Identification and Validation of a Biomarker Signature in Patients With Resectable Pancreatic Cancer via Genome-Wide Screening for Functional Genetic Variants

A Predictive Noninvasive Single-Nucleotide Variation-Based Biomarker Signature for Resectable Pancreatic Cancer: Protocol for a Prospective Validation Study

BACKGROUND

Single-nucleotide variations (SNVs; formerly SNPs) are inherited genetic variants that can be easily determined in routine clinical practice using a simple blood or saliva test. SNVs have potential to serve as noninvasive biomarkers for predicting cancer-specific patient outcomes after resection of pancreatic ductal adenocarcinoma (PDAC). Two recent analyses led to the identification and validation of three SNVs in the CD44 and CHI3L2 genes (rs187115, rs353630, and rs684559), which can be used as predictive biomarkers to help select patients most likely to benefit from pancreatic resection. These variants were associated with an over 2-fold increased risk for tumor-related death in three independent PDAC study cohorts from Europe and the United States, including The Cancer Genome Atlas cohorts (reaching a P value of 1×10-8). However, these analyses were limited by the inherent biases of a retrospective study design, such as selection and publication biases, thereby limiting the clinical use of these promising biomarkers in guiding PDAC therapy.

OBJECTIVE

To overcome the limitations of previous retrospectively designed studies and translate the findings into clinical practice, we aim to validate the association of the identified SNVs with survival in a controlled setting using a prospective cohort of patients with PDAC following pancreatic resection.

METHODS

All patients with PDAC who will undergo pancreatic resection at three participating hospitals in Switzerland and fulfill the inclusion criteria will be included in the study consecutively. The SNV genotypes will be determined using standard genotyping techniques from patient blood samples. For each genotyped locus, log-rank and Cox multivariate regression tests will be performed, accounting for the relevant covariates American Joint Committee on Cancer stage and resection status. Clinical follow-up data will be collected for at least 3 years. Sample size calculation resulted in a required sample of 150 patients to sufficiently power the analysis.

RESULTS

The follow-up data collection started in August 2019 and the estimated end of data collection will be in May 2027. The study is still recruiting participants and 142 patients have been recruited as of November 2023. The DNA extraction and genotyping of the SNVs will be performed after inclusion of the last patient. Since no SNV genotypes have been determined, no data analysis has been performed to date. The results are expected to be published in 2027.

CONCLUSIONS

This is the first prospective study of the CD44 and CHI3L2 SNV-based biomarker signature in PDAC. A prospective validation of this signature would enable its clinical use as a noninvasive predictive biomarker of survival after pancreatic resection that is readily available at the time of diagnosis and can assist in guiding PDAC therapy. The results of this study may help to individualize treatment decisions and potentially improve patient outcomes.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

DERR1-10.2196/54042.

Identification of Transcriptomic Signatures of Pancreatic Ductal Adenocarcinoma-Derived Exosomes That Promote Macrophage M2 Polarization and Predict Prognosis: S100A9 Reveals Tumor Progression

Background

Exosomes play a role in intercellular communication and participate in the interaction between pancreatic ductal adenocarcinoma (PDAC) cells and immune cells. Macrophages can receive tumor cell-derived exosomes to polarize into M2-type macrophages, which can enhance the invasion and metastasis of pancreatic cancer, leading to poor prognosis. However, the mechanism by which pancreatic cancer cell-derived exosomes promote M2-type macrophages is still unclear.

Methods

M2 macrophage-associated exosome-derived key module genes were identified by differentially expressed genes (DEGs) and weighted gene co-expression network analysis (WGCNA) analysis using exoRbase 2.0, The Cancer Genome Atlas (TCGA), and The International Cancer Genome Consortium (ICGC) databases. Multivariate Cox regression analysis was used to identify key prognostic genes and obtain regression coefficients to establish prognostic signature. Immune infiltration, tumor mutations, and GSEA among different risk groups were compared. exoRbase 2.0, Gene Expression Profiling Interactive Analysis 2 (GEPIA2), HPA, and TISCH2 databases were used to further analyze the expression pattern of S100A9 in pancreatic cancer. In vitro experiments, cell-derived exosome isolation, quantitative polymerase chain reaction (qPCR), western blot, flow cytometry analysis, cell transfection, transwell assay, and CCK-8 assay were applied to investigate the roles of S100A9 in macrophage M2 polarization and tumor progression.

