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| <?xml version="1.0" encoding="UTF-8"?><!DOCTYPE article PUBLIC "-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD v1.1d1 20130915//EN" "JATS-archivearticle1.dtd"><article xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" article-type="research-article" dtd-version="1.1d1"><front><journal-meta><journal-id journal-id-type="nlm-ta">elife</journal-id><journal-id journal-id-type="hwp">eLife</journal-id><journal-id journal-id-type="publisher-id">eLife</journal-id><journal-title-group><journal-title>eLife</journal-title></journal-title-group><issn publication-format="electronic">2050-084X</issn><publisher><publisher-name>eLife Sciences Publications, Ltd</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type="publisher-id">04037</article-id><article-id pub-id-type="doi">10.7554/eLife.04037</article-id><article-categories><subj-group subj-group-type="display-channel"><subject>Registered report</subject></subj-group><subj-group subj-group-type="heading"><subject>Biochemistry</subject></subj-group></article-categories><title-group><article-title>Registered report: Widespread potential for growth factor-driven resistance to anticancer kinase inhibitors</article-title></title-group><contrib-group><contrib contrib-type="author" id="author-16420"><name><surname>Greenfield</surname><given-names>Edward</given-names></name><xref ref-type="aff" rid="aff1">1</xref><xref ref-type="fn" rid="con1"/><xref ref-type="fn" rid="conf1"/></contrib><contrib contrib-type="author" id="author-16421"><name><surname>Griner</surname><given-names>Erin</given-names></name><xref ref-type="aff" rid="aff2">2</xref><xref ref-type="fn" rid="con2"/><xref ref-type="fn" rid="conf3"/></contrib><contrib contrib-type="author" corresp="yes"><contrib-id contrib-id-type="group-author-key">group-author-id1</contrib-id><collab>Reproducibility Project: Cancer Biology</collab><xref ref-type="corresp" rid="cor1">*</xref><xref ref-type="other" rid="par-1"/><xref ref-type="fn" rid="con3"/><xref ref-type="fn" rid="conf2"/></contrib><aff id="aff1"><label>1</label><institution content-type="dept">Monoclonal Antibody Core Facility</institution>, <institution>Dana-Farber Cancer Institute</institution>, <addr-line><named-content content-type="city">Boston</named-content></addr-line>, <country>United States</country></aff><aff id="aff2"><label>2</label><institution>University of Virginia</institution>, <addr-line><named-content content-type="city">Charlottesville</named-content></addr-line>, <country>United States</country></aff></contrib-group><contrib-group content-type="section"><contrib contrib-type="editor"><name><surname>Massagué</surname><given-names>Joan</given-names></name><role>Reviewing editor</role><aff><institution>Memorial Sloan-Kettering Cancer Center</institution>, <country>United States</country></aff></contrib></contrib-group><contrib-group><contrib contrib-type="author non-byline"><contrib-id contrib-id-type="group-author-key">group-author-id1</contrib-id><name><surname>Iorns</surname><given-names>Elizabeth</given-names></name><aff><institution>Science Exchange</institution>, <addr-line><named-content content-type="city">Palo Alto</named-content></addr-line>, <country>United States</country></aff></contrib><contrib contrib-type="author non-byline"><contrib-id contrib-id-type="group-author-key">group-author-id1</contrib-id><name><surname>Gunn</surname><given-names>William</given-names></name><aff><institution>Mendeley</institution>, <addr-line><named-content content-type="city">London</named-content></addr-line>, <country>United Kingdom</country></aff></contrib><contrib contrib-type="author non-byline"><contrib-id contrib-id-type="group-author-key">group-author-id1</contrib-id><name><surname>Tan</surname><given-names>Fraser</given-names></name><aff><institution>Science Exchange</institution>, <addr-line><named-content content-type="city">Palo Alto</named-content></addr-line>, <country>United States</country></aff></contrib><contrib contrib-type="author non-byline"><contrib-id contrib-id-type="group-author-key">group-author-id1</contrib-id><name><surname>Lomax</surname><given-names>Joelle</given-names></name><aff><institution>Science Exchange</institution>, <addr-line><named-content content-type="city">Palo Alto</named-content></addr-line>, <country>United States</country></aff></contrib><contrib contrib-type="author non-byline"><contrib-id contrib-id-type="group-author-key">group-author-id1</contrib-id><name><surname>Errington</surname><given-names>Timothy</given-names></name><aff><institution>Center for Open Science</institution>, <addr-line><named-content content-type="city">Charlottesville</named-content></addr-line>, <country>United States</country></aff></contrib></contrib-group><author-notes><corresp id="cor1"><label>*</label>For correspondence: <email>fraser@scienceexchange.com</email></corresp></author-notes><pub-date publication-format="electronic" date-type="pub"><day>10</day><month>12</month><year>2014</year></pub-date><pub-date pub-type="collection"><year>2014</year></pub-date><volume>3</volume><elocation-id>e04037</elocation-id><history><date date-type="received"><day>16</day><month>07</month><year>2014</year></date><date date-type="accepted"><day>28</day><month>09</month><year>2014</year></date><fn><p><mixed-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Wilson</surname><given-names>TR</given-names></name><name><surname>Fridlyand</surname><given-names>J</given-names></name><name><surname>Yan</surname><given-names>Y</given-names></name><name><surname>Penuel</surname><given-names>E</given-names></name><name><surname>Burton</surname><given-names>L</given-names></name><name><surname>Chan</surname><given-names>E</given-names></name><name><surname>Peng</surname><given-names>J</given-names></name><name><surname>Lin</surname><given-names>E</given-names></name><name><surname>Wang</surname><given-names>Y</given-names></name><name><surname>Sosman</surname><given-names>J</given-names></name><name><surname>Ribas</surname><given-names>A</given-names></name><name><surname>Li</surname><given-names>J</given-names></name><name><surname>Moffat</surname><given-names>J</given-names></name><name><surname>Sutherlin</surname><given-names>DP</given-names></name><name><surname>Koeppen</surname><given-names>H</given-names></name><name><surname>Merchant</surname><given-names>M</given-names></name><name><surname>Neve</surname><given-names>R</given-names></name><name><surname>Settleman</surname><given-names>J</given-names></name></person-group>. <day>26</day><month>07</month><year>2012</year>. <article-title>Widespread potential for growth-factor-driven resistance to anticancer kinase inhibitors</article-title>. <source>Nature</source> <volume>487</volume>:<fpage>505</fpage>–<lpage>509</lpage>. doi: <pub-id pub-id-type="doi">10.1038/nature11249</pub-id>.</mixed-citation></p></fn></history><permissions><copyright-statement>Copyright © 2014, Greenfield et al</copyright-statement><copyright-year>2014</copyright-year><copyright-holder>Greenfield et al</copyright-holder><license xlink:href="http://creativecommons.org/licenses/by/4.0/"><license-p>This article is distributed under the terms of the <ext-link ext-link-type="uri" xlink:href="http://creativecommons.org/licenses/by/4.0/">Creative Commons Attribution License</ext-link>, which permits unrestricted use and redistribution provided that the original author and source are credited.</license-p></license></permissions><self-uri content-type="pdf" xlink:href="elife04037.pdf"/><abstract><object-id pub-id-type="doi">10.7554/eLife.04037.001</object-id><p>The <ext-link xmlns:xlink="http://www.w3.org/1999/xlink" ext-link-type="uri" xlink:href="https://osf.io/e81xl/wiki/home/">Reproducibility Project: Cancer Biology</ext-link> seeks to address growing concerns about reproducibility in scientific research by conducting replications of 50 papers in the field of cancer biology published between 2010 and 2012. This Registered Report describes the proposed replication plan of key experiments from ‘Widespread potential for growth-factor-driven resistance to anticancer kinase inhibitors’ by Wilson and colleagues, published in Nature in 2012 (<xref ref-type="bibr" rid="bib20">Wilson et al., 2012</xref>). The experiments that will be replicated are those reported in Figure 2B and C. In these experiments, Wilson and colleagues show that sensitivity to receptor tyrosine kinase (RTK) inhibitors can be bypassed by various ligands through reactivation of downstream signaling pathways (Figure 2A; <xref ref-type="bibr" rid="bib20">Wilson et al., 2012</xref>), and that blocking the receptors for these bypassing ligands abrogates their ability to block sensitivity to the original RTK inhibitor (Figure 2C; <xref ref-type="bibr" rid="bib20">Wilson et al., 2012</xref>). The Reproducibility Project: Cancer Biology is a collaboration between the <ext-link xmlns:xlink="http://www.w3.org/1999/xlink" ext-link-type="uri" xlink:href="http://centerforopenscience.org/">Center for Open Science</ext-link> and <ext-link xmlns:xlink="http://www.w3.org/1999/xlink" ext-link-type="uri" xlink:href="https://www.scienceexchange.com/">Science Exchange</ext-link>, and the results of the replications will be published by <italic>eLife</italic>.</p><p><bold>DOI:</bold> <ext-link ext-link-type="doi" xlink:href="10.7554/eLife.04037.001">http://dx.doi.org/10.7554/eLife.04037.001</ext-link></p></abstract><kwd-group kwd-group-type="author-keywords"><title>Author keywords</title><kwd>Reproducibility Project: Cancer Biology</kwd><kwd>methodology</kwd><kwd>receptor tyrosine kinase inhibitors</kwd><kwd>signaling pathway reactivation</kwd></kwd-group><kwd-group kwd-group-type="research-organism"><title>Research organism</title><kwd>human</kwd></kwd-group><funding-group><award-group id="par-1"><funding-source><institution-wrap><institution>Laura and John Arnold Foundation</institution></institution-wrap></funding-source><principal-award-recipient>Reproducibility Project: Cancer Biology</principal-award-recipient></award-group><funding-statement>The Reproducibility Project: Cancer Biology is funded by the Laura and John Arnold Foundation, provided to the Center for Open Science in collaboration with Science Exchange. The funder had no role in study design or the decision to submit the work for publication.