Colorectal cancer risk stratification on histological slides based on survival curves predicted by deep learning.

Studies have shown that colorectal cancer prognosis can be predicted by deep learning-based analysis of histological tissue sections of the primary tumor. So far, this has been achieved using a binary prediction. Survival curves might contain more detailed information and thus enable a more fine-grained risk prediction. Therefore, we established survival curve-based CRC survival predictors and benchmarked them against standard binary survival predictors, comparing their performance extensively on the clinical high and low risk subsets of one internal and three external cohorts. Survival curve-based risk prediction achieved a very similar risk stratification to binary risk prediction for this task. Exchanging other components of the pipeline, namely input tissue and feature extractor, had largely identical effects on model performance independently of the type of risk prediction. An ensemble of all survival curve-based models exhibited a more robust performance, as did a similar ensemble based on binary risk prediction. Patients could be further stratified within clinical risk groups. However, performance still varied across cohorts, indicating limited generalization of all investigated image analysis pipelines, whereas models using clinical data performed robustly on all cohorts.

Transgenic HPV11-E2 protein modulates URR activity in vivo.

In vitro experiments have shown that the E2 protein of human papillomaviruses (HPV) binds to the upstream regulatory region (URR) of the viral genome and modulates transcription. Additionally, it seems to be a necessary component for viral DNA replication together with E1. We have developed a transgenic mouse model containing the URR region of the low-risk virus HPV11 that regulates the expression of the lacZ reporter gene. Most interestingly, in these mice, the transgene was exclusively expressed in the bulge region of the hair follicle but not in any other tissues. Further experimental data indicate that in double transgenic mice that also express the HPV11-E2 protein under the control of the Ubiquitin C-promoter, the transcription of the reporter gene is modulated. When E2 is present, the expression of the reporter gene also occurs exclusively in the bulge region of the hair follicles as it does in the single transgenic mice, but the expression of the lacZ driven by the URR is increased and the statistical spread is greater. Even if the expression of the reporter gene occurs in the hair follicles of the dorsal skin of an animal uniform, E2 obviously has the capacity for both to induce and to repress the URR activity in vivo.

Drug sensitivity profiling of 3D tumor tissue cultures in the pediatric precision oncology program INFORM.

The international precision oncology program INFORM enrolls relapsed/refractory pediatric cancer patients for comprehensive molecular analysis. We report a two-year pilot study implementing ex vivo drug sensitivity profiling (DSP) using a library of 75-78 clinically relevant drugs. We included 132 viable tumor samples from 35 pediatric oncology centers in seven countries. DSP was conducted on multicellular fresh tumor tissue spheroid cultures in 384-well plates with an overall mean processing time of three weeks. In 89 cases (67%), sufficient viable tissue was received; 69 (78%) passed internal quality controls. The DSP results matched the identified molecular targets, including BRAF, ALK, MET, and TP53 status. Drug vulnerabilities were identified in 80% of cases lacking actionable (very) high-evidence molecular events, adding value to the molecular data. Striking parallels between clinical courses and the DSP results were observed in selected patients. Overall, DSP in clinical real-time is feasible in international multicenter precision oncology programs.

Influenza A virus infection instructs hematopoiesis to megakaryocyte-lineage output.

Respiratory tract infections are among the deadliest communicable diseases worldwide. Severe cases of viral lung infections are often associated with a cytokine storm and alternating platelet numbers. We report that hematopoietic stem and progenitor cells (HSPCs) sense a non-systemic influenza A virus (IAV) infection via inflammatory cytokines. Irrespective of antiviral treatment or vaccination, at a certain threshold of IAV titer in the lung, CD41-positive hematopoietic stem cells (HSCs) enter the cell cycle while endothelial protein C receptor-positive CD41-negative HSCs remain quiescent. Active CD41-positive HSCs represent the source of megakaryocytes, while their multi-lineage reconstitution potential is reduced. This emergency megakaryopoiesis is thrombopoietin independent and attenuated in IAV-infected interleukin-1 receptor-deficient mice. Newly produced platelets during IAV infection are immature and hyper-reactive. After viral clearance, HSC quiescence is re-established. Our study reveals that non-systemic viral respiratory infection has an acute impact on HSCs via inflammatory cytokines to counteract IAV-induced thrombocytopenia.

