Prediction of Progression-Free Survival in original report Patients With Advanced, Well-Differentiated, Neuroendocrine Tumors Being Treated With a Somatostatin Analog: The GETNE-TRASGU Study Alberto Carmona-Bayonas, MD, PhD1; Paula Jiménez-Fonseca, MD, PhD2; Ángela Lamarca, MD, PhD3; Jorge Barriuso, MD, PhD3,4; Ángel Castaño, MD, PhD5; Marta Benavent, MD, PhD6; Vicente Alonso, MD, PhD7; Marı́a del Carmen Riesco-Martı́nez, MD, PhD8; Teresa Alonso-Gordoa, MD9; Ana Custodio, MD, PhD10; Manuel Sánchez Cánovas, PhD1; Jorge Hernando Cubero, MD, PhD11; Carlos López, MD, PhD12; Adelaida Lacasta, MD, PhD13; Ana Fernández Montes, MD14; Mónica Marazuela, MD, PhD15; Guillermo Crespo, MD, PhD16; Pilar Escudero, MD, PhD17; José Ángel Diaz, MD, PhD18; Eduardo Feliciangeli, MD19; Javier Gallego, MD, PhD20; Marta Llanos, MD, PhD21; Ángel Segura, MD22; Felip Vilardell, MD, PhD23; Juan Carlos Percovich, MD24; Enrique Grande, MD, PhD25; Jaume Capdevila, MD, PhD11; Juan W. Valle, MD, FRCP3,4; and Rocı́o Garcı́a-Carbonero, MD, PhD26 PURPOSE Somatostatin analogs (SSAs) are recommended for the first-line treatment of most patients with well- abstract differentiated, gastroenteropancreatic (GEP) neuroendocrine tumors; however, benefit from treatment is heterogeneous. The aim of the current study was to develop and validate a progression-free survival (PFS) prediction model in SSA-treated patients. PATIENTS AND METHODS We extracted data from the Spanish Group of Neuroendocrine and Endocrine Tumors Registry (R-GETNE). Patient eligibility criteria included GEP primary, Ki-67 of 20% or less, and first-line SSA monotherapy for advanced disease. An accelerated failure time model was developed to predict PFS, which was represented as a nomogram and an online calculator. The nomogram was externally validated in an independent series of consecutive eligible patients (The Christie NHS Foundation Trust, Manchester, United Kingdom). RESULTS We recruited 535 patients (R-GETNE, n = 438; Manchester, n = 97). Median PFS and overall survival in the derivation cohort were 28.7 (95% CI, 23.8 to 31.1) and 85.9 months (95% CI, 71.5 to 96.7 months), respectively. Nine covariates significantly associated with PFS were primary tumor location, Ki-67 percentage, neutrophil-to-lymphocyte ratio, alkaline phosphatase, extent of liver involvement, presence of bone and peritoneal metastases, documented progression status, and the presence of symptoms when initiating SSA. The GETNE-TRASGU (Treated With Analog of Somatostatin in Gastroenteropancreatic and Unknown Primary NETs) model demonstrated suitable calibration, as well as fair discrimination ability with a C-index value of 0.714 (95% CI, 0.680 to 0.747) and 0.732 (95% CI, 0.658 to 0.806) in the derivation and validation series, respectively. CONCLUSION The GETNE-TRASGU evidence-based prognostic tool stratifies patients with GEP neuroendocrine tumors receiving SSA treatment according to their estimated PFS. This nomogram may be useful when stratifying patients with neuroendocrine tumors in future trials. Furthermore, it could be a valuable tool for ASSOCIATED making treatment decisions in daily clinical practice. CONTENT J Clin Oncol 37:2571-2580. © 2019 by American Society of Clinical Oncology Data Supplement Creative Commons Attribution Non-Commercial No Derivatives 4.0 License Author affiliations and support information (if INTRODUCTION Somatostatin analogs (SSAs) are currently the recom- applicable) appear Gastroenteropancreatic neuroendocrine tumors (GEP- mended first-line treatment of most patients who are at the end of this article. NETs) are a heterogeneous group of neoplasms that diagnosed with advanced, well-differentiated GEP- Accepted on July 3, follow a variable clinical course and are defined by the NETs 3,4 as they provide prolonged progression-free 2019 and published at WHO 2010 classification into three subgroups on the survival (PFS) with an acceptable toxicity profile. jco.org on August 7, basis of proliferation index1; however, within each PFS is often used as an outcome measure of patients 2019: DOI https://doi. org/10.1200/JCO.19. category, cases with a different clinical and biologic with GEP-NETs because it is a surrogate variable for 00980 behavior continue to coexist.2 overall survival (OS). 5,6 Despite SSAs being the Volume 37, Issue 28 2571 Downloaded from ascopubs.org by 95.17.110.95 on February 17, 2022 from 095.017.110.095 Copyright © 2022 American Society of Clinical Oncology. All rights reserved. Carmona-Bayonas et al treatment of choice for many patients with GEP-NET, benefit,15 as it is already happening in other advanced benefit from treatment—in terms of PFS—is far from ho- tumors.16,17 This model would also inform discussions with mogenous across the entire spectrum of SSA-treated GEP- patients in daily practice and assist in the design of future NETs. Several observations support the need to develop clinical trials. robust multivariable models to predict PFS that are able to The current study aimed to develop and externally vali- assist clinicians in treatment decisions and investigators in date a predictive model of PFS in patients with advanced, the design of new treatment strategies. The phase III well-differentiated GEP-NETs treated with SSAs alone. CLARINET (Controlled Study of Lanreotide Antiproliferative Furthermore, we investigated the dynamic, nonlinear ef- Response in Neuroendocrine Tumors) and PROMID fects of Ki-67 percentage in combination with patients’ (Prospective, Randomized Study on the Effect of Octreotide proinflammatory status on the basis of the neutrophil-to- LAR in the Control of Tumor Growth in Patients with lymphocyte ratio. Metastatic Neuroendocrine Midgut Tumors) trials dem- onstrated the antiproliferative effect of SSAs,7-9 with