Bioinformatics services

Multi-omics databases, proprietary analytical software, and automated advanced analytical pipelines, coupled with a high-complexity molecular laboratory, deliver robust results from projects of any size. Hundreds of collaborations with leading cancer centers, dozens of peer-reviewed publications, and a strong IP portfolio validate BostonGene’s solutions.
Genomic analysis
Clonal evolution of tumor cells
Next-generation sequencing (NGS) analysis of a sample’s mutational profile determines the number of tumor clones and their ratios.
Somatic variant & translocation calling
Identification of genomic alterations enables accurate and precise interpretation of complex disease mechanisms.
Germline mutations of pathological significance
Detection of a broad range of mutation types and the integration of various tools and databases enhances the interpretation of genetic variants.
Genomic alterations
Whole exome sequencing detects somatic and germline alterations, copy number variations (CNVs), tumor mutational burden (TMB), homologous recombination deficiency (HRD), genomic/microsatellite instability (MSI), and loss of heterozygosity (LOH).
Transcriptomic analysis
Fusion transcript identification
RNA-based fusion calling detects denovo and recurrent fusions that result from alternative splicing or trans-splicing, providing information on the expression level and functional consequences of fusion transcripts.
Gene expression & pathway analysis
RNA-seq accurately measures the expression of more than 100,000 transcripts and 20,000 genes, including splice variants. RNA-seq analysis uncovers the functional and transcriptomic effects of genomic alterations.
Proprietary expression signature mapping
Machine learning and advanced algorithms correctly map gene expression patterns and identify expression signatures unique to specific disease subtypes or stages, providing valuable insights into disease mechanisms and potential therapeutic targets.
Tumor microenvironment (TME) classification
The BostonGene Tumor Portrait™ test depicts tumor activity, cellular composition, the immune microenvironment, and other associated processes identifying activated or suppressed pathways within tumors to guide therapeutic decision-making.
Cellular deconvolution (Kassandra)
Kassandra, a unique machine learning cellular deconvolution algorithm trained on artificial transcriptomes, accurately reconstructs the tumor microenvironment using bulk RNA-seq.
Adaptive immune repertoire profiling (TCR/BCR-seq)
Reactive profiling of the immune cell repertoire in the tumor microenvironment of solid cancers and the identification of major malignant clones of T and B cells in hematological malignancies enables understanding of tumor progression and evolution.
Cancer-associated viruses and bacteria
Detection of viral or bacterial sequences and analysis of their expression patterns provide insights into their potential role in tumor development and progression.
Neoantigen prediction (DNA & RNA)
Tumor antigenicity is measured by calculating the expression of neoantigen-based peptides in mutated and fusion proteins.
Immunotherapy response prediction
The bioinformatics pipeline for immunotherapy response prediction utilizes a multi-omics approach, integrating genomic, transcriptomic, and immunological data to identify biomarkers associated with immunotherapy response and resistance.
Liquid biopsy modules
Molecular response monitoring
Molecular response monitoring provides a real-time assessment of treatment efficacy by detecting changes in molecular signatures.
Minimal residual disease monitoring
Minimal residual disease monitoring provides early detection of recurrence by detecting and quantifying trace amounts of tumor nucleic acids in the blood after treatment, which enables timely intervention for improved patient outcomes.
Peripheral blood immunotype classification
Machine learning-based FACS analysis for the identification of more than 300 cell populations for deep patient immune profiling.
Immune-related adverse event prediction
Identification of changes in the frequencies of different immune cell subpopulations enables the prediction of the likelihood and severity of immune-related adverse events (irAEs) associated with immunotherapy treatments.
Immunotherapy response prediction
Proprietary flow cytometry-based immune profiling analyzes specific immune cell subsets in patient blood samples to predict response to immunotherapy, utilizing advanced machine learning technology for high-resolution, multiparameter analysis.
Multiparameter imaging
Proprietary digital pathology platform
AI-based digital imaging analysis identifies distinct characteristics of the tumor and microenvironment (i.e., tertiary lymphoid structures - TLS) with improved sensitivity and precision over current methods.
Machine learning-based multiplex immunofluorescence image analysis
MxIF analysis provides a comprehensive overview of tissue (tumor cells, active and suppressive immune cell infiltration, stromal components, and vascularization) with simultaneous detection of up to 40 multiple clinically relevant markers with single-cell resolution.
Cellular community and interaction analysis
AI-powered advanced analytics of tissue architecture identifies cellular communities based on cell-to-cell interactions.