Inflammatory exposure drives long-lived impairment of hematopoietic stem cell self-renewal activity and accelerated aging.

Hematopoietic stem cells (HSCs) mediate regeneration of the hematopoietic system following injury, such as following infection or inflammation. These challenges impair HSC function, but whether this functional impairment extends beyond the duration of inflammatory exposure is unknown. Unexpectedly, we observed an irreversible depletion of functional HSCs following challenge with inflammation or bacterial infection, with no evidence of any recovery up to 1 year afterward. HSCs from challenged mice demonstrated multiple cellular and molecular features of accelerated aging and developed clinically relevant blood and bone marrow phenotypes not normally observed in aged laboratory mice but commonly seen in elderly humans. In vivo HSC self-renewal divisions were absent or extremely rare during both challenge and recovery periods. The progressive, irreversible attrition of HSC function demonstrates that temporally discrete inflammatory events elicit a cumulative inhibitory effect on HSCs. This work positions early/mid-life inflammation as a mediator of lifelong defects in tissue maintenance and regeneration.

Biosensor for deconvolution of individual cell fate in response to ion beam irradiation.

Clonogenic survival assay constitutes the gold standard method for quantifying radiobiological effects. However, it neglects cellular radiation response variability and heterogeneous energy deposition by ion beams on the microscopic scale. We introduce "Cell-Fit-HD4D" a biosensor that enables a deconvolution of individual cell fate in response to the microscopic energy deposition as visualized by optical microscopy. Cell-Fit-HD4D enables single-cell dosimetry in clinically relevant complex radiation fields by correlating microscopic beam parameters with biological endpoints. Decrypting the ion beam's energy deposition and molecular effects at the single-cell level has the potential to improve our understanding of radiobiological dose concepts as well as radiobiological study approaches in general.

Predictive modeling of resistance to SMO inhibition in a patient-derived orthotopic xenograft model of SHH medulloblastoma.

Background: Inhibition of the sonic hedgehog (SHH) pathway with Smoothened (SMO) inhibitors is a promising treatment strategy in SHH-activated medulloblastoma, especially in adult patients. However, the problem is that tumors frequently acquire resistance to the treatment. To understand the underlying resistance mechanisms and to find ways to overcome the resistance, preclinical models that became resistant to SMO inhibition are needed. Methods: To induce SMO inhibitor resistant tumors, we have treated a patient-derived xenograft (PDX) model of SHH medulloblastoma, sensitive to SMO inhibition, with 20 mg/kg Sonidegib using an intermitted treatment schedule. Vehicle-treated and resistant models were subjected to whole-genome and RNA sequencing for molecular characterization and target engagement. In vitro drug screens (76 drugs) were performed using Sonidegib-sensitive and -resistant lines to find other drugs to target the resistant lines. One of the top hits was then validated in vivo. Results: Nine independent Sonidegib-resistant PDX lines were generated. Molecular characterization of the resistant models showed that eight models developed missense mutations in SMO and one gained an inactivating point mutation in MEGF8, which acts downstream of SMO as a repressor in the SHH pathway. The in vitro drug screen with Sonidegib-sensitive and -resistant lines identified good efficacy for Selinexor in the resistant line. Indeed, in vivo treatment with Selinexor revealed that it is more effective in resistant than in sensitive models. Conclusions: We report the first human SMO inhibitor resistant medulloblastoma PDX models, which can be used for further preclinical experiments to develop the best strategies to overcome the resistance to SMO inhibitors in patients.

Deep learning can predict lymph node status directly from histology in colorectal cancer.

BACKGROUND: Lymph node status is a prognostic marker and strongly influences therapeutic decisions in colorectal cancer (CRC). OBJECTIVES: The objective of the study is to investigate whether image features extracted by a deep learning model from routine histological slides and/or clinical data can be used to predict CRC lymph node metastasis (LNM). METHODS: Using histological whole slide images (WSIs) of primary tumours of 2431 patients in the DACHS cohort, we trained a convolutional neural network to predict LNM. In parallel, we used clinical data derived from the same cases in logistic regression analyses. Subsequently, the slide-based artificial intelligence predictor (SBAIP) score was included in the regression. WSIs and data from 582 patients of the TCGA cohort were used as the external test set. RESULTS: On the internal test set, the SBAIP achieved an area under receiver operating characteristic (AUROC) of 71.0%, the clinical classifier achieved an AUROC of 67.0% and a combination of the two classifiers yielded an improvement to 74.1%. Whereas the clinical classifier's performance remained stable on the TCGA set, performance of the SBAIP dropped to an AUROC of 61.2%. Performance of the clinical classifier depended strongly on the T stage. CONCLUSION: Deep learning-based image analysis may help predict LNM of patients with CRC using routine histological slides. Combination with clinical data such as T stage might be useful. Strategies to increase performance of the SBAIP on external images should be investigated.

