Biomarker discovery
BostonGene is dedicated to advancing biomarker development and validation, pioneering a new era in biomarker discovery and facilitating the translation of research solutions into clinical practice.
Advanced multiomics platform for biomarker discovery
BostonGene leverages an extensive array of technologies to select the most suitable options for next-generation biomarker discovery and orthogonal validation. Our AI-driven approach enables high-throughput analysis and unbiased candidate biomarker selection.
Immune system profiling
Capturing the status of the patient's immune system with just one tube of blood.
Tumor microenvironment insights
Minimally invasive genomic and transcriptomic tumor profiling is achieved through cell-free DNA (cfDNA) and cell-free RNA (cfRNA) sequencing from a blood sample.
Genomic alterations
Comprehensive analysis, from individual cells to complex spatial and biological relationships, decodes the tumor microenvironment by integrating sequencing and imaging methods.
Liquid biopsy
In-depth characterization of surface target molecules for indication selection and clinical trial support.
Target expression analysis
Novel biomarkers are identified based on multi-gene expression signatures.
Gene expression signatures
Robust assessment of expression levels for more than 20,000 genes.
Neoantigen analysis
Identification of somatic and germline alterations, as well as genomic signatures associated with therapy response and resistance from WES and RNA-seq data.
Immune system profiling
Capturing the status of the patient’s immune system with one tube of blood.
Blood immunoprofiling, using high-resolution flow cytometry coupled with whole transcriptome sequencing, evaluates functional cell states and cellular composition to enable:
Immunotherapy response prediction
Advanced machine learning technology enables high-resolution, multiparameter analysis to group patients into distinct immunophenotypes associated with therapy response.
Immune-related adverse event prediction
Identification of immune system changes allows for predicting the likelihood and severity of immune-related adverse events (irAEs) associated with immunotherapy treatments.
Deep immunophenotyping
15 Functional panels, 100+ Antibodies, 8,000+ Unique cell populations and stages of differentiation
More about flow cytometry
Immunotherapy response prediction
Advanced machine learning technology enables high-resolution, multiparameter analysis to group patients into distinct immunophenotypes associated with therapy response.
Immune-related adverse event prediction
Identification of immune system changes allows for predicting the likelihood and severity of immune-related adverse events (irAEs) associated with immunotherapy treatments.
Deep immunophenotyping
15 Functional panels, 100+ Antibodies, 8,000+ Unique cell populations and stages of differentiation
More about flow cytometry
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Tumor microenvironment insights
Comprehensive analysis, from individual cells to complex spatial and biological relationships, decodes the tumor microenvironment by integrating sequencing and imaging methods.
An ML-based tool Kassandra™ reconstructs tumor microenvironment composition
- Estimation of stromal, endothelial and immune cell content derived from bulk RNA-seq data.
- Identification of more than 70 unique cell populations achieved with single-cell resolution.
- Orthogonally validated by scRNA-seq, MxIF, IHC, flow cytometry and CyTOF.
Visit Cancer Cell publications
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Tumor microenvironment typing distinguishes between therapy responders and non-responders
- Prediction of prognosis and response to immunotherapy based on bulk RNA-seq data.
- Gene expression signatures covering key cellular and functional TME properties.
- Applicable to various cancer types.
More about RNA-seq
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Spatial proteomics provides an overview of tissue composition and architecture
- Multiplex immunofluorescence (MxIF): detection of up to 40 biomarkers from a single slide.
- Characterization of tumor cells, immune cell infiltration, stroma, vasculature and cell-cell interactions.
- Identification of tissue cellular communities potentially predictive of therapy response.
More about MxIF
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Genomic alterations
Identification of somatic and germline alterations, as well as genomic signatures associated with therapy response and resistance from WES and RNA-seq data.
For single nucleotide variants (SNVs), insertions/deletions (indels), copy number alterations (CNAs), and fusions, BostonGene can assess across multiple cancer types:
- Prevalence of genomic events that serve as potential drug targets and biomarkers, aiding in the identification of promising indications.
- Co-occurring genomic events to explore potential combination therapies and synthetic lethality.
- In addition, BostonGene identifies genomic events linked to therapy response or resistance.
More about WES
Liquid biopsy
Minimally invasive genomic and transcriptomic tumor profiling is achieved through cell-free DNA (cfDNA) and cell-free RNA (cfRNA) sequencing from a blood sample.
Circulating tumor DNA (ctDNA) analysis detects clinically relevant gene alterations
Response monitoring
Assessment of ctDNA fraction levels compared to the pre-treatment state.
Treatment resistance monitoring
Real-time assessment of treatment efficacy through the detection of changes in molecular signatures.
Residual disease monitoring
Early detection of recurrence by identifying ctDNA fraction in the blood after treatment.
General tumor profiling
Identification of actionable molecular events to guide treatment selection.
More about cfDNA sequencing
Assessment of ctDNA fraction levels compared to the pre-treatment state.
Treatment resistance monitoring
Real-time assessment of treatment efficacy through the detection of changes in molecular signatures.
Residual disease monitoring
Early detection of recurrence by identifying ctDNA fraction in the blood after treatment.
General tumor profiling
Identification of actionable molecular events to guide treatment selection.
