Detection Of Signatures Research Articles

Abstract PO5-16-05: Integrating Circulating Tumor Cells (CTCs) and Cell-free DNA (cfDNA) Signatures for Monitoring Treatment Response in Stage III/IV Inflammatory Breast Cancer

Abstract Background: Inflammatory breast cancer (IBC) is a rare and aggressive form of breast cancer, comprising only between 1-5% of all breast cancer cases in the United States. Despite its low incidence, IBC accounts for 10% of breast cancer-related deaths and is often diagnosed with metastasis, resulting in shorter survival compared to non-IBC cases. The 5-year relative survival rate for all stages of IBC is 39%, dropping to 19% for patients with distant metastasis. Liquid biopsy-based biomarkers have emerged as potential analytes for predicting prognosis and monitoring response to therapies in IBC patients. We explored the prognostic role of liquid biopsy encompassing both CTCs and cfDNA in the blood of patients with stage III/IV IBC and their clinical outcomes. Methods: CTC and cfDNA analysis was performed on 90 blood samples from 47 patients with stage III and IV IBC. Samples were collected before (pre-treatment, n=43) initiation and after (post-treatment, n=47) completion of neoadjuvant chemotherapy. CTCs were enriched and enumerated using the CellSearch® platform (Menarini-Silicon Biosystems). CfDNA was extracted from plasma using MagMax™ Cell-Free Total Nucleic Acid Isolation Kit (Applied Biosystems™) and quantified using the Qubit™ dsDNA High Sensitivity kit (Invitrogen™). Differences in cfDNA concentrations were used to plot a receiver operating characteristic (ROC) curve which was then used to determine Area Under the Curve (AUC) and a cfDNA cut-off point. ROC cut-off was calculated based on Youden Index J. Survival analysis was performed by the Kaplan-Meier estimator and Cox regression models. P-values were measured using the Student t-test. Results: Prior to neoadjuvant treatment, CTC was detected in 51% of patient samples, and the median cfDNA concentration was 5.87 ng/mL (range 0.95-24.11 ng/mL). After completion of neoadjuvant treatment, CTC was detected in 25% of patient samples, and the median cfDNA concentration was 11.19 ng/mL (range 3.41-88.13 ng/mL). The cut-off points determined for pre-treatment and post-treatment cfDNA concentration was >4.2 ng/mL and >9.5 ng/mL respectively. At pre-treatment, CTC detection and cfDNA concentration >4.2 ng/mL were independently associated with disease relapse (P=0.007, 0.016, respectively). The combination of CTC detection and cfDNA concentration >4.2 ng/mL was significantly associated with disease relapse (P=0.0004), decreased relapse free survival (RFS, P=0.005) and overall survival (OS, P=0.007). At the post-neoadjuvant treatment time point, the combination of CTC detection and cfDNA concentration >9.5 ng/mL was significantly associated with relapse regardless of pCR status (P=0.02). Conclusion: The combined presence of CTCs and elevated cfDNA concentrations in patients with stage III/IV IBC after completion of neoadjuvant chemotherapy was associated with disease relapse regardless of pCR status. A combination of elevated cfDNA and CTC detection signatures after completion of neoadjuvant chemotherapy may be useful for risk stratification and adjuvant systemic therapy planning. Citation Format: Sridevi Addanki, Salyna Meas, Vanessa Sarli, Abdulkader Almosa, Wendy Woodward, Dhruvajyoti Roy, Anthony Lucci. Integrating Circulating Tumor Cells (CTCs) and Cell-free DNA (cfDNA) Signatures for Monitoring Treatment Response in Stage III/IV Inflammatory Breast Cancer [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO5-16-05.

Enhancing pathogen detection and characterization of respiratory microbiome signatures of infection through next-generation sequencing

