The prognostic value of biological markers in paediatric Hodgkin lymphoma

European Journal of Cancer 52 (2016) 33e40

Available online at www.sciencedirect.com

ScienceDirect journal homepage: www.ejcancer.com

Original Research

The prognostic value of biological markers in paediatric Hodgkin lymphoma* Piero Farruggia a,*, Giuseppe Puccio b, Alessandra Sala c, Alessandra Todesco d, Salvatore Buffardi e, Alberto Garaventa f, Gaetano Bottigliero g, Maurizio Bianchi h, Marco Zecca i, Franco Locatelli j, Andrea Pession k, Marta Pillon d, Claudio Favre l, Salvatore D’Amico m, Massimo Provenzi n, Angela Trizzino a, Giulio Andrea Zanazzo o, Antonella Sau p, Nicola Santoro q, Giulio Murgia r, Tommaso Casini s, Maurizio Mascarin t, Roberta Burnelli u On benhalf of AIEOP (Italian Association of Pediatric Hematology and Oncology) and Hodgkin Lymphoma working group1 a

Pediatric Hematology and Oncology Unit, Oncology Department, A.R.N.A.S. Ospedali Civico, Di Cristina e Benfratelli, Palermo, Italy b Department of Sciences for Health Promotion and Mother and Child Care, University of Palermo, Palermo, Italy c Clinica Pediatrica, Universita’ MilanodBicocca A.O. San GerardodFondazione MBBM, Monza, Italy d Dipartimento di Oncoematologia Pediatrica, Universita` di Padova, Padova, Italy e Dipartimento di Oncologia Pediatrica A.O. Santobono-Pausilipon, Napoli, Italy f Dipartimento di Ematologia e Oncologia Pediatrica, Istituto G. Gaslini, Genova, Italy g Servizio di Oncologia Pediatrica, Dipartimento di Pediatria II Ateneo di Napoli, Napoli, Italy

*

Preliminary results of this study have been presented as a poster at 20th EHA Congress (Vienna, Austria, 11e14 June 2015). * Corresponding author: U.O. di Oncoematologia Pediatrica, A.R.N.A.S. Ospedali Civico, Di Cristina e Benfratelli, Piazza Nicola Leotta 4, 90127 Palermo, Italy. Tel.: þ39 91 6664309; fax: þ39 91 6664127. E-mail address: [email protected] (P. Farruggia). 1 AIEOP (Italian Association of Pediatric Hematology and Oncology) and Hodgkin Lymphoma working group: Simone Cesaro, Ada Zaccaron U.O.C Oncoematologia Pediatrica Policlinico “G.B. Rossi” (Verona); Patrizia Bertolini, U.O. di Pediatria e Oncoematologia Azienda Ospedaliera di Parma (Parma); Caterina Consarino, Unita` Operativa di Ematologia ed Oncologia Pediatrica Azienda Ospedaliera Pugliese-Ciaccio (Catanzaro); Grazia Iaria, Divisione Ematologia Ospedali Riuniti (Reggio Calabria); Roberta Pericoli, U.O. di Pediatria Ospedale Infermi - Azienda USL Rimini (Rimini); Paolo Pierani, Clinica Pediatrica, Centro Regionale Oncoematologia Pediatrica Ospedale Dei Bambini G. Salesi (Ancona); Fausto Fedeli, Divisione Pediatria ‘Mariani’ Ospedale ‘Niguarda Ca Granda’ (Milano); Monica Cellini, U.O. di Ematologia Oncologia e Trapianto Azienda Ospedaliera Universitaria (Modena); Raffaela De Santis, S.C. Oncoematologia Pediatrica Ospedale “Casa Sollievo Della Sofferenza” (San Giovanni Rotondo); Fulvio Porta, Clinica Pediatrica, Oncoematologia pediatrica e TMO Ospedale dei Bambini (Brescia); Mauro Caini, Dipartimento di Pediatria, Ostetricia e Medicina Della Riproduzione Universita` Degli Studi di Siena (Siena); Carlo Cosmi, Clinica Pediatrica Universita` (Sassari); Nadia Mirra, Clinica Pediatrica II De Marchi (Milano); Adele Civino, Unita’ Operativa Pediatria U.T.I.N. Pia Fondazione di Culto e Religione Azienda Ospedaliera “Card G Panico” (Tricase); Domenico Sperlı`, Unita Operativa Pediatria Azienda Ospedaliera Annunziata (Cosenza); Luigi Nespoli, Clinica Pediatrica Universita` Degli Studi dell’Insubria Ospedale ‘Filippo Del Ponte’ (Varese); Maurizio Caniglia, Katia Perruccio S.C. di Oncoematologia Pediatrica con Trapianto di CSE, Ospedale ‘S.M. della Misericordia’ (Perugia); Paolo D’Angelo U.O. Oncoematologia pediatrica, (Palermo); Eva Passone, Clinica Pediatrica (Udine); Roberta Caruso, Oncoematologia pediatrica, Ospedale Bambin Gesu` (Roma); Giovanni Scarzello, SOC di Radioterapia e Medicina Nucleare, IOV Istituto Oncologico Veneto, (Padova); Roberto Rondelli, Dipartimento di Oncoematologia Pediatrica, Policlinico Sant’Orsola Malpighi (Bologna) http://dx.doi.org/10.1016/j.ejca.2015.09.003 0959-8049/ª 2015 Elsevier Ltd. All rights reserved.

