TURNING CANCER BIOLOGY INTO BETTER DECISIONS.
The decision-making platform for oncology drug development.
Clyra helps biopharma teams define the right patients and biomarker strategy before a trial, then identify response and resistance biomarkers to guide the next phase of development.
Built for oncology translational and clinical development teams.
BIOLOGICAL EVIDENCE
Molecular
Cellular
Clinical
Outcomes
DEVELOPMENT DECISION
A clear population, biomarker, or next-phase strategy
USE CASES
Make the next development decision with better biological evidence.
BEFORE A TRIAL
Patient & Population Decisions
Define the right patients and biomarker strategy before your trial begins.
Characterize disease heterogeneity, define biologically grounded subgroups, and translate the evidence into a practical biomarker strategy.
Explore Patient & Population Decisions
AFTER EARLY CLINICAL DATA
Identify response and resistance biomarkers and refine your next trial phase.
Explore Clinical Trial Decisions
More data does not automatically produce a better decision.
Oncology programs still struggle with heterogeneous patient populations, small cohorts, fragmented datasets, and biomarkers that do not translate cleanly into development strategy. Clyra organizes molecular, cellular, clinical, and outcome evidence around the specific decision a program needs to make.
Evidence organized around the decision criteria
FROM QUESTION TO DECISION
Built around the decision—not the dataset.
01
Frame the decision
Define the asset, indication, development stage, and decision criteria.
02
Assemble the evidence
Connect relevant human data, program data, and complementary longitudinal evidence when it adds value.
03
Model the biology
Characterize tumor states, patient subgroups, biomarkers, and outcome associations.
04
Translate the result
Turn the evidence into a population, biomarker, or next-phase strategy—with assumptions and limitations made explicit.
CORE CAPABILITIES
One platform. Decision-specific workflows.
Multimodal cohort characterization
Understand the molecular, cellular, clinical, and outcome context of a patient population.
Tumor-state and subgroup discovery
Identify biologically distinct states that may be hidden by conventional disease classifications.
Biomarker prioritization and validation
Compare candidate biomarkers across cohorts, modalities, and relevant biological contexts.
Response and resistance analysis
Identify the features associated with sensitivity, non-response, adaptation, and recurrence when treatment-linked data are available.
DATA & MODELS
Human evidence remains central to every human oncology conclusion.
Human evidence
Primary for human oncology conclusions
Program data
Complementary longitudinal evidence
INITIAL FOCUS
Starting with osteosarcoma.
Human osteosarcoma is rare, heterogeneous, and difficult to study in large prospective cohorts. Naturally occurring canine osteosarcoma provides an additional source of longitudinal cancer evidence. Clyra uses both to study tumor states, biomarkers, and outcomes while keeping species-specific differences explicit.
We are starting with osteosarcoma and expanding into other rare and hard-to-model cancers.
RESEARCH
Scientific evidence behind the platform.
What decision does your program need to make?
If you are defining a trial population, building a biomarker strategy, or interpreting early clinical data, let’s discuss the program.