Abstract
Objective:
Restrictive eligibility criteria are a known barrier to patient enrollment into clinical trials. With the introduction of chimeric antigen receptor T-cell (CAR-T) therapy, it is imperative to ensure trials are generalizable to the intended population with appropriate safety guiderails.
Methods:
Using the U.S. National Library of Medicine’s clinical trial database, we identified 84 clinical trials and characterized inclusion/exclusion criteria for CAR-T therapy in hematologic malignancies with a focus on age, performance status, and comorbidities, and the relationship to sponsorship, disease type, and study phase.
Results:
The overwhelming majority of CAR-T trials imposed restrictions on upper age (n=54, 64%), performance status (n=72, 86%), and renal function (n=76, 90%). Institution-sponsored studies were more likely to have age restrictions (n=29) than industry-sponsored (n=20), (83% vs 45%, p<0.01). There was no relationship between study phase and use of upper age limit restriction or study phase and affiliation with performance status restrictions. Inclusion criteria for renal function was highly variable and ambiguous; creatinine <1.2–3.0 mg/dL, creatinine clearance >20–60 mL/min, and GFR >30–70 mL/min.
Conclusion:
These results suggest highly variable inclusion/exclusion criteria for early phase CAR-T studies that may limit patient accessibility to therapy and emphasize the need for a standardized, evidence-based approach to patient enrollment.
Keywords: Clinical Trial Design, chimeric antigen receptor t-cell therapy
Introduction
CAR-T has altered the therapeutic landscape of hematologic malignancies. Two CAR-T products are Food and Drug Administration (FDA) approved for use in acute lymphoblastic leukemia (ALL) and aggressive lymphoma)1–3.; numerous others are being tested in clinical trials including multiple myeloma and acute myeloid leukemia. CAR-T therapy offers a novel approach to reengineering a patient’s own immune system to detect and kill aggressive forms of cancer, but is also associated with critical toxicities that can be fatal or life-threatening reactions including Cytokine Release Syndrome (CRS) and/or immune effector cell therapy neurotoxicity (ICANS)4. New trials are advised to enroll patients across the entire age spectrum to allow for a broad application and understanding across demographics5. In general, eligibility criteria are implemented to ensure safety of trial participants. However, overly restrictive eligibility is an established barrier to patient enrollment into trials, resulting in lower patient accrual and decreased generalizability6. Many have questioned whether advancing age is a legitimate trial exclusion and emphasize careful consideration of physiologic reserves in lieu of chronologic age7. The FDA recommended approaches to foster expanded patient participation in clinical trials through broadening eligibility criteria and avoiding unnecessary exclusions8.
This study characterizes the inclusion/exclusion criteria for CAR-T therapy in hematologic malignancies with a focus on age, performance status, and comorbidities, as well as relationship to sponsorship, disease, and study phase.
Methods
We queried the U.S. National Library of Medicine’s Clinical Trial database (https://clinicaltrials.gov/) for clinical trials on CAR-T therapy for hematologic malignancies registered on or before June 25, 2019 (date of data export) with the following filters applied: “hematologic”, “recruiting”, “not yet recruiting”, “not recruiting, active, completed”, “suspended”, “terminated studies”, “interventional studies”, “CAR”, “CAR T”, “chimeric antigen receptor”, “CAR NK, adult, older adult, early phase 1”, “phase 1”, “phase 2”, “phase 3”. From this, 95 studies populated, 84 were utilized and 11 excluded due to non-hematologic malignancy (n=9), hematologic and non-hematologic malignancy (n=1), and non-malignancy (n=1). All inclusion/exclusion criteria were abstracted directly from the U.S. National Library of Medicine’s Clinical Trial database. This study was exempt from Institutional Review Board approval as all included retrospective data is publicly available and devoid of all human subject identifiers.
From eligible studies, we extracted data on inclusion/exclusion criteria, phase of study, and disease. We mapped Karnofsky Performance Status to Eastern Cooperative Oncology Group Performance Status for consistency9. We categorized as industry-sponsored versus institution-sponsored based on funding. If industry collaborated with institution, we classified as industry-sponsored (n=24). We compared the proportion of studies excluding subjects based on age, performance status, infectious disease, organ function, and neurological disorders in the setting of study sponsorship, phase, and disease using chi-squared tests using STATA 16.0 MP (StataCorp LLC, College Station, TX) for all statistical analysis. All tests were two-sided, and the level of significance was 0.05. Adolescent and Young Adult (AYA) populations were excluded from age restriction analyses.
