Stromal Content Is Correlated With Tissue Site, Contrast Retention, and Survival in Pancreatic Adenocarcinoma

Stroma and lymphocytes identified by deep learning are independent predictors for survival in pancreatic cancer

Pancreatic ductal adenocarcinoma (PDAC) is one of the most lethal cancers known to humans. However, not all patients fare equally poor survival, and a minority of patients even survives advanced disease for months or years. Thus, there is a clinical need to search corresponding prognostic biomarkers which forecast survival on an individual basis. To dig more information and identify potential biomarkers from PDAC pathological slides, we trained a deep learning (DL) model based U-net-shaped backbone. This DL model can automatically detect tumor, stroma and lymphocytes on whole slide images (WSIs) of PDAC patients. We performed an analysis of 800 PDAC scans, categorizing stroma in percentage (SIP) and lymphocytes in percentage (LIP) into two and three categories, respectively. The presented model achieved remarkable accuracy results with a total accuracy of 94.72%, a mean intersection of union rate of 78.66%, and a mean dice coefficient of 87.74%. Survival analysis revealed that SIP-mediate and LIP-high groups correlated with enhanced median overall survival (OS) across all cohorts. These findings underscore the potential of SIP and LIP as prognostic biomarkers for PDAC and highlight the utility of DL as a tool for PDAC biomarkers detecting on WSIs.

Enhanced safety and efficacy profile of CD40 antibody upon encapsulation in pHe-triggered membrane-adhesive nanoliposomes

AIM

To develop pH (pHe)-triggered membrane adhesive nanoliposome (pHTANL) of CD40a to enhance anti-tumor activity in pancreatic cancer while reducing systemic toxicity.

MATERIALS AND METHODS

A small library of nanoliposomes (NL) with various lipid compositions were synthesized to prepare pH (pHe)-triggered membrane adhesive nanoliposome (pHTANL). Physical and functional characterization of pHTANL-CD40a was performed via dynamic light scattering (DLS), Transmission Electron Microscopy (TEM), confocal microscopy, and flow cytometry. In vivo studies were performed using PDAC (Panc02) transplanted mice. Tumor tissue was analyzed by flow cytometry, and plasma cytokines and liver enzymes were analyzed by ELISA.

RESULTS

pHTANL-CD40a reduced tumor growth, enhanced tumor immune infiltration/activation, and enhanced survival compared to vehicle and free-CD40a. Importantly, pHTANL-CD40a treatment resulted in significantly lower systemic toxicity as indicated by unchanged body weight, minimal organ deformity, and reduced serum levels of liver enzyme alanine transaminase (ALT) and inflammatory cytokine IL-6.

CONCLUSION

pHTANL-CD40a is more effective than free CD40a in anti-tumor activity, especially in altering the TME immune landscape for a potential therapeutic benefit in combination with immunotherapy.

One novel transfer learning-based CLIP model combined with self-attention mechanism for differentiating the tumor-stroma ratio in pancreatic ductal adenocarcinoma

PURPOSE

To develop a contrastive language-image pretraining (CLIP) model based on transfer learning and combined with self-attention mechanism to predict the tumor-stroma ratio (TSR) in pancreatic ductal adenocarcinoma on preoperative enhanced CT images, in order to understand the biological characteristics of tumors for risk stratification and guiding feature fusion during artificial intelligence-based model representation.

MATERIAL AND METHODS

This retrospective study collected a total of 207 PDAC patients from three hospitals. TSR assessments were performed on surgical specimens by pathologists and divided into high TSR and low TSR groups. This study developed one novel CLIP-adapter model that integrates the CLIP paradigm with a self-attention mechanism for better utilizing features from multi-phase imaging, thereby enhancing the accuracy and reliability of tumor-stroma ratio predictions. Additionally, clinical variables, traditional radiomics model and deep learning models (ResNet50, ResNet101, ViT_Base_32, ViT_Base_16) were constructed for comparison.

RESULTS

The models showed significant efficacy in predicting TSR in PDAC. The performance of the CLIP-adapter model based on multi-phase feature fusion was superior to that based on any single phase (arterial or venous phase). The CLIP-adapter model outperformed traditional radiomics models and deep learning models, with CLIP-adapter_ViT_Base_32 performing the best, achieving the highest AUC (0.978) and accuracy (0.921) in the test set. Kaplan-Meier survival analysis showed longer overall survival in patients with low TSR compared to those with high TSR.

CONCLUSION

The CLIP-adapter model designed in this study provides a safe and accurate method for predicting the TSR in PDAC. The feature fusion module based on multi-modal (image and text) and multi-phase (arterial and venous phase) significantly improves model performance.

[Possibilities of post-processing of multislice computed tomography results in non-invasive diagnosis of pancreatic fibrosis]

AIM

To evaluate the possibilities of post-processing of multidetector computed tomography (CT) results in the non-invasive diagnosis of pancreatic fibrosis (PF).

MATERIALS AND METHODS

The study included 165 patients aged 57.91±13.5 years who underwent preoperative CT during surgical treatment for chronic pancreatitis and pancreatic cancer from April 2022 to February 2024. The normalized contrast ratios of pancreatic tissue in the pancreatic (NCPP) and venous (NCVP) phases, as well as the contrast ratio (CR) were measured. Pathomorphological assessment of PF performed in tissues outside neoplasm or desmoplastic reaction by the Kloppel and Maillet scale.

RESULTS

The values of post-processing CT results were compared in groups with different degrees of PF. Mean CR values were significantly higher (p=0.001) in patients with severe PF (CR 1.16±0.65 HU) than in patients with mild PF (CR 0.78±0.31 HU). CR value significant increase (p=0.03) was found in patients with signs of inflammatory changes in the pancreas tissue (CR 1.14±0.6 HU) than in those without them (CR 0.81±0.3 HU). There were no significant differences between the values of NCPP and NCVP, and the degree of PF.

CONCLUSION

The CR value increased in patients with severe degree of PF. There was a relationship between CR value increase and the radiological density of pancreatic tissue in non-contrast phase and presence of early signs of pancreatic inflammatory changes. Thus, there was a relationship between CT postprocessing results and morphological signs of PF, which can be used for pancreatic fibrosis non-invasive diagnosis and identification of additional signs of early chronic pancreatitis.

Collagen type I PET/MRI enables evaluation of treatment response in pancreatic cancer in pre-clinical and first-in-human translational studies

Pancreatic ductal adenocarcinoma (PDAC) is an invasive and rapidly progressive malignancy. A major challenge in patient management is the lack of a reliable imaging tool to monitor tumor response to treatment. Tumor-associated fibrosis characterized by high type I collagen is a hallmark of PDAC, and fibrosis further increases in response to neoadjuvant chemoradiotherapy (CRT). We hypothesized that molecular positron emission tomography (PET) using a type I collagen-specific imaging probe, 68Ga-CBP8 can detect and measure changes in tumor fibrosis in response to standard treatment in mouse models and patients with PDAC. Methods: We evaluated the specificity of 68Ga-CBP8 PET to tumor collagen and its ability to differentiate responders from non-responders based on the dynamic changes of fibrosis in nude mouse models of human PDAC including FOLFIRNOX-sensitive (PANC-1 and PDAC6) and FOLFIRINOX-resistant (SU.86.86). Next, we demonstrated the specificity and sensitivity of 68Ga-CBP8 to the deposited collagen in resected human PDAC and pancreas tissues. Eight male participant (49-65 y) with newly diagnosed PDAC underwent dynamic 68Ga-CBP8 PET/MRI, and five underwent follow up 68Ga-CBP8 PET/MRI after completing standard CRT. PET parameters were correlated with tumor collagen content and markers of response on histology. Results: 68Ga-CBP8 showed specific binding to PDAC compared to non-binding 68Ga-CNBP probe in two mouse models of PDAC using PET imaging and to resected human PDAC using autoradiography (P < 0.05 for all comparisons). 68Ga-CBP8 PET showed 2-fold higher tumor signal in mouse models following FOLFIRINOX treatment in PANC-1 and PDAC6 models (P < 0.01), but no significant increase after treatment in FOLFIRINOX resistant SU.86.86 model. 68Ga-CBP8 binding to resected human PDAC was significantly higher (P < 0.0001) in treated versus untreated tissue. PET/MRI of PDAC patients prior to CRT showed significantly higher 68Ga-CBP8 uptake in tumor compared to pancreas (SUVmean: 2.35 ± 0.36 vs. 1.99 ± 0.25, P = 0.036, n = 8). PET tumor values significantly increased following CRT compared to untreated tumors (SUVmean: 2.83 ± 0.30 vs. 2.25 ± 0.41, P = 0.01, n = 5). Collagen deposition significantly increased in response to CRT (59 ± 9% vs. 30 ± 9%, P=0.0005 in treated vs. untreated tumors). Tumor and pancreas collagen content showed a positive direct correlation with SUVmean (R2 = 0.54, P = 0.0007). Conclusions: This study demonstrates the specificity of 68Ga-CBP8 PET to tumor type I collagen and its ability to differentiate responders from non-responders based on the dynamic changes of fibrosis in PDAC. The results highlight the potential use of collagen PET as a non-invasive tool for monitoring response to treatment in patients with PDAC.

