Putative Prognostic Biomarkers Research Articles

Comparative Analysis of Breast Cancer Metabolomes Highlights Fascin's Central Role in Regulating Key Pathways Related to Disease Progression.

Omics technologies provide useful tools for the identification of novel biomarkers in many diseases, including breast cancer, which is the most diagnosed cancer in women worldwide. We and others have reported a central role for the actin-bundling protein (fascin) in regulating breast cancer disease progression at different levels. However, whether fascin expression promotes metabolic molecules that could predict disease progression has not been fully elucidated. Here, fascin expression was manipulated via knockdown (fascinKD+NORF) and rescue (fascinKD+FORF) in the naturally fascin-positive (fascinpos+NORF) MDA-MB-231 breast cancer cells. Whether fascin dysregulates metabolic profiles that are associated with disease progression was assessed using untargeted metabolomics analyses via liquid chromatography-mass spectrometry. Overall, 12,226 metabolic features were detected in the tested cell pellets. Fascinpos+NORF cell pellets showed 2510 and 3804 significantly dysregulated metabolites compared to their fascinKD+NORF counterparts. Fascin rescue (fascinKD+FORF) revealed 2710 significantly dysregulated cellular metabolites compared to fascinKD+NORF counterparts. A total of 101 overlapped cellular metabolites between fascinKD+FORF and fascinpos+NORF were significantly dysregulated in the fascinKD+NORF cells. Analysis of the significantly dysregulated metabolites by fascin expression revealed their involvement in the metabolism of sphingolipid, phenylalanine, tyrosine, and tryptophan biosynthesis, and pantothenate and CoA biosynthesis, which are critical pathways for breast cancer progression. Our findings of fascin-mediated alteration of metabolic pathways could be used as putative poor prognostic biomarkers and highlight other underlying mechanisms of fascin contribution to breast cancer progression.

Comprehensive single-cell transcriptomic profiling reveals molecular subtypes and prognostic biomarkers with implications for targeted therapy in esophageal squamous cell carcinoma

BackgroundEsophageal squamous cell carcinoma (ESCC) is a genetically heterogeneous disease with poor clinical outcomes. Identification of biomarkers linked to DNA replication stress may enable improved prognostic risk stratification and guide therapeutic decision making. We performed integrated single-cell RNA sequencing and computational analyses to define the molecular determinants and subtypes underlying ESCC heterogeneity. MethodsSingle-cell RNA sequencing was performed on ESCC samples and analyzed using Seurat. Differential gene expression analysis was used to identify esophageal cell phenotypes. DNA replication stress-related genes were intersected with single-cell differential expression data to identify potential prognostic genes, which were used to generate a DNA replication stress (DRS) score. This score and associated genes were evaluated in survival analysis. Putative prognostic biomarkers were evaluated by Cox regression and consensus clustering. Mendelian randomization analyses assessed the causal role of PRKCB. ResultsHigh DRS score associated with poor survival. Four genes (CDKN2A, NUP155, PPP2R2A, PRKCB) displayed prognostic utility. Three molecular subtypes were identified with discrete survival and immune properties. A 12-gene signature displayed robust prognostic performance. PRKCB was overexpressed in ESCC, while PRKCB knockdown reduced ESCC cell migration. ConclusionsThis integrated single-cell sequencing analysis provides new insights into the molecular heterogeneity and prognostic determinants underlying ESCC. The findings identify potential prognostic biomarkers and a gene expression signature that may enable improved patient risk stratification in ESCC. Experimental validation of the role of PRKCB substantiates the potential clinical utility of our results.

Protein expression and localization of ABC transporters in pancreatic adenocarcinoma: Prognostic role of ABCC8

BackgroundATP-binding cassette (ABC) transporters translocate various substances across cellular membranes. Their deregulation may cause cancer drug resistance or perturbations in the supply of building blocks for cancer cells and modify patients’ prognosis. This study investigated protein expression and cellular localization of the previously suggested putative prognostic biomarkers – ABCB2/TAP1, ABCC7/CFTR, ABCC8/SUR1, and ABCD4 in patients with pancreatic ductal adenocarcinoma (PDAC). MethodsProtein expression and localization were assessed by immunohistochemistry in formalin-fixed paraffin-embedded primary tumor tissue blocks of 61 PDAC patients and associated with clinical data and the survival of patients. ResultsNo CFTR protein expression was observed in PDAC, while TAP1 and ABCC8 were expressed predominantly in the cytoplasm of tumor cells. Most samples (81 %) had detectable both membranous and cytoplasmic ABCD4 staining and 42 % had ABCD4 expressed in the apical orientation. Negative membranous ABCD4 staining was significantly more frequent in advanced stage III or IV tumors (p = 0.022). Small or medium counts of individual ABCC8-positive cells in the stroma surrounding tumor tubules were also more often found in stage III or IV (p = 0.044). Patients with moderate or strong ABCC8 cytoplasmic staining intensity in tumor cells had a 3.5-fold higher risk of disease progression than those with weak staining (p = 0.002). ConclusionsThe study shows for the first time that the cytoplasmic ABCC8 protein expression has prognostic value in PDAC.

