A blood-based miRNA signature with prognostic value for overall survival in advanced stage non-small cell lung cancer treated with immunotherapy

Extracellular vesicles miR-574-5p and miR-181a-5p as prognostic markers in NSCLC patients treated with nivolumab

Immune checkpoint inhibitors (ICIs) have revolutionized the management of advanced non-small cell lung cancer (NSCLC), although patient survival is still unsatisfactory. Accurate predictive markers capable of personalizing the treatment of patients with NSCLC are still lacking. Circulating extracellular vesicles involved in cell-to-cell communications through miRNAs (EV-miRs) transfer are promising markers. Plasma from 245 patients with advanced NSCLC who received nivolumab as second-line therapy was collected and analyzed. EV-miRnome was profiled on 174/245 patients by microarray platform, and selected EV-miRs were validated by qPCR. A prognostic model combining EV-miR and clinical variables was built using stepwise Cox regression analysis and tested on an independent patient cohort (71/245). EV-PD-L1 gene copy number was assessed by digital PCR. For 54 patients with disease control, EV-miR changes at best response versus baseline were investigated by microarray and validated by qPCR. EV-miRNome profiling at baseline identified two EV-miRs (miR-181a-5p and miR-574-5p) that, combined with performance status, are capable of discriminating patients unlikely from those that are likely to benefit from immunotherapy (median overall survival of 4 months or higher than 9 months, respectively). EV-PD-L1 digital evaluation reported higher baseline copy number in patients at increased risk of mortality, without improving the prognostic score. Best response EV-miRNome profiling selected six deregulated EV-miRs (miR19a-3p, miR-20a-5p, miR-142-3p, miR-1260a, miR-1260b, and miR-5100) in responding patients. Their longitudinal monitoring highlighted a significant downmodulation already in the first treatment cycles, which lasted more than 6 months. Our results demonstrate that EV-miRs are promising prognostic markers for NSCLC patients treated with nivolumab.

MiRNAs and Microbiota in Non-Small Cell Lung Cancer (NSCLC): Implications in Pathogenesis and Potential Role in Predicting Response to ICI Treatment

Lung cancer (LC) is one of the most prevalent cancers in both men and women and today is still characterized by high mortality and lethality. Several biomarkers have been identified for evaluating the prognosis of non-small cell lung cancer (NSCLC) patients and selecting the most effective therapeutic strategy for these patients. The introduction of innovative targeted therapies and immunotherapy with immune checkpoint inhibitors (ICIs) for the treatment of NSCLC both in advanced stages and, more recently, also in early stages, has revolutionized and significantly improved the therapeutic scenario for these patients. Promising evidence has also been shown by analyzing both micro-RNAs (miRNAs) and the lung/gut microbiota. MiRNAs belong to the large family of non-coding RNAs and play a role in the modulation of several key mechanisms in cells such as proliferation, differentiation, inflammation, and apoptosis. On the other hand, the microbiota (a group of several microorganisms found in human orgasms such as the gut and lungs and mainly composed by bacteria) plays a key role in the modulation of inflammation and, in particular, in the immune response. Some data have shown that the microbiota and the related microbiome can modulate miRNAs expression and vice versa by regulating several intracellular signaling pathways that are known to play a role in the pathogenesis of lung cancer. This evidence suggests that this axis is key to predicting the prognosis and effectiveness of ICIs in NSCLC treatment and could represent a new target in the treatment of NSCLC. In this review, we highlight the most recent evidence and data regarding the role of both miRNAs and the lung/gut microbiome in the prediction of prognosis and response to ICI treatment, focusing on the link between miRNAs and the microbiome. A new potential interaction based on the underlying modulated intracellular signaling pathways is also shown.

Circulating biomarkers as predictors of response to immune checkpoint inhibitors in NSCLC: Are we on the right path?

