GMDx Genomics has developed powerful genomic oncology applications, introducing GMDx C-PAS and GMDx-IO

Predict patient response to immunotherapy treatment

Predict cancer progression

Oncology

Accurately predicting responders

“The biggest challenge we have today is predicting whether immuno-oncology will actually work”  — Prof. Mark Shackleton, Head of Oncology – Alfred Hospital, Australia.

Accurately predicting immunotherapy responders vs non-responders can potentially:

  • bring forward immunotherapy as a 1st or 2nd line treatment option

  • enable better patient outcomes

  • generate significant health economic savings 

  • empower clinicians to make more informed treatment decisions 

Our proprietary platform and machine-learning based analytics achieve up to 86% predictive accuracy across cancer types including melanoma, lung, bladder, kidney, and head & neck cancers.

GMDx-IO machine learning methods facilitate ongoing improvements to prediction accuracy with additional patient data (unlike other biomarkers such as TMB).

Cancer

Progression

GMDx was the first to use genomic data to identify Cancer Progression Associated Signatures (CPAS)* and we have developed proprietary methods to predict cancer progression for a range of cancers.

GMDx-CPAS has now verified on several cancer types. More than 10,000 patient genomes across 33 tumour types were analysed. GMDx IIF profiles were generated for all patients in this cohort that had “Progression Free Survival” (PFS) recorded (n=9,433) and a verifiable method for CPAS was developed to predict cancer progression. Accuracy, sensitivity and specificity was very high and, in some cases it was 100%.

LEGAL

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