Accuracy of models to prognosticate survival after surgery for pancreatic cancer in the era of neoadjuvant therapy

BACKGROUND

Outcomes for pancreatic adenocarcinoma (PDAC) remain difficult to prognosticate. Multiple models attempt to predict survival following the resection of PDAC, but their utility in the neoadjuvant population is unknown. We aimed to assess their accuracy among patients that received neoadjuvant chemotherapy (NAC).

METHODS

We performed a multi-institutional retrospective analysis of patients who received NAC and underwent resection of PDAC. Two prognostic systems were evaluated: the Memorial Sloan Kettering Cancer Center Pancreatic Adenocarcinoma Nomogram (MSKCCPAN) and the American Joint Committee on Cancer (AJCC) staging system. Discrimination between predicted and actual disease-specific survival was assessed using the Uno C-statistic and Kaplan-Meier method. Calibration of the MSKCCPAN was assessed using the Brier score.

RESULTS

A total of 448 patients were included. There were 232 (51.8%) females, and the mean age was 64.1 years (±9.5). Most had AJCC Stage I or II disease (77.7%). For the MSKCCPAN, the Uno C-statistic at 12-, 24-, and 36-month time points was 0.62, 0.63, and 0.62, respectively. The AJCC system demonstrated similarly mediocre discrimination. The Brier score for the MSKCCPAN was 0.15 at 12 months, 0.26 at 24 months, and 0.30 at 36 months, demonstrating modest calibration.

CONCLUSIONS

Current survival prediction models and staging systems for patients with PDAC undergoing resection after NAC have limited accuracy.

Transcriptomic analysis reveals high ITGB1 expression as a predictor for poor prognosis of pancreatic cancer

Transcriptomic analysis of cancer samples helps identify the mechanism and molecular markers of cancer. However, transcriptomic analyses of pancreatic cancer from the Japanese population are lacking. Hence, in this study, we performed RNA sequencing of fresh and frozen pancreatic cancer tissues from 12 Japanese patients to identify genes critical for the clinical pathology of pancreatic cancer among the Japanese population. Additionally, we performed immunostaining of 107 pancreatic cancer samples to verify the results of RNA sequencing. Bioinformatics analysis of RNA sequencing data identified ITGB1 (Integrin beta 1) as an important gene for pancreatic cancer metastasis, progression, and prognosis. ITGB1 expression was verified using immunostaining. The results of RNA sequencing and immunostaining showed a significant correlation (r = 0.552, p = 0.118) in ITGB1 expression. Moreover, the ITGB1 high-expression group was associated with a significantly worse prognosis (p = 0.035) and recurrence rate (p = 0.028). We believe that ITGB1 may be used as a drug target for pancreatic cancer in the future.

The enhanced cell cycle related to the response to adjuvant therapy in pancreatic ductal adenocarcinoma

A major clinical challenge for treating patients with pancreatic ductal adenocarcinoma (PDAC) is identifying those that may benefit from adjuvant chemotherapy versus those that will not. Thus, there is a need for a robust and convenient biomarker for predicting chemotherapy response in PDAC patients. In this study, network inference was conducted by integrating the differentially expressed cell cycle signatures and target genes between the basal-like subtype and classical subtype of PDAC. As a result from this statistical analysis, two dominant cell cycle genes, RASAL2 and ASPM, were identified. Based on the expression levels of these two genes, we constructed a "Enhanced Cell Cycle" scoring system (ECC score). Patients were given an ECC score, and respectively divided into ECC-high and ECC-low groups. Survival, pathway enrichment, immune environment characteristics, and chemotherapy response analysis' were performed between the two groups in a total of 891 patients across 5 cohorts. ECC-high patients exhibited shortened recurrence-free survival (RFS) and overall survival (OS) rates. In addition, it was found that adjuvant chemotherapy could significantly improve the outcome of the ECC-high patients while ECC-low patients did not benefit from adjuvant chemotherapy. It was also found that there was less CD8+ T cell, natural killer (NK) cell, M1 macrophage, and plasma cell infiltration in ECC-high patients when compared to ECC-low patients. Also, the expression of CD73, an immune suppressor gene, and it's related hypoxia pathway were elevated in the ECC-high group when compared to the ECC-low group. In conclusion, this study showed that patients characterized as ECC-high not only had reduced RFS and OS rates, but were also more sensitive to adjuvant chemotherapy and could potentially be less sensitive to immune checkpoint inhibitors. Being able to characterize patients by these parameters would allow doctors to make more informed decisions on patient treatment regimens.