striking PATIENTS AND METHODS differences observed in median PFS in both studies. Most Study Population and Design participants in the PROMID trial had a low liver tumor load and a Ki-67 index of 2% or less (expected to be a population The model was designed in a training cohort that included with less aggressive, slow-growing disease). Despite this, patients from the Spanish National Cancer Registry approximately 25% of participants assigned to SSA in this (R-GETNE; data cutoff date for analyses was December study experienced progression before 6 months, which 2018), a hospital-based registry managed by the Spanish indicated that treatment was insufficient in that subgroup. Group of Neuroendocrine and Endocrine Tumors (GETNE). Unfortunately, it was difficult to further characterize this This observational registry included consecutive patients who patient population in univariable survival analyses. In the were diagnosed with GEP-NETs. Its design, characteristics, CLARINET study, long-acting lanreotide (Somatuline and quality criteria have been reported previously.2,18,19 Autogel; Ipsen, Paris, France) prolonged PFS compared Inclusion criteria for this substudy, GETNE-TRASGU (Treated with placebo. Despite prognosis being notably different With Analog of Somatostatin in Gastroenteropancreatic and depending on the site of the primary tumor, other predictors Unknown Primary NETs) consisted of histologic diagnosis of of increased benefit from treatment were not identified.7 GEP-NET (pancreas, GI, or unknown origin), advanced Multivariable analyses have established that liver tumor disease measurable by Response Evaluation Criteria in Solid load, histologic grade, and the site of the primary tumor are Tumors (RECIST) version 1.1 and monitored using computed predictive factors for PFS, thus contributing to prognosis tomography at least every 6 months until progression,20 well- and representing factors to account for when making differentiated tumor per WHO 2010 classification with Ki-67 treatment decisions.10 However, these and other studies of 20% or less,21 and SSA monotherapy as first-line treatment failed to factor in the additive effect of multiple covariates of advanced disease maintained until progression or toxicity. that converge in the course of GEP-NETs or were in- Eligibility criteria conform to the standard for SSA adminis- sufficient to make individual predictions of PFS.9,10 tration for antiproliferative purposes, in accordance with in- ternational clinical guidelines as well as with regulatory Other targeted therapies, including antiangiogenics or conditions and clinical practice. Patients who were initially phosphatidylinositol 3-kinase/AKT/mammalian target of treated with other systemic treatments or curative surgery for rapamycin pathway inhibitors, have been incorporated into metastatic disease, or cases with poor documentation of the treatment armamentarium of GEP-NETs over the past clinical information were excluded. Sixty centers from across decade, administered concurrently or sequentially with the country participated in R-GETNE, and data from a total of SSAs. However, the optimal sequence and timing for the 3,971 registered patients were screened for eligibility with administration of each of these alternative treatments is additional data quality check—double checking all variables uncertain.11,12 It is hoped that new agents in the pipeline, of interest, monitoring for discrepancies and missing data, such as axitinib, surufatinib, lenvatinib, cabozantinib, and and using multiple filters and controls so as to minimize others, may continue to improve NET management.13 unjustified loss of data. Furthermore, recent progress that has been made in clarifying the molecular biology of pancreatic NETs—with Results were externally validated in a separate cohort from the identification of mutated or altered gene expression in The Christie NHS Foundation Trust (Manchester, United DAXX/ATRX, phosphatidylinositol 3-kinase/Akt/mamma- Kingdom), which included consecutive patients who met lian target of rapamycin, multiple endocrine neoplasia 1, inclusion criteria and for whom all items included in the and other pathways—has provided potential new candi- nomogram were available. Any patients with missing in- dates for targeted therapies.14 In this context of expanding formation, including blood results at the time of SSA ini- therapeutic options, a predictive tool of benefit from tiation, were excluded. treatment with SSAs could be useful for selecting the most All procedures followed were in accordance with the ethical adequate treatment according to individual predicted standards of the responsible committee on human 2572 © 2019 by American Society of Clinical Oncology Volume 37, Issue 28 Downloaded from ascopubs.org by 95.17.110.95 on February 17, 2022 from 095.017.110.095 Copyright © 2022 American Society of Clinical Oncology. All rights reserved. PFS Prediction in Neuroendocrine Tumors TABLE 1. Baseline Patient Characteristics research (institutional and national) and with the Helsinki TRASGU Training TRASGU Validation Declaration of 1964 and later versions. Informed consent, if Cohort Cohort applicable depending on the participating institution, was R-GETNE Christie Hospital obtained from all patients before they were included in Characteristic (n = 438) Cohort (n = 97) the study. Median age, years 62 (26-89) 64 (26-86) Objectives and Variables of Interest (range) The main end point of this study was PFS, which was Sex, female 205 (46.8) 48 (49.4) defined as the interval of time between commencing SSA Median Ki-67, % 4 (0-20) 2 (1-20) therapy and tumor progression or all-cause mortality, (range) censoring patients who were lost to follow-up. The aim of Missing values 1 0 the study was to develop and externally validate a model Median neutrophils 3,800 (290-22,000) 4,100 (1,700-13,470) enabling patients with GEP-NET to be stratified on the basis (range) of the length of PFS. Candidate predictors were initially Missing values 45 0 chosen after a review of the literature and consulting with Median lymphocytes 1,700 (110-26,400) 1,400 (300-3,400) GETNE experts. Demographic (age and sex), family (range) (multiple endocrine neoplasia 1 and other familial syn- Missing values 56 0 dromes), clinical (body mass index, Eastern Cooperative Alkaline phosphatase Oncology Group performance status, and comorbidities), documented progression status before initiating SSA (tu- Normal 272 (62.1) 67 (69.0) mor progression, stable disease not documented or , 3 1.1-2.5 ULN 90 (20.5) 23 (23.7) months, and stable disease for $ 3 months), tumor (uptake . 