Performance of different CT enhancement quantification methods as predictors of pancreatic cancer recurrence after upfront surgery

The prognosis of pancreatic cancer (PDAC) after tumor resection remains poor, mostly due to a high but variable risk of recurrence. A promising tool for improved prognostication is the quantification of CT tumor enhancement. For this, various enhancement formulas have been used in previous studies. However, a systematic comparison of these formulas is lacking. In the present study, we applied twenty-three previously published CT enhancement formulas to our cohort of 92 PDAC patients who underwent upfront surgery. We identified seven formulas that could reliably predict tumor recurrence. Using these formulas, weak tumor enhancement was associated with tumor recurrence at one and two years after surgery (p ≤ 0.030). Enhancement was inversely associated with adverse clinicopathological features. Low enhancement values were predictive of a high recurrence risk (Hazard Ratio ≥ 1.659, p ≤ 0.028, Cox regression) and a short time to recurrence (TTR) (p ≤ 0.027, log-rank test). Some formulas were independent predictors of TTR in multivariate models. Strikingly, almost all of the best-performing formulas measure solely tumor tissue, suggesting that normalization to non-tumor structures might be unnecessary. Among the top performers were also the absolute arterial/portal venous tumor attenuation values. These can be easily implemented in clinical practice for better recurrence prediction, thus potentially improving patient management.

Precision Targeting Strategies in Pancreatic Cancer: The Role of Tumor Microenvironment

Pancreatic cancer demonstrates an ever-increasing incidence over the last years and represents one of the top causes of cancer-associated mortality. Cells of the tumor microenvironment (TME) interact with cancer cells in pancreatic ductal adenocarcinoma (PDAC) tumors to preserve cancer cells' metabolism, inhibit drug delivery, enhance immune suppression mechanisms and finally develop resistance to chemotherapy and immunotherapy. New strategies target TME genetic alterations and specific pathways in cell populations of the TME. Complex molecular interactions develop between PDAC cells and TME cell populations including cancer-associated fibroblasts, myeloid-derived suppressor cells, pancreatic stellate cells, tumor-associated macrophages, tumor-associated neutrophils, and regulatory T cells. In the present review, we aim to fully explore the molecular landscape of the pancreatic cancer TME cell populations and discuss current TME targeting strategies to provide thoughts for further research and preclinical testing.

Temporally resolved proteomics identifies nidogen-2 as a cotarget in pancreatic cancer that modulates fibrosis and therapy response

Pancreatic ductal adenocarcinoma (PDAC) is characterized by increasing fibrosis, which can enhance tumor progression and spread. Here, we undertook an unbiased temporal assessment of the matrisome of the highly metastatic KPC (Pdx1-Cre, LSL-KrasG12D/+, LSL-Trp53R172H/+) and poorly metastatic KPflC (Pdx1-Cre, LSL-KrasG12D/+, Trp53fl/+) genetically engineered mouse models of pancreatic cancer using mass spectrometry proteomics. Our assessment at early-, mid-, and late-stage disease reveals an increased abundance of nidogen-2 (NID2) in the KPC model compared to KPflC, with further validation showing that NID2 is primarily expressed by cancer-associated fibroblasts (CAFs). Using biomechanical assessments, second harmonic generation imaging, and birefringence analysis, we show that NID2 reduction by CRISPR interference (CRISPRi) in CAFs reduces stiffness and matrix remodeling in three-dimensional models, leading to impaired cancer cell invasion. Intravital imaging revealed improved vascular patency in live NID2-depleted tumors, with enhanced response to gemcitabine/Abraxane. In orthotopic models, NID2 CRISPRi tumors had less liver metastasis and increased survival, highlighting NID2 as a potential PDAC cotarget.

The radiomorphological appearance of the invasive margin in pancreatic cancer is associated with tumor budding

PURPOSE

Pancreatic cancer (PDAC) is characterized by infiltrative, spiculated tumor growth into the surrounding non-neoplastic tissue. Clinically, its diagnosis is often established by magnetic resonance imaging (MRI). At the invasive margin, tumor buds can be detected by histology, an established marker associated with poor prognosis in different types of tumors.

METHODS

We analyzed PDAC by determining the degree of tumor spiculation on T2-weighted MRI using a 3-tier grading system. The grade of spiculation was correlated with the density of tumor buds quantified in histological sections of the respective surgical specimen according to the guidelines of the International Tumor Budding Consensus Conference (n = 28 patients).

RESULTS

64% of tumors revealed intermediate to high spiculation on MRI. In over 90% of cases, tumor buds were detected. We observed a significant positive rank correlation between the grade of radiological tumor spiculation and the histopathological number of tumor buds (rs = 0.745, p < 0.001). The number of tumor buds was not significantly associated with tumor stage, presence of lymph node metastases, or histopathological grading (p ≥ 0.352).

CONCLUSION

Our study identifies a readily available radiological marker for non-invasive estimation of tumor budding, as a correlate for infiltrative tumor growth. This finding could help to identify PDAC patients who might benefit from more extensive peripancreatic soft tissue resection during surgery or stratify patients for personalized therapy concepts.

Feasibility and effectiveness of automatic deep learning network and radiomics models for differentiating tumor stroma ratio in pancreatic ductal adenocarcinoma

OBJECTIVE

This study aims to compare the feasibility and effectiveness of automatic deep learning network and radiomics models in differentiating low tumor stroma ratio (TSR) from high TSR in pancreatic ductal adenocarcinoma (PDAC).

METHODS

A retrospective analysis was conducted on a total of 207 PDAC patients from three centers (training cohort: n = 160; test cohort: n = 47). TSR was assessed on hematoxylin and eosin-stained specimens by experienced pathologists and divided as low TSR and high TSR. Deep learning and radiomics models were developed including ShuffulNetV2, Xception, MobileNetV3, ResNet18, support vector machine (SVM), k-nearest neighbor (KNN), random forest (RF), and logistic regression (LR). Additionally, the clinical models were constructed through univariate and multivariate logistic regression. Kaplan-Meier survival analysis and log-rank tests were conducted to compare the overall survival time between different TSR groups.

RESULTS

To differentiate low TSR from high TSR, the deep learning models based on ShuffulNetV2, Xception, MobileNetV3, and ResNet18 achieved AUCs of 0.846, 0.924, 0.930, and 0.941, respectively, outperforming the radiomics models based on SVM, KNN, RF, and LR with AUCs of 0.739, 0.717, 0.763, and 0.756, respectively. Resnet 18 achieved the best predictive performance. The clinical model based on T stage alone performed worse than deep learning models and radiomics models. The survival analysis based on 142 of the 207 patients demonstrated that patients with low TSR had longer overall survival.

CONCLUSIONS

Deep learning models demonstrate feasibility and superiority over radiomics in differentiating TSR in PDAC. The tumor stroma ratio in the PDAC microenvironment plays a significant role in determining prognosis.

