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Pallares-Moratalla, C.

Publications and source records attributed to Pallares-Moratalla, C..

2 recordsLinked to original sources

Distinct tumor immune microenvironmental (TIME) landscapes drive divergent immunotherapy responses in glioblastoma

BackgroundImmunotherapies have improved outcomes in many cancers but show limited efficacy in glioblastoma (GBM). This study aimed to determine whether immunotherapy could be tailored to GBM by functional subtyping of vascular-immune landscapes. MethodsWe employed single-cell RNA sequencing, multiplex immunohistochemistry to characterize three distinct TIME subtypes in human and murine GBMs. We evaluated responses to combination of anti-angiogenic immunomodulating therapies (CD40 Ag, anti-PDL1, PI3K{gamma}/{delta} inhibition) in orthotopic syngeneic GBM mouse models. ResultsWe identified three distinct functional TIME subtypes with unique vascular-immune landscapes in human and murine GBM: TIME-low (immune-low/deserted, leaky vasculature), TIME-med (intermediate immune-infiltration, angiogenic), and TIME-high (heavily infiltrated with immunosuppressive myeloid cells and dysfunctional T cells). Representative mouse models of TIME-GBMs responded in subtype specific ways to anti-angiogenic immunomodulating therapies. TIME-low GBMs enhanced T-cell activity but relapsed due to emerging myeloid immunosuppression, concomitant with mesenchymal transition. TIME-med displayed the most immune-activated, yet angiogenic phenotype, and showed overall good responses to various anti-angiogenic immunomodulating therapies. TIME-high GBMs were mostly non-responsive but improved when the myeloid-cell PI3K{gamma} was targeted. However, CD40 agonist treatment, expected to enhance APC function, unexpectedly worsened survival by promoting angiogenesis and heightening immunosuppression, leading to dysfunctional T cells and reduced NK cell recruitment, and subsequent enhanced tumor propagation. ConclusionsOur study reveals three GBM TIME subtypes with distinct vascular-immune landscapes that require tailored therapies. TIME-med tumors are predicted to respond best to immunotherapies, TIME-low tumors show transient effects with anti-angiogenic immunomodulating therapies, while TIME-high tumors, due to their profound immunosuppression, can even have worse outcomes. Key pointsO_LIThree TIME subtypes were identified in GBM with distinct vascular-immune landscapes C_LIO_LITIME subtypes show divergent immunotherapy responses C_LIO_LITIME classification supports personalized treatment strategy for GBM immunotherapy C_LI Importance of the StudyThis study advances glioblastoma immunotherapy by providing the first comprehensive single-cell characterization of TIME subtypes, moving beyond bulk RNA-sequencing to reveal detailed functional states of immune cells. We establish clinically relevant murine models that recapitulate human TIME subtypes, enabling preclinical testing of TIME-targeted therapies. Our findings identify TIME-low GBM as immune deserted and TIME-med tumors as the most immunotherapy-responsive subtype that should be prioritized for clinical selection. We found that high immune infiltration correlates with non-responsiveness and even unexpected detrimental effects with CD40 agonist treatment in TIME-high tumors--critical information given ongoing clinical trials. Identifying distinct immunosuppressive mechanisms across TIME subtypes and differential treatment responses provides a framework for personalized immunotherapy selection. The immediate translational impact of this work highlights the importance of TIME classification for treatment stratification and the urgent need to consider TIME status in clinical trial design, potentially explaining variable patient responses in previous GBM immunotherapy trials.

cancer biology↗

LipidQMap - An Open-Source Tool for Quantitative Mass Spectrometry Imaging of Lipids

Mass spectrometry imaging (MSI) is a powerful tool in both basic and clinical research, enabling spatial visualization of biomolecules and drugs in tissue sections. However, factors influencing mass spectrometric response during imaging are increasingly recognized for their impact on apparent molecular distribution. Quantitative mass spectrometry imaging (qMSI) addresses this variability by incorporating analytical standards that undergo the same processes as analytes. While qMSI sample preparation protocols for omics-scale quantitative lipidomics are actively evolving, software solutions for downstream data processing remain scarce. Here, we introduce LipidQMap, the first open-source platform for processing omics-scale qMSI lipidomics data. LipidQMap applies one-point calibration, normalizing annotated lipid signals against class-specific standards on a pixel-by-pixel basis, and generates concentration heat maps in pmol/mm2. The software supports centroided data import, recalibration, and lipid identification using a built-in, user-modifiable lipid database. LipidQMap resolves Na/H adduct isobaric overlaps by leveraging sodiated-to-protonated adduct ratios of standards, validated across several MSI platforms using mouse brain sections. Type II isobaric overlaps are corrected using predicted isotopic patterns. Extensive validation demonstrates that LipidQMap is robust across MSI platforms and harmonizes qMSI data, enabling more accurate and reproducible spatial lipid quantification.

bioinformatics↗