Predictive Biomarkers for Immunotherapy in Lung Cancer: Perspective From the International Association for the Study of Lung Cancer Pathology Committee

Immunohistochemistry of Lung Cancer Biomarkers

Immunohistochemical (IHC) staining represents a comparatively inexpensive testing method that is attractive as a potential alternative to molecular sequencing methods or fluorescence in situ hybridization for pulmonary biomarker testing. While a variety of IHC tests directed at actionable genetic alterations have been developed and evaluated since the advent of targeted therapy, specific antibody clones for anaplastic lymphoma kinase, ROS-1, and potentially neurotrophic tropmyosin receptor kinase have been the primary antibodies that provide sufficiently robust results to be utilized as either a primary testing or screening method to direct targeted therapy. Antibodies for a variety of other targets such as epidermal growth factor receptors, for example, have lacked sufficient sensitivity and specificity to cover the range of mutations that may occur and are generally not recommended in lieu of molecular testing with the exception of limited resource settings. IHC is also used as a predictive marker for response to immunotherapy through evaluation of programmed death ligand 1 expression. In addition, multiple antibody-drug conjugates (ADCs) are under investigation, designed to deliver drugs directly to tumor cells through binding to specific target antigens. Some ADCs have already received accelerated FDA approval, and IHC was incorporated in many clinical trials evaluating ADC efficacy. As such, it is anticipated that ADCs may have a companion diagnostic IHC to guide patient selection.

Longitudinal Changes of CT-radiomic and Systemic Inflammatory Features Predict Survival in Advanced Non-Small Cell Lung Cancer Patients Treated With Immune Checkpoint Inhibitors

PURPOSE

This study aims to determine whether longitudinal changes in CT radiomic features (RFs) and systemic inflammatory indices outperform single-time-point assessment in predicting survival in advanced non-small cell lung cancer (NSCLC) treated with immune checkpoint inhibitors (ICIs).

MATERIALS AND METHODS

We retrospectively acquired pretreatment (T0) and first disease assessment (T1) RFs and systemic inflammatory indices from a single-center cohort of stage IV NSCLC patients and computed their delta (Δ) variation as [(T1-T0)/T0]. RFs from the primary tumor were selected for building baseline-radiomic (RAD) and Δ-RAD scores using the linear combination of standardized predictors detected by LASSO Cox regression models. Cox models were generated using clinical features alone or combined with baseline and Δ blood parameters and integrated with baseline-RAD and Δ-RAD. All models were 3-fold cross-validated. A prognostic index (PI) of each model was tested to stratify overall survival (OS) through Kaplan-Meier analysis.

RESULTS

We included 90 ICI-treated NSCLC patients (median age 70 y [IQR=42 to 85], 63 males). Δ-RAD outperformed baseline-RAD for predicting OS [c-index: 0.632 (95%CI: 0.628 to 0.636) vs. 0.605 (95%CI: 0.601 to 0.608) in the test splits]. Integrating longitudinal changes of systemic inflammatory indices and Δ-RAD with clinical data led to the best model performance [Integrated-Δ model, c-index: 0.750 (95% CI: 0.749 to 0.751) in training and 0.718 (95% CI: 0.715 to 0.721) in testing splits]. PI enabled significant OS stratification within all the models (P-value <0.01), reaching the greatest discriminative ability in Δ models (high-risk group HR up to 7.37, 95% CI: 3.9 to 13.94, P<0.01).

CONCLUSION

Δ-RAD improved OS prediction compared with single-time-point radiomic in advanced ICI-treated NSCLC. Integrating Δ-RAD with a longitudinal assessment of clinical and laboratory data further improved the prognostic performance.

Analysis of immune checkpoint inhibitors for advanced non-small cell lung cancer in patients receiving antacids

BACKGROUND

Proton pump inhibitors (PPIs) are reported to decrease the efficacy of immune checkpoint inhibitors (ICIs), but there are few reports on the association between ICI efficacy and antacids other than PPIs, and simultaneous examination of the effects of antacids, corticosteroids, and non-steroidal anti-inflammatory drugs (NSAIDs) on ICI therapy.

METHODS

We conducted a retrospective study of 381 patients with non-small cell lung cancer who received ICI therapy from January 1, 2016 to December 31, 2022. The primary endpoint was overall survival (OS) and the secondary endpoint was progression-free survival (PFS). Antacids included histamine type 2 receptor antagonists (H2RAs), PPIs, and potassium-competitive acid blockers (P-CABs).

RESULTS

Antacids were administered to 218 patients, including 168 with PPIs, 37 with P-CABs, and 13 with H2RAs. Patients with antacids had worse median PFS and OS than those without antacids (PFS, 2.9 vs. 6.2 months; OS, 12.3 vs. 24.0 months), and those with PPIs, P-CABs, or H2RAs had similar results. However, there were no significant differences between patients with and without antacids when stratified by corticosteroid and NSAID use. Multivariate analyses showed that corticosteroids and NSAIDs administered for cancer-associated symptoms were related to poor prognosis, but antacids including PPIs, P-CABs, or H2RAs were not related.

CONCLUSIONS

Antacids were not related to ICI efficacy when NSAIDs or corticosteroids were taken into consideration. This may be because the most frequent reason for administering NSAIDs and corticosteroids was cancer-associated symptoms, which are a poor prognostic factor, and most of the patients treated with these medications also received antacids.

New promises and challenges in the treatment of advanced non-small-cell lung cancer

Targeted therapies and immunotherapies have radically improved treatment for advanced non-small-cell lung cancer (NSCLC). Tyrosine kinase inhibitors targeting oncogenic driver mutations continue to evolve over multiple generations to enhance effectiveness and tackle drug resistance. Immune checkpoint inhibitors remain integral for the treatment of NSCLCs that do not have specific actionable genetic mutations. Antibody-drug conjugates and bispecific antibodies are being integrated into treatment guidelines, and emerging therapies include T-cell engagers, cellular therapies, cancer vaccines, and external devices. Despite these advances, challenges remain in identifying predictive biomarkers to individually tailor treatments, abrogate resistance, reduce costs, and ensure optimal cancer treatment accessibility.

Small molecule inhibitors as adjuvants in cancer immunotherapy: enhancing efficacy and overcoming resistance

Adjuvant therapy is essential in cancer treatment to enhance primary treatment effectiveness, reduce adverse effects, and prevent recurrence. Small molecule inhibitors as adjuvants in cancer immunotherapy aim to harness their immunomodulatory properties to optimize treatment outcomes. By modulating the tumor microenvironment, enhancing immune cell function, and increasing tumor sensitivity to immunotherapy, small molecule inhibitors have the potential to improve patient responses. This review discusses the evolving use of small molecule inhibitors as adjuvants in cancer treatment, highlighting their role in enhancing the efficacy of immunotherapy and the opportunities for advancing cancer therapies in the future.

KRAS and TP53 co-mutation predicts benefit of immune checkpoint blockade in lung adenocarcinoma

BACKGROUND

Predictive biomarkers in use for immunotherapy in advanced non-small cell lung cancer are of limited sensitivity and specificity. We analysed the potential of activating KRAS and pathogenic TP53 mutations to provide additional predictive information.

METHODS

The study cohort included 713 consecutive immunotherapy patients with advanced lung adenocarcinomas, negative for actionable genetic alterations. Additionally, two previously published immunotherapy and two surgical patient cohorts were analyzed. Therapy benefit was stratified by KRAS and TP53 mutations. Molecular characteristics underlying KRASmut/TP53mut tumours were revealed by the analysis of TCGA data.

RESULTS

An interaction between KRAS and TP53 mutations was observed in univariate and multivariate analyses of overall survival (Hazard ratio [HR] = 0.56, p = 0.0044 and HR = 0.53, p = 0.0021) resulting in a stronger benefit for KRASmut/TP53mut tumours (HR = 0.71, CI 0.55-0.92). This observation was confirmed in immunotherapy cohorts but not observed in surgical cohorts. Tumour mutational burden, proliferation, and PD-L1 mRNA were significantly higher in TP53-mutated tumours, regardless of KRAS status. Genome-wide expression analysis revealed 64 genes, including CX3CL1 (fractalkine), as specific transcriptomic characteristic of KRASmut/TP53mut tumours.

CONCLUSIONS

KRAS/TP53 co-mutation predicts ICI benefit in univariate and multivariate survival analyses and is associated with unique molecular tumour features. Mutation testing of the two genes can be easily implemented using small NGS panels.

