In contrast, combinations of PD-1 and CTLA 4 inhibitors result in higher rates of specific shared adverse events consistent with synergistic toxicity [ 25 ]. Increased ALTs, colitis, hypophysitis, hypothyroidism, and pneumonitis occur in these combinations, and can be dose-limiting and life threatening. However, the historic approach to cancer combinations using independently acting compounds would argue for combination therapies that do not include overlapping immune toxicities. At this time, less than half of patients will respond to monotherapy treatment with checkpoint inhibitors.
This unmet need could be approached either by using biomarkers for personalized treatments, or by identifying effective combination therapies. This review will focus on combination strategies for immune checkpoint inhibitors. However, the ligand receptor pairing of PD-1 provides a strong rationale for PD-L1 as a predictive biomarker, which has been a highly successful approach to bringing PD-1 checkpoint inhibitors to patients most likely to benefit from their use. Exploiting its expression on tumor cells, a companion diagnostic has been approved by the FDA to identify patients with NSCLC, bladder, gastric, head, and neck squamous cell and cervical cancers likely to benefit from treatment with pembrolizumab [ 26 ].
Unlike many gene tests, PD-L1 expression varies across a gradient in immunohistochemistry assays, requiring the development of empiric cut points derived from receiver operator curves to optimize assay use. A variety of exploratory assays in development evaluate T cell receptor clonality, tumor infiltrating lymphocytes, mutational burden, immune gene signatures, and multiplex immunohistochemistry [ 28 ]. These assays will similarly require clinical correlation to identify cut points since they also measure continuous variables. Complexities of the assay platforms used for these additional assays seem likely to slow or inhibit their use at this time compared with the success of PD-L1 as a companion diagnostic.
We have entered a new era in the development of the checkpoint inhibitors where development of more effective combination regimens will be critical to further progress.
Basic principles of cancer combination therapy were developed in the first decade of use of chemotherapeutic agents [ 29 ]. In a seminal analysis, Law articulated fundamental principles [ 30 ]. First, therapeutic selectivity for cancer cells versus normal cells is fundamental to the agents used and to their use in combination. Second, evolution of resistance has always been proposed as the major contributor to eventual treatment failure. The development of immunotherapeutic combinations may now be coming to better understand these basic principles [ 31 ].
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The manageable side effects of PD-1 checkpoint inhibitors readily address the first principle [ 32 ]. The nature of resistance to PD-1 checkpoint inhibitors is only now progressing as a topic for investigation [ 33 ]. However, in contrast to the sophisticated molecular tools being used to identify resistance mechanisms, a consideration of simpler approaches to understanding resistance may be helpful as well. It is simple to note that the probability of resistance emerging from combinations of agents with different and independent mechanisms is the product of the frequency of resistance mutations for each of the agents.
This is one powerful argument in favor of combining agents with orthogonal mechanisms of action. DeVita and colleagues further linked resistance to cancer therapy to tumor mass following simple logic [ 7 ]. Resistance will be tumor mass related simply because the likelihood of a resistant clone developing is related to the cell numbers in a tumor. These simple principles should be no less true for immunotherapy.
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If preexisting anti-tumor T cell repertoires are fundamental to the success of PD-1 therapies, and if those repertoires are limited, then the ratio of tumor targeted T cells to the net number of tumor cells will be critical to the success of PD-1 treatments. Accordingly, the role of tumor burden in immunotherapeutic resistance was recently shown by the Wherry lab [ 34 ]. Just as physicians can be trapped into approaching their next case with an eye to their immediately preceding one, the scientific community can find itself looking at new paradigms using thinking guided by any immediately preceding paradigm shift.
The clinical cancer community finds itself in a transition from the era of novel targeted therapies to the era of immune oncologic agents. By simple logic, the mathematic interaction between combination agents should start with the determination of whether both agents are individually effective, only one of them is effective, or neither is effective when combined.
