Plasma YKL-40 is associated with prognosis in patients with metastatic pancreatic cancer receiving immune checkpoint inhibitors in combination with radiotherapy.

Background: YKL-40, also known as chitinase-3-like protein 1 (CHI3L1), is a secreted glycoprotein produced by various cell types including stromal, immune, and cancer cells. It contributes to cancer progression through tumor-promoting inflammation and has been shown to inhibit the cytotoxicity of T and NK lymphocytes. In vivo studies have demonstrated synergistic anti-cancer effects of blocking YKL-40 in combination with immune checkpoint inhibitors (ICIs). Biomarkers for the prediction of the response to ICIs are highly needed. We investigated the association between plasma YKL-40 and clinical benefit and survival in patients with metastatic pancreatic cancer (mPC) receiving ICIs and stereotactic body radiotherapy (SBRT). Methods: Blood samples were collected from 84 patients with mPC who participated in the randomized phase II CheckPAC study, in which patients received nivolumab with or without ipilimumab combined with a single fraction of SBRT. Plasma YKL-40 was measured using a commercial ELISA kit. Results: Elevated baseline plasma YKL-40 was an independent predictor of shorter overall survival (OS) (HR 2.19, 95% CI 1.21-3.95). A ≥ 40% decrease in plasma YKL-40 during treatment was associated with longer progression-free survival (p = 0.009) and OS (p = 0.0028). There was no correlation between plasma YKL-40 and the tumor burden marker CA19-9 at baseline or during treatment. Conclusion: This study contributes new knowledge regarding YKL-40 as a predictor of clinical benefit from ICIs and radiotherapy. These exploratory results warrant further investigation of YKL-40 as a biomarker for patients treated with immunotherapies. Clinical trial registration: Clinicaltrials.gov, identifier NCT02866383.

A deep learning algorithm to predict risk of pancreatic cancer from disease trajectories.

Pancreatic cancer is an aggressive disease that typically presents late with poor outcomes, indicating a pronounced need for early detection. In this study, we applied artificial intelligence methods to clinical data from 6 million patients (24,000 pancreatic cancer cases) in Denmark (Danish National Patient Registry (DNPR)) and from 3 million patients (3,900 cases) in the United States (US Veterans Affairs (US-VA)). We trained machine learning models on the sequence of disease codes in clinical histories and tested prediction of cancer occurrence within incremental time windows (CancerRiskNet). For cancer occurrence within 36 months, the performance of the best DNPR model has area under the receiver operating characteristic (AUROC) curve = 0.88 and decreases to AUROC (3m) = 0.83 when disease events within 3 months before cancer diagnosis are excluded from training, with an estimated relative risk of 59 for 1,000 highest-risk patients older than age 50 years. Cross-application of the Danish model to US-VA data had lower performance (AUROC = 0.71), and retraining was needed to improve performance (AUROC = 0.78, AUROC (3m) = 0.76). These results improve the ability to design realistic surveillance programs for patients at elevated risk, potentially benefiting lifespan and quality of life by early detection of this aggressive cancer.

Randomized Phase II Study of Nivolumab With or Without Ipilimumab Combined With Stereotactic Body Radiotherapy for Refractory Metastatic Pancreatic Cancer (CheckPAC).

PURPOSE: To evaluate the clinical benefit of nivolumab with or without ipilimumab in combination with stereotactic body radiotherapy (SBRT) in patients with refractory metastatic pancreatic cancer (mPC). METHODS: Between November 2016 and December 2019, patients with refractory mPC were randomly assigned 1:1 to SBRT of 15 Gy with nivolumab or nivolumab/ipilimumab stratified by performance status (ClinicalTrials.gov identifier: NCT02866383). The primary end point was the clinical benefit rate (CBR), defined as the percentage of patients with complete or partial response (PR) or stable disease, according to RECIST 1.1. Simon's 2-stage phase II optimal design was used independently for both arms, with CBR determining expansion to the second stage. Secondary end points included safety, response rate, duration of response, progression-free survival, and overall survival. Exploratory analyses included biomarkers related to the benefits. RESULTS: Eighty-four patients (41 SBRT/nivolumab and 43 SBRT/nivolumab/ipilimumab) received at least one dose of study treatment. CBR was 17.1% (8.0 to 30.6) for patients receiving SBRT/nivolumab and 37.2% (24.0 to 52.1) for SBRT/nivolumab/ipilimumab. PR was observed in one patient receiving SBRT/nivolumab and lasted for 4.6 months. Six patients receiving SBRT/nivolumab/ipilimumab achieved a PR with a median duration of response of 5.4 months (4.2 to not reached). Grade 3 or higher treatment-related adverse events occurred in 10 (24.4%) and 13 (30.2%) patients in the SBRT/nivolumab and SBRT/nivolumab/ipilimumab groups, respectively. Programmed cell death ligand-1 expression by tumor proportion score or combined positivity score of ≥ 1% was not associated with clinical benefits. On-treatment decreased serum interleukin-6, interleukin-8, and C-reactive protein levels were associated with better overall survival. CONCLUSION: Clinically meaningful antitumor activity and favorable safety profiles were demonstrated after treatment with SBRT/nivolumab/ipilimumab in patients with refractory mPC. However, the contribution from SBRT is unknown. Further studies are warranted.

Disease trajectory browser for exploring temporal, population-wide disease progression patterns in 7.2 million Danish patients.

We present the Danish Disease Trajectory Browser (DTB), a tool for exploring almost 25 years of data from the Danish National Patient Register. In the dataset comprising 7.2 million patients and 122 million admissions, users can identify diagnosis pairs with statistically significant directionality and combine them to linear disease trajectories. Users can search for one or more disease codes (ICD-10 classification) and explore disease progression patterns via an array of functionalities. For example, a set of linear trajectories can be merged into a disease trajectory network displaying the entire multimorbidity spectrum of a disease in a single connected graph. Using data from the Danish Register for Causes of Death mortality is also included. The tool is disease-agnostic across both rare and common diseases and is showcased by exploring multimorbidity in Down syndrome (ICD-10 code Q90) and hypertension (ICD-10 code I10). Finally, we show how search results can be customized and exported from the browser in a format of choice (i.e. JSON, PNG, JPEG and CSV).

Complete Topological Mapping of a Cellular Protein Interactome Reveals Bow-Tie Motifs as Ubiquitous Connectors of Protein Complexes.

The network topology of a protein interactome is shaped by the function of each protein, making it a resource of functional knowledge in tissues and in single cells. Today, this resource is underused, as complete network topology characterization has proved difficult for large protein interactomes. We apply a matrix visualization and decoding approach to a physical protein interactome of a dendritic cell, thereby characterizing its topology with no prior assumptions of structure. We discover 294 proteins, each forming topological motifs called "bow-ties" that tie together the majority of observed protein complexes. The central proteins of these bow-ties have unique network properties, display multifunctional capabilities, are enriched for essential proteins, and are widely expressed in other cells and tissues. Collectively, the bow-tie motifs are a pervasive and previously unnoted topological trend in cellular interactomes. As such, these results provide fundamental knowledge on how intracellular protein connectivity is organized and operates.