Identification of Plausible Candidates in Prostate Cancer Using Integrated Machine Learning Approaches.

Currently, prostate-specific antigen (PSA) is commonly used as a prostate cancer (PCa) biomarker. PSA is linked to some factors that frequently lead to erroneous positive results or even needless biopsies of elderly people. In this pilot study, we undermined the potential genes and mutations from several databases and checked whether or not any putative prognostic biomarkers are central to the annotation. The aim of the study was to develop a risk prediction model that could help in clinical decision-making. An extensive literature review was conducted, and clinical parameters for related comorbidities, such as diabetes, obesity, as well as PCa, were collected. Such parameters were chosen with the understanding that variations in their threshold values could hasten the complicated process of carcinogenesis, more particularly PCa. The gathered data was converted to semi-binary data (-1, -0.5, 0, 0.5, and 1), on which machine learning (ML) methods were applied. First, we cross-checked various publicly available datasets, some published RNA-seq datasets, and our whole-exome sequencing data to find common role players in PCa, diabetes, and obesity. To narrow down their common interacting partners, interactome networks were analysed using GeneMANIA and visualised using Cytoscape, and later cBioportal was used (to compare expression level based on Z scored values) wherein various types of mutation w.r.t their expression and mRNA expression (RNA seq FPKM) plots are available. The GEPIA 2 tool was used to compare the expression of resulting similarities between the normal tissue and TCGA databases of PCa. Later, top-ranking genes were chosen to demonstrate striking clustering coefficients using the Cytoscape-cytoHubba module, and GEPIA 2 was applied again to ascertain survival plots. Comparing various publicly available datasets, it was found that BLM is a frequent player in all three diseases, whereas comparing publicly available datasets, GWAS datasets, and published sequencing findings, SPFTPC and PPIMB were found to be the most common. With the assistance of GeneMANIA, TMPO and FOXP1 were found as common interacting partners, and they were also seen participating with BLM. A probabilistic machine learning model was achieved to identify key candidates between diabetes, obesity, and PCa. This, we believe, would herald precision scale modeling for easy prognosis.

Sampling Cerebrospinal Fluid and Blood from Lateral Tail Vein in Rats during EEG Recordings.

Because the composition of body fluids reflects many physiological and pathological dynamics, biological liquid samples are commonly obtained in many experimental contexts to measure molecules of interest, such as hormones, growth factors, proteins, or small non-coding RNAs. A specific example is the sampling of biological liquids in the research of biomarkers for epilepsy. In these studies, it is desirable to compare the levels of molecules in cerebrospinal fluid (CSF) and in plasma, by withdrawing CSF and plasma in parallel and considering the time distance of the sampling from and to seizures. The combined CSF and plasma sampling, coupled with video-EEG monitoring in epileptic animals, is a promising approach for the validation of putative diagnostic and prognostic biomarkers. Here, a procedure of combined CSF withdrawal from cisterna magna and blood sampling from the lateral tail vein in epileptic rats that are continuously video-EEG monitored is described. This procedure offers significant advantages over other commonly used techniques. It permits rapid sampling with minimal pain or invasiveness, and reduced time of anesthesia. Additionally, it can be used to obtain CSF and plasma samples in both tethered and telemetry EEG recorded rats, and it may be used repeatedly across multiple days of experiment. By minimizing the stress due to sampling by shortening isoflurane anesthesia, measures are expected to reflect more accurately the true levels of investigated molecules in biofluids. Depending on the availability of an appropriate analytical assay, this technique may be used to measure the levels of multiple, different molecules while performing EEG recording at the same time.

Meta-analysis of the microbial biomarkers in the gut-lung crosstalk in COVID-19, community-acquired pneumonia and Clostridium difficile infections.

