C‐reactive protein flare‐response predicts long‐term efficacy to first‐line anti‐PD‐1‐based combination therapy in metastatic renal cell carcinoma

Objectives Immune checkpoint blockade (IO) has revolutionised the treatment of metastatic renal cell carcinoma (mRCC). Early C-reactive protein (CRP) kinetics, especially the recently introduced CRP flare-response phenomenon, has shown promising results to predict IO efficacy in mRCC, but has only been studied in second line or later. Here, we aimed to validate the predictive value of early CRP kinetics for 1st-line treatment of mRCC with αPD-1 plus either αCTLA-4 (IO+IO) or tyrosine kinase inhibitor (IO+TKI). Methods In this multicentre retrospective study, we investigated the predictive potential of early CRP kinetics during 1st-line IO therapy. Ninety-five patients with mRCC from six tertiary referral centres with either IO+IO (N = 59) or IO+TKI (N = 36) were included. Patients were classified as CRP flare-responders, CRP responders or non-CRP responders as previously described, and their oncological outcome was compared. Results Our data validate the predictive potential of early CRP kinetics in 1st-line immunotherapy in mRCC. CRP responders, especially CRP flare-responders, had significantly prolonged progression-free survival (PFS) compared with non-CRP responders (median PFS: CRP flare-responder: 19.2 months vs. responders: 16.2 vs. non-CRP responders: 5.6, P < 0.001). In both the IO+IO and IO+TKI subgroups, early CRP kinetics remained significantly associated with improved PFS. CRP flare-response was also associated with long-term response ≥ 12 months. Conclusions Early CRP kinetics appears to be a low-cost and easy-to-implement on-treatment biomarker to predict response to 1st-line IO combination therapy. It has potential to optimise therapy monitoring and might represent a new standard of care biomarker for immunotherapy in mRCC

Target heterogeneity in oncology : the best predictor for differential response to radioligand therapy in neuroendocrine tumors and prostate cancer

Tumor or target heterogeneity (TH) implies presence of variable cellular populations having different genomic characteristics within the same tumor, or in different tumor sites of the same patient. The challenge is to identify this heterogeneity, as it has emerged as the most common cause of ‘treatment resistance’, to current therapeutic agents. We have focused our discussion on ‘Prostate Cancer’ and ‘Neuroendocrine Tumors’, and looked at the established methods for demonstrating heterogeneity, each with its advantages and drawbacks. Also, the available theranostic radiotracers targeting PSMA and somatostatin receptors combined with targeted systemic agents, have been described. Lu-177 labeled PSMA and DOTATATE are the ‘standard of care’ radionuclide therapeutic tracers for management of progressive treatment-resistant prostate cancer and NET. These approved therapies have shown reasonable benefit in treatment outcome, with improvement in quality of life parameters. Various biomarkers and predictors of response to radionuclide therapies targeting TH which are currently available and those which can be explored have been elaborated in details. Imaging-based features using artificial intelligence (AI) need to be developed to further predict the presence of TH. Also, novel theranostic tools binding to newer targets on surface of cancer cell should be explored to overcome the treatment resistance to current treatment regimens.http://www.mdpi.com/journal/cancerspm2021Nuclear Medicin

Inferring causal molecular networks: empirical assessment through a community-based effort

Inferring molecular networks is a central challenge in computational biology. However, it has remained unclear whether causal, rather than merely correlational, relationships can be effectively inferred in complex biological settings. Here we describe the HPN-DREAM network inference challenge that focused on learning causal influences in signaling networks. We used phosphoprotein data from cancer cell lines as well as in silico data from a nonlinear dynamical model. Using the phosphoprotein data, we scored more than 2,000 networks submitted by challenge participants. The networks spanned 32 biological contexts and were scored in terms of causal validity with respect to unseen interventional data. A number of approaches were effective and incorporating known biology was generally advantageous. Additional sub-challenges considered time-course prediction and visualization. Our results constitute the most comprehensive assessment of causal network inference in a mammalian setting carried out to date and suggest that learning causal relationships may be feasible in complex settings such as disease states. Furthermore, our scoring approach provides a practical way to empirically assess the causal validity of inferred molecular networks