Orchestrating and sharing large multimodal data for transparent and reproducible research

Reproducibility is essential to open science, as there is limited relevance for findings that can not be reproduced by independent research groups, regardless of its validity. It is therefore crucial for scientists to describe their experiments in sufficient detail so they can be reproduced, scrutinized, challenged, and built upon. However, the intrinsic complexity and continuous growth of biomedical data makes it increasingly difficult to process, analyze, and share with the community in a FAIR (findable, accessible, interoperable, and reusable) manner. To overcome these issues, we created a cloud-based platform called ORCESTRA ( orcestra.ca ), which provides a flexible framework for the reproducible processing of multimodal biomedical data. It enables processing of clinical, genomic and perturbation profiles of cancer samples through automated processing pipelines that are user-customizable. ORCESTRA creates integrated and fully documented data objects with persistent identifiers (DOI) and manages multiple dataset versions, which can be shared for future studies.

Meta-Analysis of Microdissected Breast Tumors Reveals Genes Regulated in the Stroma but Hidden in Bulk Analysis

Transcriptome data provide a valuable resource for the study of cancer molecular mechanisms, but technical biases, sample heterogeneity, and small sample sizes result in poorly reproducible lists of regulated genes. Additionally, the presence of multiple cellular components contributing to cancer development complicates the interpretation of bulk transcriptomic profiles. To address these issues, we collected 48 microarray datasets derived from laser capture microdissected stroma or epithelium in breast tumors and performed a meta-analysis identifying robust lists of differentially expressed genes. This was used to create a database with carefully harmonized metadata that we make freely available to the research community. As predicted, combining the results of multiple datasets improved statistical power. Moreover, the separate analysis of stroma and epithelium allowed the identification of genes with different contributions in each compartment, which would not be detected by bulk analysis due to their distinct regulation in the two compartments. Our method can be profitably used to help in the discovery of biomarkers and the identification of functionally relevant genes in both the stroma and the epithelium. This database was made to be readily accessible through a user-friendly web interface.

Changes and prognostic impact of inflammatory nutritional factors during neoadjuvant chemoradiotherapy for patients with resectable and borderline resectable pancreatic cancer

Background

Inflammatory nutritional factors, such as the neutrophil/lymphocyte ratio (NLR), Glasgow Prognostic Score (GPS), modified GPS (mGPS), and C-reactive protein/albumin (CRP/Alb) ratio, have prognostic values in many types of cancer. In this study, the prognostic values of inflammatory nutritional scores were evaluated in the patients with resectable or borderline resectable pancreatic ductal adenocarcinoma (PDAC) after neoadjuvant chemoradiotherapy (NACRT).

Methods

A total of 49 patients who underwent pancreatectomy after NACRT from September 2009 to May 2016 were enrolled. The NACRT consisted of hypofractionated external-beam radiotherapy (30 Gy in 10 fractions) with concurrent S-1 (60 mg/m²) delivered 5 days/week for 2 weeks before pancreatectomy. Inflammatory nutritional scores were determined before and after NACRT in this series.

