Is translational research compatible with preclinical publication strategies?

The term "translational research" is used to describe the transfer of basic biological knowledge into practical medicine, a process necessary for motivation of public spending. In the area of cancer therapeutics, it is becoming increasingly evident that results obtained in vitro and in animal models are difficult to translate into clinical medicine. We here argue that a number of factors contribute to making the translation process inefficient. These factors include the use of sensitive cell lines and fast growing experimental tumors as targets for novel therapies, and the use of unrealistic drug concentrations and radiation doses. We also argue that aggressive interpretation of data, successful in hypothesis-building biological research, does not form a solid base for development of clinically useful treatment modalities. We question whether "clean" results obtained in simplified models, expected for publication in high-impact journals, represent solid foundations for improved treatment of patients. Open-access journals such as Radiation Oncology have a large mission to fulfill by publishing relevant data to be used for making actual progress in translational cancer research

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Inhibitors of the catalytic activity of the 20S proteasome are cytotoxic to tumor cells and are currently in clinical use for treatment of multiple myeloma, whilst the deubiquitinase activity associated with the 19S regulatory subunit of the proteasome is also a valid target for anti-cancer drugs. The mechanisms underlying the therapeutic efficacy of these drugs and their selective toxicity towards cancer cells are not known. Here, we show that increasing the cellular levels of proteasome substrates using an inhibitor of Sec61-mediated protein translocation significantly increases the extent of apoptosis that is induced by inhibition of proteasomal deubiquitinase activity in both cancer derived and non-transformed cell lines. Our results suggest that increased generation of misfolded proteasome substrates may contribute to the mechanism(s) underlying the increased sensitivity of tumor cells to inhibitors of the ubiquitin-proteasome system

A novel inhibitor of proteasome deubiquitinating activity renders tumor cells sensitive to TRAIL-mediated apoptosis by natural killer cells and T cells

The proteasome inhibitor bortezomib simultaneously renders tumor cells sensitive to killing by natural killer (NK) cells and resistant to killing by tumor-specific T cells. Here, we show that b-AP15, a novel inhibitor of proteasome deubiquitinating activity, sensitizes tumors to both NK and T cell-mediated killing. Exposure to b-AP15 significantly increased the susceptibility of tumor cell lines of various origins to NK (p<0.0002) and T cell (p=0.02) –mediated cytotoxicity. Treatment with b-AP15 resulted in increased TRAIL [tumor necrosis factor-related apoptosis-inducing ligand] receptor-2 expression (p=0.03) and decreased cFLIP expression in tumor cells in vitro. In tumor-bearing SCID/Beige mice, treatment with b-AP15 followed by infusion of either human NK cells or tumor-specific T cells resulted in a significantly delayed tumor progression compared with mice treated with NK cells (p=0.006), T cells (p<0.0001), or b-AP15 alone (p=0.003). Combined infusion of NK and T cells in tumor-bearing BALB/c mice following treatment with b-AP15 resulted in a significantly prolonged long-term survival compared with mice treated with b-AP15 and NK or T cells (p≤0.01). Our findings show that b-AP15-induced sensitization to TRAIL-mediated apoptosis could be used as a novel strategy to augment the anti-cancer effects of adoptively infused NK and T cells in patients with cancer.VetenskapsrådetEuropean Research CouncilAccepte

A natural oxadiazine isolated from cyanobacteria kills cancer cells in multicellular culture systems by impairing cellular respiration

Cyanobacteria are versatile microorganisms that ubiquitously inhabit terrestrial and aquatic ecosystems. They adapt to external threats by mainly producing secondary metabolites. Therefore, cyanobacteria have been recognized as producer of natural products with potential biotechnological applications, such as bioplastics, antifouling, antibiotics, antiprotozoal or anticancer treatment.info:eu-repo/semantics/publishedVersio

Charting calcium-regulated apoptosis pathways using chemical biology: role of calmodulin kinase II

<p>Abstract</p> <p>Background</p> <p>Intracellular free calcium ([Ca²⁺]_i) is a key element in apoptotic signaling and a number of calcium-dependent apoptosis pathways have been described. We here used a chemical biology strategy to elucidate the relative importance of such different pathways.</p> <p>Results</p> <p>A set of 40 agents ("bioprobes") that induce apoptosis was first identified by screening of a chemical library. Using p53, AP-1, NFAT and NF-κB reporter cell lines, these bioprobes were verified to induce different patterns of signaling. Experiments using the calcium chelator BAPTA-AM showed that Ca²⁺was involved in induction of apoptosis by the majority of the bioprobes and that Ca²⁺was in general required several hours into the apoptosis process. Further studies showed that the calmodulin pathway was an important mediator of the apoptotic response. Inhibition of calmodulin kinase II (CaMKII) resulted in more effective inhibition of apoptosis compared to inhibition of calpain, calcineurin/PP2B or DAP kinase. We used one of the bioprobes, the plant alkaloid helenalin, to study the role of CaMKII in apoptosis. Helenalin induced CaMKII, ASK1 and Jun-N-terminal kinase (JNK) activity, and inhibition of these kinases inhibited apoptosis.</p> <p>Conclusion</p> <p>Our study shows that calcium signaling is generally not an early event during the apoptosis process and suggests that a CaMKII/ASK1 signaling mechanism is important for sustained JNK activation and apoptosis by some types of stimuli.</p

Antiproliferative Effects of the Natural Oxadiazine Nocuolin A Are Associated With Impairment of Mitochondrial Oxidative Phosphorylation