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P. Farruggia et al. / European Journal of Cancer 52 (2016) 33e40

h

S.C. Oncoematologia Pediatria e Centro Trapianti, Ospedale Infantile Regina Margherita, Torino, Italy Oncoematologia Pediatrica, Fondazione IRCCS, Policlinico San Matteo, Pavia, Italy j Oncoematologia Pediatrica, IRCCS Ospedale Bambino Gesu`, Roma, University of Pavia, Italy k Dipartimento di Oncoematologia Pediatrica, “Lalla Seragnoli” Clinica Pediatrica Policlinico Sant’Orsola Malpighi, Bologna, Italy l Oncologia Clinica Pediatrica e Trapianto Midollo Osseo, Azienda OspedalieradUniversita`, Pisa, Italy m Oncologia Pediatrica, Clinica Pediatrica, Catania, Italy n Sezione Oncoematologia Pediatrica, Dipartimento di Pediatria, Ospedali Riuniti di Bergamo, Bergamo, Italy o U.O. Emato-Oncologia Pediatrica, Universita` degli Studi di Trieste Osp.le Infantile Burlo Garofolo, Trieste, Italy p U.O. Oncoematologia Pediatrica, Ospedale Civile Spirito Santo, Pescara, Italy q Dipartimento Biomedicina Eta’ Evolutiva, U.O. Pediatrica I Policlinico, Bari, Italy r Oncoematologia Pediatrica e Patologia della coagulazione, Ospedale Regionale per le Microcitemie, Cagliari, Italy s Dipartimento di Oncoematologia Pediatrica, A.O.U Meyer, Firenze, Italy t S.S. Radioterapia Pediatrica e Area Giovani, IRCCS, Centro di Riferimento Oncologico Aviano, Pordenone, Italy u Oncoematologia Pediatrica, Azienda Ospedaliera Universitaria, Ospedale Sant’Anna, Ferrara, Italy i

Received 15 May 2015; received in revised form 24 July 2015; accepted 3 September 2015

