Biomarker Discovery
Discover defensible biomarkers through multi-method consensus and structured knowledge annotation
The Challenge
Single-Gene Biomarkers Fail
Traditional univariate analysis (differential expression) identifies weak biomarkers that fail assessment in independent cohorts. Need multi-gene signatures with mechanistic rationale.
Black Box Models Lack Regulatory Acceptance
Deep learning models can achieve strong performance but lack interpretability. Regulatory agencies and clinicians demand explainable biomarkers with clear biological mechanisms.
Companion Diagnostic Development Is Slow
Traditional biomarker discovery, validation, and CDx development takes years and requires substantial investment. Lack of cross-platform validation and unclear IP strategy delay approvals.
Overfitting on Small Cohorts
Many pharma cohorts are small. Biomarkers discovered on limited datasets frequently fail to replicate, consuming significant time and resources in failed validation studies.
"Our lead asset failed Phase III because the biomarker we used for patient selection was discovered post-hoc on a small cohort and didn't hold up. We needed a robust signature from day one."
The Helomnix Solution
Helomnix approaches biomarker discovery as a multi-layered evidence integration problem rather than a single-model feature selection task. Candidate biomarkers are identified through complementary modeling strategies applied to multi-omics representations of disease biology, then filtered based on convergent support across methods — reducing method-specific artifacts and producing compact, reproducible panels.
Every candidate gene is contextualized through OmniRef, a structured and curated knowledge layer capturing disease-gene associations, druggability context, gene essentiality signals, and clinical evidence, with explicit versioning, provenance, and auditability. Genes lacking convergent biological support are deprioritized before further evaluation.
Final signatures are assessed within the Digital Twin Map to ensure they discriminate biological states rather than population averages, supporting robust translational interpretation. Panels are designed for qRT-PCR or NGS-based companion diagnostics.
Unique Differentiator
Multi-method consensus reduces method-specific artifacts, OmniRef annotation adds mechanistic biological rationale, and Digital Twin Map projection ensures signatures hold within patient archetypes — not just across a population average. This layered approach supports stronger IP protection and more robust translational review.
How It Works
Multi-Omics Feature Extraction
Integrate transcriptomic, genomic, and cell composition data. Unsupervised multi-omics factor modeling extracts latent biological programs that capture cross-platform interactions missed by single-modality analysis.
Consensus Feature Selection
Multiple independent modeling strategies are applied in parallel, and candidate genes supported by convergent signals across methods are retained. This consensus filter eliminates method-specific artifacts and produces compact, interpretable panels.
Biological Validation
Annotate candidates through OmniRef — integrating disease-gene associations, druggability context, essentiality signals, and clinical evidence. Genes are assessed for mechanistic coherence and convergent biological support before inclusion.
CDx-Ready Signature
Deliver a gene signature optimized for qRT-PCR or NGS panels. Includes cross-cohort comparative evaluation, Digital Twin Map projection showing state-level discrimination, and documentation supporting translational review.
Real-World Application
A pharma partner developing a novel compound for DLBCL needed a companion diagnostic to identify the ABC subtype showing the strongest mechanistic alignment. Traditional COO classification (Hans algorithm) had limited concordance with gold-standard methods.
Before
Hans algorithm (IHC-based) for ABC vs. GCB classification: limited concordance with gold-standard gene expression profiling. Not sufficient for CDx approval.
After
Helomnix multi-method consensus analysis identified a multi-gene signature combining RNA-seq and H&E image features, annotated through OmniRef and assessed on independent cohorts with strong classification performance.
Outcome
The multi-gene signature substantially outperformed the Hans algorithm and was designed for a qRT-PCR panel. A complete translational evidence package was prepared, streamlining the CDx development timeline.
Value to Your Organization
Biomarker Sensitivity
Multi-omics consensus biomarkers achieve substantially stronger discriminative performance than traditional univariate differential expression analysis.
IP & Regulatory Advantage
Interpretable biomarkers with mechanistic rationale provide stronger IP protection and support more robust translational review than black-box models.
CDx Development Efficiency
Assessed biomarkers from day one can eliminate months of post-hoc evaluation studies, substantially reducing CDx development costs and timelines.
Our Methodology
Data Inputs
- Bulk RNA-seq (required)
- miRNA profiling (enhances regulatory network coverage)
- Genomic mutation data (WES or targeted panels)
- Cell composition estimates (deconvolution or scRNA-seq)
- Clinical outcomes (response, survival, relapse)
AI/ML Techniques
- Multi-method consensus feature selection across complementary modeling strategies
- Unsupervised multi-omics factor modeling for latent program discovery
- OmniRef knowledge annotation (disease associations, druggability, essentiality, clinical evidence)
- Pathway enrichment and drug-biomarker association analysis
- Digital Twin Map projection for state-level evaluation
- Multi-cohort comparative assessment (internal atlases + public cohorts)
Deliverables
- Consensus gene signature with per-method selection evidence
- OmniRef gene evidence profiles (disease associations, druggability, essentiality, clinical evidence)
- Cross-cohort comparative evaluation summaries
- Pathway enrichment and drug-biomarker association report
- Digital Twin Map state-discrimination analysis
- qRT-PCR or NGS panel design recommendations
- Documentation supporting translational and regulatory discussions
Discuss a Translational Application
We welcome discussions about how this approach can support your translational research.