One of the first attempts at staging solid tumor malignancies was made by Pierre Denoix, a French surgeon, who used the size and extension of a tumor along with its lymphatic involvement and distant spread to detail progression of disease.1 The TNM system devised by Denoix is perhaps the most prolifically used staging system for cancer. The understanding of tumor biology and etiology has, however, remarkably changed since Denoix first described his system. Tumor microenvironments represent dynamic ecological systems in which traditional evolutionary principles are applicable.2 Cytotoxic chemotherapy, immunotherapy, targeted therapy, and radiation therapy all interact with tumor cells and the associated microenvironment to modulate selective pressures in ways that vary temporally, spatially, and stochastically. Given this improved and evolving understanding of the complexity of tumors and their interactions with treatment, the idea of static staging seems less applicable to the way we currently manage cancer. In the same way that our understanding of cancer is changing, the paradigm through which we treat these cancers must concomitantly evolve. In the past, the role of staging was to categorize patients into prognostic groups for which treatment and outcomes were both calibrated and similar. In fact, as knowledge of tumor biology and evolutionary dynamics progress, the insufficiencies from existing staging heuristics are becoming more apparent. For example, locally advanced squamous cell cancers of the head/neck (LA-SCCHN) represent a disease group for which isolated tumor staging may not fully align with prognosis and survival. Currently, these tumors are staged at diagnosis, and many times are not reassessed for several months after completion of radiation and chemotherapy, which may lead to a characterization of the disease that lags an ever-changing disease state.
The very role of staging as well as the methodology needs to change to account for the dynamic and evolving nature of cancer. Staging at a single time point gives a stationary snapshot of the current state of a complex adaptive dynamic system. A single observation of the night sky may provide useful information of where planets and stars are relative to one another, but it does not allow us to deduce the evolutionary history of the solar system, nor to predict the planetary motions in the future. Obtaining unperturbed tumor growth dynamics at multiple time points prior to therapy, such as at biopsy, second opinion, or treatment planning, may provide invaluable information about the growth trajectory of the individual disease. Although 2 patients could present in the clinic with comparable tumor burden and TNM stage, their growth trajectories and thus their treatment response may be remarkably different.3 Furthermore, complex adaptive dynamic systems are best understood when perturbed. Cancer therapy may be the most radical perturbation of intrinsic tumor growth, and thus observing how the tumor responds to treatment may hold the key for dynamic treatment adaptation and, ultimately, improved responses and outcomes. However, understanding of response dynamics may require consultation of mathematics and physics concepts that have not routinely contributed to clinical cancer care yet. We have previously argued that successful adaptive control requires a reference model for each individual patient that the clinically observed response can be compared against. Although for some patients a 25% reduction in tumor volume initially during therapy may be remarkable, for others a comparable tumor volume reduction may indicate more resistant disease than pretreatment biomarkers may have suggested.4 In this scenario, each patient serves as their own control, and the subsequent treatments may need to be remarkably different.
After considering tumor growth dynamics before and during the initial phases of therapy, cancer evolution becomes increasingly important during prolonged courses of therapy. Tumor cells have evolved to outcompete their physiologic counterparts, which allows them to grow malignant masses, invade tissues, and evade immune surveillance. During therapy, however, selective fitness rapidly changes, and cancer cells have a strong motivation to quickly evolve resistance to therapy. Recent clinical trials suggest that intermittent therapies with prolonged treatment holidays may significantly dampen evolution of resistance and increase time to resistance compared with continuous therapy with maximum tolerable doses.5 These data strongly suggest that tumors are rapidly changing during therapy, and that current pretreatment staging may not necessarily offer the best approach to select optimal treatments, because treatment regimens that remain unchanged during patient care may be ill-suited for a rapidly changing disease.
Disease dynamics need to be included as part of the staging process at 2 points in the cancer care paradigm: (1) before any treatment or intervention, to account for differences in tumor growth, immune contexture, and microenvironmental makeups that modulate cancer cell proliferation rates and thereby response to many cancer therapies; and (2) during treatment, to assess how a patient is responding to a particular therapy, which could be used as a trigger to further personalize and dynamically adapt therapy as needed. The latter may already be done implicitly (à la response criteria, such as RECIST and RANO). However, explicit integration of response to therapy into adaptive or dynamic staging may allow practitioners to personalize the dosing, scheduling, and/or sequencing of anticancer therapies. Regarding the former, if it is intuitively understood that on-treatment dynamics reveal something about the nature of the response to therapy, then it should not be a stretch to think that disease dynamics before the start of treatment may be revelatory of underlying disease characteristics, just as much as the static measures currently used for staging.
