Absolute increase in tumour size can improve the metric evaluating disease progression in metastatic colorectal cancer

Absolute increase in tumour size can improve the metric evaluating disease progression in metastatic colorectal cancer | 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 Research Article Absolute increase in tumour size can improve the metric evaluating disease progression in metastatic colorectal cancer Ari Robinson, Zvia Agur, Yuri Kogan This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3891219/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 Background The assessment of response to therapy in advanced solid cancer diseases is predicated on the Response Evaluation Criteria In Solid Tumours for the evaluation of disease state by the relative changes in lesion size. The underlying assumption is that a larger relative increase in lesion size implicates less efficacious therapy, worse prognosis and shorter survival. Methods We analyzed retrospective data of metastatic colorectal cancer patients from three clinical datasets, stratified into three cohorts by the treatment protocol. We evaluated the first relative and absolute increase in target lesion size for their association with overall survival. Results About fifty four percent of the patient population increased in target lesion size during the first-line treatment. A multivariate analysis showed that patients with larger relative increase in lesion size had slightly longer survival in all three cohorts ( \(HR = 0.85, HR=0.95 ,HR = 0.75\) for Cohorts 1–3, respectively); p-values showed no significance. In contrast, patients with larger absolute increase in total lesion size had significantly shorter survival ( \(HR = 1.1 \left(p=0.05\right), HR=1.2 \left(p=0.04\right), HR = 1.25(p=0.02)\) for Cohorts 1–3, respectively). We also found a negative correlation between the SLD at nadir and relative increase. Conclusions The different impact of the absolute and the relative increase in SLD at relapse reflects the different growth patterns of small and big tumours. Further validation of our results is required. We believe that the first absolute increase in total lesion size may become a useful metric, upon which a more precise categorization of survival-related disease progression can be based. Figures Figure 1 Figure 2 Figure 3 Introduction A crucial consideration in cancer clinical trials and in oncology practice is the efficacy of a treatment in prolonging patients’ life. Overall Survival (OS) serves as the “gold standard” primary clinical endpoint, allowing the evaluation of the long-term benefits in the treatment of advanced cancers. However, the use of OS in clinical trials has its limitations, deriving from the challenges associated with conducting long-term follow-up. This brings up the requirement for surrogate endpoints that can be assessed earlier in treatment, enabling quicker evaluation of new therapies in trials, as well as reliable prognosis and treatment navigation for the patient in clinic [1, 2]. In the advanced cancer setting, Progression-Free Survival (PFS) is currently the most common surrogate endpoint for OS [3]. But regardless of its validation in many cancer types, including in metastatic colorectal cancer (mCRC) [2, 4–7], PFS utility as an endpoint has been questioned [1, 8, 9]. PFS is aimed at capturing the time of radiological progression, which theoretically reflects the transition of the disease from a state of response to that of no response to therapy. This is determined by Response Evaluation Criteria in Solid Tumours (RECIST), which guide the estimation of disease intensity in patients with solid tumours [10]. The RECIST criteria divide response to treatment into four categories: progressive disease (PD), stable disease (SD), partial response (PR), and complete response (CR). This categorization defines thresholds on the ratio between the current assessment of total target lesion load (evaluated by the sum of longest diameters of target lesions; SLD) and the minimum SLD over the preceding assessments (nadir). The assumption underlying this approach is that the relative SLD change from nadir is correlated with the severity and the prognosis of the patient’s condition. According to RECIST, if the SLD increased by more than 20% from nadir, the disease has progressed (PD) and a shorter patient survival is to be expected; otherwise, the disease has not progressed (CR, PR or SD) and a longer patient survival is anticipated [11]. This rationale is also reflected in the clinical practice, where radiological PD prompts a switch to the next treatment line [12]. An additional endpoint, which is based on categorical RECIST evaluation, is Overall Response Rate (ORR), defined as the proportion of patients who have a partial or complete response to therapy (PR or CR), evaluated at predefined times, usually upon the first or the second radiological assessment under treatment. Its main advantage is the ability to estimate efficacy early in-trial, allowing to leverage the results of Phase II trial for the “Go/No-Go” decision [13]. This endpoint showed no predictive capacity for some modern therapies [14, 15] and, in some cases, a complementary endpoint, Disease Control Rate, was suggested, which reports the proportion of control (CR/PR/SD) vs. progression (PD), at the same timepoints. Another endpoint, Best Response, defined as the best RECIST evaluation over a fixed time period, or during the entire treatment course, is also used to summarize treatment efficacy, and was shown to be related to OS [16]. The use of these endpoints underlines RECIST criteria as an effective metric to evaluate survival-related disease state. This is predicated on the ability of RECIST to differentiate between response levels at consecutive timepoints during treatment [17–20]. Recently, shortcomings of RECIST and the related endpoints have surfaced, including reliance on discrete threshold values for categorizing the disease state, which do not necessarily correlate to OS [21, 22]. Proposals of alternative metrics include Early Tumour Shrinkage (ETS), defined as the percent reduction in SLD at the first assessment on-treatment, and Depth of Response (DpR), defined as maximal relative reduction in SLD during the entire treatment [23–27]. These metrics, which do not entail categorization, were extensively studied, especially in mCRC. Results show varied association with OS [25, 26, 28]. While ETS and DpR attempt to improve the resolution of tumour shrinkage evaluation during the initial treatment, our aim here was to test different metrics of tumour growth at the time of relapse for their ability to reflect OS in mCRC patients. Using clinical data from three cohorts of mCRC patients treated by three different first-line systemic therapies, we checked whether the magnitude of SLD change when the total lesion size begins to increase can provide valuable information regarding the anticipated OS. Methods Data source, cohorts’ definition, inclusion criteria We retrieved from the platform of Project Data Sphere ( www.projectdatasphere.org ) individual de-identified patient data from the clinical trials NCT00305188, NCT00272051 and NCT00115765, which evaluated the first-line treatments of mCRC. We unified the data of the two first clinical trials into Cohort 1 and split the patient dataset of the third clinical trial into Cohorts 2 and 3, according to the therapeutic protocol (see Table 1 ). For study design, inclusion and exclusion criteria, interventions, end points and outcomes of the three clinical trials, see the Project Data Sphere platform and the www.clinicaltrials.govwebsite . The collected data included all the SLD estimations during the follow-up, the follow-up time, time of death, the treatment protocols, sex, and age. In all the cohorts, lesion size was measured every eight weeks. Patients selection, calculation of response metrics We selected for our analysis only the patients whose baseline SLD was recorded, and whose SLD increased at some point during treatment; we defined this event as the time of the first SLD increase. We defined the nadir as the minimal SLD value preceding the first increase in SLD. Further, we calculated the First Absolute Increase (FAI), equal to the difference between the SLD at the time of the first SLD increase and the SLD at nadir (nSLD), and the First Relative SLD Increase (FRI) as the ratio of FAS to nSLD. We calculated OS as the time from treatment onset to the time of death or end of follow-up. Software and statistical methods We used the Kaplan-Meier method and the univariate and multivariate Cox proportional hazards models for survival analyses. All the variables in the proportional hazards models were normalized by subtracting the mean and dividing by standard deviation. Hazard Ratio (HR) values were reported with 95% confidence intervals (CIs). P-values are two sided. We used the R version 4.2.2 for all the statistical analyses. Table 1 Patient characteristics for the three cohorts, stratified