Rediscovering immunohistochemistry in lung cancer

Several observations indicate that protein expression analysis by immunohistochemistry (IHC) remains relevant in individuals with non-small-cell lung cancer (NSCLC) when considering targeted therapy, as an early step in diagnosis and for therapy selection. Since the advent of next-generation sequencing (NGS), the role of IHC in testing for NSCLC biomarkers has been forgotten or ignored. We discuss how protein-level investigations maintain a critical role in defining sensitivity to lung cancer therapies in oncogene- and non-oncogene-addicted cases and in patients eligible for immunotherapy, suggesting that IHC testing should be reconsidered in clinical practice. We also argue how a panel of IHC tests should be considered complementary to NGS and other genomic assays. This is relevant to current clinical diagnostic practice but with potential future roles to optimize the selection of patients for innovative therapies. At the same time, strict validation of antibodies, assays, scoring systems, and intra- and interobserver reproducibility is needed.

A combined model of serum neutrophil extracellular traps, CD8+ T cells, and tumor proportion score provides better prediction of PD-1 inhibitor efficacy in patients with NSCLC

Immune checkpoint inhibitors provide a definite survival benefit for patients with driver-negative advanced non-small cell lung cancer (NSCLC), but predictors of efficacy are still lacking. There may be a relationship between immune inflammatory state and tumor immune response. We explored the relationship of serum neutrophil extracellular traps (NETs) with infiltrating cells in the tumor tissues of patients with NSCLC as well as their relationship with the therapeutic efficacy of programmed cell death protein 1 (PD-1) inhibitors. Serum myeloperoxidase (MPO)-double-stranded DNA (dsDNA) was detected as a marker of NET serum concentration. T cells were detected by immunohistochemical staining, and neutrophils were counted by MPO immunofluorescence staining. Of the 31 patients with NSCLC, a longer progression-free survival after PD-1 inhibitor treatment was associated with higher levels of CD3+ T cells, a lower neutrophil : CD3+-T-cell ratio (NEU/CD3+) and lower neutrophil : CD8+-T-cell ratio (NEU/CD8+) in tumor tissues. Patients with higher serum NETs were more likely to develop progressive disease after treatment (P = 0.003) and to have immune-related adverse events (IrAEs) as well as higher NEU/CD3+ and NEU/CD8+. The combined model of serum NETs, CD8+ T cells, and tumor proportion score (TPS) significantly improved the prediction of PD-1 inhibitor efficacy [P = 0.033; area under the curve (AUC) = 0.881]. Our results indicate that serum NETs are effective predictors of PD-1 inhibitor response and reflect the tissue neutrophil-to-lymphocyte ratio and IrAE levels. The combined model of serum NETs, CD8+ T cells, and TPS is a powerful tool for predicting the efficacy of PD-1 inhibitor treatment in patients with NSCLC.

PIM1/NF-κB/CCL2 blockade enhances anti-PD-1 therapy response by modulating macrophage infiltration and polarization in tumor microenvironment of NSCLC

Elevated infiltration of tumor-associated macrophages (TAMs) drives tumor progression and correlates with poor prognosis for various tumor types. Our research identifies that the ablation of the Pim-1 proto-oncogene (PIM1) in non-small cell lung cancer (NSCLC) suppresses TAM infiltration and prevents them from polarizing toward the M2 phenotype, thereby reshaping the tumor immune microenvironment (TME). The predominant mechanism through which PIM1 exerts its impact on macrophage chemotaxis and polarization involves CC motif chemokine ligand 2 (CCL2). The expression level of PIM1 is positively correlated with high CCL2 expression in NSCLC, conferring a worse overall patient survival. Mechanistically, PIM1 deficiency facilitates the reprogramming of TAMs by targeting nuclear factor kappa beta (NF-κB) signaling and inhibits CCL2 transactivation by NSCLC cells. The decreased secretion of CCL2 impedes TAM accumulation and their polarization toward a pro-tumoral phenotype. Furthermore, Dual blockade of Pim1 and PD-1 collaboratively suppressed tumor growth, repolarized macrophages, and boosted the efficacy of anti-PD-1 antibody. Collectively, our findings elucidate the pivotal role of PIM1 in orchestrating TAMs within the TME of NSCLC and highlight the potential of PIM1 inhibition as a strategy for enhancing the efficacy of cancer immunotherapy.

The role of immunotherapy in patients with lung cancer and brain metastases: a narrative review of the literature

Worldwide, approximately half of the patients diagnosed with lung cancer (LC) will develop, simultaneously or asynchronously, brain metastases (BMs). The existence of BMs negatively affects the quality of life and constitutes a poor prognostic factor, linked with high mortality. Locoregional therapy with surgery or radiation is, until now, the treatment of choice, especially for symptomatic patients; however, both options are linked to a high complication rate. The question arising here is whether, in asymptomatic patients, the benefit outweighs the risk and whether an alternative method can be used to treat this special category of patients. Over the last decade, immune checkpoint inhibitors (ICIs) have represented a major breakthrough in the field of oncology, and several molecules have been approved as a treatment option for LC. This review tried to analyze the tumor microenvironment of both the primary lung tumor and the BMs in order to evaluate the intracranial activity of ICIs, outline the main challenges of including these agents in the treatment of LC with BMs, highlight the available information from the main clinical trials, and mark the potential positive effect of choosing a combination therapy. In conclusion, it appears that immunotherapy has a positive effect, inhibiting the progression of BMs, but more data should be published specifically for this category of patients.

Current status and progress of PD-L1 detection: guiding immunotherapy for non-small cell lung cancer

Non-small cell lung cancer (NSCLC) is the leading cause of cancer-related deaths and represents a substantial disease burden worldwide. Immune checkpoint inhibitors combined with chemotherapy are the standard first-line therapy for advanced NSCLC without driver mutations. Programmed death-ligand 1 (PD-L1) is currently the only approved immunotherapy marker. PD-L1 detection methods are diverse and have developed rapidly in recent years, such as improved immunohistochemical detection methods, the application of liquid biopsy in PD-L1 detection, genetic testing, radionuclide imaging, and the use of machine learning methods to construct PD-L1 prediction models. This review focuses on the detection methods and challenges of PD-L1 from different sources, and discusses the influencing factors of PD-L1 detection and the value of combined biomarkers. Provide support for clinical screening of immunotherapy-advantage groups and formulation of personalized treatment decisions.

Intersecting Blood Cytokines With Cholesterol Parameters to Profile Patients With Advanced Solid Tumors Receiving Immune Checkpoint Inhibitors

The study investigated the relationship between serum proinflammatory cytokine levels, cholesterol metabolism, and clinical outcome in cancer patients undergoing immune checkpoint inhibitors (ICIs). Peripheral blood was collected before therapy from ICI-treated advanced cancer patients. We retrospectively assessed plasma total cholesterol (TC), ABCA1- and ABCG1-mediated cholesterol efflux (CE), passive diffusion (PD), cholesterol loading capacity (CLC), and serum IL-6, IL-10, and TNF-α. The association between blood cholesterol parameters and inflammatory cytokines and their effect on overall survival (OS), progression-free survival (PFS), and clinical benefit (CB) from ICIs were statistically assessed. Among 70 consecutively enrolled patients (nonsmall cell lung cancer: 94%; renal cell carcinoma: 6%), TC, CLC, and cholesterol PD resulted significantly higher in IL-6low and IL-10low cases (P<0.05), whereas ABCA1-mediated CE was increased in IL-10high patients (P=0.018). Uni- and multivariable analysis revealed meaningfully longer OS and PFS in IL-6low (HR 2.13 and 2.97, respectively) and IL-10low (HR 3.17 and 2.62) groups. At univariate analysis all cholesterol-related indices significantly correlated with OS and PFS, whereas at multivariate only high PD was validated as a protection factor (OS, HR 0.75; PFS, HR 0.84). Finally, uni- and multivariable showed a statistically significant inverse association of CB with ABCG1-CE (OR 0.62), as with IL-6 (OR 0.13) and IL-10 (OR 0.10). In-depth characterization of the interplay between blood cholesterol metabolism and immune-inflammatory cytokines might provide novel insights into the complex relationship among cancer, inflammation, lipids profile, and response to immunotherapy.

Harnessing genetic interactions for prediction of immune checkpoint inhibitors response signature in cancer cells

Genetic interactions (GIs) refer to two altered genes having a combined effect that is not seen individually. They play a crucial role in influencing drug efficacy. We utilized CGIdb 2.0 (http://www.medsysbio.org/CGIdb2/), an updated database of comprehensively published GIs information, encompassing synthetic lethality (SL), synthetic viability (SV), and chemical-genetic interactions. CGIdb 2.0 elucidates GIs relationships between or within protein complex models by integrating protein-protein physical interactions. Additionally, we introduced GENIUS (GENetic Interactions mediated drUg Signature) to leverage GIs for identifying the response signature of immune checkpoint inhibitors (ICIs). GENIUS identified high MAP4K4 expression as a resistant signature and high HERC4 expression as a sensitive signature for ICIs treatment. Melanoma patients with high expression of MAP4K4 were associated with decreased efficacy and poorer survival following ICIs treatment. Conversely, overexpression of HERC4 in melanoma patients correlated with a positive response to ICIs. Notably, HERC4 enhances sensitivity to immunotherapy by facilitating antigen presentation. Analyses of immune cell infiltration and single-cell data revealed that B cells expressing MAP4K4 may contribute to resistance to ICIs in melanoma. Overall, CGIdb 2.0, provides integrated GIs data, thus serving as a crucial tool for exploring drug effects.