Results and Discussion
The 84 CAR-T clinical trials targeting hematologic diseases (Table 1) included leukemia (n=11), lymphoma (n=8), multiple myeloma (n=40), and a combination of disease types (n=25). Overall, 54% were industry-sponsored (n=45) and 46% were institution-sponsored (n=39). The majority of studies were phase 1 (n=47) or phase 1/2 (n=28), and the remainder (n=9) were phase 2 or higher.
Table 1.
CAR-T Clinical Trial Criteria Restrictions
| Study Restriction | Upper Age Limit Restriction | Number of studies, n (%) | ||
|---|---|---|---|---|
| Age limit (n=54/84) | Adolescent and Young Adult (AYA) | 3 months – 25 years old | 1 (1.9%) | |
| 6 months – 25 years old | 1 (1.9%) | |||
| ≤ 24 years old | 1 (1.9%) | |||
| ≤ 25 years old | 1 (1.9%) | |||
| ≥3 years old – ≤30 years old | 1 (1.9%) | |||
| ≤ 60 years old | 1 (1.9%) | |||
| ≤ 65 years old | 1 (1.9%) | |||
| ≤ 70 years old | 23 (42.6%) | |||
| ≤ 73 years old | 3 (5.6%) | |||
| ≤ 75 years old | 11 (20.4%) | |||
| ≤ 78 years old | 1 (1.9%) | |||
| ≤ 80 years old | 8 (14.8%) | |||
| ≤ 85 years old | 1 (1.9%) | |||
| Performance status, ECOG* (n=72/84) | Score | Number of studies, n (%) | ||
| 0–3 | 1 (1.4%) | |||
| 0–2 | 37 (51.4%) | |||
| 0–1 | 34 (47.2%) | |||
| Infectious diseases (n=70/84) | Disease | Number of studies, n (%) | ||
| HIV | 69 (98.6%) | |||
| Hepatitis B | 64 (91.4%) | |||
| Hepatitis C | 64 (91.4%) | |||
| Syphilis | 11 (15.7%) | |||
| Organ function (n=82/84) | Renal function n=76/84 | Creatinine (mg/dL) criteria (n=68) | Number of studies, n (%) | |
| Adequate | 24 (35.3%) | |||
| ≤1.2 or 1.25 | 6 (8.8%) | |||
| ≤1.3 | 1 (1.5%) | |||
| ≤ 1.4 | 1 (1.5%) | |||
| ≤1.5, ≤ 1.5 x ULN | 5 (7.4%), 10 (14.7%) | |||
| ≤2.0, ≤ 2.0 x ULN | 7 (10.3%), 2 (2.9%) | |||
| ≤2.5, ≤ 2.5 x ULN | 8 (11.8%), 1 (1.5%) | |||
| ≤3.0, ≤ 3.0 x ULN | 1 (1.5%), 2 (2.9%) | |||
| CrCl (mL/min) criteria (n=15) | Number of studies, n (%) | |||
| ≥ 20 | 3 (20.0%) | |||
| ≥ 30 | 2 (13.3%) | |||
| ≥ 40 | 4 (26.7%) | |||
| ≥ 50 | 2 (13.3%) | |||
| > 60 | 4 (26.7%) | |||
| GFR (mL/min) criteria (n=8) | Number of studies, n (%) | |||
| > 30 | 1 (12.5%) | |||
| > 50 | 3 (37.5%) | |||
| > 60 | 3 (37.5%) | |||
| > 70 | 1 (12.5%) | |||
| Cardiac Function n=67/84 | Manifestation | Number of studies, n (%) | ||
| Arrhythmia | 29 (43.3%) | |||
| History of Long QT | 5 (7.5%) | |||
| Unstable Angina/MI | 29 (43.3%) | |||
| Poorly Controlled HTN | 9 (13.4%) | |||
| ECG within normal limits | 7 (10.4%) | |||
| Heart Failure | 31 (46.3%) | |||
| Ejection Fraction Restrictions n=40 | Ejection Fraction | Number of studies, n (%) | ||
| ≥40% | 8 (20.0%) | |||
| ≥45% | 9 (22.5%) | |||
| ≥50% | 19 (47.5%) | |||
| ≥55% | 1 (2.5%) | |||
| ≥60% | 1 (2.5%) | |||
| Adequate | 2 (5.0%) | |||
| Pulmonary Function n=44/84 | Oxygen Requirement | Number of studies, n (%) | ||
| No Supplemental Oxygen | 24 (54.5%) | |||
| Neurologic Disorders (n=27/84) | Disorder | Number of studies, n (%) | ||
| Epilepsy | 15 (55.5%) | |||
| Brain Injuries | 10 (37.0%) | |||
| Dementia | 8 (29.6% | |||
| Parkinson’s Disease | 5 (18.5%) | |||
| Coordination Movement Disorder | 1 (3.7%) | |||
| Cerebellar Disease | 8 (29.6%) | |||
| Psychosis | 7 (25.9%) | |||
| Paresis | 6 (22.2%) | |||
| Aphasia | 6 (22.2%) | |||
| History of Stroke | 15 (55.5%) | |||
