Predictable clinical benefits without synergy in trials of combination therapies with immune checkpoint inhibitors

Predictable clinical benefits without synergy in trials of combination therapies with immune checkpoint inhibitors | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Predictable clinical benefits without synergy in trials of combination therapies with immune checkpoint inhibitors Adam (C.) Palmer, Benjamin Izar, Haeun Hwangbo, Peter Sorger This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-253936/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Hundreds of clinical trials are currently underway testing combinations of Immune Checkpoint Inhibitors (ICIs) with other cancer therapies in the hope that drug combinations will be more effective than monotherapy. Enhanced efficacy is proposed to result from drug additivity or synergy involving mechanisms such as immune priming. In this paper we re-analyze progression free survival data from thirteen recent Phase III trials of ICI combinations and find that observed benefits are fully and accurately accounted for by an increased chance that each patient will respond to a single-agent, consistent with the predictions of drug independence, with no requirement for additive or synergistic efficacy. Thus, the likely anti-tumor efficacy of new ICI combinations can be reliably predicted from monotherapy data (Pearson r=0.98, P < 5×10-9, n = 4173 patients and 8 types of cancer), although predicting adverse effects is not yet possible. Realizing the clinical potential of drug additivity or synergy is likely to require biomarkers that identify patients in whom multiple constituents of a drug combination are active. Cancer Biology Bioinformatics Computational Biology Immune Checkpoint Inhibitors drug additivity or synergy cancer therapy Figures Figure 1 Figure 2 Figure 3 Full Text Due to technical limitations, full-text HTML conversion of this manuscript could not be completed. However, the latest manuscript can be downloaded and accessed as a PDF. Additional Declarations Yes there is potential Competing Interest. PKS is a member of the SAB or Board of Directors of Glencoe Software, Applied Biomath and RareCyte Inc and has equity in these companies; he is a member of the SAB of NanoString Inc. In the last five years the Sorger lab has received research funding from Novartis and Merck. Sorger declares that none of these relationships are directly or indirectly related to the content of this manuscript. BI is a consultant for Merck and Volastra. Supplementary Files Supplement.pdf Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-253936","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":17392517,"identity":"1aa8e681-1d7f-45b1-8505-e1a2b62a4115","order_by":0,"name":"Adam (C.) Palmer","email":"","orcid":"https://orcid.org/0000-0001-5028-7028","institution":"University of North Carolina at Chapel Hill","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Adam","middleName":"(C.)","lastName":"Palmer","suffix":""},{"id":17392518,"identity":"a8e15046-1902-44bc-992c-85ddbd54e1cb","order_by":1,"name":"Benjamin Izar","email":"","orcid":"https://orcid.org/0000-0003-2379-6702","institution":"[email protected]","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Benjamin","middleName":"","lastName":"Izar","suffix":""},{"id":17392519,"identity":"80bcfca7-f4e1-43ed-a8ce-bd27c5a7174e","order_by":2,"name":"Haeun Hwangbo","email":"","orcid":"https://orcid.org/0000-0002-3687-4282","institution":"University of North Carolina at Chapel Hill","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Haeun","middleName":"","lastName":"Hwangbo","suffix":""},{"id":17392520,"identity":"8fce620a-23d6-4a5f-8c09-8e8c5331499e","order_by":3,"name":"Peter Sorger","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABMElEQVRIiWNgGAWjYBAC+wYwVQfhfQTxJCBsRiDbAJsWgwPMIOowRNVMErQcAHOYeYnScrz/4OMKhgNy5v2Lj3223bHNnkG6+dlnnpp7sg3szdsksPml5zCz4RmGOmOZG8+SZ+eeuZ3YIHPMeDbPsWLjBp5jZdi0GEgks0k2MDAnzpA4Y8yc23Y7gUEiwZhxBltCYoNEjhlWLfKP2X9CtJz/zGzZdtueQSL9M+OMf0At8m+wa5FgZmMEa+HvYWZmbLvNCDTcmOFjG8gWHuxaeJKNgQ47bCwhwWbM2Nt2O7FNIqeY4WNfgnEbT1qxBTYt7AcfAiOwTk6C//Bjhp9Ah/FLpG9mSPiWINvPfnjjDWyhDAKM/4CERAKEwwYTZcOuGAnwHyCoZBSMglEwCkYoAAB//mCUCPJYMQAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0002-3364-1838","institution":"Harvard Medical School","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Peter","middleName":"","lastName":"Sorger","suffix":""}],"badges":[],"createdAt":"2021-02-18 06:05:53","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-253936/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-253936/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":7162025,"identity":"6548732c-ff36-4300-a3ed-1855ad5bb4cd","added_by":"auto","created_at":"2021-03-19 20:48:00","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":180929,"visible":true,"origin":"","legend":"Calculating the expected activity of a combination therapy under the null hypothesis of\nindependent drug action. Solely for clarity of illustration, quanta of probability are drawn as large\n(4%), and simulated patients are few (25); in practice PFS is resolved to \u003c0.1%, and \u003e104 patients are\nsimulated. Step 4 illustrates how patient-to-patient variability in best single-agent responses causes\ncombination therapy to improve average response, but this does not occur if one therapy is so inferior to\nthe other that it is never a better option for any individual patient.","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-253936/v1/b8f2f483282f3a6b9b37a905.png"},{"id":7162026,"identity":"ba31a5a8-6851-42ee-9ba2-e93d4c57adb0","added_by":"auto","created_at":"2021-03-19 20:48:00","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":230375,"visible":true,"origin":"","legend":"Progression Free Survival for combination therapies as observed in clinical trials and as\npredicted from independent activity of the therapies comprising the combination. Progression Free\nSurvival (PFS) observed for each combination therapy (blue) was compared to that expected from the\nPFS distributions of the constituents of the combination (green and magenta) under the null hypothesis\nthat each patient’s PFS is the best of their two possible responses to constituent therapies (black line and\ngrey range, which reflects uncertainty in response correlation (ρ = 0.3 ± 0.2) or cross-resistance). 