Zoltan Lohinai

Cancer immunotherapy has significantly developed the management of diverse solid tumors, such as melanoma, lung, renal, gastrointestinal, genitourinary, and head and neck cancers. Immune checkpoint inhibitors (ICIs) are now central to modern oncology, providing durable clinical benefits for many patients. However, treatment responses are heterogeneous: some patients experience sustained disease control, others derive minimal or no benefit, and a subset develops immune-related adverse events or toxicities that could affect multiple organs and influence treatment outcomes. Given the variation in patient responses and side-effect profiles associated with ICI treatment, there is growing interest in explaining the biomarkers and biological mechanisms underlying these differences. Current evidence indicates that both the efficacy and adverse effects of immunotherapy result from complicated interactions among tumor characteristics, host immune status, inflammatory pathways, microbial communities, and environmental or treatment-related factors. Extensively studied biomarkers include Programmed Death Ligand 1 expression, mismatch repair deficiency, Tumor Mutational Burden, immune cell profiles, circulating markers (including tumor cells and DNA), and microbiome signatures. These biomarkers are intended to advance patient selection, monitor treatment response, and identify individuals at risk for toxicity. The key findings explain mechanistic and clinical evidence regarding the interaction among immune dysregulation, biomarkers, microbiome alterations, therapeutic efficacy, and immune-related toxicity in solid tumors. It also discusses the translational relevance of these outcomes, ongoing clinical trials, and the issues that must be addressed before these approaches be routinely implemented in oncology practice. The intended audience includes clinicians, researchers, regulatory scientists, graduate students, and fellows in oncology, immunology, and exact medicine.

Chronic obstructive pulmonary disease (COPD) is increasingly recognized as a systemic disorder affecting host–microbiome interactions beyond the airways. Although bacterial alterations in COPD have been documented, the gut mycobiome and its ecological integration with bacterial communities remain unexplored. In this study, we profiled the gut mycobiome of 61 non-small-cell lung cancer (NSCLC) patients stratified by COPD severity using ITS2 sequencing and analyzed 47 overlapping patients with available metagenomic data to construct cross-kingdom bacterial–fungal networks. Alpha diversity, assessed by Shannon, Simpson, and Chao1 indices, did not differ significantly between patients with and without severe COPD. Partial least squares discriminant analysis (PLS-DA) revealed partial separation of the two groups, with COPD severity explaining 6% of overall compositional variance (R²=0.06, p = 0.058). COPD-severe patients exhibited a significantly reduced Ascomycota/Basidiomycota ratio ( p = 0.039) and lower relative abundance of Mucoromycota . Analysis of compositions of microbiomes (ANCOM) identified Myrothecium and Lasiodiplodia crassispora enriched in severe COPD, while Helotiales_unclassified and Phallus atrovolvatus were more abundant in non-severe cases. Fungal co-occurrence networks demonstrated reduced connectivity and modularity in severe COPD compared with non-severe COPD. Cross-kingdom analyses integrating bacterial genera revealed strengthened Candida – Enterococcus / Clostridium hubs and weakened Faecalibacterium / Roseburia –yeast associations in severe disease. Keystone analysis showed increased centrality for Candida , Aspergillus , Enterococcus , and Clostridium , and decreased centrality for Akkermansia and Roseburia . A compositional balance classifier achieved high discriminatory power (AUC = 0.88) in distinguishing COPD-severe from non-severe patients. These findings indicate that COPD severity is not characterized by major diversity loss but by guild-specific compositional shifts and extensive network rewiring, favoring oxygen-tolerant, opportunistic taxa over short-chain fatty acid–associated commensals.