CRITICAL RELEVANCE STATEMENT

The objective was to compare the feasibility and effectiveness of automatic deep learning networks and radiomics models in identifying the tumor-stroma ratio in pancreatic ductal adenocarcinoma. Our findings demonstrate deep learning models exhibited superior performance compared to traditional radiomics models.

KEY POINTS

• Deep learning demonstrates better performance than radiomics in differentiating tumor-stroma ratio in pancreatic ductal adenocarcinoma. • The tumor-stroma ratio in the pancreatic ductal adenocarcinoma microenvironment plays a protective role in prognosis. • Preoperative prediction of tumor-stroma ratio contributes to clinical decision-making and guiding precise medicine.

Stromal inflammation, fibrosis and cancer: An old intuition with promising potential

It is now well established that the biology of cancer is influenced by not only malignant cells but also other components of the tumour microenvironment. Chronic inflammation and fibrosis have long been postulated to be involved in carcinogenesis. Chronic inflammation can promote tumorigenesis via growth factor/cytokine-mediated cellular proliferation, apoptotic resistance, immunosuppression; and free-radical-induced oxidative deoxyribonucleic acid damage. Fibrosis could cause a perturbation in the dynamics of the tumour microenvironment, potentially damaging the genome surveillance machinery of normal epithelial cells. In this review, we will provide an in-depth discussion of various diseases characterised by inflammation and fibrosis that have been associated with an increased risk of malignancy. In particular, we will present a comprehensive overview of the impact of alterations in stromal composition on tumorigenesis, induced as a consequence of inflammation and/or fibrosis. Strategies including the application of various therapeutic agents with stromal manipulation potential and targeted cancer screening for certain inflammatory diseases which can reduce the risk of cancer will also be discussed.

Pancreatic cancer epigenetics: adaptive metabolism reprograms starving primary tumors for widespread metastatic outgrowth

Pancreatic cancer is a paradigm for adaptation to extreme stress. That is because genetic drivers are selected during tissue injury with epigenetic imprints encoding wound healing responses. Ironically, epigenetic memories of trauma that facilitate neoplasia can also recreate past stresses to restrain malignant progression through symbiotic tumor:stroma crosstalk. This is best exemplified by positive feedback between neoplastic chromatin outputs and fibroinflammatory stromal cues that encase malignant glands within a nutrient-deprived desmoplastic stroma. Because epigenetic imprints are chemically encoded by nutrient-derived metabolites bonded to chromatin, primary tumor metabolism adapts to preserve malignant epigenetic fidelity during starvation. Despite these adaptations, stromal stresses inevitably awaken primordial drives to seek more hospitable climates. The invasive migrations that ensue facilitate entry into the metastatic cascade. Metastatic routes present nutrient-replete reservoirs that accelerate malignant progression through adaptive metaboloepigenetics. This is best exemplified by positive feedback between biosynthetic enzymes and nutrient transporters that saturate malignant chromatin with pro-metastatic metabolite byproducts. Here we present a contemporary view of pancreatic cancer epigenetics: selection of neoplastic chromatin under fibroinflammatory pressures, preservation of malignant chromatin during starvation stresses, and saturation of metastatic chromatin by nutritional excesses that fuel lethal metastasis.

Dysregulated miRNAs modulate tumor microenvironment associated signaling networks in pancreatic ductal adenocarcinoma

The desmoplastic and complex tumor microenvironment of pancreatic ductal adenocarcinoma (PDAC) has presented tremendous challenges for developing effective therapeutic strategies. Strategies targeting tumor stroma, albeit with great potential, have met with limited success due to the lack of knowledge on the molecular dynamics within the tumor microenvironment (TME). In pursuit of a better understanding of the influence of miRNAs on TME reprogramming and to explore circulating miRNAs as diagnostic and prognostic biomarkers for PDAC, using RNA-seq, miRNA-seq, and single-cell RNA-seq (scRNA-seq), we investigated the dysregulated signaling pathways in PDAC TME modulated by miRNAs from plasma and tumor tissue. Our bulk RNA-seq in PDAC tumor tissue identified 1445 significantly differentially expressed genes with extracellular matrix and structure organization as the top enriched pathways. Our miRNA-seq identified 322 and 49 abnormally expressed miRNAs in PDAC patient plasma and tumor tissue, respectively. We found many of the TME signaling pathways were targeted by those dysregulated miRNAs in PDAC plasma. Combined with scRNA-seq from patient PDAC tumor, our results revealed that these dysregulated miRNAs were closely associated with extracellular matrix (ECM) remodeling, cell-ECM communication, epithelial-mesenchymal transition, as well as immunosuppression orchestrated by different cellular components of TME. The findings of this study could assist the development of miRNA-based stromal targeting biomarkers or therapy for PDAC patients.

Early diagnosis of chronic pancreatitis

Chronic pancreatitis is one of the most challenging disorders from the perspective of its early diagnosis and effective treatment. Within the last decade, the diagnosis of early chronic pancreatitis has been firmly introduced into the practice of gastroenterology. The delineation of this form as an initial stage of chronic pancreatitis is based on the need in early and effective treatment that could cease the progression of the disease and reduce the possibility of its complications. The diagnostic criteria of chronic pancreatitis have been described in details in the literature; however, specifics of the diagnosis in its early stage have been scarcely highlighted. Chronic pancreatitis is commonly diagnosed with a number of imaging techniques (they can show abnormalities in morphology of the pancreas), as well as laboratory tests (showing functional organ deficit). However, morphological and imaging techniques are insufficient for the diagnosis of the early chronic pancreatitis. A new integral strategy towards early diagnosis seems necessary, that would consider not only the morphology, but also potential etiology, risk factors of the disease and its complications in patients with suspected chronic pancreatitis. The review of the literature presents the definition of the early pancreatitis and discusses the potential of imaging techniques and functional tests in its diagnosis. An adequate strategy for the diagnosis of the early pancreatitis is formulated, based on an individual patient characteristic with suspected early chronic pancreatitis, namely, risk factors, clinical manifestations, imaging results and serological biomarkers.

Spatial genomics reveals a high number and specific location of B cells in the pancreatic ductal adenocarcinoma microenvironment of long-term survivors

Background and aim

Only 10% of pancreatic ductal adenocarcinoma (PDAC) patients survive longer than five years. Factors underlining long-term survivorship in PDAC are not well understood. Therefore, we aimed to identify the key players in the tumor immune microenvironment (TIME) associated with long-term survivorship in PDAC patients.

Methods

The immune-related gene expression profiles of resected PDAC tumors of patients who survived and remained recurrence-free of disease for ≥36 months (long-term survivors, n=10) were compared to patients who had survived ≤6 months (short-term survivors, n=10) due to tumor recurrence. Validation was performed by the spatial protein expression profile of immune cells using the GeoMx™ Digital Spatial Profiler. An independent cohort of samples consisting of 12 long-term survivors and 10 short-term survivors, was used for additional validation. The independent validation was performed by combining qualitative immunohistochemistry and quantitative protein expression profiling.

Results

B cells were found to be significantly increased in the TIME of long-term survivors by gene expression profiling (p=0.018). The high tumor infiltration of B cells was confirmed by spatial protein profiling in the discovery and the validation cohorts (p=0.002 and p=0.01, respectively). The higher number of infiltrated B cells was found mainly in the stromal compartments of PDAC samples and was exclusively found within tumor cells in long-term survivors.

Conclusion

This is the first comprehensive study that connects the immune landscape of gene expression profiles and protein spatial infiltration with the survivorship of PDAC patients. We found a higher number and a specific location of B cells in TIME of long-term survivors which emphasizes the importance of B cells and B cell-based therapy for future personalized immunotherapy in PDAC patients.

Functional biomarkers derived from computed tomography and magnetic resonance imaging differentiate PDAC subgroups and reveal gemcitabine-induced hypo-vascularization

PURPOSE

Pancreatic ductal adenocarcinoma (PDAC) is a molecularly heterogeneous tumor entity with no clinically established imaging biomarkers. We hypothesize that tumor morphology and physiology, including vascularity and perfusion, show variations that can be detected by differences in contrast agent (CA) accumulation measured non-invasively. This work seeks to establish imaging biomarkers for tumor stratification and therapy response monitoring in PDAC, based on this hypothesis.

