Welfare and excess volatility of exchange rates

We study the properties of a GEI model with nominal assets, outside money (injected into the economy as in Magill and Quinzii), and multiple currencies. We analyze the existence of monetary equilibria and the structure of the equilibrium set under two different assumptions on the determination of the exchange rates. If currencies are perfect substitutes, equilibrium allocations are indeterminate and, generically, sunspot equilibria exist. Generically, given a nonsunspot equilibrium, there are Pareto improving (and Pareto worsening) sunspot equilibria associated with an increase in the volatility of the future exchange rates. We interpret this property as showing that, in general, there is no clear-cut effect on welfare of the excess volatility of exchange rates, even when due to purely extrinsic phenomena.

Time for change: a new training programme for morpho-molecular pathologists?

The evolution of cellular pathology as a specialty has always been driven by technological developments and the clinical relevance of incorporating novel investigations into diagnostic practice. In recent years, the molecular characterisation of cancer has become of crucial relevance in patient treatment both for predictive testing and subclassification of certain tumours. Much of this has become possible due to the availability of next-generation sequencing technologies and the whole-genome sequencing of tumours is now being rolled out into clinical practice in England via the 100 000 Genome Project. The effective integration of cellular pathology reporting and genomic characterisation is crucial to ensure the morphological and genomic data are interpreted in the relevant context, though despite this, in many UK centres molecular testing is entirely detached from cellular pathology departments. The CM-Path initiative recognises there is a genomics knowledge and skills gap within cellular pathology that needs to be bridged through an upskilling of the current workforce and a redesign of pathology training. Bridging this gap will allow the development of an integrated 'morphomolecular pathology' specialty, which can maintain the relevance of cellular pathology at the centre of cancer patient management and allow the pathology community to continue to be a major influence in cancer discovery as well as playing a driving role in the delivery of precision medicine approaches. Here, several alternative models of pathology training, designed to address this challenge, are presented and appraised

Gelsolin induces colorectal tumor cell invasion via modulation of the urokinase-type plasminogen activator cascade

Gelsolin is a cytoskeletal protein which participates in actin filament dynamics and promotes cell motility and plasticity. Although initially regarded as a tumor suppressor, gelsolin expression in certain tumors correlates with poor prognosis and therapy-resistance. In vitro, gelsolin has anti-apoptotic and pro-migratory functions and is critical for invasion of some types of tumor cells. We found that gelsolin was highly expressed at tumor borders infiltrating into adjacent liver tissues, as examined by immunohistochemistry. Although gelsolin contributes to lamellipodia formation in migrating cells, the mechanisms by which it induces tumor invasion are unclear. Gelsolin’s influence on the invasive activity of colorectal cancer cells was investigated using overexpression and small interfering RNA knockdown. We show that gelsolin is required for invasion of colorectal cancer cells through matrigel. Microarray analysis and quantitative PCR indicate that gelsolin overexpression induces the upregulation of invasion-promoting genes in colorectal cancer cells, including the matrix-degrading urokinase-type plasminogen activator (uPA). Conversely, gelsolin knockdown reduces uPA levels, as well as uPA secretion. The enhanced invasiveness of gelsolin-overexpressing cells was attenuated by treatment with function-blocking antibodies to either uPA or its receptor uPAR, indicating that uPA/uPAR activity is crucial for gelsolin-dependent invasion. In summary, our data reveals novel functions of gelsolin in colorectal tumor cell invasion through its modulation of the uPA/uPAR cascade, with potentially important roles in colorectal tumor dissemination to metastatic sites

Standardising RNA profiling based biomarker application in cancer - the need for robust control of technical variables

Histopathology-based staging of colorectal cancer (CRC) has utility in assessing the prognosis of patient subtypes, but as yet cannot accurately predict individual patient’s treatment response. Transcriptomics approaches, using array based or next generation sequencing (NGS) platforms, of formalin fixed paraffin embedded tissue can be harnessed to develop multi-gene biomarkers for predicting both prognosis and treatment response, leading to stratification of treatment. While transcriptomics can shape future biomarker development, currently < 1% of published biomarkers become clinically validated tests, often due to poor study design or lack of independent validation. In this review of a large number of CRC transcriptional studies, we identify recurrent sources of technical variability that encompass collection, preservation and storage of malignant tissue, nucleic acid extraction, methods to quantitate RNA transcripts and data analysis pipelines. We propose a series of defined steps for removal of these confounding issues, to ultimately aid in the development of more robust clinical biomarkers

