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The NCCN Guidelines for Hepatobiliary Cancers focus on the screening, diagnosis, staging, treatment, and management of hepatocellular carcinoma (HCC), gallbladder cancer, and cancer of the bile ducts (intrahepatic and extrahepatic cholangiocarcinoma). Due to the multiple modalities that can be used to treat the disease and the complications that can arise from comorbid liver dysfunction, a multidisciplinary evaluation is essential for determining an optimal treatment strategy. A multidisciplinary team should include hepatologists, diagnostic radiologists, interventional radiologists, surgeons, medical oncologists, and pathologists with hepatobiliary cancer expertise. In addition to surgery, transplant, and intra-arterial therapies, there have been great advances in the systemic treatment of HCC. Until recently, sorafenib was the only systemic therapy option for patients with advanced HCC. In 2020, the combination of atezolizumab and bevacizumab became the first regimen to show superior survival to sorafenib, gaining it FDA approval as a new frontline standard regimen for unresectable or metastatic HCC. This article discusses the NCCN Guidelines recommendations for HCC.

Abstract Introduction: Low-grade B-cell lymphomas (LGBCL), aside from follicular lymphoma and chronic lymphocytic leukemia/small lymphocytic lymphoma, account for approximately 10% of B-cell non-Hodgkin lymphomas and consist of several subtypes. While a majority of LGBCL cases have an overall favorable prognosis, we have previously shown that cases who have an event (relapse or progression, transformation, or re-treatment) within 24 months of diagnosis (EFS24) have an inferior overall survival (OS) compared to those achieving EFS24 (Tracy et al., AJH 2019;94:658-66). However, the underlying biological characteristics associated with early failure and aggressive disease across LGBCL subtypes are unknown. In this study, we used matched transcriptomic, genomic, and immune profiling data from LGBCL cases, the largest cohort to date, and asked whether there were unique biological phenotypes across different LGBCL subtypes and whether we could identify signatures associated with aggressive LGBCL. Validation of the prognostic utility of this signature was performed on a previously published, independent cohort of 63 pre-treatment LGBCL cases. Methods: Tumors from 64 newly diagnosed LGBCL patients from the Molecular Epidemiology Resource of the University of Iowa/Mayo Clinic Lymphoma Specialized Program of Research Excellence were included in this study (SMZL (n = 48), NMZL (n = 6), LPL (n = 5), B-NOS (n = 3), EMZL (n = 2)). RNA sequencing (RNAseq) data from 61 LGBCL tumors and 5 benign CD19+CD27+ memory B samples was subjected to NMF clustering to define groups. Differential expression and pathway analysis were used to identify biological characteristics of each cluster. CIBERSORT was used to identify immune cells in the tumor microenvironment. Whole exome sequencing (WES) was performed on 61 tumor-normal pairs. Singscore was used to assign a single score per patient representing gene expression of the survival-associated transcriptomic signature identified in this study. Results: NMF analysis of RNAseq data identified 5 clusters of patients, denoted LGBCL1-5 (Fig 1A). Patients from the same diagnostic subtype did not exclusively cluster together, with all LGBCL clusters comprised of patients from multiple subtypes (Fig 1B). Exploring the association between patient cluster and outcome, we observed significantly inferior event-free survival (EFS) (HR 2.24; 95% CI 1.01-4.98) and overall survival (OS) (HR 5.59; 95% CI 2.00-15.63) in LGBCL5 patients compared to LGBCL1-4 (Fig 1C). In addition, 80% of the transformation cases in our cohort were classified as LGBCL5 (Fig 1D). Differential expression and pathway analysis showed distinct processes significantly upregulated in each cluster (FDR < 0.05), with LGBCL5 demonstrating enrichment of cell cycle and mitosis pathways. CIBERSORT identified increased immune cell content in LGBCL3 and LGBCL5 compared to other clusters, with high frequencies of mast cells in both (p = 0.0002), increased CD8 T cells in LGBCL3 (p < 0.0001), and increased T follicular helper cells in LGBCL5 (p = 0.004). WES identified previously reported alterations in NOTCH, NFkB, and chromatin remodeling pathways and novel variants in LGBCL, including mutations in HNRNPK, CLTC, HLA-A, HLA-B and HLA-C. Assessment of alterations by cluster showed significant enrichment of TNFAIP3 (OR 5.54; 95% CI 1.20-28.14) and BCL2 alterations (OR 5.49; 95% CI 1.07-32.02) in LGBCL5 cluster. Finally, we identified a cell cycle-related transcriptomic signature of 108 genes upregulated in LGBCL5 and EFS24 failure cases. Cases with high expression of this signature showed significantly