More about cfDNA sequencing
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cfRNA analysis incorporates expression signals from all body tissues
Gene expression levels assessment
Identification of biomarkers of response and drug targets.
Predictive gene expression signatures
Includes PAM50 test for breast cancer patients and prediction of metastasis localization and organ damage.
Tumor microenvironment (TME) analysis
Comprehensive characterization of TME composition and detection of tissue-resident immune cells.
More about cfRNA sequencing
Identification of biomarkers of response and drug targets.
Predictive gene expression signatures
Includes PAM50 test for breast cancer patients and prediction of metastasis localization and organ damage.
Tumor microenvironment (TME) analysis
Comprehensive characterization of TME composition and detection of tissue-resident immune cells.
More about cfRNA sequencing
Target expression analysis
In-depth characterization of target molecules for indication selection and clinical trial support.
Single gene expression analysis from bulk RNA-seq
High-throughput analysis of gene expression:
- Correlates with qPCR, the gold standard for expression analysis.
- Correlates with IHC, offering the potential to transfer biomarkers discovered by RNA-seq to routine clinical practice.
Transcriptomic analysis of drug targets
Expression levels of drug target candidates are assessed from the BostonGene database, which contains over 100,000 transcriptomic samples:
Across cancer types
Identifying diagnoses with the highest or lowest target expression to find the most promising indications.
In normal tissue
Assessing potential on-target off-tumor effects.
In tumor microenvironment (TME)
Evaluating the effect on the TME or targets located on the surface of immune cells.
More about RNA-seq
Across cancer types
Identifying diagnoses with the highest or lowest target expression to find the most promising indications.
In normal tissue
Assessing potential on-target off-tumor effects.
In tumor microenvironment (TME)
Evaluating the effect on the TME or targets located on the surface of immune cells.
More about RNA-seq
Helenus, an ML-based tool, differentiates the expression of tumor and TME cells
Expression signals in a bulk RNA-seq sample can originate from tumor, cells within the TME or a mixture of both. Helenus separates these signals and enables:
A more accurate target expression
assessment similar to IHC by refining gene expression profiles specific to tumor and TME cells.
Superior scalability
Compared to IHC, enabling the assessment of multiple genes from a single sample.
More about RNA-seq
A more accurate target expression
assessment similar to IHC by refining gene expression profiles specific to tumor and TME cells.
Superior scalability
Compared to IHC, enabling the assessment of multiple genes from a single sample.
More about RNA-seq
Single gene expression analysis from IHC
BostonGene’s pathologists and AI-based algorithm accurately detect expression of target biomarkers in both tumor and immune cells. Automated workflow can be applied to the following markers:
More about digital pathology
- Nuclear (AR, PR, FOXP3, etc.)
- Cytoplasm / membrane (HER2, TROP2, HLA-DR, CD4/8, etc.)
- Complex membrane staining (0, 1+, 2+, and 3+ cells for HER2)
- Targets for novel antibody-drug conjugates (ADC)
More about digital pathology
Gene expression signatures
Novel biomarkers are identified based on multi-gene expression signatures.
Multi-gene expression signatures allow to get stable signal and can be leveraged as potential biomarkers. BostonGene has utilized this approach for:
- Identifying patients most likely to respond to immunotherapy based on TME subtyping.
- Discovering biomarkers associated with response to specific targeted therapies.
- Predicting prognosis in patients with solid and hematologic malignancies.
More about RNA-seq
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Neoantigen analysis
Prediction of tumor antigenicity based on expressed neoantigenic peptides.
Accurately predicting and selecting neoantigens provides insights for the effective design of personalized cancer vaccines based on:
- Precise identification of tumor-specific mutations from WES data.
- In silico prediction of immunogenic peptides.
- HLA genotyping and prediction of binding affinity to the patient's HLA molecules.
- Expression of the predicted neoantigens from RNA-seq data.
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TCR/BCR repertoire profiling
Information on the diversity and functionality of the immune repertoire.
Immune repertoire profiling of solid and hematologic malignancies encompasses:
- The assessment of diversity and clonality
- Detailed characterization of major and minor TCR/BCR clones, including hypermutations and isotype of the malignant BCR clone
- The development of biomarkers for therapy response or immune-related adverse events
Case example: Precise selection of patients that respond to the drug
Pharma challenge
Identify patients that benefit from tyrosine kinase inhibitors (TKI) in advanced clear cell renal cell carcinoma (ccRCC).
The existing biomarkers can’t reliable predict if a ccRCC patient benefits from TKI therapy versus immune checkpoint inhibitors (ICI).
BostonGene approach
Development of AI/ML framework based on genomic and transcriptomic data to identify patients responsive to ICI or TKI.
Result
Established strategy to select potential TKI responders.
Identify patients that benefit from tyrosine kinase inhibitors (TKI) in advanced clear cell renal cell carcinoma (ccRCC).
The existing biomarkers can’t reliable predict if a ccRCC patient benefits from TKI therapy versus immune checkpoint inhibitors (ICI).
BostonGene approach
Development of AI/ML framework based on genomic and transcriptomic data to identify patients responsive to ICI or TKI.
Result
Established strategy to select potential TKI responders.
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