Plasma Biomarkers and Immune Checkpoint Inhibitors in Non-Small Cell Lung Cancer: New Tools for Better Patient Selection?

Prognostic Factors in Advanced Non-Small Cell Lung Cancer Patients Treated with Immunotherapy

Taking into account the huge epidemiologic impact of lung cancer (in 2020, lung cancer accounted for 2,206,771 of the cases and for 1,796,144 of the cancer-related deaths, representing the second most common cancer in female patients, the most common cancer in male patients, and the second most common cancer in male and female patients) and the current lack of recommendations in terms of prognostic factors for patients selection and management, this article aims to provide an overview of the current landscape in terms of currently available immunotherapy treatments and the most promising assessed prognostic biomarkers, highlighting the current state-of-the-art and hinting at future challenges.

The prognostic biological markers of immunotherapy for non-small cell lung cancer: current landscape and future perspective

The emergence of immunotherapy, particularly programmed cell death 1 (PD-1) and programmed cell death ligand-1 (PD-L1) produced profound transformations for treating non-small cell lung cancer (NSCLC). Nevertheless, not all NSCLC patients can benefit from immunotherapy in clinical practice. In addition to limited response rates, exorbitant treatment costs, and the substantial threats involved with immune-related adverse events, the intricate interplay between long-term survival outcomes and early disease progression, including early immune hyperprogression, remains unclear. Consequently, there is an urgent imperative to identify robust predictive and prognostic biological markers, which not only possess the potential to accurately forecast the therapeutic efficacy of immunotherapy in NSCLC but also facilitate the identification of patient subgroups amenable to personalized treatment approaches. Furthermore, this advancement in patient stratification based on certain biological markers can also provide invaluable support for the management of immunotherapy in NSCLC patients. Hence, in this review, we comprehensively examine the current landscape of individual biological markers, including PD-L1 expression, tumor mutational burden, hematological biological markers, and gene mutations, while also exploring the potential of combined biological markers encompassing radiological and radiomic markers, as well as prediction models that have the potential to better predict responders to immunotherapy in NSCLC with an emphasis on some directions that warrant further investigation which can also deepen the understanding of clinicians and provide a reference for clinical practice.

Soluble biomarkers to predict clinical outcomes in non-small cell lung cancer treated by immune checkpoints inhibitors

Lung cancer remains the first cause of cancer-related death despite many therapeutic innovations, including immune checkpoint inhibitors (ICI). ICI are now well used in daily practice at late metastatic stages and locally advanced stages after a chemo-radiation. ICI are also emerging in the peri-operative context. However, all patients do not benefit from ICI and even suffer from additional immune side effects. A current challenge remains to identify patients eligible for ICI and benefiting from these drugs. Currently, the prediction of ICI response is only supported by Programmed death-ligand 1 (PD-L1) tumor expression with perfectible results and limitations inherent to tumor-biopsy specimen analysis. Here, we reviewed alternative markers based on liquid biopsy and focused on the most promising biomarkers to modify clinical practice, including non-tumoral blood cell count such as absolute neutrophil counts, platelet to lymphocyte ratio, neutrophil to lymphocyte ratio, and derived neutrophil to lymphocyte ratio. We also discussed soluble-derived immune checkpoint-related products such as sPD-L1, circulating tumor cells (detection, count, and marker expression), and circulating tumor DNA-related products. Finally, we explored perspectives for liquid biopsies in the immune landscape and discussed how they could be implemented into lung cancer management with a potential biological-driven decision.

