1446 Commentaries | JNCI Vol. 101, Issue 21 | November 4, 2009
Many cancer patients do not benefit from the systemic treatments
they receive. For example, adjuvant chemotherapy that is considered
highly effective may often improve the disease-free or overall survival
rate by only 5 – 10 percentage points. Also, chemotherapy for meta-
static disease often provides sustained benefit for a small portion of the
patients treated. Therefore, the practice of oncology has been very
inefficient, with exposure of far more patients than will benefit to the
cost and toxicity of these agents . Although this overtreatment is un-
derstandable in dealing with life-threatening diseases, the ability to
better “personalize” treatment decisions could have important bene-
fits for patients as well as medical costs. In spite of developments in
biotechnology and genomics, the pace of acceptance of new markers
to inform treatment decisions for patients with cancer has been slow.
The limited introduction of effective biomarkers is partly because of
the substantially lower reimbursement for tumor marker tests, as
compared with therapeutics by health insurers, but is also because of
a shortage of prospective studies of marker utility and the lack of re-
producibility and reliability among the many published retrospective
studies of prognostic and predictive markers ( 1 , 2 ).
Several committees and authors have proposed specifi c guidelines
that might be used to evaluate and report a given marker. For ex-
ample, in 1996, the members of the American Society of Clinical
Oncology Tumor Markers Guidelines Committee recommended fi ve
Levels of Evidence (LOEs) that might be used to determine the clin-
ical utility of a tumor marker ( 3 ). This LOE scale has been widely
cited and used as a template for deciding whether to recommend the
use of a tumor marker in clinical practice and for design and conduct
of tumor marker studies ( 4 , 5 ). The criteria for reporting the results of
marker studies (designated the REMARK criteria) have been pub-
lished in several journals, and at least a few journals have incorporated
REMARK into the required submission format ( 6 , 7 ).
In this article, we will address the nature of the methodological
diffi culties involved in studying tumor markers, both prognostic
(ie, predictive of prognosis, independent of treatment) and predic-
tive (ie, in terms of best choice of therapy). We will also propose
that there are conditions in which archived specimens can be used
to provide reliable evaluations of the clinical validity or medical
utility of prognostic and predictive biomarkers.
Prospective Randomized Trials to Address
Tumor Marker Utility
The gold standard for establishing clinical utility of a new medical
intervention is the prospective randomized clinical trial. Several
authors have proposed prospective randomized clinical trial designs
for evaluation of prospective or predictive diagnostic markers
( 8 – 13 ). In the latter circumstance, the medical utility of the candi-
date predictive biomarker can be established by evaluating the
benefit of the new drug according to marker status (positive or
negative) in adequately sized patient subgroups using a prospec-
tively specified analysis plan within a randomized clinical trial that
compares a regimen containing the new drug to a control.
One might consider a prospective clinical trial in which the test
itself is the investigational intervention to be the ultimate validation
Affiliations of authors: Biometric Research Branch, National Cancer Institute,
Bethesda, MD (RMS ); Division of Pathology, National Surgical Adjuvant
Breast and Bowel Project , University of Pittsburgh, Pittsburgh, PA (SP);
Breast Oncology Program, University of Michigan Comprehensive Cancer
Center, Ann Arbor, MI (DFH ) .
Correspondence to: Richard M. Simon, DSc, Biometric Research Branch,
National Cancer Institute, 9000 Rockville Pike, Bethesda, MD 20892-7434
(e-mail: email@example.com ).
See “Funding” and “Notes” following “References.”
Published by Oxford University Press 2009.
Advance Access publication on October 8, 2009.
Use of Archived Specimens in Evaluation of Prognostic and
Richard M. Simon , Soonmyung Paik , Daniel F. Hayes
The development of tumor biomarkers ready for clinical use is complex. We propose a refined system for biomarker study
design, conduct, analysis, and evaluation that incorporates a hierarchal level of evidence scale for tumor marker studies, in-
cluding those using archived specimens. Although fully prospective randomized clinical trials to evaluate the medical utility of a
prognostic or predictive biomarker are the gold standard, such trials are costly, so we discuss more efficient indirect “prospective –
retrospective” designs using archived specimens. In particular, we propose new guidelines that stipulate that 1) adequate
amounts of archived tissue must be available from enough patients from a prospective trial (which for predictive factors should
generally be a randomized design) for analyses to have adequate statistical power and for the patients included in the evalua-
tion to be clearly representative of the patients in the trial; 2) the test should be analytically and preanalytically validated for use
with archived tissue; 3) the plan for biomarker evaluation should be completely specified in writing before the performance of
biomarker assays on archived tissue and should be focused on evaluation of a single completely defined classifier; and 4) the
results from archived specimens should be validated using specimens from one or more similar, but separate, studies.
