Turn AI-enabled data into precision cancer treatment
Data-driven insights
Proven results
Patient care
Employ
Proven results
Patient care
ENLIGHT enhances Comprehensive Genomic Profiling (CGP) by using transcriptomics to predict how a patient will respond to targeted therapies and immunotherapies, offering a new era in precision medicine.
ENLIGHT consistently outperforms traditional markers, giving you reliable tools to further personalise your patients’ cancer care.
By integrating ENLIGHT into your routine practice, you can offer an additional layer of personalised, data-driven cancer treatment options tailored to each patient’s tumour biology.
In predicting treatment responses across multiple cancers.
Significantly higher odds (>2.5) to respond to the given drug.
Based on proven synthetic lethal (SL) partners of tumour suppressors and oncogenes, with just under two decades of evidence.
By predicting genome-wide mRNA expression with their proprietary deep-learning framework, which predicts response to targeted and Immune therapies from the inferred expression values, with an overall odds ratio of 2.28 and a 39,5% increased response rate among predicted responders versus the baseline rate.
Your CGP data by providing additional drug response prediction for various drugs across multiple cancer types.
Treatment options to maximise patient outcomes include using the most effective therapies earlier by using ENLIGHT as early as possible in the treatment pathway.
The need for additional biopsies or lengthy turn-around-times, either the WTS data from your patients’ CGP results and/or digitized H&E slides are sent via secure portal to the testing lab, which produces an ENLIGHT Match Score (EMS) report within 5 days.
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ENLIGHT enables deeper cancer insights
ENLIGHT goes beyond actionable genomic abnormalities by examining the molecular context in which targeted therapies operate. It looks at expression abnormalities, which happen far more often than the genetic mutations doctors usually focus on in precision cancer treatments today.
This engine processes multiple big-data repositories to infer which gene pairs are likely to be related by certain functional relationships, termed Genetic Interactions (GI).
Each gene pair is assigned an “Interaction Score,” which ranks how likely it is to represent a clinically relevant GI.
The output of the Inference Engine is a set of GI Maps, showing genes connected by high-scoring GIs.
These maps are generated on a pan-cancer level and for specific cancer indications.
This engine combines the GI Maps produced by the Inference Engine with information on drugs, biological pathways, and patient-specific multi-omics data to derive personalised predictions of response to cancer.
Based on these predictions, ENLIGHT screens and ranks dozens of potential treatments for each patient, complementing standard genomic matching biomarkers.