Results

The median NLR increased after NACRT (from 2.067 to 3.302), with statistical difference (p < 0.001). In multivariate analysis, high pre-NACRT mGPS (2 or 1; p = 0.0478) and significant increase in CRP/Alb ratio after NACRT (≧ 0.077; p = 0.0036) were associated with shorter overall survival. All patients were divided into two groups according to the ΔCRP/Alb ratio after NACRT: the group with high ΔCRP/Alb ratio (≧ 0.077) and the group with low ΔCRP/Alb ratio (< 0.077). The group with high ΔCRP/Alb ratio after NACRT (n = 13) not only had higher post-NACRT CRP levels (p < 0.001) but also had lower post-NACRT Alb levels (p = 0.002). Patients in the group with high ΔCRP/Alb ratio lost more body weight during NACRT (p = 0.03).

Conclusion

In addition to pre-NACRT mGPS, ΔCRP/Alb after NACRT could provide prognostic value in the patients with PDAC treated by NACRT.

Distinguishing Kawasaki Disease from Febrile Infectious Disease Using Gene Pair Signatures

Kawasaki disease (KD) is an acute systemic vasculitis of childhood with prolonged fever, and the diagnosis of KD is mainly based on clinical criteria, which is prone to misdiagnosis with other febrile infectious (FI) diseases. Currently, there remain no effective molecular markers for KD diagnosis. In this study, we aimed to use a relative-expression-based method k-TSP and resampling framework to identify robust gene pair signatures to distinguish KD from bacterial and virus febrile infectious diseases. Our study pool consisted of 808 childhood patients from several studies and assigned to three groups, namely, the discovery set (n = 224), validation set-1 (n = 197), and validation set-2 (n = 387). We had identified 60 biologically relevant gene pairs and developed a top-ranked gene pair classifier (TRGP) using the first seven signatures, with the area under the receiver-operating characteristic curves (AUROC) of 0.947 (95% CI, 0.918-0.976), a sensitivity of 0.936 (95% CI, 0.872-0.987), and a specificity of 0.774 (95% CI, 0.705-0.836) in the discovery set. In the validation set-1, the TRGP classifier distinguished KD from FI with AUROC of 0.955 (95% CI, 0.919-0.991), a sensitivity of 0.959 (95% CI, 0.925-0.986), and a specificity of 0.863 (95% CI, 0.764-0.961). In the validation set-2, the predictive performance of classification was with an AUROC of 0.796 (95% CI, 0.747-0.845), a sensitivity of 0.797 (95% CI, 0.720-0.864), and a specificity of 0.661 (95% CI, 0.606-0.717). Our study reveals that gene pair signatures are robust across diverse studies and can be utilized as objective biomarkers to distinguish KD from FI, helping to develop a fast, simple, and effective molecular approach to improve the diagnosis of KD.

MultiBaC: A strategy to remove batch effects between different omic data types

Diversity of omic technologies has expanded in the last years together with the number of omic data integration strategies. However, multiomic data generation is costly, and many research groups cannot afford research projects where many different omic techniques are generated, at least at the same time. As most researchers share their data in public repositories, different omic datasets of the same biological system obtained at different labs can be combined to construct a multiomic study. However, data obtained at different labs or moments in time are typically subjected to batch effects that need to be removed for successful data integration. While there are methods to correct batch effects on the same data types obtained in different studies, they cannot be applied to correct lab or batch effects across omics. This impairs multiomic meta-analysis. Fortunately, in many cases, at least one omics platform-i.e. gene expression- is repeatedly measured across labs, together with the additional omic modalities that are specific to each study. This creates an opportunity for batch analysis. We have developed MultiBaC (multiomic Multiomics Batch-effect Correction correction), a strategy to correct batch effects from multiomic datasets distributed across different labs or data acquisition events. Our strategy is based on the existence of at least one shared data type which allows data prediction across omics. We validate this approach both on simulated data and on a case where the multiomic design is fully shared by two labs, hence batch effect correction within the same omic modality using traditional methods can be compared with the MultiBaC correction across data types. Finally, we apply MultiBaC to a true multiomic data integration problem to show that we are able to improve the detection of meaningful biological effects.