Predicting the Risk of Metastases by PSMA-PET/CT-Evaluation of 335 Men with Treatment-Naïve Prostate Carcinoma.

Men diagnosed with aggressive prostate cancer are at high risk of local relapse or systemic progression after definitive treatment. Treatment intensification is highly needed for that patient cohort; however, no relevant stratification tool has been implemented into the clinical work routine so far. Therefore, the aim of the current study was to analyze the role of initial PSMA-PET/CT as a prediction tool for metastases. In total, 335 men with biopsy-proven prostate carcinoma and PSMA-PET/CT for primary staging were enrolled in the present, retrospective study. The number and site of metastases were analyzed and correlated with the maximum standardized uptake value (SUVmax) of the intraprostatic, malignant lesion. Receiver operating characteristic (ROC) curves were used to determine sensitivity and specificity and a model was created using multiple logistic regression. PSMA-PET/CT detected 171 metastases with PSMA-uptake in 82 patients. A statistically significant higher SUVmax was found for men with metastatic disease than for the cohort without distant metastases (median 16.1 vs. 11.2; p < 0.001). The area under the curve (AUC) in regard to predicting the presence of any metastases was 0.65. Choosing a cut-off value of 11.9 for SUVmax, a sensitivity and specificity (factor 1:1) of 76.0% and 58.4% was obtained. The current study confirms, that initial PSMA-PET/CT is able to detect a relatively high number of treatment-naïve men with metastatic prostate carcinoma. Intraprostatic SUVmax seems to be a promising parameter for the prediction of distant disease and could be used for treatment stratification-aspects which should be verified within prospective trials.

Efficacy and Safety of Checkpoint Inhibitor Treatment in Patients with Advanced Renal or Urothelial Cell Carcinoma and Concomitant Chronic Kidney Disease: A Retrospective Cohort Study.

Background: Checkpoint inhibitors are a standard of care in the treatment of advanced renal cell carcinoma (RCC) and urothelial carcinoma (UC). Patients with these tumors often suffer from concomitant chronic kidney disease (CKD). Limited data are available on the efficacy and toxicity of checkpoint inhibitors in patients with CKD. Methods: We retrospectively analyzed 126 patients who received checkpoint inhibitors for RCC (n = 85) or UC (n = 41) and analyzed the frequency of treatment- and immune-related adverse events (AEs). We performed a multivariate analysis to determine progression-free survival (PFS) and overall survival (OS). Results: A total of 38.9% of patients had CKD. Frequencies of general AEs (49.0% in CKD vs. 48.1%, p > 0.99999) and immune-related AEs (28.6 vs. 24.7%, p ≥ 0.9999) did not significantly differ between the groups. There was no difference in PFS for patients with RCC or UC and CKD or without CKD (RCC: 6.81 vs. 7.54 months, HR 1.000 (95%CI 0.548-01.822), p = 0.999; UC:2.33 vs. 3.67 months, HR 01.492 (95%CI 0.686-3.247), p = 0.431). CKD appeared to be a potential effect modifier for OS in both RCC and UC (RCC: NR vs. 23.9 months, HR 0.502 (95%CI 0.219-1.152), p = 0.104; UC:18.84 vs. 15.42 months, HR 0.656 (95%CI 0.296-1.454), p = 0.299). Conclusions: Checkpoint inhibitor treatment in our cohort of patients with CKD was as safe and efficient as in the cohort of patients without CKD.

Performance of [68Ga]Ga-PSMA-11 PET/CT in patients with recurrent prostate cancer after prostatectomy-a multi-centre evaluation of 2533 patients.

