Enabling Evolutionary Therapy in Metastatic Cancer Lacking Serum Biomarkers

TL;DR

This paper demonstrates that volumetric 3D imaging and automated lesion segmentation are essential for effective evolutionary therapy in cancers lacking serum biomarkers, as traditional RECIST criteria are insufficient.

Contribution

It introduces a virtual NSCLC model showing that 3D volumetric measurements outperform RECIST in tracking tumor dynamics for adaptive therapy.

Findings

3D volumetric measurements accurately track tumor burden.

Lesion selection and measurement dimensionality impact progression detection.

Automated segmentation enhances response assessment.

Abstract

Evolutionary therapy (ET) aims to steer tumor evolution by adjusting treatment timing and dosing to control rather than eradicate tumor burden. Clinical use requires reliable monitoring of tumor dynamics to inform mathematical models that guide therapy. In cancers such as metastatic castrate-resistant prostate cancer and relapsed platinum-sensitive ovarian cancer, ET models are informed by serial serum biomarkers. For cancers lacking reliable biomarkers, such as metastatic non-small cell lung cancer (NSCLC), radiographic imaging remains the primary method for treatment response assessment, typically using RECIST 1.1 criteria. RECIST, which tracks a few lesions with one-dimensional (1D) measurements and defines progression relative to the nadir, the smallest tumor burden recorded after treatment, was not designed to support ET. It may miss early regrowth, underrepresent tumor burden, and obscure disease trends. Using a virtual NSCLC patient model, we demonstrate that lesion selection and measurement dimensionality strongly affect progression detection. Two-dimensional metrics provide modest improvement, but only 3D volumetric measurements accurately capture both tumor burden and its dynamics, which are key requirements for ET. To support ET in cancers lacking biomarkers, response assessment must evolve beyond RECIST by integrating volumetric imaging, automated segmentation, and potentially liquid biopsies, alongside redefining progression criteria to enable adaptive, patient-centered treatments.