J Natl Cancer Inst 2009;101: 1446 – 1452
JNCI | Commentaries 1447
of a prognostic or predictive tumor marker. That is, a trial may be
designed so that a patient ’ s care would be determined based on ran-
dom assignment to use the test or not, as referred to as the marker
strategy design by Simon and Wang ( 14 ). In such a trial, treatment
decisions are made for patients who are randomly assigned to the
control group using standard prognostic factors and practice guide-
lines. For patients who are randomly assigned to the investigational
group, the test, or marker, is used in treatment determination, per-
haps in conjunction with standard prognostic factors. The test
would be performed only for patients who are randomly assigned to
the test group, and the trial would be evaluated by comparing out-
comes overall for the two randomization groups. The outcomes
must be compared overall because the new test is not used for the
“control” group. In many cases, this restriction seriously limits the
information that can be gleaned from the design. Results can be
particularly confounded and diluted in cases where the standard of
care is variable among physicians.
The marker strategy design is also generally very ineffi cient in
terms of the number of patients required for randomization.
Sample size requirements for randomized clinical trials are often
proportional to the reciprocal of the square of the size of the treat-
ment effect to be detected with a specifi ed statistical power. For
the marker strategy design, only the overall treatment effect
between the two randomized groups can be evaluated, and the size
of that effect is generally quite small because many patients will
receive the same treatment regardless of the group to which they
are randomized. If the analysis is to demonstrate that withholding
a standard therapy for test-negative patients is not inferior, then
sample size problems are compounded, and even with a huge
sample size, the results are unlikely to be convincing.
An alternative approach requires that all patients be tested for
marker status “upfront.” In this case, the evaluation can be focused
on subsets of patients for whom the treatment assignment that is
based on the test differs from treatment assignment that is based
on standard of care. For example, suppose the standard of care is
to use chemotherapy for stage II patients but not for stage I
patients and the test purports to identify patients who are likely to
benefi t from chemotherapy regardless of stage; test-positive
patients will receive chemotherapy and test-negative patients will
not. In this case, the only patients randomly assigned are stage I
patients with a positive test and stage II patients with a negative
test. The design enables the effectiveness of chemotherapy to be
evaluated separately for these subsets of patients. This design pre-
sumes, however, that the standard of care, as a function of standard
prognostic variables, is determined.
This strategy of testing all patients up-front is used by two
current clinical trials, the Microarray in Node-Negative Disease
may Avoid Chemotherapy (MINDACT) study in Europe ( 15 ) and
the Trial Assigning Individualized Options for Treatment (Rx)
(TAILORx) study in North America ( 16 ). Although the designs of
both trials are complex and somewhat different, they both address
the medical utility of withholding standard of care chemotherapy
from women with node-negative estrogen receptor – positive breast
cancer who have a predicted low risk of recurrence, based on a
predefi ned gene expression – based risk score. The MINDACT
study evaluates a 70-gene classifi er, and the TAILORx study eval-
uates a 21-gene classifi er. Even though these designs are more
effi cient than the randomized marker strategy trial design, both of
these studies will require many thousands of patients, and nearly a
decade each from the time, accrual was begun until the fi rst results
are anticipated. The TAILORx and MINDACT studies will cost
millions of dollars or Euros to conduct, and with the current speed
of the evolution of technology, the test being evaluated may have
become obsolete by the time such studies are completed.
It is common for a new marker to be identifi ed after the defi n-
itive trials have demonstrated benefi t for a specifi c agent or class of
agents or even type of modality (such as chemotherapy in general).
We maintain that, in many cases, it may be possible to use archived
specimens collected in the past from appropriate previously con-
ducted therapeutic trials and to preserve the focus, control of type
I error, and statistical power of properly designed fully prospective
studies. Indeed, when there is substantial preliminary evidence that
a new marker predicts benefi t from a specifi c drug, it may some-
times be possible to assay the marker in archived specimens from
randomized clinical trials that were conducted to evaluate the
drug, as was done for KRAS in colorectal cancer ( 17 , 18 ).
When suitable archived tissue is available and can be used reli-
ably, it can facilitate and expedite delivery of valuable cancer diag-
nostics that may be of considerable benefi t to patients. Nonetheless,
there are certainly also risks to patients from the unreliable use of
archived tissues. We have tried here to clarify the key features
involved in using these resources in a reliable manner, and we
propose a refi nement to the previously published LOE scale that
permits a more critical analysis of the quality of tumor marker
studies using archived specimens.
