AACR 2026 trends

AACR is a large annual oncology conference. This year it took place in San Diego, and 22,000 participants attended the conference.

In 2026, two trends were clearly visible at AACR: impressive results from large biological models, and many new experimental drugs being developed through classical approaches. AACR 2026 tried to find points of connection between these two trends.

Since BostonGene is taking part in this race from the side of the first trend, offering its expertise and tools to help advance therapies through clinical trial phases, I focused mainly on the AI sessions.

BostonGene came to the conference

Conference Opening

The opening immediately set the tone for the whole conference. Three speakers showed that challenges in oncology still remain and discoveries happen every year, while the arms race does not stop either: new drugs are also being developed every year. The question is: can AI help us with this?

Challenges in oncology still exist. Prostate cancer develops resistance to AR inhibitors by changing its histology.

Drug development is not standing still. CAR-T therapies for solid tumors are being actively tested.

How can AI help us? The SPARC model: understanding the activity of molecular patterns from images and predicting risk. The Boltz model: personalized selection of targeted therapy by modeling the interaction between a drug molecule and a mutated target protein. The MIRAI model: AI-based recognition of mammography images and 5-year risk assessment.

News from the World of Large Biological Models

The session was chaired by Jakob Nikolas Kather, a rising star and professor of clinical AI at the University of Dresden, Germany.

Jakob said that pathology foundation models are something from 2020. 2026 is the year of agents that work with databases of clinical records and answer doctors in human language.

Faisal Mahmood, the father of AI in pathology, presented several new models from his lab.

Faisal understood that it was time to move further and presented Apollo — a large foundation model whose main modalities are standard clinical records about the patient and results of routine laboratory and instrumental tests, such as blood tests and X-rays. The model can understand risk factors, predict disease progression, and predict treatment outcomes.

A new histology model, a new model for histology + transcriptomics, a new model for histology + genomics, and a new model for spatial transcriptomics.

Bo Wang, the creator of scGPT, also presented two new models from his lab.

X-Cell: a new improved version of scGPT. BioReason: a model for predicting the function of proteins for which the amino acid sequence is already known, but no wet-lab experiment has been performed.

Jure Leskovec presented Biomni — an agentic system designed for multi-step work on biological questions. We should test it.

The agent can work directly with bioinformatics tools and search databases. Examples of use: 1. the agent designed a wet-lab protocol; 2. the agent went through 458 tables and files from a study and generated hypotheses that can then be tested.

Poster Session

The following trend was visible at the poster sessions. The sections about new classes of therapies and first-in-human trials were extremely crowded. The AI sections had moderate interest. But the classical sections on clinical studies and bioinformatics were rather quiet.

1, 2 — immunology and experimental therapy; 3 — AI section; 4 — clinical research: prognostic biomarkers, molecular cancer classifiers, population studies, liquid biopsy.

I presented two works. The first one was about identifying the right therapy for a patient with multiple myeloma. The second one was about shared genetic patterns between diffuse large B-cell lymphoma and lymphomas of other histologies.

The main conclusion is that we can work on this area where AI meets the trend of advancing new types of therapies.