Introduction: Large-cell transformation (LCT) of mycosis fungoides (MF) has been associated with a higher risk of relapse and progression and, consequently, restricted prognosis. Its molecular pathogenesis has not been elucidated yet. Materials and Methods: In order to address molecular mechanisms of LCT, we performed hybrid capture panel-based sequencing of skin biopsies from 10 patients suffering from MF with LCT versus 17 patients without LCT including follow-up biopsies during clinical course, respectively (51 samples in total). The analyzed patients were attributed to three different groups based on the presence of LCT and clinical behavior. Results: While indolent MF cases without LCT did not show pathogenic driver mutations, a high rate of oncogenic alterations was detected in patients with LCT and aggressive clinical courses. Various genes of different oncogenic signaling pathways, including the MAPK and JAK-STAT signaling pathways, as well as epigenetic modifiers were affected. A high inter-individual and distinctive intra-individual mutation diversity was observed. Oncogenic RAS mutations were exclusively detected in patients with LCT. Conclusion: Our data demonstrate that LCT transition of MF is associated with increased frequency of somatic mutations in cancer-associated genes. In particular, the activation of RAS signaling — together with epigenetic dysregulation — may crucially contribute to the molecular pathogenesis of the LCT phenotype, thus conveying its adverse clinical behavior.

Background Despite advances in treatment of patients with non-small cell lung cancer, carriers of certain genetic alterations are prone to failure. One such factor frequently mutated, is the tumor suppressor PTEN. These tumors are supposed to be more resistant to radiation, chemo- and immunotherapy. Results We demonstrate that loss of PTEN led to altered expression of transcriptional programs which directly regulate therapy resistance, resulting in establishment of radiation resistance. While PTEN-deficient tumor cells were not dependent on DNA-PK for IR resistance nor activated ATR during IR, they showed a significant dependence for the DNA damage kinase ATM. Pharmacologic inhibition of ATM, via KU-60019 and AZD1390 at non-toxic doses, restored and even synergized with IR in PTEN-deficient human and murine NSCLC cells as well in a multicellular organotypic ex vivo tumor model. Conclusion PTEN tumors are addicted to ATM to detect and repair radiation induced DNA damage. This creates an exploitable bottleneck. At least in cellulo and ex vivo we show that low concentration of ATM inhibitor is able to synergise with IR to treat PTEN-deficient tumors in genetically well-defined IR resistant lung cancer models.

Background Germinal center-derived B cell lymphomas are tumors of the lymphoid tissues representing one of the most heterogeneous malignancies. Here we characterize the variety of transcriptomic phenotypes of this disease based on 873 biopsy specimens collected in the German Cancer Aid MMML (Molecular Mechanisms in Malignant Lymphoma) consortium. They include diffuse large B cell lymphoma (DLBCL), follicular lymphoma (FL), Burkitt’s lymphoma, mixed FL/DLBCL lymphomas, primary mediastinal large B cell lymphoma, multiple myeloma, IRF4-rearranged large cell lymphoma, MYC-negative Burkitt-like lymphoma with chr. 11q aberration and mantle cell lymphoma. Methods We apply self-organizing map (SOM) machine learning to microarray-derived expression data to generate a holistic view on the transcriptome landscape of lymphomas, to describe the multidimensional nature of gene regulation and to pursue a modular view on co-expression. Expression data were complemented by pathological, genetic and clinical characteristics. Results We present a transcriptome map of B cell lymphomas that allows visual comparison between the SOM portraits of different lymphoma strata and individual cases. It decomposes into one dozen modules of co-expressed genes related to different functional categories, to genetic defects and to the pathogenesis of lymphomas. On a molecular level, this disease rather forms a continuum of expression states than clearly separated phenotypes. We introduced the concept of combinatorial pattern types (PATs) that stratifies the lymphomas into nine PAT groups and, on a coarser level, into five prominent cancer hallmark types with proliferation, inflammation and stroma signatures. Inflammation signatures in combination with healthy B cell and tonsil characteristics associate with better overall survival rates, while proliferation in combination with inflammation and plasma cell characteristics worsens it. A phenotypic similarity tree is presented that reveals possible progression paths along the transcriptional dimensions. Our analysis provided a novel look on the transition range between FL and DLBCL, on DLBCL with poor prognosis showing expression patterns resembling that of Burkitt’s lymphoma and particularly on ‘double-hit’ MYC and BCL2 transformed lymphomas. Conclusions The transcriptome map provides a tool that aggregates, refines and visualizes the data collected in the MMML study and interprets them in the light of previous knowledge to provide orientation and support in current and future studies on lymphomas and on other cancer entities.

Oncogenic transformation of lung epithelial cells is a multistep process, frequently starting with the inactivation of tumour suppressors and subsequent development of activating mutations in proto-oncogenes, such as members of the PI3K or MAPK families. Cells undergoing transformation have to adjust to changes, including altered metabolic requirements. This is achieved, in part, by modulating the protein abundance of transcription factors. Here, we report that the ubiquitin carboxyl-terminal hydrolase 28 (USP28) enables oncogenic reprogramming by regulating the protein abundance of proto-oncogenes such as c-JUN, c-MYC, NOTCH and ∆NP63 at early stages of malignant transformation. USP28 levels are increased in cancer compared with in normal cells due to a feed-forward loop, driven by increased amounts of oncogenic transcription factors such as c-MYC and c-JUN. Irrespective of oncogenic driver, interference with USP28 abundance or activity suppresses growth and survival of transformed lung cells. Furthermore, inhibition of USP28 via a small-molecule inhibitor resets the proteome of transformed cells towards a ‘premalignant’ state, and its inhibition synergizes with clinically established compounds used to target EGFR\(^{L858R}\)-, BRAF\(^{V600E}\)- or PI3K\(^{H1047R}\)-driven tumour cells. Targeting USP28 protein abundance at an early stage via inhibition of its activity is therefore a feasible strategy for the treatment of early-stage lung tumours, and the observed synergism with current standard-of-care inhibitors holds the potential for improved targeting of established tumours.

Personalized oncology is a rapidly evolving area and offers cancer patients therapy options that are more specific than ever. However, there is still a lack of understanding regarding transcriptomic similarities or differences of metastases and corresponding primary sites. Applying two unsupervised dimension reduction methods (t-Distributed Stochastic Neighbor Embedding (t-SNE) and Uniform Manifold Approximation and Projection (UMAP)) on three datasets of metastases (n = 682 samples) with three different data transformations (unprocessed, log10 as well as log10 + 1 transformed values), we visualized potential underlying clusters. Additionally, we analyzed two datasets (n = 616 samples) containing metastases and primary tumors of one entity, to point out potential familiarities. Using these methods, no tight link between the site of resection and cluster formation outcome could be demonstrated, or for datasets consisting of solely metastasis or mixed datasets. Instead, dimension reduction methods and data transformation significantly impacted visual clustering results. Our findings strongly suggest data transformation to be considered as another key element in the interpretation of visual clustering approaches along with initialization and different parameters. Furthermore, the results highlight the need for a more thorough examination of parameters used in the analysis of clusters.