Systematic analysis of cell-intrinsic and extrinsic factors in chronic lymphocytic leukemia to understand functional consequences for drug response and clinical outcome

Chronic lymphocytic leukemia (CLL) is an indolent B-cell malignancy with a very heterogeneous clinical course. Even though many aspects of the biology of CLL have been thoroughly described, the underlying molecular cause for this heterogeneity has still not been completely understood. To fill this gap, this thesis presents a comprehensive analysis of cancer cell-intrinsic and extrinsic factors which modify drug response phenotypes and patient outcome in a cohort of 81 primary CLL patient samples. Some cancer cell-intrinsic factors, like the genome or transcriptome of CLL, have been comprehensively explored. However, proteomic profiling of a large CLL patient cohort and its integration with other molecular layers is currently lacking. Therefore, this study performed a thorough characterisation of multiple CLL cell-intrinsic factors, including the proteome, the transcriptome and the genome. These were additionally linked to ex-vivo drug response profiles (43 drugs). This revealed associations between the different layers and functional consequences for drug response and clinical outcome. nsupervised clustering of protein levels uncovered a previously unappreciated poor prognosis CLL subgroup, which was independent of established risk factors and characterised by a distinct protein and drug response profile. The existence of this subgroup could be validated in an external cohort. This comprehensive multi-omics analysis represents the first proteogenomic study of a large CLL patient cohort. CLL cells additionally depend on cell-extrinsic signals provided by the microenvironment, such as the bone marrow niche. Such signals can modify and reduce the activity of selected drugs. However, a systematic analysis of how the bone marrow microenvironment influences drug response and resistance is lacking, because appropriate bone marrow model systems for high-throughput drug screening do currently not exist. To this end, a high-throughput co-culture drug-sensitivity testing platform was established. During the careful evaluation of different stroma cells as CLL cell support for the system, an unexpected phenomenon was discovered. Some bone marrow stroma cells had the ability to phagocytose apoptotic cells in large amounts. Phagocytosis decreased the total amount of cells and, thus, artificially increased the percentage of alive cells. This has implications for co-culture studies in general, as phagocytosis can cause a systematic bias and the misinterpretation of results if left unconsidered. Consequently, nonphagocytic stroma cells were chosen for the final screening platform. Using this optimised system, responses to 43 different drugs were measured. A linear model was employed to distinguish between the effect of stroma cells on spontaneous and on druginduced apoptosis of CLL cells. In accordance with the literature, stroma cells protected CLL cells from spontaneous apoptosis ex-vivo. Interestingly, effect sizes varied between patients and especially samples with unmutated immunoglobulin heavy chain variable region and high degrees of spontaneous apoptosis profited from co-culturing. Moreover, the influence of stroma cells on drug responses was systematically assessed. While some drugs, like chemotherapeutics, were less active in co-cultures, other drugs had unchanged activity or were even more effcient in the context of stroma cells. Especially Janus kinase inhibitors could overcome the protective effect by stroma cells and kill CLL cells despite the presence of stroma. The systematic analysis of the impact of the bone marrow niche on drug response can help to understand and overcome microenvironment-induced resistances. In conclusions, this thesis provides a systematic overview of how leukemia cell-intrinsic layers of CLL and the microenvironment determine drug response and patient outcome

A machine learning study analyzing the association between patient-, tooth- and treatment-level factors on the outcome of root canal therapy

