Abstract Background Human papillomavirus (HPV) is a major etiological factor in a subset of head and neck squamous cell carcinomas (HNSCC), particularly in the oropharynx, and reliable detection is critical for prognosis and treatment decisions. Methods We applied droplet digital PCR (ddPCR) assays targeting HPV16 oncogenes (E6, E7) and the non-oncogenic gene E2 to tumor tissue, blood plasma, and oral rinse samples from 58 HNSCC patients. Results were compared with p16INK4a immunohistochemistry (IHC). A cohort of 44 non-cancer controls was included. Longitudinal monitoring was performed in 33 patients with serial liquid biopsies. Results ddPCR identified 41% of patients as HPV16-positive in tumor tissue, with higher specificity than p16INK4a IHC, which yielded 10 likely false positives. Baseline liquid biopsy results were highly concordant with tissue findings, and all 44 controls tested negative. Both blood plasma and oral rinse performed reliably, though oral rinse may be particularly well suited for HPV-related HNSCC due to direct mucosal shedding. In longitudinal analyses, all initially HPV-positive patients showed HPV clearance after surgery. In two cases, loss of clearance (i.e. re-detection of HPV DNA in plasma and oral rinse) preceded clinically confirmed progression, with lead times of 87 and 122 days. Conclusion ddPCR-based HPV testing provides high diagnostic specificity compared with p16INK4a IHC and allows minimally invasive monitoring. Plasma and oral rinse represent complementary liquid biopsy sources, with oral rinse offering particular advantages for mucosal tumors. Longitudinal testing revealed early molecular recurrence, supporting its potential as a prognostic tool. While promising, the use of liquid biopsy–based HPV detection for screening at-risk but undiagnosed populations remains exploratory and requires further evaluation in larger cohorts.

INTRODUCTION:Whole Exome Sequencing (WES) has emerged as an efficient tool in clinical cancer diagnostics to broaden the scope from panel-based diagnostics to screening of all genes and enabling robust determination of complex biomarkers in a single analysis. METHODS:To assess concordance, six formalin-fixed paraffin-embedded (FFPE) tissue specimens and four commercial reference standards were analyzed by WES as matched tumor-normal DNA at 21 NGS centers in Germany, each employing local wet-lab and bioinformatics. Somatic and germline variants, copy-number alterations (CNAs), and complex biomarkers were investigated. Somatic variant calling was performed in 494 diagnostically relevant cancer genes. The raw data were collected and re-analyzed with a central bioinformatic pipeline to separate wet- and dry-lab variability. RESULTS:The mean positive percentage agreement (PPA) of somatic variant calling was 76 % while the positive predictive value (PPV) was 89 % in relation to a consensus list of variants found by at least five centers. Variant filtering was identified as the main cause for divergent variant calls. Adjusting filter criteria and re-analysis increased the PPA to 88 % for all and 97 % for the clinically relevant variants. CNA calls were concordant for 82 % of genomic regions. Homologous recombination deficiency (HRD), tumor mutational burden (TMB), and microsatellite instability (MSI) status were concordant for 94 %, 93 %, and 93 % of calls, respectively. Variability of CNAs and complex biomarkers did not decrease considerably after harmonization of the bioinformatic processing and was hence attributed mainly to wet-lab differences. CONCLUSION:Continuous optimization of bioinformatic workflows and participating in round robin tests are recommended.

Immunohistochemical evaluation of estrogen receptor, progesterone receptor, and human epidermal growth factor receptor-2 status stratify the different subtypes of breast cancer and define the treatment course. Triple-negative breast cancer (TNBC), which does not register receptor overexpression, is often associated with worse patient prognosis. Mass spectrometry imaging transcribes the molecular content of tissue specimens without requiring additional tags or preliminary analysis of the samples, being therefore an excellent methodology for an unbiased determination of tissue constituents, in particular tumor markers. In this study, the proteomic content of 1191 human breast cancer samples was characterized by mass spectrometry imaging and the epithelial regions were employed to train and test machine-learning models to characterize the individual receptor status and to classify TNBC. The classification models presented yielded high accuracies for estrogen and progesterone receptors and over 95% accuracy for classification of TNBC. Analysis of the molecular features revealed that vimentin overexpression is associated with TNBC, supported by immunohistochemistry validation, revealing a new potential target for diagnosis and treatment.

