Biologic and clinical features of childhood gamma delta T-ALL: identification of STAG2/LMO2 γδ T-ALL as an extremely high risk leukemia in the very young.

PURPOSE: Gamma delta T-cell receptor-positive acute lymphoblastic leukemia (γδ T-ALL) is a high-risk but poorly characterized disease. METHODS: We studied clinical features of 200 pediatric γδ T-ALL, and compared the prognosis of 93 cases to 1,067 protocol-matched non-γδ T-ALL. Genomic features were defined by transcriptome and genome sequencing. Experimental modeling was used to examine the mechanistic impacts of genomic alterations. Therapeutic vulnerabilities were identified by high throughput drug screening of cell lines and xenografts. RESULTS: γδ T-ALL in children under three was extremely high-risk with 5-year event-free survival (33% v. 70% [age 3-<10] and 73% [age ≥10], P =9.5 x 10 -5 ) and 5-year overall survival (49% v. 78% [age 3-<10] and 81% [age ≥10], P =0.002), differences not observed in non-γδ T-ALL. γδ T-ALL in this age group was enriched for genomic alterations activating LMO2 activation and inactivating STAG2 inactivation ( STAG2/LMO2 ). Mechanistically, we show that inactivation of STAG2 profoundly perturbs chromatin organization by altering enhancer-promoter looping resulting in deregulation of gene expression associated with T-cell differentiation. Drug screening showed resistance to prednisolone, consistent with clinical slow treatment response, but identified a vulnerability in DNA repair pathways arising from STAG2 inactivation, which was efficaciously targeted by Poly(ADP-ribose) polymerase (PARP) inhibition, with synergism with HDAC inhibitors. Ex-vivo drug screening on PDX cells validated the efficacy of PARP inhibitors as well as other potential targets including nelarabine. CONCLUSION: γδ T-ALL in children under the age of three is extremely high-risk and enriched for STAG2/LMO2 ALL. STAG2 loss perturbs chromatin conformation and differentiation, and STAG2/LMO2 ALL is sensitive to PARP inhibition. These data provide a diagnostic and therapeutic framework for pediatric γδ T-ALL. SUPPORT: The authors are supported by the American and Lebanese Syrian Associated Charities of St Jude Children's Research Hospital, NCI grants R35 CA197695, P50 CA021765 (C.G.M.), the Henry Schueler 41&9 Foundation (C.G.M.), and a St. Baldrick's Foundation Robert J. Arceci Innovation Award (C.G.M.), Gabriella Miller Kids First X01HD100702 (D.T.T and C.G.M.) and R03CA256550 (D.T.T. and C.G.M.), F32 5F32CA254140 (L.M.), and a Garwood Postdoctoral Fellowship of the Hematological Malignancies Program of the St Jude Children's Research Hospital Comprehensive Cancer Center (S.K.). This project was supported by the National Cancer Institute of the National Institutes of Health under the following award numbers: U10CA180820, UG1CA189859, U24CA114766, U10CA180899, U10CA180866 and U24CA196173. DISCLAIMER: The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. The funding agencies were not directly involved in the design of the study, gathering, analysis and interpretation of the data, writing of the manuscript, or decision to submit the manuscript for publication.

Effective Response Metric: a novel tool to predict relapse in childhood acute lymphoblastic leukaemia using time-series gene expression profiling.

Accurate risk assignment in childhood acute lymphoblastic leukaemia is essential to avoid under- or over-treatment. We hypothesized that time-series gene expression profiles (GEPs) of bone marrow samples during remission-induction therapy can measure the response and be used for relapse prediction. We computed the time-series changes from diagnosis to Day 8 of remission-induction, termed Effective Response Metric (ERM-D8) and tested its ability to predict relapse against contemporary risk assignment methods, including National Cancer Institutes (NCI) criteria, genetics and minimal residual disease (MRD). ERM-D8 was trained on a set of 131 patients and validated on an independent set of 79 patients. In the independent blinded test set, unfavourable ERM-D8 patients had >3-fold increased risk of relapse compared to favourable ERM-D8 (5-year cumulative incidence of relapse 38·1% vs. 10·6%; P = 2·5 × 10-3 ). ERM-D8 remained predictive of relapse [P = 0·05; Hazard ratio 4·09, 95% confidence interval (CI) 1·03-16·23] after adjusting for NCI criteria, genetics, Day 8 peripheral response and Day 33 MRD. ERM-D8 improved risk stratification in favourable genetics subgroups (P = 0·01) and Day 33 MRD positive patients (P = 1·7 × 10-3 ). We conclude that our novel metric - ERM-D8 - based on time-series GEP after 8 days of remission-induction therapy can independently predict relapse even after adjusting for NCI risk, genetics, Day 8 peripheral blood response and MRD.