Zielsetzung: Das Erkennen und Bewerten von Risikofaktoren einer Wurzelkanalbehandlung (WKB) stellt einen entscheidenden Schritt bei der Therapieplanung zahnärztlicher Behandler*innen dar. Das maschinelle Lernen (ML) fand innerhalb der letzten Dekade vermehrt auch Anwendung in der Zahnmedizin. Das Ziel unserer Studie war es zum einen, Zusammenhänge zwischen zahn-, patienten- und behandlungsspezifischen Faktoren und der Prognose einer WKB zu detektieren und folgend ein Faktorgewichtungsranking zu erstellen; zum anderen sollte analysiert werden, inwiefern durch die Anwendung komplexer ML Modelle Vorhersagen über den Erfolg oder Misserfolg einer WKB möglich werden. Methodik: Es wurden Patientenfälle untersucht, die im Zeitraum von 2016 bis 2020 eine WKB am CharitéCentrum 03 für Zahn-, Mund- und Kieferheilkunde erhalten hatten und mindestens sechs Monate nachuntersucht worden sind. Bei der Analyse wurden sowohl Patientendaten als auch Röntgenbilder bewertet und nach zuvor festgelegten Kriterien ausgewertet. Ein Misserfolg der Behandlung war definiert durch das Bestehen klinischer Symptome und/oder radiologischer Auffälligkeiten. Mithilfe einer logistischen Regression (logR) wurden Zusammenhänge zwischen den einzelnen Faktoren detektiert. Neben der logR sollten komplexere ML Modelle (Random Forest (RF), Gradient Boosting Machine (GBM), Extreme Gradient Boosting (XGB)) eingesetzt werden, um Vorhersagen über den Erfolg oder Misserfolg einer WKB in einem Testdatensatz zu treffen. Zur Bewertung der Vorhersagegüte wurden ROC-Kurven (Receiver Operating Characteristic) und AUC-Werte (Area under the curve) eingesetzt. Anschließend wurde auf Basis der relativen Faktorgewichtung der einzelnen Modelle eine modellübergreifende Rangfolge der Faktoren erstellt, um deren Wichtigkeit für die Vorhersage zu beschreiben. Ergebnisse: Insgesamt wurden 591 Zähne von 458 Patient*innen (weiblich n = 216 (47,2%), männlich n = 242 (52,8%)) analysiert. Die Gesamterfolgsrate aller Behandlungen betrug 79,5%. LogR zeigte, dass vor allem zahnbezogene Faktoren einen signifikanten Einfluss auf den Ausgang einer WKB hatten. Die wichtigsten Variablen waren hierbei ein schwerer alveolärer Knochenabbau von 66-100% (OR 6,48; 95% CI [2,86; 14,89], p<0,001) und ein erhöhter Periapikaler Index von größer oder gleich 4 (OR 4,59 [2,44; 8,79], p<0,001). Misserfolge waren auch für Revisionstherapien signifikant häufiger (OR 1,77 [1,01; 2,86], p<0,01). Bei den patientenbezogenen Faktoren war lediglich das Rauchen mit einem Misserfolg einer WKB assoziiert (OR 2,05 [1,18; 3,53], p<0,05). Die Vorhersagegüte der verschiedenen ML Modelle blieb insgesamt stark begrenzt (ROCAUC: logR 0,63 [0,53; 0,73]; GBM 0,59 [0,50; 0,68]; RF 0,59 [0,50; 0,68]; XGB 0,60 [0,50; 0,70]). Schlussfolgerungen: Misserfolge einer WKB waren primär mit zahnbezogenen Faktoren assoziiert. Vorhersagen über den Ausgang einer Behandlung waren auch mit komplexeren ML Modellen nur eingeschränkt möglich.Objective: Identifying potential risk factors of a root canal treatment (RCT) is a crucial step in endodontic treatment planning. Machine learning (ML) was found beneficial for health care applications in recent years; it has also been applied in dentistry. We intended to detect tooth-, patient- and treatment-level covariates associated with the outcome of endodontic therapy, and rank them according to their importance. Additionally, we aimed to apply ML for predicting the outcome of RCT. Methods: We analyzed patients who received one or more RCT with at least six months follow-up at the Charité Dental Clinic between 2016 and 2020. To derive covariates, patient data including medical history and treatment protocols as well as periapical radiographs were employed. Failure was defined as persistent clinical symptoms and/or radiographical signs of persisting or progressing apical periodontitis. By using logistic regression (logR) on the full data set we analyzed associations between covariates and outcomes. LogR and more complex ML models (Random Forest (RF), Gradient Boosting Machine (GBM), Extreme Gradient Boosting (XGB)) were then trained and their performance for predicting success or failure of root canal therapy assessed on a separate test data. ROC (Receiver Operating Characteristic) curves and AUC (Area under the curve) values were employed to evaluate the predictive performance. Mean rank values were calculated to construct a ranking showing the relative importance of each factor. Results: A total of 591 teeth from 458 patients (female n = 216 (47.2%), male n = 242 (52.8%)) were examined. The overall success rate of root canal treatments was 79.5%. LogR showed that tooth-related covariates were significantly associated with the outcome of root canal therapy, with severe alveolar bone loss (ABL 66-100%) (OR 6.48, 95% CI [2.86, 14.89], p<0.001) and a PAI-Score higher or equal to 4 (OR 4.59, 95% CI [2.44, 8.79], p<0.001) increasing the risk of failure. Retreatments showed similarly increased risks (OR 1.77, 95% CI [1.01, 2.86], p<0.01) and smoking was significantly associated with failure on patient-level (OR 2.05, 95% CI [1.18, 3.53], p<0.05). The predictive performance of all ML models was limited (ROCAUC: logR 0.63 [0.53, 0.73]; GBM 0.59 [0.50, 0.68]; RF 0.59 [0.50, 0.68]; XGB 0.60 [0.50, 0.70]). Conclusions: Failure of root canal therapy was primarily associated with tooth-related factors. In general, predicting the outcome of RCT with ML models was only limitedly possible

