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Purpose: Aberrant activation of the hedgehog (Hh) pathway is implicated in pancreatic ductal adenocarcinoma (PDAC) tumorigenesis. We investigated the prognostic and predictive value of four Hh signaling proteins and of the tumor stromal density.Experimental Design: Using tissue microarray and immunohistochemistry, the expression of Shh, Gli1, SMO, and PTCH1 was assessed in 567 patients from three independent cohorts who underwent surgical resection for PDAC. In 82 patients, the tumor stromal index (SI) was calculated, and its association with overall survival (OS) and disease-free survival (DFS) was investigated.Results: Shh and Gli1 protein abundance were independent prognostic factors in resected PDACs; low expressors for those proteins experiencing a better OS and DFS. The combination of Shh and Gli1 levels was the most significant predictor for OS and defined 3 clinically relevant subgroups of patients with different prognosis (Gli1 and Shh low; HR set at 1 vs. 3.08 for Shh or Gli1 high vs. 5.69 for Shh and Gli1 high; P < 0.001). The two validating cohorts recapitulated the findings of the training cohort. After further stratification by lymph node status, the prognostic significance of combined Shh and Gli1 was maintained. The tumor SI was correlated with Shh levels and was significantly associated with OS (P = 0.023).Conclusions: Shh and Gli1 are prognostic biomarkers for patients with resected PDAC. Clin Cancer Res; 21(5); 1215–24. ©2014 AACR.

Supplementary Tables 1-6. Table 1 shows the correlation between immunohistochemical factors and clinicopathological data, Table 2: Univariate analyses of patients characteristics and IHC factors to disease-free survival, Table 3: Multivariate analysis for training and validation sets: clinicopathological variables, Table 4: Multivariate Analyses: OS for patients according to the nodal status, Table 5: Interaction between adjuvant therapy and biomarkers, Table 6: Stromal index and patients' outcomes

Pancreatic ducal adenocarcinoma is classically diagnosed in the 7th decade, but approximately 10% of patients are diagnosed under 55 years (y.o.). While the genomic and transcriptomic landscapes of late‐onset tumors (LOT) have been described, little is known about early‐onset tumors (EOT). Ageing is known to impact DNA methylation and proteome integrity through carbonylation‐related oxidative damages. We therefore aimed to assess the global molecular features of EOT. We compared 176 EOT (≤55 y.o.) and 316 LOT (≥70 y.o.) from three distinct surgical cohorts at the clinical/genomic/epigenomic/transcriptomic level. Furthermore, we assessed oxidative stress responses and oxidative proteome damages using 2D gel electrophoresis followed by mass spectrometry protein identification. There was no consistent clinical difference between EOT and LOT across the three cohorts. The mutational landscape of key driver genes and the global methylation profile were similar in the two groups. LOT did display age‐related features such as enriched DNA repair gene signatures and upregulation of oxidative stress defenses together with increased proteome carbonylation. However, these age‐related differences were more preeminent in non‐tumor tissues while tumor proteome and proteome damages were fairly comparable. In conclusion, this multiomics comparison showed that EOT harbor a comparable molecular profile to that of LOT., SCOPUS: ar.j, info:eu-repo/semantics/published

In Lynch-like syndrome, patients have tumors with microsatellite instability but no germline pathogenic variant in mismatch repair genes or somatic methylation of the MLH1 promoter. Identification of the mechanism that causes these tumors is crucial for guiding screening of the patients and their relatives. Double somatic hits are the usual explanation for these cases; however, we have previously reported a de novo mosaic pathogenic variant in a patient with Lynch-like syndrome. Using tumoral NGS analysis of a series of 16 patients with Lynch-like syndrome, we found six patients with double somatic hits, including one patient with mosaicism of a de novo pathogenic variant in MSH2. This variant was transmitted to the patient's offspring, which has significant implications for genetic counseling.