A novel senescence-related lncRNA signature that predicts prognosis and the tumor microenvironment in patients with lung adenocarcinoma

Background: Cellular senescence has recently been considered a new cancer hallmark. However, the factors regulating cellular senescence have not been well characterized. The aim of this study is to identify long non-coding RNAs (lncRNAs) associated with senescence and prognosis in patients with lung adenocarcinoma (LUAD). Methods: Using RNA sequence data from the Cancer Genome Atlas Lung Adenocarcinoma (TCGA-LUAD) and senescence genes from the CellAge database, a subset of senescence-related lncRNAs was first identified. Then, using univariate and multivariate Cox regression analyses, a senescence lncRNA signature (LUADSenLncSig) associated with LUAD prognosis was developed. Based on the median LUADSenLncSig risk score, LUAD patients were divided into high-risk and low-risk groups. Kaplan-Meier analysis was used to compare the overall survival (OS) in the high- and low-risk score subgroups. Differences in Gene Set Enrichment Analysis (GSEA), immune infiltration, tumor mutation burden (TMB), tumor immune dysfunction and exclusion (TIDE) module score, chemotherapy, and targeted therapy selection were also compared between the high-risk and low-risk groups. Results: A prognostic risk model was obtained consisting of the following nine senescence-related lncRNAs: LINC01116, AC005838.2, SH3PXD2A-AS1, VIMS-AS1, SH3BP5-AS1, AC092279.1, AC026355.1, AC027020.2, and LINC00996. The LUADSenLncSig high-risk group was associated with poor OS (hazard ratio = 1.17, 95% confidence interval = 1.102-1.242; p < 0.001). The accuracy of the model was further supported based on receiver operating characteristic (ROC), principal component analysis (PCA), and internal validation cohorts. In addition, a nomogram was developed consisting of LUADSenLncSig for LUAD prognosis, which is consistent with the actual probability of OS. Furthermore, immune infiltration analysis showed the low-risk group had a stronger anti-tumor immune response in the tumor microenvironment. Notably, the levels of immune checkpoint genes such as CTLA-4, PDCD-1, and CD274, and the TIDE scores were significantly higher in the low-risk subgroups than in high-risk subgroups (p < 0.001). This finding indicates the LUADSenLncSig can potentially predict immunotherapy efficacy. Conclusion: In this study, a lncRNA signature, LUADSenLncSig, that has dual functions of senescence phenotype identification and prognostic prediction as well as the potential to predict the LUAD response to immunotherapy was developed.

High Circulating Sonic Hedgehog Protein Is Associated With Poor Outcome in EGFR-Mutated Advanced NSCLC Treated With Tyrosine Kinase Inhibitors

Introduction

Growing preclinical evidence has suggested that the Sonic hedgehog (Shh) pathway is involved in resistance to tyrosine kinase inhibitor (TKI) therapy for EGFR-mutated (EGFRm) non-small cell lung cancer (NSCLC). However, little is known concerning the prognostic value of this pathway in this context.

Materials and Methods

We investigated the relationship between plasma levels of Shh and EGFRm NSCLC patients’ outcome with EGFR TKIs. We included 74 consecutive patients from two institutions with EGFRm advanced NSCLC treated by EGFR TKI as first-line therapy. Plasma samples were collected longitudinally for each patient and were analyzed for the expression of Shh using an ELISA assay. The activation of the Shh–Gli1 pathway was assessed through immunohistochemistry (IHC) of Gli1 and RT-qPCR analysis of the transcripts of Gli1 target genes in 14 available tumor biopsies collected at diagnosis (baseline).

Results

Among the 74 patients, only 61 had baseline (diagnosis) plasma samples, while only 49 patients had plasma samples at the first evaluation. Shh protein was detectable in all samples at diagnosis (n = 61, mean = 1,041.2 ± 252.5 pg/ml). Among the 14 available tumor biopsies, nuclear expression of Gli1 was observed in 57.1% (8/14) of patients’ biopsies. Shh was significantly (p < 0.05) enriched in youth (age < 68), male, nonsmokers, patients with a PS > 1, and patients presenting more than 2 metastatic sites and L858R mutation. Higher levels of Shh correlated with poor objective response to TKI, shorter progression-free survival (PFS), and T790M-independent mechanism of resistance. In addition, the rise of plasma Shh levels along the treatment was associated with the emergence of drug resistance in patients presenting an initial good therapy response.

