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Romero-Toledo, A.

Publications and source records attributed to Romero-Toledo, A..

3 recordsLinked to original sources

Multi-region biopsies and patient-derived neurosphere cultures reveal spatial divergence in glioblastoma.

Intratumor heterogeneity (ITH) is one of the main reasons for the lack of effective targeted therapies for glioblastoma (GBM). Imaging-guided surgical navigation allows for tumor-wide sampling to account for variation across distant regions of the tumor, but typical drug screening is performed on cell lines derived from a single biopsy and does not account for GBM heterogeneity. Here we profiled matching MRI-guided multi-region primary tumor biopsies from 6 GBM cases (n=40 biopsies) and corresponding neurosphere cultures (n=30) derived from these spatially distinct tumor samples. We found that in vitro cultures derived from distinct regions of the same tumor display divergent phenotypes, proliferative capacity and ability to accumulate 5-aminolevulinic acid, used to visualize cancer cells during surgery. The differential drug response of the multi-region neurospheres remains linked to the gene expression of the original tumor biopsies. Thus, studies with multiregion-derived neurospheres are essential to faithfully model GBM ITH for therapeutic testing. KEY POINTSO_LIMulti-region biopsy-derived neurospheres represent distinct spatial locations in the GBM tumor. C_LIO_LICultures derived from different regions of the tumor retain phenotypic diversity. C_LIO_LIParental biopsy phenotype predicts drug response better than to in vitro phenotype. C_LI IMPORTANCE OF THE STUDYCell lines developed from spatially distinct regions of glioblastoma capture its intratumor heterogeneity. We show that while the transcriptional output of these cell lines is not connected to their spatial origin, their drug response can be linked to it. Thus, spatial heterogeneity reflected in our neurosphere collection provides a new paradigm for drug screening in these highly heterogenous and difficult to treat tumors.

cancer biology↗

Selective Glucocorticoid Receptor Modulators of Immune Checkpoint Function

Glucocorticoids (GCs) coordinate immunity, inflammation, and metabolism through allosteric regulation of the glucocorticoid receptor (GR) transcription factor. GCs are indispensable anti-inflammatory drugs yet linking specific ligand-receptor structural states to specific biological outcomes has remained a major barrier to designing safer, more selective therapies. Using structure-based design, we developed selective glucocorticoid receptor modulators (SGRMs) of immune function by extending a steroidal scaffold from the ligand-binding pocket into an adjacent solvent channel. These SGRMs suppressed T cell pro-inflammatory cytokines and promoted differentiation of memory precursor T cells while showing minimal induction of M2 macrophage polarization or T cell checkpoint proteins PD-1 and CTLA-4, all key targets of immunotherapy. Molecular dynamics simulations revealed that solvent-channel substituents function as a lever arm to drive dynamic oscillations in the steroid core, thereby allosterically tuning GR activity states. Systematic perturbation of immune cells with a graded series of ligands enabled a ligand perturbation with machine learning (LPML) framework to map coregulated responses across cell types and identified effector T cell gene networks tightly coupled with immune checkpoint induction. This approach outlines a general strategy for decoding the logic of allosteric drug action, enabling the rational design of SGRMs with tailored immunomodulatory profiles.

systems biology↗

Cell sorting based on single nucleotide variation enables characterization of mutation-dependent transcriptome and chromatin states.

Single nucleotide variants (SNVs) contribute to cancer by altering the coding and the non-coding regions of the genome. Connecting SNVs to transcriptomic and epigenetic changes at the single-cell level remains challenging. To enable studies of rare cell populations harboring specific point mutations, we developed STAR-FACS, Specific-To-Allele PCR-FACS, to sort cells based on genomic allele alterations. We show that STAR-FACS can separate cells based on TERT promoter mutation status and is compatible with bulk and single-cell transcriptomic and epigenetic profiling. We demonstrate that glioblastoma cell lines derived from the same tumor but harboring distinct TERT promoter SNVs have different transcriptional programs.

cancer biology↗