inferior EFS (HR 14.25; 95% CI 4.90-41.38) and OS (HR 7.82; 95% CI 2.40-25.44) compared to cases with low expression in our discovery cohort. This observation was reproduced in an independent validation cohort, where patients with high expression of this signature demonstrated significantly inferior EFS (HR 5.70; 95% CI 1.49-21.79) and OS (HR 10.07; 95% CI 2.00-50.61). Conclusions: In this study, we are the first to define mechanisms of pathogenesis in LGBCL with shared transcriptomic, genomic, and immune profiles present across LGBCL subtypes. We then further defined a gene expression signature associated with inferior patient outcome, with application of this signature to an independent validation cohort demonstrating proof of concept and utility of this signature as a prognostic marker in LGBCL patients. Figure 1 Figure 1. Disclosures Maurer: Morphosys: Membership on an entity's Board of Directors or advisory committees, Research Funding; Genentech: Research Funding; Kite Pharma: Membership on an entity's Board of Directors or advisory committees; Pfizer: Membership on an entity's Board of Directors or advisory committees; BMS: Research Funding; Nanostring: Research Funding. Paludo: Karyopharm: Research Funding. Habermann: Tess Therapeutics: Other: Data Monitoring Committee; Morphosys: Other: Scientific Advisory Board; Incyte: Other: Scientific Advisory Board; Seagen: Other: Data Monitoring Committee; Loxo Oncology: Other: Scientific Advisory Board; Eli Lilly & Co.,: Other: Scientific Advisor. Link: MEI: Consultancy; Genentech/Roche: Consultancy, Research Funding; Novartis, Jannsen: Research Funding. Rimsza: NanoString Technologies: Other: Fee-for-service contract. Ansell: Bristol Myers Squibb, ADC Therapeutics, Seattle Genetics, Regeneron, Affimed, AI Therapeutics, Pfizer, Trillium and Takeda: Research Funding. Cerhan: Genentech: Research Funding; Regeneron Genetics Center: Other: Research Collaboration; Celgene/BMS: Other: Connect Lymphoma Scientific Steering Committee, Research Funding; NanoString: Research Funding. Novak: Celgene/BMS: Research Funding.

Abstract The tumor microenvironment plays a crucial role in mediating the tumor immune response, thereby affecting patient outcomes in follicular lymphoma (FL). Using CyTOF, we analyzed single cell suspensions created from pre-treatment biopsies in a cohort of 82 FL patients and retrospectively extracted patient clinical parameters. Hierarchical analysis of the tumor microenvironment composition stratified these patients into 4 groups: group 1 represents patients with high percentage of monocyte/macrophages/NK cells; patients from group 2 and 3 are rich of intratumoral T and B cells, respectively. Patients with intermediate number of T and B cells are classed into group 4. Intratumoral T cells from these 4 groups exhibited distinct phenotypical characteristics. Group 1 was enriched with KLRG1 +CD8 + T cells, while group 4 showed increased number of CXCR3 - T EM and CD4 + T N cells. Patients from group 2 and 3 featured with increased number of ICOS + T reg cells and CD57 + T FH cells, respectively. We observed that higher numbers of T cells significantly correlated with early disease stage, lack of B-symptoms, event-free survival at 24 months (EFS24) and a favorable overall and EFS survival in patients with histological grade 1 and 2. However, this association was not seen in patients with histological grade 3a and b, suggesting that T cell-mediated anti-tumor immunity is preserved in low grade patients, but overridden in higher grade patients. CITRUS analysis revealed that a cluster that is CD57 + and PD-1 high was significantly more abundant in patients who had disease progression than patients who had a complete response to therapy, suggesting a role of CD57 + T FH cells in promoting malignant cell growth in FL. Consistent with this finding, we observed that higher number of CD57 + T FH cells correlated with an inferior survival in FL. Using CITE-seq technology, we found that CD57 + T FH cells exhibited a substantially different transcriptome when compared to CD57 - T FH cells. Genes that were differentially upregulated in CD57 + T FH cells when compared to CD57 - T FH cells included genes involved in cell survival, compromised inflammatory response, and metabolism activation including GZMK, CCL4, CST7, DUSP2, LGALS3, CYTOR, CHI3L2, SYNE2, CXCL13, CD27 and FABP5. Taken together, our results indicate different tumor microenvironments among FL patient groups that is associated with variable T-cell phenotype. We found that CD57 + T FH cells play an important role in predicting disease progression and patient prognosis in FL. Figure 1 Figure 1. Disclosures Novak: Celgene/BMS: Research Funding. Ansell: Bristol Myers Squibb, ADC Therapeutics, Seattle Genetics, Regeneron, Affimed, AI Therapeutics, Pfizer, Trillium and Takeda: Research Funding.

