# Workshop Summary on Endpoints for Approval of Cancer Drugs for Lung Cancer

**Workshop participants:**

Paul Bunn, MD Gerard T. Kennealey, MD

Richard Pazdur, MD Ellen Stovall

Laurie Burke, PhD, MPH Steve Piantadosi, MD, PhD

Renzo Canetta, MD Sheila Ross

Martin H. Cohen, MD Scott Saxman, MD

#### Janet Dancey, MD Deborah Y. Kamin, PhD

Thomas R. Fleming, PhD Mark Somerfield, PhD

Richard J. Gralla, MD Mary Lopez Wilson

David Johnson, MD

Richard Kaplan, MD

Patricia Keegan, MD

(This document summarizes discussions among the workshop participants
and does not necessarily represent the views of the FDA.CDER)

# Abstract:

The United States Food and Drug Administration (FDA) is embarking upon a
process to evaluate endpoints used as the basis for approval of cancer
drugs. In a series of public workshops, FDA will seek input from panels
of experts on disease-specific endpoints to support drug approval.
Issues identified by the workshops will be before the Oncologic Drugs
Advisory Committee (ODAC). Subsequently, FDA will develop a series of
guidance documents on endpoints for the approval of cancer drugs.

On April 15, 2003, a public workshop was held on endpoints for lung
cancer drugs. Expert panelists, selected in consultation with ASCO and
NCI included members from FDA, academia (including two statisticians),
NCI, industry, and patient advocate organizations. The panel was charged
with \"identification and discussion of optimal endpoints for
demonstrating clinical benefit of cancer drugs used in the management of
lung cancer.\" This article summarizes the workshop discussions.

# 

# 1. Regulatory Background

# 

# FDA is responsible for determining that drugs are safe and effective before marketing. FDA approves new drug applications by two different mechanisms, regular approval and (since 1992) accelerated approval (AA). Regular approval is based on evidence of clinical benefit or a surrogate endpoint that *reliably predicts* clinical benefit (e.g., blood pressure as a surrogate endpoint for risk of stroke). AA, which can only be granted when the new drug provides an advantage over available therapy, may rely on a less-established surrogate endpoint, a surrogate that is only *reasonably likely* to predict clinical benefit. After AA, the drug manufacturer is required to perform additional post-marketing studies to evaluate whether treatment with the new drug provides clinical benefit. 

**1.1 Endpoints supporting cancer drug approvals**

# In the early 1980\'s, upon the advice of the ODAC, FDA determined that objective response rate (ORR) would not generally be an acceptable endpoint for approval. The benefit associated with modest response rates did not necessarily outweigh use of highly toxic cancer drugs. Acceptable endpoints were determined to be survival or improvement in the quality of a patient\'s life (evaluated at the time by functional assessments or tumor-related symptoms). After promulgation of the 1992 AA regulations, ORR was determined to be a "reasonably likely\" surrogate in selected settings to support AA under the 1992 Subpart H regulations. 

FDA recently summarized the endpoints supporting 71 new cancer drug
approvals over a 13-year period, from 1990 to 2002. Fourteen of these
applications received accelerated approval, 18 received regular approval
based on a survival improvement, while 39 applications received regular
approval based on other direct or indirect evidence of clinical benefit.
In some settings clinical inference allowed FDA to accept tumor
endpoints as surrogates for symptom benefit even though those benefits
were not directly measured (for instance, five drugs were approved for
treatment of leukemic disorders based on prolonged complete responses).
Occasionally effects on tumors were accepted as surrogates for clinical
benefit. For instance, in studies evaluating hormone treatment of
metastatic beast cancer, new hormone drugs were compared to tamoxifen, a
long-accepted palliative agent. In this setting, with relatively
non-toxic drugs and no proven effect of any hormone drug on survival,
tumor response, response duration and TTP were accepted as surrogates
for comparing new hormone drugs to tamoxifen. ORR and time to
progression (TTP) were supportive endpoints for about half of the
regular approvals (27 of 57). These 27 approvals were based on ORR alone
(10/27), TTP alone (1), ORR plus TTP (7), or ORR plus a favorable effect
on tumor-related signs or symptoms (9). Disease free survival in the
adjuvant setting also supported approval of five drugs for breast cancer
and for bone marrow transplantation for leukemia (Table 1). Morbidity
assessments supporting drug approval included evaluation of pain, bone
morbidity, cosmetic improvement in cutaneous lesions (Kaposi\'s sarcoma
and cutaneous T-cell lymphoma), and symptomatic relief associated with
improvement in pulmonary or esophageal obstruction.