Respiratory infections are the leading causes of mortality and the current pandemic COVID‐19 is one such trauma that imposed catastrophic devastation to the health and economy of the world. Unravelling the correlations and interplay of the human microbiota in the gut–lung axis would offer incredible solutions to the underlying mystery of the disease progression. The study compared the microbiota profiles of six samples namely healthy gut, healthy lung, COVID‐19 infected gut, COVID‐19 infected lungs, Clostridium difficile infected gut and community‐acquired pneumonia infected lungs. The metagenome data sets were processed, normalized, classified and the rarefaction curves were plotted. The microbial biomarkers for COVID‐19 infections were identified as the abundance of Candida and Escherichia in lungs with Ruminococcus in the gut. Candida and Staphylococcus could play a vital role as putative prognostic biomarkers of community‐acquired pneumonia whereas abundance of Faecalibacterium and Clostridium is associated with the C. difficile infections in gut. A machine learning random forest classifier applied to the data sets efficiently classified the biomarkers. The study offers an extensive and incredible understanding of the existence of gut–lung axis during dysbiosis of two anatomically different organs.

Daily Assessment of On-Treatment Tumor Regression by Cone Beam CT as a Prognostic Dynamic Biomarker in Nasopharyngeal Cancer

<h3>Purpose/Objective(s)</h3> Despite advancements in radiotherapy, approximately 30% of nasopharyngeal cancer (NPC) patients develop treatment failure. Dynamic on-treatment biomarkers may provide early evidence of response and improve risk stratification and treatment selection. Using daily cone beam computed tomography (CBCT) scans, we measure air volume recovery, an automated surrogate of tumor regression, and report its association with outcomes. <h3>Materials/Methods</h3> Patients with NPC treated with curable intent were included. Primary gross target volume (GTVp) was propagated from planning CTs to daily CBCTs by rigid registration and a density threshold was applied to measure the air volume in a 5 mm uniform expansion of the GTVp. Air volume was expressed as percent of planning GTVp volume. Missing values were interpolated, air volume trajectories were smoothed by sliding window averaging, and area under the curve (AUC) was calculated to provide a summary metric capturing both magnitude and rapidity of air volume recovery. Primary endpoint was recurrence free survival (RFS). <h3>Results</h3> Between 2013 and 2015, 39 consecutive NPC patients (2 stage II, 26 stage III, 11 stage IVA-B), ECOG 0-1, underwent radical radiotherapy (n=5) or chemoradiotherapy (CRT) (n=34) to a planned dose of 70 Gy in 35 fractions (Fx). Median AUC of the air volume recovery curve was 2.8 (Range: 0.05-7.4). Patients with above-median AUC had longer RFS (HR=0.28, log-rank p=0.045, median follow-up: 83 mo). Exploration of clinical factors associated with air recovery showed that AUC was higher in CRT than RT alone (OR: 1.74±1.60, p=0.04), lower in large tumors with high GTVp volume (OR: -0.037±0.18, p<0.001), and was not correlated with disease stage. Current smokers had 26% lower median AUC than never/former smokers (p=0.132). AUC was lower in patients with detectable end-of-treatment Epstein-Barr virus (EBV) circulating tumor DNA (median AUC 0.99 vs. 2.8 in undetectable EBV, p=0.021). We performed exploratory <i>post-hoc</i> analysis on air volume velocity (slope of air volume curve) and identified two independent putative positive prognostic biomarkers: high mid-treatment slope and negative end-treatment slope. Patients without failure had higher mid-treatment air recovery velocity (Fx 15-22), with Fx 18 velocity yielding the maximal RFS difference (HR: 0.12, p = 0.007). Negative velocity at the end of treatment was associated with improved regional (p=0.033) and distant metastatic (p=0.013) control. <h3>Conclusion</h3> Greater and more rapid tumor regression, inferred by the CBCT surrogate measure of air volume recovery, was associated with improved RFS. This discovery cohort also allowed us to identify promising dynamic biomarkers which might guide treatment adaptation. Specifically, we observed strong clinical outcome associations with mid- and end-treatment air recovery velocities. Validation of these biomarkers is ongoing in a large cohort of NPC patients with mature follow up.

Peripheral blood CD4posCD25posFoxP3pos cells and inflammatory cytokines as biomarkers of response in rheumatoid arthritis patients treated with CTLA4-Ig

