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Although the overall five-year survival of patients with pancreatic ductal adenocarcinoma (PDAC) is dismal, there are survival differences between cases with clinically and pathologically indistinguishable characteristics, suggesting that there are uncharacterized properties that drive tumor progression. Recent mRNA sequencing studies reported gene-expression signatures that define PDAC molecular subtypes that correlate with differences in survival. We previously identified Keratin 17 (K17) as a negative prognostic biomarker in other cancer types. Here, we set out to determine if K17 is as accurate as molecular subtyping of PDAC to identify patients with the shortest survival. K17 mRNA was analyzed in two independent PDAC cohorts for discovery (n = 124) and validation (n = 145). Immunohistochemical localization and scoring of K17 immunohistochemistry (IHC) was performed in a third independent cohort (n = 74). Kaplan-Meier and Cox proportional-hazard regression models were analyzed to determine cancer specific survival differences in low vs. high mRNA K17 expressing cases. We established that K17 expression in PDACs defines the most aggressive form of the disease. By using Cox proportional hazard ratio, we found that increased expression of K17 at the IHC level is also associated with decreased survival of PDAC patients. Additionally, within PDACs of advanced stage and negative surgical margins, K17 at both mRNA and IHC level is sufficient to identify the subgroup with the shortest survival. These results identify K17 as a novel negative prognostic biomarker that could inform patient management decisions.

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Pancreatic ductal adenocarcinoma (PDAC) is predicted to become the second leading cause of cancer-related deaths in the United States by 2020, due in part to innate resistance to widely used chemotherapeutic agents and limited knowledge about key molecular factors that drive tumor aggression. We previously reported a novel negative prognostic biomarker, keratin 17 (K17), whose overexpression in cancer results in shortened patient survival. In this study, we aimed to determine the predictive value of K17 and explore the therapeutic vulnerability in K17-expressing PDAC, using an unbiased high-throughput drug screen. Patient-derived data analysis showed that K17 expression correlates with resistance to Gemcitabine (Gem). In multiple in vitro models of PDAC, spanning human and murine PDAC cells, we determined that the expression of K17 results in a more than twofold increase in resistance to Gem and 5-fluorouracil, key components of current standard-of-care chemotherapeutic regimens. Furthermore, through an unbiased drug screen, we discovered that Podophyllotoxin (PPT), a microtubule inhibitor, showed at least two-fold higher sensitivity in K17expressing compared to K17-negative PDAC cells. In the clinic, another microtubule inhibitor, Paclitaxel (PTX), is used in combination with Gem as a first line chemotherapeutic regimen for pancreatic, breast, lung, and ovarian cancer. Surprisingly, we found that when combined with Gem, PPT but not PTX, was synergistic in inhibiting the viability of K17-expressing PDAC cells. This provides evidence that PPT or its derivatives could potentially be combined with Gem to enhance treatment efficacy for the approximately 50% of PDACs that express high levels of K17. In summary, we reported that K17 is a novel target for developing a biomarker-based personalized treatment for PDAC.3 4 2. Methods 2.1. Predictive-value analyses from patient-derived samples K17 mRNA expression levels of PDAC cases were acquired from the Australian Pancreatic Cancer Genome Initiative (APGI)13. K17 mRNA expression and survival were evaluated in 94 PDAC patients that were treated with adjuvant Gem alone or received no treatment. Based on the established cutoff of the maximum likelihood fit of a Cox proportional hazard model15, we applied the 76th percentile of mRNA expression to categorize patients into high-K17 versus low-K17 groups. Overall survival in high-versus low-K17 mRNA was determined using the Kaplan-Meier method, calculated from the date of diagnosis to the date of death. Patients still alive at the last follow-up were censored. Prognostic and predictive analyses were performed based on the criteria described by Ballman18. Adjusting for potential confounders, a multivariate analysis was performed by Cox proportional hazard regression. Statistical significance was set at p<0.05 and analysis was done using SAS 9.4 (SAS Institute) and GraphPad Prism 7 (Graph Pad Software). Compounds testedGem (purity > 99%), 5-FU (purity > 99%), Podophyllotoxin (PPT, purity > 99%), Taxol (PTX, purity > 95%), Mitoxant...

Objectives The microscopic features of urine cytology specimens are subjective and may not reliably distinguish between benign urothelial cells and low-grade urothelial carcinoma (UC). Prior studies demonstrated that keratin 17 (K17) detection in biopsies is highly sensitive for UC. The current study aimed to define K17 diagnostic test performance for initial screening and detect recurrent UC in urine specimens. Methods K17 was detected by immunocytochemistry (ICC) in consecutively collected urine specimens (2018-2019). A qualitative score for the K17 test was determined in 81 samples (discovery cohort) and validated in 98 samples (validation cohort). K17 sensitivity and specificity were analyzed in both cohorts across all grades of UC. Results Based on the discovery cohort, the presence of 5 or more K17 immunoreactive urothelial cells (area under the curve = 0.90; P &lt; .001) was the optimal threshold to define a K17-positive test. The sensitivity of the K17 ICC test for biopsy-confirmed UC was 35 of 36 (97%) and 18 of 21 (86%) in the discovery and validation cohorts, respectively. K17 was positive in 16 of 19 (84%) specimens with biopsy-confirmed low-grade UC and in 34 of 34 (100%) of specimens with high-grade UC. Conclusions K17 ICC is a highly sensitive diagnostic test for initial screening and detection of recurrence across all grades of UC.

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In times of crisis, including the current COVID-19 pandemic, the supply chain of filtering facepiece respirators, such as N95 respirators, are disrupted. To combat shortages of N95 respirators, many institutions were forced to decontaminate and reuse respirators. While several reports have evaluated the impact on filtration as a measurement of preservation of respirator function after decontamination, the equally important fact of maintaining proper fit to the users’ face has been understudied. In the current study, we demonstrate the complete inactivation of SARS-CoV-2 and preservation of fit test performance of N95 respirators following treatment with dry heat. We apply scanning electron microscopy with energy dispersive X-ray spectroscopy (SEM/EDS), X-ray diffraction (XRD) measurements, Raman spectroscopy, and contact angle measurements to analyze filter material changes as a consequence of different decontamination treatments. We further compared the integrity of the respirator after autoclaving versus dry heat treatment via quantitative fit testing and found that autoclaving, but not dry heat, causes the fit of the respirator onto the users face to fail, thereby rendering the decontaminated respirator unusable. Our findings highlight the importance to account for both efficacy of disinfection and mask fit when reprocessing respirators to for clinical redeployment.