Next-generation sequencing-based (NGS) methods for pathogen detection, which can capture the entire microbial composition in a clinical sample, are used strategically in parallel to culture-based methods. While NGS-based methods cannot confirm organism viability, culture-based methods can but are limited by the number of culture conditions routinely utilized in clinical microbiology laboratories. The relatively wide scope provided by sequencing compensates for the limited scope of culture-based methods but creates obstacles in distinguishing pathogens from an expected body-site-specific microbiome or benign transient colonizers. This study compares NGS-based methods for pathogen detection in respiratory samples to current diagnostics at Texas Children's Hospital in Houston, Texas. Current diagnostics include various culture-based assays, targeted PCR assays, and reference laboratory-generated sequencing. We hypothesize that employing sample metadata (source, type, etc.) and patient data (clinical history, chronic conditions, etc.) during data analysis will help distinguish pathogens from expected flora in respiratory samples. To develop these methods, 27 respiratory samples with suffcient volume for both routine diagnostic testing and this study were collected. Nucleic acid was extracted using the Qiagen DNeasy PowerSoil Pro Kit after collection, and targeted short-read sequencing (16S, 18S/ITS) was performed. These data were then analyzed using a custom bioinformatic pipeline built around Qiime2-2022. In brief, sequences are denoised and quality filtered using DADA2, an algorithm that uses a statistical model for correcting sequencing errors, after which similar amplicon sequence variants (ASVs) are clustered. Next, a feature table of ASVs that are 100% identical is generated, and taxonomy assigned using a naive Bayes taxonomy classifier against the Silva 13.2 database. Finally, unassigned and low abundance ASVs are removed. When species-level identification couldn't be determined through targeted short-read sequencing, untargeted long-read sequencing was used. In one case, we found that a bronchoalveolar lavage sample from the lung of a patient with a history of recurrent pneumonia contained a singular pathogenic bacterium, H. influenzae. This finding agreed with results obtained through reference laboratory-generated sequencing. In another case, we identified >14 unique bacteria at varying concentrations in a wash sample collected from the lung of an asymptomatic patient during a routine bronchoscopy. These findings, however, were determined to be expected respiratory flora. Our NGS-based methods’ enhanced pathogen detection compared to current diagnostics and discriminates between respiratory flora and active infections. Setting thresholds for pathogen detection, evaluating clinical relevance in the context of body site and presentation, and deploying additional molecular techniques when necessary are crucial steps to increase the accuracy of pathogen detection in the clinic. This study was supported by the Texas Children's Hospital Department of Pathology. This is the full abstract presented at the American Physiology Summit 2024 meeting and is only available in HTML format. There are no additional versions or additional content available for this abstract. Physiology was not involved in the peer review process.

Biogenic Sulfur Gases as Biosignatures on Temperate Sub-Neptune Waterworlds

Theoretical predictions and observational data indicate a class of sub-Neptune exoplanets may have water-rich interiors covered by hydrogen-dominated atmospheres. Provided suitable climate conditions, such planets could host surface liquid oceans. Motivated by recent JWST observations of K2-18 b, we self-consistently model the photochemistry and potential detectability of biogenic sulfur gases in the atmospheres of temperate sub-Neptune waterworlds for the first time. On Earth today, organic sulfur compounds produced by marine biota are rapidly destroyed by photochemical processes before they can accumulate to significant levels. Domagal-Goldman et al. suggest that detectable biogenic sulfur signatures could emerge in Archean-like atmospheres with higher biological production or low UV flux. In this study, we explore biogenic sulfur across a wide range of biological fluxes and stellar UV environments. Critically, the main photochemical sinks are absent on the nightside of tidally locked planets. To address this, we further perform experiments with a 3D general circulation model and a 2D photochemical model (VULCAN 2D) to simulate the global distribution of biogenic gases to investigate their terminator concentrations as seen via transmission spectroscopy. Our models indicate that biogenic sulfur gases can rise to potentially detectable levels on hydrogen-rich water worlds, but only for enhanced global biosulfur flux (≳20 times modern Earth’s flux). We find that it is challenging to identify DMS at 3.4 μm where it strongly overlaps with CH4, whereas it is more plausible to detect DMS and companion byproducts, ethylene (C2H4) and ethane (C2H6), in the mid-infrared between 9 and 13 μm.

Application of fiber loop ringdown spectroscopy technique for a new approach to beta-amyloid monitoring for Alzheimer Disease’s early detection

A novel fiber optic biosensor was purposed for a new approach to monitor amyloid beta protein fragment 1–42 (Aβ42) for Alzheimer’s Disease (AD) early detection. The sensor was fabricated by etching a part of fiber from single mode fiber loop in pure hydrofluoric acid solution and utilized as a Local Optical Refractometer (LOR) to monitor the change Aβ42 concentration in Artificial Cerebrospinal Fluid (ACSF). The Fiber Loop Ringdown Spectroscopy (FLRDS) technique is an ultra-sensitive measurement technique with low-cost, high sensitivity, real-time measurement, continuous measurement and portability features that was utilized with a fiber optic sensor for the first time for the detection of a biological signature in an ACSF environment. Here, the measurement is based on the total optical loss detection when specially fabricated sensor heads were immersed into ACSF solutions with and without different concentrations of Aβ42 biomarkers since the bulk refractive index change was performed. Baseline stability and the reference ring down times of the sensor head were measured in the air as 0.87% and 441.6 μs ± 3.9 μs, respectively. Afterward, the total optical loss of the system was measured when the sensor head was immersed in deionized water, ACSF solution, and ACSF solutions with Aβ42 in different concentrations. The lowest Aβ42 concentration of 2 ppm was detected by LOR. Results showed that LOR fabricated by single-mode fibers for FLRDS system design are promising candidates to be utilized as fiber optic biosensors after sensor head modification and have a high potential for early detection applications of not only AD but possibly also several fatal diseases such as diabetes and cancer.

