Katharina Kriegsmann

Sample processing of formalin-fixed specimens constitutes a major challenge in molecular profiling efforts. Pre-analytical factors such as fixative temperature, dehydration, and embedding media affect downstream analysis, generating data dependent on technical processing rather than disease state. In this study, we investigated two different sample processing methods, including the use of the cytospin sample preparation and automated sample processing apparatuses for proteomic analysis of multiple myeloma (MM) cell lines using imaging mass spectrometry (IMS). In addition, two sample-embedding instruments using different reagents and processing times were considered. Three MM cell lines fixed in 4% paraformaldehyde were either directly centrifuged onto glass slides using cytospin preparation techniques or processed to create paraffin-embedded specimens with an automatic tissue processor, and further cut onto glass slides for IMS analysis. The number of peaks obtained from paraffin-embedded samples was comparable between the two different sample processing instruments. Interestingly, spectra profiles showed enhanced ion yield in cytospin compared to paraffin-embedded samples along with high reproducibility compared to the sample replicate.

Artificial intelligence (AI) has shown potential for facilitating the detection and classification of tumors. In patients with non-small cell lung cancer, distinguishing between the most common subtypes, adenocarcinoma (ADC) and squamous cell carcinoma (SqCC), is crucial for the development of an effective treatment plan. This task, however, may still present challenges in clinical routine. We propose a two-modality, AI-based classification algorithm to detect and subtype tumor areas, which combines information from matrix-assisted laser desorption/ionization (MALDI) mass spectrometry imaging (MSI) data and digital microscopy whole slide images (WSIs) of lung tissue sections. The method consists of first detecting areas with high tumor cell content by performing a segmentation of the hematoxylin and eosin-stained (H&E-stained) WSIs, and subsequently classifying the tumor areas based on the corresponding MALDI MSI data. We trained the algorithm on six tissue microarrays (TMAs) with tumor samples from N = 232 patients and used 14 additional whole sections for validation and model selection. Classification accuracy was evaluated on a test dataset with another 16 whole sections. The algorithm accurately detected and classified tumor areas, yielding a test accuracy of 94.7% on spectrum level, and correctly classified 15 of 16 test sections. When an additional quality control criterion was introduced, a 100% test accuracy was achieved on sections that passed the quality control (14 of 16). The presented method provides a step further towards the inclusion of AI and MALDI MSI data into clinical routine and has the potential to reduce the pathologist’s work load. A careful analysis of the results revealed specific challenges to be considered when training neural networks on data from lung cancer tissue.

Avoidance of therapy-induced apoptosis is a hallmark of acquired resistance towards radiotherapy. Thus, breaking resistance still challenges modern cancer therapy. The Bcl-2 protein family is known for its regulatory role in apoptosis signaling, making Bcl-2, Mcl-1 and Bcl-x_L promising targets. This study evaluates the effects of highly specific inhibitors for Bcl-x_L (WEHI-539), Bcl-2 (ABT-199) and Mcl-1 (S63845) as radiosensitizers. Covering a broad spectrum of solid tumors, Non-Small-Cell Lung Cancer (NSCLC), Head and Neck Squamous Cell Carcinoma (HNSCC) and synovial sarcoma cell lines were exposed to fractionated radiation as standard therapy with or without Bcl-2 protein inhibition. Protein expression was detected by Western blot and cell death was assessed by flow cytometry measuring apoptosis. In contrast to NSCLC, a high level of Bcl-x_L and its upregulation during radiotherapy indicated radioresistance in HNSCC and synovial sarcoma. Radioresistant cell lines across all entities benefited synergistically from combined therapy with Bcl-x_L inhibition and fractionated radiation. In NSCLC cell lines, Mcl-1 inhibition significantly augmented radiotherapy independent of the expression level. Our data suggest that among antiapoptotic Bcl-2 proteins, targeting Bcl-x_L may break resistance to radiation in HNSCC, synovial sarcoma and NSCLC in vitro. In NSCLC, Mcl-1 might be a promising target that needs further investigation.

