Additional genomics research has identified JAK2 gene
rearrangements in leukemia cells from other children with
ALL ( 106). However, before ruxolitinib can become part of
the standard treatment for children with this genomically
defined form of ALL, it must be proven to be effective in
well-designed, well-conducted clinical trials.
The advent of technologies that allow researchers to
interrogate all of the changes in a patient’s cancer at one
time and to look at all of the proteins in a diseased or healthy
tissue simultaneously has revolutionized cancer research
and is poised to do so for other diseases as well. Physicians
and researchers are beginning to apply the knowledge
gained from this research and use it to benefit patients like
Luke Theodosiades, as well as Zach Witt, Warren Ringrose,
Rita Porterfield, and Maryann Anselmo [all of whom were
featured in the AACR Cancer Progress Report 2015 ( 24)].
However, as we generate more data about all aspects of a
patient’s cancer and look to integrate this with the patient’s
baseline and long-term medical information, it becomes
difficult to convert all of these various data into effective
treatment decisions, because physicians are literally
swimming in a sea of data. The enormous amount of data is
both the problem and a potential solution (see Figure 11, p. 60).
Recognizing this paradox, several groups have
independently started different efforts to address this
challenge posed by the explosion of genomic information
and the ability to link it to the clinical outcomes of the
patients whose tumors have been genetically sequenced.
Many of these groups are in the early stages of developing
The analysis of the treasure trove of sequencing data has
also revealed that the majority of tumors carry mutations
that occur very infrequently. If we are to discover which of
these mutations actually fuel tumor growth and to develop
precision therapeutics that target the consequences of
these mutations, many more patient samples will need
to be sequenced.
In fact, a comprehensive analysis estimated that to discover
all mutations that generate potential therapeutic targets
in a patient population would require several thousand
patients each with the same host of mutations ( 111). This
analysis underscores the need for even more and bigger
data than we currently have, as well as the tools necessary
to convert the data into real knowledge that could inform
Phase I studies are designed to determine the optimal dose of an investigational
therapy and how humans process it, as well as to identify any potential toxicities.
These first-in-human studies can also demonstrate early efficacy, or clinical results.
Phase II studies are designed to determine initial efficacy of an investigational therapy
in a particular disease or selected group of patients, in addition to continually
monitoring for adverse events or potential toxicities.
Phase III studies are large trials designed to determine therapeutic efficacy
as compared to standard of care (placebos are rarely used in cancer clinical trials).
When successful, the results of these trials can be used by regulators to approve
new therapeutics or new indications for existing therapeutics.
Phase IV studies are also known as post-marketing studies. They are conducted
after a therapy is provisionally approved by the FDA and provide additional
effectiveness or “real-world” data on the therapy.
PHASES OF CLINICAL TRIALS
Clinical trials evaluating potential new anticancer therapeutics have traditionally been done in successive
phases, each with an increasing number of patients.
Adapted from ( 1)