Skip to main content

Precision medicine in relapsed or refractory pediatric solid tumors: a collaborative Spanish initiative



Understanding pediatric cancer biology is a huge challenge in continuous development that is currently being implemented into the clinical practice thanks to the new high throughput technologies integrated by personalized medicine.

We present the results of the Precision Medicine program for children and adolescents with solid tumors in relapse/progression carried out in University La Fe Hospital (Valencia) from 2014. This is the first Spanish experience in precision medicine published in pediatric oncology.


Study enrollment was offered to all patients having a refractory or relapsed solid tumor and an available biopsy treated in La Fe Hospital (Valencia, Spain) or in other Spanish pediatric oncologic center. Eighty four patients were finally studied. The commercial Human Comprehensive Cancer GeneReadDNAseq Targeted genes Panel (Qiagen©) was sequenced in fresh/frozen samples. Variants considered pathogenic or likely pathogenic were classified using the algorithm published by Parsons et al. based on perceived clinical utility.


Thirteen of 84 patients (15%) received therapeutic recommendations due to an actionable variant detected and three patients received prognosis information based on sequencing results.


Precision medicine projects based on targetable gene panel approximations can obtain translatable information to pediatric patients with reasonable efforts. This approach lowers economic expenses and reduces time of response with respect to whole exome sequencing. Since the translation to the clinical practice is the main objective of these projects, limiting the number of relatively well-known biological markers will allow us to transfer similar amount of information with less economic and human effort.


Cure rates for childhood cancer have progressively improved in the last decades and are impressive relative to those for adult’s malignancies, reaching 75–80% 5-year survival rates [1]. However, cancer remains the leading cause of death by disease among children over 1 year of age in developed countries. Despite global good results, survival is still low for children with poor prognosis cancers such as high-grade glioma, brain stem tumour and metastatic medulloblastoma, sarcoma and neuroblastoma. Furthermore, progression or relapse for most pediatric patients with cancer still correlates with poor survival independently of different treatment combinations.

The exponential growth of next generation sequencing technologies (NGS) has allowed a generalized application [1, 2]. This has driven a rapid increase in molecular knowledge of tumors (especially in adults) [3], and as a result, the development of targeted treatments [4]. The new molecular targeted therapies have achieved unthinkable success in medical oncology, not so many years ago [5,6,7,8]. A significant percentage of adult patients may beneficiate from them after molecular tumor study [9]. Incidence and spectrum of cancers occurring in the pediatric population is markedly different from that seen in adults. Pediatric cancers have different type and frequency of genomic alterations compared to adult malignancies [10]. However, as recently demonstrated by Mody et al. [11], it is feasible to integrate sequencing data into clinical management of childhood cancers, aiming to characterize the landscape of genomic alterations in pediatric cancers, an essential step to improve outcome.

The need to explore the usefulness of drugs developed for adult patients arose among pediatric oncologists. The limited number of clinical trials available in children, as well as the lower biological knowledge of pediatric tumors led to the development of important precision medicine projects around the world [11,12,13,14,15,16,17]. In addition, the urgency to transfer biological information to patients in relapse/progression forced to start independent projects.

We present the results of the Precision Medicine program for children and adolescents with solid tumors in relapse/progression carried out in La Fe University Hospital (Valencia) from 2014. As far as we know this is the first published Spanish experience in precision medicine in pediatric oncology.


Study subjects and data collection

Study enrollment was offered to all patients having a refractory or relapsed solid tumor and an available biopsy treated in La Fe Hospital (Valencia, Spain) or in other Spanish pediatric oncologic center. High risk tumors were exceptionally considered and studied during first line treatment based on a probability of 5-year survival less than 20%. No other inclusion or exclusion criteria were required. The implementation and ultimate responsibility fall upon La Fe Hospital, but also several centers sent patients after approving the study protocol in the corresponding ethics committee. The program was approved by the Ethics committee of Hospital La Fe and the Pediatric Molecular Tumor Board (PMTB) was created in November 2014, composed by pediatric oncology, genetics, pharmacology, pathology, bioinformatics and molecular biology specialists. Parents signed the informed consent and were informed about the possibility of finding germ line mutations and accepting or refusing to be informed about that. When genomic and histopathological studies were completed, all members discussed the results and finally a report was transferred to the corresponding medical doctor. The workflow was based on the published INFORM pilot study [15]. The outcome of patients, including the treatment received, was updated on October 2018, through request to the responsible doctors.

