A Quantitative Clinicopathological Signature for Predicting Recurrence Risk of Pancreatic Ductal Adenocarcinoma After Radical Resection

Overcoming therapeutic resistance in pancreatic cancer: Emerging opportunities by targeting BRCAs and p53

Pancreatic cancer (PC) is characterized by (epi)genetic and microenvironmental alterations that negatively impact the treatment outcomes. New targeted therapies have been pursued to counteract the therapeutic resistance in PC. Aiming to seek for new therapeutic options for PC, several attempts have been undertaken to exploit BRCA1/2 and TP53 deficiencies as promising actionable targets. The elucidation of the pathogenesis of PC highlighted the high prevalence of p53 mutations and their connection with the aggressiveness and therapeutic resistance of PC. Additionally, PC is associated with dysfunctions in several DNA repair-related genes, including BRCA1/2, which sensitize tumours to DNA-damaging agents. In this context, poly(ADP-ribose) polymerase (PARP) inhibitors (PARPi) were approved for mutant BRCA1/2 PC patients. However, acquired drug resistance has become a major drawback of PARPi. This review emphasizes the importance of targeting defective BRCAs and p53 pathways for advancing personalized PC therapy, with particular focus on how this approach may provide an opportunity to tackle PC resistance.

Early Recurrence after Upfront Surgery for Pancreatic Ductal Adenocarcinoma

Background. Survival after surgery for pancreatic ductal adenocarcinoma (PDAC) remains poor, due to early recurrence (ER) of the disease. A global definition of ER is lacking and different cut-off values (6, 8, and 12 months) have been adopted. The aims of this study were to define the optimal cut-off for the definition of ER and predictive factors for ER. Methods. Recurrence was recorded for all consecutive patients undergoing upfront surgery for PDAC at our institute between 2010 and 2017. Receiver operating characteristic (ROC) curves were utilized, to estimate the optimal cut-off for the definition of ER as a predictive factor for poor post-progression survival (PPS). To identify predictive factors of ER, univariable and multivariable logistic regression models were used. Results. Three hundred and fifty one cases were retrospectively evaluated. The recurrence rate was 76.9%. ER rates were 29.0%, 37.6%, and 47.6%, when adopting 6, 8, and 12 months as cut-offs, respectively. A significant difference in median PPS was only shown between ER and late recurrence using 12 months as cut-off (p = 0.005). In the multivariate analysis, a pre-operative value of CA 19-9 > 70.5 UI/L (OR 3.10 (1.41–6.81); p = 0.005) and the omission of adjuvant treatment (OR 0.18 (0.08–0.41); p < 0.001) were significant predictive factors of ER. Conclusions. A twelve-months cut-off should be adopted for the definition of ER. Almost 50% of upfront-resected patients presented ER, and it significantly affected the prognosis. A high preoperative value of CA 19-9 and the omission of adjuvant treatment were the only predictive factors for ER.

Transcriptomic Profiling Identifies an Exosomal microRNA Signature for Predicting Recurrence Following Surgery in Patients With Pancreatic Ductal Adenocarcinoma

OBJECTIVE

We performed genome-wide expression profiling to develop an exosomal miRNA panel for predicting recurrence following surgery in patients with PDAC.

SUMMARY OF BACKGROUND DATA

Pretreatment risk stratification is essential for offering individualized treatments to patients with PDAC, but predicting recurrence following surgery remains clinically challenging.

METHODS

We analyzed 210 plasma and serum specimens from 4 cohorts of PDAC patients. Using a discovery cohort (n = 25), we performed genome-wide sequencing to identify candidate exosomal miRNAs (exo-miRNAs). Subsequently, we trained and validated the predictive performance of the exo-miRNAs in two clinical cohorts (training cohort: n = 82, validation cohort: n = 57) without neoadjuvant therapy (NAT), followed by a post-NAT clinical cohort (n = 46) as additional validation.

RESULTS

We performed exo-miRNA expression profiling in plasma specimens obtained before any treatment in a discovery cohort. Subsequently we optimized and trained a 6-exo-miRNA risk-prediction model, which robustly discriminated patients with recurrence [area under the curve (AUC): 0.81, 95% confidence interval (CI): 0.70-0.89] and relapse-free survival (RFS, P < 0.01) in the training cohort. The identified exo-miRNA panel was successfully validated in an independent validation cohort (AUC: 0.78, 95% CI: 0.65- 0.88, RFS: P < 0.01), where it exhibited comparable performance in the post-NAT cohort (AUC: 0.72, 95% CI: 0.57-0.85, RFS: P < 0.01) and emerged as an independent predictor for RFS (hazard ratio: 2.84, 95% CI: 1.30-6.20).

