Background: Despite recent advances in the management of metastatic renal cell carcinoma (mRCC), real-world outcomes remain heterogeneous, and early treatment failure is common. Predictive biomarkers for time to treatment failure (TTF) outside clinical trials are poorly characterized. Objective: To identify clinical and laboratory predictors associated with early treatment failure in a real-world cohort of mRCC patients treated with immune checkpoint inhibitors (ICIs), tyrosine kinase inhibitors (TKIs), or combination regimens. Methods: We conducted a retrospective, single-center analysis of patients with metastatic non-urothelial RCC treated between 2018 and 2023. Cox proportional hazards regression was used to evaluate the association between baseline biological parameters and TTF for each treatment regimen. Results: Among 137 patients receiving first-line therapy, 50 received Ipilimumab + Nivolumab, 49 Sunitinib, and 17 Avelumab + Axitinib. For Ipilimumab + Nivolumab, elevated AST was significantly associated with shorter TTF. For Avelumab + Axitinib, shorter TTF was associated with lymph node metastases, low lymphocyte count, low creatinine, low BMI, and low hemoglobin. For Cabozantinib in subsequent lines, a higher platelet count, ALT, and presence of liver metastases were associated with shorter TTF. No statistically significant predictors were found for Nivolumab used in the second-line setting. Conclusions: Routine, accessible biomarkers such as AST, hemoglobin, lymphocyte count, and creatinine may serve as predictors of treatment failure in specific therapeutic contexts. These findings support risk-adapted strategies and individualized monitoring in real-world clinical practice, though further validation in larger cohorts is warranted.

Background/Objectives: Immunotherapy has improved outcomes for selected patients with advanced non-small-cell lung cancer (NSCLC), yet the predictive value of individual biomarkers such as PD-L1 remains limited. Systemic inflammatory indices derived from routine blood tests may complement molecular and immunohistochemical features, offering a broader view of host–tumor immunobiology. Methods: We conducted a retrospective study of 298 patients with stage IIIB–IV NSCLC treated with immune checkpoint inhibitors (ICIs) at a tertiary oncology center between 2022 and 2024. Baseline neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), lymphocyte-to-monocyte ratio (LMR), and systemic immune–inflammation index (SII) were collected alongside PD-L1 expression and molecular alterations (EGFR, KRAS, ALK, TP53). Patients were stratified into inflammatory–molecular clusters integrating these parameters. Associations with objective response rate (ORR), progression-free survival (PFS), and overall survival (OS) were evaluated using Kaplan–Meier and multivariate Cox analyses. Results: Four distinct inflammatory–molecular clusters demonstrated significantly different outcomes (p < 0.001). Patients with low NLR and high PD-L1 expression (Cluster A) showed the highest ORR (41%), longest median PFS (13.0 months), and OS (22.5 months). The EGFR/ALK-driven, inflammation-dominant cluster (Cluster C) exhibited poor response (ORR 7%) and shortest survival (PFS 4.3 months). High NLR (HR 2.12), PD-L1 < 1% (HR 1.91), and EGFR mutation (HR 2.36) independently predicted shorter PFS. A combined model incorporating NLR, PD-L1, and molecular status outperformed individual biomarkers (AUC 0.82). Conclusions: Integrating systemic inflammatory indices with PD-L1 expression and molecular alterations identifies clinically meaningful NSCLC subgroups with distinct immunotherapy outcomes. This multidimensional approach improves prediction of ICI response and may enhance real-world patient stratification, particularly in settings with limited access to extended molecular profiling.

