Multiple Myeloma

Multiple myeloma (MM), the second most common blood cancer, is a hematological malignancy characterized by an abnormal accumulation of clonal plasma cells in the bone marrow. MM heterogeneity is associated with the presence of different genomic and transcriptomic profiles that have a clear impact on the prognosis of the disease. 

Advanced Approaches

Our team is working with data collected from healthy and sick individuals. The goal is to identify the proteins which lead to the formation of this cancer in the body, determine the markers which indicate the presence and progress of the disease, and define methods and software tools to sort and process the data.

Metabolomics Profiling

• Metabolism has been deeply studied in MM research.
• Identify metabolic vulnerabilities in MM based on the application of a system biology approach focus on metabolic networks and trascriptomic data from MM patients.
• Uncover novel targets for prognosis and treatment in MM patients.

Deep Transcriptome Profiling

SPECTRA is an approach to describe variation in a transcriptome as a set of unsupervised quantitative variables based on RNA sequencing results. It provides quantitative measures of transcriptome variation to deeply profile tumors.

Computational Biology Modelling (CBM)

• Consider the combined effect of individual mutations, gene copy number abnormalities, and large-scale chromosomal changes.
• Generate patient-specific protein network maps of activated and inactivated disease pathways.
• Predict drug response and resistance mechanisms.
• Reduce unnecessary costs or drug-related toxicities.

Main Resources

Cancer Cell Line Encyclopedia (CCLE)

Cytogenetics and Somatic Mutations (by targeted NGS) Results

PubMed (MEDLINE database)

Tumor-Genome Profile

Why Choose Us?

Our mission is to uncover the mechanisms of Multiple Myeloma. We utilize microarray, deep sequencing platforms, advanced biostatistical and computational analyses methods to detect biological signals in highly dimensional and often noisy genomic data. 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.

In addition, CD ComputaBio has multiple resources including academic research and preclinical works in the identification of a suitable disease target and its corresponding hit. Contact us for more service details.

References

1. Luis Vitores V. Valcárcel, et al. Computational Systems Biology Models for the Identification of Metabolic Vulnerabilities in Multiple Myeloma. Blood. 2019.
2. Rosalie Griffin Waller, et al. Deep transcriptome profiling of multiple myeloma with quantitative measures using the SPECTRA approach. medRxiv. 2020.