Spin Doctors: How to Diagnose a Hierarchical Merger Origin

Gravitational-wave observations provide the unique opportunity of studying black hole formation channels and histories—but only if we can identify their origin. One such formation mechanism is the dynamical synthesis of black hole binaries in dense stellar systems. Given the expected isotropic distribution of component spins of binary black holes in gas-free dynamical environments, the presence of antialigned or in-plane spins with respect to the orbital angular momentum is considered a tell-tale sign of a merger’s dynamical origin. Even in the scenario where birth spins of black holes are low, hierarchical mergers attain large component spins due to the orbital angular momentum of the prior merger. However, measuring such spin configurations is difficult. Here, we quantify the efficacy of the spin parameters encoding aligned-spin (χ eff) and in-plane spin (χ p ) at classifying such hierarchical systems. Using Monte Carlo cluster simulations to generate a realistic distribution of hierarchical merger parameters from globular clusters, we can infer mergers’ χ eff and χ p . The cluster populations are simulated using Advanced LIGO-Virgo sensitivity during the detector network’s third observing period and projections for design sensitivity. Using a “likelihood-ratio”-based statistic, we find that ∼2% of the recovered population by the current gravitational-wave detector network has a statistically significant χ p measurement, whereas no χ eff measurement was capable of confidently determining a system to be antialigned with the orbital angular momentum at current detector sensitivities. These results indicate that measuring spin-precession through χ p is a more detectable signature of hierarchical mergers and dynamical formation than antialigned spins.

Automatic Signature and Photo Detection from the Uploaded Document using AI and OCR

In our modern era, it is important for organizations in different industries to have efficient document processing capabilities. One key aspect is the identification of signatures and images in documents, which typically requires manual review. Fortunately, advancements in Artificial Intelligence (AI) and Optical Character Recognition (OCR) technologies present viable options for automating this task. This paper presents a novel method that utilizes AI and OCR techniques to automatically identify signatures and images in uploaded documents.The process starts with preparing documents and utilizing AI technology for recognizing signatures and photos. Optical Character Recognition (OCR) is also used for a thorough examination. This method enhances efficiency, precision, and adaptability in handling documents. It is applicable in various fields like finance, legal, and administration. The solution aims to transform document processing by providing a smooth and dependable way to detect signatures and photos in the modern age.

Serum micro-RNAs with mutation-targeted RNA modification: a potent cancer detection tool constructed using an optimized machine learning workflow.

RNA modifications affect fundamental biological processes and diseases and are a research hotspot. Several micro-RNAs (miRNAs) exhibit genetic variant-targeted RNA modifications that can greatly alter their biofunctions and influence their effect on cancer. Therefore, the potential role of these miRNAs in cancer can be implicated in new prevention and treatment strategies. In this study, we determined whether RMvar-related miRNAs were closely associated with tumorigenesis and identified cancer-specific signatures based on these miRNAs with variants targeting RNA modifications using an optimized machine learning workflow. An effective machine learning workflow, combining least absolute shrinkage and selection operator analyses, recursive feature elimination, and nine types of machine learning algorithms, was used to screen candidate miRNAs from 504 serum RMvar-related miRNAs and construct a diagnostic signature for cancer detection based on 43,047 clinical samples (with an area under the curve value of 0.998, specificity of 93.1%, and sensitivity of 99.3% in the validation cohort). This signature demonstrated a satisfactory diagnostic performance for certain cancers and different conditions, including distinguishing early-stage tumors. Our study revealed the close relationship between RMvar-related miRNAs and tumors and proposed an effective cancer screening tool.

Role of renin-angiotensin system/angiotensin converting enzyme-2 mechanism and enhanced COVID-19 susceptibility in type 2 diabetes mellitus.

