MetaGxData: Clinically Annotated Breast, Ovarian and Pancreatic Cancer Datasets and their Use in Generating a Multi-Cancer Gene Signature

Decoding the Non-coding: Tools and Databases Unveiling the Hidden World of "Junk" RNAs for Innovative Therapeutic Exploration

Non-coding RNAs are pivotal regulators of gene and protein expression, exerting crucial influences on diverse biological processes. Their dysregulation is frequently implicated in the onset and progression of diseases, notably cancer. A profound comprehension of the intricate mechanisms governing ncRNAs is imperative for devising innovative therapeutic interventions against these debilitating conditions. Significantly, nearly 80% of our genome comprises ncRNAs, underscoring their centrality in cellular processes. The elucidation of ncRNA functions is pivotal for grasping the complexities of gene regulation and its implications for human health. Modern genome sequencing techniques yield vast datasets, stored in specialized databases. To harness this wealth of information and to understand the crosstalk of non-coding RNAs, knowledge of available databases is required, and many new sophisticated computational tools have emerged. These tools play a pivotal role in the identification, prediction, and annotation of ncRNAs, thereby facilitating their experimental validation. This Review succinctly outlines the current understanding of ncRNAs, emphasizing their involvement in disease development. It also highlights the databases and tools instrumental in classifying, annotating, and evaluating ncRNAs. By extracting meaningful biological insights from seemingly "junk" data, these tools empower scientists to unravel the intricate roles of ncRNAs in shaping human health.

Clinically relevant gene signatures provide independent prognostic information in older breast cancer patients

BACKGROUND

The clinical utility of gene signatures in older breast cancer patients remains unclear. We aimed to determine signature prognostic capacity in this patient subgroup.

METHODS

Research versions of the genomic grade index (GGI), 70-gene, recurrence score (RS), cell cycle score (CCS), PAM50 risk-of-recurrence proliferation (ROR-P), and PAM50 signatures were applied to 39 breast cancer datasets (N = 9583). After filtering on age ≥ 70 years, and the presence of estrogen receptor (ER) and survival data, 871 patients remained. Signature prognostic capacity was tested in all (n = 871), ER-positive/lymph node-positive (ER + /LN + , n = 335) and ER-positive/lymph node-negative (ER + /LN-, n = 374) patients using Kaplan-Meier and multivariable Cox-proportional hazard (PH) modelling.

RESULTS

All signatures were statistically significant in Kaplan-Meier analysis of all patients (Log-rank P < 0.001). This significance remained in multivariable analysis (Cox-PH, P ≤ 0.05). In ER + /LN + patients all signatures except PAM50 were significant in Kaplan-Meier analysis (Log-rank P ≤ 0.05) and remained so in multivariable analysis (Cox-PH, P ≤ 0.05). In ER + /LN- patients all except RS were significant in Kaplan-Meier analysis (Log-rank P ≤ 0.05) but only the 70-gene, CCS, ROR-P, and PAM50 signatures remained so in multivariable analysis (Cox-PH, P ≤ 0.05).

CONCLUSIONS

We found that gene signatures provide prognostic information in survival analyses of all, ER + /LN + and ER + /LN- older (≥ 70 years) breast cancer patients, suggesting a potential role in aiding treatment decisions in older patients.

Integrating PANoptosis insights to enhance breast cancer prognosis and therapeutic decision-making

Background

Despite advancements, breast cancer outcomes remain stagnant, highlighting the need for precise biomarkers in precision medicine. Traditional TNM staging is insufficient for identifying patients who will respond well to treatment.

Methods

Our study involved over 6,900 breast cancer patients from 14 datasets, including in-house clinical data and single-cell data from 8 patients (37,451 cells). We integrated 10 machine learning algorithms in 55 combinations and analyzed 100 existing breast cancer signatures. IHC assays were conducted for validation, and potential immunotherapies and chemotherapies were explored.

Results

We pinpointed six stable Panoptosis-related genes from multi-center cohorts, leading to a robust Panoptosis-model. This model outperformed existing clinical and molecular features in predicting recurrence and mortality risks, with high-risk patients showing worse outcomes. IHC validation from 30 patients confirmed our findings, indicating the model's broader applicability. Additionally, the model suggested that low-risk patients benefit more from immunotherapy, while high-risk patients are sensitive to specific chemotherapies like BI-2536 and ispinesib.