Online information can increase patients’ competence and engagement. However, there are concerns regarding invalid information. Overall, 300 websites and 50 YouTube videos on multiple myeloma (MM) were evaluated. The websites did not differ between the search engines or search ranks. The median time since the last update was 9 months. The 63 unique websites showed a poor general quality (median JAMA score 2 of 4, only 18% with a valid HON certificate). The patient- (user-) focused quality was medium to poor (median sum DISCERN score 41 out of 80 points). The overall reading level was difficult requiring at least a 12th US school grade. The content level was low (median 24 out of 73 points). Sixteen percent contained misleading/wrong facts. Websites provided by foundation/advocacies showed a significantly higher general and patient- (user-) focused quality. For videos, the median time since upload was 18 months. Judged by the HON foundation score ~80% of videos showed a medium general quality. The patient- (user-) focused quality was medium to poor (median sum DISCERN score 43 points). The content level was very low (median 8 points). MM relevant websites and videos showed a medium to low general, patient- (user-) focused and content quality. Therefore, incorporation of quality indices and regular review is warranted.

Background: About 50% of older patients with acute myeloid leukemia (AML) fail to attain complete remission (CR) following cytarabine plus anthracycline-based induction therapy. Salvage chemotherapy regimens are based on high dose cytarabine (HiDAC), which is frequently combined with mitoxantrone (HAM regimen). However, CR rates remain low, with less than one-third of the patients achieving a CR. FLT3 -ITD has consistently been identified as an unfavorable molecular marker in both relapsed and refractory (r/r)-AML. One-quarter of patients who received midostaurin are refractory to induction therapy and relapse rate at two years exceeds 40%. The oral second-generation bis-aryl urea tyrosine kinase inhibitor quizartinib, is a very selective FLT3 inhibitor, has a high capacity for sustained FLT3 inhibition, and an acceptable toxicity profile. Methods: In this multicenter, upfront randomized phase II trial, all patients receive quizartinib combined with HAM (cytarabine 3g/m² bidaily day one to day three, mitoxantrone 10mg/m² days two and three) during salvage therapy. Efficacy is assessed by comparison to historical controls based on the matched threshold crossing approach with achievement of CR or complete remission with incomplete hematologic recovery (CRi) as primary endpoints. During consolidation therapy (chemotherapy and allogeneic hematopoietic cell transplantation), patients receive either prophylactic quizartinib therapy or measurable residual disease (MRD)-triggered preemptive continuation therapy with quizartinib according to up-front randomization. Addressing the primary endpoint, CR/ CRi, after salvage therapy, the sample size required is of maximum 80 patients, assuming a logistic regression is performed at a one-sided significance level α=0.05, the aspired power is 0.8, and the number of matching partners per intervention patient is at least 1. After enrolling 30 patients, the trial sample size will be recalculated in an interim analysis based on a conditional power argument.Ethics and Dissemination: Ethical approval and approvals from the local and federal competent authorities were granted. Trial results will be reported via peer-reviewed journals and presented at conferences and scientific meetings. TRIAL REGISTRATION: Clinicaltrials.gov identifier: NCT03989713; EudraCT Number: 2018-002675-17. KEYWORDS (LIMIT: 3-10): Quizartinib, relapse, refractory, acute myeloid leukemia, measurable residual disease. matched threshold crossing approach

Cellular senescence (CS) is a major homeostatic biological process, which plays a key role in normal tissue development and provides protection from stressful cell insults. The role of CS in mammary-gland development and breast cancer is not well understood. While there is a lack of experimental data on the role of CS in the development of the pre-pubertal mammary gland, there is evidence for a biphasic senescence response in adult normal-mammary-epithelial cells, where the bypass of the first senescence barrier (M0) seems to be a key step in the development of premalignant lesions, with genetic abnormalities that resemble in situ breast carcinoma. Further, there is accumulating evidence for the role of cellular senescence in breast-cancer response, regarding treatment and patient outcome. Here, we review the current literature on cellular senescence, in epithelial-mammary cells, breast-cancer cells, and breast-tumor-microenvironment-resident cells. Furthermore, we discuss its putative role in breast-cancer response, regarding treatment and disease progression. In addition, we provide preliminary evidence of CS in breast-cancer-microenvironment cells, such as tumor-associated fibroblasts and tumor-infiltrating lymphocytes, by employing the novel GL13 lipofuscin stain, as a marker of cellular senescence.

