Enable Patient Stratification Research Articles

BIOM-29. UNMASKING COPPER DEPENDENCE IN GLIOMA: 19-GENE SIGNATURE FOR PATIENT STRATIFICATION AND TARGETED THERAPY

Abstract Adult-type diffuse gliomas exhibit poor prognosis with limited treatment options. Although copper-targeted therapies have been previously explored in these tumors, they have yet to show significant benefits. To understand the role of copper in glioma biology we performed an in silico data analysis on the TCGA glioma cohorts to identify copper-homeostasis related genes (CHRG) with prognostic significance. This was done to enable patient stratification into appropriate treatment arms for future clinical trials involving copper targeted therapies. Furthermore, in vitro experimentation was performed to validate the functional outcomes observed from the in silico results. 19 CHRGs were identified to have prognostic association from the TCGA glioma cohorts. This gene signature regulates a broad range of cellular processes, including metal homeostasis, metabolism, extracellular matrix organization, and immune cell function. The CHRG signature enabled the development of a predictive model that allowed stratification of patients between high and low risk groups. In vitro spheroid growth assays in both U87 cell lines with or without IDH mutation elicited sustained growth inhibition after 6 days post treatment. Furthermore, copper chelation therapy modified the energy potential of these glioma cells in vitro leading to growth inhibition. This study established a predictive model using 19 CHRGs that enable patient stratification between high and low risk glioma patients. Furthermore, our in vitro experiments show the suitability of targeting intracellular copper to inhibit tumor growth. Further research is needed to understand the underlying mechanisms and explore optimal treatment combinations for improved outcomes. We aim to extend this approach to pediatric gliomas for improved risk assessment and personalized medicine.

Deciphering autism heterogeneity: a molecular stratification approach in four mouse models

Autism spectrum disorder (ASD) is a complex neurodevelopmental condition characterized by impairments in social interaction and communication, as well as restrained or stereotyped behaviors. The inherent heterogeneity within the autism spectrum poses challenges for developing effective pharmacological treatments targeting core features. Successful clinical trials require the identification of robust markers to enable patient stratification. In this study, we identified molecular markers within the oxytocin and immediate early gene families across five interconnected brain structures of the social circuit. We used wild-type and four heterogeneous mouse models, each exhibiting unique autism-like behaviors modeling the autism spectrum. While dysregulations in the oxytocin family were model-specific, immediate early genes displayed widespread alterations, reflecting global changes across the four models. Through integrative analysis, we identified Egr1, Foxp1, Homer1a, Oxt, and Oxtr as five robust and discriminant molecular markers that allowed the successful stratification of the four models. Importantly, our stratification demonstrated predictive values when challenged with a fifth mouse model or identifying subgroups of mice potentially responsive to oxytocin treatment. Beyond providing insights into oxytocin and immediate early gene mRNA dynamics, this proof-of-concept study represents a significant step toward the potential stratification of individuals with ASD. This work has implications for the success of clinical trials and the development of personalized medicine in autism.

Heterogeneous IL-9 Production by Circulating Skin-Tropic and Extracutaneous Memory T Cells in Atopic Dermatitis Patients.

Interleukin (IL)-9 is present in atopic dermatitis (AD) lesions and is considered to be mainly produced by skin-homing T cells expressing the cutaneous lymphocyte-associated antigen (CLA). However, its induction by AD-associated triggers remains unexplored. Circulating skin-tropic CLA+ and extracutaneous/systemic CLA- memory T cells cocultured with autologous lesional epidermal cells from AD patients were activated with house dust mite (HDM) and staphylococcal enterotoxin B (SEB). Levels of AD-related mediators in response to both stimuli were measured in supernatants, and the cytokine response was associated with different clinical characteristics. Both HDM and SEB triggered heterogeneous IL-9 production by CLA+ and CLA- T cells in a clinically homogenous group of AD patients, which enabled patient stratification into IL-9 producers and non-producers, with the former group exhibiting heightened HDM-specific and total IgE levels. Upon allergen exposure, IL-9 production depended on the contribution of epidermal cells and class II-mediated presentation; it was the greatest cytokine produced and correlated with HDM-specific IgE levels, whereas SEB mildly induced its release. This study demonstrates that both skin-tropic and extracutaneous memory T cells produce IL-9 and suggests that the degree of allergen sensitization reflects the varied IL-9 responses in vitro, which may allow for patient stratification in a clinically homogenous population.