Available online xxx

KEYWORDS Hodgkin lymphoma; Paediatric; Prognostic factor

Abstract Background: Many biological and inflammatory markers have been proposed as having a prognostic value at diagnosis of Hodgkin lymphoma (HL), but very few have been validated in paediatric patients. We explored the significance of these markers in a large population of 769 affected children. Patients and methods: By using the database of patients enrolled in A.I.E.O.P. (Associazione Italiana di Emato-Oncologia Pediatrica) trial LH2004 for paediatric HL, we identified 769 consecutive patients treated with curative intent from 1st June 2004 to 1st April 2014 with ABVD (doxorubicin, bleomycin, vinblastine, and dacarbazine), or hybrid COPP/ABV (cyclophosphamide, vincristine, prednisone, procarbazine, doxorubicin, bleomycin and vinblastine) regimens. Results: On multivariate analysis with categorical forms, the 5-year freedom from progression survival was significantly lower in patients with stage IV or elevated value of platelets, eosinophils and ferritin at diagnosis. Furthermore, stage IV and eosinophils seem to maintain their predictive value independently of interim (after IV cycles of chemotherapy) positron emission tomography. Conclusion: Using the combination of four simple markers such as stage IV and elevated levels of platelets, ferritin and eosinophils, it is possible to classify the patients into subgroups with very different outcomes. ª 2015 Elsevier Ltd. All rights reserved.

1. Introduction The outcome in paediatric Hodgkin lymphoma (HL) is excellent when combined-modality therapy is used. HL in Italy accounts for 6% of childhood tumours with 5year, 10-year, and 15-year cumulative survival rates of 96%, 93%, and 92%, respectively, in the period 2003e2008 [1] and a 10-year freedom from progression (FFP) survival of 88.2% has recently been reported in Europe in the period between 1995 and 2001 [2]. Over the last two decades, the main goal of clinical research has been to minimise treatment toxicity, while preserving excellent event-free survival, and investigators have

tried to identify children who are likely to benefit from reduced intensity treatment [3-5]. On the other hand, prognostic indices can also help identify patients to be considered for more intensive therapy. To help guide the choice of risk-adapted treatment, many prognostic factors (gene-expression profiling, immuno-histochemical biomarkers, circulating myeloid-derived suppressor cells) have been proposed [6-8] or have already been included in protocols for adults. The latter is the case of expensive positron emission tomography (PET) [9], and of the International Prognostic Score (IPS), consisting of seven factors: albumin, haemoglobin (Hb), gender, stage, age, leucocytosis and lymphocytopenia. Patients

P. Farruggia et al. / European Journal of Cancer 52 (2016) 33e40

with more than five risk factors (RFs) had FFP survival of 42% compared to 84% in patients with no RFs [10]. 2. Patients and methods We carried out a retrospective study of some biological variables at diagnosis in 769 patients enrolled in LH2004, a multicentre clinical trial of A.I.E.O.P. (Associazione Italiana di Emato-Oncologia Pediatrica) for treatment of paediatric HL. All patients met the following criteria: biopsy-proven classical HL (cHL), staged according to the Ann Arbor classification, age 0e18 years, and no previous history of malignancy, transplantation, immunosuppression, or anti-HIV serological positivity. From 1st June 2004 to 1st April 2014, a total of 769 patients (median follow-up [FUP] 2.95 years) entered the protocol. They were allocated to different therapeutic groups (TGs) defined as follows. TG1: stage IA or IIA without mediastinal bulky disease or involvement of lung hilum lymph nodes, and with less than four lymph nodal regions; TG3: stage IIIB or stage IV or mediastinal bulky disease (whatever stage); TG2: absence of criteria for TG1 or TG3. The patients were sub-classified as B had unexplained fever with temperature above 38  C and/or unexplained weight loss of more than 10% in the course of 6 months, and/or night sweating. According to the protocol, patients assigned to TG1 received three ABVD (adriamycin, bleomycin, vinblastine, dacarbazine) courses, those in TG2 received four COPP/ABV (cyclophosphamide, vincristine, procarbazine, prednisone, adriamycin, bleomycin, vinblastine), and those in TG3 received six COPP/ABV; radiotherapy (RT) was administered at 14.4e25.2 Gy, and omitted in TG1 patients in complete remission at the end of chemotherapy. This trial was approved by the HL Study Group of A.I.E.O.P. and by the Ethics Committee of each participating institution. Written informed consent was obtained for all patients from either parents or legal guardians. We considered the following data if available at diagnosis: age, sex, stage, white blood cell count (WBC), absolute lymphocyte count (ALC), absolute monocyte count (AMC), absolute eosinophil count (AEC), absolute neutrophil count (ANC), platelet count (Plts), 1h erythrocyte sedimentation rate (ESR), ferritin, albumin, and Hb; ALC/AMC ratio (L/M), ANC/ALC ratio (N/L), and AEC/ALC ratio (E/L) were also considered. Relapse was pathologically-confirmed recurrence of HL, and progression as the same condition diagnosed within 3 months of the end of therapy. We considered the FFP survival as an outcome variable, which is defined as the time from the date of diagnosis to that of relapse/progression or to the date of last FUP for patients without