Cancer patient's survival has gradually improved due to immune checkpoint inhibitors (ICIs). Several studies showed a possible association between the intestinal microbiome and ICI efficacy. Strategies for modifying the composition of the gut microbiome encompass various dietary interventions, which may have distinct impacts on the outcomes of ICI-treated patients. In our systematic review, we explored how dietary habits correlate with therapeutic responses in cancer patients and cancer mouse models undergoing immunotherapy. A systematic review was conducted using search terms: "cancer", "immunotherapy", "diet", and "microbiome", from Medline, Web of Science, Scopus, and Cochrane Library databases. The outcomes in the clinical studies were overall response rate (ORR), overall survival (OS), or progression-free survival (PFS) in human studies. In mouse studies, change in tumor size was the endpoint. The comparator attributions were questionnaire-based dietary interventions. Nineteen articles met the inclusion criteria and were included in the review (6 prospective cohort studies, 1 cross-sectional observational study, and 12 mouse studies). A consistent association was observed between high (vs. low) fiber consumption and improved therapeutic response with a pooled odds ratio of 5.79 when including all human prospective cohort studies. In mice, limited availability of methionine, cysteine, and low intake of leucine and glutamine was linked to reduced tumor progression. Combining ICIs with intermittent fasting or a fasting-mimicking diet significantly decreased tumor volume in mouse melanoma models. In humans, a higher relative abundance of short-chain fatty acid (SCFA) and lactic acid-producing bacteria-particularly Faecalibacterium prausnitzii and Akkermansia muciniphila-correlated with objective response rates (ORR). Similar microbiome alterations were observed in mouse models. Increased fiber intake enhanced ICI efficacy in mice by modulating the gut microbiome, primarily via elevated SCFA production-an effect also reflected in human studies. Intermittent fasting, high fiber, and low sugar consumption are significantly associated with better ICI outcomes. The studies revealed alterations in microbiota composition linked to diet, and these findings were confirmed in animal models, regarding the production of SCFAs and lactic acid, as well as an increase in Bacteroidota/Bacillota ratio and microbial diversity.

Machine learning (ML) algorithms hold the potential to outperform the selection of patients for immunotherapy (ICIs) compared to previous biomarker studies. We analyzed the predictive performance of ML models and compared them to traditional clinical biomarkers (TCBs) in the field of gastrointestinal (GI) cancers. The study has been registered in PROSPERO (number: CRD42023465917). A systematic search of PubMed was conducted to identify studies applying different ML algorithms to GI cancer patients treated with ICIs using tumor RNA gene expression profiles. The outcomes included were response to immunotherapy (ITR) or survival. Additionally, we compared the ML methodology details and predictive power inherent in the published gene sets using 5-fold cross-validation and logistic regression (LR), on an available well-defined ICI-treated metastatic gastric cancer (GC) cohort (n = 45). A set of standard clinical ICI biomarkers (MLH, MSH, and CD8 genes, plus PMS2 and PD-L1)) and de-novo calculated principal components (PCs) of the original datasets were also included as additional points of comparison. Nine articles were identified as eligible to meet the inclusion criteria. Three were pan-cancer studies, five assessed GC, and one studied colorectal cancer (CRC). Classification and regression models were used to predict ICI efficacy. Next, using LR, we validated the predictive power of applied ML algorithms on RNA signatures, using their reported receiver operating characteristics (ROC) analysis area under the curve (AUC) values on a well-defined ICI-treated gastric cancer (GC) dataset (n = 45). In two cases our method has outperformed the published results (reported/LR comparison: 0.74/0.831, 0.67/0.735). Besides the published studies, we have included two benchmarks: a set of TCBs and using principal components based on the whole dataset (PCA, 99% explained variance, 40 components). Interestingly, a study using a selected gene set (immuno-oncology panel) with AUC = 0.83 was the only one that outperformed the TCB (AUC = 0.8) and the PCA (AUC =0.81) results. Cross-validation of the predictive performance of these genes on the same GC dataset and an investigation of their prognostic role on a collated multi-cohort GC dataset of n = 375 resected, or chemotherapy-treated patients revealed that genes mannose-6-phosphate receptor (M6PR), Indoleamine 2,3-Dioxygenase 1 (IDO1), Neuropilin-1 (NRP1), and MAGEA3 performed similarly, or better than established biomarkers like PD-L1 and MSI. We found an immuno-oncology panel with an AUC = 0.83 that outperformed the clinical benchmark or the PC results. We recommend further investigation and experimental validation in the case of M6PR, IDO1, NRP1, and MAGEA3 expressions based on their strong predictive power in GC ITR. Well-designed studies with larger sample sizes and nonlinear ML models might help improve biomarker selections.

