- Email: [email protected]
Do participants in adjuvant breast cancer trials reflect the breast cancer patient population?
use of targeted systemic therapies. Prior to the 1980s, there were essentially no racial differences in BC mortality among patients with metastatic disease, but fewer treatments were available. Since that time, costly targeted therapies such as trastuzumab have significantly increased survival among BC patients, but there are barriers to access. In a recent study by Vaz-Luis and colleagues,4 black patients with human epidermal growth factor receptor 2-positive disease had poorer survival despite trastuzumab use compared with white patients, perhaps because black patients had a numerically longer time from diagnosis to the initiation of trastuzumab or because they were less likely to continue trastuzumab over time. These observations highlight that disparities in BC outcomes are not purely bio-
logical and that treatment barriers and nonadherence persist. While this observational study by Ademuyiwa and colleagues is not entirely novel, the results are still provocative. They showed that racial disparities in BC survival among young patients continue. Populationbased studies like this one are important for continuing to identify cohorts at greatest risk of poor outcomes. Once these subsets of patients are identified, additional research can focus on discerning whether these survival disparities are related to biology or to differences in healthcare access, culture, or socioeconomic barriers. Once we have a framework of causation, perhaps survival gaps like the one observed in this study will begin to close.
References 1. Howlader N, Altekruse SF, Li CI, et al. US incidence of BC subtypes defined by joint hormone receptor and HER2 status. J Natl Cancer Inst. 2014;106:dju055. 2. Parise CA, Bauer KR, Caggiano V. Variation in BC subtypes with age and race/ethnicity. Crit Rev Oncol Hematol. 2010;76:44-52. 3. Warner ET, Tamimi RM, Hughes ME, et al. Racial and ethnic differences in BC survival: mediating effect of tumor characteristics and sociodemographic and treatment factors. J Clin Oncol. 2015;33: 2254-2261. 4. Vaz-Luis I, Lin NU, Keating NL, et al. Racial differences in outcomes for patients with metastatic BC by disease subtype. Breast Cancer Res Treat. 2015;151:697-707.
A. J. Bishop, MD
Do participants in adjuvant breast cancer trials reflect the breast cancer patient population? Treweek S, Dryden R, McCowan C, et al (Univ of Aberdeen, UK; Inst for Res and Innovation in Social Services, Glasgow, UK; Univ of Glasgow, UK; et al) Eur J Cancer 51:907-914, 2015
Aim.dTo describe the proportion of women in Tayside, Scotland diagnosed with early breast cancer who would have been eligible for influential adjuvant breast cancer trials. Methods.dPhase III trials of adjuvant treatment for breast cancer referenced in five national guidelines were shortlisted by breast cancer specialists
292
to identify the twelve considered most influential. Eligibility criteria were extracted from protocols and applied to a 16-year cohort of women who had received a diagnosis of breast cancer and the proportion meeting trial criteria calculated. The criteria used clinically in Tayside to make decisions about use of the trial treatments were also applied to the cohort. Finally, the proportion of women receiving adjuvant endocrine therapy as part of their care and who would have been eligible for the trial evaluating that therapy was calculated. Results.dOf the cohort’s 4811 women, 3535 (73%) were eligible for at least one trial but eligibility for an individual trial rarely exceeded 45%. There were substantial differences between the proportion of women meeting trial eligibility criteria and
Breast Diseases: A Year BookÒ Quarterly Vol 26 No 4 2016
the proportion considered clinically eligible for the same treatment. The proportion of women receiving an endocrine therapy as part of their care who would also have been eligible for the trial evaluating that treatment ranged from 17% to 56%. Conclusion.dClinical eligibility criteria may be at variance with trial criteria. For adjuvant endocrine therapy, a substantial proportion of women who would have been ineligible for a trial nevertheless received the trial treatment as part of their care. Practice guidelines that are evidence based1 can help clinicians provide their patients with the most appropriate treatments; this is especially true for clinicians who treat patients with many different types of
cancer and may have difficulty keeping up-to-date on all of the types. However, to the extent that guidelines are based on flawed evidence or used as dogma rather than as a starting place in shared patient-clinician decision making, they can be harmful to patients. In this article, Treweek and colleagues provided an example of 1 way in which data that practice guidelines are based on may be flawed: patients who participate in clinical trials are often not the same as patients to whom practice guidelines are applied. The authors identified 12 randomized clinical trials (RCTs) viewed as most influential to current practice for adjuvant treatment of early breast cancer. They then analyzed a sample of almost 5000 women treated for early-stage breast cancer outside of trials. In most cases, women treated outside of trials would not have met the eligibility requirements for