Identification of a cancer driver gene-associated lncRNA signature for prognostic prediction and immune response evaluation in clear cell renal cell carcinoma.

Clear cell renal cell carcinoma (ccRCC) predominates among kidney cancer cases and is influenced by mutations in cancer driver genes (CDGs). However, significant obstacles persist in the early diagnosis and treatment of ccRCC. While various genetic models offer new hopes for improving ccRCC management, the relationship between CDG-related long non-coding RNAs (CDG-RlncRNAs) and ccRCC remains poorly understood. Therefore, this study aims to construct prognostic molecular features based on CDG-RlncRNAs to predict the prognosis of ccRCC patients, and aims to provide a new strategy to enhance clinical management of ccRCC patients. This study employed Cox and Least Absolute Shrinkage and Selection Operator (LASSO) regression analyses to comprehensively investigate the association between lncRNAs and CDGs in ccRCC. Leveraging The Cancer Genome Atlas (TCGA) dataset, we identified 97 prognostically significant CDG-RlncRNAs and developed a robust prognostic model based on these CDG-RlncRNAs. The performance of the model was rigorously validated using the TCGA dataset for training and the International Cancer Genome Consortium (ICGC) dataset for validation. Functional enrichment analysis elucidated the biological relevance of CDG-RlncRNA features in the model, particularly in tumor immunity. Experimental validation further confirmed the functional role of representative CDG-RlncRNA SNHG3 in ccRCC progression. Our analysis revealed that 97 CDG-RlncRNAs are significantly associated with ccRCC prognosis, enabling patient stratification into different risk groups. Development of a prognostic model incorporating key lncRNAs such as HOXA11-AS, AP002807.1, APCDD1L-DT, AC124067.2, and SNHG3 demonstrated robust predictive accuracy in both training and validation datasets. Importantly, risk stratification based on the model revealed distinct immune-related gene expression patterns. Notably, SNHG3 emerged as a key regulator of the ccRCC cell cycle, highlighting its potential as a therapeutic target. Our study established a concise CDG-RlncRNA signature and underscored the pivotal role of SNHG3 in ccRCC progression. It emphasizes the clinical relevance of CDG-RlncRNAs in prognostic prediction and targeted therapy, offering potential avenues for personalized intervention in ccRCC.

Association between resting-state connectivity patterns in the defensive system network and treatment response in spider phobia—a replication approach

Although highly effective on average, exposure-based treatments do not work equally well for all patients with anxiety disorders. The identification of pre-treatment response-predicting patient characteristics may enable patient stratification. Preliminary research highlights the relevance of inhibitory fronto-limbic networks as such. We aimed to identify pre-treatment neural signatures differing between exposure treatment responders and non-responders in spider phobia and to validate results through rigorous replication. Data of a bi-centric intervention study comprised clinical phenotyping and pre-treatment resting-state functional connectivity (rsFC) data of n = 79 patients with spider phobia (discovery sample) and n = 69 patients (replication sample). RsFC data analyses were accomplished using the Matlab-based CONN-toolbox with harmonized analyses protocols at both sites. Treatment response was defined by a reduction of >30% symptom severity from pre- to post-treatment (Spider Phobia Questionnaire Score, primary outcome). Secondary outcome was defined by a reduction of >50% in a Behavioral Avoidance Test (BAT). Mean within-session fear reduction functioned as a process measure for exposure. Compared to non-responders and pre-treatment, results in the discovery sample seemed to indicate that responders exhibited stronger negative connectivity between frontal and limbic structures and were characterized by heightened connectivity between the amygdala and ventral visual pathway regions. Patients exhibiting high within-session fear reduction showed stronger excitatory connectivity within the prefrontal cortex than patients with low within-session fear reduction. Whereas these results could be replicated by another team using the same data (cross-team replication), cross-site replication of the discovery sample findings in the independent replication sample was unsuccessful. Results seem to support negative fronto-limbic connectivity as promising ingredient to enhance response rates in specific phobia but lack sufficient replication. Further research is needed to obtain a valid basis for clinical decision-making and the development of individually tailored treatment options. Notably, future studies should regularly include replication approaches in their protocols.