Conclusion

These data support that higher levels of plasma Shh at diagnosis and increased levels of Shh along the course of the disease are related to the emergence of TKI resistance and poor outcome for EGFR-TKI therapy, suggesting that Shh levels could stand both as a prognostic and as a resistance biomarker for the management of EGFR-mutated NSCLC patients treated with EGFR-TKI.

Soluble PD-L1 as a predictive biomarker in lung cancer: a systematic review and meta-analysis

Background: We performed a meta-analysis to evaluate the association between soluble PD-L1 (sPD-L1) and survival outcomes and treatment response in lung cancer. Methods & methods: Eligible studies were obtained by searching PubMed, EMBASE and Web of Science. Pooled effect estimates were calculated for overall survival (OS), progression-free survival (PFS) and objective response rate (ORR). Results: Twelve eligible studies with 1188 lung cancer patients were included. High sPD-L1 was significantly associated with worse OS (hazard ratio [HR] = 2.20; 95% CI: 1.59-3.05; p < 0.001) and PFS (HR = 2.42; 95% CI: 1.72-3.42; p < 0.001) in patients treated with immune checkpoint inhibitors (ICIs). Meanwhile, high sPD-L1 predicted worse OS (HR = 1.60; 95% CI: 1.31-1.96; p < 0.001) and lower ORR (odds ratio = 0.52; 95% CI: 0.35-0.80; p = 0.002) in patients treated with non-ICI therapies. Conclusion: sPD-L1 is a potential predictive biomarker of lung cancer.

PD-L1 expression as a predictive biomarker for immune checkpoint inhibitors: between a dream and a nightmare

PD-L1 is an important predictive biomarker for treatment by immune checkpoint inhibitors (ICIs). ICIs are now indicated for the treatment of various cancer depending on the level of expression of PD-L1 on tumor cells. PD-L1 testing is done using immunohistochemistry with five different assays approved as companion diagnostic for ICIs. However, these assays have different score reporting methods and do not accurately measure PD-L1 expression. Exosomal PD-L1 testing has recently emerged as an alternative for cell-surface PD-L1 testing however studies are still premature and more extensive knowledge about this new potential biomarker is needed.

Soluble Programmed Death Ligand-1 (sPD-L1): A Pool of Circulating Proteins Implicated in Health and Diseases

Upon T-cell receptor stimulation, the Programmed cell Death-1 receptor (PD-1) expressed on T-cells can interact with its ligand PD-L1 expressed at the surface of cancer cells or antigen-presenting cells. Monoclonal antibodies targeting PD-1 or PD-L1 are routinely used for the treatment of cancers, but their clinical efficacy varies largely across the variety of tumor types. A part of the variability is linked to the existence of several forms of PD-L1, either expressed on the plasma membrane (mPD-L1), at the surface of secreted cellular exosomes (exoPD-L1), in cell nuclei (nPD-L1), or as a circulating, soluble protein (sPD-L1). Here, we have reviewed the different origins and roles of sPD-L1 in humans to highlight the biochemical and functional heterogeneity of the soluble protein. sPD-L1 isoforms can be generated essentially by two non-exclusive processes: (i) proteolysis of m/exoPD-L1 by metalloproteases, such as metalloproteinases (MMP) and A disintegrin and metalloproteases (ADAM), which are capable of shedding membrane PD-L1 to release an active soluble form, and (ii) the alternative splicing of PD-L1 pre-mRNA, leading in some cases to the release of sPD-L1 protein isoforms lacking the transmembrane domain. The expression and secretion of sPD-L1 have been observed in a large variety of pathologies, well beyond cancer, notably in different pulmonary diseases, chronic inflammatory and autoimmune disorders, and viral diseases. The expression and role of sPD-L1 during pregnancy are also evoked. The structural heterogeneity of sPD-L1 proteins, and associated functional/cellular plurality, should be kept in mind when considering sPD-L1 as a biomarker or as a drug target. The membrane, exosomal and soluble forms of PD-L1 are all integral parts of the highly dynamic PD-1/PD-L1 signaling pathway, essential for immune-tolerance or immune-escape.

