Future Directions of Cancer Immunotherapy in Clinical Trials

In 1891, bone surgeon William B. Coley injected streptococcal organisms into a patient with inoperable cancer based on his belief that a patient’s immune system could be stimulated to attack the malignant tumor.  Dr. Coley and other physicians who used Coley’s Toxins reported excellent treatment results, and Coley is now known as the “Father of Immunotherapy.” [The Iowa Orthopedic Journal; Volume 26, page 154-158].

Since that time, progress and innovation within oncology has rapidly accelerated from the basic treatment paradigm of finding the tumor, excising the tumor, and using doses of radiation or chemotherapy to the highest toxicity levels, to a paradigm that now includes immunotherapy and bio-genomics. For decades, physicians have known that several tumor types, such as melanoma, renal and lung; are able to generate an immune response with better clinical outcomes. Only several years ago, researchers proclaimed cancer immunotherapy as the scientific breakthrough of the year in Nature and Science magazine articles; thereby, establishing immunotherapy as one of the core components in the armamentarium to treat cancer. Recent initiative--such as National Cancer Moonshot, led by Vice President Joe Biden--will serve to further accelerate cancer research and improve patient care while focusing on breaking down clinical study barriers and sharing information. 

Clinical Trials and New Initiatives

The medical research community widely acknowledges that the oncology drug development process is slow and costly, typically taking 14 years from the time of discovery to drug approval and only 5% of drugs tested in Phase I clinical trials will ever reach approval.

Traditional study designs may not be optimal for personalized therapy. The objective of this new era of precision medicine coupled with advancing technologies is enabling new trial designs that match patients to the right genomic- or immune-targeted drugs. Randomized clinical trial designs are utilized to remove potential biases and have been the gold standard for demonstrating safety and efficacy to regulatory bodies; however, design limitations may not result in efficient drug testing strategies. For example, if you enroll patients with breast cancer without first selecting the tumor and comparing several drug therapies, the efficacious drug will be the one that hits more common targets and other treatments will appear less effective due to this design limitation. Had these latter drugs been tested in a targeted or selected population, their efficacy potential could have been better evaluated. 

Oncology trial design is moving from a drug-centric approach, which uses common features between patients in terms of a type of cancer or type of molecular aberration (e.g., anaplastic lymphoma kinase [ALK]) and places all patients on the same drug treatment, to a more customized patient-centric approach. It is this realization that even if all patients have one common aberration, the rest of the molecular portfolio will differ from patient to patient and the patient-centric approach or strategy with customized drug combinations targeted to the molecular profile selected for specific patients that fit the targeted drug regimen (i.e., molecular matching) and not solely based on a drug.

Protocol designs have evolved from large master protocols that include multiple tumor types, multiple targets, multiple treatments and typically end up only including a small number of patients likely to have a response. This approach has led medical researchers to move toward designing smaller umbrella or basket trials with patients selected based on genomic targets and more likely to respond to targeted therapy. Umbrella trials, such as the SWOG LUNG-MAP trial, are based on including a single histology (e.g., lung, breast, colorectal) and matching patients with different molecular aberrations to various drug combinations using predefined algorithms. Basket trials, such as the National Cancer Institute-Molecular Analysis for Therapy Chose (NCI-MATCH) trial, matches patients based on the same molecular aberration profile (e.g., BRAF) and allows different tumor types to be enrolled; this is useful for finding signals related to the functionality of the aberration and treatment response irrespective of histology. It is important to realize that umbrella and basket trial designs can efficiently address multiple questions under the auspice of one protocol.

