DNA methylation signatures predicting bevacizumab efficacy in metastatic breast cancer

Background: Biomarkers predicting response to bevacizumab in breast cancer are still missing. Since epigenetic modifications can contribute to an aberrant regulation of angiogenesis and treatment resistance, we investigated the influence of DNA methylation patterns on bevacizumab efficacy. Methods: Genome-wide methylation profiling using the Illumina Infinium HumanMethylation450 BeadChip was performed in archival FFPE specimens of 36 patients with HER2-negative metastatic breast cancer treated with chemotherapy in combination with bevacizumab as first-line therapy (learning set). Based on objective response and progression-free survival (PFS) and considering ER expression, patients were divided in responders (R) and non-responders (NR). Significantly differentially methylated gene loci (CpGs) with a strong change in methylation levels (>0.15 or <-0.15) between R and NR were identified and further investigated in 80 bevacizumab-treated breast cancer patients (optimization set) and in 15 patients treated with chemotherapy alone (control set) using targeted deep amplicon bisulfite sequencing. Methylated gene loci were considered predictive if there was a significant association with outcome (PFS) in the optimization set but not in the control set using Spearman rank correlation, Cox regression, and logrank test. Results: Differentially methylated loci in 48 genes were identified, allowing a good separation between R and NR (odds ratio (OR) 101, p<0.0001). Methylation of at least one cytosine in 26 gene-regions was significantly associated with progression-free survival (PFS) in the optimization set, but not in the control set. Using information from the optimization set, the panel was reduced to a 9-gene signature, which could divide patients from the learning set into 2 clusters, thereby predicting response with an OR of 40 (p<0.001) and an AUC of 0.91 (LOOCV). A further restricted 3-gene methylation model showed a significant association of predicted responders with longer PFS in the learning and optimization set even in multivariate analysis with an excellent and good separation of R and NR with AUC=0.94 and AUC=0.86, respectively. Conclusion: Both a 9-gene and 3-gene methylation signature can discriminate between R and NR to a bevacizumab-based therapy in MBC and could help identify patients deriving greater benefit from bevacizumab.(VLID)251037

Low Expression of miR-20a-5p Predicts Benefit to Bevacizumab in Metastatic Breast Cancer Patients Treated within the TANIA Phase III Trial

Background: In metastatic breast cancer (MBC) patients, no biomarker predicting benefit to a bevacizumab-containing therapy has been established yet. MicroRNAs (miRNAs) are involved in angiogenesis and treatment resistance and therefore could be of predictive value. Methods: Profiling of 754 miRNAs was performed in tumor samples of 58 MBC patients treated with a bevacizumab-containing first-line regimen (learning set). Based on progression-free survival (PFS), patients were divided into responders (R) and non-responders (NR). Differentially expressed miRNAs between R and NR were analyzed in a cohort of 57 patients treated with first-line chemotherapy without bevacizumab (control set), to exclude miRNAs providing prognostic information. MiRNA candidates significantly associated with PFS in multivariate analysis were further validated in tumor samples of 203 patients treated within the phase III trial TANIA randomizing between chemotherapy either alone or with bevacizumab after progression on first-line bevacizumab. Results: Low expression of miR-20a-5p (multivariate p = 0.035) and miR-21-5p (multivariate p = 0.004) were significantly associated with longer PFS in the learning set, but not in the control set. In samples from the TANIA trial, low expression of miR-20a-5p was also significantly associated with longer PFS (hazard ration (HR) 0.60; 95%-CI 0.37&ndash;0.89; p = 0.012) and longer overall survival (OS; HR 0.54; 95%-CI 0.32&ndash;0.83; p = 0.007) in the bevacizumab arm but not in the chemotherapy-only arm (PFS: HR 0.73, p = 0.119; OS: HR 1.01; p = 0.964). For miR-21-5p no significant association with PFS or OS in both treatment arms was observed. Conclusion: MiR-20a-5p expression in breast cancer tissue was predictive for a greater benefit from bevacizumab-containing therapy in two independent cohorts

HER2 Directed Antibody-Drug-Conjugates beyond T-DM1 in Breast Cancer

Since the discovery of the human epidermal growth factor receptor 2 (HER2) as an oncogenic driver in a subset of breast cancers and the development of HER2 directed therapies, the prognosis of HER2 amplified breast cancers has improved meaningfully. Next to monoclonal anti-HER2 antibodies and tyrosine kinase inhibitors, the antibody-drug conjugate T-DM1 is a pillar of targeted treatment of advanced HER2-positive breast cancers. Currently, several HER2 directed antibody-drug conjugates are under clinical investigation for HER2 amplified but also HER2 expressing but not amplified breast tumors. In this article, we review the current preclinical and clinical evidence of the investigational drugs A166, ALT-P7, ARX788, DHES0815A, DS-8201a, RC48, SYD985, MEDI4276 and XMT-1522

Combination Strategies for Immune-Checkpoint Blockade and Response Prediction by Artificial Intelligence

The therapeutic concept of unleashing a pre-existing immune response against the tumor by the application of immune-checkpoint inhibitors (ICI) has resulted in long-term survival in advanced cancer patient subgroups. However, the majority of patients do not benefit from single-agent ICI and therefore new combination strategies are eagerly necessitated. In addition to conventional chemotherapy, kinase inhibitors as well as tumor-specific vaccinations are extensively investigated in combination with ICI to augment therapy responses. An unprecedented clinical outcome with chimeric antigen receptor (CAR-)T cell therapy has led to the approval for relapsed/refractory diffuse large B cell lymphoma and B cell acute lymphoblastic leukemia whereas response rates in solid tumors are unsatisfactory. Immune-checkpoints negatively impact CAR-T cell therapy in hematologic and solid malignancies and as a consequence provide a therapeutic target to overcome resistance. Established biomarkers such as programmed death ligand 1 (PD-L1) and tumor mutational burden (TMB) help to select patients who will benefit most from ICI, however, biomarker negativity does not exclude responses. Investigating alterations in the antigen presenting pathway as well as radiomics have the potential to determine tumor immunogenicity and response to ICI. Within this review we summarize the literature about specific combination partners for ICI and the applicability of artificial intelligence to predict ICI therapy responses

Oxidative stress and neurodegeneration : the yeast model system

In this chapter we are treating yeast cells as a model for oxidative stress response and the consequences of oxidative stress which are one cause for a number of human diseases, including neurodegenerative diseases, which form the main part of this paper. All such model building depends on orthologous relations between highly conserved yeast and human genes, which are easily recognized by sequence comparisons, but much more difficult to prove functionally. Previously we have treated Friedreich's ataxia, while presently we are describing in detail the neuronal ceroid lipofuscinoses, among them Batten disease. A general overview is given how yeast can aid current research in three of the most devastating and at the same time quantitatively most important neurodegenerative diseases of old age: Alzheimer's, Huntington's, and Parkinson's disease. In the ensuing part of the chapter, we describe yeast as model for metabolic regulation and hence as a model for inborn errors of metabolism that are in some instances very faithfully mirrored by introducing the same point mutations into yeast cells which are known from patients