A Bitter Taste Receptor as a Novel Molecular Target on Cancer-Associated Fibroblasts in Pancreatic Ductal Adenocarcinoma

Cancer-associated fibroblasts (CAFs) execute diverse and complex functions in cancer progression. While reprogramming the crosstalk between CAFs and cancer epithelial cells is a promising avenue to evade the adverse effects of stromal depletion, drugs are limited by their suboptimal pharmacokinetics and off-target effects. Thus, there is a need to elucidate CAF-selective cell surface markers that can improve drug delivery and efficacy. Here, functional proteomic pulldown with mass spectrometry was used to identify taste receptor type 2 member 9 (TAS2R9) as a CAF target. TAS2R9 target characterization included binding assays, immunofluorescence, flow cytometry, and database mining. Liposomes conjugated to a TAS2R9-specific peptide were generated, characterized, and compared to naked liposomes in a murine pancreatic xenograft model. Proof-of-concept drug delivery experiments demonstrate that TAS2R9-targeted liposomes bind with high specificity to TAS2R9 recombinant protein and exhibit stromal colocalization in a pancreatic cancer xenograft model. Furthermore, the delivery of a CXCR2 inhibitor by TAS2R9-targeted liposomes significantly reduced cancer cell proliferation and constrained tumor growth through the inhibition of the CXCL-CXCR2 axis. Taken together, TAS2R9 is a novel cell-surface CAF-selective target that can be leveraged to facilitate small-molecule drug delivery to CAFs, paving the way for new stromal therapies

Behavioural cloning of teachers for automatic homework selection

© Springer Nature Switzerland AG 2019. We describe a machine-learning system for supporting teachers through the selection of homework assignments. Our system uses behavioural cloning of teacher activity to generate personalised homework assignments for students. Classroom use is then supported through additional mechanisms to combine these predictions into group assignments. We train and evaluate our system against 50,065 homework assignments collected over two years by the Isaac Physics platform. We use baseline policies incorporating expert curriculum knowledge for evaluation and find that our technique improves on the strongest baseline policy by 18.5% in Year 1 and by 13.3% in Year 2.Cambridge Assessmen

Dysregulation of PRMT5 in chronic lymphocytic leukemia promotes progression with high risk of Richter's transformation

: Richter's Transformation (RT) is a poorly understood and fatal progression of chronic lymphocytic leukemia (CLL) manifesting histologically as diffuse large B-cell lymphoma. Protein arginine methyltransferase 5 (PRMT5) is implicated in lymphomagenesis, but its role in CLL or RT progression is unknown. We demonstrate herein that tumors uniformly overexpress PRMT5 in patients with progression to RT. Furthermore, mice with B-specific overexpression of hPRMT5 develop a B-lymphoid expansion with increased risk of death, and Eµ-PRMT5/TCL1 double transgenic mice develop a highly aggressive disease with transformation that histologically resembles RT; where large-scale transcriptional profiling identifies oncogenic pathways mediating PRMT5-driven disease progression. Lastly, we report the development of a SAM-competitive PRMT5 inhibitor, PRT382, with exclusive selectivity and optimal in vitro and in vivo activity compared to available PRMT5 inhibitors. Taken together, the discovery that PRMT5 drives oncogenic pathways promoting RT provides a compelling rationale for clinical investigation of PRMT5 inhibitors such as PRT382 in aggressive CLL/RT cases

Atención al paciente oncológico en tiempos de COVID-19

Introduction: with the emergence of the new coronavirus and the wide worldwide distribution, its effects in people with some comorbidities are a global concern. Cancer is a disease with a high incidence and prevalence in society, included among the main causes of mortality.Objective: to describe the management of cancer patients during COVID-19Method: a literature review of articles published up to June 2020 was carried out, using the Pubmed / Medline, SCOPUS and SciELO databases. 28 references were selected for the preparation of the present.Development: cancer has variable clinical and prognostic behaviors that generally lead to states of immunosuppression caused by the therapeutics used for its treatment; Therefore, they are more vulnerable to infectious diseases. The proper care of this group of people is the responsibility of the health systems. Some measures are based on social distancing, either in reducing the number of companions of the patient in the consultation or chemotherapy sessions, the prohibition of visits to hospitalized patients and the use of technologies with the use of teleconsultations for routine follow-up, as well as the change from intravenous to oral treatmentsConclusions: the study of the behavior of COVID-19 in cancer patients is under development. The measures that the institutions take to achieve quality care for people with cancer are varied and are based mainly on social distancing.Introducción: con el surgimiento del nuevo coronavirus y la amplia distribución mundial, es una preocupación global sus efectos en personas con algunas comorbilidades. El cáncer es una enfermedad con alta incidencia y prevalencia en la sociedad, incluida entre las principales causas de mortalidad.Objetivo: describir el manejo del paciente oncológico durante la COVID-19Método: se realizó una revisión de la literatura de artículos publicados hasta junio del 2020, utilizando las bases de datos de Pubmed/Medline, SCOPUS y SciELO. Se seleccionaron 28 referencias para la elaboración de la presente.Desarrollo: el cáncer posee comportamientos clínicos y pronóstico variables que generalmente conllevan a estados de inmunosupresión causada por la terapéutica empleada para su tratamiento; por lo cual presentan mayor vulnerabilidad ante enfermedades infecciosas. Es responsabilidad de los sistemas de salud la correcta atención a este grupo de personas. Algunas medidas se basan en el distanciamiento social, ya sea en la reducción de la cantidad de acompañantes del paciente en la consulta o las sesiones de quimioterapia, la prohibición de las visitas a los pacientes hospitalizados y el empleo de las tecnologías con el uso de las teleconsultas para el seguimiento rutinario, así como el cambio de tratamientos por vía intravenosa a vía oralConclusiones: el estudio del comportamiento de la COVID-19 en pacientes oncológicos está en desarrollo. Las medidas que tomen las instituciones para lograr una atención de calidad a las personas que poseen cáncer son variadas y se basan sobre todo en el distanciamiento social

