Desmin and CD31 immunolabeling for detecting venous invasion of the pancreatobiliary tract cancers

Development of Clinical Radiomics-Based Models to Predict Survival Outcome in Pancreatic Ductal Adenocarcinoma: A Multicenter Retrospective Study

PURPOSE

This multicenter retrospective study aims to identify reliable clinical and radiomic features to build machine learning models that predict progression-free survival (PFS) and overall survival (OS) in pancreatic ductal adenocarcinoma (PDAC) patients.

METHODS

Between 2010 and 2020 pre-treatment contrast-enhanced CT scans of 287 pathology-confirmed PDAC patients from two sites of the Hopital Universitaire de Bruxelles (HUB) and from 47 hospitals within the HUB network were retrospectively analysed. Demographic, clinical, and survival data were also collected. Gross tumour volume (GTV) and non-tumoral pancreas (RPV) were semi-manually segmented and radiomics features were extracted. Patients from two HUB sites comprised the training dataset, while those from the remaining 47 hospitals of the HUB network constituted the testing dataset. A three-step method was used for feature selection. Based on the GradientBoostingSurvivalAnalysis classifier, different machine learning models were trained and tested to predict OS and PFS. Model performances were assessed using the C-index and Kaplan-Meier curves. SHAP analysis was applied to allow for post hoc interpretability.

RESULTS

A total of 107 radiomics features were extracted from each of the GTV and RPV. Fourteen subgroups of features were selected: clinical, GTV, RPV, clinical & GTV, clinical & GTV & RPV, GTV-volume and RPV-volume both for OS and PFS. Subsequently, 14 Gradient Boosting Survival Analysis models were trained and tested. In the testing dataset, the clinical & GTV model demonstrated the highest performance for OS (C-index: 0.72) among all other models, while for PFS, the clinical model exhibited a superior performance (C-index: 0.70).

CONCLUSIONS

An integrated approach, combining clinical and radiomics features, excels in predicting OS, whereas clinical features demonstrate strong performance in PFS prediction.

A primer on artificial intelligence in pancreatic imaging

Artificial Intelligence (AI) is set to transform medical imaging by leveraging the vast data contained in medical images. Deep learning and radiomics are the two main AI methods currently being applied within radiology. Deep learning uses a layered set of self-correcting algorithms to develop a mathematical model that best fits the data. Radiomics converts imaging data into mineable features such as signal intensity, shape, texture, and higher-order features. Both methods have the potential to improve disease detection, characterization, and prognostication. This article reviews the current status of artificial intelligence in pancreatic imaging and critically appraises the quality of existing evidence using the radiomics quality score.

Venous Invasion in Pancreatic Neuroendocrine Tumors Is Independently Associated With Disease-free Survival and Overall Survival

In this study, we evaluated venous invasion and its association with survival in patients with resected pancreatic neuroendocrine tumor (PanNET). Surgical Pathology Archives were searched for pancreatectomies performed for PanNET between October 1, 2005, and December 31, 2019. Hematoxylin and eosin (H&E)-stained slides were evaluated for venous invasion, and Movat's stain was performed in all cases with no venous invasion detected on H&E stains. Pathology reports and electronic medical records were also reviewed. Venous invasion was identified in 23 of 145 (15.9%) cases on H&E stains, and Movat's stain identified additional 34 cases with venous invasion (39.3% overall). Orphan arteries with adjacent well-defined tumor nodules or subtle hyalinizing nodules in hyalinizing tumors are highly specific for venous invasion. In stage I-III cases (n=122), venous invasion was associated with larger tumor size, higher World Health Organization (WHO) tumor grade, perineural invasion, extrapancreatic extension, lymph node metastasis, and liver metastasis ( P <0.05). In univariate analyses, tumor size, WHO grade, venous invasion, perineural invasion, T stage, and lymph node metastasis all correlated with disease-free survival; however, only venous invasion was associated with worse disease-free survival in multivariate analyses ( P <0.01). In all-stage cases, venous invasion was the only attributor associated with worse overall survival in multivariate analyses ( P =0.03). In summary, venous invasion in PanNET can be histologically subtle, and Movat's stain can greatly increase the detection rate. More importantly, enhanced venous invasion by Movat's stain correlates independently with disease-free survival in patients with stage I-III tumors and overall survival in all-stage patients.

