NcXHF

THE FUTURE OF MOLECULAR MEDICINE
phomas. Biopsies from approximately 3,000 patients will undergo
testing for abnormalities that may respond to targeted
drug therapies. Of these patients, up to 1,000 will then participate
in phase II clinical trials of targeted drug therapies.
The patients will be matched to the drug based solely on the genetic
abnormality, not on the type of cancer. NCI MATCH is a
master trial, meaning that new drugs can be added to the trial at
any time. The primary endpoint for this trial is response, however,
progression-free survival will also be assessed. 14
A new public/private cooperative effort is Lung-MAP
(Lung Master Protocol). This study will enroll 500 to 1,000
patients diagnosed with advanced, previously treated squamous
cell lung cancer each year. The molecular profıling is
done with a central commercial panel and places patients
into different arms with biologic therapies. One feature of
this study is that patients who do not meet the criteria for
treatment with a targeted therapy are placed into a trial involving
a nontargeted investigational treatment. The primary
objective of this trial is to determine if the effıcacy of
targeted therapy is better than that of standard therapy. 15
M-PACT
In the NCI-sponsored M-PACT (Molecular Profıling–Based
Assignment of Cancer Therapeutics trial), patients with advanced
tumors that have progressed on at least one line of
standard therapy undergo tumor biopsy to determine if a
mutation is present. Those who do not have an identifıable
mutation are removed from the trial. Those who do have
a mutation are randomly assigned to receive either treatment
with a drug known to target their mutation or treatment with
a drug not known to target their mutation. Cross-over is allowed
for those who experience disease progression after receiving
treatment with a drug not known to target their
mutation. Tumor response and 4-month progression-free
survival are the endpoints in this trial. Accrual to this trial
began in 2014 and approximately 700 patients are expected to
be screened, with over 150 to be enrolled in a treatment arm.
The goal is to determine whether therapies targeting a mutation
can work in the metastatic setting. 16
There is a need to test the approach in different settings,
leading to many umbrella and master studies required to answer
the overarching question of whether therapies targeting
molecular aberrations lead to better outcomes for patients over
standard chemotherapy, and in what types of patients, tumors,
or aberrations these treatments work (or do not work). The importance
of these efforts is not only the testing of the specifıc
targeted therapies, but the collection and storage of centralized
tissue and molecular data. These collections will allow large
amounts of data to be analyzed with the hope of someday developing
predictive treatment models for patients.
BIG DATA
Big data is defıned as any voluminous amount of structured,
semi-structured, and unstructured data that has the potential
to be mined for information. More specifıcally, big data is any
data whose scale, diversity, and complexity require new architecture,
techniques, algorithms, and analytics to manage it
as well as to extract value and hidden knowledge from it. Big
data expands across four fronts: velocity, variety, volume,
and veracity (Table 2). 17,18
Capturing big data in databases may help formulate hypotheses
for testing. Statistical testing can be performed on preexisting
data to facilitate this process. However, big data
collection can be compromised by bias in medical records, lack
of data validity and reliability, and technology challenges. The
potential for misinterpretation of the data is paramount. Additionally,
the structure of electronic medical records may be
poorly suited for adequate data abstraction and are certainly not
suited for numerous secondary analyses. Many institutions also
have different platforms, which can impede data integration.
Attention to privacy, sharing, transparency, and stewardship are
all guiding principles for big data collection and analysis.
Medicine, specifıcally cancer medicine, is encountering
this dilemma. As the amount of information exponentially
increases (patient clinical information, pathology, biomarkers,
treatment outcomes, and patient questionnaires), the
prospect of harnessing and processing this data is daunting.
However, the potential rewards make it worth the effort. Numerous
companies and researchers are working to integrate
and interrogate these data sets with expectations that they
will identify new opportunities for treatment, diagnosis,
prognosis, and prevention. Nontraditional groups are joining
the foray into this area, including Google and fınancial
institutions, because of their ability to collect a variety of data
on inordinately large numbers of individuals and variables
(e.g., search, purchasing, and other behaviors).
ASCO CANCERLINQ
The American Society of Clinical Oncology (ASCO) has
established an important initiative called CancerLinQ TM
(www.CancerLinQ.org), a health information technology
TABLE 2. The Four Fronts of Big Data
Fronts
Velocity
Variety
Volume
Veracity
Description
The velocity of cancer medicine is ever increasing as the
number of patient encounters, molecular tests, and
treatments grows.
Data platforms in cancer medicine continue to rapidly
evolve. The electronic health record is becoming a
better resource as a database. More and more
hospital systems are incorporating telemedicine which
enlist mobile connectivity. Archived data mining is
now becoming active data searches.
The amount of information that is being generated by
cancer medicine is being measured in the scope of
peta- and exabytes. Storage solutions must be
forward-thinking, including current cloud sourcing.
The reliability of data sources in the health care setting
is not always ideal, as most systems are not set up
for research. Large data sets can be subject to bias.
We should be prepared to acknowledge the limitations
of our data.
asco.org/edbook | 2015 ASCO EDUCATIONAL BOOK 25