</funding-statement></funding-group><custom-meta-group><custom-meta><meta-name>elife-xml-version</meta-name><meta-value>2.0</meta-value></custom-meta></custom-meta-group></article-meta></front><body><sec sec-type="intro" id="s1"><title>Introduction</title><p>A recurring theme in treatment of cancer is the acquisition of drug resistance. The effectiveness of therapies targeting specific mutations in receptor tyrosine kinases (RTKs) is limited by the acquisition of resistance to the drugs over the course of treatment (<xref ref-type="bibr" rid="bib11">Mok et al., 2009</xref>; <xref ref-type="bibr" rid="bib1">Camidge et al., 2014</xref>). Resistance can be acquired through new mutations that block the action of the RTK inhibitors or their uptake and/or genetic amplification of downstream target genes of the RTK (<xref ref-type="bibr" rid="bib3">Chen and Fu, 2011</xref>; <xref ref-type="bibr" rid="bib5">Garrett and Arteaga, 2011</xref>; <xref ref-type="bibr" rid="bib17">Sequist et al., 2011</xref>; <xref ref-type="bibr" rid="bib4">Gainor and Shaw, 2013</xref>; <xref ref-type="bibr" rid="bib22">Yang, 2013</xref>). Several studies, including this work by Wilson and colleagues, elucidated another mechanism for this acquisition of resistance: the engagement of parallel RTK signaling pathways that converge on common downstream survival signals via signals from the tumor microenvironment. In this study, Wilson and colleagues examined several cancer cell lines for ligand-mediated drug resistance (<xref ref-type="bibr" rid="bib20">Wilson et al., 2012</xref>).</p><p>In Figure 2B/C, Wilson and colleagues demonstrated that resistance to primary kinase inhibitor treatment can be induced by the addition of rescuing ligands that activate the PI(3)K–AKT and MAPK pro-survival signaling pathways. This resistance can be overcome with the addition of an appropriate secondary kinase inhibitor. Three different cancer cell line models were used to demonstrate this phenomenon. Treatment of A204 (a <italic>PDGFR</italic> amplified rhabdomyosarcoma cell line) with the ligand FGF activated pFRS2 and pERK, inducing resistance to sunitinib. The addition of a secondary kinase inhibitor, PD173074, blocked FGF-induced pFRS2 and pERK activation, restoring sensitivity to sunitinib. The treatment of M14 (a <italic>BRAF</italic>-mutated melanoma cell line) with the ligand NRG1 activated pHER3 and pAKT, inducing partial resistance to PLX4032. The addition of a secondary kinase inhibitor, lapatinib, blocked NRG1-induced pHER3 and pAKT activation, restoring sensitivity to PLX4032. Treatment of KHM-3S (an <italic>EGFR</italic>-mutated small cell lung cancer cell line) with the ligand HGF activated pMET and pERK, inducing resistance to Erlotinib. The addition of a secondary kinase inhibitor, crizotinib, blocked HGF-induced pMET and pERK activation, restoring sensitivity to erlotinib.</p><p>The cell viability assays examining drug sensitivity and the Western blots examining levels of phosphorylated kinases in Figures 2B and 2C, respectively, are the key experiments that demonstrate that growth factor ligands can reactivate downstream signaling components important for cancer cell survival, causing resistance to anticancer kinase inhibitors (<xref ref-type="bibr" rid="bib20">Wilson et al., 2012</xref>). These experiments are replicated in Protocols 1 and 2.</p><p>Two studies published around the same time as the work of Wilson and colleagues also support the proposed mechanism of acquired resistance to RTK inhibition by signaling from the tumor microenvironment. Straussman and colleagues demonstrated that HGF signaling derived from the tumor microenvironment could bypass EGFR inhibition by activation of MET signaling (<xref ref-type="bibr" rid="bib18">Straussman et al., 2012</xref>, also included for replication in the Reproducibility Project: Cancer Biology), and Harbinski and colleagues, in an approach similar to Wilson and colleagues, showed that multiple growth factor ligands could ‘bypass’ inhibitor-targeted RTKs (<xref ref-type="bibr" rid="bib6">Harbinski et al., 2012</xref>).</p><p>Since the publication of Wilson and colleagues' work, several publications have reported similar results to those being replicated in Protocols 1 and 2. Similar to the experiments with A204 cells above, Welti and colleagues demonstrated that FGF ligands could induce resistance to sunitinib, which could be reversed by the addition of PD173074 (<xref ref-type="bibr" rid="bib19">Welti et al., 2011</xref>). These experiments were performed in HUVEC cells, whereas A204 cells were used in the study being replicated. Similar to the experiments on M14 cells above, Montero-Conde and colleagues showed that NRG1 ligand could activate pHER3 and pAKT in the presence of PLX4032, and this activation could be reversed by the addition of lapatinib (<xref ref-type="bibr" rid="bib12">Montero-Conde et al., 2013</xref>). These experiments were performed in 8505C cells, whereas M14 cells were used in the study being replicated. Similar to the experiments performed on KHM-S3 cells above, several groups have demonstrated that HGF ligand can induce resistance to erlotinib and that this resistance can be reversed by the addition of crizotinib (<xref ref-type="bibr" rid="bib14">Nakagawa et al., 2012</xref>; <xref ref-type="bibr" rid="bib13">Nakade et al., 2014</xref>). These experiments were performed in PC-9 and HCC827 cells, whereas KHM-3S cells were used in the study being replicated.</p></sec><sec sec-type="materials|methods" id="s2"><title>Materials and methods</title><p>Unless otherwise noted, all protocol information was derived from the original paper, references from the original paper, or information obtained directly from the authors. An asterisk (*) indicates data or information provided by the Reproducibility Project: Cancer Biology core team. A hashtag (#) indicates information provided by the replicating lab.</p><sec id="s2-1"><title>Protocol 1: Cell viability assays</title><p>This protocol describes cell viability assays to determine the IC<sub>50</sub> values of three cancer cell lines treated with primary kinase inhibitor alone, primary kinase inhibitor in combination with rescuing ligand, and primary kinase inhibitor in triple combination with rescuing ligand and a drug targeting the rescuing ligand's receptor tyrosine kinase (RTK) (termed the secondary kinase inhibitor) (Figure 2B).</p><sec id="s2-1-1"><title>Sampling</title><p><list list-type="bullet"><list-item><p>The original data presented is qualitative, and the authors were unable to share the raw data values with the RP:CB core team. This prevents power calculations being performed a priori to determine the sample size (number of biological replicates). In order to determine an appropriate number of replicates to perform initially, we have estimated the sample sizes required based on a range of potential variance. We will also determine the sample size <italic>post hoc</italic> as described in Power Calculations.</p><list list-type="order"><list-item><p>Please see Power Calculations for details.</p></list-item></list></list-item><list-item><p>Each experiment has three cohorts. In each cohort, a dilution series of the primary kinase inhibitor (10<sup>−4</sup>, 10<sup>−3</sup>, 10<sup>−2</sup>, 10<sup>−1</sup>, 10<sup>0</sup>, and 10<sup>1</sup> µM) is run three times; once alone, once with the rescuing ligand, and once with both the rescuing ligand and the secondary kinase inhibitor. The effect of the secondary kinase inhibitor alone will also be assessed. Each condition will be run in triplicate.</p><list list-type="order"><list-item><p>Cohort 1: A204 cell line.</p><list list-type="bullet"><list-item><p>Media only [additional].</p></list-item><list-item><p>Vehicle control.</p></list-item><list-item><p>0.001 µM–10 µM sunitinib + no ligand.</p></list-item><list-item><p>0.001 µM–10 µM sunitinib + 50 ng/ml FGF.</p></list-item><list-item><p>0.001 µM–10 µM sunitinib + 50 ng/ml FGF + 0.5 µM PD173074.</p></list-item><list-item><p>0.5 µM PD173074 + no ligand [additional].</p></list-item></list></list-item><list-item><p>Cohort 2: M14 cell line.</p><list list-type="bullet"><list-item><p>Media only [additional].</p></list-item><list-item><p>Vehicle control.</p></list-item><list-item><p>0.001 µM–10 µM PLX4032 + no ligand.</p></list-item><list-item><p>0.001 µM–10 µM PLX4032 + 50 ng/ml NRG1.</p></list-item><list-item><p>0.001 µM–10 µM PLX4032 + 50 ng/ml NRG1 + 0.5 µM lapatinib.</p></list-item><list-item><p>0.5 µM lapatinib + no ligand [additional].</p></list-item></list></list-item><list-item><p>Cohort 3: KHM-3S cell line.</p><list list-type="bullet"><list-item><p>Media only [additional].</p></list-item><list-item><p>Vehicle control.</p></list-item><list-item><p>0.001 µM–10 µM erlotinib + no ligand.</p></list-item><list-item><p>0.001 µM–10 µM erlotinib + 50 ng/ml HGF.</p></list-item><list-item><p>0.001 µM–10 µM erlotinib + 50 ng/ml HGF + 0.5 µM crizotinib.</p></list-item><list-item><p>0.5 µM crizotinib + no ligand [additional].