Results

The key genes of PDAC-derived exosomes promoting M2-type macrophage polarization were identified, and a risk score model was established. The risk score is related to the expression of common immune checkpoints, immune score, and stromal score, and the tumor mutational burden and biological function of high- and low-risk groups were also different. S100A9 was positively correlated with M2-type macrophage marker. In addition, scRNA-seq data from the TISCH2 database revealed that S100A9 is predominantly expressed in pancreatic cancer cells and mono/macrophage cells, suggesting that S100A9 in pancreatic cancer cells could be received by macrophages, thereby inducing macrophage polarization. In vitro, we used exosomes from BxPC-3 cell lines to coculture macrophages and found that macrophages were mainly polarized toward M2 type, which further promoted the proliferation and metastasis of PDAC.

Conclusions

Our study established a reliable risk score model for PDAC-derived exosomes and M2 macrophages, identified the important role of S100A9 in macrophage M2 polarization, which provides a new strategy for the diagnosis and treatment of PDAC, and strengthened the understanding of the mechanism of tumor development and metastasis.

Prospective observational study on biomarkers of response in pancreatic ductal adenocarcinoma

Adjuvant chemotherapy benefits patients with resected pancreatic ductal adenocarcinoma (PDAC), but the compromised physical state of post-operative patients can hinder compliance. Biomarkers that identify candidates for prompt adjuvant therapy are needed. In this prospective observational study, 1,171 patients with PDAC who underwent pancreatectomy were enrolled and extensively followed-up. Proteomic profiling of 191 patient samples unveiled clinically relevant functional protein modules. A proteomics-level prognostic risk model was established for PDAC, with its utility further validated using a publicly available external cohort. More importantly, through an interaction effect regression analysis leveraging both clinical and proteomic datasets, we discovered two biomarkers (NDUFB8 and CEMIP2), indicative of the overall sensitivity of patients with PDAC to adjuvant chemotherapy. The biomarkers were validated through immunohistochemistry on an internal cohort of 386 patients. Rigorous validation extended to two external multicentic cohorts-a French multicentric cohort (230 patients) and a cohort from two grade-A tertiary hospitals in China (466 patients)-enhancing the robustness and generalizability of our findings. Moreover, experimental validation through functional assays was conducted on PDAC cell lines and patient-derived organoids. In summary, our cohort-scale integration of clinical and proteomic data demonstrates the potential of proteomics-guided prognosis and biomarker-aided adjuvant chemotherapy for PDAC.

Development of a monoclonal antibody to ITPRIPL1 for immunohistochemical diagnosis of non-small cell lung cancers: accuracy and correlation with CD8+ T cell infiltration

Introduction: Cancer biomarkers are substances or processes highly associated with the presence and progression of cancer, which are applicable for cancer screening, progression surveillance, and prognosis prediction in clinical practice. In our previous studies, we discovered that cancer cells upregulate inositol 1,4,5-triphosphate receptor-interacting protein-like 1 (ITPRIPL1), a natural CD3 ligand, to evade immune surveillance and promote tumor growth. We also developed a monoclonal ITPRIPL1 antibody with high sensitivity and specificity. Here, we explored the application of anti-ITPRIPL1 antibody for auxiliary diagnosis of non-small cell lung cancer (NSCLC). Methods: NSCLC patient tissue samples (n = 75) were collected and stained by anti-ITPRIPL1 or anti-CD8 antibodies. After excluding the flaked samples (n = 15), we evaluated the expression by intensity (0-3) and extent (0-100%) of staining to generate an h-score for each sample. The expression status was classified into negative (h-score < 20), low-positive (20-99), and high-positive (≥ 100). We compared the h-scores between the solid cancer tissue and stroma and analyzed the correlation between the h-scores of the ITPRIPL1 and CD8 expression in situ in adjacent tissue slices. Results: The data suggested ITPRIPL1 is widely overexpressed in NSCLC and positively correlates with tumor stages. We also found that ITPRIPL1 expression is negatively correlated with CD8 staining, which demonstrates that ITPRIPL1 overexpression is indicative of poorer immune infiltration and clinical prognosis. Therefore, we set 50 as the cutoff point of ITPRIPL1 expression H scores to differentiate normal and lung cancer tissues, which is of an excellent sensitivity and specificity score (100% within our sample collection). Discussion: These results highlight the potential of ITPRIPL1 as a proteomic immunohistochemical NSCLC biomarker with possible advantages over the existing NSCLC biomarkers, and the ITPRIPL1 antibody can be applied for accurate diagnosis and prognosis prediction.