Programmed Death Ligand-1 and Tumor Mutation Burden Testing of Patients With Lung Cancer for Selection of Immune Checkpoint Inhibitor Therapies: Guideline From the College of American Pathologists, Association for Molecular Pathology, International Association for the Study of Lung Cancer, Pulmonary Pathology Society, and LUNGevity Foundation

CONTEXT.—

Rapid advancements in the understanding and manipulation of tumor-immune interactions have led to the approval of immune therapies for patients with non-small cell lung cancer. Certain immune checkpoint inhibitor therapies require the use of companion diagnostics, but methodologic variability has led to uncertainty around test selection and implementation in practice.

OBJECTIVE.—

To develop evidence-based guideline recommendations for the testing of immunotherapy/immunomodulatory biomarkers, including programmed death ligand-1 (PD-L1) and tumor mutation burden (TMB), in patients with lung cancer.

DESIGN.—

The College of American Pathologists convened a panel of experts in non-small cell lung cancer and biomarker testing to develop evidence-based recommendations in accordance with the standards for trustworthy clinical practice guidelines established by the National Academy of Medicine. A systematic literature review was conducted to address 8 key questions. Using the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) approach, recommendations were created from the available evidence, certainty of that evidence, and key judgments as defined in the GRADE Evidence to Decision framework.

RESULTS.—

Six recommendation statements were developed.

CONCLUSIONS.—

This guideline summarizes the current understanding and hurdles associated with the use of PD-L1 expression and TMB testing for immune checkpoint inhibitor therapy selection in patients with advanced non-small cell lung cancer and presents evidence-based recommendations for PD-L1 and TMB testing in the clinical setting.

Prognostic value of body mass index for first-line chemoimmunotherapy combinations in advanced non-small cell lung cancer in Chinese population

Background

Few studies have examined the correlation between body mass index (BMI) and effectiveness of first-line chemoimmunotherapy in patients with advanced non-small cell lung cancer (NSCLC); moreover, the conclusion remains elusive and no such studies have been conducted in the Chinese population. Our study aimed to validate the predictive significance of BMI in Chinese patients with advanced NSCLC receiving first-line chemoimmunotherapy combinations.

Methods

Data of patients with advanced NSCLC treated with first-line chemoimmunotherapy between June 2018 and February 2022 at three centers were retrieved retrospectively. The association between baseline BMI with progression-free survival (PFS) and overall survival (OS) was evaluated using the Kaplan-Meier method and Cox regression models. BMI was categorized according to the World Health Organization criteria.

Results

Of the included 805 patients, 5.3 % were underweight, 63.4 % had normal weight, 27.8 % were overweight, and 3.5 % were obese. Survival analysis showed that patients in the high BMI group had significantly better PFS (p = 0.012) and OS (p = 0.014) than those in the low BMI group. Further, patients in the overweight subgroup had better PFS (p = 0.036) and OS (p = 0.043) compared to the normal weight population. The results of Cox regression analysis confirmed the correlations between BMI and prognosis of advanced NSCLC patients receiving first-line chemoimmunotherapy combinations.

Conclusions

Baseline BMI affected the clinical outcomes of first-line chemoimmunotherapy combinations in patients with advanced NSCLC, and was especially favorable for the overweight subgroup.

Plasmatic Inactive IL-18 Predicts a Worse Overall Survival for Advanced Non-Small-Cell Lung Cancer with Early Metabolic Progression after Immunotherapy Initiation

The aim of this study was to assess the potential value of circulating active and inactive IL-18 levels in distinguishing pseudo and true tumor progression among NSCLC patients receiving immune checkpoint inhibitor treatments (ICIs).

METHODS

This ancillary study includes 195 patients with metastatic non-small-cell lung cancer (NSCLC) treated with ICI in monotherapy, either pembrolizumab or nivolumab. Plasmatic levels of IL-18-related compounds, comprising the inhibitor IL-18 binding protein (IL-18BP), the inactive IL-18 (corresponding to IL-18/IL-18BP complex), and the active free IL-18, were assayed by ELISA. Objective tumoral response was analyzed by 18FDG PET-CT at baseline, 7 weeks, and 3 months post treatment induction, using PERCIST criteria.

RESULTS

Plasmatic IL-18BP and total IL-18 levels are increased at baseline in NSCLC patients compared with healthy controls, whereas IL-18/IL-18BP complexes are decreased, and free IL-18 levels remain unchanged. Neither of the IL-18-related compounds allowed to discriminate ICI responding to nonresponding patients. However, inactive IL-18 levels allowed to discriminate patients with a first tumor progression, assessed after 7 weeks of treatment, with worse overall survival. In addition, we showed that neutrophil concentration is also a predictive indicator of patients' outcomes with OS (HR = 2.6, p = 0.0001) and PFS (HR = 2.2, p = 0.001).

CONCLUSIONS

Plasmatic levels of inactive IL-18, combined with circulating neutrophil concentrations, can effectively distinguish ICI nonresponding patients with better overall survival (OS), potentially guiding rapid decisions for therapeutic intensification.

Immune checkpoint inhibitors in colorectal cancer: limitation and challenges

Colorectal cancer exhibits a notable prevalence and propensity for metastasis, but the current therapeutic interventions for metastatic colorectal cancer have yielded suboptimal results. ICIs can decrease tumor development by preventing the tumor's immune evasion, presenting cancer patients with a new treatment alternative. The increased use of immune checkpoint inhibitors (ICIs) in CRC has brought several issues. In particular, ICIs have demonstrated significant clinical effectiveness in patients with MSI-H CRC, whereas their efficacy is limited in MSS. Acquired resistance can still occur in patients with a positive response to ICIs. This paper describes the efficacy of ICIs currently in the clinical treatment of CRC, discusses the mechanisms by which acquired resistance occurs, primarily related to loss and impaired presentation of tumor antigens, reduced response of IFN-λ and cytokine or metabolic dysregulation, and summarizes the incidence of adverse effects. We posit that the future of ICIs hinges upon the advancement of precise prediction biomarkers and the implementation of combination therapies. This study aims to elucidate the constraints associated with ICIs in CRC and foster targeted problem-solving approaches, thereby enhancing the potential benefits for more patients.

Spatiotemporal characteristics of tissue derived small extracellular vesicles is associated with tumor relapse and anti-PD-1 response

Small extracellular vesicles (sEVs) residing at tumor tissues are valuable specimens for biopsy. Tumor heterogeneity is common across all cancer types, but the heterogeneity of tumor tissue-derived sEVs (Ti-sEVs) is undefined. This study aims to discover the spatial distributions of Ti-sEVs in oral squamous cell carcinoma (OSCC) tissues and explore how these vesicle distributions affect the patients' prognosis. Multi-regional sampling enabled us to uncover that Ti-sEVs' accumulation at peritumoral sites correlates with a higher disease-free survival rate, and conversely, sparse peritumoral Ti-sEVs tend to forecast a higher risk of relapse. Of those relapsed patients, Ti-sEVs strongly bind to extracellular matrix and subsequently degrade it for allowing themselves enter the bloodstream rather than staying in situ. In advanced OSCC patients, the quantity and spatial distribution of Ti-sEVs prior to anti-PD-1 treatment, as well as the temporal variance of Ti-sEVs before and after immunotherapy, strongly map the clinical response and can help to distinguish the patients with shrinking tumors from those with growing tumors. Our work elucidates the correlation of spatiotemporal features of Ti-sEVs with patients' therapeutic outcomes and exhibit the potential for using Ti-sEVs as a predictor to forecast prognosis and screen the responders to anti-PD-1 therapy.

Association between pretreatment emotional distress and immune checkpoint inhibitor response in non-small-cell lung cancer

Emotional distress (ED), commonly characterized by symptoms of depression and/or anxiety, is prevalent in patients with cancer. Preclinical studies suggest that ED can impair antitumor immune responses, but few clinical studies have explored its relationship with response to immune checkpoint inhibitors (ICIs). Here we report results from cohort 1 of the prospective observational STRESS-LUNG study, which investigated the association between ED and clinical efficacy of first-line treatment of ICIs in patients with advanced non-small-cell lung cancer. ED was assessed by Patient Health Questionnaire-9 and Generalized Anxiety Disorder 7-item scale. The study included 227 patients with 111 (48.9%) exhibiting ED who presented depression (Patient Health Questionnaire-9 score ≥5) and/or anxiety (Generalized Anxiety Disorder 7-item score ≥5) symptoms at baseline. On the primary endpoint analysis, patients with baseline ED exhibited a significantly shorter median progression-free survival compared with those without ED (7.9 months versus 15.5 months, hazard ratio 1.73, 95% confidence interval 1.23 to 2.43, P = 0.002). On the secondary endpoint analysis, ED was associated with lower objective response rate (46.8% versus 62.1%, odds ratio 0.54, P = 0.022), reduced 2-year overall survival rate of 46.5% versus 64.9% (hazard ratio for death 1.82, 95% confidence interval 1.12 to 2.97, P = 0.016) and detriments in quality of life. The exploratory analysis indicated that the ED group showed elevated blood cortisol levels, which was associated with adverse survival outcomes. This study suggests that there is an association between ED and worse clinical outcomes in patients with advanced non-small-cell lung cancer treated with ICIs, highlighting the potential significance of addressing ED in cancer management. ClinicalTrials.gov registration: NCT05477979 .

