Download Phase III Trials of Targeted Anti-cancer Therapies: redesigning the

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Phase III Trials of Targeted Anti-cancer Therapies: redesigning the
concept
Alberto Ocana, Eitan Amir, Francisco Vera-Badillo, Bostjan Seruga and Ian F. Tannock
From the Division of Medical Oncology and Hematology, Princess Margaret Hospital and
the University of Toronto, Toronto, Canada
Word count: Abstract 123, Text 3114
Keywords: phase III trials, adaptive design, anticancer-therapies, targeted agents, biomarkers
Running title: Phase III Trials of Targeted Anti-Cancer Therapies
Correspondence:
Ian F Tannock MD, PhD, DSc
Division of Medical Oncology and Hematology,
Princess Margaret Hospital,
610 University Avenue,
Toronto, ON M5G 2 M9,
Canada
T:
416-946-2245
F:
416-946-4563
E:
[email protected]
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Abstract
Randomized phase III trials provide the gold-standard evidence for the approval of new
drugs: an experimental treatment is compared with the current standard of care to identify
clinically relevant differences in a predefined endpoint. However, there are several problems
relating to the current role of phase III trials in drug development including the limited
clinical benefit observed for some approved agents, the necessity for large trials to detect
these differences, the inability of such trials to identify rare but important toxicities, and high
cost. The design of phase III trials evaluating drug combinations, and of those including
biomarkers, presents additional challenges. Here, we review these problems and suggest that
phase III trials with adaptive designs in selected prescreened populations could reduce these
limitations.
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Introduction
Phase III randomized clinical trials (RCTs) have been used to generate evidence in support of
the approval of most new agents in the treatment of cancer (1, 2). In phase III studies an
experimental treatment is compared with the standard of care to identify relevant differences
in predefined endpoints. In cancer drug development, these are usually time to event
endpoints such as overall (OS), progression-free (PFS) or disease-free survival (DFS) (1, 2).
If the magnitude of difference is statistically significant, the experimental treatment is usually
approved by regulatory authorities, and can then be incorporated into the therapeutic
armamentarium (1).
In the last decade, numerous targeted therapies have been evaluated in clinical trials and
many have been approved for the treatment of cancer. For most of these agents, phase III
trials were undertaken to detect significant differences in a predefined endpoint. However, for
a small selected group, impressive results observed in early studies led to approval without
the need to perform a randomized study (reviewed by Tsimberidou et al (3)).
The limited activity of some targeted agents, the necessity of large phase III trials to
demonstrate significant differences in endpoint, and the high cost at which they are marketed,
have raised questions about the way these agents are developed, including the necessity of
conducting phase III trials to demonstrate improved outcomes compared to standard
treatment (4-6). In the present article we review the role of phase III trials in the
development of targeted therapies, identify problems associated with them, and propose ideas
to improve the process of drug development. We suggest that modifications to the design of
phase II-III trials including adaptive models in a preselected screened population could help
to circumvent some of these problems (4, 5).
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Problems associated with drug development: role of randomized trials
Limited clinical benefit in large RCTs
The magnitude of difference in clinical benefit between experimental and standard treatment
is the key factor in convincing regulatory agencies, and some clinicians, of the utility of a
given drug. Although there is no consensus as to what should be considered a sufficient
magnitude of benefit from use of a new agent to recommend its adoption, for most
oncologists a gain in survival of at least 3-4 months for patients with incurable metastatic
disease is clinically important, especially if toxicity is acceptable and no deterioration in
quality of life is observed (6). However, there are examples of targeted agents that have been
approved by regulatory agencies with very limited improvement in absolute benefit: an
example was the approval of erlotinib in pancreatic cancer with an improvement in median
survival of around 10 days (see table 1) (7), a difference that was statistically significant but
less than pre-specified in the protocol as clinically important. A positive study should not be
based only on a statistical test alone but also on an improvement in a clinically relevant
endpoint that will translate into substantial patient benefit (6). This benefit should be
described in absolute terms since relatively impressive reductions in a hazard ratio (i.e. in
relative benefit) may translate into very small differences in an absolute measure such as
median survival.