Immune checkpoints inhibitors (ICIs) have markedly improved the therapeutic management of advanced NSCLC and, more recently, they have demonstrated efficacy also in the early-stage disease. Despite better survival outcomes with ICIs compared to standard chemotherapy, a large proportion of patients can derive limited clinical benefit from these agents. So far, few predictive biomarkers, including the programmed death-ligand 1 (PD-L1), have been introduced in clinical practice. Therefore, there is an urgent need to identify novel biomarkers to select patients for immunotherapy, to improve efficacy and avoid unnecessary toxicity. A deeper understanding of the mechanisms involved in antitumor immunity and advances in the field of liquid biopsy have led to the identification of a wide range of circulating biomarkers that could potentially predict response to immunotherapy. Herein, we provide an updated overview of these circulating biomarkers, focusing on emerging data from clinical studies and describing modern technologies used for their detection.

Blood-based biomarkers in patients with non-small cell lung cancer treated with immune checkpoint blockade

The paradigm of non-small cell lung cancer (NSCLC) treatment has been profoundly influenced by the development of immune checkpoint inhibitors (ICI), but the range of clinical responses observed among patients poses significant challenges. To date, analyses of tumor biopsies are the only parameter used to guide prognosis to ICI therapy. Tumor biopsies, however, are often difficult to obtain and tissue-based biomarkers are limited by intratumoral heterogeneity and temporal variability. In response, there has been a growing emphasis on the development of "liquid biopsy"‒ derived biomarkers, which offer a minimally invasive means to dynamically monitor the immune status of NSCLC patients either before and/or during the course of treatment. Here we review studies in which multiple blood-based biomarkers encompassing circulating soluble analytes, immune cell subsets, circulating tumor DNA, blood-based tumor mutational burden, and circulating tumor cells have shown promising associations with the clinical response of NSCLC patients to ICI therapy. These investigations have unveiled compelling correlations between the peripheral immune status of patients both before and during ICI therapy and patient outcomes, which include response rates, progression-free survival, and overall survival. There is need for rigorous validation and standardization of these blood-based assays for broader clinical application. Integration of multiple blood-based biomarkers into comprehensive panels or algorithms also has the potential to enhance predictive accuracy. Further research aimed at longitudinal monitoring of circulating biomarkers is also crucial to comprehend immune dynamics and resistance mechanisms and should be used alongside tissue-based methods that interrogate the tumor microenvironment to guide treatment decisions and may inform on the development of novel therapeutic strategies. The data reviewed here reinforce the opportunity to refine patient stratification, optimize treatments, and improve outcomes not only in NSCLC but also in the wider spectrum of solid tumors undergoing immunotherapy.

miR-Blood - a small RNA atlas of human blood components

miR-Blood is a high-quality, small RNA expression atlas for the major components of human peripheral blood (plasma, erythrocytes, thrombocytes, monocytes, neutrophils, eosinophils, basophils, natural killer cells, CD4+ T cells, CD8+ T cells, and B cells). Based on the purified blood components from 52 individuals, the dataset provides a comprehensive repository for the expression of 4971 small RNAs from eight non-coding RNA classes.

Early Detection of Lung Cancer Using Small RNAs

INTRODUCTION

Lung cancer remains the deadliest cancer in the world, and lung cancer survival is heavily dependent on tumor stage at the time of detection. Low-dose computed tomography screening can reduce mortality; however, annual screening is limited by low adherence in the United States of America and still not broadly implemented in Europe. As a result, less than 10% of lung cancers are detected through existing programs. Thus, there is a great need for additional screening tests, such as a blood test, that could be deployed in the primary care setting.

METHODS

We prospectively recruited 1384 individuals meeting the National Lung Screening Trial demographic eligibility criteria for lung cancer and collected stabilized whole blood to enable the pipetting-free collection of material, thus minimizing preanalytical noise. Ultra-deep small RNA sequencing (20 million reads per sample) was performed with the addition of a method to remove highly abundant erythroid RNAs, and thus open bandwidth for the detection of less abundant species originating from the plasma or the immune cellular compartment. We used 100 random data splits to train and evaluate an ensemble of logistic regression classifiers using small RNA expression of 943 individuals, discovered an 18-small RNA feature consensus signature (miLung), and validated this signature in an independent cohort (441 individuals). Blood cell sorting and tumor tissue sequencing were performed to deconvolve small RNAs into their source of origin.

