The complex therapeutic strategy of non-small cell lung cancer (NSCLC) has changed significantly in recent years. Disease-free survival increased significantly with immunotherapy and chemotherapy registered in perioperative treatments, as well as adjuvant registered immunotherapy and targeted therapy (osimertinib) in case of EGFR mutation. In oncogenic-addictive metastatic NSCLC, primarily in adenocarcinoma, the range of targeted therapies is expanding, with which the expected overall survival increases significantly, measured in years. By 2021, the FDA and EMA have approved targeted agents to inhibit EGFR activating mutations, T790 M resistance mutation, BRAF V600E mutation, ALK, ROS1, NTRK and RET fusion. In 2022, the range of authorized target therapies was expanded. With therapies that inhibit KRASG12C, EGFR exon 20, HER2 and MET. Until now, there was no registered targeted therapy for the KRAS mutations, which affect 30% of adenocarcinomas. Thus, the greatest expectation surrounded the inhibition of the KRAS G12C mutation, which occurs in ∼15% of NSCLC, mainly in smokers and is characterized by a poor prognosis. Sotorasib and adagrasib are approved as second-line agents after at least one prior course of chemotherapy and/or immunotherapy. Adagrasib in first-line combination with pembrolizumab immunotherapy proved more beneficial, especially in patients with high expression of PD-L1. In EGFR exon 20 insertion mutation of lung adenocarcinoma, amivantanab was registered for progression after platinum-based chemotherapy. Lung adenocarcinoma carries an EGFR exon 20, HER2 insertion mutation in 2%, for which the first targeted therapy is trastuzumab deruxtecan, in patients already treated with platinum-based chemotherapy. Two orally administered selective c-MET inhibitors, capmatinib and tepotinib, were also approved after chemotherapy in adenocarcinoma carrying MET exon 14 skipping mutations of about 3%. Incorporating reflex testing with next-generation sequencing (NGS) expands personalized therapies by identifying guideline-recommended molecular alterations.

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.

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.

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.

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.

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.