Venetoclax resistance in acute lymphoblastic leukemia is characterized by increased mitochondrial activity and can be overcome by co-targeting oxidative phosphorylation.

Deregulated apoptosis signaling is characteristic for many cancers and contributes to leukemogenesis and treatment failure in B-cell precursor acute lymphoblastic leukemia (BCP-ALL). Apoptosis is controlled by different pro- and anti-apoptotic molecules. Inhibition of anti-apoptotic molecules like B-cell lymphoma 2 (BCL-2) has been developed as therapeutic strategy. Venetoclax (VEN), a selective BCL-2 inhibitor has shown clinical activity in different lymphoid malignancies and is currently evaluated in first clinical trials in BCP-ALL. However, insensitivity to VEN has been described constituting a major clinical concern. Here, we addressed and modeled VEN-resistance in BCP-ALL, investigated the underlying mechanisms in cell lines and patient-derived xenograft (PDX) samples and identified potential strategies to overcome VEN-insensitivity. Leukemia lines with VEN-specific resistance were generated in vitro and further characterized using RNA-seq analysis. Interestingly, gene sets annotated to the citric/tricarboxylic acid cycle and the respiratory electron transport chain were significantly enriched and upregulated, indicating increased mitochondrial metabolism in VEN-resistant ALL. Metabolic profiling showed sustained high mitochondrial metabolism in VEN-resistant lines as compared to control lines. Accordingly, primary PDX-ALL samples with intrinsic VEN-insensitivity showed higher oxygen consumption and ATP production rates, further highlighting that increased mitochondrial activity is a characteristic feature of VEN-resistant ALL. VEN-resistant PDX-ALL showed significant higher mitochondrial DNA content and differed in mitochondria morphology with significantly larger and elongated structures, further corroborating our finding of augmented mitochondrial metabolism upon VEN-resistance. Using Oligomycin, an inhibitor of the complex V/ATPase subunit, we found synergistic activity and apoptosis induction in VEN-resistant BCP-ALL cell lines and PDX samples, demonstrating that acquired and intrinsic VEN-insensitivity can be overcome by co-targeting BCL-2 and the OxPhos pathway. These findings of reprogrammed, high mitochondrial metabolism in VEN-resistance and synergistic activity upon co-targeting BCL-2 and oxidative phosphorylation strongly suggest further preclinical and potential clinical evaluation in VEN-resistant BCP-ALL.

Prediction of resistance to bevacizumab plus FOLFOX in metastatic colorectal cancer-Results of the prospective multicenter PERMAD trial.

BACKGROUND: Anti-vascular endothelial growth factor (VEGF) monoclonal antibodies (mAbs) are widely used for tumor treatment, including metastatic colorectal cancer (mCRC). So far, there are no biomarkers that reliably predict resistance to anti-VEGF mAbs like bevacizumab. A biomarker-guided strategy for early and accurate assessment of resistance could avoid the use of non-effective treatment and improve patient outcomes. We hypothesized that repeated analysis of multiple cytokines and angiogenic growth factors (CAFs) before and during treatment using machine learning could provide an accurate and earlier, i.e., 100 days before conventional radiologic staging, prediction of resistance to first-line mCRC treatment with FOLFOX plus bevacizumab. PATIENTS AND METHODS: 15 German and Austrian centers prospectively recruited 50 mCRC patients receiving FOLFOX plus bevacizumab as first-line treatment. Plasma samples were collected every two weeks until radiologic progression (RECIST 1.1) as determined by CT scans performed every 2 months. 102 pre-selected CAFs were centrally analyzed using a cytokine multiplex assay (Luminex, Myriad RBM). RESULTS: Using random forests, we developed a predictive machine learning model that discriminated between the situations of "no progress within 100 days before radiological progress" and "progress within 100 days before radiological progress". We could further identify a combination of ten out of the 102 CAF markers, which fulfilled this task with 78.2% accuracy, 71.8% sensitivity, and 82.5% specificity. CONCLUSIONS: We identified a CAF marker combination that indicates treatment resistance to FOLFOX plus bevacizumab in patients with mCRC within 100 days prior to radiologic progress.

Introducing a machine learning algorithm for delirium prediction-the Supporting SURgery with GEriatric Co-Management and AI project (SURGE-Ahead).