METHODS AND MATERIALS

Regional CA accumulation in PDAC was correlated with tumor vascularization, stroma content, and tumor cellularity in murine and human subjects. Changes in CA distribution in response to gemcitabine (GEM) were monitored longitudinally with computed tomography (CT) Hounsfield Units ratio (HUr) of tumor to the aorta or with magnetic resonance imaging (MRI) ΔR1 area under the curve at 60 s tumor-to-muscle ratio (AUC60r). Tissue analyses were performed on co-registered samples, including endothelial cell proliferation and cisplatin tissue deposition as a surrogate of chemotherapy delivery.

RESULTS

Tumor cell poor, stroma-rich regions exhibited high CA accumulation both in human (meanHUr 0.64 vs. 0.34, p < 0.001) and mouse PDAC (meanAUC60r 2.0 vs. 1.1, p < 0.001). Compared to the baseline, in vivo CA accumulation decreased specifically in response to GEM treatment in a subset of human (HUr -18%) and mouse (AUC60r -36%) tumors. Ex vivo analyses of mPDAC showed reduced cisplatin delivery (GEM: 0.92 ± 0.5 mg/g, vs. vehicle: 3.1 ± 1.5 mg/g, p = 0.004) and diminished endothelial cell proliferation (GEM: 22.3% vs. vehicle: 30.9%, p = 0.002) upon GEM administration.

CONCLUSION

In PDAC, CA accumulation, which is related to tumor vascularization and perfusion, inversely correlates with tumor cellularity. The standard of care GEM treatment results in decreased CA accumulation, which impedes drug delivery. Further investigation is warranted into potentially detrimental effects of GEM in combinatorial therapy regimens.

Exploring the Biology of Cancer-Associated Fibroblasts in Pancreatic Cancer

Pancreatic ductal adenocarcinoma (PDAC) is a lethal malignancy characterised by a stubbornly low 5-year survival which is essentially unchanged in the past 5 decades. Despite recent advances in chemotherapy and surgical outcomes, progress continues to lag behind that of other cancers. The PDAC microenvironment is characterised by a dense, fibrotic stroma of which cancer-associated fibroblasts (CAFs) are key players. CAFs and fibrosis were initially thought to be uniformly tumour-promoting, however this doctrine is now being challenged by a wealth of evidence demonstrating CAF phenotypic and functional heterogeneity. Recent technological advances have allowed for the molecular profiling of the PDAC tumour microenvironment at exceptional detail, and these technologies are being leveraged at pace to improve our understanding of this previously elusive cell population. In this review we discuss CAF heterogeneity and recent developments in CAF biology. We explore the complex relationship between CAFs and other cell types within the PDAC microenvironment. We discuss the potential for therapeutic targeting of CAFs, and we finally provide an overview of future directions for the field and the possibility of improving outcomes for patients with this devastating disease.

Culprits of PDAC resistance to gemcitabine and immune checkpoint inhibitor: Tumour microenvironment components

Pancreatic ductal adenocarcinoma (PDAC) is an aggressive and lethal cancer with a dismal five-year survival rate of 11%. Despite remarkable advancements in cancer therapeutics, PDAC patients rarely benefit from it due to insurmountable treatment resistance. Notably, PDAC is pathologically characterized by an extensive desmoplastic reaction and an extremely immunosuppressive tumour microenvironment (TME). The PDAC TME consists of cell components (e.g., tumour, immune and stromal cells) and noncellular components (e.g., extracellular matrix), exhibiting high complexity and their interplay resulting in resistance to chemotherapeutics and immune checkpoint inhibitors. In our review, we shed light on how crosstalk of complex environmental components modulates PDAC drug resistance, and we summarize related clinical trials. Moreover, we extend our discussion on TME exploration and exosome analysis, providing new insights into clinical applications, including personalized medicine, disease monitoring and drug carriers.

Tumor fibrosis correlates with the survival of patients with pancreatic adenocarcinoma and is predictable using clinicoradiological features

OBJECTIVES

To evaluate the prognostic value of fibrosis for patients with pancreatic adenocarcinoma (PDAC) and preoperatively predict fibrosis using clinicoradiological features. Tumor fibrosis plays an important role in the chemoresistance of PDAC. However, the prognostic value of tumor fibrosis remains contradiction and accurate prediction of tumor fibrosis is required.

METHODS

The study included 131 patients with PDAC who underwent first-line surgery. The prognostic value of fibrosis and rounded cutoff fibrosis points for median overall survival (OS) and disease-free survival (DFS) were determined using Cox regression and receiver operating characteristic (ROC) analyses. Then the whole cohort was randomly divided into training (n = 88) and validation (n = 43) sets. Binary logistic regression analysis was performed to select independent risk factors for fibrosis in the training set, and a nomogram was constructed. Nomogram performance was assessed using a calibration curve and decision curve analysis (DCA).

RESULTS

Hazard ratios of fibrosis for OS and DFS were 1.121 (95% confidence interval [CI]: 1.082-1.161) and 1.110 (95% CI: 1.067-1.155). ROC analysis identified 40% as the rounded cutoff fibrosis point for median OS and DFS. Tumor diameter, carbohydrate antigen 19-9 level, and peripancreatic tumor infiltration were independent risk factors; areas under the nomogram curve were 0.810 and 0.804 in the training and validation sets, respectively. The calibration curve indicated good agreement of the nomogram, and DCA demonstrated good clinical usefulness.

CONCLUSIONS

Tumor fibrosis was associated with poor OS and DFS in patients with PDAC. The nomogram incorporating clinicoradiological features was useful for preoperatively predicting tumor fibrosis.

KEY POINTS

• Tumor fibrosis is correlated with poor prognosis in patients with pancreatic adenocarcinoma. • Tumor fibrosis can be categorized according to its association with overall survival and disease-free survival. • A nomogram incorporating carbohydrate antigen 19-9 level, tumor diameter, and peripancreatic tumor infiltration is useful for preoperatively predicting tumor fibrosis.

Heterogeneity of Cancer-Associated Fibroblasts and the Tumor Immune Microenvironment in Pancreatic Cancer

Simple Summary

Stroma-targeting therapy in pancreatic ductal adenocarcinoma (PDAC) has been extensively investigated, but no candidates have shown efficacy at the clinical trial stage. Studies of cancer-associated fibroblast (CAF) depletion in a mouse model suggested that CAFs have not only tumor-promoting function but also tumor-suppressive activity. Recently, single-cell RNA sequencing (scRNA-seq) has revealed the complex tumor microenvironment within PDAC, and subpopulations of functionally distinct CAFs and their association with tumor immunity have been reported. However, the existence of tumor suppressive CAFs and CAFs involved in the maintenance of PDAC differentiation has also been reported. In the future, therapeutic strategies should be developed considering these CAF subpopulations, with the hope of improving the prognosis of PDAC.

Abstract

Pancreatic ductal adenocarcinoma (PDAC) is one of the most lethal cancers, with a 5-year survival rate of 9%. Cancer-associated fibroblasts (CAFs) have historically been considered tumor-promoting. However, multiple studies reporting that suppression of CAFs in PDAC mouse models resulted in more aggressive tumors and worse prognosis have suggested the existence of a tumor-suppressive population within CAFs, leading to further research on heterogeneity within CAFs. In recent years, the benefits of cancer immunotherapy have been reported in various carcinomas. Unfortunately, the efficacy of immunotherapies in PDAC has been limited, and the CAF-driven cancer immunosuppressive microenvironment has been suggested as the cause. Thus, clarification of heterogeneity within the tumor microenvironment, including CAFs and tumor immunity, is urgently needed to establish effective therapeutic strategies for PDAC. In this review, we report the latest findings on the heterogeneity of CAFs and the functions of each major CAF subtype, which have been revealed by single-cell RNA sequencing in recent years. We also describe reports of tumor-suppressive CAF subtypes and the existence of CAFs that maintain a differentiated PDAC phenotype and review the potential for targeted therapy.