the trials on which practice guidelines were based. This discrepancy is largely because of trial eligibility requirements designed to limit variability among patients and to maximize the potential to show a treatment effect. But when trial eligibility requirements are restrictive, the results may be applicable only to equivalent patient populations. In addition to eligibility criteria, patients in RCTs rarely match the populations to which they are generalized with respect to age, ethnicity, socioeconomic status, geography, and health literacy. This discrepancy occurs because trials are not equally available to people along each of these dimensions. Furthermore, many patients are biased against participation in RCTs for 1 of 3 reasons: (1) some patients believe that being in a RCT is debasing (equivalent to being a “guinea pig”); (2) some patients believe that they will receive
only a placebo if they do not receive the investigational intervention; and (3) some patients do not want to be randomized and can often choose their own treatment outside of a trial. At a time when many RCTs fail to meet their accrual targets and close without completing or remain open until their findings are largely irrelevant, alternative approaches to more rapidly learn about the impact of investigational treatments must be considered. Trial designs avert some of the above concerns and help accrual have much appeal among patient advocates and some biostatisticians. Observation-enriched randomized controlled trials (OE-RCTs)2,3 fall into this category; OE-RCTs include both a randomized and a nonrandomized component. For example, an RCT with a simple 2-arm treatment versus a control would become a 2 2 factorial design, with 1 row being the 2-arm RCT and the other being nonrandomized (eg, patient and/or doctor choice). Even if there is an interaction between the 2 rows (ie, the difference between treatment and control differs when patients are randomized vs choice), unless the interaction is disordinal (ie, goes in the opposite direction for randomized vs choice), the nonrandomized patients add power to detect an overall treatment effect. Importantly, with an OE-RCT, the magnitude of the overall treatment effect is likely to be closer to what it is in a real-world setting, and the nature of any interaction could prove helpful in understanding how to generalize the findings. Compared with simple RCTs, OE-RCTs have the benefits of (1) accruing patients more rapidly, (2) providing more general conclusions, and (3) ameliorating ethical concerns about restricting crossover. Perhaps a more fundamental problem than generalizability is that
most RCTs on which practice guidelines are based were not designed for establishing optimal treatments for patients. Rather, they were designed for drug development and regulatory approval of investigational agents. These clinical trials compare the standard of care and the standard of care plus an investigational agent. Because these trials are powered to assess efficacy, toxic effects are almost always underestimated, and because their inclusion criteria are more restricted than for the general population, efficacy is almost always overestimated. I believe more trials that pit agents within the same class against each other or try to identify optimal dosing, scheduling, and/or sequencing of treatments would provide a sounder foundation on which to develop practice guidelines. It may be unreasonable to expect drug companies to fund such trials, but they should bedbut often are notdthe primary focus of trials funded by taxpayer and philanthropic dollars. A final concern, especially to patients and patient advocates, is the high potential for misuse of practice guidelines. At a time when personalized medicine is beginning to deliver on its promise, practice guidelines focus on the “average patient” and are too easily applied as dogma. Instead, they should be a starting place for shared patient-clinician treatment decisions that are heavily impacted by a particular patient’s medical conditions and personal values.4 Of additional concern is that practice guidelines are increasingly being used as the basis for reimbursement decisions that restrict patient options. For example, payers sometimes disallow reimbursement for a treatment that may be optimal for an individual patient because it is not the treatment recommended by a practice guideline for the “average
Breast Diseases: A Year BookÒ Quarterly Vol 26 No 4 2016
293
patient.” Informed, shared decisionmaking, rather than practice guidelines, should be the ultimate determiner of reimbursement. It is important to clarify that I am not advocating the elimination of practice guidelines; they generally serve patients well. However, we must be mindful of potential misuses as well as flaws in the data on which they are based. There is considerable optimism that the use of “big data,” such as CancerLinQ,5 which is based on the experiences of large numbers of patients treated outside of clinical trials, may speed and improve knowledge acquisition. However, big data approaches have their own significant flaws.6 Synthesizing evidence derived from multiple approaches is the best way to develop excellent guidelines, and focusing on incorporating individual patient information in a shared decision-making process is the best approach for using those guidelines.