Clinical trials have become larger with time (8), and large sample sizes are often used in
clinical trials to detect small benefits in predefined endpoints which meet conventional levels
of statistical significance and allow drug registration. For example, studies evaluating
erlotinib and bevacizumab for non-small cell lung cancer used large sample sizes to detect
small but statistically significant differences in outcome (9, 10). Similarly the recent approval
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of Ziv-aflibercept in metastatic colon cancer was based on a clinical trial involving 1226
patients where the difference in median OS was only 6 weeks (11).
Predicting a successful phase III trial from phase II data
The completion of a randomized phase II trial with positive results does not guarantee a
subsequent positive phase III study. This may be due to more stringent selection of patients in
phase II studies, the use of different endpoints in phase II and III studies (e.g. PFS in the
phase II and OS in the phase III),lack of understanding how they relate to each other, low
statistical power in smaller phase II studies, or simply regression to the mean. Estimation of
the sample size for a phase III trial will depend on the magnitude of benefit expected, and
will be influenced by that observed in phase II trials. Ideally however the sample size should
not be set by the likelihood of obtaining an effect that is statistically significant, but by
detecting or ruling out one that is clinically important.
Are RCTs sufficient to determine toxicity?
Phase III trials should be designed to obtain information about toxicities related to the
investigational treatment as well as benefit. Phase III trials tend to capture frequent but mild
toxicity and sometimes less frequent but more severe toxicities not identified in previous
phase I and II studies (12). However, many phase III trials fail to capture this information
adequately. Around 40% of fatal adverse drug reactions (ADR) of approved targeted agents
reported in updated drug labels were not described in any published report of the phase III
trial(s) used for the approval of that drug (13): thus phase III trials are not sufficiently
powered to detect infrequent but serious toxicities of targeted agents, or those that may occur
after completion of the trial (14). In addition, phase III trials have limited ability to detect
toxicity in vulnerable subgroups of patients, who are often excluded from phase III studies
but may receive the drug once it is approved. Furthermore, our group has reviewed data using
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meta-analyses, which show that newly approved anticancer drugs are associated with
increased drug-induced morbidity and mortality when compared with the standard treatment
received by control groups. These toxicities became evident only when data were pooled
from multiple RCTs (15). We do not suggest enlarging the sample size of RCTs to increase
their probability of demonstrating less common types of toxicity, but we do recommend strict
requirements for reporting of toxicity as a condition of marketing approval.
Pitfalls in the design of studies with drug combinations
Although the design of studies with drug combinations should be based on preclinical data to
support an interaction leading to improved therapeutic index, a recent analysis from our
group shows that to be uncommon: there is rarely preclinical information to suggest a
synergistic interaction between the agents evaluated in phase II trials (16). Since the activity
of targeted drugs as single agents has been modest, most of them have been developed in
association with standard chemotherapy, and they have usually been given concomitantly.
Scheduling of a primarily cytostatic targeted agent to be given concurrently with cycledependent cytotoxic chemotherapy makes little biological sense, since chemotherapy will be
less active if cells are put out of cycle by the targeted agent, although combined activity
against multiple pathways might overcome this antagonistic effect. Scheduling the targeted
agent between cycles of chemotherapy to inhibit tumor cell repopulation could be an option
to improve therapeutic outcome (17, 18). This approach has not been explored adequately in
clinical trials.
Phase I and early phase II trials provide opportunities to investigate dose and schedule for a
drug combination before launching a large randomized phase II or a phase III trial, but this is
often not undertaken. The FDA's Critical Path Initiative addresses the limited foundation of
early drug development studies (19). For example a recent phase IIB trial involving 229
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patients with HER2 negative breast cancer evaluated the combination of sorafenib and
capecitabine compared with capecitabine alone; the combination showed unacceptable
toxicity that could have been detected in a smaller study (20) . Similarly the phase III trial
that compared standard chemotherapy (AC or paclitaxel) versus standard chemotherapy with
trastuzumab in metastatic breast cancer showed unacceptable toxicity in the AC-containing
arm, a combination that was never evaluated in a phase I or phase II trial (21). These
examples highlight the importance for the re-design of phase II-III trials with drug
combinations to facilitate the early identification of toxic combinations and to select the most
appropriate dose and schedule to obtain the best balance between clinical benefit and toxicity.
Schedule is as important as dose, as has been demonstrated recently when evaluating
different schedules of sunitinib in renal cell carcinoma (22). The design of phase II trials to
find the best schedule is challenging as resources are not usually available to evaluate various
schedules, so that one schedule is chosen with limited prior information. However, there
could be greater emphasis on dose scheduling in preclinical studies that could be refined in
the phase I trials.