Inferring causal molecular networks: empirical assessment through a community-based effort

It remains unclear whether causal, rather than merely correlational, relationships in molecular networks can be inferred in complex biological settings. Here we describe the HPN-DREAM network inference challenge, which focused on learning causal influences in signaling networks. We used phosphoprotein data from cancer cell lines as well as in silico data from a nonlinear dynamical model. Using the phosphoprotein data, we scored more than 2,000 networks submitted by challenge participants. The networks spanned 32 biological contexts and were scored in terms of causal validity with respect to unseen interventional data. A number of approaches were effective, and incorporating known biology was generally advantageous. Additional sub-challenges considered time-course prediction and visualization. Our results suggest that learning causal relationships may be feasible in complex settings such as disease states. Furthermore, our scoring approach provides a practical way to empirically assess inferred molecular networks in a causal sense

Personalized [177Lu]Lutetium-PSMA Therapy for Patients with Pre-Treated Castration-Resistant Prostate Cancer: A Single Institution Experience from a Comprehensive Cancer Centre

Castration resistant prostate cancer (CRPC) is characterized by an aggressive biological behavior with a relatively short survival time, especially in progressive tumors pretreated with new hormonal agents and taxane chemotherapy. [177Lu]-Lutetium-PSMA (Lu-PSMA) treatment has proven efficacy in these patients. However, around 30% of the CRPC patients do not benefit from Lu-PSMA treatment, and little is known about predictive factors for treatment success if Lu-PSMA is offered in an individualized approach based on clinical and laboratory features. In this monocentric retrospective study, 86 CRPC patients receiving Lu-PSMA treatment were evaluated. The focus of the study was to describe clinical factors at baseline and during early treatment that are related to overall survival (OS). In addition, PSMA PET/CT-, PSA-response, and safety and tolerability (CTCAE adverse event reporting) were assessed. Efficacy endpoints were calculated using stratified Kaplan–Meier methods and Cox regression models. Mean applied dose was 17.7 GBq (mean 5.3 ± 1.1 GBq per cycle) with an average of 3.6 (range 1–8) therapy cycles. Patients were followed up for a mean of 12.4 months (range 1–39). The median OS was 15 months (95% CI 12.8–17.2). The best overall response rate in patients assessed with PSMA PET/CT and PSA response was 27.9%, and 50.0% had at least stable disease. Nine patients had a ≥grade 3 adverse event with anemia being the most frequent adverse event. Positive predictors for prolonged OS from baseline parameters were pre-treatment hemoglobin level of ≥10 g/dL and a lower PSA values at treatment start, while the presence of visceral or liver metastases were not significantly associated with worse prognoses in this cohort. With careful patient selection, an individualized Lu-PSMA treatment approach is feasible and patients with dose-limiting factors or visceral metastases should be included in prospective trials

CT Radiomics and Clinical Feature Model to Predict Lymph Node Metastases in Early-Stage Testicular Cancer

Accurate retroperitoneal lymph node metastasis (LNM) prediction in early-stage testicular germ cell tumours (TGCTs) harbours the potential to significantly reduce over- or undertreatment and treatment-related morbidity in this group of young patients as an important survivorship imperative. We investigated the role of computed tomography (CT) radiomics models integrating clinical predictors for the individualised prediction of LNM in early-stage TGCT. Ninety-one patients with surgically proven testicular germ cell tumours and contrast-enhanced CT were included in this retrospective study. Dedicated radiomics software was used to segment 273 retroperitoneal lymph nodes and extract features. After feature selection, radiomics-based machine learning models were developed to predict LN metastasis. The robustness of the procedure was controlled by 10-fold cross-validation. Using multivariable logistic regression modelling, we developed three prediction models: a radiomics-only model, a clinical-only model, and a combined radiomics–clinical model. The models’ performances were evaluated using the area under the receiver operating characteristic curve (AUC). Finally, decision curve analysis was performed to estimate the clinical usefulness of the predictive model. The radiomics-only model for predicting lymph node metastasis reached a greater discrimination power than the clinical-only model, with an AUC of 0.87 (±0.04; 95% CI) vs. 0.75 (±0.08; 95% CI) in our study cohort. The combined model integrating clinical risk factors and selected radiomics features outperformed the clinical-only and the radiomics-only prediction models, and showed good discrimination with an area under the curve of 0.89 (±0.03; 95% CI). The decision curve analysis demonstrated the clinical usefulness of our proposed combined model. The presented combined CT-based radiomics–clinical model represents an exciting non-invasive tool for individualised LN metastasis prediction in testicular germ cell tumours. Multi-centre validation is required to generate high-quality evidence for its clinical application

