Fragment Profiles Research Articles

Comprehensive study on degradation profile of firocoxib and structural elucidation of its key degradation products

Firocoxib is widely used in veterinary medicine as a non-steroidal analgesic and anti-inflammatory drug substance. Herein, a comprehensive study on the degradation profile of firocoxib was performed through force degradation studies to understand its degradation profile and characterize its major degradation products (DPs). Firocoxib drug substance was subjected to acidic, alkaline, oxidation (H2O2, KMnO4, and K2Cr2O7), thermal (solid and solution state), and photolytic (solid and solution state) stress degradation, as recommended in the ICH guidelines. Firocoxib and its major DPs were adequately separated by investigational HPLC method, which utilized a HALO C18 (100 × 2.1 mm, 2.0 µm) column. Mobile phase-A for the HPLC method is composed of 0.1% formic acid in water and mobile phase-B acetonitrile. A total of six major DPs were observed for firocoxib drug substance under these stress degradation conditions. Structural elucidation of the DPs performed using liquid chromatography-high resolution mass spectrometry and comparison of their fragmentation profile with that of the parent compound. For further structural confirmation of two major DPs, DP-2 and DP-6 were isolated and purified from the stressed samples using a preparative HPLC and analyzed by comprehensive nuclear magnetic resonance (NMR) spectroscopy studies. Most probable mechanistic pathways for the formation of DPs of firocoxib under various stress degradation conditions were postulated to understand its degradation profile. Based on the results from forced degradation, firocoxib was found to be quite stable under basic and thermal conditions, and somewhat unstable under acidic, oxidative, and photolytic conditions. The results of this study should facilitate quality monitoring and establish a stability profile of firocoxib drug substance and drug products. These results may also assist in the design and development of new formulations made with firocoxib drug substance with desired shelf life.

Cerebrospinal fluid and blood profiles of transfer RNA fragments show age, sex, and Parkinson's disease-related changes.

Transfer RNA fragments (tRFs) have recently been shown to be an important family of small regulatory RNAs with diverse functions. Recent reports have revealed modified tRF blood levels in a number of nervous system conditions including epilepsy, ischemic stroke, and neurodegenerative diseases, but little is known about tRF levels in the cerebrospinal fluid (CSF). To address this issue, we studied age, sex, and Parkinson's disease (PD) effects on the distributions of tRFs in the CSF and blood data of healthy controls and PD patients from the NIH and the Parkinson's Progression Markers Initiative (PPMI) small RNA-seq datasets. We discovered that long tRFs are expressed in higher levels in the CSF than in the blood. Furthermore, the CSF showed a pronounced age-associated decline in the level of tRFs cleaved from the 3'-end and anti-codon loop of the parental tRNA (3'-tRFs, i-tRFs), and more pronounced profile differences than the blood profiles between the sexes. In comparison, we observed moderate age-related elevation of blood 3'-tRF levels. In addition, distinct sets of tRFs in the CSF and in the blood segregated PD patients from controls. Finally, we found enrichment of tRFs predicted to target cholinergic mRNAs (Cholino-tRFs) among mitochondrial-originated tRFs, raising the possibility that the neurodegeneration-related mitochondrial impairment in PD patients may lead to deregulation of their cholinergic tone. Our findings demonstrate that the CSF and blood tRF profiles are distinct and that the CSF tRF profiles are modified in a sex-, age-, and disease-related manner, suggesting that they reflect the inter-individual cerebral differences and calling for incorporating this important subset of small RNA regulators into future studies.

Quantification of Grapiprant and Its Stability Testing under Changing Environmental Conditions.

Grapiprant is a new analgesic and anti-inflammatory drug belonging to the piprant class, approved in 2016 by the FDA Veterinary Medicine Center for the treatment of pain and inflammation associated with osteoarthritis in dogs. It acts as a highly selective antagonist of the EP4 receptor, one of the four prostaglandin E2 (PGE2) receptor subtypes. It has been shown to have anti-inflammatory effects in rat models of acute and chronic inflammation and clinical studies in people with osteoarthritis. The current state of knowledge suggests the possibility of using it in oncological therapy. The manuscript presents the development of conditions for the identification and quantitative determination of grapiprant by thin-layer chromatography with densitometric detection. The optimal separation of the substance occurs using silica gel 60F254 chromatographic plates and the mobile phase containing ethyl acetate-toluene-butylamine. Validation (according to ICH requirements) showed that the developed method is characterized by straightness of results in a wide concentration range with the limit of detection of 146.65 µg/mL. The %RSD values of the precision and accuracy confirm the sensitivity and reliability of the developed procedure. Next, the method was used for quantification of grapiprant in a pharmaceutical preparation, and for stability studies under various environmental conditions. Additionally, the mass studies were carried out on the stressed samples using the UPLC-MS/MS method. The degradation products were primarily characterized by comparing their mass fragmentation profiles with those of the drug. The results indicated a potential degradation pathway for grapiprant.