Cancer patients often do not have the luxury of patience. It is important to critically assess and fully understand the limitations of methods used to assess new treatments and make treatment decisions as well as to strive to identify faster and better methods for accumulating the relevant data.
Mindfulness Meditation for Younger Breast Cancer Survivors: A Randomized Controlled Trial
for younger breast cancer survivors designed to reduce stress, depression, and inflammatory activity. Women diagnosed with early stage breast cancer at or before age 50 who had completed cancer treatment were randomly assigned to a 6-week Mindful Awareness Practices (MAPS) intervention group (n ¼ 39) or to a wait-list control group (n ¼ 32). Participants completed questionnaires before and after the intervention to assess stress and depressive symptoms (primary outcomes) as well as physical symptoms, cancer-related distress, and positive outcomes. Blood samples were collected to examine genomic and circulating markers of inflammation. Participants also completed question-
Bower JE, Crosswell AD, Stanton AL, et al (Univ of California-Los Angeles) Cancer 121:1231-1240, 2015
Background.dPremenopausal women diagnosed with breast cancer are at risk for psychological and behavioral disturbances after cancer treatment. Targeted interventions are needed to address the needs of this vulnerable group. Methods.dThis randomized trial provided the first evaluation of a brief, mindfulness-based intervention
294
J. Perlmutter, PhD
References 1. Graham R, Mancher M, Miller Wolman D, Greenfield S, Steinberg E, eds. Clinical Practice Guidelines We Can Trust. Washington, DC: Institute of Medicine. National Academies Press; 2011, http://www.ncbi.nlm.nih. gov/books/NBK209539/. Accessed August 27, 2015. 2. Bradley C. Designing medical and educational interventional studies. A review of some alternatives to conventional randomized controlled
Breast Diseases: A Year BookÒ Quarterly Vol 26 No 4 2016
trials. Diabetes Care. 1993;16: 509-518. 3. Omel J, Schwartz K. A proposal for patient-selected controlled trials: good science and good medicine. ASCO Post. 2014;5, http://www. ascopost.com/issues/june-10,-2014/aproposal-for-patient-selectedcontrolled-trials-good-science-andgood-medicine.aspx. Accessed August 27, 2015. 4. Ubel PA. Medical facts versus value judgmentsetoward preferencesensitive guidelines. JAMA. 2015; 372:2475-2477. 5. American Society of Clinical Oncology. CancerLinQ. http:// cancerlinq.org/#the-need. Accessed August 27, 2015. 6. Patient-Centered Outcomes Research Institute. The PCORI Methodology Report. 2013. http://www.pcori.org/ assets/2013/11/PCORI-MethodologyReport.pdf. Accessed August 27, 2015.
