At MNM, our goal has always been to understand cancer and find the right therapy for every patient. We set out on this journey analyzing somatic variants of patient tumors to guide their therapy. Soon, we realized that there is much more valuable information than only a coding DNA sequence in the patient’s tumor; however, we needed more genomes with clinical data to fully understand cancer origins. We started a BioBank and began building the database of whole-genome sequencing data, which led to the creation of custom data processing pipelines, distributed computation, and storing results in the cloud. With rich datasets at hand, now we can use machine learning to drive our success. We have developed and patented custom CDK4/6i, HRD, and PD1i patient stratification algorithms. The analysis of patient response to existing therapies inspired our broader vision We have realized that our genome analysis technology can reveal patterns of cancer mutations related to genes that are not yet targeted by any therapy. It is this realization that lies at the heart of our unique approach. We do not follow a classical drug target discovery approach - from basic science to in vitro models and clinical trials. Our solution starts from real patient data and leverage AI and bioinformatics to understand mutation profiles and find relevant target candidates. And we are already doing it by creating our drug - MNM177!
The idea for MutationsNoMore (MNM) was born while we were at Oxford University, where we understood that a new “dictionary” for interpreting a tumor’s mutation language is needed. Over the past four years, we have built technology that translates different mutation types; from single point mutations, structural changes to patterns of mutations into biological insights. Then, we built a new AI framework for these data modalities – our most valuable asset.
We are proud that MNM’s predictive algorithms are used in clinics to help select the right treatment for cancer patients.