Study samples

Fresh tumor samples from last relapse were requested. Paraffined and/or pretreatment tumor samples were used only if fresh samples were not possible to obtain. Peripheral blood samples were simultaneously collected in all cases. All tumour samples were reviewed by a board-certified pathologist to confirm histology and estimate tumour content. Only samples with > 30% tumour cell content were submitted for further genomic testing. The Pathologist selected the most cellular region from tumor samples and performed immunochemistry and FISH techniques when requested. The selected tumor material and peripheral blood samples were sent to the Biobank for DNA extraction and the Genomics Unit for sequencing analysis.

DNA extraction

DNA extraction from tumor samples was carried out using the QIAamp® DNA Investigator kit (QIAGEN® ref.56504); the DNA obtained was dissolved in TE Low EDTA buffer (AFFIMETRIX® ref. 75,793). Because the QIAampminElute (QIAGEN®) columns co-purify both RNA and DNA, 5 μL of a solution of RNase A (10 mg) was added/mL (Mat No. R1455S MN®), since RNA-free DNA is needed.Blood samples were collected in Vacutainer® tube with anticoagulant K2EDTA 5.4 mg of adequate volume for the purpose (2 ml). DNA extraction was carried out using the commercial kit QIAamp® DNA Mini Kit (QIAGEN® ref. 51,304); the DNA obtained was dissolved in TE Low EDTA buffer (AFFIMETRIX® ref. 75,793).

DNA samples were preserved at the end of the extraction and up to the time of collection, at − 20 °C. The concentration by spectrophotometry, and absorbance ratios were measured with the NANODROP 2000®.

Sequencing studies, data interpretation and variant calling

DNA from tumor sample was studied using Ion Torrent technology (average coverage with a minimum of 1000X and homogeneity with a minimum of 85%).

Two commercial panels were used, the Ion Ampliseq Cancer Hotspot Panel v2 was applied in paraffin samples and the Human Comprehensive Cancer GeneReadDNAseq Targeted genes Panel (Qiagen©) in fresh/frozen samples. This second one allows studying through sequencing technology (NGS) the coding regions (exons) and flanking intronic regions of the 159 genes most frequently mutated in cancer (Additional file 1: Table S1). Ion Ampliseq Cancer HotSpots v2 (Thermo Fisher Scientific©) was sequenced using Ion Torrent technology (average coverage with a minimum of 1000X and uniformity with a minimum of 90%) (Additional file 2: Table S2).

Target validation with IHC/FISH and copy number assessment were undergone when needed, in order to enrich panel information.

Copy number assessment and LOH were detected by high resolution molecular karyotyping genomic array (Affymetrix CytoScan HD) in 250 ng DNA for fresh tissue and 1 μg for Formalin-Fixed Paraffin Embedded (FFPE), following manufacturer directions. Integrity was above 500 nucleotides. Threshold filters for gains and losses were set up to 100–500 kilobases (KB) depending on quality of sample (FFPE or fresh frozen tumour).

For the analysis of the panel results, a screening of genetic variants detected both in blood and tumor was firstly carried out; those selected variants were categorized as “germline variants”. Secondly, variants detected exclusively in tumour were categorized as “somatic variants”. In both cases, variants described as pathogenic or probably pathogenic in the literature or in diseases databases (ClinVar, HGMD, St Jude PeCan or CiVIC), with an MAF < 0.01, were always selected. For the rest of variants, an algorithm of filters have been applied to discard those with an allelic frequency < 5%, changes in non-coding regions (excluding splicing sites flanking the exon to +/− 10 nucleotides), synonymous variants (excluding splicing sites in +/− 4 positions), variants with high frequency in the general population (MAF > 0.01) or in our own database of Genomics Unit La Fe and polymorphic changes (SNPs) without clinical relevance found in healthy population or described as benign by several sources.

Variant calling was based in international recommendations as Pathogenic, Likely Pathogenic, Benign, Likely Benign, and Uncertain Significance [18]. In all cases the annotation of variants was based on the genome version GRCh37 (hg19). Variants considered pathogenic or likely pathogenic in multidisciplinary committee were confirmed using Sanger technique.

In order to more complete interpretation of genetic variants from previous bibliography and considering the lack of standards around the term “actionable findings or clinically relevant”, we classified variants using the algorithm published by Parsons et al. [14]. They developed a ranking of variants based on perceived clinical utility and established 4 categories: established clinical utility (I); potential clinical utility (II); mutations in consensus cancer genes (III); and all other mutations (IV). The significance that they assigned to every group was [14]:

  • -Group I: Mutations known to be diagnostic, prognostic, and/or predictive of treatment in the specific tumor type tested.