Introduction: Pancreatic adenocarcinoma (PAC) is highly heterogeneous, resulting in overall ineffectiveness of most anti-tumor treatments. Two tumor subtypes (Classical and Basal) and two stromal subtypes (“active” and “inactive”) have been described. The Basal and the active stroma have worse prognosis. These subtypes could also be predictive of the response to different chemotherapies. To date, molecular subtype classification or phenotype quantification can only be defined by RNAseq, a complex technique sensitive to the quantity and quality of samples, requiring a timescale limiting its routine application. We propose a deep learning approach (PACpAInt) to predict molecular subtypes in PAC on routine histological slides. Patients and Methods: 424 digitalized HES slides of 202 resected PAC from 3 centers with clinical and transcriptomic data were used as training cohort. 3 validation cohorts were used (i) 250 resected PAC from a 4th center including 97 cases with an exact HES/RNAseq spatial match and all tumor slides digitalized (n = 891); (ii) 126 resected PAC from the TCGA (HES + RNAseq); (iii) 25 liver biopsies from metastatic PAC (HES + RNAseq). A multi-step deep learning model was developed to recognize tumor tissue, tumor from stroma cells, and then predicts their transcriptomic molecular subtypes, either at the level of an entire slide, or at the tile level (squares of 112 μm) allowing to study intratumor heterogeneity. Results: PACpAInt correctly predicted the tumor subtype at the whole slide level (AUC = 0.86 and 0.81 in 2 validation cohorts) and improved for samples with unambiguous molecular subtype (AUC = 0.91 and 0.88) confirming the limit of a binary approach. Similar results were obtained on liver biopsies (AUC = 0.85 and 0.92 on unambiguous cases). PACpAInt independently predicted progression-free and overall survival (PFS HR=1.37 [1.16 - 1.62] and OS HR=1.27 [1.08 - 1.49]). Analysis of all tumor slides from 77 Classical cases showed that 39% were heterogeneous with a Basal contingent. These cases had shorter PFS (15 vs. 47 months, p= 0.001) and OS (31 vs. 64 months, p= 5e-5).The analysis of intratumoral heterogeneity using PACpAInt predicted the molecular subtype of tumor and stroma cells of each tile within a slide (> 6 million tiles analyzed). 61% of cases had a main subtype either Classical (42%) or Basal (19%). 39% of the cases were ambiguous could be considered either hybrid (10%) with coexistence of Basal and Classical cells, or intermediate (29%) corresponding to homogeneous tumors but of intermediate differentiation. This classification had a strong prognostic impact (OS: 45.1 vs 33.0 vs 23.4 vs 13.6 months, resp. Classical, intermediate, hybrid, Basal; p Conclusion: This study provides the first PAC subtyping tool widely usable in clinical practice, opening the possibility of molecular classification useful for routine care and clinical trials. Citation Format: Charlie Saillard, Flore Delecourt, Benoit Schmauch, Olivier Moindrot, Magali Svrcek, Armelle Bardier-Dupas, Jean Francois Emile, Mira Ayadi, Vinciane Rebours, Louis De Mestier, Pascal Hammel, Cindy Neuzillet, Jean Baptiste Bachet, Juan Iovanna, Nelson Dusetti, Yuna Blum, Magali Richard, Yasmina Kermezli, Valerie Paradis, Mikhail Zaslavskiy, Pierre Courtiol, Aurelie Kamoun, Remy Nicolle, Jerome Cros. PACpAInt: A deep learning approach to identify molecular subtypes of pancreatic adenocarcinoma on histology slides [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 472.