Cost associated with development of targeted drugs
Recently approved drugs are marketed at a high price despite many of them having only
modest activity; thus many new drugs do not fall within a range of cost-effectiveness used for
evaluating other health interventions. In the approval process of a drug by some regulatory
authorities, market price and cost-effectiveness are not taken into consideration. There are
concerns about long-term affordability of new anticancer drugs for both patients and
healthcare providers (including public health systems and private insurers), and health
providers may not be willing to fund the costs of drugs if they do not meet standards of costeffectiveness (23). What is considered cost-effective differs between countries (24). For
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developed countries up to $100,000 is often considered a reasonable maximum cost per
quality adjusted life year gained (24-27), but it will be far less in developing countries.
Renal cell carcinoma is one of the solid tumors where several new targeted agents have been
approved. Table 2 shows prices associated with each new treatment, including those for the
control and experimental arms of the pivotal trials, in addition to the clinical benefit observed
in the predefined endpoints and estimates of the incremental cost per life-year (or
progression-free if OS is not available) gained. Some of the new drugs do not fall within the
above standard of cost-benefit. One of the reasons proposed for the high market price is the
high cost of the drug development process and particularly that associated with the design of
large phase III trials.
Can adaptive phase II-III trial designs in a molecularly prescreened population help to
resolve these problems?
An adaptive trial uses data obtained while the trial is ongoing to modify the course of the trial
(28, 29). As suggested by the FDA and EMA (30), all potential adaptations should be
preplanned and registered before the trial is initiated. Examples of adaptive measures include
early stopping rules in case of lack of efficacy or unacceptable toxicity, adapting doses or
schedules that will lead to a more efficient benefit-toxicity relationship, stopping arms in a
multi-arm trial, changing accrual, selection and/or order of primary and secondary end-points,
or modification of concomitant treatments (28, 29, 30). In addition, adaptive trials can use
outcome-adaptive randomization in which the ratio of patients randomly assigned to the
experimental arm versus the control arm changes over time from the standard 1:1 to increase
the proportion of patients randomized to the arm that is doing better (28).
There are advantages to designing a study with various arms, including those with different
dose and schedule of a combination. With emerging (albeit imperfect) evidence that one arm
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of the phase II-III trial is superior (i.e. has less toxicity and more activity in a predefined
interim analysis) - increased accrual to that arm can augment the statistical power to detect a
relevant magnitude of clinical benefit. An example of this multi-arm approach is the
STAMPEDE trial in prostate cancer (31): an increased benefit in biochemical (i.e. PSA)
response or PFS as an interim endpoint is used to support increased accrual to the selected
arms while maintaining OS as the final endpoint. Accrual to a phase III trial can be designed
to identify uncommon but serious toxicities or even to increase the accrual for a specific
vulnerable population.
The incorporation of several arms in a randomized trial with different doses and/or schedules
can facilitate identification of the best ratio of benefit to toxicity. This approach could have
been used to identify the best dose and schedule for the combination of sorafenib and
capecitabine in HER2 negative metastatic breast cancer patients (20). In contrast, the large
phase IIB study concluded that the combination was clinically active but toxic and another
trial was necessary to evaluate the combination using lower doses (20). An adaptive design
would have saved time and resources as early stopping rules would have led to dropping of
toxic or ineffective arms.
Although adaptive clinical trials can be more expensive initially as they may include more
scenarios and treatment arms, their ability to prevent the undertaking of further clinical
studies can reduce the total cost of drug development.
Incorporation of biomarkers in adaptive designs
The incorporation of biomarkers in phase III studies should be exploratory unless a validated
marker is identified and it should then be evaluated (quantified) for all included patients. The
incorporation of biomarkers in studies of approved drugs in the last decade is summarized in
table 3. We reviewed previously the magnitude of relative benefit in phase III trials used for
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the approval of new drugs: although all studies were powered to detect about a 20-30%
reduction in relative risk, drugs designed to interact with a specific target, and especially
those that used predictive biomarkers, were able to produce the highest relative improvement
in OS and PFS (32) (figure 1). Thus the sample size needed to detect this difference was
smaller for these studies (32). Adaptive phase III trials using biomarkers and a preselected
population may not need a large sample to detect a relevant clinical benefit, and they can
have early stopping rules in case the study arm meets the predefined magnitude of benefit.