169P Urine tumor DNA fragmentation profiles provided a novel biomarker for early detection of prostate cancer

Prostate Cancer is the second most common malignancy worldwide in male. Serum total PSA (tPSA) testing has led to the identification of patients with indolent prostate cancer, and inevitably over-treatment has become a concern. Urine tumor (utDNA) provides new approach for non-invasive early detection of prostate cancer.

Systematic Review on Implication for DNA Assisted Technology into Molecular Medicine and the useful is the application of Genome Wide Studies

Objective: The aim of this study is to explore the role of molecular DNA, DNA Phenotyping and Polymerase Chain Reaction in samples relevant to genetic investigation. The Deoxyribonucleic Acid (DNA) is a macromolecule which has propelled our capacity of understanding the function of an organism at the cellular level, how organism reproduce and replicate, and pass their subjective genetic information from one generation to the other. DNA is also referred to as the genetic “Blueprint” of an organism and found to possess all information pertaining to the specific being. Nuclear DNA is present within the nucleus and is significant in developing immunity for the cell and depends on the information incorporated within its framework. Mitochondrial DNA exists in varying locations in frequencies of two to ten copies within mitochondria. The most familiar body fluids come across in molecular medicine laboratories is blood, semen and saliva and further more are vaginal fluids, urine and sweat. Bones, teeth, soft tissues etc. are also essential biomarkers for DNA-phenotyping. Methodology: The methods for research for this particular study is to demonstrate a review of relevant literature to examine the molecular genetics and the application of genome and DNA-amplification into molecular medicine investigations. To conduct a literature review a qualitative research design is the most suitable research design. It provides the rationale for assessing the human behaviour and assists to legalize and authenticate the data which is selectively collected from the secondary sources. Results: Every individual’s DNA consists of minor alterations a change in these restriction places would result in different profile of restriction fragments. Genome wide population substructure is large enough to determine ancestry with large number of Autosomal SNPs at the level of continental resolutions. Conclusion: The advances in human genomics and molecular genetics have provided success and advances by determining the cellular origin and estimating the age of sample and disposition time. The Phenotypical characteristics like hair and eye color demonstration in criminal cases has yielded better inferences however, DNA phenotyping illustrates a limited approach wherein incorporated into biological material analysis. The accuracy of DNA technology is incredibly useful for professionals like lawyers, anthropologists, homicide detectives etc. in the field of molecular medicines and is expected to improve in years to come. Key Words; Deoxyribonucleic Acid (DNA) , Polymerase Chain Reaction (PCR), Mitochondrial DNA (mtDNA) sequencing and Short Tardem Repeat (STR)

Associations of genome-wide cell-free DNA fragmentation profiles with blood biochemical and hematological parameters in healthy individuals

Cell-free DNA (cfDNA), as a non-invasive approach, has been introduced in a wide range of applications, including cancer diagnosis/ monitoring, prenatal testing, and transplantation monitoring. Yet, studies of cfDNA fragmentomics in physiological conditions are lacking. In this study, we aim to explore the correlation of fragmentation patterns of cfDNA with blood biochemical and hematological parameters in healthy individuals. We addressed the impact of physiological variables and abnormal blood biochemical and hematological parameters on cfDNA fragment size distribution. We also figured and validated that hematological inflammation markers, including leukocyte, lymphocyte, neutrophil, and platelet distribution width as well as aspartate transaminase levels were significantly correlated with the genome-wide cfDNA fragmentation pattern. Our findings suggest that cfDNA fragmentation profiles were associated with physiological parameters related to cardiovascular risk factors, inflammatory response and hepatocyte injury, which may provide insights for further research on the potential role of cfDNA fragmentation in diagnosis and monitor of several disease.

