As more oncology medicines are making it from the laboratory bench to the clinic, it has never been so important to be able to characterise the safety and efficacy profile of each newly introduced therapy. In different disease areas, different endpoints are used, and in oncology, there are multiple endpoints by which researchers measure the clinical efficacy of a drug. These endpoints span from initial responses through to disease progression or decisions to end treatment.1,2 As recent studies have shown, there is a renewed focus on how different lines of therapy impact survival, and more specifically, what happens after a patient experiences disease progression on a newly introduced therapy. This is measured by what are called post-progression outcomes data.2
Why is OS important?
Overall survival (OS) remains the gold-standard measure of efficacy in cancer. OS assesses the length of time from randomisation in a clinical trial to death from any cause. Randomisation refers to the point in time when patients are assigned by chance (randomly) to a treatment group. For example, in a placebo-controlled trial, patients randomised to one group will receive the therapy being tested, while the other group will receive a placebo.2-6
But assessing OS is sometimes more difficult than it would seem. Not only can it take many months or even years for researchers to gain enough data on the survival of patients undergoing therapy, but as new therapies are introduced in earlier lines of treatment, their effect becomes more difficult to measure when they are followed by subsequent medicines.4 And while clinical breakthroughs represent new hope, they also present a conundrum for ensuring timely access. For the patients not enrolled in the clinical trial, this lengthier timeline and more complex treatment pathway can result in a delay in regulatory approval and thereby limit wider access to the medicine.3
Other endpoints as predictors of clinical benefit
As a result of these complexities, additional endpoints, such as progression-free survival (PFS), are used. Starting from the point of randomisation in a clinical trial, PFS measures the length of time that a patient lives with the disease without it worsening, or without dying.1
A number of regulatory agencies worldwide, including the US, EU, Japan and China, accept PFS as a clinical endpoint.7 For some cancer types and especially for therapies used later in the treatment pathway, PFS has a correlation with OS and is considered to be a robust indicator of efficacy. PFS also often accurately demonstrates the length of the effect of the treatment, regardless of how the effects on OS are affected by later treatments.8
Post-progression outcomes: bridging the gap
For many therapies, receiving marketing approval based on the PFS benefit demonstrated by the therapy marks the end of the development process. But still, for researchers, physicians and patients, establishing the OS benefit can help to make treatment decisions.
As previously alluded, cancer treatment strategies no longer consist of just one medicine, but instead can include taking multiple chemotherapies, targeted therapies, radiotherapies or immunotherapies for set periods of time, or until a patient no longer derives benefit from the medicine. Each regimen is known as a line of therapy. Therefore, if a study is exploring the efficacy of the first treatment taken by a patient, then prolonged survival helped by later lines of therapy (all commenced after the initial PFS endpoint, where the patient’s treatment worsens on the first therapy) can impact the measurement of OS.2 PFS as an endpoint is a good predictor of OS where survival following disease progression is short, but where improved survival is further extended by therapies that are used later, there are additional endpoints that can help to characterise the OS advantage of the initial benefit provided by the first therapy.2
This is where post-progression outcomes data come in. Post-progression outcomes measure the length of time between disease progression (worsening of the disease) on the first therapy, to the commencement of 2nd-, 3rd- or later-lines of treatments.2,3 Post-progression milestones measured by researchers may include:
- “Time to first subsequent therapy (TFST)”: the time from randomisation in a trial to the time a patient starts his/her second line of treatment (first subsequent therapy)4
- “Second progression-free survival (or death) post initiation of 2nd-line treatment (PFS2)”: randomisation to the time a patient’s tumour starts to grow or spread again, or he/she dies, while the patient is on 2nd-line treatment4
- “Time to second subsequent therapy (TSST)”: the time from randomisation to the time a patient starts his/her third line of treatment (second subsequent therapy)4
By measuring these endpoints, not only can researchers accurately characterise the efficacy of the drug being tested as 1st-, 2nd- or later-line therapy, but they can also establish the optimal sequence of treatment – and this can have a real impact on patient outcomes.
Diversifying our approach
While PFS is an important predictor of efficacy, recent studies have shown that post-progression outcomes data may closely correlate with OS if a patient undergoes multiple lines of treatment.2 Therefore, it may be just as important to evaluate the effects of a treatment after the PFS endpoint, even if these data are not required as a post-marketing commitment.
Understanding exactly how patients respond to treatments at every stage of the disease continuum helps researchers identify targets for therapy and helps physicians address patient needs as quickly and effectively as possible. Ultimately, the shift in looking towards multiple endpoints, like PFS, OS and post-progression outcomes, is reflective of the shift from treating all patients with medicine to a multi-pronged, targeted approach that aims to match the right treatment with the right patient at the right time.
1. McCain J. The Ongoing Evolution of Endpoints in Oncology. Managed Care. 2010;19(5).
2. Imai H, et al. Clinical Significance of Post-progression Survival in Lung Cancer. Thoracic Cancer 8 (2017) 379-386.
3. Dancey J. Assessing Benefit in Trials: Are We Making Progress in Assessing Progression in Cancer Clinical Trials? Cancer. 2015;121(11)1728-1730.
4. Matulonis U, et al. Intermediate Clinical Endpoints: A Bridge Between Progression-Free Survival and Overall Survival in Ovarian Cancer Trials. Cancer. 2015;121(11):1737-46.
5. National Cancer Institute (NCI). Dictionary of Cancer Terms: Randomization. Available at https://www.cancer.gov/publications/dictionaries/cancer-terms/def/randomization. Accessed April 2018.
6. National Cancer Institute (NCI). Dictionary of Cancer Terms: Placebo-controlled. Available at https://www.cancer.gov/publications/dictionaries/cancer-terms/def/placebo-controlled. Accessed April 2018.
7. Schmitter S, et al. Regulatory Agencies’ Perspective on “Progression-Free Survival” (PFS). Poster-No. PCN289. Presented at ISPOR 19th Annual European Congress, 02/11/2016, Vienna.
8. Fleming TR, et al. Issues in Using Progression-Free Survival When Evaluating Oncology Products. Journal of Clinical Oncology. 2009;27(17): 2874-80.
Veeva ID: Z4-9957
Date of next review: April 2020