</p></list-item></list></list-item></list></list-item></list></p></sec><sec id="s2-1-2"><title>Materials and reagents</title><p><table-wrap id="tbl1" position="anchor"><table frame="hsides" rules="groups"><thead><tr><th>Reagent</th><th>Type</th><th>Manufacturer</th><th>Catalog #</th><th>Comments</th></tr></thead><tbody><tr><td>96-well tissue culture plates</td><td>Materials</td><td>Corning (Sigma-Aldrich)</td><td>CLS3516</td><td>Original unspecified</td></tr><tr><td>KHM-3S cells</td><td>Cells</td><td>JCRB Cell Bank</td><td>JCRB0138</td><td>Original source of the cells unspecified</td></tr><tr><td>A204</td><td>Cells</td><td>ATCC</td><td>HTB-82</td><td>Original source of the cells unspecified</td></tr><tr><td>M14</td><td>Cells</td><td>ATCC</td><td>HTB-129<xref ref-type="table-fn" rid="tblfn1">*</xref></td><td>Original source of the cells unspecified</td></tr><tr><td>Lapatinib</td><td>Drug</td><td>LC Laboratories</td><td>L-4804</td><td>Original formulation unspecified</td></tr><tr><td>Crizotinib</td><td>Drug</td><td>Sigma-Aldrich</td><td>PZ0191</td><td>Originally from Selleck Chemicals</td></tr><tr><td>PD173074</td><td>Drug</td><td>Sigma-Aldrich</td><td>P2499</td><td>Originally from Tocris Bioscience</td></tr><tr><td>PLX4032</td><td>Drug</td><td>Active Biochem</td><td>A-1130</td><td/></tr><tr><td>Sunitinib</td><td>Drug</td><td>Sigma-Aldrich</td><td>PZ0012</td><td>Originally from Selleck Chemicals, formulation unspecified</td></tr><tr><td>Erlotinib</td><td>Drug</td><td>LC Laboratories</td><td>E-4007</td><td/></tr><tr><td>HGF</td><td>Ligand</td><td>Sigma-Aldrich</td><td>H5791</td><td>Originally obtained from Peprotech</td></tr><tr><td>FGF-basic</td><td>Ligand</td><td>Sigma-Aldrich</td><td>F0291</td><td>Originally obtained from Peprotech</td></tr><tr><td>NRG1-β1</td><td>Ligand</td><td>Novus Biologicals</td><td>H00003084-P01</td><td>Originally obtained from R&D Systems</td></tr><tr><td>RPMI 1640</td><td>Media</td><td>Sigma-Aldrich</td><td>R8758</td><td>Originally from Gibco, formulation unspecified</td></tr><tr><td>FBS</td><td>Reagent</td><td>Sigma-Aldrich</td><td>F4135</td><td>Originally from Gibco</td></tr><tr><td>Penicillin</td><td>Antibiotic</td><td rowspan="2">Sigma-Aldrich</td><td rowspan="2">P4458</td><td>Original unspecified</td></tr><tr><td>Streptomycin</td><td>Antifungal</td><td>Original unspecified</td></tr><tr><td>Paraformaldehyde</td><td>Reagent</td><td>Sigma-Aldrich</td><td>158127</td><td>Original unspecified</td></tr><tr><td>Syto 60</td><td>Reagent</td><td>Life Technologies</td><td>S11342</td><td>Original unspecified</td></tr><tr><td>Odyssey scanner</td><td>Equipment</td><td>LiCOR</td><td/><td/></tr><tr><td>Odyssey application software</td><td>Software</td><td>LiCOR</td><td/><td/></tr></tbody></table><table-wrap-foot><fn id="tblfn1"><label>*</label><p>The breast cancer cell line MDA-MB-435 has been shown to be mislabeled; it is in fact identical to the M14 melanoma cell line (<xref ref-type="bibr" rid="bib15">Rae et al., 2007</xref>; <xref ref-type="bibr" rid="bib2">Chambers, 2009</xref>; <xref ref-type="bibr" rid="bib7">Holliday and Speirs, 2011</xref>).</p></fn></table-wrap-foot></table-wrap></p></sec><sec id="s2-1-3"><title>Procedure</title><sec id="s2-1-3-1"><title>Notes</title><p><list list-type="bullet"><list-item><p>All cells will be sent for mycoplasma testing and STR profiling.</p></list-item><list-item><p>Medium for all cell lines: RPMI 1640 supplemented with 10% FBS, 50 U/ml penicillin, and 50 µg/ml streptomycin.</p></list-item><list-item><p>Cells maintained at 37°C in a humidified atmosphere at 5% CO<sub>2</sub>.</p><list list-type="order"><list-item><p>Seed 3000–5000 cells per well into 96-well plates. For each condition replicate seed 1 well as the media control, 1 well as the vehicle control, 1 well for treatment with the secondary kinase inhibitor alone, and 6 wells per concentration curve (10<sup>−4</sup>, 10<sup>−3</sup>, 10<sup>−2</sup>, 10<sup>−1</sup>, 10<sup>0</sup>, and 10<sup>1</sup> µM), of which there are three.</p><list list-type="simple"><list-item><p>a. 6 wells per concentration curve × 3 concentration curves = 18 wells + 3 wells = 21 wells per cohort.</p></list-item></list></list-item><list-item><p>18–24 hr after seeding treat 3 wells per condition with appropriate treatment (see Sampling).</p><list list-type="simple"><list-item><p>a. Lab will record the vehicle used to solubilize the drugs.</p></list-item></list></list-item><list-item><p>72 hr after treatment, fix cells in 4% paraformaldehyde (PFA).</p><list list-type="simple"><list-item><p>a. Lab will record the PFA incubation time.</p></list-item></list></list-item><list-item><p>Stain with Syto 60 according to the manufacturer's recommendations and assay cell number using an Odyssey with Odyssey Application Software.</p><list list-type="simple"><list-item><p>a. Include empty wells and media only wells.</p></list-item></list></list-item><list-item><p>Calculate cell viability by dividing the fluorescence from the drug-treated cells by the fluorescence from the control (vehicle) treated cells. Fit normalized data to a sigmoidal dose–response curve.</p><list list-type="simple"><list-item><p>a. Also calculate the effect of vehicle by dividing the fluorescence from the control vehicle cells by the fluorescence from the media only treated cells [additional control].</p></list-item><list-item><p>b. Determine the IC<sub>50</sub> values for each curve.</p></list-item><list-item><p>c. Lab will document the software used to fit the data to a sigmoidal dose–response curve and calculate the IC<sub>50</sub> values.</p></list-item></list></list-item><list-item><p>Repeat independently two additional times.</p></list-item></list></list-item></list></p></sec></sec><sec id="s2-1-4"><title>Deliverables</title><p><list list-type="bullet"><list-item><p>Data to be collected:</p><list list-type="order"><list-item><p>Raw fluorescence data and calculated cell viability.</p></list-item><list-item><p>Semi-logarithmic graph for each condition of primary kinase inhibitor (log) vs normalized cell viability (linear) for each cell line [comparable to Figure 2B].</p></list-item><list-item><p>Calculated IC<sub>50</sub> for each condition.</p></list-item></list></list-item></list></p></sec><sec id="s2-1-5"><title>Confirmatory analysis plan</title><p><list list-type="bullet"><list-item><p>Statistical analysis of the Replication Data:</p><list list-type="order"><list-item><p>For each cell line compare the IC<sub>50</sub> of primary kinase inhibitor alone, primary kinase inhibitor + ligand, and primary kinase inhibitor + ligand + secondary kinase inhibitor.</p><list list-type="simple"><list-item><p>• ANOVA.</p></list-item></list></list-item></list></list-item><list-item><p>Meta-analysis of original and replication attempt effect sizes:</p><list list-type="order"><list-item><p>We will plot the replication data (mean and 95% confidence interval) and will include the original data point, calculated directly from the representative image in Figure 2B, as a single point on the same plot for comparison.</p></list-item></list></list-item></list></p></sec><sec id="s2-1-6"><title>Known differences from the original study</title><p><list list-type="bullet"><list-item><p>We are including two additional control conditions;</p><list list-type="order"><list-item><p>Media alone.</p><list list-type="simple"><list-item><p>a. To provide a baseline.</p></list-item></list></list-item><list-item><p>Treatment of the cells with the secondary kinase inhibitor alone.</p><list list-type="simple"><list-item><p>a. To assess any effects, the secondary kinase inhibitor may be independent of the ligand and primary kinase inhibitor.</p></list-item></list></list-item></list></list-item></list></p></sec><sec id="s2-1-7"><title>Provisions for quality control</title><p><list list-type="bullet"><list-item><p>All data obtained from the experiment—raw data, data analysis, control data, and quality control data—will be made publicly available, either in the published manuscript or as an open access dataset available on the Open Science Framework (<ext-link xmlns:xlink="http://www.w3.org/1999/xlink" ext-link-type="uri" xlink:href="https://osf.io/h0pnz/">https://osf.io/h0pnz/</ext-link>).</p></list-item><list-item><p>Cell lines will be validated by STR profiling and screened for mycoplasma contamination.</p></list-item><list-item><p>A lab from the Science Exchange network with extensive experience in conducting cell viability assays will perform these experiments.</p></list-item></list></p></sec></sec><sec id="s2-2"><title>Protocol 2: Western blot assays</title><p>This protocol describes Western blot assays to determine the levels of activated phosphorylated signaling pathways in three cancer cell lines treated with primary kinase inhibitor alone, primary kinase inhibitor in combination with rescuing ligand, and primary kinase inhibitor in triple combination with rescuing ligand and a drug targeting the rescuing ligand's receptor tyrosine kinase (RTK) (termed the secondary kinase inhibitor) (Figure 2C).</p><sec id="s2-2-1"><title>Sampling</title><p><list list-type="bullet"><list-item><p>The original data presented is qualitative. This prevents power calculations being performed a priori to determine the sample size (number of biological replicates). In order to determine an appropriate number of replicates to perform initially, we have estimated the sample sizes required based on a range of potential variance. We will also determine the sample size <italic>post hoc</italic> as described in Power Calculations.</p><list list-type="order"><list-item><p>Please see Power Calculations for details.</p></list-item></list></list-item><list-item><p>Each experiment has three cohorts. Each cohort will consist of cells treated with media alone, with vehicle alone, with the primary kinase inhibitor, with primary kinase inhibitor and the rescuing ligand and with the primary kinase inhibitor, the rescuing ligand and the secondary kinase inhibitor. The effect of the secondary kinase inhibitor alone will also be assessed. Each condition will be run once (i.e., no technical replicates will be performed).</p><list list-type="order"><list-item><p>Cohort 1: A204 cell line.</p><list list-type="bullet"><list-item><p>Media only [additional].