INTRODUCTION: Post-operative delirium (POD) is a common complication in older patients, with an incidence of 14-56%. To implement preventative procedures, it is necessary to identify patients at risk for POD. In the present study, we aimed to develop a machine learning (ML) model for POD prediction in older patients, in close cooperation with the PAWEL (patient safety, cost-effectiveness and quality of life in elective surgery) project. METHODS: The model was trained on the PAWEL study's dataset of 878 patients (no intervention, age ≥ 70, 209 with POD). Presence of POD was determined by the Confusion Assessment Method and a chart review. We selected 15 features based on domain knowledge, ethical considerations and a recursive feature elimination. A logistic regression and a linear support vector machine (SVM) were trained, and evaluated using receiver operator characteristics (ROC). RESULTS: The selected features were American Society of Anesthesiologists score, multimorbidity, cut-to-suture time, estimated glomerular filtration rate, polypharmacy, use of cardio-pulmonary bypass, the Montreal cognitive assessment subscores 'memory', 'orientation' and 'verbal fluency', pre-existing dementia, clinical frailty scale, age, recent falls, post-operative isolation and pre-operative benzodiazepines. The linear SVM performed best, with an ROC area under the curve of 0.82 [95% CI 0.78-0.85] in the training set, 0.81 [95% CI 0.71-0.88] in the test set and 0.76 [95% CI 0.71-0.79] in a cross-centre validation. CONCLUSION: We present a clinically useful and explainable ML model for POD prediction. The model will be deployed in the Supporting SURgery with GEriatric Co-Management and AI project.

INHBA is Enriched in HPV-negative Oropharyngeal Squamous Cell Carcinoma and Promotes Cancer Progression.

Patients with oropharyngeal squamous cell carcinoma (OPSCC) caused by human papilloma virus (HPV) exhibit a better prognosis than those with HPV-negative OPSCC. This study investigated the distinct molecular pathways that delineate HPV-negative from HPV-positive OPSCC to identify biologically relevant therapeutic targets. Bulk mRNA from 23 HPV-negative and 39 HPV-positive OPSCC tumors (n = 62) was sequenced to uncover the transcriptomic profiles. Differential expression followed by gene set enrichment analysis was performed to outline the top enriched biological process in the HPV-negative compared with HPV-positive entity. INHBA, the highest overexpressed gene in the HPV-negative tumor, was knocked down. Functional assays (migration, proliferation, cell death, stemness) were conducted to confirm the target's oncogenic role. Correlation analyses to reveal its impact on the tumor microenvironment were performed. We revealed that epithelial-to-mesenchymal transition (EMT) is the most enriched process in HPV-negative compared with HPV-positive OPSCC, with INHBA (inhibin beta A subunit) being the top upregulated gene. INHBA knockdown downregulated the expression of EMT transcription factors and attenuated migration, proliferation, stemness, and cell death resistance of OPSCC cells. We uncovered that INHBA associates with a pro-tumor microenvironment by negatively correlating with antitumor CD8+ T and B cells while positively correlating with pro-tumor M1 macrophages. We identified three miRNAs that are putatively involved in repressing INHBA expression. Our results indicate that the upregulation of INHBA is tumor-promoting. We propose INHBA as an attractive therapeutic target for the treatment of INHBA-enriched tumors in patients with HPV-negative OPSCC to ameliorate prognosis. SIGNIFICANCE: Patients with HPV-negative OPSCC have a poorer prognosis due to distinct molecular pathways. This study reveals significant transcriptomic differences between HPV-negative and HPV-positive OPSCC, identifying INHBA as a key upregulated gene in HPV-negative OPSCC's oncogenic pathways. INHBA is crucial in promoting EMT, cell proliferation, and an immunosuppressive tumor environment, suggesting its potential as a therapeutic target for HPV-negative OPSCC.

GatekeepR: an R Shiny application for the identification of nodes with high dynamic impact in Boolean networks.

