Search

Breast cancer is a heterogeneous disease comprising the estrogen receptor (ER)–positive luminal subtype which is subdivided into luminal A and luminal B and ER-negative breast cancer which includes the triple-negative subtype. This study has four aims: 1) to examine whether Minichromosome Maintenance (MCM)2, MCM4, and MCM6 can be used as markers to differentiate between luminal A and luminal B subtypes; 2) to study whether MCM2, MCM4, and MCM6 are highly expressed in triple-negative breast cancer, as there is an urgent need to search for surrogate markers in this aggressive subtype, for drug development purposes; 3) to compare the prognostic values of these markers in predicting relapse-free survival; and 4) to compare the three approaches used for scoring the protein expression of these markers by immunohistochemistry (IHC). MCM2, MCM4, MCM6, and MKI67 mRNA expression was first studied using in silico analysis of available breast cancer datasets. We next used IHC to evaluate their protein expression on tissue microarrays using three scoring methods. MCM2, MCM4, and MCM6 can help in distinction between luminal A and luminal B whose therapeutic management and clinical outcomes are different. MCM2, MCM4, MCM6, and Ki-67 are highly expressed in breast cancer of high histological grades that comprise clinically aggressive tumors such as luminal B, HER2-positive, and triple-negative subtypes. Low transcript expression of these markers is associated with increased probability of relapse-free survival. A positive relationship exists among the three scoring methods of each of the four markers. An independent validation cohort is needed to confirm their clinical utility.

The tumor suppressor gene TP53 is the most frequently mutated gene in human cancer. It encodes p53, a DNA-binding transcription factor that regulates multiple genes involved in DNA repair, metabolism, cell cycle arrest, apoptosis, and senescence. TP53 is associated with human cancer by mutations that lead to a loss of wild-type p53 function as well as mutations that confer alternate oncogenic functions that enable them to promote invasion, metastasis, proliferation, and cell survival. Identifying the discrete TP53 mutations in tumor cells may help direct therapies that are more effective. In this study, we identified the frequency of individual TP53 mutations in patients with colon adenocarcinoma (48%), non–small cell lung carcinoma (NSCLC) (36%), and glioma/glioblastoma (28%) at our institution using next-generation sequencing. We also identified the occurrence of somatic mutations in numerous actionable genes including BRAF, EGFR, KRAS, IDH1, and PIK3CA that occurred concurrently with these TP53 mutations. Of the 480 tumors examined that contained one or more mutations in the TP53 gene, 219 were colon adenocarcinomas, 215 were NSCLCs, and 46 were gliomas/glioblastomas. Among the patients positive for TP53 mutations diagnosed with colon adenocarcinoma, 50% also showed at least one mutation in pathogenic genes of which 14% were BRAF, 33% were KRAS, and 3% were NRAS. Forty-seven percent of NSCLC patients harboring TP53 mutations also had a mutation in at least one actionable pathogenic variant with the following frequencies: BRAF: 4%, EGFR: 10%, KRAS: 28%, and PIK3CA: 4%. Fifty-two percent of patients diagnosed with glioma/glioblastoma with a positive TP53 mutation had at least one concurrent mutation in a known pathogenic gene of which 9% were CDKN2A, 41% were IDH1, and 11% were PIK3CA.

Patients with metastatic prostate cancer (PCa) have a poorer prognosis than patients with organ-confined tumors. We strove to uncover the proteome signature of primary PCa and associated lymph node metastases (LNMs) in order to identify proteins that may indicate or potentially promote metastases formation. We performed a proteomic comparative profiling of PCa tissue from radical prostatectomy (RPE) of patients without nodal metastases or relapse at the time of surgical resection (n = 5) to PCa tissue from RPE of patients who suffered from nodal relapse (n = 5). For the latter group, we also included patient-matched tissue of the nodal metastases. All samples were formalin fixed and paraffin embedded. We identified and quantified more than 1200 proteins by liquid chromatography tandem mass spectrometry with subsequent label-free quantification. An increase of ribosomal or proteasomal proteins in LNM (compared to corresponding PCa) became apparent, while extracellular matrix components rather decreased. Immunohistochemistry (IHC) corroborated accumulation of poly-(ADP-ribose)-polymerase 1 and N-myc-downstream-regulated-gene 3, alpha/beta hydrolase domain-containing protein 11, and protein phosphatase slingshot homolog 3 in LNM. These findings strengthen the present interest in examining PARP inhibitors for the treatment of aggressive PCa. IHC also corroborated increased abundance of retinol dehydrogenase 11 in metastasized primary PCa compared to organ-confined PCa. Generally, metastasizing primary tumors were characterized by an enrichment of proteins involved in cellular lipid metabolic processes with concomitant decrease of cell adhesion proteins. This study highlights the usefulness of a combined proteomic-IHC approach to explore novel aspects in tumor biology. Our initial results open novel opportunities for follow-up studies.