Coronavirus disease 2019 (COVID-19) is a disease that caused a global pandemic and is caused by infection of severe acute respiratory syndrome coronavirus 2 virus. It has affected over 768 million people worldwide, resulting in approximately 6900000 deaths. High-risk groups, identified by the Centers for Disease Control and Prevention, include individuals with conditions like type 2 diabetes mellitus (T2DM), obesity, chronic lung disease, serious heart conditions, and chronic kidney disease. Research indicates that those with T2DM face a heightened susceptibility to COVID-19 and increased mortality compared to non-diabetic individuals. Examining the renin-angiotensin system (RAS), a vital regulator of blood pressure and pulmonary stability, reveals the significance of the angiotensin-converting enzyme (ACE) and ACE2 enzymes. ACE converts angiotensin-I to the vasoconstrictor angiotensin-II, while ACE2 counters this by converting angiotensin-II to angiotensin 1-7, a vasodilator. Reduced ACE2 expression, common in diabetes, intensifies RAS activity, contributing to conditions like inflammation and fibrosis. Although ACE inhibitors and angiotensin receptor blockers can be therapeutically beneficial by increasing ACE2 levels, concerns arise regarding the potential elevation of ACE2 receptors on cell membranes, potentially facilitating COVID-19 entry. This review explored the role of the RAS/ACE2 mechanism in amplifying severe acute respiratory syndrome coronavirus 2 infection and associated complications in T2DM. Potential treatment strategies, including recombinant human ACE2 therapy, broad-spectrum antiviral drugs, and epigenetic signature detection, are discussed as promising avenues in the battle against this pandemic.

Handwritten Online Signature Verification and Forgery Detection Using Hybrid Wavelet Transform-2 with HMM Classifier

Online signature verification stands out as a distinctive biometric feature, offering both static and dynamic attributes within 2D signature images. A Hybrid Wavelet Transform-2 (HWT-2) with a size of 256 is constructed by employing the Kronecker product of two orthogonal transforms: DCT, DHT, Haar, Hadamard, and Kekre, each with sizes of 4 and 64. The HWT enables the analysis of signals at both global and local levels, akin to wavelet transforms. HWT-2 is applied to 256 samples of online handwritten signatures, and the first 128 samples of the output are utilized as feature vectors for the verification and forgery detection of online handwritten signatures. These feature vectors are inputted into Left-Right and Ergodic Hidden Markov Model (HMM) classifiers for analysis. The HMMs are trained using 10 randomly selected genuine signature samples, and subsequently tested on the remaining 10 genuine signatures and 20 forged signatures from 40 users of the SVC 2004 signature database. This process is repeated 20 times, and the average values are computed. Among all possible combinations of HWT-2 using DCT, DHT, Haar, Hadamard, and Kekre transforms for the Left-Right HMM model, the combination of DCT 4 and DHT 64 demonstrates the best performance, with False Rejection Rate (FRR) and False Acceptance Rate (FAR) values of 3.96% and 1.48%, respectively, for state 5. Similarly, for the Ergodic HMM model, the combination of DCT 4 and DHT 64 exhibits the best performance, with FRR and FAR values of 1.10% and 2.88%, respectively, for state 5. These results indicate that combinations of HWT-2 outperform individual orthogonal transforms, and further, that HWT-2 combinations within the Ergodic HMM model offer superior performance compared to the Left-Right HMM model.

Two prospective, multicenter studies for the identification of biomarker signatures for early detection of pulmonary hypertension (PH): The CIPHER and CIPHER-MRI studies.

A blood test identifying patients at increased risk of pulmonary hypertension (PH) could streamline the investigative pathway. The prospective, multicenter CIPHER study aimed to develop a microRNA-based signature for detecting PH in breathless patients and enrolled adults with a high suspicion of PH who had undergone right heart catheterization (RHC). The CIPHER-MRI study was added to assess the performance of this CIPHER signature in a population with low probability of having PH who underwent cardiac magnetic resonance imaging (cMRI) instead of RHC. The microRNA signature was developed using a penalized linear regression (LASSO) model. Data were modeled both with and without N-terminal pro-brain natriuretic peptide (NT-proBNP). Signature performance was assessed against predefined thresholds (lower 98.7% CI bound of ≥0.73 for sensitivity and ≥0.53 for specificity, based on a meta-analysis of echocardiographic data), using RHC as the true diagnosis. Overall, 926 CIPHER participants were screened and 888 were included in the analysis. Of 688 RHC-confirmed PH cases, approximately 40% were already receiving PH treatment. Fifty microRNA (from 311 investigated) were algorithmically selected to be included in the signature. Sensitivity [97.5% CI] of the signature was 0.85 [0.80-0.89] for microRNA-alone and 0.90 [0.86-0.93] for microRNA+NT-proBNP, and the corresponding specificities were 0.33 [0.24-0.44] and 0.28 [0.20-0.39]. Of 80 CIPHER-MRI participants with evaluable data, 7 were considered PH-positive by cMRI whereas 52 were considered PH-positive by the microRNA signature. Due to low specificity, the CIPHER miRNA-based signature for PH (either with or without NT-proBNP in model) did not meet the prespecified diagnostic threshold for the primary analysis.

