Profile Data Research Articles

Deciphering the prognostic and therapeutic significance of BAG1 and BAG2 for predicting distinct survival outcome and effects on liposarcoma

Liposarcoma (LPS) is the second most common kind of soft tissue sarcoma, and a heterogeneous malignant tumor derived from adipose tissue. Up to now, the prognostic value of BAG1 or BAG2 in LPS has not been defined yet. Expression profiling data of LPS patients were collected from TCGA and GEO database. Survival curves were plotted to verify the outcome differences of patients based on BAG1 or BAG2 expression. Univariate and multivariate Cox regression models were used to analyze the prognostic ability of BAG1 or BAG2. Chaperone’s regulators BAG1 and BAG2 were identified as prognostic biomarkers for LPS patients, which exhibited distinct expression patterns and survival outcome prediction performances. Patients with high BAG2 expression and/or low BAG1 expression had worse prognosis. Enrichment analysis showed that BAG1 was involved in negative regulation of TGF-β signaling. Low expression of BAG1 was associated with high abundance of regulatory T cells (Tregs). The 2-gene signature model further confirmed the improved risk assessment performance of BAG1 and BAG2: high risk patients displayed poor prognosis. BAG1 and BAG2 are supposed to be potential prognostic biomarkers for LPS and have impacts on liposarcomagenesis and immune infiltration in distinctive manners, which may function as potential therapy targets (BAG1 agonists/BAG2 inhibitors) for LPS.

A novel wheel wear indicator in regard to wheel-rail contact parameters and vehicle hunting stability

It is well known that the abnormal equivalent conicity caused by wheel and rail wear is the main root of hunting instability, but it is still difficult to trace the underlying factor when the dynamic instability occurs, that is, wheel wear induced, rail wear induced or both. To face this challenge problem, this paper constructed a novel wheel wear indicator based on the moved wear area difference. The long-term tracking test data of different wheel profiles were used to analyze the correlation between the wear indicators and the wheel-rail contact parameters. A fully detailed dynamic model of one typical high-speed railway vehicle was developed to investigate the evolution of vehicle hunting stability under the variation of the proposed wear indicator. The results indicate that the proposed wear indicator based on the moved wear area difference has a strong correlation with the equivalent conicity and can be used for the estimation of wheel-rail contact conditions. Furthermore, the hunting stability and the instability form is closely related to the value of the proposed wheel wear indicator, which is helpful to trace the underlying factor of hunting instability, thereby supporting intelligent operation and maintenance of railway vehicles.

Translation complex profile sequencing allows discrimination of leaky scanning and reinitiation in upstream open reading frame-controlled translation.

Upstream open reading frames (uORFs) are a class of translated regions (translons) in mRNA 5' leaders. uORFs are believed to be pervasive regulators of the translation of mammalian mRNAs. Some uORFs are highly repressive but others have little or no impact on downstream mRNA translation either due to inefficient recognition of their start codon(s) or/and due to efficient reinitiation after uORF translation. While experiments with uORF reporter constructs proved to be instrumental in the investigation of uORF-mediated mechanisms of translation control, they can have serious limitations as manipulations with uORF sequences can yield various artefacts. Here we propose a general approach for using translation complex profiling (TCP-seq) data for exploring uORF regulatory characteristics. Using several examples, we show how TCP-seq could be used to estimate both repressiveness and modes of action of individual uORFs. We demonstrate how this approach could be used to assess the mechanisms of uORF-mediated translation control in the mRNA of several human genes, including EIF5, IFRD1, MDM2, MIEF1, PPP1R15B, TAF7, and UCP2.

Plasma Profiling of Acute Myeloid Leukemia With Fever- and Infection-Related Complications During Chemotherapy-Induced Neutropenia.