Prospective vs Retrospective Studies: A
Matter of Semantics
Although biomedical scientists and biostatisticians are taught
that “prospective” studies are preferable to “retrospective”
studies, the distinction between prospective and retrospective is
often confused with the distinction between “experimental” and
“observational.” We propose that for studies of prognostic and
predictive biomarkers in oncology, the term retrospective is in
some cases misleading.
In cancer epidemiology, both retrospective case – control studies
and prospective cohort studies are observational, rather than ex-
perimental, studies. Neither type of study involves random assign-
ment of exposure, and hence, observed associations between
exposures and disease do not provide as strong a basis for claims
of causality as in experimental studies. The most serious limitation
of epidemiological studies is their nonexperimental nature, not
whether they are retrospective or prospective.
In therapeutics, many retrospective analyses are also nonexper-
imental, with treatment selection based on patient factors and re-
ferral pattern rather than on randomization. Such studies are also
often conducted without a written protocol and are unfocused,
with numerous patient subsets and endpoints compared without
control for the overall chance of a false-positive conclusion. In
contrast, prospective randomized clinical trials contain internal
control of treatment assignment, careful and proscribed data col-
lection (including outcomes and endpoints), and a focused analysis
plan that is developed before the data are examined.
1448 Commentaries | JNCI Vol. 101, Issue 21 | November 4, 2009
Many biomarker studies are conducted with convenience sam-
ples of specimens, which just happen to be available and are
assayed for the marker, with no prospectively determined subject
eligibility, power calculations, marker cut-point specifi cation, or
analytical plans. Such studies are very likely to result in highly
biased conclusions and truly deserve to be pejoratively labeled as
“retrospective.” However, if a “retrospective” study is designed to
use archived specimens from a previously conducted prospective
trial, and if certain conditions are prospectively delineated in a
written protocol before the marker study is performed, we argue
that it might be considered a “prospective – retrospective” study.
Such a study should carry considerably more weight toward deter-
mination of clinical utility of the marker than a simple study of
convenience, in which specimens and an assay happen to be avail-
able. Having multiple studies of different candidate biomarkers
based on archived tissues from the same prospective trial would,
however, present a greater opportunity for false-positive conclu-
sions than a single fully prospective trial focused on a specifi c
biomarker. Consequently, independent confi rmation of fi ndings
for specifi c biomarkers in multiple prospective – retrospective
studies is important (see below).
Using Archived Tissue to Establish the
Medical Utility of a Marker
In assessing the use of archived specimens in the evaluation of
prognostic and predictive biomarkers, it is useful to consider
the three requirements for clinical acceptance of a tumor
marker that were first proposed by Henry and Hayes ( 2 ): 1) the
specific setting and utility of the marker must be clear, 2) the
magnitude in either outcomes or treatment effects between
those patients who are “positive” for a marker must be suffi-
ciently different from those who are “negative” for that marker
that the clinician and/or patient would accept different treat-
ment strategies for the two patients, and 3) the estimates of that
magnitude must be reliable.
These criteria relate to establishing the clinical utility of the
marker. It is useful to clarify the use of the term “validation” as
applied to diagnostic tests. Hunter et al. ( 19 ) distinguished three
types of validity in terms of genetic tests: “First, there is the ques-
tion of a test’s analytic validity, its ability to accurately and reli-
ability measure the genotype of interest . . . . Second, one must
consider clinical validity, or the ability of the test to detect or pre-
dict the associated disorder . . . . Finally, there is the issue of the
test’s clinical utility, or the balance of its associated risks and ben-
efi ts if it were to be introduced into clinical practice.” Clinical
utility requires that the test is “actionable,” that the clinical context
and medical indication for use of the test is clear, and that the
magnitude of outcomes or treatment effects associated with dif-
ferent results of the test are suffi ciently great as to infl uence treat-
ment decisions. A serious defect of most retrospective studies of
prognostic markers is that the patients are not selected for address-
ing a defi ned medical indication for use of the marker. Such studies
may establish a correlation with clinical outcome but not the med-
ical utility of the marker.
The consideration of reliably establishing the magnitude of
marker effect may be further divided into the following three
conditions: 1) the technical and analytical properties of the marker
assay must be accurate and/or robust and reproducible; 2) the clin-
ical study design and analysis must be appropriate and adequate to
address the utility of a precise intended clinical use; and 3) the
results should be verifi ed, or validated, in more than one study set,
with similar estimates of the magnitude in separate populations of
patients that resemble each other. Each of these conditions is po-
tentially subject to considerable bias in most retrospective studies
using archived specimens, especially those of convenience. Even if
the investigation is a prospective – retrospective study, careful at-
tention to each of these concerns will reduce the bias and inconsis-
tent results obtained with studies of convenience, and we believe
that it will further hasten the introduction of useful tumor markers
into clinical practice.