2.5 ULN 40 (9.1) 7 (7.2) on scintigraphy with octreotide, clinical status at SSA Missing values 36 (8.2) 0 treatment initiation, functionality, hepatic tumor burden, SSA number of organs affected, and sites of metastases), his- topathologic (Ki-67 percentage, histologic differentiation, Sandostatin LAR, 215 (49.1) 50 (51.5) mg primary tumor site), and laboratory (alkaline phosphatase, bilirubin, albumin, lactate dehydrogenase, chromogranin 30 165 (77.7) 48 (96.0) A, 5-hydroxyindoleacetic acid, neutrophil-lymphocyte ratio 20 45 (20.9) 1 (2.0) [NLR], hemoglobin, and platelets) variables were included. 10 1 (0.4) 1 (2.0) Statistics Missing values 3 (1.3) 0 Lanreotide Autogel, 223 (50.9) 47 (48.4) Time-varying, nonlinear effects were explored using sur- mg vival analysis with generalized additive models,22,23 repre- sented as three-dimensional surfaces and contour maps. 120 186 (83.4) 45 (95.8) No data-driven method was used in the final selection of 90 24 (10.7) 1 (2.1) variables.24 We performed a redundancy analysis with 60 10 (4.4) 1 (2.1) flexible parametric additive models to eliminate any co- Missing values 3 (1.3) 0 variate that could be predicted by the remaining variables. Primary tumor site To examine PFS, we used a log-normal accelerated failure Small intestine 174 (40.0) 67 (69.1) time (AFT) model, given the nonproportionality of haz- ards.25 In AFT models, survival times are multiplied by Pancreas 166 (37.9) 11 (11.3) a constant effect under this formulation such that the Unknown 43 (9.8) 12 (12.3) exponentiated coefficients, exp(b), are referred to as time Rectum 22 (5.0) 3 (3.1) ratios (TRs). A TR of more than 1 for the covariate implies Colon 19 (4.3) 4 (4.1) that this slows or prolongs the time to event, whereas a TR Stomach 7 (1.6) 0 of less than 1 indicates that an event is more likely to occur earlier. Thus, the regression coefficient of a binary predictor Appendix 6 (1.4) 0 equal to log(0.5) means that the median of time to event is Median time from 2.0 (0-84) 2.9 (0.4-72.3) halved in its presence. Time was measured as a continuous diagnosis of metastasis to the variable and is expressed in months. beginning We checked the adequacy of the log-normal parametric of SSA treatment, model by computing the Kaplan-Meier estimate of the months (range) distribution of residuals against the theoretical Gaussian (continued on following page) one. Nonlinear effects were modeled by restricted cubic splines. Variables with more than 20% missing data were Journal of Clinical Oncology 2573 Downloaded from ascopubs.org by 95.17.110.95 on February 17, 2022 from 095.017.110.095 Copyright © 2022 American Society of Clinical Oncology. All rights reserved. Carmona-Bayonas et al TABLE 1. Baseline Patient Characteristics (continued) 40%) and a smaller proportion of tumors with confirmed TRASGU Training TRASGU Validation progression status at the time of SSA initiation (11% v 40%) Cohort Cohort in the external validation cohort (v the R-GETNE training R-GETNE Christie Hospital set). In the R-GETNE training cohort, a median of 21 doses Characteristic (n = 438) Cohort (n = 97) of SSA were administered (range, one to 168 doses). Documented progression Treatment was initiated at antiproliferative doses in 77% status before SSA (octreotide long-acting release 30 mg every 4 weeks) and Tumor progression 179 (40.8) 11 (11.3) 83% (Lanreotide Autogel 120 mg every 4 weeks; Ipsen, Paris, France) of patients. Dose was increased during Stable disease ND or 216 (49.3) 80 (82.4) , 3 months follow-up in 14% of cases starting at lower dose; in no case was it decreased. SSA was discontinued for more than Stable disease for 43 (9.8) 6 (6.1) $ 3 months 1 month in only 3.6% of patients. In both cohorts, ap- proximately half of participants received Lanreotide Autogel Functioning tumor 120 (27.3) 39 (40.2) and the other half octreotide long-acting release as SSA Metastasis site treatment. Liver 376 (85.8) 78 (80.4) Outcomes Lymph nodes 153 (34.9) 76 (78.3) Three-hundred progression events were recorded in the Peritoneum 85 (19.4) 21 (21.6) R-GETNE training cohort, with a median follow-up of Bone 38 (8.7) 12 (12.3) 33.1 months in patients who did not experience progres- Lung 24 (5.5) 8 (8.2) sion (95% CI, 30.1 to 36.3 months). Median PFS and OS Other 24 (5.5) 6 (6.1) were 28.7 months (95% CI, 23.8 to 31.1 months) and Median No. of organs 1 (1-6) 2 (1-4) 85.9 months (95% CI, 71.5 to 96.7 months), respectively. involved (range) Curves for both PFS and OS in the R-GETNE cohort have Octreotide scan, 389 (88.8) 84 (86.7%) been incorporated into the Data Supplement. Fifty pro- performed gression events were detected in the external valida- Positive 353 (90.7) 78 (92.8%) tion series, with median follow-up and PFS values of 31.5 months (95% CI, 15.9 to 41.9 months) and NOTE. Data are given as No. (%), unless otherwise noted. 