Abstract Background: Follicular lymphoma (FL) exhibits significant clinical, cellular, molecular, and genetic heterogeneity. Our understanding of FL biology and molecular classifications of FL, to date, has largely been driven by pathologic classification (Grade 1-3b), genetic classification (m7-FLIPI), or gene expression profiling (IR-1/2; Huet-23), along with limited studies on small cohorts or targeted panels. In order to further understand the biological underpinnings and complexity of FL, large-scale and integrated whole exome sequencing (WES) and RNA sequencing (RNAseq) studies are needed. Using a highly-annotated cohort of 93 FL tumors with matched RNAseq, WES, and CyTOF data, we have explored the transcriptomic signature of purified FL B cells and identified unique molecular subsets that are defined by distinct pathway activation, immune content, and genomic signatures. Methods: Frozen cell suspensions from 93 untreated FL (Grade 1-3b) patients' tumor biopsies, enrolled in the University of Iowa/Mayo Clinic Lymphoma SPORE, were used for the study. DNA was isolated from whole tumor cell suspensions, and RNA was isolated from both whole tumor and B cell-enriched cell suspensions. RNAseq and WES were performed in the Mayo Clinic Genome Analysis Core. RNAseq and WES data were processed using a standard pipeline and novel driver genes were identified using Chasm+ driver analysis. Copy number variants were identified from WES data using GISTIC 2.0. NMF clustering and single sample gene set testing for B cell lineage and tumor microenvironment (TME) signatures were performed in R using the NMF and singscore packages. Results: Unsupervised clustering of RNAseq data identified three distinct expression programs which separated patient B cell samples into 3 groups: Group 1 (G1, n=20), Group 2 (G2, n=24), Group 3 (G3, n=43). While no clinical attributes were defined by any single group, G1 consisted of cases that had less aggressive characteristics (63% Stage I-II, 79% FLIPI 0-1). To identify unique transcriptional pathways driving the three expression programs, we scored gene level contributions to NMF expression programs and employed gene set enrichment analysis. This revealed significant pathway association with type-I IFN signaling (G1), DNA repair and stress response (G2), and epigenetic modulation (G3) as differentiating programs between the 3 groups (FDR q<0.001). VIPER master regulator activity inferencing revealed that these pathways were likely being controlled by differential activity in NF-kB, IRFs, STAT1, BCL6, and FOXO1. Each program significantly enriched for, but were not defined by, portions of specific germinal center programs, such as pre-memory (G1), light-zone-to-dark-zone transition (G2), and a pre-light-zone intermediate (G3). We next assessed the connection between B cell programs and the tumor microenvironment (TME) using available paired CyTOF data on 67 cases, which revealed an active TME in G1, with an abundance of CD8 T cell and NK cell populations, a wide variety of immune content in G2 that consisted mostly of Tfh and myeloid cells, and a poorly populated immune compartment in G3 compared to G1 and G2. Finally, somatic driver mutations and copy number alterations from WES were identified and associated with the three clusters. The three groups distinguished themselves by significant enrichment of copy number alterations (TNFAIP3-loss , 1q23-gain, 1q32-gain) in G2, while 10q-loss and mutations in BCL2 and chromatin modifiers (KMT2D and CREBBP) enriched in G3. G1, overall, had lower alteration burden and had weak associations with any specific alterations, suggesting an alternative mechanism for driving the G1 program. Conclusion: In this study, we have identified three unique FL tumor B cell groups, defined by specific transcriptional programs. Pathways such as inflammation, DNA damage response, and