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Sinonasal tumour diagnosis can be complicated by the heterogeneity of disease and classification systems. Here, the authors use machine learning to classify sinonasal undifferentiated carcinomas into 4 molecular classe with differences in differentiation state and clinical outcome.

Background Therapies based on targeting immune checkpoints have revolutionized the treatment of metastatic melanoma in recent years. Still, biomarkers predicting long-term therapy responses are lacking.Methods A novel approach of reference-free deconvolution of large-scale DNA methylation data enabled us to develop a machine learning classifier based on CpG sites, specific for latent methylation components (LMC), that allowed for patient allocation to prognostic clusters. DNA methylation data were processed using reference-free analyses (MeDeCom) and reference-based computational tumor deconvolution (MethylCIBERSORT, LUMP).Results We provide evidence that DNA methylation signatures of tumor tissue from cutaneous metastases are predictive for therapy response to immune checkpoint inhibition in patients with stage IV metastatic melanoma.Conclusions These results demonstrate that LMC-based segregation of large-scale DNA methylation data is a promising tool for classifier development and treatment response estimation in cancer patients under targeted immunotherapy.

Abstract Background Correct staging and grading of patients with clear cell renal cell carcinoma (cRCC) is of clinical relevance for the prediction of operability and for individualized patient management. As partial or radial resection with postoperative tumor grading currently remain the methods of choice for the classification of cRCC, non-invasive preoperative alternatives to differentiate lower grade from higher grade cRCC would be beneficial. Methods This institutional-review-board approved cross-sectional study included twenty-seven patients (8 women, mean age ± SD, 61.3 ± 14.2) with histopathologically confirmed cRCC, graded according to the International Society of Urological Pathology (ISUP). A native, balanced steady-state free precession T2 mapping sequence (TrueFISP) was performed at 1.5 T. Quantitative T2 values were measured with circular 2D ROIs in the solid tumor portion and also in the normal renal parenchyma (cortex and medulla). To estimate the optimal cut-off T2 value for identifying lower grade cRCC, a Receiver Operating Characteristic Curve (ROC) analysis was performed and sensitivity and specificity were calculated. Students’ t-tests were used to evaluate the differences in mean values for continuous variables, while intergroup differences were tested for significance with two-tailed Mann-Whitney-U tests. Results There were significant differences between the T2 values for lower grade (ISUP 1–2) and higher grade (ISUP 3–4) cRCC (p

3D tumor models clearly outperform 2D cell cultures in recapitulating tissue architecture and drug response. However, their potential in understanding treatment efficacy and resistance development should be better exploited if also long-term effects of treatment could be assessed in vitro. The main disadvantages of the matrices commonly used for in vitro culture are their limited cultivation time and the low comparability with patient-specific matrix properties. Extended cultivation periods are feasible when primary human cells produce the extracellular matrix in situ. Herein, we adapted the hyalograft-3D approach from reconstructed human skin to normal and tumor oral mucosa models and compared the results to bovine collagen-based models. The hyalograft models showed similar morphology and cell proliferation after 7 weeks compared to collagen-based models after 2 weeks of cultivation. Tumor thickness and VEGF expression increased in hyalograft-based tumor models, whereas expression of laminin-332, tenascin C, and hypoxia-inducible factor 1α was lower than in collagen-based models. Taken together, the in situ produced extracellular matrix better confined tumor invasion in the first part of the cultivation period, with continuous tumor proliferation and increasing invasion later on. This proof-of-concept study showed the successful transfer of the hyalograft approach to tumor oral mucosa models and lays the foundation for the assessment of long-term drug treatment effects. Moreover, the use of an animal-derived extracellular matrix is avoided.

Abstract Background Rearrangements of the anaplastic lymphoma kinase (ALK) belong to the promising targets in the therapy of advanced non-small cell lung cancer (NSCLC) and are predominantly detected by immunohistochemistry (IHC) and/or fluorescence in-situ hybridization (FISH). However, both methods occasionally produce discordant results, especially in so-called borderline (BL) cases, showing ALK FISH-positive signals in 10–20% of the tumor nuclei around the cutoff (15%). This leads to a diagnostic and thus to a therapeutic dilemma. Methods We selected 18 unequivocal (12 ALK IHC/FISH-negative; 6 ALK IHC/FISH-positive) and 15 equivocal samples with discordant results between FISH (Abbott, Vysis LSI ALK Dual Color) and IHC (Ventana, D5F3), including cases with FISH-BL results, for further RNA based-analysis. To detect ALK rearrangement at the transcriptional level, RNA was analyzed using a targeted multiplex-PCR panel followed by IonTorrent sequencing and by direct transcript counting using a digital probe-based assay (NanoString). Sensitivity of both methods was defined using RNA obtained from an ALK-positive cell line dilution series. Results Cases with unequivocal IHC/FISH results showed concordant data with both RNA-based methods, whereas the three IHC-negative/FISH-positive samples were negative. The four IHC-negative/FISH-BL-negative cases, as well as the five IHC-negative/FISH-BL-positive samples showed negative results by massive parallel sequencing (MPS) and digital probe-based assay. The two IHC-positive/FISH-BL-positive cases were both positive on the RNA-level, whereas a tumor with questionable IHC and FISH-BL-positive status displayed no ALK fusion transcript. Conclusions The comparison of methods for the confirmation of ALK rearrangements revealed that the detection of ALK protein by IHC and ALK fusion transcripts on transcriptional level by MPS and the probe-based assay leads to concordant results. Only a small proportion of clearly ALK FISH-positive cases are unable to express the ALK protein and ALK fusion transcript which might explain a non-responding to ALK inhibitors. Therefore, our findings led us to conclude that ALK testing should initially be based on IHC and/or RNA-based methods.