CONCLUSIONS

We identified a novel, noninvasive exo-miRNA signature that robustly predicts recurrence following surgery in patients with PDAC; highlighting its potential clinical impact for optimized patient selection and improved individualized treatment strategies.

Preoperative recurrence prediction in pancreatic ductal adenocarcinoma after radical resection using radiomics of diagnostic computed tomography

BACKGROUND

The high recurrence rate after radical resection of pancreatic ductal adenocarcinoma (PDAC) leads to its poor prognosis. We aimed to develop a model to preoperatively predict the risk of recurrence based on computed tomography (CT) radiomics and multiple clinical parameters.

METHODS

Datasets were retrospectively collected and analysed of 220 PDAC patients who underwent contrast-enhanced computed tomography (CE-CT) and received radical resection at 3 institutions in China between 2013 and 2017, with 153 from one institution as a training set, the remaining 67 as a validation set. For each patient, CT radiomics features were extracted from intratumoral and peritumoral regions to establish intratumoral, peritumoral and combined radiomics models using artificial neural network (ANN) algorithm. By incorporating clinical factors, radiomics-clinical nomograms were finally built by multivariable logistic regression analysis to predict 1- and 2-year recurrence risk.

FINDINGS

The developed radiomics model integrating intratumoral and peritumoral radiomics features was superior to the conventionally constructed model merely using intratumoral radiomics features. Further, radiomics-clinical nomograms outperformed other models in predicting 1-year recurrence with an area under the receiver operating characteristic curve (AUROC) of 0.916 (95%CI, 0.860-0.955) in the training set and 0.764 (95%CI, 0.644-0.859) in the validation set, and 2-year recurrence with an AUROC of 0.872 (95%CI: 0.809-0.921) in the training set and 0.773 (95%CI, 0.654-0.866) in the validation set.

INTERPRETATION

This study has developed and externally validated a radiomics-clinical nomogram integrating intra- and peritumoral CT radiomics signature as well as clinical factors to predict the recurrence risk of PDAC after radical resection, which will facilitate optimized and individualized treatment strategies.

FUNDING

This work was supported by the National Key R&D Program of China [grant number: 2018YFE0114800], the General Program of National Natural Science Foundation of China [grant number: 81772562, 2017; 81871351, 2018], the Fundamental Research Funds for the Central Universities [grant number: 2021FZZX005-08], and Zhejiang Provincial Key Projects of Technology Research [grant number: WKJ-ZJ-2033].

Distinct Survival Outcomes in Subgroups of Stage III Pancreatic Cancer Patients: Taiwan Cancer Registry and Surveillance, Epidemiology and End Results registry

Purpose

Pancreatic cancer is one of the most malignant cancers with poor survival. The latest edition of the American Joint Committee on Cancer (AJCC) staging system classifies the majority of operable pancreatic cancer patients as stage-III, while dramatic heterogeneity is observed among these patients. Therefore, subgrouping is required to accurately predict their prognosis and define a treatment plan. This study conducts a cohort study to provide a more precise classification system for stage-III pancreatic cancer patients by utilizing clinical variables.

Methods

We analyzed survival using log-rank tests, univariate Cox-regression models, and Kaplan-Meier survival curves for stage-III pancreatic ductal adenocarcinoma (PDAC) patients from the Taiwan Cancer Registry (TCR). Patients were further divided into subgroups using classification and regression tree (CART) algorithm. All results were validated using the SEER database.

Results

Among stage-III PDAC patients, lymph node and tumor grade showed significant association with survival. Patients with N2 stage had higher mortality risks (hazard ratio [HR] = 2.30, 95% confidence interval [CI] 1.71–3.08, p < 0.0001) than N0 patients. Patients with grade 3 also had higher risk of mortality (HR = 3.80, 95% CI 2.25–6.39, p < 0.0001) than grade 1 patients. The CART algorithm stratified stage-III patients into four subgroups with significantly different survival rates. The median survival of the four subgroups was 23.5, 18.4, 14.5, and 9.0 months, respectively (p < 0.0001). Similar results were observed with SEER data.

Conclusions

Lymph node involvement and tumor grade are predictive factors for survival in stage-III PDAC patients. This new precise classification system can be used to guide treatment planning in advanced-stage pancreatic cancer.

Supplementary Information

The online version contains supplementary material available at 10.1245/s10434-021-11030-w.

Comparative Recurrence Analysis of Pancreatic Adenocarcinoma after Resection

Purpose

The relation between tumor sites of pancreatic ductal adenocarcinoma (PDAC) and recurrence was not fully investigated before. We aimed to describe the differences of recurrent patterns in PDAC of head and body/tail after curative surgery.