naires at a 3-month follow-up assessment. Results.dIn linear mixed models, the MAPS intervention led to significant reductions in perceived stress (P ¼ .004) and marginal reductions in depressive symptoms (P ¼ .094), as well as significant reductions in proinflammatory gene expression (P ¼ .009) and inflammatory signaling (P ¼.001) at postintervention. Improvements in secondary outcomes included reduced fatigue, sleep disturbance, and vasomotor symptoms and increased peace and meaning and positive affect (P < .05 for all). Intervention effects on psychological and behavioral measures were not maintained at the 3-month follow-up assessment, although reductions in
logical and that treatment barriers and nonadherence persist. While this observational study by Ademuyiwa and colleagues is not entirely novel, the results are still provocative. They showed that racial disparities in BC survival among young patients continue. Populationbased studies like this one are important for continuing to identify cohorts at greatest risk of poor outcomes. Once these subsets of patients are identified, additional research can focus on discerning whether these survival disparities are related to biology or to differences in healthcare access, culture, or socioeconomic barriers. Once we have a framework of causation, perhaps survival gaps like the one observed in this study will begin to close.
References 1. Howlader N, Altekruse SF, Li CI, et al. US incidence of BC subtypes defined by joint hormone receptor and HER2 status. J Natl Cancer Inst. 2014;106:dju055. 2. Parise CA, Bauer KR, Caggiano V. Variation in BC subtypes with age and race/ethnicity. Crit Rev Oncol Hematol. 2010;76:44-52. 3. Warner ET, Tamimi RM, Hughes ME, et al. Racial and ethnic differences in BC survival: mediating effect of tumor characteristics and sociodemographic and treatment factors. J Clin Oncol. 2015;33: 2254-2261. 4. Vaz-Luis I, Lin NU, Keating NL, et al. Racial differences in outcomes for patients with metastatic BC by disease subtype. Breast Cancer Res Treat. 2015;151:697-707.
A. J. Bishop, MD
Do participants in adjuvant breast cancer trials reflect the breast cancer patient population? Treweek S, Dryden R, McCowan C, et al (Univ of Aberdeen, UK; Inst for Res and Innovation in Social Services, Glasgow, UK; Univ of Glasgow, UK; et al) Eur J Cancer 51:907-914, 2015
Aim.dTo describe the proportion of women in Tayside, Scotland diagnosed with early breast cancer who would have been eligible for influential adjuvant breast cancer trials. Methods.dPhase III trials of adjuvant treatment for breast cancer referenced in five national guidelines were shortlisted by breast cancer specialists
292
to identify the twelve considered most influential. Eligibility criteria were extracted from protocols and applied to a 16-year cohort of women who had received a diagnosis of breast cancer and the proportion meeting trial criteria calculated. The criteria used clinically in Tayside to make decisions about use of the trial treatments were also applied to the cohort. Finally, the proportion of women receiving adjuvant endocrine therapy as part of their care and who would have been eligible for the trial evaluating that therapy was calculated. Results.dOf the cohort’s 4811 women, 3535 (73%) were eligible for at least one trial but eligibility for an individual trial rarely exceeded 45%. There were substantial differences between the proportion of women meeting trial eligibility criteria and
Breast Diseases: A Year BookÒ Quarterly Vol 26 No 4 2016
the proportion considered clinically eligible for the same treatment. The proportion of women receiving an endocrine therapy as part of their care who would also have been eligible for the trial evaluating that treatment ranged from 17% to 56%. Conclusion.dClinical eligibility criteria may be at variance with trial criteria. For adjuvant endocrine therapy, a substantial proportion of women who would have been ineligible for a trial nevertheless received the trial treatment as part of their care. Practice guidelines that are evidence based1 can help clinicians provide their patients with the most appropriate treatments; this is especially true for clinicians who treat patients with many different types of
cancer and may have difficulty keeping up-to-date on all of the types. However, to the extent that guidelines are based on flawed evidence or used as dogma rather than as a starting place in shared patient-clinician decision making, they can be harmful to patients. In this article, Treweek and colleagues provided an example of 1 way in which data that practice guidelines are based on may be flawed: patients who participate in clinical trials are often not the same as patients to whom practice guidelines are applied. The authors identified 12 randomized clinical trials (RCTs) viewed as most influential to current practice for adjuvant treatment of early breast cancer. They then analyzed a sample of almost 5000 women treated for early-stage breast cancer outside of trials. In most cases, women treated outside of trials would not have met the eligibility requirements for the trials on which practice guidelines were based. This discrepancy is largely because of trial eligibility requirements designed to limit variability among patients and to maximize the potential to show a treatment effect. But when trial eligibility requirements are restrictive, the results may be applicable only to equivalent patient populations. In addition to eligibility criteria, patients in RCTs rarely match the populations to which they are generalized with respect to age, ethnicity, socioeconomic status, geography, and health literacy. This discrepancy occurs because trials are not equally available to people along each of these dimensions. Furthermore, many patients are biased against participation in RCTs for 1 of 3 reasons: (1) some patients believe that being in a RCT is debasing (equivalent to being a “guinea pig”); (2) some patients believe that they will receive
