Osimertinib-Induced Cutaneous Vasculitis Responsive to Low-Dose Dapsone Without Interruption of Anticancer Therapy: A Case Report and Review of the Literature.

A 45-year-old woman with a history of lung adenocarcinoma treated with osimertinib developed purpuric plaques and vesicles on the lower extremities after 5 months of therapy. Skin biopsy revealed leukocytoclastic vasculitis (LCV). A workup for systemic involvement was unremarkable. The patient was treated with oral dapsone while continuing osimertinib without interruption. Skin lesions cleared within 2 weeks of therapy with no recurrence after titrating off dapsone. To the best of our knowledge, this is the first reported case of LCV induced by a small-molecule EGFR inhibitor in which therapy was not interrupted. This is also the first reported case treated with dapsone rather than systemic corticosteroids. We suggest consideration of dapsone to treat skin-limited LCV induced by EGFR inhibitors in patients with lung cancer without features of systemic vasculitis. In addition, this case highlights that it may not be necessary to stop EGFR inhibitor therapy in the absence of severe features such as ulceration, bullae, necrosis, or severe pain. Dapsone is an effective targeted therapy for cutaneous LCV that does not globally impair the immune system and may allow for uninterrupted treatment of the underlying malignancy.

Understanding the impact of chemotherapy on the immune landscape of high-grade serous ovarian cancer.

OBJECTIVES: We quantitatively characterized the change in temporospatial expression of repressive and stimulatory checkpoints across immune cell populations in the tumor microenvironment in a cohort of high grade serous ovarian carcinomas (HGSOC) using matched samples before and after neoadjuvant platinum-based chemotherapy. METHODS: Using retrospectively collected matched tissue samples from 9 patients, cell populations were assessed using multiplex immunofluorescence using the Vectra Multispectral Imaging System (Perkin Elmer). We used multiple panels to assess: tumor (AE1/AE3), T cells (CD3, CD8, FOXP3), macrophages (CD68) as well as immune checkpoints (C3aR, PD-1, PD-L1, LAG3, IDO, ICOS, GITR). IHC staining was performed for folate receptor status. Changes in immune cell populations as well as intensities of associated repressive and stimulatory proteins were assessed pre- to post-treatment. RESULTS: We observed a consistently high pre-treatment stromal macrophage population which is reduced post-chemotherapy with post-treatment enrichment in macrophage PD-L1 expression. While inhibitory checkpoint expression on T cells was heterogeneous post-chemotherapy, we observed a change in the ThICOS+:Treg ratio which resulted in ThICOS+ cells outnumbering Treg cells post-treatment. Spatial analysis revealed the proximity of Treg cells to ThICOS+ T cells decreased post-treatment. We also observed upward shifts in Teff:Treg T cell ratios with retention of immune checkpoints PD-1, LAG3 and GITR. CONCLUSIONS: In this unique dataset of pre and post matched chemotherapy treated HGSOC patients, we observed changes in immune cell subsets expressing repressive or stimulatory proteins resulting in immune compositions more favorable to checkpoint modulations, suggesting novel therapeutic strategies in the recurrent setting.

A woman presenting with an unusual cause of fulminant liver failure and sepsis.

We present the case of a 61-year-old woman who presented with acutely worsening right upper quadrant pain and was found to be in acute liver failure with Klebsiella pneumoniae bacteremia. Despite aggressive intensive care management, the patient ultimately died of refractory shock attributed to sepsis and fulminant liver failure. On autopsy, she was found unexpectedly to have diffuse intrahepatic cholangiocarcinoma with metastases to regional lymph nodes and intravascular spread to the lungs. The case highlights a rare instance where intrahepatic cholangiocarcinoma presents with acute liver failure and discusses key intensive care management principles of this clinical syndrome.

Extramammary Paget disease shows differential expression of B7 family members B7-H3, B7-H4, PD-L1, PD-L2 and cancer/testis antigens NY-ESO-1 and MAGE-A.

Extramammary Paget disease (EMPD) is a rare cutaneous adenocarcinoma of the anogenital region most commonly treated with surgical excision. Surgical margin clearance is often problematic and recurrence rates remain high indicating the need for additional therapeutic options. Topical immunomodulators have been used with reported success suggesting EMPD may respond to other immunotherapies. This study investigates EMPD protein expression of targetable B7 family members and cancer/testis antigens (CTAs) B7-H3, B7-H4, PD-L1, PD-L2, MAGE-A, and NY-ESO-1 and components of antigen presenting machinery B2M and MHC-I. Fifty-seven specimens from 48 patients (31 female and 17 male), representing in situ, invasive, and metastatic disease of primary and secondary origin were stained and scored (627 total slides). The percentage of cases expressing each immune regulatory molecule in the in situ followed by invasive tumor components was: B7-H3 (94, 90), B7-H4 (82, 78), PD-L1 (6, 10), MAGE-A (39, 50), NY-ESO-1 (16, 20), B2M (100, 89), and MHC-I (78, 79). PD-L2 was negative in all cases. There was high correlation between marker expression within the in situ and invasive tumor components of the same case. B7-H4 was preferentially expressed in primary cutaneous EMPD. Co-expression of B7 family members B7-H3 and B7-H4 was found within the in situ and invasive tumor components of 74% and 48% of cases, respectively. These findings provide an initial characterization of EMPD tumor cell expression of B7-H3, B7-H4, PD-L1, PD-L2, MAGE-A, and NY-ESO-1 and indicate the potential for new immunotherapeutic options for patients with EMPD.