MOTIVATION: Boolean networks can serve as straightforward models for understanding processes such as gene regulation, and employing logical rules. These rules can either be derived from existing literature or by data-driven approaches. However, in the context of large networks, the exhaustive search for intervention targets becomes challenging due to the exponential expansion of a Boolean network's state space and the multitude of potential target candidates, along with their various combinations. Instead, we can employ the logical rules and resultant interaction graph as a means to identify targets of specific interest within larger-scale models. This approach not only facilitates the screening process but also serves as a preliminary filtering step, enabling the focused investigation of candidates that hold promise for more profound dynamic analysis. However, applying this method requires a working knowledge of R, thus restricting the range of potential users. We, therefore, aim to provide an application that makes this method accessible to a broader scientific community. RESULTS: Here, we introduce GatekeepR, a graphical, web-based R Shiny application that enables scientists to screen Boolean network models for possible intervention targets whose perturbation is likely to have a large impact on the system's dynamics. This application does not require a local installation or knowledge of R and provides the suggested targets along with additional network information and visualizations in an intuitive, easy-to-use manner. The Supplementary Material describes the underlying method for identifying these nodes along with an example application in a network modeling pancreatic cancer. AVAILABILITY AND IMPLEMENTATION: https://www.github.com/sysbio-bioinf/GatekeepR https://abel.informatik.uni-ulm.de/shiny/GatekeepR/.

Multicentric pilot study to standardize clinical whole exome sequencing (WES) for cancer patients.

A growing number of druggable targets and national initiatives for precision oncology necessitate broad genomic profiling for many cancer patients. Whole exome sequencing (WES) offers unbiased analysis of the entire coding sequence, segmentation-based detection of copy number alterations (CNAs), and accurate determination of complex biomarkers including tumor mutational burden (TMB), homologous recombination repair deficiency (HRD), and microsatellite instability (MSI). To assess the inter-institution variability of clinical WES, we performed a comparative pilot study between German Centers of Personalized Medicine (ZPMs) from five participating institutions. Tumor and matched normal DNA from 30 patients were analyzed using custom sequencing protocols and bioinformatic pipelines. Calling of somatic variants was highly concordant with a positive percentage agreement (PPA) between 91 and 95% and a positive predictive value (PPV) between 82 and 95% compared with a three-institution consensus and full agreement for 16 of 17 druggable targets. Explanations for deviations included low VAF or coverage, differing annotations, and different filter protocols. CNAs showed overall agreement in 76% for the genomic sequence with high wet-lab variability. Complex biomarkers correlated strongly between institutions (HRD: 0.79-1, TMB: 0.97-0.99) and all institutions agreed on microsatellite instability. This study will contribute to the development of quality control frameworks for comprehensive genomic profiling and sheds light onto parameters that require stringent standardization.

Liquid biopsy: an examination of platelet RNA obtained from head and neck squamous cell carcinoma patients for predictive molecular tumor markers.

Aim: Recently, a tumor cell-platelet interaction was identified in different tumor entities, resulting in a transfer of tumor-derived RNA into platelets, named further "tumor-educated platelets (TEP)". The present pilot study aims to investigate whether such a tumor-platelet transfer of RNA occurs also in patients suffering from head and neck squamous cell carcinoma (HNSCC). Methods: Sequencing analysis of RNA derived from platelets of tumor patients (TPs) and healthy donors (HDs) were performed. Subsequently, quantitative reverse transcription-polymerase chain reaction (qRT-PCR) was used for verification of differentially expressed genes in platelets from TPs and HDs in a second cohort of patients and HDs. Data were analyzed by applying bioinformatic tools. Results: Sequencing of RNA derived from the tumor as well as from platelets of TPs and HDs revealed 426 significantly differentially existing RNA, at which 406 RNA were more and 20 RNA less abundant in platelets from TPs in comparison to that of HDs. In TPs' platelets, abundantly existing RNA coding for 49 genes were detected, characteristically expressed in epithelial cells and RNA, the products of which are involved in tumor progression. Applying bioinformatic tools and verification on a second TP/HD cohort, collagen type I alpha 1 chain (COL1A1) and zinc finger protein 750 (ZNF750) were identified as the strongest potentially platelet-RNA-sequencing (RNA-seq)-based biomarkers for HNSCC. Conclusions: These results indicate a transfer of tumor-derived messenger RNA (mRNA) into platelets of HNSCC patients. Therefore, analyses of a patient's platelet RNA could be an efficient option for liquid biopsy in order to diagnose HNSCC or to monitor tumorigenesis as well as therapeutic responses at any time and in real time.