4141 Background: Pancreatic adenocarcinoma (PAC) is predicted to be the second cause of death by cancer in 2030 and its prognosis has seen little improvement in the last decades. PAC is a very heterogeneous tumor with preeminent stroma and multiple histological aspects. Omic studies confirmed its molecular heterogeneity, possibly one of the main factors explaining the failure of most clinical trials. Two and three transcriptomic subtypes of tumor cells and stroma respectively, were described with major prognostic and predictive implications. The tumor subtypes, Basal-like and Classical, have been shown by several groups to be predictive of the response to first line chemotherapy. As of today, these subtypes can only be defined by RNA profiling which is limited by the quantity and quality of the samples (formalin fixation and low cellularity) as well as by the analytical delay that may restrict its application in routine care. In addition, tumors may harbor a mixture of several subtypes limiting their interpretation using bulk transcriptomic approaches and thereby their clinical use. Here, we propose a multistep approach using deep learning models to predict tumor components and their molecular subtypes on routine histological preparations. Methods: 728 whole-slide digitized histological slides corresponding to 350 consecutive resected PAC from four centers with clinical and transcriptomic data were assembled and used as a discovery set. PAC from TCGA (n = 134) was used as a validation set. Tumor regions from slides of the discovery set were annotated to train a multistep deep learning model that first recognizes tumor tissue and then predicts tumor and stroma cells molecular subtypes assessed by the published PurIST algorithm. Results: The tumor detection model was very efficient (AUC = 0.98 in the TCGA validation cohort). In the discovery set, the Basal-like/Classical classification performance of the model by cross validation was 0.79 (AUC) and reached 0.86 when restricted to samples with a high-confidence RNA-defined molecular subtype.Subtypes defined by the model were independently associated with overall survival in multivariate analysis (HR = 2.56 [1.87 - 3.49], pval < 0.001), and association was higher relatively to PurIST RNA subtypes (HR = 1.60 [1.17 - 2.19] pval < 0.001). In the validation cohort, the model had an overall AUC of 0.82, and 0.89 in the subset of “subtype-pure” tumors. In addition to demonstrating the value of histology-based deep learning models for tumor subtyping in PAC, these results also show the limit of molecular-based subtyping in highly heterogeneous samples. Conclusions: This study provides the first PAC subtyping tool usable worldwide in clinical practice, finally opening the possibility of patient molecular stratification in routine care and clinical trials.

Background Retroperitoneal schwannomas are extremely rare, as they account for only 3% of retroperitoneal tumors. Clinical symptoms are nonspecific and of late onset, meaning that these tumors are diagnosed at an advanced stage. Surgical resection is required for histological diagnosis and to prevent possible malignant transformation. Celioscopy offers numerous benefits, reducing postoperative pain and speeding up the patient’s return to autonomy, but it can pose a real challenge due to the size of these lesions. Case presentation We report a case of laparoscopic resection of a very large right-sided retroperitoneal schwannoma, with a particular histological form. Conclusion Surgical resection in a single unit remains the golden rule, and a laparoscopy can be proposed when the diagnosis is beyond doubt. The large size of the retroperitoneal melanotic schwannomas, a common feature, increases surgical difficulties but is not a contraindication to this approach.

International audience; Microsatellite instability (MSI) caused by mismatch repair deficiency (dMMR) is detected in a small proportion of pancreatic ductal adenocarcinomas (PDACs). dMMR and MSI have been associated with responses of metastatic tumors, including PDACs, to immune checkpoint inhibitor therapy. We performed immunohistochemical analyses of a 445 PDAC specimens, collected from consecutive patients at multiple centers, to identify those with dMMR, based on loss of mismatch repair proteins MLH1, MSH2, MSH6, and/or PMS2. We detected dMMR in 1.6% of tumor samples; we found dMMR in a larger proportion of intraductal papillary mucinous neoplasms-related tumors (4/58, 6.9%) than non- intraductal papillary mucinous neoplasms PDAC (5/385, 1.3%) (P = .02). PDACs with dMMR contained potentially immunogenic mutations because of MSI in coding repeat sequences. PDACs with dMMR or MSI had a higher density of CD8+ T cells at the invasive front than PDACs without dMMR or MSI (P = .08; Fisher exact test). A higher proportion of PDACs with dMMR or MSI expressed the CD274 molecule (PD-L1, 8/9) than PDACs without dMMR or MSI (4/10) (P = .05). Times of disease-free survival and overall survival did not differ significantly between patients with PDACs with dMMR or MSI vs without dMMR or MSI. Studies are needed to determine whether these features of PDACs with dMMR or MSI might serve as prognostic factors.