| Active autoimmune or inflammatory disease of CNS | 3 (11.1%) | |||
Trials with Karnofsky Performance Status metrics were mapped to ECOG performance status metrics
The scientific basis of restricting entry into clinical trials by specific age has not been well established, yet upper age limit restrictions were in place for 54/84 (64%) trials (Figure 1). Institution-sponsored studies were more likely to have age restrictions (n=29) than industry-sponsored (n=20), (83% vs 45%, p<0.01). There was no relationship between study phase and use of upper age limit restriction (58%, 76%, and 44% for phase 1, 1/2, and 2 or higher respectively, p=0.16), suggesting as trials move through subsequent phases, age restrictions were not loosened. Across disease types, myeloma trials were encouragingly less likely to have age restrictions than non-myeloma trials (50% vs 74%, p=0.03), as myeloma is a disease of older adults. Restrictions on older age are likely utilized as a surrogate of toxicity risks, especially in light of the potential dangers of cytokine release syndrome and neurotoxicity after CAR-T. However, in one study, nearly half of patients treated with CAR-T in routine practice would have not met eligibility criteria, and the outcome remained the same with safety comparable in both the trial and routine practice10.
Figure 1.
Impact of Disease, Study Phase and Sponsorship on Age Restriction among CAR-T Trials *AYA excluded
Of the 84 trials, 72 had performance status restrictions. Most common inclusion criteria were ECOG 0–2 (n=37) or ECOG 0–1 (n=34), with the remaining having no restrictions (n=12) or a restriction of ECOG 0–3 (n=1). There was no difference in sponsorship of studies with more restrictive performance status criteria [ECOG 0–1 (n=34)] versus less restrictive [including ECOG 0–2, 0–3, and no restriction (n=50)], with 38% (n=15) of institution-sponsored studies restricting to ECOG 0–1, compared with 42% (n=19) of industry-sponsored studies (p=0.73). Comparing ECOG restrictions across phases, no significant difference was identified by trial phase: 38% (n=18) of phase 1 trials, 36% (n=10) of phase 1/2, and 67% (n=6) of phase 2 or higher restricted to ECOG 0–1 (p=0.23). Analyzing ECOG restrictions across disease types showed no significant difference with ECOG 0–1 restrictions for myeloma 43% (n=17) and other 39% (n=17), (p=0.72). This data was analyzed to compare ECOG restriction 0–2 versus others and found similar results.
Highly variable patterns of renal impairment were defined for enrollment criteria. Inclusion criteria for renal function included creatinine <1.2–3.0 mg/dL, creatinine clearance >20–60 mL/min, and GFR >30–70 mL/min. Across phases, 68% of phase 1 studies (n=32), 54% of phase 1/2 (n=15), and 56% of phase 2 or higher (n=5) had restrictions for renal impairment (p=0.42). Across diseases, 60% of myeloma (n=24) and 64% of others (n=28) had renal restrictions (p=0.73). Across sponsorship, 72% (n=28) of institution-sponsored studies and 53% (n=24) of industry-sponsored studies had renal restrictions (p=.08). Interestingly, most renal restrictions utilized serum creatinine alone, which is known to not accurately reflect renal function. Rather creatinine clearance should be prioritized, particularly among older adults11.
