# Serological Response Patterns to Assess Treatment Outcomes in Advanced Non-Small Cell Lung Cancer: A Real-World Exploratory Multi-Center Observational Cohort Study

## TL;DR

This study shows that tracking changes in blood tumor markers during treatment can help predict outcomes for lung cancer patients receiving immune therapy.

## Contribution

This is the first study to demonstrate that serum tumor marker dynamics, not just single measurements, can improve treatment outcome predictions in NSCLC patients.

## Key findings

- Serum tumor marker dynamics were associated with progression-free and overall survival in NSCLC patients.
- Distinct serological response patterns could differentiate durable responses from treatment resistance and oligoprogression.
- The findings suggest potential applicability of these dynamics to other cancers and treatment types.

## Abstract

Previous studies mainly investigated singular serum tumor marker (STM) measurements for the management of advanced cancer patients, resulting in differences between recommended cut-off points and associated accuracies in evaluating treatment outcomes. Our exploratory multi-center observational cohort study is the first to show that monitoring of STM dynamics during treatment can help to identify advanced non-small cell lung cancer (NSCLC) patients who are more or less likely to have long-term favorable treatment outcomes upon receiving immune checkpoint inhibitor (ICI)-containing treatment. This way, response classification and decision-making in clinical practice can be improved without the need to use specific cut-off points besides the measured STM’s upper reference limit. Importantly, the dynamics identified in our study can potentially also be applied for other tumor- and systemic treatment-types and other tumor cell analytes facing similar challenges to improve response classification and decision-making across multiple indications.

Background: Previous studies mainly investigated singular serum tumor marker (STM) measurements for the management of advanced cancer patients, resulting in differences between recommended cut-off points and associated accuracies in evaluating treatment outcomes. We aimed to determine which STM dynamics recur during treatment in advanced non-small cell lung cancer (NSCLC) patients with disease control three months after starting with immune checkpoint inhibitor (ICI)-containing treatment and explore whether these dynamics retain information on treatment outcomes. Methods: This real-world exploratory multi-center observational cohort study included advanced NSCLC patients with clinical and radiological disease control three months after starting with ICI-containing treatment and at least three STM measurements for at least one STM during treatment. STM dynamics visualized for all patients were subclassified into three serological response patterns by two investigators who were blinded for treatment outcomes. Results: Between March 2013 and January 2023, 256 patients were included at two thoracic oncology outpatient clinics in The Netherlands. Kaplan–Meier survival analyses showed a significant association between the serological response patterns and both progression-free survival (PFS) and overall survival (OS). Additionally, the serological response patterns could be used to distinguish a durable response versus secondary treatment resistance, and oligoprogression versus systemic progression. Conclusions: Our findings underscore the value of monitoring STM dynamics in advanced NSCLC patients during ICI-containing treatment to improve response classification and decision-making in clinical practice. Future studies should explore the value of the identified dynamics in other tumor- and systemic treatment-types and tumor cell analytes for assessing treatment outcomes across multiple indications.