  • -Group II: Mutations in members of targetable cancer pathways, gene families, or functional groups, regardless of tumor type.

  • -Group III: Mutations in other consensus cancer genes, not currently considered targetable.

  • -Group IV: All other mutations.

The term actionable variant causes important controversies depending on the specific tumor type and clinical-biological characteristics of patients. In our work, an actionable variant is referred as a genomic change that suggests an alteration with biological activity that could be targeted with a concrete therapy already used in vivo. Targeted therapies were preferentially recommended to be administered within clinical trials but also as compassionate use when this was not possible.

Other biologic studies

Based on previous bibliography and according to the sequencing results obtained for each tumor type, SNP CGH array, immunochemistry techniques (p-AKT, PDL1, p-EGFR, c-KIT, PTEN, Her2neu, p53) and/or FISH (NTRK1 / 3, ALK, BRAF) were also performed if required by the study doctor and the PMTB.

The number of pathogenic or likely pathogenic variants that were detected using Comprehensive or HotSpot panels and classified as categories I, II or III (Parsons et al. [14]) were contrasted. We have done a description of our results and also we have compared them with Parsons and other published data.


These results are referred to sequencing results. Histopathological contributions and SNP array information are not communicated.

Patient and sample characteristics

Peripheral blood and tumor samples of 98 patients with solid tumors, from 19 Spanish pediatric oncology units (POU), were remitted to our program from November 2014 to October 2018 (48 months). Finally, 14 patients were not studied because of poor quality of tumor samples. Therefore, tumor and peripheral blood samples from 84 patients were analyzed. The most represented tumor was neuroblastoma with 29.8% of cases (25/84). The rest of the cohort was composed by nine ewing Sarcomas (10.7%), seven rhabdomyosarcomas (8.3%), six osteosarcoma samples (7.1%), three high grade glioma (3.5%) and others (34/84, 40.5%).

Seventy one patients were in relapse or progression, 48 in first and 24 in second or higher relapse or progression (second relapse/progression 12, third 8, fourth 4 and one patient in fifth relapse/progression). Another 12 patients were studied at debut (because high risk disease; 12/84, 14%) and one refractory patient completed the cohort. Tumor samples studied were obtained at relapse in 55 cases and the remaining 29 at tumor diagnosis. The median patient age was 9,9 years; 54% of patients were male and 46% female (Table 1).

Table 1 Patient characteristics

Pathological diagnosis was modified after pathologist review in two cases (a patient from embryonal to alveolar rhabdomyosarcoma based on the PAX3-FOXO1A translocation and a patient from neuroblastoma to undifferentiated sarcoma). Median time between biopsy/surgery and molecular tumour board recommendation was 25 days (12–80 days).

Tumor sequencing

Fifty Tumor samples (60%) were studied through Comprehensive gene panel and 34 (40%) with HotSpot gene panel (Table 1). Variants classified as pathogenic or likely pathogenic using international system classification [18] were also distributed in the four Parsons categories. We detected a total number of 38 pathogenic or likely pathogenic variants in the overall patients (Table 2). A number of six patients (6/84, 7%) carried on pathogenic or likely pathogenic somatic variants with established clinical utility (category I): four Neuroblastoma patients with ALK mutations and two Aggressive Fibromatoses patients with CTNNB1 mutations (Table 2). Pathogenic or likely pathogenic variants in potential utility genes (category II) were detected in eight patients (8/84, 9,5%) (Table 2). Nineteen patients (19/84, 22,6%) carried variants in consensus cancer genes (category III) (Table 2). Therefore, 31 of 84 patients (36,9%) carried at least one variant considered as category I, II or III. Genetic variants classified at these categories may be directly actionable, considered as prognosis markers in specific tumor histologies and/or considered as biologically informative. Thus, these variants could be useful when taking therapeutic decisions. Two patients carried variants included in category IV of Parsons et al. classification (variants in TERT and TERT plus ERBB3 in two Neuroblastoma patients) (Additional file 3).

Table 2 Tumor sequencing results; Pathogenic or likely pathogenic variants distributed in Parsons et al. categories

The number of variants (categories I, II and III) detected in tumor samples by Comprehensive gene panel was higher than those detected by HotSpot gene panel. Indeed, we detected at least one variant classified as category I, II or III in 11 patients (11/34; 32.4%) using HotSpot gene panel. However, Comprehensive panel picked up at least one of these variants (category I, II or III) in 24 patient samples (24/50; 48%).

