Precision Oncology in Surgery: Patient Selection for Operable Pancreatic Cancer

A clinical-radiomics nomogram based on dual-layer spectral detector CT to predict cancer stage in pancreatic ductal adenocarcinoma

BACKGROUND

This study aimed to evaluate the efficacy of radiomics signatures derived from polyenergetic images (PEIs) and virtual monoenergetic images (VMIs) obtained through dual-layer spectral detector CT (DLCT). Moreover, it sought to develop a clinical-radiomics nomogram based on DLCT for predicting cancer stage (early stage: stage I-II, advanced stage: stage III-IV) in pancreatic ductal adenocarcinoma (PDAC).

METHODS

A total of 173 patients histopathologically diagnosed with PDAC and who underwent contrast-enhanced DLCT were enrolled in this study. Among them, 49 were in the early stage, and 124 were in the advanced stage. Patients were randomly categorized into training (n = 122) and test (n = 51) cohorts at a 7:3 ratio. Radiomics features were extracted from PEIs and 40-keV VMIs were reconstructed at both arterial and portal venous phases. Radiomics signatures were constructed based on both PEIs and 40-keV VMIs. A radiomics nomogram was developed by integrating the 40-keV VMI-based radiomics signature with selected clinical predictors. The performance of the nomogram was assessed using receiver operating characteristic (ROC) curves, calibration curves, and decision curves analysis (DCA).

RESULTS

The PEI-based radiomics signature demonstrated satisfactory diagnostic efficacy, with the areas under the ROC curves (AUCs) of 0.92 in both the training and test cohorts. The optimal radiomics signature was based on 40-keV VMIs, with AUCs of 0.96 and 0.94 in the training and test cohorts. The nomogram, which integrated a 40-keV VMI-based radiomics signature with two clinical parameters (tumour diameter and normalized iodine density at the portal venous phase), demonstrated promising calibration and discrimination in both the training and test cohorts (0.97 and 0.91, respectively). DCA indicated that the clinical-radiomics nomogram provided the most significant clinical benefit.

CONCLUSIONS

The radiomics signature derived from 40-keV VMI and the clinical-radiomics nomogram based on DLCT both exhibited exceptional performance in distinguishing early from advanced stages in PDAC, aiding clinical decision-making for patients with this condition.

Development of a radiomics-based model to predict occult liver metastases of pancreatic ductal adenocarcinoma: a multicenter study

BACKGROUND

Undetectable occult liver metastases block the long-term survival of pancreatic ductal adenocarcinoma (PDAC). This study aimed to develop a radiomics-based model to predict occult liver metastases and assess its prognostic capacity for survival.

MATERIALS AND METHODS

Patients who underwent surgical resection and were pathologically proven with PDAC were recruited retrospectively from five tertiary hospitals between January 2015 and December 2020. Radiomics features were extracted from tumors, and the radiomics-based model was developed in the training cohort using LASSO-logistic regression. The model's performance was assessed in the internal and external validation cohorts using the area under the receiver operating curve (AUC). Subsequently, the association of the model's risk stratification with progression-free survival (PFS) and overall survival (OS) was then statistically examined using Cox regression analysis and the log-rank test.

RESULTS

A total of 438 patients [mean (SD) age, 62.0 (10.0) years; 255 (58.2%) male] were divided into the training cohort ( n =235), internal validation cohort ( n =100), and external validation cohort ( n =103). The radiomics-based model yielded an AUC of 0.73 (95% CI: 0.66-0.80), 0.72 (95% CI: 0.62-0.80), and 0.71 (95% CI: 0.61-0.80) in the training, internal validation, and external validation cohorts, respectively, which were higher than the preoperative clinical model. The model's risk stratification was an independent predictor of PFS (all P <0.05) and OS (all P <0.05). Furthermore, patients in the high-risk group stratified by the model consistently had a significantly shorter PFS and OS at each TNM stage (all P <0.05).

CONCLUSION

The proposed radiomics-based model provided a promising tool to predict occult liver metastases and had a great significance in prognosis.

Novel piperazine-1,2,3-triazole leads for the potential treatment of pancreatic cancer

From lead 1, (N-(4-((4-(3-(4-(3-methoxyphenyl)-1H-1,2,3-triazol-1-yl)propyl)piperazin-1-yl)sulfonyl)-phenyl)acetamide), a S100A2-p53 protein-protein interaction inhibitor based on an in silico modelling driven hypothesis, four focused libraries were designed and synthesised. Growth inhibition screening was performed against 16 human cancer cell lines including the pancreatic cell lines MiaPaCa2, BxPC3, AsPC-1, Capan-2, HPAC, PANC-1 and the drug resistant CFPAC1. Modification of 1's phenylacetamide moiety, gave Library 1 with only modest pancreatic cancer activity. Modification of the 3-OCH3Ph moiety (Library 2) gave 4-CH3 (26), 4-CH2CH3 (27), 4-CF3 (31) and 4-NO2 (32) with sterically bulky groups more active. A 4-CF3 acetamide replacement enhanced cytotoxicity (Library 3). The 4-C(CH3)336 resulted in a predicted steric clash in the S100A2-p53 binding groove, with a potency decrease. Alkyl moieties afforded more potent analogues, 34 (4-CH3) and 35 (CH2CH3), a trend evident against pancreatic cancer: GI50 3.7 (35; BxPC-3) to 18 (40; AsPC-1) μM. Library 4 analogues with a 2-CF3 and 3-CF3 benzenesulfonamide moiety were less active than the corresponding Library 3 analogues. Two additional analogues were designed: 51 (4-CF3; 4-OCH3) and 52 (4-CF3; 2-OCH3) revealed 52 to be 10-20 fold more active than 51, against the pancreatic cancer cell lines examined with sub-micromolar GI50 values 0.43 (HPAC) to 0.61 μM (PANC-1). MOE calculated binding scores for each pose are also consistent with the observed biological activity with 52. The obtained SAR data is consistent with the proposed interaction within the S100A2-p53 bonding groove.

Randomized controlled trials and alternative study designs in surgical oncology

Surgery is central to the cure of most solid cancers and an integral part of modern multimodal cancer management for early and advanced stage cancers. Decisions made by surgeons and multidisciplinary team members are based on best available knowledge for the defined clinical situation at hand. While surgery is both an art and a science, good decision-making requires data that are robust, valid, representative and, applicable to most if not all patients with a specific cancer. Such data largely comes from clinical observations and registries, and more preferably from trials conducted with the specific purpose of arriving at new answers. As part of the ESSO core curriculum development an increased focus has been put on the need to enhance research literacy among surgical candidates. As an expansion of the curriculum catalogue list and to enhance the educational value, we here present a set of principles and emerging concepts which applies to surgical oncologist for reading, understanding, planning and contributing to future surgeon-led cancer trials.

