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Pancreatic ductal adenocarcinoma (PDAC) subtypes have been identified using various methodologies. However, it is a challenge to develop classification system applicable to routine clinical evaluation. We aimed to identify risk subgroups based on molecular features and develop a classification model that was more suited for clinical applications.

We collected whole dissected specimens from 225 patients who underwent surgery at Seoul National University Hospital [Seoul, Republic of Korea (South)], between October 2009 and February 2018. Target proteins with potential relevance to tumor progression or prognosis were quantified with robust quality controls. We used hierarchical clustering analysis to identify risk subgroups. A random forest classification model was developed to predict the identified risk subgroups, and the model was validated using transcriptomic datasets from external cohorts (

= 700), with survival analysis.

We identified 24 protein features that could classify the four risk subgroups level. This clinical system may improve the accuracy of risk prediction and treatment guidelines.See related commentary by Thakur and Singh, p. 3272.

Next-generation sequencing studies and CRISPR-Cas9 screens have established mutations in the IFNγ-JAK-STAT pathway as an immune checkpoint inhibitor (ICI) resistance mechanism in a subset of patients with melanoma. We hypothesized ICI resistance mutations in the IFNγ pathway would simultaneously render melanomas susceptible to oncolytic virus (OV) therapy.

Cytotoxicity experiments were performed with a number of OVs on a matched melanoma cell line pair generated from a baseline biopsy and a progressing lesion with complete

loss from a patient that relapsed on anti-PD-1 therapy, in melanoma lines following JAK1/2 RNA interference (RNAi) and pharmacologic inhibition and in

knockout (KO) B16-F10 mouse melanomas. Disodium Cromoglycate cell line Furthermore, we estimated the frequency of genetic alterations in the IFNγ-JAK-STAT pathway in human melanomas.

The melanoma line from an anti-PD-1 progressing lesion was 7- and 22-fold more sensitive to the modified OVs, herpes simplex virus 1 (HSV1-dICP0) and vesicular stomatitis virus (VSVaïve melanomas without IFN signaling defects.See related commentary by Kaufman, p. 3278.

While immune checkpoint inhibitors (ICI) have revolutionized the treatment of cancer by producing durable antitumor responses, only 10%-30% of treated patients respond and the ability to predict clinical benefit remains elusive. Several studies, small in size and using variable analytic methods, suggest the gut microbiome may be a novel, modifiable biomarker for tumor response rates, but the specific bacteria or bacterial communities putatively impacting ICI responses have been inconsistent across the studied populations.

We have reanalyzed the available raw 16S rRNA amplicon and metagenomic sequencing data across five recently published ICI studies (

= 303 unique patients) using a uniform computational approach.

Herein, we identify novel bacterial signals associated with clinical responders (R) or nonresponders (NR) and develop an integrated microbiome prediction index. Unexpectedly, the NR-associated integrated index shows the strongest and most consistent signal using a random effects model and in a sensitivity and specificity analysis (

< 0.01). We subsequently tested the integrated index using validation cohorts across three distinct and diverse cancers (

= 105).

Our analysis highlights the development of biomarkers for nonresponse, rather than response, in predicting ICI outcomes and suggests a new approach to identify patients who would benefit from microbiome-based interventions to improve response rates.

Our analysis highlights the development of biomarkers for nonresponse, rather than response, in predicting ICI outcomes and suggests a new approach to identify patients who would benefit from microbiome-based interventions to improve response rates.

Triple-negative breast cancer (TNBC) is an aggressive disease with limited therapeutic options. Antibodies targeting programmed cell death protein 1 (PD-1)/PD-1 ligand 1 (PD-L1) have entered the therapeutic landscape in TNBC, but only a minority of patients benefit. A way to reliably enhance immunogenicity, T-cell infiltration, and predict responsiveness is critically needed.

Using mouse models of TNBC, we evaluate immune activation and tumor targeting of intratumoral IL12 plasmid followed by electroporation (tavokinogene telseplasmid; Tavo). We further present a single-arm, prospective clinical trial of Tavo monotherapy in patients with treatment refractory, advanced TNBC (OMS-I140). Finally, we expand these findings using publicly available breast cancer and melanoma datasets.

Single-cell RNA sequencing of murine tumors identified a CXCR3 gene signature (CXCR3-GS) following Tavo treatment associated with enhanced antigen presentation, T-cell infiltration and expansion, and PD-1/PD-L1 expression. Assesith improved outcomes and conversion of nonresponsive tumors, potentially even beyond TNBC.

We have previously identified tissue methylated DNA markers (MDMs) associated with pancreatic ductal adenocarcinoma (PDAC). In this case-control study, we aimed to assess the diagnostic performance of plasma MDMs for PDAC.

Thirteen MDMs (

, and

) were identified on the basis of selection criteria applied to results of prior tissue experiments and assays were optimized in plasma. Next, 340 plasma samples (170 PDAC cases and 170 controls) were assayed using target enrichment long-probe quantitative amplified signal method. Initially, 120 advanced-stage PDAC cases and 120 healthy controls were used to train a prediction algorithm at 97.5% specificity using random forest modeling. Subsequently, the locked algorithm derived from the training set was applied to an independent blinded test set of 50 early-stage PDAC cases and 50 controls. Finally, data from all 340 patients were combined, and cross-validated.

The cross-validated area under the receiver operating characteristic curve (AUC) for the training sCA19-9 detect PDAC with significantly higher accuracy compared with either biomarker individually.

Plasma MDMs in combination with CA19-9 detect PDAC with significantly higher accuracy compared with either biomarker individually.