Abstract Genetically engineered mouse models of cancer can be used to filter genome‐wide expression datasets generated from human tumours and to identify gene expression alterations that are functionally important to cancer development and progression. In this study, we have generated RNAseq data from tumours arising in two established mouse models of prostate cancer, PB‐Cre/PtenloxP/loxP and p53loxP/loxPRbloxP/loxP, and integrated this with published human prostate cancer expression data to pinpoint cancer‐associated gene expression changes that are conserved between the two species. To identify potential therapeutic targets, we then filtered this information for genes that are either known or predicted to be druggable. Using this approach, we revealed a functional role for the kinase MELK as a driver and potential therapeutic target in prostate cancer. We found that MELK expression was required for cell survival, affected the expression of genes associated with prostate cancer progression and was associated with biochemical recurrence.

Background: Radiofrequency ablation (RFA) is an established treatment option for malignancies located in the liver. RFA-induced irreversible coagulation necrosis leads to the release of danger signals and cellular content. Hence, RFA may constitute an endogenous in situ tumor vaccination, stimulating innate and adaptive immune responses, including tumor-antigen specific T cells. This may explain a phenomenon termed abscopal effect, namely tumor regression in untreated lesions evidenced after distant thermal ablation or irradiation. In this study, we therefore assessed systemic and local immune responses in individual patients treated with RFA.Methods: For this prospective clinical trial, patients with liver metastasis from colorectal carcinoma (mCRC) receiving RFA and undergoing metachronous liver surgery for another lesion were recruited (n = 9) during a 5-year period. Tumor and non-malignant liver tissue samples from six patients were investigated by whole transcriptome sequencing and tandem-mass spectrometry, characterizing naturally presented HLA ligands. Tumor antigen-derived HLA-restricted peptides were selected by different predefined approaches. Further, candidate HLA ligands were manually curated. Peripheral blood mononuclear cells were stimulated in vitro with epitope candidate peptides, and functional T cell responses were assessed by intracellular cytokine staining. Immunohistochemical markers were additionally investigated in surgically resected mCRC from patients treated with (n = 9) or without RFA (n = 7).Results: In all six investigated patients, either induced immune responses and/or pre-existing T cell immunity against the selected targets were observed. Multi-cytokine responses were inter alia directed against known tumor antigens such as cyclin D1 but also against a (predicted) mutation contained in ERBB3. Immunohistochemistry did not show a relevant influx of immune cells into distant malignant lesions after RFA treatment (n = 9) as compared to the surgery only mCRC group (n = 7).Conclusions: Using an individualized approach for target selection, RFA induced and/or boosted T cell responses specific for individual tumor antigens were more frequently detectable as compared to previously published observations with well-characterized tumor antigens. However, the witnessed modest RFA-induced immunological effects alone may not be sufficient for the rejection of established tumors. Therefore, these findings warrant further clinical investigation including the assessment of RFA combination therapies e.g., with immune stimulatory agents, cancer vaccination, and/or immune checkpoint inhibitors.

