Citations

One of the signicant obstacles to donor heart utilization is that increasingly prolonged cold ischemic times are associated with worsened short- and long-term outcomes. As a result, most transplant centers aim to limit cold ischemic times to less than four hours. The TransMedics Organ Care System (OCS) (Trans Medics, Andover MA) is a portable ex vivo perfusion system designed to store a donor heart in a beating, metabolically active state, until it reaches the recipient, thereby allowing extended time outside the body while minimizing the effects of cold storage. We present a case of a heart transplant recipient supported with the Impella 5.5 as a bridge to transplantation who received a donation after circulatory death (DCD) heart supported by OCS. To our knowledge, this is the rst report of such a case.

Study Design A retrospective cohort study was conducted using the Kids’ Inpatient Database from 2000 to 2014. Subjects were included if they were 18 years and younger and suffered any type of facial fracture. Objective The purpose this study was to determine the risk factors for incurring panfacial fractures among the pediatric population. Methods The primary predictor variables were a set of heterogenous variables that included patient characteristics, injury characteristics, hospitalization outcomes. The primary outcome variable was panfacial fracture. Logistic regression was used to determine the independent risk factors for panfacial fractures. Results Relative to infants and toddlers, teenagers were nearly three times more likely to sustain panfacial fractures ( P < .01). Relative to no chronic conditions, patients with one or more chronic conditions were more likely to incur panfacial fractures. Motorcycle accidents were over three times more likely ( P < .01) to result in panfacial fractures while car accidents were over two times more likely ( P < .01) to result in panfacial fractures. Falls were less likely (OR, .39; P < .01) to result in panfacial fractures. Conclusions Motor vehicle accidents was a major risk factor for panfacial fractures. Teenagers are also found to have an increased risk for panfacial fractures relative to infants and toddlers. Each additional chronic condition was a significant risk factor for suffering panfacial fractures relative to not having any chronic condition at all. In contrast, falls independently decreased the risk of incurring a panfacial fractures. Special attention should be given to safety precautions when occupying a motor vehicle.

Chemotherapy-induced cognitive impairment (CICI) has emerged as a significant medical problem without therapeutic options. Using the platinum-based chemotherapy cisplatin to model CICI, we revealed robust elevations in the adenosine A 2A receptor (A 2A R) and its downstream effectors, cAMP and CREB, by cisplatin in the adult mouse hippocampus, a critical brain structure for learning and memory. Notably, A 2A R inhibition by the Food and Drug Administration–approved A 2A R antagonist KW-6002 prevented cisplatin-induced impairments in neural progenitor proliferation and dendrite morphogenesis of adult-born neurons, while improving memory and anxiety-like behavior, without affecting tumor growth or cisplatin’s antitumor activity. Collectively, our study identifies A 2A R signaling as a key pathway that can be therapeutically targeted to prevent cisplatin-induced cognitive impairments.

Abstract Histological distribution and density of tumor infiltrating lymphocytes (TILs) are known to be informative to cancer patients’ outcome. However, due to the heterogeneity and huge size of digitized pathology slides, it has been a challenging problem to quantify TILs-related features in whole slide images (WSI). This study aimed to explore the impact of spatial distribution of TILs quantified by deep learning (DL) approaches based on digitalized WSI stained with hematoxylin and eosin in patients with colorectal cancer (CRC). The cancer and TILs regions are first separately identified by deep learning models in the WSI. The TILs densities at tumor invasive margins are then quantified by image analysis algorithms and used for prognosis of progression-free survival (PFS) in CRC patients. The prognostic impact of spatial distributions of TILs in CRC patients was explored in the Yonsei cohort (n=180) and validated in the TCGA cohort (n=268). Two experienced pathologists manually measured TILs at the most invasive margin as 0-3 by the Klintrup-Mäkinen (KM) grading method and compared to DL approaches. On multivariate analysis, TILs densities within 200µm of the invasive margin (f_im200) was remained as the most significant prognostic factor for PFS (HR 0.004 [95% CI, 0.0001-0.1502], p=.002) in the Yonsei cohort. On multivariate analysis using the TCGA dataset, f_im200 retained prognostic significance for PFS (HR 0.031, [95% CI 0.001-0.645], p=.024). Inter-observer agreement of manual KM grading was insignificant in both cohorts. The survival analysis by pathologists’ manual KM grading showed statistically significant different PFS from the TCGA cohort, but not the Yonsei cohort. In summary, automated quantification of TILs at the invasive margin showed a prognostic utility to predict PFS, and could provide reproducible TILs density measurement in CRC patients. Citation Format: Hongming Xu, Yoon Jin Cha, Jean Clemenceau, Jinhwan Choi, Sung Hak Lee, Jeonghyun Kang, Tae Hyun Hwang. Spatial analysis of tumor infiltrating lymphocytes in histological images using deep learning predicts progression-free survival in colorectal cancer [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 642.