The Performance of Health Workers

Washington DCsch_iihpub3186pu

Proteogenomics refines the molecular classification of chronic lymphocytic leukemia

Cancer heterogeneity at the proteome level may explain differences in therapy response and prognosis beyond the currently established genomic and transcriptomic-based diagnostics. The relevance of proteomics for disease classifications remains to be established in clinically heterogeneous cancer entities such as chronic lymphocytic leukemia (CLL). Here, we characterize the proteome and transcriptome alongside genetic and ex-vivo drug response profiling in a clinically annotated CLL discovery cohort (n = 68). Unsupervised clustering of the proteome data reveals six subgroups. Five of these proteomic groups are associated with genetic features, while one group is only detectable at the proteome level. This new group is characterized by accelerated disease progression, high spliceosomal protein abundances associated with aberrant splicing, and low B cell receptor signaling protein abundances (ASB-CLL). Classifiers developed to identify ASB-CLL based on its characteristic proteome or splicing signature in two independent cohorts (n = 165, n = 169) confirm that ASB-CLL comprises about 20% of CLL patients. The inferior overall survival in ASB-CLL is also independent of both TP53- and IGHV mutation status. Our multi-omics analysis refines the classification of CLL and highlights the potential of proteomics to improve cancer patient stratification beyond genetic and transcriptomic profiling

Human resources for health interventions in high- and middle-income countries: Findings of an evidence review

Sophie Witter - ORCID: 0000-0002-7656-6188 https://orcid.org/0000-0002-7656-6188Many high- and middle-income countries face challenges in developing and maintaining a health workforce which can address changing population health needs. They have experimented with interventions which overlap with but have differences to those documented in low- and middle-income countries, where many of the recent literature reviews were undertaken. The aim of this paper is to fill that gap. It examines published and grey evidence on interventions to train, recruit, retain, distribute, and manage an effective health workforce, focusing on physicians, nurses, and allied health professionals in high- and middle-income countries. A search of databases, websites, and relevant references was carried out in March 2019. One hundred thirty-one reports or papers were selected for extraction, using a template which followed a health labor market structure. Many studies were cross-cutting; however, the largest number of country studies was focused on Canada, Australia, and the United States of America. The studies were relatively balanced across occupational groups. The largest number focused on availability, followed by performance and then distribution. Study numbers peaked in 2013–2016. A range of study types was included, with a high number of descriptive studies. Some topics were more deeply documented than others—there is, for example, a large number of studies on human resources for health (HRH) planning, educational interventions, and policies to reduce in-migration, but much less on topics such as HRH financing and task shifting. It is also evident that some policy actions may address more than one area of challenge, but equally that some policy actions may have conflicting results for different challenges. Although some of the interventions have been more used and documented in relation to specific cadres, many of the lessons appear to apply across them, with tailoring required to reflect individuals’ characteristics, such as age, location, and preferences. Useful lessons can be learned from these higher-income settings for low- and middle-income settings. Much of the literature is descriptive, rather than evaluative, reflecting the organic way in which many HRH reforms are introduced. A more rigorous approach to testing HRH interventions is recommended to improve the evidence in this area of health systems strengthening.This work was supported by the Saudi Health Council and World Bank.https://doi.org/10.1186/s12960-020-00484-w18pubpu

The Performance of Health Workers

Washington DCsch_iihpub3186pu

How to attract and retain health workers in rural areas of a fragile state: Findings from a labour market survey in Guinea