Circulating tumor DNA in lung cancer: real-time monitoring of disease evolution and treatment response

Lung cancer is one of the leading causes of all cancer-related deaths. Circulating tumor DNA (ctDNA) is released from apoptotic and necrotic tumor cells. Several sensitive techniques have been invented and adapted to quantify ctDNA genomic alterations. Applications of ctDNA in lung cancer include early diagnosis and detection, prognosis prediction, detecting mutations and structural alterations, minimal residual disease, tumor mutational burden, and tumor evolution tracking. Compared to surgical biopsy and radiographic imaging, the advantages of ctDNA are that it is a non-invasive procedure, allows real-time monitoring, and has relatively high sensitivity and specificity. Given the massive research on non-small cell lung cancer, attention should be paid to small cell lung cancer.

Combining liquid biopsy and radiomics for personalized treatment of lung cancer patients. State of the art and new perspectives

Lung cancer has become a paradigm for precision medicine in oncology, and liquid biopsy (LB) together with radiomics may have a great potential in this scenario. They are both minimally invasive, easy to perform, and can be repeated during patient's follow-up. Also, increasing evidence suggest that LB and radiomics may provide an efficient way to screen and diagnose tumors at an early stage, including the monitoring of any change in the tumor molecular profile. This could allow treatment optimization, improvement of patients' quality of life, and healthcare-related costs reduction. Latest reports on lung cancer patients suggest a combination of these two strategies, along with cutting-edge data analysis, to decode valuable information regarding tumor type, aggressiveness, progression, and response to treatment. The approach seems more compatible with clinical practice than the current standard, and provides new diagnostic companions being able to suggest the best treatment strategy compared to conventional methods. To implement radiomics and liquid biopsy directly into clinical practice, an artificial intelligence (AI)-based system could help to link patients' clinical data together with tumor molecular profiles and imaging characteristics. AI could also solve problems and limitations related to LB and radiomics methodologies. Further work is needed, including new health policies and the access to large amounts of high-quality and well-organized data, allowing a complementary and synergistic combination of LB and imaging, to provide an attractive choice e in the personalized treatment of lung cancer.

Anticipating metastasis through electrochemical immunosensing of tumor hypoxia biomarkers

Metastasis is responsible for about 90% of cancer-associated deaths. In the context of solid tumors, the low oxygen concentration in the tumor microenvironment (hypoxia) is one of the key factors contributing to metastasis. Tumor cells adapt to these conditions by overexpressing certain proteins such as programmed death ligand 1 (PD-L1) and hypoxia-inducible factor 1 alpha (HIF-1α). However, the determination of these tumor hypoxia markers that can be used to follow-up tumor progression and improve the efficiency of therapies has been scarcely addressed using electrochemical biosensors. In this work, we report the first electrochemical bioplatform for the determination of PD-L1 as well as the first one allowing its simultaneous determination with HIF-1α. The target proteins were captured and enzymatically labeled on magnetic microbeads and amperometric detection was undertaken on the surface of screen-printed dual carbon electrodes using the hydrogen peroxide/peroxidase/hydroquinone system. Sandwich immunoassays were implemented for both the HIF-1α and PD-L1 sensors and the analytical characteristics were evaluated providing LOD values of 86 and 279 pg mL^-1 for the amperometric determination of PD-L1 and HIF-1α standards, respectively. The developed electrochemical immunoplatforms are competitive versus the only electrochemical immunosensor reported for the determination of HIF-1α and the "gold standard" ELISA methodology for the single determination of both proteins in terms of assay time, compatibility with the simultaneous determination of both proteins making their use suitable for untrained users at the point of attention. The dual amperometric immunosensor was applied to the simultaneous determination of HIF-1α and PD-L1 in cancer cell lysates. The analyses lasted only 2 h and just 0.5 μg of the sample was required.

