology and drug discovery.</a> Nat Chem Biol. 9 (4), 232-240 (2013).</li></ol>

Publication fees for this article are paid by H3 Biomedicine.

Selection of cell line(s): Characterization of genetic state and genomic instability 
Undoubtedly, the single most critical factor in successfully uncovering clinically relevant resistance mechanisms is the initial cell line selection. Two factors should be considered. First, aim to select cell line(s) of the same lineage/subtype harboring the defining genetic traits of the disease (e.g., BRAFV600E in melanoma). Interrogation of publically available transcriptomic and mutation data for both cell lines30-32 and primary/metastasis tumors for a variety of indications33,34 will facilitate the selection process. Although identification of cell lines with clinically relevant genetic alterations is ideal, in some instances this may not be possible due to lack of available cell lines or feasible due to factors such as difficulty and length of the screening process.
With regards to the aforementioned point, a second factor to consider then during cell line selection is the ease or feasibility of resistance screening using the desired line. For example, factors such as proliferation and intrinsic mutation rates can greatly impact on the speed of discovery. To this end, cell lines can be utilized with faster growth kinetics and deficient in DNA MMR mechanisms in the hopes that emergence of spontaneous resistance can be accelerated35. Based on the COSMIC database, several candidate cell lines exist with deficiency in one of two frequently mutated MMR genes, MSH2 or MLH1 (Tables 2 and 3). Alternatively, if cell lines of interest do not exist bearing defects in MMR, acute treatment with physical or DNA reactive chemical mutagens such as the alkylating agent N-ethyl-N-nitrosourea (ENU) can be used to enhance genomic instability. Although both approaches may significantly shorten the time to attain resistant clones and follow-up sequencing, stringent functional testing should be performed on candidate genes as a greater number of non-functional, passenger mutations are likely to emerge. SNVs can be rank ordered to maximize chances for identifying functionally relevant mutations. Firstly, choosing those mutations that are recurrent in independent clones will increase the likelihood these mutations are drivers of resistance. In the event recurrent mutations are not identified, focusing on SNVs that fit the mechanism of action of the drug (for example, the drug target or a known downstream effector of the drug target) may be meaningful. Ultimately, the gold standard is always experimental evaluation of resistance-conferring activity of the candidate SNVs by ectopic cDNA expression in drug-sensitive parental cells.
DNA vs RNA sequencing
Once resistant clones have been generated, DNA and/or RNA can be sequenced depending on the need. DNA sequencing, either exome or whole-genome sequencing, will allow identification of germline and somatic variants, such as SNPs, indels and copy number variants. Whereas the more cost-friendly exome sequencing focuses on generating reads from known coding regions, whole-genome sequencing will generate sequencing data for the entire genome which may facilitate identification of mutations in non-coding elements such as enhancers or miRNAs36. However, since gene expression data is not gauged during DNA sequencing, it is difficult to predict which mutation(s) is likely to be a functional driver. In this regard, sequencing of RNA, although more costly, offers this advantage. The fact that mutation calling is only performed on expressed RNA species enhances the likelihood that the mutation of interest may be a functional driver. In addition to allowing a more focused survey of mutations for follow-up functional testing, RNA sequencing also offers the added advantage of being able to identify gene expression alterations, alternative splicing and novel chimeric RNA species, including gene fusions that may also serve as potent drivers of resistance.
Bioinformatics pipeline
Example commands are provided for illustration purposes, but more detailed documentation and tutorials are available from the Broad Institute16 and should be read thoroughly before beginning NGS analysis. The following commands are designed for a UNIX shell environment on a system on which all tools and reference data have been pre-installed. These commands also assume FASTQ files containing paired-end sequence reads from two samples, named &#34;parental&#34; and &#34;resistant,&#34; have been received from the vendor and placed in the &#34;data&#34; directory. In most cases these commands should be adapted or optimized for a specific application using additional command-line arguments (e.g., adding &#34;-t 8&#34; to the bwa command allows multithreaded operation across 8 CPU cores). Read groups (which assign alignments to biological samples), must often be added to BAM files even if there is only one sample per bam file, in order to comply with file format requirements for certain tools. Read group parameters RGID, RGSM, RGPL, RGPU, and RGLB can be arbitrary strings describing the sample name, sequencing platform, and library strategy.
In vitro vs in vivo assays
Although several resistance mechanisms identified by in vitro selection have been verified to be clinically relevant, there exists a possibility that the mechanisms may not serve as relevant or predominant mechanisms of clinical resistance. One reason for this may include an essential role for the micro-environment in driving resistance to therapy, a component that is devoid in the experimental protocol/setup discussed thus far. Indeed, several studies have shown that anti-cancer agents that are capable of killing tumor cells are rendered ineffective when the tumor cells are cultured in the presence of stromal cells implying innate mechanisms of resistance conferred by the stroma37,38. To identify such stroma-induced acquired resistance mechanisms, one may consider performing in vitro co-culture or in vivo tumor resistance assays. Since the former assay is quite complex, many have resorted to generating drug-resistant tumor xenografts to address the potential role of the stroma in driving resistance. Such studies have uncovered both identical5 and unique39 mechanisms of resistance relative to in vitro selection, implying that the stroma may indeed play a role in the latter. However, one must be mindful of the length of time it may take to generate such resistant tumors and the complexity of the follow-up genomic analysis-complexities due to the intra-tumoral molecular and cellular heterogeneity.
Target identification
In addition to uncovering drug resistance mechanisms, this NGS-based genomic profiling approach can also be applied to identify cellular targets of chemical probes. Historically, multiple unbiased methods have been used to identify the cellular mechanisms of action and targets of low-molecular weight chemicals with biological activities, including affinity purification coupled with quantitative proteomics, yeast genomic methods, RNAi screening, and computational inference approaches40. As an extension to elucidation of drug-resistance mechanisms using NGS-based genomic or transcriptomic profiling of phenotypically resistant cell populations, identification of unique recurrent single nucleotide variations (SNVs) or expression alterations that enable resistance can offer insights into functional cellular targets of compounds. This is based on the idea that a subset of resistance mechanisms observed may involve recurrent mutations in genes that encode the direct protein targets of the small molecule. Recently, several reports validated the utility of the approach, particularly by combining with other approaches including large-scale cancer cell line sensitivity profiling, to revealing the cellular targets of small-molecule probes9,10.