Photoimmunotherapy Retains Its Anti-Tumor Efficacy with Increasing Stromal Content in Heterotypic Pancreatic Cancer Spheroids

Pancreatic ductal adenocarcinoma (PDAC) is an aggressive disease characterized by increased levels of desmoplasia that contribute to reduced drug delivery and poor treatment outcomes. In PDAC, the stromal content can account for up to 90% of the total tumor volume. The complex interplay between stromal components, including pancreatic cancer-associated fibroblasts (PCAFs), and PDAC cells in the tumor microenvironment has a significant impact on the prognoses and thus needs to be recapitulated in vitro when evaluating various treatment strategies. This study is a systematic evaluation of photodynamic therapy (PDT) in 3D heterotypic coculture models of PDAC with varying ratios of patient-derived PCAFs that simulate heterogeneous PDAC tumors with increasing stromal content. The efficacy of antibody-targeted PDT (photoimmunotherapy; PIT) using cetuximab (a clinically approved anti-EGFR antibody) photoimmunoconjugates (PICs) of a benzoporphyrin derivative (BPD) is contrasted with that of liposomal BPD (Visudyne), which is currently in clinical trials for PDT of PDAC. We demonstrate that both Visudyne-PDT and PIT were effective in heterotypic PDAC 3D spheroids with a low stromal content. However, as the stromal content increases above 50% in the 3D spheroids, the efficacy of Visudyne-PDT is reduced by up to 10-fold, while PIT retains its efficacy. PIT was found to be 10-, 19-, and 14-fold more phototoxic in spheroids with 50, 75, and 90% PCAFs, respectively, as compared to Visudyne-PDT. This marked difference in efficacy is attributed to the ability of PICs to penetrate and distribute homogeneously within spheroids with a higher stromal content and the mechanistically different modes of action of the two formulations. This study thus demonstrates how the stromal content in PDAC spheroids directly impacts their responsiveness to PDT and proposes PIT to be a highly suited treatment option for desmoplastic tumors with particularly high degrees of stromal content.

Predictive value of collagen in cancer

Cancer is a complex disease and a significant cause of mortality worldwide. Over the course of nearly all cancer types, collagen within the tumor microenvironment influences emergence, progression, and metastasis. This review discusses collagen regulation within the tumor microenvironment, pathological involvement of collagen, and predictive values of collagen and related extracellular matrix components in main cancer types. A survey of predictive tests leveraging collagen assays using clinical cohorts is presented. A conclusion is that collagen has high predictive value in monitoring cancer processes and stratifying by outcomes. New approaches should be considered that continue to define molecular facets of collagen related to cancer.

Magnetic Resonance Radiomics and Machine-learning Models: An Approach for Evaluating Tumor-stroma Ratio in Patients with Pancreatic Ductal Adenocarcinoma

OBJECTIVE

To develop and validate a magnetic resonance imaging (MRI)-based machine learning classifier for evaluating the tumor-stroma ratio (TSR) in patients with pancreatic ductal adenocarcinoma (PDAC).

MATERIALS AND METHODS

In this retrospective study, 148 patients with PDAC underwent an MR scan and surgical resection. We used hematoxylin and eosin to quantify the TSR. For each patient, we extracted 1,409 radiomics features and reduced them using the least absolute shrinkage and selection operator logistic regression algorithm. The extreme gradient boosting (XGBoost) classifier was developed using a training set comprising 110 consecutive patients, admitted between December 2016 and December 2017. The model was validated in 38 consecutive patients, admitted between January 2018 and April 2018. We determined the performance of the XGBoost classifier based on its discriminative ability, calibration, and clinical utility.

RESULTS

A log-rank test revealed significantly longer survival in the TSR-low group. The prediction model displayed good discrimination in the training (area under the curve [AUC], 0.82) and validation set (AUC, 0.78). While the sensitivity, specificity, accuracy, positive predictive value, and negative predictive value for the training set were 77.14%, 75.00%, 0.76%, 0.84%, and 0.65%, respectively, those for the validation set were 58.33%, 92.86%, 0.71%, 0.93%, and 0.57%, respectively.

CONCLUSION

We developed an XGBoost classifier based on MRI radiomics features, a non-invasive prediction tool that can evaluate the TSR of patients with PDAC. Moreover, it will provide a basis for interstitial targeted therapy selection and monitoring.

[Epithelial-stromal interactions in pancreatic adenocarcinoma: the role of stroma in disease progression]

Pancreatic ductal adenocarcinoma (PDAC) is the most common and difficult to treat form of pancreas cancer. PDAC and other solid cancers contain both tumor cells and normal connective tissue cells called stromal cells, which are responsible for the excess production of extracellular matrix. It is known that in more than 90% of PDAC tumors and in many other types of cancer, mutations of the KRAS gene are observed, the reciprocal signaling of which has been shown between tumor and stromal cells in vitro. Pancreatic stromal stellate cells are considered precursors of activated or tumor-associated fibroblasts (CAFs), which are an increasing population of cells that proliferate in situ or are recruited into the tumor. CAFs are a heterogeneous population of stromal fibroblasts with different molecular profiles that change during tumorigenesis. Both immunosuppressive and immunosuppressive subsets of CAFs can coexist in the stroma of a single tumor. Based on the heterogeneity of the intertumor stroma, attempts are being made to classify PDAC and predict the course of the disease.

Clinical and therapeutic relevance of cancer-associated fibroblasts

Cancer-associated fibroblasts (CAFs) found in primary and metastatic tumours are highly versatile, plastic and resilient cells that are actively involved in cancer progression through complex interactions with other cell types in the tumour microenvironment. As well as generating extracellular matrix components that contribute to the structure and function of the tumour stroma, CAFs undergo epigenetic changes to produce secreted factors, exosomes and metabolites that influence tumour angiogenesis, immunology and metabolism. Because of their putative pro-oncogenic functions, CAFs have long been considered an attractive therapeutic target; however, clinical trials of treatment strategies targeting CAFs have mostly ended in failure and, in some cases, accelerated cancer progression and resulted in inferior survival outcomes. Importantly, CAFs are heterogeneous cells and their characteristics and interactions with other cell types might change dynamically as cancers evolve. Studies involving single-cell RNA sequencing and novel mouse models have increased our understanding of CAF diversity, although the context-dependent roles of different CAF populations and their interchangeable plasticity remain largely unknown. Comprehensive characterization of the tumour-promoting and tumour-restraining activities of CAF subtypes, including how these complex bimodal functions evolve and are subjugated by neoplastic cells during cancer progression, might facilitate the development of novel diagnostic and therapeutic approaches. In this Review, the clinical relevance of CAFs is summarized with an emphasis on their value as prognosis factors and therapeutic targets.

CT Radiomics and Machine-Learning Models for Predicting Tumor-Stroma Ratio in Patients With Pancreatic Ductal Adenocarcinoma

Purpose

To develop and validate a machine learning classifier based on multidetector computed tomography (MDCT), for the preoperative prediction of tumor-stroma ratio (TSR) expression in patients with pancreatic ductal adenocarcinoma (PDAC).

Materials and Methods

In this retrospective study, 227 patients with PDAC underwent an MDCT scan and surgical resection. We quantified the TSR by using hematoxylin and eosin staining and extracted 1409 arterial and portal venous phase radiomics features for each patient, respectively. Moreover, we used the least absolute shrinkage and selection operator logistic regression algorithm to reduce the features. The extreme gradient boosting (XGBoost) was developed using a training set consisting of 167 consecutive patients, admitted between December 2016 and December 2017. The model was validated in 60 consecutive patients, admitted between January 2018 and April 2018. We determined the XGBoost classifier performance based on its discriminative ability, calibration, and clinical utility.

Results

We observed low and high TSR in 91 (40.09%) and 136 (59.91%) patients, respectively. A log-rank test revealed significantly longer survival for patients in the TSR-low group than those in the TSR-high group. The prediction model revealed good discrimination in the training (area under the curve [AUC]= 0.93) and moderate discrimination in the validation set (AUC= 0.63). While the sensitivity, specificity, accuracy, positive predictive value, and negative predictive value for the training set were 94.06%, 81.82%, 0.89, 0.89, and 0.90, respectively, those for the validation set were 85.71%, 48.00%, 0.70, 0.70, and 0.71, respectively.