Abstract Background: Microsatellite Instability (MSI) is a prognostic and predictive biomarker which can guide treatments including immunotherapy for patients with gastrointestinal cancer. Universal MSI testing would benefit to patients to receive a better therapy, but many patients remain untested. To deliver broadly accessible MSI testing, deep learning models have demonstrated their feasibility for MSI prediction using H&E-stained whole-slide images (WSIs). However, to be deployed into clinical routine care, prediction models should be validated in datasets from multicenter and multiethnic groups. In addition, these models should provide uncertainty of prediction to help clinicians to make informed decisions. Method: We develop a MSI prediction model using WSIs based on Deep Gaussian process (DGP), a Bayesian model being able to model the uncertainty in prediction. We implement a DGP model in the transfer learning, where a WPI is decomposed into multiple non-overlap image patches which are converted to feature vectors by a pretrained convolutional neural network (CNN). Then, the DGP model makes a prediction for an image by averaging score function values at all the feature vectors in the image in weakly supervised learning (WSL). To test our method, we collected H&E stained colorectal/stomach cancer slides (n=1,619) from multi-institutions and multiethnic groups, including TCGA Colorectal (CRC; n=351) and Stomach Adenocarcinoma (STAD; n=174) and six datasets from tertiary hospitals in Korea (i.e., Yonsei-1 (n=174), Yonsei-1-remade (n=146), Yonsei-2 (n=95), St. Mary-1 (n=48), St. Mary-2 (n=50), and Yonsei-CLASSIC trial (n=581)). We also compare our method to multiple deep learning methods, including Densenet, Resnet, Shufflenet, Googlenet, Squeezenet and two WSL models, MIL (Campanell et al., 2019) and CLAM (Lu et al., 2021). Result: In each cancer type, we train each model using TCGA dataset as discovery cohort and tested on the rest of datasets as validation cohort. We use the area under of roc curve (AUC) for the evaluation metric. For the CRC cohorts, our DGP method achieves overall best performance with 0.81 (Yonsei-1), 0.82 (Yonsei-1-remade), 0.89 (Yonsei-2), 0.85 (St. Mary-1) 0.75 (St. Mary-2) AUCs compared to the state-of-the art methods. For the STAD cohorts, our method also achieves overall best performance with 0.74 AUC (Yonsei-classic). In addition, incorporating the uncertainty in prediction measured by our method improves the model’s performance to predict MSI. Specifically, removing cases with prediction results with high uncertainty, which could lead false positive prediction, significantly improves MSI prediction performance. Taken together, these results demonstrate the robustness and generalisability of the DGP model for MSI prediction across multicenter and multiethnic datasets. Citation Format: Sunho Park, Hongming Xu, Sung Hak Lee, Jeonghyun Kang, Tae Hyun Hwang. Deep Gaussian process with uncertainty estimation Improves microsatellite instability prediction based on whole slide image: A retrospective multicenter and multiethnic cohort study [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 5012.