Background/Objectives: Persistent gastrointestinal (GI) symptoms following oncologic treatment represent a major unmet need in survivorship care, often managed symptomatically without addressing underlying biological mechanisms. This study aimed to evaluate the clinical efficacy and biological correlates of an artificial intelligence (AI)-guided, personalized microbiome modulation strategy in oncology patients with chronic secondary GI dysfunction. Methods: We conducted a prospective, single-arm, open-label validation study including 29 adult female oncology patients with persistent GI symptoms lasting ≥3 months. Participants underwent baseline multidimensional assessment integrating shotgun metagenomic sequencing, inflammatory and nutritional biomarkers, and clinical symptom profiling. An AI-guided platform generated individualized dietary, supplement, and lifestyle recommendations. Outcomes were assessed at baseline and after a 3-month intervention, focusing on intra-individual changes in stool frequency (primary endpoint), self-reported energy, microbiome composition, and metabolic biomarkers. Paired statistical analyses, correlation testing, and multivariable regression were performed. Results: After three months, stool frequency significantly decreased (4.69 ± 2.41 to 2.07 ± 1.19 episodes/day; p < 0.0001), accompanied by a marked increase in energy levels (4.00 ± 1.04 to 7.24 ± 1.12; p < 0.0001). Microbiome analysis revealed consistent enrichment of butyrate-producing and barrier-supportive taxa, including Faecalibacterium prausnitzii, Eubacterium rectale, Roseburia intestinalis, Akkermansia muciniphila, and Bifidobacterium longum. Butyrate-related biomarkers and vitamin-associated parameters (B-complex, vitamin D) showed significant improvement, while lactate levels normalized. Changes in Bifidobacterium longum were independently associated with stool frequency reduction (β = −0.783, p = 0.0082). Conclusions: AI-guided personalized microbiome modulation was associated with significant clinical improvement and biologically coherent microbial and metabolic shifts in oncology patients with persistent GI symptoms. These findings support a precision supportive-care approach targeting microbiome restoration, warranting further validation in randomized controlled trials.

Background: Non-small-cell lung cancer (NSCLC) is often diagnosed at stage IV, when prognosis depends on metastatic spread. The impact of histopathology on the first metastatic site remains underexplored. Methods: We retrospectively analyzed 364 patients with stage IV NSCLC diagnosed at OncoHelp Medical Center, Timișoara, Romania (2020–2024). All underwent baseline CT chest–abdomen–pelvis, whole-body FDG PET-CT, and brain MRI within seven days of histological confirmation. Patients were stratified into adenocarcinoma (n = 164), squamous cell carcinoma (n = 112), and large-cell carcinoma (n = 88). The first metastatic site was defined as the earliest confirmed location. Associations were evaluated using Fisher’s exact test and multinomial logistic regression. Results: Histology was associated with the first metastatic site (global p = 0.013). Adenocarcinoma was more likely than squamous carcinoma to present with brain metastases (RRR 3.74, 95% CI 1.48–9.45; p = 0.005; p_FDR = 0.053) and showed directional signals toward bone and adrenal involvement. Squamous carcinoma more frequently spread to the pleura as the first site (adjusted p = 0.008). Large-cell carcinoma showed no consistent differences compared to squamous carcinoma. In adenocarcinoma subgroups, EGFR-mutant tumors most often metastasized to the brain (55.6%), KRAS-mutant tumors to the liver (44.4%), and ALK-rearranged tumors to bone (100%). Conclusions: The first metastatic site in NSCLC follows histology-specific patterns, with adenocarcinoma favoring hematogenous spread and squamous carcinoma showing locoregional involvement. Molecular status further refines these patterns in adenocarcinoma. Incorporating histology into baseline staging may improve diagnostic efficiency and prognostic accuracy.

