Disease Mechanism Discovery
Compare tumor differentiation to normal B→PC trajectory to uncover disease mechanisms
The Challenge
Unknown Differentiation Blockade
Why is myeloma blocked at plasmablast stage while Waldenström macroglobulinemia (WM) blocks at memory B-cell stage? Understanding the differentiation arrest would reveal therapeutic targets.
No Normal Reference Trajectory
Tumor scRNAseq shows gene expression profiles, but without a normal differentiation reference, you can't identify WHICH stage is disrupted or HOW the differentiation program is hijacked.
Subtype Heterogeneity Unexplained
Patients with the same diagnosis (e.g., WM) have vastly different outcomes. Are there molecular subtypes based on different differentiation blockades? Traditional clustering methods don't reveal this.
Missed Therapeutic Opportunities
Drugs that restore normal differentiation (differentiating agents like ATRA in AML) are highly effective but rarely discovered because mechanism-based approaches aren't used.
"We knew WM patients had heterogeneous outcomes, but we didn't understand why until we compared their tumor cells to normal B-cell differentiation stages. Two completely different subtypes emerged."
The Helomnix Solution
Helomnix offers a unique in-vitro model of normal B-cell to plasma cell differentiation (Memory B-cell → Pre-plasmablast → Plasmablast → Plasma cell, 10 days). This provides the gold-standard reference trajectory for comparing tumor cells to normal differentiation.
We perform scRNA-seq on tumor samples and project them onto our normal differentiation trajectory. This reveals: (1) which differentiation stage the tumor is blocked at, (2) which transcriptional programs are disrupted (e.g., key differentiation-stage master regulators), and (3) whether different patient subtypes arrest at different stages.
This approach has been validated in published research: a landmark WM study used our publicly available in-vitro differentiation scRNAseq data to discover two distinct WM subtypes based on MBC-like vs. PC-like differentiation blockade, with different prognosis and pathway dependencies.
Unique Differentiator
Our in-vitro differentiation model is the ONLY commercially available normal plasma cell differentiation platform with published validation. Researchers worldwide use our scRNAseq reference data—now available for pharma partnerships.
How It Works
Tumor ScRNA-Seq Profiling
Perform single-cell RNA-seq on patient tumor samples (bone marrow aspirates, lymph node biopsies). Isolate malignant B-cells/plasma cells for profiling.
Normal Reference Trajectory
Use our in-vitro B→PC differentiation model scRNAseq atlas as a comprehensive multi-stage reference. This captures the full transcriptional roadmap from MBC to PC.
Trajectory Comparison
Project tumor cells onto normal differentiation trajectory using pseudotime analysis. Identify which stage tumor cells are blocked at and which differentiation genes are dysregulated.
Subtype Discovery
Cluster patients by differentiation blockade patterns. Identify molecular subtypes (e.g., MBC-like vs. PC-like) with distinct prognosis and therapeutic vulnerabilities.
Real-World Application
Published research: "A multiomic analysis of Waldenström macroglobulinemia defines distinct disease subtypes." Researchers needed to understand WM heterogeneity and prognosis variation.
Before
Traditional approach: Cluster WM patients by mutation status or gene expression. Found heterogeneity but no clear biological interpretation or therapeutic implications.
After
Leveraged Helomnix publicly available in-vitro B→PC differentiation scRNAseq data. Compared WM tumor cells to normal differentiation trajectory. Discovered 2 distinct WM subtypes: MBC-like (early blockade) vs. PC-like (late blockade).
Outcome
MBC-like subtype had distinct prognosis and pathway dependencies. This classification is now being validated in clinical trials. Demonstrates power of differentiation-based disease subtyping.
View Published ResearchValue to Your Organization
Mechanistic Insights
Identify exact differentiation stage where tumor is blocked and which master regulators are dysregulated along the differentiation axis, providing mechanistic context for target prioritization.
Subtype Discovery
Discover molecular subtypes based on differentiation blockade patterns, supporting subgroup definition and companion diagnostic development.
Published Validation
Approach validated in published WM research. De-risk your R&D investment by using proven methodology with academic track record.
Our Methodology
Data Inputs
- Patient tumor samples (bone marrow, lymph node)
- Single-cell RNA-seq data (10X Genomics or similar)
- Clinical metadata (diagnosis, stage, treatment, outcomes)
- Disease type (MM, WM, MGUS, or other B-cell malignancy)
AI/ML Techniques
- ScRNA-seq on tumor samples (10X Genomics)
- Integration with Helomnix in-vitro differentiation reference atlas
- Pseudotime trajectory analysis
- Differential gene expression at each differentiation stage
- Transcription factor activity inference
- Patient clustering by differentiation blockade patterns
- Survival analysis by subtype
Deliverables
- Tumor differentiation stage classification (MBC/Pre-PB/PB/PC)
- Dysregulated differentiation genes and transcription factors
- Patient subtype classification based on blockade patterns
- Survival analysis by subtype with Kaplan-Meier curves
- Mechanistic context and prioritized features per subtype
- Comparison to published WM subtypes for validation
- Publication-ready figures and mechanistic summary report
Discuss a Translational Application
We welcome discussions about how this approach can support your translational research.