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Gene-expression deconvolution is used to quantify different types of cells in a mixed population. It provides a highly promising solution to rapidly characterize the tumor-infiltrating immune landscape and identify cold cancers. However, a major challenge is that gene-expression data are frequently contaminated by many outliers that decrease the estimation accuracy. Thus, it is imperative to develop a robust deconvolution method that automatically decontaminates data by reliably detecting and removing outliers. We developed a new machine learning tool, F ast A nd R obust DE convolution of E xpression P rofiles (FARDEEP), to enumerate immune cell subsets from whole tumor tissue samples. To reduce noise in the tumor gene expression datasets, FARDEEP utilizes an adaptive least trimmed square to automatically detect and remove outliers before estimating the cell compositions. We show that FARDEEP is less susceptible to outliers and returns a better estimation of coefficients than the existing methods with both numerical simulations and real datasets. FARDEEP provides an estimate related to the absolute quantity of each immune cell subset in addition to relative percentages. Hence, FARDEEP represents a novel robust algorithm to complement the existing toolkit for the characterization of tissue-infiltrating immune cell landscape. The source code for FARDEEP is implemented in R and available for download at https://github.com/YuningHao/FARDEEP.git .

Gene-expression deconvolution is used to quantify different types of cells in a mixed population. It provides a highly promising solution to rapidly characterize the tumor-infiltrating immune landscape and identify cold cancers. However, a major challenge is that gene-expression data are frequently contaminated by many outliers that decrease the estimation accuracy. Thus, it is imperative to develop a robust deconvolution method that automatically decontaminates data by reliably detecting and removing outliers. We developed a new machine learning tool, Fast And Robust DEconvolution of Expression Profiles (FARDEEP), to enumerate immune cell subsets from whole tumor tissue samples. To reduce noise in the tumor gene expression datasets, FARDEEP utilizes an adaptive least trimmed square to automatically detect and remove outliers before estimating the cell compositions. We show that FARDEEP is less susceptible to outliers and returns a better estimation of coefficients than the existing methods with both numerical simulations and real datasets. FARDEEP provides the absolute quantitation of each immune cell subset in addition to relative percentages. Hence, FARDEEP represents a novel robust algorithm to complement the existing toolkit for the characterization of tissueinfiltrating immune cell landscape. The source code for FARDEEP as implemented in R is available for download at https://goo.gl/SqGKuo.

Rationale: The endemic of peri-implantitis affects over 25% of dental implants. Current treatment depends on empirical patient and site-based stratifications and lacks a consistent risk grading system. Methods: We investigated a unique cohort of peri-implantitis patients undergoing regenerative therapy with comprehensive clinical, immune, and microbial profiling. We utilized a robust outlier-resistant machine learning algorithm for immune deconvolution. Results: Unsupervised clustering identified risk groups with distinct immune profiles, microbial colonization dynamics, and regenerative outcomes. Low-risk patients exhibited elevated M1/M2-like macrophage ratios and lower B-cell infiltration. The low-risk immune profile was characterized by enhanced complement signaling and higher levels of Th1 and Th17 cytokines. Fusobacterium nucleatum and Prevotella intermedia were significantly enriched in high-risk individuals. Although surgery reduced microbial burden at the peri-implant interface in all groups, only low-risk individuals exhibited suppression of keystone pathogen re-colonization. Conclusion: Peri-implant immune microenvironment shapes microbial composition and the course of regeneration. Immune signatures show untapped potential in improving the risk-grading for peri-implantitis.

A workforce that understands principles of geriatric medicine is critical to addressing the care needs of the growing elderly population. This will be impossible without a substantial increase in academicians engaged in education and aging research. Limited support of early‐career clinician–educators is a major barrier to attaining this goal. The Geriatric Academic Career Award (GACA) was a vital resource that benefitted 222 junior faculty members. GACA availability was interrupted in 2006, followed by permanent discontinuation after the Geriatrics Workforce Education Program (GWEP) subsumed it in 2015, leaving aspiring clinician–educators with no similar alternatives. GACA recipients were surveyed in this cross‐sectional, multimethod study to assess the effect of the award on career development, creation and dissemination of educational products, funding discontinuation consequences, and implications of program closure for the future of geriatric health care. Uninterrupted funding resulted in fulfillment of GACA goals (94%) and overall career success (96%). Collectively, awardees reached more than 40,700 learners. Funding interruption led to 55% working additional hours over and above an increased clinical workload to continue their GACA‐related research and scholarship. Others terminated GACA projects (36%) or abandoned academic medicine altogether. Of respondents currently at GWEP sites (43%), only 13% report a GWEP budget including GACA‐like support. Those with GWEP roles attributed their current standing to experience gained through GACA funding. These consequences are alarming and represent a major setback to academic geriatrics. GACA's singular contribution to the mission of geriatric medicine must prompt vigorous efforts to restore it as a distinct funding opportunity.