by treatment protocol Datasets Dataset characteristics Study cohort characteristics Clinical trial (study cohort) Treatment Total number of patients Age mean (SD) Sex F/M (% male) Patients with first increase in SLD (% of the entire data) Age mean (SD) Sex F/M (% male) NCT00272051 + NCT00305188 (cohort 1) Oxaliplatin/5-FU (5-fluorouracil )/LV (leucovorin) 756 60.51 (11.28) 304/452 (59.8) 401 (53%) 60.98 (11.08) 165/236 (58.8) NCT00115765, (cohort 2) Bevacizumab with platinum-based chemotherapy 421 60.5 (12.19) 172/249 (59.1) 227 (54%) 60.98 (11.08) 87/140 (61.6) NCT00115765, (cohort 3) Panitumumab plus bevacizumab with platinum-based chemotherapy 421 60.9 (11.89) 190/231 (54.8) 232 (55%) 59.67 (11.45) 100/132 (56.9) Results Patients characteristics and study cohorts We have organized the clinical data of mCRC patients into three patient cohorts according to the treatment protocol: Cohort 1, including patients treated by chemotherapy doublet, Cohort 2, including patients treated by chemotherapy doublet and Bevacizumab, Cohort 3, including patients treated by chemotherapy doublet, Bevacizumab and Panitimumab. We included in the analysis all patients that had baseline records of target lesions SLD and at least one assessment with SLD increase. The numbers and demographic characteristics of the patients appear in Table 1 . All the analyses were conducted separately in each cohort. The relative increase in SLD, which underlies RECIST evaluation, is weakly associated with survival The use of RECIST relies on the assumption that the relative growth of SLD is negatively correlated with the severity of prognosis: larger SLD increase should imply shorter overall survival. We checked this assumption by computing the relative SLD change at the time of the first SLD increase (FRI) and evaluating its relation to the expected survival, by Cox Proportional Hazard model. In all three cohorts the HR of FRI was less than 1, meaning a positive, albeit insignificant, correlation between this measure and patient survival: \(HR = 0.8 (p=0.22), HR=0.85 (p=0.13), HR = 0.69 (p=0.07)\) in Cohorts 1, 2, and 3, respectively. This result was consistent with the Kaplan-Meier survival analysis (Fig. 1 ) of patients with FRI 20% (defined by RECIST as PD), as shown in Fig. 1 separately for each cohort. These findings remained valid in a multivariate analysis, considering FRI, sex, age and SLD at baseline (Table 2 A). Our results show that even though the relative increase in SLD underlies the definition of PD, its magnitude is weakly associated with the OS of the patient, and in an unexpected direction: larger FRI implicates longer survival. Figure 1 : Overall survival of the patients that had FRI 20% (red), compared by Kaplan-Meier curves. Panels A, B, and C represent Cohorts 1, 2 and 3 respectively. Table 2 A multivariate analysis of the association between the SLD increase metrics and OS. Hazard Ratio (HR) and corresponding p-values for the Cox regression of OS on FRI, Age, Sex and Baseline ( A ) and on FAI, Age, Sex and Baseline ( B ) are shown, separately for each cohort. A Variable Cohort 1 (n = 401) Cohort 2 (n = 227) Cohort 3 (n = 232) HR (CI) p HR (CI) p HR (CI) p FRI 0.850 (0.617–1.172) 0.322 0.931 (0.765–1.134) 0.480 0.788 (0.608–1.021) 0.072 Age 0.886 (0.770–1.021) 0.094 1.390 (1.163–1.663) < 0.001 1.142 (0.953–1.367) 0.150 Sex (M) 0.924 (0.806–1.059) 0.255 0.878 (0.738–1.045) 0.144 0.866 (0.731–1.025) 0.093 Baseline SLD 1.308 (1.158–1.478) < 0.001 1.508 (1.275–1.784) < 0.001 1.322 (1.133–1.543) < 0.001 B Variable Cohort 1 (n = 401) Cohort 2 (n = 227) Cohort 3 (n = 232) HR (CI) p HR (CI) p HR (CI) p FAI 1.102 (1.002–1.212) 0.045 1.181 (0.996–1.399) 0.055 1.253 (1.032–1.519) 0.022 Age 0.915 (0.793–1.055) 0.222 1.377 (1.154–1.643) < 0.001 1.106 (0.923–1.325) 0.277 Sex (M) 0.901 (0.786–1.034) 0.138 0.862 (0.723–1.026) 0.095 0.826 (0.694–0.983) 0.032 Baseline SLD 1.252 (1.096–1.430) 0.001 1.485 (1.253–1.759) < 0.001 1.360 (1.170–1.580) < 0.001 The absolute increase in SLD is significantly associated with survival We evaluated the relationship of SLD at nadir of individual patients (nSLD) to their FRI. Results are shown in Fig. 2 , which suggests that FRI is inversely correlated with nSLD ( \(\rho \left(FRI, nSL{D}^{-1}\right)=0.8, 0.58, 0.57\) for the Cohorts 1, 2, and 3, respectively). This means that the relative increase, and hence the current categorization of PD, are statistically different for patients with large vs. small nSLD. This led us to hypothesize that the absolute value of the first SLD increase, FAI, may be an alternative metric to test for its correlation with OS. This measure is related to the first relative SLD increase as follows: \(FRI = FAI/nSLD\) . Whereas this formula does not imply statistical dependence between any pair of the three variables, FRI in our data was correlated with nSLD, and FAI was independent of it: ( \(\rho \left(FAI, nSL{D}^{-1}\right)=0,-0.15,-0.14\) , see Fig. 2 ). In the univariate analysis, FAI was associated with OS, both as a continuous variable ( \(HR = 1.2 (p<0.001), HR=1.29 (p=0.002), HR = 1.37(p=0.001)\) ) and as a ranked variable (Fig. 3 ). This effect remained significant also in the multivariate Cox model, considering FAI, age, sex and baseline SLD (Table 2 B). Figure 2 : A-C : Scatter plots of FRI vs. SLD at nadir across patients of Cohorts 1–3. D-F : Scatter plots of FAI vs. SLD at nadir across patients of Cohorts 1–3. Note that FRI tends to have higher values when SLD at nadir is small; no such dependance exists for FAI. Figure 3 : Overall survival of the patients who had larger FAI (larger than median value; blue) vs. the patients that had smaller FAI (smaller than median; red), compared by Kaplan-Meier curves. Panels A, B, and C represent Cohorts 1, 2 and 3 respectively. Discussion In advanced solid cancer diseases, cancer initially responds to treatment by reduction, stabilization, or increase in size. These patterns are usually evaluated by different metrics, such as Response Rate, Best Response, or PFS. All these metrics are based on the approach of RECIST, evaluating relative changes in tumour load. Here we aimed at identifying the metric that can be best associated with survival and focuses on the evaluation of cancer relapse. The first metric we tested is FRI, the relative increase in SLD, which underlies the RECIST categorization of PD. We found that this metric was positively (albeit weakly) related to OS, thus challenging the rationale of response definition by RECIST. We suggest that this effect is due to the statistical dependence between FRI and the SLD at nadir, which serves as a reference for its estimation. Our results show that patients, whose FRI was larger than 20% (defined as PD), had smaller SLD at nadir than that of patients, whose FRI was smaller than 20% (defined as SD). Our interpretation is that the relative SLD change is insufficient for capturing the differences between the growth patterns of small and big tumours. The dependence of OS on patient’s baseline SLD has been reported in many studies [29–31]. However, current endpoints and treatment guidelines ignore the possible differences in the response patterns of patients with diverse tumour sizes. For example, the response differences may stem from the distinct anatomical and physiological characteristics of small and large tumours. We believe that to address this deficiency, the metric of size changes should be absolute rather than relative. Accordingly, we suggest the FAI, which computes the absolute change in SLD at the first increase. We found this metric to be negatively associated with OS, both alone and in a multivariate analysis accounting for the known influential baseline parameters. This implies that the absolute values of SLD at nadir and at the following increase can be related to survival, while their ratio is not. Our results may have ramifications for the development of endpoints for clinical trials, and for treatment navigation in the clinic. On the practical level, we propose a new metric, which can serve as an early endpoint for evaluating the efficacy of a new therapy and can also be used for more precise prognostication during the treatment in clinic. In a general research perspective, our results challenge the exclusive use of relative changes in SLD to define categories of response. We suggest that the absolute changes in SLD may provide additional (sometimes more relevant) information about the status of the disease, regarding the patient’s survival. This implies that response criteria that take into account the absolute changes in SLD may prove useful for defining better surrogate endpoints, improved OS prediction, and timely decision-making junctions in the clinical practice. Our work has several important limitations. First and foremost, we have analyzed data of mCRC patients only, who were treated by three of the several drug protocols available at the first line. To verify our findings, one should study additional treatments and additional advanced cancer