chromatin modification contribute most to the differences between B cell samples and group membership. Additionally, each program associated with specific genetic events as well as TME composition, highlighting potential drivers of these B cell states. This study improves the understanding of the mechanisms driving FL tumors and motivates further investigation into transcriptional consequences of genetic events as well as potential tumor intrinsic factors that may influence the TME. Figure 1 Figure 1. Disclosures Maurer: BMS: Research Funding; Genentech: Research Funding; Morphosys: Membership on an entity's Board of Directors or advisory committees, Research Funding; Kite Pharma: Membership on an entity's Board of Directors or advisory committees; Pfizer: Membership on an entity's Board of Directors or advisory committees; Nanostring: Research Funding. Rimsza: NanoString Technologies: Other: Fee-for-service contract. Link: MEI: Consultancy; Genentech/Roche: Consultancy, Research Funding; Novartis, Jannsen: Research Funding. Habermann: Tess Therapeutics: Other: Data Monitoring Committee; Seagen: Other: Data Monitoring Committee; Incyte: Other: Scientific Advisory Board; Morphosys: Other: Scientific Advisory Board; Loxo Oncology: Other: Scientific Advisory Board; Eli Lilly & Co.,: Other: Scientific Advisor. Ansell: Bristol Myers Squibb, ADC Therapeutics, Seattle Genetics, Regeneron, Affimed, AI Therapeutics, Pfizer, Trillium and Takeda: Research Funding. King: Celgene/BMS: Research Funding. Cerhan: Genentech: Research Funding; Regeneron Genetics Center: Other: Research Collaboration; Celgene/BMS: Other: Connect Lymphoma Scientific Steering Committee, Research Funding; NanoString: Research Funding. Novak: Celgene/BMS: Research Funding.

Background: Optimal patient selection for radiotherapy in pancreatic ductal adenocarcinoma (PDAC) is unestablished. Molecular profiling may select patients at high risk for locoregional recurrence (LRR) who would benefit from radiation. Methods: We included resectable pancreatic cancer (R-PDAC) patients, divided into training and validation cohorts, treated among three institutions with surgery and adjuvant chemotherapy, and borderline resectable or locally advanced pancreatic cancer (BR/LA-PDAC) patients treated with chemotherapy with or without radiation at the primary study institution. We isolated RNA from R-PDAC surgical specimens. Using NanoString, we identified miRNAs differentially expressed between normal and malignant pancreatic tissue. ElasticNet regression identified two miRNAs most predictive of LRR in the training cohort, miR-181b/d and miR-575, which were used to generate a risk score (RS). We evaluated the association of the median-dichotomized RS with recurrence and overall survival (OS). Results: We identified 183 R-PDAC and 77 BR/LA-PDAC patients with median follow up of 37 months treated between 2001 and 2014. On multivariable analysis of the R-PDAC training cohort (n = 90), RS was associated with worse LRR (HR = 1.34; 95%CI 1.27–11.38; p = 0.017) and OS (HR = 2.89; 95%CI 1.10–4.76; p = 0.027). In the R-PDAC validation cohort, RS was associated with worse LRR (HR = 2.39; 95%CI 1.03–5.54; p = 0.042), but not OS (p = 0.087). For BR/LA-PDAC, RS was associated with worse LRR (HR = 2.71; 95%CI 1.14–6.48; p = 0.025), DR (HR = 1.93; 95%CI 1.10–3.38; p = 0.022), and OS (HR = 1.97; 95%CI 1.17–3.34; p = 0.011). Additionally, after stratifying by RS and receipt of radiation in BR/LA-PDAC patients, high RS patients who did not receive radiation had worse LRR (p = 0.018), DR (p = 0.006), and OS (p < 0.001) compared to patients with either low RS or patients who received radiation, irrespective of RS. Conclusions: RS predicted worse LRR and OS in R-PDAC and worse LRR, DR, and OS in BR/LA-PDAC. This may select patients who would benefit from radiation and should be validated prospectively.

