Revisiting a Null Hypothesis: Exploring the Parameters of Oligometastasis Treatment

Treatment of evolving cancers will require dynamic decision support

Cancer research has traditionally focused on developing new agents, but an underexplored question is that of the dose and frequency of existing drugs. Based on the modus operandi established in the early days of chemotherapies, most drugs are administered according to predetermined schedules that seek to deliver the maximum tolerated dose and are only adjusted for toxicity. However, we believe that the complex, evolving nature of cancer requires a more dynamic and personalized approach. Chronicling the milestones of the field, we show that the impact of schedule choice crucially depends on processes driving treatment response and failure. As such, cancer heterogeneity and evolution dictate that a one-size-fits-all solution is unlikely-instead, each patient should be mapped to the strategy that best matches their current disease characteristics and treatment objectives (i.e. their 'tumorscape'). To achieve this level of personalization, we need mathematical modeling. In this perspective, we propose a five-step 'Adaptive Dosing Adjusted for Personalized Tumorscapes (ADAPT)' paradigm to integrate data and understanding across scales and derive dynamic and personalized schedules. We conclude with promising examples of model-guided schedule personalization and a call to action to address key outstanding challenges surrounding data collection, model development, and integration.

Intermetastatic and Intrametastatic Heterogeneity Shapes Adaptive Therapy Cycling Dynamics

Adaptive therapies that alternate between drug applications and drug-free vacations can exploit competition between sensitive and resistant cells to maximize the time to progression. However, optimal dosing schedules depend on the properties of metastases, which are often not directly measurable in clinical practice. Here, we proposed a framework for estimating features of metastases through tumor response dynamics during the first adaptive therapy treatment cycle. Longitudinal prostate-specific antigen (PSA) levels in 16 patients with metastatic castration-resistant prostate cancer undergoing adaptive androgen deprivation treatment were analyzed to investigate relationships between cycle dynamics and clinical variables such as Gleason score, the change in the number of metastases over a cycle, and the total number of cycles over the course of treatment. The first cycle of adaptive therapy, which consists of a response period (applying therapy until 50% PSA reduction), and a regrowth period (removing treatment until reaching initial PSA levels), delineated several features of the computational metastatic system: larger metastases had longer cycles; a higher proportion of drug-resistant cells slowed the cycles; and a faster cell turnover rate sped up drug response time and slowed regrowth time. The number of metastases did not affect cycle times, as response dynamics were dominated by the largest tumors rather than the aggregate. In addition, systems with higher intermetastasis heterogeneity responded better to continuous therapy and correlated with dynamics from patients with high or low Gleason scores. Conversely, systems with higher intrametastasis heterogeneity responded better to adaptive therapy and correlated with dynamics from patients with intermediate Gleason scores.

SIGNIFICANCE

Multiscale mathematical modeling combined with biomarker dynamics during adaptive therapy helps identify underlying features of metastatic cancer to inform treatment decisions.

Mathematical prediction with pretreatment growth rate of metastatic cancer on outcomes: implications for the characterization of oligometastatic disease

Background

Oligometastatic disease (OMD) represents an indolent cancer status characterized by slow tumor growth and limited metastatic potential. The use of local therapy in the management of the condition continues to rise. This study aimed to investigate the advantage of pretreatment tumor growth rate in addition to baseline disease burden in characterizing OMDs, generally defined by the presence of ≤ 5 metastatic lesions.

Methods

The study included patients with metastatic melanoma treated with pembrolizumab. Gross tumor volume of all metastases was contoured on imaging before (TP_-1) and at the initiation of pembrolizumab (TP₀). Pretreatment tumor growth rate was calculated by an exponential ordinary differential equation model using the sum of tumor volumes at TP_-1 and TP₀ and the time interval between TP_-1. and TP₀. Patients were divided into interquartile groups based on pretreatment growth rate. Overall survival, progression-free survival, and subsequent progression-free survival were the study outcomes.