BackgroundPrognostic biomarkers of treatment response to distinct biologic disease-modifying anti-rheumatic drugs (b-DMARDs) are still lacking within the management of rheumatoid arthritis (RA).MethodsThirty-four b-DMARDs naive RA patients, divided by disease duration into early (cohort 1) and long standing (cohort 2), received CTLA4-Ig. At study entry, and every 3 months for 1 year, each patient underwent peripheral blood (PB)-derived CD4pos cell subpopulation assessment by flow cytometry, STAT3 and STAT5 expression by RT-PCR and IL-6, IL-12p70, TGFβ, and IL-10 serum levels by ELISA. The DAS and CDAI remission was assessed at 6 and 12 months.ResultsDAS- and CDAI-defined remission within 12 months was achieved by 16 (47.1%) and 8 (23.5%) RA patients, respectively. Considering the whole RA cohort, CTLA4-Ig induced a significant decrease of IL-6 serum levels from baseline to 6 and 12 months, as well as of PB CD4posCD25posFoxP3pos cells at 6 and 12 months, and of CD4posIL17pos cells after 12 months. PB CD4pos cells of RA patients showed higher STAT3 and STAT5 expression than healthy controls, which remained unchanged within 12 months of treatment. At study entry, RA patients achieving DAS remission had significantly lower IL-6 serum levels than RA patients not achieving this outcome. In particular, having baseline IL-6 serum levels ≤ 8.4 pg/ml, significantly identified naïve to b-DMARDs RA patients more likely to achieve DAS-remission under CTLA4-Ig at 6 months (66.7%) compared to RA patients with baseline IL-6 serum levels > 8.4 pg/ml [15.4%, OR (95%Cis) 11.00 (1.75–55.82)]. Moreover, having CD4posCD25posFoxP3pos cells rate ≥ 6.0% significantly identifies naïve to b-DMARDs early RA patients more likely to achieve DAS remission at 6 months (83.3%) compared to RA patients with baseline CD4posCD25posFoxP3pos cells < 6.0% [16.7%, OR (95% Cis) 25.00 (1.00–336.81)].ConclusionsBaseline IL-6 serum levels and peripheral blood-derived CD4pos subpopulations are putative novel prognostic biomarkers of CTLA4-Ig response in RA patients.

Comprehensive Analysis of the Differential Expression and Prognostic Value of Histone Deacetylases in Glioma.

Gliomas are the most common and aggressive malignancies of the central nervous system. Histone deacetylases (HDACs) are important targets in cancer treatment. They regulate complex cellular mechanisms that influence tumor biology and immunogenicity. However, little is known about the function of HDACs in glioma. The Oncomine, Human Protein Atlas, Gene Expression Profiling Interactive Analysis, Broad Institute Cancer Cell Line Encyclopedia, Chinese Glioma Genome Atlas, OmicShare, cBioPortal, GeneMANIA, STRING, and TIMER databases were utilized to analyze the differential expression, prognostic value, and genetic alteration of HDAC and immune cell infiltration in patients with glioma. HDAC1/2 were considerable upregulated whereas HDAC11 was significantly downregulated in cancer tissues. HDAC1/2/3/4/5/7/8/11 were significantly correlated with the clinical glioma stage. HDAC1/2/3/10 were strongly upregulated in 11 glioma cell lines. High HDCA1/3/7 and low HDAC4/5/11 mRNA levels were significantly associated with overall survival and disease-free survival in glioma. HDAC1/2/3/4/5/7/9/10/11 are potential useful biomarkers for predicting the survival of patients with glioma. The functions of HDACs and 50 neighboring genes were primarily related to transcriptional dysregulation in cancers and the Notch, cGMP-PKG, and thyroid hormone signaling pathways. HDAC expression was significantly correlated with the infiltration of B cells, CD4+ T cells, CD8+ T cells, macrophages, neutrophils, and dendritic cells in glioma. Our study indicated that HDACs are putative precision therapy targets and prognostic biomarkers of survival in glioma patients.

Identification of long non-coding RNAs and RNA binding proteins in breast cancer subtypes

Breast cancer is a heterogeneous disease classified into four main subtypes with different clinical outcomes, such as patient survival, prognosis, and relapse. Current genetic tests for the differential diagnosis of BC subtypes showed a poor reproducibility. Therefore, an early and correct diagnosis of molecular subtypes is one of the challenges in the clinic. In the present study, we identified differentially expressed genes, long non-coding RNAs and RNA binding proteins for each BC subtype from a public dataset applying bioinformatics algorithms. In addition, we investigated their interactions and we proposed interacting biomarkers as potential signature specific for each BC subtype. We found a network of only 2 RBPs (RBM20 and PCDH20) and 2 genes (HOXB3 and RASSF7) for luminal A, a network of 21 RBPs and 53 genes for luminal B, a HER2-specific network of 14 RBPs and 30 genes, and a network of 54 RBPs and 302 genes for basal BC. We validated the signature considering their expression levels on an independent dataset evaluating their ability to classify the different molecular subtypes with a machine learning approach. Overall, we achieved good performances of classification with an accuracy >0.80. In addition, we found some interesting novel prognostic biomarkers such as RASSF7 for luminal A, DCTPP1 for luminal B, DHRS11, KLC3, NAGS, and TMEM98 for HER2, and ABHD14A and ADSSL1 for basal. The findings could provide preliminary evidence to identify putative new prognostic biomarkers and therapeutic targets for individual breast cancer subtypes.

