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Spatial Proteomics for Precision Oncology

Tumors are complex cellular ecosystems in which cancer cell phenotypes, their spatial organization, and their interactions with immune and stromal compartments determine disease progression and therapy response. To resolve this complexity, the use of highly multiplexed Imaging Mass Cytometry™ (IMC™) together with multiomic profiling and artificial intelligence (AI) can identify clinically relevant features of the tumor microenvironment, providing a rich framework for patient stratification and targeted therapy in precision oncology.

In this GEN webinar, Bernd Bodenmiller, PhD (University of Zürich), illustrates this integrated approach through two recent examples. In a study of 323 epithelial ovarian cancers, tumors with more diverse cell phenotypes and spatial organization recurred faster. His lab also identified a therapy-resistant tumor cell phenotype (TUBB3+/pRB+) associated with recurrence and poor response to standard therapy. Spatial transcriptomics and ex vivo drug screening of 3D sphere models and patient samples showed that these cells resist standard-of-care agents but are sensitive to specific inhibitors. In 498 clear cell renal cell carcinomas, the use of AI revealed three survival ecotypes that predict survival better than basic clinical data and molecular stratification models. Each ecotype has its own genomic and metabolic features and is predictive of treatment outcome. Deep learning predicted the ecotypes directly from routine H&E slides, and the findings were validated across five cohorts comprising more than 2,500 patients.

Webinar attendees will learn how integrating spatial single-cell proteomics by IMC with multiomics and AI identifies therapy-resistant cell populations and prognostic tumor ecosystems. This approach provides a detailed framework for patient stratification and targeted therapy in precision oncology.

OpenAI’s Greg Brockman Says AI Will Allow Death To Become A Choice, Echoing Elon Musk’s Take That “Longevity Or Semi-Immortality Is An Extremely Solvable Problem”

Elon Musk made the claim in January 2026, then declared in October that “longevity is coming,” a view that OpenAI’s Brockman now echoes.

An AI-native experimental laboratory for autonomous biomolecular operations

China built an AI that runs an entire biology lab on it’s own.

Researchers from Shangha published a paper that feels like a massive leap toward artificial scientists.

They built AutoDNA, an “AI-native experimental laboratory”

Until now, if you asked an AI to design a biology experiment, it could give you a great protocol. But a human still had to walk into a lab, hold the pipettes, run the thermal cyclers, and tweak the process when things inevitably went wrong.

AutoDNA removes the human.

You just type a natural language prompt, and a swarm of specialized AI agents takes over.

Here’s how wild this multi-agent architecture is:

Connecting artificial intelligence with the tools of astrophysical research

Investigating a scientific question is often a complex process. As an astrophysicist studying some of the most energetic phenomena in the universe, I know that finding answers requires much more than reading scientific papers or understanding existing theories. Astrophysical research involves collecting observations, analyzing data, building physical models and comparing their predictions with observations. These tasks require different scientific tools, databases and modeling software and can take considerable time even for experienced researchers.

This challenge motivated the development of AstroGenesis, a multi-agent AI system designed to connect different stages of astrophysical research within a single environment. Our goal is to make scientific investigations faster and more accessible. A paper describing the work is posted to the arXiv preprint server.