indications. Secondly, we have included only patients who experienced an increase in target lesions size during the treatment – about half of the patient population in these studies. The other patients either had documented progression due to new lesion or non-target lesions, whose size was not reported, or left the study before declared PD. In the future, we plan to develop a combined metric of response which considers the absolute change in SLD and the non-numeric evaluation of new/non-target lesions. Conclusion We believe that an additional analysis of the retrospective datasets will lead to initial validation of our suggested metric in other treatments and cancer diseases. Next, it should be evaluated in prospective trials, including comparison to evaluation by RECIST, and estimation of its ability to timely recommend treatment change and predict the OS. If proven successful, it should be iteratively refined and prospectively tested to establish utility. We believe that taking account of the absolute changes in tumour size under treatment will improve the reliability of early clinical endpoints in clinical trials and in oncological practice. Abbreviations FRI – First Relative Increase, FAI – First Absolute Increase, nSLD – SLD at nadir Declarations Ethics approval and consent to participate. Not applicable Consent for publication Not applicable Competing interests The authors declares that they have no competing interests. Availability of data and materials All the data for this research is available at the platform of Project Data Sphere (www.projectdatasphere.org). Funding The research was self-funded. Author’s Contribution A.R- Analysis, interpretation of data, writing the manuscript; Z.A- conception, design of the work , interpretation of data, revising the manuscript; Y.K- conception, design of the work, analysis, interpretation of data, writing the manuscript. References Amir, E., et al., Poor correlation between progression-free and overall survival in modern clinical trials: are composite endpoints the answer? European Journal of Cancer, 2012. 48 (3): p. 385-388 . Poad, H., et al., The Validity of Surrogate Endpoints in Sub Groups of Metastatic Colorectal Cancer Patients Defined by Treatment Class and KRAS Status. Cancers, 2022. 14 (21): p. 5391 . Korn, R.L. and J.J. Crowley, Overview: progression-free survival as an endpoint in clinical trials with solid tumors. Clinical cancer research, 2013. 19 (10): p. 2607-2612 . Aprile, G., et al., Timing and extent of response in colorectal cancer: critical review of current data and implication for future trials. Oncotarget, 2015. 6 (30): p. 28716 . Buyse, M., et al., Progression-free survival is a surrogate for survival in advanced colorectal cancer. Journal of clinical oncology, 2007. 25 (33): p. 5218-5224 . Cicero, G., R. De Luca, and F. Dieli, Progression-free survival as a surrogate endpoint of overall survival in patients with metastatic colorectal cancer. OncoTargets and Therapy, 2018. 11 (null): p. 3059-3063 DOI: 10.2147/OTT.S151276 . Giessen, C., et al., Progression-free survival as a surrogate endpoint for median overall survival in metastatic colorectal cancer: literature-based analysis from 50 randomized first-line trials. Clinical Cancer Research, 2013. 19 (1): p. 225-235 . Broglio, K.R. and D.A. Berry, Detecting an Overall Survival Benefit that Is Derived From Progression-Free Survival. JNCI: Journal of the National Cancer Institute, 2009. 101 (23): p. 1642-1649 DOI: 10.1093/jnci/djp369 . Wilkerson, J. and T. Fojo, Progression-free survival is simply a measure of a drug’s effect while administered and is not a surrogate for overall survival. The Cancer Journal, 2009. 15 (5): p. 379-385 . Eisenhauer, E.A., et al., New response evaluation criteria in solid tumours: Revised RECIST guideline (version 1.1). European Journal of Cancer, 2009. 45 (2): p. 228-247 DOI: https://doi.org/10.1016/j.ejca.2008.10.026 . Fournier, L., et al., Twenty years on: RECIST as a biomarker of response in solid tumours an EORTC imaging group–ESOI joint paper. Frontiers in oncology, 2022. 11 : p. 800547 . NCCN, Colon cancer NCCN Clinical Practice Guidelines in Oncology (NCCN Guidelines®). 2023. Version 4.2023 . Mettu, N., et al., BACCI: A phase II randomized, double-blind, multicenter, placebo-controlled study of capecitabine (C) bevacizumab (B) plus atezolizumab (A) or placebo (P) in refractory metastatic colorectal cancer (mCRC): An ACCRU network study. Annals of oncology, 2019. 30 : p. v203 . Sidhu, R., A. Rong, and S. Dahlberg, Evaluation of progression-free survival as a surrogate endpoint for survival in chemotherapy and targeted agent metastatic colorectal cancer trials. Clinical Cancer Research, 2013. 19 (5): p. 969-976 . Azam, F. and A. Vazquez, Trends in phase ii trials for cancer therapies. Cancers, 2021. 13 (2): p. 178 . Dy, G.K., et al., Impact of complete response to chemotherapy on overall survival in advanced colorectal cancer: results from Intergroup N9741. Journal of clinical oncology, 2007. 25 (23): p. 3469-3474 . Suzuki, C., et al., The initial change in tumor size predicts response and survival in patients with metastatic colorectal cancer treated with combination chemotherapy. Annals of oncology, 2012. 23 (4): p. 948-954 . Jain, R.K., et al., Change in tumor size by RECIST correlates linearly with overall survival in phase I oncology studies. J Clin Oncol, 2012. 30 (21): p. 2684-90 DOI: 10.120 0/ jco.2011.36.4752 . Tang, P.A., et al., Surrogate End Points for Median Overall Survival in Metastatic Colorectal Cancer: Literature-Based Analysis From 39 Randomized Controlled Trials of First-Line Chemotherapy. Journal of Clinical Oncology, 2007. 25 (29): p. 4562-4568 DOI: 10.1200/jco.2006.08.1935 . Litière, S., et al., RECIST 1.1 for Response Evaluation Apply Not Only to Chemotherapy-Treated Patients But Also to Targeted Cancer Agents: A Pooled Database Analysis. J Clin Oncol, 2019. 37 (13): p. 11 02-1110 DOI: 10.1200/jco.18.01100 . Sharma, M.R., M.L. Maitland, and M.J. Ratain, RECIST: no longer the sharpest tool in the oncology clinical trials toolbox—point. Cancer research, 2012. 72 (20): p. 5145-5149 . Land, W.H., et al., Improving CT prediction of treatment response in patients with metastatic colorectal carcinoma using statistical learning theory. BMC Genomics, 2010. 11 (3): p. S15 DOI: 10.1186/1471-2164-11-S3-S15 . Giessen, C., et al., Early tumor shrinkage in metastatic colorectal cancer: retrospective analysis from an irinotecan ‐ based randomized first ‐ line trial. Cancer science, 2013. 104 (6): p. 718-724 . Mansmann, U., et al., Quantitative analysis of the impact of deepness of response on post-progression survival time following first-line treatment in patients with mCRC. Annals of Oncology, 2013. 24 : p. iv14 . Heinemann, V., et al., Early tumour shrinkage (ETS) and depth of response (DpR) in the treatment of patients with metastatic colorectal cancer (mCRC). European Journal of Cancer, 2015. 51 (14): p. 1927-1936 DOI: https://doi.org/10.1016/j.ejca.2015.06.116 . Cremolini, C., et al., Early tumor shrinkage and depth of response predict long-term outcome in metastatic colorectal cancer patients treated with first-line chemotherapy plus bevacizumab: results from phase III TRIBE trial by the Gruppo Oncologico del Nord Ovest. Annals of Oncology, 2015. 26 (6): p. 1188-1194 DOI: https://doi.org/10.1093/annonc/mdv112 . Manca, P., et al., Impact of early tumor shrinkage and depth of response on the outcomes of panitumumab-based maintenance in patients with RAS wild-type metastatic colorectal cancer. European Journal of Cancer, 2021. 144 : p. 31-40 . Douillard, J.Y., et al., Impact of early tumour shrinkage and resection on outcomes in patients with wild-type RAS metastatic colorectal cancer. Eur J Cancer, 2015. 51 (10): p. 1231-42 DOI: 10.1016/j.ejca.2015.03.026 . Stein, A., et al., Survival Prediction in Everolimus-treated Patients with Metastatic Renal Cell Carcinoma Incorporating Tumor Burden Response in the RECORD-1 Trial. European Urology, 2013. 64 (6): p. 994-1002 DOI: https://doi.org/10.1016/j.eururo.2012.11.032 . Hopkins, A.M., et al., Baseline tumor size and survival outcomes in lung cancer patients treated with immune checkpoint inhibitors. Seminars in Oncology, 2019. 46 (4): p. 380-384 DOI: https://doi.org/10.1053/j.seminoncol.2019.10.002 . Sakata, Y., et al., Comparisons between tumor burden and other prognostic factors that influence survival of patients with non-small cell lung cancer treated with immune checkpoint inhibitors. Thoracic Cancer, 2019. 10 (12): p. 2259-2266 DOI: https://doi.org/10.1111/1759-7714.13214 . Additional Declarations No competing interests reported. 