Germline sequencing

The germ line’s approach was completed only in those patients studied through the Comprehensive panel, since February 2016. Comprehensive panel includes 90 predisposing pediatric cancer genes, based on Zhang et al. published data [19]. Then, 40 patients were studied. Globally, genetic changes certainly responsible of tumor development were detected in 4 cases (4/40; 10%). Specifically, 3 Li-Fraumeni patients were detected, two de novo patients (High Grade Glioma: TP53 p.Arg273His and Choroid plexus carcinoma: TP53 p.Arg273Cys) and one member of a Li-Fraumeni family no previously diagnosed (malignant meningioma: TP53 p.144Ter). (Table 3). Their families were studied in Genetic Counseling Unit. Moreover, one PTEN hamartoma syndrome was diagnosed. Other pathogenic or likely pathogenic germline variants were detected in three patients, but the relationship with tumor predisposition was doubtable and therefore, this information was not translated to patient or family (Table 3).

Table 3 Germline results (n=40); pathogenic or likely pathogenic variants detected

Clinical translation

When variants were discussed in the PMTB, a report was prepared. Thirteen patients received therapeutic recommendations due to an actionable variant detected and three patients received prognosis information based on these sequencing results. Clinical recommendations from the PMTB according to the results from molecular studies at different tumor types are presented in Table 4. Clinical evolution in treated patients is summarized in Table 4.

Table 4 Clinical translation


Advances in molecular biology and targeted therapies development have led to the evolution of precision medicine (also named personalized medicine) thanks to decreases in cost and time of sequencing technologies. The principle of precision medicine in this new era consists on identifying genomic actionable alterations in the patient’s tumour. Over 20% of children with cancer will die from disease, either by relapse or non-response to standard treatment. Hence, improving knowledge on their tumour biological profile at relapse/progression may identify potential actionable alterations for which molecularly matched treatments are available and poor prognosis patients could benefit from [20]. In this context, different personalized medicine projects have been developed around the world and common difficulties discovered are well summarized by Moody et al. [21].

The first challenge in precision medicine is to decide how to look for potential targetable markers. Few biomarker-driven treatments are in clinical use in pediatric oncology currently, such as specific BRAF inhibition in BRAF V600E pediatric gliomas [14, 15] and ALK kinase inhibitors in ALK translocated anaplastic large cell lymphomas [22], ALK translocated inflammatory myofibroblastic tumours and ALK mutated neuroblastomas [16]. Thus, ALK and BRAF testing in selected tumours at relapse/progression is a standard of care in most pediatric oncology centres. However, consecutive single-marker testing is not compatible with clinical practice because of limited amount of tumour available (mostly diagnostic tru-cut), the intrinsic delays in each analysis and the overall cost [17]. The rapid development of high-throughput technologies and computational frameworks enables the examination of biological systems in unprecedented detail. It allows identify less common but targetable alterations across diseases. Nowadays, many pediatric institutions are starting to build molecular screening programs in order to treat patients according to their genomic alterations. The extent of genomic studies ranges from commercial hotspot cancer panels developed for adult tumours to whole exome sequencing (WES), RNA sequencing and methylation arrays [5, 19]. Noteworthy, the exome represents approximately 1% of the genome and harbour about 85% of the disease driven mutations. However, the larger analysis difficult its interpretation and a comparison with normal tissue is recommended to separate somatic from constitutional variants. After evaluating the different ways to face this first challenge we opted to use a panel of genes.