Patients were excluded for a history of a separate or concurrent malignancy in 62% trials, central nervous system (CNS) involvement of cancer in 54% trials, and infectious disease in 83% trials; HIV (n=69) and Hepatitis B/C (n=64). Many studies had restrictions for impaired organ function; renal impairment (n=76), cardiac deficits (n=67), and pulmonary function (n=44). With increasing age, potential trial candidates may have acquired comorbidities, which would further limit trial accessibility. The validity of excluding patients due to organ function was recently explored and found to not reflect expected toxicities in hematologic malignancy trials studied12. Rather, a geriatric assessment tool has been found to be a better predictor of toxicity than standard oncology assessment13.
A high incidence of neurotoxicity has been reported in CAR-T therapy. Reflective of this, 27/84 trials had restrictions for neurological disorders: epilepsy (n=15), brain injury (n=10), dementia (n=8), Parkinson’s Disease (n=5), coordination/movement disorder (n=1), cerebellar disease (n=8), psychosis (n=7), paresis (n=6), stroke (n=15), aphasia (n=6), and active autoimmune or inflammatory CNS disease (n=3). A thoughtful approach in establishing neurological eligibility criteria is imperative to assure safe enrollment of patients with pre-existing neurologic disease while not unnecessarily excluding potential candidates. There is an urgent need to develop and standardize screening tools to identify those at higher risk of neurotoxicity14.
Conclusion
The use of CAR-T as a standard of care must be tempered by the understanding that CAR-T trials have overt age caps, variable performance and comorbidity exclusions, and neurologic exclusions that play a role in limiting generalizability and patient accessibility to novel therapy. We acknowledge CAR-T therapy is relatively intensive and trials have significant focus on safety. We identified differences of age-restriction by sponsorship, and query the scientific rationale of upper age limits for CAR-T clinical trials and question if this difference is related to accrual concerns, safety or other. Limitations of this study include a limited snapshot of the trial data, restriction to HM alone, and are not reflective of the actual population enrolled. Exploration of this therapeutic modality will need to be extended to a more representative population, including aging adults. Our study supports uptake of FDA guidance on eliminating trial barriers is lagging. A coordinated effort across institutions, cooperative groups, regulatory agencies and societies is required to overcome aging biases in clinical trial design.
Acknowledgements
This study was supported in part by research funding from the National Institutes of Health to A.E.R. K23 CA208010-01.
Support:
Research reported in this publication was supported by the National Cancer Institute of the National Institutes of Health under the Award Number K23 CA208010-01 (PI Rosko). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.
Conflict of Interest Disclosures
Dr. Rosko reports grants from National Cancer Institute of the National Institutes of Health, during the conduct of the study; grants from National Cancer Institute of the National Institutes of Health, other from Association of Community Cancer Centers, personal fees from Vyxeous, other from Janssen, other from Millenium, other from Regeneron, outside the submitted work.
Dr. Olin reports other from Daiichi Sankyo, other from Astellas, other from Genentech, other from Pfizer, other from Takeda, other from Novartis, other from Astrazeneca, other from Medimmune, other from Spectrum, other from Mirati, other from Jazz, other from Genentech, other from Amgen, other from Revolution Medicine, outside the submitted work.
Dr. William reports other from Celgene, other from Kite, other from Giliead, outside the submitted work.
Dr. Jaglowski reports other from Novartis, other from Kite, other from Unum Therapeutics, other from Novartis, other from Kite, other from Juno, other from CRISPR Therapeutics, outside the submitted work.
Dr. Klepin reports other from Uptodate, other from Genentech, outside the submitted work.
Dr. Giri reports other from Carevive Systems, other from Pack Health LLC, other from Walter B. Frommeyer Jr, Fellowship in Investigative Medicine at University of Alabama at Birmingham, outside the submitted work.
Dr. Artz, Dr. Wall, Dr. Benson, Dr. Jaggers report no disclosures. Dr. Wildes TBD.
Footnotes
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