Second Malignant Neoplasms in Childhood Cancer Survivors Treated in a Tertiary Paediatric Oncology Centre.

Introduction: One of the most feared complications of childhood cancer treatment is second malignant neoplasms (SMNs). This study evaluates the incidence, risk factors and outcomes of SMNs in a tertiary paediatric oncology centre in Singapore. Materials and Methods: A retrospective review was conducted on patients diagnosed with childhood cancer under age 21 and treated at the National University Hospital, Singapore, from January 1990 to 15 April 2012. Case records of patients with SMNs were reviewed. Results: We identified 1124 cases of childhood cancers with a median follow-up of 3.49 (0 to 24.06) years. The most common primary malignancies were leukaemia (47.1%), central nervous system tumours (11.7%) and lymphoma (9.8%). Fifteen cases developed SMNs, most commonly acute myeloid leukaemia/myelodysplastic syndrome (n = 7). Median interval between the first and second malignancy was 3.41 (0.24 to 18.30) years. Overall 20-year cumulative incidence of SMNs was 5.3% (95% CI, 0.2% to 10.4%). The 15-year cumulative incidence of SMNs following acute lymphoblastic leukaemia was 4.4% (95% CI, 0% to 8.9%), significantly lower than the risk after osteosarcoma of 14.2% (95% CI, 0.7% to 27.7%) within 5 years (P <0.0005). Overall 5-year survival for SMNs was lower than that of primary malignancies. Conclusion: This study identified factors explaining the epidemiology of SMNs described, and found topoisomerase II inhibitor use to be a likely risk factor in our cohort. Modifications have already been made to our existing therapeutic protocols in osteosarcoma treatment. We also recognised the importance of other risk management strategies, including regular long-term surveillance and early intervention for detected SMNs, to improve outcomes of high risk patients.

Health-related quality of life in children with cancer undergoing treatment: a first look at the Singapore experience.

INTRODUCTION: With intensive chemotherapy and increased survival, quality of life in our paediatric population is of increasing concern. The aim of this study was to assess the children's quality of life during the treatment process. MATERIALS AND METHODS: Patients between the ages of 7 and 18 years old who are undergoing cancer treatment in the Division of Paediatric Haematology-Oncology, Department of Paediatrics, National University Health System, were identified. The child self-reported his/her health-related quality of life (HRQOL) using the PedsQL Paediatric Quality of Life Inventory and Cancer module as a validated assessment tool. RESULTS: Thirty-two patients were enrolled over a 3-week period in November 2007. The median age was 11 years (range, 7 to 17). There was 1 non-responder (3%). Fourteen (45%) boys and 17 (55%) girls were interviewed. There were 8 (26%) and 23 (74%) patients with solid and haematologic malignancies, respectively. For the Cognitive Problem Dimension score, 86% of patients with haematologic malignancy and 50% of those with solid malignancy scored below the 75th percentile (82), [OR 0.72 (0.01-0.8), P = 0.03]. For the Physical Health Summary score, patients with solid malignancy scored worse, 25% below the 10th percentile, as compared to 4.3% of patients with haematologic malignancy. This is reflected by a worse Pain and Hurt Dimension score for patients with solid malignancy. For the Perceived Appearance Dimension score, patients with solid malignancy (75%) scored lower than the median score (67) compared to those with haematologic malignancy (44%). CONCLUSIONS: The domains of HRQOL are affected to different extents for the patients with solid and those with haematologic malignancy. This is most likely to be due to the differences in treatment strategies and clinical course. Healthcare professionals should be aware of the effects of treatment on HRQOL and take practical steps to address these issues.