Purpose In the prospective study 02/96 on primary GI lymphoma, we have collected data on histology, clinical features, and treatment results. In particular, in stages I and II localized primary gastric lymphoma (PGL), our objectives were to reduce treatment intensity and to confirm our hypothesis from study 01/92, which maintained that an organ-preserving approach is not inferior to primary surgery. Patients and Methods Patients receiving radiotherapy and/or chemotherapy were stratified for histologic grade, stage, and whether surgery had been carried out or not (as decided by each participating center). Patients with aggressive PGL received six cycles of CHOP-14 (cyclophosphamide, doxorubicin, vincristine, and prednisone) followed by involved-field radiotherapy (40 Gy). Patients with indolent PGL (including patients experiencing treatment failure with antibiotic therapy for Helicobacter pylori) were treated with extended-field radiotherapy. The volume depended on stage. The irradiation dose was 30 Gy, followed by a boost of 10 Gy (the latter omitted after complete resection) to the tumor region. Results Seven hundred forty-seven patients were accrued. Of these patients, 393 with localized PGL were treated with radiotherapy and/or chemotherapy only or additional surgery between December 1996 and December 2003. The survival rate at 42 months for patients treated with surgery was 86% compared with 91.0% for patients without surgery. Conclusion In this nonrandomized study (02/96), we reproduced the previous results of study 01/92 showing no disadvantage for an organ-preserving treatment. Therefore, primary stomach resection should be questioned.

Circulating tumor cells (CTCs) hold great potential to answer key questions of how non-small cell lung cancer (NSCLC) evolves and develops resistance upon anti-PD-1/PD-L1 treatment. Currently, their clinical utility in NSCLC is compromised by a low detection rate with the established, Food and Drug Administration (FDA)-approved, EpCAM-based CellSearch^® System. We tested an epitope-independent method (Parsortix^TM system) and utilized it to assess PD-L1 expression of CTCs from NSCLC patients. We prospectively collected 127 samples, 97 of which were analyzed with the epitope-independent system in comparison to the CellSearch system. CTCs were determined by immunocytochemistry as intact, nucleated, CD45⁻, pankeratins (K)⁺ cells. PD-L1 status of CTCs was evaluated from 89 samples. With the epitope-independent system, ≥1 CTC per blood sample was detected in 59 samples (61%) compared to 31 samples (32%) with the EpCAM-based system. Upon PD-L1 staining, 47% of patients harbored only PD-L1⁺CTCs, 47% had PD-L1⁺ and PD-L1⁻CTCs, and only 7% displayed exclusively PD-L1⁻CTCs. The percentage of PD-L1⁺CTCs did not correlate with the percentage of PD-L1⁺ in biopsies determined by immunohistochemistry (p = 0.179). Upon disease progression, all patients showed an increase in PD-L1⁺CTCs, while no change or a decrease in PD-L1⁺CTCs was observed in responding patients (n = 11; p = 0.001). Our data show a considerable heterogeneity in the PD-L1 status of CTCs from NSCLC patients. An increase of PD-L1⁺CTCs holds potential to predict resistance to PD-1/PD-L1 inhibitors.

Background: An increasing number of studies within digital pathology show the potential of artificial intelligence (AI) to diagnose cancer using histological whole slide images, which requires large and diverse data sets. While diversification may result in more generalizable AI-based systems, it can also introduce hidden variables. If neural networks are able to distinguish/learn hidden variables, these variables can introduce batch effects that compromise the accuracy of classification systems. Objective: The objective of the study was to analyze the learnability of an exemplary selection of hidden variables (patient age, slide preparation date, slide origin, and scanner type) that are commonly found in whole slide image data sets in digital pathology and could create batch effects. Methods: We trained four separate convolutional neural networks (CNNs) to learn four variables using a data set of digitized whole slide melanoma images from five different institutes. For robustness, each CNN training and evaluation run was repeated multiple times, and a variable was only considered learnable if the lower bound of the 95% confidence interval of its mean balanced accuracy was above 50.0%. Results: A mean balanced accuracy above 50.0% was achieved for all four tasks, even when considering the lower bound of the 95% confidence interval. Performance between tasks showed wide variation, ranging from 56.1% (slide preparation date) to 100% (slide origin). Conclusions: Because all of the analyzed hidden variables are learnable, they have the potential to create batch effects in dermatopathology data sets, which negatively affect AI-based classification systems. Practitioners should be aware of these and similar pitfalls when developing and evaluating such systems and address these and potentially other batch effect variables in their data sets through sufficient data set stratification.