RESULTS

We generated diagnostic models and report a median receiver-operating characteristic area under the curve of 0.86 (95% confidence interval [CI]: 0.84-0.86) in the discovery cohort and generalized performance of 0.83 in the validation cohort. Diagnostic performance increased in a stage-dependent manner ranging from 0.73 (95% CI: 0.71-0.76) for stage I to 0.90 (95% CI: 0.89-0.90) for stage IV in the discovery cohort and from 0.76 to 0.86 in the validation cohort. We identified a tumor-shed, plasma-bound ribosomal RNA fragment of the L1 stalk as a dominant predictor of lung cancer. The fragment is decreased after surgery with curative intent. In additional experiments, results of dried blood spot collection and sequencing revealed that small RNA analysis could potentially be conducted through home sampling.

CONCLUSIONS

These data suggest the potential of a small RNA-based blood test as a viable alternative to low-dose computed tomography screening for early detection of smoking-associated lung cancer.

Multiomic Approaches for Cancer Biomarker Discovery in Liquid Biopsies: Advances and Challenges

Cancer is a complex and heterogeneous disease that poses a significant threat to global health. Early diagnosis and treatment are critical for improving patient outcomes, and the use of liquid biopsies has emerged as a promising approach for cancer detection and monitoring. Traditionally, cancer diagnosis has relied on invasive tissue biopsies, the collection of which can prove challenging for patients and the results of which may not always provide accurate results due to tumor heterogeneity. Liquid biopsies have gained increasing attention as they provide a non-invasive and accessible source of cancer biomarkers, which can be used to diagnose cancer, monitor treatment response, and detect relapse. The integration of -omics technologies, such as proteomics, genomics, and metabolomics, has further enhanced the capabilities of liquid biopsies by introducing precision oncology and enabling the tailoring of treatment for individual patients based on their unique tumor biology. In this review, we will discuss the challenges and advances in the field of cancer liquid biopsies and the integration of -omics technologies for different types of liquid biopsies, including blood, tear, urine, sweat, saliva, and cerebrospinal fluid.

DNA-framework-based multidimensional molecular classifiers for cancer diagnosis

A molecular classification of diseases that accurately reflects clinical behaviour lays the foundation of precision medicine. The development of in silico classifiers coupled with molecular implementation based on DNA reactions marks a key advance in more powerful molecular classification, but it nevertheless remains a challenge to process multiple molecular datatypes. Here we introduce a DNA-encoded molecular classifier that can physically implement the computational classification of multidimensional molecular clinical data. To produce unified electrochemical sensing signals across heterogeneous molecular binding events, we exploit DNA-framework-based programmable atom-like nanoparticles with n valence to develop valence-encoded signal reporters that enable linearity in translating virtually any biomolecular binding events to signal gains. Multidimensional molecular information in computational classification is thus precisely assigned weights for bioanalysis. We demonstrate the implementation of a molecular classifier based on programmable atom-like nanoparticles to perform biomarker panel screening and analyse a panel of six biomarkers across three-dimensional datatypes for a near-deterministic molecular taxonomy of prostate cancer patients.

Tobacco exposure primes the secretion of CCL21 positively associated with tertiary lymphoid structure and response to immunotherapy

BACKGROUND

It has been reported that smoking history as a predictor of immunotherapy efficacy in patients with advanced lung cancer, however, the underlying mechanisms of this phenomenon remain largely unknown.

METHODS

The patients with lung adenocarcinoma's (LUAD) cohort and the orthotopical transplanted mouse model were used to explore the correlation between smoking status and tertiary lymphoid structure (TLS) and chemokine CCL21, respectively. Cell adhesion and co-immunoprecipitation assays were performed to explore the interaction between CD4+T cells and CD20+B cells under tobacco exposure. Chromatin immunoprecipitation-PCR was used to dissect the mechanism of upregulated CCL21 secretion in tobacco treatment. Serum CCL21 level was recorded in patients with LUAD treated with immunotherapy.

RESULTS

Here we observed that individuals with a smoking history exhibit an increased quantity and maturation level of TLS compared with non-smokers, along with higher levels of CCL21 secretion. Tobacco exposure promoted CCL21 expression in an epithelial cell-intrinsic manner, of which BaP, the main component of tobacco, facilitated the nuclear retention of the aryl hydrocarbon receptor that occupied the promoter of CCL21. Additionally, the activated CCL21/CCR7 axis increased the CD11a expression of CD4+T cells, boosting the interaction with CD20+B cells dependent on ICAM1, which potentially induced the TLSs formation. Patients with elevated serum levels of CCL21 benefited more from immunotherapy.

