The AIS, based on initial CT scan, provides useful prognostic information in patients with severe head injury.

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Twenty-five years after its first description the p53 protein has been shown to play a key role in both cancer and ageing. The p53 protein is activated by many different stress pathways, including oncogene action and DNA damage. The elucidation of the p53 response, which is aberrant in most cancers (including breast, lung, stomach and colorectal cancer), has provided many new targets for drug development and p53 gene therapy is now approved in China. In tumours where p53 is mutant small molecules may be able to restore its function. In many tumours the wild-type p53 gene remains intact but its function is compromised by loss of upstream signalling pathways or downstream effectors.A key regulator is Mdm2, an E3 ubiquitin ligase, that binds and ubiquitinates p53 and directs its degradation via the proteosome. Small potent peptides that can block the p53 Mdm2 interaction and activate the p53 response have been described. Growing selections of lead small molecules that mimic the action of these peptides have also been recently discovered. Cell-based screens have revealed that inhibitors of nuclear export and inhibitors of transcription (one of which is in clinical trial) can also activate the p53 response therapeutically. The pharmaceutical regulation of the p53 pathway offers great hope for improved treatment of human cancer. 2Mammographic screening with a breast cancer prevention programme Because of the heterogeneity of breast cancer from nodule to nodule, single findings cannot achieve the sensitivity or the negative predictive value necessary to identify a low-risk group that can be offered the option of follow-up (ACR Breast Imaging Reporting and Data System [BIRADS] 3 group). However, by using multiple findings in a strict algorithm, such a group can be identified. It is also important to keep in mind that breast cancer can be heterogeneous within an individual nodule. Part of the nodule may have circumscribed features that simulate a benign lesion, while another part may be spiculated and obviously malignant. Only by scanning the whole surface and substance of the nodule in two orthogonal planes (radial and anti-radial) can the presence of suspicious findings be excluded, and if there is a mixture of benign and suspicious findings, the benign findings should be ignored.These studies show that sonography is useful in the characterization of solid breast masses. Characterizing solid breast nodules into BIRADS categories defines carcinomas that might have been missed clinically or mammographically. It identifies a BIRADS 3 group that has far less than 2% risk of being malignant and can offer the patient the option of followup rather than biopsy. Currently, approximately 80% of patients with BIRADS 3 solid nodules are electing to be followed rather than to undergo biopsy. It improves the accuracy of the diagnosis of malignant breast lesions. Importantly, it also accurately defines a population of benign solid breast lesions that do not require biopsy when strict sonographic criteria of benignity are present.To a...

Background: Late recurrence is characteristic of ER+ breast cancers. Despite an apparently effective adjuvant endocrine therapy, many breast cancers recur years after their initial endocrine treatment. Why some tumors recur early (<3 years) and some recur later (>5 years) is poorly understood. If systemic endocrine therapies killed all cells, recurrence would reflect only the appearance of new disease. Thus, we hypothesized that cells that survive and lie dormant may be driven, in part, by altered wiring of their cell death signaling. We, therefore, studied how cell death signaling is differentially wired in primary tumors that will recur early versus those that will recur later. Method: Genes involved in apoptosis, autophagy, ferroptosis, necrosis, and pyroptosis were identified from KEGG to initiate network feature analysis of gene expression data from public and our first in-house gene expression dataset. Data were collected from ER+ breast cancer pre-endocrine treatment samples with up to 20 years follow-up. Publicly available datasets used were GSE6532, GSE2034, GSE7390, GSE17705, GSE12093, and TCGA. We applied our Knowledge-fused Differential Dependency Network (KDDN) analysis tool to the public datasets; KDDN has provided powerful new insights into signaling in breast and other cancers. Common gene-gene interactions (edges) predicted in at least two different datasets were extracted from all KDDN analyses results. To strengthen the relevance of these features, predicted network edges that represent known protein-protein interactions (PPI) were identified from the STRING database, and these edges were noted in the signaling graphs. Final network graphs were constructed using the common edges from all overlaid networks. We conducted IPA analysis on all nodes in the final network and selected those incorporating network hubs. We took a similar approach to our second in-house dataset, which we used for independent testing. Here, patients were included if their tumor exhibited an initial reduction in volume of at least 40% by four months in response to neo-adjuvant Letrozole. Patients were then classified into two groups during follow-up of up to 3.7 years: i) initial tumor size reduction followed by continued response (expected to recur late); ii) initial reduction followed by tumor regrowth (expected to recur early). KDDN analysis was performed on pretreatment samples from these two groups and a network created annotated with PPI information. Results: MAPK8 and CYCS (Molecular Mechanisms of Cancer, p=1.58E-52), TNFRSF1A Neuroinflammation Signaling Pathway, p=1.26E-54), RELA, and NFKB1 (Colorectal Cancer Metastasis Signaling, p=7.94E-35), were identified as hubs. Hubs may be critical signaling components driving the differences between tumors that will become dormant and recur late. Connections between SLC25A6 and SQSTM1 (p = 0.008), BIRC2 and GABARAP (p = 0.021) in the early group, and AKT3 and IRS2 (p = 0.014) in the late group, were shared between the two final networks. With longer follow-up time on the second in-house dataset, we will better define the two groups and identify additional common phenotype specific gene-gene interactions. Citation Format: Clarke R, Dixon M, Jin L, Pearce D, Turnbull A, Selli C, Hu R, Zwart A, Wang Y, Xuan J, Sengupta S, Sims A, Liu MC. Local network topology differences between early and late recurrence in ER+ breast cancers [abstract]. In: Proceedings of the 2018 San Antonio Breast Cancer Symposium; 2018 Dec 4-8; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2019;79(4 Suppl):Abstract nr P5-04-17.