29.0 months (95% CI, 20.9 to 50.1 months), respectively. Abbreviations: LAR, long-acting release; ND, not documented; R-GETNE, Median OS was not reached. Spanish Group of Neuroendocrine and Endocrine Tumors Registry; SSA, somatostatin analog; TRASGU, Treated With Analog of Somatostatin in Development of a Predictive Model of PFS Gastroenteropancreatic and Unknown Primary Neuroendocrine Tumors; ULN, Both the NLR and Ki-67 percentage index had a pro- upper limit of normal. nounced time-varying effect that rapidly faded over time. The hazard ratio surface and the corresponding contour eliminated—for example, mitosis, chromogranin A, and map for Ki-67 percentage are illustrated in Figures 1A and 5-hydroxyindoleacetic acid. Remaining missing data were 1B. In the case of Ki-67 percentage, the effect was clearly then imputed by predictive mean matching.26 The Data nonlinear (P = .030) and found to be the predictor that Supplement provides an at-a-glance plot of the pattern of correlated most closely with PFS (Somers’ Dxy rank cor- missing data in the data set. Regarding sample size, the relations in the Data Supplement). For example, the model number of predictors was chosen so that there would be at illustrates that the residual effect of a Ki-67 percentage of least 15 events per covariate.24 The model was represented 20% after 20 months of follow-up is approximately as a nomogram; calibration curves were plotted and dis- equivalent to the effect of a Ki-67 percentage of 7% crimination assessed using Harrell’s C-index, which takes evaluated at time zero, which must be determined if we are into account the right-censored data. Analyses were per- to clarify the true effect of Ki-67 percentage as a predictor of formed using R version 3.5.1,27 including the Hmisc, rms, PFS. In contrast, no clear evidence of nonlinearity was mgcv, visreg, and pammtools packages.22,28-31 detected for NLR (Figs 1C and 1D; P = .420). RESULTS The log-normal parametric survival time model adequately fitted the data to the most relevant prognostic factors (Data Patients and Treatments Supplement). Nine covariates were significantly associated The final GETNE-TRASGU database contained 535 with PFS: primary tumor site, Ki-67 percentage, NLR, al- patients—438 from the R-GETNE training cohort and 97 kaline phosphatase, percentage of hepatic tumor load, from the external validation subset. Table 1 lists baseline presence of bone and peritoneal metastases, documented characteristics of both subsets. Demographic data and progression status, and presence of symptoms at the time therapies were similar in both cohorts, with the exception of of the initiation of SSA therapy. No redundancy or in- a higher representation of small bowel primaries (69% v teraction between covariates was detected. 2574 © 2019 by American Society of Clinical Oncology Volume 37, Issue 28 Downloaded from ascopubs.org by 95.17.110.95 on February 17, 2022 from 095.017.110.095 Copyright © 2022 American Society of Clinical Oncology. All rights reserved. PFS Prediction in Neuroendocrine Tumors Estimated PFS time ratios are listed in Table 2 and shown in 0.714 (95% CI, 0.680 to 0.747) in the R-GETNE training Figure 2. An increase in Ki-67 percentage from 2% to 8% cohort. The final model is graphically represented in decreased median PFS by 36% (95% CI, 18% to 49%). a nomogram (Fig 4) and a Web-based calculator has been The GETNE-TRASGU model had an adequate calibration designed (http://www.iricom.es/prognostictools/trasgu). The (Fig 3A) and fair discrimination ability with a C-index of underlying equation and example of its use are specified in A B 2.5 80 2.5 2.0 Hazard Ratio 60 1.5 2.0 Time (months) Hazard Ratio 1.0 0.5 40 1.5 50 1.0 20 0 Tim 100 5 e( 10 mo 0.5 7( %) nth 15 150 Ki-6 s) 20 0 5 10 15 20 Ki-67 (%) C D 4 80 4 Hazard Ratio 3 60 Time (months) 3 Hazard Ratio 2 1 40 2 20 40 2 20 Tim 4 e( 60 1 mo 6 R NL nth 80 8 s) 0 2 4 6 8 NLR FIG 1. Three-dimensional surface and contour map showing the time-varying effects of Ki-67 percentage and neutrophil-to-lymphocyte ratio (NLR). (A) Three-dimensional surface plot showing the relation between Ki-67 percentage and time on the y- and x-axes, respectively. The z-axis shows the contribution of each combination on the hazard ratio for progression-free survival. (B) Contour map that uses a color gradient to visualize the effect of the combination on the hazard ratio, where the darker shades of blue indicate a decrease and the darker shades of red denote an increase on the hazard ratio. (C) Three-dimensional surface for NLR. (D) NLR contour map. The plot shows how the prognostic effect of Ki-67 percentage and NLR is diluted if the patient survives long enough without experiencing tumor progression. The nonlinear effect consists of higher levels of Ki-67 percentage being associated with a higher risk of progression, although there is a maximum level around 15% at which subsequent increases no longer correlate with greater risk. In contrast, NLR has a time-varying effect, but in this case the relation with hazard ratio is approximately linear. Journal of Clinical Oncology 2575 Downloaded from ascopubs.org by 95.17.110.95 on February 17, 2022 from 095.017.110.095 Copyright © 2022 American Society of Clinical Oncology. All rights reserved. Carmona-Bayonas et al the Data Supplement. The model is capable of capturing DISCUSSION a remarkable prognostic heterogeneity, stratifying patients The current study describes the development and in- according to their risk (Data Supplement). dependent validation of the GETNE-TRASGU PFS pre- External Validation diction tool for its use in individuals diagnosed with advanced, well-differentiated GEP-NETs starting treat- The GETNE-TRASGU nomogram was validated in an in- ment with SSA. With 536 patients, it is the largest series dependent series. In this external