**1.2 Endpoints supporting approval of drugs for lung cancer**

Drugs approved in the past decade for first-line treatment of NSCLC are
navelbine, paclitaxel, gemcitabine, and docetaxel. Docetaxel was also
approved for second-line treatment of NSCLC. In most cases, the primary
basis of approval was a statistically significant improvement in
survival demonstrated in one or two randomized controlled studies. In
most instances the survival advantage was 6-8 weeks at the median (Table
2). The first-line docetaxel application was approved based on survival
benefit established though a non-inferiority comparison to navelbine.
All of the approved first-line regimens were cisplatin doublets except
navelbine, which was also approved for single-agent use. Recently
gefitinib received accelerated approval for third-line treatment of
NSCLC (after failure of platinum and docetaxel regimens) based on a
response rate of 10% and a median response duration of 7 months.
Photofrin was approved for local treatment (intrabronchial photodynamic
therapy) of patients with symptomatic obstructing NSCLC. The endpoints
supporting approval of Photofrin were the intrabronchial tumor response
rate and assessments on a pulmonary symptom severity scale (evaluating
cough, hemoptysis, and dyspnea). Over the past decade, only one drug new
drug application has been approved for the treatment of small cell lung
cancer (SCLC). Topotecan was approved for second-line treatment based
primarily on ORR (considered to represent benefit in that setting) with
supportive evidence from pulmonary symptom assessments.

**2. Lung cancer endpoints**

**2.1 Objective response rate**

Effects on ORR have been used twice as the basis of lung cancer drug
approval, once as a \"reasonably likely\" surrogate endpoint to support
AA in NSCLC refractory to available therapy (gefitinib) , and once as a
full surrogate to support regular approval of second-line treatment in
small cell lung cancer (topotecan). Recently RECIST criteria have
provided a widely accepted common standard method for ORR measurement.
Effects on ORR have not been demonstrated to reliably predict effects on
survival in NSCLC, although some studies with higher ORR rates have
reported higher levels of symptom benefit. Inclusion of stable disease
in the endpoint definition has been suggested to improve the prediction
of survival effects. For regulatory purposes, meaningful evaluation of
such an endpoint would need to be done in randomized controlled studies.

**2.2 Time to Progression**

TTP has often been poorly defined and not rigorously evaluated. In most
NSCLC studies submitted for drug approval where a survival benefit was
demonstrated, a TTP effect was also noted. However, more importantly, it
has not been established that benefit on TTP reliably predicts benefit
on survival. In Cancer Cooperative Group studies of NSCLC, median
overall survival (OS) is usually about twice the median TTP. Simplified
analyses of progression have been suggested, such as percent progression
at a single pre-specified time. Retrospective analyses of a Cooperative
Group data base suggest that such an progression endpoint may predict
effects on OS. In NSCLC, the relationship between surrogates such as TTP
and survival have been difficult to evaluate because drugs have produced
only minimal effects on survival. With the advent of more effective
agents, these relationships need to be re-examined.

**2.3 Survival**

As discussed earlier, most lung cancer drug approvals have been based on
a significant improvement in survival. Survival is an optimal endpoint
because measurement is easy and accurate, and the value of a survival
improvement is unquestioned. Given the variation in survival among
different study populations, survival must be assessed in randomized
controlled studies. The most efficient design evaluates the potential
superiority of the new drug regimen compared to a standard control
regimen. In \"add-on\" designs (typically cisplatin plus or minus new
drug) the new drug does not have to \"beat\" a standard drug, it must
only demonstrate additional benefit in combination. Because multiple
NSCLC treatment doublets are now marketed and are associated with small
survival benefits, it may no longer be possible to have a platinum-alone
control arm. To date no new 3 drug combination has shown constant
superiority over 2 drug combinations in either SCLC or NCSC and no
approval has been applied for using this approach.