Conclusion

The Panoptosis-model represents a major advancement in breast cancer prognosis and treatment personalization, offering significant insights for effectively managing a wide range of breast cancer patients.

Large-scale phenogenomic analysis of human cancers uncovers frequent alterations affecting SMC5/6 complex components in breast cancer

Cancer cells often experience large-scale alterations in genome architecture because of DNA damage and replication stress. Whether mutations in core regulators of chromosome structure can also lead to cancer-promoting loss in genome stability is not fully understood. To address this question, we conducted a systematic analysis of mutations affecting a global regulator of chromosome biology -the SMC5/6 complex- in cancer genomics cohorts. Analysis of 64 959 cancer samples spanning 144 tissue types and 199 different cancer genome studies revealed that the SMC5/6 complex is frequently altered in breast cancer patients. Patient-derived mutations targeting this complex associate with strong phenotypic outcomes such as loss of ploidy control and reduced overall survival. Remarkably, the phenotypic impact of several patient mutations can be observed in a heterozygous context, hence providing an explanation for a prominent role of SMC5/6 mutations in breast cancer pathogenesis. Overall, our findings suggest that genes encoding global effectors of chromosome architecture can act as key contributors to cancer development in humans.

A20 affects macrophage polarization through the NLRP3 inflammasome signaling pathway and promotes breast cancer progression

Breast cancer is the most common malignant tumor in females, and the majority of patients succumb to metastasis. The present study aimed to investigate the association between tumor necrosis factor alpha-induced protein 3 (A20), NOD-, LRR- and pyrin domain-containing protein 3 (NLRP3) and tumor-associated macrophage polarization, and their effects on the proliferation and metastasis of breast cancer cells. The expression of A20 in breast cancer cells was analyzed by reverse transcription-quantitative PCR (RT-qPCR) and western blotting. RT-qPCR and western blotting were also used to confirm the transfection efficiency. The viability, clone formation, migration, invasion and angiogenesis of transfected breast cancer cells were detected by Cell Counting Kit-8, colony formation, wound healing, Transwell and tube formation assays, respectively. Activated macrophages, namely M1 and M2 type macrophages, were observed by double staining immunofluorescence. The levels of M1 and M2 macrophage markers were analyzed by qPCR. The expression of angiogenesis-related proteins and NLRP3 inflammasome activation-associated proteins was detected by western blotting. The results revealed that A20 was highly expressed in breast cancer cells. Interference with A20 inhibited the proliferation, invasion, migration and angiogenesis of breast cancer cells, and inhibited the M2-like polarization of macrophages. Interference with A20 promoted the activation of the NLRP3 inflammasome. The NLRP3 inhibitor MCC950 alleviated the effect of interference with A20 to promote macrophage proliferation and recruitment, as well as M2-like polarization. In conclusion, interference with A20 inhibited macrophage proliferation and M2-like polarization through the NLRP3 inflammasome signaling pathway to inhibit breast cancer progression.

Integrated multiomics analyses unveil the implication of a costimulatory molecule score on tumor aggressiveness and immune evasion in breast cancer: A large-scale study through over 8,000 patients

BACKGROUND

Although immunotherapy has revolutionised cancer management, reliable genomic biomarkers for identifying eligible patient subpopulations are lacking. Costimulatory molecules play a crucial role in mounting anti-tumour responses, and clinical trials targeting these novel biomarkers are underway. However, whether these molecules can determine tumour aggressiveness and the risk of tumour evasion in breast cancer (BC) remains largely unknown.

METHODS

The whole-tissue transcriptomic data of 8236 patients with BC from 15 independent cohorts were extracted. An integrated scoring system named 'costimulatory molecule score' (CMS) was constructed and sufficient validated using least absolute shrinkage and selection operator regression (1000 iterations) and the random survival forest algorithm (1000 trees). The correlation among CMSs, cancer genotypes and clinicopathological characteristics was examined. Extensive multiomics and immunogenomic analyses were performed to investigate and verify the association among CMSs, enriched pathways, potential intrinsic and extrinsic immune escape mechanisms, immunotherapy response and therapeutic options.

RESULTS

The predictive role of CMS model that relies on expression pattern of merely 5 costimulatory genes for prognosis is almost universally applicable to BC patients in a platform-independent manner. Through internal and external in silico validation, high CMS was characterized by favorable genotypes but decreased tumor immunogenicity, activation of stroma, immune-suppressive states and potential immunotherapeutic resistance. Similar results were observed in a real-world immunotherapy cohort and Pan-Cancer analysis.