Mineralogical Characterization of the Lunar South Polar Region: 1. The Artemis Exploration Zone

AbstractThe lunar south pole is a region of focused scientific and exploration interest, with several crewed and robotic missions to this region planned within the next decade. Understanding the mineralogy of the region is essential to inform landing site characterization and selection and provides the key context for interpreting samples and in situ observations. At high latitudes, extreme illumination conditions (high phase angles) can negatively impact the data quality of orbital instruments. This is especially true for passive near‐infrared spectrometers such as the Moon Mineralogy Mapper (M3) and the Kaguya Spectral Profiler, which measure the spectral properties of the surface using reflected sunlight. Using Moon Mineralogy Mapper data, we observed that the south polar region is associated with a detectable mafic signature consistent with the presence of pyroxenes. The strongest mafic signatures are associated with the South Pole—Aitken Basin, suggesting that impact melt and basin ejecta from the lower crust and upper mantle are present within this region. This observation is validated in several ways: (a) comparisons between M3 data acquired during different mission phases, (b) comparisons between multiple spectral parameters sensitive to the presence of mafic minerals, (c) comparisons between the north and south lunar polar regions, and (d) comparisons with publicly available Kaguya polar mineralogy maps and Lunar Prospector elemental abundances. We also investigate the nature of an anomalous high‐albedo region within 2–3° of the south pole observed in Lunar Orbiter Laser Altimeter reflectance data exhibiting a spatially conflicting apparent FeO abundance pattern between several data sets.

Unveiling the Genetic Mechanism of Meat Color in Pigs through GWAS, Multi-Tissue, and Single-Cell Transcriptome Signatures Exploration.

Meat color traits directly influence consumer acceptability and purchasing decisions. Nevertheless, there is a paucity of comprehensive investigation into the genetic mechanisms underlying meat color traits in pigs. Utilizing genome-wide association studies (GWAS) on five meat color traits and the detection of selection signatures in pig breeds exhibiting distinct meat color characteristics, we identified a promising candidate SNP, 6_69103754, exhibiting varying allele frequencies among pigs with different meat color characteristics. This SNP has the potential to affect the redness and chroma index values of pork. Moreover, transcriptome-wide association studies (TWAS) analysis revealed the expression of candidate genes associated with meat color traits in specific tissues. Notably, the largest number of candidate genes were observed from transcripts derived from adipose, liver, lung, spleen tissues, and macrophage cell type, indicating their crucial role in meat color development. Several shared genes associated with redness, yellowness, and chroma indices traits were identified, including RINL in adipose tissue, ENSSSCG00000034844 and ITIH1 in liver tissue, TPX2 and MFAP2 in lung tissue, and ZBTB17, FAM131C, KIFC3, NTPCR, and ENGSSSCG00000045605 in spleen tissue. Furthermore, single-cell enrichment analysis revealed a significant association between the immune system and meat color. This finding underscores the significance of the immune system associated with meat color. Overall, our study provides a comprehensive analysis of the genetic mechanisms underlying meat color traits, offering valuable insights for future breeding efforts aimed at improving meat quality.

A Comprehensive Review of Dimensionality Reduction Techniques for Real time Network Intrusion Detection with Applications in Cybersecurity

This paper reviews popular signature and anomaly-based intrusion detection systems (IDS). Dimensionality reduction techniques (DRT) are used to increase the efficiency of such systems for real-time operation. Autoencoder-based IDS is rapidly gaining in popularity, primarily due to its inherent ability to denoise data and reduce dimensionality. In addition to the efficiency, we also look at the classification techniques used by various authors, and the overall impact of a model in terms of performance metrics. This article is written for novices in cybersecurityto get a jumpstart on the latest IDS algorithms. The purpose is to give useful insights into the broad and progressive view of various techniques in wide use, expose high-impact future research areas and to summarize classic IDS methods and feature selection techniques.

Search for a Neutron Dark Decay in ^{6}He.

Neutron dark decays have been suggested as a solution to the discrepancy between bottle and beam experiments, providing a dark matter candidate that can be searched for in halo nuclei. The free neutron in the final state following the decay of ^{6}He into ^{4}He+n+χ provides an exceptionally clean detection signature when combined with a high efficiency neutron detector. Using a high-intensity ^{6}He^{+} beam at Grand Accélérateur National d'Ions Lourds, a search for a coincident neutron signal resulted in an upper limit on a dark decay branching ratio of Br_{χ}≤4.0×10^{-10} (95% C.L.). Using the dark neutron decay model proposed originally by Fornal and Grinstein, we translate this into an upper bound on a dark neutron branching ratio of O(10^{-5}), improving over global constraints by one to several orders of magnitude depending on m_{χ}.