Detailed molecular characterization of tumor genomes by robust sequencing technologies and improved data analytical tools has led to the discovery of key genetic alterations in specific cancer types1,2. Development of targeted therapies aimed at these genetic lesions, such as HER2, BCR-ABL, EGFR and ALK, have significantly improved quality of life for patients1,2. However, despite the specificity of this approach, the clinical response to most single therapies has been sub-optimal as resistance ultimately emerges. Recently, significant progress has been made in understanding the molecular underpinnings of resistance to targeted therapeutics. Intriguingly, it is becoming evident that a prominent mechanism of resistance involves persistent target/pathway activity. As a case in point, androgen receptor (AR)-directed enzalutamide treatment of prostate cancer leads to enrichment of activating mutations in AR itself, maintaining AR-signaling output in the presence of the inhibitor3-5. This knowledge has led to an aggressive campaign to 1) develop third generation antagonists that can continue to suppress both WT and mutant-AR function in enzalutamide-resistant PCa3 and 2) identify potential downstream nodes of AR signaling that may be targeted for therapeutic intervention. Similarly, resistance to other classes of inhibitors such as those targeting EGFR, BRAF and ABL often lead to mutations that reactivate the original addicting kinase pathway1.
With the knowledge that resistance inevitably emerges in most patients, developing approaches to expeditiously bring these mechanisms to light will allow development of effective follow-up therapies. One approach that is being widely used is to analyze the genomics of clinical refractory tumors relative to treatment-na&#239;ve or -sensitive tumors to identify enrichments/depletions in genetic lesions that may be amenable to drug discovery. Despite its promise, there are two major liabilities of this approach that hamper quick discovery. Firstly, gaining timely access to tumor material for genomic interrogation may serve as a significant hurdle in moving from therapy to cure. Secondly, deconvolution of the myriad of genetic lesions in the resistant setting may be challenging since tumors can present significant intra-tumoral heterogeneity 6,7.
In light of these challenges, there has been increased reliance on preclinical discovery of resistance mechanisms. This approach may allow identification of prominent resistance mechanisms prior to the clinical trials1 that may guide alternative clinical management strategies in those patients that bear these mechanisms either prior to therapy or following onset of resistance.
One such preclinical discovery tool that is being widely used is to apply unbiased functional RNAi screens. For example, Whittaker and colleagues applied a genome-scale RNAi screen to identify that NF1 loss mediates resistance to RAF and MEK inhibitors through sustained MAPK pathway activation8. These findings were found to be clinically relevant as loss-of-function mutations in NF1 were observed in BRAF-mutant tumor cells that are intrinsically resistant to RAF inhibition and in melanoma tumors resistant to vemurafenib activation8. However, despite the success of this approach, many clinically relevant targets are often not identified, presumably due to the loss-of-function bias of this approach.
In contrast, a less biased tool for preclinical discovery of resistance mechanisms involves the generation of resistant cell lines through prolonged exposure to compound of interest coupled with NGS-based genomic or transcriptomic profiling. This approach has been successfully implemented by several groups to identify spontaneous recurrent single nucleotide variants or expression alterations that enable resistance5,9,10. For example, a recurrent F876L mutation in AR was recently discovered in resistant clones in vitro3-5 and in xenograft tumors in vivo5 prior to identification of this mutation in the clinic4. Very recently, Bhang and colleagues (2015)11 used ClonTracer in two clinically relevant models to show that majority of resistant clones that arise during prolonged drug exposure were part of a pre-existing subpopulation suggesting that most functionally relevant mutations are likely already pre-existing that become selected for during selection11.
In contrast to the genomic profiling of tumors discussed earlier, this approach benefits from less heterogeneity as &#39;homogenous&#39; resistant clones are used for analysis facilitating more accurate genetic dissection of potential drivers. Furthermore, excitingly, in addition to the potential for uncovering mechanisms of resistance, this method can also be applied to identify the cellular mechanisms of action and targets of bioactive small molecules for which this information is unknown10. Given the clear advantages and the multiple uses of this approach, here we present a protocol detailing successful implementation of such a preclinical screen to maximize the potential for clinically meaningful discoveries.

