Biology Reference
In-Depth Information
future where we will be able to utilize targeted therapies much more effectively in cancer
patients, and will be able to identify mechanisms of drug resistance and areas of unmet need
much more quickly. However, it is sobering to realize how difficult it has been to develop
biomarkers that actually impact clinical practice. Currently, most of our successful exam-
ples in oncology are limited to a few drugs inhibiting signal transduction pathways. These
drugs have become models for biomarker strategies and have several unique advantages
that are not always available for other classes of drug. So, what makes signal transduction
pathways good opportunities for biomarker development? Signal transduction pathways
are often activated by 'driver' mutations that can be easily detected by sequencing, PCR or
FISH. Consequently, it is possible to surmise that those tumor cells with the driver mutation
may be susceptible to a therapeutic intervention that blocks signaling through this pathway
identified by the cognate mutation. For example, the 50% of melanomas with BRAF signaling
activated by the V600E mutation will respond to inhibition of BRAF by vemurafenib, while
this drug will have no effect on melanomas without BRAF activation. So, pathway activation
biomarkers have two enormous advantages for the development of predictive biomarkers.
First, the markers are easy to identify by scanning for recurrent mutations at the target gene
or downstream signal transduction genes, and, secondly, there is an enormous difference in
effect size between the subgroups which enables the development of highly predictive mark-
ers to differentiate the clinical response between the marker positive and negative classes.
The development of molecular profiles has proved particularly challenging for oncology
drugs. Indeed, the only FDA approved molecular profiles are for prognostic markers and not
predictive biomarkers. These prognostic tests (MammaPrint - Agendia, Tumor of Origin -
Pathwork Diagnostics, and Allomap - XDx) were all developed against large retrospective
databases with known disease outcome or tumor of origin; data sets that do not exist for
first-in-class drugs during development. A further complexity for the development of oncol-
ogy drugs is that the endpoint changes during development. Early clinical studies are done
using response (or tumor shrinkage) as the primary endpoint. Late development and regis-
trational trials are done using progression-free survival (PFS) or overall survival (OS) as end-
points. Unfortunately, predictive markers for response developed in the early clinical trials
have often not predicted PFS or OS accurately.
Accelerating personalized healthcare for cancer patients will require thoughtful implemen-
tation of new technologies (notably NGS) in larger, innovative clinical trials. We are entering a
positive reinforcement cycle where new targeted therapies drive the need for more predictive
biomarkers and more predictive biomarkers create niches for new targeted therapies. Today,
we have 12 targeted drugs in approved in oncology against only receptor tyrosine kinase
or signal transduction targets. Ongoing Phase IIB and III clinical trials will likely lead to the
approval of many more targeted therapies in the next few years, resulting in the need to profile
all cancer patients to determine eligibility for treatment with a targeted therapy. Once on treat-
ment with a targeted therapy, it will become important to use serum DNA and CTCs to moni-
tor treatment response and the development of acquired drug resistance and to alter therapy
accordingly. Cancer gene panel, exome, and eventually whole genome sequencing will soon be
routinely applied to all metastatic cancer patients, and we will be able to determine the success
of personalized healthcare strategies in oncology by demonstrating increased PFS and OS for
patients being treated with targeted therapies guided in real time by predictive biomarkers.
Search WWH ::
Custom Search