Ken Herrmann

Transforming growth factor-beta (TGFβ) has dual roles in cancer, initially suppressing tumors but later promoting metastasis and immune evasion. Efforts to inhibit TGFβ have been largely unsuccessful due to significant toxicity and indiscriminate immunosuppression. Leucine-rich repeat-containing protein 15 (LRRC15) is a TGFβ-regulated antigen expressed by cancer cells of mesenchymal origin and cancer-associated fibroblasts (CAFs). In preclinical studies, ablation of TGFβ-driven LRRC15+ CAFs enhances effector functions of CD8+ T cells. However, the pathobiological mechanisms associated with TGFβ’s upregulation of LRRC15 expression in cancer cells remain unclear. Using an integrated approach combining functional compound screening with scRNA-seq, we reveal key genomic features regulating TGFβ’s ability to increase LRRC15 expression on cancer cells. Construction of gene regulatory networks converged our analyses on four key genes ( MMP2, SPARC, TGFβR2, and WNT5B ) central to TGFβ-induced LRRC15 pathobiology in cancer cells. Validation of these genes in cell models and their use in predicting immunotherapy responses highlight their potential in refining immunotherapy strategies and personalizing cotreatment options.

Background Prostate cancer remains under-prioritised from a policy and advocacy perspective, despite a growing global disease burden. Objective To identify system-level barriers that prevent patients with prostate cancer from receiving high-quality care and to generate expert consensus on priority areas for health system action. Methods A targeted evidence review was conducted to identify barriers across the prostate cancer care pathway. Consensus was generated using a structured process informed by Delphi methodology. Two advisory panels comprising 37 experts, including multidisciplinary healthcare professionals, health policy experts, and patient advocacy representatives from Europe, the Americas, and Asia, assessed and prioritised the barriers by impact and feasibility of addressing. Following the panels, the experts developed a set of position statements to inform system-level action on prostate cancer. Results The expert consensus highlighted nine priority areas for prostate cancer policy, including elevating prostate cancer on health agendas; recognising broader societal and economic consequences; better-organised, risk-adapted approaches to screening; stronger diagnostic pathways with expanded access to biomarker testing, digital pathology, and standardised data integration; more evidence-based treatment practices, including multidisciplinary decision-making, timely treatment intensification, clearer guidance on treatment sequencing and reduced inequitable treatment access; and more structured, holistic survivorship care. Conclusions Health systems face challenges across the continuum of care in prostate cancer. While tailored approaches are required at the country level, global expert recommendations provide guidance on prioritising shared challenges across geographies to move towards more integrated, data-driven, and patient-centred prostate cancer pathways.

Background/Objectives: To evaluate the diagnostic potential of apparent diffusion coefficient (ADC) values for classifying lymph nodes as benign or malignant in breast cancer patients undergoing [¹⁸F]FDG-PET/MRI staging. Methods: Mean ADC values and short-axis diameters (±standard deviation) of 199 thoracic lymph nodes in 113 newly diagnosed breast cancer patients were retrospectively analyzed. All patients underwent [¹⁸F]FDG-PET/MRI staging, between July 2017 and June 2021. A node-by-node comparison was performed with respect to pathological node status. Nodal FDG uptake in whole-body [¹⁸F]FDG-PET/MRI served as reference standard for nodal malignancy. Group comparison using Mann–Whitney U test, receiver operating characteristic curve (ROC) analysis and diagnostic performance were calculated. p values below 0.05 were defined as statistically significant. Confidence intervals (CI; 95%) were calculated. Results: Ninety-three lymph nodes were FDG-negative while 106 lymph nodes were FDG-positive. FDG-negative lymph nodes had significantly lower short-axis diameters ((5.1 ± 1.5 mm versus 12.3 ± 5.3 mm); p < 0.01; U: 405.50; Z: −11.24). ADC values were significantly lower in FDG-positive lymph nodes (0.72 ± 0.14 × 10⁻³ mm²/s) than in FDG-negative lymph nodes ((1.18 ± 0.18 × 10⁻³ mm²/s); p < 0.01; U: 173.00; Z: −11.80). ROC analysis and Youden’s index revealed an ADC cut-off of 0.95 × 10⁻³ mm²/s (AUC: 0.98; p < 0.01; 95% CI: 0.96–1.01). According to the calculated cut-off, sensitivity, specificity, and accuracy of ADC values for differentiating FDG-negative from FDG-positive lymph nodes were 98%, 97% and 97%, respectively. Conclusions: ADC values derived from MRI were significantly associated with FDG uptake in this retrospective cohort and may serve as a complementary imaging biomarker for lymph node characterization.