Carboxypeptidase A3 (CPA3) is a specific mast cell (MC) protease with variable expression. This protease is one of the preformed components of the secretome. During maturation of granules, CPA3 becomes an active enzyme with a characteristic localization determining the features of the cytological and ultrastructural phenotype of MC. CPA3 takes part in the regulation of a specific tissue microenvironment, affecting the implementation of innate immunity, the mechanisms of angiogenesis, the processes of remodeling of the extracellular matrix, etc. Characterization of CPA3 expression in MC can be used to refine the MC classification, help in a prognosis, and increase the effectiveness of targeted therapy.

The development of targeted therapies has improved survival rates for patients with advanced non-small cell lung cancer (NSCLC). However, tissue biopsy is unfeasible or inadequate in many patients, limiting biomarker testing and access to targeted therapies. The increasing numbers of established and emerging biomarkers with available targeted treatments highlights the challenges associated with sequential single-gene testing and limited tissue availability. Multiplex next-generation sequencing (NGS) offers an attractive alternative and represents a logical next step, and in cases where the tumour is inaccessible, tissue biopsy yields insufficient tumour content, or when the patient’s performance status does not allow a tissue biopsy, liquid biopsy can provide valuable material for molecular diagnosis. Here, we explore the role of liquid biopsy (i.e., circulating cell-free DNA analysis) in Europe. Liquid biopsies could be used as a complementary approach to increase rates of molecular diagnosis, with the ultimate aim of improving patient access to appropriate targeted therapies. Expert opinion is also provided on potential future applications of liquid biopsy in NSCLC, including for cancer prevention, detection of early stage and minimum residual disease, monitoring of response to therapy, selection of patients for immunotherapy, and monitoring of tumour evolution to enable optimal adaptation/combination of drug therapies.

The worldwide approval of the combination maintenance therapy of olaparib and bevacizumab in advanced high-grade serous ovarian cancer requires complex molecular diagnostic assays that are sufficiently robust for the routine detection of driver mutations in homologous recombination repair (HRR) genes and genomic instability (GI), employing formalin-fixed (FFPE) paraffin-embedded tumor samples without matched normal tissue. We therefore established a DNA-based hybrid capture NGS assay and an associated bioinformatic pipeline that fulfils our institution’s specific needs. The assay´s target regions cover the full exonic territory of relevant cancer-related genes and HRR genes and more than 20,000 evenly distributed single nucleotide polymorphism (SNP) loci to allow for the detection of genome-wide allele specific copy number alterations (CNA). To determine GI status, we implemented an %CNA score that is robust across a broad range of tumor cell content (25–85%) often found in routine FFPE samples. The assay was established using high-grade serous ovarian cancer samples for which BRCA1 and BRCA2 mutation status as well as Myriad MyChoice homologous repair deficiency (HRD) status was known. The NOGGO (Northeastern German Society for Gynecologic Oncology) GIS (GI-Score) v1 assay was clinically validated on more than 400 samples of the ENGOT PAOLA-1 clinical trial as part of the European Network for Gynaecological Oncological Trial groups (ENGOT) HRD European Initiative. The “NOGGO GIS v1 assay” performed using highly robust hazard ratios for progression-free survival (PFS) and overall survival (OS), as well a significantly lower dropout rate than the Myriad MyChoice clinical trial assay supporting the clinical utility of the assay. We also provide proof of a modular and scalable routine diagnostic method, that can be flexibly adapted and adjusted to meet future clinical needs, emerging biomarkers, and further tumor entities.