Some of the past research on neoantigen and p53 antibodies in immunity has been encouraging. The data is enormously complex, but keeps pointing to TP53's great potential. To this end, we were anxious to start our mega-experiment, but were delayed by C19, now I'm glad to report we are well underway. In co-operation with researchers at UCLA we aim to determine whether Codondex transcript analysis, of TP53 can predict the best tumor tissue selection for most effective Natural Killer (NK) cell priming, activation and cell killing, including in autologous tumor micro environments.
We're hoping to to achieve a result along the path toward our ambitious clinical goal. We aim to prove that a specifically selected section from biopsied tissue can be used to effectively prime autologus NK cells for patient reapplication and disease treatment.
This co-culture vs. sequencing challenge uses sections (T1-T8) taken from each of two tumors. Each section is co-cultured with 2 treated NK cell and one naive NK cell line and tests the efficacy of NK cell cytotoxicity against tumor cell and tumor tissue in killing assays. Separately, by sequencing TP53 of each selection and computing Codondex iScore(TM) algorithm we hope to identify specific features of each tissue selection that point computed results to research outcomes.
|Co-culture vs. Sequencing Challenge|
To better understand the analysis and encourage research contributions we are inviting applicants for first grants directed toward this objective.
Codondex tools analyse genetic sequences at an arbitrary number of nucleotides. The tool provides an easy way to observe fine repetitive details of small subsequences contained within a gene. We compute various metrics for each subsequence including 'Inclusiveness', which measures the total occurrences of every computed smaller subsequence is found within the subsequence of interest.
Our primary interest is intronic, non-coding DNA in multi-transcript genes. In these systems we create a transcript list, which we call the Vector, that is sorted by Codondex i-Score. This metric looks at Inclusiveness scaled by the length of the subsequence, to better account for intrinsic probability of finding smaller subsequences within progressively longer ones. Using this we look at the way order of this vector changes from subsequence to subsequence. Large changes in these vectors then prompts us to tag them for further investigation as it represents large deviation from transcript similarity, with this subsequence being labelled a Key Sequence.
Codondex is proposing 3 grants for open problems to aid in our journey towards a more biologically useful platform. These 3 problems span statistical analysis, data acquisition and biological relevance of various aspects that are integral to our platform.