Incorporating lesion-to-lesion heterogeneity into early oncology decision making

How Does Target Lesion Selection Affect RECIST? A Computer Simulation Study

OBJECTIVES

Response Evaluation Criteria in Solid Tumors (RECIST) is grounded on the assumption that target lesion selection is objective and representative of the change in total tumor burden (TTB) during therapy. A computer simulation model was designed to challenge this assumption, focusing on a particular aspect of subjectivity: target lesion selection.

MATERIALS AND METHODS

Disagreement among readers and the disagreement between individual reader measurements and TTB were analyzed as a function of the total number of lesions, affected organs, and lesion growth.

RESULTS

Disagreement rises when the number of lesions increases, when lesions are concentrated on a few organs, and when lesion growth borders the thresholds of progressive disease and partial response. There is an intrinsic methodological error in the estimation of TTB via RECIST 1.1, which depends on the number of lesions and their distributions. For example, for a fixed number of lesions at 5 and 15, distributed over a maximum of 4 organs, the error rates are observed to be 7.8% and 17.3%, respectively.

CONCLUSIONS

Our results demonstrate that RECIST can deliver an accurate estimate of TTB in localized disease, but fails in cases of distal metastases and multiple organ involvement. This is worsened by the "selection of the largest lesions," which introduces a bias that makes it hardly possible to perform an accurate estimate of the TTB. Including more (if not all) lesions in the quantitative analysis of tumor burden is desirable.

Virtual patient analysis identifies strategies to improve the performance of predictive biomarkers for PD-1 blockade

Patients with metastatic triple-negative breast cancer (TNBC) show variable responses to PD-1 inhibition. Efficient patient selection by predictive biomarkers would be desirable, but is hindered by the limited performance of existing biomarkers. Here, we leveraged in-silico patient cohorts generated using a quantitative systems pharmacology model of metastatic TNBC, informed by transcriptomic and clinical data, to explore potential ways to improve patient selection. We tested 90 biomarker candidates, including various cellular and molecular species, by a cutoff-based biomarker testing algorithm combined with machine learning-based feature selection. Combinations of pre-treatment biomarkers improved the specificity compared to single biomarkers at the cost of reduced sensitivity. On the other hand, early on-treatment biomarkers, such as the relative change in tumor diameter from baseline measured at two weeks after treatment initiation, achieved remarkably higher sensitivity and specificity. Further, blood-based biomarkers had a comparable ability to tumor- or lymph node-based biomarkers in identifying a subset of responders, potentially suggesting a less invasive way for patient selection.

Eliciting the antitumor immune response with a conditionally activated PD-L1 targeting antibody analyzed with a quantitative systems pharmacology model

Conditionally activated molecules, such as Probody therapeutics (PbTx), have recently been investigated to improve antitumoral response while reducing systemic toxicity. PbTx are engineered to be proteolytically activated by proteases that are preferentially active locally in the tumor microenvironment (TME). Here, we perform an exploratory study using our recently published quantitative systems pharmacology model, previously validated for other drugs, to evaluate the effectiveness and targeting specificity of an anti-PD-L1 PbTx compared to the non-modified antibody. We have informed the model using the PbTx dynamics and pharmacokinetics published in the literature for anti-PD-L1 in patients with triple-negative breast cancer (TNBC). Our results suggest masking of the antibody slightly decreases its efficacy, while increasing the localization of active therapeutic component in the TME. We also perform a parameter optimization for the PbTx design and drug dosing regimens to maximize the response rate. Although our results are specific to the case of TNBC, our findings are generalizable to any conditionally activated PbTx molecule in solid tumors and suggest that design of a highly effective and selective PbTx is feasible.