Risk SNP in a transcript of RP11-638I2.4 increases lncRNA-YY1 interaction and pancreatic cancer susceptibility

Tens of thousands of long non-coding RNAs (lncRNAs) have been identified through RNA-seq analysis, but the biological and pathological significance remains unclear. By integrating the genome-wide lncRNA data with a cross-ancestry meta-analysis of PDAC GWASs, we depicted a comprehensive atlas of pancreatic ductal adenocarcinoma (PDAC)-associated lncRNAs, containing 1,204 lncRNA (445 novel lncRNAs and 759 GENCODE annotated lncRNAs) and 4,368 variants. Furthermore, we found that PDAC-associated lncRNAs could function by altering chromatin activity, transcription factors, and RNA-binding proteins binding affinity. Importantly, genetic variants linked to PDAC are preferentially found at PDAC-associated lncRNA regions, supporting the biological and clinical relevance of PDAC-associated lncRNAs. Finally, we prioritized a novel transcript (MICT00000110172.1) of RP11-638I2.4 as a potential tumor promoter. MICT00000110172.1 is able to reinforce the interaction with YY1, which could reverse the effect of YY1 on pancreatic cancer cell cycle arrest to promote the pancreatic cancer growth. G > A change at rs2757535 in the second exon of MICT00000110172.1 induces a spatial structural change and creates a target region for YY1 binding, which enforces the effect of MICT00000110172.1 in an allele-specific manner, and thus confers susceptibility to tumorigenesis. In summary, our results extend the repertoire of PDAC-associated lncRNAs that could act as a starting point for future functional explorations, and the identification of lncRNA-based target therapy.

Predictive factors for survival in borderline resectable and locally advanced pancreatic cancer: are these really two different entities?

BACKGROUND

The treatment of borderline resectable (BR) and locally advanced (LA) pancreatic ductal adenocarcinoma (PDAC) has evolved with a wider application of neoadjuvant chemotherapy (NACHT). The aim of this study was to identify predictive factors for survival in BR and LA PDAC.

METHODS

Clinicopathologic data of patients with BR and LA PDAC who underwent surgical exploration between January 2011 and June 2021 were retrospectively collected. Survival from the date of surgery was estimated using the Kaplan-Meier method. Simple and multiple Cox proportional hazards models were fitted to identify factors associated with survival. Surgical resection was analyzed in combination with the involvement of lymph nodes as this last was only known after a formal resection.

RESULTS

Ninety patients were surgically explored (BR: 45, LA: 45), of which 51 (57%) were resected (BR: 31, LA: 20). NACHT was administered to 43 patients with FOLFIRINOX being the most frequent regimen applied (33/43, 77%). Major complications (Clavien-Dindo grade III and IV) occurred in 7.8% of patients and 90-day mortality rate was 3.3%. The median overall survival since surgery was 16 months (95% CI 12-20) in the group which underwent surgical resection and 10 months (95% CI 7-13) in the group with an unresectable tumor (p=0.001). Cox proportional hazards models showed significantly lower mortality hazard for surgical resection compared to no surgical resection, even after adjusting for National Comprehensive Cancer Network  (NCCN) classification and administration of NACHT [surgical resection with involved lymph nodes vs no surgical resection (cHR 0.49; 95% CI 0.29-0.82; p=0.007)]. There was no significant difference in survival between patients with BR and LA disease (cHR= 1.01; 95% CI 0.63-1.62; p=0.98).

CONCLUSIONS

Surgical resection is the only predictor of survival in patients with BR and LA PDAC, regardless of their initial classification as BR or LA. Our results suggest that surgery should not be denied to patients with LA PDAC a priori. Prospective studies including patients from the moment of diagnosis are required to identify biologic and molecular markers which may allow a better selection of patients who will benefit from surgery.

The PANcreatic Disease ReseArch (PANDoRA) consortium: Ten years' experience of association studies to understand the genetic architecture of pancreatic cancer

Pancreatic cancer has an incidence that almost matches its mortality. Only a small number of risk factors and 33 susceptibility loci have been identified. so Moreover, the relative rarity of pancreatic cancer poses significant hurdles for research aimed at increasing our knowledge of the genetic mechanisms contributing to the disease. Additionally, the inability to adequately power research questions prevents small monocentric studies from being successful. Several consortia have been established to pursue a better understanding of the genetic architecture of pancreatic cancers. The Pancreatic disease research (PANDoRA) consortium is the largest in Europe. PANDoRA is spread across 12 European countries, Brazil and Japan, bringing together 29 basic and clinical research groups. In the last ten years, PANDoRA has contributed to the discovery of 25 susceptibility loci, a feat that will be instrumental in stratifying the population by risk and optimizing preventive strategies.

Gene signature developed for predicting early relapse and survival in early-stage pancreatic cancer

BACKGROUND

The aim of this study was to construct a predictive signature integrating tumour-mutation- and copy-number-variation-associated features using machine learning to precisely predict early relapse and survival in patients with resected stage I-II pancreatic ductal adenocarcinoma.

