After performing bioinformatic analysis in a personalised manner on a large set of different cancer patients, big data analytics return associations between the gathered samples. These inter-patient associations are used to identify alterations that are frequently shared amongst patients and can be used as targets for broadly applicable therapies (off-the-shelf). Drugs against these shared targets are often preferred over personalised target discovery per patient as they are less expensive to develop and often incur a lesser cost to the patient.
A similar approach to the extensive cohort screening for shared target discovery is taken in the discovery of novel markers for diagnostic and prognostic purposes.
Example case target discovery: lncRNA
It is long known that lncRNAs have a tumour-specific expression (indication-specific), and they enable functional regulation in these cells on a transcriptional level (antisense silencing, scaffolding, etc.). Recent MS analyses have shown however that, although the full sequence of these lncRNAs is not being translated, there are certain small Open Reading Frames (ORFs) present in the lncRNA that are being translated into small Peptides (10-40aa). Detection of these smPeps on the surface of tumour cells has opened up our interest in using these peptides as a new target type for immunotherapy.
myNEO is partnering with different companies in the discovery process of new tumour-specific alterations shared amongst patients, for diagnostic and therapeutic purposes.
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