Targeted Sequencing of Circulating Cell Free DNA Can Be Used to Monitor Therapeutic Efficacy of Tyrosine Kinase Inhibitors in Non-small Cell Lung Cancer Patients

BACKGROUND/AIM

Circulating tumor DNA (ctDNA) bears specific mutations derived from tumor cells. The amount of mutant ctDNA may reflect tumor burden. In this study, we detected epidermal growth factor receptor (EGFR) mutations in ctDNA as a monitoring marker for the response of non-small cell lung cancer (NSCLC) patients to tyrosine kinase inhibitors (TKIs).

PATIENTS AND METHODS

Serial plasma samples from eight NSCLC patients during TKI treatment were collected. Libraries with barcoded adapters were constructed from ctDNA of these plasma samples using a PCR-based targeted DNA panel. The libraries were then sequenced for measuring EGFR mutations. In addition, carcinoembryonic antigen (CEA) was also measured in these patients.

RESULTS

In six patients who suffered disease progression (PD), five had elevated EGFR mutation reads before PD. In the two patients who did not develop PD, EGFR mutations remained undetectable in their plasma. The CEA levels were higher than the cutoff value in most samples and had a poor correlation with disease status.

CONCLUSION

The mutation count of tumor-specific mutations can be a monitoring marker of TKI treatment in NSCLC patients.

Microbiome: A Supportive or a Leading Actor in Lung Cancer?

Lung cancer is still the leading cause of cancer death worldwide. Despite the major diagnostic and therapeutic innovations, the effect on mortality has been modest and the overall survival is still poor. Better understanding of the pathology of these tumors is necessary in order to develop personalized therapeutic strategies in lung cancer patients. Human microbiome has been associated with normal physiology and function, and increasing evidence points towards a key role of the microbiome in promoting the progression of lung disease. Studies have shown that although poorly understood, lung has a distinctive microbiome that may an important role in lung cancer development and progression, and interactions between microbial populations have the potential to influence disease, suggesting that microbiome can be an emerging target in cancer therapeutics. We will review mechanisms how the lung microbiota influences carcinogenesis, focusing on the bacterial dysbiosis and inflammation. Moreover, we will discuss the link between the microbiome and cancer and the consequences induced by the immune system, as the host microbiota plays an essential role in activating and modulating the immune response. We summarize current research advances in the lung microbiome and demonstrate the potential to exploit microbiome as a mechanism to prevent carcinogenesis and modulate therapeutic strategy, suggesting microbiome as a valuable approach in lung cancer patients.

Clinically relevant prognostic and predictive markers for immune-checkpoint-inhibitor (ICI) therapy in non-small cell lung cancer (NSCLC)

BACKGROUND

Immune checkpoint inhibitors (ICI) either alone or in combination with chemotherapy have expanded our choice of agents for the palliative treatment of non-small cell lung cancer (NSCLC) patients. Unfortunately, not all patients will experience favorable response to treatment with ICI and may even suffer from severe side effects. Therefore, prognostic and predictive markers, beyond programmed death ligand 1 (PD-L1) expression status, are of utmost importance for decision making in the palliative treatment. This review focuses on clinical, laboratory and genetic markers, most of them easily to obtain in the daily clinical practice.

RESULTS

Recently, a number of prognostic and predictive factors in association to palliative ICI therapy have been described in NSCLC. Besides biometric parameters and clinical characteristics of the tumor, there are useful markers from routine blood sampling as well as innovative soluble genetic markers which can be determined before and during ICI treatment. Additionally, the level of evidence is noted.

CONCLUSIONS

These factors can be helpful to predict patients' outcome and tumor response to ICI. They should be implemented prospectively in ICI based clinical trials to develop reliable algorithms for palliative NSCLC treatment.

