Biology subjects

Kamburov, A.

Publications and source records attributed to Kamburov, A..

3 recordsLinked to original sources

bioRxiv2024

Transcriptome Analysis of Archived Tumor Tissues by Visium, GeoMx DSP, and Chromium Methods Reveals Inter- and Intra-Patient Heterogeneity

Recent advancements in probe-based, full-transcriptome, high-resolution technologies for Formalin-Fixed Paraffin-Embedded (FFPE) tissues, such as Visium CytAssist, Chromium Flex (10X Genomics), and GeoMx DSP (Nanostring), have opened new opportunities for studying decades-old archival samples in biobanks, facilitating the generation of data from extensive cohorts. However, the experimental protocols can be labor-intensive and costly; therefore, it is thus essential for researchers to carefully evaluate the strengths and limitations of each technology in relation to their specific research objectives. Here, we report the results of a comparative analysis of the three methods mentioned above on FFPE archival tumor samples from four non-small cell lung cancer, four breast cancer and six diffuse large B-cell lymphoma. We highlight some relative advantages and disadvantages of each method in the context of operational challenges, bioinformatic analysis and biological discovery. Our results show that: 1) all three methods yielded good-quality, highly reproducible transcriptomic data from serial sections of the same FFPE block; 2) GeoMx data contained mixtures of cell types, even when pre-selecting areas with cell type-specific markers; 3) high-throughput spot-level (Visium) or cell-level (Chromium) data enabled the identification of tumor heterogeneity within and between patients, which could be used to identify targeted therapies. Our data support the use of Visium and Chromium for high-throughput and discovery-driven projects, while the GeoMx platform could be suited for addressing specialized questions on targeted regions. All data generated from this study, including GeoMx, Visium, Chromium, H&E, and expert annotations are publicly available.

bioinformatics↗

bioRxiv2023

Novel YAP1/TAZ pathway inhibitors identified through phenotypic screening with potent anti-tumor activity via blockade of GGTase-I / Rho-GTPase signaling

This study describes the identification and target deconvolution of novel small molecule inhibitors of oncogenic YAP1/TAZ activity with potent anti-tumor activity in vivo. A high-throughput screen (HTS) of 3.8 million compounds was conducted using a cellular YAP1/TAZ reporter assay. Target deconvolution studies identified the geranylgeranyltransferase-I (GGTase-I) complex, as the direct target of YAP1/TAZ pathway inhibitors. The novel small molecule inhibitors block the activation of Rho-GTPases, leading to subsequent inactivation of YAP1/TAZ and inhibition of cancer cell proliferation in vitro. Multi-parameter optimization resulted in BAY-593, an in vivo probe with favorable PK properties, which demonstrated anti-tumor activity and blockade of YAP1/TAZ signaling in vivo. SIGNIFICANCEYAP1/TAZ have been shown to be aberrantly activated oncogenes in several human solid tumors, resulting in enhanced cell proliferation, metastasis and provision of a pro-tumorigenic microenvironment, making YAP1/TAZ targets for novel cancer therapies. Yet, the development of effective inhibitors of these potent oncogenes has been challenging. In this work, we break new ground in this direction through the identification of novel inhibitors of YAP1/TAZ activity. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=192 HEIGHT=200 SRC="FIGDIR/small/555331v2_ufig1.gif" ALT="Figure 1"> View larger version (33K): org.highwire.dtl.DTLVardef@4aac3forg.highwire.dtl.DTLVardef@728b6forg.highwire.dtl.DTLVardef@203a2eorg.highwire.dtl.DTLVardef@1cbc5f9_HPS_FORMAT_FIGEXP M_FIG C_FIG HIGHLIGHTSO_LINovel YAP1/TAZ pathway inhibitors identified by phenotypic high-throughput screen C_LIO_LITarget deconvolution identifies GGTase-I as the direct target of the novel YAP1/TAZ pathway inhibitors C_LIO_LIGGTase-I inhibitors block Rho-GTPase signaling and downstream YAP1/TAZ C_LIO_LIGGTase-I inhibitor BAY-593 demonstrates potent anti-tumor activity in vivo C_LI

cancer biology↗

bioRxiv2017

Expanding discovery from cancer genomes by integrating protein network analyses with in vivo tumorigenesis assays

Approaches that integrate molecular network information and tumor genome data could complement gene-based statistical tests to identify likely new cancer genes, but are challenging to validate at scale and their predictive value remains unclear. We developed a robust statistic (NetSig) that integrates protein interaction networks and data from 4,742 tumor exomes and used it to accurately classify known driver genes in 60% of tested tumor types and to predict 62 new candidates. We designed a quantitative experimental framework to compare the in vivo tumorigenic potential of NetSig candidates, known oncogenes and random genes in mice showing that NetSig candidates induce tumors at rates comparable to known oncogenes and 10-fold higher than random genes. By reanalyzing nine tumor-inducing NetSig candidates in 242 patients with oncogene-negative lung adenocarcinomas, we find that two (AKT2 and TFDP2) are significantly amplified. Overall, we illustrate a scalable integrated computational and experimental workflow to expand discovery from cancer genomes.

cancer biology↗