Primary cutaneous CD30+ T-cell lymphoproliferative disorders (CD30+ LPD) are the second most common cutaneous lymphomas. According to the World Health Organization (WHO), CD30+ LPD include primary cutaneous anaplastic large cell lymphoma (pcALCL) and lymphomatoid papulosis (LyP) as well as borderline lesions. pcALCL and LyP is thought to represent two ends of a spectrum of diseases that have different clinical presentations, clinical courses, and prognoses in their classic forms, but share the same histology of medium to large CD30+ atypical lymphoid cell infiltrates. Because the behavior of these entities is different clinically and prognostically, we aim to search for oncogenic genomic variants using whole exome sequencing (WES) that drive the development of LyP and pcALCL. Clinical information, pathology, immunohistochemistry, and T-cell rearrangements on six cases of LyP and five cases of pcALCL were reviewed to confirm the rendered diagnosis prior to WES of all specimens. All cases of CD30 LPD had recurrent mutations in at least one of the epigenetic modifying genes, with the most frequent mutations found in the mixed lineage methyltransferase family involved in the methylation of H3K4: CREBBP (27%), KMT2A (36%), KMT2D (36%), SETD2 (27%), and SMARCA4 (27%) (Figure). Within the JAK/STAT pathway, mutations of STAT3 were observed in 2 cases of pcALCL (n=2, 18%), STAT5B in one case of LyP (n=1, 9%) and JAK1 mutation in one case of LyP(n=1, 9%). Lastly, mutations were also identified in the T-cell signaling pathway. 3/5 cases of pcALCL demonstrated loss of function mutations in EOMES, a T-box transcription factor important in lymphocyte development. We had two cases of C-ALCL and one case of LyP with NOTCH1 mutations supporting the importance of this gene in T-cell lymphomas. TP53 mutations and copy number loss is more characteristic of aggressive lymphomas and these abnormalities were absent in pcALCL and LyP and may explain the indolent nature of CD30+ LPD. While the JAK/STAT pathway, T-signaling, and epigenetic alterations possibly play a role in the pathogenesis of these diseases, genes involved in cell-cycle control and apoptosis (ie. TP53) that are characteristic of more aggressive diseases were not identified. Extending this investigation to a larger number of samples will allow us to detect additional mutations that may help distinguish between CD30+ LPDs of LyP and pcALCL as well as other histologic mimics such as systemic ALCL and large cell transformation of MF. Figure 1 Figure 1. Disclosures Abdulla: Caris Life Licenses: Current Employment. Zain: Kiyoaw Kirin, Secura Bio, Seattle Genetics: Honoraria; Secura Bio, DaichiSankyo, Abbvie: Research Funding; Secura Bio, Ono , Legend, Kiyowa Kirin, Myeloid Therapeutics Verastem Daichi Sankyo: Consultancy.

Deregulated SRC/FAK signaling leads to enhanced migration and invasion in many types of tumors. In myxoid and round cell liposarcoma (MRCLS), an adipocytic tumor characterized by the expression of the fusion oncogene FUS-CHOP, SRC have been found as one of the most activated kinases. Here we used a cell-of-origin model of MRCLS and an MRCLS cell line to thoroughly characterize the mechanisms of cell invasion induced by FUS-CHOP using in vitro (3D spheroid invasion assays) and in vivo (chicken chorioallantoic membrane model) approaches. FUS-CHOP expression activated SRC-FAK signaling and increased the invasive ability of MRCLS cells. In addition, FAK expression was found to significantly correlate with tumor aggressiveness in sarcoma patient samples. The involvement of SRC/FAK activation in FUS-CHOP-mediated invasion was further confirmed using the SRC inhibitor dasatinib, the specific FAK inhibitor PF-573228, and FAK siRNA. Notably, dasatinib and PF573228 could also efficiently block the invasion of cancer stem cell subpopulations. Downstream of SRC/FAK signaling, we found that FUS-CHOP expression increases the levels of the RHO/ROCK downstream effector phospho-MLC2 (T18/S19) and that this activation was prevented by dasatinib or PF573228. Moreover, the ROCK inhibitor RKI-1447 was able to completely abolish invasion in FUS-CHOP-expressing cells. These data uncover the involvement of SRC/FAK/RHO/ROCK signaling axis in FUS-CHOP-mediated invasion, thus providing a rationale for testing inhibitors of this pathway as potential novel antimetastatic agents for MRCLS treatment.