Focus on Therapeutic Options for Surgically Resectable Pancreatic Adenocarcinoma Based on Novel Biomarkers

Pancreatic ductal adenocarcinoma remains associated with a poor prognosis, even when diagnosed at an early stage. Consequently, it is imperative to carefully consider the available therapeutic options and tailor them based on clinically relevant biomarkers. In our comprehensive review, we specifically concentrated on the identification of novel predictive and prognostic markers that have the potential to be integrated into multiparametric scoring systems. These scoring systems aim to accurately predict the efficacy of neoadjuvant chemotherapy in surgically resectable pancreatic cancer cases. By identifying robust predictive markers, we can enhance our ability to select patients who are most likely to benefit from neoadjuvant chemotherapy. Furthermore, the identification of prognostic markers can provide valuable insights into the overall disease trajectory and inform treatment decisions. The development of multiparametric scoring systems that incorporate these markers holds great promise for optimizing the selection of patients for neoadjuvant chemotherapy, leading to improved outcomes in resectable pancreatic neoplasia. Continued research efforts are needed to validate and refine these markers and scoring systems, ultimately advancing the field of personalized medicine in pancreatic adenocarcinoma management.

Deep Learning for Fully Automated Prediction of Overall Survival in Patients Undergoing Resection for Pancreatic Cancer: A Retrospective Multicenter Study

OBJECTIVE

To develop an imaging-derived biomarker for prediction of overall survival (OS) of pancreatic cancer by analyzing preoperative multiphase contrast-enhanced computed topography (CECT) using deep learning.

BACKGROUND

Exploiting prognostic biomarkers for guiding neoadjuvant and adjuvant treatment decisions may potentially improve outcomes in patients with resectable pancreatic cancer.

METHODS

This multicenter, retrospective study included 1516 patients with resected pancreatic ductal adenocarcinoma (PDAC) from 5 centers located in China. The discovery cohort (n=763), which included preoperative multiphase CECT scans and OS data from 2 centers, was used to construct a fully automated imaging-derived prognostic biomarker-DeepCT-PDAC-by training scalable deep segmentation and prognostic models (via self-learning) to comprehensively model the tumor-anatomy spatial relations and their appearance dynamics in multiphase CECT for OS prediction. The marker was independently tested using internal (n=574) and external validation cohorts (n=179, 3 centers) to evaluate its performance, robustness, and clinical usefulness.

RESULTS

Preoperatively, DeepCT-PDAC was the strongest predictor of OS in both internal and external validation cohorts [hazard ratio (HR) for high versus low risk 2.03, 95% confidence interval (CI): 1.50-2.75; HR: 2.47, CI: 1.35-4.53] in a multivariable analysis. Postoperatively, DeepCT-PDAC remained significant in both cohorts (HR: 2.49, CI: 1.89-3.28; HR: 2.15, CI: 1.14-4.05) after adjustment for potential confounders. For margin-negative patients, adjuvant chemoradiotherapy was associated with improved OS in the subgroup with DeepCT-PDAC low risk (HR: 0.35, CI: 0.19-0.64), but did not affect OS in the subgroup with high risk.

CONCLUSIONS

Deep learning-based CT imaging-derived biomarker enabled the objective and unbiased OS prediction for patients with resectable PDAC. This marker is applicable across hospitals, imaging protocols, and treatments, and has the potential to tailor neoadjuvant and adjuvant treatments at the individual level.

Cancer-associated fibroblasts: A key target to snatch victory from defeat in therapy resistance associated with the pancreatic cancer stroma

The stroma plays a dual role in the tumour microenvironment (TME), where it can both promote or restrict tumour growth. These effects are significantly modulated by the presence of cancer-associated fibroblasts (CAFs), key components of the TME. The stroma and CAFs influence pancreatic cancer (PC) both physically and functionally. The physical impact involves the deposition of a wall-like matrix, creating a solid barrier that prevents the escape of materials from the inside and the entry of substances from the outside. Functionally, the stroma influences PC treatment through crosstalk between CAFs, cancer cells, and immune cells. Transformation of the "CAFs wall", however, may reduce the original benefit of limiting PC metastasis. In this review, we found that targeting the CAFs and designing novel carriers allowing the entry of drugs or therapeutic agents into the TME are alternative strategies to effectively treat PC. This article aims to provide a specific review focusing on the possibly therapeutic markers and its novel therapeutic strategies of CAFs in PC, discussing the concise treatment methods and its new challenging in current advanced researches.

Serum DUPAN-2 could be an Alternative Biological Marker for CA19-9 Nonsecretors with Pancreatic Cancer

OBJECTIVE

This study investigates the use of serum DUPAN-2 in predicting the PC progression in CA19-9 nonsecretors.

BACKGROUND

Although we previously reported that serum CA19-9 >500U/ mL is a poor prognostic factor and an indication for enhanced neoadjuvant treatment, there is not a biomarker surrogate that equivalently predicts prognosis for CA19-9 nonsecretors.

METHODS

We evaluated consecutive PC patients who underwent pancreatectomy from 2005 to 2019. All patients were categorized as either nonsecretor or secretor (CA19-9 ≤ or >2.0U/mL).

RESULTS

Of the 984 resected PC patients, 94 (9.6%) were nonsecretors and 890 (90.4%) were secretors. The baseline characteristics were not statistically different between the 2 groups except for the level of DUPAN-2 (720 vs. 100U/mL, P < 0.001). Survival curves after resection were similar between the 2 groups (29.4 months vs. 31.3 months, P = 0.900). Survival curves of patients with DUPAN-2 >2000U/mL in the nonsecretors and patients with CA19-9 >500U/mL in the secretors were nearly equivalent as well (hazard ratio 2.08 vs. 1.89). In the multivariate analysis, DUPAN-2 >2000U/mL (hazard ratio 2.53, P = 0.010) was identified as independent prognostic factor after resection.

CONCLUSION

DUPAN-2 >2000U/mL in CA19-9 nonsecretors can be an unfavorable factor that corresponds to CA19-9 >500U/mL in CA19-9 secretors which is an indicator for enhanced neoadjuvant treatment. The current results shed light on the subset of nonsecretors with poor prognosis that were traditionally categorized in a group with a more favorable prognosis group.

Robotic pancreaticoduodenectomy for pancreatic ductal adenocarcinoma: Analysis of surgical outcomes and long-term prognosis in a high-volume center

BACKGROUND

Robotic pancreaticoduodenectomy (RPD) has been reported to be safe and feasible for patients with pancreatic ductal adenocarcinoma (PDAC) of the pancreatic head. This study aimed to analyze the surgical outcomes and risk factors for poor long-term prognosis of these patients.

METHODS

Data from patients who underwent RPD for PDAC of pancreatic head were retrospectively analyzed. Multivariate Cox regression analysis was used to seek the independent prognostic factors for overall survival (OS), and an online nomogram calculator was developed based on the independent prognostic factors.

RESULTS

Of the 273 patients who met the inclusion criteria, the median operative time was 280.0 minutes, the estimated blood loss was 100.0 mL, the median OS was 23.6 months, and the median recurrence-free survival (RFS) was 14.4 months. Multivariate analysis showed that preoperative carbohydrate antigen 19-9 (CA19-9) [hazard ratio (HR) = 2.607, 95% confidence interval (CI): 1.560-4.354, P < 0.001], lymph node metastasis (HR = 1.429, 95% CI: 1.005-2.034, P = 0.047), tumor moderately (HR = 3.190, 95% CI: 1.813-5.614, P < 0.001) or poorly differentiated (HR = 5.114, 95% CI: 2.839-9.212, P < 0.001), and Clavien-Dindo grade ≥ III (HR = 1.657, 95% CI: 1.079-2.546, P = 0.021) were independent prognostic factors for OS. The concordance index (C-index) of the nomogram constructed based on the above four independent prognostic factors was 0.685 (95% CI: 0.640-0.729), which was significantly higher than that of the AJCC staging (8th edition): 0.541 (95% CI: 0.493-0.589) (P < 0.001).

