LIFE SCIENCES /// RESEARCH

Unravel the complexities of cancer types by defining heterogeneity, progression patterns, and clinical behaviors across patient populations.

• Analyze multimodal real-world data to characterize molecular and clinical attributes of fit-for-purpose cohorts, including gene expression, biomarkers, and drug resistance.
• Capture the genetic, molecular, and phenotypic complexity of tumors by leveraging tumor organoid models.

Identify patient populations with insufficient treatment options, understand the complex interplay of tumor biology and treatment resistance, and quantify disease burden to justify the value of novel therapies.

• Guide the development of more effective therapeutics through real-world data analyses of treatment gaps, disease progression, and outcomes.
• Model treatment response and mechanisms of resistance on patient-derived tumor organoids to reveal previously unrecognized disease biology across unmet populations.

Assess clinical relevance of your target across a diverse patient population while accounting for tumor heterogeneity, and optimize lead compounds to effectively overcome resistance mechanisms and enhance safety and efficacy.

• Surface insights into drug resistance, safety, and patient-specific responses through real-world data analyses to refine lead compounds and validate the relevance of therapeutic targets.
• Screen for drug viability, payload sensitivity, and pathway perturbation with patient-derived tumor organoids to evaluate your desired therapeutic effect.
• Profile the effects of compounds on various omic layers to fine-tune efficacy and safety, as well as reveal biomarkers and molecular pathways involved in disease progression.

Enhance clinical trial design by refining biomarkers and thresholds for response by while unraveling mechanisms of resistance and overcoming tumor heterogeneity.

• Analyze real-world, multimodal data to surface clinically-relevant insights regarding tumor heterogeneity, prevalence, treatment response, and resistance mechanisms that may help to inform clinical trial design and strategy.
• Model tumor biology on patient-derived tumor organoids to test therapeutic candidates and assess how biomarkers correlate with drug efficacy to optimize for clinical relevance and inform patient stratification strategies.

Explore effective and specific molecular targets while aiming to minimize off-target effects and resistance within diverse patient populations.

• Understand disease progression, monitor treatment response, and detect resistance patterns within de-identified cohorts of real-world patient data. Identify additional cancer types or subpopulations that may be relevant for your mechanism of action (MoA).
• Study tumor heterogeneity, drug responses, and tumor microenvironment interactions via physiologically relevant systems to uncover potential new therapeutic targets.
• Establish a comprehensive view of tumor biology through analyses of genomic, transcriptomic, proteomic, and epigenetic data to investigate complex molecular alterations and dysregulated pathways.
• Leverage AI-developed algorithms to predict outcomes, resistance mechanisms, and treatment responses to support patient stratification and biomarker refinement.