Detection and monitoring of HBV-related hepatocellular carcinoma from plasma cfDNA fragmentation profiles

Most hepatocellular carcinomas (HCCs) are associated with hepatitis B virus infection (HBV) in China. Early detection of HCC can significantly improve prognosis but is not yet fully clinically feasible. This study aims to develop methods for detecting HCC and studying the carcinogenesis of HBV using plasma cell-free DNA (cfDNA) whole-genome sequencing (WGS) data. Low coverage WGS was performed for 452 participants, including healthy individuals, hepatitis B patients, cirrhosis patients, and HCC patients. Then the sequencing data were processed using various machine learning models based on cfDNA fragmentation profiles for cancer detection. Our best model achieved a sensitivity of 87.10% and a specificity of 88.37%, and it showed an increased sensitivity with higher BCLC stages of HCC. Overall, this study proves the potential of a non-invasive assay based on cfDNA fragmentation profiles for the detection and prognosis of HCC and provides preliminary data on the carcinogenic mechanism of HBV.

HL-370 Fragmentóme Profiling Reveals Distinct Molecular Subgroups of Classic Hodgkin Lymphoma

Classic Hodgkin lymphoma (cHL) is a phenotypically heterogeneous disease with enigmatic biology and pathogenesis. By leveraging ctDNA analysis, here we identify molecular groups of cHL with phenotype- and outcome-associated signatures. Identification and biological characterization of novel cHL subgroups. We sequenced 202 cHL patients targeting ~344 kb of genomic space. The length of ctDNA mapped reads was extracted, and unsupervised clustering was performed. PET/CT scan and 71 plasma cytokines were obtained for clinical correlation. Single-cell RNA sequencing was performed for 8 patients. To identify molecular subgroups within cHL, we focused on the fragmentation profile of cfDNA, a non-genetic way of detecting tumor-derived DNA in the cfDNA samples. We identified two distinct clusters, one with a fragmentation profile close to healthy (N=135), named mono-nucleosomal, and a submono-nucleosomal cluster characterized by a global shift toward shorter fragments, whose length was less than the typical wrap around the nucleosome (N=67). We explored whether cHL subtypes defined by the cfDNA fragmentation profile are clinically and biologically validated. Patients with the submono-nucleosomal fragmentome showed more frequent advanced-stage B symptoms and elevated ESR at the clinical level, higher tumor volume (TMTV and TLG) at the radiomic level, and lower PFS in terms of outcome. Tumor mutations and immune microenvironment are pathophysiologic features of cHL. The submono-nucleosomal cluster carried a higher tumor mutation burden because of heavier aberrant somatic hypermutation (ASHM). The submono-nucleosomal cluster had an immune-suppressive microenvironment enriched of T-regs and higher plasmatic levels of T-regs chemoattractants and inducers of PD1 expression (IL6, CCL2, CCL4). Conversely, mono-nucleosomal cHL had a microenvironment enriched of TFH cells and cytotoxic CD8 TEM cells and associated with cytokines and chemokines that are known for being attractants and activators of effector T cells (IL-1a, CCL21, CXCL12, IL-17A). Our observations indicate that cHL subgroups belonging to the submono-nucleosomal cluster have a more aggressive disease, a higher mutation load and more neoantigens likely due to denser ASHM, and an immune-suppressive microenvironment. Instead, cHL subgroups belonging to the mono-nucleosomal cluster have a less aggressive disease, a lower mutation load and lower neoantigens likely due to lighter ASHM, and a tumor-suppressive microenvironment.

1669P Monitoring immune checkpoint inhibition in advanced solid tumors using genome-wide cfDNA fragmentomes