Immune Checkpoint Inhibitor-Related Pneumonitis

Immune checkpoint inhibitors are novel agents that have been proved efficacious in a variety of cancer types, but they are associated with a unique set of organ-specific, immune-related adverse events. Among them, immune-related pneumonitis requires special attention because it is difficult to diagnose and potentially lethal. Accumulating real-world epidemiological data suggest that immune-related pneumonitis is more frequent than previously reported. Its diagnosis requires exclusion of other causes and assessment of radiographic features on high-resolution CT of the chest. Management of immune-related pneumonitis is based on the use of immunosuppressants. Future research should be focused on finding predictive biomarkers for immune-related pneumonitis as well as optimizing its management.

Soluble Immune Checkpoints, Gut Metabolites and Performance Status as Parameters of Response to Nivolumab Treatment in NSCLC Patients

Patients with non-small cell lung cancer (NSCLC) have been shown to benefit from the introduction of anti-PD1 treatment. However, not all patients experience tumor regression and durable response. The identification of a string of markers that are direct or indirect indicators of the immune system fitness is needed to choose optimal therapeutic schedules in the management of NSCLC patients. We analyzed 34 immuno-related molecules (14 soluble immune checkpoints, 17 cytokines/chemokines, 3 adhesion molecules) released in the serum of 22 NSCLC patients under Nivolumab treatment and the gut metabolomic profile at baseline. These parameters were correlated with performance status (PS) and/or response to treatment. Nivolumab affected the release of soluble immune checkpoints (sICs). Patients with a better clinical outcome and with an optimal PS (PS = 0) showed a decreased level of PD1 and maintained low levels of several sICs at first clinical evaluation. Low levels of PDL1, PDL2, Tim3, CD137 and BTLA4 were also correlated with a long response to treatment. Moreover, responding patients showed a high proportion of eubiosis-associated gut metabolites. In this exploratory study, we propose a combination of immunological and clinical parameters (sICs, PS and gut metabolites) for the identification of patients more suitable for Nivolumab treatment. This string of parameters validated in a network analysis on a larger cohort of patients could help oncologists to improve their decision-making in an NSCLC setting.

Immune checkpoint blockade and biomarkers of clinical response in non-small cell lung cancer

Immunotherapy with PD‐1 and PD‐L1 inhibitors has revolutionized the treatment for patients with NSCLC the last years with increased overall survival and in particular increased number of long‐time survivors in patients with metastatic disease. It is now a treatment of choice for patients with distant metastases (stage IV) and in conjunction with chemoradiotherapy for patients with limited spread confined to the chest (stage III). PD‐1 inhibition has been proven to be superior to standard chemotherapy, both as a single treatment and when combined with either chemotherapy or CTLA‐4 inhibition. Despite the success of immunotherapy, the majority of patients do not respond or relapse within a short time frame. Biomarkers that would help to properly select patients with a high likelihood of clinical response to PD‐1 and PD‐L1 inhibitors are scarce and far from optimal, and only one (PD‐L1 expression) has reached clinical practice. Thus for immunotherapy to be effective, the discovery and validation of additional biomarkers is critical for patient selection and prediction of clinical response. In this mini‐review, we give an overview of current clinical management of NSCLC including treatment landscape with regard to immunotherapy, as well as discuss the current genetic and immune cell biomarker studies and their potential for introduction into clinical practice.

PD-1/PD-L1 immune checkpoint inhibitors in glioblastoma: clinical studies, challenges and potential

Immune checkpoint inhibitors (CIs) have changed the landscape of tumor immunotherapy. Glioblastoma (GBM) remains the most common primary malignant brain tumor in adults and has a very poor prognosis. Due to the high invasiveness and aggressiveness of GBM, there is considerable interest in programmed cell death-1 (PD-1)/programmed cell death ligand-1 (PD-L1) treatment. However, the immunosuppressive and immune-privileged characteristics of GBM limit the efficacy of CIs. While clinical studies of CI monotherapies have shown unsatisfactory survival benefits, new treatment strategies have received attention. Multiple clinical studies have focused on combination of standard therapy (temozolomide, radiotherapy), targeted therapy and other immunotherapies, and some have reported results. Here, we reviewed recent clinical trials of anti-PD-1/PD-L1 monotherapy, studies with neoadjuvant strategies, and preclinical and clinical studies of combination immunotherapies for GBM. The preliminary clinical reports in certain subsets of patients with hypermutated or mismatch repair system deficiency GBM are also discussed.