Emerging Role of isomiRs in Cancer: State of the Art and Recent Advances.

The advent of Next Generation Sequencing technologies brought with it the discovery of several microRNA (miRNA) variants of heterogeneous lengths and/or sequences. Initially ascribed to sequencing errors/artifacts, these isoforms, named isomiRs, are now considered non-canonical variants that originate from physiological processes affecting the canonical miRNA biogenesis. To date, accurate IsomiRs abundance, biological activity, and functions are not completely understood; however, the study of isomiR biology is an area of great interest due to their high frequency in the human miRNome, their putative functions in cooperating with the canonical miRNAs, and potential for exhibiting novel functional roles. The discovery of isomiRs highlighted the complexity of the small RNA transcriptional landscape in several diseases, including cancer. In this field, the study of isomiRs could provide further insights into the miRNA biology and its implication in oncogenesis, possibly providing putative new cancer diagnostic, prognostic, and predictive biomarkers as well. In this review, a comprehensive overview of the state of research on isomiRs in different cancer types, including the most common tumors such as breast cancer, colorectal cancer, melanoma, and prostate cancer, as well as in the less frequent tumors, as for example brain tumors and hematological malignancies, will be summarized and discussed.

Neutrophil NETworking in ENL: Potential as a Putative Biomarker: Future Insights.

Erythema nodosum leprosum (ENL), also known as type 2 reaction (T2R) is an immune complex mediated (type III hypersensitivity) reactional state encountered in patients with borderline lepromatous and lepromatous leprosy (BL and LL) either before, during, or after the institution of anti-leprosy treatment (ALT). The consequences of ENL may be serious, leading to permanent nerve damage and deformities, constituting a major cause of leprosy-related morbidity. The incidence of ENL is increasing with the increasing number of multibacillary cases. Although the diagnosis of ENL is not difficult to make for physicians involved in the care of leprosy patients, its management continues to be a most challenging aspect of the leprosy eradication program: the chronic and recurrent painful skin lesions, neuritis, and organ involvement necessitates prolonged treatment with prednisolone, thalidomide, and anti-inflammatory and immunosuppressive drugs, which further adds to the existing morbidity. In addition, the use of immunosuppressants like methotrexate, azathioprine, cyclosporine, or biologics carries a risk of reactivation of persisters (Mycobacterium leprae), apart from their own end-organ toxicities. Most ENL therapeutic guidelines are primarily designed for acute episodes and there is scarcity of literature on management of patients with chronic and recurrent ENL. It is difficult to predict which patients will develop chronic or recurrent ENL and plan the treatment accordingly. We need simple point-of-care or ELISA-based tests from blood or skin biopsy samples, which can help us in identifying patients who are likely to require prolonged treatment and also inform us about the prognosis of reactions so that appropriate therapy may be started and continued for better ENL control in such patients. There is a significant unmet need for research for better understanding the immunopathogenesis of, and biomarkers for, ENL to improve clinical stratification and therapeutics. In this review we will discuss the potential of neutrophils (polymorphonuclear granulocytes) as putative diagnostic and prognostic biomarkers by virtue of their universal abundance in human blood, functional versatility, phenotypic heterogeneity, metabolic plasticity, differential hierarchical cytoplasmic granule mobilization, and their ability to form NETs (neutrophil extracellular traps). We will touch upon the various aspects of neutrophil biology relevant to ENL pathophysiology in a step-wise manner. We also hypothesize about an element of metabolic reprogramming of neutrophils by M. leprae that could be investigated and exploited for biomarker discovery. In the end, a potential role for neutrophil derived exosomes as a novel biomarker for ENL will also be explored.

Fluctuating biomarkers in primary sclerosing cholangitis: A longitudinal comparison of alkaline phosphatase, liver stiffness, and ELF