Abstract B055: Metabolic stratification of human breast tumors reveal subtypes of clinical and therapeutic relevance

Abstract Metabolic heterogeneity in breast cancer has limited the deployment of metabolic therapies. To enable patient stratification, we studied global metabolic landscape of breast cancer (N>3000 combined) and identified three metabolic subtypes with increasing degrees of metabolic deregulation. M1 exhibited deregulated bile acid biosynthesis whereas M2 showed reliance on methionine pathway and M3 was found to be the most metabolically aggressive engaging fatty, nucleotide, and glucose metabolism. Extent of metabolic deregulation correlated strongly with tumor aggressiveness and patient outcome Moreover bile acid biosynthesis predicted less aggressive tumors with better prognosis while pyrimidine metabolism predicted the opposite trend. Notably this pattern was reproducible in independent datasets and in vivo tumor metabolite data. Genomic characterization of metabolic subtypes revealed TP53 mutation and MYC amplification as the strongest predictor of metabolic deregulation. Using machine learning, we identified robust and generalizable signatures that recaptured metabolic subtypes in tumors and cell lines. Surprisingly, the least deregulated M1 subtype was not found to be represented by any of the 50 cell line models analyzed. Experimental inhibition of metabolic pathways demonstrated subtype specific sensitivity and improved combinatorial efficacy of chemotherapeutic drugs. In addition, breast cancer cell lines representing metabolic subtypes revealed therapeutically relevant drugs. Taken together, this work presents metabolic stratification of breast cancer patients that may have implications in predicting patient outcome and designing precision therapies. Citation Format: Mohammad A Iqbal, Shumaila Siddiqui, Kirk Smith, Prithvi Singh, Bhupender Kumar, Salem Chouaib, Sriram Chandrasekaran. Metabolic stratification of human breast tumors reveal subtypes of clinical and therapeutic relevance [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Advances in Breast Cancer Research; 2023 Oct 19-22; San Diego, California. Philadelphia (PA): AACR; Cancer Res 2024;84(3 Suppl_1):Abstract nr B055.

Cuproptosis-Related lncRNAs Modulate the Prognosis of MIBC by Regulating the Expression Pattern of Immunosuppressive Molecules Within the Tumor Microenvironment.

Cuproptosis-related gene and long non-coding RNA (lncRNA) modulation of cancer regulation is well-established. This investigation aimed to elucidate the prognostic implications of cuproptosis-associated lncRNAs in muscle-invasive bladder cancer (MIBC). Employing the Cancer Genome Atlas (TCGA) and IMvigor210 cohorts, bioinformatics and statistical analyses probed the prognostic relevance of cuproptosis-related lncRNAs. Co-expression analysis revealed tight associations between lncRNA expression and cuproptosis-linked genes, with 13 cuproptosis-related lncRNAs found to correlate with MIBC prognosis. Lasso regression identified a six-lncRNA prognostic signature, enabling patient stratification into high- and low-risk categories. Tissue validation substantiated differential expression of FAM13A-AS1, GHRLOS, LINC00456, OPA1-AS1, RAP2C-AS1, and UBE2Q1-AS1 between MIBC tumor and normal tissues. Comparative analyses of tumor microenvironments and immune profiles between risk groups disclosed elevated immunosuppressive molecule expression, including programmed cell death-1 (PD-L1) and T-cell immunoglobulin-3 (TIM-3), in high-risk individuals. These findings suggest that cuproptosis-related lncRNAs may modulate the expression of immunosuppressive molecules, thereby influencing MIBC tumorigenesis and progression. Further exploration is warranted to unveil novel therapeutic targets for MIBC based on the expression patterns of cuproptosis-related lncRNAs and their impact on immune responses in the tumor microenvironment.

Metabolic stratification of human breast tumors reveal subtypes of clinical and therapeutic relevance

Extensive metabolic heterogeneity in breast cancers has limited the deployment of metabolic therapies. To enable patient stratification, we studied the metabolic landscape in breast cancers (∼3000 patients combined) and identified three subtypes with increasing degrees of metabolic deregulation. Subtype M1 was found to be dependent on bile-acid biosynthesis, whereas M2 showed reliance on methionine pathway, and M3 engaged fatty-acid, nucleotide, and glucose metabolism. The extent of metabolic alterations correlated strongly with tumor aggressiveness and patient outcome. This pattern was reproducible in independent datasets and using invivo tumor metabolite data. Using machine-learning, we identified robust and generalizable signatures of metabolic subtypes in tumors and cell lines. Experimental inhibition of metabolic pathways in cell lines representing metabolic subtypes revealed subtype-specific sensitivity, therapeutically relevant drugs, and promising combination therapies. Taken together, metabolic stratification of breast cancers can thus aid in predicting patient outcome and designing precision therapies.