METHODS

Patients with microscopically confirmed stage I-II pancreatic ductal adenocarcinoma undergoing R0 resection at the Chinese PLA General Hospital between March 2015 and December 2016 were enrolled. Whole exosome sequencing was performed, and genes with different mutation or copy number variation statuses between patients with and without relapse within 1 year were identified using bioinformatics analysis. A support vector machine was used to evaluate the importance of the differential gene features and to develop a signature. Signature validation was performed in an independent cohort. The associations of the support vector machine signature and single gene features with disease-free survival and overall survival were assessed. Biological functions of integrated genes were further analysed.

RESULTS

Overall, 30 and 40 patients were included in the training and validation cohorts, respectively. Some 11 genes with differential patterns were first identified; using a support vector machine, four features (mutations of DNAH9, TP53, and TUBGCP6, and copy number variation of TMEM132E) were further selected and integrated to construct a predictive signature (the support vector machine classifier). In the training cohort, the 1-year disease-free survival rates were 88 per cent (95 per cent c.i. 73 to 100) and 7 per cent (95 per cent c.i. 1 to 47) in the low-support vector machine subgroup and the high-support vector machine subgroup respectively (P < 0.001). Multivariable analyses showed that high support vector machine was significantly and independently associated with both worse overall survival (HR 29.20 (95 per cent c.i. 4.48 to 190.21); P < 0.001) and disease-free survival (HR 72.04 (95 per cent c.i. 6.74 to 769.96); P < 0.001). The area under the curve of the support vector machine signature for 1-year disease-free survival (0.900) was significantly larger than the area under the curve values of the mutations of DNAH9 (0.733; P = 0.039), TP53 (0.767; P = 0.024), and TUBGCP6 (0.733; P = 0.023), the copy number variation of TMEM132E (0.700; P = 0.014), TNM stage (0.567; P = 0.002), and differentiation grade (0.633; P = 0.005), suggesting higher predictive accuracy for prognosis. The value of the signature was further validated in the validation cohort. The four genes included in the support vector machine signature (DNAH9, TUBGCP6, and TMEM132E were novel in pancreatic ductal adenocarcinoma) were significantly associated with the tumour immune microenvironment, G protein-coupled receptor binding and signalling, cell-cell adhesion, etc.

CONCLUSION

The newly constructed support vector machine signature precisely and powerfully predicted relapse and survival in patients with stage I-II pancreatic ductal adenocarcinoma after R0 resection.

Systematic analysis on expression quantitative trait loci identifies a novel regulatory variant in ring finger and WD repeat domain 3 associated with prognosis of pancreatic cancer

BACKGROUND

Pancreatic adenocarcinoma (PAAD) is an extremely lethal malignancy. Identification of the functional genes and genetic variants related to PAAD prognosis is important and challenging. Previously identified prognostic genes from several expression profile analyses were inconsistent. The regulatory genetic variants that affect PAAD prognosis were largely unknown.

METHODS

Firstly, a meta-analysis was performed with seven published datasets to systematically explore the candidate prognostic genes for PAAD. Next, to identify the regulatory variants for those candidate genes, expression quantitative trait loci analysis was implemented with PAAD data resources from The Cancer Genome Atlas. Then, a two-stage association study in a total of 893 PAAD patients was conducted to interrogate the regulatory variants and find the prognostic locus. Finally, a series of biochemical experiments and phenotype assays were carried out to demonstrate the biological function of variation and genes in PAAD progression process.

RESULTS

A total of 128 genes were identified associated with the PAAD prognosis in the meta-analysis. Fourteen regulatory loci in 12 of the 128 genes were discovered, among which, only rs4887783, the functional variant in the promoter of Ring Finger and WD Repeat Domain 3 ( RFWD3 ), presented significant association with PAAD prognosis in both stages of the population study. Dual-luciferase reporter and electrophoretic mobility shift assays demonstrated that rs4887783-G allele, which predicts the worse prognosis, enhanced the binding of transcript factor REST, thus elevating RFWD3 expression. Further phenotypic assays revealed that excess expression of RFWD3 promoted tumor cell migration without affecting their proliferation rate. RFWD3 was highly expressed in PAAD and might orchestrate the genes in the DNA repair process.

CONCLUSIONS

RFWD3 and its regulatory variant are novel genetic factors for PAAD prognosis.