Pediatric cancer present a low mutation rate comparing with the adults [23, 24], however, the availability of specific drugs for the identified genetic alterations found is still limited, making difficult the approximation of precision medicine to pediatric tumors [25,26,27]. Only few genetic alterations are really actionable in concrete tumors and in small patient cohorts [28], besides that the availability of targeted drugs for children with cancer is scarce. Therefore, deeper tumor knowledge and specialized drugs are necessary. However, target drugs developed in adulthood tumors can be useful in some pediatric patients, justifying the huge effort invested in transferring this knowledge into pediatric oncology. This is why our group also joined the Personalized Medicine initiative in pediatric oncology. The identification of potentially actionable genetic variants in relapsed pediatric cancers varies from 30 to 60% between studies [21]. Unfortunately, patients receiving targeted therapy is even lower (3–18% of study populations) [29] and our study obtains similar rates as previously described results. The sequencing method performed is an important point to consider regarding the rate of identified genetic variants. For instance, we observed different percentages of genetic variants classified as category I, II or III, depending on the sequencing panel used (32% of patients had at least one mutation by HotSpot vs 48% of patients by Comprehensive panel). In our results, this difference is mainly due to ALK gene mutations that are not completely covered in HotSpot panel. We also compared our results with Parsons et al. conclusions. They used whole exome sequencing (WES) to study 121 patients’samples. They found four patients with category I genetic variants (3%), 29 patients with category II genetic variants (24%) and 24 patients with at least one category III genetic variant (20%). Therefore, 47% (57/121) of their patients had at least one genetic variant classified as either grade I, II or III. Although statistical comparisons with our results is not possible because the sequencing method and cancer population were different, it is interesting to remark that most of variants classified as category I, II and III in Parsons study were located in genes included in Comprehensive panel (Table 5). PEDS-MIONCO SEQ, Basic3, PIPseq, MAPPYACTS and INFORM Precision Medicine programs in pediatric oncology also use WES and RNA-seq and in some of them metilation and pharmacogenetic studies. Their results have been of outstanding importance to discover the genetic alterations of the pediatric tumors, and have permitted that other centers with more limited technical and/or economical resources could implement programs of Precision Medicine for their patients. Therefore, many genes studied in these projects have never been described mutated in pediatric cancer and consequently the clinical utility of WES or RNAseq compared to gene panels is inconclusive [30, 31]. Our results support the clinical utility of gene panels in pediatric oncology. However, copy number variations (CNV) have been considered an important aspect in pediatric tumors and we were not able to study it in many of our patients. This is considered a program weakness. Indeed, we have designed a specific targeted gene panel that encloses also CNV and some frequent translocations in pediatric cancer in order to maximize the coverage of genetic variants in the pediatric cancer population, maintaining an affordable budget for personalized clinical care.

Table 5 Genes not included in Comprehensive panel where at least one variant (cathegory I, II or III) was detected in Parsons et al. study

The second challenge is how interpret detected variants. Based on this work, we want to underline the importance of the multidisciplinary PMTB in order to interpret genetic variations and give clinical recommendations. In our case PMTB was able to provide therapeutic interpretations of molecular profiles for the majority of patients, and targeted therapies were recommended for 13 of the cases. PMTBs ability to offer clinically useful interpretations of current molecular profiles required (i) the synthesis of published evidence about the prevalence of observed alleles and their documented pathogenicity, (ii) inference of potential pathogenicity based on molecular and signaling pathway modeling, and (iii) inference of potential therapeutic susceptibility based on the apparent allelic frequencies of observed mutations and known drug mechanisms of action. In precision medicine, additional questions can contribute to the challenge when a therapeutic decision must be taken by a MTB: which role has a specific genetic alteration in the biology of the tumor?; what response rate to a targeted therapy is possible in a tumor type and in a concrete patient?; what bypass mechanism is present in the tumor when progressing again during target therapy?. In order to clarify these issues, biomarker standardization efforts have been started [32, 33]. Although the exact utility of targeted drugs is still in growing knowledge, umbrella or basket trials derived from personalized medicine projects are necessary [29]. Although taking decision in an MTB from preclinical or clinical previous limited information in pediatric patient results very difficult is the best way to take conclusions.

Another challenge in precision medicine is also how to proceed with germ line variants. Significant family information is derived from personalized medicine studies and in some cases that is discovered without clinical suspicion. Therefore, both patients and parents must be properly informed about the possibility of being carriers of a germline variant, consequences or therapeutic possibilities in that case, with previous signed consent. In our work, we carried out family segregation studies in 4 patients with germline variants known as cancer predisposition variants.

The last challenge in middle size centers is the lack of investment in precision medicine platforms and in bioinformatic expert’s support. Nevertheless, our efforts could get important information to our patients. We have compared both, our results and Parsons’ in order to decide the best way to continue. Although more translatable information might be obtained from studies of whole exome sequencing, RNA sequencing or epigenomics, these broad studies are not economically achievable in our work context. We must decide if the best option is working with panels no bigger than few hundred genes and design specific strategies for every tumor. An alternative strategy could be sequencing a small number of genes, analyzing the expression of certain proteins and performing analysis of copy number variations (CNV) or epigenomics on concrete genes (based on preclinical and published clinical data), according to each tumor type. Therefore, we have designed a directed gene panel, which includes all known predisposing genes to pediatric cancer [19] and actionable genes (direct or indirectly) in pediatric malignant solid tumors. This panel will enable the analysis of CNV as well as specific targetable genomic translocations. Finally, for the best of our knowledge, it is obvious that all required biologic data from clinical trials must be tested in personalized medicine projects.