CONCLUSIONS

This large-scale study indicated that RPD was feasible for PDAC of pancreatic head. Preoperative CA19-9, lymph node metastasis, tumor poorly differentiated, and Clavien-Dindo grade ≥ III were independent prognostic factors for OS. The online nomogram calculator could predict the OS of these patients in a simple and convenient manner.

S100 family proteins are linked to organoid morphology and EMT in pancreatic cancer

Epithelial-mesenchymal transition (EMT) is a continuum that includes epithelial, partial EMT, and mesenchymal states, each of which is associated with cancer progression, invasive capabilities, and ultimately, metastasis. We used a lineage-traced sporadic model of pancreatic cancer to generate a murine organoid biobank from primary and secondary tumors, including sublines that underwent partial EMT and complete EMT. Using an unbiased proteomics approach, we found that organoid morphology predicts the EMT state, and the solid organoids are associated with a partial EMT signature. We also observed that exogenous TGFβ1 induces solid organoid morphology that is associated with changes in the S100 family, complete EMT, and the formation of high-grade tumors. S100A4 may be a useful biomarker for predicting EMT state, disease progression, and outcome in patients with pancreatic cancer.

The Impact of Molecular Subtyping on Pathological Staging of Pancreatic Cancer

BACKGROUND

The long-term outcomes following surgical resection for pancreatic ductal adenocarcinoma (PDAC) remains poor, with only 20% of patients surviving 5 years after pancreatectomy. Patient selection for surgery remains suboptimal largely due to the absence of consideration of aggressive tumor biology.

OBJECTIVE

The aim of this study was to evaluate traditional staging criteria for PDAC in the setting of molecular subtypes.

METHODS

Clinicopathological data were obtained for 5 independent cohorts of consecutive unselected patients, totaling n = 1298, including n = 442 that underwent molecular subtyping. The main outcome measure was disease-specific survival following surgical resection for PDAC stratified according to the American Joint Commission for Cancer (TNM) staging criteria, margin status, and molecular subtype.

RESULTS

TNM staging criteria and margin status confers prognostic value only in tumors with classical pancreatic subtype. Patients with tumors that are of squamous subtype, have a poor outcome irrespective of favorable traditional pathological staging [hazard ratio (HR) 1.54, 95% confidence interval (CI) 1.04-2.28, P = 0.032]. Margin status has no impact on survival in the squamous subtype (16.0 vs 12.1 months, P = 0.374). There were no differences in molecular subtype or gene expression of tumors with positive resection margin status.

CONCLUSIONS

Aggressive tumor biology as measured by molecular subtype predicts poor outcome following pancreatectomy for PDAC and should be utilized to inform patient selection for surgery.

O Uso do Sequenciamento Total do Exoma no Diagnóstico do Adenocarcinoma Ductal Pancreático

  Introdução: O adenocarcinoma ductal pancreático (PDAC) é uma doença agressiva responsável no Brasil por 2% das neoplasias e 5% das mortes por câncer. A análise do exoma – parte do DNA que codifica as proteínas – permite identificar as variantes somáticas do tumor e as germinativas do paciente. Essa informação é necessária para implementar a terapia-alvo para o PDAC, pois fornece evidência para selecionar, ou excluir, tratamentos para a doença. Objetivo: Identificar as variantes de interesse clínico e farmacológico presentes no PDAC de quatro pacientes, por meio da técnica de sequenciamento total do exoma (WES). Método: Foram utilizados dados públicos de quatro amostras de pares tumor-normal de PDAC, localizados na cabeça do pâncreas de pacientes caucasianos, estádio T3N1M0, sequenciadas e publicizadas pelo Texas Cancer Research Biobank. Para identificar as variações somáticas e germinativas, utilizou-se o software GATK. As consequências clínicas e farmacológicas dessas variações foram anotadas por meio do software VEP e analisadas mediante o software estatístico R. Resultados: Dos quatro tumores, um possui variante estrutural com duplicação do gene AKT2; outro, variantes nos genes da via das ciclinas CDK14 e CDKN2C, o que altera o regime quimioterápico; na linhagem germinativa, um paciente tem variantes no gene XRCC1, que sugere aumento da resposta à platina. Conclusão: Embora a patologia classifique todos os tumores como PDAC, cada paciente – bem como o respectivo tumor – apresenta especificidades que afetam o diagnóstico e as possibilidades terapêuticas. O WES permite identificá-las a um custo baixo, o que amplia as possibilidades de tratamento do PDAC.

Exosome-derived FGD5-AS1 promotes tumor-associated macrophage M2 polarization-mediated pancreatic cancer cell proliferation and metastasis

Inflammatory molecules and exosomes are crucial for signal transduction between tumor-associated macrophages and tumor cells. IL-6, a key inflammatory molecule secreted by M2 macrophages after polarization, can mediate malignant progression of pancreatic cancer (PC). However, the functions and mechanisms of IL-6 and tumor-derived exosomes in tumor-associated macrophages and PC remain unclear. Transcriptome chip and quantitative reverse transcription PCR experiments indicated that FGD5-AS1 induced IL-6 and high FGD5-AS1 expression correlated with the poor prognosis in PC patients. RNA pulldown, mass spectrometry, and dual luciferase reporter assays were used to identify the mechanism of exosomal FGD5-AS1 in promoting PC progression and M2 macrophage polarization. FGD5-AS1 exerted cancer-promoting functions when co-cultured with M2 macrophages. PC-derived exosomal FGD5-AS1 stimulated M2 macrophage polarization by activating STAT3/NF-κB pathway. FGD5-AS1 interacts with p300, resulting in STAT3 acetylation, thus promoting nuclear localization and transcriptional activity of STAT3/NF-κB. These data indicated that PC cells generate FGD5-AS1-rich exosomes, which cause M2 macrophage polarization to promote the malignant behaviors of PC cells. Targeting exosomal FGD5-AS1 may provide a potential diagnosis and treatment strategy for PC.

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.

Management of Advanced Pancreatic Cancer through Stromal Depletion and Immune Modulation

Pancreatic cancer is one of the leading causes of cancer-related deaths worldwide. Unfortunately, therapeutic gains in the treatment of other cancers have not successfully translated to pancreatic cancer treatments. Management of pancreatic cancer is difficult due to the lack of effective therapies and the rapid development of drug resistance. The cytotoxic agent gemcitabine has historically been the first-line treatment, but combinations of other immunomodulating and stroma-depleting drugs are currently undergoing clinical testing. Moreover, the treatment of pancreatic cancer is complicated by its heterogeneity: analysis of genomic alterations and expression patterns has led to the definition of multiple subtypes, but their usefulness in the clinical setting is limited by inter-tumoral and inter-personal variability. In addition, various cell types in the tumor microenvironment exert immunosuppressive effects that worsen prognosis. In this review, we discuss current perceptions of molecular features and the tumor microenvironment in pancreatic cancer, and we summarize emerging drug options that can complement traditional chemotherapies.

Prognostic models to predict survival in patients with pancreatic cancer: a systematic review

BACKGROUND

Pancreatic ductal adenocarcinoma (PDAC) has poor survival. Current treatments offer little likelihood of cure or long-term survival. This systematic review evaluates prognostic models predicting overall survival in patients diagnosed with PDAC.

