In multiple malignancies, checkpoint inhibitor therapy has an established role in the first-line treatment setting. However, only a subset of patients benefit from checkpoint inhibition, and as a result, the field of biomarker research is active. Molecular imaging with the use of positron emission tomography (PET) is one of the biomarkers that is being studied. PET tracers such as conventional ¹⁸F-FDG but also PD-(L)1 directed tracers are being evaluated for their predictive power. Furthermore, the use of artificial intelligence is under evaluation for the purpose of response prediction. Response evaluation during checkpoint inhibitor therapy can be challenging due to the different response patterns that can be observed compared to traditional chemotherapy. The additional information provided by PET can potentially be of value to evaluate a response early after the start of treatment and provide the clinician with important information about the efficacy of immunotherapy. Furthermore, the use of PET to stratify between patients with a complete response and those with a residual disease can potentially guide clinicians to identify patients for which immunotherapy can be discontinued and patients for whom the treatment needs to be escalated. This review provides an overview of the use of positron emission tomography (PET) to predict and evaluate treatment response to immunotherapy.

Circulating tumor cells (CTCs) have been detected in many patients with different solid malignancies. It has been reported that presence of CTCs correlates with worse survival in patients with multiple types of cancer. Several techniques have been developed to detect CTCs in liquid biopsies. Currently, the only method for CTC detection that is approved by the Food and Drug Administration is CellSearch. Due to low abundance of CTCs in certain cancer types and in early stages of disease, its clinical application is currently limited to metastatic colorectal cancer, breast cancer and prostate cancer. Therefore, we aimed to develop a new method for the detection of CTCs using the Attune NxT—a flow cytometry-based application that was specifically developed to detect rare events in biological samples without the need for enrichment. When healthy donor blood samples were spiked with variable amounts of different EpCAM+EGFR+ tumor cell lines, recovery yield was on average 75%. The detection range was between 1000 and 10 cells per sample. Cell morphology was confirmed with the Attune CytPix. Analysis of blood samples from metastatic colorectal cancer patients, as well as lung cancer patients, demonstrated that increased EpCAM+EGFR+ events were detected in more than half of the patient samples. However, most of these cells showed no (tumor) cell-like morphology. Notably, CellSearch analysis of blood samples from a subset of colorectal cancer patients did not detect CTCs either, suggesting that these blood samples were negative for CTCs. Therefore, we anticipate that the Attune NxT is not superior to CellSearch in detection of low amounts of CTCs, although handling and analysis of samples is easier. Moreover, morphological confirmation is essential to distinguish between CTCs and false positive events.

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.