<table>
	<tbody>
		<tr>
			<td>48-well plates</td>
			<td>Fisher Scientific</td>
			<td>07-200-86&#160;</td>
			<td>Expanding clones</td>
		</tr>
		<tr>
			<td>96-well plates</td>
			<td>Fisher Scientific</td>
			<td>07-200-588</td>
			<td>Generating GI50 curves</td>
		</tr>
		<tr>
			<td>CellTiter-Glo</td>
			<td>Promega</td>
			<td>G7572</td>
			<td>Viability testing</td>
		</tr>
		<tr>
			<td>Cloning discs (3 mm)</td>
			<td>Sigma</td>
			<td>Z374431</td>
			<td>Picking clones</td>
		</tr>
		<tr>
			<td>Sterile forceps</td>
			<td>Unomedical</td>
			<td>DF8088S</td>
			<td>Picking clones</td>
		</tr>
		<tr>
			<td>RNeasy Plus RNA extraction kit</td>
			<td>Qiagen</td>
			<td>74134</td>
			<td>Isolating RNA</td>
		</tr>
		<tr>
			<td>Blood and Tissue DNeasy extraction kit</td>
			<td>Qiagen</td>
			<td>69581</td>
			<td>Isolating gDNA</td>
		</tr>
		<tr>
			<td>GATK</td>
			<td>The Broad Institute</td>
			<td></td>
			<td>Indel realigner</td>
		</tr>
		<tr>
			<td>MuTect</td>
			<td>The Broad Institute</td>
			<td></td>
			<td>Paired variant calling tool</td>
		</tr>
		<tr>
			<td>Oncotator</td>
			<td>The Broad Institute</td>
			<td></td>
			<td>Variant annotation tool</td>
		</tr>
		<tr>
			<td>MutationAccessor</td>
			<td>The Broad Institute</td>
			<td></td>
			<td>Functional impact prediction tool</td>
		</tr>
	</tbody>
</table>

implementation of in vitro drug resistance assays: maximizing the potential for uncovering clinically relevant resistance mechanisms

Although targeted therapies are initially effective, resistance inevitably emerges. Several methods, such as genetic analysis of resistant clinical specimens, have been applied to uncover these resistance mechanisms to facilitate follow-up care. Although these approaches have led to clinically relevant discoveries, difficulties in attaining the relevant patient material or in deconvoluting the genomic data collected from these specimens have severely hampered the path towards a cure. To this end, we here describe a tool for expeditious discovery that may guide improvement in first-line therapies and alternative clinical management strategies. By coupling preclinical in vitro or in vivo drug selection with next-generation sequencing, it is possible to identify genomic structural variations and/or gene expression alterations that may serve as functional drivers of resistance. This approach facilitates the spontaneous emergence of alterations, enhancing the probability that these mechanisms may be observed in the patients. In this protocol we provide guidelines to maximize the potential for uncovering single nucleotide variants that drive resistance using adherent lines.