AMBAR - Interactive Alteration annotations for molecular tumor boards.

MOTIVATION: Personalized decision-making for cancer therapy relies on molecular profiling from sequencing data in combination with database evidence and expert knowledge. Molecular tumor boards (MTBs) bring together clinicians and scientists with diverse expertise and are increasingly established in the clinical routine for therapeutic interventions. However, the analysis and documentation of patients data are still time-consuming and difficult to manage for MTBs, especially as few tools are available for the amount of information required. RESULTS: To overcome these limitations, we developed an interactive web application AMBAR (Alteration annotations for Molecular tumor BoARds), for therapeutic decision-making support in MTBs. AMBAR is an R shiny-based application that allows customization, interactive filtering, visualization, adding expert knowledge, and export to clinical systems of annotated mutations. AVAILABILITY: AMBAR is dockerized, open source and available at https://sysbio.uni-ulm.de/?Software:Ambar Contact:hans.kestler@uni-ulm.de.

A systems biology approach to define mechanisms, phenotypes, and drivers in PanNETs with a personalized perspective.

Pancreatic neuroendocrine tumors (PanNETs) are a rare tumor entity with largely unpredictable progression and increasing incidence in developed countries. Molecular pathways involved in PanNETs development are still not elucidated, and specific biomarkers are missing. Moreover, the heterogeneity of PanNETs makes their treatment challenging and most approved targeted therapeutic options for PanNETs lack objective responses. Here, we applied a systems biology approach integrating dynamic modeling strategies, foreign classifier tailored approaches, and patient expression profiles to predict PanNETs progression as well as resistance mechanisms to clinically approved treatments such as the mammalian target of rapamycin complex 1 (mTORC1) inhibitors. We set up a model able to represent frequently reported PanNETs drivers in patient cohorts, such as Menin-1 (MEN1), Death domain associated protein (DAXX), Tuberous Sclerosis (TSC), as well as wild-type tumors. Model-based simulations suggested drivers of cancer progression as both first and second hits after MEN1 loss. In addition, we could predict the benefit of mTORC1 inhibitors on differentially mutated cohorts and hypothesize resistance mechanisms. Our approach sheds light on a more personalized prediction and treatment of PanNET mutant phenotypes.

Supporting SURgery with GEriatric Co-Management and AI (SURGE-Ahead): A study protocol for the development of a digital geriatrician.

INTRODUCTION: Geriatric co-management is known to improve treatment of older adults in various clinical settings, however, widespread application of the concept is limited due to restricted resources. Digitalization may offer options to overcome these shortages by providing structured, relevant information and decision support tools for medical professionals. We present the SURGE-Ahead project (Supporting SURgery with GEriatric co-management and Artificial Intelligence) addressing this challenge. METHODS: A digital application with a dashboard-style user interface will be developed, displaying 1) evidence-based recommendations for geriatric co-management and 2) artificial intelligence-enhanced suggestions for continuity of care (COC) decisions. The development and implementation of the SURGE-Ahead application (SAA) will follow the Medical research council framework for complex medical interventions. In the development phase a minimum geriatric data set (MGDS) will be defined that combines parametrized information from the hospital information system with a concise assessment battery and sensor data. Two literature reviews will be conducted to create an evidence base for co-management and COC suggestions that will be used to display guideline-compliant recommendations. Principles of machine learning will be used for further data processing and COC proposals for the postoperative course. In an observational and AI-development study, data will be collected in three surgical departments of a University Hospital (trauma surgery, general and visceral surgery, urology) for AI-training, feasibility testing of the MGDS and identification of co-management needs. Usability will be tested in a workshop with potential users. During a subsequent project phase, the SAA will be tested and evaluated in clinical routine, allowing its further improvement through an iterative process. DISCUSSION: The outline offers insights into a novel and comprehensive project that combines geriatric co-management with digital support tools to improve inpatient surgical care and continuity of care of older adults. TRIAL REGISTRATION: German clinical trials registry (Deutsches Register für klinische Studien, DRKS00030684), registered on 21st November 2022.

