Introduction

Two pancreatic cancer(PDAC) transcriptome subtypes have been defined (basal-like and classical) that seem related to different prognosis. Nevertheless, RNA-extraction from pancreatic tissue is cumbersome and has been performed mainly on surgical samples, representative of <20% of cases. Most PDAC patients undergo Endoscopic-UltraSound(EUS)-guided tissue acquisition(EUS-TA), but RNA-sequencing on such samples has been rarely performed or limited to paraffin-embedded samples with low RNA quality. Furthermore, the association between molecular subtype and patient-related factors such as BMI, smoking, drug use, tumor location and Ca19.9 expression is uninvestigated.

Aims & Methods

The aim was to correlate PDAC molecular subtypes identified by RNA-sequencing on EUS-TA samples with prognosis and evaluate whether they are associated to patients’ factors.
Consecutive patients with non-metastatic PDAC who underwent EUS-TA at diagnosis were enrolled in a prospective biobanking study with snap-frozen samples. Those with adequate quantity and quality of RNA underwent RNA-sequencing with Illumina Nova-Seq. PURIST score was applied to define transcriptional subtype and association with patient-related factors and overall survival (OS) investigated. Categorical and continuous variables were investigated by Fisher’s exact test or Mann-Whitney test, correlation analyses with Pearson test

Results

In 44/45 samples, RNA was of quantity and integrity allowing successful RNA-sequencing. According with PURIST score 3 patients were classified as basal-like (6.8%) and the other 41 as classical. Basal-like patients had a significantly lower median OS compared to classical (3 vs 16 months; p=0.01) and a basal-like phenotype was associated to increased risk of death (HR 7.79; p=0.006). PURIST score also significantly correlated inversely with OS (r=-0.6; p=0.0007). Concerning patients’ variables, patients with basal-like tumors were more frequently current smokers (66.6% vs 12.2%; p=0.05) and had a lower baseline Ca19.9 (80 vs 1243 IU/ml; p=0.001). No differences were found concerning age, gender, BMI, diabetes history, disease stage, tumor location, use of aspirin or statins.

Conclusion

Molecular subtype identification on EUS-TA PDAC cases at diagnosis is feasible and a basal-like phenotype is rare but associated to worse prognosis. Furthermore, current smoking seems to be associated to a more aggressive molecular subtype, which in turn does not seem to express a high Ca19.9. Further studies on a larger cohort are needed to confirm such findings.

Introduction

Pancreatic cancer (PC) is one of the fatal malignant tumors, with a 5-year survival rate is approximately 6%, and is expected to become the second leading cause of cancer deaths by 2030. The poor prognosis of PC is associated with the tumour microenvironment, characterized by excessive fibrosis and extracellular matrix deposition, known as desmoplasia. PC’s highly desmoplastic property not only increases the malignancy of the tumor but also inhibits the penetration and diffusion of anti-cancer drugs, related to drug resistance. Chauhan et al. demonstrated that the angiotensin inhibitor (ASI), losartan reduced stromal collagen and hyaluronan production, thus improving the efficacy of chemotherapy in PC models. Several retrospective studies have reported that the ASI usage group showed higher overall survival (OS) and progression-free survival compared to the non-ASI usage group in PC patients. However, most of the previous studies on ASI in PC patients were based on retrospective data from single institutions. Recently, real-world big data study could explain this subject.

Aims & Methods

In this study, we aimed to conduct whether ASI use in PC patients improve OS using National Health Information Database of the National Health Insurance Service, which represents the entire Korean population. We assessed 209,723 participants for eligibility, diagnosed with pancreatic cancer between 2002 and 2020. A total of 64,219 PC patients were enrolled and divided into 3 groups [chronic ASI users with hypertension (HTN), n=13,239; non-ASI users with HTN, n=5,963; non-HTN group, n=45,017]. We performed propensity score matching to balance the baseline characteristics of the 3 groups and to reduce potential confounders using a logistic regression model with adjustment for the following: age; sex; region of residence (urban or rural); household income; underlying disease of HTN, diabetes mellitus, cardiovascular disease, cerebrovascular disease, chronic kidney disease or chronic pancreatitis; Charlson Comorbidity Index (0, 1 or ≥2); concurrent use of aspirin, metformin, statin; PC surgery type; radiotherapy; and chemotherapy.