To maximize the potential for discovering key functional drivers of resistance, select single cell clones for expansion, phenotypic testing and sequencing. As illustrated in Figure 4A, HCT116 cells treated for a prolonged period with cytotoxic compound #1 led to the spontaneous emergence of resistant clones that continued to grow during treatment (dashed black circles). These clones were picked using approach #1 highlighted in Figure 3 and subsequently expanded for phenotypic analysis. As shown in Figure 4B, resistant clones 1-3 all showed significant resistance to compound #1 and its close analog compound #2 (greater viability/growth), whereas all clones showed sensitivity to an unrelated cytotoxic compound velcade. Following confirmation of phenotypic resistance, gDNA was isolated and submitted for whole-exome sequencing analysis. Bioinformatics tools were applied to narrow in on those structural variants that are 1) recurrent and 2) have the potential for functional impact (Figure 5A). Structural variants that met these two criteria were confirmed by an independent sequencing tool. As illustrated in Figure 5B, a heterozygous missense mutation that was identified using whole-exome sequencing was confirmed using the Sanger sequencing method (upper panel, WT sequence; lower panel, mutant sequence). Following sequence confirmation, the parental cell line originally used for the resistance assay was genetically engineered to express the mutant cDNA to functionally confirm the role of the mutation. As illustrated in Figure 6, whereas overexpression of the WT cDNA failed to confer resistance, forced expression of the mutant cDNA significantly conferred phenotypic resistance to compound #1, confirming the functional role of this structural variation as a driver of resistance. All reagents used for this experiment are outlined in Table 1.
<img alt="Figure 1" src="/files/ftp_upload/52879/52879fig1.jpg" /> 
Figure 1. Layout of assay and control plates. Blue shade, compound treatment wells. White shade, medium only. Compounds are serially diluted 1:4 and are administered in triplicates (B-D or E-G). 2 compounds can be applied per plate.
<img alt="Figure 2" src="/files/ftp_upload/52879/52879fig2.jpg" /> 
Figure 2. Representative viability curves. Red dotted line represents the day 0 reading. x-axis indicates increasing doses of compound to the right and y-axis represents viability relative to DMSO control wells. GI100= dose used to achieve 100% growth inhibition; GI50= dose used to reduce viability to 50% of DMSO control.
<img alt="Figure 3" src="/files/ftp_upload/52879/52879fig3.jpg" /> 
Figure 3. Two approaches for picking resistant clones for expansion. Approach 1- use pipettor to pick up and transfer well-defined clones to 48-well plates. Approach 2- use trypsin-soaked cloning discs to lift clones and transfer to 48-well plates.
<img alt="Figure 4" src="/files/ftp_upload/52879/52879fig4.jpg" /> 
Figure 4. Confirmation of the resistance achieved for HCT116 clones to compound #1 in vitro.&#160;(A) Compound #1-resistant HCT116 clones emerged following continuous three-week selection. (B) Viability of control and resistant clones was tested after 72 hr treatment with various compounds. Compound #2 is a close analog of compound #1. Velcade was used as a control cytotoxic agent. Data are shown as average + standard deviation of three biological replicates.
<img alt="Figure 5" src="/files/ftp_upload/52879/52879fig5.jpg" /> 
Figure 5.&#160;Identification of a unique, recurrent mutation (single nucleotide variants, SNVs) in gene A in compound #1-resistant HCT116 clones.&#160;(A) Work flow to identify SNVs present in all resistant clones and predicted to have a high functional impact by MutationAssessor. (B) Confirmation of the mutation in gene A by Sanger Sequencing.
<img alt="Figure 6" src="/files/ftp_upload/52879/52879fig6.jpg" /> 
Figure 6. Re-expression of mutated gene A conferred resistance to compound #1 in vitro. Viability of the engineered HCT116 cell lines was tested after 72 hr treatment with compound #1. HCT116-WT or mutant cell lines are stably expressing WT or mutant cDNAs of gene A, respectively. Data are shown as average + standard deviation of three biological replicates.
<table>
	<tbody>
		<tr>
			<td>Sample Name</td>
			<td>Amino Acid</td>
			<td colspan="2">Primary Tissue</td>
			<td>Zygosity</td>
		</tr>
		<tr>
			<td>C-33-A</td>
			<td>p.E768fs*44</td>
			<td colspan="2">cervix</td>
			<td>Heterozygous</td>
		</tr>
		<tr>
			<td>C-33-A</td>
			<td>p.S860*</td>
			<td colspan="2">cervix</td>
			<td>Heterozygous</td>
		</tr>
		<tr>
			<td>CML-T1</td>
			<td>p.?</td>
			<td colspan="2">haematopoietic_and_lymphoid_tissue</td>
			<td>Homozygous</td>
		</tr>
		<tr>
			<td>CP66-MEL</td>
			<td>p.C822F</td>
			<td colspan="2">skin</td>
			<td>Heterozygous</td>
		</tr>
		<tr>
			<td>CTV-1</td>
			<td>p.0?</td>
			<td colspan="2">haematopoietic_and_lymphoid_tissue</td>
			<td>Homozygous</td>
		</tr>
		<tr>
			<td>EFO-27</td>
			<td>p.Q130fs*2</td>
			<td colspan="2">ovary</td>
			<td>Heterozygous</td>
		</tr>
		<tr>
			<td>EFO-27</td>
			<td>p.?</td>
			<td colspan="2">ovary</td>
			<td>Heterozygous</td>
		</tr>
		<tr>
			<td>HCC2218</td>
			<td>p.E467K</td>
			<td colspan="2">breast</td>
			<td>Heterozygous</td>
		</tr>
		<tr>
			<td>J-RT3-T3-5</td>
			<td>p.R711*</td>
			<td colspan="2">haematopoietic_and_lymphoid_tissue</td>
			<td>Homozygous</td>
		</tr>
		<tr>
			<td>LNCaP</td>
			<td>p.?</td>
			<td colspan="2">prostate</td>
			<td>Homozygous</td>
		</tr>
		<tr>
			<td>LoVo</td>
			<td>p.?</td>
			<td colspan="2">large_intestine</td>
			<td>Homozygous</td>
		</tr>
		<tr>
			<td>MOLT-13</td>
			<td>p.R711*</td>
			<td colspan="2">haematopoietic_and_lymphoid_tissue</td>
			<td>Homozygous</td>
		</tr>
		<tr>
			<td>NALM-6</td>
			<td>p.?</td>
			<td colspan="2">haematopoietic_and_lymphoid_tissue</td>
			<td>Homozygous</td>
		</tr>
		<tr>
			<td>NCI-H630</td>
			<td>p.R680*</td>
			<td colspan="2">large_intestine</td>
			<td>Heterozygous</td>
		</tr>
		<tr>
			<td>SKUT-1</td>
			<td>p.L787fs*11</td>
			<td colspan="2">endometrium</td>
			<td>Homozygous</td>
		</tr>
		<tr>
			<td>SKUT-1B</td>
			<td>p.L787fs*11</td>
			<td colspan="2">endometrium</td>
			<td>Homozygous</td>
		</tr>
		<tr>
			<td>SUP-T1</td>
			<td>p.?</td>
			<td colspan="2">haematopoietic_and_lymphoid_tissue</td>
			<td>Homozygous</td>
		</tr>
	</tbody>
</table>
Table 1.&#160;MSH2-mutated Cell Lines. Cell line (sample name), amino acid substitution, the lineage of origin and the zygosity are indicated.
<table>
	<tbody>
		<tr>
			<td>Sample Name</td>
			<td>Amino Acid</td>
			<td colspan="2">Primary Tissue</td>
			<td>Zygosity</td>
		</tr>
		<tr>
			<td>CCRF-CEM</td>
			<td>p.R100*</td>
			<td colspan="2">haematopoietic_and_lymphoid_tissue</td>
			<td>Heterozygous</td>
		</tr>
		<tr>
			<td>CCRF-CEM</td>
			<td>p.?</td>
			<td colspan="2">haematopoietic_and_lymphoid_tissue</td>
			<td>Heterozygous</td>
		</tr>
		<tr>
			<td>CW-2</td>
			<td>p.Y130fs*6</td>
			<td colspan="2">large_intestine</td>
			<td>Homozygous</td>
		</tr>
		<tr>
			<td>DU-145</td>
			<td>p.?</td>
			<td colspan="2">prostate</td>
			<td>Homozygous</td>
		</tr>
		<tr>
			<td>GR-ST</td>
			<td>p.?</td>
			<td colspan="2">haematopoietic_and_lymphoid_tissue</td>
			<td>Homozygous</td>
		</tr>
		<tr>
			<td>HCT-116</td>
			<td>p.S252*</td>
			<td colspan="2">large_intestine</td>
			<td>Homozygous</td>
		</tr>
		<tr>
			<td>IGROV-1</td>
			<td>p.S505fs*3</td>
			<td colspan="2">ovary</td>
			<td>Homozygous</td>
		</tr>
		<tr>
			<td>MN-60</td>
			<td>p.?</td>
			<td colspan="2">haematopoietic_and_lymphoid_tissue</td>
			<td>Homozygous</td>
		</tr>
		<tr>
			<td>NCI-SNU-1</td>
			<td>p.R226*</td>
			<td colspan="2">stomach</td>
			<td>Homozygous</td>
		</tr>
		<tr>
			<td>P30-OHK</td>
			<td>p.?</td>
			<td colspan="2">haematopoietic_and_lymphoid_tissue</td>
			<td>Homozygous</td>
		</tr>
		<tr>
			<td>PR-Mel</td>
			<td>p.?</td>
			<td colspan="2">skin</td>
			<td>Homozygous</td>
		</tr>
		<tr>
			<td>REH</td>
			<td>p.?</td>
			<td colspan="2">haematopoietic_and_lymphoid_tissue</td>
			<td>Homozygous</td>
		</tr>
		<tr>
			<td>SK-OV-3</td>
			<td>p.0?</td>
			<td colspan="2">ovary</td>
			<td>Homozygous</td>
		</tr>
		<tr>
			<td>SNU-1544</td>
			<td>p.S2L</td>
			<td colspan="2">large_intestine</td>
			<td>Heterozygous</td>
		</tr>
		<tr>
			<td>SNU-1746</td>
			<td>p.E523K</td>
			<td colspan="2">large_intestine</td>
			<td>Homozygous</td>
		</tr>
		<tr>
			<td>SNU-324</td>
			<td>p.C233R</td>
			<td colspan="2">pancreas</td>
			<td>Heterozygous</td>
		</tr>
		<tr>
			<td>SNU-324</td>
			<td>p.V384D</td>
			<td colspan="2">pancreas</td>
			<td>Heterozygous</td>
		</tr>
		<tr>
			<td>SNU-478</td>
			<td>p.V384D</td>
			<td colspan="2">pancreas</td>
			<td>Heterozygous</td>
		</tr>
	</tbody>
</table>
Table 2.&#160;MLH1-mutated Cell Lines. Cell line (sample name), amino acid substitution, the lineage of origin and the zygosity are indicated.