Acute myeloid leukemia (AML) is a heterogenous and complex blood cancer requiring aggressive treatment. Early identification and prediction of the complications following treatment is vital for effective disease management. We explored associations between plasma protein levels and fever- and infection-related complications in 26 AML patients during chemotherapy-induced neutropenia. Longitudinal plasma profiling was conducted using data-dependent mass spectrometry analysis. Mass spectrometry-based plasma profiling data correlated well with laboratory parameters, including C-reactive protein, and revealed a broader inflammation protein network associated with fever- and infection-related complications. These data indicate the potential of longitudinal plasma profiling in AML patients for identifying and predicting complications that may aid in improved disease monitoring and treatment.

Self‐Potential Tomography Preconditioned by Particle Swarm Optimization—Application to Monitoring Hyporheic Exchange in a Bedrock River

AbstractA self‐potential (SP) data‐inversion algorithm was developed and tested on an analytical model of electrical‐potential profile data attributed to single and multiple polarized electrical sources. The developed algorithm was then validated by an application to SP‐monitoring field data measured on the floodplain of East Fork Poplar Creek, Oak Ridge, Tennessee, to image electrical sources in areas conducive to preferential flow into the flood plain from the bedrock‐lined riverbed. The algorithm combined stochastic source‐localization by particle‐swarm‐optimization (PSO) of electrical sources characterized by simplified geometries with source tomography by regularized weighted least‐squares minimization of a quadratic objective function. Prior information was incorporated by preconditioning the tomography algorithm by PSO results. Variable percentages of random noise were added to analytical‐model data to evaluate the algorithm performance. Results indicated that true parameters of single‐source models were inverted and approximated with small residual error, whereas inversion of analytical‐model data representing multiple electrical sources accurately approximated the locations of the sources but miscalculated some parameters because of the non‐uniqueness of the inverse‐model solution. Source tomography applied to analytical model data during testing produced a spatially continuous parameter field that identified the locations of point‐scale synthetic dipole sources of electrical current flow with varying degrees of accuracy depending on the prior information incorporated into the tomography. When applied to SP‐monitoring field data, the algorithm imaged electrical sources within a known fault that intersects the bedrock riverbed and flood plain of East Fork Poplar Creek and depicted dynamic electrical conditions attributed to hyporheic exchange.

APFC: Adaptive Particle Filter for Change Point Detection of Profile Data in Manufacturing Systems

APFC: Adaptive Particle Filter for Change Point Detection of Profile Data in Manufacturing Systems

Bioactive Compounds and Valorization of Coffee By-Products from the Origin: A Circular Economy Model from Local Practices in Zongolica, Mexico.

The by-products of green coffee processing are rich in compounds that can be recycled for their possible use in the production of beverages, fertilizers and weed control in production areas. The objective of this work was to identify the organic and inorganic bioactive compounds of green coffee and the coffee by-products related to the production of origin, such as dried cascara (skin-pulp), parchment and silverskin (unroasted), in order to investigate the role their biomolecules may have in reuse through practices and local knowledge, not yet valued. The metabolomic profile by HPLC-ESI-HRMS of the aqueous extract of the dried cascara highlighted 93 non-volatile molecules, the highest number reported for dried cascara. They belong to groups of organic acids (12), alkaloids (5), sugars (5), fatty acids (2), diglycerides (1), amino acids (18), phospholipids (7), vitamins (5), phenolic acids (11), flavonoids (8), chlorogenic acids (17), flavones (1) and terpenes (1). For the first time, we report the use of direct analysis in real-time mass spectrometry (DART-MS) for the identification of metabolites in aqueous extracts of dried cascara, parchment, silverskin and green coffee. The DART analysis mainly showed the presence of caffeine and chlorogenic acids in all the extracts; additionally, sugar adducts and antioxidant compounds such as polyphenols were detected. The mineral content (K, Ca, P, S, Mg and Cl) by EDS spectrometry in the by-products and green coffee showed a relatively high content of K in the dried cascara and green coffee, while Ca was detected in double quantity in the silverskin. These metabolomic and mineral profile data allow enhancement of the link between the quality of green coffee and its by-products and the traditional local practices in the crop-growing area. This consolidates the community's experience in reusing by-products, thereby minimizing the impact on the environment and generating additional income for coffee growers' work, in accordance with the principles of circular economy and bioeconomy.