Abstract 5028: Detection of non-small cell lung and bladder cancer signatures in peripheral blood using a novel antibody-free chromatin capture assay

Abstract Immune checkpoint inhibition (ICI) via anti-PD-1/PD-L1 has improved clinical outcomes for multiple cancers, including bladder and non-small cell lung cancer (NSCLC). Yet, the response rates remain low highlighting a significant knowledge gap in the mechanisms driving response and resistance. While the expression of PD-L1 has been established as a predictive biomarker of ICI response, other features, including molecular tumor burden, tumor microenvironment (TME), and the host’s immune mechanisms may contribute to overall response. The complex interactions between a tumor and the immune system underscore the need for a more comprehensive approach to explore novel biological and immunological insights of cancer progression. Circulating tumor DNA assays, while integral to contemporary decision-making in oncology, fall short in delineating the transcriptional programs that dictate cancer phenotypes and their evolution over the disease trajectory. To bridge this gap, we have developed a plasma-based active chromatin capture method that enables the comprehensive epigenetic and transcriptomic interrogation of both tumor and immune components. In this study, we quantified epigenetic profiles from plasma samples (n=100) of patients with bladder cancer (n=20) and NSCLC (n=24) undergoing ICI therapy, along with samples (n=70) from healthy individuals (n=5). As expected, the total cfDNA yield (ng) of lung (1.7x, p=2.3e-08) and bladder (1.98x, p=1.6e-11) cancer samples were higher than those of healthy individuals. More Interestingly, the longer cfDNA fragments derived from regulatory-active chromatin, also showed a significantly higher abundance in plasma samples from both lung (2.0x, p=2.5e-09) and bladder (3.2x, p=3.2e-14) cancer patients. Furthermore, the patterns of epigenetic regulations across cancer and healthy conditions showed distinct and stable clusters, consistent between gene bodies and transcription start sites, highlighting the epigenetic changes that may be driving cancer progression. We also observed a common enrichment of immune epigenetic signatures in both lung and bladder cancer samples, likely attributed to shared mechanisms associated with the modulation of the tumor microenvironment and immune interactions. Understanding these changes at an epigenetic level could provide insights into novel therapeutic approaches that target the tumor microenvironment and the immune interactions. Citation Format: Kevin Lai, Katharine Dilger, Rachael Cunningham, Timothy Barnes, Khiet Truong, Kathy Lam, Rhea Boquiren, Maggie C. Louie, Diana Abdueva. Detection of non-small cell lung and bladder cancer signatures in peripheral blood using a novel antibody-free chromatin capture assay [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 5028.

Abstract 4433: Secondary electrospray ionization mass spectrometry for volatile analysis of lung cancer

Abstract Lung cancer remains a leading cause of cancer-related mortality globally, necessitating innovative approaches for early detection and improved understanding of its metabolic landscape and consequences. Secondary Electrospray Ionization Mass Spectrometry (SESI-MS), an innovative analytical technique, has emerged as a powerful tool for the analysis of volatile organic compounds (VOCs) associated with various diseases, from metabolic disorders to bacterial infections. In this proof-of-concept study, we first utilized SESI-MS analyses of four representative lung cancer cells of both non-small cell lung cancer (NSCLC) and small cell lung cancer (SCLC) to demonstrate its capability to profile VOCs released by lung cancer cells. The technique allows for real-time, non-invasive analysis of the headspace above cultured cells, enabling the detection of unique VOC signatures associated with different lung cancer subtypes. Meanwhile, SESI-MS also facilitated the discrimination specific cells based on distinct VOC patterns. Furthermore, we also performed cisplatin treatment experiments and observed the metabolic signature shift post drug treatment. These results collectively offered potential applications in early diagnosis and personalized treatment strategies. Next, we continued with in vivo investigations to further extend the utility of SESI-MS in lung cancer research. Animal models, mimicking human lung cancer pathophysiology, have been subjected to SESI-MS analysis to identify volatile biomarkers indicative of tumor development and progression. Several representative cancer drug treatments were performed to the lung cancer mice model, and the drug-induced responses were also monitored via SESI-MS breath analyses. The in vivo SESI-MS approach detected many interesting breath VOC features that could provide valuable insights into the dynamic changes in VOC profiles during various stages of lung cancer, and therefore has the potential to offer a deeper understanding of the disease's metabolic alterations and potential avenues for targeted therapeutic interventions. In conclusion, SESI-MS-based volatile analysis presents a robust and versatile approach for investigating lung cancer, and our ongoing work has the potential to bridge the gap between in vitro and in vivo models for better translational research outcomes. The detection of unique VOC signatures associated with lung cancer cells and tumors holds significant promise for early detection, subtype classification, and monitoring of treatment responses. As technology continues to advance, SESI-MS stands poised to play a pivotal role in transforming the landscape of lung cancer diagnostics and research. Citation Format: Jiangjiang (Chris) Zhu, Fouad Choueiry. Secondary electrospray ionization mass spectrometry for volatile analysis of lung cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 4433.