Watch this Scientific Journal Video about Implementation of In Vitro Drug Resistance Assays: Maximizing the Potential for Uncovering Clinically Relevant Resistance Mechanisms at JoVE.com

Implementation of In Vitro Drug Resistance Assays: Maximizing the Potential for Uncovering Clinically Relevant Resistance Mechanisms

Drug resistance to targeted therapeutics is widespread and the need to identify mechanisms of resistance--prior to or following clinical onset--is critical for guiding alternative clinical management strategies. Here, we present a protocol to couple derivation of drug-resistant lines in vitro with sequencing to expedite discovery of these mechanisms.

Implementation of In Vitro Drug Resistance Assays: Maximizing the Potential for Uncovering Clinically Relevant Resistance Mechanisms

Although targeted therapies are initially effective, resistance inevitably emerges. Several methods, such as genetic analysis of resistant clinical ...

implementation-vitro-drug-resistance-assays-maximizing-potential-for

Universidad de Cartagena UDEC

Research

JoVE Journal

Medicine

10.5K Views.  H3 Biomedicine. Drug resistance to targeted therapeutics is widespread and the need to identify mechanisms of resistance--prior to or following clinical onset--is critical for guiding alternative clinical management strategies. Here, we present a protocol to couple derivation of drug-resistant lines in vitro with sequencing to expedite discovery of these mechanisms.

Video: Implementation of In Vitro Drug Resistance Assays: Maximizing the Potential for Uncovering Clinically Relevant Resistance Mechanisms

Characterization of Molecular Mechanisms of In vivo UVR Induced Cataract

Cataract is the leading cause of blindness in the world 1. The World Health Organization defines cataract as a clouding of the lens of the eye which impedes the transfer of light. Cataract is a multi-factorial disease associated with diabetes, smoking, ultraviolet radiation (UVR), alcohol, ionizing radiation, steroids and hypertension. There is strong experimental 2-4 and epidemiological evidence 5,6 that UVR causes cataract. We developed an animal model for UVR B induced cataract in both anesthetized 7 and non-anesthetized animals 8.
The only cure for cataract is surgery but this treatment is not accessible to all. It has been estimated that a delay of onset of cataract for 10 years could reduce the need for cataract surgery by 50% 9. To delay the incidence of cataract, it is needed to understand the mechanisms of cataract formation and find effective prevention strategies. Among the mechanisms for cataract development, apoptosis plays a crucial role in initiation of cataract in humans and animals 10. Our focus has recently been apoptosis in the lens as the mechanism for cataract development 8,11,12. It is anticipated that a better understanding of the effect of UVR on the apoptosis pathway will provide possibilities for discovery of new pharmaceuticals to prevent cataract.
In this article, we describe how cataract can be experimentally induced by in vivo exposure to UVR-B. Further RT-PCR and immunohistochemistry are presented as tools to study molecular mechanisms of UVR-B induced cataract.

