Towards a Platform for Cost-Effective Personalized Cancer Immunotherapy, Tailored to the Patient and Tumor
If you live long enough, you will get cancer. It's just a matter of time and odds, and thus any future rejuvenation toolkit must include robust medical technology capable of curing cancer. The principal challenge of cancer research is that every tumor has a different biochemistry, different enough to cause great variation in the effectiveness any one narrow strategy based on targeting a single protein or cellular process. Ways around this issue include (a) focusing on one of the few mechanisms that are the same in all cancers, such as the need to lengthen telomeres, and (b) developing some means to cost-effectively target a different set of proteins and mechanisms in every cancer patient. This early-stage research takes the second approach:
A tailored immunotherapy approach that could be used as a "universally applicable blueprint" was found to be effective in three independent tumour mouse models, a new study reports. Tumour-specific mutations represent ideal targets for cancer immunotherapy as they lack expression in healthy tissues and can potentially be recognised by the body's immune system. However, systematic targeting by vaccine approaches have been hampered by each patient's tumour possessing a unique set of mutations - the mutanome - that must be identified first.In the current study, researchers established a process by which mutations identified by exome sequencing could be selected as vaccine targets through bioinformation prioritisation based on both expression levels and major histocompatibility complex (MHC) class II-binding capacity for rapid production. The team undertook work on three separate mouse models of lung, skin, and colon cancer. The investigators generated vaccines that delivered customised synthetic mRNA sequences which encouraged CD4 T cells to attack the target mutations, and showed improved survival in mice treated with the vaccines compared to untreated mice. Finally, they demonstrated an abundance of mutations predicted to bind to MHC class II in human cancers by employing the same predictive algorithm on corresponding human cancer types.
"The tailored immune-therapy approach introduced here may be regarded as a universally applicable blueprint for comprehensive exploitation of the substantial neo-epitope target repertoire of cancers, enabling the effective targeting of every patient's tumour with vaccines produced 'just in time.'"
Link: http://ecancer.org/news/7219-mouse-models-show-tailored-immunotherapy-to-be-effective.php
I think this can be quite effective. The key of course is in the use of the word "personalized." Too much of health care is a one size fits all which amounts to a one size fits none.
If in fact, treatments become geared toward the individual instead of the disease, there can be much optimism here.
I hope very much that we can get closer to 100% cure rate or at least controlled for cancer. And, this to be done w/o surgery, radiation, or chemo.
As the scientists improve on getting better results, it would be nice to see a year to year cure/control rate for cancer published and maybe break it down for each type of cancer. I'd love to see this rate to improve so that say 2016 has a 70% cure/control for all cancer, then 2017 to be at 73% rate and 2018 to be at 80%. Or maybe I am just dreaming.
An article about some practical problems with this approach, waiting three weeks for results was too long for pancreatic cancer patients:
http://www.the-scientist.com/?articles.view/articleNo/42744/title/Time-Bungles-Precision-Medicine/
These problems could be overcome with better priority procedures, and some evidence of efficiency so that doctors aren't so reluctant to do a biopsy in patients that with present medical technology are almost certain to die anyway.
@Robert Church:
Not year by year, but better than nothing: http://www.eurocare.it/Database/tabid/77/Default.aspx
@Robert Church:
See also: http://www.cancer.gov/statistics/find#stat
Perhaps this is already being done, but having the ability to assess the natural history/progression of genomic and molecular changes in tumor cells of large numbers of individual patients could perhaps provide a great deal of insight in terms of how certain malignancies evolve during disease progression.
Ascertaining how different treatments affect the selection process of tumor cells could provide more accurate probabilistic models to inform or predict future treatment approaches (if required). As a simple example, perhaps targeting one pathway or molecule has been shown to preferentially select for an escape variant in an entirely different but targetable pathway. Hitting both pathways (or more) at once may be more effective in dictating the path of disease progression. Issues with specificity come into play here, but knowing the current and highly probable future molecular states of tumor cells in question would help in this regard.
All of this could be done in the context of combined biological (including vaccine-based)/small molecule approaches. I am pretty sure combinatorial approaches (aside from radiation and chemo) are currently utilized, but most likely not in a highly informed, probabilistic manner. (Well, aside from you have a 50% chance to live 6-9 months, because we have absolutely no idea as to how to effectively treat your cancer...)