The rapid identification of disease progression in patients receiving immune checkpoint inhibitors (ICIs) is challenging using methods that rely on imaging assessments. Here, we investigated genome-wide cell-free DNA (cfDNA) fragmentation profiles to molecularly detect disease progression in the phase I study of entinostat and nivolumab +/- ipilimumab in advanced solid tumors (ETCTN-9844, NCT02453620). Patients with metastatic or unresectable solid tumors received an entinostat run-in 2 weeks prior to the addition of anti-PD-1 and anti-CTLA-4 ICIs. Blood was collected at baseline (50/50 patients) and at 10 weeks after treatment initiation (27/50 patients). Low-coverage (1-2x) whole genome sequencing was performed on cfDNA from plasma samples. Genome-wide cfDNA fragmentation features were included in a Bayesian model to approximate a tumor fraction as compared to best response by RECIST v1.1. Molecular response was defined as reduction (≥ 30%) of the fragmentation based tumor fraction between the baseline and 10-week time point. 50 patients with breast (n = 28), salivary gland (n = 7), gastrointestinal (n = 6), gynecological (n = 4), sarcoma (n = 3), and lung cancer (n = 2) received a median of 4 prior lines of therapy (0-14). Response assessment using molecular analyses and best overall response by RECIST were reported at a mean of 9.3 (5-10.7) and 14.3 weeks (5-73). Molecular responders and non-responders at week 10 experienced a median PFS that was not reached (NR) and 4.5 months (HR = 5.4; 95% CI 1.7-17; p = 0.004) while patients with RECIST response and non-response at week 10 had similar PFS (median 6.4 vs. 6.7 months; HR = 1.7; 95% CI 0.54-5.3; p = 0.36). Among the subgroup of radiographic non-responders with stable disease, PFS for molecular responders was NR while for molecular non-responders it was 5.2 months (p = 0.021). There is a clinical need for non-invasive tests to identify primary or acquired resistance to ICIs, sparing patients from ineffective treatments associated with immune-related adverse events. Here, we demonstrate a cfDNA fragmentation based approach for molecular detection of disease progression that could guide future trials with ICIs.

Expression profiles of exosomal tRNA-derived fragments and their biological functions in lipomas.

There is evidence that exosomes derived from the lipoma tissue (Exo-LT) have a stronger capacity to promote the proliferation and migration of adipose-derived stem cells (ADSCs) than those from the adipose tissue (Exo-AT). But the Exo-LT do not have a significant effect on the adipogenic differentiation of the ADSCs. Recently, certain exosomal tRNA-derived fragments (tRFs) have been shown to play a crucial role in the pathogenesis of certain tumors. Therefore, it is necessary to identify the differently expressed tRFs in Exo-LT to further elucidate their molecular functions in lipomas. High-throughput sequencing was performed to examine the tRFs and mRNAs from the all samples belonging to the Exo-LT and Exo-AT groups. Target prediction and bioinformatics analysis were performed to explore their downstream mRNAs and biological functions. In total, 456 differently expressed tRFs and tiRNAs were identified in the Exo-LT group, 12 of which were up-regulated and 12 were down-regulated, respectively. Notably, tRF-1001 was most obviously down-regulated and tRF-3004a was most obviously up-regulated in the Exo-LT group. Moreover, among the target genes of tRF-1001 and tRF-3004a, both JAG2 and VSIG4 were significantly down-regulated in the Exo-LT group, while WNT5A, COL1A1, and PPARGC1A were highly expressed in both the Exo-LT and Exo-AT groups. The significant down-regulation of JAG2 and VSIG4 in the Exo-LT group could be due to the fact that Exo-LT had a stronger capacity to promote the proliferation and migration of ADSCs compared to the Exo-AT. The high expression of WNT5A, COL1A1, and PPARGC1A in both the Exo-LT and Exo-AT groups could be due to the similar ability of Exo-LT and Exo-AT to promote the adipogenic differentiation of ADSCs.

Database of video images of natural emotional facial expressions: perception of emotions and automated analysis of facial structure

Subject of study. The development of methods for automated recognition of emotions on a face requires the creation of new stimuli sets that comprise dynamic natural emotional expressions. To date, no such databases have been designed or validated on the Russian population. Aim of study. The aims of this study were to develop a database of dynamic recordings of natural emotional facial expressions and to compare the profiles of subjective evaluation of these recordings with the results of an automated analysis of facial gestures. Method. Emotional profiles of video images of facial expressions were based on their evaluation by human observers. Study participants (N=250) rated the intensity of 33 differential emotions on a 5-point scale in each of 210 video fragments containing the natural expressions of 5 models. An automated analysis of facial structure in the video fragments of expressions was performed using OpenFace 2.0 to quantify the dynamic changes on the models’ faces. The emotional profiles of video fragments were compared with the results of automated mapping using representational similarity analysis. We calculated the rank correlation coefficient between matrices that represent the structure of subjective evaluation of expressions and their formal description. Additionally, we performed k-means cluster analysis based on subjective evaluation to identify the categorical structure of perceived emotional states. Main results. The representational similarity analysis demonstrated a significant positive correlation between subjective evaluation of expressions and their description in terms of facial actions. However, the correlation was low (0.214), which suggested a substantial variability of mimic patterns that can be subjectively perceived as similar emotions. A cluster analysis revealed five clusters corresponding to basic emotions: attention, joy, surprise, sadness, and disgust. Practical significance. The developed database of natural emotional expressions will be of interest to researchers in the field of affective computing, particularly, for the development of more effective methods for the recognition of users’ emotional states and more accurate simulation of emotional responses in robotic systems.