“Analytical validation” generally refers to reproducibility and
robustness of the test or assay value. This generally includes
minimizing variation with regard to both preanalytical factors,
such as tissue collection, processing, storage, and preparation, as
well as analytical factors, such as reagent choice, incubation time
and conditions, and method of readout (including cut-point
determination) ( 20 , 21 ).
For a clinical biomarker evaluation using archived tissues to be
interpretable, it is necessary that the assay results from the archived
sample refl ect what would happen in a true clinical setting. The
following are examples of how archived tissue might differ from
true clinical specimens.
1) Preanalytical issues. It is possible that samples collected in
the past, and specifi cally for the bank in hand, might be handled
differently than they are in current practice. Examples of differ-
ences might include whether a precollection diagnostic biopsy
was performed (which might affect various gene expression and
tissue processes), the time after the sample was removed from the
patient and processed (fi xed, frozen, etc), procedures for fi xation
or freezing, how the sample was stored (temperature, exposed to
room air, as a tissue block or a section on a slide, etc), and how
many cycles it was frozen and thawed.
2) Analytical issues. For a tumor marker study to be suffi cient
to change clinical practice, the test itself should be ready for
clinical practice. For studies to change clinical practice, the inves-
tigator should carefully and prospectively plan to use reagents,
conditions, and cut points that have been previously determined
to be accurate and reproducible. These considerations include
fi xed reagent supply sources, concentrations, and incubation times
among many other possible variables. In addition, the investi-
gator should have demonstrated with statistical confi dence the
analytical concordance of results between archived specimens
and clinical samples for that specifi c assay. Examples of these
concerns include whether the sample was prepared for analysis
in a tissue microarray or as a whole section, and whether and
how it was subjected to antigen retrieval.
As a precaution against bias that may result from incomplete
analytical and preanalytical validation, marker studies using
archived specimens should have the assays performed blinded to all
clinical data, including treatment and patient outcome.
JNCI | Commentaries 1449
Clinical Study Design
As noted in the first required condition, the investigator should have
a clear idea of the specific intended use for the assay. In general, this
will be as a prognostic factor to decide if any further treatment is
necessary or as a predictive factor to determine whether a particular
type of therapy is likely to be effective. To establish medical utility of
a prognostic marker, a randomized trial is sometimes not necessary.
For example, a prospective single-arm trial in which chemotherapy is
withheld from patients at a low risk of recurrence is used in the por-
tion of the TAILORx clinical trial designed to validate the very favor-
able prognostic outcomes in the low recurrence score population.
Assuming that preanalytical factors are well controlled and match
current practice activities and that the clinical data are collected in a
fashion typical of a clinical trial, archived tissue from a sufficiently
large population of untreated patients may be adequate to permit ac-
curate estimates of recurrence based on tumor marker subgroups for
determination of clinical utility of the marker.
Tumor response data from a single-arm phase II clinical trial of
a specifi ed treatment can be used to establish the clinical validity of
Table 1 . Elements of tumor marker studies that constitute Levels of Evidence determination *
Clinical trial PCT designed to address
Prospective trial not designed
to address tumor marker, but
design accommodates tumor
Accommodation of predictive
marker requires PRCT
Prospectively enrolled, treated,
and followed in clinical trial and,
especially if a predictive utility
is considered, a PRCT addressing
the treatment of interest
aspect to study
Prospectively enrolled, treated,
and followed in PCT
in registry, but
stipulation of treatment
or follow-up; patient
data collected by
processed and archived
with no prospective
and assayed for
in real time
processed, and archived
prospectively using generic
SOPs. Assayed after trial
using generic SOPs.
Assayed after trial
Study not prospectively
powered at all.
design confounded by
Focused analysis plan
for marker question
Result very likely to be
play of chance
Study powered to address
tumor marker question
Study powered to address
therapeutic question and
underpowered to address
tumor marker question
Study not prospectively
powered at all.
design confounded by
selection of specimens
Focused analysis plan for
marker question developed
before doing assays
No focused analysis
plan for marker question
developed before doing
ValidationResult unlikely to be play of
Result more likely to be play
of chance that A but less likely
Requires one or more
Result very likely to be
play of chance
* PCT = prospective controlled trial; PRCT = prospective randomized controlled trial; SOPs = standard operating practices.
a biomarker for predicting response to that treatment, but a larger
randomized trial with a survival or progression-free survival end-
point is generally required to establish the medical utility of the
Suggested Revision of LOEs
In the original American Society of Clinical Oncology LOE scale,
“retrospective studies” were determined to be LOE II or worse ( 3 ).
