Immuno-oncology

A tumor dataset does not answer a clinical question on its own. Which patients respond to checkpoint blockade, what separates their microenvironment from everyone else’s, and whether that signature survives the next cohort: this is the work GenXMap’s computational team takes on.

Predictive Biomarker Discovery & Validation Programme

A designed programme rather than an analysis order: GenXMap integrates genomic, transcriptomic, proteomic and tumor-microenvironment data to identify molecular signatures associated with treatment response or resistance and, where an independent cohort is available, tests those signatures in data they were not derived from.

• Genomic profilingWGS or WES to characterise tumor mutations, genomic alterations and mutational patterns relevant to treatment response or resistance
• Targeted mutation validationtargeted sequencing to confirm and quantify selected variants identified during discovery or required for follow-up
• Bulk RNA sequencingtranscriptomic profiling of tumor tissue to identify differential expression, immune-response programmes and molecular signatures across treatment conditions
• Proteomicsmass-spectrometry-based protein profiling, including TIMS-TOF MS, to characterise changes at the protein level and identify signatures associated with treatment response
• cfDNA / cfRNA profilingdetecting and monitoring tumor- or treatment-associated molecular signals from liquid biopsy samples
• Tumor microenvironment analysisimmune deconvolution of bulk transcriptomic data using appropriate references, complemented where relevant by single-cell and spatial transcriptomic data
• Single-cell and spatial integrationadding cellular and spatial resolution to refine cell populations, localise molecular signals and interpret bulk signatures in their biological context
• Multi-cohort integrationcorrecting for batch effects and integrating datasets across cohorts, platforms and study batches
• Signature discovery and validationidentifying molecular features associated with response or resistance, with validation cohorts defined independently from the discovery analysis

The work starts before the analysis: defining which molecular layers are needed to answer the biological question, selecting appropriate references, determining how cohorts will be integrated, and separating discovery from validation from the outset.

The questions we take on

• Which patients respond to checkpoint blockade, and what separates them molecularly
• How the immune composition of the microenvironment shifts under treatment
• Whether a candidate signature holds on an independent cohort
• How molecular findings line up with treatment arm and clinical outcome

How the analysis runs

What the report contains

From Data to Biological Evidence

The laboratory work behind these analyses runs on OMICS4, GenXMap’s platform site, with sample requirements, logistics and turnaround defined for each study.

But the analysis does not have to start in our laboratory.

We also work with data generated elsewhere. Existing transcriptomics, proteomics, metabolomics, microbiome or extracellular-vesicle datasets , from an earlier study, an internal R&D programme or another provider, can be analysed and integrated on their own terms.

What determines the analysis is the biological question, the study design and the metadata available, not who generated the data or where the sequencing was performed.

Where multiple omics layers are available, we integrate them to identify converging biological signals, distinguish robust effects from technical or donor variability, and build a coherent evidence package around the claim.

The data may come from different platforms, providers or experiments. The biological question remains the starting point.

Which cells moved, and where they were

In immuno-oncology, knowing that expression changed is rarely enough. You need to know which immune subsets moved, and where they sit relative to the tumour. That is three modalities, not one: bulk RNA-seq on our own platforms, single-cell with Parean Biotechnologies in Saint-Malo, and spatial transcriptomics with Explicyte, a precision oncology and immuno-oncology CRO in Bordeaux. Run as one programme, on one design and one sample set, they are read together rather than reconciled afterwards.

GenXMap is also joining the Paris-Saclay Cancer Cluster, and is joining the Marseille Immunology Biocluster. For a programme that needs a specific cohort or a clinical collaborator, that is the difference between a conversation and a search.

Case studies for this industry are in preparation: get in touch to discuss a project directly.