Conclusions

The CT radiomics-based XGBoost classifier provides a potentially valuable noninvasive tool to predict TSR in patients with PDAC and optimize risk stratification.

The PDAC Extracellular Matrix: A Review of the ECM Protein Composition, Tumor Cell Interaction, and Therapeutic Strategies

Pancreatic ductal adenocarcinoma (PDAC) is notorious for a dense fibrotic stroma that is interlaced with a collagen-based extracellular matrix (ECM) that plays an important role in tumor biology. Traditionally thought to only provide a physical barrier from host responses and systemic chemotherapy, new studies have demonstrated that the ECM maintains biomechanical and biochemical properties of the tumor microenvironment (TME) and restrains tumor growth. Recent studies have shown that the ECM augments tumor stiffness, interstitial fluid pressure, cell-to-cell junctions, and microvascularity using a mix of biomechanical and biochemical signals to influence tumor fate for better or worse. In addition, PDAC tumors have been shown to use ECM-derived peptide fragments as a nutrient source in nutrient-poor conditions. While collagens are the most abundant proteins found in the ECM, several studies have identified growth factors, integrins, glycoproteins, and proteoglycans in the ECM. This review focuses on the dichotomous nature of the PDAC ECM, the types of collagens and other proteins found in the ECM, and therapeutic strategies targeting the PDAC ECM.

The Heterogeneity of the Tumor Microenvironment as Essential Determinant of Development, Progression and Therapy Response of Pancreatic Cancer

Pancreatic ductal adenocarcinoma (PDAC) is commonly diagnosed at advanced stages and most anti-cancer therapies have failed to substantially improve prognosis of PDAC patients. As a result, PDAC is still one of the deadliest tumors. Tumor heterogeneity, manifesting at multiple levels, provides a conclusive explanation for divergent survival times and therapy responses of PDAC patients. Besides tumor cell heterogeneity, PDAC is characterized by a pronounced inflammatory stroma comprising various non-neoplastic cells such as myofibroblasts, endothelial cells and different leukocyte populations which enrich in the tumor microenvironment (TME) during pancreatic tumorigenesis. Thus, the stromal compartment also displays a high temporal and spatial heterogeneity accounting for diverse effects on the development, progression and therapy responses of PDAC. Adding to this heterogeneity and the impact of the TME, the microbiome of PDAC patients is considerably altered. Understanding this multi-level heterogeneity and considering it for the development of novel therapeutic concepts might finally improve the dismal situation of PDAC patients. Here, we outline the current knowledge on PDAC cell heterogeneity focusing on different stromal cell populations and outline their impact on PDAC progression and therapy resistance. Based on this information, we propose some novel concepts for treatment of PDAC patients.

Mirage or long-awaited oasis: reinvigorating T-cell responses in pancreatic cancer

Pancreatic ductal adenocarcinoma (PDAC) is plagued by a dismal 5-year survival rate, early onset of metastasis and limited efficacy of systemic therapies. This scenario highlights the need to fervently pursue novel therapeutic strategies to treat this disease. Recent research has uncovered complicated dynamics within the tumor microenvironment (TME) of PDAC. An abundant stroma provides a framework for interactions between cancer-associated fibroblasts, suppressive myeloid cells and regulatory lymphocytes, which together create an inhospitable environment for adaptive immune responses. This accounts for the poor infiltration and exhausted phenotypes of effector T cells within pancreatic tumors. Innovative studies in genetically engineered mouse models have established that with appropriate pharmacological modulation of suppressive elements in the TME, T cells can be prompted to regress pancreatic tumors. In light of this knowledge, innovative combinatorial strategies involving immunotherapy and targeted therapies working in concert are rapidly emerging. This review will highlight recent advances in the field related to immune suppression in PDAC, emerging preclinical data and rationale for ongoing immunotherapy clinical trials. In particular, we draw attention to foundational findings involving T-cell activity in PDAC and encourage development of novel therapeutics to improve T-cell responses in this challenging disease.

ALDH1 expression predicts progression of premalignant lesions to cancer in Type I endometrial carcinomas

In type 1 endometrial cancer, unopposed estrogen stimulation is thought to lead to endometrial hyperplasia which precedes malignant progression. Recent data from our group and others suggest that ALDH activity mediates stemness in endometrial cancer, but while aldehyde dehydrogenase 1 (ALDH1) has been suggested as a putative cancer stem cell marker in several cancer types, its clinical and prognostic value in endometrial cancer remains debated. The aim of this study was to investigate the clinical value of ALDH1 expression in endometrial hyperplasia and to determine its ability to predict progression to endometrial cancer. Interrogation of the TCGA database revealed upregulation of several isoforms in endometrial cancer, of which the ALDH1 isoforms collectively constituted the largest group. To translate its expression, a tissue microarray was previously constructed which contained a wide sampling of benign and malignant endometrial samples. The array contained a metachronous cohort of samples from individuals who either developed or did not develop endometrial cancer. Immunohistochemical staining was used to determine the intensity and frequency of ALDH1 expression. While benign proliferative and secretory endometrium showed very low levels of ALDH1, slightly higher expression was observed within the stratum basalis. In disease progression, cytoplasmic ALDH1 expression showed a step-wise increase between endometrial hyperplasia, atypical hyperplasia, and endometrial cancer. ALDH1 was also shown to be an early predictor of EC development, suggesting that it can serve as an independent prognostic indicator of patients with endometrial hyperplasia with or without atypia who would progress to cancer (p = 0.012).

New approaches to non-invasive diagnosis of pancreatic fibrosis in chronic pancreatitis

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Pancreatic ductal adenocarcinoma progression is restrained by stromal matrix

Desmoplasia describes the deposition of extensive extracellular matrix and defines primary pancreatic ductal adenocarcinoma (PDA). The acellular component of this stroma has been implicated in PDA pathogenesis and is being targeted therapeutically in clinical trials. By analyzing the stromal content of PDA samples from numerous annotated PDA data sets and correlating stromal content with both anatomic site and clinical outcome, we found PDA metastases in the liver, the primary cause of mortality to have less stroma, have higher tumor cellularity than primary tumors. Experimentally manipulating stromal matrix with an anti-lysyl oxidase like-2 (anti-LOXL2) antibody in syngeneic orthotopic PDA mouse models significantly decreased matrix content, led to lower tissue stiffness, lower contrast retention on computed tomography, and accelerated tumor growth, resulting in diminished overall survival. These studies suggest an important protective role of stroma in PDA and urge caution in clinically deploying stromal depletion strategies.

The Emerging Role of Microbiota and Microbiome in Pancreatic Ductal Adenocarcinoma

Pancreatic ductal adenocarcinoma (PDAC) is one of the most aggressive malignant tumors due to the absence of biomarkers for early-stage detection and poor response to therapy. Since mounting evidence supports the role of microbiota composition in tumorigenesis and cancer treatment, the link between microbiome and PDAC has been described. In this review, we summarize the current knowledge regarding the impact of the gut and oral microbiome on the risk of PDAC development. Microenvironment-driven therapy and immune system interactions are also discussed. More importantly, we provide an overview of the clinical trials evaluating the microbiota role in the risk, prognosis, and treatment of patients suffering from PDAC and solid tumors. According to the research findings, immune tolerance might result from the microbiota-derived remodeling of pancreatic tumor microenvironment. Thus, microbiome profiling and targeting represent the potential trend to enhance antitumor immunity and improve the efficacy of PDAC treatment.

Stroma-Targeting Therapy in Pancreatic Cancer: One Coin With Two Sides?