The HLA ligandome of oropharyngeal squamous cell carcinomas reveals shared tumour-exclusive peptides for semi-personalised vaccination.

BACKGROUND: The immune peptidome of OPSCC has not previously been studied. Cancer-antigen specific vaccination may improve clinical outcome and efficacy of immune checkpoint inhibitors such as PD1/PD-L1 antibodies. METHODS: Mapping of the OPSCC HLA ligandome was performed by mass spectrometry (MS) based analysis of naturally presented HLA ligands isolated from tumour tissue samples (n = 40) using immunoaffinity purification. The cohort included 22 HPV-positive (primarily HPV-16) and 18 HPV-negative samples. A benign reference dataset comprised of the HLA ligandomes of benign haematological and tissue datasets was used to identify tumour-associated antigens. RESULTS: MS analysis led to the identification of naturally HLA-presented peptides in OPSCC tumour tissue. In total, 22,769 peptides from 9485 source proteins were detected on HLA class I. For HLA class II, 15,203 peptides from 4634 source proteins were discovered. By comparative profiling against the benign HLA ligandomic datasets, 29 OPSCC-associated HLA class I ligands covering 11 different HLA allotypes and nine HLA class II ligands were selected to create a peptide warehouse. CONCLUSION: Tumour-associated peptides are HLA-presented on the cell surfaces of OPSCCs. The established warehouse of OPSCC-associated peptides can be used for downstream immunogenicity testing and peptide-based immunotherapy in (semi)personalised strategies.

The chromatin remodeling protein ATRX positively regulates IRF3-dependent type I interferon production and interferon-induced gene expression.

The chromatin remodeling protein alpha thalassemia/mental retardation syndrome X-linked (ATRX) is a component of promyelocytic leukemia nuclear bodies (PML-NBs) and thereby mediates intrinsic immunity against several viruses including human cytomegalovirus (HCMV). As a consequence, viruses have evolved different mechanisms to antagonize ATRX, such as displacement from PML-NBs or degradation. Here, we show that depletion of ATRX results in an overall impaired antiviral state by decreasing transcription and subsequent secretion of type I IFNs, which is followed by reduced expression of interferon-stimulated genes (ISGs). ATRX interacts with the transcription factor interferon regulatory factor 3 (IRF3) and associates with the IFN-ß promoter to facilitate transcription. Furthermore, whole transcriptome sequencing revealed that ATRX is required for efficient IFN-induced expression of a distinct set of ISGs. Mechanistically, we found that ATRX positively modulates chromatin accessibility specifically upon IFN signaling, thereby affecting promoter regions with recognition motifs for AP-1 family transcription factors. In summary, our study uncovers a novel co-activating function of the chromatin remodeling factor ATRX in innate immunity that regulates chromatin accessibility and subsequent transcription of interferons and ISGs. Consequently, ATRX antagonization by viral proteins and ATRX mutations in tumors represent important strategies to broadly compromise both intrinsic and innate immune responses.

Identification of dynamic driver sets controlling phenotypical landscapes.

Controlling phenotypical landscapes is of vital interest to modern biology. This task becomes highly demanding because cellular decisions involve complex networks engaging in crosstalk interactions. Previous work on control theory indicates that small sets of compounds can control single phenotypes. However, a dynamic approach is missing to determine the drivers of the whole network dynamics. By analyzing 35 biologically motivated Boolean networks, we developed a method to identify small sets of compounds sufficient to decide on the entire phenotypical landscape. These compounds do not strictly prefer highly related compounds and show a smaller impact on the stability of the attractor landscape. The dynamic driver sets include many intervention targets and cellular reprogramming drivers in human networks. Finally, by using a new comprehensive model of colorectal cancer, we provide a complete workflow on how to implement our approach to shift from in silico to in vitro guided experiments.

Discrete Logic Modeling of Cell Signaling Pathways.

Cell signaling pathways often crosstalk generating complex biological behaviors observed in different cellular contexts. Frequently, laboratory experiments focus on a few putative regulators, alone unable to predict the molecular mechanisms behind the observed phenotypes. Here, systems biology complements these approaches by giving a holistic picture to complex signaling crosstalk. In particular, Boolean network models are a meaningful tool to study large network behaviors and can cope with incomplete kinetic information. By introducing a model describing pathways involved in hematopoietic stem cell maintenance, we present a general approach on how to model cell signaling pathways with Boolean network models.