Differentiation of Patients with Balance Insufficiency (Vestibular Hypofunction) versus Normal Subjects Using a Low-Cost Small Wireless Wearable Gait Sensor.

Balance disorders present a significant healthcare burden due to the potential for hospitalization or complications for the patient, especially among the elderly population when considering intangible losses such as quality of life, morbidities, and mortalities. This work is a continuation of our earlier works where we now examine feature extraction methodology on Dynamic Gait Index (DGI) tests and machine learning classifiers to differentiate patients with balance problems versus normal subjects on an expanded cohort of 60 patients. All data was obtained using our custom designed low-cost wireless gait analysis sensor (WGAS) containing a basic inertial measurement unit (IMU) worn by each subject during the DGI tests. The raw gait data is wirelessly transmitted from the WGAS for real-time gait data collection and analysis. Here we demonstrate predictive classifiers that achieve high accuracy, sensitivity, and specificity in distinguishing abnormal from normal gaits. These results show that gait data collected from our very low-cost wearable wireless gait sensor can effectively differentiate patients with balance disorders from normal subjects in real-time using various classifiers. Our ultimate goal is to be able to use a remote sensor such as the WGAS to accurately stratify an individual's risk for falls.

Multicenter Study Using Desorption-Electrospray-Ionization-Mass-Spectrometry Imaging for Breast-Cancer Diagnosis.

The histological and molecular subtypes of breast cancer demand distinct therapeutic approaches. Invasive ductal carcinoma (IDC) is subtyped according to estrogen-receptor (ER), progesterone-receptor (PR), and HER2 status, among other markers. Desorption-electrospray-ionization-mass-spectrometry imaging (DESI-MSI) is an ambient-ionization MS technique that has been previously used to diagnose IDC. Aiming to investigate the robustness of ambient-ionization MS for IDC diagnosis and subtyping over diverse patient populations and interlaboratory use, we report a multicenter study using DESI-MSI to analyze samples from 103 patients independently analyzed in the United States and Brazil. The lipid profiles of IDC and normal breast tissues were consistent across different patient races and were unrelated to country of sample collection. Similar experimental parameters used in both laboratories yielded consistent mass-spectral data in mass-to-charge ratios ( m/ z) above 700, where complex lipids are observed. Statistical classifiers built using data acquired in the United States yielded 97.6% sensitivity, 96.7% specificity, and 97.6% accuracy for cancer diagnosis. Equivalent performance was observed for the intralaboratory validation set (99.2% accuracy) and, most remarkably, for the interlaboratory validation set independently acquired in Brazil (95.3% accuracy). Separate classification models built for ER and PR statuses as well as the status of their combined hormone receptor (HR) provided predictive accuracies (>89.0%), although low classification accuracies were achieved for HER2 status. Altogether, our multicenter study demonstrates that DESI-MSI is a robust and reproducible technology for rapid breast-cancer-tissue diagnosis and therefore is of value for clinical use.

Nondestructive tissue analysis for ex vivo and in vivo cancer diagnosis using a handheld mass spectrometry system.

Conventional methods for histopathologic tissue diagnosis are labor- and time-intensive and can delay decision-making during diagnostic and therapeutic procedures. We report the development of an automated and biocompatible handheld mass spectrometry device for rapid and nondestructive diagnosis of human cancer tissues. The device, named MasSpec Pen, enables controlled and automated delivery of a discrete water droplet to a tissue surface for efficient extraction of biomolecules. We used the MasSpec Pen for ex vivo molecular analysis of 20 human cancer thin tissue sections and 253 human patient tissue samples including normal and cancerous tissues from breast, lung, thyroid, and ovary. The mass spectra obtained presented rich molecular profiles characterized by a variety of potential cancer biomarkers identified as metabolites, lipids, and proteins. Statistical classifiers built from the histologically validated molecular database allowed cancer prediction with high sensitivity (96.4%), specificity (96.2%), and overall accuracy (96.3%), as well as prediction of benign and malignant thyroid tumors and different histologic subtypes of lung cancer. Notably, our classifier allowed accurate diagnosis of cancer in marginal tumor regions presenting mixed histologic composition. Last, we demonstrate that the MasSpec Pen is suited for in vivo cancer diagnosis during surgery performed in tumor-bearing mouse models, without causing any observable tissue harm or stress to the animal. Our results provide evidence that the MasSpec Pen could potentially be used as a clinical and intraoperative technology for ex vivo and in vivo cancer diagnosis.

Metabolic Markers and Statistical Prediction of Serous Ovarian Cancer Aggressiveness by Ambient Ionization Mass Spectrometry Imaging.