Self-organized metabotyping of obese individuals identifies clusters responding differently to bariatric surgery.

Weight loss through bariatric surgery is efficient for treatment or prevention of obesity related diseases such as type 2 diabetes and cardiovascular disease. Long term weight loss response does, however, vary among patients undergoing surgery. Thus, it is difficult to identify predictive markers while most obese individuals have one or more comorbidities. To overcome such challenges, an in-depth multiple omics analyses including fasting peripheral plasma metabolome, fecal metagenome as well as liver, jejunum, and adipose tissue transcriptome were performed for 106 individuals undergoing bariatric surgery. Machine leaning was applied to explore the metabolic differences in individuals and evaluate if metabolism-based patients' stratification is related to their weight loss responses to bariatric surgery. Using Self-Organizing Maps (SOMs) to analyze the plasma metabolome, we identified five distinct metabotypes, which were differentially enriched for KEGG pathways related to immune functions, fatty acid metabolism, protein-signaling, and obesity pathogenesis. The gut metagenome of the most heavily medicated metabotypes, treated simultaneously for multiple cardiometabolic comorbidities, was significantly enriched in Prevotella and Lactobacillus species. This unbiased stratification into SOM-defined metabotypes identified signatures for each metabolic phenotype and we found that the different metabotypes respond differently to bariatric surgery in terms of weight loss after 12 months. An integrative framework that utilizes SOMs and omics integration was developed for stratifying a heterogeneous bariatric surgery cohort. The multiple omics datasets described in this study reveal that the metabotypes are characterized by a concrete metabolic status and different responses in weight loss and adipose tissue reduction over time. Our study thus opens a path to enable patient stratification and hereby allow for improved clinical treatments.

DOP06 Deep characterization of IL-23 pathway in different gut segments and associated plasma biomarkers in inflammatory bowel disease

Abstract Background The role of Interleukin 23 (IL-23) in the pathogenesis of both Crohn’s disease (CD) and ulcerative colitis (UC) is well documented. However, more than half of patients do not respond or lose response to current anti-IL-23 treatments. Thus, we aimed to identify patients with IL-23-driven disease using gut segment-specific transcriptome analysis and quantification of IL-23 pathway cytokines in plasma to enable patient stratification in inflammatory bowel disease (IBD). Methods Analyses of real-world data from the Study of a Prospective Adult Research Cohort with IBD (SPARC IBD) obtained from the IBD Plexus program of the Crohn’s & Colitis Foundation including clinical, plasma biomarker (IL-22, IL-23, INF-γ, and IL-17A), and mucosal ileum and colon transcriptome data. Results To differentiate IL-23 from TNF-driven disease, we performed gene set variation analysis (GSVA) using IL-23 and TNF gene sets and ileal and colonic transcriptome data from 1230 CD and 664 UC patients. An IL-23, but not TNF, transcriptional module was enriched in ileal compared to colonic biopsies in CD (p < 0.001​). IL-23 pathway activity was significantly higher in uninflamed Ileal compared to colonic biopsies from CD patients (p < 0.001)​). Importantly, IL-23 pathway activity was increased in intestinal biopsies from macroscopically inflamed compared to uninflamed regions, from all gut segments in both CD and UC and correlated with the endoscopic score for both diseases (Fig 1). We also observed higher plasma IL-22 levels in both CD and UC patients with moderate to severely active disease compared with patients in remission (Fig 2). We found that tissue IL-23 pathway activity was linked to plasma biomarker levels in general. The subgroup of CD patients with elevated plasma IL-22 levels showed higher IL-23 pathway activity in the ileum (p=0.003) and colon (p=0.013). In UC, patients with elevated plasma IL-22 levels showed higher IL-23 pathway activity in the sigmoid colon (p=0.001), but not the cecum (p=0.141) (Fig 3). Conclusion IL-23 pathway activity in different segments of the gut may contribute differently to pathogenesis in CD, while IL-23 pathway activity is similar across colonic segments in UC. The association between plasma biomarker levels and gut tissue IL-23 pathway activity suggests that quantifying plasma IL-22 levels could be a valuable tool to assess the efficacy of anti-IL-23 therapy in IBD.