From PLOS via Jisc Publications RouterSophie Witter - ORCID: 0000-0002-7656-6188 https://orcid.org/0000-0002-7656-6188Most countries face challenges attracting and retaining health staff in remote areas but this is especially acute in fragile and shock-prone contexts, like Guinea, where imbalances in staffing are high and financial and governance arrangements to address rural shortfalls are weak. The objective of this study was to understand how health staff could be better motivated to work and remain in rural, under-served areas in Guinea. In order to inform the policy dialogue on strengthening human resources for health, we conducted three nationally representative cross-sectional surveys, adapted from tools used in other fragile contexts. This article focuses on the health worker survey. We found that the locational job preferences of health workers in Guinea are particularly influenced by opportunities for training, working conditions, and housing. Most staff are satisfied with their work and with supervision, however, financial aspects and working conditions are considered least satisfactory, and worrying findings include the high proportion of staff favouring emigration, their high tolerance of informal user payments, as well as their limited exposure to rural areas during training. Based on our findings, we highlight measures which could improve rural recruitment and retention in Guinea and similar settings. These include offering upgrading and specialization in return for rural service; providing greater exposure to rural areas during training; increasing recruitment from rural areas; experimenting with fixed term contracts in rural areas; and improving working conditions in rural posts. The development of incentive packages should be accompanied by action to tackle wider issues, such as reforms to training and staff management.Funder: World Bank Group; funder-id: http://dx.doi.org/10.13039/10000442116pubpub1

Comparing the value of mono- vs coculture for high-throughput compound screening in hematological malignancies

Large-scale compound screens are a powerful model system for understanding variability of treatment response and discovering druggable tumor vulnerabilities of hematological malignancies. However, as mostly performed in a monoculture of tumor cells, these assays disregard modulatory effects of the in vivo microenvironment. It is an open question whether and to what extent coculture with bone marrow stromal cells could improve the biological relevance of drug testing assays over monoculture. Here, we established a high-throughput platform to measure ex vivo sensitivity of 108 primary blood cancer samples to 50 drugs in monoculture and coculture with bone marrow stromal cells. Stromal coculture conferred resistance to 52% of compounds in chronic lymphocytic leukemia (CLL) and 36% of compounds in acute myeloid leukemia (AML), including chemotherapeutics, B-cell receptor inhibitors, proteasome inhibitors, and Bromodomain and extraterminal domain inhibitors. Only the JAK inhibitors ruxolitinib and tofacitinib exhibited increased efficacy in AML and CLL stromal coculture. We further confirmed the importance of JAK-STAT signaling for stroma-mediated resistance by showing that stromal cells induce phosphorylation of STAT3 in CLL cells. We genetically characterized the 108 cancer samples and found that drug-gene associations strongly correlated between monoculture and coculture. However, effect sizes were lower in coculture, with more drug-gene associations detected in monoculture than in coculture. Our results justify a 2-step strategy for drug perturbation testing, with large-scale screening performed in monoculture, followed by focused evaluation of potential stroma-mediated resistances in coculture

Individual performance-based incentives for health care workers in organization for economic co-operation and development member countries: A systematic literature review

Sophie Witter - ORCID: 0000-0002-7656-6188 https://orcid.org/0000-0002-7656-6188In response to rising costs and growing concerns about safety, quality, equity and affordability of health care, many countries have now developed and deployed performance-based incentives, targeted at facilities as well as individuals. Evidence of the effect of these efforts has been mixed; it remains unclear how effective strategies of varying design and magnitude (relative to provider salary) are at incentivizing individual-level performance. This study reviews the current evidence on effectiveness of individual-level performance-based incentives for health care in organization for Economic Co-operation and Development countries, which are relatively well situated to implement, monitor and evaluate performance-based incentives programs. We delineate the conditions under which sanctions or rewards – in the context of gain-seeking, loss aversion, and increased social pressure to modify behaviors – may be more effective. We find that programs that utilized positive reinforcement methods are most commonly observed – with slightly more overall bonus incentives than payment per output or outcome achieved incentives. When comparing the outcomes from negative reinforcement methods with positive reinforcement methods, we found more evidence that positive reinforcement methods are effective at improving health care worker performance. Overall, just over half of the studies reported positive impacts, indicating the need for care in designing and adopting performance-based incentives programs.This work was supported by the Saudi Health Council and World Bank. Financing for the analysis was provided by the Saudi Health Council and the Health, Nutrition and Population Reimbursable Advisory Services Program (P172148)between the World Bank and the Ministry of Finance in Saudi Arabia.https://doi.org/10.1016/j.healthpol.2022.03.016aheadofprintaheadofprin