Norwegian Development Aid: A Paradigm Shift in the Making?

Norway has remained committed to allocating one percent of its Gross National Income in developing aid. Poverty reduction has also stayed as an overarching objective for the aid budget. The commitment to these traditional pillars in Norwegian aid is also accompanied by major changes: reduced focus on partner countries and bilateral aid and an expanding emphasis on supporting global action, the rise of a flourishing number of new thematic priorities and delivery through multilateral channels. This article maps the evolving continuities and changes in Norwegian development aid since the early 1990s. It relies on an analytical framework based on Peter Halls work on paradigm shifts and his distinction between first, second and third order changes. The explanation for the evolving profile of Norwegian aid is found in a combination of domestic driving forces. Based on Matthew Wood’s concepts of puzzling and powering dynamics behind paradigm shifts the article identifies the forces behind evolving aid policies. One is depolitisation behind Norwegian aid – a broad political and popular support and consensus but limited debate leaving much space for foreign policy and self-interest to shape implementation and selection of instruments. A second is linked to a change in the policy arena and the growing dominance of the Ministry of Foreign Affairs. This has led to a multifaceted and fragmented aid profile allowing various dimensions of development aid policies to be shaped by different interests and priorities. It has not yet reached a stage where we can identify a major break with the past and a radical policy transformation. The new focus on climate issues and migration may have the potential to change that and give self-interests a stronger influence over long-term development aid and its objectives. Changes in coalition politics may have the potential to change the majority behind the one percent target and the commitment to poverty reduction.

Untargeted metabolomics analysis reveals improved phenolic profile in whole wheat bread with yerba mate and the effects of the bread-making process

Untargeted metabolomics analysis was applied to evaluate the phenolic profile of whole wheat bread with yerba mate (YM) during the bread-making process (flour, dough and bread). The free, bound and total phenolic contents of the samples evaluated by the Folin-Ciocalteu method showed the highest values for the flour, dough and bread samples prepared with 4.5% YM in fine and medium particle sizes (flour 181.48 – 175.26 mg GAE/g; dough 149.62 – 141.40 mg GAE/g; and bread 148.32 – 147.00 mg GAE/g). Globally, 104 phenolic compounds were tentatively identified, belonging to the five subclasses: flavonoids (35), phenolic acids (32), other polyphenols (10), stilbenes (2) and lignan (1). Of these compounds, 24 had the same m/z but showed different fragmentation profiles. A higher number of polyphenols was identified in the bound extracts (77%) than in the free extracts (59%). The addition of 4.5% of YM promoted an improved and more abundant profile of phenolic compounds in the dough and bread. The major compounds found in the samples containing YM were 5-caffeoylquinic acid and caffeic acid. The baking process did not adversely affect the abundance of phenolic compounds. The bread-making process positively affected the phenolic profile due to the release of bound phenolic compounds. At the same time, the addition of YM as a natural ingredient promoted an increase in the polyphenols in the bread.

Abstract 5159: Towards population-scale screening of human cancer using genome-wide fragmentation profiles of cell-free DNA