only a placebo if they do not receive the investigational intervention; and (3) some patients do not want to be randomized and can often choose their own treatment outside of a trial. At a time when many RCTs fail to meet their accrual targets and close without completing or remain open until their findings are largely irrelevant, alternative approaches to more rapidly learn about the impact of investigational treatments must be considered. Trial designs avert some of the above concerns and help accrual have much appeal among patient advocates and some biostatisticians. Observation-enriched randomized controlled trials (OE-RCTs)2,3 fall into this category; OE-RCTs include both a randomized and a nonrandomized component. For example, an RCT with a simple 2-arm treatment versus a control would become a 2 2 factorial design, with 1 row being the 2-arm RCT and the other being nonrandomized (eg, patient and/or doctor choice). Even if there is an interaction between the 2 rows (ie, the difference between treatment and control differs when patients are randomized vs choice), unless the interaction is disordinal (ie, goes in the opposite direction for randomized vs choice), the nonrandomized patients add power to detect an overall treatment effect. Importantly, with an OE-RCT, the magnitude of the overall treatment effect is likely to be closer to what it is in a real-world setting, and the nature of any interaction could prove helpful in understanding how to generalize the findings. Compared with simple RCTs, OE-RCTs have the benefits of (1) accruing patients more rapidly, (2) providing more general conclusions, and (3) ameliorating ethical concerns about restricting crossover. Perhaps a more fundamental problem than generalizability is that
most RCTs on which practice guidelines are based were not designed for establishing optimal treatments for patients. Rather, they were designed for drug development and regulatory approval of investigational agents. These clinical trials compare the standard of care and the standard of care plus an investigational agent. Because these trials are powered to assess efficacy, toxic effects are almost always underestimated, and because their inclusion criteria are more restricted than for the general population, efficacy is almost always overestimated. I believe more trials that pit agents within the same class against each other or try to identify optimal dosing, scheduling, and/or sequencing of treatments would provide a sounder foundation on which to develop practice guidelines. It may be unreasonable to expect drug companies to fund such trials, but they should bedbut often are notdthe primary focus of trials funded by taxpayer and philanthropic dollars. A final concern, especially to patients and patient advocates, is the high potential for misuse of practice guidelines. At a time when personalized medicine is beginning to deliver on its promise, practice guidelines focus on the “average patient” and are too easily applied as dogma. Instead, they should be a starting place for shared patient-clinician treatment decisions that are heavily impacted by a particular patient’s medical conditions and personal values.4 Of additional concern is that practice guidelines are increasingly being used as the basis for reimbursement decisions that restrict patient options. For example, payers sometimes disallow reimbursement for a treatment that may be optimal for an individual patient because it is not the treatment recommended by a practice guideline for the “average
Breast Diseases: A Year BookÒ Quarterly Vol 26 No 4 2016
293
patient.” Informed, shared decisionmaking, rather than practice guidelines, should be the ultimate determiner of reimbursement. It is important to clarify that I am not advocating the elimination of practice guidelines; they generally serve patients well. However, we must be mindful of potential misuses as well as flaws in the data on which they are based. There is considerable optimism that the use of “big data,” such as CancerLinQ,5 which is based on the experiences of large numbers of patients treated outside of clinical trials, may speed and improve knowledge acquisition. However, big data approaches have their own significant flaws.6 Synthesizing evidence derived from multiple approaches is the best way to develop excellent guidelines, and focusing on incorporating individual patient information in a shared decision-making process is the best approach for using those guidelines.
