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Introduction The bursa subacromialis (BS) provides the gliding mechanism of the shoulder and regenerates itself after surgical removal. Therefore, we explored the presence of mesenchymal stem cells (MSCs) within the human adult BS tissue and characterized the BS cells compared to MSCs from bone marrow (BMSCs) on a molecular level. Methods BS cells were isolated by collagenase digest from BS tissues derived from patients with degenerative rotator cuff tears, and BMSCs were recovered by adherent culture from bone-marrow of patients with osteoarthritis of the hip. BS cells and BMSCs were compared upon their potential to proliferate and differentiate along chondrogenic, osteogenic and adipogenic lineages under specific culture conditions. Expression profiles of markers associated with mesenchymal phenotypes were comparatively evaluated by flow cytometry, immunohistochemistry, and whole genome array analyses. Results BS cells and BMSCs appeared mainly fibroblastic and revealed almost similar surface antigen expression profiles, which was CD44 + , CD73 + , CD90 + , CD105 + , CD106 + , STRO-1 + , CD14 − , CD31 − , CD34 − , CD45 − , CD144 − . Array analyses revealed 1969 genes upregulated and 1184 genes downregulated in BS cells vs. BMSCs, indicating a high level of transcriptome similarity. After 3 weeks of differentiation culture, BS cells and BMSCs showed a similar strong chondrogenic, adipogenic and osteogenic potential, as shown by histological, immunohistochemical and RT-PCR analyses in contrast to the respective negative controls. Conclusions Our in vitro characterizations show that BS cells fulfill all characteristics of mesenchymal stem cells, and therefore merit further attention for the development of improved therapies for various shoulder pathologies.

When ruptured, the anterior cruciate ligament (ACL) of the human knee has limited regenerative potential. However, the goal of this report was to show that the cells that migrate out of the human ACL constitute a rich population of progenitor cells and we hypothesize that they display mesenchymal stem cell (MSC) characteristics when compared with adherent cells derived from bone marrow or collagenase digests from ACL. We show that ACL outgrowth cells are adherent, fibroblastic cells with a surface immunophenotype strongly positive for cluster of differentiation (CD)29, CD44, CD49c, CD73, CD90, CD97, CD105, CD146, and CD166, weakly positive for CD106 and CD14, but negative for CD11c, CD31, CD34, CD40, CD45, CD53, CD74, CD133, CD144, and CD163. Staining for STRO-1 was seen by immunohistochemistry but not flow cytometry. Under suitable culture conditions, the ACL outgrowth-derived MSCs differentiated into chondrocytes, osteoblasts, and adipocytes and showed capacity to self-renew in an in vitro assay of ligamentogenesis. MSCs derived from collagenase digests of ACL tissue and human bone marrow were analyzed in parallel and displayed similar, but not identical, properties. In situ staining of the ACL suggests that the MSCs reside both aligned with the collagenous matrix of the ligament and adjacent to small blood vessels. We conclude that the cells that emigrate from damaged ACLs are MSCs and that they have the potential to provide the basis for a superior, biological repair of this ligament.

The anterior-cruciate-ligament (ACL) contains mesenchymal stem cells (ACL-MSCs), suggesting the feasibility of regenerative treatments of this tissue. The immortalization of isolated cells results in cell-lines applicable to develop cell-based therapies. Immortal cell lines eliminate the need for frequent cell isolation from donor tissues. The objective of this study was to characterize cell lines that were generated from isolated ACL-MSCs using TERT gene transfer. We isolated ACL-MSCs from human ACLs derived at the time of ACL reconstruction surgery or total knee arthroplasty. We generated cell lines and compared them to non-immortalized ACL-MSCs. We assessed the cellular morphology and we detected surface antigen expression. The resistance to senescence was inferred using the beta galactosidase activity. Histology, immunohistochemistry, and reverse transcriptase polymerase chain reaction (RT-PCR) were used to evaluate the multilineage differentiation capacity. The morphology of hTERT-ACL-MSCs was similar to ACL up to the last assessment at passage eight. We detected a strong surface expression of CD44, CD90, CD105, and STRO-1 in hTERT-ACL-MSCs. No substantial reduction in the ATP activity was observed in hTERT-ACL-MSCs. Cell lines generated from ACL-MSCs maintain their morphology, surface antigen expression profile, and proliferative capacity; while markers of senescence appear to be reduced. These cell-lines maintained their multilineage differentiation capacity. The demonstrated model systems can be used for further development of new cell-based regenerative approaches in anterior cruciate ligament research, which may lead to new therapeutic strategies in the future.

