Inflammatory properties are known to promote tumor progression leading to an impaired median overall survival (mOS). Various small studies have focused on a wide range of inflammation-based prognostic indicators. By using sufficient data from 1294 out of 2323 patients diagnosed with pancreatic cancer between 2009 and 2021 at our cancer center, inflammatory markers such as the neutrophil to lymphocyte ratio (NRL), the platelet to lymphocyte ratio (PLR), the lymphocyte to monocyte ratio (LMR) and the CRP to albumin ratio (CAR) were evaluated. We identified a new combined score, termed the inflammatory benchmark index (IBI). We performed univariate and multivariate overall survival analyses and identified optimal prognostic cut-off values for each parameter. In univariate analyses, advanced age (p < 0.001), gender (p < 0.001), tumor stage (p < 0.001), CA19-9 (p = 0.001), NLR (p = 0.001), LMR (p = 0.004), PLR (p = 0.004), CAR (p = 0.001) and IBI (p = 0.001) were identified as prognostic markers. In multivariate analyses advanced age (p < 0.001), gender (p = 0.001), tumor stage (p < 0.001), CA19-9 (p < 0.001), NLR (p = 0.001), LMR (p = 0.038), CAR (p < 0.001) and IBI (p < 0.001) were independent prognostic markers. These findings emphasize the impact of inflammation in pancreatic cancer, provide easily accessible prognostic values for the clinician, and may be useful as stratification parameters for trials aimed at patient inflammation or immune response.

Pancreatic cancer is the seventh leading cause of cancer death in both sexes. The aim of this study is to analyze baseline CT body composition using artificial intelligence to identify possible imaging predictors of survival. We retrospectively included 103 patients. First, the presence of surgical treatment and cut-off values for sarcopenia and obesity served as independent variates. Second, the presence of surgery, subcutaneous adipose tissue (SAT), visceral adipose tissue (VAT), and skeletal muscle index (SMI) served as independent variates. Cox regression analysis was performed for 1-year, 2-year, and 3-year survival. Possible differences between patients undergoing surgical versus nonsurgical treatment were analyzed. Presence of surgery significantly predicted 1-year, 2-year, and 3-year survival (p = 0.01, <0.001, and <0.001, respectively). Across the follow-up periods of 1-year, 2-year, and 3-year survival, the presence of sarcopenia became an equally important predictor of survival (p = 0.25, 0.07, and <0.001, respectively). Additionally, increased VAT predicted 2-year and 3-year survival (p = 0.02 and 0.04, respectively). The impact of sarcopenia on 3-year survival was higher in the surgical treatment group (p = 0.02 and odds ratio = 2.57) compared with the nonsurgical treatment group (p = 0.04 and odds ratio = 1.92). Fittingly, a lower SMI significantly affected 3-year survival only in patients who underwent surgery (p = 0.02). Especially if surgery is performed, AI-derived sarcopenia and reduced muscle mass are unfavorable imaging predictors.

Background: In the treatment of advanced urothelial (aUC) and renal cell carcinoma (aRCC), biomarkers such as PD-1 and PD-L1 are not robust prognostic markers for immunotherapy (IO) response. Previously, a significant association between IO and a change in splenic volume (SV) was described for several tumour entities. To the best of our knowledge, this study presents the first correlation of SV to IO in aUC and aRCC. Methods: All patients with aUC (05/2017–10/2021) and aRCC (01/2012–05/2022) treated with IO at our academic centre were included. SV was measured at baseline, 3 and 9 months after initiation of IO using an in-house developed convolutional neural network-based spleen segmentation method. Uni- and multivariate Cox regression models for overall survival (OS) and progression-free survival (PFS) were used. Results: In total, 35 patients with aUC and 30 patients with aRCC were included in the analysis. Lower SV at the three-month follow-up was significantly associated with improved OS in the aRCC group. Conclusions: We describe a new, innovative artificial intelligence-based approach of a radiological surrogate marker for IO response in aUC and aRCC which presents a promising new predictive imaging marker. The data presented implicate improved OS with lower follow-up SV in patients with aRCC.