Methods

The recurrent patterns of PDAC were compared and the associations with clinical characteristics were analyzed in these patients. Prognostic factors of overall survival (OS) and progression-free survival (PFS) were analyzed and validated. Predictive systems were constructed and measured by the area under the AUC curve and concordance index (C-index).

Results

A total of 302 PDAC patients were included in this study, including 247 patients with PDAC of head and another 55 patients with PDAC of body/tail. Patients who developed tumor recurrence within 24 months after resection had significantly shorter OS in both groups. Liver metastasis occupied most of the tumor progressions and diminished while local recurrence increased gradually over time. The variation trends were similar for patients in both groups while these changes were more pronounced for patients in the head group. Local recurrence and liver-only metastasis seemed to indicate a better OS. Furthermore, predictive systems for OS and PFS prediction based on independent risk factors were established and showed significant higher values of AUC and C-indexes compared with the TNM stage system.

Conclusions

Different characteristics of progressions for PDAC of head and body/tail suggested biological heterogeneity. The exploration of these variations helps to provide personalized management of recurrence in PDAC.

Multi-institutional development and external validation of machine learning-based models to predict relapse risk of pancreatic ductal adenocarcinoma after radical resection

BACKGROUND

Surgical resection is the only potentially curative treatment for pancreatic ductal adenocarcinoma (PDAC) and the survival of patients after radical resection is closely related to relapse. We aimed to develop models to predict the risk of relapse using machine learning methods based on multiple clinical parameters.

METHODS

Data were collected and analysed of 262 PDAC patients who underwent radical resection at 3 institutions between 2013 and 2017, with 183 from one institution as a training set, 79 from the other 2 institution as a validation set. We developed and compared several predictive models to predict 1- and 2-year relapse risk using machine learning approaches.

RESULTS

Machine learning techniques were superior to conventional regression-based analyses in predicting risk of relapse of PDAC after radical resection. Among them, the random forest (RF) outperformed other methods in the training set. The highest accuracy and area under the receiver operating characteristic curve (AUROC) for predicting 1-year relapse risk with RF were 78.4% and 0.834, respectively, and for 2-year relapse risk were 95.1% and 0.998. However, the support vector machine (SVM) model showed better performance than the others for predicting 1-year relapse risk in the validation set. And the k neighbor algorithm (KNN) model achieved the highest accuracy and AUROC for predicting 2-year relapse risk.

CONCLUSIONS

By machine learning, this study has developed and validated comprehensive models integrating clinicopathological characteristics to predict the relapse risk of PDAC after radical resection which will guide the development of personalized surveillance programs after surgery.

An Inflammation-Index Signature Predicts Prognosis of Patients with Intrahepatic Cholangiocarcinoma After Curative Resection

Background

The prognosis of patients with intrahepatic cholangiocarcinoma (ICC) after resection is at great variance. We aimed to establish a novel prognostic nomogram in facilitating the risk stratification for these patients.

Methods

A total of 82 high-dimensional radiological and pathological data were analyzed by LASSO-penalized Cox regression analyses and the panels with the best predictive performance were selected. Specific nomograms were established based on the selected panels and were validated in both primary (n=292) and validation cohorts (n=107). The area under the receiver operating characteristic curve (AUC) and the concordance index (C-index) were used to compare the predictive ability of nomograms and other staging systems.

Results

The modified Glasgow Prognostic Score (mGPS) was identified as the prognostic factor for both overall survival (OS) and progression-free survival (PFS). The nomograms built on the prognostic factors showed powerful efficacy in survival prediction, with C-indexes of 0.800 (95% CI 0.767-0.833) and 0.752 (95% CI 0.718-0.786) for OS and PFS in the primary cohort, 0.659 (95% CI 0.586-0.732) and 0.638 (95% CI 0.571-0.705) for OS and PFS in the validation cohort, respectively. Compared with tumor-node-metastasis stage, Barcelona Clinic Liver Cancer staging score, Cancer of the Liver Italian Program score, and Okuda staging system, the nomograms had significantly higher values of AUC and C-indexes in survival prediction in the primary and validation cohorts.

Conclusion

Compared with currently used staging systems, the nomograms showed significantly higher efficacy in predicting survival of ICC patients after resection. The nomograms provide new versions of personalized management for these patients.

Tumour and stroma RNA signatures predict more accurately distant recurrence than clinicopathological factors in resected pancreatic adenocarcinoma

BACKGROUND

Few patients with pancreatic adenocarcinoma (PAC) are eligible for surgery. Patients with early relapse have a poor prognosis and might be better candidates for a medical approach. Clinical and pathological parameters only partially predict recurrence and are only obtained after surgery. PAC subtypes based on gene expression were proposed, and we assessed if they could predict the risk and type of recurrence independently of clinicopathological parameters.

