Citations

ABSTRACT A 65-year-old man presented with hematuria, night sweats, nausea, intermittent nonbloody diarrhea, and abdominal pain. Computed tomography angiogram with enterography showed retroperitoneal fibrosis surrounding both kidneys and ureters without any evidence of vascular obstruction or hydronephrosis. Laparoscopic biopsy demonstrated fibroadipose tissue involved by a subtle histiocytic infiltrate in a background of marked fibrosis, scattered lymphocytes, and plasma cells. The histiocytes strongly expressed CD163, Factor XIIIa, and BRAF V600E . He was diagnosed with Erdheim-Chester disease, a rare histiocytic neoplasm uncommonly presenting with gastroenterological manifestations.

ImportanceIncreased cancer risk in first-degree relatives of probands with pancreatic ductal adenocarcinoma (PDAC probands) who carry pathogenic or likely pathogenic germline variants (PGVs) in cancer syndrome–associated genes encourages cascade genetic testing. To date, unbiased risk estimates for the development of cancers on a gene-specific basis have not been assessed.ObjectiveTo quantify the risk of development of PDAC and extra-PDAC among first-degree relatives of PDAC probands who carry a PGV in 1 of 9 cancer syndrome–associated genes—ATM, BRCA1, BRCA2, PALB2, MLH1, MSH2, MSH6, PMS2, and CDKN2A.Design, Setting, and ParticipantsThis case series focused on first-degree relatives of PDAC probands carrying PGVs in specific cancer syndrome–associated genes. The cohort comprised clinic-ascertained patients enrolled in the Mayo Clinic Biospecimen Resource for Pancreas Research registry with germline genetic testing. In total, 234 PDAC probands carrying PGVs were drawn from the prospective research registry of 4562 participants who had undergone genetic testing of cancer syndrome–associated genes. Demographic and cancer-related family histories were obtained by questionnaire. The data were collected from October 1, 2000, to December 31, 2021.Main Outcomes and MeasuresFor the PDAC probands, the genetic test results of the presence of PGVs in 9 cancer syndrome–associated genes were obtained by clinical testing. Cancers (ovary, breast, uterus or endometrial, colon, malignant melanoma, and pancreas) among first-degree relatives were reported by the probands. Standardized incidence ratios (SIRs) were used to estimate cancer risks among first-degree relatives of PDAC probands carrying a PGV.ResultsIn total, 1670 first-degree relatives (mean [SD] age, 58.1 [17.8] years; 853 male [51.1%]) of 234 PDAC probands (mean [SD] age, 62.5 [10.1] years; 124 male [53.0%]; 219 [94.4%] White; 225 [98.7%] non-Hispanic or non-Latino]) were included in the study. There was a significantly increased risk of ovarian cancer in female first-degree relatives of probands who had variants in BRCA1 (SIR, 9.49; 95% CI, 3.06-22.14) and BRCA2 (SIR, 3.72; 95% CI, 1.36-8.11). Breast cancer risks were higher with BRCA2 variants (SIR, 2.62; 95% CI, 1.89-3.54). The risks of uterine or endometrial cancer (SIR, 6.53; 95% CI, 2.81-12.86) and colon cancer (SIR, 5.83; 95% CI, 3.70-8.75) were increased in first-degree relatives of probands who carried Lynch syndrome mismatch repair variants. Risk of PDAC was also increased for variants in ATM (SIR, 4.53; 95% CI, 2.69-7.16), BRCA2 (SIR, 3.45; 95% CI, 1.72-6.17), CDKN2A (SIR, 7.38; 95% CI, 3.18-14.54), and PALB2 (SIR, 5.39; 95% CI, 1.45-13.79). Melanoma risk was elevated for first-degree relatives of probands with CDKN2A variants (SIR, 7.47; 95% CI, 3.97-12.77).Conclusions and RelevanceIn this case series, the presence of PGVs in 9 cancer syndrome–associated genes in PDAC probands was found to be associated with increased risk of 6 types of cancers in first-degree relatives. These gene-specific PDAC and extra-PDAC cancer risks may provide justification for clinicians to counsel first-degree relatives about the relevance and importance of genetic cascade testing, with the goal of higher uptake of testing.