validation cohort, the published to date with this objective. To our knowledge, model retained adequate calibration (Fig 3B) and contin- GETNE-TRASGU is the first statistical model to stratify ued to enable prognosis-based discrimination of patients and make individualized predictions regarding the with a C-index of 0.732 (95% CI, 0.658 to 0.806). Kaplan- prognosis of patients with NETs being treated with Meier curves stratified on the basis of 2-year PFS pre- SSAs.9,10 The model has been independently validated in dictions are presented in the Data Supplement. an external validation sample of consecutive patients, maintaining an adequate calibration and capacity to discriminate PFS. Two of the applicability criteria are particularly noteworthy: the absence of metastases that can potentially be cured with TABLE 2. AFT Model to Predict Progression-Free Survival AFT Model to Predict Progression-Free surgery or locoregional therapies and the presence of a Ki-67 Survival TR (95% CI) P percentage index of 20% or less. The GETNE-TRASGU model assigns an estimation of PFS on the basis of the Ki-67, 8% v 2% 0.64 (0.51 to 0.82) , .001 additive effect of clinical, analytical, and pathologic variables. NLR, 2 v 8 1.45 (1.04 to 2.00) .026 All variables included in this nomogram have been studied Primary tumor site previously in GEP-NETs and have a well-known biologic Pancreas, gastric Ref — substrate. They are all established prognostic factors; how- Small intestine 1.89 (1.45 to 2.46) , .001 ever, their predictive ability has not been described before.9 Unknown 1.39 (0.95 to 2.04) .088 In the phase III PROMID study, median PFS ranged from Colorectal 1.30 (0.88 to 1.91) .174 4.6 months to 29.4 months, depending on the extent of liver involvement.8 Likewise, in the phase III CLARINET trial, Hepatic tumor burden, % midgut, hindgut, and pancreatic NETs that were treated No liver metastases Ref — with SSA behaved differently, with median PFS of 61.5 . 0-24 0.88 (0.64 to 1.21) .451 months, 55.0 months, and 29.7 months, respectively.7,9 25-50 0.67 (0.49 to 0.92) .013 Documented disease progression status before SSA was . 50 0.54 (0.37 to 0.78) .001 a controlled variable in the CLARINET study, which at- tributed part of the favorable PFS to the fact that 96% of Peritoneal metastases 0.63 (0.47 to 0.84) .002 patients had stable disease at the time of inclusion.9 In an Bone metastases 0.63 (0.43 to 0.90) .013 observational study, Laskaratos et al10 reported that the Alkaline phosphatase antiproliferative effect of SSAs depended on the location Normal Ref — and histologic differentiation of the primary tumor. Both 1.1-2.5 ULN 0.84 (0.64 to 1.09) .200 alkaline phosphatase and NLR have been correlated with . 2.5 ULN 0.61 (0.41 to 0.91) .015 OS in some series of NETs and general tumors.32-34 The presence of bone metastases is associated with a poor Documented progression status before prognosis in NETs and other neoplasms.35 In the GETNE- SSA TRASGU model, after controlling for multiple confounding Tumor progression Ref — factors, peritoneal involvement diminished PFS with a TR of Stable disease , 3 months or not 1.15 (0.91 to 1.44) .224 0.63 (95% CI, 0.43 to 0.90). In other series, such an documented adverse impact has not been explicitly reported, although Stable disease for $ 3 months 1.65 (1.10 to 2.49) .015 there might be a relationship between peritoneal metas- Symptoms, asymptomatic 1.57 (1.18 to 2.10) .001 tases and NETs that originate from the ileum or from the appendix, thus confounding those findings.36 The func- NOTE. Interpretation of adjusted TRs: TR . 1 means that an increase in the tioning status variable has a highly complex meaning, as it value of the covariate is associated with longer survival; TR , 1 means that an may correlate with both high tumor burden and histologic increase in the value of the covariate is associated with shorter survival. Adjusted TRs are derived from a multivariable log-normal AFT model and represent its differentiation. Although the previous series exhibited exponentiated coefficients. variable results overall, once Ki-67 percentage and tumor Abbreviations: AFT, accelerated failure time; NLR, neutrophil-to-lymphocyte burden were controlled for and, above all, insulinomas ratio; Ref, reference; SSA, somatostatin analog; TR, time ratio; ULN, upper limit of excluded, available data did not corroborate function- normal. ality itself as having a favorable effect in this patient 2576 © 2019 by American Society of Clinical Oncology Volume 37, Issue 28 Downloaded from ascopubs.org by 95.17.110.95 on February 17, 2022 from 095.017.110.095 Copyright © 2022 American Society of Clinical Oncology. All rights reserved. PFS Prediction in Neuroendocrine Tumors Time Ratios (90%, 95%, 99% CIs) 0.40 0.50 0.60 0.75 1.00 1.20 1.40 1.60 1.80 2.20 2.60 Ki-67% − 8:2 NLR − 2:8 Peritoneal metastases Bone metastases Truly asymptomatic Pancreas/stomach v small intestine Colorectal v small intestine Unknown v small intestine No liver metastases v liver involvement 25% to 50% Liver involvement 1% to 24% v 25% to 50% Liver involvement > 50% v 25% to 50% Alkaline phosphatase 1.1-2.5 ULN v normal Alkaline phosphatase > 2.5 ULN v normal PD v SD not documented or < 3 months SD 3 months v SD not documented or < 3 months FIG 2. Effect of GETNE-TRASGU (Spanish Group of Neuroendocrine and Endocrine Tumors–Treated With Analog of Somatostatin in Gastroenteropancreatic and Unknown Primary Neuroendocrine Tumors) covariates on progression- free survival. Adjusted time ratios are derived from a multivariable log-normal accelerated failure time model and represent its exponentiated coefficients (Table 2). Interpretation of the adjusted time ratios (TR): TR . 