Abstract 6090: Integration of innate and adaptive immune signatures for early detection of cancer

Abstract Up to 50% of cancer patients are diagnosed at a late stage with tumors that are often unresectable, leading to intensive treatments and a preventable loss of life. Whilst multiple assays have been developed to tackle the issue of disease mortality, these approaches have serious limitations. For example, circulating tumor DNA (ctDNA) preferentially detects hard-to-treat tumors and micrometastatic disease, with limited sensitivity and specificity for early-stage malignancy, underscoring an urgent need for novel early detection strategies. Substantial evidence exists to demonstrate that signals related to innate (e.g. pro-inflammatory cytokines) and adaptive (e.g. antibodies and T cell receptors [TCRs]) immune engagement arise early in the development of cancer. The immune system therefore offers potential as an exquisitely sensitive and highly specific intrinsic early detection system for cancer. Here we propose the development of a pan-cancer novel early detection assay, using peripheral blood to measure three key cancer-specific components of the anti-tumor immune response: i) T cell receptor (TCR) sequences, ii) antibody signatures, iii) cytokine markers. Our preliminary data demonstrates that an age and smoking-matched cohort of lung cancer patients can be distinguished from healthy donors using TCR sequencing data processed through a published machine learning approach (p<0.01). In support of this, a pilot study on a custom peptide microarray comprised of lung cancer associated antigens and recurrent neoantigens demonstrated that lung cancer patient samples show significantly higher antibody intensities than healthy donors. Thirdly, using data from a prospective cohort study, we have demonstrated that inflammatory markers can be leveraged to predict future cancer diagnosis, and identified key protein markers for future work. These data provide proof-of-principle for the use of these analytes as a tool for early detection. We are actively curating a unique cohort of samples for this work from a prospective study, Nodule Immunophenotyping Biomarker for Lung Cancer Early Diagnosis (NIMBLE) (NCT05432739), which recruits patients with indeterminate lung nodules that may represent early cancers (>280 patients to date). This malignancy has high mortality rates and a significant need for early detection strategies. Through this, we aim to understand the early immunobiological response to cancer and leverage this information to design a novel multi-parametric immunopredictor assay for early detection of cancer. Citation Format: Evelyn Fitzsimons, Alexander Coulton, Hongchang Fu, Marcellus Augustine, Richard Lee, James Reading, Kevin Litchfield. Integration of innate and adaptive immune signatures for early detection of cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 6090.

Abstract 1268: Immune activation characterization via amino acid concentration signatures for multi-cancer early detection and CDKi treatment response prediction

Abstract Early detection of cancer can increase survival rates by 5-10 times. In the US, a significant portion of cancers are still detected at a late stage, for example, 32.1% of breast cancer, 45.5% of lung cancer, 22.8% of colon cancer patients were diagnosed at advanced stage. Early detection is challenging because many cancers are asymptomatic in early stages, although cancer screening can detect cancer before symptoms emerge. Current standard-of-care screening programs are limited and directed to single types of cancer. Non-invasive multi-cancer early detection (MCED) is urgently needed because it can screen for several cancer types with a single blood test, increasing cancer screening coverage while avoiding cumulative false positive rates. Developmental MCED approaches usually rely on detection of circulating tumor DNA (ctDNA) released from tumors. However, ctDNA signals scale with tumor burden limiting sensitivity and specificity, particularly for early-stage cancers, while also limiting applicability to precision oncology. Here we report a MCED approach with superior sensitivity and specificity, which can also be used to predict treatment response. We introduce a novel strategy for detecting cancers from blood plasma, profiling the immune response to disease development rather than relying on detection of emerging diseased tissue. The platform is designed to detect alterations in the ratio of immunoglobulins and albumin, as well as class-switching among immunoglobulins, by measuring the overall concentration of amino acid residues incorporated into proteins in patient plasma. By using our biorthogonal chemistry labeling tool, we were able to measure and characterize amino acid concentration signatures (AACS) within the neat blood samples, including cysteine, free cysteine, lysine, tryptophan and tyrosine, which allowed us to observe distinct signatures for varying cancers including breast, prostate, colorectal and pancreatic cancers. We could identify 84% of cancers with <0.5% false positive rate (N=93). In this study, cancer AACS signatures were distinct from signatures measured with non-cancerous immune activation, including autoimmune diseases and infection. Moreover, AACS was also correlated to clinical features, such as cancer metastatic statuses and therapy response. With Cyclin-dependent Kinase 4/6 Inhibitors-treated advanced breast cancer patients as an example, we achieved 100% correct prediction of responding patients and 91.7% accurate predictions of non-responding patients by using AACS in treatment-naïve samples. Altogether, the novel AACS approach is a powerful combination of bioorthogonal chemistry and machine learning analysis in clinical practice, and it can be potentially used for cancer screening and support clinical decisions for treatment selection and patient stratification in new indications. Citation Format: Cong Tang, Patrícia Corredeira, Sandra Casimiro, Wesley Sukdao, Luís Costa, Emma Yates, Gonçalo Bernardes. Immune activation characterization via amino acid concentration signatures for multi-cancer early detection and CDKi treatment response prediction [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 1268.