</p></list-item><list-item><p>Vehicle control.</p></list-item><list-item><p>1 µM sunitinib + no ligand.</p></list-item><list-item><p>1 µM sunitinib + 50 ng/ml FGF.</p></list-item><list-item><p>1 µM sunitinib + 50 ng/ml FGF + 0.5 µM PD173074.</p></list-item><list-item><p>1 µM PD173074 + no ligand [additional].</p></list-item></list></list-item><list-item><p>Cohort 2: M14 cell line.</p><list list-type="bullet"><list-item><p>Media only [additional].</p></list-item><list-item><p>Vehicle control.</p></list-item><list-item><p>1 µM PLX4032 + no ligand.</p></list-item><list-item><p>1 µM PLX4032 + 50 ng/ml NRG1.</p></list-item><list-item><p>1 µM PLX4032 + 50 ng/ml NRG1 + 0.5 µM lapatinib.</p></list-item><list-item><p>1 µM lapatinib + no ligand [additional].</p></list-item></list></list-item><list-item><p>Cohort 3: KHM-3S cell line.</p><list list-type="bullet"><list-item><p>Media only [additional].</p></list-item><list-item><p>Vehicle control.</p></list-item><list-item><p>1 µM erlotinib + no ligand.</p></list-item><list-item><p>1 µM erlotinib + 50 ng/ml HGF.</p></list-item><list-item><p>1 µM erlotinib + 50 ng/ml HGF + 0.5 µM Crizotinib.</p></list-item><list-item><p>1 µM crizotinib + no ligand [additional].</p></list-item></list></list-item><list-item><p>Cohort 4: positive control cell lines.</p><list list-type="bullet"><list-item><p>For Cohort 1: HL60 cells treated with FGF [additional control].</p></list-item><list-item><p>For Cohort 2: MCF7 cells treated with NRG1 [additional control].</p></list-item><list-item><p>For Cohort 3: HEK293 cells treated with HGF [additional control].</p><list list-type="simple"><list-item><p>a. Treatment of these cell lines with their cognate growth factor ligands will serve as a positive control for ligand activity.</p></list-item></list></list-item></list></list-item></list></list-item></list></p></sec><sec id="s2-2-2"><title>Materials and reagents:</title><p><table-wrap id="tbl2" position="anchor"><table frame="hsides" rules="groups"><thead><tr><th>Reagent</th><th>Type</th><th>Manufacturer</th><th>Catalog #</th><th>Comments</th></tr></thead><tbody><tr><td>96-well Tissue culture plates</td><td>Materials</td><td>Corning (Sigma-Aldrich)</td><td>CLS3596</td><td>Original unspecified</td></tr><tr><td>6-well tissue culture plates</td><td>Materials</td><td>Corning (Sigma-Aldrich)</td><td>CLS3516</td><td>Original unspecified</td></tr><tr><td>KHM-3S cells</td><td>Cells</td><td>JCRB Cell Bank</td><td>JCRB0138</td><td>Original source of the cells unspecified</td></tr><tr><td>A204 cells</td><td>Cells</td><td>ATCC</td><td>HTB-82</td><td>Original source of the cells unspecified</td></tr><tr><td>M14 cells</td><td>Cells</td><td>ATCC</td><td>HTB-129</td><td>Original source of the cells unspecified</td></tr><tr><td>HL60 cells</td><td>Cells</td><td>ATCC</td><td>CCL-240</td><td/></tr><tr><td>MCF7 cells</td><td>Cells</td><td>ATCC</td><td>HTB-22</td><td/></tr><tr><td>HEK293 cells</td><td>Cells</td><td>ATCC</td><td>CRL-1573</td><td/></tr><tr><td>Lapatinib</td><td>Drug</td><td>LC Laboratories</td><td>L-4804</td><td>Original formulation unspecified</td></tr><tr><td>Crizotinib</td><td>Drug</td><td>Sigma-Aldrich</td><td>PZ0191</td><td>Originally from Selleck Chemicals</td></tr><tr><td>PD173074</td><td>Drug</td><td>Sigma-Aldrich</td><td>P2499</td><td>Originally from Tocris Bioscience</td></tr><tr><td>PLX4032</td><td>Drug</td><td>Active Biochem</td><td>A-1130</td><td/></tr><tr><td>Sunitinib</td><td>Drug</td><td>Sigma-Aldrich</td><td>PZ0012</td><td>Originally from Selleck Chemicals, formulation unspecified</td></tr><tr><td>Erlotinib</td><td>Drug</td><td>LC Laboratories</td><td>E-4007</td><td/></tr><tr><td>HGF</td><td>Ligand</td><td>Sigma-Aldrich</td><td>H5791</td><td>Originally obtained from Peprotech</td></tr><tr><td>FGF-basic</td><td>Ligand</td><td>Sigma-Aldrich</td><td>F0291</td><td>Originally obtained from Peprotech</td></tr><tr><td>NRG1-β1</td><td>Ligand</td><td>Novus Biologicals</td><td>P1426</td><td>Originally obtained from R&D Systems</td></tr><tr><td>RPMI 1640</td><td>Media</td><td>Sigma-Aldrich</td><td>R8758</td><td>Originally from Gibco, formulation unspecified</td></tr><tr><td>FBS</td><td>Reagent</td><td>Sigma-Aldrich</td><td>F4135</td><td>Originally from Gibco</td></tr><tr><td>Penicillin</td><td>Antibiotic</td><td rowspan="2">Sigma-Aldrich</td><td rowspan="2">P4458</td><td>Original unspecified</td></tr><tr><td>Streptomycin</td><td>Antifungal</td><td>Original unspecified</td></tr><tr><td>Halt protease and phosphatase cocktail inhibitor</td><td>Reagent</td><td>Thermo Scientific</td><td>78440</td><td/></tr><tr><td>Image J</td><td>Software</td><td>National Institutes of Health (NIH)</td><td>N/A</td><td/></tr><tr><td>p-PDGFRα</td><td>Antibody</td><td>Santa Cruz</td><td>SC-12911</td><td>190 kDa</td></tr><tr><td>PDGFRα</td><td>Antibody</td><td>Cell Signaling</td><td>5241</td><td>190 kDa</td></tr><tr><td>p-AKT S473</td><td>Antibody</td><td>Invitrogen</td><td>44-621 G</td><td>65 kDa</td></tr><tr><td>AKT</td><td>Antibody</td><td>Cell Signaling</td><td>9272</td><td>65 kDa</td></tr><tr><td>p-ERK T202/Y204</td><td>Antibody</td><td>Cell Signaling</td><td>9101</td><td>44,42 kDa</td></tr><tr><td>ERK</td><td>Antibody</td><td>Cell Signaling</td><td>9102</td><td>44,42 kDa</td></tr><tr><td>pFRS2α Y196</td><td>Antibody</td><td>Cell Signaling</td><td>3864</td><td>85 kDa</td></tr><tr><td>FRS2α</td><td>Antibody</td><td>Santa Cruz</td><td>SC-8318</td><td>85 kDa</td></tr><tr><td>β-tubulin</td><td>Antibody</td><td>Cell Signaling</td><td>2146</td><td>55 kDa</td></tr><tr><td>pHER3 Y1289</td><td>Antibody</td><td>Cell Signaling</td><td>4791</td><td>185 kDa</td></tr><tr><td>HER3</td><td>Antibody</td><td>Santa Cruz</td><td>SC-285</td><td>185 kDa</td></tr><tr><td>p-EGFR Y1068</td><td>Antibody</td><td>Abcam</td><td>ab5644</td><td>185 kDa</td></tr><tr><td>EGFR</td><td>Antibody</td><td>BD Biosciences</td><td>610017</td><td>185 kDa</td></tr><tr><td>p-MET Y1234/5</td><td>Antibody</td><td>Cell Signaling</td><td>3126</td><td>145 kDa</td></tr><tr><td>MET</td><td>Antibody</td><td>Santa Cruz</td><td>SC-10</td><td>145 kDa</td></tr><tr><td>Anti-Mouse IgG-HRP</td><td>Antibody</td><td>Cell Signaling Technology</td><td>7076P2</td><td>Original unspecified</td></tr><tr><td>Anti-Rabbit IgG-HRP</td><td>Antibody</td><td>Cell Signaling Technology</td><td>7074P2</td><td>Original unspecified</td></tr><tr><td>Anti-Goat IgG-HRP</td><td>Antibody</td><td>Santa Cruz Biotechnology</td><td>sc-2020</td><td>Original unspecified</td></tr><tr><td>Trypsin-EDTA solution (1X)</td><td>Reagent</td><td>Sigma-Aldrich</td><td>T3924</td><td>Original unspecified</td></tr><tr><td>Dulbecco’s Phosphate Buffered Saline</td><td>Reagent</td><td>Sigma-Aldrich</td><td>D1408</td><td>Original unspecified</td></tr><tr><td>Mini Protean TGX 4–15% Tris-Glycine gels; 15-well; 15 μl</td><td>Reagent</td><td>Bio-Rad</td><td>456-1086</td><td>Original unspecified</td></tr><tr><td>2X Laemmli sample buffer</td><td>Reagent</td><td>Sigma-Aldrich</td><td>S3401</td><td>Original unspecified</td></tr><tr><td>ECL DualVue Western Markers (15 to 150 kDa)</td><td>Reagent</td><td>Sigma-Aldrich</td><td>GERPN810</td><td>Original unspecified</td></tr><tr><td>Nitrocellulose membrane; 0.45 μm, 20 × 20 cm</td><td>Reagent</td><td>Bio-Rad</td><td>162-0113</td><td>Original unspecified</td></tr><tr><td>Ponceau S</td><td>Reagent</td><td>Sigma-Aldrich</td><td>P7170</td><td>Original unspecified</td></tr><tr><td>Tris Buffered Saline (TBS); 10X solution</td><td>Reagent</td><td>Sigma-Aldrich</td><td>T5912</td><td>Original unspecified</td></tr><tr><td>Tween 20</td><td>Reagent</td><td>Sigma-Aldrich</td><td>P1379</td><td>Original unspecified</td></tr><tr><td>Nonfat-Dried Milk</td><td>Reagent</td><td>Sigma-Aldrich</td><td>M7409</td><td>Original unspecified</td></tr><tr><td>Super Signal West Pico Substrate</td><td>Reagent</td><td>Thermo-Fisher (Pierce)</td><td>34087</td><td/></tr></tbody></table></table-wrap></p></sec><sec id="s2-2-3"><title>Procedure</title><sec id="s2-2-3-1"><title>Notes</title><p><list list-type="bullet"><list-item><p>All cells will be sent for mycoplasma testing and STR profiling.</p></list-item><list-item><p>Medium for cell lines: RPMI 1640 supplemented with 10% FBS, 50 U/ml penicillin, and 50 µg/ml streptomycin.</p><list list-type="order"><list-item><p>MCF7 cells and HEK293 cells are maintained in DMEM + 10% FBS.</p></list-item></list></list-item><list-item><p>Cells maintained at 37°C in a humidified atmosphere at 5% CO<sub>2</sub>.</p><list list-type="order"><list-item><p>Seed cells in plates.</p><list list-type="simple"><list-item><p>a. Two control and four experimental wells (6 wells total) are needed for each cell line in Cohorts 1–3.</p><list list-type="simple"><list-item><p>i. Lab will determine and record the number of cells seeded and well size used.</p></list-item></list></list-item><list-item><p>b. *For Cohort 4 seed cells as needed into wells of a 6-well plate.</p></list-item></list></list-item><list-item><p>18–24 hr after seeding treat wells in Cohorts 1–3 with conditions as described in the Sampling section.</p><list list-type="simple"><list-item><p>a. Lab will determine and record vehicle for preparation of drug solutions.</p></list-item><list-item><p>b. Harvest protein as in Step 5 after 2 hr of treatment.</p></list-item></list></list-item><list-item><p>Simultaneously treat cells in Cohort 4 as follows:</p><list list-type="simple"><list-item><p>a. HL60 cells. Note: This protocol is based on <xref ref-type="bibr" rid="bib9">Krejci et al. (2003)</xref>.</p><list list-type="simple"><list-item><p>i. Serum starve HL60 cells for 24 hr prior to protein harvesting.</p><list list-type="bullet"><list-item><p>Serum starve = DMEM + 0% FBS.</p></list-item></list></list-item><list-item><p>ii. Treat cells for 10 min with 100 ng/ml FGF.</p></list-item><list-item><p>iii. Harvest cell lysates as noted in Step 5.