Identification of TGF-β signaling-related molecular patterns, construction of a prognostic model, and prediction of immunotherapy response in gastric cancer

Background: TGF-β signaling pathway plays an essential role in tumor progression and immune responses. However, the link between TGF-β signaling pathway-related genes (TSRGs) and clinical prognosis, tumor microenvironment (TME), and immunotherapy in gastric cancer is unclear. Methods: Transcriptome data and related clinical data of gastric cancer were downloaded from the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases, and 54 TSRGs were obtained from the Molecular Signatures Database (MSigDB). We systematically analyzed the expression profile characteristics of 54 TSRGs in 804 gastric cancer samples and examined the differences in prognosis, clinicopathological features, and TME among different molecular subtypes. Subsequently, TGF-β-related prognostic models were constructed using univariate and least absolute shrinkage and selection operator (LASSO) Cox regression analysis to quantify the degree of risk in each patient. Patients were divided into two high- and low-risk groups based on the median risk score. Finally, sensitivity to immune checkpoint inhibitors (ICIs) and anti-tumor agents was assessed in patients in high- and low-risk groups. Results: We identified two distinct TGF-β subgroups. Compared to TGF-β cluster B, TGF-β cluster A exhibits an immunosuppressive microenvironment with a shorter overall survival (OS). Then, a novel TGF-β-associated prognostic model, including SRPX2, SGCE, DES, MMP7, and KRT17, was constructed, and the risk score was demonstrated as an independent prognostic factor for gastric cancer patients. Further studies showed that gastric cancer patients in the low-risk group, characterized by higher tumor mutation burden (TMB), the proportion of high microsatellite instability (MSI-H), immunophenoscore (IPS), and lower tumor immune dysfunction and exclusion (TIDE) score, had a better prognosis, and linked to higher response rate to immunotherapy. In addition, the risk score and anti-tumor drug sensitivity were strongly correlated. Conclusion: These findings highlight the importance of TSRGs, deepen the understanding of tumor immune microenvironment, and guide individualized immunotherapy for gastric cancer patients.

Epigenetic Alterations of Repeated Relapses in Patient-matched Childhood Ependymomas

Recurrence is frequent in pediatric ependymoma (EPN). Our longitudinal integrated analysis of 30 patient-matched repeated relapses (3.67 ± 1.76 times) over 13 years (5.8 ± 3.8) reveals stable molecular subtypes (RELA and PFA) and convergent DNA methylation reprogramming during serial relapses accompanied by increased orthotopic patient derived xenograft (PDX) (13/27) formation in the late recurrences. A set of differentially methylated CpGs (DMCs) and DNA methylation regions (DMRs) are found to persist in primary and relapse tumors (potential driver DMCs) and are acquired exclusively in the relapses (potential booster DMCs). Integrating with RNAseq reveals differentially expressed genes regulated by potential driver DMRs (CACNA1H, SLC12A7, RARA in RELA and HSPB8, GMPR, ITGB4 in PFA) and potential booster DMRs (PLEKHG1 in RELA and NOTCH, EPHA2, SUFU, FOXJ1 in PFA tumors). DMCs predicators of relapse are also identified in the primary tumors. This study provides a high-resolution epigenetic roadmap of serial EPN relapses and 13 orthotopic PDX models to facilitate biological and preclinical studies.

A phase I/II study of ivaltinostat combined with gemcitabine and erlotinib in patients with untreated locally advanced or metastatic pancreatic adenocarcinoma

This phase I/II study evaluated the safety and efficacy of a new histone deacetylase (HDAC) inhibitor, ivaltinostat, in combination with gemcitabine and erlotinib for advanced pancreatic ductal adenocarcinoma (PDAC). Patients diagnosed with unresectable, histologically confirmed PDAC who had not undergone previous therapy were eligible. Phase I had a 3 + 3 dose escalation design to determine the maximum tolerable dose (MTD) of ivaltinostat (intravenously on days 1, 8 and 15) with gemcitabine (1000 mg/m2 intravenously on days 1, 8 and 15) and erlotinib (100 mg/day, orally) for a 28-day cycle. In phase II, patients received a six-cycle treatment with the MTD of ivaltinostat determined in phase I. The primary endpoint was the objective response rate (ORR). Secondary endpoints included overall survival (OS), disease control rate (DCR) and progression-free survival (PFS). The MTD of ivaltinostat for the phase II trial was determined to be 250 mg/m2 . In phase II, 24 patients were enrolled. The median OS and PFS were 8.6 (95% confidence interval [CI]: 5.3-11.2) and 5.3 months (95% CI: 3.7-5.8). Of the 16 patients evaluated for response, ORR and DCR were 25.0% and 93.8% with a median OS/PFS of 10.8 (95% CI: 8.3-16.7)/5.8 (95% CI: 4.6-6.7) months. Correlative studies showed that mutation burden detected by cfDNA and specific blood markers such as TIMP1, pro-MMP10, PECAM1, proMMP-2 and IGFBP1 were associated with clinical outcomes. Although the result of a small study, a combination of ivaltinostat, gemcitabine and erlotinib appeared to be a potential treatment option for advanced PDAC.