Background & AimsPrimary sclerosing cholangitis (PSC) is a progressive liver disease characterised by fluctuating liver biochemistries and highly variable disease progression. The Enhanced Liver Fibrosis (ELF®) test and liver stiffness measurements (LSMs) reflect fibrosis and predict clinical outcomes in PSC; however, longitudinal assessments are missing. We aimed to characterise the systematic change in ELF and LSM over time in a prospective cohort of patients with PSC, along with their longitudinal relationship to alkaline phosphatase (ALP) and bilirubin.MethodsWe included 113 non-transplant PSC patients (86 males [76.1%]; mean age 43.3 ± 15.7 years) with annual study visits between 2013 and 2019 at 2 Norwegian centres. ELF test, LSM, clinical data, liver biochemistries, and revised Mayo risk score were measured. We used linear mixed-effects models to estimate change over time, intraclass correlations (ICCs), and their relationship with ALP and bilirubin.ResultsAt baseline, the median (range) ELF test was 9.3 (7.5–12.9) and median LSM 1.26 m/s (0.66–3.04 m/s). ELF and LSM increased over time (0.09 point/year, 95% CI [0.03, 0.15], p = 0.005, vs. 0.12 point/year, 95% CI [0.03, 0.21], p = 0.009). Between-patient effects explained 78% of ELF variation (ICC 0.78) and 56% of LSM variation (ICC 0.56). ALP also increased and showed the highest ICC (0.86).ConclusionsELF and LSM increased over a 5-year period. Longitudinal analyses demonstrated differences regarding within- and between-patient effects, suggesting that the ELF test may have superior reliability for risk stratification compared with LSM in PSC.Lay summaryPrimary sclerosing cholangitis (PSC) is characterised by substantial disease variability between patients and fluctuating liver biochemistries. Hence, new biomarkers are needed to identify individuals with an increased risk of developing end-stage liver disease. We explore the change over time of 2 putative prognostic biomarkers in PSC, the serum Enhanced Liver Fibrosis (ELF®) test and LSMs by ultrasound, demonstrating differences that may reflect differing abilities to discriminate risk.

Predicting outcomes of COVID-19 from admission biomarkers: a prospective UK cohort study

IntroductionCOVID-19 has an unpredictable clinical course, so prognostic biomarkers would be invaluable when triaging patients on admission to hospital. Many biomarkers have been suggested using large observational datasets but sample...

Abstract PR08: The genomic landscape of metastatic urothelial carcinoma from circulating tumor DNA

Abstract The recent expansion of treatment options for patients with metastatic urothelial carcinoma (mUC) has emphasized the need for molecular biomarkers in this setting. However, knowledge of the somatic genome landscape in mUC is limited; large-scale sequencing efforts have relied on primary tumor tissue from muscle-invasive bladder cancer (MIBC), revealing a molecularly heterogeneous disease characterized in particular by high somatic mutation rates. Putative prognostic and predictive biomarkers described from primary tissue need validation in metastatic tumors. Given the challenge of obtaining metastatic tissue, we sought to utilize circulating tumor DNA (ctDNA) to characterize the somatic landscape in mUC. We collected 162 whole-blood samples from 90 mUC patients. Targeted next-generation sequencing was performed on cell-free DNA (cfDNA) and matched leukocyte DNA utilizing a custom 50-gene panel. Somatic alteration frequencies in our metastatic cohort were compared to those reported for primary MIBC by TCGA (Cell 2017, n=412), with data obtained via cBioPortal. Our cohort of 90 mUC patients included 14% with upper tract disease. The median cfDNA sequencing depth was 986x, with ctDNA detectable in at least one blood collection for 81% (73/90) of mUC patients. In 10/73 patients, the estimated tumor mutation burden was ≥30 mutations per Mb. TP53 was the most frequently mutated gene (46/73 ctDNA-positive patients). Chromatin modifiers were also frequently altered: ARID1A 27%, and 25% for KDM6A and KMT2D. Genes mutated in the PI3K pathway included PIK3CA (22%), PTEN (5.5%), and PIK3R1 (1.4%). FGFR3 mutations were identified in 8.2% of patients. ERBB2 mutations were present in 12.3% of patients, and amplification was detected in eight patients. Thus, the metastatic somatic landscape closely resembles primary disease; however, comparison to TCGA dataset revealed mutations in TP53 and FGFR1 were enriched for in the metastatic setting (TP53: 63.0% vs. 48.1%, p = 0.022; FGFR1: 5.5% vs. 1.5%, p = 0.049; two-sided Fisher's exact test). Profiling of ctDNA from a large mUC cohort suggests a relatively similar somatic landscape to primary MIBC. However, alterations in key driver genes are potentially over- or underrepresented in metastatic disease, which has prognostic implications. In mUC, ctDNA profiling is a powerful alternative to metastatic tissue biopsy. This abstract is also being presented as Poster B19. Citation Format: Gillian R. Vandekerkhove, Matti Annala, Jean-Michel Lavoie, Nora Sundahl, Simon Walz, Takeshi Sano, Andrew Murtha, Tilman Todenhöfer, Piet Ost, Kim N. Chi, Peter C. Black, Bernhard Eigl, Alexander W. Wyatt. The genomic landscape of metastatic urothelial carcinoma from circulating tumor DNA [abstract]. In: Proceedings of the AACR Special Conference on Bladder Cancer: Transforming the Field; 2019 May 18-21; Denver, CO. Philadelphia (PA): AACR; Clin Cancer Res 2020;26(15_Suppl):Abstract nr PR08.