Exploring the prognostic value of T follicular helper cell levels in chronic lymphocytic leukemia

Chronic lymphocytic leukemia (CLL) presents with heterogeneous clinical outcomes, suggesting varied underlying pathogenic mechanisms. This study aims to elucidate the impact of T follicular helper (Tfh) cells on CLL progression and prognosis. Gene expression profile data for CLL were collected from GSE22762 and GSE39671 datasets. Patients were divided into high and low groups using Tfh levels using the optimal cutoff value based on overall survival (OS) and time-to-first treatment (TTFT). Differential expression analysis was performed between these groups, followed by co-expression network analysis and single-sample Gene Set Enrichment Analysis (ssGSEA). Marker genes of Tfh cells were used to construct prognostic models. Additionally, 40 CLL patients were recruited and categorized based on median Tfh levels. Marker gene expression was assessed using RT-qPCR and Western Blot, and immune cell levels were determined through flow cytometry. The high group showed better prognosis compared to the low group. Among the 1121 differentially expressed genes identified, five co-expression networks were constructed, with the turquoise module showing the highest correlation with Tfh cells. Genes within this module significantly participate in cytokine-cytokine receptor interaction, PI3K-Akt signaling pathway, and natural killer cell mediated cytotoxicity. Tfh cells were significantly negatively correlated with activated B cells and positively correlated with Tregs. The Random Survival Forest (RSF) model identified 10 marker genes, and further analysis using Lasso regression and nomogram selected CLEC4A, RAE1, CD84, and PRDX1 as prognostic markers. In the high group, levels of CLEC4A and RAE1 were higher than in the low group, whereas CD84 and PRDX1 were lower. Flow cytometry revealed that the level of activated B cells in the high Tfh group was significantly lower than in the low Tfh group, while the level of Tregs is significantly higher in the high Tfh group. This study seeks to contribute to a more detailed understanding of the pathogenesis of CLL, delving into the prognostic significance of Tfh.

Top to bottom interactive velocity analysis using travel-time ellipses and first break times from walkaway vertical seismic profiling data: a synthetic study

Top to bottom interactive velocity analysis using travel-time ellipses and first break times from walkaway vertical seismic profiling data: a synthetic study

Genome-wide impact of codon usage bias on translation optimization in Drosophila melanogaster

Accuracy and efficiency are fundamental to mRNA translation. Codon usage bias is widespread across species. Despite the long-standing association between optimized codon usage and improved translation, our understanding of its evolutionary basis and functional effects remains limited. Drosophila is widely used to study codon usage bias, but genome-scale experimental data are scarce. Using high-resolution mass spectrometry data from Drosophila melanogaster, we show that optimal codons have lower translation errors than nonoptimal codons after accounting for these biases. Genomic-scale analysis of ribosome profiling data shows that optimal codons are translated more rapidly than nonoptimal codons. Although we find no long-term selection favoring synonymous mutations in D. melanogaster after diverging from D. simulans, we identify signatures of positive selection driving codon optimization in the D. melanogaster population. These findings expand our understanding of the functional consequences of codon optimization and serve as a foundation for future investigations.

Profile of Oral Corticosteroid Use in Patients in Madison City Pharmacies

Hyperglycemia induced by corticosteroid use depends on the dose, indication and setting of use. The type of corticosteroid that causes a greater degree of hyperglycemia is the use of Deksametasonor metilprednisolonrednisolone compared to prednisolone or hydrocortisone. High risk of hyperglycemia due to the use of oral corticosteroids, especially Deksametasonor metilprednisolonrednisolone. The aim of this study was to determine the profile of oral corticosteroid use in Madiun City pharmacies. This research is an observational study using retrospective data. Where data was obtained from profile data on the use of oral corticosteroids in one of the pharmacies in Madiun City. Then the data is analyzed, percentages are calculated and presented in the form of tables, graphs and diagrams. The results obtained were that the number of oral corticosteroids used in Madiun City pharmacies in 2022 was 44,270 tablets and increased in 2023, namely 50,680 tablets. Meanwhile, the type of oral corticosteroid that is widely used is metilprednisolonrednisone 4 mg, namely 13,090 tablets (29.57%) in 2022 and 14,950 tablets (29.50%) in 2023. The increase in the use of metilprednisolonrednisolone 4mg from 2022-2023 is 1,860 tablets . The conclusion of this research is that the use of oral corticosteroids in Madiun City pharmacies in 2022 and 2023 will increase and the most common type of corticosteroid is metilprednisolonrednisolone 4 mg.