BACKGROUND Cytometry by time‐of‐flight (CyTOF) or mass cytometry is a form of flow cytometry. CyTOF uses antibodies bound to metal isotopes as tags instead of fluorochromes normally used in standard flow cytometry. These nonradioactive isotopes of heavy metals allow mass cytometry to analyze up to 50 different parameters without significant signal overlap. Due to the accuracy of mass cytometry, researches are able to investigate multiple cell markers at once, which is not possible with standard fluorescent flow cytometry (FFC). CyTOF works by feeding a cell sample tagged with conjugated antibodies into a nebulizer where cells are aerosolized. The aerosolized samples pass through an argon plasma torch and become an ionized atom cloud. This cloud passes through a quadrupole where low mass ions are filtered out resulting in a cloud enriched for the heavy metal reporter tags. The tags enter the time‐of‐flight chamber and further separated by mass as they reach the detector AIMS The main aim of this project was to optimize the stimulation conditions that are best suited for use with a CyTOF T cell functional panel. This would provide a method for decreased cell loss in sample preparation. METHODS T‐cell‐enriched samples underwent stimulation using 4 different condition: 4‐hour stimulation of PMA/Ionomycin, 1:1 ratio of Dynabeads at 24h, 48h, and 72h. Brefeldin A was placed in every well containing cells to act as transport inhibitor. FFC was used to test cell recovery and cell death between stimulation techniques and stimulation strength was observed using CyTOF. RESULTS There was no significant difference in 4h unstimulated cells and 24h unstimulated cells in both cell recovery and cell viability. However, comparing the PMA/Ionomycin unstimulated cells to the 4h stimulated cells did produce a significant decrease in cell recovery and cell death. Because this technique is a non‐physiological stimulation, this result was expected. The use of FFC also allowed us to see the significant loss of cells in recovery and death after 24h stimulation with the 48h and 72h Dynabead assays. The “sledge hammer” effect is clearly visible with the PMA/Ionomycin with 2.73% of IL‐4 cells being stimulated compared to only 0.28% in the 24h Dynabead sample. This trend of PMA/Ionomycin producing a strong stimulation signal was a trend. CONCLUSIONS We determined that T cell culture with Brefeldin A beyond 24 h significantly decreases cell viability and recovery. In observing the stimulation for Dynabeads at 24h, the cells are not directly affected while PMA/Ionomycin does cause a significant loss in cell viability and total cells recovered. PMA/Ionomycin is capable of stimulating the production of all measurable cytokines, even rare populations such as IL‐10‐ or IL‐6‐producing T cells. Dynabeads resemble a more physiological stimulation condition and is able to activate all T cell subsets except IL‐6‐producing T cells. Support or Funding Information The Berner Charitable and Scholarship Foundation, Funded by the Immune Monitoring Core and The Division of Hematology This abstract is from the Experimental Biology 2019 Meeting. There is no full text article associated with this abstract published in The FASEB Journal .

Background: Follicular Lymphoma (FL) is the second most common non-Hodgkin lymphoma and presents with significant clinical, cellular, molecular, and genetic heterogeneity. Despite the name and defining microanatomic location, the genetic and molecular identity and pathogenesis of the FL tumor cell is largely uncharacterized. Prior clinical and molecular classifications of FL have been primarily driven by pathologic classification (Grade 1-3b), genetic classification (M7-FLIPI), or gene expression profiling (IR-1/2). Using a unique cohort of 93 FL tumors, we have explored the transcriptomic signature of purified FL B cells, along with their matched whole tumor, and identified unique molecular subsets which are defined by distinct pathway activation, immune content, and genomic signatures identified through whole exome sequencing (WES). Methods: Frozen tumor biopsies from 93 untreated FL (Grade 1-3b) patients enrolled in the University of Iowa/Mayo Clinic Lymphoma SPORE were used for the study. DNA was isolated from whole tumor cell suspensions and RNA was isolated from both whole tumor and B cell enriched cell suspensions. RNA sequencing (RNAseq) and WES was performed in the Mayo Clinic Genome Analysis Core. RNAseq and WES data were processed using the Mayo Clinic standard pipeline and novel driver genes were identified using 20/20+ driver analysis. Copy number variants were identified using GISTIC 2.0. NMF clustering and single sample gene set testing, for B cell lineage and tumor microenvironment (TME) signatures, was performed in R using the NMF and SingScore packages. Results: NMF consensus clustering of FL B cell RNAseq data identified two distinct subsets, C1 (n=32) and C2 (n=57). Clinically, C1 was associated with being FL grade 3 (p&lt;0.001) and more aggressive therapy (e.g. immunochemotherapy) at diagnosis while other clinical factors were similar between the groups. To determine the biologic underpinnings that drove the NMF analysis, the top NMF metagenes were incorporated (n=389 for C1 and n=293 for C2) for GO analysis. Among those genes were AICDA, IRF4, and EZH2 in C1, and BCL2 in C2. Additionally, we identified significant gene set enrichment with NMF metagenes and calculated differential gene set expression comparing C1, C2, and normal B cells (n=5). Together, these analyses suggest C1 displays significantly (p&lt;0.05) enhanced DNA replication and metabolic activity, whereas C2 displays significantly enhanced cytoskeleton rearrangement, repressed inflammatory cytokine signaling, and NF-kB activity. Based on recent scRNAseq data suggesting that FL derives from distinct germinal center B cells, the B lineage difference between C1 and C2 was explored. Both subgroups were heterogeneous for dark (DZ), intermediate (INT), light (LZ) zones, and plasmablast (PBL) gene signatures. When compared to benign B cell samples, C1 was enriched for a pre-PBL (p&lt;0.01), and C2 for an INT phenotype related to a LZ signature (p&lt;0.01). To define if a genomic signature was associated with C1 and C2, mutations and copy number variants (&gt;5% frequency) were examined. TNFAIP3, TP53, and BCL6 alterations were enriched in C1 samples, whereas C2 associated with alterations in BCL2, KMT2D, CREBBP, REL, and MYC. Finally, B cell clusters were analyzed for TME signatures. C1 samples displayed significant enrichment of macrophage, cytotoxic cell, gamma-delta-Tcell, and endothelial cell TME elements (p&lt;0.05) consistent with an inflamed/hot tumor. In contrast, C2 samples exhibited significantly lower signatures (p&lt;0.05) in all entities except for the fibroblast signature, compared to C1 and normal, consistent with an immune desert phenotype. Conclusion: Our results suggest that B cells from FL patients display two distinct transcriptomic signatures. C1 identifies an immunologically active tumor, driven by TNFAIP3 alterations, with pre-PBL characteristics, DNA replication and repair, inflammatory cytokine secretion/signaling, and hyper-metabolic characteristics. C2 identifies an immunologically quiet tumor, driven by alterations in BCL2 and chromatin modifiers, with an intermediate GC phenotype, repressed cytokine signaling, and active cell cycle progression and cytoskeleton rearrangement. This study improves our understanding of the mechanisms driving FL tumors and motivates further investigation into the relationship between tumor intrinsic factors that may influence the TME. Disclosures Maurer: Morphosys: Membership on an entity's Board of Directors or advisory committees; Kite: Membership on an entity's Board of Directors or advisory committees; Pfizer: Membership on an entity's Board of Directors or advisory committees; Nanostring: Research Funding; Celgene / BMS: Research Funding. Ansell:ADC Therapeutics: Research Funding; Trillium: Research Funding; Affimed: Research Funding; Regeneron: Research Funding; AI Therapeutics: Research Funding; Takeda: Research Funding; Seattle Genetics: Research Funding; Bristol Myers Squibb: Research Funding. Cerhan:NanoString: Research Funding; BMS/Celgene: Research Funding. Novak:Celgene/BMS: Research Funding.

Abstract In response to the coronavirus disease 2019 (COVID-19) pandemic, the Cardio-Oncology and Imaging Councils of the American College of Cardiology offers recommendations to clinicians regarding the cardiovascular care of cardio-oncology patients in this expert consensus statement. Cardio-oncology patients—individuals with an active or prior cancer history and with or at risk of cardiovascular disease—are a rapidly growing population who are at increased risk of infection, and experiencing severe and/or lethal complications by COVID-19. Recommendations for optimizing screening and monitoring visits to detect cardiac dysfunction are discussed. In addition, judicious use of multimodality imaging and biomarkers are proposed to identify myocardial, valvular, vascular, and pericardial involvement in cancer patients. The difficulties of diagnosing the etiology of cardiovascular complications in patients with cancer and COVID-19 are outlined, along with weighing the advantages against risks of exposure, with the modification of existing cardiovascular treatments and cardiotoxicity surveillance in patients with cancer during the COVID-19 pandemic.