A methodological framework to enhance the clinical success of cancer immunotherapy

Hoos, Axel; Britten, Cedrik M.; Huber, Christoph; O’Donnell-Tormey, Jill

doi:10.1038/nbt.2000

Cited by 54 publications

(46 citation statements)

References 16 publications

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“…Theoretical foundation and sporadic empirical evidence for efficacy of cancer immunotherapy abound, but numerous biologic and other obstacles impede the development of predictably effective immune treatment of malignancy (4,11,(13)(14). Development of treatments, particularly those using live or inactivated cells, is in addition hampered by inadequacy of standard preclinical in vitro and animal testing methods (as inadequate models of human immunity) and by regulatory requirements of a priori proofs of potency (cf.…”

Section: Discussionmentioning

confidence: 99%

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Reconsidering the Paradigm of Cancer Immunotherapy by Computationally Aided Real-time Personalization

et al. 2012

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Although therapeutic vaccination often induces markers of tumor-specific immunity, therapeutic responses remain rare. An improved understanding of patient-specific dynamic interactions of immunity and tumor progression, combined with personalized application of immune therapeutics would increase the efficacy of immunotherapy. Here, we developed a method to predict and enhance the individual response to immunotherapy by using personalized mathematical models, constructed in the early phase of treatment. Our approach includes an iterative real-time in-treatment evaluation of patient-specific parameters from the accruing clinical data, construction of personalized models and their validation, model-based simulation of subsequent response to ongoing therapy, and suggestion of potentially more effective patient-specific modified treatment. Using a mathematical model of prostate cancer immunotherapy, we applied our model to data obtained in a clinical investigation of an allogeneic whole-cell therapeutic prostate cancer vaccine. Personalized models for the patients who responded to treatment were derived and validated by data collected before treatment and during its early phase. Simulations, based on personalized models, suggested that an increase in vaccine dose and administration frequency would stabilize the disease in most patients. Together, our findings suggest that application of our method could facilitate development of a new paradigm for studies of in-treatment personalization of the immune agent administration regimens (P-trials), with treatment modifications restricted to an approved range, resulting in more efficacious immunotherapies. Cancer Res; 72(9); 2218-27. Ó2012 AACR.

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Section: Discussionmentioning

confidence: 99%

“…Cancer immunotherapy does not fit the standard paradigm of development typical of most modern therapeutics (13)(14)(15). Conventional cancer agents are designed to target the tumor directly, whereas immunotherapy should affect cancer indirectly, by manipulating the immune system.…”

Section: Introductionmentioning

confidence: 99%

Reconsidering the Paradigm of Cancer Immunotherapy by Computationally Aided Real-time Personalization

et al. 2012

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“…the balance between tumor elimination on the one hand, and tumor escape on the other hand [11,12]. Immunotherapies aim at enhancing immune reactivity against cancer and push the escape-elimination balance towards tumor elimination.…”

Section: B Early Action -Late Benefit Modelmentioning

confidence: 99%

“…The phenomenon has usually been interpreted as a delayed effect of the treatment, being in contrast to the immediate effect and early curve divergence seen in chemotherapy [9,12]. For instance, treatment with the immunotherapeutic drug ipilimumab resulted in effects that were not graphically observable until several months after initiation of treatment, resulting in late divergence of the survival curves [1].…”

Section: Introductionmentioning

confidence: 99%

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Late divergence of survival curves in cancer immunotherapy trials: interpretation and implications

Thorén

Anderson

Strannegård

2013

Cancer Immunol Immunother

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Late divergence of survival curves of treated patients and controls is commonly seen in successful cancer immunotherapy trials. Although late survival curve divergence may be caused by a delayed action of therapy, it may also be related to early effects of the treatment.We suggest that late survival divergence most often reflects a specific benefit of therapy for patients who suffer from a more slowly progressing disease. The occurrence of delayed survival curve divergence has important implications for the statistical analysis of immunotherapy trials. Thus, it leads to non-proportional hazard ratios that make commonly used statistical tests, e.g. the logrank test, suboptimal. It is therefore suggested that the statistical analysis of immunotherapy trials primarily should be based on a test that compares the survival curves at or after a pre-specified, fixed, late time point. PrécisLate survival curve divergence in immunotherapy trials has important implications for the statistical analysis and is commonly misinterpreted to be a consequence of delayed therapeutic effects.3

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Improving Dendritic Cell Cancer Vaccine Potency Using RNA Interference

Sæbøe-Larssen

Sioud

2020

RNA Interference and CRISPR Technologies

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A methodological framework to enhance the clinical success of cancer immunotherapy

Cited by 54 publications

References 16 publications

Reconsidering the Paradigm of Cancer Immunotherapy by Computationally Aided Real-time Personalization

Reconsidering the Paradigm of Cancer Immunotherapy by Computationally Aided Real-time Personalization

Late divergence of survival curves in cancer immunotherapy trials: interpretation and implications

Improving Dendritic Cell Cancer Vaccine Potency Using RNA Interference

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