Abstract Increased pancreatic cancer (PC) risk in first-degree relatives (FDRs) of PC patients with germline pathogenic/likely pathogenic variants (PLPV) is a motivator for cascade genetic testing. To date, unbiased FDR risk estimates for gene-specific risks of genetic cancer syndrome-associated cancers are unknown. These estimates would enhance genetic counseling of at-risk FDRs in families where the PC proband has been found to carry a PLPV. Study Aim: To objectively quantify gene-specific risks of six relevant cancers among FDRs of PC patients with germline PLPV in ATM, BRCA1, BRCA2, CDKN2A, MLH1, MSH2, MSH6, PALB2, and PMS2. Methods: In the prospective, clinic-based Mayo Clinic Biospecimen Resource for Pancreas Research registry, 4,562 PC patients had previously undergone germline genetic testing for pancreatic cancer-associated genes through either research studies or clinical testing. Of these, 234 PC probands were found to carry PLPV among 9 genes of interest and had provided detailed demographic and cancer data on their FDRs by questionnaire. We focused on six cancer types (ovary, breast, uterus, pancreas, colon, and malignant melanoma) in FDRs as reported by the probands. Standardized incidence ratios estimated risk by gene of six cancers among FDRs of PC patients carrying PLPV. Results: 1,670 FDRs (mean age 58.1+17.8SD; 48.9% female) were included in the study. We found significantly increased risk of ovarian cancer in female FDRs of PC probands who carry PLPV in BRCA1 (SIR 9.49, 95%CI:3.06-22.14) or BRCA2 (3.72, 95%CI:1.36-8.11), and breast cancer risks were higher with BRCA2 (2.62, 95%CI:1.89-3.54). Uterine cancer risk was increased in FDRs of PC probands who carry PLPV for Lynch Syndrome mismatch repair (MMR) (6.53, 95%CI:2.81-12.86). PC risk was also increased (ATM 4.53, 95% CI:2.69-7.16; BRCA2 3.45, 95%CI:1.72-6.17; CDKN2A 7.38, 95%CI:3.18-14.54; PALB2 5.39, 95%CI:1.45-13.79). Increased colon cancer risk was observed in FDRs of probands who carried MMR PLPV (5.83, 95%CI:3.70-8.75), while melanoma risk was elevated for FDRs of probands with CDKN2A PLPV (7.47, 95%CI:3.97-12.77). Conclusion: PLPV in nine syndrome-associated genes in PC probands are associated with increased risk of six cancers in FDRs in PC families. The findings underscore the importance of risk estimation of various other cancers in PC families for screening, early detection, and intervention. The gene-specific quantified cancer risks for FDRs of PC patients with syndrome-associated germline PLPV can inform counseling regarding extra-PC risks when considering cascade genetic testing. Citation Format: Xuan Chen, Kari Rabe, Margaret Meyer, Jennnifer Kemppainen, Masayasu Horibe, Shruti Chandra, Shounak Majumder, Gloria Petersen. Gene-specific risk of syndrome-associated cancers in first-degree relatives of pancreatic cancer patients with pathogenic/likely pathogenic variants [abstract]. In: Proceedings of the AACR Special Conference on Pancreatic Cancer; 2022 Sep 13-16; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2022;82(22 Suppl):Abstract nr A033.

