AI ONCOLOGY SOLUTIONS

AI oncology is increasingly constrained not by model architecture, but by the biology available to train and validate those models. Most oncology datasets are built from static snapshots of disease—genomics, transcriptomics, pathology images, clinical records or retrospective outcomes—which describe what a tumor looks like but provide limited information about how it behaves when therapeutically challenged.

Matched functional response, preserved tumor–immune–stromal context, controlled treatment perturbations and sufficiently deep multimodal characterization of the same patient tumor are all often missing. As a result, AI models can identify correlations and predict associations, but have far less direct biological grounding for learning treatment response, resistance, combination effects or the interactions between tumor cells and their surrounding microenvironment. This limitation becomes particularly important for emerging applications such as tumor twins, virtual perturbation, target discovery and patient-response prediction, where models need training and validation data that connect molecular and spatial states to experimentally observed changes in real human tumors.

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Farcast addresses this gap by generating proprietary, treatment-linked datasets directly from clinically sourced patient tumors and by continuously adding to it. Through its Tumor Data Supply Chain, Farcast provisions fresh human tumors, preserves near-native tumor biology, exposes matched tissue from the same tumor to multiple therapies and combinations, profiles resulting functional, molecular, cellular and spatial changes, biologically labels those responses, and increasingly links them with patient-level clinical information.

We partner with AI labs, techbios and AI-focused biotechs who can use this data to train and fine-tune oncology models, create tumor twins incorporating tumor, immune and stromal compartments, validate model predictions against experimentally observed human biology. Supporting target discovery and confirmation by connecting digital perturbations with physical perturbation experiments. By giving AI systems access not simply to more static data, but to structured observations of how real human tumors change under defined therapeutic pressure, our data can provide a more biologically grounded substrate for building the next generation of predictive and mechanistic oncology models.