Background/Objectives: Small cell lung cancer (SCLC) is one of the malignancies with the worst prognosis, and there have been no major breakthroughs in its treatment for a long time. The majority of patients are diagnosed at the extensive stage, where the only option is chemotherapy, and even the addition of immune checkpoint inhibitors results in only modest benefits. The characterization of the molecular mechanisms behind therapy resistance has relevance in finding novel therapeutic approaches. Previous studies showed the possibility of annexin A1’s (ANXA1) involvement in the immunosuppressive tumor microenvironment in SCLC, and there are studies showing the direct effects of ANXA1 modulation on cancer cell aggressiveness. Methods: We aimed to characterize the roles of ANXA1 expression using publicly available transcriptomic data, the RNA-seq-based predictive algorithms EPIC and ESTIMATE, and immunohistochemistry on patient samples. For the in vitro studies, we silenced ANXA1 expression with short hairpin RNA in three SCLC cell lines, measured the growth rate with the trypan blue exclusion assay, assessed the chemosensitivity to cisplatin and etoposide with the Presto Blue^TM viability assay, and performed Western blots to assess changes in the levels of metabolic and mesenchymal markers and transcriptional drivers. Results: ANXA1-high tumors are associated with significantly increased immune infiltrates, stromality, and tumor-associated macrophages (TAMs). The ANXA1 protein is expressed on tumor cells and TAMs at the tissue level. ANXA1 silencing in H841 cells did not affect the growth rate; in SW1271 cells, shANXA1 cells grew significantly slower than shCTRL cells. Meanwhile, in H1048 cells, proliferation was significantly faster. Despite the different growth rates of the tested cell lines, ANXA1 silencing decreased the chemosensitivity to both cisplatin and etoposide in all three cell lines. Gene expression changes in mesenchymal markers, metabolic markers, dominant transcriptional drivers, and immune-relevant molecules were also characterized. Conclusions: This is the first comprehensive characterization of ANXA1 in SCLC to reveal its role in the tumor’s cell biology and the TME, aiming to boost further research in the field.

Introduction: Urine samples are non-invasive approaches to study potential circulating biomarkers from the host organism. Specific proteins cross the bloodstream through the intestinal barrier and may also derive from gut microbiota. In this study, we aimed to evaluate the predictive role of the host and bacterial urine extracellular vesicle (EV) proteomes in patients with non-small cell lung cancer (NSCLC) treated with anti-PD1 immunotherapy.Methods: We analyzed the urine EV proteome of 33 advanced-stage NSCLC patients treated with anti-PD1 immunotherapy with LC-MS/MS, stratifying patients according to long (>6 months) and short (≤6 months) progression-free survival (PFS). Gut microbial communities on a subcohort of 23 patients were also analyzed with shotgun metagenomics. Internal validation was performed using the Random Forest (RF) machine learning (ML) algorithm. RF was validated with a non-linear Bayesian ML model. Gene enrichment, and pathway analysis of host urine proteins were analyzed using the Reactome and Gene Ontology databases.Results: We identified human (n=3513), bacterial (n=2647), fungal (n=19), and viral (n=4) proteins. 186 human proteins showed differential abundance (pE. coli and E. faecalis proteins in the urine positively correlates with their gut metagenomic abundance. RF ML model supported the reliability in predicting PFS for critical human urine proteins (AUC=0.89), accuracy (95%) and Bacterial proteins (AUC=0.74).Conclusion: To our knowledge, this is the first study to depict the predictive role of the host and bacterial urine proteome in anti-PD1-treated advanced NSCLC.