Introduction:The rapidly aging US population is resulting in major challenges including delivering quality care at lower costs in the face of a critical health-care workforce shortage. The movement toward home care has dramatically increased the need for qualified, paid personal care aides (PCAs). Adequate PCA training that focuses on skills for person-centered, at home support is an imperative. This study provides evidence that clients of PCAs who have completed a comprehensive, evidence-based PCA training program, titled Building Training…Building Quality (BTBQ), report higher satisfaction and better health outcomes, compared to clients of PCAs with lesser or other training.Methods:A mixed-methods, quasi-experimental design was used to compare self-reported survey responses from clients of BTBQ-trained PCAs (treatment group) with responses from clients of non-BTBQ-trained PCAs (control group).Results:Clients of BTBQ-trained PCAs had significantly fewer falls and emergency department visits compared to clients whose PCAs had no BTBQ training (P < .05). Conclusion: BTBQ-like PCA training reduces costly adverse events.

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Purpose: Current immunotherapy response rates in high-grade serous carcinoma (HGSC) of the ovary, fallopian tube, and peritoneum are 10-15%. We sought to determine if decreasing levels of the DNA damage repair protein DEK or inhibiting its downstream effector aurora kinase A (AURKA) induces type I interferon (IFN-I) signaling to improve the immune sensing of HGSC. Experimental Procedures: RNA-Seq analysis was performed on HGSC cell lines with stable expression of shRNA targeting DEK (shDEK) or control. Gene expression patterns were analyzed by gene set enrichment analysis and confirmed by RT-PCR. As AURKA/B were identified to be downregulated by shDEK, cell lines were treated with aurora kinase inhibitors and analyzed for DNA damage and apoptosis. IFN-I signature transcripts were analyzed by RT-PCR following shDEK therapy or aurora kinase inhibitor therapy compared to controls. Tumor-infiltrating lymphocyte (TIL) profiles in HGSC from The Cancer Genome Atlas (TCGA) were characterized. Using the ID8 immunocompetent ovarian cancer mouse model, IFN-I signature transcripts were quantified after transfection with siRNA targeting Dek or control without or with overexpression of the DNA damage sensing protein Sting. In vivo studies were performed by injecting ID8 cells intraperitoneally and then treating with the AURKA inhibitor alisertib, anti-PD-L1 antibody, combination therapy or control. TILs were analyzed by flow cytometry. Results: RNA-Seq analysis identified interferon-alpha response as an upregulated pathway in the setting of DEK deficiency. In vitro validation revealed that decreasing DEK levels increases IFN-I signaling. RNA-Seq analysis also showed decreased AURKA/B following shDEK treatment. A positive correlation between DEK and AURKA/B transcript levels was also found in primary patient samples. AURKA/B inhibitor therapy resulted in increased DNA damage and apoptosis, and increased IFN-I signature gene transcripts including IFNB1, IFNA4, ISG15, and MX1. TCGA analysis showed that elevated levels of IFN-I genes including chemokines CXCL9 and CXCL10 are correlated with TIL subsets essential for antitumor immunity. In ID8 cells, Dek-deficiency enhanced Sting-mediated induction of Ifnb1, Cxcl9, and Cxcl10. In the ID8 in vivo studies, AURKA inhibitor therapy resulted in increased TCRβ and TCRγδ TIL subpopulations. Combinatorial therapy animal studies are ongoing. Conclusions: Decreasing levels of the DNA damage repair protein DEK or inhibiting its effector AURKA/B induces DNA damage and increases IFN-I signaling in both HGSC cells lines and primary patient samples. Our TCGA analysis supports the hypothesis that IFN-I signaling is pivotal for the HGSC immunogenicity. Inhibition of AURKA in the ID8 mouse model system results in a shift in the immune phenotype, and further preclinical combinatorial studies are under way. Our results identify a new synthetic immune toxicity combination by priming HGSC with AURKA-targeted therapy with the goal of increasing immunotherapy responses. Citation Format: Danielle E. Bolland, Yuning Hao, Yee Sun Tan, Jake Reske, Lijun Tan, Ronald L. Chandler, Yuying Xie, Yu L. Lei, Karen McLean. Induction of DNA damage in high-grade serous carcinoma induces type I interferon signaling [abstract]. In: Proceedings of the AACR Special Conference on Advances in Ovarian Cancer Research; 2019 Sep 13-16, 2019; Atlanta, GA. Philadelphia (PA): AACR; Clin Cancer Res 2020;26(13_Suppl):Abstract nr B05.