Radiomics and Clinicopathological Characteristics for Predicting Lymph Node Metastasis in Testicular Cancer

Accurate prediction of lymph node metastasis (LNM) in patients with testicular cancer is highly relevant for treatment decision-making and prognostic evaluation. Our study aimed to develop and validate clinical radiomics models for individual preoperative prediction of LNM in patients with testicular cancer. We enrolled 91 patients with clinicopathologically confirmed early-stage testicular cancer, with disease confined to the testes. We included five significant clinical risk factors (age, preoperative serum tumour markers AFP and B-HCG, histotype and BMI) to build the clinical model. After segmenting 273 retroperitoneal lymph nodes, we then combined the clinical risk factors and lymph node radiomics features to establish combined predictive models using Random Forest (RF), Light Gradient Boosting Machine (LGBM), Support Vector Machine Classifier (SVC), and K-Nearest Neighbours (KNN). Model performance was assessed by the area under the receiver operating characteristic (ROC) curve (AUC). Finally, the decision curve analysis (DCA) was used to evaluate the clinical usefulness. The Random Forest combined clinical lymph node radiomics model with the highest AUC of 0.95 (±0.03 SD; 95% CI) was considered the candidate model with decision curve analysis, demonstrating its usefulness for preoperative prediction in the clinical setting. Our study has identified reliable and predictive machine learning techniques for predicting lymph node metastasis in early-stage testicular cancer. Identifying the most effective machine learning approaches for predictive analysis based on radiomics integrating clinical risk factors can expand the applicability of radiomics in precision oncology and cancer treatment

Inclusion of non-CO2 effects of aviation in the EU ETS: Testing of a Monitoring Reporting & Verification (MRV) Scheme for non-CO2 aviation effects

Darstellung des Projekts "Testing of a Monitoring Reporting & Verification (MRV) Scheme for non-CO2 aviation effect

Xpert bladder cancer monitor to predict the need for a second TURB (MoniTURB trial)

To determine whether Xpert bladder cancer monitor, a noninvasive PCR-based biomarker test, can predict the need for 2nd transurethral resection of the bladder (TURB) better than clinical assessment. Patients scheduled for TURB were prospectively screened. After initial TURB, patients were assigned to 2nd TURB or follow-up cystoscopy at 3 months (FU) by clinicians’ discretion. Central urine cytology and Xpert monitor tests were performed prior to the 1st TURB and 2nd TURB or FU, respectively. Statistical analysis to compare clinical assessment and Xpert monitor comprised sensitivity (SENS), specificity (SPEC), NPV and PPV. Of 756 screened patients, 171 were included (114 with 2nd TURB, 57 with FU). Residual tumors were detected in 34 patients who underwent 2nd TURB, and recurrent tumors were detected in 2 patients with FU. SENS and SPEC of Xpert monitor were 83.3% and 53.0%, respectively, PPV was 32.6% and NPV was 92.1%. Clinical risk assessment outperformed Xpert monitor. In patients with pTa disease at initial TURB, Xpert monitor revealed a NPV of 96%. Xpert monitor was not superior than clinical assessment in predicting the need for 2nd TURB. It might be an option to omit 2nd TURB for selected patients with pTa disease