Identification and expression analysis of novel splice variants of the human carcinoembryonic antigen-related cell adhesion molecule 19 (CEACAM19) gene using a high-throughput sequencing approach

Alternative splicing is commonly involved in carcinogenesis, being highly implicated in differential expression of cancer-related genes. Recent studies have shown that the human CEACAM19 gene is overexpressed in malignant breast and ovarian tumors, possessing significant biomarker attributes. In the present study, 3′ rapid amplification of cDNA ends (3′ RACE) and next-generation sequencing (NGS) were used for the detection and identification of novel CEACAM19 transcripts. Bioinformatical analysis of our NGS data revealed novel splice junctions between previously annotated exons and ultimately new exons. Next, fifteen novel CEACAM19 transcripts were identified with Sanger sequencing. Additionally, their expression profile was investigated in a wide panel of human cell lines, using nested PCR with variant-specific primers. The broad expression pattern of the CEACAM19 gene, along with the fact that its overexpression has previously been associated with ovarian and breast cancer progression, indicate the potential of novel CEACAM19 transcripts as putative diagnostic and/or prognostic biomarkers.

Multiplexed MRM-based protein quantification of putative prognostic biomarkers for chronic kidney disease progression in plasma

Current diagnostic measures for Chronic Kidney Disease (CKD) include detection of reduced estimated glomerular filtration rate (eGFR) and albuminuria, which have suboptimal accuracies in predicting disease progression. The disease complexity and heterogeneity underscore the need for multiplex quantification of different markers. The goal of this study was to determine the association of six previously reported CKD-associated plasma proteins [B2M (Beta-2-microglobulin), SERPINF1 (Pigment epithelium-derived factor), AMBP (Protein AMBP), LYZ (Lysozyme C), HBB (Hemoglobin subunit beta) and IGHA1 (Immunoglobulin heavy constant alpha 1)], as measured in a multiplex format, with kidney function, and outcome. Antibody-free, multiple reaction monitoring mass spectrometry (MRM) assays were developed, characterized for their analytical performance, and used for the analysis of 72 plasma samples from a patient cohort with longitudinal follow-up. The MRM significantly correlated (Rho = 0.5–0.9) with results from respective ELISA. Five proteins [AMBP, B2M, LYZ, HBB and SERPINF1] were significantly associated with eGFR, with the three former also associated with unfavorable outcome. The combination of these markers provided stronger associations with outcome (p < 0.0001) compared to individual markers. Collectively, our study describes a multiplex assay for absolute quantification and verification analysis of previously described putative CKD prognostic markers, laying the groundwork for further use in prospective validation studies.

Impact of CDKN2A/b status in pancreatic cancer (PC).

759 Background: PC is a lethal disease with limited treatment options. We utilized Comprehensive Genomic Profiling (CGP) to identify putative prognostic and/or predictive biomarkers. Methods: We retrospectively reviewed PC patients (pts) at our institution who underwent CGP utilizing the Foundation One assay. CGP was performed on hybrid-capture, adaptor ligation-based libraries for up to 315 genes plus 47 introns from 19 genes frequently rearranged in cancer. PC pts were categorized by clinical stage – localized (resectable and borderline resectable PC; LPC), locally advanced (LAPC) and metastatic (mPC). Effect of gene alterations (GAs) with at least 10% prevalence were analyzed. The marginal effect of each gene on radiographic response and survival outcomes was estimated using proportional odds and multivariate Cox regression analysis, respectively, adjusting for stage. Results: Ninety-three pts were identified - median age was 63, 55% were male, and 50% were smokers. Clinical stage at diagnosis was LPC, LAPC and mPC in 42 (45%), 23 (25%) and 28 (30%) pts, respectively. The most commonly altered genes were KRAS (94%), TP53 (75%), CDKN2A (41.2%) and SMAD4 (32.9%). All patients were microsatellite stable and the median tumor mutational burden was 1.7. 5-FU (52%) or Gemcitabine (46%) based chemotherapy combinations were utilized as the first systemic therapy. Median overall survival for patients with LPC, LAPC and mPC were 30.7, 28.8 and 9.6 months respectively. Thirty-eight (91%) pts with LPC underwent curative intent surgery compared to 15 (65%) pts with LAPC (p = 0.019). Thirty-five (95%) pts with wild type (WT) CDKN2A and 47 (94%) pts with WT CDKN2B underwent curative intent surgery compared to 13 (65%) and 1(14%) pt(s) with GAs in CDKN2A and CDKN2B respectively (p = 0.003 and p &lt; 0.0001 respectively). The response to chemotherapy was statistically significantly higher in pts with WT CDKN2A (53%) and CDKN2B (48%) compared to pts with GAs in CDKN2A (19%) and CDKN2B (12%) (p = 0.03 and p = 0.05, respectively). Conclusions: GAs in CDKN2A/B may have a predictive and possibly a prognostic impact. The clinical validity and biological relevance of these findings need to be further explored in larger studies.