The above features can be included in a continuous phase II-III study design (figure 2). This
approach would exclude the majority of single arm trials that have no intention to proceed to
a phase III trial; this is a positive feature as most such trials represent a waste of resources
(33). In the phase II part, different arms can be evaluated but only the arm with signs of
major activity and acceptable toxicity will be included in the final phase III part that will be
compared with standard treatment. An example of this approach is the I-SPY2 trial in breast
cancer (34). The phase II part is exploratory and gives information about how to design the
phase III and what might be the expected magnitude of benefit and its uncertainty. In
addition, the endpoint for the phase II part could be a surrogate for the endpoint used in the
phase III trial. However, this assumption is often not justified: for example, there is rather
poor correlation between improvements in PFS and in OS. The total number of patients to be
included can also be higher as they will be combined with those included in the phase II part,
so the identification of a meaningful ratio of clinical benefit to toxicity will be easier (figure
2). This approach can reduce time and cost. An example will be a multi-arm trial in
metastatic breast cancer in which a specific experimental treatment arm shows an increase in
PFS of more than 6-8 months, like the results recently reported with pertuzumab in HER2positive breast cancer that has lead to FDA approval of this drug (35). The preliminary result
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could give confidence to continue or increase accrual in that arm and stop the other
experimental arms, and aim for an improvement in OS of at least 3-4 months.
The design of phase II-III trials in a molecularly prescreened population can also help to
overcome some of the problems described earlier. Impressive clinical activity observed in a
phase I-II trial in a subgroup of patients with a specific molecular alteration could be the basis
for the design of a phase II-III trial. This transition from a phase I-II trial to a phase III trial
will save time and resources thus speeding the development process. A phase I trial is
designed to confirm a safe dose and an extension cohort at the recommended phase II dose
can evaluate efficacy in a specific subpopulation of prescreened patients - as performed in the
development of vemurafenib in b-RAF mutated metastatic melanoma and crizotinib in
anaplastic lymphoma kinase-mutated non-small cell lung cancer (36, 37). The design is not
necessarily based on the selection of patients by tumor subtypes and might be based on
molecular alterations. Different tumor types with a similar molecular phenotype could be
included in the phase I-II trial, and based on the clinical efficacy observed, a series of phase
II-III trials with adaptive design could be launched in specific tumor subtypes.
Patients with newly diagnosed metastatic cancer could be prescreened, and when they
progress on standard treatment, the molecular information could be used to randomize them
in an adaptive phase II-III trial with drug combinations (figure 2). The trial might evaluate
different arms that include a targeted agent alone or in combination with another modulator
of a defective molecular pathway, or with chemotherapy if there is preclinical evidence of a
therapeutically beneficial interaction. Different doses or schedules might also be explored in
different arms of the phase II part. A surrogate endpoint like PFS can be used in the phase II
trial but with an ambitious magnitude of benefit to select the best study arm for further
development of the phase III trial. Those arms with lack of efficacy or substantial toxicity
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will be dropped but the arm with more activity and better safety profile can be expanded to a
phase III trial with increased accrual and with a more appropriate endpoint such as OS.
Adaptive designs also have limitations. There is no way to capture very rare toxicities or
those that appear later after the study has finished, and the logistics and interpretation of
results can be more complicated. Indeed, their implementation has been slower than
expected. Different reasons may explain this lack of endorsement by investigators, including
the more complicated logistics, concerns about security, or increased chance of erroneous
positive conclusions (see FDA guideline for adaptive designs). The incorporation of
biomarkers also adds cost and requires a process of validation. Phase III trials could also be
large, depending on the magnitude of the expected benefit. In addition, the final marketed
price of a drug will be selected to maximize the profit that the pharmaceutical company has
estimated for that drug, and has little or no relationship to the cost of drug development.
However, even with these limitations, phase III trials with adaptive designs can overcome
some of the problems associated with traditional phase III trials.
Conflict of Interest Statement: All authors of this manuscript declare no conflicts of
interest.
Acknowledgement: No funding sources to be declared.
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Table 1. RCTs used for the approval of agents from 2000 to 2011 that reported both OS
and PFS (or TTP, indicated by *) if this is given instead of PFS).