Characterization of Molecular Mechanisms of In vivo UVR Induced Cataract

Cataract is the leading cause of blindness in the world. Solar ultraviolet radiation (UVR) is the main risk factor for cataract development. An animal model of far UVR-B induced cataract was developed. In this article we describe methods for investigation of cataract formation: exposure to UVR, quantitative RT-PCR and immunohistochemistry.

Cataract is the leading cause of blindness in the world 1. The World Health Organization defines cataract as a clouding of the lens of the eye which ...

Assessing Myogenic Response and Vasoactivity In Resistance Mesenteric Arteries Using Pressure Myography

Small resistance arteries constrict and dilate respectively in response to increased or decreased intraluminal pressure; this phenomenon known as myogenic response is a key regulator of local blood flow. In isobaric conditions small resistance arteries develop sustained constriction known as myogenic tone (MT), which is a major determinant of systemic vascular resistance (SVR). Hence, ex vivo&#160;pressurized preparations of small resistance arteries are major tools to study microvascular function in near-physiological states. To achieve this, a freshly isolated intact segment of a small resistance artery (diameter ~260 &#956;m) is mounted onto two small glass cannulas and pressurized. These arterial preparations retain most in vivo characteristics and permit assessment of vascular tone in real-time. Here we provide a detailed protocol for assessing vasoactivity in pressurized small resistance mesenteric arteries from rats; these arteries develop sustained vasoconstriction - approximately 25% of maximal diameter - when pressurized at 70 mmHg. These arterial preparations may be used to study the effect of investigational compounds on relationship between intra-arterial pressure and vasoactivity and determine changes in microvascular function in animal models of various diseases.

Pressure myography is used to assess vasoactivity of small arteries that develop sustained constriction when pressurized. This manuscript provides a detailed protocol to assess in isolated segments of small mesenteric arteries from rats, vasoactivity and the effect of intraluminal pressure on vascular diameter.

Small resistance arteries constrict and dilate respectively in response to increased or decreased intraluminal pressure; this phenomenon known as myogenic ...

Optimization of High Grade Glioma Cell Culture from Surgical Specimens for Use in  Clinically Relevant Animal Models and 3D Immunochemistry

Glioblastomas, the most common and aggressive form of astrocytoma, are refractory to therapy, and molecularly heterogeneous. The ability to establish cell cultures that preserve the genomic profile of the parental tumors, for use in patient specific in vitro and in vivo models, has the potential to revolutionize the preclinical development of new treatments for glioblastoma tailored to the molecular characteristics of each tumor.
Starting with fresh high grade astrocytoma tumors dissociated into single cells, we use the neurosphere assay as an enrichment method for cells presenting cancer stem cell phenotype, including expression of neural stem cell markers, long term self-renewal in vitro, and the ability to form orthotopic xenograft tumors. This method has been previously proposed, and is now in use by several investigators. Based on our experience of dissociating and culturing 125 glioblastoma specimens, we arrived at the detailed protocol we present here, suitable for routine neurosphere culturing of high grade astrocytomas and large scale expansion of tumorigenic cells for preclinical studies. We report on the efficiency of successful long term cultures using this protocol&#160;and suggest affordable alternatives for culturing dissociated glioblastoma cells that fail to grow as neurospheres. We also describe in detail a protocol for preserving the neurospheres 3D architecture for immunohistochemistry. Cell cultures enriched in CSCs, capable of generating orthotopic xenograft models that preserve the molecular signatures and heterogeneity of GBMs, are becoming increasingly popular for the study of the biology of GBMs and for the improved design of preclinical testing of potential therapies.

Protocol for propagation of dissociated high grade glioma surgical specimens in serum-free neurosphere medium to select for cells with cancer stem cell phenotype. For specimens that fail to grow as neurospheres, an alternative protocol is suggested. A paraffin embedding technique for maintaining the 3D neurosphere architecture for immunocytochemistry is described.

Glioblastomas, the most common and aggressive form of astrocytoma, are refractory to therapy, and molecularly heterogeneous. The ability to establish cell ...

An Affordable HIV-1 Drug Resistance Monitoring Method for Resource Limited Settings

HIV-1 drug resistance has the potential to seriously compromise the effectiveness and impact of antiretroviral therapy (ART). As ART programs in sub-Saharan Africa continue to expand, individuals on ART should be closely monitored for the emergence of drug resistance. Surveillance of transmitted drug resistance to track transmission of viral strains already resistant to ART is also critical. Unfortunately, drug resistance testing is still not readily accessible in resource limited settings, because genotyping is expensive and requires sophisticated laboratory and data management infrastructure. An open access genotypic drug resistance monitoring method to manage individuals and assess transmitted drug resistance is described. The method uses free open source software for the interpretation of drug resistance patterns and the generation of individual patient reports. The genotyping protocol has an amplification rate of greater than 95% for plasma samples with a viral load &#62;1,000 HIV-1 RNA copies/ml. The sensitivity decreases significantly for viral loads &#60;1,000 HIV-1 RNA copies/ml. The method described here was validated against a method of HIV-1 drug resistance testing approved by the United States Food and Drug Administration (FDA), the Viroseq genotyping method. Limitations of the method described here include the fact that it is not automated and that it also failed to amplify the circulating recombinant form CRF02_AG from a validation panel of samples, although it amplified subtypes A and B from the same panel.

Drug resistance testing for HIV-1 infected individuals failing antiretroviral therapy (ART) can guide future therapies and improve treatment outcomes. Optimizing individual and population health outcomes in high HIV prevalence but resource-limited settings will ultimately require affordable and accessible drug resistance genotyping and interpretation methods.

