Monitoring biomarkers in non-small cell lung cancer patients treated with immune checkpoint inhibitors

The introduction of immune checkpoint inhibitors (ICIs) has revolutionized the approach to advanced non-small cell lung cancer (NSCLC) by offering durable disease control with less side effects than traditional chemotherapy.1 However, as most patients do not benefit from ICIs, it is important to identify potential well-responders.1 Response assessment by conventional imaging is frequently unable to identify patients who will achieve durable clinical benefit (DCB), and the radiologic assessment of ICI response is neither accurate nor prompt.2,3 Dr. Qing Zhou, Guangdong Lung Cancer Institute, China, explained that tissue biomarkers are traditionally obtained from tissue biopsies which require invasive, risky and costly surgical interventions.2 Also, sufficient tumor tissue molecular analysis may not be obtainable in a substantial number of patients.2 As a result, it may be more difficult to find monitoring biomarkers to predict drug response, resistance and disease relapse for immunotherapy than for genomic or proteomic therapy.

To address the unmet needs from tissue biomarkers, circulating tumor DNA (ctDNA) is a promising biomarker that is expected to have greater specificity than most serum protein markers as it is a byproduct of dying cancer cells - its level provides a real-time snapshot of active tumor cell death.1 In a study that evaluated the longitudinal changes in ctDNA levels among NSCLC patients receiving ICIs, ctDNA response was found to precede and correlate with radiographic response of tumors.1 In addition, a reduction in ctDNA level to half its pre-treatment value was associated with improved patient survival, indicating that ctDNA monitoring could provide an early measure of therapeutic efficacy.1 Peripheral CD8 T-cell levels were also found to be independently associated with DCB in stage IV NSCLC patients receiving programmed death ligand-1 (PD-L1) blockade-based ICIs.2 As such, the benefit of ICIs can be better predicted by integrating pre-treatment ctDNA, pretreatment circulating immune cell profiling and early on-treatment ctDNA dynamics in a Bayesian model than assessing the individual parameters.2 Using this model, subsequent treatment strategies following PD-L1 or other ICIs can be better personalized.2

Apart from predicting the drug response, ctDNA can also be used to identify patients at high risk of disease recurrence by monitoring the post-surgical minimal residual disease (MRD).6 As MRD is defined as cancer that persists after treatment, ctDNA can be used to monitor this occult stage of cancer progression.4 In addition, circulating tumor cells (CTCs) can enable subsequent analyses at the DNA, RNA and protein levels to compliment ctDNA analyses that identify genetic and epigenetic changes in the DNA.4 The use of CTCs and ctDNA for detecting micro-metastasis would also enable the testing of new adjuvant or post-adjuvant treatment strategies to delay or prevent disease progression, and Dr. Zhou shared that there are several ongoing clinical trials that investigate MRD monitoring after adjuvant ICI treatment.4

However, Dr. Zhou also pointed out that ctDNA monitoring remains a clinical challenge in practice. As only a small amount of ctDNA is shed by tumors during the early cancer stage, the most sensitive NGS-based method to detect ctDNA, i.e.,CAPP-seq method, can only detect 50% of stage I cancer. ctDNA levels need to be quantified using maximum or mean variant allele frequencies or ctDNA concentration and do not have a consistent definition. Additionally, a positive ctDNA response is variably defined as any decrease from baseline to up to 90% decrease from baseline. Similarly, the challenges of monitoring MRD also include determining the detection threshold, as well as optimal detection time and follow-up intervals. “In the future, we need to do the clinical decision-making model based on big data and artificial intelligence mechanistic learning. We can combine the baseline data, dynamic biomarker data and long-term survival data in one model, so that we can make the best clinical decision for a specific patient,” concluded Dr. Zhou.

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