Background Prognosis of patients with pancreatic adenocarcinoma (PAC) remains poor. S100A2 has been recently suggested as a negative prognostic biomarker in PAC. We aimed to investigate its prognostic and/or predictive value in a large independent multicentric cohort of patients with resected PAC. Methods Sequential samples of 471 patients were retrospectively collected; 142 patients did not receive adjuvant treatment (30%) and 329 (70%) received an adjuvant treatment. We measured protein levels of S100A2 by semiquantitative immunohistochemistry with tissue microarrays and correlated with patients’ overall survival (OS) and disease-free survival (DFS). Results S100A2 protein status was obtained in 462 (98%) patients. Its expression was low, moderate or high in 59%, 12% and 2% of cases, respectively. It was not correlated with DFS or OS in the whole population, neither in the subgroup of patients who did not receive adjuvant treatment. However among patients who received an adjuvant therapy, moderate/high levels of S100A2 were significantly associated with longer OS and DFS in multivariate analysis (hazard ratios of 0.63, p = 0.022 and 0.67, p = 0.017, respectively), whereas low S100A2 was not. Interaction tests for adjuvant therapy were statistically significant both for the OS and the DFS ( p = 0.001 and p = 0.023, respectively). On multivariate analysis, S100A2 retained independent predictive values (OS: p p = 0.003) with a significant benefit of adjuvant therapy for those patients with moderate/high S100A2. Conclusions S100A2 expression predicts longer DFS and OS in patients treated with adjuvant therapy and should be evaluated as a predictive biomarker.

Some patients happen to have a colorectal cancer with microsatellite instability (MSI), but without any alteration in Mismatch Repair (MMR) system (germline mutation/promoter methylation). We aimed to identify the mechanism of inactivation of MMR genes in those cases. We studied 18 patients with MSI CCR and loss of expression of a MMR protein. DNA was extracted from tumoral and normal colonic material. We studied the 3 main MMR genes in tumors, by sequencing and large rearrangement analysis, and looked for mosaicism. Seven patients lost expression of MLH1, we found 1 mutation in the tumor for 3 patients and 2 mutations in one. Eight patients lost expression of MSH2: we found 1 mutation in 2 patients and 2 mutations in four. In the 5 cases with 2 hits, MSI was due to double somatic hits (n = 3), mosaicism (n = 1) and missed germline mutation (n = 1). Mosaicism was confirmed by HRM analysis, and by finding a germline mutation in one patient's son. We could explain MSI in the tumors of 5 patients (27.8 %). Their follow up and family's surveillance could be adjusted, as the sporadic cases don't require intensive surveillance. We describe the first case of somatic mosaicism after de novo mutation in MSH2.

Prognosis of patients with pancreatic adenocarcinoma is poor. Many prognostic biomarkers have been tested, but most studies included heterogeneous patients. We aimed to investigate the prognostic and/or predictive values of four relevant biomarkers in a multicentric cohort of patients.A total of 471 patients who had resected pancreatic adenocarcinoma were included. Using tissue microarray, we assessed the relationship of biomarker expressions with the overall survival: Smad4, type II TGF-β receptor, CXCR4, and LKB1.High CXCR4 expression was found to be the only independent negative prognostic biomarker [hazard ratio (HR) = 1.74; P0.0001]. In addition, it was significantly associated with a distant relapse pattern (HR = 2.19; P0.0001) and was the strongest prognostic factor compared with clinicopathological factors. In patients who did not received adjuvant treatment, there was a trend toward decrease in the overall survival for negative Smad4 expression. Loss of Smad4 expression was not correlated with recurrence pattern but was shown to be predictive for adjuvant chemotherapy (CT) benefit (HR = 0.59; P = 0.002).CXCR4 is a strong independent prognostic biomarker associated with distant metastatic recurrence and appears as an attractive target to be evaluated in pancreatic adenocarcinoma. Negative SMAD4 expression should be considered as a potential predictor of adjuvant CT benefit.

