Developing new cancer therapies remains costly, lengthy and high risk, in part because conventional translational models often fail to capture the biological complexity of human tumors. Simplified biomodel systems can miss critical interactions among tumor, immune and stromal compartments, while genomic data alone provides only a partial view of therapeutic response. Drug developers also lack sufficiently rich functional biosignatures that can be carried through clinical development to connect an individual tumor’s multimodal biology with accumulated response patterns and historical evidence using modern artificial intelligence.

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Farcast helps address this gap through more than a decade of experience processing nearly 24,000 live human tumors. Our approach generates both biological breadth and multimodal depth: across tumor types, patient populations, cell compartments and therapeutic mechanisms, as well as across functional, molecular, cellular and spatial readouts. Multiple therapies and combinations can be tested in parallel in tissue derived from the same patient tumor, creating treatment-linked datasets that capture not simply what a tumor is, but how its biology changes in response to therapeutic pressure. These measurements can then be integrated and analyzed to identify biosignatures associated with response, resistance, mechanism of action and combination effects.

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By studying therapeutic response directly in human tumor biomodels while retaining native tumor–immune–stromal context, Farcast provides drug developers with a more biologically relevant view of how novel agents behave before and throughout clinical development. Histopathology, IHC, gene-expression signatures, cytokine profiling, flow cytometry and spatial biology can be combined to characterize response across multiple biological levels. Applying AI and data science to these rich, treatment-linked datasets can support the development of functional biosignatures, biomarker hypotheses, patient-stratification strategies and combination rationale—helping development teams make better-informed decisions and potentially improve the probability of clinical success.