Machine Learning Analysis Using RNA Sequencing to Distinguish Neuromyelitis Optica from Multiple Sclerosis and Identify Therapeutic Candidates

This study aims to identify RNA biomarkers distinguishing neuromyelitis optica (NMO) from relapsing-remitting multiple sclerosis (RRMS) and explore potential therapeutic applications leveraging machine learning (ML). An ensemble approach was developed using differential gene expression analysis and competitive ML methodologies, interrogating total RNA sequencing datasets from peripheral whole blood of treatment-naïve RRMS and NMO patients and healthy individuals. Pathway analysis of candidate biomarkers informed the biological context of disease, transcription factor activity, and small-molecule therapeutic potential. ML models differentiated between NMO and RRMS patients, with the performance of certain models exceeding 90% accuracy. RNA biomarkers driving model performance were associated with ribosomal dysfunction and viral infection. Regulatory networks of kinases and transcription factors informed biological context and identified potential therapeutic targets. Small-molecule candidates capable of reversing perturbed gene expression were uncovered. Two small molecules identified, mitoxantrone and vorinostat, reinforced discovered expression signatures and highlighted the potential to identify new therapeutic candidates, as their use in NMO patients and experimental autoimmune encephalomyelitis have been previously described. Putative RNA biomarkers were identified that accurately distinguish NMO from RRMS and healthy individuals. The application of multivariate approaches in analysis of RNA sequencing data enhances the discovery of unique RNA biomarkers, accelerating the development of new methods for disease detection, monitoring, and therapeutics. Integrating biological understanding enhances detection of disease-specific signatures and possible therapeutic targets.

Exploring the impact of microlensing on gravitational wave signals: Biases, population characteristics, and prospects for detection

ABSTRACT In this study, we investigate the impact of microlensing on gravitational wave (GW) signals in the LIGO−Virgo sensitivity band. Microlensing caused by an isolated point lens, with (redshifted) mass ranging from MLz ∈ (1, 105) M⊙ and impact parameter y ∈ (0.01, 5), can result in a maximum mismatch of $\sim 30~{{\ \rm per\ cent}}$ with their unlensed counterparts. When y < 1, it strongly anticorrelates with the luminosity distance enhancing the detection horizon and signal-to-noise ratio (SNR). Biases in inferred source parameters are assessed, with in-plane spin components being the most affected intrinsic parameters. The luminosity distance is often underestimated, while sky-localization and trigger times are mostly well-recovered. Study of a population of microlensed signals due to an isolated point lens primarily reveals: (i) using unlensed templates during the search causes fractional loss (20 per cent to 30 per cent) of potentially identifiable microlensed signals; (ii) the observed distribution of y challenges the notion of its high improbability at low values (y ≲ 1), especially for y ≲ 0.1; (iii) Bayes factor analysis of the population indicates that certain region in MLz − y parameter space have a higher probability of being detected and accurately identified as microlensed. Notably, the microlens parameters for the most compelling candidate identified in previous microlensing searches, GW200208_130117, fall within a 1σ range of the aforementioned higher probability region. Identifying microlensing signatures from MLz < 100 M⊙ remains challenging due to small microlensing effects at typical SNR values. Additionally, we also examined how microlensing from a population of microlenses influences the detection of strong lensing signatures in pairs of GW events, particularly in the posterior-overlap analysis.