METHODS

Patients with curative-intent surgery for PAC without pretreatment were selected and divided into two independent cohorts defined as discovery (n = 381) and validation (n = 149) cohorts. Transcriptomic analyses were performed on formalin-fixed paraffin-embedded surgical samples to characterise tumour and stroma compartments using previously defined signatures. We associated molecular and clinicopathological characteristics with general, distant, and local recurrences using Cox regression analyses.

RESULTS

We found that tumour biology predicted distant recurrence contrary to local recurrence, which was directly related to resection margin status. Pure basal-like and stroma-activated subtypes were strongly associated with distant recurrence, independently of clinicopathological factors (hazard ratios [HRs] = 5.85, p < 0.001 and HR = 1.75, p = 0.007, respectively). By dissecting tumoural and stromal compartments, we demonstrated that the basal-like tumour component positively correlated with distant recurrence in both cohorts (HR = 1.45, p < 0.001 and HR = 1.90, p < 0.001), whereas the inactive structural stroma component was protective against distant recurrence (HR = 0.68, p < 0.001 and HR = 0.72, p < 0.001).

CONCLUSIONS

In addition to suggesting a different mechanism for local and distant relapse (incomplete resection and high metastatic potential, respectively), our results show the potency of molecular phenotype to predict patient outcome regarding distant recurrences.

A Novel Nomogram to Predict Survival in Patients With Recurrence of Pancreatic Ductal Adenocarcinoma After Radical Resection

The post-progression survival (PPS) of patients with pancreatic ductal adenocarcinoma (PDAC) after radical resection is varied and influenced by the characteristics of tumor progression. We aimed to establish and validate a nomogram to predict PPS for PDAC patients after surgery. A total of 302 PDAC patients who had undergone curative resection from 2008 to 2018 were enrolled in this study and randomly divided into training and validation cohorts at a ratio of 3:1. The nomogram was established based on independent prognostic factors selected by LASSO and Cox regression and measured by the area under the receiver operating characteristic curve (AUC) and the concordance index (C-index). Significant prognostic factors included carbohydrate antigen 19-9 (CA19-9), lymph node (LN)9 metastasis, LN14 metastasis, LN16 metastasis, tumor differentiation, imaging-detected tumor size, local progression, liver-only metastasis, lung-only metastasis, and multiple metastases. The nomogram built on these factors showed powerful efficacy in PPS prediction, with C-index values of 0.751 (95% CI 0.692–0.0.810) and 0.710 (95% CI 0.645–0.755) for the training and validation cohorts, respectively. The AUC values for the 1-year and 2-year PSS rates were 0.745, 0.747, and 0.783, 0.748, respectively; these values were higher than those of the 8th tumor–node–metastasis (TNM) stage system. The exploration of risk factors and the establishment of a nomogram can provide new versions of personalized recurrence management for PDAC patients after surgery.

Score for the Overall Survival Probability of Patients With Pancreatic Adenocarcinoma of the Body and Tail After Surgery: A Novel Nomogram-Based Risk Assessment

Pancreatic adenocarcinoma of the body and tail often has a dismal prognosis and lacks a specific prognostic stage. The aim of this study was to construct a nomogram for predicting survival of patients with pancreatic adenocarcinoma of the body and tail after surgery. Data of patients were selected from the Surveillance, Epidemiology, and End Results (SEER) database and from medical records of Sun Yat-sen University Cancer Center (SYSUCC). In a multivariate analysis for overall survival (OS), the following six variables were identified as independent predictors and incorporated into the nomogram: age, tumor differentiation, tumor size, lymph node ratio (LNR), and chemotherapy. A nomogram was built based on independent risk predictors. The concordance index (C-index) for nomogram, Tumor-Node-Metastasis (TNM) 7th and 8th stage system were 0.775 [95% confidence interval (CI), 0.731–0.819], 0.617 (95%CI, 0.575–0.659), and 0.632 (95%CI, 0.588–0.676), respectively. The calibrated nomogram predicted survival rates which closely corresponded to the actual survival rates. Furthermore, the values of the area under receiver operating characteristic (ROC) curves (AUC) of the nomograms were higher than those of the TNM 7th or 8th stage system in predicting 1-, 2-, and 3-year survival of patients in training and external validation cohorts. The well-calibrated nomogram could be used to predict prognosis for patients with pancreatic adenocarcinoma of the body and tail after surgery.