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-3891219","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":271169631,"identity":"d399039a-b2b4-4140-acde-c11d2871e308","order_by":0,"name":"Ari Robinson","email":"","orcid":"","institution":"Institute for Medical BioMathematics","correspondingAuthor":false,"prefix":"","firstName":"Ari","middleName":"","lastName":"Robinson","suffix":""},{"id":271169632,"identity":"70cb6aa6-0494-4eaa-b8f2-43aa4632b3d4","order_by":1,"name":"Zvia Agur","email":"","orcid":"","institution":"Institute for Medical BioMathematics","correspondingAuthor":false,"prefix":"","firstName":"Zvia","middleName":"","lastName":"Agur","suffix":""},{"id":271169633,"identity":"abcae5a5-252a-4586-9f9d-31a1245321da","order_by":2,"name":"Yuri Kogan","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAmUlEQVRIiWNgGAWjYBACgwMMDAc+GDAkkKbl4AyStEg2MDAw8zCQooVfIvfhYZuCbXm6DczHPn4hRgubRLrB4RyD28VmB9iSZ8sQpyWNAaQlcdsBHmNmCaK1WJCuhQGqhfEDUVp4njEc7AH55TBbMjMxOhjY2NOYP/z4czvP7HjzYcYfROmBA2ZIBJEISLVlFIyCUTAKRggAADBxMQLKSqrdAAAAAElFTkSuQmCC","orcid":"","institution":"Institute for Medical BioMathematics","correspondingAuthor":true,"prefix":"","firstName":"Yuri","middleName":"","lastName":"Kogan","suffix":""}],"badges":[],"createdAt":"2024-01-23 13:37:58","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3891219/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3891219/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":50752190,"identity":"bfab37ef-8658-44bc-a659-9db04a2f85c9","added_by":"auto","created_at":"2024-02-06 17:47:35","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":212030,"visible":true,"origin":"","legend":"\u003cp\u003eOverall survival of the patients that had FRI \u0026lt; 20% (blue) vs. the patients that had FRI \u0026gt; 20% (red), compared by Kaplan-Meier curves. Panels A, B, and C represent Cohorts 1, 2 and 3 respectively.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-3891219/v1/9d2667bc3696e81ea46a7260.png"},{"id":50752740,"identity":"866ae369-a45a-4df6-b4d9-c175baadc227","added_by":"auto","created_at":"2024-02-06 17:55:35","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":319285,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eA-C\u003c/strong\u003e: Scatter plots of FRI vs. SLD at nadir across patients of Cohorts 1-3. \u003cstrong\u003eD-F\u003c/strong\u003e: Scatter plots of FAI vs. SLD at nadir across patients of Cohorts 1-3. Note that FRI tends to have higher values when SLD at nadir is small; no such dependance exists for FAI.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-3891219/v1/4bc65ddbb222e781bd64b66a.png"},{"id":50752191,"identity":"7b161457-4f5b-4115-923e-043e486668ea","added_by":"auto","created_at":"2024-02-06 17:47:35","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":218430,"visible":true,"origin":"","legend":"\u003cp\u003eOverall survival of the patients who had larger FAI (larger than median value; blue) vs. the patients that had smaller FAI (smaller than median; red), compared by Kaplan-Meier curves. Panels A, B, and C represent Cohorts 1, 2 and 3 respectively.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-3891219/v1/e9e4a1abf7a12446783ed6bc.png"},{"id":58556043,"identity":"2fbef47b-7cbb-403f-b8f0-5c4f2ac8a8c5","added_by":"auto","created_at":"2024-06-18 08:02:53","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1388472,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3891219/v1/e5db10c6-f5ca-4d81-85cb-f90daaef07d2.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Absolute increase in tumour size can improve the metric evaluating disease progression in metastatic colorectal cancer","fulltext":[{"header":"Introduction","content":"\u003cp\u003eA crucial consideration in cancer clinical trials and in oncology practice is the efficacy of a treatment in prolonging patients\u0026rsquo; life. Overall Survival (OS) serves as the \u0026ldquo;gold standard\u0026rdquo; primary clinical endpoint, allowing the evaluation of the long-term benefits in the treatment of advanced cancers. However, the use of OS in clinical trials has its limitations, deriving from the challenges associated with conducting long-term follow-up. This brings up the requirement for surrogate endpoints that can be assessed earlier in treatment, enabling quicker evaluation of new therapies in trials, as well as reliable prognosis and treatment navigation for the patient in clinic [1, 2].\u003c/p\u003e \u003cp\u003eIn the advanced cancer setting, Progression-Free Survival (PFS) is currently the most common surrogate endpoint for OS [3]. But regardless of its validation in many cancer types, including in metastatic colorectal cancer (mCRC) [2, 4\u0026ndash;7], PFS utility as an endpoint has been questioned [1, 8, 9]. PFS is aimed at capturing the time of radiological progression, which theoretically reflects the transition of the disease from a state of response to that of no response to therapy. This is determined by Response Evaluation Criteria in Solid Tumours (RECIST), which guide the estimation of disease intensity in patients with solid tumours [10]. The RECIST criteria divide response to treatment into four categories: progressive disease (PD), stable disease (SD), partial response (PR), and complete response (CR). This categorization defines thresholds on the ratio between the current assessment of total target lesion load (evaluated by the sum of longest diameters of target lesions; SLD) and the minimum SLD over the preceding assessments (nadir). The assumption underlying this approach is that the relative SLD change from nadir is correlated with the severity and the prognosis of the patient\u0026rsquo;s condition. According to RECIST, if the SLD increased by more than 20% from nadir, the disease has progressed (PD) and a shorter patient survival is to be expected; otherwise, the disease has not progressed (CR, PR or SD) and a longer patient survival is anticipated [11]. This rationale is also reflected in the clinical practice, where radiological PD prompts a switch to the next treatment line [12].\u003c/p\u003e \u003cp\u003eAn additional endpoint, which is based on categorical RECIST evaluation, is Overall Response Rate (ORR), defined as the proportion of patients who have a partial or complete response to therapy (PR or CR), evaluated at predefined times, usually upon the first or the second radiological assessment under treatment. Its main advantage is the ability to estimate efficacy early in-trial, allowing to leverage the results of Phase II trial for the \u0026ldquo;Go/No-Go\u0026rdquo; decision [13]. This endpoint showed no predictive capacity for some modern therapies [14, 15] and, in some cases, a complementary endpoint, Disease Control Rate, was suggested, which reports the proportion of control (CR/PR/SD) vs. progression (PD), at the same timepoints. Another endpoint, Best Response, defined as the best RECIST evaluation over a fixed time period, or during the entire treatment course, is also used to summarize treatment efficacy, and was shown to be related to OS [16]. The use of these endpoints underlines RECIST criteria as an effective metric to evaluate survival-related disease state. This is predicated on the ability of RECIST to differentiate between response levels at consecutive timepoints during treatment [17\u0026ndash;20].\u003c/p\u003e \u003cp\u003eRecently, shortcomings of RECIST and the related endpoints have surfaced, including reliance on discrete threshold values for categorizing the disease state, which do not necessarily correlate to OS [21, 22]. Proposals of alternative metrics include Early Tumour Shrinkage (ETS), defined as the percent reduction in SLD at the first assessment on-treatment, and Depth of Response (DpR), defined as maximal relative reduction in SLD during the entire treatment [23\u0026ndash;27]. These metrics, which do not entail categorization, were extensively studied, especially in mCRC. Results show varied association with OS [25, 26, 28].\u003c/p\u003e \u003cp\u003eWhile ETS and DpR attempt to improve the resolution of tumour shrinkage evaluation during the initial treatment, our aim here was to test different metrics of tumour growth at the time of relapse for their ability to reflect OS in mCRC patients. Using clinical data from three cohorts of mCRC patients treated by three different first-line systemic therapies, we checked whether the magnitude of SLD change when the total lesion size begins to increase can provide valuable information regarding the anticipated OS.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eData source, cohorts\u0026rsquo; definition, inclusion criteria\u003c/h2\u003e \u003cpa\u003eWe retrieved from the platform of Project Data Sphere (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ewww.projectdatasphere.org\u003c/span\u003e\u003cspan address=\"http://www.projectdatasphere.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) individual de-identified patient data from the clinical trials NCT00305188, NCT00272051 and NCT00115765, which evaluated the first-line treatments of mCRC. We unified the data of the two first clinical trials into Cohort 1 and split the patient dataset of the third clinical trial into Cohorts 2 and 3, according to the therapeutic protocol (see Table \u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). For study design, inclusion and exclusion criteria, interventions, end points and outcomes of the three clinical trials, see the Project Data Sphere platform and the \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ewww.clinicaltrials.govwebsite\u003c/span\u003e\u003cspan address=\"http://www.clinicaltrials.govwebsite\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. The collected data included all the SLD estimations during the follow-up, the follow-up time, time of death, the treatment protocols, sex, and age. In all the cohorts, lesion size was measured every eight weeks.