A variety of cancer entities are driven by KRAS mutations, which remain difficult to target clinically. Survival pathways, such as resistance to cell death, may represent a promising treatment approach in KRAS mutated cancers. Based on the frequently observed genomic deletions of BCL-2-related ovarian killer (BOK) in cancer patients, we explored the function of BOK in a mutant Kras G12D -driven murine model of lung cancer. Using Kras G12D /+ Bok −/− mice, we observed an overall tumor-promoting function of BOK in vivo. Specifically, loss of BOK reduced proliferation both in cell lines in vitro as well as in Kras G12D -driven tumor lesions in vivo. During tumor development in vivo, loss of BOK resulted in a lower tumor burden, with fewer, smaller, and less advanced tumors. Using Kras G12D /+ Tp53 Δ/Δ Bok −/− mice, we identified that this phenotype was entirely dependent on the presence of functional p53. Furthermore, analysis of a human dataset of untreated early-stage lung tumors did not identify any common deletion of the BOK locus, independently of the TP53 status or the histopathological classification. Taken together our data indicate that BOK supports tumor progression in Kras -driven lung cancer.

Cancer-related deaths are very commonly attributed to complications from metastases to neighboring as well as distant organs. Dissociate response in the treatment of pancreatic adenocarcinoma is one of the main causes of low treatment success and low survival rates. This behavior could not be explained by transcriptomics or genomics; however, differences in the composition at the protein level could be observed. We have characterized the proteomic composition of primary pancreatic adenocarcinoma and distant metastasis directly in human tissue samples, utilizing mass spectrometry imaging. The mass spectrometry data was used to train and validate machine learning models that could distinguish both tissue entities with an accuracy above 90%. Model validation on samples from another collection yielded a correct classification of both entities. Tentative identification of the discriminative molecular features showed that collagen fragments (COL1A1, COL1A2, and COL3A1) play a fundamental role in tumor development. From the analysis of the receiver operating characteristic, we could further advance some potential targets, such as histone and histone variations, that could provide a better understanding of tumor development, and consequently, more effective treatments.

Currently, pathological evaluation of stage I/II colon cancer, following the Union Internationale Contre Le Cancer (UICC) guidelines, is insufficient to identify patients that would benefit from adjuvant treatment. In our study, we analyzed tissue samples from 276 patients with colon cancer utilizing mass spectrometry imaging. Two distinct approaches are herein presented for data processing and analysis. In one approach, four different machine learning algorithms were applied to predict the tendency to develop metastasis, which yielded accuracies over 90% for three of the models. In the other approach, 1007 m/z features were evaluated with regards to their prognostic capabilities, yielding two m/z features as promising prognostic markers. One feature was identified as a fragment from collagen (collagen 3A1), hinting that a higher collagen content within the tumor is associated with poorer outcomes. Identification of proteins that reflect changes in the tumor and its microenvironment could give a very much-needed prediction of a patient’s prognosis, and subsequently assist in the choice of a more adequate treatment.

Somatic mutations in genes such as ASXL1, RUNX1, TP53 or EZH2 adversely affect the outcome of patients with myelodysplastic syndromes (MDS). Since selective BCL-2 inhibition is a promising treatment strategy in hematologic malignancies, we tested the therapeutic impact of ABT-199 on MDS patient samples bearing an adverse mutational profile. By gene expression, we found that the level of pro-apoptotic BIM significantly decreased during MDS disease progression in line with an acquired resistance to cell death. Supporting the potential for ABT-199 treatment in MDS, high-risk MDS patient samples specifically underwent cell death in response to ABT-199 even when harbouring mutations in ASXL1, RUNX1, TP53 or EZH2. ABT-199 effectively targeted the stem- and progenitor compartment in advanced MDS harbouring mutations in ASXL1, RUNX1, TP53 or EZH2 and even proved effective in patients harbouring more than one of the defined high-risk mutations. Moreover, we utilized the protein abundance of BCL-2 family members in primary patient samples using flow cytometry as a biomarker to predict ABT-199 treatment response. Our data demonstrate that ABT-199 effectively induces apoptosis in progenitors of high-risk MDS/sAML despite the presence of adverse genetic mutations supporting the notion that pro-apoptotic intervention will hold broad therapeutic potential in high-risk MDS patients with poor prognosis.