A Practical In Silico Method for Predicting Compound Brain Concentration-Time Profiles: Combination of PK Modeling and Machine Learning.

Given the aging populations in advanced countries globally, many pharmaceutical companies have focused on developing central nervous system (CNS) drugs. However, due to the blood-brain barrier, drugs do not easily reach the target area in the brain. Although conventional screening methods for drug discovery involve the measurement of (unbound fraction of drug) brain-to-plasma partition coefficients, it is difficult to consider nonequilibrium between plasma and brain compound concentration-time profiles. To truly understand the pharmacokinetics/pharmacodynamics of CNS drugs, compound concentration-time profiles in the brain are necessary; however, such analyses are costly and time-consuming and require a significant number of animals. Therefore, in this study, we attempted to develop an in silico prediction method that does not require a large amount of experimental data by combining modeling and simulation (M&S) with machine learning (ML). First, we constructed a hybrid model linking plasma concentration-time profile to the brain compartment that takes into account the transit time and brain distribution of each compound. Using mouse plasma and brain time experimental values for 103 compounds, we determined the brain kinetic parameters of the hybrid model for each compound; this case was defined as scenario I (a positive control experiment) and included the full brain concentration-time profile data. Next, we built an ML model using chemical structure descriptors as explanatory variables and rate parameters as the target variable, and we then input the predicted values from 5-fold cross-validation (CV) into the hybrid model; this case was defined as scenario II, in which no brain compound concentration-time profile data exist. Finally, for scenario III, assuming that the brain concentration is obtained at only one time point, we used the brain kinetic parameters from the result of the 5-fold CV in scenario II as the initial values for the hybrid model and performed parameter refitting against the observed brain concentration at that time point. As a result, the RMSE/R2-values of the brain compound concentration-time profiles over time were 0.445/0.517 in scenario II and 0.246/0.805 in scenario III, indicating the method provides high accuracy and suggesting that it is a practical method for predicting brain compound concentration-time profiles.

RPFdb v3.0: an enhanced repository for ribosome profiling data and related content.

RPFdb (http://www.rpfdb.org or http://sysbio.gzzoc.com/rpfdb/) is a comprehensive repository dedicated to hosting ribosome profiling (Ribo-seq) data and related content. Herein, we present RPFdb v3.0, a significant update featuring expanded data content and improved functionality. Key enhancements include (i) increased data coverage, now encompassing 5018 Ribo-seq datasets and 2343 matched RNA-seq datasets from 496 studies across 34 species; (ii) implementation of translation efficiency, combining Ribo-seq and RNA-seq data to provide gene-specific translation efficiency; (iii) addition of pausing score, facilitating the identification of condition-specific triplet amino acid motifs with enhanced ribosome enrichment; (iv) refinement of open reading frame (ORF) annotation, leveraging RibORF v2.0 for more sensitive detection of actively translated ORFs; (v) introduction of a resource hub, curating advances in translatome sequencing techniques and data analytics tools to support a panoramic overview of the field; and (vi) redesigned web interface, providing intuitive navigation with dedicated pages for streamlined data retrieval, comparison and visualization. These enhancements make RPFdb a more powerful and user-friendly resource for researchers in the field of translatomics. The database is freely accessible and regularly updated to ensure its continued relevance to the scientific community.