Tumor Type
Study design
HR: PFS/OS
0.70/0.78
CRC (43)
Gemcitabine+Paclitaxel vs
Paclitaxel*
Albumin-bound paclitaxel vs
Paclitaxel*
Ixabepilone+Capecitabine vs
Capecitabine
Capecitabine+Docetaxel vs
Docetaxel*
Letrozole + Lapatinib
vs Letrozole + Placebo
Capecitabine vs 5FU-LV*
CRC (44)
IFL vs FOLFOX vs IROX*
0.74/0.66
CRC (45)
IFC vs FC
NR/0.59
CRC (46)
IFL vs FL vs I
0.64/0.78
Gastric
cancer (47)
HNC (48)
Capecitabine or FU + Cisplatin +/Trastuzumab
TPF vs PF
0.71/0.74
0.72/0.73
HCC (49)
Sorafenib vs Placebo
1.08/0.69
Mesothe
lioma (50)
NSCLC (10)
Pemetrexed+Cisplatin vs Cisplatin*
0.68/0.77
Erlotinib vs Placebo
0.61/0.70
NSCLC (9)
Paclitaxel –Carboplatin+/Bevacizumab
Gemcitabine + Erlotinib vs
Gemcitabine + Placebo
Mitoxantrone + pred vs
Cabazitaxel+pred
Temsirolimus vs Interferon alfa vs
combination
0.66/0.79
Breast (38)
Breast (39)
Breast (40)
Breast (41)
Breast (42)
Pancreas (7)
Prostate (51)
Renal (52)
0.73/0.75
0.79/0.90
0.65/0.77
0.84
0.71/0.74
0.95
NR/0.92
0.77/0.82
0.74/0.70
NR/0.73
median PFS
(mos)
exp/control
difference
6.1/4.0
2.1
5.8/4.2
1.6
6.2/4.2
2.0
6.1/4.2
1.9
8.2/3.0
5.2
5.2-4.7
0.5
8.7/6.9
1.8
6.7/4.4
2.3
7.0/4.3
2.7
6.7/5.5
1.2
11/8.2
2.8
4.9/4.1
0.8
5.7/3.9
1.8
2.2/1.8
0.4
6.2/4.5
1.7
3.8/3.6
0.2
2.8/1.4
1.4
3.8/1.9
1.9
median OS (mos)
exp/control
difference
18.6/15.8
2.8
16.3/13.9
2.4
16.4/15.6
0.8
14.5/11.5
3.0
33.3/32.3
1.0
13.2/12.1
1.1
19.5/15.0
4.5
17.4/14.1
3.3
14.8/12.6
2.2
13.8/11.1
2.7
18.8/14.5
4.3
10.7/7.9
2.8
12.1/9.3
2.8
6.7/4.7
2.0
12.3/10.3
2.0
6.2/5.9
0.3
15.1/12.7
2.4
10.9/7.3
3.6
Ratio of
median
differences in
OS/PFS
1.33
1.50
0.40
1.58
0.19
2.20
2.50
1.43
0.81
2.25
1.53
3.50
1.55
5.00
1.18
1.50
1.71
1.89
Abbreviations: OS: overall survival; PFS: progression free survival; mos: months; NR: not recorded; CRC:
colorectal Cancer; HNC: Head and Neck Cancer; NSCLC: Non-small Cell Lung Cancer; *TTP: time to
treatment progression used instead of PFS; FU: fluorouracil; LV: leucovorin; I: irinotecan; IROX: irinotecan
and oxaliplatin; TPF: taxotere, platinum compound, fluorouracil; PF: platinum compound, fluorouracil.