Cancer stem cell markers for liver cancer and pancreatic cancer

Cancer stem cells (CSC) theory has ushered in a new era of cancer research. Tumor recurrence, metastasis and chemotherapy resistance are all related to the existence of cancer stem cells. Further understanding of tumor heterogeneity will contribute to targeted treatment. Liver cancer and pancreatic cancer are common digestive gland tumors with high lethality. This article reviews the identification and isolation of CSC markers in hepatocellular carcinoma and pancreatic cancer. The markers related signal pathways are involved in the occurrence and development of tumors, and have a significant impact on the proliferation, metastasis and invasion of cancer cells, which can be used as potential molecular therapeutic targets. This study will be helpful to understand cancer stem cell like cells.

Combined Identification of Novel Markers for Diagnosis and Prognostic of Classic Hodgkin Lymphoma

BACKGROUND

An effective diagnostic and prognostic marker based on the gene expression profile of classic Hodgkin lymphoma (cHL) has not yet been developed. The aim of the present study was to investigate potential markers for the diagnosis and prediction of cHL prognosis.

METHODS

The gene expression profiles with all available clinical features were downloaded from the Gene Expression Omnibus (GEO) database. Then, multiple machine learning algorithms were applied to develop and validate a diagnostic signature by comparing cHL with normal control. In addition, we identified prognostic genes and built a prognostic model with them to predict the prognosis for 130 patients with cHL which were treated with first-line treatment (ABVD chemotherapy or an ABVD-like regimen).

RESULTS

A diagnostic prediction signature was constructed and showed high specificity and sensitivity (training cohort: AUC=0.981,95% CI 0.933-0.998, P<0.001, validation cohort: AUC=0.955,95% CI 0.895-0.986, P<0.001). Additionally, nine prognostic genes (LAMP1, STAT1, MMP9, C1QB, ICAM1, CD274, CCL19, HCK and LILRB2) were screened and a prognostic prediction model was constructed with them, which had been confirmed effectively predicting prognosis (P<0.001). Furthermore, the results of the immune infiltration assessment indicated that the high scale of the fraction of CD8 + T cells, M1 macrophages, resting mast cells associated with an adverse outcome in cHL, and naive B cells related to prolonged survival. In addition, a nomogram that combined the prognostic prediction model and clinical characteristics is also suggested to have a good predictive value for the prognosis of patients.

CONCLUSION

The new markers found in this study may be helpful for the diagnosis and prediction of the prognosis of cHL.

Inflammatory Biomarker Score Identifies Patients with Six-Fold Increased Risk of One-Year Mortality after Pancreatic Cancer

Simple Summary

For 20 years, the CA 19-9 blood test has been the only broadly used biomarker of pancreatic ductal adenocarcinoma (PDAC). We lack easily available biomarkers to help differentiate patients between good, intermediate and poor survivors at the time of PDAC diagnosis. Using one of the largest studies of patients with PDAC, we found that a simple combination of blood tests, namely CRP, CA 19-9 and IL-6, into a single biomarker score was a better marker of one-year survival than the currently recommended CA 19-9 alone or any other combination of the four inflammatory biomarkers examined (CRP, CA 19-9, IL-6 and YKL-40). However, since this is the first study examining this inflammatory biomarker score, future validation studies are needed. Moreover, CRP outperformed CA 19-9 in the majority of patients, thus questioning the routine use of CA 19-9 in patients with PDAC.

Abstract

We examined whether elevated plasma C-reactive protein (CRP), carbohydrate antigen (CA) 19-9, interleukin-6 (IL-6) and YKL-40, individually or combined, can identify poor survivors among patients with pancreatic ductal adenocarcinoma (PDAC). We measured CRP, CA 19-9, IL-6 and YKL-40 in 993 patients at the time of PDAC diagnosis. The biomarker score was the sum of biomarker categories, coded 0, 1 and 2 for low, intermediate and high plasma concentrations, respectively. High vs. low levels of CRP, CA 19-9 and IL-6 were each independently associated with a two-fold increased risk of one-year mortality. CRP performed best in patients with advanced and CA 19-9 in patients with low cancer stages. YKL-40 was not associated with mortality and, therefore, was not included in the biomarker score. Compared to the biomarker score = 0, the multifactorially adjusted hazard ratios for one-year mortality were 1.56 (95% confidence interval: 0.99–2.44) for score = 1, 2.22 (1.41–3.49) for score = 2, 3.44 (2.20–5.38) for score = 3, 5.13 (3.21–8.17) for score = 4 and 6.32 (3.84–10.41) for score = 5–6 (p-value for trend = 3 × 10⁻³¹). This score performed better than any single biomarker or combination of biomarkers when examined in similarly sized or other categories. In conclusion, a combination score of elevated CRP, CA 19-9 and IL-6 identified patients with six-fold higher one-year mortality.

Neoadjuvant Treatment for Pancreatic Adenocarcinoma: A False Promise or an Opportunity to Improve Outcome?

