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AACR 2025 — Portrai abstracts

Eleven posters spanning bispecific ADC target pairs, image-based ST platform comparisons, in-silico ADC modeling, CAF-targeting antibodies, CELLama, and AI lymphocyte identification in NSCLC.

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Daeseung Lee April 8, 2025 - 5 min read
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AACR 2025 — Portrai posters covering spatial transcriptomics and precision oncology
AACR 2025 — Portrai posters covering spatial transcriptomics and precision oncology

Explore our latest spatial transcriptomics innovations for precision oncology

Discover Portrai’s groundbreaking AACR posters highlighting our cutting-edge spatial transcriptomics and AI-driven approaches. From precise antibody-drug conjugate design and advanced tumor microenvironment profiling to novel biomarker discovery, explore how Portrai is redefining therapeutic strategies for cancer treatment through spatial biology.

Posters

  1. #2503 — Analytic platform for optimal target pair selection in bispecific antibody-drug conjugates using scRNAseq and spatial transcriptomics data
  2. #6276 — Comparative analysis and integration of image-based and high-resolution sequence-based spatial transcriptomics platforms for tumor microenvironment analysis
  3. #6259 — Spatial transcriptomics-driven in-silico modeling of therapeutic effects of antibody-drug conjugates and their association with treatment response in lung adenocarcinoma
  4. #2443 — High-resolution AI-based spatial biology tool for lung cancer trained by image-based spatial transcriptomics data to analyze tumor microenvironment
  5. #6752 — Identification and validation of human antibodies targeting a novel pan-adenocarcinoma target discovered via spatial transcriptomics
  6. #5456 — Development of human antibodies targeting a novel cancer-associated fibroblast protein identified from spatial transcriptomics
  7. #5074 — Estimation of homologous recombination deficiency map from spatial transcriptomics
  8. #5075 — Detection and spatial analysis of mRNA delivered by lipid nanoparticles through spatial transcriptomics
  9. #8745 — CELLama: Cell Embedding Leveraging Language Model Abilities for analyzing cell types and spatial relationships in tumor
  10. #2502 — Biologically informed cell typing in the tumor microenvironment with lightweight LLMs
  11. #2421 — Development and validation of an AI-based model for lymphocyte identification in NSCLC H&E image using spatial transcriptomics

Visit Portrai at AACR 2025 and join us in shaping the future of precision oncology through spatial transcriptomics. Connect with our scientific team, explore collaborative opportunities, and learn how our innovations can accelerate your research and therapeutic development.

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