</p></list-item></list></list-item><list-item><p>b. MCF7 cells. Note: This protocol is based on <xref ref-type="bibr" rid="bib16">Sarup et al. (2008)</xref>.</p><list list-type="simple"><list-item><p>i. Serum starve cells for 48 hr prior to protein harvesting.</p><list list-type="bullet"><list-item><p>Serum starve = DMEM + 0.1% BSA.</p></list-item></list></list-item><list-item><p>ii. Treat cells with 1 nmol/l NRG1 for 10 min at 37°C.</p></list-item><list-item><p>iii. Harvest cell lysates as noted in Step 5.</p></list-item></list></list-item><list-item><p>c. HEK293 cells. Note: This protocol is based on <xref ref-type="bibr" rid="bib21">Wright et al. (2012)</xref>.</p><list list-type="simple"><list-item><p>i. Serum starve HEK293 cells for 24 hr prior to protein harvesting.</p><list list-type="bullet"><list-item><p>Serum starve = DMEM + 0% FBS.</p></list-item></list></list-item><list-item><p>ii. Treat cells with 29 ng/ml HGF for 10 min at 37°C.</p></list-item><list-item><p>iii. Harvest cell lysates as noted in Step 5.</p></list-item></list></list-item></list></list-item><list-item><p><sup>#</sup>Preparation of cell lysate:</p><list list-type="simple"><list-item><p>a. Note: from here on, the replicating lab will use their in-house Western blot protocol, as recommended by the original authors.</p></list-item><list-item><p>b. Harvest cells from the tissue culture plate using 1× trypsin–EDTA.</p></list-item><list-item><p>c. Wash cells with 1× cold PBS and spin at 1200 rpm for 5 min.</p></list-item><list-item><p>d. Decant the PBS and add lysis buffer to the cell pellet and resuspend well.</p></list-item><list-item><p>e. Incubate at room temperature for 5 min.</p></list-item><list-item><p>f. Spin solution at 13,000 rpm for 30 min at 4°C using a benchtop centrifuge.</p></list-item><list-item><p>g. Collect the lysate/protein sample and store at −20°C or −80°C for later use.</p></list-item></list></list-item><list-item><p><sup>#</sup>SDS-PAGE separation:</p><list list-type="simple"><list-item><p>a. Prepare the lysate sample by adding SDS reducing loading dye to ∼25–30 µg of protein sample and boiling at 95°C–100°C for 5 min.</p><list list-type="simple"><list-item><p>i. Lab will record exact amount of protein loaded and provide data from determining protein concentration.</p></list-item></list></list-item><list-item><p>b. Let samples cool on ice and quick-spin the tubes to collect any droplets on the cap of the tube.</p></list-item><list-item><p>c. Prepare the gel for sample loading—insert the gel in the gel box with 1× running buffer and ensure there is no leak.</p><list list-type="simple"><list-item><p>i. Based on the expected MWs of the targets, lab will determine the optimal percentage gel to use.</p></list-item></list></list-item><list-item><p>d. Load 16 µl of sample (25–30 µg/lane) in each well of the Tris–glycine gel.</p></list-item><list-item><p>e. Run the sample at 175 V for 25 min.</p></list-item><list-item><p>f. Remove the gel from the cassette and rinse with water.</p></list-item></list></list-item><list-item><p><sup>#</sup>Transfer and blocking:</p><list list-type="simple"><list-item><p>a. Transfer protein on the gel to a nitrocellulose membrane for 1 hr at 12 V using a semi-dry transfer apparatus, 1× transfer buffer, and blotting sheets.</p></list-item><list-item><p>b. Verify the efficiency of the transfer by Ponceau staining of the membrane.</p><list list-type="simple"><list-item><p>i. Lab will record an image of the Ponceau-stained membrane.</p></list-item></list></list-item><list-item><p>c. Incubate the blots in 5% non-fat skim milk for 1 hr at room temperature.</p></list-item></list></list-item><list-item><p><sup>#</sup>Antibody probing:</p><list list-type="simple"><list-item><p>a. Dilute the primary antibodies according to the manufacturer's recommendations, as suggested by the original authors.</p><list list-type="simple"><list-item><p>i. If the manufacturer recommends a range of dilutions, lab will use a dilution in the middle of the recommended dilution range.</p></list-item><list-item><p>ii. A204:</p><list list-type="bullet"><list-item><p>p-PDGFRα.</p></list-item><list-item><p>PDGFRα.</p></list-item><list-item><p>p-AKT S473.</p></list-item><list-item><p>AKT.</p></list-item><list-item><p>p-ERK T202/Y204.</p></list-item><list-item><p>ERK.</p></list-item><list-item><p>pFRS2α Y196.</p></list-item><list-item><p>FRS2α.</p></list-item><list-item><p>β-tubulin [additional control].</p><list list-type="simple"><list-item><p>A. Loading control.</p></list-item></list></list-item></list></list-item><list-item><p>iii. M14:</p><list list-type="bullet"><list-item><p>pHER3 Y1289.</p></list-item><list-item><p>HER3.</p></list-item><list-item><p>p-AKT S473.</p></list-item><list-item><p>AKT.</p></list-item><list-item><p>p-ERK T202/Y204.</p></list-item><list-item><p>ERK.</p></list-item><list-item><p>β-tubulin [additional control].</p><list list-type="simple"><list-item><p>A. Loading control.</p></list-item></list></list-item></list></list-item><list-item><p>iv. KHM-3S:</p><list list-type="bullet"><list-item><p>p-EGFR Y1068.</p></list-item><list-item><p>EGFR.</p></list-item><list-item><p>p-AKT S473.</p></list-item><list-item><p>AKT.</p></list-item><list-item><p>p-ERK T202/Y204.</p></list-item><list-item><p>ERK.</p></list-item><list-item><p>p-MET Y1234/5.</p></list-item><list-item><p>MET.</p></list-item><list-item><p>β-tubulin [additional control].</p><list list-type="simple"><list-item><p>A. Loading control.</p></list-item></list></list-item></list></list-item><list-item><p>v. HL60:</p><list list-type="bullet"><list-item><p>pERK T202/Y204.</p></list-item><list-item><p>ERK.</p></list-item><list-item><p>β-tubulin [additional control].</p><list list-type="simple"><list-item><p>A. Loading control.</p></list-item></list></list-item></list></list-item><list-item><p>vi. MCF7:</p><list list-type="bullet"><list-item><p>pHER3.</p></list-item><list-item><p>HER3.</p></list-item><list-item><p>β-tubulin [additional control].</p><list list-type="simple"><list-item><p>A. Loading control.</p></list-item></list></list-item></list></list-item><list-item><p>vii. HEK293:</p><list list-type="bullet"><list-item><p>pMET.</p></list-item><list-item><p>MET.</p></list-item><list-item><p>β-tubulin [additional control].</p><list list-type="simple"><list-item><p>A. Loading control.</p></list-item></list></list-item></list></list-item></list></list-item><list-item><p>b. Add the antibody solutions to the membranes and incubate them for 12–16 hr at 4°C.</p></list-item><list-item><p>c. Wash the blots with Tris-buffered saline (TBS) and with 0.5% Tween-20 three times for 10 min each.</p></list-item><list-item><p>d. Dilute HRP-secondary antibody in 5% milk and add to the blots.</p><list list-type="simple"><list-item><p>i. Lab will record the dilution factor of the secondary antibody.</p></list-item></list></list-item><list-item><p>e. Incubate at room temperature for 1 hr.</p></list-item><list-item><p>f. Wash the blots with TBS +0.5% Tween-20 four times for 15 min each.</p></list-item></list></list-item><list-item><p><sup>#</sup>Developing:</p><list list-type="simple"><list-item><p>a. Remove as much wash buffer as possible.</p></list-item><list-item><p>b. Mix Super Signal West Pico Chemiluminescent Substrate solutions in equal proportions and add it to the blot.</p></list-item><list-item><p>c. Incubate for ∼1 min.</p></list-item><list-item><p>d. Insert the blot in the developing cassette and develop the blot in the dark.</p></list-item><list-item><p>e. Expose the blot to the film at three time points, starting with 15 s. Determine the other two time points based on the strength of the signal in the 15 s exposure.</p></list-item></list></list-item><list-item><p><sup>#</sup>Scan film and quantify band intensity using densitometric analysis software.</p></list-item><list-item><p>Repeat independently two additional times.</p></list-item></list></list-item></list></p></sec></sec><sec id="s2-2-4"><title>Deliverables</title><p><list list-type="bullet"><list-item><p>Data to be collected:</p><list list-type="order"><list-item><p>Images of probed membranes (images of full films with molecular weight ladders).</p></list-item><list-item><p>Scanned image of Ponceau-stained membranes after protein transfer.</p></list-item><list-item><p>Quantified signal intensities and bar graphs of mean signal intensities normalized for β-tubulin loading and total pan-protein levels.</p></list-item></list></list-item></list></p></sec><sec id="s2-2-5"><title>Confirmatory analysis plan</title><p><list list-type="bullet"><list-item><p>Statistical analysis of the Replication Data:</p><list list-type="order"><list-item><p>For each cell line compare the following normalized phosphorylated kinase levels of primary kinase inhibitor alone, primary kinase inhibitor + ligand, and primary kinase inhibitor + ligand + secondary kinase inhibitor.</p><list list-type="simple"><list-item><p>• One-way ANOVA.</p></list-item><list-item><p>• Note: at the time of analysis, we will generate a histogram of all the data to determine if it follows a Gaussian distribution or not. If it is skewed, we will perform the appropriate transformation in order to proceed with the proposed statistical analysis.</p></list-item></list></list-item></list></list-item><list-item><p>Meta-analysis of original and replication attempt effect sizes:</p><list list-type="order"><list-item><p>We will plot the replication data (mean and 95% confidence interval) and will include the original data point, calculated directly from the representative image in Figure 2C, as a single point on the same plot for comparison.</p></list-item></list></list-item></list></p></sec><sec id="s2-2-6"><title>Known differences from the original study</title><p><list list-type="bullet"><list-item><p>We are including three additional control conditions;</p><list list-type="order"><list-item><p>Media alone.