Citations

Subarachnoid hemorrhage (SAH) is one of the deadliest types of strokes with high rates of morbidity and permanent injury. Fluctuations in the levels of cerebral metabolites following SAH can be indicators of brain injury severity. Specifically, the changes in the levels of key metabolites involved in cellular metabolism, lactate and pyruvate, can be used as a biomarker for patient prognosis and tailor treatment to an individual’s needs. Here, clinical research is reviewed on the usefulness of cerebral lactate and pyruvate measurements as a predictive tool for SAH outcomes and their potential to guide a precision medicine approach to treatment.

Abstract 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.

Abstract Background:Late recurrence (emergence from dormancy) is characteristic of ER+ breast cancers. Despite adjuvant endocrine therapy, many breast cancers recur decades after their initial diagnosis and treatment. Why this occurs is poorly understood. Methods: We studied 2 independent datasets of endocrine treated, ER+ breast cancers with up to 20 years follow-up. The 1st comprised matched samples from the primary tumor pretreatment at diagnosis and the first recurrence after or during adjuvant endocrine therapy (all FFPE). The 2nd dataset comprised pretreatment biopsies only (all snap frozen). For both datasets, high quality RNA was amplified, labelled, and subjected to transcriptome analysis using the Affymetrix technology (U133 Plus 2.0). Low quality data were identified using 'simpleaffy' and 'ffpe', and removed; all tools were from the R package unless otherwise noted. Remaining data were normalized using 'frma'. Genes differentially expressed between early (≤3 years) and late (≥ 5 years) were selected using limma. Unsupervised hierarchical clustering and PCA explored the structure of the data. A similar molecular analysis was done on the 2nd dataset. A classification scheme that robustly separated early from late recurrences was validated in an independent public dataset of comparable patients, array platform, and frozen tissues. We also explored features in pretreatment samples that predetermined response duration. Results: Genes that separated pretreatment specimens by recurrence time did not separate posttreatment specimens. Specimens did not cluster in patient pairs or by site of recurrence. 8245 genes were differentially expressed between early and late recurrences in the FFPE samples, while 2400 genes were significantly different in the same comparison in the frozen samples. Initial pathway analysis was done on each dataset independently using IPA (Ingenuity® Systems, www.ingenuity.com). 70 canonical pathways were identified in common between the two datasets (pretreatment). We then looked for genes regulated in both datasets (ignores FFPE and frozen tissue as source). There were 279 genes in common that differentially regulated in the same direction (upregulated; downregulated). IPA analysis of these genes identified 49 canonical pathways. We also explored the differentially expressed gene sets using 'GSEA' (www.software.broadinstitute.org/gsea/index.jsp). Pathways consistently associated with early vs. late recurrence include integrin signaling, the unfolded protein response, endoplasmic reticulum stress, actin-based motility, and estrogen biosynthesis. Conclusion: Analysis of pretreatment tumors can predict early recurrences from those that will remain dormant and recur much later. Recurrent tumors exhibit a remodeled molecular landscape that likely reflects the effects of treatments and/or a recreation of a niche with potentially common features at the site of recurrence. Changes in molecular signaling associated with duration of recurrence are consistent with our experimental model studies in vitro implicating UPR signaling as a major integrator of response to endocrine therapy and duration of survival. Additional data sets are being arrayed and more detailed molecular signaling studies are in progress. Citation Format: Clarke R, Dixon MJ, Jin L, Turnbull A, Hu R, Zwart A, Wang Y, Xuan J, Sengupta S, Renshaw L, Sims A, Liu MC. Molecular features of dormancy in ER+ breast cancers [abstract]. In: Proceedings of the 2017 San Antonio Breast Cancer Symposium; 2017 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2018;78(4 Suppl):Abstract nr P4-04-10.

Intravenous infusion of oncolytic reovirus in patients leads to infection of brain tumors, infiltration by cytotoxic T cells, and up-regulation of PD-L1.

Abstract Identifying genetic variants with pleiotropic associations can uncover common pathways influencing multiple cancers. We took a two-stage approach to conduct genome-wide association studies for lung, ovary, breast, prostate, and colorectal cancer from the GAME-ON/GECCO Network (61,851 cases, 61,820 controls) to identify pleiotropic loci. Findings were replicated in independent association studies (55,789 cases, 330,490 controls). We identified a novel pleiotropic association at 1q22 involving breast and lung squamous cell carcinoma, with eQTL analysis showing an association with ADAM15/THBS3 gene expression in lung. We also identified a known breast cancer locus CASP8/ALS2CR12 associated with prostate cancer, a known cancer locus at CDKN2B-AS1 with different variants associated with lung adenocarcinoma and prostate cancer, and confirmed the associations of a breast BRCA2 locus with lung and serous ovarian cancer. This is the largest study to date examining pleiotropy across multiple cancer-associated loci, identifying common mechanisms of cancer development and progression. Cancer Res; 76(17); 5103–14. ©2016 AACR.

Abstract Rationale Rehabilitation started early during an intensive care unit (ICU) stay is associated with improved outcomes and is the basis for many quality improvement (QI) projects showing important changes in practice. However, little evidence exists regarding whether such changes are sustainable in real-world practice. Objectives To evaluate the sustained effect of a quality improvement project on the timing of initiation of active physical therapy intervention in patients with acute lung injury (ALI). Methods This was a pre–post evaluation using prospectively collected data involving consecutive patients with ALI admitted pre–quality improvement (October 2004–April 2007, n = 120) versus post–quality improvement (July 2009–July 2012, n = 123) from a single medical ICU. Measurements and Main Results The primary outcome was time to first active physical therapy intervention, defined as strengthening, mobility, or cycle ergometry exercises. Among ICU survivors, more patients in the post–quality improvement versus pre–quality improvement group received physical therapy in the ICU (89% vs. 24%, P < 0.001) and were able to stand, transfer, or ambulate during physical therapy in the ICU (64% vs. 7%, P < 0.001). Among all patients in the post–quality improvement versus pre–quality improvement group, there was a shorter median (interquartile range) time to first physical therapy (4 [2, 6] vs. 11 d [6, 29], P < 0.001) and a greater median (interquartile range) proportion of ICU days with physical therapy after initiation (50% [33, 67%] vs. 18% [4, 47%], P = 0.003). In multivariable regression analysis, the post–quality improvement period was associated with shorter time to physical therapy (adjusted hazard ratio [95% confidence interval], 8.38 [4.98, 14.11], P < 0.001), with this association significant for each of the 5 years during the post–quality improvement period. The following variables were independently associated with a longer time to physical therapy: higher Sequential Organ Failure Assessment score (0.93 [0.89, 0.97]), higher Fi O2 (0.86 [0.75, 0.99] for each 10% increase), use of an opioid infusion (0.47 [0.25, 0.89]), and deep sedation (0.24 [0.12, 0.46]). Conclusions In this single-site, pre–post analysis of patients with ALI, an early rehabilitation quality improvement project was independently associated with a substantial decrease in the time to initiation of active physical therapy intervention that was sustained over 5 years. Over the entire pre–post period, severity of illness and sedation were independently associated with a longer time to initiation of active physical therapy intervention in the ICU.