Target Discovery and Independent Orthogonal Validation for Small Cell Lung Carcinoma

Small cell lung carcinoma (SCLC) is a rapidly proliferating lung malignancy, with a doubling time of 3 months. It is most commonly associated with a history of tobacco exposure. Compared to non-small cell lung cancer, 70% of SCLC patients are metastatic at diagnosis, underscoring the importance of timely diagnosis and treatment.

Researchers Spend 80% of Time Scrubbing Data, Not Analyzing It

Integrated multi-omics analyses require expertise across a wide range of data modalities, from genomic mutations to mass spectrometry–based proteomics.

Researchers often spend months identifying relevant datasets and curating, cleaning, and harmonizing heterogeneous data sources.
Significant effort is required to define appropriate and comparable cohorts across modalities.
Downstream analytical workflows must then be executed to extract biologically meaningful signals.
Collectively, these steps frequently take more than six months before actionable insights are produced and hypotheses can be confidently generated or rejected.

Target Identification and Prioritization using Knowledge Graphs

Knowledge graphs facilitate multi-omics analyses by systematically integrating and organizing heterogeneous biological datasets, enabling efficient representation of complex relationships across data modalities. Knowledge Graphs enhance supports advanced analytical workflows, and ultimately accelerates drug discovery and precision medicine initiatives. The time take for insight generation is dramatically reduced.

PollyKG has 20+ Disease - Gene relationships curated for understanding driver mutations, gene essentiality and druggability of genes.

*Subgraph for Disease (SCLC) — Gene Association for GoF driver mutations in genes curated from literature.*

Disease - Gene Associations that are Evidence Backed

At Elucidata we use domain expertise to mine evidence from literature and store all relevant information as edge properties that users can query intuitively using NLQ or Graph Explorer or using our Python SDK.

**Table 1 — List of Genes Identified from PollyKG that are evidence backed**

**Table 2 — Edge properties stored on PollyKG that allows users to filter Gene lists**

Key Aspects of Target Prioritization

PollyKG provides the following information out of the box:

Gene Tractability information for prioritising genes that can be 'drugged'
Literature evidence supporting the Disease — Gene Association
Gene dependency information for cell line based evidence

Key Takeaway

PollyKG enables users to rapidly test hypotheses without running complex bioinformatics analysis. PollyKG can be further modified based on user needs for aspects like predicting toxicity.