Abstract Introduction: Earlier detection is a critical clinical intervention to reduce cancer-related mortality. The DELFI liquid biopsy approach (DNA evaluation of fragments for early interception) utilizing low coverage (1-2x) whole genome sequencing (WGS) to analyze cell-free DNA (cfDNA) fragmentation provides a promising avenue for cancer detection. As sequencing costs remain a barrier to adoption of liquid biopsy approaches for early detection, we evaluated WGS for DELFI using 2-channel Illumina NovaSeq sequencing as a more affordable (~7-fold cost savings) alternative to 4-channel HiSeq instruments. Methods: We performed WGS on the prospectively collected LUCAS cohort of 365 individuals at risk for lung cancer using both HiSeq and NovaSeq platforms (Mathios et. al., Nature Communications 2021). Genome-wide fragmentation was summarized in non-overlapping 5 Mb bins by ratio of short (100-150 bp) to long (151-220 bp) fragments.To measure within-sequencer repeatability, we compared fragmentation profiles of non-cancer individuals to the median non-cancer fragmentation profile by Spearman correlation. Principal component analyses were performed to assess the extent to which the sequencer explains variation of fragmentation profiles across samples.For cancer prediction, we used a penalized logistic regression model with fragmentation profiles and other genome-wide characteristics as features. Machine learning performance was assessed by cross-validation and area under the receiver operator characteristic curve (AUC). To evaluate whether we could have developed the classifier from a combination of NovaSeq with HiSeq sequenced samples, we evaluated performance trained on 90:10%, 75:25%, 50:50%, 25:75%, and 10:90% HiSeq:NovaSeq mixtures, respectively. Results: cfDNA fragmentation profiles were highly concordant among non-cancer individuals for both platforms with median correlations of 0.96 (IQR: 0.95 - 0.97) and 0.95 (IQR: 0.94 - 0.96). Visualization of fragmentation principal components did not reveal separation by sequencing platform. The DELFI approach applied to samples sequenced by NovaSeq recapitulated previously published performance measures based on HiSeq (AUC 0.90, 95% CI 0.86 - 0.94). In simulations of mixed-platform datasets, we found the same qualitative performance (AUC range: 0.893-0.902). Conclusions: cfDNA fragmentation profiles were similar between HiSeq and NovaSeq platforms, and classification accuracies from machine learning models trained on these platforms were equivalent. Our results indicate HiSeq and NovaSeq sequenced samples can be combined in models with no discernible loss in classification accuracy provided a balance of non-cancers and cancers are sequenced on both platforms. The lower cost of NovaSeq sequencing may enable wider adoption of genome-wide fragmentation-based approaches for cancer detection. Citation Format: Akshaya V. Annapragada, Dimitrios Mathios, Stephen Cristiano, Jamie E. Medina, Vilmos Adleff, Noushin Niknafs, Jacob Carey, Nic Dracopoli, Peter Bach, Jillian Phallen, Victor E. Velculescu, Robert B. Scharpf. Towards population-scale screening of human cancer using genome-wide fragmentation profiles of cell-free DNA [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 5159.

Abstract 5150: Multimodal analysis of plasma cell-free DNA methylome for sensitive multi-cancer detection

Abstract Introduction: Genomic scale copy number, methylation, and fragmentation aberrations are proven genetic and epigenetic biomarkers of circulating tumor DNA (ctDNA). We developed a novel cell-free DNA (cfDNA) methylome sequencing assay that allows for integrative analysis of these genomic features for sensitive detection of multiple types of cancer. Methods: Whole methylome sequencing (WMS) libraries were generated from enzymatically converted cfDNA. Low-pass (~2X) paired-end NGS sequencing was performed on WMS libraries and paired whole genome sequencing (WGS) libraries of unconverted cfDNA for technical comparison and analytical validation. For development of cancer detection models, we profiled the genome-wide methylation density (MD), fragment size index (FSI), fragment end motif (motif) and chromosome instability (CIN) based on WMS data from a discovery cohort of 352 healthy controls and 559 newly diagnosed cancer patients (45 breast, 105 colorectal, 44 esophageal, 79 gastric, 79 liver, 110 lung, 83 pancreatic, and 14 others), 34.5% of which were at stage I or II. Machine learning models, including KNN, SVM, LR, GBDT, and random forest were trained and tested for individual biomarker types, with a final ensemble classifier to integrate all biomarkers. Performance of the predictive model was confirmed on an independent validation cohort consisting of 145 healthy controls and 236 cancer patients (21 breast, 45 colorectal, 18 esophageal, 35 gastric, 34 liver, 47 lung, and 36 pancreatic), among which 31.8% were at early stages (I or II). Results: WMS and WGS data from 512 cfDNA samples showed high concordance in CIN (R=0.988, 95% CI: 0.986-0.990) and FSI (R=0.961, 0.954-0.967) profiles. On the independent validation cohort, the optimal model selected for each of individual genomic features achieved following area under the ROC curve (AUC) values for cancer detection: MD-KNN, 0.830 (0.789-0.870); FSI-SVM, 0.904 (0.874-0.933); motif-SVM, 0.943 (0.920-0.966); and CIN-PAscore, 0.812 (0.770-0.854). The ensemble classifier based on linear SVM outperformed individual biomarkers, with an AUC value of 0.952 (0.934-0.971), which translated to, at 95% specificity, detection sensitivity of 66.7% for breast, 77.8% for colorectal, 83.3% for esophageal, 62.9% for gastric, 82.4% for liver, 66.0% for lung, and 77.8% for pancreatic cancers. Noteworthily, the overall sensitivity on early-stage cancer was 74.7%. Conclusions: These results demonstrate the first proof of principle on the feasibility of integrating multiple genomic cancer markers on the same WMS technical platform. Low-pass WMS on plasma cfDNA from 10ml of blood with integrative multimodal analysis of methylation, fragmentation, and CNV profiles yields in satisfactory sensitivity and specificity for detection of multiple types of cancer, warranting a forthcoming prospective study to further assess its clinical performance in a larger cohort. Citation Format: Yulong Li, Fenglong Bie, Fengwei Tan, Tiancheng Han, Shunli Yang, Fang Lv, Peiyao Nie, Qi Zhang, Yuanyuan Hong, Zhijie Wang, Ji He, Weizhi Chen, Liang Zhao, Shugeng Gao. Multimodal analysis of plasma cell-free DNA methylome for sensitive multi-cancer detection [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 5150.

