Martin Zacharias

The Hallmarks of Cancer framework distilled the complexity of tumour biology into a shared set of capabilities, but in its static form it cannot capture where in a tumour these traits occur, when they arise, or how they reorganize during cancer initiation and progression. We propose Spatiotemporal Hallmark Ecosystems (SHEs) as a unifying lens that integrates spatial architecture, temporal dynamics, and hallmark biology into a single operational unit of tumour evolution. In this view, hallmarks are not fixed consequences of specific mutations, but emergent, context-dependent programs shaped by interactions between genetic drivers, tissue structure, and systemic cues. SHEs help provide insight into why identical mutations can yield divergent outcomes, why some premalignant states persist for years, and how transitions, whether toward invasion, metastasis, or resistance, reflect coordinated reconfiguration of multiple hallmarks. By reframing cancer as a set of evolving ecosystems rather than isolated traits, this perspective outlines a path toward more predictive, adaptive, and context-aware oncology.

Aim: This study evaluates the comparative effectiveness of pathologists versus artificial intelligence (AI) algorithms in scoring PD‐L1 expression in non‐small cell lung carcinoma (NSCLC). Immune‐checkpoint inhibitors have revolutionized NSCLC treatment, with PD‐L1 expression, measured as the tumour proportion score (TPS), serving as a critical predictive biomarker for therapeutic response.Methods and Results: In our analysis, 51 SP263‐stained NSCLC cases were scored by six pathologists using light microscopy and whole‐slide images (WSI), alongside evaluations by two commercially available software tools: uPath software (Roche) and the PD‐L1 Lung Cancer TME application (Visiopharm). The study examined intra‐ and interobserver agreement among pathologists at TPS cutoffs of 1% and 50%, revealing moderate interobserver agreement (Fleiss' kappa 0.558) for TPS <1% and almost perfect agreement (Fleiss' kappa 0.873) for TPS ≥50%. Intraobserver consistency was high, with Cohen's kappa ranging from 0.726 to 1.0. Comparisons between the AI algorithms and the median pathologist scores showed fair agreement for uPath (Fleiss' kappa 0.354) and substantial agreement for the Visiopharm application (Fleiss' kappa 0.672) at the 50% TPS cutoff.Conclusion: These results indicate that while there is strong interobserver concordance among pathologists at higher TPS levels, the performance of AI algorithms is less consistent. The study underscores the need for further refinement of AI tools to match the reliability of expert human evaluation, particularly in critical clinical decision‐making contexts.

Due to the success story of biomarker-driven targeted therapy, most NSCLC guidelines agree that molecular reflex testing should be performed in all cases with non-squamous cell carcinoma (non-SCC). In contrast, testing recommendations for squamous cell carcinoma (SCC) vary considerably, specifically concerning the exclusion of patients of certain age or smoking status from molecular testing strategies. We performed a retrospective single-center study examining the value of molecular reflex testing in an unselected cohort of 316 consecutive lung SCC cases, tested by DNA- and RNA-based next-generation sequencing (NGS) at our academic institution between 2019 and 2023. Clinicopathological data from these cases were obtained from electronic medical records and correlated with sequencing results. In 21/316 (6.6%) cases, we detected an already established molecular target for an approved drug. Among these were seven cases with an EGFR mutation, seven with a KRAS G12C mutation, four with an ALK fusion, two with an EGFR fusion and one with a METex14 skipping event. All patients harboring a targetable alteration were >50 years of age and most of them had >15 pack-years, questioning restrictive molecular testing strategies. Based on our real-world data, we propose a reflex testing workflow using DNA- and RNA-based NGS that includes all newly diagnosed NSCLC cases, irrespective of histology, but also irrespective of age or smoking status.