CONCLUSION

This comprehensive characterization indicates CMS model may be complemented for predicting tumor aggressiveness and immune evasion in BC patients, underlining the future clinical potential for further exploration of resistance mechanisms and optimization of immunotherapeutic strategies.

Overview of Bioinformatics Software and Databases for Metabolic Engineering

The explosion of the "omics" era has introduced a growing number of sets and tools that facilitate molecular interrogation of the metabolome. These include various bioinformatics and pharmacogenomics resources that can be utilized independently or collectively to facilitate metabolic engineering across disease, clinical oncology, and understanding of molecular changes across larger systems. This review provides starting points for accessing publicly available data and computational tools that support assessment of metabolic profiles and metabolic regulation, providing both a depth-and-breadth approach toward understanding the metabolome. We focus in particular on pathway databases and tools, which provide in-depth analysis of metabolic pathways, which is at the heart of metabolic engineering.

A pairwise strategy for imputing predictive features when combining multiple datasets

MOTIVATION

In the training of predictive models using high-dimensional genomic data, multiple studies' worth of data are often combined to increase sample size and improve generalizability. A drawback of this approach is that there may be different sets of features measured in each study due to variations in expression measurement platform or technology. It is often common practice to work only with the intersection of features measured in common across all studies, which results in the blind discarding of potentially useful feature information that is measured in individual or subsets of studies.

RESULTS

We characterize the loss in predictive performance incurred by using only the intersection of feature information available across all studies when training predictors using gene expression data from microarray and sequencing datasets. We study the properties of linear and polynomial regression for imputing discarded features and demonstrate improvements in the external performance of prediction functions through simulation and in gene expression data collected on breast cancer patients. To improve this process, we propose a pairwise strategy that applies any imputation algorithm to two studies at a time and averages imputed features across pairs. We demonstrate that the pairwise strategy is preferable to first merging all datasets together and imputing any resulting missing features. Finally, we provide insights on which subsets of intersected and study-specific features should be used so that missing-feature imputation best promotes cross-study replicability.

AVAILABILITY AND IMPLEMENTATION

The code is available at https://github.com/YujieWuu/Pairwise_imputation.

SUPPLEMENTARY INFORMATION

Supplementary information is available at Bioinformatics online.

Toll-like receptor 2 promotes breast cancer progression and resistance to chemotherapy

Cancer stem cells (CSCs) are the main drivers of disease progression and chemotherapy resistance in breast cancer. Tumor progression and chemoresistance might then be prevented by CSC-targeted therapies. We previously demonstrated that Toll-like Receptor (TLR)2 is overexpressed in CSCs and fuels their self-renewal. Here, we show that high TLR2 expression is linked to poor prognosis in breast cancer patients, therefore representing a candidate target for breast cancer treatment. By using a novel mammary cancer-prone TLR2^KO mouse model, we demonstrate that TLR2 is required for CSC pool maintenance and for regulatory T cell induction. Accordingly, cancer-prone TLR2^KO mice display delayed tumor onset and increased survival. Transplantation of TLR2^WT and TLR2^KO cancer cells in either TLR2^WT or TLR2^KO hosts shows that tumor initiation is mostly sustained by TLR2 expression in cancer cells. TLR2 host deficiency partially impairs cancer cell growth, implying a pro-tumorigenic effect of TLR2 expression in immune cells. Finally, we demonstrate that doxorubicin-induced release of HMGB1 activates TLR2 signaling in cancer cells, leading to a chemotherapy-resistant phenotype. Unprecedented use of TLR2 inhibitors in vivo reduces tumor growth and potentiates doxorubicin efficacy with no negative impact on the host immune system, opening new perspectives for the treatment of breast cancer patients.

