Cross-species models of cancer biology.

Cross-species models of cancer biology.

Cross-species models of cancer biology.

Clyra models human and canine cancer data within their respective biological and clinical contexts, then aligns relevant molecular features and outcome-linked tumor states across species. The platform is designed to study conserved biology while retaining species-specific differences.

Clyra models human and canine cancer data within their respective biological and clinical contexts, then aligns relevant molecular features and outcome-linked tumor states across species. The platform is designed to study conserved biology while retaining species-specific differences.

Data inputs

Data inputs

The specific inputs depend on the research question and available cohorts.

The specific inputs depend on the research question and available cohorts.

Human cancer data

Molecular profiles

Tumor and clinical annotations

Treatment context

Response, progression, and survival outcomes when available

Canine cancer data

Longitudinal molecular profiles

Diagnosis and disease history

Treatment exposure

Progression, recurrence, and survival outcomes when available

Modeling approach

Modeling approach

01

Model each cohort in context

Model each cohort in context

Model each cohort in context

The platform first represents molecular, clinical, treatment, and outcome relationships within each species and cohort.

02

Align relevant biology across species

Align relevant biology across species

Align relevant biology across species

Homologous genes, pathways, cell states, tumor states, and outcome associations can then be compared across human and canine cancers.

03

Adapt the model to a program question

Adapt the model to a program question

Adapt the model to a program question

The resulting representations can be evaluated for patient stratification, biomarker discovery, response and resistance analysis, or target and indication research.

Model outputs

Model outputs

Depending on the dataset and study design, an analysis may produce:

Depending on the dataset and study design, an analysis may produce:

Tumor-state representations

Outcome-linked subgroup definitions

Ranked biomarker or pathway candidates

Response and resistance associations

Cross-cohort and cross-species concordance analyses

Evaluation

Evaluation

Evaluation should match the program question and the quality of the available data. Relevant analyses may include held-out cohort testing, cross-cohort replication, cross-species concordance, outcome association, sensitivity analysis, and assessment of potential confounding factors.

Evaluation should match the program question and the quality of the available data. Relevant analyses may include held-out cohort testing, cross-cohort replication, cross-species concordance, outcome association, sensitivity analysis, and assessment of potential confounding factors.

Findings generated from canine data should be evaluated against relevant human evidence. The website must not imply that canine evidence replaces human validation.

Findings generated from canine data should be evaluated against relevant human evidence. The website must not imply that canine evidence replaces human validation.

Discuss a program

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