Molecular profiling of signet ring cell colorectal cancer provides a strong rationale for genomic targeted and immune checkpoint inhibitor therapies

We would like to thank all patients whose samples were used in this study. We are also thankful to the Northern Ireland Biobank and Grampian Biorepository for providing us with tissue blocks and patient data; and Dr HG Coleman (Queen’s University Belfast) for her advice on statistical analyses. This work has been carried out with financial support from Cancer Research UK (grant: C11512/A18067), Experimental Cancer Medicine Centre Network (grant: C36697/A15590 from Cancer Research UK and the NI Health and Social Care Research and Development Division), the Sean Crummey Memorial Fund and the Tom Simms Memorial Fund. The Northern Ireland Biobank is funded by HSC Research and Development Division of the Public Health Agency in Northern Ireland and Cancer Research UK through the Belfast CRUK Centre and the Northern Ireland Experimental Cancer Medicine Centre; additional support was received from Friends of the Cancer Centre. The Northern Ireland Molecular Pathology Laboratory which is responsible for creating resources for the Northern Ireland Biobank has received funding from Cancer Research UK, Friends of the Cancer Centre and Sean Crummey Foundation.Peer reviewedPublisher PD

Activation of STING-Dependent Innate Immune Signaling By S-Phase-Specific DNA Damage in Breast Cancer

Background: Previously we identified a DNA damage response–deficient (DDRD) molecular subtype within breast cancer. A 44-gene assay identifying this subtype was validated as predicting benefit from DNA-damaging chemotherapy. This subtype was defined by interferon signaling. In this study, we address the mechanism of this immune response and its possible clinical significance. Methods: We used immunohistochemistry (IHC) to characterize immune infiltration in 184 breast cancer samples, of which 65 were within the DDRD subtype. Isogenic cell lines, which represent DDRD-positive and -negative, were used to study the effects of chemokine release on peripheral blood mononuclear cell (PBMC) migration and the mechanism of immune signaling activation. Finally, we studied the association between the DDRD subtype and expression of the immune-checkpoint protein PD-L1 as detected by IHC. All statistical tests were two-sided. Results: We found that DDRD breast tumors were associated with CD4+ and CD8+ lymphocytic infiltration (Fisher’s exact test P < .001) and that DDRD cells expressed the chemokines CXCL10 and CCL5 3.5- to 11.9-fold more than DNA damage response–proficient cells (P < .01). Conditioned medium from DDRD cells statistically significantly attracted PBMCs when compared with medium from DNA damage response–proficient cells (P < .05), and this was dependent on CXCL10 and CCL5. DDRD cells demonstrated increased cytosolic DNA and constitutive activation of the viral response cGAS/STING/TBK1/IRF3 pathway. Importantly, this pathway was activated in a cell cycle–specific manner. Finally, we demonstrated that S-phase DNA damage activated expression of PD-L1 in a STING-dependent manner. Conclusions: We propose a novel mechanism of immune infiltration in DDRD tumors, independent of neoantigen production. Activation of this pathway and associated PD-L1 expression may explain the paradoxical lack of T-cell-mediated cytotoxicity observed in DDRD tumors. We provide a rationale for exploration of DDRD in the stratification of patients for immune checkpoint–based therapies

Cancer-related ectopic expression of the bone-related transcription factor RUNX2 in non-osseous metastatic tumor cells is linked to cell proliferation and motility

10.1186/bcr2762Breast Cancer Research125-BCRR

Encrypted federated learning for secure decentralized collaboration in cancer image analysis.

Artificial intelligence (AI) has a multitude of applications in cancer research and oncology. However, the training of AI systems is impeded by the limited availability of large datasets due to data protection requirements and other regulatory obstacles. Federated and swarm learning represent possible solutions to this problem by collaboratively training AI models while avoiding data transfer. However, in these decentralized methods, weight updates are still transferred to the aggregation server for merging the models. This leaves the possibility for a breach of data privacy, for example by model inversion or membership inference attacks by untrusted servers. Somewhat-homomorphically-encrypted federated learning (SHEFL) is a solution to this problem because only encrypted weights are transferred, and model updates are performed in the encrypted space. Here, we demonstrate the first successful implementation of SHEFL in a range of clinically relevant tasks in cancer image analysis on multicentric datasets in radiology and histopathology. We show that SHEFL enables the training of AI models which outperform locally trained models and perform on par with models which are centrally trained. In the future, SHEFL can enable multiple institutions to co-train AI models without forsaking data governance and without ever transmitting any decryptable data to untrusted servers