20
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2. Total cost of control and experimental arms, and estimated incremental cost per lifeyear gained for OS (or PFS if cross-over or if OS was not given), for new agents approved
for the treatment of renal cell carcinoma
Targeted agent
Design
Total
price:
control
arm (US$)
Total price:
experimental
arm (US$)
Primary
Endpoint
(PE)
Δ in median
OS/PFS
(months)
Incremental
cost per lifeyear gained
(US$)
Bevacizumab
(Bev)(53)
Everolimus (54)
Bev + IFN-α vs
IFN-α + Placebo
Everolimus vs
Placebo
31,260
157,220
PFS
314,900
Placebo
27,730
PFS
Not
reported/4.8
Not
reached/2.1
Sorafenib (55)
Sorafenib vs
Placebo
Sunitinib vs IFNα
Temsirolimus vs
IFN-α
Placebo
34,700
OS
3.4/2.7
122,500
7,720
52,260
PFS
89,000
5,870
21,440
OS
Not
reached/6.0
3.6/1.9
Sunitinib (56)
Temsirolimus
(52)
158,450
51,900
21
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Table 3. Evaluation of biomarkers for approved drugs from 2000 to 2011, from phase I
to phase III studies
Biomarker
included in
phase II-III
trials
Yes
No
Approved
indication
Phase III
trial for
approval
HER2
None
Selection of
patients based
on biomarker
in phase I
Yes
No
Breast
Lung
Slamon (21)
None
No
No
Lung
Shepherd (10)
EGFR for
CP02-9502 and
CP02-9401
Yes
No
Colon
Jonker (61)
Cunningham
(62)
Targeted agent
Publicati
on Year
Phase I
Target
Tumor type
Biomarker
Trastuzumab (57)
Gefitinib (58)
1999
2002
HER2
EGFR
Erlotinib (59)
2001
EGFR
Breast
Solid express
EGFR
Solid tumors
Cetuximab (60)
2002
EGFR
Solid tumors
with
EGFR overexpression
Head and
Neck cancer
Bevacizumab (64)
2001
VEGF
Solid tumors
None
No
No
Colon
NSCLC
Vermorken
(63)
Hurwitz (65)
Giantonio
(66)
Sandler (9)
RCC
Sorafenib (67)
2005
Sunitinib (68)
2006
Multi TKI
Multi TKI
Solid tumors
Solid tumors
pERK in PBL
VEGF, VGFR2
Panitumumab
(70)
Temsirolimus
(72)
2008
EGFR
Solid tumors
None
2004
mTOR
Solid tumors
None
Everolimus (73)
2008
mTOR
Solid tumors
pS6 in PBMC
Vemurafenib (37)
2010
B-RAF
Solid tumors
Erk1/2
Crizotinib (36)
2009
ALK
Solid tumors
No
(Signs of
activity in HCC
and RCC)
No
(Signs of
activity in RCC,
neuro-endocrine
and GIST)
None
No
(Signs of
activity in breast
cancer and
RCC)
No
(Signs of
activity in RCC)
No
(Signs of
activity in
melanoma)
No
(Signs of
activity in
sarcoma and
NSCLC)
HCC
Escudier (53)
Llovet (49)
RCC
Escudier (55)
RCC
Motzer (56)
GIST
Demetri (69)
No
Colon
No
RCC
Van Cutsem
(71)
Hudes (52)
No
RCC
Motzer (54)
Yes
Melanoma
Chapman (74)
Yes
NSCLC
Kwak (75)
No
No
Abbreviations: TKI: tyrosine kinase inhibitor; RCC: renal cell carcinoma; EGFR: epidermal growth factor
receptor; PBMC: peripheral blood monoclonal cells; RCC: renal cell carcinoma; NSCLC: non-small cell lung
cancer; GIST: gastrointestinal stromal tumors; PBL: peripheral blood lymphocytes; VEGFR: vascular
endothelial growth factor receptor; HCC: hepatocelular carcinoma.
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Figure 1. Forest plot showing meta-analyses of hazard ratios for progression-free
survival based on mechanism of action of targeted agents. Reproduced from “Oncogenic
Targets, Magnitude of Benefit, and Market Pricing of Antineoplastic Drugs” by Amir E, et al.
J Clin Oncol. 2011 Jun 20;29(18):2543-9, under permission of the American Society of
Clinical Oncology (number: 3173531452218)
Figure 2. Schematic representation of phase II- III adaptive designs using prescreened
populations.