Natural products are interesting sources for drug discovery. The natural product oxadiazine Nocuolin A (NocA) was previously isolated from the cyanobacterial strain Nodularia sp. LEGE 06071 and here we examined its cytotoxic effects against different strains of the colon cancer cell line HCT116 and the immortalized epithelial cell line hTERT RPE-1. NocA was cytotoxic against colon cancer cells and immortalized cells under conditions of exponential growth but was only weakly active against non-proliferating immortalized cells. NocA induced apoptosis by mechanism(s) resistant to overexpression of BCL family members. Interestingly, NocA affected viability and induced apoptosis of HCT116 cells grown as multicellular spheroids. Analysis of transcriptome profiles did not match signatures to any known compounds in CMap but indicated stress responses and induction of cell starvation. Evidence for autophagy was observed, and a decrease in various mitochondrial respiration parameter within 1 h of treatment. These results are consistent with previous findings showing that nutritionally compromised cells in spheroids are sensitive to impairment of mitochondrial energy production due to limited metabolic plasticity. We conclude that the antiproliferative effects of NocA are associated with effects on mitochondrial oxidative phosphorylation

Detection of breast cancer lymph node metastases in frozen sections with a point-of care low-cost microscope scanner

Background Detection of lymph node metastases is essential in breast cancer diagnostics and staging, affecting treatment and prognosis. lntraoperative microscopy analysis of sentinel lymph node frozen sections is standard for detection of axillary metastases but requires access to a pathologist for sample analysis. Remote analysis of digitized samples is an alternative solution but is limited by the requirement for high-end slide scanning equipment. Objective To determine whether the image quality achievable with a low-cost, miniature digital microscope scanner is sufficient for detection of metastases in breast cancer lymph node frozen sections. Methods Lymph node frozen sections from 79 breast cancer patients were digitized using a prototype miniature microscope scanner and a high-end slide scanner. Images were independently reviewed by two pathologists and results compared between devices with conventional light microscopy analysis as ground truth. Results Detection of metastases in the images acquired with the miniature scanner yielded an overall sensitivity of 91% and specificity of 99% and showed strong agreement when compared to light microscopy (k = 0.91). Strong agreement was also observed when results were compared to results from the high-end slide scanner (k = 0.94). A majority of discrepant cases were micrometastases and sections of which no anticytokeratin staining was available. Conclusion Accuracy of detection of metastatic cells in breast cancer sentinel lymph node frozen sections by visual analysis of samples digitized using low-cost, point-of-care microscopy is comparable to analysis of digital samples scanned using a high-end, whole slide scanner. This technique could potentially provide a workflow for digital diagnostics in resource-limited settings, facilitate sample analysis at the point-of-care and reduce the need for trained experts on-site during surgical procedures.Peer reviewe

Identification of tumor epithelium and stroma in tissue microarrays using texture analysis

<p>Abstract</p> <p>Background</p> <p>The aim of the study was to assess whether texture analysis is feasible for automated identification of epithelium and stroma in digitized tumor tissue microarrays (TMAs). Texture analysis based on local binary patterns (LBP) has previously been used successfully in applications such as face recognition and industrial machine vision. TMAs with tissue samples from 643 patients with colorectal cancer were digitized using a whole slide scanner and areas representing epithelium and stroma were annotated in the images. Well-defined images of epithelium (n = 41) and stroma (n = 39) were used for training a support vector machine (SVM) classifier with LBP texture features and a contrast measure C (LBP/C) as input. We optimized the classifier on a validation set (n = 576) and then assessed its performance on an independent test set of images (n = 720). Finally, the performance of the LBP/C classifier was evaluated against classifiers based on Haralick texture features and Gabor filtered images.</p> <p>Results</p> <p>The proposed approach using LPB/C texture features was able to correctly differentiate epithelium from stroma according to texture: the agreement between the classifier and the human observer was 97 per cent (kappa value = 0.934, <it>P </it>< 0.0001) and the accuracy (area under the ROC curve) of the LBP/C classifier was 0.995 (CI95% 0.991-0.998). The accuracy of the corresponding classifiers based on Haralick features and Gabor-filter images were 0.976 and 0.981 respectively.</p> <p>Conclusions</p> <p>The method illustrates the capability of automated segmentation of epithelial and stromal tissue in TMAs based on texture features and an SVM classifier. Applications include tissue specific assessment of gene and protein expression, as well as computerized analysis of the tumor microenvironment.</p> <p>Virtual slides</p> <p>The virtual slide(s) for this article can be found here: <url>http://www.diagnosticpathology.diagnomx.eu/vs/4123422336534537</url></p

Deep learning based tissue analysis predicts outcome in colorectal cancer

Image-based machine learning and deep learning in particular has recently shown expert-level accuracy in medical image classification. In this study, we combine convolutional and recurrent architectures to train a deep network to predict colorectal cancer outcome based on images of tumour tissue samples. The novelty of our approach is that we directly predict patient outcome, without any intermediate tissue classification. We evaluate a set of digitized haematoxylin-eosin-stained tumour tissue microarray (TMA) samples from 420 colorectal cancer patients with clinicopathological and outcome data available. The results show that deep learning-based outcome prediction with only small tissue areas as input outperforms (hazard ratio 2.3; CI 95% 1.79-3.03; AUC 0.69) visual histological assessment performed by human experts on both TMA spot (HR 1.67; CI 95% 1.28-2.19; AUC 0.58) and whole-slide level (HR 1.65; CI 95% 1.30-2.15; AUC 0.57) in the stratification into low-and high-risk patients. Our results suggest that state-of-the-art deep learning techniques can extract more prognostic information from the tissue morphology of colorectal cancer than an experienced human observer.Peer reviewe