**2.4 Non-inferiority survival studies in NSCLC**

In many disease settings, efficacy can be established by showing that a
new drug is \"non-inferior\" to an effective standard drug. As discussed
below non-inferiority (NI) studies are difficult to perform in NSCLC at
the current time.

One can never prove that two treatments are equal. One can, however,
show that a treatment is not worse than a standard treatment by more
than some specified acceptable amount (margin). A critical issue in
determining this NI margin relates to identifying what treatment effect
(TE) can reliably be attributed to the standard drug. The goal of the NI
study in a regulatory setting is to compare the new drug to the standard
drug and, through inference and statistical methodology, determine the
*fraction* of *TE* that is *demonstrated* to be *retained* (FTEDR).
Through clinical and regulatory judgment, one determines whether this is
an acceptable fraction (AF) of the TE for the specific clinical setting.
Some of the important considerations in this judgment are the level of
improvement that the new drug provides in safety, tolerability or
convenience relative to the standard treatment.

The field of NI analysis is still developing so that there are ongoing
research and discussions about the most reliable and reasonable methods
for determining FTEDR and regarding the most appropriate AF for
different clinical settings.

NI trials are most readily conducted in settings in which the standard
intervention provides a large TE, where the size of this TE has been
precisely established in earlier randomized trials, and where these
estimates of the TE from earlier trials are unbiased with respect to the
effect the comparator regimen will actually have in the active control
trial, (with this latter condition often referred to as the "constancy
assumption"). In the NSCLC setting, NI designs are difficult because of
the small and poorly documented survival benefit associated with the
active control treatments (standard first-line chemotherapy). Evidence
documenting the TE of currently approved treatments in NSCLC generally
consists of one or two trials showing a 1-2 month median survival
difference of marginal statistical significance. Taxotere is the only
drug approved for second line treatment of NSCLC. The only data
available to estimate the taxotere TE is a 104 patient study comparing
taxotere to best supportive care. The small size of this study does not
allow a precise estimate of treatment effect (HR .56, C.I. 0.35, 0.88).

**2.5 Disease-free survival**

FDA has long stated that for adjuvant treatment, disease-free survival
(DFS) would be an adequate approval endpoint in disease settings where
most patients are symptomatic or where effects on DFS is a reliable
predictor of effects on survival. Most lung cancer patients are
symptomatic when disease recurs, so it seems reasonable for ODAC to
discuss whether delay of symptoms is a reasonable basis to justify the
use of DFS for lung cancer drug approval, and, if so, whether drug
approval based on DFS should be restricted to less toxic therapies.
Another approach would be to actually measure the treatment-related
and/or tumor-related symptoms using HRQOL scales to determine whether
treatment toxicity outweighed DFS benefit. The adequacy of DFS as a
survival surrogate in lung cancer cannot be rigorously tested with
existing data due to the lack of effective adjuvant treatments. Two
small studies studies with a significant or near significant DFS benefit
showed similar effects on survival and one study showed a significant
survival effect but no effect on DFS.

**2.6 Patient-reported outcomes (PRO)**

At presentation, over 90% of lung cancer patients with Stage III or IV
extent report two or more disease-related symptoms. These commonly
include pulmonary effects such as cough and dyspnea, and the general
symptoms of fatigue, pain, and anorexia; additionally, patients have
high degrees of psychological distress. Consequently, in addition to
survival outcomes, information about treatment effects on the patient
reported outcomes of health-related quality of life (HRQOL) and symptom
benefit is important.