Purpose This study aimed to compare accuracy and efficiency of a convolutional neural network (CNN)–enhanced workflow for pancreas segmentation versus radiologists in the context of interreader reliability. Methods Volumetric pancreas segmentations on a data set of 294 portal venous computed tomographies were performed by 3 radiologists (R1, R2, and R3) and by a CNN. Convolutional neural network segmentations were reviewed and, if needed, corrected (“corrected CNN [c-CNN]” segmentations) by radiologists. Ground truth was obtained from radiologists' manual segmentations using simultaneous truth and performance level estimation algorithm. Interreader reliability and model's accuracy were evaluated with Dice-Sorenson coefficient (DSC) and Jaccard coefficient (JC). Equivalence was determined using a two 1-sided test. Convolutional neural network segmentations below the 25th percentile DSC were reviewed to evaluate segmentation errors. Time for manual segmentation and c-CNN was compared. Results Pancreas volumes from 3 sets of segmentations (manual, CNN, and c-CNN) were noninferior to simultaneous truth and performance level estimation–derived volumes [76.6 cm3 (20.2 cm3), P < 0.05]. Interreader reliability was high (mean [SD] DSC between R2-R1, 0.87 [0.04]; R3-R1, 0.90 [0.05]; R2-R3, 0.87 [0.04]). Convolutional neural network segmentations were highly accurate (DSC, 0.88 [0.05]; JC, 0.79 [0.07]) and required minimal-to-no corrections (c-CNN: DSC, 0.89 [0.04]; JC, 0.81 [0.06]; equivalence, P < 0.05). Undersegmentation (n = 47 [64%]) was common in the 73 CNN segmentations below 25th percentile DSC, but there were no major errors. Total inference time (minutes) for CNN was 1.2 (0.3). Average time (minutes) taken by radiologists for c-CNN (0.6 [0.97]) was substantially lower compared with manual segmentation (3.37 [1.47]; savings of 77.9%–87% [P < 0.0001]). Conclusions Convolutional neural network–enhanced workflow provides high accuracy and efficiency for volumetric pancreas segmentation on computed tomography.

INTRODUCTION: Observational studies have suggested an increased risk of pancreatic ductal adenocarcinoma (PDAC) in patients with acute and chronic pancreatitis. We conducted a systematic review and meta-analysis to evaluate the magnitude of this association and summarize the published epidemiological evidence. METHODS: We searched electronic databases (MEDLINE, Embase, Web of Science, Cochrane, and Scopus) and reference lists until January 18, 2021. Studies reporting quantitative association between pancreatitis and PDAC were included and assessed for eligibility, data abstraction, and risk of bias. Standardized incidence ratios (SIRs) were pooled using the random-effects model. RESULTS: Twenty-five cohort and case-control studies met inclusion criteria. Meta-analysis of 12 chronic pancreatitis (CP) studies demonstrated an increased risk of PDAC in patients with CP (SIR: 22.61, 95% confidence interval [CI]: 14.42–35.44). This elevated risk persisted in subgroup analysis of studies that excluded patients diagnosed with PDAC within 2 years of CP diagnosis (SIR: 21.77, 95% CI: 14.43–32.720). The risk was higher in hereditary pancreatitis (SIR: 63.36, 95% CI: 45.39–88.46). The cumulative incidence rates of PDAC in CP increased with follow-up duration. Limited evidence in acute pancreatitis indicates higher PDAC risk in the subset of patients eventually diagnosed with CP. PDAC seems to be uncommon in patients with autoimmune pancreatitis, with 8 reported cases in 358 patients with autoimmune pancreatitis across 4 studies. DISCUSSION: There is an increased risk of PDAC in patients with CP, and incidence rates increase with CP disease duration. Our results indicate that PDAC surveillance may be considered in individuals with long-standing CP.

Purpose of review Pancreatic ductal adenocarcinoma (PDAC) is third leading cause of cancer death in the United States, a lethal disease with no screening strategy. Although diagnosis at an early stage is associated with improved survival, clinical detection of PDAC is typically at an advanced symptomatic stage when best in class therapies have limited impact on survival. Recent findings In recent years this status quo has been challenged by the identification of novel risk factors, molecular markers of early-stage disease and innovations in pancreatic imaging. There is now expert consensus that screening may be pursued in a cohort of individuals with increased likelihood of developing PDAC based on genetic and familial risk. Summary The current review summarizes the known risk factors of PDAC, current knowledge and recent observations pertinent to early detection of PDAC in these risk groups and outlines future approaches that will potentially advance the field.