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recurrent disease; second malignant neoplasm and death before recurrence were censored at the time of the event. All prognostic factors were first analysed as continuous variables, except for the A-B categorization, sex and stage. However, since these continuous variables are more likely to be of clinical significance if categorised as threshold, we applied a statistical procedure to find some reasonable cutoff allowing us to also perform a categorical analysis. The procedure to find the cutoff was as follows: - we considered a subset of the data including all patients who had FUP of at least 2 years; - we considered the events as outcome variable (relapses þ progressions) in that population at 2 years from diagnosis; - we performed an Receiver Operating Characteristic (ROC) analysis of each prognostic variable for the binary outcome (event/no event) at 2 years, using cutoff-parameterised 2D performance curves, and a chi square analysis at each cutoff; - for each variable, an appropriate cutoff was determined as the value corresponding to the highest chi square value in the curve. Bivariate survival analysis was performed on the whole data sample, using KaplaneMeier curves and log-rank test. Multivariate survival analysis was performed using a Cox proportional hazards model. All P values were two-sided and values <0.05 were considered to be statistically significant. Statistical analysis was performed using the open source statistical software R [11]. Survival analysis was performed using the R package survival [12]. ROC curve analysis was performed using the R package ROCR [13]. An internal bootstrap validation was performed by the R package rms [14]. 3. Results There were (Table 1) 361 female patients (46.9%), and 408 males (53.0%); 454 patients (59.0%) were in category A and 315 (40.9%) in category B. There were 27 stage I patients (3.5%), 397 stage II (51.6%), 177 stage III (23.0%) and 168 stage IV (21.8%). Histologically, 636 patients (82.7%) were classified as nodular sclerosis, 81 (10.5%) as mixed cellularity, seven (0.9%) as lymphocyte depleted, and eight (1.0%) as lymphocyte rich, and in 37 (4.8%) no histological subtype was reported. Ninetynine patients (12.8%) were included in TG1, 171 (22.2%) in TG2, and 499 (64.8%) in TG3. There were 108 (14.0%) events: 37 were progressions and 71 were relapses. Since it is important that the possible prognostic continuous variables are tested for associations, which

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P. Farruggia et al. / European Journal of Cancer 52 (2016) 33e40

Table 1 Characteristics of the patients.

Age (years) Ferritin (ng/ml) 1-h ESR WBC  109/l ALC  109/l AMC  109/l AEC  109/l ANC  109/l Plts  109/l L/M N/L E/L Hb (g/dl) Albumin (g/dl)

Mean

Standard deviation

Median

Range

13.25 181.87 59.53 11.662 2.015 0.771 0.440 8.512 381.391 3.36 5.47 0.24 11.62 3.93

3.07 288.41 35.60 5.529 1.008 0.458 1.915 4.589 129.751 3.03 5.73 0.57 1.73 0.63

13.85 108 54 10.855 1.879 0.700 0.236 7.736 371.500 2.62 4.25 0.13 11.7 4.00

1.26e18.00 2e3724 0e138 0.547e74.9 0.095e8.100 0.020e4.494 0.001e44.940 0.345e36.864 43.200e1152.0 0.10e35 0.56e80 0.01e8.5 5.7e21.1 1.8e6.2

