- Liquid biopsy of low burden tumors using circulating cell-free DNA
- Chimeric Protein-Protein Interactions (ChiPPI) analysis and their role in altering cancer-specific phenotypes.
- Pan-Cancer data analysis to study the similarities and differences across diverse tumor types.
- Liquid Biopsy using cell free DNA in Glioblastoma.
- Comparative genomics and protein domain evolution.
- Codon-usage analysis and cell-cycle regulation.
- Analysis of miRNA sequences for evolutionary differences.
Liquid biopsy of low burden tumors using circulating cell-free DNA
Gliomas are the most frequent brain tumors worldwide. Gliomas make up about 30% of all brain and central nervous system tumors, and 80% of all malignant brain tumors. Diagnosis of the glioma tumor type and its grade is a most essential step in order to suggest a right treatment for the glioma patients. We present a comprehensive study of the different types of the tumors with a low burden in plasma matched with the cfDNA extracted from a clinical cohort of patients’ plasma in order to find unique tumor mutations as biomarkers. We successfully detected the glioma specific mutations for the highly frequently mutated genes such as IDH1, IDH2, PDGFRA, NOTCH1, PIK3R1 and 30 other genes. We identified the particular mutations of the cfDNA isolated from the plasma of the glioma patients, followed by the DNA-sequencing and our predictive bioinformatics analysis. We have collected the matched tumor and cfDNA mutations to uncover the tumor grade as well as its heterogeneity using our unique measurement of the mutations coverage by the DNA-seq reads. Moreover, we used our previously published methods to uncover unique fusions in the glioma patients and its alterations in the protein-protein interactions networks to understand the tumor prognosis. For the best of our knowledge, our study is the most advanced study in the field of the liquid biopsy for the brain cancer tumors, and it will provide a quick and safe non-invasive diagnostic method for the glioma patients, as it uncovers the tumour sub-types using unique biomarkers. This will provide the best personalized treatment for the highly complicate disease and will eventually bypass the existing “wait-and- see” method for prognosis.
Chimeric Protein-Protein Interactions (ChiPPI) analysis and their role in altering cancer-specific phenotypes
We seek to repeat the successful development of Imatinib, the miracle drug used to treat chronic myeloid leukemia (CML) that target BCR/ABL protein. Use of Imatinib nearly doubled five year survival rate for people with chronic myeloid leukemia, from 31% in 1993 to 59% for those diagnosed between 2003 and 2009. Discovering a successful drug match to cancer-associated fusion proteins could produce a breakthrough treatment that attacks and kills cancer with minimal damage to healthy cells and few side-effects.
Dr. Frenkel-Morgenstern has worked on cancer fusion transcripts and proteins with 2009, during at the Spanish National Cancer Research Centre (CNIO). She has significantly contributed to the field by discovering fusion transcripts and proteins in several organisms and in cancer cells culminating in the establishment of the ChiTaRS database, comprising a collection of more than 50,000 fusions transcripts in eight organisms (today ChiTaRS-3.1). Next, we developed a ChiPPI server that predicts protein interactions of fusion proteins using alterations of protein domains and Domain-domain co-occurrence scores (DDCO) for more than 29,000 fusions (ChiPPI method).
Pan-Cancer data analysis to study the similarities and differences across diverse tumor types
Recent advances in RNA and DNA sequencing technologies make available whole genome and whole transcriptome sequencing to all interested research laboratories, at an affordable price. Use of high-throughput sequencing technologies is now an essential component of research in many fields and critical for publishing results in select journals. Often, research can no longer focus on one biological effect of interest; rather the specific effect must be analyzed in relation to all other biological processes in parallel to be deemed worthy of publication. Utilizing sequencing data is no longer a niche; it has become mainstream.
Out lab is an active member of the PanCancer (ICGC consortium) project seeking to understand structural variants at the DNA levels and chimeric transcripts at the RNA levels. We developed a new approach using Probabilistic, Bayesian and Percolation-based models to study changes in the protein interaction networks to study different cancer phenotypes.