PURPOSE: The continuous development of new imaging approaches, molecular phenotyping, genetic subtypes, prognosis assessments, and effective therapies across a range of disease states has created a need to redefine terminology and best practices for clinical trial conduct in patients with advanced prostate cancer. Methods: We convened an international expert committee of diverse working groups, the Prostate Cancer Working Group 4 (PCWG4), between 2016 and 2025. Our objective was to formulate updated criteria based on emerging evidence and clinical trial data in a biomarker context to provide guidance for clinical trial design, eligibility, and end point assessments for patients with advanced prostate cancer. RESULTS: PCWG4 redefines terminology around the disease state and previous therapies in a patient-centric context and terminology focused on androgen pathway modulation. We consider imaging, with a particular focus on positron emission tomography (PET)–defined disease. New recommendations are provided for disease state terminology, defining eligibility criteria, response and delay/prevent end points, intervals for reassessments including imaging, and patient-reported outcome determination. We provide recommendations in a biomarker-based context of use for the intended indication, reflective of patient benefit for specific interventions. We emphasize the need for development of validated PET imaging and molecular and phenotypic criteria as well as trial designs to appropriately risk stratify patients, predict and assess benefit, and measure post-treatment outcomes reliably in a trial framework. CONCLUSION: PCWG4 updates recommendations on patient and tumor characterization, therapy development, and imaging criteria and extends guidance into earlier androgen pathway modulator–naïve/sensitive disease states to reflect an evolving, heterogeneous, and diverse patient population to optimize treatment benefits for all patients.

Adverse drug effects remain a major barrier to safe and effective cancer therapy, underscoring the need for tools that predict treatment-related toxicities. We analyzed multimodal real-world data from 14,596 cancer patients across 38 cancer entities, encompassing 330 clinical, tumor, and imaging characteristics, along with 89 anticancer agents. Hematological adverse events (HAE), defined by nadirs of hemoglobin, leukocyte, neutrophil, and platelet values within two months of treatment initiation, were highly prevalent (87.7%; 33.1% severe). We developed Toxix , an explainable artificial intelligence (xAI) framework modeling interactions between patient characteristics and drug combinations. Toxix achieved strong predictive performance for severe toxicities (median AUROC 0.85 for anemia; >0.76 for leukopenia, neutropenia, and thrombocytopenia) and was validated in an external cohort of 2,768 patients with non-small cell lung cancer. Model explainability enabled systematic characterization of drug-patient interactions underlying HAEs. Toxix provides a real-world informed framework for personalized and toxicity-aware cancer therapy planning.