Abstract 6202: Plasma cell-free DNA fragmentation patterns combined with tumor mutation detection in diagnosis of lung cancer

Abstract Background: Lung cancer is one of the deadliest types of cancer in China. Its 5-year survival rates at early stages are significantly higher than advanced stages. LDCT has been adopted for lung cancer screening in high-risk individuals; however, debate regarding its accuracy is still ongoing. Mutation detection on cell free DNA (cfDNA) has traditionally been used to monitor DNA molecular changes derived from lung cancer cells in blood, while recently fragmentation pattern profiling of cfDNA has been shown as a promising alternative for early cancer detection. We aimed to combine mutation detection and fragmentation pattern analysis on cfDNA to develop a non-invasive assay to screen early lung cancer. Methods: Candidate DNA mutations were curated from literature and public databases, including COSMIC and TCGA. DNA fragmentation markers were collected from literature and whole genome sequencing (WGS) datasets. A panel of 407 primers covering selected mutation and fragmentation markers was developed to distinguish lung adenocarcinoma (ADC) plasma and normal plasma. We enrolled a total of 122 plasma samples (64 normal, 58 ADC) for this study. 49 normal samples and 44 ADC samples were used to construct a mutation-based classification model and a fragmentation-based classification model separately. The tuning parameters and features were determined by inner 4-fold cross validation. For the mutation-based model, baseline was set using normal samples in training set. Maximum allele frequency was calculated for each sample in test data (15 normal, 14 ADC), which was filtered by the background baseline. For the fragmentation-based model, we used the DELFI fragment score to construct fragmentation profiles, which was the ratio between short fragments (100-150bp) and long fragments (151-220bp). After optimization, the two models were integrated by Logistic Regression to create a combined model, which was validated by 4-fold nested cross validation. Results: ADC and normal plasma were sequenced by the aforementioned panel at an average depth of 2,000X to ensure the reliability of model construction and classification results. In classifying normal and ADC plasma, the mutation model alone is only modestly accurate as it produced an AUC of 0.69. But the fragmentation model demonstrated significantly higher accuracy, achieving AUC of 0.85. Furthermore, the combined model performed better than either model along, achieving an elevated AUC of 0.87. Conclusions: We demonstrate that DNA mutation and fragmentation pattern of cfDNA can classify lung cancer and normal plasmas separately, but fragmentation pattern are more accurate than mutation in this task. Combining the two models further improved prediction accuracy, suggesting they complement each other. Although this is a pilot study of limited cases, it demonstrated the potential of combining markers for accurate lung ADC detection in plasma. Citation Format: Zhoufeng Wang, Minjie Xu, Hua Chen, Kehui Xie, Minyang Su, Qiye He, Zhixi Su, Rui Liu, Weimin Li. Plasma cell-free DNA fragmentation patterns combined with tumor mutation detection in diagnosis of lung 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 6202.

Hellenistic and Roman Gerasa: The Archaeology and History of a Decapolis City. Edited by Achim Lichtenberger and Rubina Raja. Turnhout: Brepols 2020. Pp. xviii + 390. €110. ISBN 978-2-503-58504-8 (paper).

<i>Hellenistic and Roman Gerasa: The Archaeology and History of a Decapolis City</i>. Edited by Achim Lichtenberger and Rubina Raja. Turnhout: Brepols 2020. Pp. xviii + 390. €110. ISBN 978-2-503-58504-8 (paper).