METHODS

We conducted a comprehensive search of eight electronic databases from their date of inception through to December 2019. Studies that published models predicting survival in patients with PDAC were identified.

RESULTS

3297 studies were identified; 187 full-text articles were retrieved and 54 studies of 49 unique prognostic models were included. Of these, 28 (57.1%) were conducted in patients with advanced disease, 17 (34.7%) with resectable disease, and four (8.2%) in all patients. 34 (69.4%) models were validated, and 35 (71.4%) reported model discrimination, with only five models reporting values >0.70 in both derivation and validation cohorts. Many (n = 27) had a moderate to high risk of bias and most (n = 33) were developed using retrospective data. No variables were unanimously found to be predictive of survival when included in more than one study.

CONCLUSION

Most prognostic models were developed using retrospective data and performed poorly. Future research should validate instruments performing well locally in international cohorts and investigate other potential predictors of survival.

The Right Treatment Strategy for the Right Patient: A Biomarker-Driven Approach to Neoadjuvant vs. Surgery-First Management of Resectable and Borderline Resectable Pancreatic Cancer

Simple Summary

Routine neoadjuvant therapy for resectable and borderline resectable pancreatic cancer is gaining popularity, but its true oncological benefit remains disputed. Whilst the genotypic and phenotypic heterogeneity of pancreatic cancer is becoming increasingly appreciated, there is currently no method to determine whether certain patients will benefit from a neoadjuvant approach and whether others will benefit from a surgery-first approach. In this study, a previously validated prognostic triple biomarker panel is shown to predict genetic subtypes and clinical phenotypes of pancreatic cancer and also the optimal treatment strategy (neoadjuvant vs. surgery-first) for patients with resectable and borderline resectable pancreatic cancer.

Abstract

The genomic heterogeneity of pancreatic ductal adenocarcinoma (PDAC) is becoming increasingly appreciated. We aimed to evaluate the ability of a triple biomarker panel (S100A4, Ca-125, and mesothelin) to predict: (i) genetic PDAC subtypes; (ii) clinical phenotypes; and (iii) the optimal treatment strategy (neoadjuvant vs. surgery-first) in resectable and borderline resectable PDAC. Patients who underwent resection for resectable and borderline resectable PDAC were included from one single-institutional cohort and one multi-institutional cohort from the Australian Pancreatic Genome Initiative (APGI). Tumors were immunohistochemically evaluated for S100A4, Ca-125, and mesothelin, and a subset from the APGI cohort underwent RNA sequencing. This study included 252 and 226 patients from the single institution and the APGI cohorts, respectively. Triple-negative biomarker status correlated with non-squamous PDAC genotypes (p = 0.020), lower rates of distant recurrence (p = 0.002), and longer median overall survival (mOS) with the surgery-first approach compared with neoadjuvant treatment (33.3 vs. 22.2 mths, p = 0.038) in resectable PDAC. In contrast, the triple-positive disease was associated with longer mOS with neoadjuvant treatment compared with the surgery-first approach (29.5 vs. 13.7 mths, p = 0.021) in resectable and borderline resectable PDAC. In conclusion, the triple biomarker panel predicts genetic PDAC subtypes, clinical phenotypes, and optimal treatment strategies in resectable and borderline resectable PDAC.

New criteria of resectability for pancreatic cancer: A position paper by the Japanese Society of Hepato-Biliary-Pancreatic Surgery (JSHBPS)

The symposium "New criteria of resectability for pancreatic cancer" was held during the 33nd meeting of the Japanese Society of Hepato-Biliary-Pancreatic Surgery (JSHBPS) in 2021 to discuss the potential modifications that could be made in the current resectability classification. The meeting focused on setting the foundation for developing a new prognosis-based resectability classification that is based on the tumor biology and the response to neoadjuvant treatment (NAT). The symposium included selected experts from Western and Eastern high-volume centers who have discussed their concept of resectability status through published literature. During the symposium, presenters reported new resectability classifications from their respective institutions based on tumor biology, conditional status, pathology, and genetics, in addition to anatomical tumor involvement. Interestingly, experts from all the centers reached the agreement that anatomy alone is insufficient to define resectability in the current era of effective NAT. On behalf of the JSHBPS, we would like to summarize the content of the conference in this position paper. We also invite global experts as internal reviewers of this paper for intercontinental cooperation in creating an up-to-date, prognosis-based resectability classification that reflects the trends of contemporary clinical practice.

[Better Therapy for Pancreatic Cancer through More Radical Surgery?]

Despite advances in the treatment of pancreatic cancer, the survival of affected patients remains limited. A more radical surgical therapy could help to improve the prognosis, in particular by reducing the local recurrence rate, which is around 45% in patients with resected pancreatic cancer. In addition, patients with oligometastatic pancreatic cancer could also benefit from a more radical indication for surgery.Based on an analysis of the literature, important principles of pancreatic cancer surgery were examined.Even if even more radical surgical approaches such as an "extended" lymphadenectomy or a standard complete pancreatectomy do not bring any survival advantage, complete resection of the tumour (R0), a thorough locoregional lymphadenectomy and an adequate radical dissection in the area of the peripancreatic vessels including periarterial nerve plexuses should be the standard of pancreatic carcinoma resections. Whenever necessary to achieve an R0 resection, resections of the pancreas have to be extended, as well as additional venous vascular resections and multivisceral resections had to be performed. Simultaneous arterial vascular resections as part of pancreatic resections as well as surgical resections in oligometastatic patients should, however, be reserved for selected patients. These aspects of the surgical technique in pancreatic carcinoma mentioned above must not be neglected from the point of view of an "existing limited prognosis". On the contrary, they form the absolutely necessary basis in order to achieve good survival results in combination with system therapy. However, it may always be necessary to adapt these standards according to the age, comorbidities and wishes of the patient.

Integrative Analysis of Biomarkers and Mechanisms in Adamantinomatous Craniopharyngioma

Craniopharyngioma is a benign tumor, and the predominant treatment methods are surgical resection and radiotherapy. However, both treatments may lead to complex complications, seriously affecting patients’ survival rate and quality of life. Adamantinomatous craniopharyngioma (ACP), as one of the histological subtypes of craniopharyngioma, is associated with a high incidence and poor prognosis, and there is a gap in the targeted therapy of immune-related genes for ACP. In this study, two gene expression profiles of ACP, namely GSE68015 and GSE94349, were downloaded from the Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) were identified by the Limma package, and 271 differentially expressed immune-related genes (DEIRGs) were obtained from the Immport database. The gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Gene Set Enrichment Analysis (GSEA) were performed for annotation, visualization, and integrated discovery. Five hub genes, including CXCL6, CXCL10, CXCL11, CXCL13, and SAA1, were screened out through protein-protein interaction (PPI) network interaction construction. Two diagnostic markers, namely S100A2 and SDC1 (both of which have the Area Under Curve value of 1), were screened by the machine learning algorithm. CIBERSORT analysis showed that M2 macrophages, activated NK cells, and gamma delta T cells had higher abundance in ACP infiltration, while CD8+ T cells, regulatory T cells, and Neutrophils had less abundance in ACP infiltration. The expression of gamma delta T cells was positively correlated with CXCL6, S100A2, SDC1, and SAA1, while CD8+ T cells expression was negatively correlated with CXCL6, S100A2, SDC1, and CXCL10. ACP with high CXCL6 showed remarkable drug sensitivity to Pentostatin and Wortmannin via CellMiner database analysis. Our results deepened the understanding of the molecular immune mechanism in ACP and provided potential biomarkers for the precisely targeted therapy for ACP.