Mixed adenoneuroendocrine carcinomas of the gastrointestinal tract are until today poorly understood and thus very challenging for interdisciplinary therapy. We herewith report the first case series of patients with a primary mixed adenoneuroendocrine carcinoma of the rectum. Both cases were initially diagnosed as adenocarcinoma and only secondarily with mixed adenoneuroendocrine carcinoma and had a poor outcome due to a rapid tumor progression and resistance to chemotherapy. A 65-year-old female presented with local tumor recurrence and hepatopulmonary metastasis 1 year after primary surgery for adenocarcinoma of the rectum and consecutive radiochemotherapy regimen. Fluorouracil (5-FU) was followed by bevacizumab- and capecitabine-based chemotherapy but had to be discontinued due to side effects and progressive disease. Progressive local pain syndrome accompanied by recurrent bleeding episodes led to a local tumor-debulking operation. Afterward, mixed adenoneuroendocrine carcinoma as the underlying diagnosis in the final histopathological examination was detected. The patient died 3 months after the operation in the context of a fulminant tumor progress. A 63-year-old male patient underwent neoadjuvant radiochemotherapy and laparoscopic rectum resection. After 5 months, postoperative oxaliplatin/capecitabine-based adjuvant chemotherapy was switched to carboplatin/etopsid due to a progressive polyneuropathy and biopsy-proven pulmonary metastasis. The patient then had to be switched to local radiation of cerebral metastases and Topotecan due to cerebral bleeding episodes but died 18 months after the initial diagnosis. In conclusion of our case series, mixed adenoneuroendocrine carcinomas of the rectum should be considered as a rare but aggressive tumor entity. An early and detailed histopathological diagnosis is required in order to establish an individual interdisciplinary treatment concept.

Abstract Castrate‐resistant prostate cancer (CRPC) is poorly characterized and heterogeneous and while the androgen receptor (AR) is of singular importance, other factors such as c‐Myc and the E2F family also play a role in later stage disease. HES6 is a transcription co‐factor associated with stem cell characteristics in neural tissue. Here we show that HES6 is up‐regulated in aggressive human prostate cancer and drives castration‐resistant tumour growth in the absence of ligand binding by enhancing the transcriptional activity of the AR, which is preferentially directed to a regulatory network enriched for transcription factors such as E2F1. In the clinical setting, we have uncovered a HES6‐associated signature that predicts poor outcome in prostate cancer, which can be pharmacologically targeted by inhibition of PLK1 with restoration of sensitivity to castration. We have therefore shown for the first time the critical role of HES6 in the development of CRPC and identified its potential in patient‐specific therapeutic strategies.

This study investigates the predictive capability of radiomics in determining programmed cell death ligand 1 (PD-L1) expression (>=1%) status in non-small cell lung cancer (NSCLC) patients using a newly collected [18F]FDG PET/CT dataset. We aimed to replicate and validate the radiomics-based machine learning (ML) model proposed by Zhao et al. [1] predicting PD-L1 status from PET/CT-imaging. An independent cohort of 254 NSCLC patients underwent [18F]FDG PET/CT imaging, with primary tumor segmentation conducted using lung tissue window (LTW) and more conservative soft tissue window (STW) methods. Radiomics models ("Rad-score" and "complex model") and a clinical-stage model from Zhao et al. were evaluated via 10-fold cross-validation and AUC analysis, alongside a benchmark-study comparing different ML-model pipelines. Clinicopathological data were collected from medical records. On our data, the Rad-score model yielded mean AUCs of 0.593 (STW) and 0.573 (LTW), below Zhao et al.'s 0.761. The complex model achieved mean AUCs of 0.505 (STW) and 0.519 (LTW), lower than Zhao et al.'s 0.769. The clinical model showed a mean AUC of 0.555, below Zhao et al.'s 0.64. All models performed significantly lower than Zhao et al.'s findings. Our benchmark study on four ML pipelines revealed consistently low performance across all configurations. Our study failed to replicate original findings, suggesting poor model performance and questioning predictive value of radiomics features in classifying PD-L1 expression from PET/CT imaging. These results highlight challenges in replicating radiomics-based ML models and stress the need for rigorous validation., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