Precision medicine projects based on targetable gene panel approximations can obtain translatable information to pediatric patients with reasonable efforts. Since the translation to the clinical practice is the main objective of these projects, limiting the number of relatively well-known biological markers will allow us to transfer similar amount of information with less economic and human effort.

The presented experience performed under limited human and financial resources, explores differences within published data. Our results support alternative ways on how to apply a pediatric precision medicine aiming the transfer of as much information as possible with the lowest possible expenses.

Availability of data and materials

Yes. Supplementary material.



Copy number variations


Molecular Tumor Board


Next generation sequencing


Pediatric Molecular Tumor Board


Pediatric oncology units


International Society of Pediatric Oncology /Europe Neuroblastoma


Whole exome sequencing


  1. 1.

    Van Dijk EL, Auger H, Jaszczyszyn Y, Thermes C. Ten years of next-generation sequencing technology. Trends Genet. 2014;30(9):418–26.

    CAS  Article  Google Scholar 

  2. 2.

    Ross JS, Cronin M. Whole cancer genome sequencing by next-generation methods. Am J Clin Pathol. 2011;136(4):527–39.

    CAS  Article  Google Scholar 

  3. 3.

    Weinstein JN, Collisson EA, Mills GB, Shaw KR, Ozenberger BA, Ellrott K, et al. The Cancer genome atlas pan-Cancer analysis project. Nat Genet. 2013;45(10):1113–20.

    Article  Google Scholar 

  4. 4.

    Banerji U, Workman P. Critical parameters in targeted drug development: the pharmacological a1.udit trail. SeminOncol. 2016;43(4):436–45.

    CAS  Google Scholar 

  5. 5.

    Bartholomew C, Eastlake L, Dunn P, Yiannakis D. EGFR targeted therapy in lung cancer; an evolving story. Respir Med Case Rep. 2017;20:137–40.

    CAS  PubMed  PubMed Central  Google Scholar 

  6. 6.

    Zimmer AS, Gillard M, Lipkowitz S, Lee JM. Update on PARP inhibitors in breast Cancer. Curr Treat Options in Oncol. 2018;19(5):21.

    Article  Google Scholar 

  7. 7.

    Maximiano S, Magalhães P, Guerreiro MP, Morgado M. Trastuzumab in the treatment of breast Cancer. BioDrugs. 2016;30(2):75–86.

    CAS  Article  Google Scholar 

  8. 8.

    Mackiewicz J, Mackiewicz A. BRAF and MEK inhibitors in the era of immunotherapy in melanoma patients. Contemp Oncol (Pozn). 2018;22(1A):68–72.

    Google Scholar 

  9. 9.

    Dizon DS, Krilov L, Cohen E, Gangadhar T, Ganz PA, Hensing TA, et al. Clinical Cancer advances 2016: annual report on progress against Cancer from the American Society of Clinical Oncology. J Clin Oncol. 2016;34(9):987–1011.

    CAS  Article  Google Scholar 

  10. 10.

    Alexandrov LB, Nik-Zainal S, Wedge DC, Aparicio SA, Behjati S, Blankin AV, et al. Signatures of mutational processes in human cancer. Nature. 2013;500:415–21.

    CAS  Article  Google Scholar 

  11. 11.

    Mody RJ, Wu YM, Lonigro RJ, Cao X, Roychowdhury S, Vats P, et al. Integrative clinical sequencing in themanagement of refractory or relapsed cancer in youth. JAMA. 2015;314:913–25.

    CAS  Article  Google Scholar 

  12. 12.

    Harris MH, DuBois SG, Glade Bender JL, Kim A, Crompton BD, Parker E, et al. Multicenter feasibility study oftumor molecular profiling to inform therapeutic decisions in advanced pediatricsolid tumors: the individualized Cancer therapy (iCat) study. JAMA Oncol. 2016;2:608–15.

    Article  Google Scholar 

  13. 13.

    Oberg JA, Glade Bender JL, Sulis ML, Pendrick D, Sireci AN, Hsiao SJ, et al. Implementation of next generationsequencing into pediatric hematology-oncology practice: moving beyondactionable alterations. Genome Med. 2016;8:133.