Imaging-based Machine-learning Models to Predict Clinical Outcomes and Identify Biomarkers in Pancreatic Cancer: A Scoping Review

OBJECTIVE

To perform a scoping review of imaging-based machine-learning models to predict clinical outcomes and identify biomarkers in patients with PDAC.

SUMMARY OF BACKGROUND DATA

Patients with PDAC could benefit from better selection for systemic and surgical therapy. Imaging-based machine-learning models may improve treatment selection.

METHODS

A scoping review was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-analyses-scoping review guidelines in the PubMed and Embase databases (inception-October 2020). The review protocol was prospectively registered (open science framework registration: m4cyx). Included were studies on imaging-based machine-learning models for predicting clinical outcomes and identifying biomarkers for PDAC. The primary outcome was model performance. An area under the curve (AUC) of ≥0.75, or a P-value of ≤0.05, was considered adequate model performance. Methodological study quality was assessed using the modified radiomics quality score.

RESULTS

After screening 1619 studies, 25 studies with 2305 patients fulfilled the eligibility criteria. All but 1 study was published in 2019 and 2020. Overall, 23/25 studies created models using radiomics features, 1 study quantified vascular invasion on computed tomography, and one used histopathological data. Nine models predicted clinical outcomes with AUC measures of 0.78-0.95, and C-indices of 0.65-0.76. Seventeen models identified biomarkers with AUC measures of 0.68-0.95. Adequate model performance was reported in 23/25 studies. The methodological quality of the included studies was suboptimal, with a median modified radiomics quality score score of 7/36.

CONCLUSIONS

The use of imaging-based machine-learning models to predict clinical outcomes and identify biomarkers in patients with PDAC is increasingly rapidly. Although these models mostly have good performance scores, their methodological quality should be improved.

3,5-Bis(trifluoromethyl)phenylsulfonamides, a novel pancreatic cancer active lead. Investigation of the terminal aromatic moiety

Virtual screening identified N-(6-((4-bromobenzyl)amino)hexyl)-3,5-bis(trifluoromethyl)benzenesulfonamide (1) a lead compound that bound to the S100A2-p53 binding groove. S100A2 is a Ca²⁺ binding protein with implications in cell signaling and is known to be upregulated in pancreatic cancer. It is a validated pancreatic cancer drug target. Lead 1, inhibited the growth of the MiaPaCa-2 pancreatic cancer cell line (GI₅₀ = 2.97 μM). Focused compound libraries were developed to explore the SAR of this compound class with 4 libraries and 43 compounds total. Focused library (Library 1) development identified lipophillic sulfonamides as preferred for MiaPaCa-2 activity, with -CF₃ and -C(CH₃)₃ substituents well tolerated (MiaPaCa-2 GI₅₀ < 6 μM). Contraction of the hexylamino spacer to ethyl (Library 2) and propyl (Library 3) proved beneficial to activity against a broad spectrum panel of cancer cell lines: HT29 (lung), MCF-7 (breast), A2780 (ovarian), H460 (colon), A431 (skin), Du145 (prostate), BE2-C (neuroblastoma), U87 and SJ-G2 (glioblastoma) (cohort-1); and a pancreatic cancer cell line panel: MiaPaCa-2, BxPC-3, AsPC-1, Capan-2, HPAC and PANC-1 (cohort-2). With a marked preference for a propyl linker the observed GI₅₀ values ranged from 1.4 - 18 μM against cohort-1 and 1.4-18 μM against cohort-2 cell lines. In Library 4 the terminal aromatic moiety was explored with 4-substituted analogues preferred (with activity of 48 (4-Cl) >47 (3-Cl) >46 (2-Cl)) against the cell lines examined. The introduction of bulky aromatic moieties was well tolerated, e.g. dihydrobenzo[b]^1,4dioxine (51) returned cohort-2 GI₅₀ values of 1.7 - 3.4 μM. In all instances the observed docked binding poses and binding scores were consistent with the observed cytotoxicity. This in turn supports, but does not prove, that these analogues function via S100A2-p53 binding groove inhibition.

S100A2 Is a Prognostic Biomarker Involved in Immune Infiltration and Predict Immunotherapy Response in Pancreatic Cancer

Pancreatic cancer (PC) is a highly fatal and aggressive disease with its incidence and mortality quite discouraging. It is of great significance to construct an effective prognostic signature of PC and find the novel biomarker for the optimization of the clinical decision-making. Due to the crucial role of immunity in tumor development, a prognostic model based on nine immune-related genes was constructed, which was proved to be effective in The Cancer Genome Atlas (TCGA) training set, TCGA testing set, TCGA entire set, GSE78229 set, and GSE62452 set. Furthermore, S100A2 (S100 Calcium Binding Protein A2) was identified as the gene occupying the most paramount position in risk model. Gene set enrichment analysis (GSEA), ESTIMATE and CIBERSORT algorithm revealed that S100A2 was closely associated with the immune status in PC microenvironment, mainly related to lower proportion of CD8+T cells and activated NK cells and higher proportion of M0 macrophages. Meanwhile, patients with high S100A2 expression might get more benefit from immunotherapy according to immunophenoscore algorithm. Afterwards, our independent cohort was also used to demonstrate S100A2 was an unfavorable marker of PC, as well as its remarkably positive correlation with the expression of PD-L1. In conclusion, our results demonstrate S100A2 might be responsible for the preservation of immune-suppressive status in PC microenvironment, which was identified with significant potentiality in predicting prognosis and immunotherapy response in PC patients.

MCT4 as a potential therapeutic target to augment gemcitabine chemosensitivity in resected pancreatic cancer

BACKGROUND

Pancreatic cancer is a devastating disease with a high relapse rate, even in case of resectable pancreatic cancer. Here, we aimed to identify the prognostic significance and therapeutic options of metabolic subtypes of resectable pancreatic cancer.

METHOD

Transcriptomic data were obtained from the TCGA-PAAD cohort in the The Cancer Genome Atlas (TCGA) data portal (n = 182). After integrative analysis of transcriptomic data in the discovery cohort, immunohistochemical (IHC) staining was performed in an independent cohort (n = 51) to validate the molecules of interest. Experimental testing for the molecules of interest was performed in vitro using pancreatic cancer cell line models AsPC1, BxPC3, MIA PaCa-2 and PANC-1.

RESULTS

Two subtypes showing distinct gene expression patterns in the TCGA-PAAD dataset were identified. Of these, the active glucose metabolism subtype showed a significantly lower survival rate related to relapse after surgical resection. The genes SLC2A1 (GLUT1) and SLC16A3 (MCT4) were highly enriched in this subtype. The validation cohort showed a high MCT4 staining and a high relapse rate (p = 0.01). Several molecular pathways associated with aggressive tumor biology, including cell cycle regulation and Myc and mTOR downstream signaling, were highly enriched in the active glucose metabolism subtype, as well as with distinct responses to immunotherapy. MCT4 inhibition suppressed the in vitro malignant characteristics of pancreatic cancer cells and showed a synergistic effect with gemcitabine treatment.

CONCLUSIONS

From our data we conclude that MCT4 may serve as a potential therapeutic target in resectable pancreatic cancer. The precision medicine strategy for resectable pancreatic cancer should be validated in a clinical setting with a prospective study design.