Development and Characterization of Ethanolic Extract of Cleome gynandra L. Loaded Solid Lipid Nanoparticles for the Management of Diabetes Mellitus

The bioactive components of the botanical remedy Cleome gynandra L., which is famous for its antidiabetic effects, were extracted using ethanol. To improve its stability, bioavailability, and therapeutic potential, the extract was subsequently encapsulated within solid lipid nanoparticles (SLNs) using a high-pressure homogenization method with varying concentrations of lipids. Zeta potential, entrapment efficiency, kinetics of drug release, and particle size were among the numerous characteristics of the SLNs that were thoroughly examined. One important measure for determining whether bioactive compounds were successfully encapsulated within the SLNs is the entrapment efficiency. An impressive entrapment efficiency of more than 82.13% was shown by the optimized formulation F4. The optimized formulation F4 exhibited a mean particle size ranging from 243.5 to 261.7 nm, according to particle size analysis. This size range is ideal for improving cellular uptake and bioavailability. A negative charge was observed in the zeta potential measurements, which means that the SLNs are stable and do not aggregate. A PDI of 0.416 provided additional evidence of the nanoparticles’ stability and uniformity. A sustained release of about 62.57% over 7 hours was shown in the in-vitro drug release profile data, indicating a long-lasting therapeutic effect. Improved therapeutic efficacy of antidiabetic treatment and more stable blood glucose levels are both benefits of the sustained release profile. The in-vitro results of formulation F4 suggest that SLNs have the good stability, bioavailability, and therapeutic potential of C. gynandra L.’s ethanolic extract when used as a delivery system.

Is there a ubiquitous spectrolaminar motif of local field potential power across primate neocortex?

Mendoza-Halliday, Major et al., 2024 ("The Paper")1 advocates a local field potential (LFP)-based approach to functional identification of cortical layers during "laminar" (simultaneous recordings from all cortical layers) multielectrode recordings in nonhuman primates (NHPs). The Paper describes a "ubiquitous spectrolaminar motif" in the primate neocortex: 1) 75-150 Hz power peaks in the supragranular layers, 2) 10-19 Hz power peaks in the infragranular layers and 3) the crossing point of their laminar power gradients identifies Layer 4 (L4). Identification of L4 is critical in general, but especially for The Paper as the "motif" discovery is couched within a framework whose central hypothesis is that gamma activity originates in the supragranular layers and reflects feedforward activity, while alpha-beta activity originates in the infragranular layers and reflects feedback activity. In an impressive scientific effort, The Paper analyzed laminar data from 14 cortical areas in 2 prior macaque studies and compared them to marmoset, mouse, and human data to further bolster the canonical nature of the motif. Identification of such canonical principles of brain operation is clearly a topic of broad scientific interest. Similarly, a reliable online method for L4 identification would be of broad scientific value for the rapidly increasing use of laminar recordings using numerous evolving technologies. Despite The Paper's strengths, and its potential for scientific impact, a series of concerns that are fundamental to the analysis and interpretation of laminar activity profile data in general, and local field potential (LFP) signals in particular, led us to question its conclusions. We thus evaluated the generality of The Paper's methods and findings using new sets of data comprised of stimulus-evoked laminar response profiles from primary and higher-order auditory cortices (A1 and belt cortex), and primary visual cortex (V1). The rationale for using these areas as a test bed for new methods is that their laminar anatomy and physiology have already been extensively characterized by prior studies, and there is general agreement across laboratories on key matters like L4 identification. Our analyses indicate that The Paper's findings do not generalize well to any of these cortical areas. In particular, we find The Paper's methods for L4 identification to be unreliable. Moreover, both methodological and statistical concerns, outlined below and in the supplement, question the stated prevalence of the motif in The Paper's published dataset. After summarizing our findings and related broader concerns, we briefly critique the evidence from biophysical modeling studies cited to support The Paper's conclusions. While our findings are at odds with the proposition of a ubiquitous spectrolaminar motif in the primate neocortex, The Paper already has, and will continue to spark debate and further experimentation. Hopefully this countervailing presentation will lead to robust collegial efforts to define optimal strategies for applying laminar recording methods in future studies.