We now propose an updated revision of the LOE scale, in which
more precise definitions are provided for the types of studies that
might be used to analyze the clinical utility of a biomarker and in
which retrospective studies using archived specimens might reach
level I evidence. The LOE for the medical utility of a biomarker
relates to key factors involving patients, specimens, assays, and statis-
tical analysis plans ( Tables 1 and 2 ).
Scientifi cally, the clinical utility of a biomarker in a particular
situation is best addressed by a prospective randomized clinical trial
( Table 1 , category A). Patients are entered, treated, and followed
1450 Commentaries | JNCI Vol. 101, Issue 21 | November 4, 2009
prospectively according to a prewritten protocol; the study is pro-
spectively powered specifi cally to address the tumor marker ques-
tion; and specimens are collected, processed, and assayed for the
marker in real time. The randomized trial will generally not use a
“marker strategy design” as described above, however, because of
the serious limitations of that design. Although further confi rmation
in a separate trial of the results gained from a category A prospective
trial is always welcome, compelling results from such a trial would
be considered defi nitive and no other validating trial would be
required. This strategy was included in the original LOE scale pro-
posed by American Society of Clinical Oncology as LOE I and
continues to be the “gold standard.”
In the revised LOE scale, a second strategy to obtain level I data
would be to perform a tumor marker study using archived speci-
mens from a prospective trial that addresses a therapeutic question
(or another marker question) and accommodates the current
marker question ( Table 1 , category B). To evaluate prognostic
markers that are intended to identify patients for whom prognosis
is so good that further therapy would be withheld, the clinical trial
in some cases may not need to be randomized. For example, in the
TAILORx study, the low recurrence score group receives only
endocrine therapy and is followed to determine if risk of recur-
rence is as low as predicted by the 21-gene recurrence score. To
evaluate a predictive marker, the prospective trial would generally
need to be a randomized trial that compares the treatment with an
appropriate control treatment. As in study design A, patients are
prospectively enrolled, treated, and followed, and specimens are
prospectively collected, processed, and archived using generic
standard operating procedures. The tumor marker question might
be identifi ed during the conduct of the trial or after its completion,
but the specifi cation of the tumor marker hypothesis should be
based on results completely external to the trial. In fact, tissues
archived from the trial should not be assayed until a new protocol
has been written that focuses on the evaluation of the specifi ed new
marker with a completely specifi ed statistical analysis plan. Before
undertaking the study, the assay should be analytically and preana-
lytically validated for use with archived tissue, and the assay should
be performed blinded to the clinical data. Because the trial was
designed to address the therapeutic question, it will often be
underpowered to establish the statistical signifi cance of treatment
by marker interaction ( 22 ). It may, however, be adequately sized to
reliably identify a large treatment effect in “test-positive” patients,
as might be expected for a predictive biomarker. Nevertheless,
even with these caveats, results from such a study will be more
likely to arise from chance than those from a fully prospective
It is clearly desirable that the available specimens from the
archived bank should be representative of the patients who were
accrued to the study as a whole, although there is no guarantee that
the study patients are themselves representative of the general
population of patients. Although there are no minimal require-
ments that can be universally applicable, we suggest that the cor-
relative study should include at least two-thirds of the total accrued
patients or that the patients be selected in a way that strives to
avoid selection bias. For example, if the investigator wishes to
minimize resource utilization, or wishes to use intrastudy specimen
sets for test and validation, one might use a mathematical random-
ization scheme to select a sample of specimens for study that
mirror the known important prognostic and predictive factors of
the population as a whole ( 5 ).
For a category B study to be suffi cient to change practice, we
maintain that the results must be confi rmed using specimens from
a second category B study based on archived tissue from a different
trial that has been designed, conducted, and analyzed in a similar,
if not identical, manner. The results of these two studies must be
equally compelling to change clinical practice. Furthermore, these
validation studies need to be performed using the same assay or
similar assays that clearly identify the same marker. For example,
different investigators have used several different assays for p53
status, including direct sequencing for genetic abnormalities, im-
munohistochemistry to determine protein expression, or even
functional assays. These assays provide very different indications of
p53, and therefore, the available data are very diffi cult to interpret
( 5 ). Validation studies must also address the same endpoint and
that endpoint should refl ect medical utility.