CONCLUSIONS

Patients with a smoking history exhibited higher levels of TLS via the CCL21-dependent mechanism, serum CCL21 was identified as a reliable biomarker for predicting the efficacy of immunotherapy.

Predicting response and toxicity to immune checkpoint inhibitors in lung cancer using antibodies to frameshift neoantigens

PURPOSE

To evaluate a new class of blood-based biomarkers, anti-frameshift peptide antibodies, for predicting both tumor responses and adverse immune events to immune checkpoint inhibitor (ICI) therapies in advanced lung cancer patients.

EXPERIMENTAL DESIGN

Serum samples were obtained from 74 lung cancer patients prior to palliative PD-(L)1 therapies with subsequently recorded tumor responses and immune adverse events (irAEs). Pretreatment samples were assayed on microarrays of frameshift peptides (FSPs), representing ~ 375,000 variant peptides that tumor cells can be informatically predicted to produce from translated mRNA processing errors. Serum-antibodies specifically recognizing these ligands were measured. Binding activities preferentially associated with best-response and adverse-event outcomes were determined. These antibody bound FSPs were used in iterative resampling analyses to develop predictive models of tumor response and immune toxicity.

RESULTS

Lung cancer serum samples were classified based on predictive models of ICI treatment outcomes. Disease progression was predicted pretreatment with ~ 98% accuracy in the full cohort of all response categories, though ~ 30% of the samples were indeterminate. This model was built with a heterogeneous sample cohort from patients that (i) would show either clear response or stable outcomes, (ii) would be administered either single or combination therapies and (iii) were diagnosed with different lung cancer subtypes. Removing the stable disease, combination therapy or SCLC groups from model building increased the proportion of samples classified while performance remained high. Informatic analyses showed that several of the FSPs in the all-response model mapped to translations of variant mRNAs from the same genes. In the predictive model for treatment toxicities, binding to irAE-associated FSPs provided 90% accuracy pretreatment, with no indeterminates. Several of the classifying FSPs displayed sequence similarity to self-proteins.

CONCLUSIONS

Anti-FSP antibodies may serve as biomarkers for predicting ICI outcomes when tested against ligands corresponding to mRNA-error derived FSPs. Model performances suggest this approach might provide a single test to predict treatment response to ICI and identify patients at high risk for immunotherapy toxicities.

Predictive Biomarkers for Checkpoint Inhibitor Immune-Related Adverse Events

Immune-checkpoint inhibitors (ICIs) are antagonists of inhibitory receptors in the immune system, such as the cytotoxic T-lymphocyte-associated antigen-4, the programmed cell death protein-1 and its ligand PD-L1, and they are increasingly used in cancer treatment. By blocking certain suppressive pathways, ICIs promote T-cell activation and antitumor activity but may induce so-called immune-related adverse events (irAEs), which mimic traditional autoimmune disorders. With the approval of more ICIs, irAE prediction has become a key factor in improving patient survival and quality of life. Several biomarkers have been described as potential irAE predictors, some of them are already available for clinical use and others are under development; examples include circulating blood cell counts and ratios, T-cell expansion and diversification, cytokines, autoantibodies and autoantigens, serum and other biological fluid proteins, human leucocyte antigen genotypes, genetic variations and gene profiles, microRNAs, and the gastrointestinal microbiome. Nevertheless, it is difficult to generalize the application of irAE biomarkers based on the current evidence because most studies have been retrospective, time-limited and restricted to a specific type of cancer, irAE or ICI. Long-term prospective cohorts and real-life studies are needed to assess the predictive capacity of different potential irAE biomarkers, regardless of the ICI type, organ involved or cancer site.