Comparative Panel Sequencing of DNA Variants in cf-, ev- and tumorDNA for Pancreatic Ductal Adenocarcinoma Patients.

Pancreatic ductal adenocarcinomas (PDACs) are tumors with poor prognosis and limited treatment options. Personalized medicine aims at characterizing actionable DNA variants by next-generation sequencing, thereby improving treatment strategies and outcomes. Fine-needle tumor biopsies are currently the gold standard to acquire samples for DNA profiling. However, liquid biopsies have considerable advantages as they are minimally invasive and frequently obtainable and thus may help to monitor tumor evolution over time. However, which liquid analyte works best for this purpose is currently unclear. Our study aims to directly compare tumor-, circulating free (cf-) and extracellular vesicle-derived (ev)DNA by panel sequencing of matching patient material. We evaluated copy number variations (CNVs), single nucleotide variants (SNVs) and insertions and deletions (indels). Our data show that evDNA contains significantly larger DNA fragments up to 5.5 kb, in line with previous observations. Stringent bioinformatic processing revealed a significant advantage of evDNA with respect to cfDNA concerning detection performance for SNVs and a numerical increase for indels. A combination of ev- and cfDNA was clearly superior for SNV detection, as compared to either single analyte, thus potentially improving actionable variant prediction upon further optimization. Finally, calling of CNVs from liquid biopsies still remained challenging and uninformative.

CHD5 inhibits metastasis of neuroblastoma.

CHD5, a tumor suppressor at 1p36, is frequently lost or silenced in poor prognosis neuroblastoma (NB) and many adult cancers. The role of CHD5 in metastasis is unknown. We confirm that low expression of CHD5 is associated with stage 4 NB. Forced expression of CHD5 in NB cell lines with 1p loss inhibited key aspects of the metastatic cascade in vitro: anchorage-independent growth, migration, and invasion. In vivo, formation of bone marrow and liver metastases developing from intravenously injected NB cells was delayed and decreased by forced CHD5 expression. Genome-wide mRNA sequencing revealed reduction of genes and gene sets associated with metastasis when CHD5 was overexpressed. Known metastasis-suppressing genes preferentially upregulated in CHD5-overexpressing NB cells included PLCL1. In patient NB, low expression of PLCL1was associated with metastatic disease and poor survival. Knockdown of PLCL1 and of p53 in IMR5 NB cells overexpressing CHD5 reversed CHD5-induced inhibition of invasion and migration in vitro. In summary, CHD5 is a metastasis suppressor in NB.

Reconstructing Boolean network ensembles from single-cell data for unraveling dynamics in the aging of human hematopoietic stem cells.

Regulatory dependencies in molecular networks are the basis of dynamic behaviors affecting the phenotypical landscape. With the advance of high throughput technologies, the detail of omics data has arrived at the single-cell level. Nevertheless, new strategies are required to reconstruct regulatory networks based on populations of single-cell data. Here, we present a new approach to generate populations of gene regulatory networks from single-cell RNA-sequencing (scRNA-seq) data. Our approach exploits the heterogeneity of single-cell populations to generate pseudo-timepoints. This allows for the first time to uncouple network reconstruction from a direct dependency on time series measurements. The generated time series are then fed to a combined reconstruction algorithm. The latter allows a fast and efficient reconstruction of ensembles of gene regulatory networks. Since this approach does not require knowledge on time-related trajectories, it allows us to model heterogeneous processes such as aging. Applying the approach to the aging-associated NF-κB signaling pathway-based scRNA-seq data of human hematopoietic stem cells (HSCs), we were able to reconstruct eight ensembles, and evaluate their dynamic behavior. Moreover, we propose a strategy to evaluate the resulting attractor patterns. Interaction graph-based features and dynamic investigations of our model ensembles provide a new perspective on the heterogeneity and mechanisms related to human HSCs aging.

Shorter Leukocyte Telomere Length Is Associated with Worse Survival of Patients with Bladder Cancer and Renal Cell Carcinoma.

