Challenges in Treating Childhood Infratentorial Ependymoma: A Low- and Middle-Income Country Experience.

PURPOSE: Patients and physicians in low- and middle-income countries (LMICs) face challenges owing to limited expertise and suboptimal access to appropriate diagnostic and treatment modalities. We report our experience in treating posterior fossa ependymoma (PFE) at MAHAK, a charity organization in Iran whose radiation oncology department is the only one exclusively dedicated to childhood cancer in the whole country. METHODS AND MATERIALS: Pediatric patients with PFE referred to MAHAK between November 2008 and January 2016 were identified. Details on investigations and management done before referral were collected. Management at MAHAK and patient outcomes were analyzed. RESULTS: Of 80 patients diagnosed as having ependymoma, 54 with PFE were identified. Forty-three patients received adjuvant radiation therapy, and 11 were irradiated initially after recurrence. At a median follow-up of 5.1 years (range, 0.3-9.7 years), the latter group had the worst outcome, with a 5-year overall survival (OS) rate of 27% (95% CI, 7%-54%). Patients who started radiation therapy within 77 days after initial surgery had a better outcome compared with those who started later (5-year OS: 74% vs 32%; P = .05). Compliance with follow-up recommendations was poor. Only 22% of the patients had at least 2 IQ test assessments, and 50% showed some decline over time. Three cases of growth hormone deficiency were detected, but none of the patients received replacement therapy. CONCLUSIONS: Access to pediatric neurosurgery, anesthesia, and timely radiation therapy are among the most challenging obstacles to be overcome in LMICs. Our series confirmed that chemotherapy is not an appropriate option for delaying radiation therapy, especially in young children. The importance of long-term follow-up should be acknowledged by the parents and medical team.

RENT+: an improved method for inferring local genealogical trees from haplotypes with recombination.

Motivation: : Haplotypes from one or multiple related populations share a common genealogical history. If this shared genealogy can be inferred from haplotypes, it can be very useful for many population genetics problems. However, with the presence of recombination, the genealogical history of haplotypes is complex and cannot be represented by a single genealogical tree. Therefore, inference of genealogical history with recombination is much more challenging than the case of no recombination. Results: : In this paper, we present a new approach called RENT+ for the inference of local genealogical trees from haplotypes with the presence of recombination. RENT+ builds on a previous genealogy inference approach called RENT , which infers a set of related genealogical trees at different genomic positions. RENT+ represents a significant improvement over RENT in the sense that it is more effective in extracting information contained in the haplotype data about the underlying genealogy than RENT . The key components of RENT+ are several greatly enhanced genealogy inference rules. Through simulation, we show that RENT+ is more efficient and accurate than several existing genealogy inference methods. As an application, we apply RENT+ in the inference of population demographic history from haplotypes, which outperforms several existing methods. Availability and Implementation: : RENT+ is implemented in Java, and is freely available for download from: https://github.com/SajadMirzaei/RentPlus . Contacts: : sajad@engr.uconn.edu or ywu@engr.uconn.edu. Supplementary information: : Supplementary data are available at Bioinformatics online.

Fast Construction of Near Parsimonious Hybridization Networks for Multiple Phylogenetic Trees.

Hybridization networks represent plausible evolutionary histories of species that are affected by reticulate evolutionary processes. An established computational problem on hybridization networks is constructing the most parsimonious hybridization network such that each of the given phylogenetic trees (called gene trees) is "displayed" in the network. There have been several previous approaches, including an exact method and several heuristics, for this NP-hard problem. However, the exact method is only applicable to a limited range of data, and heuristic methods can be less accurate and also slow sometimes. In this paper, we develop a new algorithm for constructing near parsimonious networks for multiple binary gene trees. This method is more efficient for large numbers of gene trees than previous heuristics. This new method also produces more parsimonious results on many simulated datasets as well as a real biological dataset than a previous method. We also show that our method produces topologically more accurate networks for many datasets.