Fish proteins as potential precursors of taste-active compounds: an in silico study.

Fish protein is a good source of amino acids and peptides with sensory properties. Theoretically, the type of protein affects the taste quality of the protein hydrolysates. To better use fish protein in the food ingredients industry, an in silico approach was adopted to evaluate the potential of fish protein to release taste-active compounds. Six types of protein from seven commercial fishes were screened from the Uniprot knowledge base. The results showed that a remarkable number of umami fragments presented in myosin and parvalbumin (PB), such as glutamic acid (Glu), aspartic acid (Asp), and Asp- and Glu- containing peptides, whereas sweet amino acids and bitter peptides (e.g., Pro- and Gly- containing peptides) were mainly found in collagen (CGI) in all fish samples. After the in silico proteolysis by papain, a difference in the profile of taste-active fragments was observed among the six types of proteins. Amino acids were the main hydrolysis products of these proteins, especially umami, sweet, and bitter amino acids, significantly contributing to the taste formation of protein hydrolysates. Besides, the myosin and CGI hydrolysates were abundant in taste active peptides both in types and quantities. Myosin is a promising protein source for producing umami fragments, and CGI seems to be a good precursor of sweet and bitter fragments. Different types of protein have an essential effect on the taste of protein hydrolysates. © 2022 Society of Chemical Industry.

Ultra-deep sequencing with unique molecular identifier(UMI) for detection of ctDNA by fragment profiling using machine learning.

e15508 Background: Liquid biopsy has been well known for its potential in cancer detection, non-invasive tumor genotyping and disease surveillance. However, ctDNA levels are low in the early stages of monitoring and postoperative progression of most tumors, which makes detection and analysis of ctDNA quite complicated. Methods: In our study, fixed-sequence UMI double-ended sequencing with a 1123-gene panel was used both on blood samples from 200 healthy donors and tissue-plasma samples from 1000 colorectal cancer (CRC) patients. First, GATK mutation detection was performed on tissue and plasma samples from 700 CRC patients to obtain trustable positive mutation sites in DBSNP138 database, meanwhile Samtools MPileUP analysis was performed on 200 healthy samples to obtain negative sites at low frequencies below 1%. Then, six features were extracted from the supporting variation sequences for the two above types of loci: IS (insert size), VBQ (variation base quality), MRBQ (mean read base quality), PIR (position in read), MQ (mapping quality) and R1&amp;R2 (read 1 and read 2 of the paired-end sequencing). All the above data were used to establish the training set model; after that, SNV background noise was modeled by SVM and optimized through five-fold cross-validation. Last, plasma samples from 300 CRC patients were used as a validation set to verify the accuracy of the model. Results: The test set of 300 CRC patients was used to verify the accuracy of the model, in which， the effectiveness of VAF mutation frequency above 0.2% was 95%, and above 0.02% was 80% in plasma. Conclusions: We established a specific method based on UMI two-terminal capture with machine learning modeling, which was significantly superior to any available method in eliminating noise background errors and filtering false-positive low-frequency mutation.

Structural elucidation of major degradation products of milbemycin oxime drug substance using LC-MS and NMR

Milbemycin oxime (MO) drug substance is a 16-membered macrocyclic lactone that exhibits a broad spectrum of biological activity and high potency towards parasites. In this study, a comprehensive forced degradation study was carried out on MO drug substance to identify and characterize its major degradation products (DPs). MO drug substance was subjected to acid, base, oxidation (H2O2), heat (solid and solution state), and photolytic (solid and solution state) stress degradation as per the ICH guidelines. Chromatographic separation of the drug substance (MO A3 and MO A4) and its DPs was achieved using a gradient elution on a HALO C18 column (100 × 4.6 mm, 2.7 µm). Mobile phase A consisted of water/acetonitrile (60/40, v/v) and mobile phase B consisted of ethanol/isopropanol (50/50, v/v). A total of twelve major DPs were observed for MO drug substance under various stress conditions. These DPs were further identified and characterized using liquid chromatography-high resolution mass spectrometry and comparison of their fragmentation profile with MO A4 and MO A3 using tandem mass spectrometry. Of these, H2O2 induced oxidative degradation product (3,4-dihydroperoxide MO A4) was isolated using semi-preparative HPLC and characterized by comparison of its nuclear magnetic resonance spectroscopy data with MO A4. The proposed structures of the DPs have been rationalized by appropriate degradation pathways for MO A4 and MO A3.