Results

At baseline, median cumulative volume and number of metastases were 28.4 cc (range, 0.4-1194.8 cc) and 7 (range, 1-73), respectively. The median interval between TP_-1 and TP₀ was -90 days and pretreatment tumor growth rate (×10^-2 days^-1) was median 4.71 (range -0.62 to 44.1). The slow-paced group (pretreatment tumor growth rate ≤ 7.6 ×10^-2 days^-1, the upper quartile) had a significantly higher overall survival rate, progression-free survival, and subsequent progression-free survival compared to those of the fast-paced group (pretreatment tumor growth rate > 7.6 ×10^-2 days^-1). Notably, these differences were prominent in the subgroup with >5 metastases.

Conclusion

Pretreatment tumor growth rate is a novel prognostic metric associated with overall survival, progression-free survival, and subsequent progression-free survival among metastatic melanoma patients, especially patients with >5 metastases. Future prospective studies should validate the advantage of disease growth rate plus disease burden in better defining OMDs.

Role of local ablative treatment in oligometastatic non-small cell lung cancer: a meta-analysis

INTRODUCTION

This meta-analysis analyzed the oncologic role of local ablative treatment (LAT) in oligometastatic nonsmall cell lung cancer.

METHOD

Pubmed, MEDLINE, Embase, and Cochrane Library were searched until October, 2022. Studies comparing LAT with standard care (control) were included. Sensitivity analyses were performed including randomized controlled studies (RCTs). Subgroup analyses were performed according to specific categories and metastatic burden. The primary endpoints were overall survival (OS) and progression-free survival (PFS). Considering the median OS and PFS from landmark studies, 2-year OS and 1-year PFS rates were used to calculate pooled odds ratios (ORs).

RESULTS

A total of 20 studies (four RCTs) encompassing 1750 patients were included. Surgery and radiotherapy (60 and 90% of studies) were mainly used as LATs. Pooled ORs of OS and PFS were 3.492 (95% CI:2.612-4.699, P <0.001) and 3.743 (95% CI: 2.586-5.419, P <0.001), favoring LAT, respectively. Sensitivity analyses, including RCTs showed ORs of 4.111 ( P <0.001) and 4.959 ( P =0.001) regarding OS and PFS, favoring LCT, respectively. Pooled 1-year and 2-year OS rates were 83.8 and 58.4% in LAT arms, whereas 64.4 and 31% in control arms; pooled 1-year and 2-year PFS rates were 64.6 and 32.8% in LAT arms, and 36.1 and 10% in control arms. In subgroup analyses, the pooled ORs were 3.981 ( P <0.001), 3.355 ( P <0.001), and 1.726 ( P =0.373) in synchronous, oligopersistence, and oligoprogression/recurrence subgroups, respectively. Regarding PFS comparison, pooled ORs were 5.631 ( P <0.001), 3.484 ( P <0.001), and 1.777 ( P =0.07), respectively. According to metastatic burden categories, pooled ORs favored LAT arms in both analyses including low-metastatic and high-metastatic burden subgroups.

CONCLUSION

The present study supports the role of LAT in treating nonsmall cell lung cancer oligometastasis. The oligoprogression/recurrence disease could have less LAT benefit than synchronous or oligopersistent disease.

Inferring density-dependent population dynamics mechanisms through rate disambiguation for logistic birth-death processes

Density dependence is important in the ecology and evolution of microbial and cancer cells. Typically, we can only measure net growth rates, but the underlying density-dependent mechanisms that give rise to the observed dynamics can manifest in birth processes, death processes, or both. Therefore, we utilize the mean and variance of cell number fluctuations to separately identify birth and death rates from time series that follow stochastic birth-death processes with logistic growth. Our nonparametric method provides a novel perspective on stochastic parameter identifiability, which we validate by analyzing the accuracy in terms of the discretization bin size. We apply our method to the scenario where a homogeneous cell population goes through three stages: (1) grows naturally to its carrying capacity, (2) is treated with a drug that reduces its carrying capacity, and (3) overcomes the drug effect to restore its original carrying capacity. In each stage, we disambiguate whether the dynamics occur through the birth process, death process, or some combination of the two, which contributes to understanding drug resistance mechanisms. In the case of limited sample sizes, we provide an alternative method based on maximum likelihood and solve a constrained nonlinear optimization problem to identify the most likely density dependence parameter for a given cell number time series. Our methods can be applied to other biological systems at different scales to disambiguate density-dependent mechanisms underlying the same net growth rate.