Mutational analysis of pulmonary large cell neuroendocrine carcinoma: APC gene mutations identify a good prognostic factor

Pulmonary large cell neuroendocrine carcinoma (LCNEC) is a highly aggressive neoplasm with biological heterogeneity. Mutations in multiple genes have been identified in LCNEC. However, associations between gene alterations, histopathological characteristics, and prognosis remain ambiguous. Here, we investigated the clinicopathologic, immunohistochemical, and genomic characteristics of 19 patients with LCNEC and 9 patients with atypical carcinoid (AC). We revealed high mutation frequencies of TP53 (89.5 %), RB1 (42.1 %), APC (31.6 %), and MCL1 (31.6 %) in LCNEC, while genetic alterations were rarely found in AC. APC alterations mainly occurred to the exon 16 and were only identified in LCNEC with wild-type RB1. The 19 LCNEC were further subgrouped into APC wild-type (LCNEC-APCMT, 6/19) and APC-mutated (LCNEC-APCWT, 13/19) subgroups. In comparison with LCNEC-APCWT, LCNEC-APCMT displayed lower TMB (median: 12.64 vs 4.20, P = 0.045), and relatively mild cytologic atypia. In addition, LCNEC-APCMT distinguished itself from AC and LCNEC-APCWT by obviously downregulated expression of neuroendocrine markers (CD56 and Syn, P < 0.01) and significantly altered expression of genes downstream of APC (β-catenin migrating into the cytoplasm and nucleus, P < 0.001; c-Myc upregulating, P = 0.005). The OS of LCNEC-APCMT was numerically intermediate between AC and LCNEC-APCWT. We first proposed that APC alterations were common in LCNEC with wild-type RB1 and that LCNEC-APCMT was associated with lower TMB and better OS in comparison with LCNEC-APCWT.

Liquid biopsy for human cancer: cancer screening, monitoring, and treatment

Currently, tumor treatment modalities such as immunotherapy and targeted therapy have more stringent requirements for obtaining tumor growth information and require more accurate and easy-to-operate tumor information detection methods. Compared with traditional tissue biopsy, liquid biopsy is a novel, minimally invasive, real-time detection tool for detecting information directly or indirectly released by tumors in human body fluids, which is more suitable for the requirements of new tumor treatment modalities. Liquid biopsy has not been widely used in clinical practice, and there are fewer reviews of related clinical applications. This review summarizes the clinical applications of liquid biopsy components (e.g., circulating tumor cells, circulating tumor DNA, extracellular vesicles, etc.) in tumorigenesis and progression. This includes the development process and detection techniques of liquid biopsies, early screening of tumors, tumor growth detection, and guiding therapeutic strategies (liquid biopsy-based personalized medicine and prediction of treatment response). Finally, the current challenges and future directions for clinical applications of liquid biopsy are proposed. In sum, this review will inspire more researchers to use liquid biopsy technology to promote the realization of individualized therapy, improve the efficacy of tumor therapy, and provide better therapeutic options for tumor patients.

IFN-α/β/IFN-γ/IL-15 pathways identify GBP1-expressing tumors with an immune-responsive phenotype

Immunotherapy is widely used in cancer treatment; however, only a subset of patients responds well to it. Significant efforts have been made to identify patients who will benefit from immunotherapy. Successful anti-tumor immunity depends on an intact cancer-immunity cycle, especially long-lasting CD8+ T-cell responses. Interferon (IFN)-α/β/IFN-γ/interleukin (IL)-15 pathways have been reported to be involved in the development of CD8+ T cells. And these pathways may predict responses to immunotherapy. Herein, we aimed to analyze multiple public databases to investigate whether IFN-α/β/IFN-γ/IL-15 pathways could be used to predict the response to immunotherapy. Results showed that IFN-α/β/IFN-γ/IL-15 pathways could efficiently predict immunotherapy response, and guanylate-binding protein 1 (GBP1) could represent the IFN-α/β/IFN-γ/IL-15 pathways. In public and private cohorts, we further demonstrated that GBP1 could efficiently predict the response to immunotherapy. Functionally, GBP1 was mainly expressed in macrophages and strongly correlated with chemokines involved in T-cell migration. Therefore, our study comprehensively investigated the potential role of GBP1 in immunotherapy, which could serve as a novel biomarker for immunotherapy and a target for drug development.

Predicting therapeutic response to neoadjuvant immunotherapy based on an integration model in resectable stage IIIA (N2) non-small cell lung cancer

OBJECTIVE

Accurately predicting response during neoadjuvant chemoimmunotherapy for resectable non-small cell lung cancer remains clinically challenging. In this study, we investigated the effectiveness of blood-based tumor mutational burden (bTMB) and a deep learning (DL) model in predicting major pathologic response (MPR) and survival from a phase 2 trial.

METHODS

Blood samples were prospectively collected from 45 patients with stage IIIA (N2) non-small cell lung cancer undergoing neoadjuvant chemoimmunotherapy. An integrated model, combining the computed tomography-based DL score, bTMB, and clinical factors, was developed to predict tumor response to neoadjuvant chemoimmunotherapy.

RESULTS

At baseline, bTMB were detected in 77.8% (35 of 45) of patients. Baseline bTMB ≥11 mutations/megabase was associated with significantly greater MPR rates (77.8% vs 38.5%, P = .042), and longer disease-free survival (P = .043), but not overall survival (P = .131), compared with bTMB <11 mutations/megabase in 35 patients with bTMB available. The developed DL model achieved an area under the curve of 0.703 in all patients. Importantly, the predictive performance of the integrated model improved to an area under the curve of 0.820 when combining the DL score with bTMB and clinical factors. Baseline circulating tumor DNA (ctDNA) status was not associated with pathologic response and survival. Compared with ctDNA residual, ctDNA clearance before surgery was associated with significantly greater MPR rates (88.2% vs 11.1%, P < .001) and improved disease-free survival (P = .010).

CONCLUSIONS

The integrated model shows promise as a predictor of tumor response to neoadjuvant chemoimmunotherapy. Serial ctDNA dynamics provide a reliable tool for monitoring tumor response.

Artificial Intelligence and Machine Learning in Predicting the Response to Immunotherapy in Non-small Cell Lung Carcinoma: A Systematic Review

Non-small cell lung carcinoma (NSCLC) is a prevalent and aggressive form of lung cancer, with a poor prognosis for metastatic disease. Immunotherapy, particularly immune checkpoint inhibitors (ICIs), has revolutionized the management of NSCLC, but response rates are highly variable. Identifying reliable predictive biomarkers is crucial to optimize patient selection and treatment outcomes. This systematic review aimed to evaluate the current state of artificial intelligence (AI) and machine learning (ML) applications in predicting the response to immunotherapy in NSCLC. A comprehensive literature search identified 19 studies that met the inclusion criteria. The studies employed diverse AI/ML techniques, including deep learning, artificial neural networks, support vector machines, and gradient boosting methods, applied to various data modalities such as medical imaging, genomic data, clinical variables, and immunohistochemical markers. Several studies demonstrated the ability of AI/ML models to accurately predict immunotherapy response, progression-free survival, and overall survival in NSCLC patients. However, challenges remain in data availability, quality, and interpretability of these models. Efforts have been made to develop interpretable AI/ML techniques, but further research is needed to improve transparency and explainability. Additionally, translating AI/ML models from research settings to clinical practice poses challenges related to regulatory approval, data privacy, and integration into existing healthcare systems. Nonetheless, the successful implementation of AI/ML models could enable personalized treatment strategies, improve treatment outcomes, and reduce unnecessary toxicities and healthcare costs associated with ineffective treatments.

Clinical significance of inter-assay discrepancy in PD-L1 evaluation for the efficacy of pembrolizumab in advanced NSCLC with high PD-L1 expression

INTRODUCTION

Programmed cell death ligand-1 (PD-L1) expression is a predictive biomarker for the efficacy of anti-programmed cell death receptor-1/PD-L1 antibodies in advanced non-small cell lung cancer (NSCLC). Although several assays have been approved for evaluating PD-L1 expression status, inter-assay discordance has been observed between some assays. The clinical significance of these discrepancies is still unclear.

