[Sex-specific differences of special tumor diseases]. / Geschlechtsspezifische Unterschiede spezieller Tumorerkrankungen.

BACKGROUND: Numerous data show that sex and gender have gained increasing importance in precision medicine as relevant modulators of specific oncological and hematological diseases. The purpose of this article is to provide a summary of the current state of knowledge on sex differences in the incidence and outcome of specific malignancies and to further elucidate possible underlying causes. MATERIAL AND METHODS: Evaluation and discussion of basic research studies, meta-analyses, and clinical trials. RESULTS: There are significant sex-specific differences in the incidence, response rates, and mortality for a variety of oncological diseases. For the most part, men have poorer outcomes, whereas women have higher treatment-associated toxicities and distinct presentations at younger ages. Hormonal, immunological, and pharmacological causes are suspected. CONCLUSION: Advanced patient-individualized treatment in oncology and hematology will be measured in the future by the implementation of the existing relevant sex differences in the clinical practice and further investigations on underlying mechanisms in studies in order to guarantee and to optimize the best possible treatment for oncological patients in the future.

Effective high-throughput isolation of fully human antibodies targeting infectious pathogens.

As exemplified by the ongoing severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic, there is a strong demand for rapid high-throughput isolation pipelines to identify potent neutralizing antibodies for prevention and therapy of infectious diseases. However, despite substantial progress and extensive efforts, the identification and production of antigen-specific antibodies remains labor- and cost-intensive. We have advanced existing concepts to develop a highly efficient high-throughput protocol with proven application for the isolation of potent antigen-specific antibodies against human immunodeficiency virus 1, hepatitis C virus, human cytomegalovirus, Middle East respiratory syndrome coronavirus, SARS-CoV-2 and Ebola virus. It is based on computationally optimized multiplex primer sets (openPrimeR), which guarantee high coverage of even highly mutated immunoglobulin gene segments as well as on optimized antibody cloning and production strategies. Here, we provide the detailed protocol, which covers all critical steps from sample collection to antibody production within 12-14 d.

openPrimeR for multiplex amplification of highly diverse templates.

To study the diversity of immune receptors and pathogens, multiplex PCR has become a central approach in research and diagnostics. However, insufficient primer design against highly diverse templates often prevents amplification and therefore limits the correct understanding of biological processes. Here, we present openPrimeR, an R-based tool for evaluating and designing multiplex PCR primers. openPrimeR provides a functional and intuitive interface and uses either a greedy algorithm or an integer linear program to compute the minimal set of primers that performs full target coverage. As proof of concept, we used openPrimeR to find optimal primer sets for the amplification of highly mutated immunoglobulins. Comprehensive analyses on specifically generated immunoglobulin variable gene segment libraries resulted in the composition of highly effective primer sets (oPR-IGHV, oPR-IGKV and oPR-IGLV) that demonstrated to be particularly suitable for the isolation of novel human antibodies.

Modeling the Amplification of Immunoglobulins through Machine Learning on Sequence-Specific Features.

Successful primer design for polymerase chain reaction (PCR) hinges on the ability to identify primers that efficiently amplify template sequences. Here, we generated a novel Taq PCR data set that reports the amplification status for pairs of primers and templates from a reference set of 47 immunoglobulin heavy chain variable sequences and 20 primers. Using logistic regression, we developed TMM, a model for predicting whether a primer amplifies a template given their nucleotide sequences. The model suggests that the free energy of annealing, ΔG, is the key driver of amplification (p = 7.35e-12) and that 3' mismatches should be considered in dependence on ΔG and the mismatch closest to the 3' terminus (p = 1.67e-05). We validated TMM by comparing its estimates with those from the thermodynamic model of DECIPHER (DE) and a model based solely on the free energy of annealing (FE). TMM outperformed the other approaches in terms of the area under the receiver operating characteristic curve (TMM: 0.953, FE: 0.941, DE: 0.896). TMM can improve primer design and is freely available via openPrimeR ( http://openPrimeR.mpi-inf.mpg.de ).