bioRxiv Science⌕ Search

Biology subjects

Tanhaemami, M.

Publications and source records attributed to Tanhaemami, M..

3 recordsLinked to original sources

Clustered structural variant hotspots enable oncogenic addiction and plasticity in osteosarcoma

The genomic landscape of osteosarcoma, the most common bone cancer worldwide, is among the most structurally complex of all human malignancies. The identification of recurrent and functionally consequential patterns has thus remained a challenge. Across 236 whole-genome sequencing osteosarcoma samples, we uncovered five genomic hotspots of clustered structural variation collectively altered in 58% of tumors. Four were associated with amplification of oncogenes ( MYC , CCND3 , CCNE1 , CDK4) , while the fifth mapped largely upstream of TP53 . Hotspot events showed coordinated patterns of co-occurrence and mutual exclusivity with each other and with tumor suppressor alterations, suggesting genomic context-specific selection. We found localized transcriptional dysregulation at hotspot event loci, and single cells harboring these events converged on a neural crest-like program, linking these structural alterations to a less differentiated cell state. These events were also detectable non-invasively through liquid biopsies and displayed ongoing structural evolution throughout disease progression, a finding with potential clinical utility. Our results provide novel insight into how complex rearrangements shape oncogenesis in osteosarcoma, with broader relevance to other cancers characterized by complex genomes.

cancer biology↗

A novel combination of CDK4/6 and PI3K inhibitors exhibits highly synergistic activity and translational potential in Ewing sarcoma.

Ewing sarcoma is a highly aggressive solid malignancy affecting children and young adults. Ewing sarcoma is driven primarily by EWSR1::FLI1, a fusion oncoprotein that has been notoriously difficult to target with traditional pharmacologic agents. There are numerous examples of preclinical promising combinations of small molecules that are never tested in pediatric clinical trials because agents fail to reach the market due to limited efficacy for common adult cancers. Moreover, the effectiveness of single-agent therapies for cancer treatment is often limited. To address these limitations, we selected 28 compounds that were largely FDA approved or in late stages of clinical development and known to regulate important pathways in Ewing sarcoma. We performed a drug screen in Ewing sarcoma cell lines with 180 combinations of tyrosine kinase inhibitors, cell cycle inhibitors, and conventional chemotherapy. The results of the screen revealed that a PI3K inhibitor, copanlisib, combined with a CDK4/6 inhibitor, ribociclib, exhibited strong synergistic anti-Ewing sarcoma activity. Using proteomic methods such as a reverse-phase protein array and western immunoblotting, we demonstrated that this combination induced a downregulation of the PI3K/AKT pathway as well as proteins involved in cell cycle regulation. We further confirmed these in vitro data using bulk RNA-sequencing. To evaluate the phenotypic effect of the PI3K/CDK4/6 inhibition in Ewing sarcoma lines, we performed apoptosis and cell cycle analyses using flow cytometry and demonstrated that ribociclib primarily induced a G0/G1 arrest with minimal effect on Ewing cell viability but significantly enhanced the apoptotic effect of copanlisib treatment. In a xenograft model of Ewing sarcoma, we demonstrated that the combination therapy significantly prolonged survival compared to treatment with either vehicle or single-agent therapy alone. Our findings identify a new candidate therapy combination for Ewing sarcoma using FDA-approved drugs and provide a resource of additional potential synergistic combinations for future validation. Statement of translational relevanceTreatment of patients with newly diagnosed Ewing sarcoma involves intensive multi- agent chemotherapy, radiation, and surgery, often leading to long-term toxicities. Children with relapsed or metastatic disease experience poor outcomes with five-year overall survival rates of only 15 to 30 percent. Therefore, new therapeutic approaches are urgently needed. Our drug screen revealed several promising combinations with synergistic anti-Ewing sarcoma activity including the PI3K inhibitor copanlisib with the CDK4/6 inhibitor ribociclib. This combination also significantly improved survival in a mouse xenograft model of Ewing sarcoma compared to treatment with vehicle or either drug alone. Pre-clinical validation of a combination therapy composed of two FDA- approved drugs nominates this combination for early phase trials for patients with with relapsed or refractory Ewing sarcoma, a group of patients greatly in need of new therapeutic opportunities. The data generated by our drug screen may also be used as a resource to identify other promising combinations for future validation as Ewing sarcoma therapies.

cancer biology↗

Using Flow Cytometry and Multistage Machine Learning to Discover Label-Free Signatures of Algal Lipid Accumulation

--Most applications of flow cytometry or cell sorting rely on the conjugation of fluorescent dyes to specific biomarkers. However, labeled biomarkers are not always available, they can be costly, and they may disrupt natural cell behavior. Label-free quantification based upon machine learning approaches could help correct these issues, but label replacement strategies can be very difficult to discover when applied labels or other modifications in measurements inadvertently modify intrinsic cell properties. Here we demonstrate a new, but simple approach based upon feature selection and linear regression analyses to integrate statistical information collected from both labeled and unlabeled cell populations and to identify models for accurate label-free single-cell quantification. We verify the methods accuracy to predict lipid content in algal cells (Picochlorum soloecismus) during a nitrogen starvation and lipid accumulation time course. Our general approach is expected to improve label-free single-cell analysis for other organisms or pathways, where biomarkers are inconvenient, expensive, or disruptive to downstream cellular processes.

systems biology↗