Acute Lymphocytic Leukemia

Acute lymphocytic leukemia (ALL) is a type of cancer of the blood and bone marrow — the spongy tissue inside bones where blood cells are made. Acute lymphocytic leukemia occurs when a bone marrow cell develops changes (mutations) in its genetic material or DNA. Normally, the DNA tells the cell to grow at a set rate and to die at a set time. In acute lymphocytic leukemia, the mutations allow the bone marrow cell to continue growing and dividing. Common inherited risk factors include mutations in ARID5B, CDKN2A/2B, CEBPE, IKZF1, GATA3, PIP4K2A and, more rarely, TP53. These genes play important roles in cellular development, proliferation, and differentiation.

Computational Biology Modeling

Use computational biology modeling (CBM) to create intracellular protein network maps. CBM is based on numerous PubMed references and online sources, and includes more than thousands of genes, unique biomarkers, and various functional interactions associated with signaling pathways important in cancer.

• Use cytogenetic profiling by spectral karyotyping to identify chromosomal aberrations.
• Use array comparative genomic hybridization (aCGH) to assess copy number variations (CNV).
• Use whole-exome sequencing (WES) for identifying genetic variants to interpret the genomic signature of each patient’s disease.
• Compile the resulting data to create a list of genes with mutations and CNVs in the patient’s genome.
• Extract the genes found on loci of the affected regions of chromosomes from the human reference genome (ENSEMBL).
• Match the complete gene list with the Cancer CBM to determine the subset to be represented in the model.

Digital Drug Models

A digital drug library of FDA-approved and investigational agents has been created for CBM by programming each agent’s mechanism of action (MOA), as well as effects on specific protein targets and pathways determined from published literature.

CD ComputaBio has multiple resources including academic research and preclinical works in the identification of a suitable disease target and its corresponding hit. We are also interested in how the machine learning-based integration of multi-omic datasets can aid in the discovery of new cancer subgroups and biomarkers. Contact us for more service details.

Reference

1. Christopher R.Cogle, et al. Computational modeling of early T-cell precursor acute lymphoblastic leukemia (ETP-ALL) to identify personalized therapy using genomics. Leukemia Research. 2019.