\u003c/p\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003ePatients selection, calculation of response metrics\u003c/h2\u003e \u003cp\u003eWe selected for our analysis only the patients whose baseline SLD was recorded, and whose SLD increased at some point during treatment; we defined this event as the time of the first SLD increase. We defined the nadir as the minimal SLD value preceding the first increase in SLD. Further, we calculated the First Absolute Increase (FAI), equal to the difference between the SLD at the time of the first SLD increase and the SLD at nadir (nSLD), and the First Relative SLD Increase (FRI) as the ratio of FAS to nSLD. We calculated OS as the time from treatment onset to the time of death or end of follow-up.\u003c/p\u003e \u003cdiv id=\"Sec5\" class=\"Section3\"\u003e \u003ch2\u003eSoftware and statistical methods\u003c/h2\u003e \u003cp\u003eWe used the Kaplan-Meier method and the univariate and multivariate Cox proportional hazards models for survival analyses. All the variables in the proportional hazards models were normalized by subtracting the mean and dividing by standard deviation. Hazard Ratio (HR) values were reported with 95% confidence intervals (CIs). P-values are two sided. We used the R version 4.2.2 for all the statistical analyses.\u003c/p\u003e \u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1 \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePatient characteristics for the three cohorts, stratified by treatment protocol\u003c/p\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e \u003cdiv class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e \u003cdiv class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e \u003cdiv class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e \u003cthead\u003e \u003ctr\u003e \u003cth colname=\"c1\"\u003e \u003cp\u003eDatasets\u003c/p\u003e \u003c/th\u003e \u003cth colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003eDataset characteristics\u003c/p\u003e \u003c/th\u003e \u003cth colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003eStudy cohort characteristics\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth colname=\"c1\"\u003e \u003cp\u003eClinical trial\u003c/p\u003e \u003cp\u003e(study cohort)\u003c/p\u003e \u003c/th\u003e \u003cth colname=\"c2\"\u003e \u003cp\u003eTreatment\u003c/p\u003e \u003c/th\u003e \u003cth colname=\"c3\"\u003e \u003cp\u003eTotal number of patients\u003c/p\u003e \u003c/th\u003e \u003cth colname=\"c4\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003cp\u003emean (SD)\u003c/p\u003e \u003c/th\u003e \u003cth colname=\"c5\"\u003e \u003cp\u003eSex F/M\u003c/p\u003e \u003cp\u003e(% male)\u003c/p\u003e \u003c/th\u003e \u003cth colname=\"c6\"\u003e \u003cp\u003ePatients with first increase in SLD (% of the entire data)\u003c/p\u003e \u003c/th\u003e \u003cth colname=\"c7\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003cp\u003emean (SD)\u003c/p\u003e \u003c/th\u003e \u003cth colname=\"c8\"\u003e \u003cp\u003eSex F/M\u003c/p\u003e \u003cp\u003e(% male)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd colname=\"c1\"\u003e \u003cp\u003eNCT00272051\u0026thinsp;+\u0026thinsp;NCT00305188\u003c/p\u003e \u003cp\u003e(cohort 1)\u003c/p\u003e \u003c/td\u003e \u003ctd colname=\"c2\"\u003e \u003cp\u003eOxaliplatin/5-FU (5-fluorouracil )/LV (leucovorin)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e756\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e60.51 (11.28)\u003c/p\u003e \u003c/td\u003e \u003ctd colname=\"c5\"\u003e \u003cp\u003e304/452\u003c/p\u003e \u003cp\u003e(59.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e401\u003c/p\u003e \u003cp\u003e(53%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e60.98 (11.08)\u003c/p\u003e \u003c/td\u003e \u003ctd colname=\"c8\"\u003e \u003cp\u003e165/236 (58.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd colname=\"c1\"\u003e \u003cp\u003eNCT00115765,\u003c/p\u003e \u003cp\u003e(cohort 2)\u003c/p\u003e \u003c/td\u003e \u003ctd colname=\"c2\"\u003e \u003cp\u003eBevacizumab with platinum-based chemotherapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e421\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e60.5 (12.19)\u003c/p\u003e \u003c/td\u003e \u003ctd colname=\"c5\"\u003e \u003cp\u003e172/249 (59.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e227\u003c/p\u003e \u003cp\u003e(54%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e60.98 (11.08)\u003c/p\u003e \u003c/td\u003e \u003ctd colname=\"c8\"\u003e \u003cp\u003e87/140\u003c/p\u003e \u003cp\u003e(61.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd colname=\"c1\"\u003e \u003cp\u003eNCT00115765,\u003c/p\u003e \u003cp\u003e(cohort 3)\u003c/p\u003e \u003c/td\u003e \u003ctd colname=\"c2\"\u003e \u003cp\u003ePanitumumab plus bevacizumab with platinum-based chemotherapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e421\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e60.9 (11.89)\u003c/p\u003e \u003c/td\u003e \u003ctd colname=\"c5\"\u003e \u003cp\u003e190/231\u003c/p\u003e \u003cp\u003e(54.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e232\u003c/p\u003e \u003cp\u003e(55%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e59.67 (11.45)\u003c/p\u003e \u003c/td\u003e \u003ctd colname=\"c8\"\u003e \u003cp\u003e100/132\u003c/p\u003e \u003cp\u003e(56.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e \u003c/p\u003e "},{"header":"Results","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003ePatients characteristics and study cohorts\u003c/h2\u003e \u003cp\u003eWe have organized the clinical data of mCRC patients into three patient cohorts according to the treatment protocol: Cohort 1, including patients treated by chemotherapy doublet, Cohort 2, including patients treated by chemotherapy doublet and Bevacizumab, Cohort 3, including patients treated by chemotherapy doublet, Bevacizumab and Panitimumab. We included in the analysis all patients that had baseline records of target lesions SLD and at least one assessment with SLD increase. The numbers and demographic characteristics of the patients appear in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. All the analyses were conducted separately in each cohort.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eThe relative increase in SLD, which underlies RECIST evaluation, is weakly associated with survival\u003c/h2\u003e \u003cp\u003eThe use of RECIST relies on the assumption that the relative growth of SLD is negatively correlated with the severity of prognosis: larger SLD increase should imply shorter overall survival. We checked this assumption by computing the relative SLD change at the time of the first SLD increase (FRI) and evaluating its relation to the expected survival, by Cox Proportional Hazard model.\u003c/p\u003e \u003cp\u003eIn all three cohorts the HR of FRI was less than 1, meaning a positive, albeit insignificant, correlation between this measure and patient survival: \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(HR = 0.8 (p=0.22), HR=0.85 (p=0.13), HR = 0.69 (p=0.07)\\)\u003c/span\u003e\u003c/span\u003e in Cohorts 1, 2, and 3, respectively. This result was consistent with the Kaplan-Meier survival analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) of patients with FRI \u0026lt; 20% (defined by RECIST as SD) vs. the patients with FRI \u0026gt; 20% (defined by RECIST as PD), as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e separately for each cohort. These findings remained valid in a multivariate analysis, considering FRI, sex, age and SLD at baseline (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). Our results show that even though the relative increase in SLD underlies the definition of PD, its magnitude is weakly associated with the OS of the patient, and in an unexpected direction: larger FRI implicates longer survival.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e: Overall survival of the patients that had FRI\u0026thinsp;\u0026lt;\u0026thinsp;20% (blue) vs. the patients that had FRI\u0026thinsp;\u0026gt;\u0026thinsp;20% (red), compared by Kaplan-Meier curves. Panels A, B, and C represent Cohorts 1, 2 and 3 respectively.