Results

Multivariate analysis using Cox regression analysis for matched cohort presented ASI usage, female, younger, history of chronic pancreatitis, CCI ≥2, concurrent use of statin, and receiving distal pancreatectomy were significantly associated with longer OS. In addition, Kaplan-Meier curve for OS of 3 groups according to ASI usage showed that chronic ASI users presented significantly longer OS than non-ASI users and non-HTN group [Hazard ratio, chronic ASI users (control) versus non-ASI users 1.447; chronic ASI users (control) versus non-HTN group 1.518; P value < 0.001].

Conclusion

We found that the use of ASI after PC diagnosis is associated with longer survival in PC patients. Additional well-designed randomized controlled studies are needed to confirm these findings in the future.

References

1. Rahib, L., B. D. Smith, R. Aizenberg, A. B. Rosenzweig, J. M. Fleshman and L. M. Matrisian (2014). "Projecting cancer incidence and deaths to 2030: the unexpected burden of thyroid, liver, and pancreas cancers in the United States" Cancer Res 74(11): 2913-2921.
2. Encarnación-Rosado, J. and A. C. Kimmelman (2021). "Harnessing metabolic dependencies in pancreatic cancers." Nature Reviews Gastroenterology & Hepatology 18(7): 482-492.
3. Thomas, D. and P. Radhakrishnan (2019). "Tumor-stromal crosstalk in pancreatic cancer and tissue fibrosis." Molecular cancer 18: 1-15.
4. Chauhan, V. P., J. D. Martin, H. Liu, D. A. Lacorre, S. R. Jain, S. V. Kozin, T. Stylianopoulos, A. S. Mousa, X. Han, P. Adstamongkonkul, Z. Popović, P. Huang, M. G. Bawendi, Y. Boucher and R. K. Jain (2013). "Angiotensin inhibition enhances drug delivery and potentiates chemotherapy by decompressing tumour blood vessels." Nat Commun 4: 2516.
5. Pinter, M. and R. K. Jain (2017). "Targeting the renin-angiotensin system to improve cancer treatment: Implications for immunotherapy." Sci Transl Med 9(410).

Introduction

Pancreatic ductal adenocarcinoma (PDAC) is a highly metastatic disease refractory to all targeted and immune therapies. However, our understanding of PDAC microenvironment especially the metastatic microenvironment is very limited partly due to the inaccessibility to metastatic tumor tissues. Here, we aim to carry out the first single-cell transcriptome analysis of PDAC metastatic microenvironment in matched primary tumor and liver metastasis tissues.

Aims & Methods

We collected synchronously resected PDAC primary tumors and matched liver metastases from four PDAC patients and performed single-cell RNA sequencing. We performed comparative analysis on both cellular composition and functional phenotyping between primary and metastatic tumors. We also performed trajectory analysis and cellular interactome analysis in the metastasis microenvironment.

Results

Epithelial cancer cells exhibited distinct transcriptomic profile in liver metastasis with clearly defined evolutionary routes from primary tumor. We identified specific subtypes of stromal and immune cells critical to the formation of the pro-tumor microenvironment in metastatic lesions, including RGS5⁺ cancer-associated fibroblasts, CCL18⁺ lipid-associated macrophages, S100A8⁺ neutrophils and FOXP3⁺ regulatory T cells. We also identified several new biomarkers associated with PDAC metastasis and poor prognosis, such as LITAF, SPRR1B, KLK7 and LGALS9. Cellular interactome analysis further revealed that the lack of tumor-immune cell interaction in metastatic tissues contributes to the formation of the immunosuppressive microenvironment.

Conclusion

Our study provides a comprehensive characterization of the transcriptional landscape of PDAC liver metastasis, which deepens our understanding of the mechanisms underlying the immunosuppressive microenvironment in metastatic PDAC and serves as a valuable resource for the design of novel targeted therapies.

Disclosure

No

Introduction

Pancreatic cancer (PC) is characterized by an exceptionally aggressive biology and high tumor heterogeneity causing considerable variations in therapy response. Patient-derived organoids (PDOs) reflect parental tumor features and represent a powerful preclinical tool to predict drug response and harness personalized treatment. In clinical practice, PDO-guided selection of effective drugs may provide substantial benefit and improve outcomes.

