Fighting viral infections and virus-driven tumors with cytotoxic CD4+ T cells

CD4+ T cells have been and are still largely regarded as the orchestrators of immune responses, being able to differentiate into distinct T helper cell populations based on differentiation signals, transcription factor expression, cytokine secretion, and specific functions. Nonetheless, a growing body of evidence indicates that CD4+ T cells can also exert a direct effector activity, which depends on intrinsic cytotoxic properties acquired and carried out along with the evolution of several pathogenic infections. The relevant role of CD4+ T cell lytic features in the control of such infectious conditions also leads to their exploitation as a new immunotherapeutic approach. This review aims at summarizing currently available data about functional and therapeutic relevance of cytotoxic CD4+ T cells in the context of viral infections and virus-driven tumors

Mechanism of KMT5B haploinsufficiency in neurodevelopment in humans and mice.

Pathogenic variants in KMT5B, a lysine methyltransferase, are associated with global developmental delay, macrocephaly, autism, and congenital anomalies (OMIM# 617788). Given the relatively recent discovery of this disorder, it has not been fully characterized. Deep phenotyping of the largest (n = 43) patient cohort to date identified that hypotonia and congenital heart defects are prominent features that were previously not associated with this syndrome. Both missense variants and putative loss-of-function variants resulted in slow growth in patient-derived cell lines. KMT5B homozygous knockout mice were smaller in size than their wild-type littermates but did not have significantly smaller brains, suggesting relative macrocephaly, also noted as a prominent clinical feature. RNA sequencing of patient lymphoblasts and Kmt5b haploinsufficient mouse brains identified differentially expressed pathways associated with nervous system development and function including axon guidance signaling. Overall, we identified additional pathogenic variants and clinical features in KMT5B-related neurodevelopmental disorder and provide insights into the molecular mechanisms of the disorder using multiple model systems

Pitfalls in machine learning‐based assessment of tumor‐infiltrating lymphocytes in breast cancer: a report of the international immuno‐oncology biomarker working group

The clinical significance of the tumor-immune interaction in breast cancer (BC) has been well established, and tumor-infiltrating lymphocytes (TILs) have emerged as a predictive and prognostic biomarker for patients with triple-negative (estrogen receptor, progesterone receptor, and HER2 negative) breast cancer (TNBC) and HER2-positive breast cancer. How computational assessment of TILs can complement manual TIL-assessment in trial- and daily practices is currently debated and still unclear. Recent efforts to use machine learning (ML) for the automated evaluation of TILs show promising results. We review state-of-the-art approaches and identify pitfalls and challenges by studying the root cause of ML discordances in comparison to manual TILs quantification. We categorize our findings into four main topics; (i) technical slide issues, (ii) ML and image analysis aspects, (iii) data challenges, and (iv) validation issues. The main reason for discordant assessments is the inclusion of false-positive areas or cells identified by performance on certain tissue patterns, or design choices in the computational implementation. To aid the adoption of ML in TILs assessment, we provide an in-depth discussion of ML and image analysis including validation issues that need to be considered before reliable computational reporting of TILs can be incorporated into the trial- and routine clinical management of patients with TNBC

A Reuse Technique for Performance Testing of Software Product Lines

Testing that the applications of a software product line comply with their functional as well as with their nonfunctional requirements (for example performance) is important for achieving the desired product quality. Existing approaches for software product line testing only deal with testing an application against its functional requirements. In this paper we present a technique that supports the development of reusable performance test case scenarios in domain engineering and the reuse of these scenarios in application engineering. The technique is an extension of the ScenTED technique for system testing from our previous work. The technique focuses on load testing and performance profiling, two types of performance testing, and it has been validated in a case study at Siemens Medical Solutions HS IM. 1

Integration testing in software product line engineering: a model-based technique*

The development process in software product line engineering is divided into domain engineering and application engineering. As a consequence of this division, tests should be performed in both processes. However, existing testing techniques for single systems cannot be applied during domain engineering, because of the variability in the domain artifacts. Existing software product line test techniques only cover unit and system tests. Our contribution is a model-based, automated integration test technique that can be applied during domain engineering. For generating integration test case scenarios, the technique abstracts from variability and assumes that placeholders are created for variability. The generated scenarios cover all interactions between the integrated components, which are specified in a test model. Additionally, the technique reduces the effort for creating placeholders by minimizing the number of placeholders needed to execute the integration test case scenarios. We have experimentally measured the performance of the technique and the potential reduction of placeholders

Testing Variabilities in Use Case Models 1

Abstract. The derivation of system test cases for product families is difficult due to variability in the requirements, since each variation point multiplies the number of possible behaviors to be tested. This paper proposes an approach to develop domain test cases from use cases that contain variabilities and to derive application test cases from them. The basic idea to avoid combinatorial explosion is to preserve the variability in domain test cases. New strategies to capture variability in test cases are suggested, which in combination help dealing with all basic types of variability in a use case and in its relationships (e.g., <<include>>). 1

Model-based testing of software product lines

Due to the rising demand for individualised software products and software-intensive systems (e.g.,mobile phone or automotive software), organizations are faced with the challenge to provide a diversity of software systems at low costs, in short time, and with high quality. Software product line engineering is the approach for tackling this challenge and has proven its effectiveness in numerous industrial success stories, including Siemens, ABB, Boeing, Hewlett-Packard, Philips, and Bosch [Pohl et al. 2005]