Using nearly 1500 archived specimens collected within a pro-
spective randomized clinical trial, Hayes et al. ( 23 ) reported that
node-positive, estrogen receptor – positive, and human epidermal
growth factor receptor 2 – negative patients did not appear to ben-
efi t from addition of adjuvant paclitaxel chemotherapy after four
cycles of doxorubicin and cyclophosphamide. Although these ob-
servations were provocative, results from a completely separate,
but similarly designed, prospective randomized clinical trial did
not confi rm these fi ndings ( 24 ), and the question regarding selec-
tion of patients for adjuvant paclitaxel remains open ( 25 ). Thus,
this issue is still considered to be LOE II in Table 2 . By contrast,
the recently observed association of presence of KRAS mutations
with lack of benefi t from monoclonal antibodies directed against
the epidermal growth factor receptor , such as cetuximab and pani-
tumumab ( 17 , 18 ), provides an example of successful use of cate-
gory B archived samples to establish medical utility. Several
prospective randomized trials have demonstrated a small but sta-
tistically signifi cant benefi t from these antibodies, either alone or
in combination with chemotherapy, for treatment of patients with
advanced colorectal cancer ( 26 ). Preliminary, LOE II or III studies
suggested that cetuximab and panitumumab are only active in
Table 2 . Revised determination of Levels of Evidence using
elements of tumor marker studies *
One or more with consistent
None or inconsistent results
2 or more with consistent
None or 1 with consistent
results or inconsistent
IV – VD
* Levels of Evidence (LOEs) revised from those originally proposed by Hayes
et al. ( 3 ).
† NA = not applicable because LOE IV and V studies will never be satisfactory
for determination of medical utility.
JNCI | Commentaries 1451
patients whose cancers carry a wild-type KRAS ( 27 ). These data
have now been validated in a retrospectively performed study using
archived samples from large prospectively randomized clinical
trials and therefore would achieve LOE I in our modifi ed scale
( Tables 1 and 2 ) ( 28 ).
Category C ( Table 1 ) biomarker studies use prospective patient
registries in which subjects are treated and followed according to
standards of care. Specimens are collected, processed, and archived
prospectively, using generic standard operating procedures, but
are assayed after the study has completed patient accrual. Tumor
marker studies conducted using these specimens are often not
prospectively powered at all. Because of the lack of control of
treatment assignment, specimen collection, and data collection,
such settings are generally more susceptible to selection biases for
patients, specimens, and clinical data that include outcomes. This
concern may not be the case in some tightly controlled population-
based registries. Category C studies are more likely confounded by
unrecognized biases, and their results are more likely to result
from chance than those of categories A and B. Category C studies
may be validated to LOE II if two or more subsequent studies
provide similar results ( Table 2 ). However, it is unlikely that cate-
gory C studies would ever be suffi cient to change practice, except
under particularly compelling circumstances.
Category D studies ( Table 1 ) are the most common type of
reported tumor marker analyses: studies of convenience in which
specimens were collected for unknown reasons, processed and
stored in a variety of ways, and happen to be available for assay.
The results from these types of studies are highly unstable and
likely to be because of chance alone.
Ideally, any new medical intervention will be adopted into clinical
practice only in the setting of level I evidence, and ideally, such
evidence is generated in a prospective randomized clinical trial.
However, such trials are not always practical. In the case of tumor
markers, practice guidelines and the availability of other diagnostic
procedures can sometimes make it very difficult to perform new
clinical trials because such trials may involve withholding of
therapy that is considered standard of care. Even when they are
considered ethical, such trials usually require many years to con-
duct and are quite expensive. For new drug development, in many
cases, an analytically validated companion diagnostic test will not
be available or the appropriate biological measurement may not be
clear at the time that the pivotal trials of the drug are initiated, as
for the use of KRAS mutation as a predictive biomarker for EGFR
inhibitors in colorectal cancer ( 17 , 18 , 28 ).
Archived tissue specimens from high-quality datasets can therefore
be of great importance for establishing the medical utility of a prog-
nostic or predictive biomarker. We argue that it is appropriate to use
archived tissue specimens from large prospective clinical trials to do
so. For such an evaluation to be more useful than just for generating
hypotheses, however, several conditions must be satisfi ed:
1) Archived tissue, adequate for a successful assay, must be
available on a suffi ciently large number of patients from the piv-
otal trials to permit appropriately powered analyses and to ensure
that the patients included in the biomarker evaluation are clearly
representative of the patients in the pivotal trials. Although no
minimal requirement can be stated as universally applicable, we
would suggest that samples from at least two-thirds of the
patients be available for analysis.
2) Substantial data on analytical validity of the test must exist
that ensure that results obtained from the archived specimens
will closely resemble those that would have been obtained from
analysis of specimens collected in real time. Assays should be
conducted blinded to the clinical data.