Double Ki-67 and synaptophysin labeling in pancreatic neuroendocrine tumor biopsies

BACKGROUND

Pancreatic neuroendocrine tumors (PanNETs) are frequently detected on endoscopic ultrasound-guided fine-needle aspiration biopsy (EUS-FNAB) specimens. The conventional methods for evaluating the Ki-67 labeling index (Ki67LI) in EUS-FNAB specimens are laborious, and their results are difficult to interpret. More practical and easy methods for evaluating the Ki67LI in PanNETs from EUS-FNAB specimens is increasing in need.

METHODS

We used double Ki-67 and synaptophysin (double Ki-Syn) antibody cocktail; Ki67LI, total Ki-67 positive cells, and total tumor cells were counted and compared with those detected on conventional single Ki-67 immunostaining (single Ki-67) of 96 PanNETs [Grade 1 (G1), 68 cases (71%); G2, 26 (27%); G3, 2 (2%)] from EUS-FNAB specimens.

RESULTS

The tumor grading between double Ki-Syn and single Ki-67 immunolabeling was highly concordant (correlation, 0.95; Fisher's exact test, P < 0.001). Seven EUS-FNAB specimens (7%) had discrepant results, of which 2 were removed through surgical resection and showed the same tumor grade as that detected on double Ki-Syn immunolabeling. Fifty-four specimens (56%) had higher Ki-67 positive tumor cell counts on single Ki-67 immunolabeling. Sixty-two specimens (65%) had higher total tumor cell counts on double Ki-Syn immunolabeling. The number of specimens with less than 500 total counted tumor cells were significantly reduced when double Ki-Syn immunolabeling was applied [P = 0.046; single Ki-67, 17 specimens (18%); double Ki-Syn, 9 specimens (9%)].

CONCLUSION

Double Ki-Syn immunolabeling enables the accurate counting of the number of proliferating tumor cells without including inflammatory and contaminant epithelial cells compared with single Ki-67 immunolabeling in PanNETs from EUS-FNAB specimens.

Hybrid membrane-coated nanosuspensions for multi-modal anti-glioma therapy via drug and antigen delivery

BACKGROUND

Glioma is one of the deadliest human cancers. Although many therapeutic strategies for glioma have been explored, these strategies are seldom used in the clinic. The challenges facing the treatment of glioma not only involve the development of chemotherapeutic drugs and immunotherapeutic agents, but also the lack of a powerful platform that could deliver these two moieties to the targeted sites. Herein, we developed chemoimmunotherapy delivery vehicles based on C6 cell membranes and DC membranes to create hybrid membrane-coated DTX nanosuspensions (DNS-[C6&DC]m).

RESULTS

Results demonstrated successful hybrid membrane fusion and nanosuspension functionalization, and DNS-[C6&DC]m could be used for different modes of anti-glioma therapy. For drug delivery, membrane coating could be applied to target the source cancer cells via a homotypic-targeting mechanism of the C6 cell membrane. For cancer immunotherapy, biomimetic nanosuspension enabled an immune response based on the professional antigen-presenting characteristic of the dendritic cell membrane (DCm), which carry the full array of cancer cell membrane antigens and facilitate the uptake of membrane-bound tumor antigens for efficient presentation and downstream immune n.

CONCLUSION

DNS-[C6&DC]m is a multifunctional biomimetic nano-drug delivery system with the potential to treat gliomas through tumor-targeted drug delivery combined with immunotherapy, thereby presenting a promising approach that may be utilized for multiple modes of cancer therapy.

Organoids in cancer research: a review for pathologist-scientists

The use of three-dimensional (3D) culture models for cancer research has expanded greatly in recent years, with studies in almost every tumor type addressing a wide variety of research questions. Multiple distinct 3D culture approaches are now available, each with its own advantages and disadvantages, as well as most effective applications. In this review, we focus on one of these 3D culture models, organoids, in which multicellular units are isolated from primary or metastatic tumors and cultured in extracellular matrix gels. Organoids can be studied in acute cultures for short times after isolation, or passaged and biobanked for long-term use. We define this model system and describe some key studies in which organoid culture models were used to investigate cellular strategies and molecular mechanisms driving cancer initiation and progression, highlighting research questions for which this model is particularly well suited. In addition, as interest in implementing organoid systems continues to expand, we discuss key considerations in developing a new organoid research program. Our goal is to demonstrate the power and utility of organoid models and provide guidance for investigators who are considering implementation of these models in their own research programs. © 2021 The Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd.