bioRxiv Science⌕ Search

Biology subjects

Garvalov, B. K.

Publications and source records attributed to Garvalov, B. K..

2 recordsLinked to original sources

The coumarin derivative X6632 is a pan-ID protein inhibitor that suppresses tumor growth by targeting cancer cells and the tumor-associated microvasculature

Inhibitor of DNA binding (ID) proteins are key regulators of tumor cell stemness, therapy resistance and pathological angiogenesis in multiple cancer types and other diseases. Here, we characterize the coumarin-derived compound X6632 as a pan-ID inhibitor with dual activity against tumor cells and the tumor-associated microvasculature in a number of human and murine models. X6632 efficiently suppressed ID protein expression, inhibited the proliferation, migration, invasion of melanoma cells, and impaired multiple endothelial cell functions, including proliferation, migration, invasion, tube formation and sprouting in vitro. In back-to-back comparisons, X6632 exhibited an approximately ten-fold higher efficacy compared to the first-generation ID antagonist AGX51. In vivo, X6632 potently reduced pathological (neo)vascularization in established angiogenesis models, including oxygen-induced retinopathy and in Matrigel plug assays. It also significantly decreased blood vessel density in syngeneic melanoma models, delayed tumor growth and, when combined with immune checkpoint blockade, achieved superior tumor control compared with either monotherapy. Moreover, X6632 inhibited clonogenic growth in several breast cancer models, and robustly suppressed the growth of triple negative breast cancer in vivo, both in the highly aggressive 4T1 syngeneic model and in patient-derived xenografts. Collectively, these data establish X6632 as a second-generation, pan-ID protein inhibitor that can simultaneously target malignant cells and the tumor-supporting vasculature, and support the further pre-clinical development of the compound for the treatment of melanoma, breast cancer and potentially additional ID-dependent malignancies, as well as diseases driven by pathological neoangiogenesis.

cancer biology↗

Deep learning reveals cancer metastasis and therapeutic antibody targeting in whole body

Reliable detection of disseminated tumor cells and of the biodistribution of tumor-targeting therapeutic antibodies within the entire body has long been needed to better understand and treat cancer metastasis. Here, we developed an integrated pipeline for automated quantification of cancer metastases and therapeutic antibody targeting, named DeepMACT. First, we enhanced the fluorescent signal of tumor cells more than 100-fold by applying the vDISCO method to image single cancer cells in intact transparent mice. Second, we developed deep learning algorithms for automated quantification of metastases with an accuracy matching human expert manual annotation. Deep learning-based quantifications in a model of spontaneous metastasis using human breast cancer cells allowed us to systematically analyze clinically relevant features such as size, shape, spatial distribution, and the degree to which metastases are targeted by a therapeutic monoclonal antibody in whole mice. DeepMACT can thus considerably improve the discovery of effective therapeutic strategies for metastatic cancer. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=137 SRC="FIGDIR/small/541862v1_ufig1.gif" ALT="Figure 1"> View larger version (49K): org.highwire.dtl.DTLVardef@ffe1b9org.highwire.dtl.DTLVardef@13c55eborg.highwire.dtl.DTLVardef@2ccb6corg.highwire.dtl.DTLVardef@df55e9_HPS_FORMAT_FIGEXP M_FIG C_FIG Supplementary Movies and deep learning algorithms of DeepMACT are available at http://discotechnologies.org/DeepMACT/

cancer biology↗