Analyses of sea turtle landing behavior based on frequently observed coastal profile data - A case study in Enshu Coast, Japan

This study analyses sea turtle landing behavior along a 10 km sandy beach on the Enshu Coast, Japan, facing the Pacific Ocean. Data for the analysis are sea turtles' landing positions and crawl tracks recorded by a handheld GNSS device from May to September (2011–2016), noting spawning or no-spawning activity simultaneously, supplemented by weekly cross-shore beach profiles surveyed at four locations, satellite images, and other geographical data. At the onset, landing positions on the beach are visualized to determine local concentrations along the beach. Subsequently, we estimate the distance of spawning and no-spawning positions from the waterline during landing and evaluate beach stability through the temporal elevation change standard deviation. The study found that preferred spawning locations are 40–70 m from the waterline, above the intertidal zone, on bare sand, with a standard deviation of 0.1–0.6 m. This study also highlights the impact of beach infrastructure on sea turtle spawning failures and the influence of significant wave height on sea turtle landing.

HMPA: a pioneering framework for the noncanonical peptidome from discovery to functional insights.

Advancements in peptidomics have revealed numerous small open reading frames with coding potential and revealed that some of these micropeptides are closely related to human cancer. However, the systematic analysis and integration from sequence to structure and function remains largely undeveloped. Here, as a solution, we built a workflow for the collection and analysis of proteomic data, transcriptomic data, and clinical outcomes for cancer-associated micropeptides using publicly available datasets from large cohorts. We initially identified 19586 novel micropeptides by reanalyzing proteomic profile data from 3753 samples across 8 cancer types. Further quantitative analysis of these micropeptides, along with associated clinical data, identified 3065 that were dysregulated in cancer, with 370 of them showing a strong association with prognosis. Moreover, we employed a deep learning framework to construct a micropeptide-protein interaction network for further bioinformatics analysis, revealing that micropeptides are involved in multiple biological processes as bioactive molecules. Taken together, our atlas provides a benchmark for high-throughput prediction and functional exploration of micropeptides, providing new insights into their biological mechanisms in cancer. The HMPA is freely available at http://hmpa.zju.edu.cn.

M6ATM: a deep learning framework for demystifying the m6A epitranscriptome with Nanopore long-read RNA-seq data.

N6-methyladenosine (m6A) is one of the most abundant and well-known modifications in messenger RNAs since its discovery in the 1970s. Recent studies have demonstrated that m6A is involved in various biological processes, such as alternative splicing and RNA degradation, playing an important role in a variety of diseases. To better understand the role of m6A, transcriptome-wide m6A profiling data are indispensable. In recent years, the Oxford Nanopore Technology Direct RNA Sequencing (DRS) platform has shown promise for RNA modification detection based on current disruptions measured in transcripts. However, decoding current intensity data into modification profiles remains a challenging task. Here, we introduce the m6A Transcriptome-wide Mapper (m6ATM), a novel Python-based computational pipeline that applies deep neural networks to predict m6A sites at a single-base resolution using DRS data. The m6ATM model architecture incorporates a WaveNet encoder and a dual-stream multiple-instance learning model to extract features from specific target sites and characterize the m6A epitranscriptome. For validation, m6ATM achieved an accuracy of 80% to 98% across in vitro transcription datasets containing varying m6A modification ratios and outperformed other tools in benchmarking with human cell line data. Moreover, we demonstrated the versatility of m6ATM in providing reliable stoichiometric information and used it to pinpoint PEG10 as a potential m6A target transcript in liver cancer cells. In conclusion, m6ATM is a high-performance m6A detection tool, and our results pave the way for future advancements in epitranscriptomic research.