Abstract Background: Gastric carcinogenesis has been found to be associated with intragastric microbiome, gastric mucosal gene expression, and immune cells comprising the tumor microenvironment separately in independent studies. However, to date, there has been no multi-omic investigations analyzing these biological traits simultaneously. Thus, there is a paucity in our understanding of the intricate relationship between intragastric microbes and the host in gastric disease. To address this gap, we aimed to comprehensively investigate the microbiome, host gene expression, and immune cells in the stomach across a range of different states along the gastric carcinogenesis pathway. Methods: Gastric biopsy samples were obtained from healthy individuals or patients with gastritis or gastric cancer (biopsy cohort, n=30). Surgical samples (of cancer and adjacent severe gastritis tissues) were obtained from gastric cancer patients (surgery cohort, n=40). Using 16S rRNA gene sequencing, RNA-seq, and cell-type enrichment analysis of each participant’s biospecimens, we identified associations between the microbiome and host transcriptome or inferred immune cell types. Results: Microbiome composition and gene expression patterns showed significant differences between disease states (healthy, gastritis, or cancer). Expression of ‘Cell cycle’ pathway genes was enriched in gastric cancer in both biopsy and surgery cohorts. In the biopsy cohort, Helicobacteraceae, which was abundant in gastritis, was highly correlated with FAM3D, which was previously found to be involved in gastrointestinal inflammation. In the surgery cohort, Lachnospiraceae, which was abundant in gastric cancer, was highly correlated with UBD, which is known to regulate mitosis and reduce cell cycle time. Cell-type enrichment analysis revealed lower B cell infiltration in gastric cancer than in gastritis (both cohorts). Tissue-infiltrating B cells were associated with Helicobacteraceae. Conclusions: Associations between the intragastric microbiome, human gene expression, and tissue-infiltrating immune cells are unique to gastric disease state. The findings herein may aid the discovery of novel biomarkers or therapeutic targets of gastric cancer. Citation Format: Chan Hyuk Park, Changjin Hong, A-reum Lee, Jaeyun Sung, Tae Hyun Hwang. Integrated analysis of intragastric microbiome and human gene expression uncovers genetic, microbial, and immunological associations in gastritis and gastric cancer [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 5013.

Abstract Prostate-specific antigen (PSA) is widely used clinically for prostate cancer diagnostics and as an indicator of therapeutic efficacy and recurrence. Several human chemoprevention trials are being conducted to validate the prostate cancer prevention efficacy of selenium and PSA is used in these trials as a biomarker of response. A better understanding of the effects of selenium metabolites on the kinetics of PSA turnover and secretion in prostate cancer cells treated with selenium at concentrations which are achievable physiologically will be important for interpreting the results of these trials. This study addresses whether the putative active anticancer selenium metabolite methylselenol or its precursor methylseleninic acid (MSeA) specifically inhibits PSA expression in the androgen-responsive LNCaP prostate cancer cell model. The results show that exposure to sub-apoptotic concentrations of MSeA and methylselenol inhibited PSA protein expression and secretion, whereas sodium selenite and selenomethionine lacked inhibitory effect. The inhibition was detectable at 3 h of exposure and required a threshold level of MSeA to sustain. Turnover experiments showed that MSeA caused rapid PSA degradation, which was partially blocked by lysosomal inhibitors, but not by a proteasomal inhibitor. Furthermore, MSeA treatment reduced PSA mRNA level, down-regulated androgen receptor protein expression, and inhibited androgen-stimulated PSA promoter transcription. In summary, methylselenol or MSeA specifically and rapidly inhibited PSA expression through two mechanisms of action: inducing PSA protein degradation and suppressing androgen-stimulated PSA transcription. These findings may have important mechanistic implications for the prostate specific cancer chemopreventive action of selenium.

