This is a really great point srunni thank you for bringing it up. We agree that the current approach to identifying predictive cancer biomarkers has many challenges. That's why we believe in a different approach.
There have been four subtypes found in the disease, but all four are still treated ineffectively with the same standard of care chemotherapy (Temozolomide). To date none of these predictive biomarkers have led to a successful targeted therapy.
This was a personal frustration for me, as I had my dad's tumor sequenced for genetic mutations, but there were no targeted therapies that reach the brain in high enough concentrations to be effective.
This is an important distinction in personalized medicine specific to brain cancer, as many drugs do not cross the blood brain barrier. For example the most prevalent mutation in Glioblastoma is a mutation in the Epidermal Growth Factor Receptor (EGFR) pathway. This is also a driver mutation in lung cancer that has approved targeted therapies like Afatinib.
Unfortunately none of these drugs have worked in trials for brain cancer, in large part because they do not cross the blood brain barrier well. For example here is a study recently published on the poor results with Afatinib in brain tumor patients.
http://www.ncbi.nlm.nih.gov/pubmed/25140039
There are currently very few targeted therapies in development for brain cancer as it is a small disease for pharma companies to market drugs.
This is why our approach is to correlate tumor response to existing therapies with empirically identified biomarkers. In contrast using tumors alone to predict cancer biomarkers is challenging because it requires a hypothesis or target to be tested one at a time with a novel therapy in a trial.
For underserved diseases like glioblastoma, this could result in many years passing by as each hypothesis driven therapy fails in clinical trials.
Definitely not a stupid question it is really important!
There are several novel ways to deliver drugs to tumors in the brain in trials. Some use specially designed pumps, nanotechnology, and intra-arterial delivery (where a catheter is snaked to the tumor through blood vessels).
The problem is that all of these methods are experimental so they can't be used in combination. We're very hopeful that they are successful though!
Glioblastoma was actually the first tumor profiled for the Cancer Genome Atlas Project. http://cancergenome.nih.gov/cancersselected/glioblastomamult...
There have been four subtypes found in the disease, but all four are still treated ineffectively with the same standard of care chemotherapy (Temozolomide). To date none of these predictive biomarkers have led to a successful targeted therapy.
This was a personal frustration for me, as I had my dad's tumor sequenced for genetic mutations, but there were no targeted therapies that reach the brain in high enough concentrations to be effective.
This is an important distinction in personalized medicine specific to brain cancer, as many drugs do not cross the blood brain barrier. For example the most prevalent mutation in Glioblastoma is a mutation in the Epidermal Growth Factor Receptor (EGFR) pathway. This is also a driver mutation in lung cancer that has approved targeted therapies like Afatinib.
Unfortunately none of these drugs have worked in trials for brain cancer, in large part because they do not cross the blood brain barrier well. For example here is a study recently published on the poor results with Afatinib in brain tumor patients. http://www.ncbi.nlm.nih.gov/pubmed/25140039
There are currently very few targeted therapies in development for brain cancer as it is a small disease for pharma companies to market drugs.
This is why our approach is to correlate tumor response to existing therapies with empirically identified biomarkers. In contrast using tumors alone to predict cancer biomarkers is challenging because it requires a hypothesis or target to be tested one at a time with a novel therapy in a trial.
For underserved diseases like glioblastoma, this could result in many years passing by as each hypothesis driven therapy fails in clinical trials.