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eA multivariate analysis of the association between the SLD increase metrics and OS. Hazard Ratio (HR) and corresponding p-values for the Cox regression of OS on FRI, Age, Sex and Baseline (\u003cb\u003eA\u003c/b\u003e) and on FAI, Age, Sex and Baseline (\u003cb\u003eB\u003c/b\u003e) are shown, separately for each cohort. \u003cb\u003eA\u003c/b\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eCohort 1 (n\u0026thinsp;=\u0026thinsp;401)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eCohort 2 (n\u0026thinsp;=\u0026thinsp;227)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eCohort 3 (n\u0026thinsp;=\u0026thinsp;232)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHR (CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHR (CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eHR (CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFRI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.850 (0.617\u0026ndash;1.172)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.322\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.931 (0.765\u0026ndash;1.134)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.480\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.788 (0.608\u0026ndash;1.021)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.072\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.886 (0.770\u0026ndash;1.021)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.094\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.390 (1.163\u0026ndash;1.663)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.142 (0.953\u0026ndash;1.367)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.150\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex (M)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.924 (0.806\u0026ndash;1.059)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.255\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.878 (0.738\u0026ndash;1.045)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.144\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.866 (0.731\u0026ndash;1.025)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.093\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBaseline SLD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.308 (1.158\u0026ndash;1.478)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.508 (1.275\u0026ndash;1.784)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.322 (1.133\u0026ndash;1.543)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eB\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Taba\" border=\"1\"\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eCohort 1 (n\u0026thinsp;=\u0026thinsp;401)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eCohort 2 (n\u0026thinsp;=\u0026thinsp;227)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eCohort 3 (n\u0026thinsp;=\u0026thinsp;232)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHR (CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHR (CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eHR (CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFAI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.102 (1.002\u0026ndash;1.212)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.045\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.181 (0.996\u0026ndash;1.399)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.055\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.253 (1.032\u0026ndash;1.519)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.022\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.915 (0.793\u0026ndash;1.055)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.222\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.377 (1.154\u0026ndash;1.643)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.106 (0.923\u0026ndash;1.325)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.277\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex (M)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.901 (0.786\u0026ndash;1.034)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.138\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.862 (0.723\u0026ndash;1.026)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.095\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.826 (0.694\u0026ndash;0.983)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.032\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBaseline SLD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.252 (1.096\u0026ndash;1.430)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.485 (1.253\u0026ndash;1.759)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.360 (1.170\u0026ndash;1.580)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eThe absolute increase in SLD is significantly associated with survival\u003c/h2\u003e \u003cp\u003eWe evaluated the relationship of SLD at nadir of individual patients (nSLD) to their FRI. Results are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, which suggests that FRI is inversely correlated with nSLD (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\rho \\left(FRI, nSL{D}^{-1}\\right)=0.8, 0.58, 0.57\\)\u003c/span\u003e\u003c/span\u003e for the Cohorts 1, 2, and 3, respectively). This means that the relative increase, and hence the current categorization of PD, are statistically different for patients with large vs. small nSLD. This led us to hypothesize that the absolute value of the first SLD increase, FAI, may be an alternative metric to test for its correlation with OS. This measure is related to the first relative SLD increase as follows: \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(FRI = FAI/nSLD\\)\u003c/span\u003e\u003c/span\u003e. Whereas this formula does not imply statistical dependence between any pair of the three variables, FRI in our data was correlated with nSLD, and FAI was independent of it: (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\rho \\left(FAI, nSL{D}^{-1}\\right)=0,-0.15,-0.14\\)\u003c/span\u003e\u003c/span\u003e, see Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn the univariate analysis, FAI was associated with OS, both as a continuous variable (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(HR = 1.2 (p\u0026lt;0.001), HR=1.29 (p=0.002), HR = 1.37(p=0.001)\\)\u003c/span\u003e\u003c/span\u003e) and as a ranked variable (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). This effect remained significant also in the multivariate Cox model, considering FAI, age, sex and baseline SLD (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e: \u003cb\u003eA-C\u003c/b\u003e: Scatter plots of FRI vs. SLD at nadir across patients of Cohorts 1\u0026ndash;3. \u003cb\u003eD-F\u003c/b\u003e: Scatter plots of FAI vs. SLD at nadir across patients of Cohorts 1\u0026ndash;3. Note that FRI tends to have higher values when SLD at nadir is small; no such dependance exists for FAI.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e: Overall survival of the patients who had larger FAI (larger than median value; blue) vs. the patients that had smaller FAI (smaller than median; red), compared by Kaplan-Meier curves. Panels A, B, and C represent Cohorts 1, 2 and 3 respectively.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn advanced solid cancer diseases, cancer initially responds to treatment by reduction, stabilization, or increase in size. These patterns are usually evaluated by different metrics, such as Response Rate, Best Response, or PFS. All these metrics are based on the approach of RECIST, evaluating relative changes in tumour load. Here we aimed at identifying the metric that can be best associated with survival and focuses on the evaluation of cancer relapse.\u003c/p\u003e \u003cp\u003eThe first metric we tested is FRI, the relative increase in SLD, which underlies the RECIST categorization of PD. We found that this metric was positively (albeit weakly) related to OS, thus challenging the rationale of response definition by RECIST. We suggest that this effect is due to the statistical dependence between FRI and the SLD at nadir, which serves as a reference for its estimation. Our results show that patients, whose FRI was larger than 20% (defined as PD), had smaller SLD at nadir than that of patients, whose FRI was smaller than 20% (defined as SD). Our interpretation is that the relative SLD change is insufficient for capturing the differences between the growth patterns of small and big tumours.\u003c/p\u003e \u003cp\u003eThe dependence of OS on patient\u0026rsquo;s baseline SLD has been reported in many studies [29\u0026ndash;31]. However, current endpoints and treatment guidelines ignore the possible differences in the response patterns of patients with diverse tumour sizes. For example, the response differences may stem from the distinct anatomical and physiological characteristics of small and large tumours. We believe that to address this deficiency, the metric of size changes should be absolute rather than relative. Accordingly, we suggest the FAI, which computes the absolute change in SLD at the first increase. We found this metric to be negatively associated with OS, both alone and in a multivariate analysis accounting for the known influential baseline parameters. This implies that the absolute values of SLD at nadir and at the following increase can be related to survival, while their ratio is not.