METHODS

We retrospectively reviewed treatment-naïve NSCLC patients whose PD-L1 expression was evaluated using both 22C3 and SP142 assays. Among those, efficacy analysis was performed for patients with PD-L1 tumor proportion score (TPS) ≥ 50 % (22C3), who had received first-line pembrolizumab monotherapy. Additionally, transcriptome analysis was conducted in the available tumors with TPS ≥ 50 % to investigate the distinct immune profiles that accompany inter-assay discordance.

RESULTS

In total, 611 patients were eligible. Among 198 patients with TPS ≥ 50 %, 91 (46 %) had tumor cell score ≤ 1 (SP142, i.e., inter-assay discrepancy). In the 52 patients who received first-line pembrolizumab monotherapy, treatment efficacy was significantly lower in patients with the discrepancy than that in those without (objective response rate: 18 % vs. 83 %, p < 0.001; median progression-free survival [months]: 3.2 vs. 8.3, p < 0.001). Transcriptome analysis revealed significantly more CD274 splice variants with aberrant 3'-terminal sequences in tumors with the inter-assay discrepancy than in those without.

CONCLUSION

The inter-assay discrepancy in the PD-L1 status of tumor cells between the 22C3 and SP142 assays, reflecting an imbalance in the CD274 splice variants, could be a biomarker for primary resistance against pembrolizumab monotherapy in high PD-L1-expressing NSCLCs.

5-Hydroxymethylcytosine in Cell-Free DNA Predicts Immunotherapy Response in Lung Cancer

Immune checkpoint inhibitors (ICIs) drastically improve therapeutic outcomes for lung cancer, but accurately predicting individual patient responses to ICIs remains a challenge. We performed the genome-wide profiling of 5-hydroxymethylcytosine (5hmC) in 85 plasma cell-free DNA (cfDNA) samples from lung cancer patients and developed a 5hmC signature that was significantly associated with progression-free survival (PFS). We built a 5hmC predictive model to quantify the 5hmC level and validated the model in the validation, test, and control sets. Low weighted predictive scores (wp-scores) were significantly associated with a longer PFS compared to high wp-scores in the validation [median 7.6 versus 1.8 months; p = 0.0012; hazard ratio (HR) 0.12; 95% confidence interval (CI), 0.03-0.54] and test (median 14.9 versus 3.3 months; p = 0.00074; HR 0.10; 95% CI, 0.02-0.50) sets. Objective response rates in patients with a low or high wp-score were 75.0% (95% CI, 42.8-94.5%) versus 0.0% (95% CI, 0.0-60.2%) in the validation set (p = 0.019) and 80.0% (95% CI, 44.4-97.5%) versus 0.0% (95% CI, 0.0-36.9%) in the test set (p = 0.0011). The wp-scores were also significantly associated with PFS in patients receiving single-agent ICI treatment (p < 0.05). In addition, the 5hmC predictive signature demonstrated superior predictive capability to tumor programmed death-ligand 1 and specificity to ICI treatment response prediction. Moreover, we identified novel 5hmC-associated genes and signaling pathways integral to ICI treatment response in lung cancer. This study provides proof-of-concept evidence that the cfDNA 5hmC signature is a robust biomarker for predicting ICI treatment response in lung cancer.

Soluble immune-checkpoint factors: a potential immunotherapy biomarker

There is unmet need for additional biomarkers to better select patients with non-small cell lung cancer (NSCLC) that are likely to benefit from immunotherapy in order to improve patient outcomes, reduce patient toxicity, and relieve the growing burden of healthcare costs. In this issue of the JCI, Hayashi and colleagues evaluated soluble forms of the immune checkpoint molecules PD-L1, PD-1, and CTLA-4 in the plasma of patients with advanced NSCLC who had been treated with anti-PD-1/L1 therapy. The findings suggest that these soluble immune-checkpoint factors may provide a complementary biomarker to PD-L1 IHC, although application into the clinic may not be straightforward.

Identification of immune-related gene signature for non-small cell lung cancer patients with immune checkpoint inhibitors

Background

The utilization of immune checkpoint inhibitors (ICIs) has become the established protocol for treating advanced non-small cell lung cancer (NSCLC). This work aimed to identify the immune-related gene signature that can predict the prognosis of NSCLC patients receiving ICI treatment.

Methods

The ImmPort database was queried to obtain a list of immune-related genes (IRGs). Differentially expressed IRGs in NSCLC patients were identified using the TCGA database. RNA-seq data and clinical information from NSCLC patients receiving immunotherapy were obtained from the GEO database (GSE93157 and ////). A gene signature was generated through multivariate Cox and LASSO regression analyses. The prognostic value and function of this gene signature were thoroughly investigated using comprehensive bioinformatics analyses.

Results

A total of 6 prognostic-related genes were identified from 617 differentially expressed genes, and two prognostic-related differentially expressed genes (CAMP and IL17A) were determined to construct gene signature. Our gene signature demonstrated superior performance compared to other clinicopathological parameters in predicting the prognosis of NSCLC patients receiving immunotherapy, with an area under the ROC curve (AUC) of 0.812. Furthermore, immune infiltration analysis indicated that the high-risk group was enriched with resting CD4 T cell memory, while the low-risk group showed a "hot" tumor microenvironment that promotes anti-tumor immunity in NSCLC patients.

Conclusion

Gene signatures based on immune-related genes exhibited excellent indicator performance of prognosis and immune infiltration, which has the potential to be an effective biomarker for NSCLC with ICI treatment.

Sterotactic Ablative Radiotherapy in a Multicentric Series of Oligometastatic SCLC: The SAMOS Cohort

AIMS

SCLC is the most aggressive lung cancer histology with a 5-year OS <10%. At the diagnosis, almost two-thirds of the SCLC an Extended Disease presentation. Two randomized studies (CASPIAN and ImPower133) demonstrated an OS improvement, when immunotherapy was prescribed as maintenance therapy after standard chemotherapy. To date, SABR has had a limited indication in managing metastatic SCLC, although recent reports proposed it as a valid treatment option in selected patients. We propose a retrospective multicentric analysis of patients treated with SABR for oligometastatic SCLC.

METHOD

Data of patients affected by oligometastatic-SCLC treated with SABR between 2017 and 2022 in 11 Italian centers were collected. Clinical and therapeutic variables together with OS and time to next treatment were analyzed. Univariate analysis with Kaplan-Meier curve were calculated, and log-rank test were applied. Cox proportional hazard model was used for multivariate analysis.

RESULTS

Data from 93 patients and 132 metastatic lesions were analyzed. The median age was 64 years (36-86) and all but 1 had Performance Status 0 or 1. Fifty-two patients presented ED at diagnosis. The first line treatment was radiochemotherapy in 42%, CHT alone in 24% and CHT-IO in 28%, others treatment accounts for 4% and only 2% of patients underwent best supportive care. Of the 132 lesions treated with SBRT 55 were in brain, 27 in lung, 11 in liver, 10 in lymph nodes, 8 in bones and 20 in adrenal gland. Median OS was 14 months, 1 year-OS and 2 years OS were 53% and 27%, respectively. The median TtNT was 14 months for the entire population. Of all the analyzed variables only, the anatomical site of the metastases and their number showed statistical significance in the univariate analysist, confirmed in the subsequent multivariate.

CONCLUSION

SABR seems to play a role in delaying further systemic lines in oligometastatic disease and to extend the use of ongoing treatment in oligoprogressive state. Prospective studies are needed to confirm these findings.

Prognostic and predictive biomarkers in non-small cell lung carcinoma

Lung cancer is the most common cause of cancer-related deaths globally, with the highest mortality rates among both men and women. Most lung cancers are diagnosed at late stages, necessitating systemic therapy. Modern clinical management of lung cancer relies heavily upon application of biomarkers, which guide the selection of systemic treatment. Here, we provide an overview of currently approved and emerging biomarkers of non-small cell lung cancer (NSCLC), including EGFR, ALK, ROS1, RET, NTRK1-3, KRAS, BRAF, MET, ERBB2/HER2, NRG1, PD-L1, TROP2, and CEACAM5. For practical purposes, we divide these biomarkers into genomic and protein markers, based on the tested substrate. We review the biology and epidemiology of the genomic and proteomic biomarkers, discuss optimal diagnostic assays for their detection, and highlight their contribution to the contemporary clinical management of NSCLC.

Dynamic surveillance of lymphocyte subsets in patients with non-small cell lung cancer during chemotherapy or combination immunotherapy for early prediction of efficacy

Background

Non-small cell lung cancer (NSCLC) remains the leading cause of cancer-related deaths worldwide. Lymphocytes are the primary executors of the immune system and play essential roles in tumorigenesis and development. We investigated the dynamic changes in peripheral blood lymphocyte subsets to predict the efficacy of chemotherapy or combination immunotherapy in NSCLC.

