See How We Apply Our Technology
Explore real-world applications across clinical validation, target discovery, and drug development
Hidden Subtype Detection in Breast Cancer
Revealing Misdiagnosed Cancer Populations at Single-Cell Resolution
Discovered hidden cancer subtypes missed by traditional bulk diagnostics. Found ER+ cells in TNBC-diagnosed patients and TNBC cells in ER+-diagnosed patients, preventing inappropriate treatment selection.
Key Findings
- Detected ER+ subclusters within TNBC-diagnosed tumors
- Identified TNBC populations in ER+-diagnosed patients
- Confirmed findings through biomarker validation
- Explained treatment resistance and recurrence patterns
Methodology
- 1Single-cell RNA sequencing analysis
- 2AI-powered subtype classification
- 3Biomarker expression validation
- 4Comparison with bulk diagnostic results
Results
- Revealed false negatives in traditional diagnostics
- Identified patients at risk of treatment failure
- Enabled more precise treatment selection
Clinical Impact
Prevents inappropriate treatment by detecting hidden subtypes, reducing therapy failure and recurrence risk through accurate patient stratification.
The Hidden Enemy in HER2+ Breast Cancer
Identifying Pre-Existing Resistant Clones (TOP2A+/RAD21+) Invisible to Traditional Profiling
Identified pre-existing drug-resistant cancer cell populations (TOP2A+/RAD21+) in treatment-naïve HER2+ breast cancer patients that are invisible to traditional bulk sequencing methods. T-DXd resistance isn't just acquired—it's often waiting there before treatment begins.
Key Findings
- Discovered TOP2A+/RAD21+ resistant cell populations in untreated patients
- T-DXd resistance exists before treatment initiation, not just acquired
- Current bulk-seq tools cannot detect these rare resistant cells
- Enhanced DNA repair mechanisms drive drug resistance in specific cells
- Resistant cells exhibit distinct cell cycle control and drug resistance patterns
Methodology
- 1Single-cell RNA sequencing of treatment-naïve HER2+ samples
- 2TOP2A and RAD21 expression profiling at cellular resolution
- 3DNA repair pathway enrichment analysis
- 4Gene regulatory network (GRN) analysis (TFDP1/E2F regulons)
- 5Comparison with bulk RNA-seq sensitivity limitations
- 6B2SC-based HER2+ cluster prediction and validation
Results
- Pre-existing resistant clones identified across multiple untreated patients
- TOP2A regulon activity significantly elevated in resistant populations
- TFDP1/E2F pathway enrichment confirmed in resistant clones
- Predictive biomarkers for T-DXd treatment response established
- B2SC successfully predicted and validated HER2+ related clusters
- Potential synergistic agents with T-DXd identified for resistant clones
Clinical Impact
Enables pre-treatment identification of patients likely to develop T-DXd resistance, allowing personalized therapy selection before resistance emerges.
Mechanism-Based Patient Stratification
Beyond Expression: Identifying Functionally Active Targets
Distinguished between high expression and high functional activity using Gene Regulatory Network analysis. Eliminates false positive targets by confirming which pathways are actually driving disease.
Key Findings
- Identified functionally active regulatory networks
- Distinguished passenger from driver molecular changes
- Mapped differential pathway activity across subtypes
- Validated mechanistic link to disease progression
Methodology
- 1Gene Regulatory Network (GRN) inference
- 2Transcription factor activity quantification
- 3Single-cell pathway analysis
- 4Functional validation of target activity
Results
- Confirmed drug targets with functional activity
- Eliminated false positive stratification markers
- Enabled mechanism-based patient selection
Clinical Impact
Reduces clinical trial failures by ensuring treatments target functionally active pathways, not just highly expressed genes.
bsADC Target Identification
Bispecific ADC Target Discovery
Identified validated druggable gene pairs across cancer subtypes with minimal off-tumor toxicity.
Key Findings
- Discovered 6+ validated gene pairs across multiple cancers
- Achieved cancer cell specificity with low toxicity risk
- Validated through wet lab experiments
Methodology
- 1Single-cell gene expression analysis
- 2Cancer specificity simulation
- 3Toxicity validation against healthy organs
Clinical Impact
Enabled rapid bsADC program advancement with validated, cancer-specific targets.
CDK-inhibitor MoA Analysis
Pathway Understanding in Breast Cancer
Identified transcription factors regulating CDK-inhibitor expression through regulatory network analysis.
Key Findings
- Mapped complete regulatory landscape
- Identified novel regulatory candidates
- Characterized differential activity patterns
Methodology
- 1Gene Regulatory Network inference
- 2Single-cell regulon quantification
- 3Pathway analysis
Clinical Impact
Provided mechanistic insights for therapeutic intervention targeting.
PROTAC E3 Ligase Optimization
Cell-Type Specific Ligase Selection
Optimized E3 ligase selection at cellular resolution for PROTAC development.
Key Findings
- Profiled cell-type specific E3 ligase expression
- Identified optimal ligase-target pairs
Methodology
- 1Single-cell E3 ligase profiling
- 2Cell-type specific expression analysis
Clinical Impact
Enables rational PROTAC design with improved degradation efficiency.
ICI Response Biomarkers
T-cell Trajectory Analysis
Mapped T-cell trajectories to identify immune checkpoint inhibitor response biomarkers.
Key Findings
- Mapped T-cell activation to exhaustion trajectory
- Identified novel biomarker candidates
Methodology
- 1Pseudotime trajectory analysis
- 2TME profiling
Clinical Impact
Provides predictive biomarkers for better patient selection.
TKI Resistance Mechanisms
Resistance Pathway Analysis
Decoded cellular heterogeneity in TKI response to identify resistance mechanisms.
Key Findings
- Mapped alternative resistance pathways
- Identified combination therapy targets
Methodology
- 1In-silico drug perturbation
- 2Pathway activation analysis
Clinical Impact
Guides combination therapy design to overcome resistance.
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