Medulloblastoma is the most common malignant brain tumor in children. Therapeutic approaches to medulloblastoma (combination of surgery, radiotherapy, and chemotherapy) have led to significant improvements, but these are achieved at a high cost to quality of life. Alternative therapeutic approaches are needed. Genetic mutations leading to the activation of the Hedgehog pathway drive tumorigenesis in ~30% of medulloblastoma. In a yeast two-hybrid proteomic screen, we discovered a novel interaction between GLI1, a key transcription factor for the mediation of Hedgehog signals, and PIN1, a peptidylprolyl cis/trans isomerase that regulates the postphosphorylation fate of its targets. The GLI1/PIN1 interaction was validated by reciprocal pulldowns using epitope-tagged proteins in HEK293T cells as well as by co-immunoprecipiations of the endogenous proteins in a medulloblastoma cell line. Our results support a molecular model in which PIN1 promotes GLI1 protein abundance, thus contributing to the positive regulation of Hedgehog signals. Most importantly, in vivo functional analyses of Pin1 in the GFAP-tTA;TRE-SmoA1 mouse model of Hedgehog-driven medulloblastoma demonstrate that the loss of Pin1 impairs tumor development and dramatically increases survival. In summary, the discovery of the GLI1/PIN1 interaction uncovers PIN1 as a novel therapeutic target in Hedgehog-driven medulloblastoma tumorigenesis.

Graphical abstract, Predictive tools, utilising biomarkers, aim to objectively assess the potential response to a particular clinical intervention in order to direct treatment. Conventional cancer therapy remains poorly served by predictive biomarkers, despite being the mainstay of treatment for most patients. In contrast, targeted therapy benefits from a clearly defined protein target for potential biomarker assessment. We discuss potential data sources of predictive biomarkers for conventional and targeted therapy, including patient clinical data and multi-omic biomarkers (genomic, transcriptomic and protein expression). Key examples, either clinically adopted or demonstrating promise for clinical translation, are highlighted. Following this, we provide an outline of potential barriers to predictive biomarker development; broadly discussing themes of approaches to translational research and study/trial design, and the impact of cellular and molecular tumor heterogeneity. Future avenues of research are also highlighted.

A multitude of efforts worldwide aim to create a single-cell reference map of the human body, for fundamental understanding of human health, molecular medicine, and targeted treatment. Antibody-based proteomics using immunohistochemistry (IHC) has proven to be an excellent technology for integration with large-scale single-cell transcriptomics datasets. The golden standard for evaluation of IHC staining patterns is manual annotation, which is expensive and may lead to subjective errors. Artificial intelligence holds much promise for efficient and accurate pattern recognition, but confidence in prediction needs to be addressed. Here, the aim was to present a reliable and comprehensive framework for automated annotation of IHC images. We developed a multilabel classification of 7848 complex IHC images of human testis corresponding to 2794 unique proteins, generated as part of the Human Protein Atlas (HPA) project. Manual annotation data for eight different cell types was generated as a basis for training and testing a proposed Hybrid Bayesian Neural Network. By combining the deep learning model with a novel uncertainty metric, DeepHistoClass (DHC) Confidence Score, the average diagnostic performance improved from 86.9% to 96.3%. This metric not only reveals which images are reliably classified by the model, but can also be utilized for identification of manual annotation errors. The proposed streamlined workflow can be developed further for other tissue types in health and disease and has important implications for digital pathology initiatives or large-scale protein mapping efforts such as the HPA project., Graphical Abstract, Highlights • A novel method for automated annotation of immunohistochemistry images. • Introduction of an uncertainty metric, the DeepHistoClass (DHC) confidence score. • Increased accuracy of automated image predictions. • Identification of manual annotation errors., In Brief A novel method for automated annotation of immunohistochemistry images, combining the predictions with an uncertainty metric, the DeepHistoClass (DHC) confidence score. This metric not only reveals which images are reliably classified by the model, but can also be utilized for identification of manual annotation errors. The proposed streamlined workflow can be developed further for other tissue types in health and disease and has important implications for digital pathology initiatives or large-scale protein mapping efforts such as the HPA project.