</p><list list-type="simple"><list-item><p>i. To provide a baseline.</p></list-item></list></list-item><list-item><p>Treatment of the cells with the secondary kinase inhibitor alone.</p><list list-type="simple"><list-item><p>i. To assess any effects, the secondary kinase inhibitor may be independent of the ligand and primary kinase inhibitor.</p></list-item></list></list-item><list-item><p>Treatment of a control cell line with the growth factor ligand alone.</p><list list-type="simple"><list-item><p>i. To ensure the growth factor ligand is active.</p><list list-type="bullet"><list-item><p>FGF should cause phosphorylation of ERK1/2 in HL60 cells.</p></list-item><list-item><p>NRG1 should cause phosphorylation of HER3 in MCF7 cells.</p></list-item><list-item><p>HGF should cause phosphorylation of MET in HEK293 cells.</p></list-item></list></list-item></list></list-item></list></list-item><list-item><p>The original authors recommended that the replicating lab follows a standard Western blot protocol.</p></list-item></list></p></sec><sec id="s2-2-7"><title>Provisions for quality control</title><p><list list-type="bullet"><list-item><p>All data obtained from the experiment—raw data, data analysis, control data and quality control data—will be made publicly available, either in the published manuscript or as an open access dataset available on the Open Science Framework (<ext-link xmlns:xlink="http://www.w3.org/1999/xlink" ext-link-type="uri" xlink:href="https://osf.io/h0pnz/">https://osf.io/h0pnz/</ext-link>).</p></list-item><list-item><p>Cell lines will be validated by STR profiling and screened for mycoplasma contamination.</p></list-item><list-item><p>A lab from the Science Exchange network with extensive experience in conducting Western blot assays for phosphorylated proteins will perform these experiments.</p></list-item></list></p></sec></sec><sec id="s2-3"><title>Power Calculations</title><sec id="s2-3-1"><title>Protocol 1</title><p>The original data presented is qualitative (images of survival curves) and the authors were unable to share the raw data values with the RP:CB core team. To estimate original effect sizes, we determined approximate IC<sub>50</sub> concentrations from the original survival curve images.</p><p>Summary of the original data.<table-wrap id="tbl3" position="anchor"><table frame="hsides" rules="groups"><thead><tr><th>A204 cells</th><th>IC<sub>50</sub></th></tr></thead><tbody><tr><td>Sunitinib</td><td>0.05 μM</td></tr><tr><td>Sunitinib + FGF</td><td>2.5 μM</td></tr><tr><td>Sunitinib + FGF + PD173074</td><td>0.025 μM</td></tr></tbody></table><table-wrap-foot><fn><p>• FGF induces resistance to Sunitinib.</p></fn><fn><p>• PD173074 blocks FGF-induced resistance to Sunitinib, restoring sensitivity.</p></fn></table-wrap-foot></table-wrap><table-wrap id="tbl4" position="anchor"><table frame="hsides" rules="groups"><thead><tr><th>M14</th><th>IC<sub>50</sub></th></tr></thead><tbody><tr><td>PLX4032</td><td>0.1 μM</td></tr><tr><td>PLX4032 + NRG1</td><td>0.2 μM</td></tr><tr><td>PLX4032 + NRG1 + Lapatinib</td><td>0.1 μM</td></tr></tbody></table><table-wrap-foot><fn><p>• NRG1 induces partial resistance to PLX4032.</p></fn><fn><p>• Lapatinib blocks NRG1-induced resistance to PLX4032, restoring sensitivity.</p></fn></table-wrap-foot></table-wrap><table-wrap id="tbl5" position="anchor"><table frame="hsides" rules="groups"><thead><tr><th>KHM-3S</th><th>IC<sub>50</sub></th></tr></thead><tbody><tr><td>Erlotinib</td><td>0.5 μM</td></tr><tr><td>Erlotinib + HGF</td><td>>10 μM</td></tr><tr><td>Erlotinib + HGF + Crizotinib</td><td>0.3 μM</td></tr></tbody></table><table-wrap-foot><fn><p>• HGF induces resistance to Erlotinib.</p></fn><fn><p>• Crizotinib blocks HGF-induced resistance to Erlotinib, restoring sensitivity.</p></fn></table-wrap-foot></table-wrap></p><p>We have calculated the projected sample size based on a variety of different possible levels of variance using a one-way ANOVA test with an alpha error of 0.05.<list list-type="bullet"><list-item><p>These power calculations were performed with G*Power software, version 3.1.7 (<xref ref-type="bibr" rid="bib3a">Faul et al., 2007</xref>).</p></list-item><list-item><p>The F statistic was calculated at <ext-link xmlns:xlink="http://www.w3.org/1999/xlink" ext-link-type="uri" xlink:href="http://statpages.org/anova1sm.html">http://statpages.org/anova1sm.html</ext-link>.</p></list-item><list-item><p>The η<sub>P</sub><sup>2</sup> was calculated using the formula on the spreadsheet accessed from Lakens and colleagues (<xref ref-type="bibr" rid="bib10">Lakens, 2013</xref>).</p></list-item></list><table-wrap id="tbl6" position="anchor"><table frame="hsides" rules="groups"><thead><tr><th colspan="6">A204</th></tr><tr><th>Variance</th><th>F (2, 6)</th><th>η<sub>P</sub><sup>2</sup></th><th>Effect size <italic>f</italic></th><th>Power</th><th>Total sample size across all groups</th></tr></thead><tbody><tr><td>2%</td><td>7273.6132</td><td>0.999588</td><td>49.25631</td><td>99.99%</td><td>6</td></tr><tr><td>15%</td><td>129.3087</td><td>0.977326</td><td>6.565316</td><td>99.99%</td><td>6</td></tr><tr><td>28%</td><td>37.1103</td><td>0.925206</td><td>3.517109</td><td>98.53%</td><td>6</td></tr><tr><td>40%</td><td>18.184</td><td>0.858384</td><td>2.461981</td><td>85.32%</td><td>6</td></tr></tbody></table></table-wrap></p><p>For each percent variance, the relative standard deviation of the approximated IC<sub>50</sub> was used to calculate the F statistic from a one-way ANOVA analysis, which was converted to η<sub>P</sub><sup>2</sup> (the ratio of variance attributed to the effect and the effect plus its associate error variance from the ANOVA), and then used to determine the effect size (Cohen's <italic>f</italic>) and the needed sample size to obtain at least 80% power. The actual power obtained is listed.<table-wrap id="tbl7" position="anchor"><table frame="hsides" rules="groups"><thead><tr><th colspan="6">M14</th></tr><tr><th>Variance</th><th>F (2, 6)</th><th>η<sub>P</sub><sup>2</sup></th><th>Effect size <italic>f</italic></th><th>Power</th><th>Total sample size across all groups</th></tr></thead><tbody><tr><td>2%</td><td>1250</td><td>0.997606</td><td>20.4135</td><td>99.99%</td><td>6</td></tr><tr><td>15%</td><td>22.2222</td><td>0.881057</td><td>2.721652</td><td>90.90%</td><td>6</td></tr><tr><td>28%</td><td>6.3776</td><td>0.680089</td><td>1.458036</td><td>85.39%</td><td>9</td></tr><tr><td>40%</td><td>3.125</td><td>0.510204</td><td>1.020621</td><td>88.33%</td><td>15</td></tr></tbody></table><table frame="hsides" rules="groups"><thead><tr><th colspan="6">KHM-S3</th></tr><tr><th>Variance</th><th>F (2, 6)</th><th>η<sub>P</sub><sup>2</sup></th><th>Effect size <italic>f</italic></th><th>Power</th><th>Total sample size across all groups</th></tr></thead><tbody><tr><td>2%</td><td>6890.8212</td><td>0.999565</td><td>47.9359</td><td>99.99%</td><td>6</td></tr><tr><td>15%</td><td>122.5035</td><td>0.976096</td><td>6.390149</td><td>99.99%</td><td>6</td></tr><tr><td>28%</td><td>35.1573</td><td>0.921378</td><td>3.423315</td><td>98.12%</td><td>6</td></tr><tr><td>40%</td><td>17.2271</td><td>0.851684</td><td>2.396322</td><td>83.59%</td><td>6</td></tr></tbody></table></table-wrap></p><p>In order to produce quantitative replication data, we will run the experiment three times. Each time we will quantify the IC<sub>50</sub>. We will determine the standard deviation of the IC<sub>50</sub> across the three biological replicates and combine this with the means from the original study to simulate an effect size. Using this simulated effect size, we will then determine the number of replicates necessary to reach a power of at least 80%. We will then perform additional replicates, if required, to ensure that the experiment has more than 80% power to detect the original effect.</p></sec><sec id="s2-3-2"><title>Protocol 2</title><p>The original data presented is qualitative (images of Western Blots). We used Image Studio Lite v. 4.0.21 (LICOR) to perform densitometric analysis of the presented bands to quantify the original effect size. Levels of phospho-protein were normalized to total protein and then normalized to the control.</p><p>Summary of original data.<table-wrap id="tbl8" position="anchor"><table frame="hsides" rules="groups"><thead><tr><th>A204 cells</th><th>pPDGFR</th><th>pAKT</th><th>pERK</th><th>pFRS2</th></tr></thead><tbody><tr><td>Control</td><td>1</td><td>1</td><td>1</td><td>1</td></tr><tr><td>Sunitinib alone</td><td>0.264</td><td>0.0845</td><td>1.952</td><td>1.473</td></tr><tr><td>Sunitinib + FGF</td><td>0.337</td><td>0.092</td><td>5.350</td><td>8.069</td></tr><tr><td>Sunitinib + FGF + PD173074</td><td>0.304</td><td>0.071</td><td>0.369</td><td>1.013</td></tr></tbody></table><table-wrap-foot><fn><p>• FGF activates pFRS2 and pERK in the presence of Sunitinib.</p></fn><fn><p>• PD173074 blocks FGF-induced pFRS2 and pERK activation.</p></fn></table-wrap-foot></table-wrap><table-wrap id="tbl9" position="anchor"><table frame="hsides" rules="groups"><thead><tr><th>M14 cells</th><th>pHER3</th><th>pAKT</th><th>pERK</th></tr></thead><tbody><tr><td>Control</td><td>1</td><td>1</td><td>1</td></tr><tr><td>PLX4032 alone</td><td>0.3667</td><td>1.8645</td><td>0.0524</td></tr><tr><td>PLX4032 + NRG1</td><td>3.9447</td><td>11.211</td><td>0.0539</td></tr><tr><td>PLX4032 + NRG1 + Lapatinib</td><td>1.0666</td><td>1.7863</td><td>0.0571</td></tr></tbody></table><table-wrap-foot><fn><p>• NRG1 activates pHER3 and pAKT in the presence of PLX4032.</p></fn><fn><p>• Lapatinib blocks NRG1-induced pHER3 and pAKT activation.