Ovarian high-grade serous carcinoma (HGSC) results in the highest mortality among gynecological cancers, developing rapidly and aggressively. Dissimilarly, serous borderline ovarian tumors (BOT) can progress into low-grade serous carcinomas and have relatively indolent clinical behavior. The underlying biological differences between HGSC and BOT call for accurate diagnostic methodologies and tailored treatment options, and identification of molecular markers of aggressiveness could provide valuable biochemical insights and improve disease management. Here, we used desorption electrospray ionization (DESI) mass spectrometry (MS) to image and chemically characterize the metabolic profiles of HGSC, BOT, and normal ovarian tissue samples. DESI-MS imaging enabled clear visualization of fine papillary branches in serous BOT and allowed for characterization of spatial features of tumor heterogeneity such as adjacent necrosis and stroma in HGSC. Predictive markers of cancer aggressiveness were identified, including various free fatty acids, metabolites, and complex lipids such as ceramides, glycerophosphoglycerols, cardiolipins, and glycerophosphocholines. Classification models built from a total of 89,826 individual pixels, acquired in positive and negative ion modes from 78 different tissue samples, enabled diagnosis and prediction of HGSC and all tumor samples in comparison with normal tissues, with overall agreements of 96.4% and 96.2%, respectively. HGSC and BOT discrimination was achieved with an overall accuracy of 93.0%. Interestingly, our classification model allowed identification of three BOT samples presenting unusual histologic features that could be associated with the development of low-grade carcinomas. Our results suggest DESI-MS as a powerful approach for rapid serous ovarian cancer diagnosis based on altered metabolic signatures. Cancer Res; 77(11); 2903-13. ©2017 AACR.

Predictability of Genetic Interactions from Functional Gene Modules.

Characterizing genetic interactions is crucial to understanding cellular and organismal response to gene-level perturbations. Such knowledge can inform the selection of candidate disease therapy targets, yet experimentally determining whether genes interact is technically nontrivial and time-consuming. High-fidelity prediction of different classes of genetic interactions in multiple organisms would substantially alleviate this experimental burden. Under the hypothesis that functionally related genes tend to share common genetic interaction partners, we evaluate a computational approach to predict genetic interactions in Homo sapiens, Drosophila melanogaster, and Saccharomyces cerevisiae By leveraging knowledge of functional relationships between genes, we cross-validate predictions on known genetic interactions and observe high predictive power of multiple classes of genetic interactions in all three organisms. Additionally, our method suggests high-confidence candidate interaction pairs that can be directly experimentally tested. A web application is provided for users to query genes for predicted novel genetic interaction partners. Finally, by subsampling the known yeast genetic interaction network, we found that novel genetic interactions are predictable even when knowledge of currently known interactions is minimal.

Computational discovery of pathway-level genetic vulnerabilities in non-small-cell lung cancer.

MOTIVATION: Novel approaches are needed for discovery of targeted therapies for non-small-cell lung cancer (NSCLC) that are specific to certain patients. Whole genome RNAi screening of lung cancer cell lines provides an ideal source for determining candidate drug targets. RESULTS: Unsupervised learning algorithms uncovered patterns of differential vulnerability across lung cancer cell lines to loss of functionally related genes. Such genetic vulnerabilities represent candidate targets for therapy and are found to be involved in splicing, translation and protein folding. In particular, many NSCLC cell lines were especially sensitive to the loss of components of the LSm2-8 protein complex or the CCT/TRiC chaperonin. Different vulnerabilities were also found for different cell line subgroups. Furthermore, the predicted vulnerability of a single adenocarcinoma cell line to loss of the Wnt pathway was experimentally validated with screening of small-molecule Wnt inhibitors against an extensive cell line panel. AVAILABILITY AND IMPLEMENTATION: The clustering algorithm is implemented in Python and is freely available at https://bitbucket.org/youngjh/nsclc_paper CONTACT: marcotte@icmb.utexas.edu or jon.young@utexas.edu SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Efforts to make and apply humanized yeast.

Despite a billion years of divergent evolution, the baker's yeast Saccharomyces cerevisiae has long proven to be an invaluable model organism for studying human biology. Given its tractability and ease of genetic manipulation, along with extensive genetic conservation with humans, it is perhaps no surprise that researchers have been able to expand its utility by expressing human proteins in yeast, or by humanizing specific yeast amino acids, proteins or even entire pathways. These methods are increasingly being scaled in throughput, further enabling the detailed investigation of human biology and disease-specific variations of human genes in a simplified model organism.

Bacterium in a box: sensing of quorum and environment by the LuxI/LuxR gene regulatory circuit.

The chemical signaling mechanism known as "bacterial quorum sensing" (QS) is normally interpreted as allowing bacteria to detect their own population density, in order to coordinate gene expression across a colony. However, the release of the chemical signal can also be interpreted as a means for one or a few cells to probe the local physical properties of their microenvironment. We have studied the behavior of the LuxI/LuxR QS circuit of Vibrio fischeri in tightly confining environments where individual cells detect their own released signals. We find that the lux genes become activated in these environments, although the activation onset time shows substantial cell-to-cell variability and little sensitivity to the confining volume. Our data suggest that noise in gene expression could significantly impact the utility of LuxI/LuxR as a probe of the local physical environment.