Background & Aims MicroRNA (miRNA) is highly stable in biospecimens and provides tissue-specific profiles, making it a useful biomarker of carcinogenesis. We aimed to discover a set of miRNAs that could accurately discriminate Barrett’s esophagus (BE) from normal esophageal tissue and to test its diagnostic accuracy when applied to samples collected by a noninvasive esophageal cell sampling device. Methods We analyzed miRNA expression profiles of 2 independent sets of esophageal biopsy tissues collected during endoscopy from 38 patients with BE and 26 patients with normal esophagus (controls) using Agilent microarray and Nanostring nCounter assays. Consistently up-regulated miRNAs were quantified by real-time polymerase chain reaction in esophageal tissues collected by Cytosponge from patients with BE vs without BE. miRNAs were expressed from plasmids and antisense oligonucleotides were expressed in normal esophageal squamous cells; effects on proliferation and gene expression patterns were analyzed. Results We identified 15 miRNAs that were significantly up-regulated in BE vs control tissues. Of these, 11 (MIR215, MIR194, MIR 192, MIR196a, MIR199b, MIR10a, MIR145, MIR181a, MIR30a, MIR7, and MIR199a) were validated in Cytosponge samples. The miRNAs with the greatest increases in BE tissues (7.9-fold increase in expression or more, P < .0001: MIR196a, MIR192, MIR194, and MIR215) each identified BE vs control tissues with area under the curve (AUC) values of 0.82 or more. We developed an optimized multivariable logistic regression model, based on expression levels of 6 miRNAs (MIR7, MIR30a, MIR181a, MIR192, MIR196a, and MIR199a), that identified patients with BE with an AUC value of 0.89, 86.2% sensitivity, and 91.6% specificity. Expression level of MIR192, MIR196a, MIR199a, combined that of trefoil factor 3, identified patients with BE with an AUC of 0.93, 93.1% sensitivity, and 93.7% specificity. Hypomethylation was observed in the promoter region of the highly up-regulated cluster MIR192–MIR194. Overexpression of these miRNAs in normal esophageal squamous cells increased their proliferation, via GRHL3 and PTEN signaling. Conclusions In analyses of miRNA expression patterns of BE vs non-BE tissues, we identified a profile that can identify Cytosponge samples from patients with BE with an AUC of 0.93. Expression of MIR194 is increased in BE samples via epigenetic mechanisms that might be involved in BE pathogenesis., Graphical abstract

Receptor activator of nuclear factor kappa-B ligand (RANKL) is an essential mediator of osteoclast formation, function and survival. In patients with solid tumor metastasis to the bone, targeting the bone microenvironment by inhibition of RANKL using denosumab, a fully human monoclonal antibody (mAb) specific to RANKL, has been demonstrated to prevent tumor-induced osteolysis and subsequent skeletal complications. Recently, a prominent functional role for the RANKL pathway has emerged in the primary bone tumor giant cell tumor of bone (GCTB). Expression of both RANKL and RANK is extremely high in GCTB tumors and denosumab treatment was associated with tumor regression and reduced tumor-associated bone lysis in GCTB patients. In order to address the potential role of the RANKL pathway in another primary bone tumor, this study assessed human RANKL and RANK expression in human primary osteosarcoma (OS) using specific mAbs, validated and optimized for immunohistochemistry (IHC) or flow cytometry.Our results demonstrate RANKL expression was observed in the tumor element in 68% of human OS using IHC. However, the staining intensity was relatively low and only 37% (29/79) of samples exhibited≥10% RANKL positive tumor cells. RANK expression was not observed in OS tumor cells. In contrast, RANK expression was clearly observed in other cells within OS samples, including the myeloid osteoclast precursor compartment, osteoclasts and in giant osteoclast cells. The intensity and frequency of RANKL and RANK staining in OS samples were substantially less than that observed in GCTB samples. The observation that RANKL is expressed in OS cells themselves suggests that these tumors may mediate an osteoclastic response, and anti-RANKL therapy may potentially be protective against bone pathologies in OS. However, the absence of RANK expression in primary human OS cells suggests that any autocrine RANKL/RANK signaling in human OS tumor cells is not operative, and anti-RANKL therapy would not directly affect the tumor.

DNA sequencing continues to evolve quickly even after >30years. Many new platforms suddenly appeared and former established systems have vanished in almost the same manner. Since establishment of next-generation sequencing devices, this progress gains momentum due to the continually growing demand for higher throughput, lower costs and better quality of data. In consequence of this rapid development, standardized procedures and data formats as well as comprehensive quality management considerations are still scarce. Here, we listed and summarized current standardization efforts and quality management initiatives from companies, organizations and societies in form of published studies and ongoing projects. These comprise on the one hand quality documentation issues like technical notes, accreditation checklists and guidelines for validation of sequencing workflows. On the other hand, general standard proposals and quality metrics are developed and applied to the sequencing workflow steps with the main focus on upstream processes. Finally, certain standard developments for downstream pipeline data handling, processing and storage are discussed in brief. These standardization approaches represent a first basis for continuing work in order to prospectively implement next-generation sequencing in important areas such as clinical diagnostics, where reliable results and fast processing is crucial. Additionally, these efforts will exert a decisive influence on traceability and reproducibility of sequence data.