Circulating Biomarkers for Prediction of Immunotherapy Response in NSCLC

Non-small cell lung cancer (NSCLC) constitutes the majority of the lung cancer population and the prognosis is poor. In recent years, immunotherapy has become the standard of care for advanced NSCLC patients as numerous trials demonstrated that immune checkpoint inhibitors (ICI) are more efficacious than conventional chemotherapy. However, only a minority of NSCLC patients benefit from this treatment. Therefore, there is an unmet need for biomarkers that could accurately predict response to immunotherapy. Liquid biopsy allows repeated sampling of blood-based biomarkers in a non-invasive manner for the dynamic monitoring of treatment response. In this review, we summarize the efforts and progress made in the identification of circulating biomarkers that predict immunotherapy benefit for NSCLC patients. We also discuss the challenges with future implementation of circulating biomarkers into clinical practice.

Therapeutic strategies for non-small cell lung cancer: Experimental models and emerging biomarkers to monitor drug efficacies

While new targeted therapies have considerably changed the treatment and prognosis of non-small cell lung cancer (NSCLC), they are frequently unsuccessful due to primary or acquired resistances. Chemoresistance is a complex process that combines cancer cell intrinsic mechanisms including molecular and genetic abnormalities, aberrant interactions within the tumor microenvironment, and the pharmacokinetic characteristics of each molecule. From a pharmacological point of view, two levers could improve the response to treatment: (i) developing tools to predict the response to chemo- and targeted therapies and (ii) gaining a better understanding of the influence of the tumor microenvironment. Both personalized medicine approaches require the identification of relevant experimental models and biomarkers to understand and fight against chemoresistance mechanisms. After describing the main therapies in NSCLC, the scope of this review will be to identify and to discuss relevant in vitro and ex vivo experimental models that are able to mimic tumors. In addition, the interests of these models in the predictive responses to proposed therapies will be discussed. Finally, this review will evaluate the involvement of novel secreted biomarkers such as tumor DNA or micro RNA in predicting responses to anti-tumor therapies.

MicroRNAs and Drug Resistance in Non-Small Cell Lung Cancer: Where Are We Now and Where Are We Going

Lung cancer is the leading cause of cancer-related mortality in the world. The development of drug resistance represents a major challenge for the clinical management of patients. In the last years, microRNAs have emerged as critical modulators of anticancer therapy response. Here, we make a critical appraisal of the literature available on the role of miRNAs in the regulation of drug resistance in non-small cell lung cancer (NSCLC). We performed a comprehensive annotation of miRNAs expression profiles in chemoresistant versus sensitive NSCLC, of the drug resistance mechanisms tuned up by miRNAs, and of the relative experimental evidence in support of these. Furthermore, we described the pros and cons of experimental approaches used to investigate miRNAs in the context of therapeutic resistance, to highlight potential limitations which should be overcome to translate experimental evidence into practice ultimately improving NSCLC therapy.

Serum cytokines predict efficacy and toxicity, but are not useful for disease monitoring in lung cancer treated with PD-(L)1 inhibitors

Introduction

PD-(L)1 inhibitors (IO) have improved the prognosis of non-small-cell lung cancer (NSCLC), but more reliable predictors of efficacy and immune-related adverse events (irAE) are urgently needed. Cytokines are important effector molecules of the immune system, whose potential clinical utility as biomarkers remains unclear.

Methods

Serum samples from patients with advanced NSCLC receiving IO either alone in the first (1L, n=46) and subsequent lines (n=50), or combined with chemotherapy (ICT, n=108) were analyzed along with age-matched healthy controls (n=15) at baseline, after 1 and 4 therapy cycles, and at disease progression (PD). Patients were stratified in rapid progressors (RP, progression-free survival [PFS] <120 days), and long-term responders (LR, PFS >200 days). Cytometric bead arrays were used for high-throughput quantification of 20 cytokines and other promising serum markers based on extensive search of the current literature.

Results

Untreated NSCLC patients had increased levels of various cytokines and chemokines, like IL-6, IL-8, IL-10, CCL5, G-CSF, ICAM-1, TNF-RI and VEGF (fold change [FC]=1.4-261, p=0.026-9x10^-7) compared to age-matched controls, many of which fell under ICT (FC=0.2-0.6, p=0.014-0.002), but not under IO monotherapy. Lower baseline levels of TNF-RI were associated with longer PFS (hazard ratio [HR]= 0.42-0.54; p=0.014-0.009) and overall survival (HR=0.28-0.34, p=0.004-0.001) after both ICT and IO monotherapy. Development of irAE was associated with higher baseline levels of several cytokines, in particular of IL-1β and angiogenin (FC=7-9, p=0.009-0.0002). In contrast, changes under treatment were very subtle, there were no serum correlates of radiologic PD, and no association between dynamic changes in cytokine concentrations and clinical outcome. No relationship was noted between the patients' serologic CMV status and serum cytokine levels.