Background and Objectives: Systemic inflammatory markers from an ordinary complete blood count (CBC) may foreshadow where non-small-cell lung cancer (NSCLC) will first spread, but organ-specific signatures remain poorly defined. Materials and Methods: We retrospectively reviewed 302 adults (mean age 60.7 ± 13.4 years; 80.8% men) with stage IV NSCLC managed at OncoHelp Medical Center, Timișoara, between January 2022 and December 2024. Eligibility demanded a single radiologically confirmed distant site at diagnosis and pre-treatment CBC. Neutrophil-to-lymphocyte (NLR), platelet-to-lymphocyte (PLR), and lymphocyte-to-monocyte (LMR) ratios were compared across pleural (n = 52), bone (n = 86), liver (n = 66), and brain (n = 98) metastases using Kruskal–Wallis tests with Bonferroni adjustment; z-standardized logistic models identified independent predictors. Results: Metastases clustered most often in brain (32.5%), followed by bone (28.5%), liver (21.9%), and pleura (17.2%). Median PLR rose selectively in pleural disease (274 vs. 217–253 in other sites; p = 0.006). LMR fell to 2.0 in bone but climbed to 2.8 in brain lesions (p = 0.032 and 0.008, respectively). NLR was globally elevated (6.7–7.6), yet differed significantly only for bone and liver deposits. Logistic modeling showed that each standard-deviation rise in absolute neutrophil count quadrupled the odds of hepatic involvement (Odd Ratio (OR) 4.26; 99% Confidence inerval (CI) 2.20–6.25), monocytosis nearly doubled bone risk (OR 1.83; 1.01–3.33), while higher erythrocytes, eosinophils, and lymphocytes independently protected against pleural seeding (all p < 0.01). Age-stratified analysis revealed that osseous and cerebral metastases predominated in patients ≤ 50 years, whereas inflammatory indices were age-invariant. Conclusions: Routine CBC ratios encode distinct “inflammatory fingerprints” that mirror the first metastatic destination in NSCLC: platelets herald pleural spread, neutrophils favor liver and bone, and divergent lymphocyte–monocyte balances separate bone from brain. Although no substitute for cross-sectional imaging, these low-cost markers could refine clinical suspicion, guide targeted work-up, and illuminate the biology of organ-selective dissemination, particularly in resource-limited settings.

Background/Objectives: The fundamental classification based on white, brown, pink, and beige adipose tissue morphology together with fat vacuole content released into the tumor microenvironment incompletely defines breast cancer-associated adipose tissue (BCAAT) heterogeneity and does not sufficiently explain its controversial impact on invasion, recurrence, or survival in breast cancer (BC). We aim to expand BCAAT characterization by systematically evaluating stromal cellular elements within peritumoral adipose tissue, including CD34-positive fibroblasts, smooth muscle actin (SMA)-positive myofibroblasts, inflammatory cells, and microvascular structures to define distinct BCAAT subgroups. Methods: CD34 and smooth muscle actin (SMA) double immunohistochemistry was performed on 109 BC tissue specimens from patients aged 35 to 79 years old, followed by microscopic evaluation of cellular and vascular components inside peritumor adipose tissue. Microscopic findings were then correlated to age, body mass index (BMI), lymphovascular (LVI) and perineural invasion (PnI), recurrence (R), and tertiary lymphoid structures (TLSs). Results: Four BCAAT subtypes have been identified as fibroblast-rich (F^Rich_BCAAT), myofibroblast-rich (MyoF^Rich_BCAAT), vascular-rich (V^Rich_BCAAT), and mixed-vascular and inflammatory-rich (VI^Rich_BCAAT). The F^Rich_BCAAT subtype predominates for the age subgroup 35 to 49 years old and is a significantly worse prognostic factor for survival (p = 0.022). For the age subgroup of 50 to 69 years old, the VI^Rich_BCAAT subtype significantly influences PnI (p = 0.05) but not survival (Log-rank test, z = 0.57, p = 0.57). V^Rich_BCAAT was significantly impactful for BC patient survival aged 70 to 75 years old (p = 0.043). BMI did not correlate with any of the BCAAT subtypes but was strongly correlated with prognostic markers for each BCAAT subtype. Conclusions: Based on immunohistochemically detected cellular and vascular components, four microscopic BCAAT subtypes were identified. Three of four BCCAT subtypes specifically affect BC patient prognosis and survival depending on age.