\u003c/p\u003e \u003cp\u003eOur results may have ramifications for the development of endpoints for clinical trials, and for treatment navigation in the clinic. On the practical level, we propose a new metric, which can serve as an early endpoint for evaluating the efficacy of a new therapy and can also be used for more precise prognostication during the treatment in clinic.\u003c/p\u003e \u003cp\u003eIn a general research perspective, our results challenge the exclusive use of relative changes in SLD to define categories of response. We suggest that the absolute changes in SLD may provide additional (sometimes more relevant) information about the status of the disease, regarding the patient\u0026rsquo;s survival. This implies that response criteria that take into account the absolute changes in SLD may prove useful for defining better surrogate endpoints, improved OS prediction, and timely decision-making junctions in the clinical practice.\u003c/p\u003e \u003cp\u003eOur work has several important limitations. First and foremost, we have analyzed data of mCRC patients only, who were treated by three of the several drug protocols available at the first line. To verify our findings, one should study additional treatments and additional advanced cancer indications. Secondly, we have included only patients who experienced an increase in target lesions size during the treatment \u0026ndash; about half of the patient population in these studies. The other patients either had documented progression due to new lesion or non-target lesions, whose size was not reported, or left the study before declared PD. In the future, we plan to develop a combined metric of response which considers the absolute change in SLD and the non-numeric evaluation of new/non-target lesions.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eWe believe that an additional analysis of the retrospective datasets will lead to initial validation of our suggested metric in other treatments and cancer diseases. Next, it should be evaluated in prospective trials, including comparison to evaluation by RECIST, and estimation of its ability to timely recommend treatment change and predict the OS. If proven successful, it should be iteratively refined and prospectively tested to establish utility. We believe that taking account of the absolute changes in tumour size under treatment will improve the reliability of early clinical endpoints in clinical trials and in oncological practice.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eFRI \u0026ndash; First Relative Increase, FAI \u0026ndash; First Absolute Increase, nSLD \u0026ndash; SLD \u0026nbsp;at nadir\u003c/p\u003e\n"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate.\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declares that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll the data for this research is available at the platform of Project Data Sphere (www.projectdatasphere.org).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe research was self-funded.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor\u0026rsquo;s Contribution\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA.R- Analysis, interpretation of data, writing the manuscript; Z.A- conception, design of the work , interpretation of data, revising the manuscript; Y.K- \u0026nbsp;conception, design of the work, analysis, interpretation of data, writing the manuscript. \u0026nbsp; \u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAmir, E., et al., \u003cem\u003ePoor correlation between progression-free and overall survival in modern clinical trials: are composite endpoints the answer?\u003c/em\u003e European Journal of Cancer, 2012. \u003cstrong\u003e48\u003c/strong\u003e(3): p. 385-388\u003cspan dir=\"RTL\"\u003e.\u003c/span\u003e\u003c/li\u003e\n\u003cli\u003ePoad, H., et al., \u003cem\u003eThe Validity of Surrogate Endpoints in Sub Groups of Metastatic Colorectal Cancer Patients Defined by Treatment Class and KRAS Status.\u003c/em\u003e Cancers, 2022. \u003cstrong\u003e14\u003c/strong\u003e(21): p. 5391\u003cspan dir=\"RTL\"\u003e.\u003c/span\u003e\u003c/li\u003e\n\u003cli\u003eKorn, R.L. and J.J. Crowley, \u003cem\u003eOverview: progression-free survival as an endpoint in clinical trials with solid tumors.\u003c/em\u003e Clinical cancer research, 2013. \u003cstrong\u003e19\u003c/strong\u003e(10): p. 2607-2612\u003cspan dir=\"RTL\"\u003e.\u003c/span\u003e\u003c/li\u003e\n\u003cli\u003eAprile, G., et al., \u003cem\u003eTiming and extent of response in colorectal cancer: critical review of current data and implication for future trials.\u003c/em\u003e Oncotarget, 2015. \u003cstrong\u003e6\u003c/strong\u003e(30): p. 28716\u003cspan dir=\"RTL\"\u003e.\u003c/span\u003e\u003c/li\u003e\n\u003cli\u003eBuyse, M., et al., \u003cem\u003eProgression-free survival is a surrogate for survival in advanced colorectal cancer.\u003c/em\u003e Journal of clinical oncology, 2007. \u003cstrong\u003e25\u003c/strong\u003e(33): p. 5218-5224\u003cspan dir=\"RTL\"\u003e.\u003c/span\u003e\u003c/li\u003e\n\u003cli\u003eCicero, G., R. De Luca, and F. Dieli, \u003cem\u003eProgression-free survival as a surrogate endpoint of overall survival in patients with metastatic colorectal cancer.\u003c/em\u003e OncoTargets and Therapy, 2018. \u003cstrong\u003e11\u003c/strong\u003e(null): p. 3059-3063 DOI: 10.2147/OTT.S151276\u003cspan dir=\"RTL\"\u003e.\u003c/span\u003e\u003c/li\u003e\n\u003cli\u003eGiessen, C., et al., \u003cem\u003eProgression-free survival as a surrogate endpoint for median overall survival in metastatic colorectal cancer: literature-based analysis from 50 randomized first-line trials.\u003c/em\u003e Clinical Cancer Research, 2013. \u003cstrong\u003e19\u003c/strong\u003e(1): p. 225-235\u003cspan dir=\"RTL\"\u003e.\u003c/span\u003e\u003c/li\u003e\n\u003cli\u003eBroglio, K.R. and D.A. Berry, \u003cem\u003eDetecting an Overall Survival Benefit that Is Derived From Progression-Free Survival.\u003c/em\u003e JNCI: Journal of the National Cancer Institute, 2009. \u003cstrong\u003e101\u003c/strong\u003e(23): p. 1642-1649 DOI: 10.1093/jnci/djp369\u003cspan dir=\"RTL\"\u003e.\u003c/span\u003e\u003c/li\u003e\n\u003cli\u003eWilkerson, J. and T. Fojo, \u003cem\u003eProgression-free survival is simply a measure of a drug\u0026rsquo;s effect while administered and is not a surrogate for overall survival.\u003c/em\u003e The Cancer Journal, 2009. \u003cstrong\u003e15\u003c/strong\u003e(5): p. 379-385\u003cspan dir=\"RTL\"\u003e.\u003c/span\u003e\u003c/li\u003e\n\u003cli\u003eEisenhauer, E.A., et al., \u003cem\u003eNew response evaluation criteria in solid tumours: Revised RECIST guideline (version 1.1).\u003c/em\u003e European Journal of Cancer, 2009. \u003cstrong\u003e45\u003c/strong\u003e(2): p. 228-247 DOI: https://doi.org/10.1016/j.ejca.2008.10.026\u003cspan dir=\"RTL\"\u003e.\u003c/span\u003e\u003c/li\u003e\n\u003cli\u003eFournier, L., et al., \u003cem\u003eTwenty years on: RECIST as a biomarker of response in solid tumours an EORTC imaging group\u0026ndash;ESOI joint paper.\u003c/em\u003e Frontiers in oncology, 2022. \u003cstrong\u003e11\u003c/strong\u003e: p. 800547\u003cspan dir=\"RTL\"\u003e.\u003c/span\u003e\u003c/li\u003e\n\u003cli\u003eNCCN, \u003cem\u003eColon cancer NCCN Clinical Practice Guidelines in Oncology (NCCN Guidelines\u0026reg;).\u003c/em\u003e 2023. \u003cstrong\u003eVersion 4.2023\u003c/strong\u003e\u003cspan dir=\"RTL\"\u003e.\u003c/span\u003e\u003c/li\u003e\n\u003cli\u003eMettu, N., et al., \u003cem\u003eBACCI: A phase II randomized, double-blind, multicenter, placebo-controlled study of capecitabine (C) bevacizumab (B) plus atezolizumab (A) or placebo (P) in refractory metastatic colorectal cancer\u003c/em\u003e\u003cspan dir=\"RTL\"\u003e \u003c/span\u003e\u003cem\u003e(mCRC): An ACCRU network study.\u003c/em\u003e Annals of oncology, 2019. \u003cstrong\u003e30\u003c/strong\u003e: p. v203\u003cspan dir=\"RTL\"\u003e.\u003c/span\u003e\u003c/li\u003e\n\u003cli\u003eSidhu, R., A. Rong, and S. Dahlberg, \u003cem\u003eEvaluation of progression-free survival as a surrogate endpoint for survival in chemotherapy and targeted agent metastatic colorectal cancer trials.\u003c/em\u003e Clinical Cancer Research, 2013. \u003cstrong\u003e19\u003c/strong\u003e(5): p. 969-976\u003cspan dir=\"RTL\"\u003e.\u003c/span\u003e\u003c/li\u003e\n\u003cli\u003eAzam, F. and A. Vazquez, \u003cem\u003eTrends in phase ii trials for cancer therapies.\u003c/em\u003e Cancers, 2021. \u003cstrong\u003e13\u003c/strong\u003e(2): p. 178\u003cspan dir=\"RTL\"\u003e.\u003c/span\u003e\u003c/li\u003e\n\u003cli\u003eDy, G.K., et al., \u003cem\u003eImpact of complete response to chemotherapy on overall survival in advanced colorectal cancer: results from Intergroup N9741.\u003c/em\u003e Journal of clinical oncology, 2007. \u003cstrong\u003e25\u003c/strong\u003e(23): p. 3469-3474\u003cspan dir=\"RTL\"\u003e.\u003c/span\u003e\u003c/li\u003e\n\u003cli\u003eSuzuki, C., et al., \u003cem\u003eThe initial change in tumor size predicts response and survival in patients with metastatic colorectal cancer treated with combination chemotherapy.