Introduction: Small cell lung cancer (SCLC) is a highly aggressive malignancy with poor survival outcomes. The CD70-CD27 axis has been implicated in immune regulation and tumor progression across cancers, but its role in SCLC has not yet been elucidated. This research explores the expression patterns and prognostic significance of CD70 and CD27 in early-stage SCLC. Methods: In this retrospective study, we analyzed 190 surgically resected SCLC tumor samples using immunohistochemistry (IHC) for CD70 and CD27 expression and RNAscope for CD70 RNA detection. Immune infiltration was assessed using CD45, CD8, and CD20 staining. Quantification of RNAscope signals was performed using QPath software. Kaplan–Meier survival analysis and multivariate Cox regression were used to assess the prognostic impact of CD70, CD27, and immune cell infiltrates on overall survival (OS). Results: CD70 was expressed in 46% of tumors, primarily within tumor nests, with lower expression in stromal areas. High CD70 expression correlated with significantly decreased OS (p = 0.0078, HR: 1.795) without any correlation with CD45 + , CD8 + or CD20 + immune cell infiltrates. CD27 expression was mainly confined to the stroma, and it did not show a significant association with OS (p = 0.582). Importantly, high CD27 expression was linked to reduced CD45 + and CD8 + cell densities in the stroma. Both CD70 and CD27 were expressed on CD68 + macrophages, CD27 was expressed on CAFs, and both molecules exhibited a partial coexpression with CD3. Furthermore, patients with high CD20 + B-cell densities or the presence of tertiary lymphoid structures (TLS) had significantly improved OS (p = 0.0017, HR: 0.491), suggesting the importance of B-cell-related immune responses in SCLC prognosis. Conclusion: CD70, B-cell density and the presence of TLSs, but not CD27, emerged as a significant prognostic biomarker for OS in surgically treated SCLC, suggesting its potential as a therapeutic target.

Background: Colonization of the human gut and tumor tissue by non-pathogenic fungi has emerged as a potential risk factor associated with cancer epidemics. Therefore, we aimed to conduct a systematic review to assess the role of fungal colonization in gastrointestinal (GI) tumors in increasing diagnostic efficiency.Methods: A PubMed citation search was conducted for publications up to and including March 2023, followed by full-text screening. Results were reported according to Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines. According to the Patient, Intervention, Comparison, Outcome (PICO) framework, patients diagnosed with early-and advanced-stage GI cancers, GI adenoma patients, and healthy subjects were included with metagenomic (MG) or internal transcribed spacer (ITS) sequencing on tumor tissue, adjacent normal tissue, stool, and blood samples.Results: Fourteen studies were eligible based on the inclusion criteria and methodological quality. Studies were conducted in stool (n = 8) or tissue (n = 7) as the most common specimens to be used for molecular analysis. In the collected data, ITS was used in n = 10 cases and metagenomic analyses in n = 3 cases. Observing the interindividual variability, we found that the Ascomycota/Basidiomycota (A/B) ratio from healthy to cancer state decreased in n = 2, increased in n = 1 cases, and did not change significantly in n = 2 studies. An increase in the relative abundance of Malassezia was identified in n = 4, of Candida in n = 5, of Saccharomyces in n = 2, and of Aspergillus in n = 2 cases. Intraindividual differences in the A/B ratio were identified in cancer and adjacent tissue (n = 4) and cancer vs. stool (n = 1) studies. Intraindividual variability of the A/B ratio showed an increase in n = 2 and no change in n = 3 studies for cancer tissue.Conclusion: In conclusion, the advent of highly sensitive sequencing methods may aid in the identification and the differentiation of cancerous from healthy human fungal colonizations with potential future diagnostic applications. Further studies are needed to establish reliable biomarkers for GI cancer screening.

