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ObjectiveComplex phenotypes captured on histological slides represent the biological processes at play in individual cancers, but the link to underlying molecular classification has not been clarified or systematised. In colorectal cancer (CRC), histological grading is a poor predictor of disease progression, and consensus molecular subtypes (CMSs) cannot be distinguished without gene expression profiling. We hypothesise that image analysis is a cost-effective tool to associate complex features of tissue organisation with molecular and outcome data and to resolve unclassifiable or heterogeneous cases. In this study, we present an image-based approach to predict CRC CMS from standard H&E sections using deep learning.DesignTraining and evaluation of a neural network were performed using a total of n=1206 tissue sections with comprehensive multi-omic data from three independent datasets (training on FOCUS trial, n=278 patients; test on rectal cancer biopsies, GRAMPIAN cohort, n=144 patients; and The Cancer Genome Atlas (TCGA), n=430 patients). Ground truth CMS calls were ascertained by matching random forest and single sample predictions from CMS classifier.ResultsImage-based CMS (imCMS) accurately classified slides in unseen datasets from TCGA (n=431 slides, AUC)=0.84) and rectal cancer biopsies (n=265 slides, AUC=0.85). imCMS spatially resolved intratumoural heterogeneity and provided secondary calls correlating with bioinformatic prediction from molecular data. imCMS classified samples previously unclassifiable by RNA expression profiling, reproduced the expected correlations with genomic and epigenetic alterations and showed similar prognostic associations as transcriptomic CMS.ConclusionThis study shows that a prediction of RNA expression classifiers can be made from H&E images, opening the door to simple, cheap and reliable biological stratification within routine workflows.

Prostate cancer (PCa) is the most prevalent cancer in men. Hyperactive STAT3 is thought to be oncogenic in PCa. However, targeting of the IL-6/STAT3 axis in PCa patients has failed to provide therapeutic benefit. Here we show that genetic inactivation of Stat3 or IL-6 signalling in a Pten -deficient PCa mouse model accelerates cancer progression leading to metastasis. Mechanistically, we identify p19 ARF as a direct Stat3 target. Loss of Stat3 signalling disrupts the ARF–Mdm2–p53 tumour suppressor axis bypassing senescence. Strikingly, we also identify STAT3 and CDKN2A mutations in primary human PCa. STAT3 and CDKN2A deletions co-occurred with high frequency in PCa metastases. In accordance, loss of STAT3 and p14 ARF expression in patient tumours correlates with increased risk of disease recurrence and metastatic PCa. Thus, STAT3 and ARF may be prognostic markers to stratify high from low risk PCa patients. Our findings challenge the current discussion on therapeutic benefit or risk of IL-6/STAT3 inhibition. IL6-STAT3 signaling is activated in prostate cancer, however inhibiting this pathway has not lead to a survival advantage in patients. Here, Pencik et al. show that loss of the IL6-STAT3 axis in mice and humans leads to metastasis due to loss of ARF, unravelling STAT3 and ARF as potential prognostic markers in prostate cancer.

BackgroundThe DNA-damage immune-response (DDIR) signature is an immune-driven gene expression signature retrospectively validated as predicting response to anthracycline-based therapy. This feasibility study prospectively evaluates the use of this assay to predict neoadjuvant chemotherapy response in early breast cancer.MethodsThis feasibility study assessed the integration of a novel biomarker into clinical workflows. Tumour samples were collected from patients receiving standard of care neoadjuvant chemotherapy (FEC + /−taxane and anti-HER2 therapy as appropriate) at baseline, mid- and post-chemotherapy. Baseline DDIR signature scores were correlated with pathological treatment response. RNA sequencing was used to assess chemotherapy/response-related changes in biologically linked gene signatures.ResultsDDIR signature reports were available within 14 days for 97.8% of 46 patients (13 TNBC, 16 HER2 + ve, 27 ER + HER2-ve). Positive scores predicted response to treatment (odds ratio 4.67 for RCB 0-1 disease (95% CI 1.13–15.09, P = 0.032)). DDIR positivity correlated with immune infiltration and upregulated immune-checkpoint gene expression.ConclusionsThis study validates the DDIR signature as predictive of response to neoadjuvant chemotherapy which can be integrated into clinical workflows, potentially identifying a subgroup with high sensitivity to anthracycline chemotherapy. Transcriptomic data suggest induction with anthracycline-containing regimens in immune restricted, “cold” tumours may be effective for immune priming.Trial registrationNot applicable (non-interventional study). CRUK Internal Database Number 14232.

ObjectiveCurrent strategies to guide selection of neoadjuvant therapy in oesophageal adenocarcinoma (OAC) are inadequate. We assessed the ability of a DNA damage immune response (DDIR) assay to predict response following neoadjuvant chemotherapy in OAC.DesignTranscriptional profiling of 273 formalin-fixed paraffin-embedded prechemotherapy endoscopic OAC biopsies was performed. All patients were treated with platinum-based neoadjuvant chemotherapy and resection between 2003 and 2014 at four centres in the Oesophageal Cancer Clinical and Molecular Stratification consortium. CD8 and programmed death ligand 1 (PD-L1) immunohistochemical staining was assessed in matched resection specimens from 126 cases. Kaplan-Meier and Cox proportional hazards regression analysis were applied according to DDIR status for recurrence-free survival (RFS) and overall survival (OS).ResultsA total of 66 OAC samples (24%) were DDIR positive with the remaining 207 samples (76%) being DDIR negative. DDIR assay positivity was associated with improved RFS (HR: 0.61; 95% CI 0.38 to 0.98; p=0.042) and OS (HR: 0.52; 95% CI 0.31 to 0.88; p=0.015) following multivariate analysis. DDIR-positive patients had a higher pathological response rate (p=0.033), lower nodal burden (p=0.026) and reduced circumferential margin involvement (p=0.007). No difference in OS was observed according to DDIR status in an independent surgery-alone dataset.DDIR-positive OAC tumours were also associated with the presence of CD8+ lymphocytes (intratumoural: p<0.001; stromal: p=0.026) as well as PD-L1 expression (intratumoural: p=0.047; stromal: p=0.025).ConclusionThe DDIR assay is strongly predictive of benefit from DNA-damaging neoadjuvant chemotherapy followed by surgical resection and is associated with a proinflammatory microenvironment in OAC.