1 means that an increase in the value of the covariate is associated with longer survival. TR , 1 means that an increase in the value of the covariate is associated with shorter survival. NLR, neutrophil-to-lymphocyte ratio; PD, progressive disease; SD, stable disease; ULN, upper limit of normal. population.37 In the GETNE-TRASGU study, the presence occurs in other series.7 Consequently, these dynamic ef- of both tumor and endocrine symptoms was associated fects should not be dismissed. Our data show that the with a similarly higher risk of disease progression. formal verification of the assumption of proportionality of In the GETNE-TRASGU study, we chose an AFT model with the hazard ratios is essential when studying NETs. In log-normal distribution, as opposed to the more usual contrast, analysis of the nonlinear effect of Ki-67 per- proportional hazards model. The rationale for this choice is centage indicates that the cutoffs typically applied in the based on the fact that the Ki-67 percentage effect was the WHO classification are not compatible with the structure most heavily weighted variable; however, its importance of these data. Analysis of the categorized histologic grade gradually faded with prolonged follow-up. Slow growth therefore entails a substantial loss of information.2 In fact, exhibited by well-differentiated GEP-NETs that were treated categorizing the Ki-67 percentage by cutoffs has no with SSA—with expectations of PFS sometimes exceeding known biologic basis. As a result, it must clearly be 60 months—suggests that the decreasing magnitude of evaluated as a continuous variable that allows for non- impact of Ki-67 percentage on PFS, as observed here, likely linear effects. A B 1.0 1.0 Observed 12-Month PFS Observed 12-Month PFS 0.8 0.8 0.6 0.6 0.4 0.4 0.2 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 0.3 0.4 0.5 0.6 0.7 0.8 0.9 Predicted 12-Month PFS Predicted 12-Month PFS FIG 3. Calibration curves in the training and validation cohorts. (A) GETNE-TRASGU cohort. (B) Christie Hospital cohort. Calibration method consisted of obtaining estimates at intervals of the observed values versus model- predicted values. The term predicted means the probability of progression-free survival (PFS) at a fixed point of time, whereas observed refers to the Kaplan-Meier survival estimate stratified by intervals. Journal of Clinical Oncology 2577 Downloaded from ascopubs.org by 95.17.110.95 on February 17, 2022 from 095.017.110.095 Copyright © 2022 American Society of Clinical Oncology. All rights reserved. Carmona-Bayonas et al 0 10 20 30 40 50 60 70 80 90 100 Points Ki-67% 20 16 12 10 8 6 4 2 0 Neutrophil-to-lymphocyte ratio 16 14 12 10 8 6 4 2 0 Colorectal Small intestine Primary tumor site Pancreas, stomach Unknown 25% to 50% No liver metastases Liver involvement > 50% 1% to 24% No Peritoneal metastases Yes No Bone metastases Yes 1.1-2.5 ULN Alkaline phosphatase > 2.5 ULN Normal Stable disease not documented, or < 3 months Documented progression status before SSA Tumor progression Stable disease for 3 months No symptoms Symptoms Tumor or endocrine-related symptoms Total points 0 50 100 150 200 250 300 350 400 450 500 550 54 66 78 90 Median progression-free survival, months 6 12 18 24 30 36 42 48 60 72 84 12-month progression-free survival, % 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 24-month progression-free survival, % 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 FIG 4. GETNE-TRASGU (Spanish Group of Neuroendocrine and Endocrine Tumors–Treated With Analog of Somatostatin in Gastroenteropancreatic and Unknown Primary Neuroendocrine Tumors) nomogram. SSA, somatostatin analog; ULN, upper limit of normal. Some limitations to the GETNE-TRASGU study should be aware that, as prognosis for patients with well-differentiated noted. First, it is based on retrospective data, with the NETs is quantified in several years, the number of events is consequent loss of accuracy that this entails. To diminish low, which makes the OS data more uncertain in the long the negative impact of its retrospective design, we term. Consequently, estimations entail greater uncertainty requested that researchers confirm the quality of the data on the curve tails. Insofar as interpretation is concerned, by monitoring any possible inconsistency. Second, the it must be remembered that the model does not make chosen end point—researcher-defined PFS—may vary a one-off prediction of what will happen to a given patient at from centrally reviewed RECIST v1.1-defined PFS reported a given point in time, but rather it establishes an estimation in prospective randomized studies.10 We could argue the of a covariate-adjusted survival function. Inclusion of the appropriateness of the chosen end point with the following. proposed model as a key stratification factor in future First, there are well-known limitations of RECIST for GEP- clinical trials is strongly encouraged for a definitive pro- NETs.38 In addition, researcher-defined PFS may reflect spective validation. a more clinically relevant definition of progression to treatment with the inclusion of information on the need for In conclusion, the GETNE-TRASGU nomogram is an alternative therapeutic strategy. The reader must also be evidence-based tool that is based on nine clinical variables 2578 © 2019 by American Society of Clinical Oncology Volume 37, Issue 28 Downloaded from ascopubs.org by 95.17.110.95 on February 17, 2022 from 095.017.110.095 Copyright © 2022 American Society of Clinical Oncology. All rights reserved. PFS Prediction in Neuroendocrine Tumors that make it possible to stratify patients with advanced GEP- treatment decisions in the individualized management of NETs according to PFS estimated during SSA treatment. GEP-NETs and to provide risk stratification in future clinical The GETNE-TRASGU tool can contribute to making trials with SSA. AFFILIATIONS SUPPORT 1 Hospital Universitario Morales Meseguer, Universidad de Murcia, Funded by a restricted grant from Novartis Farmacéutica. This entity has Instituto Murciano de Investigación Biosanitaria, Murcia, Spain not contributed to the design of the study, analysis, interpretation of the 2 Hospital Universitario Central de Asturias, Oviedo, Spain results, drafting of the document, or the decision regarding publication. 