ESR: erythrocyte sedimentation rate; WBC: white blood cell count; ALC: absolute lymphocyte count; AMC: absolute monocyte count; AEC: absolute eosinophil count; ANC: absolute neutrophil count; Plts: platelet count; L/M: ALC/AMC ratio; N/L: ANC/ALC ratio; E/ L: AEC/ALC ratio; Hb: haemoglobin.

must be taken into account when evaluating the results of multiple regression analysis, we performed a correlation matrix analysis, shown in Table A.1, where the corresponding adjusted P values (according to Holm’s method) are also reported. Many of the variables present strong correlations; AMC, ALC and especially AEC are more independent. The correlations with derived parameters (L/M ratio, N/L ratio and E/L ratio) are expected. Most continuous variables are strongly associated with the AeB categorization (Table A.2): the

Fig. 1. The curve shows the “true positive rate”/“false positive rate” tradeoff at each tested cutoff. Cutoff scale can be read on the colourized scale. Area under curve is a cutoff independent measure which is equal to the value of the WilcoxoneManneWhitney test statistic and corresponds to the probability that the classifier will score a randomly drawn positive sample higher than randomly drawn negative sample.

association is lower for AMC, ALC and especially AEC. Finally, we looked at the distribution of those continuous variables in the four stages (data not shown): only Plts and especially AEC are not significantly associated with stage. We looked for appropriate cutoffs in a subset population with pre-determined FUP (2 years). Fig. 1 shows the cutoff-parameterised ROC curve for ferritin. The “best” cutoff was determined by a chi square analysis for each different cutoff, choosing the cutoff value corresponding to the highest chi square. A graph of chi square values for ferritin cutoff is shown in Fig. A.1. The same procedure was performed for each of the continuous prognostic variables considered. Table A.3 sums up the results: all the variables considered in this subpopulation model are statistically significant, except the E/L ratio. The most significant variable is a ferritin value 209 ng/ml. We used the above cutoff values to perform a bivariate analysis. Fig. A.2 shows the FFP survival curve of the overall population, with 95% CI: the 5-year FFP rate was 81.6% (95% CI: 78.3e84.9). Fig. 2 shows the KaplaneMeier survival curve for ferritin as categorised at the 209 cutoff. The 5-year FFP survival was better in the low ferritin group than that in the high ferritin group (86.5% versus 66.0%), and the difference is highly significant. Table A.4 shows the results of the survival analysis for all the variables. All the variables, except for histology, are statistically significant. Stage was analysed as a binary variable (stage IV versus all the others), because this analysis showed the highest significance. Finally, a multivariate survival analysis was performed using a Cox proportional hazards model. As a first step, we performed a continuous analysis on all

Fig. 2. KaplaneMeier survival curve for ferritin categorised at the 209 cutoff.

P. Farruggia et al. / European Journal of Cancer 52 (2016) 33e40

Table 2 Final Cox multiple regression model for freedom from progression with variables in binary form.

Stage (IV versus others) Ferritin AEC Plts

p

Hazard ratio

95% confidence interval

0.007682

2.084

1.215e3.576

0.000306 6.13  10e5 0.003275

2.7241 3.053 2.656

1.581e4.694 1.769e5.270 1.385e5.092

Concordance Z 0.716 (se Z 0.036). Rsquare Z 0.087 (max possible Z 0.777). Likelihood ratio test Z 41.02 on 4 df, p Z 2.656  10e8. Wald test Z 42.8 on 4 df, p Z 1.139  10e8. Score (logrank) test Z 49.23 on 4 df, p Z 5.219  10e10. AEC: absolute eosinophil count; Plts: platelet count.

