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Cancer research currently is heavily skewed toward high-income countries (HICs), with little research conducted in, and relevant to, the problems of low- and middle-income countries (LMICs). This regional discordance in cancer knowledge generation and application needs to be rebalanced. Several gaps in the research enterprise of LMICs need to be addressed to promote regionally relevant research, and radical rethinking is needed to address the burning issues in cancer care in these regions. We identified five top priorities in cancer research in LMICs based on current and projected needs: reducing the burden of patients with advanced disease; improving access and affordability, and outcomes of cancer treatment; value-based care and health economics; quality improvement and implementation research; and leveraging technology to improve cancer control. LMICs have an excellent opportunity to address important questions in cancer research that could impact cancer control globally. Success will require collaboration and commitment from governments, policy makers, funding agencies, health care organizations and leaders, researchers and the public.

Medulloblastoma is a WHO grade IV neoplasm that is usually restricted to the central nervous system, with metastasis occurring along the neuraxis through the ventricular system and cerebrospinal fluid. Extraneural metastasis is rare, usually occurs in bones/lymph nodes, and carries poor prognosis. We report multimodal imaging in a 14-year-old boy with desmoplastic medulloblastoma having multiple extraneural nodal and skeletal metastases, which was misdiagnosed as lymphoma. This case highlights the importance of understanding tumor biology with active clinical and radiological surveillance that is useful in early detection of extraneural metastasis of this aggressive tumor that helps in advocating appropriate and timely treatment.

Vali: Cell Works Group Inc.: Employment. Cogle:Celgene: Other: Steering Committee Member of Connect MDS/AML Registry. Singh:Cellworks Research India Private Limited: Employment. Usmani:Cellworks Research India Private Limited: Employment. Pampana:Cellworks Research India Private Limited: Employment. Shyamasundar:Cellworks Research India Private Limited: Employment. Sikora:Cellworks Research India Private Limited: Employment. Kumar:Cellworks Research India Private Limited: Employment. Abbasi:Cell Works Group Inc.: Employment.

Background:Monosomy of chromosome 7/Del 7 (-7) or its long arm (del(7q)) is one of the most common cytogenetic abnormalities in pediatric and adult myeloid malignancies, particularly in adverse-risk acute myeloid leukemias (AMLs). Monosomy 7 with complex karyotype further worsens the prognosis. Therefore, predicting response of therapies in this segment of patients is urgently needed to improve disease management by customizing therapy to the profile genomics instead of the conventional method of trial and error or one-size-fits all treatments.

ETP-ALL, an orphan disease, is a subtype of T-ALL with poor prognosis and high risk of relapse after standard of care (SOC) chemotherapy. ETP-ALL is a genomically heterogeneous disease with many cases harboring complex karyotype and genetic mutations typically seen in myeloid neoplasias, such as acute myeloid leukemia (AML). Targeted therapies for ETP-ALL are limited because identified gene mutations are not actionable by currently available drugs. Unfortunately, empirical chemotherapy trials have yet to show significant improvements in clinical outcomes of ETP-ALL patients (pts), which makes treating ETP-ALL challenging. Ideally, selecting treatment for ETP-ALL is rationally designed from the panoply of unique molecular abnormalities observed in ETP-ALL cases. Recently, we developed a predictive simulation modeling method that projects protein network maps from the hundreds of leukemia genomic mutations observed in individual pts (PMID 27855285). Our computational modeling enables multi-gene/multi-drug simulations. We hypothesized that ETP-ALL is comprised of protein network disturbances distinct from other T-ALL cases. We furthermore hypothesized these unique ETP-ALL protein networks would lead to the discovery of new treatment combinations for pts with ETP-ALL.

ETP-ALL is a high-risk subtype of T-ALL with poor prognosis and high risk of relapse in patients treated with standard of care (SOC). ETP-ALL patients have more complex cytogenetic profiles and a unique immunophenotype compared to other T-ALL cases. ETP-ALL is characterized by the expression of myeloid and hematopoietic stem cell (HSC) surface proteins, as well as gene expression profiles similar to myeloid progenitors and HSCs, including frequent mutations in myeloid oncogenes. Targeted therapies for ETP-ALL are limited because identified gene mutations are not actionable by currently available drugs. Recently we developed a predictive simulation modeling method that projects cancer patient-specific protein network maps based on the patient's unique cancer genomics (PMID 27855285). Here, we present a computational biology approach to analyze the genomic profile of ETP-ALL and identify novel treatment strategies using available FDA approved drugs. In this study, we used the LOUCY cell line as a model of ETP-ALL due to its high expression of characteristic ETP-ALL markers HHEX, LYL1, MEF2C and ZEB2.

Background: Early T-cell precursor acute lymphoblastic leukemia (ETP-ALL) is an aggressive hematological malignancy with poor prognosis after chemotherapy compared to other ALLs. ETP-ALL cases typically display a high burden of chromosomal copy number variations (CNVs) and myeloid gene mutations. To enhance the understanding of this disease, a computational biological modeling approach was developed to classify ETP-ALL vs. non-ETP-ALL patient based on genomic abnormalities and protein biomarkers associated with ETP-ALL and non ETP-ALL profiles.