Optimization is ubiquitous in our daily lives. In the past, (sub-)optimal solutions to any problem have been derived by trial and error, sheer luck, or the expertise of knowledgeable individuals. In our contemporary age, there thankfully exists a plethora of different algorithms that can find solutions more reliably than ever before. Yet, choosing an appropriate algorithm for any given problem is challenging in itself. The field of automated algorithm selection provides various approaches to tackle this latest problem. This is done by delegating the selection of a suitable algorithm for a given problem to a complex computer model. This computer model is generated through the use of Artificial Intelligence. Many of these computer models rely on some sort of information about the problem to make a reasonable selection. Various methods exist to provide this informative input to the computer model in the form of numerical data. In this cumulative dissertation, I propose several improvements to the different variants of informative inputs. This in turn enhances and refines the current state-of-the-art of automated algorithm selection. Specifically, I identify and address current issues with the existing body of work to strengthen the foundation that future work builds upon. Furthermore, the rise of deep learning offers ample opportunities for automated algorithm selection. In several joint works, my colleagues and I developed and evaluated several different methods that replace the existing methods to extract an informative input. Lastly, automated algorithm selection approaches have been restricted to certain types of problems. I propose a method to extend the generation of informative inputs to other problem types and provide an outlook on further promising research directions.

Exploratory landscape analysis and fitness landscape analysis in general have been pivotal in facilitating problem understanding, algorithm design and endeavors such as automated algorithm selection and configuration. These techniques have largely been limited to search spaces of a single domain. In this work, we provide the means to compute exploratory landscape features for mixed-variable problems where the decision space is a mixture of continuous, binary, integer, and categorical variables. This is achieved by utilizing existing encoding techniques originating from machine learning. We provide a comprehensive juxtaposition of the results based on these different techniques. To further highlight their merit for practical applications, we design and conduct an automated algorithm selection study based on a hyperparameter optimization benchmark suite. We derive a meaningful compartmentalization of these benchmark problems by clustering based on the used landscape features. The identified clusters mimic the behavior the used algorithms exhibit. Meaning, the different clusters have different best performing algorithms. Finally, our trained algorithm selector is able to close the gap between the single best and the virtual best solver by 57.5% over all benchmark problems.

Exploratory landscape analysis (ELA) is a well-established tool to characterize optimization problems via numerical features. ELA is used for problem comprehension, algorithm design, and applications such as automated algorithm selection and configuration. Until recently, however, ELA was limited to search spaces with either continuous or discrete variables, neglecting problems with mixed variable types. This gap was addressed in a recent study that uses an approach based on target-encoding to compute exploratory landscape features for mixedvariable problems. In this work, we investigate an alternative encoding scheme based on SHAP values. While these features do not lead to better results in the algorithm selection setting considered in previous work, the two different encoding mechanisms exhibit complementary performance. Combining both feature sets into a hybrid approach outperforms each encoding mechanism individually. Finally, we experiment with two different ways of meta-selecting between the two feature sets. Both approaches are capable of taking advantage of the performance complementarity of the models trained on target-encoded and SHAP-encoded feature sets, respectively.

Exploratory Landscape Analysis is a powerful technique for numerically characterizing landscapes of single-objective continuous optimization problems. Landscape insights are crucial both for problem understanding as well as for assessing benchmark set diversity and composition. Despite the irrefutable usefulness of these features, they suffer from their own ailments and downsides. Hence, in this work we provide a collection of different approaches to characterize optimization landscapes. Similar to conventional landscape features, we require a small initial sample. However, instead of computing features based on that sample, we develop alternative representations of the original sample. These range from point clouds to 2D images and, therefore, are entirely feature-free. We demonstrate and validate our devised methods on the BBOB testbed and predict, with the help of Deep Learning, the high-level, expert-based landscape properties such as the degree of multimodality and the existence of funnel structures. The quality of our approaches is on par with methods relying on the traditional landscape features. Thereby, we provide an exciting new perspective on every research area which utilizes problem information such as problem understanding and algorithm design as well as automated algorithm configuration and selection.

Hyperparameter optimization (HPO) is a key component of machine learning models for achieving peak predictive performance. While numerous methods and algorithms for HPO have been proposed over the last years, little progress has been made in illuminating and examining the actual structure of these black-box optimization problems. Exploratory landscape analysis (ELA) subsumes a set of techniques that can be used to gain knowledge about properties of unknown optimization problems. In this paper, we evaluate the performance of five different black-box optimizers on 30 HPO problems, which consist of two-, three- and five-dimensional continuous search spaces of the XGBoost learner trained on 10 different data sets. This is contrasted with the performance of the same optimizers evaluated on 360 problem instances from the black-box optimization benchmark (BBOB). We then compute ELA features on the HPO and BBOB problems and examine similarities and differences. A cluster analysis of the HPO and BBOB problems in ELA feature space allows us to identify how the HPO problems compare to the BBOB problems on a structural meta-level. We identify a subset of BBOB problems that are close to the HPO problems in ELA feature space and show that optimizer performance is comparably similar on these two sets of benchmark problems. We highlight open challenges of ELA for HPO and discuss potential directions of future research and applications.