Conclusions

Untreated NSCLC is characterized by increased blood levels of several pro-inflammatory and angiogenic effectors, which decrease under ICT. Baseline serum cytokine levels could be exploited for improved prediction of subsequent IO benefit (in particular TNF-RI) and development of irAE (e.g. IL-1β or angiogenin), but they are not suitable for longitudinal disease monitoring. The potential utility of IL-1/IL-1β inhibitors in the management and/or prevention of irAE in NSCLC warrants investigation.

Identifying tumor immunity-associated molecular features in liver hepatocellular carcinoma by multi-omics analysis

Background: Although current immunotherapies have achieved some successes for hepatocellular carcinoma (HCC) patients, their benefits are limited for most HCC patients. Therefore, the identification of biomarkers for promoting immunotherapeutic responses in HCC is urgently needed.

Methods: Using the TCGA HCC cohort, we investigated correlations of various molecular features with antitumor immune signatures (CD8⁺ T cell infiltration and cytolytic activity) and an immunosuppressive signature (PD-L1 expression) in HCC. These molecular features included mRNAs, microRNAs (miRNAs), long non-coding RNAs (lncRNAs), proteins, and pathways.

Results: We found that the mutations of several oncogenes and tumor suppressor genes significantly correlated with reduced antitumor immune signatures, including TTN, CTNNB1, RB1, ZFHX4, and TP53. It indicates that these genes’ mutations may inhibit antitumor immune responses in HCC. Four proteins (Syk, Lck, STAT5, and Caspase-7) had significant positive expression correlations with CD8⁺ T cell enrichment, cytolytic activity, and PD-L1 expression in HCC. It suggests that these proteins’ expression could be useful biomarkers for the response to immune checkpoint inhibitors Similiarly, we identified other types of biomarkers potentially useful for predicting the response to ICIs, including miRNAs (hsa-miR-511-5p, 150-3p, 342-3p, 181a-3p, 625-5p, 4772-3p, 155-3p, 142-5p, 142-3p, 155-5p, 625-3p, 1976, 7702), many lncRNAs, and pathways (apoptosis, cytokine-cytokine receptor interaction, Jak-STAT signaling, MAPK signaling, PI3K-AKT signaling, HIF-1 signaling, ECM receptor interaction, focal adhesion, and estrogen signaling). Further, tumor mutation burden showed no significant correlation with antitumor immunity, while tumor aneuploidy levels showed a significant negative correlation with antitumor immunity.

Conclusion: The molecular features significantly associated with HCC immunity could be predictive biomarkers for immunotherapeutic responses in HCC patients. They could also be potential intervention targets for boosting antitumor immunity and immunotherapeutic responses in HCC.

Prognostic scores including peripheral blood-derived inflammatory indices in patients with advanced non-small-cell lung cancer treated with immune checkpoint inhibitors

Peripheral blood inflammatory indices, like the neutrophil-to-lymphocyte ratio (NLR), may reflect the host's pro-inflammatory status and systemic immune response to cancer-related inflammation. We reviewed 22 combined prognostic scores based on peripheral blood-derived inflammatory indices for aNSCLC patients treated with single-agent or combination immune-checkpoint inhibitors (ICI) as first-line or subsequent therapy lines and attempted evidence strength assessment and scoring. The Lung Immune Prognostic Index (LIPI), consisting of derived NLR and LDH, was the most studied score with validated prognostic value in over five thousand aNSCLC ICI-naïve or pretreated patients. The combination of NLR and tumour programmed-cell-death-ligand1 (PD-L1) expression showed a predictive value. The Lung-Immune-Prognostic score (LIPS) might help identify patients with poor performance status but a favourable outcome following first-line ICI. These non-expensive scores can help clinicians discuss the prognosis with aNSCLC patients approaching ICI, identify those less likely to benefit from single-agent ICI and orient future clinical research.