Malignant duodenal neoplasms are relatively rare, and the diagnosis is often delayed because of their vague and nonspecific symptoms. We report the case of a 79-year-old female who had a medical history of malignant melanoma of the cheek that had initially been diagnosed at 53 years of age. Work-up revealed severe stenosis of the duodenum caused by a large mass with ulceration at the tip of its mucosal surface. Tumor biopsy led to a histological diagnosis of extremely poorly differentiated carcinoma, but it was impossible to determine whether the lesion was a primary neoplasm or represented secondary involvement. Pancreatoduodenectomy was performed, and the surgical specimen showed a protuberant tumor in the nonampullary region of the second portion of the duodenum. Final diagnosis of metastatic duodenal melanoma was made by immunohistological examination. She is currently alive without recurrence 28 months after the surgical treatment.

Asparaginase encapsulated in erythrocytes (ERY-ASP) is a potentially effective drug in patients with pancreatic adenocarcinoma (PAC) with null/low asparagine synthetase (ASNS) expression. Our aims were to assess ASNS expression in PAC from a large cohort and its prognostic and/or predictive value and to conduct a phase I trial with ERY-ASP in patients with metastatic PAC.Asparagine synthetase expression was evaluated using immunohistochemistry in resected PAC (471 patients) and in pairs of primary tumor and metastases (55 patients). Twelve patients were included in the phase I trial and received a single administration of ERY-ASP (25-150 IU/kg).Null/low ASNS expression was found in 79.4% of the resected PAC with a high concordance between primary tumor and metastases. Asparagine synthetase expression was significantly correlated with sex and CXCR4 expression. In the phase I trial, ERY-ASP was well tolerated by patients with metastatic PAC. No patient had DLTs, and 6 patients had at least 1 ERY-ASP causally related adverse event out of the 12 adverse events reported.Given the high rate of PAC with null/low ASNS expression and the good tolerability profile of ERY-ASP, ERY-ASP should be evaluated in further clinical studies in metastatic PAC.

Purpose: Aberrant activation of the hedgehog (Hh) pathway is implicated in pancreatic ductal adenocarcinoma (PDAC) tumorigenesis. We investigated the prognostic and predictive value of four Hh signaling proteins and of the tumor stromal density. Experimental Design: Using tissue microarray and immunohistochemistry, the expression of Shh, Gli1, SMO, and PTCH1 was assessed in 567 patients from three independent cohorts who underwent surgical resection for PDAC. In 82 patients, the tumor stromal index (SI) was calculated, and its association with overall survival (OS) and disease-free survival (DFS) was investigated. Results: Shh and Gli1 protein abundance were independent prognostic factors in resected PDACs; low expressors for those proteins experiencing a better OS and DFS. The combination of Shh and Gli1 levels was the most significant predictor for OS and defined 3 clinically relevant subgroups of patients with different prognosis (Gli1 and Shh low; HR set at 1 vs. 3.08 for Shh or Gli1 high vs. 5.69 for Shh and Gli1 high; P < 0.001). The two validating cohorts recapitulated the findings of the training cohort. After further stratification by lymph node status, the prognostic significance of combined Shh and Gli1 was maintained. The tumor SI was correlated with Shh levels and was significantly associated with OS (P = 0.023). Conclusions: Shh and Gli1 are prognostic biomarkers for patients with resected PDAC. Clin Cancer Res; 21(5); 1215–24. ©2014 AACR.