STAT3 induces breast cancer growth via ANGPTL4, MMP13 and STC1 secretion by cancer associated fibroblasts

In the tumor microenvironment, Cancer Associated Fibroblasts (CAFs) become activated by cancer cells and increase their secretory activity to produce soluble factors that contribute to tumor cells proliferation, invasion and dissemination to distant organs. The pro-tumorigenic transcription factor STAT3 and its canonical inducer, the pro-inflammatory cytokine IL-6, act conjunctly in a positive feedback loop that maintains high levels of IL-6 secretion and STAT3 activation in both tumor and stromal cells. Here, we demonstrate that STAT3 is essential for the pro-tumorigenic functions of murine breast cancer CAFs both in vitro and in vivo, and identify a STAT3 signature significantly enriched for genes encoding for secreted proteins. Among these, ANGPTL4, MMP13 and STC-1 were functionally validated as STAT3-dependent mediators of CAF pro-tumorigenic functions by different approaches. Both in vitro and in vivo CAFs activities were moreover impaired by MMP13 inhibition, supporting the feasibility of a therapeutic approach based on inhibiting STAT3-induced CAF-secreted proteins. The clinical potential of such an approach is supported by the observation that an equivalent CAF-STAT3 signature in humans is expressed at high levels in breast cancer stromal cells and characterizes patients with a shorter disease specific survival, including those with basal-like disease.

Targeted siRNA nanotherapeutics against breast and ovarian metastatic cancer: a comprehensive review of the literature

Metastasis is considered the major cause of unsuccessful cancer therapy. The metastatic development requires tumor cells to leave their initial site, circulate in the blood stream, acclimate to new cellular environments at a remote secondary site and endure there. There are several steps in metastasis, including invasion, intravasation, circulation, extravasation, premetastatic niche formation, micrometastasis and metastatic colonization. siRNA therapeutics are appreciated for their usefulness in treatment of cancer metastasis. However, siRNA therapy as a single therapy may not be a sufficient option for control of metastasis. By combining siRNA with targeting, functional agents or small-molecule drugs have shown potential effects that enhance therapeutic effectiveness. This review addresses multidrug resistance and metastasis in breast and ovarian cancers and highlights drug-delivery strategies using siRNA therapeutics.

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.

A systematic review of datasets that can help elucidate relationships among gene expression, race, and immunohistochemistry-defined subtypes in breast cancer

Scholarly requirements have led to a massive increase of transcriptomic data in the public domain, with millions of samples available for secondary research. We identified gene-expression datasets representing 10,214 breast-cancer patients in public databases. We focused on datasets that included patient metadata on race and/or immunohistochemistry (IHC) profiling of the ER, PR, and HER-2 proteins. This review provides a summary of these datasets and describes findings from 32 research articles associated with the datasets. These studies have helped to elucidate relationships between IHC, race, and/or treatment options, as well as relationships between IHC status and the breast-cancer intrinsic subtypes. We have also identified broad themes across the analysis methodologies used in these studies, including breast cancer subtyping, deriving predictive biomarkers, identifying differentially expressed genes, and optimizing data processing. Finally, we discuss limitations of prior work and recommend future directions for reusing these datasets in secondary analyses.

Moving pan-cancer studies from basic research toward the clinic

Although all cancers share common hallmarks, we have long realized that there is no silver-bullet treatment for the disease. Many clinical oncologists specialize in a single cancer type, based predominantly on the tissue of origin. With advances brought by genetics and cancer genomic research, we now know that cancers are profoundly different, both in origins and in genetic alterations. At the same time, commonalities such as key driver mutations, altered pathways, mutational, immune and microbial signatures and other areas (many revealed by pan-cancer studies) point to the intriguing possibility of targeting common traits across diverse cancer types with the same therapeutic strategies. Studies designed to delineate differences and similarities across cancer types are thus critical in discerning the basic dynamics of oncogenesis, as well as informing diagnoses, prognoses and therapies. We anticipate growing emphases on the development and application of therapies targeting underlying commonalities of different cancer types, while tailoring to the unique tissue environment and intrinsic molecular fingerprints of each cancer type and subtype. Here we summarize the facets of pan-cancer research and how they are pushing progress toward personalized medicine.