23
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Figure 1:
Study or subgroup
Hazard ratio, 99% CI
Agents directed against a specific molecular target
Amado 2008
Demetri 2006
Geyer 2006
Karapetis 2008
Maemondo 2010
Mok 2009
Slamon 2001
Subtotal
3.0%
2.6%
2.5%
2.9%
2.9%
2.8%
3.3%
20.0%
0.45 [0.34, 0.59]
0.33 [0.23, 0.47]
0.49 [0.34, 0.71]
0.40 [0.30, 0.54]
0.30 [0.22, 0.41]
0.48 [0.35, 0.66]
0.51 [0.41, 0.63]
0.42 [0.36, 0.49]
Test for overall effect: Z = 11.00 (P<0.001)
Less specific biological targeted agents
Bonner 2006
3.0%
0.68 [0.52, 0.89]
Escudier 2007
3.1%
0.44 [0.34, 0.56]
Escudier 2007
3.4%
0.63 [0.52, 0.77]
Glantonio 2007
3.2%
0.61 [0.49, 0.77]
Hurwitz 2004
2.8%
0.54 [0.39, 0.74]
Jonker 2007
3.5%
0.68 [0.58, 0.80]
Llovet 2008
3.2%
0.58 [0.46, 0.73]
Miller 2007
3.5%
0.60 [0.51, 0.70]
Moore 2007
3.4%
0.77 [0.64, 0.93]
Motzer 2007
3.1%
0.42 [0.33, 0.54]
Motzer 2008
3.0%
0.31 [0.24, 0.41]
Sandler 2006
3.5%
0.66 [0.56, 0.77]
Shepherd 2005
3.4%
0.61 [0.51, 0.74]
Van Cutsem 2007
3.4%
0.54 [0.44, 0.66]
Subtotal
45.6% 0.57 [0.51, 0.64]
Test for overall effect: Z = 10.10 (P<0.001)
Chemotherapies
Albain 2008
3.5%
0.70 [0.59, 0.84]
Culeanu 2009
3.4%
0.50 [0.41, 0.61]
Goldberg 2004
3.4%
0.74 [0.61, 0.90]
Gradishar 2005
3.5%
0.75 [0.64, 0.89]
Hanna 2004
3.4%
0.97 [0.80, 1.18]
O’Shaughnessy 2002
3.4%
0.65 [0.53, 0.79]
Scagliotti 2008
3.6%
1.04 [0.92, 1.18]
Sparano 2010
3.6%
0.79 [0.69, 0.91]
Thomas 2007
3.5%
0.75 [0.64, 0.88]
Vogelzang 2003
3.2%
0.68 [0.54, 0.86]
Subtotal
34.4% 0.75 [0.66, 0.85]
Test for overall effect: Z = 4.39 (P<0.001)
Total (95% CI)
100.0% 0.59 [0.53, 0.65]
0.5 0.7 1 1.5 2
Favors experimental Favors control
Test for overall effect: Z = 10.46 (P<0.001)
Test for subgroup differences: ChF = 31.43, df = 2 (P<0.001)
CCR Reviews
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Research.
Standard treatment:
Chemotherapy
Biomarker +
Progression
Arm X: Standard
treatment
Experimental treatment:
Biological agent
Experimental treatment:
8_ebe]_YWbW][djiY^[Zkb[7
+ Chemotherapy
Experimental treatment:
8_ebe]_YWbW][djiY^[Zkb[8
+ Chemotherapy
Prescreened
population
Arm Y: Best
experimental treatment
Phase II
randomized part
Phase III part
š ;Whboijeff_d]hkb[i\eh
ineffective or toxic arms
š Ikhhe]Wj[[dZfe_dji
like PFS
š ?Z[dj_\ocW]d_jkZ[e\
X[d[\_j\ehf^Wi[???Z[i_]d
š I[b[YjX[ijZei[_\
_dYbkZ[Z_dWdoWhc
š 8[ijWhc_Z[dj_\_[Z_d
f^Wi[??fWhj
š 7ffhefh_Wj[[dZfe_djb_a[EI
š 7h[b[lWdjcW]d_jkZ[e\X[d[\_j
š 7Z[gkWj[iWcfb[i_p[je
detect clinical differences
WdZ[lWbkWj[jen_Y_jofhe\_b[
(rare toxicities)
© 2013 American Association for Cancer Research
CCR Reviews
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Downloaded from clincancerres.aacrjournals.org on April 28, 2017. © 2013 American Association for Cancer
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Figure 2:
Author Manuscript Published OnlineFirst on July 23, 2013; DOI: 10.1158/1078-0432.CCR-13-1222
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Phase III Trials of Targeted Anti-cancer Therapies: redesigning
the concept
Alberto Ocana, Eitan Amir, Francisco Vera-Badillo, et al.
Clin Cancer Res Published OnlineFirst July 23, 2013.
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