Abstract C156: Examining the role of T cell responses in colorectal cancer sex disparities

Abstract Purpose: Persistent sex disparities in colorectal cancer (CRC) incidence and mortality exist and vary by race and ethnicity. The overarching goal of this proposal is to quantify differences in the CRC tumor immune microenvironment by sex in the Latino Colorectal Cancer Consortium (LC3). Methods: The LC3 is a consortium of pooled individual-level data from Latino participants diagnosed with CRC from California, Florida, and Puerto Rico. Biospecimens, epidemiologic data, and clinical outcomes were collected from each contributing study. DNA was extracted from tumor tissue and paired germline or normal tissue samples. Whole exome sequencing was performed using established protocols. Global genetic ancestry was estimated from whole exome sequence data. High-throughput T cell receptor (TCR) sequencing was used to estimate features of the T cell receptor (TCR) repertoire (e.g. abundance, clonality), and tumor-infiltrating lymphocyte (TIL) counts were obtained through pathology review. We evaluated differences in TCR abundance and TILs by sex using linear models for continuous measurements and generalized linear models for binary or count measurements. Primary analyses were unadjusted, and secondary analyses adjusted for age at diagnosis, stage at diagnosis, tumor location (right colon vs. left colorectum), and microsatellite instability (MSI) status. To examine how genetic ancestry modified the relationship between sex and T cell responses, we fit separate models with an interaction term between each genetic ancestry proportion variable (i.e. African, European, Native American) and sex, and assessed effect modification using a likelihood ratio test. Results: To date, 240 participants had TCR repertoire profiling and whole exome sequencing data generated. 52% were male, with a mean age of 58. 17% of participants had rectal tumors. With respect to Hispanic ethnic heritage or country of origin, 49% of participants were of Mexican descent, followed by 13% of Central or South American descent, and 10% were of Puerto Rican descent. Mean T cell abundance was 0.18 (standard deviation (SD): 0.16) and mean clonality was 0.05 (SD: 0.05). Further analysis is underway. Conclusions: This project leverages within-Latino genetic diversity to disentangle the contributions of sex and genetic ancestry to variability in immune function and to understand how this may further sex disparities in CRC. We anticipate that results from this analysis will provide insight on the relationship between the tumor immune microenvironment and sex disparities in CRC. Citation Format: Nicole C. Loroña, Ya-Yu Tsai, Daniel Sobieski, Eric Cockman, Stephanie L. Schmit, Jane C. Figueiredo. Examining the role of T cell responses in colorectal cancer sex disparities [abstract]. In: Proceedings of the 17th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2024 Sep 21-24; Los Angeles, CA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2024;33(9 Suppl):Abstract nr C156.

Identification of primary mediastinal B-cell lymphomas with higher clonal dominance and poorer outcome using 5′RACE

AbstractThere is a scarcity of data on the tumor B-cell receptor (BCR) repertoire and lymphoid microenvironment in primary mediastinal B-cell lymphoma (PMBL). We applied 5ʹ rapid amplification of complimentary DNA ends (5′RACE) to tumor RNA samples from 137 patients with PMBL with available gene expression profiling and next-generation sequencing data. We obtained 5′RACE results for 75 of the 137 (54.7%) patients with the following clinical characteristics: median age (range), 33 years (18-64); female, 53.3%; performance status score 0 to 1, 86.7%; stage I to II, 57.3%; first-line treatment with anti-CD20 plus doxorubicin-based chemotherapy, 100%. Among the 60 biopsies that expressed a productive BCR, we highlighted a strong somatic hypermutation profile, defined as <98% identity to the germ line sequence, with 58 (96.7%) patients carrying mutated IgVH. We then identified a subgroup of 12 of the 75 patients (16%) with a worse prognosis (progression-free survival [PFS]: hazard ratio [HR], 17; overall survival [OS]: HR, 21) that was associated with the highest clonal dominance (HCD) status, defined as the dominant clonotype representing >81.1% and >78.6% of all complementarity-determining region 3 sequences for IgVH and IgVL, respectively. When compared with other patients, this subgroup had similar clinical characteristics but a greater median allele frequency for all somatic variants, a decreased BCR diversity, and greater expression of PDL1/PDL2 and MS4A1 genes, suggesting greater tumoral infiltration. We confirmed this poorer prognosis in a multivariate model and in an independent validation cohort in which 6 of 37 (16%) PMBL patients exhibited HCD (PFS: HR, 12; OS: HR, 17).