Expression of Immune Checkpoint Regulators IDO, VISTA, LAG3, and TIM3 in Resected Pancreatic Ductal Adenocarcinoma

Pancreatic cancer features elaborate mechanisms of immune evasion. The potential of new immune molecules was explored to restore the antitumor immune response. If these immune molecules are associated with poor survival, specific drugs could take effect. Here, we analyze the expression of VISTA, LAG3, IDO, and TIM3 on tumor-infiltrating lymphocytes (TILs) and its impact on patient survival. We analyzed 153 pancreatic cancer patients from the prospectively managed database of the multicentered PANCALYZE study. Immunohistochemistry on a tissue microarray assessed VISTA, LAG3, IDO, and TIM3 expression of TILs from the patients undergoing primary resection. Complementarily, we analyzed publicly available transcriptomic data (n = 903). Successful completion of chemotherapy, and lymph node status were independent predictors of survival in the multivariate analysis of the clinicopathologic parameters. Fifteen tumors were exclusively VISTA-positive, thirteen tumors expressed VISTA together with TIM3, and ten tumors expressed VISTA together with IDO. Patients featuring tumors with high numbers of IDO-positive TILs had better patient survival (p = 0.037). VISTA, LAG3, and TIM3 expression did not correlate with survival. The analysis of publicly available data did not show survival differences. Tumors rarely co-express more than two immune molecules at the same time, and VISTA is most frequently co-expressed. Although IDO generally inhibits T-cell proliferation, a high expression of IDO was associated with improved survival. We expect immune checkpoint inhibitors against VISTA, LAG3, and TIM3 to be inefficient in a clinical application.

High-dimensional analysis of the adenosine pathway in high-grade serous ovarian cancer

BACKGROUND

Hydrolysis of extracellular ATP to adenosine (eADO) is an important immune checkpoint in cancer immunology. We here investigated the impact of the eADO pathway in high-grade serous ovarian cancer (HGSC) using multiparametric platforms.

METHODS

We performed a transcriptomic meta-analysis of eADO-producing CD39 and CD73, an eADO signaling gene signature, immune gene signatures and clinical outcomes in approximately 1200 patients with HGSC. Protein expression, localization and prognostic impact of CD39, CD73 and CD8 were then performed on approximately 1000 cases on tissue microarray, and tumor-infiltrating lymphocytes (TILs) were analyzed by flow cytometry and single-cell RNA sequencing on a subset of patients.

RESULTS

Concomitant CD39 and CD73 gene expression, as well as high levels of an eADO gene signature, were associated with worse prognosis in patients with HGSC, notably in the immunoregulatory molecular subtype, characterized by an immune-active microenvironment. CD39 was further associated with primary chemorefractory and chemoresistant human HGSC and platinum-based chemotherapy of murine HGSC was significantly more effective in CD39-deficient mice. At protein level, CD39 and CD73 were predominantly expressed by cancer-associated fibroblasts, and CD39 was expressed on severely exhausted, clonally expanded and putative tissue-resident memory TILs.

CONCLUSIONS

Our study revealed the clinical, immunological, subtype-specific impacts of eADO signaling in HGSC, unveiled the chemoprotective effect of CD39 and supports the evaluation of eADO-targeting agents in patients with ovarian cancer.

WDR82/PNUTS-PP1 Prevents Transcription-Replication Conflicts by Promoting RNA Polymerase II Degradation on Chromatin

Transcription-replication (T-R) conflicts cause replication stress and loss of genome integrity. However, the transcription-related processes that restrain such conflicts are poorly understood. Here, we demonstrate that the RNA polymerase II (RNAPII) C-terminal domain (CTD) phosphatase protein phosphatase 1 (PP1) nuclear targeting subunit (PNUTS)-PP1 inhibits replication stress. Depletion of PNUTS causes lower EdU uptake, S phase accumulation, and slower replication fork rates. In addition, the PNUTS binding partner WDR82 also promotes RNAPII-CTD dephosphorylation and suppresses replication stress. RNAPII has a longer residence time on chromatin after depletion of PNUTS or WDR82. Furthermore, the RNAPII residence time is greatly enhanced by proteasome inhibition in control cells but less so in PNUTS- or WDR82-depleted cells, indicating that PNUTS and WDR82 promote degradation of RNAPII on chromatin. Notably, reduced replication is dependent on transcription and the phospho-CTD binding protein CDC73 after depletion of PNUTS/WDR82. Altogether, our results suggest that RNAPII-CTD dephosphorylation is required for the continuous turnover of RNAPII on chromatin, thereby preventing T-R conflicts.

Interferon-Induced Protein With Multiple Tetratricopeptide Repeats 3 Is Associated With Response to Chemotherapy and Recurrence but Not With Survival

OBJECTIVES

The interferon-induced protein with multiple tetratricopeptide repeats 3 (IFIT3) seems to be associated with the prognosis in pancreatic cancer. Here we clarify whether the heterogeneity of IFIT3 expression affects previous IFIT3 analysis.

