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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 and in vivo models of PDAC, spanning human and murine PDAC cells, and orthotopic xenografts, 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 significantly higher sensitivity in K17‐positive compared to K17‐negative PDAC cell lines and animal models. In the clinic, another microtubule inhibitor, paclitaxel (PTX), is used in combination with Gem as a first‐line chemotherapeutic regimen for PDAC. Surprisingly, we found that when combined with Gem, PPT, but not PTX, was synergistic in inhibiting the viability of K17‐expressing PDAC cells. Importantly, in preclinical models, PPT in combination with Gem effectively decreased tumor growth and enhanced the survival of mice bearing K17‐expressing tumors. This provides evidence that PPT and its derivatives could potentially be combined with Gem to enhance treatment efficacy for the ~ 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., Keratin 17, a signature gene overexpressed in the most lethal subtype of pancreatic ductal adenocarcinoma (PDAC), is both prognostic and predictive, and it directly promotes chemoresistance. We discover a novel therapeutic regimen that provides higher efficacy for Keratin 17‐expressing PDAC. These studies bring us closer to the frontline of translational research to develop a biomarker‐based targeted therapy for cancers.

SF3B1 is the most commonly mutated RNA splicing factor in cancer1–4, but the mechanisms by which SF3B1 mutations promote malignancy are poorly understood. Here we integrated pan-cancer splicing analyses with a positive-enrichment CRISPR screen to prioritize splicing alterations that promote tumorigenesis. We report that diverse SF3B1 mutations converge on repression of BRD9, which is a core component of the recently described non-canonical BAF chromatin-remodelling complex that also contains GLTSCR1 and GLTSCR1L5–7. Mutant SF3B1 recognizes an aberrant, deep intronic branchpoint within BRD9 and thereby induces the inclusion of a poison exon that is derived from an endogenous retroviral element and subsequent degradation of BRD9 mRNA. Depletion of BRD9 causes the loss of non-canonical BAF at CTCF-associated loci and promotes melanomagenesis. BRD9 is a potent tumour suppressor in uveal melanoma, such that correcting mis-splicing of BRD9 in SF3B1-mutant cells using antisense oligonucleotides or CRISPR-directed mutagenesis suppresses tumour growth. Our results implicate the disruption of non-canonical BAF in the diverse cancer types that carry SF3B1 mutations and suggest a mechanism-based therapeutic approach for treating these malignancies. A range of SF3B1 mutations promote tumorigenesis through the repression of BRD9, a core component of the non-canonical BAF complex, and correcting BRD9 mis-splicing in these SF3B1-mutant cells suppresses tumour growth.

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.

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 < .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.

Background Multiplex immunohistochemistry (mIHC) permits the labeling of six or more distinct cell types within a single histologic tissue section. The classification of each cell type requires detection of uniquely colored chromogens localized to cells expressing biomarkers of interest. The most comprehensive and reproducible method to evaluate such slides is to employ digital pathology and image analysis pipelines to whole-slide images (WSIs). Our suite of deep learning tools quantitatively evaluates the expression of six biomarkers in mIHC WSIs. These methods address the current lack of readily available methods to evaluate more than four biomarkers and circumvent the need for specialized instrumentation to spectrally separate different colors. The use case application for our methods is a study that investigates tumor immune interactions in pancreatic ductal adenocarcinoma (PDAC) with a customized mIHC panel. Methods Six different colored chromogens were utilized to label T-cells (CD3, CD4, CD8), B-cells (CD20), macrophages (CD16), and tumor cells (K17) in formalin-fixed paraffin-embedded (FFPE) PDAC tissue sections. We leveraged pathologist annotations to develop complementary deep learning-based methods: (1) ColorAE is a deep autoencoder which segments stained objects based on color; (2) U-Net is a convolutional neural network (CNN) trained to segment cells based on color, texture and shape; and (3) ensemble methods that employ both ColorAE and U-Net, collectively referred to as ColorAE:U-Net. We assessed the performance of our methods using: structural similarity and DICE score to evaluate segmentation results of ColorAE against traditional color deconvolution; F1 score, sensitivity, positive predictive value, and DICE score to evaluate the predictions from ColorAE, U-Net, and ColorAE:U-Net ensemble methods against pathologist-generated ground truth. We then used prediction results for spatial analysis (nearest neighbor). Results We observed that (1) the performance of ColorAE is comparable to traditional color deconvolution for single-stain IHC images (note: traditional color deconvolution cannot be used for mIHC); (2) ColorAE and U-Net are complementary methods that detect six different classes of cells with comparable performance; (3) combinations of ColorAE and U-Net in ensemble methods outperform ColorAE and U-Net alone; and (4) ColorAE:U-Net ensemble methods can be employed for detailed analysis of the tumor microenvironment (TME). Summary We developed a suite of scalable deep learning methods to analyze 6 distinctly labeled cell populations in mIHC WSIs. We evaluated our methods and found that they reliably detected and classified cells in the PDAC tumor microenvironment. We also utilized the ColorAE:U-Net ensemble method to analyze 3 mIHC WSIs with nearest neighbor spatial analysis. We demonstrate a proof of concept that these methods can be employed to quantitatively describe the spatial distribution of immune cells within the tumor microenvironment. These complementary deep learning methods are readily deployable for use in clinical research studies.