Aims: Mucormycosis is a fast‐progressing disease with a high mortality rate. The most important factor determining survival of patients is early and accurate diagnosis. Although histopathology often recognises invasive mould infections at first, histomorphology alone is insufficient in providing an accurate diagnosis. Unbiased molecular methods to detect and identify fungi are promising, yet their role in complementing routine histopathological workflows has not been studied sufficiently.Methods and results: We performed a retrospective single‐centre study examining the clinical value of complementing histopathology with internal transcribed spacer (ITS) sequencing of fungal DNA in the routine diagnosis of mucormycosis. At our academic centre, we identified 14 consecutive mucormycosis cases diagnosed by histopathology and subsequent ITS sequencing. Using histomorphological examination, fungal hyphae could be detected in all cases; however, morphological features were unreliable regarding specifying the taxa. Subsequent ITS sequencing identified a remarkable phylogenetic diversity among Mucorales: the most common species was Rhizopus microsporus (six of 14; 42.9%), followed by Lichtheimia corymbifera (three of 14, 21.4%) and single detections of Rhizopus oryzae, Actinomucor elegans, Mucor circinelloides, Rhizomucor pusillus and Rhizomucor miehei (one of 14; 7.1%, respectively). In one case, we additionally detected Pneumocystis jirovecii in the same lung tissue specimen, suggesting a clinically relevant co‐infection. Fungal culture was performed in 10 cases but yielded positive results in only two of 10 (20%), revealing its limited value in the diagnosis of mucormycosis.Conclusions: Our study demonstrates that a combination of histopathology and ITS sequencing is a practically feasible approach that outperforms fungal culture in detecting Mucorales in tissue‐associated infections. Therefore, pathologists might adapt diagnostic workflows accordingly when mucormycosis is suspected.

Hypertrophic scars continue to be a major burden, especially after burns. Persistent inflammation during wound healing appears to be the precipitating aspect in pathologic scarring. The lack of a standardized model hinders research from fully elucidating pathophysiology and therapy, as most therapeutic approaches have sparse evidence. The goal of this project was to investigate the mechanisms of scar formation after prolonged wound inflammation and to introduce a method for generating standardized hypertrophic scars by inducing prolonged inflammation. Four wound types were created in Duroc pigs: full-thickness wounds, burn wounds, and both of them with induced hyperinflammation by resiquimod. Clinical assessment (Vancouver Scar Scale), tissue oxygenation by hyperspectral imaging, histologic assessment, and gene expression analysis were performed at various time points during the following five months. Native burn wounds as well as resiquimod-induced full-thickness and burn wounds resulted in more hypertrophic scars than full-thickness wounds. The scar scale showed significantly higher scores in burn- and resiquimod-induced wounds compared with full-thickness wounds as of day 77. These three wound types also showed relative hypoxia compared with uninduced full-thickness wounds in hyperspectral imaging and increased expression of HIF1a levels. The highest number of inflammatory cells was detected in resiquimod-induced full-thickness wounds with histologic features of hypertrophic scars in burn and resiquimod-induced wounds. Gene expression analysis revealed increased inflammation with only moderately altered fibrosis markers. We successfully created hypertrophic scars in the Duroc pig by using different wound etiologies. Inflammation caused by burns or resiquimod induction led to scars similar to human hypertrophic scars. This model may allow for the further investigation of the exact mechanisms of pathological scars, the role of hypoxia and inflammation, and the testing of therapeutic approaches.

The treatment of chronic wounds still challenges modern medicine because of these wounds’ heterogenic pathophysiology. Processes such as inflammation, ischemia and bacterial infection play major roles in the progression of a chronic wound. In recent years, preclinical wound models have been used to understand the underlying processes of chronic wound formation. However, the wound models used to investigate chronic wounds often lack translatability from preclinical models to patients, and often do not take exaggerated inflammation into consideration. Therefore, we aimed to investigate prolonged inflammation in a porcine wound model by using resiquimod, a TLR7 and TLR8 agonist. Pigs received full thickness excisional wounds, where resiquimod was applied daily for 6 days, and untreated wounds served as controls. Dressing change, visual documentation and wound scoring were performed daily. Biopsies were collected for histological as well as gene expression analysis. Resiquimod application on full thickness wounds induced a visible inflammation of wounds, resulting in delayed wound healing compared to non-treated control wounds. Gene expression analysis revealed high levels of IL6, MMP1 and CD68 expression after resiquimod application, and histological analysis showed increased immune cell infiltration. By using resiquimod, we were able to show that prolonged inflammation delayed wound healing, which is often observed in chronic wounds in patients. The model we used shows the importance of inflammation in wound healing and gives an insight into the progression of chronic wounds.

Rapid Antigen Test for Postmortem Evaluation of SARS-CoV-2 Carriage