Simple Summary

Pancreatic cancer usually presents late when it has spread to distant sites. In a small proportion of patients, the cancer can be removed by surgery. Surgery is usually followed by chemotherapy, as studies have shown that this improves survival. However, due to complications after surgery and a decline in fitness, some patients do not start their chemotherapy and many do not complete the planned course. The cancer returns in the majority of patients. Chemotherapy or a combination of chemotherapy and radiotherapy before surgery are being investigated to improve survival. The best treatment regime and patient selection for different treatment strategies remains to be defined and is discussed here.

Abstract

Pancreatic ductal adenocarcinoma (PDAC) has an aggressive tumor biology and is associated with poor survival outcomes. Most patients present with metastatic or locally advanced disease. In the 10–20% of patients with upfront resectable disease, surgery offers the only chance of cure, with the addition of adjuvant chemotherapy representing an established standard of care for improving outcomes. Despite resection followed by adjuvant chemotherapy, at best, 3-year survival reaches 63.4%. Post-operative complications and poor performance mean that around 50% of the patients do not commence adjuvant chemotherapy, and a significant proportion do not complete the intended treatment course. These factors, along with the advantages of early treatment of micrometastatic disease, the ability to downstage tumors, and the increase in R0 resection rates, have increased interest in neo-adjuvant treatment strategies. Here we review biomarkers for early diagnosis of PDAC and patient selection for a neo-adjuvant approach. We also review the current evidence for different chemotherapy regimens in this setting, as well as the role of chemoradiotherapy and immunotherapy, and we discuss ongoing trials.

Susceptibility loci for pancreatic cancer in the Brazilian population

BACKGROUND

Pancreatic adenocarcinoma (PA) is a very aggressive cancer and has one of the poorest prognoses. Usually, the diagnosis is late and resistant to conventional treatment. Environmental and genetic factors contribute to the etiology, such as tobacco and alcohol consumption, chronic pancreatitis, diabetes and obesity. Somatic mutation in pancreatic cancer cells are known and SNP profile by GWAS could access novel genetic risk factors for this disease in different population context. Here we describe a SNP panel for Brazilian pancreatic cancer, together with clinical and epidemiological data.

METHODS

78 pancreatic adenocarcinoma and 256 non-pancreatic cancer subjects had 25 SNPs genotyped by real-time PCR. Unconditional logistic regression methods were used to assess the main effects on PA risk, using allelic, co-dominant and dominant inheritance models.

RESULTS

9 SNPs were nominally associated with pancreatic adenocarcinoma risk, with 5 of the minor alleles conferring protective effect while 4 related as risk factor. In epidemiological and clinical data, tobacco smoking, diabetes and pancreatitis history were significantly related to pancreatic adenocarcinoma risk. Polygenic risk scores computed using the SNPs in the study showed strong associations with PA risk.

CONCLUSION

We could assess for the first time some SNPs related with PA in Brazilian populations, a result that could be used for genetic screening in risk population such as familial pancreatic cancer, smokers, alcohol users and diabetes patients.

Lack of association of CD44-rs353630 and CHI3L2-rs684559 with pancreatic ductal adenocarcinoma survival

Although pancreatic ductal adenocarcinoma (PDAC) survival is poor, there are differences in patients' response to the treatments. Detection of predictive biomarkers explaining these differences is of the utmost importance. In a recent study two genetic markers (CD44-rs353630 and CHI3L2-rs684559) were reported to be associated with survival after PDAC resection. We attempted to replicate the associations in 1856 PDAC patients (685 resected with stage I/II) from the PANcreatic Disease ReseArch (PANDoRA) consortium. We also analysed the combined effect of the two genotypes in order to compare our results with what was previously reported. Additional stratified analyses considering TNM stage of the disease and whether the patients received surgery were also performed. We observed no statistically significant associations, except for the heterozygous carriers of CD44-rs353630, who were associated with worse OS (HR = 5.01; 95% CI 1.58-15.88; p = 0.006) among patients with stage I disease. This association is in the opposite direction of those reported previously, suggesting that data obtained in such small subgroups are hardly replicable and should be considered cautiously. The two polymorphisms combined did not show any statistically significant association. Our results suggest that the effect of CD44-rs353630 and CHI3L2-rs684559 cannot be generalized to all PDAC patients.