</p></fn></table-wrap-foot></table-wrap><table-wrap id="tbl10" position="anchor"><table frame="hsides" rules="groups"><thead><tr><th>KHM-3S cells</th><th>pEGFR</th><th>pAKT</th><th>pERK</th><th>pMET</th></tr></thead><tbody><tr><td>Control</td><td>1</td><td>1</td><td>1</td><td>1</td></tr><tr><td>Erlotinib alone</td><td>0.008</td><td>0.609</td><td>0.18</td><td>1.098</td></tr><tr><td>Erlotinib + HGF</td><td>0.014</td><td>1.381</td><td>0.979</td><td>11.66</td></tr><tr><td>Erlotinib + HGF + Crizotinib</td><td>0.023</td><td>0.417</td><td>0.085</td><td>1.095</td></tr></tbody></table><table-wrap-foot><fn><p>• HGF activates pMET and pERK in the presence of Erlotinib.</p></fn><fn><p>• Crizotinib blocks HGF-induced pMET and pERK activation.</p></fn></table-wrap-foot></table-wrap></p><p>We have calculated the projected sample size based on a variety of different possible levels of variance (<xref ref-type="bibr" rid="bib8">Koller and Wätzig, 2005</xref>) using a one-way ANOVA test with an alpha error of 0.05.<list list-type="bullet"><list-item><p>These power calculations were performed with G*Power software, version 3.1.7 (Faul et al., 2007).</p></list-item><list-item><p>The F statistic was calculated at <ext-link xmlns:xlink="http://www.w3.org/1999/xlink" ext-link-type="uri" xlink:href="http://statpages.org/anova1sm.html">http://statpages.org/anova1sm.html</ext-link>.</p></list-item><list-item><p>The η<sub>P</sub><sup>2</sup> was calculated using the formula on the spreadsheet accessed from Lakens and colleagues (<xref ref-type="bibr" rid="bib10">Lakens, 2013</xref>).</p></list-item></list><table-wrap id="tbl11" position="anchor"><table frame="hsides" rules="groups"><thead><tr><th colspan="5">A204 cells</th></tr><tr><th>2% Variance</th><th>pPDGFR</th><th>pAKT</th><th>pERK</th><th>pFRS2</th></tr></thead><tbody><tr><td>F(3, 8)</td><td>2884.5133</td><td>6189.0064</td><td>4400.8341</td><td>5183.0738</td></tr><tr><td>η<sub>p</sub>²</td><td>0.999076377</td><td>0.999569314</td><td>0.999394421</td><td>0.999485769</td></tr><tr><td>Effect size <italic>f</italic></td><td>32.8891</td><td>48.17548</td><td>40.62403</td><td>44.08686</td></tr><tr><td>Power</td><td>99.99%</td><td>99.99%</td><td>99.99%</td><td>99.99%</td></tr><tr><td>Total sample size across all groups</td><td>8</td><td>8</td><td>8</td><td>8</td></tr></tbody></table><table frame="hsides" rules="groups"><thead><tr><th>15% variance</th><th>pPDGFR</th><th>pAKT</th><th>pERK</th><th>pFRS2</th></tr></thead><tbody><tr><td>F(3, 8)</td><td>51.28023644</td><td>110.0267804</td><td>78.23705067</td><td>92.14353422</td></tr><tr><td>η<sub>p</sub>²</td><td>0.950568679</td><td>0.976336986</td><td>0.967039009</td><td>0.971873631</td></tr><tr><td>Effect size <italic>f</italic></td><td>4.385212</td><td>6.423398</td><td>5.416539</td><td>5.87825</td></tr><tr><td>Power</td><td>99.99%</td><td>99.99%</td><td>99.99%</td><td>99.99%</td></tr><tr><td>Total sample size across all groups</td><td>8</td><td>8</td><td>8</td><td>8</td></tr></tbody></table><table frame="hsides" rules="groups"><thead><tr><th>28% variance</th><th>pPDGFR</th><th>pAKT</th><th>pERK</th><th>pFRS2</th></tr></thead><tbody><tr><td>F(3, 8)</td><td>14.71690459</td><td>31.57656327</td><td>22.4532352</td><td>26.44425408</td></tr><tr><td>η<sub>p</sub>²</td><td>0.846598456</td><td>0.922125726</td><td>0.893842473</td><td>0.908396348</td></tr><tr><td>Effect size <italic>f</italic></td><td>2.349221</td><td>3.441106</td><td>2.901717</td><td>3.149063</td></tr><tr><td>Power</td><td>91.97%</td><td>99.79%</td><td>98.43%</td><td>99.35%</td></tr><tr><td>Total sample size</td><td>8</td><td>8</td><td>8</td><td>8</td></tr></tbody></table><table frame="hsides" rules="groups"><thead><tr><th>40% variance</th><th>pPDGFR</th><th>pAKT</th><th>pERK</th><th>pFRS2</th></tr></thead><tbody><tr><td>F(3, 8)</td><td>7.21128325</td><td>15.472516</td><td>11.00208525</td><td>12.9576845</td></tr><tr><td>η<sub>p</sub>²</td><td>0.73003845</td><td>0.852988598</td><td>0.804907816</td><td>0.829326246</td></tr><tr><td>Effect size <italic>f</italic></td><td>1.644455</td><td>2.408774</td><td>2.031202</td><td>2.204344</td></tr><tr><td>Power</td><td>96.95%</td><td>93.12%</td><td>83.18%</td><td>88.55%</td></tr><tr><td>Total sample size across all groups</td><td>12</td><td>8</td><td>8</td><td>8</td></tr></tbody></table></table-wrap></p><p>For each percent variance, the relative standard deviation of the approximated phospho-protein level was used to calculate the F statistic from a one-way ANOVA analysis, which was converted to η<sub>P</sub><sup>2</sup> (the ratio of variance attributed to the effect and the effect plus its associated error variance from the ANOVA), and then used to determine the effect size (Cohen's <italic>f</italic>) and the needed sample size to obtain at least 80% power. The actual power obtained is listed.<table-wrap id="tbl12" position="anchor"><table frame="hsides" rules="groups"><thead><tr><th colspan="4">M14 cells</th></tr><tr><th>2% Variance</th><th>pHER3</th><th>pAKT</th><th>pERK</th></tr></thead><tbody><tr><td>F(3, 8)</td><td>4297.4601</td><td>5283.2994</td><td>6645.7378</td></tr><tr><td>η<sub>p</sub>²</td><td>0.999379863</td><td>0.99949552</td><td>0.999598901</td></tr><tr><td>Effect size <italic>f</italic></td><td>40.14408</td><td>44.51111</td><td>49.92144</td></tr><tr><td>Power</td><td>99.99%</td><td>99.99%</td><td>99.99%</td></tr><tr><td>Total sample size across all groups</td><td>8</td><td>8</td><td>8</td></tr></tbody></table><table frame="hsides" rules="groups"><thead><tr><th>15% variance</th><th>pHER3</th><th>pAKT</th><th>pERK</th></tr></thead><tbody><tr><td>F(3, 8)</td><td>76.39929067</td><td>93.92532267</td><td>118.1464498</td></tr><tr><td>η<sub>p</sub>²</td><td>0.966272885</td><td>0.972392466</td><td>0.977927341</td></tr><tr><td>Effect size <italic>f</italic></td><td>5.352545</td><td>5.934812</td><td>6.656194</td></tr><tr><td>Power</td><td>99.99%</td><td>99.99%</td><td>99.99%</td></tr><tr><td>Total sample size across all groups</td><td>8</td><td>8</td><td>8</td></tr></tbody></table><table frame="hsides" rules="groups"><thead><tr><th>28% variance</th><th>pHER3</th><th>pAKT</th><th>pERK</th></tr></thead><tbody><tr><td>F(3, 8)</td><td>21.92581684</td><td>26.95560918</td><td>33.90682551</td></tr><tr><td>η<sub>p</sub>²</td><td>0.891565784</td><td>0.909977657</td><td>0.927087448</td></tr><tr><td>Effect size <italic>f</italic></td><td>2.867435</td><td>3.179364</td><td>3.565818</td></tr><tr><td>Power</td><td>98.24%</td><td>99.42%</td><td>99.88%</td></tr><tr><td>Total sample size</td><td>8</td><td>8</td><td>8</td></tr></tbody></table><table frame="hsides" rules="groups"><thead><tr><th>40% variance</th><th>pHER3</th><th>pAKT</th><th>pERK</th></tr></thead><tbody><tr><td>F(3, 8)</td><td>10.74365025</td><td>13.2082485</td><td>16.6143445</td></tr><tr><td>η<sub>p</sub>²</td><td>0.801148125</td><td>0.8320201</td><td>0.861694667</td></tr><tr><td>Effect size <italic>f</italic></td><td>2.007204</td><td>2.225555</td><td>2.496073</td></tr><tr><td>Power</td><td>82.32%</td><td>89.11%</td><td>94.57%</td></tr><tr><td>Total sample size across all groups</td><td>8</td><td>8</td><td>8</td></tr></tbody></table><table frame="hsides" rules="groups"><thead><tr><th colspan="5">KHM-S3 cells</th></tr><tr><th>2% Variance</th><th>pEGFR</th><th>pAKT</th><th>pERK</th><th>pMET</th></tr></thead><tbody><tr><td>F(3, 8)</td><td>7271.894</td><td>1594.1561</td><td>3697.7822</td><td>6041.5258</td></tr><tr><td>η<sub>p</sub>²</td><td>0.999633426</td><td>0.998330017</td><td>0.999279367</td><td>0.999558805</td></tr><tr><td>Effect size <italic>f</italic></td><td>52.22032</td><td>24.45012</td><td>37.238</td><td>47.59802</td></tr><tr><td>Power</td><td>99.99%</td><td>99.99%</td><td>99.99%</td><td>99.99%</td></tr><tr><td>Total sample size across all groups</td><td>8</td><td>8</td><td>8</td><td>8</td></tr></tbody></table><table frame="hsides" rules="groups"><thead><tr><th>15% variance</th><th>pEGFR</th><th>pAKT</th><th>pERK</th><th>pMET</th></tr></thead><tbody><tr><td>F(3, 8)</td><td>129.2781156</td><td>28.34055289</td><td>65.73835022</td><td>107.4049031</td></tr><tr><td>η<sub>p</sub>²</td><td>0.979789525</td><td>0.913998523</td><td>0.961016505</td><td>0.975773338</td></tr><tr><td>Effect size <italic>f</italic></td><td>6.962707</td><td>3.260016</td><td>4.965066</td><td>6.346404</td></tr><tr><td>Power</td><td>99.99%</td><td>99.57%</td><td>99.99%</td><td>99.99%</td></tr><tr><td>Total sample size across all groups</td><td>8</td><td>8</td><td>8</td><td>8</td></tr></tbody></table><table frame="hsides" rules="groups"><thead><tr><th>28% variance</th><th>pEGFR</th><th>pAKT</th><th>pERK</th><th>pMET</th></tr></thead><tbody><tr><td>F(3, 8)</td><td>37.1015</td><td>8.13344949</td><td>18.86623571</td><td>30.82411122</td></tr><tr><td>η<sub>p</sub>²</td><td>0.932944692</td><td>0.753089075</td><td>0.876158512</td><td>0.920376091</td></tr><tr><td>Effect size <italic>f</italic></td><td>3.730022</td><td>1.746437</td><td>2.659857</td><td>3.399859</td></tr><tr><td>Power</td><td>99.94%</td><td>98.31%</td><td>96.62%</td><td>99.75%</td></tr><tr><td>Total sample size across all groups</td><td>8</td><td>12</td><td>8</td><td>8</td></tr></tbody></table><table frame="hsides" rules="groups"><thead><tr><th>40% variance</th><th>pEGFR</th><th>pAKT</th><th>pERK</th><th>pMET</th></tr></thead><tbody><tr><td>F(3, 8)</td><td>18.179735</td><td>3.98539025</td><td>9.2444555</td><td>15.1038145</td></tr><tr><td>η<sub>p</sub>²</td><td>0.872080242</td><td>0.59912149</td><td>0.776119611</td><td>0.84993841</td></tr><tr><td>Effect size <italic>f</italic></td><td>2.611015</td><td>1.222506</td><td>1.8619</td><td>2.379901</td></tr><tr><td>Power</td><td>96.09%</td><td>94.83%</td><td>99.19%</td><td>92.58%</td></tr><tr><td>Total sample size across all groups</td><td>8</td><td>16</td><td>12</td><td>8</td></tr></tbody></table></table-wrap></p><p>In order to produce quantitative replication data, we will run the experiment three times. Each time we will quantify band intensity. We will determine the standard deviation of band intensity across the three biological replicates and combine this with the mean from the original study to simulate the original effect size. We will use this simulated effect size to determine the number of replicates necessary to reach a power of at least 80%. We will then perform additional replicates, if required, to ensure that the experiment has more than 80% power to detect the original effect.