**2.6.1** **Symptom assessments**

To date, patient morbidity assessments used by FDA as direct support for
cancer drug approval have consisted of measurements or observations that
allowed FDA to infer symptom benefit, such as tumor responses paired
with reported improvements in tumor-related symptoms or signs. The
perceived advantages of a targeted assessment of symptoms include the
potential relative brevity of instruments compared to some
multidimensional tools, and the assumption that improvement in
measurements could reflect true patient benefit. However, several
problems exist for this approach: a) the definition of effective patient
benefit is not always agreed upon, b) clinical correlates for changes in
symptom measures are not always known, c) if the tool used is not
validated, uncertainty in the accuracy of the endpoint could be a
problem. Instruments assessing improvements in symptoms require that
patients have the symptoms at study entry thus restricting patient
eligibility.

**2.6.2** **Health Related Quality of Life Instruments (HRQOL)**

When evaluating the role of HRQOL instruments in the lung cancer
drug-approval process, important issues are the instrument\'s relevance
and validity in that setting. Psychometric properties are important to
consider. Do the instruments available for evaluating lung cancer meet
the well-established criteria for acceptability? Several existing
measures applicable to lung cancer patients have undergone extensive
psychometric evaluation. Because HRQOL attempts to assess the impact of
treatment and disease on multiple dimensions of importance to patients,
these instruments vary in length. Typically, quality of life scales are
longer than scales that only evaluate single or a few symptoms. This
greater length must be considered in the context of the problems of
missing data. Additionally, differentiating between cancer-related
symptoms, side-effects of treatment and symptoms or problems not related
to cancer can often be difficult and perhaps the impact of these can be
best assessed using a multi-dimensional instrument.

Regulatory context will affect the appropriate use of instruments. HRQOL
instruments have been proposed as primary efficacy endpoints determining
whether a new drug is approved, as co-primary endpoints supporting drug
approval, or as secondary endpoints to be described in drug labeling or
to guide future research. For drug approval, FDA must find that the drug
is both safe and effective. Therefore, assessments that reflect
primarily a difference in drug toxicity cannot support approval without
separate demonstration of effectiveness. PROs that blend together
assessments of efficacy and safety may be acceptable as primary
endpoints when the comparator drug is relatively non-toxic. When the
comparator drug is toxic, however, it may be necessary to separately
assess tumor-related PRO benefit and toxicity-related PRO benefit, or to
measure effectiveness by non-PRO endpoints.

There are currently three lung cancer-specific instruments with
published and acceptable psychometrics in peer-reviewed literature,
using previously established criteria. All three are in common use, the
EORTC-LC13, the FACT-L, and the LCSS. They share several factors, but
also have some differences, which have been discussed in some detail in
a recent comprehensive review (Drs. C. Earle and J. Weeks for the NCI
sponsored Clinical Outcomes Working Group (COMWOG)). Although these
questionnaires differ in the number and format of their scales, all ask
patients to measure the impact of lung cancer specific symptoms and
treatment related complications on several dimensions of quality of
life; all are brief, easy to administer, and have acceptable
psychometric properties (feasibility, reliability and validity). They
are likely to be able to measure accurately the positive and negative
impact of disease and treatment, as expressed by patients, on the
various dimensions of quality of life. All three of these instruments
have undergone fairly extensive field testing, and have been used in
many trials in many countries. In trials using more than one instrument,
the different instruments tend to show convergent results. It appears
that the EORTC instrument is more frequently used in Europe, while the
LCSS and FACT-L predominate in the US.

-   [EORTC QLQ-LC13]{.underline}. The EORTC QLQ-C30 consists of 30
    general cancer-related questions in Likert and numerical analogue
    scale (NAS) formats, covering the week leading up to its
    administration. The LC13 adds 13 lung cancer related questions (thus
    43 in total). The core instrument combined with the lung cancer
    subscale is estimated to take about 11 minutes to complete. It has
    been translated into 23 major languages.

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-   [LCSS]{.underline}. The LCSS is a lung cancer-specific instrument.
    It concentrates on the symptoms of lung cancer, capturing overall
    quality of life only by a global question. It does not have a
    "general cancer" component and does not attempt to assess the
    toxicity of treatment directly. It consists only of 9 visual
    analogue scales (VAS) and 6 optional items for an observer to fill
    out for further context if desired, and asks about HRQOL in the
    previous 24 hours. Of the three lung cancer-specific HRQOL
    instruments, the LCSS has the most published literature documenting
    its psychometric properties.