The potential for using both pretreatment and on-treatment dynamics to inform treatment decisions and to better understand the underlying disease state has already been investigated in multiple settings. This has ranged from monitoring growth dynamics in chronic lymphocytic leukemia to correlate with relevant genetic drivers,6 to using prostate-specific antigen dynamics to predict response to androgen deprivation therapy.5 There have been a number of studies using serial MRI data to make predictions about how a particular tumor will change over time under different therapeutic conditions.7,8 Studies involving mathematical modeling to simulate volume regression profiles during radiation therapy to allow for personalized therapies have been done in lung cancer9 and SCCHN.10 Expanding on these studies would allow real-time assessments of responses to therapies and for the allowance of adaptability of treatments. In one SCCHN study using a proliferation saturation index, assessing responses at certain timepoints was prognostically important and helped to provide individualized radiation prescriptions.10 Additionally, tumor dynamics are already being considered as important inputs in oncology drug development,11 which adds to the case for moving toward a paradigm of cancer staging that incorporates disease dynamics.
SCCHN provides a representative case study of the potential difficulties of static staging. Although traditionally response assessments after chemoradiation are not performed until approximately 2 to 3 months after completion of treatment, some studies have already demonstrated that treatment response at 4 weeks is highly prognostic of outcome.10 In this clinical trial, midtreatment response did not correlate with initial T stage, suggesting a biologic heterogeneity in radiation response, which also suggests an opportunity for adaptation of treatment. Furthermore, plasma HPV levels are being studied for oropharyngeal disease as a potential biomarker for disease diagnosis, recurrence, and potentially for adaptive treatment escalation or de-escalation. As these studies mature and potentially change standard of care, the relevance of static staging will diminish significantly.
In summary, our knowledge of tumor biology and evolutionary dynamics should inform staging. Consequently, for many cancers, staging should become a dynamic process that can inform maintenance, escalation, or de-escalation of therapies. In the same way that biomarkers and molecular alterations at diagnosis affect stage for many cancers, we believe that assessing changes in disease state can, will, and should be a hallmark of the way we manage patients with cancer.
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Prokopiou S, Moros EG, Poleszczuk J, et al. A proliferation saturation index to predict radiation response and personalize radiotherapy fractionation. Radiat Oncol 2015;10:159.
Enderling H, Alfonso JCL, Moros E, et al. Integrating mathematical modeling into the roadmap for personalized adaptive radiation therapy. Trends Cancer 2019;5:467–474.
Brady-Nicholls R, Nagy JD, Gerke TA, et al. Prostate-specific antigen dynamics predict individual responses to intermittent androgen deprivation. Nat Commun 2020;11:1750.
Gruber M, Bozic I, Leshchiner I, et al. Growth dynamics in naturally progressing chronic lymphocytic leukaemia. Nature 2019;570:474–479.
Rockne R, Rockhill JK, Mrugala M, et al. Predicting the efficacy of radiotherapy in individual glioblastoma patients in vivo: a mathematical modeling approach. Phys Med Biol 2010;55:3271–3285.
Jarrett AM, Kazerouni AS, Wu C, et al. Quantitative magnetic resonance imaging and tumor forecasting of breast cancer patients in the community setting. Nat Protoc 2021;16:5309–5338.
Sunassee ED, Tan D, Ji N, et al. Proliferation saturation index in an adaptive Bayesian approach to predict patient-specific radiotherapy responses. Int J Radiat Biol 2019;95:1421–1426.
Zahid MU, Mohamed AS, Latifi K, et al. Proliferation saturation index to characterize response to radiation therapy and evaluate altered fractionation in head and neck cancer. Appl Radiat Oncol 2021;10:32–39.
Bruno R, Bottino D, de Alwis DP, et al. Progress and opportunities to advance clinical cancer therapeutics using tumor dynamic models. Clin Cancer Res 2020;26:1787–1795.