HIV-1 drug resistance has the potential to seriously compromise the effectiveness and impact of antiretroviral therapy (ART). As ART programs in ...

A Novel in vivo Gene Transfer Technique and in vitro Cell Based Assays for the Study of Bone Loss in Musculoskeletal Disorders

Differentiation and activation of osteoclasts play a key role in the development of musculoskeletal diseases as these cells are primarily involved in bone resorption. Osteoclasts can be generated in vitro from monocyte/macrophage precursor cells in the presence of certain cytokines, which promote survival and differentiation. Here, both in vivo and in vitro techniques are demonstrated, which allow scientists to study different cytokine contributions towards osteoclast differentiation, signaling, and activation. The minicircle DNA delivery gene transfer system provides an alternative method to establish an osteoporosis-related model is particularly useful to study the efficacy of various pharmacological inhibitors in vivo. Similarly, in vitro culturing protocols for producing osteoclasts from human precursor cells in the presence of specific cytokines enables scientists to study osteoclastogenesis in human cells for translational applications. Combined, these techniques have the potential to accelerate drug discovery efforts for osteoclast-specific targeted therapeutics, which may benefit millions of osteoporosis and arthritis patients worldwide.

A Novel in vivo Gene Transfer Technique and in vitro Cell Based Assays for the Study of Bone Loss in Musculoskeletal Disorders

Differentiation of precursor cells into osteoclasts is regulated by cytokines and growth factors. Here, a novel gene transfer technique for differentiation of osteoclasts in vivo and cell culture protocols for differentiating precursor cells into osteoclasts in vitro as a method to study the effects of cytokines on osteoclastogenesis are described.

Differentiation and activation of osteoclasts play a key role in the development of musculoskeletal diseases as these cells are primarily involved in bone ...

Evaluating the Effectiveness of Cancer Drug Sensitization In Vitro and In Vivo

Due to the high level of heterogeneity and mutations inherent in human cancers, single agent therapies, or combination regimens which target the same pathway, are likely to fail. Emphasis must be placed upon the inhibition of pathways that are responsible for intrinsic and/or adaptive resistance to therapy. An active field of investigation is the development and testing of DNA repair inhibitors that promote the action of, and prevent resistance to, commonly used chemotherapy and radiotherapy. We used a novel protocol to evaluate the effectiveness of BRCA2 inhibition as a means to sensitize tumor cells to the DNA damaging drug cisplatin. Tumor cell metabolism (acidification and respiration) was monitored in real-time for a period of 72 hr to delineate treatment effectiveness on a minute by minute basis. In combination, we performed an assessment of metastatic frequency using a chicken embryo chorioallantoic membrane (CAM) model of extravasation and invasion. This protocol addresses some of the weaknesses of commonly used in vitro and in vivo methods to evaluate novel cancer therapy regimens. It can be used in addition to common methods such as cell proliferation assays, cell death assays, and in vivo murine xenograft studies, to more closely discriminate amongst candidate targets and agents, and select only the most promising candidates for further development.

Evaluating the Effectiveness of Cancer Drug Sensitization In Vitro and In Vivo

Here, real-time monitoring of tumor cell metabolism, combined with an in vivo chicken embryo chorio-allantoic membrane (CAM) model of metastasis, are used to evaluate novel anti-cancer targets/agents for their ability to sensitize tumor cells to DNA damaging chemotherapeutics.

Due to the high level of heterogeneity and mutations inherent in human cancers, single agent therapies, or combination regimens which target the same ...

Adapted Resistance Training Improves Strength in Eight Weeks in Individuals with Multiple Sclerosis

Hip weakness is a common symptom affecting walking ability in people with multiple sclerosis (MS). It is known that resistance strength training (RST) can improve strength in individuals with MS, however; it remains unclear the duration of RST that is needed to make strength gains and how to adapt hip strengthening exercises for individuals of varying strength using only resistance bands. This paper describes the methodology to set up and implement an adapted resistance strength training program, using resistance bands, for individuals with MS. Directions for pre- and post-strength tests to evaluate efficacy of the strength-training program are included. Safety features and detailed instructions outline the weekly program content and progression. Current evidence is presented showing that significant strength gains can be made within 8 weeks of starting a RST program. Evidence is also presented showing that resistance strength training can be successfully adapted for individuals with MS of varying strength with little equipment.

Hip weakness is a common symptom affecting walking ability in people with multiple sclerosis. Isolated muscle strengthening is a useful method to target specific weaknesses. This protocol describes a progressive resistance-training program using exercise bands to increase hip muscle strength.

Hip weakness is a common symptom affecting walking ability in people with multiple sclerosis (MS). It is known that resistance strength training (RST) can ...

A Model to Simulate Clinically Relevant Hypoxia in Humans

In case of apnea, arterial partial pressure of oxygen (pO2) decreases, while partial pressure of carbon dioxide (pCO2) increases. To avoid damage to hypoxia sensitive organs such as the brain, compensatory circulatory mechanisms help to maintain an adequate oxygen supply. This is mainly achieved by increased cerebral blood flow. Intermittent hypoxia is a commonly seen phenomenon in patients with obstructive sleep apnea. Acute airway obstruction can also result in hypoxia and hypercapnia. Until now, no adequate model has been established to simulate these dynamics in humans. Previous investigations focusing on human hypoxia used inhaled hypoxic gas mixtures. However, the resulting hypoxia was combined with hyperventilation and is therefore more representative of high altitude environments than of apnea. Furthermore, the transferability of previously performed animal experiments to humans is limited and the pathophysiological background of apnea induced physiological changes is poorly understood. In this study, healthy human apneic divers were utilized to mimic clinically relevant hypoxia and hypercapnia during apnea. Additionally, pulse-oximetry and Near Infrared Spectroscopy (NIRS) were used to evaluate changes in cerebral and peripheral oxygen saturation before, during, and after apnea.