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-   [FACT-L]{.underline}. The FACT-G (the general component) consists of
    34 questions, while the FACT-L (for lung cancer) currently adds 7
    questions. The FACT has well-documented content validity. As with
    the LCSS, it was developed using patient input as well as that of
    medical professionals for item generation and review. The FACT-L
    emphasizes social and emotional well-being, enhancing its
    multidimensional in scope. The FACT may be best in situations where
    patients are not as ill. It does not have as comprehensive an
    assessment of symptoms as the other two lung cancer-specific
    instruments and, therefore, has been most successful in monitoring
    patients receiving supportive care rather than aggressive
    anti-cancer treatment.

**2.6.3 Conclusions regarding PROs in lung cancer**

There is a clear need to evaluate patient-reported outcomes (PRO) in
patients with lung cancer. While evaluation difficulties remain, recent
trials indicate that initiatives to overcome these problems are meeting
with some success. Studies using symptom endpoints and quality of life
have been successful in selected indications, but present difficulties.
Attention to the areas specified in the companion article (Gralla et al)
is necessary if trials are to overcome common problems in PRO
evaluation. Education of investigators in the importance and conduct of
PRO research is needed prior to the initiation of trials. Patients must
understand the study requirements as part of the consenting process.
Steps must be taken to ensure that as little data as possible are
missing. The endpoints and analysis plan need to be specified prior to
the initiation of the trial. Trials need to be properly powered and
adequately controlled for the specified endpoints.

Further research in the evaluation and analysis of PROs will enhance
this important component of the cancer drug evaluation. Concordant
evidence of anti-tumor activity (either as survival data, response rates
or as prolongation of TTP) can be desirable. Indeed PROs should be
viewed as components of the total value of a treatment, and together
with these other cancer endpoints, provide a mutually enhanced picture
of the benefits and risks of anticancer therapies.

Table 1: Approved treatment indications based on disease free survival
(DFS), 1990-1992

  ------------- ----------------------- ----------------- -----------------
  Drug          Indication              Signficant        Year of approval
                                        findings other    
                                        than DFS          

  Anastrazole   Adjuvant therapy of                       2002
                post menopausal breast                    
                cancer                                    

  Busulfan      Induction therapy for   Time to           1999
                bone marrow             engraftment       
                transplantation in CML                    

  Paclitaxel    Node positive breast    Survival benefit  1999
                cancer                                    

  Epirubicin    Node positive breast    Survival benefit  1999
                cancer                                    

  Tamoxifen     Node negative breast                      1990
                cancer                                    