Heat selection enables highly scalable methylome profiling in cell-free DNA for noninvasive monitoring of cancer patients.

Genome-wide analysis of cell-free DNA methylation profile is a promising approach for sensitive and specific detection of many cancers. However, scaling such assays for clinical translation is impractical because of the high cost of whole-genome bisulfite sequencing. We show that the small fraction of GC-rich genome is highly enriched in CpG sites and disproportionately harbors most of the cancer-specific methylation signature. Here, we report on the simple and effective heat enrichment of CpG-rich regions for bisulfite sequencing (Heatrich-BS) platform that allows for focused methylation profiling in these highly informative regions. Our novel method and bioinformatics algorithm enable accurate tumor burden estimation and quantitative tracking of colorectal cancer patient’s response to treatment at much reduced sequencing cost suitable for frequent monitoring. We also show tumor epigenetic subtyping using Heatrich-BS, which could enable patient stratification. Heatrich-BS holds great potential for highly scalable screening and monitoring of cancer using liquid biopsy.

Longitudinal dynamics of clonal hematopoiesis identifies gene-specific fitness effects

Clonal hematopoiesis of indeterminate potential (CHIP) increases rapidly in prevalence beyond age 60 and has been associated with increased risk for malignancy, heart disease and ischemic stroke. CHIP is driven by somatic mutations in hematopoietic stem and progenitor cells (HSPCs). Because mutations in HSPCs often drive leukemia, we hypothesized that HSPC fitness substantially contributes to transformation from CHIP to leukemia. HSPC fitness is defined as the proliferative advantage over cells carrying no or only neutral mutations. If mutations in different genes lead to distinct fitness advantages, this could enable patient stratification. We quantified the fitness effects of mutations over 12 years in older age using longitudinal sequencing and developed a filtering method that considers individual mutational context alongside mutation co-occurrence to quantify the growth potential of variants within individuals. We found that gene-specific fitness differences can outweigh inter-individual variation and, therefore, could form the basis for personalized clinical management.

From imaging a single cell to implementing precision medicine: an exciting new era.

In the age of high-throughput, single-cell biology, single-cell imaging has evolved not only in terms of technological advancements but also in its translational applications. The synchronous advancements of imaging and computational biology have produced opportunities of merging the two, providing the scientific community with tools towards observing, understanding, and predicting cellular and tissue phenotypes and behaviors. Furthermore, multiplexed single-cell imaging and machine learning algorithms now enable patient stratification and predictive diagnostics of clinical specimens. Here, we provide an overall summary of the advances in single-cell imaging, with a focus on high-throughput microscopy phenomics and multiplexed proteomic spatial imaging platforms. We also review various computational tools that have been developed in recent years for image processing and downstream applications used in biomedical sciences. Finally, we discuss how harnessing systems biology approaches and data integration across disciplines can further strengthen the exciting applications and future implementation of single-cell imaging on precision medicine.

COVID-19 Mortality Prediction From Deep Learning in a Large Multistate Electronic Health Record and Laboratory Information System Data Set: Algorithm Development and Validation.

BackgroundCOVID-19 is caused by the SARS-CoV-2 virus and has strikingly heterogeneous clinical manifestations, with most individuals contracting mild disease but a substantial minority experiencing fulminant cardiopulmonary symptoms or death. The clinical covariates and the laboratory tests performed on a patient provide robust statistics to guide clinical treatment. Deep learning approaches on a data set of this nature enable patient stratification and provide methods to guide clinical treatment.ObjectiveHere, we report on the development and prospective validation of a state-of-the-art machine learning model to provide mortality prediction shortly after confirmation of SARS-CoV-2 infection in the Mayo Clinic patient population.MethodsWe retrospectively constructed one of the largest reported and most geographically diverse laboratory information system and electronic health record of COVID-19 data sets in the published literature, which included 11,807 patients residing in 41 states of the United States of America and treated at medical sites across 5 states in 3 time zones. Traditional machine learning models were evaluated independently as well as in a stacked learner approach by using AutoGluon, and various recurrent neural network architectures were considered. The traditional machine learning models were implemented using the AutoGluon-Tabular framework, whereas the recurrent neural networks utilized the TensorFlow Keras framework. We trained these models to operate solely using routine laboratory measurements and clinical covariates available within 72 hours of a patient’s first positive COVID-19 nucleic acid test result.ResultsThe GRU-D recurrent neural network achieved peak cross-validation performance with 0.938 (SE 0.004) as the area under the receiver operating characteristic (AUROC) curve. This model retained strong performance by reducing the follow-up time to 12 hours (0.916 [SE 0.005] AUROC), and the leave-one-out feature importance analysis indicated that the most independently valuable features were age, Charlson comorbidity index, minimum oxygen saturation, fibrinogen level, and serum iron level. In the prospective testing cohort, this model provided an AUROC of 0.901 and a statistically significant difference in survival (P<.001, hazard ratio for those predicted to survive, 95% CI 0.043-0.106).ConclusionsOur deep learning approach using GRU-D provides an alert system to flag mortality for COVID-19–positive patients by using clinical covariates and laboratory values within a 72-hour window after the first positive nucleic acid test result.