Predictive value of neutrophil-lymphocyte ratio and platelet-lymphocyte ratio in non-small cell lung cancer patients treated with immune checkpoint inhibitors: A meta-analysis

BACKGROUND

High neutrophil-lymphocyte ratio (NLR) and platelet-lymphocyte ratio (PLR) are associated with poor prognosis in cancer patients treated with Immune checkpoint inhibitors (ICIs). However, whether this relationship exists in non-small cell lung cancer (NSCLC) patients remains unclear. Thus, this meta-analysis was conducted to investigate the prognostic role of NLR and PLR in NSCLC treated with ICIs.

METHODS

Eligible studies that evaluated the value of pre-treatment or post-treatment NLR/PLR in NSCLC patients received ICIs were obtained by searching PubMed, Web of Science, Cochrane Library, and EMBASE. The pooled hazard ratio (HR) and 95% confidence interval (CI) were used to assess the relationship between NLR/PLR and overall survival (OS) and progression-free survival (PFS). Subgroup analysis and publication bias were conducted to investigate heterogeneity.

RESULTS

1845 NSCLC patients from 21 studies were included and three ICIs(nivolumab, pembrolizumab, and atezolizumab) were used. Overall, high NLR was associated with poor OS (HR: 2.50, 95% CI:1.79-3.51, P < 0.001) and PFS (HR: 1.77, 95% CI:1.51-2.01, P < 0.001). Subgroup analyses were consistent with the pooled results. Similarly, the pooled results for PLR showed that elevated PLR was related to inferior OS (HR: 1.93, 95% CI: 1.51-2.01, P < 0.001) and PFS (HR: 1.57, 95%CI: 1.30-1.90, P < 0.001). However, the subgroup analysis based on test time indicated that there was no significant correlation between post-treatment PLR and survival outcomes.

CONCLUSION

NLR and pre-treatment PLR could serve as prognostic biomarkers in NSCLC patients treated with ICIs. However, the value of post-treatment PLR needs further to be evaluated.

Immunotherapy by Immune Checkpoint Inhibitors and Nuclear Medicine Imaging: Current and Future Applications

Immunotherapy by using immune checkpoint inhibitors is a revolutionary development in oncology. Medical imaging is also impacted by this new therapy, particularly nuclear medicine imaging (also called radionuclide imaging), which uses radioactive tracers to visualize metabolic functions. Our aim was to review the current applications of nuclear medicine imaging in immunotherapy, along with their limitations, and the perspectives offered by this imaging modality. Method: Articles describing the use of radionuclide imaging in immunotherapy were researched using PubMed by April 2019 and analyzed. Results: More than 5000 articles were analyzed, and nearly 100 of them were retained. Radionuclide imaging, notably 18F-FDG PET/CT, already has a major role in many cancers for pre-therapeutic and therapeutic evaluation, diagnoses of adverse effects, called immune-related adverse events (IrAE), and end-of-treatment evaluations. However, these current applications can be hindered by immunotherapy, notably due to atypical response patterns such as pseudoprogression, which is defined as an increase in the size of lesions, or the visualization of new lesions, followed by a response, and hyperprogression, which is an accelerated tumor growth rate after starting treatment. To overcome these difficulties, new opportunities are offered, particularly therapeutic evaluation criteria adapted to immunotherapy and immuno-PET allowing us to predict responses to immunotherapy. Moreover, some new technological solutions are also promising, such as radiomic analyses and body composition on associated anatomical images. However, more research has to be done, notably for the diagnosis of hyperprogression and pseudoprogression. Conclusion: Immunotherapy, by its major impact on cancer and by the new patterns generated on images, is revolutionary in the field of medical images. Nuclear medicine imaging is already established and will be able to help meet new challenges through its plasticity.