Cancer patients often do not have the luxury of patience. It is important to critically assess and fully understand the limitations of methods used to assess new treatments and make treatment decisions as well as to strive to identify faster and better methods for accumulating the relevant data.
Mindfulness Meditation for Younger Breast Cancer Survivors: A Randomized Controlled Trial
for younger breast cancer survivors designed to reduce stress, depression, and inflammatory activity. Women diagnosed with early stage breast cancer at or before age 50 who had completed cancer treatment were randomly assigned to a 6-week Mindful Awareness Practices (MAPS) intervention group (n ¼ 39) or to a wait-list control group (n ¼ 32). Participants completed questionnaires before and after the intervention to assess stress and depressive symptoms (primary outcomes) as well as physical symptoms, cancer-related distress, and positive outcomes. Blood samples were collected to examine genomic and circulating markers of inflammation. Participants also completed question-
Bower JE, Crosswell AD, Stanton AL, et al (Univ of California-Los Angeles) Cancer 121:1231-1240, 2015
Background.dPremenopausal women diagnosed with breast cancer are at risk for psychological and behavioral disturbances after cancer treatment. Targeted interventions are needed to address the needs of this vulnerable group. Methods.dThis randomized trial provided the first evaluation of a brief, mindfulness-based intervention
294
J. Perlmutter, PhD
References 1. Graham R, Mancher M, Miller Wolman D, Greenfield S, Steinberg E, eds. Clinical Practice Guidelines We Can Trust. Washington, DC: Institute of Medicine. National Academies Press; 2011, http://www.ncbi.nlm.nih. gov/books/NBK209539/. Accessed August 27, 2015. 2. Bradley C. Designing medical and educational interventional studies. A review of some alternatives to conventional randomized controlled
Breast Diseases: A Year BookÒ Quarterly Vol 26 No 4 2016
trials. Diabetes Care. 1993;16: 509-518. 3. Omel J, Schwartz K. A proposal for patient-selected controlled trials: good science and good medicine. ASCO Post. 2014;5, http://www. ascopost.com/issues/june-10,-2014/aproposal-for-patient-selectedcontrolled-trials-good-science-andgood-medicine.aspx. Accessed August 27, 2015. 4. Ubel PA. Medical facts versus value judgmentsetoward preferencesensitive guidelines. JAMA. 2015; 372:2475-2477. 5. American Society of Clinical Oncology. CancerLinQ. http:// cancerlinq.org/#the-need. Accessed August 27, 2015. 6. Patient-Centered Outcomes Research Institute. The PCORI Methodology Report. 2013. http://www.pcori.org/ assets/2013/11/PCORI-MethodologyReport.pdf. Accessed August 27, 2015.
naires at a 3-month follow-up assessment. Results.dIn linear mixed models, the MAPS intervention led to significant reductions in perceived stress (P ¼ .004) and marginal reductions in depressive symptoms (P ¼ .094), as well as significant reductions in proinflammatory gene expression (P ¼ .009) and inflammatory signaling (P ¼.001) at postintervention. Improvements in secondary outcomes included reduced fatigue, sleep disturbance, and vasomotor symptoms and increased peace and meaning and positive affect (P < .05 for all). Intervention effects on psychological and behavioral measures were not maintained at the 3-month follow-up assessment, although reductions in