BACKGROUND: Telomeres are protein-DNA complexes at the tips of linear chromosomes. They protect the DNA from end-to-end fusion and exonucleolytic degradation. Shortening of telomeric DNA during aging can generate dysfunctional telomeres, promoting tumorigenesis. More recent data indicate that both short and long telomeres of peripheral blood leukocyte (PBL) cells can serve as prognostic biomarkers for cancer risk and may be associated with survival of patients with solid cancers. Telomere length in PBL cells could also be a potential prognostic biomarker for survival in bladder cancer (BC) or renal cell carcinoma (RCC). METHODS: The relative telomere length (RTL) of PBL cells was assessed in patients with BC (n = 144) and RCC (n = 144) by using qPCR. A control population of patients without malignant disease (NC, n = 73) was included for comparison. The correlation and association of RTL with histopathological parameters and overall survival (OS) were evaluated. RESULTS: Patients with BC and RCC had significantly shorter telomeres compared to patients without malignant disease. Within the cancer cohorts, multivariate analysis revealed that short RTL is an independent predictor of worse survival in BC (p = 0.039) and RCC (p = 0.041). CONCLUSION: Patients with BC and RCC had significantly shorter telomeres compared to the normal population. Shorter RTL in BC and RCC was an independent predictor of reduced survival.

Digitalization of adverse event management in oncology to improve treatment outcome-A prospective study protocol.

The occurrence of adverse events frequently accompanies tumor treatments. Side effects should be detected and treated as soon as possible to maintain the best possible treatment outcome. Besides the standard reporting system Common Terminology Criteria for Adverse Events (CTCAE), physicians have recognized the potential of patient-reporting systems. These are based on a more subjective description of current patient reporting symptoms. Patient-reported symptoms are essential to define the impact of a given treatment on the quality of life and the patient's wellbeing. They also act against an underreporting of side effects which are paramount to define the actual value of a treatment for the individual patient. Here, we present a study protocol for a clinical trial that assesses the potential of a smartphone application for CTCAE conform symptom reporting and tracking that is adjusted to the standard clinical reporting system rather than symptom oriented descriptive trial tools. The presented study will be implemented in two parts, both lasting over six months. The first part will assess the feasibility of the application with 30 patients non-randomly divided into three equally-sized age groups (<55years, 55-75years, >75years). In the second part 36 other patients will be randomly assigned to two groups, one reporting using the smartphone and one not. This prospective second part will compare the impact of smartphone reported adverse events regarding applied therapy doses and quality of life to those of patients receiving standard care. We aim for early detection and treatment of adverse events in oncological treatment to improve patients' safety and outcomes. For this purpose, we will capture frequent adverse events of chemotherapies, immunotherapies, or other targeted therapies with our smartphone application. The presented trial is registered at the U.S. National Library of Medicine ClinicalTrials.gov (NCT04493450) on July 30, 2020.

A Prospective Feasibility Trial to Challenge Patient-Derived Pancreatic Cancer Organoids in Predicting Treatment Response.

Real-time isolation, propagation, and pharmacotyping of patient-derived pancreatic cancer organoids (PDOs) may enable treatment response prediction and personalization of pancreatic cancer (PC) therapy. In our methodology, PDOs are isolated from 54 patients with suspected or confirmed PC in the framework of a prospective feasibility trial. The drug response of single agents is determined by a viability assay. Areas under the curves (AUC) are clustered for each drug, and a prediction score is developed for combined regimens. Pharmacotyping profiles are obtained from 28 PDOs (efficacy 63.6%) after a median of 53 days (range 21-126 days). PDOs exhibit heterogeneous responses to the standard-of-care drugs, and are classified into high, intermediate, or low responder categories. Our developed prediction model allows a successful response prediction in treatment-naïve patients with an accuracy of 91.1% for first-line and 80.0% for second-line regimens, respectively. The power of prediction declines in pretreated patients (accuracy 40.0%), particularly with more than one prior line of chemotherapy. Progression-free survival (PFS) is significantly longer in previously treatment-naïve patients receiving a predicted tumor sensitive compared to a predicted tumor resistant regimen (mPFS 141 vs. 46 days; p = 0.0048). In conclusion, generation and pharmacotyping of PDOs is feasible in clinical routine and may provide substantial benefit.