Background: Primary objective of this present trial was to define the maximum tolerable dose of lapatinib in combination with oxaliplatin, 5-fluorouracil, and folinic acid (OFF) in refractory pancreatic cancer. The secondary objective was to assess the safety and efficacy of lapatinib plus OFF. Methods: We conducted a phase I trial using an accelerated dose escalation design in patients with refractory pancreatic cancer. Lapatinib was given on days 1 to 42 in combination with folinic acid 200 mg/m² day + 5-fluorouracil 2000 mg/m² (24 h) on days 1, 8, 15, and 22, and oxaliplatin 85 mg/m² days 8 and 22 of a 43-day cycle (OFF). Toxicity and efficacy were evaluated. Results: In total, eighteen patients were enrolled: dose level 1 (1000 mg) was assigned to seven patients, dose level 2 (1250 mg), five patients; and dose level 3 (1500 mg), six patients. Dose-limiting toxicities were diarrhea and/or neutropenic enterocolitis observed in two of six patients: one diarrhea III°, one diarrhea IV°, as well as neutropenic enterocolitis. The maximum tolerable dose of lapatinib was 1250 mg OD. Conclusions: The combination of lapatinib 1250 mg OD with platinum-containing chemotherapy is safe and feasible in patients with refractory pancreatic cancer and warrants further investigation.

Patient-Derived Organoids (PDOs) represent a promising technology for therapy prediction in pancreatic cancer, with the potential of enhancing treatment outcomes and allowing more effective, personalized treatment choices. However, classification approaches into sensitive and resistant models remain very variable and are based on single-agent testing only, neglecting interactive effects of multi-drug combinations. Here, we established 13 PDOs and performed both single-agent and multi-drug testing. By comparing different clustering approaches of drug-response metrics and establishing a new classification approach based on pharmacokinetic modelling, we were able to evaluate which score best predicts the clinical response of patients. Our newly developed score considered the Area Under The Curve (AUC) of cell viability curves and reached a prediction accuracy of 85%. Our data supports previous findings for PDOs to constitute an effective platform for translational drug testing. Furthermore, our results suggest that the AUC is a more accurate drug-response metric than the half maximal inhibitory concentration (IC₅₀), and that multi-drug testing yields a higher accuracy than single-agent testing. The methodology and outcomes presented in this study are of critical relevance for future PDO-based translational trials as they allow a new physiology-based approach towards multi-drug testing and classification of organoid response, which improves PDO prediction accuracy.

Background: Pancreatic adenocarcinoma (PDAC) is still a complex, devastating disease. Cachexia symptoms frequently impair patient survival. This accompanying syndrome is commonly diagnosed late, when clinical signs become evident. Early diagnosis using conventional measurement methods is often difficult, and the discrimination of this disease from cancer progression is challenging and often overlaps. The aim of this study was to analyze whether conventional nutritional assessments or laboratory biomarkers are better predictive tools for the early detection of patients at risk of reduced survival. Methods: We analyzed a prospective predefined cohort of 182 patients with gastrointestinal cancer, 120 patients with PDAC and—as controls—62 patients with other gastrointestinal adenocarcinoma (oAC), from whom we have sufficient data of protocol-defined conventional nutritional assessments, clinical data, and specific laboratory parameters. Results: at the time of tumor diagnosis, high inflammatory biomarkers (c-reactive protein (CRP), interleukin-6 (IL-6)) and albumin serum levels were associated with impaired OS in PDAC patients, but not in patients with oAC. Hemoglobin, body mass index (BMI), and bioelectrical assessments alone did not have a prognostic impact at the time of diagnosis. In a multivariate analysis, only CRP (HR 1.91 (1.25–2.92), p = 0.003) was found to be an independent prognostic factor in PDAC patients. Over the course of the disease in PDAC patients, inflammatory biomarkers, albumin, hemoglobin, and bioelectrical assessments were associated with impaired OS. In multivariate testing, CRP (HR 2.21 (1.38–3.55), p < 0.001) and albumin (HR 1.71 (1.05–2.77), p = 0.030) were found to be independent prognostic factors in PDAC patients. Conclusion: Specifically for PDAC patients, high inflammatory index and albumin serum levels potentially represent a sufficient early surrogate marker to detect patients at high risk of impaired OS better than complex conventional methods. These findings could help to identify patients who may benefit from early therapeutic interventions.