4. Discussion
NSCLC, cancers, RET, cancer, lung cancer, PD, primary lung tumor
CANCERS, CANCER, DISEASE, LUNG CANCER, NSCLC, REGRESSION, DISEASES
The present results show, in advanced NSCLC patients who are eligible for the currently standard first-line pembrolizumab monotherapy, their baseline burden of disease estimated with CT using the “number of involved organs” and smoking history/pack years as independent predictors of an adverse response (i.e., progressive disease, PD) to this treatment. Those who have no smoking history and a higher burden of disease on the baseline CT are at an increased risk of PD. Although smoking is associated with the response to pembrolizumab, compared to smokers with >40 pack years, those with ≤40 pack years have a higher risk for developing PD. For these high risk of progression patients, physicians may consider offering chemotherapy immunotherapy, or immunotherapy combinations upfront to provide more effective early control of the disease.Studies have shown that some clinical parameters could facilitate response identification, such as age, gender, ECOG, and smoking status [From a clinical perspective, misclassifying patients belonging to the PD group (i.e., misclassifying a PD patient into the low-risk of progression group) is arguably more detrimental than misclassifying a DC patient. Therefore, we think the Youden cut-off may be preferred when using this MV prediction model to identify patients at a higher risk to progress, because the corresponding SN with the Youden cut-off is 0.93, which indicates that 93% of patients who are indeed in the PD group will be correctly predicted by the model as the “high-risk of progression group”, in contrast to only 71% if using the “topleft” cut-off.In the current study, we used the “number of involved organs” (by diseases) counted using the baseline CT as the surrogate for the “overall disease burden”, and its positive association with PD suggested a relationship between widespread disease (i.e., more organ involvement) and a poor response to pembrolizumab monotherapy. This measure is not precisely how RECIST 1.1 describes the “disease burden”, as it does not consider the size of each target lesion. However, the multivariate logistic regression showed that it was significantly associated with the response outcome, while the “size of the primary lung tumor” was not. In addition, although it may not be as accurate as RECIST that was commonly used in the trial, in clinical practice, the “number of involved organs” could easily be quantified during the radiological review without labor-intensive measurements for each lesion. We think “the number of involved organs” on a CT could be a simple, meaningful, and practical measures that have the potential to guide the selection of therapy in clinical practice, with the consideration of adding chemotherapy or another immunotherapy. To our knowledge, this has not been reported, and this CT measure may expand our knowledge of the clinicoradiological characteristics of the subsets of this high PD-L1 cohort.The impact of smoking history on the therapeutic response to immunotherapy has been reported, while there is limited data about the effect of pack years and the response in smokers. A recent meta-analysis supported that smoking history could be a simple index to guide the selection of potential responders to immunotherapy among NSCLC patients [Our study endpoint was the treatment response (i.e., PD vs. DC) rather than survival. We chose so, because treatment switching often relies on the response assessed radiographically at the FU CT in clinical practice, and it was the PD patients that we aimed to identify at the baseline before commencing treatment. In addition, we used the disease control (DC) rate (sum of CR, PR, or SD) to reflect the “response to treatment” [Since the discovery of a checkpoint blockade through the anti-PD-1- and anti-PD-L1-based immunotherapies and their successful application in cancers, including lung cancer, identifying which patients are most likely to respond remains a challenge [From a medical imaging perspective, artificial intelligence (AI) and the CT imaging radiomic analysis have demonstrated remarkable progress and are expected to identify wide applications in assessing treatment responses. Future studies using radiomics in this cohort may identify radiomic features that can provide added value to the current results and serve as a “digital biomarker” to predict the responses to immunotherapy in this cohort.The strength of our study is in having a homogeneous patient population (i.e., all patients had PDL1 > 50%, no EGFR and ALK aberrant, received upfront pembrolizumab monotherapy, and had baseline and subsequent CT scans to assess the responses), which enabled us to study this important question. Our study had several limitations. First, it was a retrospective study based at a single cancer center with a relatively small sample size, and most of the patients included were Caucasians. Whether these findings are applicable to other patient populations remains unknown. Secondly, except for EGFR and ALK, the mutation status of many targetable mutations that may impact benefit from ICI are unknown. There is evidence indicating ROS1, RET fusion, and MET exon 14 mutations could associate with an inadequate response to ICIs despite high PD-L1 expression [