    Article  Google Scholar 

  14. 14.

    Parsons DW, Roy A, Yang Y, Wang T, Scollon S, Bergstrom K, et al. Diagnostic yield of clinical tumor andgermline whole-exome sequencing for children with solid tumors. JAMA Oncol. 2016;2:616–24.

    Article  Google Scholar 

  15. 15.

    Worst BC, van Tilburg CM, Balasubramanian GP, Fiesel P, Witt R, Freitag A, et al. Next-generationpersonalised medicine for high-risk paediatric cancer patients – the INFORM pilot study. Eur J Cancer. 2016;65:91–101.

    Article  Google Scholar 

  16. 16.

    Harttrampf AC, Lacroix L, Deloger M, Deschamps F, Puget S, Auger N, et al. MOlecular Screening for Cancer Treatment Optimization (MOSCATO-01) in pediatric patients: a single institutional prospective molecular stratification trial. Clin Cancer Res. 2017;23:6101–12.

    CAS  Article  Google Scholar 

  17. 17.

    Pincez T, Clément N, Lapouble E, Pierron G, Kamal M, Bieche I, et al. Feasibility and clinical integration ofmolecular profiling for target identification in pediatric solid tumors. Pediatr Blood Cancer. 2017;64:e26365.

    Article  Google Scholar 

  18. 18.

    Richards S, Aziz N, Bale S, Bick D, Das S, Gastier-Foster J, et al. Standards and guidelines for the interpretation of sequence variants: a joint consensus recommendation of the American College of Medical Genetics and Genomics and the Association for Molecular Pathology. Genet Med. 2015;17(5):405–24.

    Article  Google Scholar 

  19. 19.

    Zhang J, Walsh MF, Wu G, Edmonson MN, Gruber TA, Easton J, et al. Germline mutations in predisposition genes in pediatric Cancer. N Engl J Med. 2015;373(24):2336–46.

    CAS  Article  Google Scholar 

  20. 20.

    Seibel NL, Janeway K, Allen CE, Chi SN, Cho YJ, Glade Bender JL, et al. Pediatric oncology enters an era of precision medicine. Curr Probl Cancer. 2017;41(3):194–200.

    Article  Google Scholar 

  21. 21.

    Mody R, Prensner JR, Everett J, Parsons DW, Chinnaiyan AM. Precision medicine in pediatric oncology: lessons learned and next steps. Pediatr Blood Cancer. 2017;64(3).

    Article  Google Scholar 

  22. 22.

    Zeng Y, Feldman AL. Genetics of anaplastic large cell lymphoma. Leuk Lymphoma. 2016;57(1):21–7.

    CAS  Article  Google Scholar 

  23. 23.

    Lawrence MS, Stojanov P, Polak P, Kryukov GV, Cibulskis K, Sivachenko A, et al. Mutational heterogeneity in cancer and thesearch for new cancer associated genes. Nature. 2013;499(7457):214e8.

    Article  Google Scholar 

  24. 24.

    Vogelstein B, Papadopoulos N, Velculescu VE, Zhou S, Diaz LA Jr, Kinzler KW. Cancer genome landscapes science. 2013;339(6127):1546e58.

    CAS  Article  Google Scholar 

  25. 25.

    Eleveld TF, Oldridge DA, Bernard V, Koster J, Colmet Daage L, Diskin SJ, et al. Relapsed neuroblastomas show frequent RAS-MAPK pathway mutations. Nat Genet. 2015;47(8):864–71.

    CAS  Article  Google Scholar 

  26. 26.

    Schramm A, Köster J, Assenov Y, Althoff K, Peifer M, Mahlow E, et al. Mutational dynamics between primary and relapse neuroblastomas. Nat Genet. 2015;47(8):872–7.

    CAS  Article  Google Scholar 

  27. 27.

    Morrissy AS, Garzia L, Shih DJ, Zuyderduyn S, Huang X, Skowron P, et al. Divergent clonal selection dominates medulloblastoma at recurrence. Nature. 2016;529(7586):351–7.

    CAS  Article  Google Scholar 

  28. 28.

    Tran TH, Shah AT, Loh ML. Precision medicine in pediatric oncology: translating genomic discoveries into optimized therapies. Clin Cancer Res. 2017;23(18):5329–38.

    CAS  Article  Google Scholar 

  29. 29.

    Forrest SJ, Geoerger B, Janeway KA. Precision medicine in pediatric oncology. Curr Opin Pediatr. 2018;30(1):17–24.