cases that were complete for all the variables considered. We included all the variables in continuous form (except for sex, stage, and A-B group), and then selected the best model by backward elimination, based on the Bayesian information criterion (BIC). The best model included only two variables (stage and Plts). The same analysis was then repeated with all variables in categorical form. The best model included four variables: stage, ferritin, Plts, and AEC. The categorical model was definitely better than the continuous model: lower p values from likelihood ratio analysis (p Z 3.21  10e7 versus p Z 0.000258), and lower BIC (542.64 versus 550.05). The model, as applied to all cases complete for those four variables, is shown in Table 2: it has a highly significant likelihood ratio test (p Z 2.656  10e8), Wald (p Z 1.139  10e8) and logrank (p Z 5.219  10e10), and all 4 variables are individually significant, especially AEC and ferritin. We performed an internal bootstrap validation of the model with 800 repetitions. The corrected R2 shrinked from 0.1119 to 0.0954 (optimism 0.0165), while the Area under curve (AUC; concordance) shrinked from 0.7158 to 0.7065 (optimism 0.0093). The low values of optimism and the low reduction of AUC show that the model is reliable. Stage, ferritin, AEC and Plts are confirmed as significant independent prognostic factors. Under the identified cutoffs, 5-year FFP was lower in patients with stage IV or elevated values of ferritin, AEC and Plts: 73.4% (95% CI: 66.1e81.5) versus 83.9% (95% CI:

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80.3e87.6) for stage IV, 66.0% (95% CI: 57.3e76.1) versus 86.5% (95% CI: 82.7e90.4) for ferritin, 71.6% (95% CI: 62.3e82.4) versus 84.9% (95% CI: 81.0e89.0) for AEC and 66.3% (95% CI: 55.3e79.6) versus 82.4% (95% CI: 78.9e86.1) for Plts. The percentage of progressions versus relapses was not significantly different when each of the four RFs was used to categorise patients (Table A.5): progressions represent about one third of the total events, and the progression/relapse ratio is not significantly affected by any of the RFs we considered. We also assessed the distribution of categorical ferritin, AEC and Plts in the three TGs (stage IV is by default associated with TG3): Table A.6 shows this distribution. While AEC has a similar distribution in the TGs, both ferritin and Plts show great differences (“positive” values are much more common in TG3). Cox analysis by the final model, when performed for each TG, is not significant in TG1 (three ABVD) and TG2 (four COPP/ABV) with p Z 0.6157 for TG1 and p Z 0.4536 for TG2. On the contrary, Cox multiple regression is extremely significant when performed for TG3 (six COPP/ABV): the hazard ratio for stage (IV versus others), ferritin, AEC and Plts is 1.841 (p Z 0.046), 3.068 (p Z 0.00019), 3.246 (p Z 0.00016) and 2.276 (p Z 0.02207), respectively. We also considered a Cox analysis of FFP survival based on the total number of RFs in each patient. The results are shown in Table 3 and Fig. 3. Only 12 patients had three RFs (no patient had four): of these, eight had an event after less than 18 months, and four had no events, but they have an FUP of only 7e11 months. In the LH2004 trial a metabolic evaluation, initially performed with gallium scan, was scheduled in TG3, after four COPP/ABV, and after the end of chemotherapy in TG1 and TG2. We tried to apply our model to the subset of patients (497) with available PET results. Categorical ferritin was the variable with the highest collinearity with PET, while Plts showed lower collinearity and AEC and stage (IV versus others) were independent. In the subset of 312 patients in TG3 with PET data, 27 were positive, and 11 of them (40.7%) had an event at the time of the analysis. Applying our findings to the remaining 16 “false positive” patients,

Table 3 Cox regression model for total number of risk factors from our categorical model (versus 0 risk factors). Number of risk factors

Number of cases

0 1 2 3

227 135 77 12

p 0.00989 2.57  10e5 7.68  10e14

Concordance Z 0.719 (se Z 0.035). Rsquare Z 0.099 (max possible Z 0.782). Likelihood ratio test Z 47.21 on 3 df, p Z 3.12910e10. Wald test Z 59.1 on 3 df, p Z 9.14410e13. Score (logrank) test Z 100.4 on 3 df, p Z 0. CI: confidence interval; FFP: freedom from progression.