METHODS

This retrospective study analyzes pancreatic cancer tissue samples retrieved by surgery from 2 independent patient cohorts. Patients underwent either primary surgery (n = 72) or received neoadjuvant chemotherapy (n = 12). Immunohistochemistry assessed IFIT3 expression and its heterogeneity. Complementarily, we analyzed publicly available transcriptomic data (n = 903).

RESULTS

Of the primarily resected tumors, 16.4% were heterogeneous. Patients with IFIT3-negative tumors did not survive longer compared with patients with IFIT3-positive tumors. An analysis of publicly available data confirmed this result. Patients developing lung metastases had the best prognosis (4.8 years) with significantly lower IFIT3 expression compared with liver metastasis (P = 0.0117). Patients receiving neoadjuvant therapy who are IFIT3 negative had a longer disease-free survival (1.2 vs 0.3 years, P = 0.0081).

CONCLUSIONS

Low IFIT3 expression is not associated with longer survival. Divergent results from tissue microarray analyses could be explained with tumor heterogeneity. As a single biomarker, IFIT3 is not suitable for predicting disease prognosis. Recurrence of lung metastases and response to neoadjuvant chemotherapy are associated with low IFIT3 expression.

Transcriptomic data helps refining classification of pulmonary carcinoid tumors with increased mitotic counts

Pulmonary neuroendocrine neoplasms are classified by WHO as either typical or atypical carcinoids, large cell (LCNEC) or small cell (SCLC) neuroendocrine carcinoma based on mitotic count, morphology, and necrosis assessment. LCNEC with low mitotic count and sharing morphologic features with carcinoids are in a gray zone for classification and their rare prevalence and the paucity of studies precludes proper validation of the current grading system. In this study, we aim to investigate their clinicopathological and transcriptomic profiles. Lung resection specimens obtained from 18 patients diagnosed with carcinoids or LCNEC were selected. Four of them were characterized as borderline tumors based on a mitotic rate ranging between 10 and 30 mitoses per 2 mm². Comprehensive morphological and immunohistochemical (IHC) evaluation was performed and tumor-based transcriptomic profiles were analyzed through unsupervised clustering. Clustering analysis revealed two distinct molecular groups characterized by low (C1) and high (C2) proliferation. C1 was comprised of seven carcinoids and three borderline tumors, while C2 was comprised of seven LCNEC and one borderline tumor. Furthermore, patients in cluster C1 had a better recurrence-free survival compared with patients in cluster C2 (20% vs 75%). Histological features, IHC profile, and molecular analysis showed that three out of four borderline tumors showed features consistent with carcinoids. Therefore, our findings convey that the current diagnostic guidelines are suboptimal for classification of pulmonary neuroendocrine tumors with increased proliferative index and carcinoid-like morphology. These results support the emerging concept that neuroendocrine tumors with carcinoid-like features and mitotic count of <20 mitoses per 2 mm² should be regarded as pulmonary carcinoids instead of LCNEC.

The IL-17A/IL-17RA Axis Is Not Related to Overall Survival and Cancer Stem Cell Modulation in Pancreatic Cancer

(1) Background: IL-17A accelerates pancreatic intraepithelial neoplasia (PanIN) progression. In this study, we examined whether IL-17A/IL-17RA promotes pancreatic ductal adenocarcinoma (PDAC) aggressiveness in terms of survival and cancer stem cell modulation. (2) Methods: In vitro, the wound-healing assay, the sphere formation assay, and flow cytometry were applied to assess cancer stem cell features. In vivo, pancreatic tumors were induced in C57BL/6 mice using electroporation with oncogenic plasmids (P53-/- R172H; KrasG12V). Anti-IL-17 antibodies were administered as immunotherapy. We analyzed IL-17A/IL-17RA related survival using publicly available transcriptomic data (n = 903). (3) Results: IL-17A/IL-17RA expression was not related to survival in PDAC patients. IL-17A neither induces stem cell markers nor increases sphere formation and cell motility in vitro. Blocking the IL-17A/IL-17RA axis in a murine pancreatic cancer model did not improve the survival of mice, but reduced the tumor burden slightly. (4) Conclusions: IL-17A does not promote stem cell expansion in PDAC cell lines. Blocking IL-17A/IL-17RA signaling does not interfere with pancreatic cancer development and progression and may not be considered as a promising monotherapy for PDAC.