Effective treatments for patients with cholangiocarcinoma after progression on gemcitabine-based chemotherapy are urgently needed. Mutations in the BRAF gene have been found in 5% of biliary tract tumours. The combination of dabrafenib and trametinib has shown activity in several BRAFV600E-mutated cancers. We aimed to assess the activity and safety of dabrafenib and trametinib combination therapy in patients with BRAFV600E-mutated biliary tract cancer. This study is part of an ongoing, phase 2, open-label, single-arm, multicentre, Rare Oncology Agnostic Research (ROAR) basket trial in patients with BRAFV600E-mutated rare cancers. Patients were eligible for the biliary tract cancer cohort if they were aged 18 years or older, had BRAFV600E-mutated, unresectable, metastatic, locally advanced, or recurrent biliary tract cancer, an Eastern Cooperative Oncology Group performance status of 0–2, and had received previous systemic treatment. All patients were treated with oral dabrafenib 150 mg twice daily and oral trametinib 2 mg once daily until disease progression or intolerance of treatment. The primary endpoint was the overall response rate, which was determined by Response Evaluation Criteria in Solid Tumors version 1.1 in the intention-to-treat evaluable population, which comprised all enrolled patients regardless of receiving treatment who were evaluable (ie, had progression, began a new anticancer treatment, withdrew consent, died, had stable disease for 6 weeks or longer, or had two or more post-baseline assessments). The ROAR trial is registered with ClinicalTrials.gov, NCT02034110. These results are based on an interim analysis; the study is active but not recruiting. Between March 12, 2014, and July 18, 2018, 43 patients with BRAFV600E-mutated biliary tract cancer were enrolled to the study and were evaluable. Median follow-up was 10 months (IQR 6–15). An investigator-assessed overall response was achieved by 22 (51%, 95% CI 36–67) of 43 patients. An independent reviewer-assessed overall response was achieved by 20 (47%, 95% CI 31–62) of 43 patients. The most common grade 3 or worse adverse event was increased γ-glutamyltransferase in five (12%) patients. 17 (40%) patients had serious adverse events and nine (21%) had treatment-related serious adverse events, the most frequent of which was pyrexia (eight [19%]). No treatment-related deaths were reported. Dabrafenib plus trametinib combination treatment showed promising activity in patients with BRAFV600E-mutated biliary tract cancer, with a manageable safety profile. Routine testing for BRAFV600E mutations should be considered in patients with biliary tract cancer. GlaxoSmithKline and Novartis.

Effective treatments are needed to improve outcomes for high-grade glioma and low-grade glioma. The activity and safety of dabrafenib plus trametinib were evaluated in adult patients with recurrent or progressive BRAFV600E mutation-positive high-grade glioma and low-grade glioma. This study is part of an ongoing open-label, single-arm, phase 2 Rare Oncology Agnostic Research (ROAR) basket trial at 27 community and academic cancer centres in 13 countries (Austria, Belgium, Canada, France, Germany, Italy, Japan, the Netherlands, Norway, South Korea, Spain, Sweden, and the USA). The study enrolled patients aged 18 years or older with an Eastern Cooperative Oncology Group performance status of 0, 1, or 2. Patients with BRAFV600E mutation-positive high-grade glioma and low-grade glioma received dabrafenib 150 mg twice daily plus trametinib 2 mg once daily orally until unacceptable toxicity, disease progression, or death. In the high-grade glioma cohort, patients were required to have measurable disease at baseline using the Response Assessment in Neuro-Oncology high-grade glioma response criteria and have been treated previously with radiotherapy and first-line chemotherapy or concurrent chemoradiotherapy. Patients with low-grade glioma were required to have measurable non-enhancing disease (except pilocytic astrocytoma) at baseline using the Response Assessment in Neuro-Oncology low-grade glioma criteria. The primary endpoint, in the evaluable intention-to-treat population, was investigator-assessed objective response rate (complete response plus partial response for high-grade glioma and complete response plus partial response plus minor response for low-grade glioma). This trial is ongoing, but is closed for enrolment, NCT02034110. Between April 17, 2014, and July 25, 2018, 45 patients (31 with glioblastoma) were enrolled into the high-grade glioma cohort and 13 patients were enrolled into the low-grade glioma cohort. The results presented here are based on interim analysis 16 (data cutoff Sept 14, 2020). In the high-grade glioma cohort, median follow-up was 12·7 months (IQR 5·4–32·3) and 15 (33%; 95% CI 20–49) of 45 patients had an objective response by investigator assessment, including three complete responses and 12 partial responses. In the low-grade glioma cohort, median follow-up was 32·2 months (IQR 25·1–47·8). Nine (69%; 95% CI 39–91) of 13 patients had an objective response by investigator assessment, including one complete response, six partial responses, and two minor responses. Grade 3 or worse adverse events were reported in 31 (53%) patients, the most common being fatigue (five [9%]), decreased neutrophil count (five [9%]), headache (three [5%]), and neutropenia (three [5%]). Dabrafenib plus trametinib showed clinically meaningful activity in patients with BRAFV600E mutation-positive recurrent or refractory high-grade glioma and low-grade glioma, with a safety profile consistent with that in other indications. BRAFV600E testing could potentially be adopted in clinical practice for patients with glioma. Novartis.