Hazard ratio 2.457 4.578 29.303

95% CI

5-year FFP

95% CI

1.241e4.865 2.254e9.298 12.087e71.042

90.4% 81.5% 71.1% 0%

85.5e95.6 74.0e89.7 60.2e84.1

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P. Farruggia et al. / European Journal of Cancer 52 (2016) 33e40

Fig. 3. KaplaneMeier curve based on the number of risk factors.

whose median FUP was 1.4 years, 11 were evaluable in our model (three RFs available): six were not at risk (five patients with no RF and one with one RF). Among the 285 patients in TG3 with a negative interim PET, 39 had an event: 18 were evaluable in our model and eight were at risk (three had three RFs and five had two RFs). 4. Discussion We analysed data from the LH2004 trial database to assess the prognostic relevance of some biological markers: gender, stage, sex, ESR, albumin, ferritin, Hb, WBC, ANC, ALC, AMC, AEC, Plts, ANC/ALC ratio, AEC/ALC ratio, and ALC/AMC ratio. Albumin less than 4 g/dl, Hb less than 10.5 g/dl, leucocytosis (WBC at least 15  109/l), and lymphocytopenia (ALC less than 0.6  109/l, and/or less than 8% of the WBC, or both) are included in the IPS of adult HL [10]. Recent gene-expression profiling studies have demonstrated that tumour-infiltrating myeloid-derived cells predict outcome in cHL [15]. Since tumour-associated macrophages are derived from circulating monocytes, and peripheral monocytosis has been associated with a worse outcome in patients with solid neoplasms [16], an additional prognostic marker in cHL could be the ALC/ AMC ratio. It has been found to be an independent prognostic factor for overall survival in adult cHL in two recent studies [17,18] but it was not confirmed in another analysis [19]. An elevated ANC has also been proposed to facilitate neoplasm growth [20], and an ANC/ALC ratio >4.3 has been proposed as an adverse prognostic factor in adult cHL [21]. Blood and tissue eosinophilia have been noted in about 15% [22] and 38% of HL patients [23], respectively. In an old study [22] eosinophilia seemed to offer a better prognosis, although in an extremely selected population, but in

another study it correlated with a shorter relapse-free survival [24]. Furthermore, tissue eosinophilia in some studies seemed to have no influence on prognosis [25], whereas in some others, it was associated with a worse outcome [26]. A definite explanation for worse outcome in our HL patients with eosinophilia cannot be given but in the past it was postulated that eosinophilia could mean necrosis and dissemination of the tumour [27] and it has been proved that eosinophils induce the proliferation of CD30e and CD40e Reed Sternberg cells [28,29]. Thrombocytosis is encountered in about 24% of HL patients [30] but nothing has been published about its possible prognostic significance. Elevated ESR is a wellknown adverse prognostic factor [31] and, in the past, high serum ferritin was found related to the spread of the tumour [32,33] and, in a paediatric report, to a poor outcome [34]. In this study we analysed many biological factors in paediatric cHL. Stage IV and increased values of ferritin, ESR, WBC, ANC, AMC, AEC and Plts, along with reduced values of albumin, Hb, ALC and ALC/ AMC, revealed an adverse prognostic significance in bivariate analysis; ferritin, AEC, Plts and stage IV retained strong significance on multivariate analysis with a hazard ratio of 3.436, 3.212, 2.941, and 2.084, respectively. Moreover, the final model behaves very well at the internal bootstrap validation procedure. Variables which present a strong association, especially albumin, ferritin, Hb, ESR (Table A.1), and that are strongly associated with the B category (Table A.2) are likely to express some measure of the inflammatory state induced by the HL. While most of these RFs are strongly significant at the bivariate analysis, it is expected because of their collinearity that most of them lose their significance in a multiple regression model. That can also explain why some RFs included in the IPS in adults are not present in our final model. According to our data, the 5-year FFP survival is significantly better in patients with a stage other than IV, low values of ferritin, AEC and Plts (83.9% versus 73.4%, 88.5% versus 65.5%, 87.1% versus 73%, and 84.5% versus 66.4%, respectively), and by combining these four RFs (no patients had four RFs), it is possible to divide children into four groups with significantly different outcomes, the 5 year FFP survival being 91.6%, 79.8%, 53% and 0% in patients with zero, one, two and three RFs, respectively (even though only 12 patients had three RFs). One of the concerns about the IPS is that it has less utility in children and adults with limited or lower risk disease [9,10]. We can see a similar phenomenon here, because our RFs correlate more strongly with the more advanced stage TG3 patients, with the noticeable exception of AEC which is independent of the others, relatively unrelated to systemic symptoms, and with no association with higher stages or TGs. It could be argued that the stronger association of