Molecular markers related to immunosurveillance as predictive and monitoring tools in non-small cell lung cancer: recent accomplishments and future promises

Introduction: The landscape of systemic treatment options for lung cancer has rapidly evolved with the emergence of immunomodulatory agents such as neutralizing antibodies targeting the programmed cell death protein 1 (PD-1) and its ligand (PD-L1). Another major breakthrough was the introduction of biomarkers, such as PD-L1 expression and tumor mutational burden (TMB), predicting response to immunotherapy. However, markers for monitoring treatment response are still lacking.Areas covered: PD-L1 and TMB represent static pre-treatment evaluations. Dynamic biomarkers are required, along with static ones, to accurately predict and monitor immunotherapy response and to discriminate between responders and non-responders early in the course of treatment. The tumor immune contexture offers potential candidates that can be tested through the liquid biopsy approach, such as circulating tumor cells (CTCs), circulating tumor DNA (ctDNA), exosomes, microRNAs (miRNAs), circular RNAs (circRNAs), RNA splice variants, and immune cell subsets.Expert opinion: A holistic approach combining information from tissue at the time of diagnosis and serial liquid biopsy data could lead to a novel combinatorial biomarker panel with enhanced treatment monitoring potential. Incorporating information from additional parts of the tumor-host ecosystem, such as metabolic markers and the microbiome is expected to provide added value to this strategy.

A Pan-Cancer Approach to Predict Responsiveness to Immune Checkpoint Inhibitors by Machine Learning

Immunotherapy by using immune checkpoint inhibitors (ICI) has dramatically improved the treatment options in various cancers, increasing survival rates for treated patients. Nevertheless, there are heterogeneous response rates to ICI among different cancer types, and even in the context of patients affected by a specific cancer. Thus, it becomes crucial to identify factors that predict the response to immunotherapeutic approaches. A comprehensive investigation of the mutational and immunological aspects of the tumor can be useful to obtain a robust prediction. By performing a pan-cancer analysis on gene expression data from the Cancer Genome Atlas (TCGA, 8055 cases and 29 cancer types), we set up and validated a machine learning approach to predict the potential for positive response to ICI. Support vector machines (SVM) and extreme gradient boosting (XGboost) models were developed with a 10×5-fold cross-validation schema on 80% of TCGA cases to predict ICI responsiveness defined by a score combining tumor mutational burden and TGF- β signaling. On the remaining 20% validation subset, our SVM model scored 0.88 accuracy and 0.27 Matthews Correlation Coefficient. The proposed machine learning approach could be useful to predict the putative response to ICI treatment by expression data of primary tumors.

Current progress in the clinical use of circulating tumor cells as prognostic biomarkers

The process of metastasis is characterized by the shedding of tumor cells into the bloodstream, where they are transported to other parts of the body to seed new tumors. These cells, known as circulating tumor cells (CTCs), have the potential to reveal much about an individual cancer case, and theoretically can aid in the prediction of outcomes and design of precision treatments. Recent advances in technology now allow for the robust and reproducible characterization of CTCs from a simple blood draw. Both the number of circulating cells and important molecular characteristics correlated with clinical phenotypes such as drug resistance can be obtained and used for real-time prognostic analysis. Molecular characterization can provide a snapshot of the activity of the main tumor (serving as a "liquid biopsy") and early warnings concerning changes such as the development of resistance, and aid in predicting the efficacy of different therapeutic approaches for treatment optimization. Herein, the authors review the current clinical use of CTCs as prognostic biomarkers for several different cancers. The quantification of CTCs can lead to more accurate staging and decision making regarding options such as adjuvant chemotherapy.