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Pulmonary enteric adenocarcinoma is a rare non-small cell lung cancer subtype. It is poorly characterized and cannot be distinguished from metastatic colorectal or upper gastrointestinal adenocarcinomas by means of routine pathological methods. As DNA methylation patterns are known to be highly tissue specific, we aimed to develop a methylation-based algorithm to differentiate these entities. To this end, genome-wide methylation profiles of 600 primary pulmonary, colorectal, and upper gastrointestinal adenocarcinomas obtained from The Cancer Genome Atlas and the Gene Expression Omnibus database were used as a reference cohort to train a machine learning algorithm. The resulting classifier correctly classified all samples from a validation cohort consisting of 680 primary pulmonary, colorectal and upper gastrointestinal adenocarcinomas, demonstrating the ability of the algorithm to reliably distinguish these three entities. We then analyzed methylation data of 15 pulmonary enteric adenocarcinomas as well as four pulmonary metastases and four primary colorectal adenocarcinomas with the algorithm. All 15 pulmonary enteric adenocarcinomas were reliably classified as primary pulmonary tumors and all four metastases as well as all four primary colorectal cancer samples were identified as colorectal adenocarcinomas. In a t-distributed stochastic neighbor embedding analysis, the pulmonary enteric adenocarcinoma samples did not form a separate methylation subclass but rather diffusely intermixed with other pulmonary cancers. Additional characterization of the pulmonary enteric adenocarcinoma series using fluorescence in situ hybridization, next-generation sequencing and copy number analysis revealed KRAS mutations in nine of 15 samples (60%) and a high number of structural chromosomal changes. Except for an unusually high rate of chromosome 20 gain (67%), the molecular data was mostly reminiscent of standard pulmonary adenocarcinomas. In conclusion, we provide sound evidence of the pulmonary origin of pulmonary enteric adenocarcinomas and in addition provide a publicly available machine learning-based algorithm to reliably distinguish these tumors from metastatic colorectal cancer.

Background The antibody targeting platelet-derived growth factor receptor alpha (PDGFRA), olaratumab, was approved in 2016 for metastatic soft tissue sarcoma (STS) in combination with doxorubicin based on promising results of a phase Ib/II trial by the Food and Drug Administration (FDA). However, recently the phase III ANNOUNCE trial could not confirm the additional value of olaratumab in this context. Methods Here, in a retrospective analysis we share our single-centre experience with olaratumab/doxorubicin in STS by including n  = 32 patients treated with olaratumab/doxorubicin between 2016 and 2019. Results Median progression-free survival (PFS) in the overall cohort was 3.1 months (range 0.6–16.2). A response [complete remission (CR), partial remission (PR) or stable disease (SD)] was seen in n  = 11 (34%) cases, whereas n  = 21 (66%) patients showed progressive disease (PD). In n  = 9 patients surgery was performed subsequently in an individual therapeutic approach. Out of n  = 5 patients receiving additional regional hyperthermia, n  = 3 achieved PR or SD. Conclusions This single-centre experience does also not support the promising phase Ib/II results for olaratumab/doxorubicin in STS. However, our findings do not preclude that olaratumab combination therapy could be valuable in a neoadjuvant setting. This warrants further exploration also taking into account the heterogeneous nature of STS.

Background Risk assessment on the molecular level is important in predictive pathology to determine the risk of metastatic disease for ERpos, HER2neg breast cancer. The gene expression test EndoPredict (EP) was trained and validated for prediction of a 10-year risk of distant recurrence to support therapy decisions regarding endocrine therapy alone or in combination with chemotherapy. The EP test provides the 12-gene Molecular Score (MS) and the EPclin-Score (EPclin), which combines the molecular score with tumor size and nodal status. In this project we investigated the correlation of 12-gene MS and EPclin scores with classical pathological markers. Methods EndoPredict-based gene expression profiling was performed prospectively in a total of 1652 patients between 2017 and 2020. We investigated tumor grading and Ki67 cut-offs of 20% for binary classification as well as 10% and 30% for three classes (low, intermediate, high), based on national and international guidelines. Results 410 (24.8%) of 1652 patients were classified as 12-gene MS low risk and 626 (37.9%) as EPclin low risk. We found significant positive associations between 12-gene MS and grading ( p  < 0.001), EPclin and grading ( p  = 0.001), 12-gene MS and Ki67 ( p  < 0.001), and EPclin and Ki67 ( p  < 0.001). However, clinically relevant differences between EP test results, Ki67 and tumor grading were observed. For example, 118 (26.3%) of 449 patients with Ki67 > 20% were classified as low risk by EPclin. Same differences were seen comparing EP test results and tumor grading. Conclusion In this study we could show that EP risk scores are distributed differentially among Ki67 expression groups, especially in Ki67 low and high tumors with a substantial proportion of patients with EPclin high risk results in Ki67 low tumors and vice versa. This suggests that classical pathological parameters and gene expression parameters are not interchangeable, but should be used in combination for risk assessment.

RANK ligand (RANKL) is crucial for the development of mouse mammary glands during pregnancy. RANKL functions as a major paracrine effector of the mitogenic action of progesterone in mammary epithelium via its receptor RANK and has a role in expansion and regenerative potential of mammary stem cells. Pharmacologic inhibition of RANKL attenuates the development of mammary carcinoma and inhibits metastatic progression in multiple mouse models. Primary breast carcinoma samples from the neoadjuvant GeparTrio study were analyzed to correlate the expression of human RANK and RANKL with pathological complete response (pCR), disease-free (DFS), and overall (OS) survival. Pre-treatment FFPE core biopsies ( n  = 601) were analyzed for percentage and intensity of immunohistochemical RANK and RANKL expression. Antibodies against human RANK (N-1H8; Amgen) and human RANKL (M366; Amgen) were used. RANK protein was expressed in 160 (27 %) patients. Increased RANK expression was observed in 14.5 % of patients and correlated with high tumor grade ( p  < 0.023) and negative hormone receptor (HR) status ( p  < 0.001). Patients with high RANK expression showed a higher pCR rate (23.0 % vs. 12.6 %, p  = 0.010), shorter DFS ( p  = 0.038), and OS ( p  = 0.011). However, prognostic and predictive information was not an independent parameter. Only 6 % of samples expressed RANKL, which was not correlated with any clinical features. Higher RANK expression in the primary tumor is associated with a higher sensitivity to chemotherapy, but also a higher risk of relapse and death. Our study provides a basis for further exploration of the antitumor activity of clinical antibodies against RANKL.