Paradigm shift for defining the resectability of pancreatic cancer

Supported by the expanding indications for neoadjuvant therapy (NAT) for advanced pancreatic cancer (PC), the concept of resectability has evolved from being mostly based on the anatomical tumor extent to considering the biological and conditional factors relevant to prognosis. Therefore, it is more reasonable to define the “criteria for surgical resection” instead of using the “(technical) resectability criteria.” NAT has been used in resectable PCs (RPC) with a high risk of early systemic recurrence, as predicted by various biological or anatomical markers. Moreover, the indications for NAT followed by conversion surgery or adjuvant surgery for borderline resectable or locally advanced PC (LAPC) are gradually expanding. Therefore, it is important to define the RPC group that will benefit from NAT and the LAPC group that will benefit from post-NAT surgery. At diagnosis, population-based approaches, such as prognostic stratification and staging systems and personalized outcome-based approaches using prognostic prediction models can be used to determine the criteria for treatment options. Standardized indications for conversion surgery are needed for patients who are initially treated with NAT. In addition to imaging-based morphological criteria, biological criteria, including CA19-9, and various metabolic criteria can be used to establish predicted outcome-based criteria. Multicenter collaboration is required to develop a large database with standardized data collection for various biomarkers and response data after NAT to establish more accurate outcome prediction models to define the new resectability criteria.

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.

S100 Proteins in Pancreatic Cancer: Current Knowledge and Future Perspectives

Pancreatic cancer (PC) is a highly malignant tumor occurring in the digestive system. Currently, there is a lack of specific and effective interventions for PC; thus, further exploration regarding the pathogenesis of this malignancy is warranted. The S100 protein family, a collection of calcium-binding proteins expressed only in vertebrates, comprises 25 members with high sequence and structural similarity. Dysregulated expression of S100 proteins is a biomarker of cancer progression and prognosis. Functionally, these proteins are associated with the regulation of multiple cellular processes, including proliferation, apoptosis, growth, differentiation, enzyme activation, migration/invasion, Ca2+ homeostasis, and energy metabolism. This review highlights the significance of the S100 family in the diagnosis and prognosis of PC and its vital functions in tumor cell metastasis, invasion and proliferation. A further understanding of S100 proteins will provide potential therapeutic targets for preventing or treating PC.

[Influence of molecular pathology on oncological surgery of pancreatic cancer]

Pancreatic cancer is a very aggressive malignant disease with an extremely poor prognosis; however, the survival of patients at all tumor stages is highly variable. Standard therapies, which are based predominantly on the TNM classification, patient's general condition and comorbidities, are highly variable in their effectiveness. In recent years, new technologies with multi-omics tumor characterizations have revealed the molecular heterogeneity of pancreatic cancer; however, in routine clinical practice, pancreatic cancer is usually considered as a uniform disease without paying attention to the individual tumor biology. Recent clinical studies have shown that molecular analyses can identify pharmacological targets and prognosis-relevant or treatment-relevant subtypes. Better methods for prognosis prediction and stratification based on clinical and molecular parameters could help to create more effective personalized multimodal therapy concepts and replace uniform standard therapies.

Single-cell transcriptomics reveals heterogeneous progression and EGFR activation in pancreatic adenosquamous carcinoma

Pancreatic adenosquamous carcinoma (PASC) — a rare pathological pancreatic cancer (PC) type — has a poor prognosis due to high malignancy. To examine the heterogeneity of PASC, we performed single-cell RNA sequencing (scRNA-seq) profiling with sample tissues from a healthy donor pancreas, an intraductal papillary mucinous neoplasm, and a patient with PASC. Of 9,887 individual cells, ten cell subpopulations were identified, including myeloid, immune, ductal, fibroblast, acinar, stellate, endothelial, and cancer cells. Cancer cells were divided into five clusters. Notably, cluster 1 exhibited stem-like phenotypes expressing UBE2C, ASPM, and TOP2A. We found that S100A2 is a potential biomarker for cancer cells. LGALS1, NPM1, RACK1, and PERP were upregulated from ductal to cancer cells. Furthermore, the copy number variations in ductal and cancer cells were greater than in the reference cells. The expression of EREG, FCGR2A, CCL4L2, and CTSC increased in myeloid cells from the normal pancreas to PASC. The gene sets expressed by cancer-associated fibroblasts were enriched in the immunosuppressive pathways. We demonstrate that EGFR-associated ligand-receptor pairs are activated in ductal-stromal cell communications. Hence, this study revealed the heterogeneous variations of ductal and stromal cells, defined cancer-associated signaling pathways, and deciphered intercellular interactions following PASC progression.

Optimal Upfront Treatment in Surgically Resectable Pancreatic Cancer Candidates: A High-Volume Center Retrospective Analysis

Pancreatic adenocarcinoma is a devastating disease with only 15-20% of patients resectable at diagnosis. Neoadjuvant chemotherapy for this cohort is becoming increasingly popular; however, there are no published randomized trials that support the use of neoadjuvant chemotherapy over upfront surgery in resectable disease. This retrospective cohort analysis was conducted to compare both treatment pathways and to identify any potential prognostic markers. Medical records from one large volume pancreatic cancer center from 2013-2019 were reviewed and 126 patients with upfront resectable disease were analyzed. Due to a change in practice in our center patients treated prior to December 2016 received upfront surgery and those treated after this date received neoadjuvant chemotherapy. Of these, 86 (68%) patients were treated with upfront surgery and 40 (32%) of patients were treated with neoadjuvant chemotherapy. Our results demonstrated that patients treated with upfront surgery with early-stage (1a) disease had a longer median OS compared to those treated with neoadjuvant chemotherapy (24 vs. 21 months, p = 0.028). This survival difference was not evident for all patients (regardless of stage). R0 resections were similar between groups (p = 0.605). We identified that both tumor viability (in neoadjuvant chemotherapy-treated patients) and tumor grade were useful prognostic markers. Upfront surgery for certain patients with low volume disease may be suitable despite the global trend towards neoadjuvant chemotherapy for all upfront resectable patients. A prospective clinical trial in this cohort incorporating biomarkers is needed to determine optimal therapy pathway.

Predicting early recurrence for resected pancreatic ductal adenocarcinoma: a multicenter retrospective study in China

A precise classification of early recurrence (ER) after radical surgery of pancreatic ductal adenocarcinoma (PDAC) has not been standardized. We aim to develop an optimal cut-off based on scientific evidence to distinguish early and late recurrence (LR) for PDAC after radical surgery and develop a predictive model for ER of PDAC. The best threshold for recurrence-free survival (RFS) was assessed with a minimum P-value method, and patients were categorized into ER and LR groups. We used a logistic regression model to assess potential risk factors for ER and develop a predictive model for ER risk. The best threshold between high-risk and intermediate-high-risk groups was identified by using the receiver operating characteristic curve. Among 3,279 patients included, 1,234 (37.6%) experienced ER. The RFS of 9 months is the optimal threshold to distinguish ER and LR. Univariable and multivariable analysis identified four preoperative risk factors for ER, including larger tumor maximal diameter on computed tomography (CT), enlarged lymph nodes on CT, carbohydrate antigen (CA) 125 > 35 U/ml, and CA19-9 > 235 U/ml. The concordance index (C-index) for the predictive model in the training cohort and the validation cohort was 0.651 (95% confidence interval (CI): 0.624-0.678), and 0.636 (95% CI: 0.593-0.679), respectively, showing promising predictive ability. The high-risk group had a score above 203, and the corresponding risk of ER for this group was 56.7%. An RFS of 9 months is the best threshold to distinguish ER and LR. The model can accurately predict the risk of ER in PDAC after radical resection, and risk grouping can predict the patients who could benefit from upfront surgery.