Pancreatic ductal adenocarcinoma (PDAC) is a malignancy with one of the worst prognoses worldwide and has an overall 5-year survival rate of only 9%. Although chemotherapy is the recommended treatment for patients with advanced PDAC, its efficacy is not satisfactory. The dense dysplastic stroma of PDAC is a major obstacle to the delivery of chemotherapy drugs and plays an important role in the progression of PDAC. Therefore, stroma-targeting therapy is considered a potential treatment strategy to improve the efficacy of chemotherapy and patient survival. While several preclinical studies have shown encouraging results, the anti-tumor potential of the PDAC stroma has also been revealed, and the extreme depletion might promote tumor progression and undermine patient survival. Therefore, achieving a balance between stromal abundance and depletion might be the further of stroma-targeting therapy. This review summarized the current progress of stroma-targeting therapy in PDAC and discussed the double-edged sword of its therapeutic effects.

Building towards Precision Oncology for Pancreatic Cancer: Real-World Challenges and Opportunities

The advent of next-generation sequencing (NGS) has provided unprecedented insight into the molecular complexity of pancreatic ductal adenocarcinoma (PDAC). This has led to the emergence of biomarker-driven treatment paradigms that challenge empiric treatment approaches. However, the growth of sequencing technologies is outpacing the development of the infrastructure required to implement precision oncology as routine clinical practice. Addressing these logistical barriers is imperative to maximize the clinical impact of molecular profiling initiatives. In this review, we examine the evolution of precision oncology in PDAC, spanning from germline testing for cancer susceptibility genes to multi-omic tumor profiling. Furthermore, we highlight real-world challenges to delivering precision oncology for PDAC, and propose strategies to improve the generation, interpretation, and clinical translation of molecular profiling data.

Pros and Cons: High Proportion of Stromal Component Indicates Better Prognosis in Patients With Pancreatic Ductal Adenocarcinoma-A Research Based on the Evaluation of Whole-Mount Histological Slides

The study aimed to investigate the potential of tumor–stroma ratio (TSR) on digitalized whole-mount histopathology to predict prognosis in patients with pancreatic ductal adenocarcinoma (PDAC). The effectiveness were evaluated through internal validation. Data were retrospectively collected from consecutive patients who underwent primary pancreatic resection from December 2016 to August 2017 (developing cohort) and from September 2017 to April 2018 (validation cohort). Digitalized whole-mount slide images were used to evaluate TSR by both pathologists and a computerized model based on Conditional Generative Adversarial Model (cGAN), respectively. TSR>1 and ≤ 1 denoted low and high stromal component. Logistic regression analysis revealed intratumoral necrosis and R1 independently associated with low stromal component in the developing cohort. Cox regression analysis revealed tumor–node–metastasis (TNM) stage [II vs. I: hazard ratio (HR), 2.584; 95% CI, 1.386–4.819; P = 0.003; III vs. I: HR, 4.384; 95% CI, 2.285–8.411; P < 0.001], stromal component (low vs. high: HR, 1.876; 95% CI, 1.227–2.870; P = 0.004), tumor grade (G3 vs. G1/2: HR, 2.124; 95% CI, 1.419–3.179; P < 0.001), and perineural invasion (with vs. without: HR, 2.147; 95% CI, 1.187–3.883; P = 0.011) were independent prognostic factors in the developing cohort. Stromal component categories could classify patients into subgroups within TNM stages I, II, and III based on over survival. All results were validated in the validation cohort. The weighted kappa value for categorical assessments between pathologists' evaluation and computer-aided evaluation was 0.804 (95% CI, 0.573–0.951). TSR represents a simple and reliable metric for combining the prognostic value of TNM stage in patients with PDAC.

Crosstalk between Tumor and Stromal Cells in Pancreatic Ductal Adenocarcinoma

Pancreatic ductal adenocarcinoma (PDAC) remains a lethal cancer. The poor prognosis calls for a more detailed understanding of disease biology in order to pave the way for the development of effective therapies. Typically, the pancreatic tumor is composed of a minority of malignant cells within an excessive tumor microenvironment (TME) consisting of extracellular matrix (ECM), fibroblasts, immune cells, and endothelial cells. Research conducted in recent years has particularly focused on cancer-associated fibroblasts (CAFs) which represent the most prominent cellular component of the desmoplastic stroma. Here, we review the complex crosstalk between CAFs, tumor cells, and other components of the TME, and illustrate how these interactions drive disease progression. We also discuss the emerging field of CAF heterogeneity, their tumor-supportive versus tumor-suppressive capacity, and the consequences for designing stroma-targeted therapies in the future.

Alignment of stroma fibers, microvessel density and immune cell populations determine overall survival in pancreatic cancer-An analysis of stromal morphology

Introduction

The aim of this study was to define histo-morphological stroma characteristics by analyzing stromal components, and to evaluate their impact on local and systemic tumor spread and overall survival in pancreatic ductal adenocarcinoma (PDAC).

Methods and materials

Patients who underwent oncologic resections with curative intent for PDAC were identified from a prospectively maintained database. Histological specimens were re-evaluated for morphological stroma features as stromal fibers, fibroblast morphology, stroma matrix density, microvessel density and distribution of immune cell populations.

Results

A total of 108 patients were identified undergoing curative resection for PDAC in the period from 2011–2016. 33 (30.6%) patients showed parallel alignment of stroma fibers while 75 (69.4%) had randomly oriented stroma fibers. As compared to parallel alignment, random orientation of stroma fibers was associated with larger tumor size (median 3.62 cm vs. median 2.87cm, p = 0.037), nodal positive disease (76.0% vs. 54.5%, p = 0.040), higher margin positive resection rates (41.9% vs. 15.2%, p = 0.008) and a trend for higher rates of T3/4 tumors (33.3% vs. 15.2%, p = 0.064). In univariate analysis, patients with parallel alignment of stroma fibers had improved overall survival rates as compared to patients with random orientation of stroma fibers (42 months vs. 22 months, p = 0.046). The combination of random orientation of stroma fibers and low microvessel density was associated with impaired overall survival rates (16 months vs. 36 months, p = 0.019). A high CD4/CD3 ratio (16 months vs. 33 months, p = 0.040) and high stromal density of CD163 positive cells were associated with reduced overall survival (27 months vs. 34 months, p = 0.039). In multivariable analysis, the combination of random orientation of stroma fibers and low microvessel density (HR 1.592, 95%CI 1.098–2.733, p = 0.029), high CD4/CD3 ratio (HR 2.044, 95%CI 1.203–3.508, p = 0.028) and high density of CD163 positive cells (HR 1.596, 95%CI 1.367–1.968, p = 0.036) remained independent prognostic factors.

Conclusion

Alignment of stroma fibers and microvessel density are simple histomorphological features serving as surrogate markers of local tumor progression dissemination and surgical resectability and determine prognosis in PDAC patients. High CD4/CD3 ratio and CD163 positive cell counts determine poor prognosis.

Fibrotic Response to Neoadjuvant Therapy Predicts Survival in Pancreatic Cancer and Is Measurable with Collagen-Targeted Molecular MRI

PURPOSE

To evaluate the prognostic value of posttreatment fibrosis in human PDAC patients, and to compare a type I collagen targeted MRI probe, CM-101, to the standard contrast agent, Gd-DOTA, for their abilities to identify FOLFIRINOX-induced fibrosis in a murine model of PDAC.

EXPERIMENTAL DESIGN

Ninety-three chemoradiation-treated human PDAC samples were stained for fibrosis and outcomes evaluated. For imaging, C57BL/6 and FVB mice were orthotopically implanted with PDAC cells and FOLFIRINOX was administered. Mice were imaged with Gd-DOTA and CM-101.

RESULTS

In humans, post-chemoradiation PDAC tumor fibrosis was associated with longer overall survival (OS) and disease-free survival (DFS) on multivariable analysis (OS P = 0.028, DFS P = 0.047). CPA increased the prognostic accuracy of a multivariable logistic regression model comprised of previously established PDAC risk factors [AUC CPA (-) = 0.76, AUC CPA (+) = 0.82]. In multiple murine orthotopic PDAC models, FOLFIRINOX therapy reduced tumor weight (P < 0.05) and increased tumor fibrosis by collagen staining (P < 0.05). CM-101 MR signal was significantly increased in fibrotic tumor regions. CM-101 signal retention was also increased in the more fibrotic FOLFIRINOX-treated tumors compared with untreated controls (P = 0.027), consistent with selective probe binding to collagen. No treatment-related differences were observed with Gd-DOTA imaging.