3 The Christie NHS Foundation Trust, Manchester, United Kingdom 4 University of Manchester, Manchester, United Kingdom AUTHORS’ DISCLOSURES OF POTENTIAL CONFLICTS OF INTEREST 5 Hospital Universitario de Fuenlabrada, Madrid, Spain AND DATA AVAILABILITY STATEMENT 6 Hospital Universitario Virgen del Rocı́o, Instituto de Biomedicina de Disclosures provided by the authors and data availability statement (if Sevilla, Sevilla, Spain applicable) are available with this article at DOI https://doi.org/10.1200/ 7 Hospital Universitario Miguel Servet, Zaragoza, Spain JCO.19.00980. 8 Hospital Universitario Doce de Octubre, Madrid, Spain 9 Hospital Universitario Ramón y Cajal, Madrid, Spain 10 AUTHOR CONTRIBUTIONS Hospital Universitario La Paz, Centro de Investigación Biomédica en Red Cáncer, CB16/12/00398, Madrid, Spain Conception and design: Alberto Carmona-Bayonas, Paula Jiménez- 11 Hospital Universitario Vall d’Hebron, Vall Hebron Institute of Oncology, Fonseca, Guillermo Crespo, Pilar Escudero, José Ángel Diaz, Juan Carlos Autonomous University of Barcelona, Barcelona, Spain Percovich, Enrique Grande, Rocı́o Garcı́a-Carbonero 12 Hospital Universitario Marqués de Valdecilla, Santander, Spain Administrative support: Paula Jiménez-Fonseca 13 Hospital Universitario Donostia, San Sebastián, Spain Provision of study material or patients: Alberto Carmona-Bayonas, Paula 14 Complexo Hospitalario Universitario de Ourense, Orense, Spain Jiménez-Fonseca, Ángela Lamarca, Jorge Barriuso, Marta Benavent, 15 Hospital Universitario de la Princesa, Madrid, Spain Vicente Alonso, Teresa Alonso-Gordoa, Ana Custodio, Ana Fernández 16 Complejo Asistencial Universitario de Burgos, Burgos, Spain Montes, Pilar Escudero, Eduardo Feliciangeli, Felip Vilardell, Juan Carlos 17 Hospital Clı́nico Universitario Lozano Blesa, Zaragoza, Spain Percovich, Jaume Capdevila, Juan W. Valle, Rocı́o Garcı́a-Carbonero 18 Hospital Universitario Clı́nico San Carlos, Madrid, Spain Collection and assembly of data: Alberto Carmona-Bayonas, Paula 19 Hospital Universitario Santa Lucia, Cartagena, Spain Jiménez-Fonseca, Ángela Lamarca, Jorge Barriuso, Ángel Castaño, Marta 20 Hospital General Universitario de Elche, Elche, Spain Benavent, Vicente Alonso, Teresa Alonso-Gordoa, Ana Custodio, Manuel 21 Hospital Universitario de Canarias, Universidad de La Laguna, Sánchez-Cánovas, Jorge Hernando Cubero, Carlos López, Adelaida Tenerife, Spain Lacasta, Ana Fernández Montes, Mónica Marazuela, Guillermo Crespo, 22 Hospital Universitario La Fe, Valencia, Spain Pilar Escudero, José Ángel Diaz, Eduardo Feliciangeli, Javier Gallego, 23 Hospital Universitari Arnau de Vilanova, Lleida, Spain Marta Llanos, Ángel Segura, Felip Vilardell, Juan Carlos Percovich, 24 Hospital Universitario Gregorio Marañón, Madrid, Spain Jaume Capdevila, Rocı́o Garcı́a-Carbonero 25 MD Anderson Cancer Center Madrid, Madrid, Spain Data analysis and interpretation: Alberto Carmona-Bayonas, Paula 26 Hospital Universitario Doce de Octubre, Universidad Complutense de Jiménez-Fonseca, Ángela Lamarca, Jorge Barriuso, Vicente Alonso, Madrid, Centro Nacional de Investigaciones Oncológicas, Centro de Marı́a del Carmen Riesco-Martı́nez, Ana Custodio, Guillermo Crespo, José Investigación Biomédica en Red Cáncer, Madrid, Spain Ángel Diaz, Juan Carlos Percovich, Enrique Grande, Jaume Capdevila, Juan W. Valle, Rocı́o Garcı́a-Carbonero CORRESPONDING AUTHOR Manuscript writing: All authors Alberto Carmona-Bayonas, MD, PhD, Hematology and Medical Oncology Final approval of manuscript: All authors Department, Hospital Universitario Morales Meseguer, Universidad de Accountable for all aspects of the work: All authors Murcia, Instituto Murciano de Investigación Biosanitaria, Avenida Marqués de los Vélez, 30007, Murcia, Spain; Twitter: ACKNOWLEDGMENT @crh_MoralesIMIB; @GrupoGetne; e-mail: alberto.carmonabayonas@ The authors thank GETNE for sponsoring this study and generating the gmail.com. necessary network for this collaborative work. The authors also thank Priscilla Chase Duran for editing and translating the manuscript and the EQUAL CONTRIBUTION mFAR team for the support of the Web site registry. The authors thank A.C.-B. and P.J.-F. contributed equally to this work. Andreas Bender for help in the analysis of time-varying effects. REFERENCES 1. 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Lamarca A, Barriuso J, Kulke M, et al: Determination of an optimal response cut-off able to predict progression-free survival in patients with well-differentiated advanced pancreatic neuroendocrine tumours treated with sunitinib: An alternative to the current RECIST-defined response. Br J Cancer 118:181-188, 2018 n n n 2580 © 2019 by American Society of Clinical Oncology Volume 37, Issue 28 Downloaded from ascopubs.org by 95.17.110.95 on February 17, 2022 from 095.017.110.095 Copyright © 2022 American Society of Clinical Oncology. All rights reserved. PFS Prediction in Neuroendocrine Tumors AUTHORS’ DISCLOSURES OF POTENTIAL CONFLICTS OF INTEREST Prediction of Progression-Free Survival in Patients With Advanced, Well-Differentiated, Neuroendocrine Tumors Being Treated With a Somatostatin Analog: The GETNE-TRASGU Study The following represents disclosure information provided by authors of this manuscript. All relationships are considered compensated. Relationships