Background: Timely detection is crucial to improve outcomes in patients with cardiac amyloidosis (CA) by initiation of life-saving treatments. Although confirmatory bone scintigraphy is highly accurate for CA detection, identifying at-risk patients for referral remains challenging. Objectives: This study aimed to develop and validate a machine learning model, Amylo-Detect , using structured multimodal electronic health record (EHR) data to guide referrals for confirmatory scintigraphy and monoclonal protein testing. Methods: Consecutive all-comer patients (n=11,616) referred for bone scintigraphy at the Vienna General Hospital (2010-2023) were retrospectively included. Patients referred before August 2020 formed the development cohort. The remaining patients comprised the internal validation cohort. External validation was performed at the University Hospital Essen (n=1,521). Amylo-Detect was trained using 50 routinely available parameters to predict CA-suggestive uptake (Perugini grade ≥2) and compared with an existing score and clinical routine. Results: High-grade uptake was present in 388 patients (3.0%). Amylo-Detect demonstrated excellent performance in development (AUC 0.93), independent internal validation (AUC 0.91), and external validation cohort (AUC 0.91), outperforming existing scoring systems and clinical routine. Results were consistent across subgroups, even when crucial predictors were missing. Of the 42/388 (10.8%) patients missed in clinical routine, 12/42 (29%) were additionally detected by Amylo-Detect . The model further conveyed significant prognostic value for mortality and heart failure hospitalization. Conclusions: We present Amylo-Detect , a validated EHR-based tool for CA risk prediction, available as a web app, allowing application and further evaluation. By improving timely detection and referral, Amylo-Detect promises to address diagnostic delays and improve outcomes. Author summary: Cardiac amyloidosis is a progressive and often fatal heart disease that is frequently diagnosed too late, even though effective treatments are now available. A major challenge is recognizing which patients should be referred for specialized testing, because early symptoms are diverse and often non-specific. In this study, we developed and validated a machine learning prediction system, called Amylo-Detect, that uses routinely collected information from electronic health records to identify patients who may be at risk for cardiac amyloidosis. We trained and externally validated the tool using data from 13,137 patients across two hospitals. We found that Amylo-Detect was highly accurate in identifying patients with disease-indicative findings on confirmatory scans and identified a substantial number of patients who were not initially suspected of having cardiac amyloidosis. Amylo-Detect, which we make publicly available via a web app, consistently outperformed existing risk scores and routine clinical decision-making. Our findings suggest that automated analysis of electronic health records can support clinicians in recognizing cardiac amyloidosis earlier, reduce diagnostic delays, and potentially improve patient outcomes by enabling timely treatment.

Summary: Radioligand therapy exemplifies precision oncology, yet its consequences remain incompletely understood. A recent article shows that [177Lu]Lu-PSMA promotes emergence and expansion of clonal hematopoiesis enriched for DNA damage response gene mutations, reframing bone marrow effects as treatment-emergent clonal selection rather than collateral toxicity and informing biology-guided patient monitoring and theranostics. See related article by Munzur et al., p. 2644

Background/Objectives: Analysis of the density and spatial distribution of pulmonary infiltrates of patients with high-grade (≥3) pneumonitis after radiochemotherapy and durvalumab consolidation (RT/CTx + IO) was performed in order to define dosimetric hallmarks of the development of infiltrates following this multimodality treatment. Methods: Consecutive patients treated with RT/CTx + IO for stage III NSCLC were retrospectively reviewed with respect to the occurrence of grade ≥ 3 pneumonitis. Lung infiltrates were contoured on follow-up CT scans acquired around the time of maximum pneumonitis expression. The applied dose distribution was overlaid with the follow-up CT using elastic deformation, and infiltrates were binned according to their density in density strata of 50 HU. The dose and density dependence of partial infiltrate volumes per unit lung volume was analyzed using a mixed fixed and random effect model adjusting for patient, density and dose-dependent random effects. Results: Six patients with grade ≥ 3 pneumonitis were identified from 132 patients treated with RT/CT + IO at a comprehensive cancer center. Partial volumes of lung infiltrates captured by follow-up CT with maximum pneumonitis expression ranged from 15.5 to 60.0% (median 39.8%). A significant, systematic dose–response relationship was found for partial lung infiltrate volumes per dose and density bin. A unimodal density distribution of partial lung infiltrate volumes was also found over the infiltrate density range of −1000 to 100 HU. This was determined using a mixed model that adjusted for random effects (p < 0.0001 for both effects, F-test). There was no interaction effect between systematic dose and infiltrate density dependence of the partial infiltrate volumes. The proportion of infiltrate volumes that are attributable to the systematic dose–response relation amounts to a mean of 16.6% of the total infiltrate volume per patient according to this model. Compared to patients with pneumonitis of grade ≤ 2, patients with high-risk pneumonitis had higher partial infiltrate volumes, particularly in the low-dose regions in five grade dose bins up to 20 Gy (AUC = 1.0, p < 0.0001, likelihood-ratio test). Conclusions: Dose-dependent and -independent partial lung infiltrate volumes were found in patients with high-grade pneumonitis after RT/CTx + IO. These results indicate that pneumonitis involves contributions from both radiochemotherapy-induced and immunotherapy-related mechanisms.