3) The analysis plan for the biomarker evaluation must be com-
pletely developed before the performance of the biomarker as-
says. Both the analysis plan for the biomarker study and the
design of the trial(s) whose samples were selected for analysis
should be appropriate for the evaluation of a companion diagnos-
tic had it been undertaken at the outset. The analysis should be
focused on a single, completely defi ned, diagnostic classifi er. For
multigene classifi ers, the mathematical form of combining the
individual components, weights, and cut points should be speci-
fi ed beforehand. In general, the analysis should not be explor-
atory, and practices that might lead to a false-positive conclusion
should be avoided.
4) The results must be validated in at least one or more simi-
larly designed studies using the same assay techniques.
Physicians need improved tools for selecting treatments for
individual patients. Cancers of the same primary site are in many
cases heterogeneous in molecular pathogenesis, clinical course,
and treatment responsiveness. Current approaches for treatment
development, evaluation, and use result in treatment of many
patients with ineffective drugs. Advances in cancer genomics and
biotechnology are providing increased opportunities for develop-
ment of more effective therapeutics and prognostic and predictive
biomarkers to inform their use. These opportunities have enor-
mous potential benefi ts for patients and for containing health-care
costs. However, the complexity of cancer biology and the increased
complexity of development of biomarkers with drugs offer formi-
dable challenges to the transition to a more predictive oncology. In
some cases, it is either ethically or practically impossible to eval-
uate the medical utility of prognostic and predictive biomarkers in
a fully prospective manner.
It is essential to ensure that cancer patients are offered the ben-
efi ts of valuable prognostic and predictive tests as soon as they are
rigorously and reliably evaluated. In this article, we have tried to
clarify some of the uncertainty in the fi eld about the validation of
prognostic and predictive biomarkers and to propose an update of
a LOE schema that has been widely used for evaluating the med-
ical utility of biomarkers in oncology. We believe that this update
is important for improving the conduct of validation studies and,
in some cases, for expediting the adoption of important diagnostic
1. Ransohoff DE . Rules of evidence for cancer molecular-marker discovery
and validation . Nat Rev Cancer . 2004 ; 4 ( 4 ): 309 – 314 .
2. Henry NL , Hayes DF . Uses and abuses of tumor markers in the diagnosis,
monitoring and treatment of primary and metastatic breast cancer .
Oncologist . 2006 ; 11 ( 6 ): 541 – 552 .
1452 Commentaries | JNCI Vol. 101, Issue 21 | November 4, 2009 Download full-text
3. Hayes DF , Bast RC , Desch CE , et al . Tumor marker utility grading
system: a framework to evaluate clinical utility of tumor markers . J Natl
Cancer Inst . 1996 ; 88 ( 20 ): 1456 – 1466 .
4. Locker GY , Hamilton S , Harris J , et al . ASCO 2006 update of recommen-
dations for the use of tumor markers in gastrointestinal cancer . J Clin
Oncol . 2006 ; 24 ( 33 ): 5313 – 5327 .
5. Harris L , Fritsche H , Mennel R , et al . American Society of Clinical
Oncology 2007 update of recommendations for the use of tumor markers
in breast cancer . J Clin Oncol . 2007 ; 25 ( 33 ): 5287 – 5310 .
6. McShane LM , Altman DG , Sauerbrei W , Taube SE , Gion M , Clark GM .
Reporting recommendations for tumor marker prognostic studies
(REMARK) . J Natl Cancer Inst . 2005 ; 97 ( 16 ): 1180 – 1184 .
7. Hayes D , Ethier S , Lippman M . New guidelines for reporting tumor
marker studies in breast cancer research and treatment: REMARK . Breast
Cancer Research and Treatment . 2006 ; 100 ( 1 ) : 237 – 238 .
8. Simon R . A roadmap for developing and validating therapeutically rele-
vant genomic classifi ers . J Clin Oncol . 2005 ; 23 ( 29 ) : 7332 – 7341 .
9. Sargent DJ , Conley BA , Allegra C , Collette L . Clinical trial designs for
predictive marker validation in cancer treatment trials . J Clin Oncol .
2005 ; 23 ( 9 ): 2020 – 2027 .
10. Freidlin B , Simon R . Adaptive signature design: an adaptive clinical trial
design for generating and prospectively testing a gene expression signature
for sensitive patients . Clin Cancer Res. 2005 ; 11 ( 21 ) : 7872 – 7878 .
11. Jiang W , Freidlin B , Simon R . Biomarker adaptive threshold design: a
procedure for evaluating treatment with possible biomarker-defi ned sub-
set effect . J Natl Cancer Inst . 2007 ; 99 ( 13 ) : 1036 – 1043 .
12. Simon R . Using genomics in clinical trial design . Clin Cancer Res.
2008 ; 14 ( 19 ) : 5984 – 5993 .