2L and\n2L+ indicate data from patients treated at second-line or later; all other data are from patients previously\nuntreated for metastatic or advanced cancer. TPS: PD-L1 Tumor Proportion Score. CPS: PD-L1\nCombined Proportion Score. Combination therapy data from: a CheckMate 06712, b KEYNOTE-18913,\nc KEYNOTE-40714, d IMpassion13017, e KEYNOTE-42616, f JAVELIN Renal 10115, g IMpower13310\nand CASPIAN19, h IMpower13023, i KEYNOTE-04822, j KEYNOTE-06218, k IMpower15024 (note that\nthe difference between the prediction of independence – black line – and observed PFS – blue line - is\nsignificant for left panel and not for right panel; see Figure 3), l NCT0032415520. m KEYNOTE-02221.\nData sources, patient characteristics, and limitations are described in Supplementary Tables 1 and 2.","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-253936/v1/dbcfb61e14dc110f29f89c85.jpg"},{"id":7161639,"identity":"0849438c-84b0-40be-86ad-a3a6c7f309d9","added_by":"auto","created_at":"2021-03-19 20:45:00","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":194918,"visible":true,"origin":"","legend":"Trials combining Immune Checkpoint Inhibitors with other cancer therapies are\nconsistent with, or inferior to, independent drug action. a. Patient events (progression or censoring) \nfrom clinical trials were compared to the prediction of independent drug action by computing the Hazard\nRatio (Cox Proportional Hazards model). Error bars are 95% confidence intervals. Superiority to\nindependence would indicate ‘drug additivity or synergy’, while inferiority could occur for multiple\nreasons including antagonistic interaction, toxicity necessitating dose reduction/interruption, or strong\ncross-resistance. HNSCC: Head and Neck Squamous Cell Carcinoma; NSCLC: Non-Small-Cell Lung\nCancer; TPS: PD-L1 Tumor Proportion Score; CPS: PD-L1 Combined Proportion Score. Data in this\nand subsequent panels are color coded to denote deviation from the ideal case of a combination in which\ndirectly comparable monotherapies are available. See text for details. b, c. Observed PFS has a strong\nlinear correlation with PFS expected under the null hypothesis of independent drug action, both at a\nlandmark of 12 months (Pearson’s r = 0.98, P \u003c 10–8\n, all trials) and over all measured times; r = 0.99 for\ntrials excluding KEYNOTE-02221 of pembrolizumab + dabrafenib+trametinib for BRAFV600 melanoma,\nwhich was inferior to independence and for which dosing violated model assumptions (green points). 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In the last five years the Sorger lab has received research funding from Novartis and Merck. Sorger declares that none of these relationships are directly or indirectly related to the content of this manuscript. BI is a consultant for Merck and Volastra.","formattedTitle":"Predictable clinical benefits without synergy in trials of combination therapies with immune checkpoint inhibitors","fulltext":[{"header":"Full Text","content":"Due to technical limitations, full-text HTML conversion of this manuscript could not be completed. 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Enhanced efficacy is proposed to result from drug additivity or synergy involving mechanisms such as immune priming. In this paper we re-analyze progression free survival data from thirteen recent Phase III trials of ICI combinations and find that observed benefits are fully and accurately accounted for by an increased chance that each patient will respond to a single-agent, consistent with the predictions of drug independence, with no requirement for additive or synergistic efficacy. Thus, the likely anti-tumor efficacy of new ICI combinations can be reliably predicted from monotherapy data (Pearson r=0.98, P \u003c 5×10-9, n = 4173 patients and 8 types of cancer), although predicting adverse effects is not yet possible. Realizing the clinical potential of drug additivity or synergy is likely to require biomarkers that identify patients in whom multiple constituents of a drug combination are active.","manuscriptTitle":"Predictable clinical benefits without synergy in trials of combination therapies with immune checkpoint inhibitors","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2021-03-19 20:44:58","doi":"10.21203/rs.3.rs-253936/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"46af345a-64f5-4c78-a29f-78516f55c22b","owner":[],"postedDate":"March 19th, 2021","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":3096593,"name":"Cancer Biology"},{"id":3096594,"name":"Bioinformatics"},{"id":3096595,"name":"Computational Biology"}],"tags":[],"updatedAt":"2021-03-31T21:45:51+00:00","versionOfRecord":[],"versionCreatedAt":"2021-03-19 20:44:58","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-253936","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-253936","identity":"rs-253936","version":["v1"]},"buildId":"7rjqhiLT3MXkJMwkYKINL","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}