are self-held unless noted. I = Immediate Family Member, Inst = My Institution. Relationships may not relate to the subject matter of this manuscript. For more information about ASCO’s conflict of interest policy, please refer to www.asco.org/rwc or ascopubs.org/jco/site/ifc. Alberto Carmona-Bayonas Guillermo Crespo Consulting or Advisory Role: Pfizer, Genentech Consulting or Advisory Role: Pfizer, Bristol-Myers Squibb, Ipsen, Roche, MSD, Speakers’ Bureau: Rovi Eisai, Sanofi, Novartis, Janssen, Bayer, Astellas Pharma Research Funding: LEO Pharma (Inst) Travel, Accommodations, Expenses: MSD, Ipsen Travel, Accommodations, Expenses: Ipsen, Genentech, Novartis José Ángel Diaz Paula Jiménez-Fonseca Consulting or Advisory Role: Ipsen, Merck, Roche, Merck Serono, MSD, Servier Other Relationship: Ipsen Ángela Lamarca Eduardo Feliciangeli Honoraria: Eisai, Nutricia, Nutricia (I) Honoraria: Astellas Pharma Speakers’ Bureau: Merck, Ipsen, Ipsen (I), Pfizer, Pfizer (I), Novartis (I), Incyte Consulting or Advisory Role: Merck Serono Research Funding: Ipsen, Ipsen (I) Travel, Accommodations, Expenses: Roche, MSD Oncology, Servier, Pfizer, Travel, Accommodations, Expenses: Abbott Nutrition, Ipsen, Pfizer, Celgene, Merck Serono Novartis, Advanced Accelerator Applications, Sirtex Medical, Bayer, Ipsen (I), Delcath Systems, Mylan, NanoString Technologies (I) Javier Gallego Consulting or Advisory Role: Amgen, Bayer, Roche, Bristol-Myers Squibb, Jorge Barriuso Celgene Consulting or Advisory Role: Nutricia, Nutricia (I), Eisai (I) Speakers’ Bureau: Amgen, Ipsen, Eli Lilly Speakers’ Bureau: Ipsen (I), Pfizer (I), Merck Serono (I), Pfizer, Novartis Travel, Accommodations, Expenses: Amgen, Novartis Research Funding: Pfizer Travel, Accommodations, Expenses: Advanced Accelerator Applications, Ipsen, Marta Llanos Novartis, NanoString Technologies, Ipsen (I), Pfizer (I), Novartis (I), Bayer (I), Honoraria: Servier, Roche, Bristol-Myers Squibb, Ipsen, Sanofi, Eisai, Eli Lilly Mylan (I), Advanced Accelerator Applications (I), Celgene (I), Pfizer Consulting or Advisory Role: Amgen Marta Benavent Consulting or Advisory Role: Pfizer, Ipsen Juan Carlos Percovich Travel, Accommodations, Expenses: Pfizer, Ipsen, Novartis Honoraria: Pfizer, Novartis, Ipsen, Novo Nordisk, Sanofi Vicente Alonso Enrique Grande Consulting or Advisory Role: Roche, Merck Serono, Amgen, Sanofi, Regeneron, Honoraria: Pfizer, Bristol-Myers Squibb, Ipsen, Roche, Eisai, Eusa Pharma, Bayer, Novartis, Ipsen, Servier MSD, Genzyme, Adacap, Novartis, Pierre Fabre, Lexicon, Celgene, Janssen- Travel, Accommodations, Expenses: Merck Serono, Roche, Novartis, Amgen, Cilag, Astellas Pharma Sanofi, Regeneron Consulting or Advisory Role: MSD, Pfizer, Ipsen Teresa Alonso-Gordoa Research Funding: MTEM/Threshold (Inst), Roche, Pfizer (Inst), AstraZeneca (Inst), Ipsen (Inst) Consulting or Advisory Role: Ipsen, Pfizer, Novartis, Roche, Bristol-Myers Travel, Accommodations, Expenses: Bristol-Myers Squibb, Genentech, Pfizer, Squibb, Sanofi, Janssen-Cilag, Astellas Pharma, AstraZeneca Research Funding: Roche Janssen-Cilag Travel, Accommodations, Expenses: Pfizer, Sanofi Jaume Capdevila Manuel Sánchez Cánovas Consulting or Advisory Role: Bayer, Eisai, Sanofi, Exelixis, Novartis, Ipsen, Consulting or Advisory Role: KyowaKirin Pfizer, Merck Serono, Advanced Accelerator Applications Research Funding: Leo Pharma (Inst) Speakers’ Bureau: Bayer, Eisai, Sanofi, Novartis, Ipsen, Pfizer, Merck Serono Travel, Accommodations, Expenses: MSD Oncology, Sanofi, Servier, LEO Research Funding: Eisai (Inst), AstraZeneca (Inst), Advanced Accelerator Pharma, Sanofi Applications (Inst), Novartis (Inst), Ipsen (Inst), Bayer (Inst), Pfizer (Inst) Other Relationship: KyowaKirin, Mylan, Angelini Pharma, Sanofi, Rovi Travel, Accommodations, Expenses: Pfizer, Ipsen, Eisai Jorge Hernando Cubero Juan W. Valle Speakers’ Bureau: Eisai, Ipsen, Roche, Angelini Pharma Honoraria: Ipsen Travel, Accommodations, Expenses: Ipsen, Novartis, Advanced Accelerator Consulting or Advisory Role: Ipsen, Novartis, AstraZeneca, Merck, Delcath Applications, Roche, AstraZeneca, Eisai Systems, Agios, Pfizer, PCI Biotech, Incyte, Keocyt, QED, Pieris Carlos López Pharmaceuticals, Genoscience Pharma, Mundipharma, Wren Laboratories, Honoraria: Roche, Merck, Sanofi, Novartis, Pfizer, Eisai, Ipsen, Bayer, NuCana BioMed AstraZeneca, Servier Speakers’ Bureau: Novartis, Ipsen, Nucana, Imaging Equipment Consulting or Advisory Role: Amgen, Roche, Sanofi, Merck, Servier, Pfizer, Research Funding: Novartis (Inst) Ipsen, Bayer, Eisai Travel, Accommodations, Expenses: Celgene, Nucana, Pfizer Research Funding: Amgen, Roche, Merck, Merck Sharp & Dohme, AstraZeneca, Sanofi, Bayer, Ipsen, Eisai, Celgene Rocı́o Garcı́a-Carbonero Travel, Accommodations, Expenses: Roche, Pfizer, Merck, Servier, Amgen, Honoraria: Ipsen, Roche, Sanofi, Bristol-Myers Squibb, Servier, Novartis, Pfizer, Ipsen Merck, PharmaMar, Adacap, Merck (I), PharmaMar (I), Roche (I), Bristol-Myers Squibb (I), AstraZeneca (I), Eli Lilly (I), Boehringer Ingelheim (I), Gilead Sciences Adelaida Lacasta (I), Servier (I), Sysmex (I) Travel, Accommodations, Expenses: Amgen, Roche, Ipsen Consulting or Advisory Role: Ipsen, Novartis, Pfizer, Advanced Accelerator Ana Fernández Montes Applications Consulting or Advisory Role: Sanofi, Amgen Speakers’ Bureau: Ipsen, Pfizer Speakers’ Bureau: Sanofi, Roche, Servier, Eli Lilly, Celgene Research Funding: Pfizer (Inst), Bristol-Myers Squibb (Inst), MSD (Inst), AstraZeneca (Inst) Mónica Marazuela Travel, Accommodations, Expenses: Roche, Merck Consulting or Advisory Role: Novartis, Ipsen, Pfizer Speakers’ Bureau: Novartis, Ipsen, Pfizer No other potential conflicts of interest were reported. 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