Predicting Survival in Patients with Pancreatic Cancer by Integrating Bone Marrow FDG Uptake and Radiomic Features of Primary Tumor in PET/CT.

Simple SummaryFDG uptake of bone marrow (BM) is known to reflect the degree of host inflammatory response to cancer cells and showed significant association with survival in diverse kinds of cancers. The aim of this retrospective study was to evaluate the prognostic significance of FDG uptake of BM and to investigate whether integrating FDG uptake of BM and radiomic features of primary tumors could improve the prediction of overall survival (OS) in patients with pancreatic cancer. In multivariable survival analysis, along with total lesion glycolysis (TLG) and first-order entropy of primary tumor lesions, FDG uptake of BM was an independent predictor of OS. We designed a PET/CT scoring system based on the cumulative scores of tumor factors (TLG and first-order entropy) and host factors (FDG uptake of BM). This scoring system was able to stratify the patients with three distinct prognostic groups independent of clinical stage and treatment modality.The purpose of this study was to evaluate the prognostic significance of FDG uptake of bone marrow (BM SUV) and to investigate its role combined with radiomic features of primary tumors in improving the prediction of overall survival (OS) in patients with pancreatic cancer. We retrospectively enrolled 65 pancreatic cancer patients with staging FDG PET/CT. BM SUV and conventional imaging parameters of primary tumors including total lesion glycolysis (TLG) were measured. First-order and higher-order textural features of primary cancer were extracted using PET textural analysis. Associations of PET/CT parameters of bone marrow (BM) and primary cancer with OS were assessed. BM SUV as well as TLG and first-order entropy of pancreatic cancer were significant independent predictors of OS in multivariable analysis. A PET/CT scoring system based on the cumulative scores of these three independent predictors enabled patient stratification into three distinct prognostic groups. The scoring system yielded a good prognostic stratification based on subgroup analysis irrespective of tumor stage and treatment modality. BM SUV was an independent predictor of OS in pancreatic cancer patients. The PET/CT scoring system that integrated PET/CT parameters of primary tumors and BM can provide prognostic information in pancreatic cancer independent of tumor stage and treatment.

Predictive value of RAD51 on the survival and drug responsiveness of ovarian cancer

BackgroundOvarian cancer has greatly endangered and deteriorated female health conditions worldwide. Refinement of predictive biomarkers could enable patient stratification and help optimize disease management.MethodsRAD51 expression profile, target-disease associations, and fitness scores of RAD51 were analyzed in ovarian cancer using bioinformatic analysis. To further identify its role, gene enrichment analysis was performed, and a regulatory network was constructed. Survival analysis and drug sensitivity assay were performed to evaluate the effect of RAD51 expression on ovarian cancer prognosis. The predictive value of RAD51 was then confirmed in a validation cohort immunohistochemically.ResultsOvarian cancer expressed more RAD51 than normal ovary. RAD51 conferred ovarian cancer dependency and was associated with ovarian cancer. RAD51 had extensive target-disease associations with various diseases, including ovarian cancer. Genes that correlate with and interact with RAD51 were involved in DNA damage repair and drug responsiveness. High RAD51 expression indicated unfavorable survival outcomes and resistance to platinum, taxane, and PARP inhibitors in ovarian cancer. In the validation cohort (126 patients), high RAD51 expression indicated platinum resistance, and platinum-resistant patients expressed more RAD51. Patients with high RAD51 expression had shorter OS (HR = 2.968, P < 0.0001) and poorer PFS (HR = 2.838, P < 0.0001). RAD51 expression level was negatively correlated with patients’ survival length.ConclusionsOvarian cancer had pronounced RAD51 expression and RAD51 conferred ovarian cancer dependency. High RAD51 expression indicated poor survival and decreased drug sensitivity. RAD51 has predictive value in ovarian cancer and can be exploited as a predictive biomarker.