\u003c/em\u003e Annals of oncology, 2012. \u003cstrong\u003e23\u003c/strong\u003e(4): p. 948-954\u003cspan dir=\"RTL\"\u003e.\u003c/span\u003e\u003c/li\u003e\n\u003cli\u003eJain, R.K., et al., \u003cem\u003eChange in tumor size by RECIST correlates linearly with overall survival in phase I oncology studies.\u003c/em\u003e J Clin Oncol, 2012. \u003cstrong\u003e30\u003c/strong\u003e(21): p. 2684-90 DOI: 10.120\u003cspan dir=\"RTL\"\u003e0/\u003c/span\u003ejco.2011.36.4752\u003cspan dir=\"RTL\"\u003e.\u003c/span\u003e\u003c/li\u003e\n\u003cli\u003eTang, P.A., et al., \u003cem\u003eSurrogate End Points for Median Overall Survival in Metastatic Colorectal Cancer: Literature-Based Analysis From 39 Randomized Controlled Trials of First-Line Chemotherapy.\u003c/em\u003e Journal of Clinical Oncology, 2007. \u003cstrong\u003e25\u003c/strong\u003e\u003cspan dir=\"RTL\"\u003e(29): \u003c/span\u003ep. 4562-4568 DOI: 10.1200/jco.2006.08.1935\u003cspan dir=\"RTL\"\u003e.\u003c/span\u003e\u003c/li\u003e\n\u003cli\u003eLiti\u0026egrave;re, S., et al., \u003cem\u003eRECIST 1.1 for Response Evaluation Apply Not Only to Chemotherapy-Treated Patients But Also to Targeted Cancer Agents: A Pooled Database Analysis.\u003c/em\u003e J Clin Oncol, 2019. \u003cstrong\u003e37\u003c/strong\u003e(13): p. 11\u003cspan dir=\"RTL\"\u003e02-1110 \u003c/span\u003eDOI: 10.1200/jco.18.01100\u003cspan dir=\"RTL\"\u003e.\u003c/span\u003e\u003c/li\u003e\n\u003cli\u003eSharma, M.R., M.L. Maitland, and M.J. Ratain, \u003cem\u003eRECIST: no longer the sharpest tool in the oncology clinical trials toolbox\u0026mdash;point.\u003c/em\u003e Cancer research, 2012. \u003cstrong\u003e72\u003c/strong\u003e(20): p. 5145-5149\u003cspan dir=\"RTL\"\u003e.\u003c/span\u003e\u003c/li\u003e\n\u003cli\u003eLand, W.H., et al., \u003cem\u003eImproving CT prediction of treatment response in patients with metastatic colorectal carcinoma using statistical learning theory.\u003c/em\u003e BMC Genomics, 2010. \u003cstrong\u003e11\u003c/strong\u003e(3): p. S15 DOI: 10.1186/1471-2164-11-S3-S15\u003cspan dir=\"RTL\"\u003e.\u003c/span\u003e\u003c/li\u003e\n\u003cli\u003eGiessen, C., et al., \u003cem\u003eEarly tumor shrinkage in metastatic colorectal cancer: retrospective analysis from an irinotecan\u003c/em\u003e\u003cem\u003e‐\u003c/em\u003e\u003cem\u003ebased randomized first\u003c/em\u003e\u003cem\u003e‐\u003c/em\u003e\u003cem\u003eline trial.\u003c/em\u003e Cancer science, 2013. \u003cstrong\u003e104\u003c/strong\u003e(6): p. 718-724\u003cspan dir=\"RTL\"\u003e.\u003c/span\u003e\u003c/li\u003e\n\u003cli\u003eMansmann, U., et al., \u003cem\u003eQuantitative analysis of the impact of deepness of response on post-progression survival time following first-line treatment in patients with mCRC.\u003c/em\u003e Annals of Oncology, 2013. \u003cstrong\u003e24\u003c/strong\u003e: p. iv14\u003cspan dir=\"RTL\"\u003e.\u003c/span\u003e\u003c/li\u003e\n\u003cli\u003eHeinemann, V., et al., \u003cem\u003eEarly tumour shrinkage (ETS) and depth of response (DpR) in the treatment of patients with metastatic colorectal cancer (mCRC).\u003c/em\u003e European Journal of Cancer, 2015. \u003cstrong\u003e51\u003c/strong\u003e(14): p. 1927-1936 DOI: https://doi.org/10.1016/j.ejca.2015.06.116\u003cspan dir=\"RTL\"\u003e.\u003c/span\u003e\u003c/li\u003e\n\u003cli\u003eCremolini, C., et al., \u003cem\u003eEarly tumor shrinkage and depth of response predict long-term outcome in metastatic colorectal cancer patients treated with first-line chemotherapy plus bevacizumab: results from phase III TRIBE trial by the Gruppo Oncologico del Nord Ovest.\u003c/em\u003e Annals of Oncology, 2015. \u003cstrong\u003e26\u003c/strong\u003e(6): p. 1188-1194 DOI: https://doi.org/10.1093/annonc/mdv112\u003cspan dir=\"RTL\"\u003e.\u003c/span\u003e\u003c/li\u003e\n\u003cli\u003eManca, P., et al., \u003cem\u003eImpact of early tumor shrinkage and depth\u003c/em\u003e\u003cspan dir=\"RTL\"\u003e \u003c/span\u003e\u003cem\u003eof response on the outcomes of panitumumab-based maintenance in patients with RAS wild-type metastatic colorectal cancer.\u003c/em\u003e European Journal of Cancer, 2021. \u003cstrong\u003e144\u003c/strong\u003e: p. 31-40\u003cspan dir=\"RTL\"\u003e.\u003c/span\u003e\u003c/li\u003e\n\u003cli\u003eDouillard, J.Y., et al., \u003cem\u003eImpact of early tumour shrinkage and resection on outcomes in patients with wild-type RAS metastatic colorectal cancer.\u003c/em\u003e Eur J Cancer, 2015. \u003cstrong\u003e51\u003c/strong\u003e(10): p. 1231-42 DOI: 10.1016/j.ejca.2015.03.026\u003cspan dir=\"RTL\"\u003e.\u003c/span\u003e\u003c/li\u003e\n\u003cli\u003eStein, A., et al., \u003cem\u003eSurvival Prediction in Everolimus-treated Patients with Metastatic Renal Cell Carcinoma Incorporating Tumor Burden Response in the RECORD-1 Trial.\u003c/em\u003e European Urology, 2013. \u003cstrong\u003e64\u003c/strong\u003e(6): p. 994-1002 DOI: https://doi.org/10.1016/j.eururo.2012.11.032\u003cspan dir=\"RTL\"\u003e.\u003c/span\u003e\u003c/li\u003e\n\u003cli\u003eHopkins, A.M., et al., \u003cem\u003eBaseline tumor size and survival outcomes in lung cancer patients treated with immune checkpoint inhibitors.\u003c/em\u003e Seminars in Oncology, 2019. \u003cstrong\u003e46\u003c/strong\u003e(4): p. 380-384 DOI: https://doi.org/10.1053/j.seminoncol.2019.10.002\u003cspan dir=\"RTL\"\u003e.\u003c/span\u003e\u003c/li\u003e\n\u003cli\u003eSakata, Y., et al., \u003cem\u003eComparisons between tumor burden and other prognostic factors that influence survival of patients with\u003c/em\u003e\u003cspan dir=\"RTL\"\u003e \u003c/span\u003e\u003cem\u003enon-small cell lung cancer treated with immune checkpoint inhibitors.\u003c/em\u003e Thoracic Cancer, 2019. \u003cstrong\u003e10\u003c/strong\u003e(12): p. 2259-2266 DOI: https://doi.org/10.1111/1759-7714.13214\u003cspan dir=\"RTL\"\u003e.\u003c/span\u003e\u003c/li\u003e\n\u003c/ol\u003e\n"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"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},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-3891219/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3891219/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eThe assessment of response to therapy in advanced solid cancer diseases is predicated on the Response Evaluation Criteria In Solid Tumours for the evaluation of disease state by the relative changes in lesion size. The underlying assumption is that a larger relative increase in lesion size implicates less efficacious therapy, worse prognosis and shorter survival.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe analyzed retrospective data of metastatic colorectal cancer patients from three clinical datasets, stratified into three cohorts by the treatment protocol. We evaluated the first relative and absolute increase in target lesion size for their association with overall survival.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eAbout fifty four percent of the patient population increased in target lesion size during the first-line treatment. A multivariate analysis showed that patients with larger relative increase in lesion size had slightly longer survival in all three cohorts (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(HR = 0.85, HR=0.95 ,HR = 0.75\\)\u003c/span\u003e\u003c/span\u003e for Cohorts 1\u0026ndash;3, respectively); p-values showed no significance. In contrast, patients with larger absolute increase in total lesion size had significantly shorter survival (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(HR = 1.1 \\left(p=0.05\\right), HR=1.2 \\left(p=0.04\\right), HR = 1.25(p=0.02)\\)\u003c/span\u003e\u003c/span\u003e for Cohorts 1\u0026ndash;3, respectively). We also found a negative correlation between the SLD at nadir and relative increase.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThe different impact of the absolute and the relative increase in SLD at relapse reflects the different growth patterns of small and big tumours. Further validation of our results is required. We believe that the first absolute increase in total lesion size may become a useful metric, upon which a more precise categorization of survival-related disease progression can be based.\u003c/p\u003e","manuscriptTitle":"Absolute increase in tumour size can improve the metric evaluating disease progression in metastatic colorectal cancer","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-02-06 17:47:30","doi":"10.21203/rs.3.rs-3891219/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":"9395917b-59c4-4292-934e-d11171a47d4d","owner":[],"postedDate":"February 6th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-06-18T07:54:45+00:00","versionOfRecord":[],"versionCreatedAt":"2024-02-06 17:47:30","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3891219","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3891219","identity":"rs-3891219","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}