PURPOSE: To evaluate the performance of [68Ga]Ga-PSMA-11 PET/CT in the diagnosis of recurrent prostate cancer (PC) after prostatectomy in a large multicentre cohort. METHODS: The centres, which contributed to this study, were the departments of nuclear medicine of Heidelberg (Germany), Technical University of Munich (Germany) and Albert Einstein Hospital of São Paulo (Brazil). A total of 2533 patients who were scanned with [68Ga]Ga-PSMA-11 PET/CT at 1 h p.i. due to recurrent PC after prostatectomy were included in this retrospective analysis. Exclusion criteria were as follows: patients with untreated primary tumour, previous chemotherapy or Xofigo®; those previously treated with exclusively external beam radiation therapy or HIFU; those referred for PSMA-therapy; and those treated with ADT (including first- and second-generation ADT) within the last 6 months. Potential influences of different factors such as PSA level, PSA doubling-time (PSADT), PSA velocity (PSAVel), Gleason Score (GSC, including the separate analysis of 7a and 7b), age and amount of injected tracer were evaluated in a multivariable analysis. RESULTS: The rate of pathologic PET/CT-scans was 43% for PSA ≤ 0.2 ng/ml, 58% for PSA > 0.2 to ≤ 0.5, 72% for PSA > 0.5 to ≤ 1.0 and increased to a maximum of 93% for PSA > 10 ng/ml. A pathological PET/CT was significantly (p = 0.001) associated with PSA level and higher GSC. Amount of injected tracer, age, PSADT and PSAVel were not associated with a higher probability of a pathological scan. CONCLUSION: [68Ga]Ga-PSMA-11 PET/CT at 1 h p.i. confirmed its high performance in the largest patient cohort yet analysed. Tumour detection showed a clear association with higher PSA and higher GSC. No association was found between a pathological [68Ga]Ga-PSMA-11 PET/CT and age, amount of injected tracer, PSADT or PSAVel.

Potential therapeutic effect of low-dose paclitaxel in melanoma patients resistant to immune checkpoint blockade: A pilot study.

The low dose application of chemotherapeutic agents such as paclitaxel was previously shown to initiate anti-tumor activity by neutralizing myeloid-derived suppressor cells (MDSCs) in melanoma mouse models. Here, we investigated immunomodulating effects of low-dose paclitaxel in 9 metastatic melanoma patients resistant to prior treatments. Three patients showed response to therapy (two partial responses and one stable disease). In responding patients, paclitaxel decreased the frequency and immunosuppressive pattern of MDSCs in the peripheral blood and skin metastases. Furthermore, paclitaxel modulated levels of inflammatory mediators in the serum. In addition, responders displayed enhanced frequencies of tumor-infiltrating CD8+ T cells and their activity indicated by the upregulation of CD25 and TCR ζ-chain expression. Our study suggests that low-dose paclitaxel treatment could improve clinical outcome of some advanced melanoma patients by enhancing anti-tumor immunity and might be proposed for combined melanoma immunotherapy.

Diagnostic Accuracy of 18F-PSMA-1007 PET/CT Imaging for Lymph Node Staging of Prostate Carcinoma in Primary and Biochemical Recurrence.

Prostate-specific membrane antigen (PSMA)-ligand PET/CT is performed on patients with prostate cancer to stage the disease initially or to identify sites of recurrence after definitive therapy. On the basis of clinical results, 18F-PSMA-1007 is a promising PSMA PET tracer, but detailed histologic confirmation has been lacking. Methods: Ninety-six patients with prostate cancer underwent 18F-PSMA-1007 PET/CT followed by either radical prostatectomy with lymphadenectomy or salvage lymphadenectomy. The histologic findings of PSMA PET-positive nodes were analyzed retrospectively. A lesion-based and patient-based analysis was performed comparing all positive lesions and only lesions larger than 3 mm on histopathology. Results: Of the patients, 90.6% received 18F-PSMA-1007 PET/CT for staging before the primary treatment, whereas 9.4% underwent imaging for biochemical recurrence. In 34.4% of the cohort, positive lymph nodes were present on imaging. In total, 1,746 lymph nodes were dissected in 96 patients. 18F-PSMA-1007 PET had a lesion-based sensitivity of 81.7%, a specificity of 99.6%, a positive predictive value of 92.4%, and a negative predictive value of 98.9% for detecting positive lymph nodes larger than 3 mm. In the analysis of all malignant nodes regardless of size, the overall sensitivity, specificity, positive predictive value, and negative predictive value on lesion-based analysis were 71.2%, 99.5%, 91.3%, and 97.9%, respectively. The patient-based analysis showed a sensitivity of 85.9% and a specificity of 99.5% for lymph nodes larger than 3 mm. Conclusion:18F-PSMA-1007 PET/CT reliably detects malignant lymph nodes and has an exceptional specificity of more than 99% for nodal metastases.

