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Cover letter for Postdoctoral Research Fellow, Translational Cancer Research in Genomics Job ID 10416

I am excited that my application is under close consideration for this position, because, if hired, I could contribute towards revolutionizing patient-centered cancer treatment approaches. I am happy about the translational part of this research, because it allows patients to benefit from even the most recent scientific breakthrough discoveries.

For me cancer is an exciting fascinating, but simultaneously fear-inducing phenomenon, similar to playing with fire, because it is nature’s proof that immortality is not only possible, but wide-spread evolved biological reality. This gives us plenty of opportunities to observe options for delaying and eventually stopping all adverse effects of aging while simultaneously claiming the lives of far too many patients every single day. If we want to cure cancer, we must reverse aging into physiological, metabolic cellular rejuvenation, because otherwise, if no action is taken, the aging immune system can eventually no longer maintain its comprehensive anti-cancer surveillance.

However, the cancerous tissue is only one of the at least two major variables affecting treatment outcome, because the specific physiological and metabolic constitution of the still non-cancerous somatic cells is at least equally important. If we cannot yet maximize the benefits of anti-aging and rejuvenating interventions extending lifespan and health-span due to their unfortunately still way too little acceptance by the general public, healthcare providers and even research professionals, we must live with the inevitable consequence that with advancing age cancer cannot be cured permanently yet, because it keeps evolving until initially very effective interventions gradually fail. The thus induced evolutionary race between cancer, medical interventions, physiological and immunological conditions, which deviate widely even among patients suffering from genetically identical tumors, will challenge medical providers to discover unique very patient-specific interventions faster than the cancer can kill. E.g. younger and otherwise still healthy patients can tolerate much higher dosage of toxic anti-cancer drugs than fragile older patients who therefore need completely different medical interventions.

Hence, as long as rejuvenation and immortality are not yet actively pursued, we will have lots of cancer patients, who relapse every few years and who therefore, require their own cancer chaser or cancer suppressing scientist, who must develop novel and effective live-saving anti-cancer treatments for each of his/her patients faster than the relapsing cancer can kill, e.g. by taking advantage of the unique differences between tumors and cancer-free tissues. Cancers can only develop by differing from the remaining cells giving them a replicative advantage. Fortunately, this makes cancers more vulnerable to any intervention, which harms the more rapidly dividing tumor faster than cancer-free cells.

In the near future better collaborations between patients and their healthcare providers allows patients to actively choose any combination of interventions offered by their physicians. This makes current FDA approval procedures obsolete, because realistically, the lives of our patients depend much more on timely discovering and exploiting novel distinctive fitness differences between tumor and healthy tissues in the presence of external stressors than on unlikely adverse drug reactions. Most unexpected adverse effects of such kind of rapidly evolving experimental cancer and patient-specific treatment won’t kill immediately and can be stopped on time. Currently, no drug, which harms 10%, but saves the lives of 90%, can get approved despite helping 9 times more people. Soon lots of these rare adverse drug reactions can be much more reliably predicted based on genotypic data.

The pivotal role of our research at Fred Hutchinson is to discover, explore and evaluate the effects of novel interventions, because medical providers can add them to their expanding and cancer-discriminating toolboxes, out of which they can let their patients choose their preferred interventions based on their side-effects and other personal preferences. We at Fred Hutchinson want to give our healthcare providers a head start by supplying them with as many cancer-discriminating techniques far enough in advance to have enough time for discovering the best way to force an aggressively metastasizing tumor back into remission before it kills.

From an economic commercial perspective, this rapid co-evolution between interventions and relapsing metastasizing or rapidly growing tumors is generating life-saving medical demands from which even the privately funded sector can commercially benefit until the effective reversal of aging has become a feasible alternative to chasing cancers for the rest of the patients’ lives.

At Fred Hutchinson we can provide proof-of-principle that above outlined adaptations to develop ever changing cancer-suppressing combinations will allow adding many more years to the lives of cancer victims compared to current health policy practices. We will soon need realistic and goal-oriented - instead of procedure-oriented - healthcare and research policies to save the lives of many more people for much longer. This would cause a new powerful, innovative and self-directed group of professional cancer suppressors to thrive while gradually transitioning to fully cancer-curing anti-aging interventions, which are caused by eliminating the age-dependent and exponentially rising risk for developing cancer, because of the gradual overall metabolic, physiological and cognitive decline due to aging. Aging is the true master disease of all age-related degenerative diseases because it raises susceptibility to its many symptoms, e.g. Alzheimer, Parkinson, Cancer, Diabetes, Macular Degeneration, Depression, Infections, Pneumonia, Dementia, Hearing Loss, Cardiovascular Diseases, Strokes, etc.

I am fascinated by the prospective of true ageless immortality. That is why I used machine learning techniques to predict, model and reverse-engineer aging in yeast. Like yeast, cancer cells are constantly dividing. I expect an even better gene-function-specific time series clustering outcome for cancer because of much more abundant cancer training data. My most extensive time series yeast RNA microarray dataset had only 81 time points, taken in 4 minute intervals spanning only 3 complete cell cycles during 6 hours. Since there is more interest in cancer than in yeast research, I hope to get to analyze high temporal resolution cancer datasets with at least 12 transcriptome, proteome, metabolome, and epi-genetic time points per cell cycle over many cell cycles to test my aging hypothesis not only on yeast but also on human cells.