Methods

This retrospective study collected data from 81 patients with NSCLC who received treatments at the First Affiliated Hospital of Zhengzhou University from May 2021 to May 2023. Patients were divided into response and non-response groups, chemotherapy and combination immunotherapy groups, and first-line and multiline groups. We analyzed the absolute counts of each lymphocyte subset in the peripheral blood at baseline and after each treatment cycle. Within-group and between-group differences were analyzed using paired Wilcoxon signed-rank and Mann-Whitney U tests, respectively. The ability of lymphocyte subsets to predict treatment efficacy was analyzed using receiver operating characteristic curve and logistic regression.

Results

The absolute counts of lymphocyte subsets in the response group significantly increased after the first cycle of chemotherapy or combination immunotherapy, whereas those in the non-response group showed persistent decreases. Ratios of lymphocyte subsets after the first treatment cycle to those at baseline were able to predict treatment efficacy early. Combination immunotherapy could increase lymphocyte counts compared to chemotherapy alone. In addition, patients with NSCLC receiving chemotherapy or combination immunotherapy for the first time mainly presented with elevated lymphocyte levels, whereas multiline patients showed continuous reductions.

Conclusion

Dynamic surveillance of lymphocyte subsets could reflect a more actual immune status and predict efficacy early. Combination immunotherapy protected lymphocyte levels from rapid decrease and patients undergoing multiline treatments were more prone to lymphopenia than those receiving first-line treatment. This study provides a reference for the early prediction of the efficacy of clinical tumor treatment for timely combination of immunotherapy or the improvement of immune status.

CT radiomics-based model for predicting TMB and immunotherapy response in non-small cell lung cancer

BACKGROUND

Tumor mutational burden (TMB) is one of the most significant predictive biomarkers of immunotherapy efficacy in non-small cell lung cancer (NSCLC). Radiomics allows high-throughput extraction and analysis of advanced and quantitative medical imaging features. This study develops and validates a radiomic model for predicting TMB level and the response to immunotherapy based on CT features in NSCLC.

METHOD

Pre-operative chest CT images of 127 patients with NSCLC were retrospectively studied. The 3D-Slicer software was used to outline the region of interest and extract features from the CT images. Radiomics prediction model was constructed by LASSO and multiple logistic regression in a training dataset. The model was validated by receiver operating characteristic (ROC) curves and calibration curves using external datasets. Decision curve analysis was used to assess the value of the model for clinical application.

RESULTS

A total of 1037 radiomic features were extracted from the CT images of NSCLC patients from TCGA. LASSO regression selected three radiomics features (Flatness, Autocorrelation and Minimum), which were associated with TMB level in NSCLC. A TMB prediction model consisting of 3 radiomic features was constructed by multiple logistic regression. The area under the curve (AUC) value in the TCGA training dataset was 0.816 (95% CI: 0.7109-0.9203) for predicting TMB level in NSCLC. The AUC value in external validation dataset I was 0.775 (95% CI: 0.5528-0.9972) for predicting TMB level in NSCLC, and the AUC value in external validation dataset II was 0.762 (95% CI: 0.5669-0.9569) for predicting the efficacy of immunotherapy in NSCLC.

CONCLUSION

The model based on CT radiomic features helps to achieve cost effective improvement in TMB classification and precise immunotherapy treatment of NSCLC patients.

Cancer testis antigen burden (CTAB): a novel biomarker of tumor-associated antigens in lung cancer

BACKGROUND

Cancer-testis antigens (CTAs) are tumor antigens that are normally expressed in the testes but are aberrantly expressed in several cancers. CTA overexpression drives the metastasis and progression of lung cancer, and is associated with poor prognosis. To improve lung cancer diagnosis, prognostic prediction, and drug discovery, robust CTA identification and quantitation is needed. In this study, we examined and quantified the co-expression of CTAs in lung cancer to derive cancer testis antigen burden (CTAB), a novel biomarker of immunotherapy response.

METHODS

Formalin fixed paraffin embedded (FFPE) tumor samples in discovery cohort (n = 5250) and immunotherapy and combination therapy treated non-small cell lung cancer (NSCLC) retrospective (n = 250) cohorts were tested by comprehensive genomic and immune profiling (CGIP), including tumor mutational burden (TMB) and the mRNA expression of 17 CTAs. PD-L1 expression was evaluated by IHC. CTA expression was summed to derive the CTAB score. The median CTAB score for the discovery cohort of 170 was applied to the retrospective cohort as cutoff for CTAB "high" and "low". Biomarker and gene expression correlation was measured by Spearman correlation. Kaplan-Meier survival analyses were used to detect overall survival (OS) differences, and objective response rate (ORR) based on RECIST criteria was compared using Fisher's exact test.

RESULTS

The CTAs were highly co-expressed (p < 0.05) in the discovery cohort. There was no correlation between CTAB and PD-L1 expression (R = 0.011, p = 0.45) but some correlation with TMB (R = 0.11, p = 9.2 × 10-14). Kaplan-Meier survival analysis of the immunotherapy-treated NSCLC cohort revealed better OS for the pembrolizumab monotherapy treated patients with high CTAB (p = 0.027). The combination group demonstrated improved OS compared to pembrolizumab monotherapy group (p = 0.04). The pembrolizumab monotherapy patients with high CTAB had a greater ORR than the combination therapy group (p = 0.02).

CONCLUSIONS

CTA co-expression can be reliably measured using CGIP in solid tumors. As a biomarker, CTAB appears to be independent from PD-L1 expression, suggesting that CTAB represents aspects of tumor immunogenicity not measured by current standard of care testing. Improved OS and ORR for high CTAB NSCLC patients treated with pembrolizumab monotherapy suggests a unique underlying aspect of immune response to these tumor antigens that needs further investigation.

Comparison of the Efficacy and Safety of Perioperative Immunochemotherapeutic Strategies for Resectable Non-small Cell Lung Cancer: a Systematic Review and Network Meta-analysis

AIMS

The aim of this network meta-analysis was to elucidate the efficacy and safety of various immune checkpoint inhibitors (ICIs) used in combination with chemotherapy for the treatment of non-small cell lung cancer (NSCLC).

MATERIALS AND METHODS

Data from randomised controlled trials comparing perioperative ICI-chemotherapy and chemotherapy alone were acquired from the EMBASE, Web of Science, Cochrane Library databases, PubMed, and meeting abstracts from inception until August 2023. The endpoints for this analysis were pathological complete response, event-free survival and treatment-related adverse events of any grade or adverse events of grade 3 or higher.

RESULTS

In total, six randomised controlled trials with 2538 NSCLC patients were selected for this network meta-analysis. Compared with other ICIs, toripalimab + chemotherapy demonstrated increased pathological complete response rates and prolonged event-free survival in NSCLC. In patients with negative/low PD-L1 expression or squamous cell pathology, toripalimab + chemotherapy was the most effective regimen. In contrast, nivolumab + chemotherapy was preferable for patients with high PD-L1 expression or non-squamous cell pathology. Among the analysed regimens, toripalimab + chemotherapy presented the highest risk of adverse events of any grade, whereas nivolumab + chemotherapy showed the highest risk of grade 3-4 adverse events. Conversely, durvalumab + chemotherapy exhibited the lowest risk of grade 3-4 adverse events.

CONCLUSIONS

Among the evaluated perioperative immunochemotherapy regimens, toripalimab + chemotherapy indicated a significantly increased survival benefit for most resectable NSCLC patients. However, for high PD-L1 expression and non-squamous NSCLC patients, nivolumab + chemotherapy provided the most potent outcomes. Perioperative durvalumab + chemotherapy is a relatively safe treatment. The findings of this investigation are expected to assist clinicians in making informed decisions among promising treatment options.

Lipidomics reveals immune-related adverse events in NSCLC patients receiving immune checkpoint inhibitor

There is a lack of reliable biomarkers to predict and identify the risk of immune-related adverse events (irAEs) in non-small cell lung cancer (NSCLC) patients undergoing immune checkpoint inhibitor (ICI) treatment. This study aims to explore potential biomarkers using lipidomics to identify and predict the risk of irAEs in NSCLC patients receiving ICI treatment. This prospective study enrolled 94 NSCLC patients with IIIB/IV stage NSCLC who underwent first-line chemotherapy in combination with ICI treatment. The prediction cohort consisted of plasma samples collected from 60 patients before ICI treatment, and the occurrence of irAE was monitored within 6 months of initiating first-line ICI therapy. The validation cohort comprised 34 patients, with plasma samples obtained from 15 patients who did not develop irAE at 6 months of ICI treatment and plasma samples collected from 19 irAE patients at the onset of irAE. Through non-targeted lipidomics and semi-targeted lipid quantification analysis, we identify 11 differentially metabolized lipids and further screened these lipids with the area under the curve (AUC) > 0.7 to predict the occurrence of irAEs in NSCLC patients following ICI treatment. The results showed that the biomarker panel consisting of 9 lipids (LPC-18:2, PC-40:6, LPC-22:6, LPC-O-18:0, PS-38:0, PC-38:6, PC-37:6, PC-36:5,LPC-17:0) exhibited a good AUC of 0.859 in the prediction and 0.940 in the validation cohort phase of the receiver operating characteristic curve; The study utilizes plasma lipidomics to develop a rapid and effective prediction model for identifying irAEs in advanced NSCLC patients who treatment with first-line chemotherapy combined with immunotherapy.