Integrative analysis identifies an immune-relevant epigenetic signature for prognostication of non-G-CIMP glioblastomas

The clinical and molecular implications of DNA methylation alterations remain unclear among the majority of glioblastomas (GBMs) without glioma-CpGs island methylator phenotype (G-CIMP); integrative multi-level molecular profiling may provide useful information. Independent cohorts of non-G-CIMP GBMs or IDH wild type (wt) lower-grade gliomas (LGGs) from local and public databases with DNA methylation and gene expression microarray data were included for discovery and validation of a multimarker signature, combined using a RISK score model. Bioinformatic and in vitro functional analyses were employed for biological validation. Using a strict multistep selection approach, we identified eight CpGs, each of which was significantly correlated with overall survival (OS) of non-G-CIMP GBMs, independent of age, the O-6-methylguanine-DNA methyltransferase (MGMT) methylation status, treatments and other identified CpGs. An epigenetic RISK signature of the 8 CpGs was developed and validated to robustly and independently prognosticate prognosis in different cohorts of not only non-G-GIMP GBMs, but also IDHwt LGGs. It also showed good discriminating value in stratified cohorts by current clinical and molecular factors. Bioinformatic analysis revealed consistent correlation of the epigenetic signature to distinct immune-relevant transcriptional profiles of GBM bulks. Functional experiments showed that S100A2 appeared to be epigenetically regulated by one identified CpG and was associated with GBM cell proliferation, apoptosis, invasion, migration and immunosuppression. The prognostic 8-CpGs RISK score signature may be of promising value for refining current glioma risk classification, and its potential links to distinct immune phenotypes make it a promising biomarker candidate for predicting response to anti-glioma immunotherapy.

S100A4 overexpression in pancreatic ductal adenocarcinoma: imaging biomarkers from whole-tumor evaluation with MRI and texture analysis

OBJECTIVE

To investigate the relationship between imaging findings and S100A4 overexpression in pancreatic ductal adenocarcinoma (PDAC) and to determine imaging biomarkers of S100A4 overexpression from whole-tumor evaluation with MRI and texture analysis.

METHODS

A total of 60 patients with pathologically confirmed PDAC were included in the study. All patients underwent preoperative abdominal contrast-enhanced MRI examination with Magnetom Aera (Siemens Healthcare, Germany, 1.5 T) at our institute. Whole-tumor evaluation including texture analysis was performed. Sections of specimens were reviewed, and the S100A4 expression status was quantitatively evaluated. Univariate and multivariate logistic regression analyses were conducted to find imaging biomarkers that could predict S100A4 overexpression.

RESULTS

Twenty-four tumors (40.0%) had negative results for S100A4 overexpression, and 36 tumors (60.0%) exhibited overexpression. After univariate and multivariate analysis, distal pancreatic duct dilatation, T₁WI_10th percentile and the enhancement rate difference between delayed phase (DP) and portal venous phase (PVP) were identified to predict S100A4 overexpression in PDAC independently (p = 0.009, 0.012 and 0.044), with odds ratios (ORs) of 0.102, 0.139 and 4.645, respectively. The area under the ROC curve (AUC) values were 0.715, 0.707 and 0.691. The AUC value of the proposed model was 0.877 with a sensitivity of 80.6% and specificity of 75.0%.

CONCLUSION

A model including distal pancreatic duct dilatation, T₁WI_10th percentile and the enhancement rate difference between the DP and PVP could predict S100A4 overexpression in PDAC as imaging biomarkers.

Clinical translation and implementation of optical imaging agents for precision image-guided cancer surgery

INTRODUCTION

The field of tumor-specific fluorescence-guided surgery has seen a significant increase in the development of novel tumor-targeted imaging agents. Studying patient benefit using intraoperative fluorescence-guided imaging for cancer surgery is the final step needed for implementation in standard treatment protocols. Translation into phase III clinical trials can be challenging and time consuming. Recent studies have helped to identify certain waypoints in this transition phase between studying imaging agent efficacy (phase I-II) and proving patient benefit (phase III).

TRIAL INITIATION

Performing these trials outside centers of expertise, thus involving motivated clinicians, training them, and providing feedback on data quality, increases the translatability of imaging agents and the surgical technique. Furthermore, timely formation of a trial team which oversees the translational process is vital. They are responsible for establishing an imaging framework (camera system, imaging protocol, surgical workflow) and clinical framework (disease stage, procedure type, clinical research question) in which the trial is executed. Providing participating clinicians with well-defined protocols with the aim to answer clinically relevant research questions within the context of care is the pinnacle in gathering reliable trial data.

OUTLOOK

If all these aspects are taken into consideration, tumor-specific fluorescence-guided surgery is expected be of significant value when integrated into the diagnostic work-up, surgical procedure, and follow-up of cancer patients. It is only by involving and collaborating with all stakeholders involved in this process that successful clinical translation can occur.

AIM

Here, we discuss the challenges faced during this important translational phase and present potential solutions to enable final clinical translation and implementation of imaging agents for image-guided cancer surgery.

Reshaping preoperative treatment of pancreatic cancer in the era of precision medicine

This review summarises the recent evidence on preoperative therapeutic strategies in pancreatic cancer and discusses the rationale for an imminent need for a personalised therapeutic approach in non-metastatic disease. The molecular diversity of pancreatic cancer and its influence on prognosis and treatment response, combined with the failure of 'all-comer' treatments to significantly impact on patient outcomes, requires a paradigm shift towards a genomic-driven approach. This is particularly important in the preoperative, potentially curable setting, where a personalised treatment allocation has the substantial potential to reduce pancreatic cancer mortality.

Targeting DNA Damage Response and Replication Stress in Pancreatic Cancer

BACKGROUND & AIMS

Continuing recalcitrance to therapy cements pancreatic cancer (PC) as the most lethal malignancy, which is set to become the second leading cause of cancer death in our society. The study aim was to investigate the association between DNA damage response (DDR), replication stress, and novel therapeutic response in PC to develop a biomarker-driven therapeutic strategy targeting DDR and replication stress in PC.

METHODS

We interrogated the transcriptome, genome, proteome, and functional characteristics of 61 novel PC patient-derived cell lines to define novel therapeutic strategies targeting DDR and replication stress. Validation was done in patient-derived xenografts and human PC organoids.

RESULTS

Patient-derived cell lines faithfully recapitulate the epithelial component of pancreatic tumors, including previously described molecular subtypes. Biomarkers of DDR deficiency, including a novel signature of homologous recombination deficiency, cosegregates with response to platinum (P < .001) and PARP inhibitor therapy (P < .001) in vitro and in vivo. We generated a novel signature of replication stress that predicts response to ATR (P < .018) and WEE1 inhibitor (P < .029) treatment in both cell lines and human PC organoids. Replication stress was enriched in the squamous subtype of PC (P < .001) but was not associated with DDR deficiency.