The Era of Radiogenomics in Precision Medicine: An Emerging Approach to Support Diagnosis, Treatment Decisions, and Prognostication in Oncology

With the rapid development of new technologies, including artificial intelligence and genome sequencing, radiogenomics has emerged as a state-of-the-art science in the field of individualized medicine. Radiogenomics combines a large volume of quantitative data extracted from medical images with individual genomic phenotypes and constructs a prediction model through deep learning to stratify patients, guide therapeutic strategies, and evaluate clinical outcomes. Recent studies of various types of tumors demonstrate the predictive value of radiogenomics. And some of the issues in the radiogenomic analysis and the solutions from prior works are presented. Although the workflow criteria and international agreed guidelines for statistical methods need to be confirmed, radiogenomics represents a repeatable and cost-effective approach for the detection of continuous changes and is a promising surrogate for invasive interventions. Therefore, radiogenomics could facilitate computer-aided diagnosis, treatment, and prediction of the prognosis in patients with tumors in the routine clinical setting. Here, we summarize the integrated process of radiogenomics and introduce the crucial strategies and statistical algorithms involved in current studies.

Identification of genetic variants in m6A modification genes associated with pancreatic cancer risk in the Chinese population

N6-Methyladenosine (m⁶A) is the most prevalent modification of RNA in eukaryotes, and is associated with many cellular processes and even the development of cancers. We hypothesized that single-nucleotide polymorphisms (SNPs) in m⁶A modification genes, including its "writers", "erasers" and "readers", might affect the m⁶A functions and associate with the susceptibility to pancreatic ductal adenocarcinoma (PDAC). We first conducted a two-stage case-control study in Chinese population to interrogate all SNPs in 22 m⁶A modification genes. In the discovery stage, a total of 2735 SNPs were genotyped in 980 patients and 1991 controls. Then, the promising SNP was replicated in another independent population consisting of 858 cases and 2084 controls. As a result, we found the rs7495 in 3'UTR of hnRNPC was significantly associated with increased risk of PDAC in both stages (combined odds ratio = 1.22, 95% confidence interval = 1.12-1.32, P = 2.39 × 10^-6). To further reveal the biological function of rs7495 and hnRNPC, we performed a series of biochemical experiments. Luciferase reporter assays indicated that rs7495G allele promoted hnRNPC expression through disrupting a putative binding site for has-miR-183-3p. Cell viability assay demonstrated that knockdown of hnRNPC suppressed the proliferation of PDAC cells. RNA-seq analysis suggested that as an m⁶A "reader", hnRNPC played an important role in RNA biological processes. In conclusion, our findings elucidated that rs7495G could confer higher risk of PDAC via promoting the expression of hnRNPC through a miRNA-mediated manner. These results provided a novel insight into the critical role of m⁶A modification in tumorigenesis.

Prognostic and predictive value of a mRNA signature in peripheral T-cell lymphomas: A mRNA expression analysis

Current international prognostic index is widely questioned on the risk stratification of peripheral T‐cell lymphoma and does not accurately predict the outcome for patients. We postulated that multiple mRNAs could combine into a model to improve risk stratification and helping clinicians make treatment decisions. In this study, the gene expression profiles were downloaded from the Gene Expression Omnibus (GEO) database. Weighted gene co‐expression network analysis (WGCNA) was used to screening genes in selected module which most closely related to PTCLs, and then built a mRNA signature using a LASSO Cox regression model and validated the prognostic accuracy of it. Finally, a nomogram was constructed and the performance was assessed. A total of 799 WGCNA‐selected mRNAs in black module were identified, and a mRNA signature which based on DOCK2, GSTM1, H2AFY, KCNAB2, LAPTM5 and SYK for PTCLs was developed. Significantly statistical difference can be seen in overall survival of PTCLs between low‐risk group and high‐risk group (training set:hazard ratio [HR] 4.3, 95% CI 2.4‐7.4, P < .0001; internal testing set:hazard ratio [HR] 2.4, 95% CI 1.2‐4.8, P < .01; external testing set:hazard ratio [HR] 2.3, 95% CI 1.10‐4.7, P = .02). Furthermore, multivariate regression demonstrated that the signature was an independently prognostic factor. Moreover, the nomogram which combined the mRNA signature and multiple clinical factors suggesting that predicted survival probability agreed well with the actual survival probability. The signature is a reliable prognostic tool for patients with PTCLs, and it has the potential for clinicians to implement personalized therapeutic regimen for patients with PTCLs.

Updated risk factors to inform early pancreatic cancer screening and identify high risk patients

Pancreatic adenocarcinoma (PDAC) is associated with poor clinical outcomes and incomplete responses to conventional therapy. Therefore, there is an unmet clinical need to better understand the predisposing factors for pancreatic cancer in hopes of providing early screening to high-risk patients. While select risk factors such as age, race, and family history, or predisposing syndromes are unavoidable, there are several new and established risk factors that allow for intervention, namely by counseling patients to make the appropriate lifestyle modifications. Here, we discuss the best-studied risk factors for PDAC such as tobacco use and chronic pancreatitis, as well as newly emerging risk factors including select nutritional deficits, bacterial infections, and psychosocial factors. As several of these risk factors appear to be additive or synergistic, by understanding their relationships and offering coordinated, multidisciplinary care to high-risk patients, it may be possible to reduce pancreatic cancer incidence and improve clinical outcomes through early detection.