A novel sub-regional radiomics model to predict immunotherapy response in non-small cell lung carcinoma

BACKGROUND

Identifying precise biomarkers of immunotherapy response for non-small cell lung carcinoma (NSCLC) before treatment is challenging. This study aimed to construct and investigate the potential performance of a sub-regional radiomics model (SRRM) as a novel tumor biomarker in predicting the response of patients with NSCLC treated with immune checkpoint inhibitors, and test whether its predictive performance is superior to that of conventional radiomics, tumor mutational burden (TMB) score and programmed death ligand-1 (PD-L1) expression.

METHODS

We categorized 264 patients from retrospective databases of two centers into training (n = 159) and validation (n = 105) cohorts. Radiomic features were extracted from three sub-regions of the tumor region of interest using the K-means method. We extracted 1,896 features from each sub-region, resulting in 5688 features per sample. The least absolute shrinkage and selection operator regression method was used to select sub-regional radiomic features. The SRRM was constructed and validated using the support vector machine algorithm. We used next-generation sequencing to classify patients from the two cohorts into high TMB (≥ 10 muts/Mb) and low TMB (< 10 muts/Mb) groups; immunohistochemistry was performed to assess PD-L1 expression in formalin-fixed, paraffin-embedded tumor sections, with high expression defined as ≥ 50% of tumor cells being positive. Associations between the SRRM and progression-free survival (PFS) and variant genes were assessed.

RESULTS

Eleven sub-regional radiomic features were employed to develop the SRRM. The areas under the receiver operating characteristic curve (AUCs) of the proposed SRRM were 0.90 (95% confidence interval [CI] 0.84-0.96) and 0.86 (95% CI 0.76-0.95) in the training and validation cohorts, respectively. The SRRM (low vs. high; cutoff value = 0.936) was significantly associated with PFS in the training (hazard ratio [HR] = 0.35 [0.24-0.50], P < 0.001) and validation (HR = 0.42 [0.26-0.67], P = 0.001) cohorts. A significant correlation between the SRRM and three variant genes (H3C4, PAX5, and EGFR) was observed. In the validation cohort, the SRRM demonstrated a higher AUC (0.86, P < 0.001) than that for PD-L1 expression (0.66, P = 0.034) and TMB score (0.54, P = 0.552).

CONCLUSIONS

The SRRM had better predictive performance and was superior to conventional radiomics, PD-L1 expression, and TMB score. The SRRM effectively stratified the progression-free survival (PFS) risk among patients with NSCLC receiving immunotherapy.

DCAF13 inhibits the p53 signaling pathway by promoting p53 ubiquitination modification in lung adenocarcinoma

BACKGROUND

Lung cancer is a malignant tumor with the highest mortality worldwide. Abnormalities in the ubiquitin proteasome system are considered to be contributed to lung cancer progression with deleterious effects. DDB1 and CUL4 associated factor 13 (DCAF13) is a substrate receptor of the E3 ubiquitin ligase CRL4, but its role in lung cancer remains unknown. In this study, we aimed to investigate the regulatory mechanisms of DCAF13 in lung adenocarcinoma (LUAD).

METHODS

So as to investigate the effect of DCAF13 on lung adenocarcinoma cell function using in vivo and in vitro. Mechanistically, we have identified the downstream targets of DCAF13 by using RNA-sequencing, as well as ubiquitination assays, co-immunoprecipitation, immunofluorescence, immunohistochemistry and chromatin immunoprecipitation - qPCR experiments.

RESULTS

Our findings reveal that DCAF13 is a carcinogenic factor in LUAD, as it is highly expressed and negatively correlated with clinical outcomes in LUAD patients. Through RNA-sequencing, it has been shown that DCAF13 negatively regulates the p53 signaling pathway and inhibits p53 downstream targets including p21, BAX, FAS, and PIDD1. We also demonstrate that DCAF13 can bind to p53 protein, leading to K48-linked ubiquitination and degradation of p53. Functionally, we have shown that DCAF13 knockdown inhibits cell proliferation and migration. Our results highlight the significant role of DCAF13 in promoting LUAD progression by inhibiting p53 protein stabilization and the p53 signaling pathway. Furthermore, our findings suggest that high DCAF13 expression is a poor prognostic indicator in LUAD, and DCAF13 may be a potential therapeutic target for treating with this aggressive cancer.

CONCLUSIONS

The DCAF13 as a novel negative regulator of p53 to promote LUAD progression via facilitating p53 ubiquitination and degradation, suggesting that DCAF13 might be a novel biomarker and therapeutical target for LUAD.

Advances and challenges in the treatment of lung cancer

Lung cancer accounts for a relatively high proportion of malignant tumors. As the most prevalent type of lung cancer, non-small cell lung cancer (NSCLC) is characterized by high morbidity and mortality. Presently, the arsenal of treatment strategies encompasses surgical resection, chemotherapy, targeted therapy and radiotherapy. However, despite these options, the prognosis remains distressingly poor with a low 5-year survival rate. Therefore, it is urgent to pursue a paradigm shift in treatment methodologies. In recent years, the advent of sophisticated biotechnologies and interdisciplinary integration has provided innovative approaches for the treatment of lung cancer. This article reviews the cutting-edge developments in the nano drug delivery system, molecular targeted treatment system, photothermal treatment strategy, and immunotherapy for lung cancer. Overall, by systematically summarizing and critically analyzing the latest progress and current challenges in these treatment strategies of lung cancer, we aim to provide a theoretical basis for the development of novel drugs for lung cancer treatment, and thus improve the therapeutic outcomes for lung cancer patients.

Utilization of cancer immunotherapy in sub-Saharan Africa

Introduction

The Lancet Oncology Commission for sub-Saharan Africa (SSA) predicts that cancer deaths will double from 520,158 per year to more than 1 million per year by the year 2040. These striking figures indicate a need to urgently evaluate cancer treatment infrastructure and resources in the region. Studies have found immunotherapy to be effective for the treatment of advanced-stage cancer, which almost 70% of patients in SSA present with. Despite immunotherapy's significant therapeutic potential, its utilization in SSA is not well documented. The purpose of this study was to evaluate the landscape of immunotherapy in SSA.

Methods

A Qualtrics survey assessing the existing infrastructure and training for safe immunotherapy administration was developed and distributed online via email and WhatsApp to 3,231 healthcare providers across SSA, with a target audience of healthcare providers serving patients with cancer. The survey contained 22 questions evaluating the accessibility, use, knowledge, and training on immunotherapy in SSA. Responses were collected between January and February 2023. Microsoft Excel was used to summarize and visually present the distribution of responses as counts and proportions.

Results

292 responses were included from 28 countries in SSA. 29% of all respondents indicated their clinic has easy access to cancer immunotherapy and 46% indicated their clinic currently practices it. Of clinics that practiced immunotherapy (n = 133), 12% used genomic sequencing to assess the tumor mutational burden biomarker, and 44% assessed expression of the PD-L1 biomarker prior to immunotherapy administration. 46% of all respondents were familiar with immunotherapy. 11% indicated being adequately trained to administer it. Of these (n=33), 52% indicated also being trained to manage immune-related adverse events related to immunotherapy administration.

Conclusion

Immunotherapy utilization and training is low in SSA and insufficient for the rising cancer burden. Increased accessibility and usage of biomarker testing to predict immunotherapy response, incorporation of immunotherapy training into continuous medical education, and increased access to immunotherapy drugs may be prerequisites for expanded utilization of immunotherapy in SSA.

Identification of ACBD3 as a new molecular biomarker in pan-cancers through bioinformatic analysis: a preclinical study

BACKGROUND

Acyl-CoA-binding domain-containing 3 (ACBD3) is a multifunctional protein, that plays essential roles in cellular signaling and membrane domain organization. Although the precise roles of ACBD3 in various cancers remain unclear. Thus, we aimed to determine the diverse roles of ACBD3 in pan-cancers.

METHODS

Relevant clinical and RNA-sequencing data for normal tissues and 33 tumors from The Cancer Genome Atlas (TCGA) database, the Human Protein Atlas, and other databases were applied to investigate ACBD3 expression in various cancers. ACBD3-binding and ACBD3-related target genes were obtained from the STRING and GEPIA2 databases. The possible functions of ACBD3-binding genes were explored using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses. We also applied the diagnostic value and survival prognosis analysis of ACBD3 in pan-cancers using R language. The mutational features of ACBD3 in various TCGA cancers were obtained from the cBioPortal database.