4020 Background: High grade (G3) GEPNENs are a rare and heterogeneous disease entity for which there is little prospective treatment data. EP chemotherapy is the treatment standard but this may not be appropriate for all G3 GEPNEN pts. CAPTEM has demonstrated activity in G3 GEPNENs and may be a promising alternative. EA2142 aimed to determine if CAPTEM was superior to EP in pts with G3 GEPNENs. Methods: This was a multicenter, randomized (1:1) phase II trial for pts with a locally advanced and unresectable or metastatic well differentiated G3 neuroendocrine tumor (NET) or a poorly differentiated, non-small cell G3 neuroendocrine carcinoma (NEC) of suspected gastrointestinal origin and an ECOG PS of 0-2. Pathology must have demonstrated a Ki-67 of 20-100% or at least 10 mitoses/10 high powered field. Pts were randomized to receive capecitabine 750 mg/m2 orally every 12 hours on days 1-14 and temozolomide 200 mg/m2 orally once daily on days 10-14 of a 28-day treatment cycle (Arm A) or etoposide 100 mg/m2 daily on days 1-3 with either cisplatin 25 mg/m2 daily on days 1-3 or carboplatin AUC 5 on day 1 of a 21-day treatment cycle (Arm B). Restaging scans were performed every 8 weeks and toxicity monitored per CTCAEv4. Final statistical plan was to accrue 80 pts to detect a 67% improvement in progression free survival (PFS) (primary endpoint) with CAPTEM as compared to EP, 80% power and one-sided significance level of 0.10. A planned interim analysis for efficacy and futility was conducted. Results: A total of 67 pts were enrolled (Arm A, n=32; Arm B, n=35). Male 58%, African American 4%, Asian 3%. Mean age 61. Among 63 eligible pts, primary tumor site pancreatic 56%, non-pancreatic 43%. Poorly differentiated 57%, well differentiated 33%, unknown 10%. Mean Ki-67 48% (Arm A), 60% (Arm B). The study was closed prior to full accrual due to futility at 57.7% information time. In the interim analysis, among 62 eligible pts, PFS, overall survival and response rate with CAPTEM were 2.43 months (mos) (95% CI 2.04, 7.72), 12.6 mos, 9% respectively vs 5.36 mos (95% CI 2.14, 7.23), 13.6 mos and 10% with EP. Toxicity was evaluable in 57 pts with Grade (G) 3/4 events occurring in 29% of pts on Arm A, 66% of pts on Arm B. G3/4 events occurring in more than 5% of pts on Arm A—febrile neutropenia (n=2); abdominal pain (n=2); diarrhea (n=2); nausea (n=2); neutropenia (n=2); dehydration (n=2) and on Arm B—anemia (n=8); febrile neutropenia (n=2); fatigue (n=2); lymphopenia (n=2); neutropenia (n=12); thrombocytopenia (n=4); leukopenia (n=6). There was one G5 event on Arm A due to sepsis. Conclusions: CAPTEM does not appear to be superior to EP chemotherapy as front-line treatment for pts with G3 NENs but does demonstrate a more favorable toxicity profile. Studies assessing G3 NET independently of G3 NEC are needed. Clinical trial information: NCT02595424.

Abstract A key question in human gut microbiome research is what are the robust structural patterns underlying its taxonomic composition. Herein, we use whole metagenomic datasets from healthy human guts to show that such robust patterns do exist, albeit not in the conventional enterotype sense. We first introduce the concept of mixed-membership enterotypes using a network inference approach based on stochastic block models. We find that gut microbiomes across a group of people (hosts) display a nested structure, which has been observed in a number of ecological systems. This finding led us to designate distinct ecological roles to both microbes and hosts: generalists and specialists. Specifically, generalist hosts have microbiomes with most microbial species, while specialist hosts only have generalist microbes. Moreover, specialist microbes are only present in generalist hosts. From the nested structure of microbial taxonomies, we show that these ecological roles of microbes are generally conserved across datasets. Our results show that the taxonomic composition of healthy human gut microbiomes is associated with robustly structured combinations of generalist and specialist species.