Aims & Methods

We have derived >40 PDOs from treatment-naïve and pretreated PC patient primary tumor and metastases (after ultrasound-guided biopsy, endoscopic ultrasound-guided biopsy or surgical resection) with a reliable efficacy. PDO drug response was evaluated using a cytotoxicity assay. Using Jenks natural breaks classification method, we clustered response (AUC) for each drug and correlated it to patient response.

Results

The implementation of an automated pipetting system and the subsequent miniaturization of drug screenings significantly enhanced our process capacity, conferring the possibility to extend our panel to a second list of approved targeted substances and further reducing the time before pharmacotyping. The validity of our approach was assessed via re-pharmacotyping a set of PDOs. Following up our feasibility trial (Beutel, 2021), we validated the accuracy of our model on a greater number of PC patients. Our model allowed overall a successful drug-response prediction in naïve patients with an accuracy of 85.7% for both first and second-line regimens. Prediction power remained lower for pretreated patients, with a precision of 57.1% for subsequent lines. A trend was also observed towards a better performance of the system in prognosticating chemoresponsiveness vs unresponsiveness (89.5% vs 68.8%). Finally, the administration of a regimen predicted to be efficient translated into a significantly longer progression-free survival. The access to longitudinal biopsies allowed us to conduct whole exome sequencing on 14 PDOs to capture a comprehensive genetic profiling over the time of treatment, notably revealing a CHEK2-mutated patient who responded over time upon PARP inhibitor maintenance therapy, in line with our PDO-based response prediction, further highlighting the robustness of our method and algorithm.

Conclusion

Overall, we report a robust and clinically-relevant preclinical tool for drug response prediction, paving the way towards an PDO-based true precision medicine in clinical routine.

Introduction

Pancreatic cancer (PC) accounts for 0.5 million new cases and 4.7% of the world's cancer-related deaths in 2020^1,2. Endoscopic ultrasound (EUS) guided Fine Needle Biopsies (FNB) are the current standard of care for confirming suspicious lesions, however the diagnostic accuracy is reported below 70%^3-5. Rapid on-site evaluation (ROSE) improves the diagnostic performances of biopsies via an on-site pathological assessment to reduce the number of needle passes, complication rates, and the need for additional procedures⁶. However, availability of an on-site pathologist is often not feasible. NovaScan has pioneered the use of an impedance spectroscopy biomarker for cancer detection in different tissues based on the Cole Relaxation Frequency (CRF). NovaScan has developed a ”smart ROSE” device that assesses PC presence in FNBs obtained during endoscopic procedures ex vivo, in real-time and on-site, without altering the tissue sample or affecting the workflow.

Aims & Methods

Data were collected in a double-blind, IRB approved study from a cohort of 21 patients (9 males, 12 females) undergoing EUS procedures for suspected pancreatic lesions at the Texas International Endoscopy Center. FNBs were performed with a 22 gauge biopsy from a transduodenal approach. Each biopsy sample was transferred onto a disposable strainer allowing for any excess fluid to drain. An electrode for scanning was placed onto the core samples for data collection with the "smart ROSE," a low cost, portable device that can be used in the operating room. The device measures impedance spectroscopy in the 1 kHz - 10 MHz frequency range and derives a bio impedance biomarker via custom-designed hardware. The assessment of the sample is completed in a few seconds and subsequently, the sample is flushed into a pathology jar to undergo the standard workflow for cytology. The algorithm for extracting the bioimpedance biomarker from the measurement was developed based on our previous preclinical study with transgenic (KPC) mice⁷; pancreas samples with a CRF biomarker below 1 MHz were considered noncancerous, while biomarker values above 1MHz or the absence of a biomarker reading were considered cancerous.

Results

PC assessment provided by nsCanary was validated against pathology outcomes for each biopsy sample. Findings showing a sensitivity of 80% and a specificity of 87.5% are reported in Table 1.

Conclusion

We believe the "smart ROSE" device will provide immediate feedback to the endoscopist on cancer presence and on sample adequacy. This device is intended to support the decision-making process by enabling clinicians to overcome the challenge of proving cancer presence during the first biopsy procedure, ultimately avoiding revisits and unnecessary trauma.