YKL-39 as a Potential New Target for Anti-Angiogenic Therapy in Cancer

YKL-39 belongs to the evolutionarily conserved family of Glyco_18-containing proteins composed of chitinases and chitinase-like proteins. Chitinase-like proteins (CLPs) are secreted lectins that lack hydrolytic activity due to the amino acid substitutions in their catalytic domain and combine the functions of cytokines and growth factors. One of the major cellular sources that produce CLPs in various pathologies, including cancer, are macrophages. Monocytes recruited to the tumor site and programmed by tumor cells differentiate into tumor-associated macrophages (TAMs), which are the primary source of pro-angiogenic factors. Tumor angiogenesis is a crucial process for supplying rapidly growing tumors with essential nutrients and oxygen. We recently determined that YKL-39 is produced by tumor-associated macrophages in breast cancer. YKL-39 acts as a strong chemotactic factor for monocytes and stimulates angiogenesis. Chemotherapy is a common strategy to reduce tumor size and aggressiveness before surgical intervention, but chemoresistance, resulting in the relapse of tumors, is a common clinical problem that is critical for survival in cancer patients. Accumulating evidence indicates that TAMs are essential regulators of chemoresistance. We have recently found that elevated levels of YKL-39 expression are indicative of the efficiency of the metastatic process in patients who undergo neoadjuvant chemotherapy. We suggest YKL-39 as a new target for anti-angiogenic therapy that can be combined with neoadjuvant chemotherapy to reduce chemoresistance and inhibit metastasis in breast cancer patients.

Applying a radiomics-based strategy to preoperatively predict lymph node metastasis in the resectable pancreatic ductal adenocarcinoma

PURPOSE

This retrospective study is designed to develop a Radiomics-based strategy for preoperatively predicting lymph node (LN) status in the resectable pancreatic ductal adenocarcinoma (PDAC) patients.

METHODS

Eighty-five patients with histopathological confirmed PDAC are included, of which 35 are LN metastasis positive and 50 are LN metastasis negative. Initially, 1,124 radiomics features are computed from CT images of each patient. After a series of feature selection, a Radiomics logistic regression (LOG) model is developed. Subsequently, the predictive efficiency of the model is validated using a leave-one-out cross-validation method. The model performance is evaluated on discrimination and compared with the conventional CT evaluation method based on subjective CT image features.

RESULTS

Radiomics LOG model is developed based on eight most related radiomics features. Remarkable differences are demonstrated between patients with LN metastasis positive and LN metastasis negative in Radiomics LOG scores namely, 0.535±1.307 (mean±standard deviation) vs. -1.514±1.800 (mean±standard deviation) with p < 0.001. Radiomics LOG model shows significantly higher predictive efficiency compared to the conventional evaluation method of LN status in which areas under ROC curves are AUC = 0.841 with 95% confidence interval (CI: 0.758∼0.925) vs. AUC = 0.682 with (95% CI: 0.566∼0.798). Leave-one-out cross validation indicates that the Radiomics LOG model correctly classifies 70.3% cases, while the conventional CT evaluation method only correctly classifies 57.0% cases.

CONCLUSION

A radiomics-based strategy provides an individualized LN status evaluation in PDAC patients, which may help clinicians implement an optimal personalized patient treatment.

Detection and analysis of stable and flexible genes towards a genome signature framework in cancer

Comparison and detection of stable cancer genes across cancer types is of interest. The gene expression data of 6 different cancer types (colon, breast, lung, ovarian, brain and renal) and a control group from The Cancer Genome Atlas (TCGA) database were used in this study. The comparison of gene expression data together with the calculation standard deviations of such data was completed using a statistical model for the detection of stable genes. Genes having similar expression (referred as flexible genes) pattern to the control group in four out of six cancer types are PATE, NEUROD4 and TRAFD1. Moreover, 13 genes showed low difference compared to the control group with low standard deviation across cancer types (referred as stable genes). Among them, genes GDF2, KCNT1 and RNF151 showed consistent low expression while ODF4, OR5I1, MYOG and OR2B11 showed consistent high expression. Thus, the detection and analysis of stable and flexible cancer genes help towards the design and development of a framework (outline) for specific genome signature (biomarker) in cancer.