Hypoxia simulation in humans has usually been performed by inhaling hypoxic gas mixtures. For this study, apneic divers were used to simulate dynamic hypoxia in humans. Additionally, physiological changes in desaturation and re-saturation kinetics were evaluated with non-invasive tools such as Near-Infrared-Spectroscopy (NIRS) and peripheral oxygenation saturation (SpO2).

In case of apnea, arterial partial pressure of oxygen (pO2) decreases, while partial pressure of carbon dioxide (pCO2) increases. To avoid damage to ...

A Cell Culture Model of Resistance Arteries

The myoendothelial junction (MEJ), a unique signaling microdomain in small diameter resistance arteries, exhibits localization of specific proteins and signaling processes that can control vascular tone and blood pressure. As it is a projection from either the endothelial or smooth muscle cell, and due to its small size (on average, an area of ~1 &#181;m2), the MEJ is difficult to study in isolation. However, we have developed a cell culture model called the vascular cell co-culture (VCCC) that allows for in vitro MEJ formation, endothelial cell polarization, and dissection of signaling proteins and processes in the vascular wall of resistance arteries. The VCCC has a multitude of applications and can be adapted to suit different cell types. The model consists of two cell types grown on opposite sides of a filter with 0.4 &#181;m pores in which the in vitro MEJs can form. Here we describe how to create the VCCC via plating of cells and isolation of endothelial, MEJ, and smooth muscle fractions, which can then be used for protein isolation or activity assays. The filter with intact cell layers can be fixed, embedded, and sectioned for immunofluorescent analysis. Importantly, many of the discoveries from this model have been confirmed using intact resistance arteries, underscoring its physiological relevance.

A cell culture model of resistance arteries is described, allowing for the dissection of signaling pathways in endothelium, smooth muscle, or between endothelium and smooth muscle (the myoendothelial junction). The selective application of agonists or protein isolation, electron microscopy, or immunofluorescence can be utilized using this cell culture model.

The myoendothelial junction (MEJ), a unique signaling microdomain in small diameter resistance arteries, exhibits localization of specific proteins and ...

Evaluation of Vascular Control Mechanisms Utilizing Video Microscopy of Isolated Resistance Arteries of Rats

This protocol describes the use of in vitro television microscopy to evaluate vascular function in isolated cerebral resistance arteries (and other vessels), and describes techniques for evaluating tissue perfusion using Laser Doppler Flowmetry (LDF) and microvessel density utilizing fluorescently labeled Griffonia simplicifolia (GS1) lectin. Current methods for studying isolated resistance arteries at transmural pressures encountered in vivo and in the absence of parenchymal cell influences provide a critical link between in vivo studies and information gained from molecular reductionist approaches that provide limited insight into integrative responses at the whole animal level. LDF and techniques to selectively identify arterioles and capillaries with fluorescently-labeled GS1 lectin provide practical solutions to enable investigators to extend the knowledge gained from studies of isolated resistance arteries. This paper describes the application of these techniques to gain fundamental knowledge of vascular physiology and pathology in the rat as a general experimental model, and in a variety of specialized genetically engineered &#34;designer&#34; rat strains that can provide important insight into the influence of specific genes on important vascular phenotypes. Utilizing these valuable experimental approaches in rat strains developed by selective breeding strategies and new technologies for producing gene knockout models in the rat, will expand the rigor of scientific premises developed in knockout mouse models and extend that knowledge to a more relevant animal model, with a well understood physiological background and suitability for physiological studies because of its larger size.

This manuscript describes in vitro video microscopy protocols for evaluating vascular function in rat cerebral resistance arteries. The manuscript also describes techniques for evaluating microvessel density with fluorescently labeled lectin and tissue perfusion using Laser Doppler Flowmetry.

This protocol describes the use of in vitro television microscopy to evaluate vascular function in isolated cerebral resistance arteries (and other ...

Implementation of In Vitro Drug Resistance Assays: Maximizing the Potential for Uncovering Clinically Relevant Resistance Mechanisms

Podsumowanie

Przeglądaj więcej filmów

Rozdziały w tym wideo

Characterization of Molecular Mechanisms of In vivo UVR Induced Cataract

Assessing Myogenic Response and Vasoactivity In Resistance Mesenteric Arteries Using Pressure Myography

Optimization of High Grade Glioma Cell Culture from Surgical Specimens for Use in Clinically Relevant Animal Models and 3D Immunochemistry

An Affordable HIV-1 Drug Resistance Monitoring Method for Resource Limited Settings

A Novel in vivo Gene Transfer Technique and in vitro Cell Based Assays for the Study of Bone Loss in Musculoskeletal Disorders

Evaluating the Effectiveness of Cancer Drug Sensitization In Vitro and In Vivo

Adapted Resistance Training Improves Strength in Eight Weeks in Individuals with Multiple Sclerosis

A Model to Simulate Clinically Relevant Hypoxia in Humans

A Cell Culture Model of Resistance Arteries

Evaluation of Vascular Control Mechanisms Utilizing Video Microscopy of Isolated Resistance Arteries of Rats