References

1. S. Kato and K. Honda. Use of biomarkers and imaging for early detection of pancreatic cancer. Cancers, 12(7):1965, 2020.
2. M. R. Young, N. Abrams, S. Ghosh, J. A. S. Rinaudo, G. Marquez, and S. Srivastava. Prediagnostic image data, artificial intelligence, and pancreatic cancer: a tell-tale sign to early detection. Pancreas, 49(7):882–886,2020.
3. S. Varadarajulu and M. B. Wallace. Applications of endoscopic ultrasonography in pancreatic cancer. Cancer Control, 11(1):15–22, 2004.
4. P. Michl et al. UEG position paper on pancreatic cancer. Bringing pancreatic cancer to the 21st century: Prevent, detect,and treat the disease earlier and better. UEG journal, 9(7):860–871, 2021
5. D. T. de Moura et al. Evaluation of endoscopic ultrasound fine-needle aspiration versus fine-needle biopsy and impact of rapid on-site evaluation for pancreatic masses. Endoscopy International Open, 8(06):E738–E747, 2020.
6. J. B. Klapman, R. Logrono, C. E. Dye, and I. Waxman. Clinical impact of on-site cytopathology interpretation on endoscopic ultrasound-guided fine needle aspiration. The American journal of gastroenterology, 98(6):1289–1294, 2003.
7. Bogdanowicz, Les, et al. Cole Relaxation Frequency: A Parameter for Pancreatic Cancer Detection. The American Journal of Gastroenterology 117(10S):p e24-e25, October 2022

Introduction

Single-molecule fluorescence microscopy has been researched for diagnosis of disease and detecting drugs or substances at the molecular level. It is known to detect the target proteins more precisely than ELISA and has a higher multiple detection rate, with small sample volume and at low cost. Pancreatic cancer often presents at an advanced stage. So there is a demand for early detection.

Aims & Methods

The aim of this pilot study is to check the feasibility of this technique to diagnose the pancreatic cancer. From April 2016 to June 2021, 16 pancreatic cancer patients and 21 non-pancreatic cancer patients were enrolled in the study at Seoul National University of Bundang Hospital. 15 markers (CA19-9, CRP, CA-125, D-dimer, Lumican, Cathepsin-D, IL-6, LRG-1, IFN-r, Beta-2, CEA, Clusterin, TTR, IL-4, and C5a) were detected from blood sample using single-molecule fluorescence microscopy. To create a train and test set for our research, we split the enrolled patients into halves. The primary outcome of the study is the accuracy, which is defined as the number of correct predictions, where patients are predicted as patients and controls are predicted as controls, divided by the total count. We analyzed the classification accuracy of each group and examined the accuracy based on the combination and number of markers used. Although the sizes of the training set and the testing set were equal, we randomly split each sample into either the training or testing set, and repeated this process 1,000 times.

Results

Eight out of 16 pancreatic cancer patients were used for training and the remaining 8 for testing. For the non-pancreatic cancer patients, 11 of the 21 blood samples were used for training, and the other 10 for testing.
In the classification accuracy of diagnosis, we tested the accuracy by utilizing Gaussian Naïve Bayes and Support Vector Machine. In case of Gaussian Naive Bayes for the train set, the average accuracy was 99.2%, the standard deviation was 0.019. When applied to the test set, the accuracy was 86.3%, with a standard deviation of 0.074. In case of Support Vector Machine for the train set, the average accuracy was 75.6%, the standard deviation was 0.004. When tested on the test set, the accuracy was 77.6% with a standard deviation of 0.076.
For the accuracy according to the number of biomarkers used, the accuracy was calculated by adding one biomarker at a time in the order previously listed. Accuracy tended to increase as markers were added, the highest accuracy was achieved when using 10 biomarkers (88.4%), and adding more biomarkers from the 11th to 15th actually decreased the accuracy (lowest 85.5% to highest 87.8%).

Conclusion

In this pilot study, we identified that single molecule counting technology showed a feasibility for the new technique to differentiate the patients with pancreatic cancer from healthy control. Further studies with more samples and appropriate selection of biomarkers, and optimization of classification model are necessary to fit the best performance of this novel technique. Utilizing this novel technique with multiple biomarkers in combination is expect to enable low-cost, small-sample volume, and high- accuracy diagnosis in pancreatic cancer.