13. Simon R . Designs and adaptive analysis plans for pivotal clinical trials of
therapeutics and companion diagnostics . Expert Rev Mol Diagn . 2008 ;
2 ( 6 ): 721 – 729 .
14. Simon R , Wang SJ . Use of genomic signatures in therapeutics develop-
ment . Pharmacogenomics J . 2006 ; 6 ( 3 ) : 166 – 173 .
15. Bogaerts J , Cardoso F , Buyse M , et al . Gene signature evaluation as a
prognostic tool: challenges in the design of the MINDACT trial . Nat Clin
Pract Oncol . 2006 ; 3 ( 10 ): 540 – 551 .
16. Sparano JA . TAILORx: Trial Assigning Individualized Options for
Treatment(Rx) . Clin Breast Cancer . 2008 ; 7 ( 4 ): 347 – 350 .
17. Amado RG , Wolf M , Peeters M , et al . Wild-type KRAS is required for
panitumumab effi cacy in patients with metastatic colorectal cancer . J Clin
Oncol . 2008 ; 26 ( 10 ): 1626 – 1634 .
18. Karapetis CS , Khambata-Ford S , Jonker DJ , et al . K-ras mutations and
benefi t from cetuximab in advanced colorectal cancer . N Engl J Med .
2008 ; 359 ( 17 ): 1757 – 1765 .
19. Hunter DJ , Khoury MJ , Drazen JM . Letting the genome out of the
bottle — will we get our wish . N Engl J Med . 2008 ; 358 ( 2 ): 105 – 107 .
20. Goldstein NS , Hewitt SM , Taylor CR , Yaziji H , Hicks DG .
Recommendations for improved standardization of immunohistochem-
istry . Appl Immunohistochem Mol Morphol . 2007 ; 15 ( 2 ): 124 – 133 .
21. Goldstein NS , Hunter S , Forbes S , Odish E , Tehrani M . Estrogen re-
ceptor antibody incubation time and extent of immunoreactivity in inva-
sive carcinoma of the breast: the importance of optimizing antibody
avidity . Appl Immunohistochem Mol Morphol . 2007 ; 15 ( 2 ): 203 – 207 .
22. Peterson B , George SL . Sample size requirements and length of study for
testing interaction in a 1 x k factorial design when time-to-failure is the
outcome . Control Clin Trials . 1993 ; 14 ( 6 ) : 511 – 522 .
23. Hayes DF , Thor AD , Dressler LG , et al . HER2 and response to paclitaxel
in node-positive breast cancer . N Engl J Med . 2007 ; 357 ( 14 ) : 1496 – 1506 .
24. Martin M , Rodriguez-Lescure A , Ruiz A , et al . Randomized phase 3 trial
of fl uorouracil, epirubicin, and cyclophosphamide alone or followed by
paclitaxel for early breast cancer . J Natl Cancer Inst . 2008 ; 100 ( 11 ):
805 – 814 .
25. Hudis C , Dang C . The taxane limbo: how low can we go? J Natl Cancer
Inst . 2008 ; 100 ( 11 ): 761 – 763 .
26. Labianca R , La Verde N , Garassino MC . Development and clinical indi-
cations of cetuximab . Int J Biol Markers . 2007 ; 22 ( 1 suppl 4 ): S40 – S46 .
27. Messersmith WA , Ahnen DJ . Targeting EGFR in colorectal cancer .
N Engl J Med . 2008 ; 359 ( 17 ): 1834 – 1836 .
28. Allegra C , Jessup JM , Somerfi eld MR , et al . American Society of Clinical
Oncology provisional clinical opinion: testing for KRAS gene mutations in
patients with metastatic colorectal carcinoma to predict response
to anti-epidermal growth factor receptor monoclonal antibody therapy
[published online ahead of print] . J Clin Oncol . 2009 ; 27 ( 12 ) : 2091 – 2096 .
Supported in part by the Fashion Footwear Charitable Foundation of New
York/QVC Presents Shoes on Sale (D.F.H.).
The authors take full responsibility for the analysis and interpretation of the
data, the writing of the manuscript, and the decision to submit the manuscript
The authors would like to acknowledge very helpful discussions with their
colleagues and members of the American Society of Clinical Oncology Tumor
Markers Guidelines Committee that contributed to much of this work. D. F.
Hayes reports research and other support from Astra Zeneca, GlaxoSmithKline,
Pfi zer, Novartis, Ayerst-Wyeth, Veridex (Johnson & Johnson), and Sanofi -
Aventis, Predictive Biosciences, Curalogie SAB, Incyte Corporation, DNA
Repair, and Compendia Bioscience.
Manuscript received December 5 , 2008 ; revised August 4 , 2009 ; accepted
August 27 , 2009 .