</p></sec></sec></sec></body><back><ack id="ack"><title>Acknowledgements</title><p>The Reproducibility Project: Cancer Biology core team would like to thank the original authors, in particular Dr Jeff Settleman, for generously sharing critical information to ensure the fidelity and quality of this replication attempt. We would also like to thank the following companies for generously donating reagents to the Reproducibility Project: Cancer Biology: American Type Culture Collection (ATCC), BioLegend, Charles River Laboratories, Corning Incorporated, DDC Medical, EMD Millipore, Harlan Laboratories, LI-COR Biosciences, Mirus Bio, Novus Biologicals, and Sigma-Aldrich.</p></ack><sec sec-type="additional-information"><title>Additional information</title><fn-group content-type="competing-interest"><title>Competing interests</title><fn fn-type="conflict" id="conf1"><p>EG: The Monoclonal Antibody Core Facility is a Science Exchange associated laboratory.</p></fn><fn fn-type="conflict" id="conf2"><p>RP:CB: EI, FT and JL are employed and holds shares in Science Exchange Inc.</p></fn><fn fn-type="conflict" id="conf3"><p>The other authors declare that no competing interests exist.</p></fn></fn-group><fn-group content-type="author-contribution"><title>Author contributions</title><fn fn-type="con" id="con1"><p>EG, Drafting or revising the article</p></fn><fn fn-type="con" id="con2"><p>EG, Drafting or revising the article</p></fn><fn fn-type="con" id="con3"><p>RP:CB, Conception and design, Drafting or revising the article</p></fn></fn-group></sec><ref-list><title>References</title><ref id="bib1"><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Camidge</surname><given-names>DR</given-names></name><name><surname>Pao</surname><given-names>W</given-names></name><name><surname>Sequist</surname><given-names>LV</given-names></name></person-group><year>2014</year><article-title>Acquired resistance to TKIs in solid tumours: learning from lung cancer</article-title><source>Nature Reviews. 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letter</article-title></title-group><contrib-group content-type="section"><contrib contrib-type="editor"><name><surname>Massagué</surname><given-names>Joan</given-names></name><role>Reviewing editor</role><aff><institution>Memorial Sloan-Kettering Cancer Center</institution>, <country>United States</country></aff></contrib></contrib-group></front-stub><body><boxed-text><p>eLife posts the editorial decision letter and author response on a selection of the published articles (subject to the approval of the authors). An edited version of the letter sent to the authors after peer review is shown, indicating the substantive concerns or comments; minor concerns are not usually shown. Reviewers have the opportunity to discuss the decision before the letter is sent (see <ext-link ext-link-type="uri" xlink:href="http://elifesciences.org/review-process">review process</ext-link>). Similarly, the author response typically shows only responses to the major concerns raised by the reviewers.</p></boxed-text><p>Thank you for sending your work entitled “Registered report: Widespread potential for growth-factor-driven resistance to anticancer kinase inhibitors” for consideration at <italic>eLife</italic>. Your article has been favorably evaluated by Tony Hunter (Senior editor) and 3 reviewers, one of whom is a member of our Board of Reviewing Editors.</p><p>The Reviewing editor and the other reviewers discussed their comments before we reached this decision, and the Reviewing editor has assembled the following comments to help you prepare a revised submission.</p><p>1) The experimental design to test the reproducibility of <xref ref-type="bibr" rid="bib20">Wilson et al. (2012)</xref> is thorough and well-articulated, with some exceptions. First, it will be important to perform positive controls to assess the performance of the growth factors or the kinase inhibitors that will be used.</p><p>2) Second, given that the western blots in the original manuscript are not quantified and that quantification is derived from the published work, the authors should describe how they are going to determine whether the data are “reproducible” or not.</p><p>3) Third, it is not immediately clear whether the distribution of the data (IC50 for Protocol 1 and band intensity for Protocol 2) will exhibit any skew. Therefore, at the time of analysis, it may be useful to plot histograms of the data to examine their distributions, and, if necessary, consider suitable transformations (for example, the Box–Cox family of transformations) of the data to obtain (approximately) symmetric distributions so that the testing procedures are valid.</p><p>4) Lastly, the authors should either include or explain the reason for excluding in the replication study the role of HGF-MET signaling in resistance to BRAF inhibition that was observed in some melanomas in the original study and other reports.</p></body></sub-article><sub-article article-type="reply" id="SA2"><front-stub><article-id pub-id-type="doi">10.7554/eLife.04037.003</article-id><title-group><article-title>Author response</article-title></title-group></front-stub><body><p><italic>1) The experimental design to test the reproducibility of</italic> <xref ref-type="bibr" rid="bib20"><italic>Wilson et al (2012)</italic></xref> <italic>is thorough and well-articulated, with some exceptions. First, it will be important to perform positive controls to assess the performance of the growth factors or the kinase inhibitors that will be used</italic>.</p><p>We agree that verifying the activity of the reagents prior to their use in our experiments is an important step. We have three classes of reagent: primary RTK inhibitors, growth factor ligands, and secondary RTK inhibitors. Each cohort includes a positive control where the cell line of interest is treated solely with its cognate primary RTK inhibitor. This should demonstrate that the drug is active as anticipated, and the quality control data (for both primary and secondary RTK inhibitors) provided by the manufacturers will be included in the materials publicly available through the Open Science Framework. However, as indicated by the reviewers, there is known lot-to-lot variation in growth factors, so we have added steps to test the growth factors we are using for activity. In Protocol 2, we have added in additional cell lines that have a known response to treatment with the ligand alone, as evidenced by phosphorylation of downstream targets. We will treat these positive control cell lines with the growth factors and assess phosphorylation of their cognate target by Western blot. The manuscript has been updated to reflect this additional work.</p><p><italic>2) Second, given that the western blots in the original manuscript are not quantified and that quantification is derived from the published work, the authors should describe how they are going to determine whether the data are “reproducible” or not</italic>.</p><p>We will present both the original data and replication data for side-by-side comparison. We will plot the mean value of our replication data along with the 95% confidence interval. We will then include the original data point (IC<sub>50</sub> or quantified Western blot band intensity) on the same plot to demonstrate if the original data falls within the 95% confidence interval of the replication data. We have also updated the language of the manuscript to reflect this change.</p><p><italic>3) Third, it is not immediately clear whether the distribution of the data (IC50 for Protocol 1 and band intensity for Protocol 2) will exhibit any skew. Therefore, at the time of analysis, it may be useful to plot histograms of the data to examine their distributions, and, if necessary, consider suitable transformations (for example, the Box–Cox family of transformations) of the data to obtain (approximately) symmetric distributions so that the testing procedures are valid</italic>.</p><p>Thank you for this suggestion. At the time of analysis, we will generate a histogram of all the data to determine if it follows a Gaussian distribution or not. If it is skewed, we will perform the appropriate transformation in order to proceed with the proposed statistical analysis. We will note any changes or transformations made. We have also updated the manuscript to address this point.</p><p><italic>4) Lastly, the authors should either include or explain the reason for excluding in the replication study the role of HGF-MET signaling in resistance to BRAF inhibition that was observed in some melanomas in the original study and other reports</italic>.</p><p>We agree that all of the experiments included in the original study are important, and choosing which experiments to replicate has been one of the great challenges of this project. In this case, the RP:CB core team felt that the most impactful information in <xref ref-type="bibr" rid="bib20">Wilson et al., 2012</xref> was that bypassing RTK inhibition by ligand-mediated activation of parallel signaling pathways was a mechanism applicable to many different types of cancer, each with its own constellation of addictive mutations and cognate inhibitors. The experiments addressing the role of HGF in activating MET signaling to bypass EGFR inhibition provide a more detailed exploration of this mechanism in one specific cancer type scenario, and support the larger conclusion drawn from the experiments we chose for replication. As such, we will restrict our analysis to the experiments being replicated and will not include discussion of experiments not being replicated in this study.</p></body></sub-article></article> |