Inflammatory hepatocellular adenomas (IHCAs) are benign liver lesions that can be characterized histologically by the presence of an inflammatory infiltrate and at the molecular level by the overexpression of acute phase inflammatory response genes. Recurrent somatic mutations of the interleukin-6 (IL-6) signal transducer (IL6ST) locus, encoding the critical component of the IL-6 signal transduction machinery gp130, are present in 60% of IHCAs and in a subset (2%) of hepatocellular carcinoma (HCCs). By screening of 256 human hepatic adenoma specimens (the largest genetic analysis of IL6ST performed to date in this setting), we identified 24 distinct somatic IL6ST mutations among 66 mutant adenomas. The functional analysis of nine different gp130 mutants expressed in hepatic cancer cell lines consistently revealed the constitutive and IL-6-independent activation of the JAK/STAT signaling pathway. We further demonstrated that the signaling activity of mutant gp130 in IHCA remains responsive to suppressor of cytokine signaling 3 (SOCS3), a physiological gp130 inhibitor. Specifically, cells expressing a double mutant variant of gp130 with a disrupted SOCS3-binding site at residue 759 (Y186/Y759F) displayed a hyperactivation of signal transducer and activator of transcription 3 (STAT3) as compared with cells expressing the endogenous IHCA-associated Y186 gp130 mutant. Notably, we identified that constitutive signaling via gp130 in IHCA requires the Janus kinase family member JAK1, but not JAK2 or tyrosine kinase 2. In support of this notion, AG490, a tyrosine kinase inhibitor that selectively blocks JAK2, had no effect on gp130 activity. In stark contrast, we showed that ruxolitinib, a JAK1/JAK2-selective tyrosine kinase inhibitor used to treat patients with myelofibrosis, dramatically impaired JAK1-STAT signaling downstream of all IHCA-associated gp130 mutants. In conclusion, our findings provide a rationale for the use of JAK1 inhibitors for the treatment of HCAs expressing mutant gp130 as well as a subset of HCCs that bear similar mutations.

Background & Aims Patients who undergo surgery for pancreatic ductal adenocarcinoma (PDAC) frequently receive adjuvant gemcitabine chemotherapy. Key determinants of gemcitabine cytotoxicity include the activities of the human equilibrative nucleoside transporter 1 (hENT1), deoxycytidine kinase (dCK), and ribonucleotide reductase subunit 1 (RRM1). We investigated whether tumor levels of these proteins were associated with efficacy of gemcitabine therapy following surgery. Methods Sequential samples of resected PDACs were retrospectively collected from 434 patients at 5 centers; 142 patients did not receive adjuvant treatment (33%), 243 received adjuvant gemcitabine-based regimens (56%), and 49 received nongemcitabine regimens (11%). We measured protein levels of hENT1, dCK, and RRM1 by semiquantitative immunohistochemistry with tissue microarrays and investigated their relationship with patients' overall survival time. Results The median overall survival time of patients was 32.0 months. Among patients who did not receive adjuvant treatment, levels of hENT1, RRM1, and dCK were not associated with survival time. Among patients who received gemcitabine, high levels of hENT1 and dCK were significantly associated with longer survival time (hazard ratios of 0.34 [ P P = .012], respectively). Interaction tests for gemcitabine administration and hENT1 and dCK status were statistically significant ( P = .0007 and P = .016, respectively). On multivariate analysis of this population, hENT1 and dCK retained independent predictive values, and those patients with high levels of each protein had the longest survival times following adjuvant therapy with gemcitabine. Conclusions High levels of hENT1 and dCK in PDAC predict longer survival times in patients treated with adjuvant gemcitabine.

Context.—Splenic metastases from solid tumors, defined as parenchymal lesion, are considered exceptional. Nevertheless, the number of case reports has been increasing due to the improvement of imaging techniques and the long-term follow-up of patients with cancer. Splenic metastases occur in a context of multivisceral disseminated cancer or as a solitary lesion. Objective.—To provide a general overview of the clinicopathologic features, differential diagnosis, and pathogenesis of splenic metastases. Data Sources.—Relevant articles indexed in PubMed (National Library of Medicine) database. The search was based on the following terms: (metastasis or metastases) and spleen. Conclusions.—The most common primary sources of splenic metastasis are breast, lung, colorectal, and ovarian carcinomas and melanoma in cases of multivisceral cancer and colorectal and ovarian carcinomas in cases of solitary splenic lesion. Splenectomy can be replaced by less aggressive methods such as fine-needle aspiration or percutaneous biopsy for establishing the diagnosis of solitary splenic metastasis. The main differential diagnoses are primary lymphoma, vascular tumors, and infectious lesions of the spleen. The relative rarity of splenic metastases could be explained by anatomic factors and the inhibitory effect of the splenic microenvironment on the growth of metastatic cells. The analysis of clinical case reports suggests that solitary splenic metastases may result from the growth of an early blood-borne micrometastasis following a period of clinical latency, often several years after the diagnosis of the primary tumor.