                                                          
  ------------- ----------------------- ----------------- -----------------

**Table 2:NSCLC Approved First and Second Line Treatments**

**Advanced or Metastatic Disease**

+---------------+------+----+-------+--------------------------------+
| *             | **\# | ** | **    | **Result**                     |
| *Treatments** | Tria | \# | Endpo |                                |
|               | ls** | of | int** |                                |
|               |      | P  |       |                                |
|               |      | ts |       |                                |
|               |      | ** |       |                                |
+---------------+------+----+-------+--------------------------------+
| *             |      |    |       |                                |
| *First-Line** |      |    |       |                                |
+---------------+------+----+-------+--------------------------------+
| Navelbine     | 1    | 2  | Sur   | Median surv. 30w vs 22w, p=.06 |
|               |      | 11 | vival |                                |
| vs. 5-FU/LV   |      |    |       | 1 year surv. 24% vs 16%        |
|               |      | 2  |       |                                |
|               |      | :1 |       | RR 12% vs 3%                   |
|               |      | ra |       |                                |
|               |      | nd |       |                                |
+---------------+------+----+-------+--------------------------------+
| Nave          | 2    | 4  | Sur   | Median surv. 7.8m vs 6.2m,     |
| lbine/cisplat |      | 32 | vival | p=.01                          |
| vs cisplat &  |      |    |       |                                |
|               |      | 6  | Sur   | 1 year surv. 38% vs 22%        |
| Nave          |      | 12 | vival |                                |
| lbine/cisplat |      |    |       | RR 19% vs 8%, p\<.001          |
| vs navelbine  |      |    |       |                                |
| vs            |      |    |       | Median surv. [9.2m vs          |
| vind          |      |    |       | 7.2m]{.underline} vs 7.4m,     |
| esine/cisplat |      |    |       | p=.05                          |
|               |      |    |       |                                |
|               |      |    |       | 1 yr surv. 35% vs 30% vs 27%   |
|               |      |    |       |                                |
|               |      |    |       | RR [28% vs 14%]{.underline} vs |
|               |      |    |       | 15%, p\<.001                   |
+---------------+------+----+-------+--------------------------------+
| G             | 2    | 5  | Sur   | Median surv. 9.0m vs 7.6m,     |
| emzar/cisplat |      | 22 | vival | p=.008                         |
| vs cisplat &  |      |    |       |                                |
|               |      | 1  | Sur   | TTP 5.2m vs 3.7m, p=.009       |
| G             |      | 35 | vival |                                |
| emzar/cisplat |      |    |       | RR 26% vs 10%, p\<.001         |
| vs            |      |    |       |                                |
| VP16/cisplat  |      |    |       | Median surv. 8.7m vs 7.0m,     |
|               |      |    |       | p=.18                          |
|               |      |    |       |                                |
|               |      |    |       | TTP 5.0m vs 4.1m, p=.015       |
|               |      |    |       |                                |
|               |      |    |       | RR 33% vs 14%, p\<.01          |
+---------------+------+----+-------+--------------------------------+
| Paclitaxel    | 1    | 5  | Sur   | Median surv. 9.3m vs 10.0m vs  |
| 135mg/m2 or   |      | 99 | vival | 7.4m, NS                       |
| 200           |      |    |       |                                |
| mg/m2/cisplat |      |    |       | TTP 4.3m vs 4.9m, vs 2.7m,     |
| vs            |      |    |       | p=.05, .08                     |
| VP16/cisplat  |      |    |       |                                |
|               |      |    |       | RR 25% vs 23% vs 12%, p=.001,  |
|               |      |    |       | \<.001                         |
+---------------+------+----+-------+--------------------------------+
| Doce          | 1    | 12 | Sur   | Median surv. 10.9m vs 10.0m vs |
| taxel/cisplat |      | 18 | vival | 9.1m, NS                       |
| vs            |      |    |       |                                |
| Nave          |      |    |       | Efficacy established by a      |
| lbine/cisplat |      |    |       | non-inferiority analysis.      |
| vs Docetaxel/ |      |    |       | Docetax/ carboplat did not     |
| carboplat     |      |    |       | demonstrate preservation of    |
|               |      |    |       | 50% of the survival effect of  |
|               |      |    |       | Navelbine/cisplat.             |
+---------------+------+----+-------+--------------------------------+
| **Second      |      |    |       |                                |
| Line**        |      |    |       |                                |
+---------------+------+----+-------+--------------------------------+
| Docetaxel vs  | 2    | 1  | Sur   | Median surv. 7.5m vs 4.6m,     |
| Best          |      | 04 | vival | p=.01                          |
| Supportive    |      |    |       |                                |
| Care &        |      | 2  | Sur   | TTP 12.3w vs 7.0w, p\<.05      |
|               |      | 48 | vival |                                |
| Docetaxel vs  |      |    |       | RR 5.5%                        |
| Investigator  |      |    |       |                                |
| choice        |      |    |       | Median surv. 5.7m vs 5.6m, NS  |
|               |      |    |       |                                |
|               |      |    |       | 1 year surv. 30% vs 20%,       |
|               |      |    |       | p\<.05                         |
|               |      |    |       |                                |
|               |      |    |       | RR 5.7% vs 0.8%                |
+---------------+------+----+-------+--------------------------------+

m=months, NS=nonsignificant, RR= response rate, TTP=time to progression,
w=weeks