OSbrca: A Web Server for Breast Cancer Prognostic Biomarker Investigation With Massive Data From Tens of Cohorts.

Potential prognostic mRNA biomarkers are exploited to assist in the clinical management and treatment of breast cancer, which is the first life-threatening tumor in women worldwide. However, it is technically challenging for untrained researchers to process high dimensional profiling data to screen and validate the potential prognostic values of genes of interests in multiple cohorts. Our aim is to develop an easy-to-use web server to facilitate the screening, developing, and evaluating of prognostic biomarkers in breast cancers. Herein, we collected more than 7,400 cases of breast cancer with gene expression profiles and clinical follow-up information from The Cancer Genome Atlas and Gene Expression Omnibus data, and built an Online consensus Survival analysis web server for Breast Cancers, abbreviated OSbrca, to generate the Kaplan–Meier survival plot with a hazard ratio and log rank P-value for given genes in an interactive way. To examine the performance of OSbrca, the prognostic potency of 128 previously published biomarkers of breast cancer was reassessed in OSbrca. In conclusion, it is highly valuable for biologists and clinicians to perform the preliminary assessment and validation of novel or putative prognostic biomarkers for breast cancers. OSbrca could be accessed at http://bioinfo.henu.edu.cn/BRCA/BRCAList.jsp.

Evaluation of quantitative imaging parameters in head and neck squamous cell carcinoma.

Functional imaging such as 18F-fluoro-deoxy-glucose positron emission tomography/computed tomography (FDG-PET/CT), 18F-fluoro-misonidazole (F-MISO)-PET/CT, and diffusion-weighted magnetic resonance imaging (DW-MRI) can assess complex biological phenomena in tumors reflecting underlying disease biology. The aim of this prospective observational study was to correlate quantitative imaging parameters derived from pretreatment biological imaging such as FDG-PET/CT, F-MISO-PET/CT, and DW-MRI with each other and with clinical outcomes in patients with head and neck squamous cell carcinoma (HNSCC) treated with definitive radio(chemo)therapy. Twenty patients with pharyngo-laryngeal cancers underwent pretreatment biological imaging. Gross tumor volume (GTV) was delineated on axial planning CT (GTV<inf>CT</inf>). Quantitative FDG-PET/CT parameters included maximum, mean, minimum standardized uptake values (SUV<inf>max-FDG</inf>, SUV<inf>mean-FDG</inf>, SUV<inf>min-FDG</inf>); metabolic tumor volume (MTV); and total lesion glycolysis (TLG). F-MISO-PET/CT parameters included hypoxic tumor volume (HTV); maximum, mean, minimum SUV; and fractional hypoxic volume (FHV). Mean apparent diffusion coefficient (ADC<inf>mean</inf>) was derived from DW-MRI. There was moderately strong positive correlation (r=0.616, P=0.005) between GTV<inf>CT</inf> and MTV. HTV derived from F-MISO-PET/CT at 3-hours (HTV<inf>3hrs-F-MISO</inf>) showed strong positive correlation with GTV<inf>CT</inf> (r=0.753, P<0.0001) and MTV (r=0.796, P<0.0001) respectively. ADC<inf>mean</inf> showed strong positive correlations with SUV<inf>mean-5hrs-F-MISO</inf> (r=0.713, P=0.021) and SUV<inf>min-5hrs-F-MISO</inf> (r=0.731, P=0.016) respectively. A moderate negative correlation (r=-0.500, P=0.049) was observed between ADC<inf>mean</inf> and MTV. At a median follow up of 44 months, the 5-year Kaplan-Meier estimates of loco-regional control, disease-free survival, and overall survival were 53%, 43%, and 40% respectively. Larger volume of primary tumor (GTV<inf>CT</inf>>22cc and MTV>7.9cc) and increasing hypoxia (HTV<inf>3hr-F-MSO</inf>>4.9cc) were associated with worse outcomes. Functional imaging represents an attractive and non-invasive modality to assess complex biological phenomena in solid tumors. Larger tumor volume and increasing hypoxia emerged as putative prognostic imaging biomarkers in HNSCC.