Regulatory agencies, pharmaceutical companies and public groups have been actively working together to develop approaches that simplify the drug development process to enable patients earlier access to therapies and improved patient care. As these new trial designs focus on selecting the right patients, members of various oncology groups, such as the American Society of Clinical Oncology (ASCO), have met with the FDA to discuss simplifying clinical trials. Some of the proposed initiatives include lowering regulatory hurdles to allow earlier patient access to trials, accelerating approvals, improving informed consent requirement, use of biomarkers, requirements for safety and efficacy of therapeutic combinations, and steps to simplify compassionate use of therapy drugs. These groups plan to release several instructive white papers later this year and their cooperative efforts are critical to advancing cancer care as exemplified in the accelerated approval of Keytruda® (pembrolizumab) based the results of the KEYNOTE-001, a biomarker-based Phase IB trial.

Another evident change in the oncology landscape is data sharing and the collaborative participation of multiple pharmaceutical companies and oncology groups to offer a variety of available targeted therapies in a single clinical protocol. Other innovative approaches from pharmaceutical companies and publicly sponsored clinical trial groups, such as the National Clinical Trial Network which is comprised of NRG Oncology Group, American College of Radiology Imaging Network (ACRIN), SWOG, ACTION (Alliance for Clinical Trials in Oncology), Children’s Oncology Group (COG), Eastern Cooperative Oncology Group (ECOG) and SWOG (formerly the Southwest Oncology Group); that are focused on generating new hypotheses that will define informative signals for identifying patient tumor characteristics and matching treatments according to patients’ genomic signatures and determining those patients most likely to benefit with improved outcomes.

Collaborative efforts such as these are essential to helping oncologists identify patient subsets, using leukemia cell banks established by cooperative groups over the last 30 years, from patients who participated in past Cancer and Leukemia Group B (CALGB) trials (now part of the Alliance of Clinical Trials in Oncology). The data from these well-designed studies with properly defined study treatments and documented clinical outcomes have provided the basis for the identification and understanding of various genomic subtypes, and the impact of these subtypes on prognosis in hematological malignancies (Mrozek, et al. Blood 2007, 109; 431-448).

Role of Collaboration and Big Data

Experience has shown that cancer is difficult to treat due to the fact that cancer does not represent a single disease, but represents multiple genomic subsets. In order to treat cancer properly, this heterogeneous group needs to be sorted by subtype and treated with targeted agents shown to have effectiveness to the aberration.

The genomic complexity of cancer defines the role of big data analysis and the future requirements for access to a super-commuting power capable of analyzing huge amounts of data compiled across past and future trial databases to establish “benchmarks” of the effectiveness of new treatment combinations that can be compared to the standard of care or other experimental regimes under consideration.

The value of interrogating large databases was exemplified in a recent presentation by Dr. Maria Schwaerderle during ASCO’s Annual Meeting in 2015. She presented an analysis of more than 570 Phase II studies, including  32,149 patients, demonstrating that personalized-targeted therapies were associated with better outcomes than cytotoxic agents, which in turn were better than non-personalized targeted therapies (median response rates of 30%, 12% and 4% respectively) and therefore, the implications on study design can be readily understood. (Schwaerderle, et al., Impact of Precision Medicine in Diverse Cancers: A Meta-analysis of Phase 2 Clinical Trials; JCO 2015, 61, 5997).

As institutions and oncology groups (e.g., ASCO, COP, SWOG) continue open collaboration and sharing of advances in clinical treatment protocols, the NCI established the Genomic Data Commons as a means to expand the cancer knowledge network by merging genomic and clinical data from an international community of cancer researchers into a unified data repository with standardized mapping and nomenclature that enables data sharing and analysis across studies in support of precision medicine.

Cancer can be viewed as a complicated pathologic process and trial design innovation will continue to be molecularly driven, focusing on understanding the genomic aberrations. Future trial designs will evaluate more combinations of targeted drugs as patients are found to have more genomic aberrations. Collaborative approaches will continue to transform treatments in oncology and improve patient quality of life and overall survival rates.

Joseph Pierro, MD is Chief Medical Officer with Biomedical Systems. Donald Cooper is Senior Director, Imaging Systems, with Biomedical Systems.