Biology subjects

Schlicker, A.

Publications and source records attributed to Schlicker, A..

3 recordsLinked to original sources

bioRxiv2026

The ITCC-P4 PDX platform of pediatric cancers for preclinical testing

Cancer is the leading cause of disease-related deaths among children in high-income countries. Tumor heterogeneity and lack of mechanism-of-action-based therapeutic options are key challenges to overcome in order to improve pediatric cancer patients survival. Here, we report the EU-IMI-2 funded public-private partnership "ITCC-Pediatric Preclinical Proof-of-Concept Platform" (ITCC-P4), which has built a large repertoire of patient-derived xenograft (PDX) models, representing all major solid pediatric cancer types, for in vivo drug testing. Three-hundred-fifty-three PDX models from diagnostic and relapsed pediatric cancers have been established and molecularly characterized, together with matched germline/tumor samples. As proof-of-concept, we present in vivo drug screening data in neuroblastoma and rhabdomyosarcoma models. PDX data, accessible at http://r2platform.com/itcc-p4, allow the selection of models based on oncogenic drivers and/or potential biomarkers for preclinical testing. Operated by a non-profit entity (www.itccp4.com), this sustainable platform aids academic and industrial researchers in developing and prioritizing innovative therapies for pediatric cancer. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=112 SRC="FIGDIR/small/703023v1_ufig1.gif" ALT="Figure 1"> View larger version (41K): org.highwire.dtl.DTLVardef@195ba30org.highwire.dtl.DTLVardef@f2c2d9org.highwire.dtl.DTLVardef@1d63f4dorg.highwire.dtl.DTLVardef@d60027_HPS_FORMAT_FIGEXP M_FIG C_FIG

cancer biology↗

bioRxiv2025

Benchmarking Chemical, Genetic, and Cell Line Encodings for Cancer Perturbation Response Prediction

Estimating the response of tumor cells to specific perturbations is crucial for identifying effective treatments that selectively target cancer cells while sparing healthy ones, enabling personalized medicine approaches. Large-scale initiatives, such as DepMap, have profiled cancer cell line responses to various drug treatments and gene knockouts, facilitating the development of computational models that predict sensitivity of cancer cells to different perturbations. Existing models utilize diverse methods for encoding perturbations, including various chemical fingerprints and types of gene-gene relationships. They also rely on different architectures and are often trained on distinct datasets. This variability makes it unclear which chemical, genetic, or cell line encoding is most informative for predicting cancer cell viability following perturbation treatment. To address this gap, we systematically evaluated various approaches to encode chemical and genetic perturbations and cell lines on the tasks of predicting cell viability and gene dependency. We found that for genetic perturbations, STRING-based encodings yield the highest performance, considerably outperforming GO-term and protein language model based encodings, which showed promising results in previous perturbation prediction studies. For chemical perturbations, while most encoders showed comparable performance, those pre-trained on other bio-assay data yielded the highest performance. Finally, we found that for cell line encodings, raw gene expression features outperformed more sophis-ticated approaches, such as transcriptomics foundation model embeddings, as well as genotype-based encodings. Together, our results identify promising approaches for encoding chemical and genetic perturbations and enable virtual screening for perturbations with selective toxicity.

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↗