Radical radiotherapy, often in combination with hormone ablation, is a safe and effective treatment option for localised or locally-advanced prostate cancer. However, up to 30% of patients with locally advanced PCa will go on to develop biochemical failure, within 5 years, following initial radiotherapy. Improving radiotherapy response is clinically important since patients exhibiting biochemical failure develop castrate-resistant metastatic disease for which there is no curative therapy and median survival is 8–18 months. The aim of this research was to determine if loss of PTEN (highly prevalent in advanced prostate cancer) is a novel therapeutic target in the treatment of advanced prostate cancer. Previous work has demonstrated PTEN-deficient cells are sensitised to inhibitors of ATM, a key regulator in the response to DSBs. Here, we have shown the role of PTEN in cellular response to IR was both complex and context-dependent. Secondly, we have confirmed ATM inhibition in PTEN-depleted cell models, enhances ionising radiation-induced cell killing with minimal toxicity to normal prostate RWPE-1 cells. Furthermore, combined treatment significantly inhibited PTEN-deficient tumour growth compared to PTEN-expressing counterparts, with minimal toxicity observed. We have further shown PTEN loss is accompanied by increased endogenous levels of ROS and DNA damage. Taken together, these findings provide pre-clinical data for future clinical evaluation of ATM inhibitors as a neoadjuvant/adjuvant in combination with radiation therapy in prostate cancer patients harbouring PTEN mutations.

Nature Communications 6: Article number:7736 (2015); Published: 22 July 2015; Updated: 26 October 2015 The affiliation details for Jan Pencik are incorrect in this Article. The correct affiliation details for this author are given below: Ludwig Boltzmann Institute for Cancer Research, Waehringerstrasse 13A, 1090 Vienna, Austria.

The Air Force expects its leaders to stay involved with their Airmen, a collective term used for Air Force service members. The leader's daily involvement requires interactions such as mentorship, guidance, and instruction. In addition, the leader should provide career counseling concerning benefits, entitlements, and opportunities. Finally, leaders must promote a culture of Airmen who are capable of mastering multiple tasks and promote professional military schools and continued civilian education. Here, Richardson et al discuss the servant-leadership among technical sergeants in the US Air Force.

Over the past four decades, crises such as 9/11, Hurricane Katrina, the 2008 Great Recession, the COVID19 pandemic, and the 2025 Los Angeles wildfires have exposed the strengths and shortcomings of leadership during unprecedented challenges. This article presents a thematic analysis of empirical peer-reviewed literature from 1985 to 2025, synthesizing lessons learned across diverse crises. By focusing on five core dimensions-decision-making under uncertainty, emotional intelligence, communication strategies, resilience-building, and ethical leadership-this analysis provides actionable insights for leaders navigating volatile, uncertain, complex, and ambiguous (VUCA) environments. The findings emphasize adaptability, transparency, empathy, and ethical stewardship as key factors that distinguish successful crisis leaders. The article also proposes a \"Crisis Leadership Framework\" to guide future leaders in addressing the dynamic demands of a rapidly changing world. Through this synthesis of research and practice, this article bridges academic insights with pragmatic tools, equipping leaders to respond effectively to global disruptions while fostering long-term resilience.

Complexity leadership, complex adaptive leadership, and adaptive leadership are distinct yet interconnected research areas, originating in the early 1980s. This article extends a systematic review, focusing on the third decade of literature in these fields. The authors examined 778 business-related articles, narrowing down to 91 published between 2003 and 2012 for detailed deductive analysis. Findings from this decade highlight a shift from traditional, leader-centric models to adaptive, holistic frameworks that emphasize emergence, nonlinearity, feedback loops, and interdependence. Key themes include adaptive capacity, self-organization, and distributed cognition, which underscore the importance of collaborative leadership in managing complex, volatile environments. These insights offer practical guidance, illustrating how organizations can use these principles to foster continuous innovation, adaptability, and resilience-laying the groundwork for the most recent fifteen years of complexity leadership research.

Image analysis is a cost-effective tool to associate complex features of tissue organisation with molecular and outcome data. Here we predict consensus molecular subtypes (CMS) of colorectal cancer (CRC) from standard H&E sections using deep learning. Domain adversarial training of a neural classification network was performed using 1,553 tissue sections with comprehensive multi-omic data from three independent datasets. Image-based consensus molecular subtyping (imCMS) accurately classified CRC whole-slide images and preoperative biopsies, spatially resolved intratumoural heterogeneity and provided accurate secondary calls with higher discriminatory power than bioinformatic prediction. In all three cohorts imCMS established sensible classification in CMS unclassified samples, reproduced expected correlations with (epi)genomic alterations and effectively stratified patients into prognostic subgroups. Leveraging artificial intelligence for the development of novel biomarkers extracted from histological slides with molecular and biological interpretability has remarkable potential for clinical translation.