Open-source curation of a pancreatic ductal adenocarcinoma gene expression analysis platform (pdacR) supports a two-subtype model

Torre-Healy, Luke A.; Kawalerski, Ryan; Oh, Ki; Chrastecka, Lucie; Peng, Xianlu L.; Aguirre, Andrew J.; Rashid, Naim U.; Yeh, Jen Jen; Moffitt, Richard A.

doi:10.1038/s42003-023-04461-6

Cited by 10 publications

(4 citation statements)

References 59 publications

(79 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, challenges with data quality and availability persist. While most reports up to date use statistical methods and platforms, the AI applications to predictive PDAC precursor evaluation in pancreatic cysts and tumors are emerging [ 96 , 97 , 98 , 99 ]. Cross-disciplinary collaborations can advance data sharing and model interpretability, making continued studies on complete multi-omics data integration and incorporation with imaging data essential focuses of current research in PDAC.…”

Section: Discussionmentioning

confidence: 99%

Pancreatic Ductal Adenocarcinoma (PDAC): A Review of Recent Advancements Enabled by Artificial Intelligence

Mukund,

Afridi,

Karolak

et al. 2024

Cancers

View full text Add to dashboard Cite

Pancreatic Ductal Adenocarcinoma (PDAC) remains one of the most formidable challenges in oncology, characterized by its late detection and poor prognosis. Artificial intelligence (AI) and machine learning (ML) are emerging as pivotal tools in revolutionizing PDAC care across various dimensions. Consequently, many studies have focused on using AI to improve the standard of PDAC care. This review article attempts to consolidate the literature from the past five years to identify high-impact, novel, and meaningful studies focusing on their transformative potential in PDAC management. Our analysis spans a broad spectrum of applications, including but not limited to patient risk stratification, early detection, and prediction of treatment outcomes, thereby highlighting AI’s potential role in enhancing the quality and precision of PDAC care. By categorizing the literature into discrete sections reflective of a patient’s journey from screening and diagnosis through treatment and survivorship, this review offers a comprehensive examination of AI-driven methodologies in addressing the multifaceted challenges of PDAC. Each study is summarized by explaining the dataset, ML model, evaluation metrics, and impact the study has on improving PDAC-related outcomes. We also discuss prevailing obstacles and limitations inherent in the application of AI within the PDAC context, offering insightful perspectives on potential future directions and innovations.

show abstract

Section: Discussionmentioning

confidence: 99%

Pancreatic Ductal Adenocarcinoma (PDAC): A Review of Recent Advancements Enabled by Artificial Intelligence

Mukund,

Afridi,

Karolak

et al. 2024

Cancers

View full text Add to dashboard Cite

show abstract

“…Gene expression and somatic mutation data was obtained from The Cancer Genome Atlas (TCGA) https://www.cancer.gov/tcga [PDAC 30 , LUAD 31 , LUSC 32 ]. The dataset and code utilized in our research are available at https://github.com/rmoffitt/pdacR 33,34 .…”

Section: Declarationsmentioning

confidence: 99%

“…All other mutations were categorized as loss of function (LOF), and samples with other mutation data that included no mutation in the TP53 gene were considered wild-type. Expression values were log2 transformed, then the average was computed among established normal and activated stroma gene sets for each sample (https://github.com/rmoffitt/pdacR 33,34 ). Comparisons between TP53 mutation status were preformed using Welch's two sample t-test comparing average expression of loss of function group with hotspot group.…”

Section: Gene Expression Analysismentioning

confidence: 99%

Clinically relevant orthotopic pancreatic cancer models for adoptive T cell therapy

Horvat,

Karpovsky,

Phillips

et al. 2023

Preprint

Self Cite

View full text Add to dashboard Cite

Pancreatic ductal adenocarcinoma (PDAC) is an aggressive tumor. Prognosis is poor and survival is low in patients diagnosed with this disease; ∼12% at 5 years. Immunotherapy, including adoptive T cell transfer therapy, has not impacted outcomes in PDAC patients, due in part to the hostile tumor microenvironment (TME) which limits T cell trafficking and persistence. We posit that murine models serve as useful tools to study the fate of T cell therapy. Currently, genetically engineered mouse models (GEMM) for PDAC are considered a “gold-standard” as they recapitulate many aspects of human disease. However, these models have limitations, including marked tumor variability across individual mice and cost of colony maintenance. We characterized the immunologic features and trafficking patterns of adoptively transferred T cells in orthotopic PDAC models using two mouse cell lines, KPC-Luc and MT-5, isolated from KPC-GEMM mouse models (KrasLSL-G12D/+p53-/-and KrasLSL-G12D/+p53LSL-R172H/+, respectively). The MT-5 orthotopic model best recapitulates the cellular and stromal features of the TME in the PDAC GEMM. In contrast, far more host immune cells infiltrate KPC-Luc tumors, which have less stroma. Albeit CD4+ T cells were similarly detected in MT-5 tumors compared to KPC-GEMM in mice. Interestingly, we found that CAR T cells redirected to recognize mesothelin on these tumors that signal via CD3σ and 41BB (Meso-41BBσ-CAR T cells) post antigen recognition infiltrated the tumors of mice bearing stroma-devoid KPC-Luc orthotopic tumors, but not MT-5 tumors. Our data establish for the first time a reproducible and realistic clinical system useful for modeling stroma-rich and stroma-devoid PDAC tumors. These models shall serve in-depth study of how to overcome barriers that limit anti-tumor activity of adoptively transferred T cells.

show abstract

“…Gene signatures such as these have been important in preliminary trials aimed at therapeutic decision support 8 – 10 . Exploration of the interplay of these signatures was recently explored and made accessible online 11 .…”

Section: Introductionmentioning

confidence: 99%

Coordinated single-cell tumor microenvironment dynamics reinforce pancreatic cancer subtype

Oh,

Yoo,

Torre-Healy

et al. 2023

Nat Commun

Self Cite

View full text Add to dashboard Cite

Bulk analyses of pancreatic ductal adenocarcinoma (PDAC) samples are complicated by the tumor microenvironment (TME), i.e. signals from fibroblasts, endocrine, exocrine, and immune cells. Despite this, we and others have established tumor and stroma subtypes with prognostic significance. However, understanding of underlying signals driving distinct immune and stromal landscapes is still incomplete. Here we integrate 92 single cell RNA-seq samples from seven independent studies to build a reproducible PDAC atlas with a focus on tumor-TME interdependence. Patients with activated stroma are synonymous with higher myofibroblastic and immunogenic fibroblasts, and furthermore show increased M2-like macrophages and regulatory T-cells. Contrastingly, patients with ‘normal’ stroma show M1-like recruitment, elevated effector and exhausted T-cells. To aid interoperability of future studies, we provide a pretrained cell type classifier and an atlas of subtype-based signaling factors that we also validate in mouse data. Ultimately, this work leverages the heterogeneity among single-cell studies to create a comprehensive view of the orchestra of signaling interactions governing PDAC.

show abstract

Open-source curation of a pancreatic ductal adenocarcinoma gene expression analysis platform (pdacR) supports a two-subtype model

Cited by 10 publications

References 59 publications

Pancreatic Ductal Adenocarcinoma (PDAC): A Review of Recent Advancements Enabled by Artificial Intelligence

Pancreatic Ductal Adenocarcinoma (PDAC): A Review of Recent Advancements Enabled by Artificial Intelligence

Clinically relevant orthotopic pancreatic cancer models for adoptive T cell therapy

Coordinated single-cell tumor microenvironment dynamics reinforce pancreatic cancer subtype

Contact Info

Product

Resources

About