Alterations in BAP1 Are Associated with Cisplatin Resistance through Inhibition of Apoptosis in Malignant Pleural Mesothelioma.

The clinical standard treatment for patients with malignant pleural mesothelioma (MPM) includes a cisplatin-based chemotherapy, leading to reduction of tumor size in only a minority of patients. Predicting response to chemotherapy in patients with MPM by using a genetic marker would, therefore, enable patient stratification. In this retrospective biomarker study, eligible patients had resectable MPM, measurable disease, and available primary MPM tissue. All patients underwent first-line treatment with cisplatin and pemetrexed, followed by surgery. Thorough molecular analysis was performed (whole-exome and targeted deep sequencing, and copy-number analyses), and also mechanistic in vitro data (viability assays, Western blots, and immunoprecipitation) using mesothelioma cell lines with and without siRNA-mediated BRCA1-associated protein 1 (BAP1) knockdown were provided. In a training cohort of patients with MPM (n = 28), mutations or deletions of BAP1 each predicted resistance to chemotherapy in patients with primary MPM. The negative predictive value of BAP1 loss in patients with MPM was confirmed by amplicon sequencing and copy-number array technology in an independent test cohort (n = 39). Preliminary mechanistic studies using siRNA-based knockdown of BAP1 in MPM cell culture models along with immunoprecipitation assays confirmed chemoresistance in vitro, possibly through inhibition of apoptosis and transcriptional regulation of the BAP1/HCF1/E2F1 axis. Alterations in BAP1 in MPM were a negative predictor for response to chemotherapy and could possibly be used as a companion biomarker for treatment decision.

Radiotherapy enhances uptake and efficacy of 90Y-cetuximab: A preclinical trial

Background and purposeSystemic molecular radiotherapy utilizes internal irradiation by radionuclide-labeled tumor-targeting agents with the potential to destroy (micro-)metastases. However, doses that are applicable in solid tumors do not reach the levels nessecary for tumor control. Thus, the combination of molecular and external radiotherapy is a promising treatment strategy, as enhanced tumor doses can be delivered with and without minor overlapping toxicities. Here, we combined a 90Y-labeled anti-EGFR antibody (Cetuximab) with clinically relevant fractionated radiotherapy in a preclinical trial using head and neck squamous cell carcinoma xenograft tumors. Materials and methodsTo model 90Y-Cetuximab uptake for treatment schedule optimization, FaDu-bearing mice were injected with near-infrared-labeled-Cetuximab at different time points during radiotherapy with differing doses. Cetuximab uptake was longitudinally followed by in vivo-optical imaging. Tumor control probability experiments with fractionated radiotherapy (30 fx, 6 weeks, 8 dose groups/ arm) in combination with 90Y-Cetuximab were performed to test the curative potential. ResultsImaging of near-infrared-labeled-Cetuximab uptake revealed that low to moderate external beam doses can enhance antibody uptake. Using the optimized schedule, combination of molecular and external radiotherapy using 90Y-Cetuximab at a dose that did not result in permanent tumor inactivation in previous experiments, led to substantially increased tumor control compared to radiotherapy alone. ConclusionOur results indicate that combination of radiolabeled therapeutics with clinically relevant fractionated radiotherapy has a remarkable potential to improve curative treatment outcome. Application of some radiation dose prior to injection may improve drug uptake and enable patient stratification and treatment personalization via a corresponding PET-tracer during therapy.

The Immune Phenotype of Isolated Lymphoid Structures in Non-Tumorous Colon Mucosa Encrypts the Information on Pathobiology of Metastatic Colorectal Cancer.