RESULTS

When compared with normal tissues, ACBD3 expression was statistically upregulated in eleven cancers and downregulated in three cancers. ACBD3 expression was remarkably different among various pathological stages of tumors, immune and molecular subtypes of cancers, cancer phosphorylation levels, and immune cell infiltration. The survival of four tumors was correlated with the expression level of ACBD3, including pancreatic adenocarcinoma, adrenocortical carcinoma, sarcoma, and glioma. The high accuracy in diagnosing multiple tumors and its correlation with prognosis indicated that ACBD3 may be a potential biomarker of pan-cancers.

CONCLUSION

According to our pan-cancer analysis, ACBD3 may serve as a remarkable prognostic and diagnostic biomarker of pan-cancers as well as contribute to tumor development. ACBD3 may also provide new directions for cancer treatment targets in the future.

Clinical markers predict the efficacy of several immune checkpoint inhibitors in patients with non-small cell lung cancer in China

Objectives

Immune checkpoint inhibitors (ICIs) are one of the most significant oncological treatment modalities as a result of the rapid advancement of immunotherapy. Programmed Cell Death-Ligand 1 (PD-L1) and tumor mutational burden (TMB) have emerged as key markers for predicting the efficacy and prognosis of ICIs in non-small cell lung cancer (NSCLC), and the predictive role of tumor-infiltrating lymphocytes (TILs) has also received significant attention. However, the prognosis of some individuals cannot be determined by these indicators; for instance, some patients with low PD-L1 expression also benefit from longer survival. Therefore, the purpose of this research was to investigate the connection between new haematological and pathological markers and clinical outcomes in NSCLC patients receiving ICIs.

Methods

Seventy-six patients with stage III-IV NSCLC treated with ICIs were included in this study. We used the Mann-Whitney test, COX regression and Kaplan-Meier analysis to retrospectively analyze peripheral blood indicators and survival prognostic data of 76 patients in order to investigate the relationship between baseline neutrophil-to-lymphocyte ratio (NLR) and the efficacy of ICIs. To investigate the correlation between CXCL13, CXCR5, CD8 and the efficacy of ICIs, we assessed the expression levels of aforementioned indicators in biopsied tissues of 10 non-small cell lung tumors by immunohistochemistry (IHC) and immunofluorescence (IF) and performed statistical analysis.

Results

Disease control rate (DCR) was higher in patients with baseline NLR <3.4 (p=0.016) and neutrophil percentage <71% (P=0.015). Baseline NLR (HR=2.364, P=0.003) and neutrophil percentage (HR=2.824, P=0.013) had the greatest influence on patients' survival prognosis, with baseline NLR exhibiting a stronger predictive value (AUC=0.717), according to univariate and multifactorial COX regression analyses of progression-free survival (PFS) and overall survival (OS). In NSCLC tissues, higher expression of CXCL13 was associated with better clinical outcomes (P=0.032) and higher expression of CD8 was associated with prolonged survival (P=0.022).

Conclusion

Low baseline NLR in peripheral blood and high expression of CD8 in tissues are associated with longer PFS and may have a potential predictive value for patients with stage III-IV NSCLC using ICIs.

The Promise and Limitations of Neoadjuvant Immune-Checkpoint Blockade in Resectable Non-Small Cell Lung Cancer

SUMMARY

The landscape of neoadjuvant immune-checkpoint blockade for resectable non-small cell lung cancer has become an exciting area of clinical and translational exploration. Cascone and colleagues present a platform study of one cycle of novel immunomodulatory agents prior to surgical resection, offering a unique opportunity to perform translational biomarker studies, though many questions remain regarding the ultimate application to a broader patient population. See related article by Cascone et al., p. 2394 (1).

Association between pretreatment C-reactive protein level and survival in non-small cell lung cancer patients treated with immune checkpoint inhibitors: A meta-analysis

BACKGROUND

Current evidence suggests that C-reactive protein (CRP) levels may affect cancer prognosis. However, the effect of CRP has not been validated in immunotherapy recipients with non-small cell lung cancer (NSCLC). Therefore, we performed a meta-analysis to explore the prognostic value of CRP level in patients with NSCLC treated with immune checkpoint inhibitors.

METHODS

PubMed, Web of Science, Embase, and Scopus databases were systematically retrieved for eligible publications, and hazard ratios (HRs) with corresponding 95% confidence intervals (95%CIs) were extracted and merged to evaluate the correlation between pretreatment CRP levels and overall survival (OS) and progression-free survival (PFS). Subgroup and sensitivity analyses were conducted to confirm these findings.

RESULTS

Thirty-five cohorts consisting of 4698 patients were included in the primary analysis. Pooled results demonstrated that a higher pretreatment CRP level is associated with worse OS and PFS (OS: HR = 1.13, 95 %CI:1.09-1.18; PFS: HR = 1.16, 95 %CI:1.10-1.22). These findings remained robust after further statistical analyses.

CONCLUSION

Pretreatment CRP level could be a promising biomarker for NSCLC immunotherapy. However, prospective studies are required to validate these findings.

Defining Tumor Microenvironment as a Possible Target for Effective GEP-NENs Immunotherapy-A Systematic Review

Neuroendocrine neoplasms (NENs) are a heterogenous and recurrent group of malignancies originating from neuroendocrine secretory cells diffused on all parts of the human body. Gastro-entero-pancreatic neuroendocrine tumors (GEP-NETs) account for most NENs. Considering the abundance of possible origins, locations, and tumor specifications, there is still no consensus about optimal treatment options for these neoplasms. In light of the escalating immunotherapeutic approaches, it is crucial to define indications for such therapy in GEP-NETs. Bearing in mind the significance of pathophysiological mechanisms and tumor microenvironment (TME) impact on carcinogenesis, defining TME structure and correlation with the immune system in GEP-NETs appears essential. This paper aimed to assess the characterization of the tumor immune microenvironment for a better understanding of the possible therapeutic options in GEP-NETS. The authors performed a systematic review, extracting papers from the PubMed, Web of Science, and Scopus databases according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Among 3800 articles identified through database searching, 292 were assessed for eligibility. Ultimately, 28 articles were included in the qualitative synthesis. This paper sums up the research on the immune cell infiltrates, immune checkpoint expression, cytokine profile, neoangiogenesis, and microbiome in the TME of GEP-NETs.

The Gut Microbiome from a Biomarker to a Novel Therapeutic Strategy for Immunotherapy Response in Patients with Lung Cancer

The gastrointestinal microbiome has been shown to play a key role in determining the responses to cancer immunotherapy, including immune checkpoint inhibitor (ICI) therapy and CAR-T. In patients with non-small cell lung cancer (NSCLC), increasing evidence suggests that a microbiome composition signature is associated with clinical response to ICIs as well as with the development of immune-related adverse events. In support of this, antibiotic (ATB)-related dysbiosis has been consistently linked with the deleterious impact of ICI response, shortening the overall survival (OS) among patients on ATBs prior to ICI initiation. In parallel, several preclinical experiments have unravelled various strategies using probiotics, prebiotics, diet, and fecal microbiota transplantation as new therapeutic tools to beneficially shift the microbiome and enhance ICI efficacy. These approaches are currently being evaluated in clinical trials and have achieved encouraging preliminary results. In this article, we reviewed the recent studies on the gut microbiome as a potential biomarker and an adjuvant therapy to ICIs in NSCLC patients.

Quantitative multiplexed imaging technologies for single-cell analysis to assess predictive markers for immunotherapy in thoracic immuno-oncology: promises and challenges

The past decade has witnessed a revolution in cancer treatment by the shift from conventional drugs (chemotherapies) towards targeted molecular therapies and immune-based therapies, in particular the immune-checkpoint inhibitors (ICIs). These immunotherapies selectively release the host immune system against the tumour and have shown unprecedented durable remission for patients with cancers that were thought incurable such as advanced non-small cell lung cancer (aNSCLC). The prediction of therapy response is based since the first anti-PD-1/PD-L1 molecules FDA and EMA approvals on the level of PD-L1 tumour cells expression evaluated by immunohistochemistry, and recently more or less on tumour mutation burden in the USA. However, not all aNSCLC patients benefit from immunotherapy equally, since only around 30% of them received ICIs and among them 30% have an initial response to these treatments. Conversely, a few aNSCLC patients could have an efficacy ICIs response despite low PD-L1 tumour cells expression. In this context, there is an urgent need to look for additional robust predictive markers for ICIs efficacy in thoracic oncology. Understanding of the mechanisms that enable cancer cells to adapt to and eventually overcome therapy and identifying such mechanisms can help circumvent resistance and improve treatment. However, more than a unique universal marker, the evaluation of several molecules in the tumour at the same time, particularly by using multiplex immunostaining is a promising open room to optimise the selection of patients who benefit from ICIs. Therefore, urgent further efforts are needed to optimise to individualise immunotherapy based on both patient-specific and tumour-specific characteristics. This review aims to rethink the role of multiplex immunostaining in immuno-thoracic oncology, with the current advantages and limitations in the near-daily practice use.