    Article  Google Scholar 

  30. 30.

    Ma X, Liu Y, Liu Y, Alexandrov LB, Edmonson MN, Gawad C, et al. Pan-cancer genome and transcriptome analyses of 1,699paediatricleukaemias and solid tumours. Nature. 2018;555(7696):371–6.

    CAS  Article  Google Scholar 

  31. 31.

    Gröbner SN, Worst BC, Weischenfeldt J, Buchhalter I, Kleinheinz K, Rudneva VA, et al. The landscape of genomic alterations across childhood Cancers. Nature. 2018;555(7696):321–7.

    Article  Google Scholar 

  32. 32.

    Lyman GH, Moses HL. Biomarker tests for molecularly targeted therapies-the key to unlocking precision medicine. N Engl J Med. 2016;375(1):4–6.

    Article  Google Scholar 

  33. 33.

    Graig LA, Phillips JK, Moses HL. Biomarker tests for molecularly targeted therapies: key to unlocking precision medicine. N Engl J Med. 2016;375(1):4–6.

    Article  Google Scholar 

Download references


Désirée Ramal has contributed in an essential way in the study development and management of the data derived from the project.

This work was supported by “Sumemos muchas manos por los niños enfermos”, “Cancercare Xàvia”, “Asociación Cáncer Algemesí”.

Pablo Gargallo is a research Fellow of Post-MIR program IIS La Fe, Valencia Spain.

The Pediatric Hemato-Oncolgy Unit of University Hospital La Fe is a node of the European Network for Research in Pediatric Cancer (ERN-PAEDCAN).

Contributing hospitals

- Hospital General (Alicante): Carlos Esquembre.

- Clínico (Valencia): Francisco Mares.

-Virgen del Rocío (Sevilla): Catalina Márquez.

- Virgen de las Nieves (Granada): María José Moreno.

- Carlos Haya (Málaga): Laura García.

- Arrixaca (Murcia): Mar Bermúdez.

- Clínico (Santiago de Compostela): Alexandra Regueiro.

-Xeral y Cies (Vigo):María Tallón.

- Son Espases (Mallorca): Blanca Martínez.

- Cruces (Baracaldo): Ricardo López.

- Niño Jesús (Madrid): Francisco Bautista.

- La Paz (Madrid): Pedro Rubio.

- 12 Octubre (Madrid): Vanesa Perez.

- San Rafael (Madrid): Amalia Varo.

- Monte Príncipe (Madrid): Blanca López.

- Materno-infantil (Badajoz): Isabel Pintor.

- Complejo hospitalario de Navarra (Navarra): María Sagaseta.

- Hospital Miguel Servet (Zaragoza): Carlota Calvo.

-Hospital de Torrecárdenas: M° Ángeles Vázquez.


Funding has been obtained from private micro-donations, and these from patient associations.

Author information




All authors read, corrected and approved the final manuscript. All authors are official members of “Molecular Tumor Board” in La Fe Hospital (Valencia, Spain). All the authors have contributed in an essential way obtaining data, joint analysis of the same and obtaining the conclusions in the text collected and transferred to the families (each one from the point of view of their specialty).

Corresponding author

Correspondence to Pablo Gargallo.

Ethics declarations

Ethics approval and consent to participate

Every patient or legal guardian, in the case, were informed adequately and signed the informed consent. The Project was approved for Ethics committee for biomedical research (La Fe Hospital, Valencia, Spain).

Consent for publication

None individual person’s data is included, and therefore, not applicable.

Competing interests

The authors declare that they have no competing interests

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Additional files

Additional file 1:

Table S1. Genes included in Human Comprehensive Cancer GeneRead DNAseq Targeted genes Panel (Qiagen©). (PPTX 63 kb)

Additional file 2:

Table S2. Genes included in Ion Ampliseq Cancer Hot Spots v2 (Thermo Fisher Scientific©). (PPTX 46 kb)

Additional file 3:

Reference database. (XLSX 21 kb)

Rights and permissions

Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (, which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver ( applies to the data made available in this article, unless otherwise stated.

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Gargallo, P., Font de Mora, J., Berlanga, P. et al. Precision medicine in relapsed or refractory pediatric solid tumors: a collaborative Spanish initiative. transl med commun 4, 10 (2019).

Download citation


  • Precision medicine
  • Relapsed/refractory solid tumors
  • Genomics
  • Actionable pathways
  • Target therapy
  • Clinical translation