P. Farruggia et al. / European Journal of Cancer 52 (2016) 33e40

platelets and ferritin with TG3 patients minimises the value of identifying these new RFs. The number of events in TG1 and TG2 is too low, in our series, to assess if these variables could help identify patients requiring treatment modifications in these groups. However, these variables could certainly be useful to predict which TG3 patients are at higher risk of unfavourable prognosis even with current aggressive treatment protocols, and could benefit from revised therapeutic approaches. If applied only to the patients in TG3, our model remains strongly predictive, as shown by the Cox analysis (see Results section). Even if PET data were not available for all patients, we tried to get some information about the relationship between PET and our model, using a limited set of PET results after four cycles of chemotherapy in TG3 and after the end of chemotherapy in TG1 and TG2. Our results suggest that this predictor is related to ferritin, and Plts, but completely independent of AEC and stage IV. New biological markers are being sought to supplement RFs used in clinical practice. This is the first study to simultaneously evaluate a high number of potentially prognostic biological factors in paediatric cHL and we think that the reasonably sized cohort of analysed patients gives strength to the study; furthermore it has to be remarked that all the markers identified in the present analysis are inexpensive and easy to use in clinical practice. While they certainly need to be validated in further studies and the possible correlation with interim PET results should be analysed in a larger population, we believe that the present model provides an opportunity to better assess the initial level of risk, and possibly identify subgroups of patients with potentially worse outcomes. It has recently been reported that reducing scheduled treatment in patients with potentially worse outcomes, such as RT withdrawal on the basis of a negative interim PET, could result in an increase of early relapse rate [35]. If our data are confirmed, they could be integrated into the initial staging with the aim, as a first application, of reducing the risk of under-treatment; on these basis we could imagine a possible future scenario where, for example, in presence of a negative interim PET, RT would not be omitted in children classified as “at risk” on the basis of our score. Funding No specific funding was received for this study. Conflict of interest statement The authors report no potential conflicts of interest, including specific financial interest, relationships, or affiliations relevant to the subject of this manuscript.

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Acknowledgements The authors would like to thank Dr Valentino Conter (Pediatrics Unit, University of Milano-Bicocca, Fondazione MBBM, Ospedale San Gerardo, Monza, Italy) for his critical review and recommendations on a previous draft of the manuscript. The Foundation BCC Pordenonese and the Sicilian Primary Immunodeficiency Association are acknowledged for supporting the activity of the S.S. Radioterapia Pediatrica e Area Giovani - Pordenone and of the Pediatric OncoHematology Unit of Civico Hospital - Palermo respectively. No specific funding was received for this study. Appendix A. Supplementary data Supplementary data related to this article can be found at http://dx.doi.org/10.1016/j.ejca.2015.09.003

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