Liquid Biopsy using cell free DNA in Glioblastoma
We selected glioblastoma as a model to develop an advanced methodology for monitoring patients using cfDNA or circulating tumour DNA (ctDNA) in the blood plasma. Gliomas, the most frequent brain tumours, comprise ~80% of all primary malignant brain tumours. Glioblastoma multiforme (GBM) is the most common type of glioma, and is uniformly fatal. The median survival of treated GBM patients is in the range of 12-15 months. Standard modalities of therapy are unselective and include surgery, radiation therapy and chemotherapy. Temozolomide (TMZ) is an alkylating agent that methylates DNA and is used as the standard chemotherapy for glioblastoma. Enrolling patients for clinical trials and tailoring therapies based on treatment response can be challenging in the absence of an objective method for measuring tumour burden and determining its genomic composition.
Currently, tumour heterogeneity, clonal diversity and mutation acquisition are major impedances for tailoring personalized therapy in gliomas. Thus, a Liquid Biopsy Diagnostics Platform that applies cfDNA diagnostics may identify the most appropriate therapeutic approach for each glioblastoma patient, by drugs or by radiotherapy, and may help determine the optimal combinations. We isolated cfDNA of 18 of 20 glioma patients vs. 10 healthy individuals. Moreover, we identified mutations, nucleosome maps and fusion biomarkers with 80% sensitivity and 90% specificity.
Comparative genomics and protein domain evolution
Subterranean species are those living underground, have adapted to the extreme stresses in comparison to the fossorial and the aboveground species. Here we present a comparative analysis of protein-protein interactions (PPI) of all genes including stress-response genes among four subterranean, seven fossorial and 12 aboveground species (PASTORAL). We found that organisms adapted by modulation of the PPI networks and by “shuffling” protein domains rather than sequence alterations. The orthologous hypoxia, heat-shock and circadian clock proteins have been found to form clusters which correspond to their ecology association, based on the PPI conservation, rather than protein domains conservation. Proteins containing these domains were found to be essential nodes, or hubs in the corresponding PPI sub-networks.
Thus, the distribution of hubs in ecology-speciﬁc PPI sub-networks, is readily predicted by clustering of all the PASTORAL organisms using their PPI networks’ properties. Moreover, we looked on the codon usage preferences. In general, rare proteins with disordered regions prefer less optimal codon usage and form less interactions relative to abundant proteins having ordered and mixed regions. Particularly, proteins of subterranean animals adopt less optimal codon usage compared with animals of other ecologies. Thus, we assumed that evolution built domains from “disordered” toward “ordered” and constructed more condense networks to have aboveground species. Finally, we organized our data for the subterranean, fossorial and aboveground species in a user-friendly accessible database, PASTORAL, which contains 23 organisms, their predicted PPIs from diverse taxa confined to specific ecologies. As a result, our collections enable evolutionary biologists to study convergent evolution related to stress response and other essential cellular processes in an extended and comparative manner.
Codon-usage analysis and cell-cycle regulation
The cell cycle is a temporal program that regulates DNA synthesis and cell division. When we compared the codon usage of cell cycle-regulated genes with that of other genes, we discovered that there is a significant preference for non-optimal codons. Moreover, genes encoding proteins that cycle at the protein level exhibit non-optimal codon preferences. Remarkably, cell cycle-regulated genes expressed in different phases display different codon preferences. Here, we show empirically that transfer RNA (tRNA) expression is indeed highest in the G2 phase of the cell cycle, consistent with the non-optimal codon usage of genes expressed at this time, and lowest toward the end of G1, reflecting the optimal codon usage of G1 genes.
Accordingly, protein levels of human glycyl-, threonyl-, and glutamyl-prolyl tRNA synthetases were found to oscillate, peaking in G2/M phase. In light of our findings, we propose that non-optimal (wobbly) matching codons influence protein synthesis during the cell cycle. We describe a new mathematical model that shows how codon usage can give rise to cell-cycle regulation. In summary, our data indicate that cells exploit wobbling to generate cell cycle-dependent dynamics of proteins.