Simple SummaryToday, the presence of well-organized functional structures of immune cells at tumor sites, known as ectopic lymphoid structures, and their strong association with patient survival have been reported in more than ten different cancer types. We aimed to investigate whether there is a link between the patient-specific characteristics of pre-formed isolated lymphoid structures in non-tumorous colon tissue and the disease pathobiology for patients with metastatic colorectal cancer. The study employed a powerful approach of quantitative tissue image cytometry to compare lymphoid structures of different anatomical locations within the same patients. We showed that the properties of isolated lymphoid structures in non-tumorous colon tissue predefine the immune phenotype of ectopic lymphoid structures at primary and metastatic sites. We discovered that B-cell-enriched and highly proliferative lymphoid structures are prognostic towards an improved clinical outcome. The knowledge gained from this study expands our understanding of tumor-immune interactions and draws particular attention to the anti-tumor immune response guided by isolated lymphoid structures outside of tumor tissue.The gut-associated lymphoid tissue represents an integral part of the immune system. Among the powerful players of the mucosa-associated lymphoid tissue are isolated lymphoid structures (ILSs), which as information centers, drive the local (and systemic) adaptive immune responses. Germinal center reactions, taking place within ILSs, involve the coordinated action of various immune cell types with a central role given to B cells. In the current study, we aimed at dissecting the impact of ILSs within non-tumorous colon tissue (NT) on the pathobiology of colorectal cancer (CRC) with metastasis in the liver (CRCLM). In particular, we focused on the immune phenotypes of ILSs and ectopic lymphoid structures (ELSs), built up at matching primary and metastatic tumor sites. We implemented an integrative analysis strategy on the basis of tissue image cytometry and clonality assessment to explore the immune phenotype of ILS/ELS at three tissue entities: NT, CRC, and CRCLM (69 specimens in total). Applying a panel of lineage markers used for immunostaining, we characterized and compared the anatomical features, the cellular composition, the activation, and proliferation status of ILSs and ELSs, and assessed the clinical relevance of staining-derived data sets. Our major discovery was that ILS characteristics at the NT site predefine the immune phenotype of ELSs at CRC and CRCLM. Thereby, B-cell-enriched (CD20) and highly proliferative (Ki67) ILSs and ELSs were found to be associated with improved clinical outcome in terms of survival and enabled patient stratification into risk groups. Moreover, the data revealed a linkage between B-cell clonality at the NT site and the metastatic characteristics of the tumor in the distant liver tissue. Consolidation of immunostaining-based findings with the results of compendium-wide transcriptomic analysis furthermore proposed CD27 as a novel marker of T follicular helper cells within lymphoid structures. Overall, the study nominates the ILS immune phenotype as a novel prognostic marker for patients with metastatic CRC.

Heterogeneous addiction to transforming growth factor-beta signalling in recessive dystrophic epidermolysis bullosa-associated cutaneous squamous cell carcinoma.

Recessive dystrophic epidermolysis bullosa (RDEB) is associated with a high mortality rate due to the development of life-threatening, metastatic cutaneous squamous cell carcinoma (cSCC). Elevated transforming growth factor-beta (TGF-β) signalling is implicated in cSCC development and progression in patients with RDEB. To determine the effect of exogenous and endogenous TGF-β signalling in RDEB cSCC with a view to assessing the potential of targeting TGF-β signalling for RDEB cSCC therapy. A panel of 11 patient-derived RDEB cSCC primary tumour keratinocyte cell lines (SCCRDEBs) were tested for their signalling and proliferation responses to exogenous TGF-β. Their responses to TGF-β receptor type-1 (TGFBR1) kinase inhibitors [SB-431542 and AZ12601011 (AZA01)] were tested using in vitro proliferation, clonogenicity, migration and three-dimensional invasion assays, and in vivo tumour xenograft assays. All SCCRDEBs responded to exogenous TGF-β by activation of canonical SMAD signalling and proliferative arrest. Blocking endogenous signalling by treatment with SB-431542 and AZ12601011 significantly inhibited proliferation (seven of 11), clonogenicity (six of 11), migration (eight of 11) and invasion (six of 11) of SCCRDEBs. However, these TGFBR1 kinase inhibitors also promoted proliferation and clonogenicity in two of 11 SCCRDEB cell lines. Pretreatment of in vitro TGFBR1-addicted SCCRDEB70 cells with SB-431542 enhanced overall survival and reduced tumour volume in subcutaneous xenografts but had no effect on nonaddicted SCCRDEB2 cells in these assays. Targeting TGFBR1 kinase activity may have therapeutic benefit in the majority of RDEB cSCCs. However, the potential tumour suppressive role of TGF-β signalling in a subset of RDEB cSCCs necessitates biomarker identification to enable patient stratification before clinical intervention.