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., Competing Interests: Declaration of Competing Interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: MMT reports speaker and travel Expenses from Twist. JS reports speaker honoraria from DLS, Molecular Health, AstraZeneca and Biocartis, outside the submitted work. UL reports speaker fees from AstraZeneca, GSK, Novartis, Menarini, advisory board from AstraZeneca and Novartis. DH reports speaker honorary AstraZeneca, adboard BMS, WD speaker honoraries BMS & Novartis. SMB reports speaker honoraria, advisory board fees and research grants from AstraZeneca, Daiichi, Menarini, Novartis, Roche, BMS, Pfizer, Bayer, MSD, Merck, Amgen, Molecular Health, Targos, DLS, Janssen, GSK, QuIP, outside the submitted work. SL reports research grant from BMS, advisory board/speaker invitation from AstraZeneca, Eli Lilly, Roche and Takeda outside of this work. NTG reports research support from Janssen-Cilag and Advisory Boards from Janssen-Cilag, AstraZeneca, Daiichi-Sankyo and BMS outside the submitted work. WW reports research grants from Roche, MSD, BMS and AstraZeneca. Advisory board, lectures and speaker bureau fees from Roche, MSD, BMS, AstraZeneca, Pfizer, Merck, Lilly, Boehringer, Novartis, Takeda, Bayer, Janssen, Amgen, Astellas, Illumina, Eisai, Siemens, Agilent, ADC, GSK und Molecular Health. SO received reimbursement for travel expenses and payment for conference presentations from Illumina Inc. and Oxford Nanopore Technologies. CS reports research funding from BMS Stiftung Immunonkologie and institutional grants from Illumina outside the submitted work. CPS reports an investigator-initiated grant from Illumnia outside of the submitted work. PS reports grants from Inctye, BMS, Gilead, Falk, speakers bureau/advisory board from MSD, BMS, AstraZeneca, Incyte, Astellas, Janssen, Eisai, Amgen, Boehringer Ingelheim. DK reports personal fees for speaker honoraria from AstraZeneca, and Pfizer, personal fees for Advisory Board from Bristol-Myers Squibb, outside the submitted work. NP reports speaker fees from Novartis, Bayer, Roche, AstraZeneca, Illumina, BMS, MSD, PGDX/Labcorp, advisory board from Novartis, Lilly, Roche, Janssen, travel expenses from Novartis, AstraZeneca, Illumina, BMS, MSD, PGDX/Labcorp, Research grants from Illumina. JB reports grants from German Cancer Aid and consulting from MSD, outside the submitted work. AS reports participation in Advisory Board/Speaker’s Bureau for Astra Zeneca, AGCT, Bayer, Bristol-Myers Squibb, Eli Lilly, Illumina, Janssen, MSD, Novartis, Pfizer, Roche, Seattle Genetics, Takeda, and Thermo Fisher, grants from Bayer, Bristol-Myers Squibb, and Chugai, outside the submitted work. All other authors report no conflicts of interest., (Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Aims: DNA methylation profiling, recently endorsed by the World Health Organisation (WHO) as a pivotal diagnostic tool for brain tumours, most commonly relies on bead arrays. Despite its widespread use, limited data exist on the technical reproducibility and potential cross-institutional differences. The LOGGIC Core BioClinical Data Bank registry conducted a prospective laboratory comparison trial with 12 international laboratories to enhance diagnostic accuracy for paediatric low-grade gliomas, focusing on technical aspects of DNA methylation data generation and profile interpretation under clinical real-time conditions., Methods: Four representative low-grade gliomas of distinct histologies were centrally selected, and DNA extraction was performed. Participating laboratories received a DNA aliquot and performed the DNA methylation-based classification and result interpretation without knowledge of tumour histology. Additionally, participants were required to interpret the copy number profile derived from DNA methylation data and conduct DNA sequencing of the BRAF hotspot p.V600 due to its relevance for low-grade gliomas. Results had to be returned within 30 days., Results: High technical reproducibility was observed, with a median pairwise correlation of 0.99 (range 0.94-0.99) between coordinating laboratory and participants. DNA methylation-based tumour classification and copy number profile interpretation were consistent across all centres, and BRAF mutation status was accurately reported for all cases. Eleven out of 12 centres successfully reported their analysis within the 30-day timeframe., Conclusion: Our study demonstrates remarkable concordance in DNA methylation profiling and profile interpretation across 12 international centres. These findings underscore the potential contribution of DNA methylation analysis to the harmonisation of brain tumour diagnostics., (© 2024 The Author(s). Neuropathology and Applied Neurobiology published by John Wiley & Sons Ltd on behalf of British Neuropathological Society.)

Cellular myxoma is a benign soft tissue tumor frequently associated with GNAS mutation that may morphologically resemble low-grade myxofibrosarcoma. This study aimed to identify the undescribed methylation profile of cellular myxoma and compare it to myxofibrosarcoma. We performed molecular analysis on twenty cellular myxomas and nine myxofibrosarcomas and analyzed the results using the methylation-based DKFZ sarcoma classifier. A total of 90% of the cellular myxomas had GNAS mutations (four loci had not been previously described). Copy number variations were found in all myxofibrosarcomas but in none of the cellular myxomas. In the classifier, none of the cellular myxomas reached the 0.9 threshold. Unsupervised t-SNE analysis demonstrated that cellular myxomas form their own clusters, distinct from myxofibrosarcomas. Our study shows the diagnostic potential and the limitations of molecular analysis in cases where morphology and immunohistochemistry are not sufficient to distinguish cellular myxoma from myxofibrosarcoma, particularly regarding GNAS wild-type tumors. The DKFZ sarcoma classifier only provided a valid prediction for one myxofibrosarcoma case; this limitation could be improved by training the tool with a more considerable number of cases. Additionally, the classifier should be introduced to a broader spectrum of mesenchymal neoplasms, including benign tumors like cellular myxoma, whose distinct methylation pattern we demonstrated.