The design heatmap: A simple visualization of D -optimality design problems.

Optimal experimental designs are often formal and specific, and not intuitively plausible to practical experimenters. However, even in theory, there often are many different possible design points providing identical or nearly identical information compared to the design points of a strictly optimal design. In practical applications, this can be used to find designs that are a compromise between mathematical optimality and practical requirements, including preferences of experimenters. For this purpose, we propose a derivative-based two-dimensional graphical representation of the design space that, given any optimal design is already known, will show which areas of the design space are relevant for good designs and how these areas relate to each other. While existing equivalence theorems already allow such an illustration in regard to the relevance of design points only, our approach also shows whether different design points contribute the same kind of information, and thus allows tweaking of designs for practical applications, especially in regard to the splitting and combining of design points. We demonstrate the approach on a toxicological trial where a D -optimal design for a dose-response experiment modeled by a four-parameter log-logistic function was requested. As these designs require a prior estimate of the relevant parameters, which is difficult to obtain in a practical situation, we also discuss an adaption of our representations to the criterion of Bayesian D -optimality. While we focus on D -optimality, the approach is in principle applicable to different optimality criteria as well. However, much of the computational and graphical simplicity will be lost.

Drawing statistical conclusions from experiments with multiple quantitative measurements per subject.

In experiments with multiple quantitative measurements per subject, for example measurements on multiple lesions per patient, the additional measurements on the same patient provide limited additional information. Treating these measurements as independent observations will produce biased estimators for standard deviations and confidence intervals, and increases the risk of false positives in statistical tests. The problem can be remedied in a simple way by first taking the average of all observations of each specific patient, and then doing all further calculations only on the list of these patient means. A more sophisticated statistical modeling of the experiment, for example in a linear mixed model, is only required if (i) there is a large imbalance in the number of observations per patient or (ii) there is a specific interest in actually identifying the various sources of variation in the experiment.

Artificial Intelligence in Skin Cancer Diagnostics: The Patients' Perspective.

Background: Artificial intelligence (AI) has shown promise in numerous experimental studies, particularly in skin cancer diagnostics. Translation of these findings into the clinic is the logical next step. This translation can only be successful if patients' concerns and questions are addressed suitably. We therefore conducted a survey to evaluate the patients' view of artificial intelligence in melanoma diagnostics in Germany, with a particular focus on patients with a history of melanoma. Participants and Methods: A web-based questionnaire was designed using LimeSurvey, sent by e-mail to university hospitals and melanoma support groups and advertised on social media. The anonymous questionnaire evaluated patients' expectations and concerns toward artificial intelligence in general as well as their attitudes toward different application scenarios. Descriptive analysis was performed with expression of categorical variables as percentages and 95% confidence intervals. Statistical tests were performed to investigate associations between sociodemographic data and selected items of the questionnaire. Results: 298 individuals (154 with a melanoma diagnosis, 143 without) responded to the questionnaire. About 94% [95% CI = 0.91-0.97] of respondents supported the use of artificial intelligence in medical approaches. 88% [95% CI = 0.85-0.92] would even make their own health data anonymously available for the further development of AI-based applications in medicine. Only 41% [95% CI = 0.35-0.46] of respondents were amenable to the use of artificial intelligence as stand-alone system, 94% [95% CI = 0.92-0.97] to its use as assistance system for physicians. In sub-group analyses, only minor differences were detectable. Respondents with a previous history of melanoma were more amenable to the use of AI applications for early detection even at home. They would prefer an application scenario where physician and AI classify the lesions independently. With respect to AI-based applications in medicine, patients were concerned about insufficient data protection, impersonality and susceptibility to errors, but expected faster, more precise and unbiased diagnostics, less diagnostic errors and support for physicians. Conclusions: The vast majority of participants exhibited a positive attitude toward the use of artificial intelligence in melanoma diagnostics, especially as an assistance system.

