Biology subjects

Thaler, S.

Publications and source records attributed to Thaler, S..

4 recordsLinked to original sources

bioRxiv2026

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↗

bioRxiv2026

Loss of either RASSF1A alone or in combination with Caveolin-1 inhibition is associated with different premalignant histopathological alterations in the mammary glands of transgenic mice

Analyses of human breast carcinomas (BCs) and premalignant breast lesions show that the loss of RASSF1A is an early event in the development of ER+ BCs, which correlates linearly with malignant progression. This observation suggests that RASSF1A inhibition is important for the development and progression of ER+ BCs. In addition to RASSF1A, concurrent caveolin-1 (Cav-1) inhibition may further promote ER+ breast carcinogenesis. In the present study, transgenic Rassf1a-/- and Cav-1(-/-) single as well as Rassf1a-/-, Cav-1(-/-) double knockout mice were used to investigate the impact of single or combined Rassf1a and Cav-1 inactivation on BC initiation. Loss of either one or both proteins led to different, pre-malignant histopathological alterations within the mammary glands of the mice, but not to fully developed BC, confirming that Rassf1a and Cav-1 are both important for maintaining the integrity of mammary gland epithelial structure, but suggesting that further intracellular changes or extracellular factors are required for the development of luminal BC when both genes are lost.

cancer biology↗

bioRxiv2025

Boltz-2: Towards Accurate and Efficient Binding Affinity Prediction

Accurately modeling biomolecular interactions is a central challenge in modern biology. While recent advances, such as AlphaFold3 and Boltz-1, have substantially improved our ability to predict biomolecular complex structures, these models still fall short in predicting binding affinity, a critical property underlying molecular function and therapeutic efficacy. Here, we present Boltz-2, a new structural biology foundation model that exhibits strong performance for both structure and affinity prediction. Boltz-2 introduces controllability features including experimental method conditioning, distance constraints, and multi-chain template integration for structure prediction, and is, to our knowledge, the first AI model to approach the performance of free-energy perturbation (FEP) methods in estimating small molecule-protein binding affinity. Crucially, it achieves strong correlation with experimental readouts on many benchmarks, while being at least 1000x more computationally efficient than FEP. By coupling Boltz-2 with a generative model for small molecules, we demonstrate an effective workflow to find diverse, synthesizable, high-affinity binders, as estimated by absolute FEP simulations on the TYK2 target. To foster broad adoption and further innovation at the intersection of machine learning and biology, we are releasing Boltz-2 weights, inference, and training code 1 under a permissive open license, providing a robust and extensible foundation for both academic and industrial research.

molecular biology↗

bioRxiv2020

Feasibility of Integrating Canine Olfaction with Chemical and Microbial Profiling of Urine to Detect Lethal Prostate Cancer

Prostate cancer is the second leading cause of cancer death in men in the developed world. A more sensitive and specific detection strategy for lethal prostate cancer beyond serum prostate specific antigen (PSA) population screening is urgently needed. Diagnosis by canine olfaction, using dogs trained to detect cancer by smell, has been shown to be both specific and sensitive. While dogs themselves are impractical as scalable diagnostic sensors, machine olfaction for cancer detection is testable. However, studies bridging the divide between clinical diagnostic techniques, artificial intelligence, and molecular analysis remains difficult due to the significant divide between these disciplines. We tested the clinical feasibility of a cross-disciplinary, integrative approach to early prostate cancer biosensing in urine using trained canine olfaction, volatile organic compound (VOC) analysis by gas chromatography-mass spectroscopy (GC-MS) artificial neural network (ANN)-assisted examination, and microbial profiling in a double-blinded pilot study. Two dogs were trained to detect Gleason 9 prostate cancer in urine collected from biopsy-confirmed patients. Biopsy-negative controls were used to assess canine specificity as prostate cancer biodetectors. Urine samples were simultaneously analyzed for their VOC content in headspace via GC-MS and urinary microbiota content via 16S rDNA Illumina sequencing. In addition, the dogs diagnoses were used to train an ANN to detect significant peaks in the GC-MS data. The canine olfaction system was 71% sensitive and between 70-76% specific at detecting Gleason 9 prostate cancer. We have also confirmed VOC differences by GC-MS and microbiota differences by 16S rDNA sequencing between cancer positive and biopsy-negative controls. Furthermore, the trained ANN identified regions of interest in the GC-MS data, informed by the canine diagnoses. Methodology and feasibility are established to inform larger-scale studies using canine olfaction, urinary VOCs, and urinary microbiota profiling to develop machine olfaction diagnostic tools. Scalable multi-disciplinary tools may then be compared to PSA screening for earlier, non-invasive, more specific and sensitive detection of clinically aggressive prostate cancers in urine samples.

cancer biology↗