Abstract Purpose: We have previously identified tissue methylated DNA markers (MDMs) associated with pancreatic ductal adenocarcinoma (PDAC). In this case–control study, we aimed to assess the diagnostic performance of plasma MDMs for PDAC. Experimental Design: Thirteen MDMs (GRIN2D, CD1D, ZNF781, FER1L4, RYR2, CLEC11A, AK055957, LRRC4, GH05J042948, HOXA1, PRKCB, SHISA9, and NTRK3) were identified on the basis of selection criteria applied to results of prior tissue experiments and assays were optimized in plasma. Next, 340 plasma samples (170 PDAC cases and 170 controls) were assayed using target enrichment long-probe quantitative amplified signal method. Initially, 120 advanced-stage PDAC cases and 120 healthy controls were used to train a prediction algorithm at 97.5% specificity using random forest modeling. Subsequently, the locked algorithm derived from the training set was applied to an independent blinded test set of 50 early-stage PDAC cases and 50 controls. Finally, data from all 340 patients were combined, and cross-validated. Results: The cross-validated area under the receiver operating characteristic curve (AUC) for the training set was 0.93 (0.89–0.96) for the MDM panel alone, 0.91 (95% confidence interval, 0.87–0.96) for carbohydrate antigen 19-9 (CA19-9) alone, and 0.99 (0.98–1) for the combined MDM-CA19-9 panel. In the test set of early-stage PDAC, the AUC for MDMs alone was 0.84 (0.76–0.92), CA19-9 alone was 0.87 (0.79–0.94), and combined MDM-CA19-9 panel was 0.90 (0.84–0.97) significantly better compared with either MDMs alone or CA19-9 alone (P = 0.0382 and 0.0490, respectively). At a preset specificity of 97.5%, the sensitivity for the combined panel in the test set was 80% (28%–99%) for stage I disease and 82% (68%–92%) for stage II disease. Using the combined datasets, the cross-validated AUC was 0.9 (0.86–0.94) for the MDM panel alone and 0.89 for CA19-9 alone (0.84–0.93) versus 0.97 (0.94–0.99) for the combined MDM-CA19-9 panel (P ≤ 0.0001). Overall, cross-validated sensitivity of MDM-CA19-9 panel was 92% (83%–98%), with an observed specificity of 92% at the preset specificity of 97.5%. Conclusions: Plasma MDMs in combination with CA19-9 detect PDAC with significantly higher accuracy compared with either biomarker individually.

Objective The diagnosis of autoimmune pancreatitis (AIP) is challenging. Sonographic and cross-sectional imaging findings of AIP closely mimic pancreatic ductal adenocarcinoma (PDAC) and techniques for tissue sampling of AIP are suboptimal. These limitations often result in delayed or failed diagnosis, which negatively impact patient management and outcomes. This study aimed to create an endoscopic ultrasound (EUS)-based convolutional neural network (CNN) model trained to differentiate AIP from PDAC, chronic pancreatitis (CP) and normal pancreas (NP), with sufficient performance to analyse EUS video in real time. Design A database of still image and video data obtained from EUS examinations of cases of AIP, PDAC, CP and NP was used to develop a CNN. Occlusion heatmap analysis was used to identify sonographic features the CNN valued when differentiating AIP from PDAC. Results From 583 patients (146 AIP, 292 PDAC, 72 CP and 73 NP), a total of 1 174 461 unique EUS images were extracted. For video data, the CNN processed 955 EUS frames per second and was: 99% sensitive, 98% specific for distinguishing AIP from NP; 94% sensitive, 71% specific for distinguishing AIP from CP; 90% sensitive, 93% specific for distinguishing AIP from PDAC; and 90% sensitive, 85% specific for distinguishing AIP from all studied conditions (ie, PDAC, CP and NP). Conclusion The developed EUS-CNN model accurately differentiated AIP from PDAC and benign pancreatic conditions, thereby offering the capability of earlier and more accurate diagnosis. Use of this model offers the potential for more timely and appropriate patient care and improved outcome.