CONCLUSIONS

Replication stress and DDR deficiency are independent of each other, creating opportunities for therapy in DDR-proficient PC and after platinum therapy.

A gene expression signature for predicting response to neoadjuvant chemoradiotherapy in pancreatic ductal adenocarcinoma

In patients with pancreatic ductal adenocarcinoma (PDAC), optimal treatment selection, including multimodality regimens such as neoadjuvant chemoradiotherapy (NACRT), can be clinically transformative. Unfortunately, currently no predictive biomarkers are available that can guide the use of NACRT in PDAC patients. Accordingly, herein we developed a novel gene signature that can preoperatively predict NACRT-sensitivity in PDAC patients. Herein, we evaluated the performance of a 10-gene panel in 749 PDAC cases, which included two public datasets (The Cancer Genome Atlas and International Cancer Genome Consortium; n = 276), and three clinical specimen cohorts (n = 417), and a pre-NACRT endoscopic ultrasound-guided fine-needle aspiration (EUS-FNA) biopsy cohort (n = 56). The potential predictive performance of this signature was evaluated and compared to CA-19-9 levels and key clinicopathological factors. We first evaluated the prognostic potential of a 10-gene panel which significantly predicted overall survival in both public datasets (P < .01, P < .01), and two in-house patient cohorts (P < .01, P = .04). In the pre-NACRT EUS-FNA cohort, we established a radio-sensitivity gene panel (RSGP) which yielded highly robust (area under the curve [AUC] = 0.91; 95% CI: 0.81-0.97) for predicting response to gemcitabine-based NACRT. Multivariate logistic regression analysis revealed that RSGP was an independent predictor for response to NACRT (OR = 2.70; 95% CI: 1.25-5.85), and this response-prediction was even more robust when CA-19-9 levels were included into the model. In conclusion, we have validated and developed a novel gene signature that is highly robust in predicting response to NACRT, even in preoperative settings, highlighting its clinical significance for optimizing and personalizing treatment strategies in PDAC patients.

A MicroRNA Signature Identifies Pancreatic Ductal Adenocarcinoma Patients at Risk for Lymph Node Metastases

BACKGROUND & AIMS

Pancreatic ductal adenocarcinomas (PDACs) frequently metastasize to the lymph nodes; strategies are needed to identify patients at highest risk for lymph node metastases. We performed genome-wide expression profile analyses of PDAC specimens, collected during surgery or endoscopic ultrasound-guided fine-need aspiration (EUS-FNA), to identify a microRNA (miRNA) signature associated with metastasis to lymph nodes.

METHODS

For biomarker discovery, we analyzed miRNA expression profiles of primary pancreatic tumors from 3 public data sets (The Cancer Genome Atlas, GSE24279, and GSE32688). We then analyzed 157 PDAC specimens (83 from patients with lymph node metastases and 74 without) from Japan, collected from 2001 through 2017, for the training cohort and 107 PDAC specimens (63 from patients with lymph node metastases and 44 without) from a different medical center in Japan, from 2002 through 2016, for the validation cohort. We also analyzed samples collected by EUS-FNA before surgery from 47 patients (22 patients with lymph node metastases and 25 without; 17 for the training cohort and 30 from the validation cohort) and 62 specimens before any treatment from patients who received neoadjuvant chemotherapy (9 patients with lymph node metastasis and 53 without) for additional validation. Multivariate logistic regression analyses were used to evaluate the statistical differences in miRNA expression between patients with vs without metastases.

RESULTS

We identified an miRNA expression pattern associated with diagnosis of PDAC metastasis to lymph nodes. Using logistic regression analysis, we optimized and trained a 6-miRNA risk prediction model for the training cohort; this model discriminated patients with vs without lymph node metastases with an area under the curve (AUC) of 0.84 (95% confidence interval [CI], 0.77-0.89). In the validation cohort, the model identified patients with vs without lymph node metastases with an AUC of 0.73 (95% CI, 0.64-0.81). In EUS-FNA biopsy samples, the model identified patients with vs without lymph node metastases with an AUC of 0.78 (95% CI, 0.63-0.89). The miRNA expression pattern was an independent predictor of PDAC metastasis to lymph nodes in the validation cohort (odds ratio, 17.05; 95% CI, 2.43-119.57) and in the EUS-FNA cohort (95% CI, 0.65-0.87).

CONCLUSIONS

Using data and tumor samples from 3 independent cohorts, we identified an miRNA signature that identifies patients at risk for PDAC metastasis to lymph nodes. The signature has similar levels of accuracy in the analysis of resected tumor specimens and EUS-FNA biopsy specimens. This model might be used to select treatment and management strategies for patients with PDAC.

Development and external validation of a prediction model for survival in patients with resected ampullary adenocarcinoma

INTRODUCTION

Ampullary adenocarcinoma (AAC) is a rare malignancy with great morphological heterogeneity, which complicates the prediction of survival and, therefore, clinical decision-making. The aim of this study was to develop and externally validate a prediction model for survival after resection of AAC.

MATERIALS AND METHODS

An international multicenter cohort study was conducted, including patients who underwent pancreatoduodenectomy for AAC (2006-2017) from 27 centers in 10 countries spanning three continents. A derivation and validation cohort were separately collected. Predictors were selected from the derivation cohort using a LASSO Cox proportional hazards model. A nomogram was created based on shrunk coefficients. Model performance was assessed in the derivation cohort and subsequently in the validation cohort, by calibration plots and Uno's C-statistic. Four risk groups were created based on quartiles of the nomogram score.

RESULTS

Overall, 1007 patients were available for development of the model. Predictors in the final Cox model included age, resection margin, tumor differentiation, pathological T stage and N stage (8th AJCC edition). Internal cross-validation demonstrated a C-statistic of 0.75 (95% CI 0.73-0.77). External validation in a cohort of 462 patients demonstrated a C-statistic of 0.77 (95% CI 0.73-0.81). A nomogram for the prediction of 3- and 5-year survival was created. The four risk groups showed significantly different 5-year survival rates (81%, 57%, 22% and 14%, p < 0.001). Only in the very-high risk group was adjuvant chemotherapy associated with an improved overall survival.

CONCLUSION

A prediction model for survival after curative resection of AAC was developed and externally validated. The model is easily available online via www.pancreascalculator.com.

Neo-adjuvant therapy for pancreatic cancer: hope for the future

In 2018, pancreatic ductal adenocarcinoma (PDAC) was the 3rd highest cause cancer related death in the United States. Worldwide estimates in 2018 indicate 458,918 cases diagnosed with 432,242 deaths. Standard therapy for decades for localized PDAC has been to pursue surgical resection for localized disease. For the individuals who are diagnosed with localized PDAC and undergo surgical resection, historical survival has been reported to be around 24 months. While recent advancements in the use of multiagent systemic therapy has allowed for greater survival benefit, adjuvant therapy does have limitations. Recently, neo-adjuvant therapy for PDAC has become more accepted in practice. Areas covered: In this review, we will discuss the current guidelines for treatment of localized PDAC, the pros and cons of neo-adjuvant versus adjuvant therapy for PDAC, the utilization of available biomarkers for the management of PDAC, and future possibilities for clinical trials. Expert commentary: Neo-adjuvant therapy for localized PDAC has tremendous promise in leading to greater survival by treating for micro-metastatic disease along with selecting for patients for better outcomes. Further work based upon molecular insights will lead to better biomarkers for treatment assessment along with improvements in treatment.