Alectinib is a standard initial treatment for patients with advanced anaplastic lymphoma kinase (ALK) rearranged non-small-cell lung cancer (NSCLC). The current study analyzed a prospective cohort of 24 consecutive alectinib-treated patients and controls in order to comprehensively characterize longitudinal erythrocyte changes under treatment with ALK inhibitors. Upon starting alectinib, all examined patients developed reticulocytosis and abnormal erythrocyte morphology with anisocytosis and a predominance of acanthocytes (64% of red blood cells on average, range 36–100%) in the peripheral blood smear within approximately 2 weeks. Changes were accompanied by a gradual reduction in Eosin-5-maleimide (EMA) binding, which became pathologic (ANK1, EPB42, SLC4A1, SPTA1, or SBTB genes. The direct Coombs test was also negative in 11/11 tested cases. Besides, anemia, increased LDH, and increased bilirubin were noted in a fraction of patients only, ranging between 42 and 68%. Furthermore, haptoglobin decreases were infrequent, occurring in approximately 1/3 of cases only, and mild, with an average value of 0.93 g/L within the normal range of 0.3–2 g/dL, suggesting that hemolysis occurred predominantly in the extravascular compartment, likely due to splenic trapping of the deformed erythrocytes. These changes showed no association with progression-free survival under alectinib or molecular features, i.e., ALK fusion variant or TP53 status of the disease, and resolved upon a switch to an alternative ALK inhibitor. Thus, alectinib induces mild, reversible erythrocyte changes in practically all treated patients, whose most sensitive signs are aberrant red cell morphology in the peripheral smear, a pathologic EMA test, and reactive reticulocytosis. Frank hemolytic anemia is rare, but mild subclinical hemolysis is very frequent and poses differential-diagnostic problems. Alectinib can be continued under the regular control of hemolysis parameters, but the risk of long-term complications, such as cholelithiasis due to increased serum bilirubin in most patients, remains unclear at present.

Background: Segmentation is, in many Pathomics projects, an initial step. Usually, in supervised settings, well-annotated and large datasets are required. Regarding the rarity of such datasets, unsupervised learning concepts appear to be a potential solution. Against this background, we tested for a small dataset on lung cancer tissue microarrays (TMA) if a model (i) first can be in a previously published unsupervised setting and (ii) secondly can be modified and retrained to produce meaningful labels, and (iii) we finally compared this approach to standard segmentation models. Methods: (ad i) First, a convolutional neuronal network (CNN) segmentation model is trained in an unsupervised fashion, as recently described by Kanezaki et al. (ad ii) Second, the model is modified by adding a remapping block and is retrained on an annotated dataset in a supervised setting. (ad iii) Third, the segmentation results are compared to standard segmentation models trained on the same dataset. Results: (ad i–ii) By adding an additional mapping-block layer and by retraining, models previously trained in an unsupervised manner can produce meaningful labels. (ad iii) The segmentation quality is inferior to standard segmentation models trained on the same dataset. Conclusions: Unsupervised training in combination with subsequent supervised training offers for histological images here no benefit.

Among neonates, tested positive for SARS-CoV-2, the majority of infections occur through postpartum transmission. Only few reports describe intrauterine or intrapartum SARS-CoV-2 infections in newborns. To understand the route of transmission, detection of the virus or virus nucleic acid in the placenta and amniotic tissue are of special interest. Current methods to detect SARS-CoV-2 in placental tissue are immunohistochemistry, electron microscopy, in-situ hybridization, polymerase chain reaction (PCR) and next-generation sequencing. Recently, we described an alternative method for the detection of viral ribonucleic acid (RNA), by combination of reverse transcriptase-PCR and mass spectrometry (MS) in oropharyngeal and oral swabs. In this report, we could detect SARS-CoV-2 in formal-fixed and paraffin-embedded (FFPE) placental and amniotic tissue by multiplex RT-PCR MS. Additionally, we could identify the British variant (B.1.1.7) of the virus in this tissue by the same methodology. Combination of RT-PCR with MS is a fast and easy method to detect SARS-CoV-2 viral RNA, including specific variants in FFPE tissue.