Sarcomas are malignant soft tissue and bone tumours affecting adults, adolescents and children. They represent a morphologically heterogeneous class of tumours and some entities lack defining histopathological features. Therefore, the diagnosis of sarcomas is burdened with a high inter-observer variability and misclassification rate. Here, we demonstrate classification of soft tissue and bone tumours using a machine learning classifier algorithm based on array-generated DNA methylation data. This sarcoma classifier is trained using a dataset of 1077 methylation profiles from comprehensively pre-characterized cases comprising 62 tumour methylation classes constituting a broad range of soft tissue and bone sarcoma subtypes across the entire age spectrum. The performance is validated in a cohort of 428 sarcomatous tumours, of which 322 cases were classified by the sarcoma classifier. Our results demonstrate the potential of the DNA methylation-based sarcoma classification for research and future diagnostic applications.

Background: The prognosis in patients with relapsed Ewing sarcoma is unfavorable. Our investigation identifies factors predicting for the outcome following relapse.Procedure: We analyzed type of relapse, time to relapse and overall survival after relapse (OSr) in 714 patients with first recurrence. All patients had been treated within the Cooperative Ewing Sarcoma Studies (CESS) 81 or 86, or the European Intergroup CESS (EICESS 92). OSr time was calculated from diagnosis of first relapse to last follow‐up or death.Results: Median follow‐up time from diagnosis of primary disease was 2.2 years (mean = 4.0; range: 0.2–24.9). Relapse sites were local in 15%, combined local and systemic in 12%, and systemic in 73%. Among patients with a localized primary tumor, 20% relapsed locally, while 12% showed combined and 68% systemic relapse. When the primary disease was disseminated, 82% developed systemic, 13% combined, and 5% local relapse. Five‐year OSr was 0.13 (SE = 0.01). Outcome following local relapse, with a 5‐year survival rate of 0.24 (P < 0.001), was superior to outcome after systemic or combined recurrence. Five‐year OSr was 0.07 (SE = 0.01) in patients who relapsed 0–2 years after the diagnosis of primary disease, as compared to a 5‐year OSr of 0.29 (SE = 0.03) when relapse occurred later.Conclusions: 5‐year OSr in Ewing sarcoma is poor (2 years) and strictly localized relapse. Pediatr Blood Cancer 2011; 57: 549–553.

Ewing sarcoma, a highly aggressive bone and soft-tissue cancer, is considered a prime example of the paradigms of a translocation-positive sarcoma: a genetically rather simple disease with a specific and neomorphic-potential therapeutic target, whose oncogenic role was irrefutably defined decades ago. This is a disease that by definition has micrometastatic disease at diagnosis and a dismal prognosis for patients with macrometastatic or recurrent disease. International collaborations have defined the current standard of care in prospective studies, delivering multiple cycles of systemic therapy combined with local treatment; both are associated with significant morbidity that may result in strong psychological and physical burden for survivors. Nevertheless, the combination of non-directed chemotherapeutics and ever-evolving local modalities nowadays achieve a realistic chance of cure for the majority of patients with Ewing sarcoma. In this review, we focus on the current standard of diagnosis and treatment while attempting to answer some of the most pressing questions in clinical practice. In addition, this review provides scientific answers to clinical phenomena and occasionally defines the resulting translational studies needed to overcome the hurdle of treatment-associated morbidities and, most importantly, non-survival.

INFORM is a prospective, multinational registry gathering clinical and molecular data of relapsed, progressive, or high-risk pediatric patients with cancer. This report describes long-term follow-up of 519 patients in whom molecular alterations were evaluated according to a predefined seven-scale target prioritization algorithm. Mean turnaround time from sample receipt to report was 25.4 days. The highest target priority level was observed in 42 patients (8.1%). Of these, 20 patients received matched targeted treatment with a median progression-free survival of 204 days [95% confidence interval (CI), 99–not applicable], compared with 117 days (95% CI, 106–143; P = 0.011) in all other patients. The respective molecular targets were shown to be predictive for matched treatment response and not prognostic surrogates for improved outcome. Hereditary cancer predisposition syndromes were identified in 7.5% of patients, half of which were newly identified through the study. Integrated molecular analyses resulted in a change or refinement of diagnoses in 8.2% of cases. Significance: The pediatric precision oncology INFORM registry prospectively tested a target prioritization algorithm in a real-world, multinational setting and identified subgroups of patients benefiting from matched targeted treatment with improved progression-free survival, refinement of diagnosis, and identification of hereditary cancer predisposition syndromes. See related commentary by Eggermont et al., p. 2677 . This article is highlighted in the In This Issue feature, p. 2659

Sequencing of cell-free DNA in the blood of cancer patients (liquid biopsy) provides attractive opportunities for early diagnosis, assessment of treatment response, and minimally invasive disease monitoring. To unlock liquid biopsy analysis for pediatric tumors with few genetic aberrations, we introduce an integrated genetic/epigenetic analysis method and demonstrate its utility on 241 deep whole-genome sequencing profiles of 95 patients with Ewing sarcoma and 31 patients with other pediatric sarcomas. Our method achieves sensitive detection and classification of circulating tumor DNA in peripheral blood independent of any genetic alterations. Moreover, we benchmark different metrics for cell-free DNA fragmentation analysis, and we introduce the LIQUORICE algorithm for detecting circulating tumor DNA based on cancer-specific chromatin signatures. Finally, we combine several fragmentation-based metrics into an integrated machine learning classifier for liquid biopsy analysis that exploits widespread epigenetic deregulation and is tailored to cancers with low mutation rates. Clinical associations highlight the potential value of cfDNA fragmentation patterns as prognostic biomarkers in Ewing sarcoma. In summary, our study provides a comprehensive analysis of circulating tumor DNA beyond recurrent genetic aberrations, and it renders the benefits of liquid biopsy more readily accessible for childhood cancers.