CONCLUSIONS

In humans, post-chemoradiation tumor fibrosis is associated with OS and DFS. In mice, our MR findings indicate that translation of collagen molecular MRI with CM-101 to humans might provide a novel imaging technique to monitor fibrotic response to therapy to assist with prognostication and disease management.

Beyond Stiffness: Collagen Signaling in Pancreatic Cancer and Pancreas Regeneration

This commentary highlights the article by Ruggeri et al that reports the importance of discoidin domain receptor 1 in tissue homeostasis in pancreatic injury and pancreatic ductal adenocarcinoma pathogenesis.

Fibroblast Heterogeneity in the Pancreatic Tumor Microenvironment

The poor prognosis for patients with pancreatic ductal adenocarcinoma (PDAC) impels an improved understanding of disease biology to facilitate the development of better therapies. PDAC typically features a remarkably dense stromal reaction, featuring and established by a prominent population of cancer-associated fibroblasts (CAF). Genetically engineered mouse models and increasingly sophisticated cell culture techniques have demonstrated important roles for fibroblasts in PDAC progression and therapy response, but these roles are complex, with strong evidence for both tumor-supportive and tumor-suppressive or homeostatic functions. Here, we review the recent literature that has improved our understanding of heterogeneity in fibroblast fate and function in this disease including the existence of distinct fibroblast populations, and highlight important avenues for future study. SIGNIFICANCE: Although the abundant stromal reaction associated with pancreatic cancer has long been appreciated, the functions of the CAF cells that establish this stromal reaction remain unclear. An improved understanding of the transcriptional and functional heterogeneity of pancreatic CAFs, as well as their tumor-supportive versus tumor-suppressive capacity, may facilitate the development of effective therapies for this disease.

A unifying paradigm for transcriptional heterogeneity and squamous features in pancreatic ductal adenocarcinoma

Pancreatic cancer expression profiles largely reflect a classical or basal-like phenotype. The extent to which these profiles vary within a patient is unknown. We integrated evolutionary analysis and expression profiling in multiregion-sampled metastatic pancreatic cancers, finding that squamous features are the histologic correlate of an RNA-seq-defined basal-like subtype. In patients with coexisting basal and squamous and classical and glandular morphology, phylogenetic studies revealed that squamous morphology represented a subclonal population in an otherwise classical and glandular tumor. Cancers with squamous features were significantly more likely to have clonal mutations in chromatin modifiers, intercellular heterogeneity for MYC amplification and entosis. These data provide a unifying paradigm for integrating basal-type expression profiles, squamous histology and somatic mutations in chromatin modifier genes in the context of clonal evolution of pancreatic cancer.

A machine learning model for the prediction of survival and tumor subtype in pancreatic ductal adenocarcinoma from preoperative diffusion-weighted imaging

Background

To develop a supervised machine learning (ML) algorithm predicting above- versus below-median overall survival (OS) from diffusion-weighted imaging-derived radiomic features in patients with pancreatic ductal adenocarcinoma (PDAC).

Methods

One hundred two patients with histopathologically proven PDAC were retrospectively assessed as training cohort, and 30 prospectively accrued and retrospectively enrolled patients served as independent validation cohort (IVC). Tumors were segmented on preoperative apparent diffusion coefficient (ADC) maps, and radiomic features were extracted. A random forest ML algorithm was fit to the training cohort and tested in the IVC. Histopathological subtype of tumor samples was assessed by immunohistochemistry in 21 IVC patients. Individual radiomic feature importance was evaluated by assessment of tree node Gini impurity decrease and recursive feature elimination. Fisher’s exact test, 95% confidence intervals (CI), and receiver operating characteristic area under the curve (ROC-AUC) were used.

Results

The ML algorithm achieved 87% sensitivity (95% IC 67.3–92.7), 80% specificity (95% CI 74.0–86.7), and ROC-AUC 90% for the prediction of above- versus below-median OS in the IVC. Heterogeneity-related features were highly ranked by the model. Of the 21 patients with determined histopathological subtype, 8/9 patients predicted to experience below-median OS exhibited the quasi-mesenchymal subtype, whilst 11/12 patients predicted to experience above-median OS exhibited a non-quasi-mesenchymal subtype (p < 0.001).

Conclusion

ML application to ADC radiomics allowed OS prediction with a high diagnostic accuracy in an IVC. The high overlap of clinically relevant histopathological subtypes with model predictions underlines the potential of quantitative imaging in PDAC pre-operative subtyping and prognosis.

Electronic supplementary material

The online version of this article (10.1186/s41747-019-0119-0) contains supplementary material, which is available to authorized users.

New Era of Endoscopic Ultrasound-Guided Tissue Acquisition: Next-Generation Sequencing by Endoscopic Ultrasound-Guided Sampling for Pancreatic Cancer

Pancreatic cancer is a lethal cancer with an increasing incidence. Despite improvements in chemotherapy, patients with pancreatic cancer continue to face poor prognoses. Endoscopic ultrasound-guided tissue acquisition (EUS-TA) is the primary method for obtaining tissue samples of pancreatic cancer. Due to advancements in next-generation sequencing (NGS) technologies, multiple parallel sequencing can be applied to EUS-TA samples. Genomic biomarkers for therapeutic stratification in pancreatic cancer are still lacking, however, NGS can unveil potential predictive genomic biomarkers of treatment response. Thus, the importance of NGS using EUS-TA samples is becoming recognized. In this review, we discuss the recent advances in EUS-TA application for NGS of pancreatic cancer.

Fibroblasts in Pancreatic Ductal Adenocarcinoma: Biological Mechanisms and Therapeutic Targets

The desmoplastic reaction of pancreas cancer may begin as a wound healing response to the nascent neoplasm, but it soon creates an insidious shelter that can sustain the growing tumor and rebuff therapy. Among the many cell types subverted by transformed epithelial cells, fibroblasts are recruited and activated to lay a foundation of extracellular matrix proteins and glycosaminoglycans that alter tumor biophysics and signaling. Their near-universal presence in pancreas cancer and ostensible support of disease progression make fibroblasts attractive therapeutic targets. More recently, however, it has also become apparent that diverse subpopulations of fibroblasts with distinct phenotypes and secretomes inhabit the stroma, and that targeted depletion of particular fibroblast subsets could either provide substantial therapeutic benefit or accelerate disease progression. An improved characterization of these fibroblast subtypes, along with their potential relationships to tumor subtypes and mutational repertoires, is needed in order to make anti-fibroblast therapies clinically viable.

Real-time Genomic Characterization of Advanced Pancreatic Cancer to Enable Precision Medicine

Clinically relevant subtypes exist for pancreatic ductal adenocarcinoma (PDAC), but molecular characterization is not yet standard in clinical care. We implemented a biopsy protocol to perform time-sensitive whole-exome sequencing and RNA sequencing for patients with advanced PDAC. Therapeutically relevant genomic alterations were identified in 48% (34/71) and pathogenic/likely pathogenic germline alterations in 18% (13/71) of patients. Overall, 30% (21/71) of enrolled patients experienced a change in clinical management as a result of genomic data. Twenty-six patients had germline and/or somatic alterations in DNA-damage repair genes, and 5 additional patients had mutational signatures of homologous recombination deficiency but no identified causal genomic alteration. Two patients had oncogenic in-frame BRAF deletions, and we report the first clinical evidence that this alteration confers sensitivity to MAPK pathway inhibition. Moreover, we identified tumor/stroma gene expression signatures with clinical relevance. Collectively, these data demonstrate the feasibility and value of real-time genomic characterization of advanced PDAC.Significance: Molecular analyses of metastatic PDAC tumors are challenging due to the heterogeneous cellular composition of biopsy specimens and rapid progression of the disease. Using an integrated multidisciplinary biopsy program, we demonstrate that real-time genomic characterization of advanced PDAC can identify clinically relevant alterations that inform management of this difficult disease. Cancer Discov; 8(9); 1096-111. ©2018 AACR.See related commentary by Collisson, p. 1062This article is highlighted in the In This Issue feature, p. 1047.