Formation and role of exosomes in cancer

Exosomes offer new insight into cancer biology with both diagnostic and therapeutic implications. Because of their cell-to-cell communication, exosomes influence tumor progression, metastasis, and therapeutic efficacy. They can be isolated from blood and other bodily fluids to reveal disease processes occurring within the body, including cancerous growth. In addition to being a reservoir of cancer biomarkers, they can be re-engineered to reinstate tumor immunity. Tumor exosomes interact with various cells of the microenvironment to confer tumor-advantageous changes that are responsible for stromal activation, induction of the angiogenic switch, increased vascular permeability, and immune escape. Exosomes also contribute to metastasis by aiding in the epithelial-to-mesenchymal transition and formation of the pre-metastatic niche. Furthermore, exosomes protect tumor cells from the cytotoxic effects of chemotherapy drugs and transfer chemoresistance properties to nearby cells. Thus, exosomes are essential to many lethal elements of cancer and it is important to understand their biogenesis and role in cancer

Summary of CAF screens.

<p>Positive and negative screens were carried out for a cell target, CAFs, and processed with PHASTpep.</p

Normalization strategy and sorting of PHASTpep.

<p>(A) For each screen, the frequencies were divided by the total number of reads of the screen, followed by the frequency of that sequence in the reference library. (B) In order to demonstrate the sorting process, small libraries were created that represented a reference library, two positive screens, and 2 negative screens. For each sequence, a qualitative ranking was determined (predicted ranking) based on the level of frequency assigned in each library. For example, GVTHKLQ was absent in the reference library, high in both positive screens, and absent in both negative screens. Therefore, it was predicted to be ranked very high. Conversely, TPSIYFL was only high in the negative screens and absent elsewhere. Thus it was predicted to rank very low. For each test case (sequence), the predicted ranking was compared to the actual ranking after running the test libraries through our sorting software. R, reference; PS, positive screen; NS, negative screen; A, absent; L, low; H, high.</p

Peptide signatures of various cells and tissues.

<p>Peptides identified from screens performed on cell lines, ex vivo tissue specimens and in vivo screens were processed and analyzed using PHASTpep. They are presented as a heat map generated via conditional formatting in Excel. PDEC, pancreatic ductal epithelial cell; gl, glucose; B, b cells; TIL, tumor infiltrating lymphocyte; Eff, effector; Omm, ommental; SVF, stromal vascular fraction; Ob, obese; CHO, chinese hamster ovary.</p

<i>In vitro</i> and <i>in vivo</i> peptide sequence validation.

<p>(A) An ELISA compares the binding of phage displaying the peptides to CAFs versus normal fibroblasts (MRC5). The first three sequences were selected using our selectivity analysis; whereas, the next six sequences were found using a traditional phage display approach. The dashed line indicates a fold change of 1.2. (B) Flow cytometry was performed by binding fluorescently-labeled phage to cells with a live-dead violet stain. Data was gated on cell population, live cells, and phage positive cells. (C) An ELISA compares binding of phage to HPSC and MRC5. Statistical significance was measured with a student t-test between HPSC and MRC5 where ^#p<0.01 and *p<0.02. (D) An ELISA compares binding of phage to HPSC and BXPC3. Statistical significance was measured with a student t-test between HPSC and BXPC3 where *p<0.02 and ^Φp<0.06. (E) Fluorescently-labeled phage were injected into mice bearing subcutaneous admix CAF/BXPC3 tumors or BXPC3-only tumors (n = 6 tumors per group) and tumor accumulation was measured on an FMT using a region-of-interest around the tumor area. Statistical significance was determined using student’s t-test of each type of displayed peptide versus KE with ^#p<0.01 and *p<0.02. (F) FMT images of mice with admix CAF/BXPC3 tumors scanned 4 h post-injection. Tumor regions have been circled with dashed lines. (G) Tumor sections of admix tumors were fixed, sectioned, and stained with anti-αSMA (green), then mounted with prolong gold anti-fade with DAPI (blue). The fluorescent labeling of the phage is colored red. Mander’s correlation coefficients (M) are indicated at the bottom of each image. For each phage type, images are representative of two tumors, three tumor sections each. Scale bars, 10 um.</p

Approach to finding candidate peptide sequences.

<p>(A) The Illumina sequencer outputs fastq files that are separated by barcodes. For each of these files, the portion of DNA corresponding to the displayed peptides was isolated and translated. The number of times each sequence was read in a run was summed to obtain the frequency associated with that sequence, which was subsequently divided by the total number of reads from the run and then by the frequency of that sequence in the reference library. This processing resulted in a normalized frequency for each sequence of a run. (B) Sequences present in one screen but absent in another were set to the non-zero mode of the absent screen rather than zero to prevent later division by zero. The normalized frequencies across all positive screens were averaged as well as across all negative screens. The average positive normalized frequency was divided by the average negative normalized frequency and this ratio was used to sort the sequences so that sequences high across positive screens and low across negative screens distilled to the top fraction. Sequences ordered by ratio created the rows of the comparison matrix showing all of the normalized frequencies for each sequence across all screens, facilitating identification of the most selective sequences. * PhD libraries from NEB are generated with constrained codons. When using this library, sequences containing codons not represented in the library are removed.</p