Multiplex quantitation of 270 plasma protein markers to identify a signature for early detection of colorectal cancer.

Blood-based protein biomarker signatures might be an alternative or supplement to existing methods for early detection of colorectal cancer (CRC) for population-based screening. The objective of this study was to derive a protein biomarker signature for early detection of CRC and its precursor advanced adenoma (AA). In a two-stage design, 270 protein markers were measured by liquid chromatography/multiple reaction monitoring/mass spectrometry in plasma samples of discovery and validation sets. In the discovery set consisting of 100 newly diagnosed CRC cases and 100 age- and sex-matched controls free of neoplasm at screening colonoscopy, the algorithms predicting the presence of early- or late-stage CRC were derived by Lasso regression and .632 + bootstrap. The prediction algorithms were then externally validated in an independent validation set consisting of participants of screening colonoscopy including 56 participants with CRC, 99 with AA and 99 controls without any colorectal neoplasms. Three different signatures for all-, early- and late-stage CRC consisting of five-, three- and eight-protein markers were obtained in the discovery set with areas under the curves (AUCs) after .632 + bootstrap adjustment of 0.85, 0.83 and 0.96, respectively. External validation in the representative screening population yielded AUCs of 0.79 (95% CI, 0.70-0.86), 0.79 (95% CI, 0.66-0.89) and 0.80 (95% CI, 0.70-0.89) for all-, early- and late-stage CRCs, respectively. The three-marker early-stage algorithm yielded an AUC of 0.65 (95% CI, 0.56-0.73) for detection of AA in the validation set. Although not yet competitive with available stool-based tests for CRC early detection, the identified proteins may contribute to the development of powerful blood-based tests for early detection of CRC and its precursors AAs.

Lymph Node Involvement in Treatment-Naïve Prostate Cancer Patients: Correlation of PSMA PET/CT Imaging and Roach Formula in 280 Men in Radiotherapeutic Management.

The importance of prostate-specific membrane antigen (PSMA) PET/CT for primary staging of treatment-naïve prostate cancer patients is still under debate. Therefore, the present study aimed to evaluate the role of PSMA PET/CT in detecting nodal metastases in a large cohort of men and compare imaging results with the risk of lymph node involvement based on the Roach formula. Methods: In total, 280 men with newly diagnosed prostate carcinoma were included in the present study. For all patients, PSMA PET/CT was performed for primary staging. Median age was 67 y (range, 38-84 y), and 84% of all patients were classified as high-risk according to the d'Amico criteria. The risk of lymph node involvement was calculated using the Roach formula and compared with the PSMA PET/CT results. Results: PSMA-positive nodes were detected in 90 of 280 men (32.1%). Although most nodal metastases occurred within the pelvis, 36.0% were in extrapelvic sites. In 9 patients (3.2%), nodal metastases occurred in the Virchow node. After comparison of PSMA data with the results of the Roach formula, an area under the curve of 0.781 was obtained for the Roach predictions. Conclusion: For treatment-naïve prostate cancer patients, PSMA PET/CT is well suited for the detection of nodal metastases. However, the original Roach formula can still be used for a quick assessment of potential lymphatic spread in daily clinical routine.

Modeling dose-response functions for combination treatments with log-logistic or Weibull functions.

Dose-response curves of new substances in toxicology and related areas are commonly fitted using log-logistic functions. In more advanced studies, an additional interest is often how these substances will behave when applied in combination with a second substance. Here, an essential question for both design and analysis of these combination experiments is whether the resulting dose-response function will still be a member of the class of log-logistic functions, and, if so, what function parameters will result for the combined substances. Different scenarios might be considered in regard to whether a true interaction between the substances is expected, or whether the combination will simply be additive. In this paper, it is shown that the resulting function will in general not be a log-logistic function, but can be approximated very closely with one. Parameters for this approximation can be predicted from the parameters of both ingredients. Furthermore, some simple interaction structures can still be represented with a single log-logistic function. The approach can also be applied to Weibull-type dose-response functions, and similar results are obtained. Finally, the results were applied to a real data set obtained from cell culture experiments involving two cancer treatments, and the dose-response curve of a combination treatment was predicted from the properties of the singular substances.