Additivity when only one agent is active has also been described as inertism.
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This is often envisioned by a simple box diagram Fig. The equation is a test of independence since the combined response rate should be less than the simple sum if the drugs have no interaction. However, clinical synergy is often confused by the aspirations of in vitro studies, with hopes of predicting synergy in the clinic.
The in vitro field is substantially more complex than clinical development since it is possible to identify true synergy through the use of response surfaces like those of Chou and Talaly [ 36 ]. Since it is rare in clinical practice, and especially in clinical oncology, to be willing to sacrifice the full effect of either of two agents if used at sub therapeutic doses, the evaluation of surface response interactions is rare to nonexistent in clinical practice.
In contrast, Palmer and Sorger recently applied the principles of Bliss independence to analyze the contributions of ipilimumab and nivolumab to their combination efficacy in treating advanced melanoma [ 31 ]. They applied Bliss independence predictions to measures of overall tumor shrinkage Waterfall plots and progression-free survival PFS to argue for the absence of rigorous mathematical synergy in that combination despite broad claims for biological synergy. PFS has proven to be a less than ideal endpoint to measure the clinical effect of immune oncology agents, often failing to show an effect where the ORR coupled with durability of response, and overall survival more clearly measure the benefit of immunotherapeutics to patients.
Using several case studies, this review will assess contributions of combinations of PD-1 therapies for a subset of the most promising combinations to better understand the potential to obtain greater benefit than that achieved independently by either agent or regimen alone.
Z can be either positive or negative, depending on whether the Bliss independent prediction is less than or greater than the measured ORR for a combination. The empiric combination of newly emerging agents with standard of care therapies has moved oncology forward since the chemotherapy era began [ 37 ]. Platinum-based chemotherapy doublets were established as a standard of care for the first line treatment of NSCLC prior to the emergence of PD-1 therapies [ 38 ].
A complex but steady line of experimentation has also proposed immunologic contributions of standard chemotherapy to their clinical activity [ 40 ], and these experiments provide evidence that some chemotherapy is less effective in immune deficient animal models. Thus, these combinations had the potential to benefit both from independent activity as well as beneficial immune interactions.
In Table 2 , the components of the Bliss independent contributions to the total combination ORR were identified and calculated for these six combination studies in initial phase 1 trials. From this simple calculation, it is evident that the independent contributions of the agents might predict a large portion of the combination ORR, and that an additional contribution can be seen as well.
These calculations should be considered measures of central tendency given the limitations of phase 1—2 data. With the small numbers of subjects involved, which preclude controlling for variability, these conclusions should be considered directional.
Nevertheless, this framework to evaluating clinical data offers an objective approach to the assessment of combination effects. By subtracting the Bliss prediction from the actual combination ORR, an additional combination specific contribution Z can be assessed. Since this value is most positive for the pemetrexed-platinum combinations, the clinical trial evidence here supports a long line of investigations evaluating the immunological effects of platinum therapies [ 42 ].
Limits to the interpretation of phase 1 trials are well known. Their small numbers limit statistical rigor, and the typical use of single arm structures precludes rigorous comparisons. Importantly, however, the overall clinical benefit of this combination has been fully supported by the randomized trials that followed. These responses were quite durable, since the median duration of response was not reached in the initial publication.
These phases 1 and 2 proof of concept studies were followed by a large randomized phase 3 trial, Keynote [ 44 ]. Not you? Forgotten password? Forgotten password Use the form below to recover your username and password. New details will be emailed to you. Simply reserve online and pay at the counter when you collect.
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Available in shop from just two hours, subject to availability. Your order is now being processed and we have sent a confirmation email to you at. This item can be requested from the shops shown below. New, Expanded 2nd Edition. G: Clinical research refers to all research carried out on humans healthy or sick people. The evaluations are done using studies called clinical trials. Clinical Trials and Clinical research are pivitol in medical studies and research papers, find a multitude of clinical journals at OAText.
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