Introduction: Changes in the human gut microbiome have been linked to various chronic diseases, including chronic obstructive pulmonary disease (COPD). While substantial knowledge is available on the genomic features of fecal communities, little is known about the microbiome’s transcriptional activity. Here, we analyzed the metatranscriptomic (MTR) abundance of MetaCyc pathways, SuperPathways, and protein domain families (PFAM) represented by the gut microbiome in a cohort of non-small cell lung cancer (NSCLC) patients with- or without COPD comorbidity.Methods: Fecal samples of 40 NSCLC patients with- or without COPD comorbidity were collected at the time of diagnosis. Data was preprocessed using the Metaphlan3/Humann3 pipeline and BioCyc^© to identify metabolic SuperPathways. LEfSe analysis was conducted on Pathway- and PFAM abundance data to determine COPD- and non-COPD-related clusters.Results: Key genera Streptococcus, Escherichia, Gemella, and Lactobacillus were significantly more active transcriptionally compared to their metagenomic presence. LEfSe analysis identified 11 MetaCyc pathways that were significantly overrepresented in patients with- and without COPD comorbidity. According to Spearman’s rank correlation, Smoking PY showed a significant negative correlation with Glycolysis IV, Purine Ribonucleoside Degradation and Glycogen Biosynthesis I, and a significant positive correlation with Superpathway of Ac-CoA Biosynthesis and Glyoxylate cycle, whereas forced expiratory volume in the first second (FEV1) showed a significant negative correlation with Glycolysis IV and a significant positive correlation with Glycogen Biosynthesis I. Furthermore, COPD patients showed a significantly increased MTR abundance in ~60% of SuperPathways, indicating a universally increased MTR activity in this condition. FEV1 showed a significant correlation with SuperPathways Carbohydrate degradation, Glycan biosynthesis, and Glycolysis. Taxonomic analysis suggested a more prominent MTR activity from multiple Streptococcus species, Enterococcus (E.) faecalis, E. faecium and Escherichia (E.) coli than expected from their metagenomic abundance. Multiple protein domain families (PFAMs) were identified as more associated with COPD, E. faecium, E.coli, and Streptococcus salivarius, contributing the most to these PFAMs.Conclusion: Metatranscriptome analysis identified COPD-related subsets of lung cancer with potential therapeutic relevance.

Advanced-stage non-small cell lung cancer (NSCLC) poses treatment challenges, with immune checkpoint inhibitors (ICIs) as the main therapy. Emerging evidence suggests the gut microbiome significantly influences ICI efficacy. This study explores the link between the gut microbiome and ICI outcomes in NSCLC patients, using metatranscriptomic (MTR) signatures. We utilized a de novo assembly-based MTR analysis on fecal samples from 29 NSCLC patients undergoing ICI therapy, segmented according to progression-free survival (PFS) into long (> 6 months) and short (≤ 6 months) PFS groups. Through RNA sequencing, we employed the Trinity pipeline for assembly, MMSeqs2 for taxonomic classification, DESeq2 for differential expression (DE) analysis. We constructed Random Forest (RF), Support Vector Machine (SVM), and Extreme Gradient Boosting (XGBoost) machine learning (ML) algorithms and comprehensive microbial profiles. We detected no significant differences concerning alpha-diversity, but we revealed a biologically relevant separation between the two patient groups in beta-diversity. Actinomycetota was significantly overrepresented in patients with short PFS (vs long PFS, 36.7% vs. 5.4%, p < 0.001), as was Euryarchaeota (1.3% vs. 0.002%, p = 0.009), while Bacillota showed higher prevalence in the long PFS group (66.2% vs. 42.3%, p = 0.007), when comparing the abundance of corresponding RNA reads. Among the 120 significant DEGs identified, cluster analysis clearly separated a large set of genes more active in patients with short PFS and a smaller set of genes more active in long PFS patients. Protein Domain Families (PFAMs) were analyzed to identify pathways enriched in patient groups. Pathways related to DNA synthesis and Translesion were more enriched in short PFS patients, while metabolism-related pathways were more enriched in long PFS patients. E. coli-derived PFAMs dominated in patients with long PFS. RF, SVM and XGBoost ML models all confirmed the predictive power of our selected RNA-based microbial signature, with ROC AUCs all greater than 0.84. Multivariate Cox regression tested with clinical confounders PD-L1 expression and chemotherapy history underscored the influence of n = 6 key RNA biomarkers on PFS. According to ML models specific gut microbiome MTR signatures’ associate with ICI treated NSCLC outcomes. Specific gene clusters and taxa MTR gene expression might differentiate long vs short PFS.