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de la Nava, D.

Publications and source records attributed to de la Nava, D..

2 recordsLinked to original sources

Replication competent adenoviral platform for in situ production of immunotherapeutic RNA aptamers targeting 4 1BB

Viroimmunotherapy leverages oncolytic viruses to induce antitumor immunity and is increasingly explored for solid tumors. Their activity can be enhanced by arming them with immunostimulatory payloads, but most approaches rely on protein-based transgenes that are constrained by viral genome packaging limits. Here, we establish a replication-competent Delta-24-RGD-based platform for localized production of immunotherapeutic RNA aptamers at the tumor site. RNA aptamers provide compact, highly specific ligands that can, in principle, target diverse immune receptors. As a model, we engineered a Delta-24-RGD derivative encoding circular 4-1BB targeting aptamers and show that infected tumor cells sustain aptamer transcription and release, which is associated with a pro-inflammatory remodeling of the tumor microenvironment and measurable antitumor activity in different mouse models with a comparable effect to that achieved with a 4-1BBL-expressing adenovirus used as a benchmark. Overall, this work delivers a proof of concept that replication-competent adenoviruses can serve as in situ factories for extracellularly active RNA aptamers, supporting their development as flexible platforms for localized non-coding cancer immunotherapy.

cancer biology↗

Mapping Tumor Microenvironment and Treatment Response of Diffuse Midline Glioma Using Multiplexed Immunofluorescence and AI Models

BackgroundDespite its clinical promise in non-solid tumor, immunotherapy is yet to show significant clinical efficacy for brain tumors including pediatric diffuse midline glioma (DMG). This indicated the need to fully explore DMG immune tumor microenvironment (TME). MethodWhole brains (49 DMGs, 20 non-DMG, 10 non-malignant) from 79 pediatric patients were used to establish a tissue microarray (918 cores) representing primary, metastatic, and adjacent healthy sites. CellDIVE MxIF multiplex assay was used to probe for 33 immune and cell type markers. RNA sequencing (n=62 patients) defined additional immune signatures. Findings were validated using patient plasma and DMG PDX models. Our annotated single-cell atlas was used to train a spatial AI model to predict antigens from H&E staining. FindingsWe found enrichment of M1-activated microglia in primary versus adjacent healthy tissue. PD1 positive cells were significantly (p<0.01) higher in tumor compared to adjacent controls. This was validated by mRNA profiling, further indicating two distinct groups with top 35 significant (p<0.05) genes revealing synaptic signature in the metastatic cohort. We stratified the patient cohort by treatment. Imipridone cohort (n=5) showed decreased progenitor (Nestin+, Vimentin+, and SOX2+) and increased macrophages/microglia infiltration. Increased T and B cells was validated in patient plasma following imipridone therapy. Combination therapy of imipridone and immunotherapy (n=7) resulted in increased myeloid (Iba1, CD68, CD163) and lymphoid (CD3, CD8) cells. Enhanced immune engagement was validated in DMG PDX models. Machine learning resulted in a spatial AI model capable of predicting 22 antigens using H&E slides. InterpretationsDMG tumors maintain a cold immune microenvironment, which is nevertheless dynamic and responsive to therapy, indicating the need to explore combination therapies. AI-assisted antigen detection is suitable for rapid interpretation of clinical biospecimens. FundingThis work was supported by Rising Tide, SNF, LilaBean Foundation, Swifty Foundation, Swiss to Cure DIPG and Yuvaan Tiwari Foundation. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=45 SRC="FIGDIR/small/644698v1_ufig1.gif" ALT="Figure 1"> View larger version (24K): org.highwire.dtl.DTLVardef@198b382org.highwire.dtl.DTLVardef@312e0forg.highwire.dtl.DTLVardef@c71b82org.highwire.dtl.DTLVardef@1df1c35_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOGraphical AbstractC_FLOATNO C_FIG

cancer biology↗