Non-invasive Cancer Detection Research Articles

Tyndall, Rayleigh, Mei, and Raman scattering: Understanding their role in aesthetics.

The role of various light scattering phenomena in aesthetics and clinical practice is explored in this review. Four main types of light scattering, Tyndall, Rayleigh, Mie, and Raman, are discussed. Each type is explained in terms of its physical principles and its applications in aesthetic medicine. Tyndall scattering is relevant in understanding the blue appearance of certain dermal fillers. Rayleigh scattering contributes to skin tone perception and plays a role in certain laser treatments. Mie scattering is important in laser hair removal and the appearance of skin conditions like melasma. Raman scattering, while primarily used in research, shows promise for non-invasive skin analysis, personalized skincare, treatment monitoring, and early skin cancer detection. It is important to understand these scattering phenomena for optimizing light-based aesthetic procedures and developing effective treatments. Properly applying the appropriate scattering theory based on relative particle size is crucial in clinical aesthetic practice.

Rapid detection of lung cancer based on serum Raman spectroscopy and a support vector machine: a case-control study

BackgroundEarly screening and detection of lung cancer is essential for the diagnosis and prognosis of the disease. In this paper, we investigated the feasibility of serum Raman spectroscopy for rapid lung cancer screening.MethodsRaman spectra were collected from 45 patients with lung cancer, 45 with benign lung lesions, and 45 healthy volunteers. And then the support vector machine (SVM) algorithm was applied to build a diagnostic model for lung cancer. Furthermore, 15 independent individuals were sampled for external validation, including 5 lung cancer patients, 5 benign lung lesion patients, and 5 healthy controls.ResultsThe diagnostic sensitivity, specificity, and accuracy were 91.67%, 92.22%, 90.56% (lung cancer vs. healthy control), 92.22%,95.56%,93.33% (benign lung lesion vs. healthy) and 80.00%, 83.33%, 80.83% (lung cancer vs. benign lung lesion), repectively. In the independent validation cohort, our model showed that all the samples were classified correctly.ConclusionTherefore, this study demonstrates that the serum Raman spectroscopy analysis technique combined with the SVM algorithm has great potential for the noninvasive detection of lung cancer.

Canine Cancer Diagnostics by X-ray Diffraction of Claws.

We report the results of X-ray diffraction (XRD) measurements of the dogs' claws and show the feasibility of using this approach for early, non-invasive cancer detection. The obtained two-dimensional XRD patterns can be described by Fourier coefficients, which were calculated for the radial and circular (angular) directions. We analyzed these coefficients using the supervised learning algorithm, which implies optimization of the random forest classifier by using samples from the training group and following the calculation of mean cancer probability per patient for the blind dataset. The proposed algorithm achieved a balanced accuracy of 85% and ROC-AUC of 0.91 for a blind group of 68 dogs. The transition from samples to patients additionally improved the ROC-AUC by 10%. The best specificity and sensitivity values for 68 patients were 97.4% and 72.4%, respectively. We also found that the structural parameter (biomarker) most important for the diagnostics is the intermolecular distance.

Non-invasive colorectal cancer detection using multimodal deep learning ensemble classifier.

Non-invasive colorectal cancer detection using multimodal deep learning ensemble classifier.

Deliberate inclusion of multiple co-correlated gene target sets during discovery for improved biomarker translation to a clinical assay in early-stage breast cancer.

e12560 Background: Co-correlation between gene targets provides a more robust, and often nonlinear, understanding of human biology, resulting in more accurate and reliable diagnostics. When translating from discovery to a clinical assay, detectability of promising targets often hinders diagnostic development. Translating a single set of co-correlated targets becomes more challenging, as the success of the program is dependent on the interrelationship between all targets and the ability of the entire set to translate clinically. Given target translation uncertainty, we hypothesized that the deliberate inclusion of multiple independent sets of co-correlated targets during discovery could bridge the gap between promising biology and a clinically viable assay. We set out to discover and clinically validate multiple sets of co-correlated targets required for noninvasive breast cancer detection. Methods: Nonlinear statistical methods were repeatedly applied to microarray gene expression profiles from peripheral blood (PBMCs) obtained from 140 patients with and without breast cancer. Four independent cohorts were used for subsequent studies to assess efficacy and reproducibility of discovered targets. In total, five independent sets of highly efficacious co-correlated targets, totaling 26 unique genes, were discovered and analytically validated across the five patient cohorts (n=357). A sixth independent cohort of 197 plasma samples (breast cancer n=116, controls n=81) was obtained and used to clinically translate and validate the 26 targets using real-time qPCR (qPCR). Results: qPCR revealed strong and reproducible clinical signal from 8/26 previously discovered targets (Table). To evaluate the efficacy of the targets, an 8-gene signature was trained on the PBMC discovery cohort, achieving >99% sensitivity, 83% specificity, indicating a promising classifier could be trained from the 8 qPCR targets. During CLIA validation of the assay, in normalized qPCR data from 87 patients, the 8-gene assay achieved a statistically significant >99% sensitivity, 89% specificity in stage I breast cancer. Conclusions: Given the performance observed from the 8-gene qPCR assay, we conclude that the deliberate inclusion of multiple sets of co-correlated targets, including those considered mathematically redundant during discovery, contributed to the success of a clinically viable assay for the detection of early-stage breast cancer in plasma. [Table: see text]

Assessment of plasma cell-free DNA fragmentation for multi-cancer early detection: An independent clinical validation study.

e15037 Background: Cell-free DNA (cfDNA) in the circulation has gained significant attention due to its potential applications for non-invasive early cancer detection. Fragmentomics of cfDNA is emerging as a promising field of biomarker research, exhibiting the capability to sensitively detect multiple cancer types. In previous retrospective research, we developed an approach called PatternWGS for comprehensive analysis of cfDNA fragmentation patterns and presented a multi-cancer detection model. In this study, we aim to evaluate the performance of PatternWGS in an independent validation cohort including lung, breast, colorectal, liver, and gastric cancers from a new clinical center. Methods: Participants with confirmed diagnosis of lung, breast, colorectal or liver cancer were enrolled in the cancer group. Non-cancer participants without known presence of malignancies were recruited from the same clinical center. Plasma samples from patients with lung (n=47), breast (n=19), colorectal (n=14), liver (n=8) and gastric (n=6) cancers, along with 101 non-cancer individuals were collected in this study. cfDNA was isolated from plasma and stored within a month prior to the whole genome sequencing. The characteristics of cfDNA fragmentation integrating fragment size profiles, motif patterns, genomic coverage distributions were utilized to evaluate the performance for cancer signal detection. In addition, the performance of estimated cfDNA tumor fraction, using copy number aberrations (ichorCNA), was compared with our approach. Results: 94 patients with one of five types of cancers and 101 non-cancer controls were prospectively enrolled between July 2023 and November 2023. Among the cancer group, 64% were at early stages (Stage I: 48%, Stage II: 16%). A significantly higher proportion of shorter fragments was observed in cancer group, and the degree increased with increasing cancer stage. Overall sensitivity across cancer types and stages was 72.3%, and the specificity for non-cancer controls was 80%. As expected, the sensitivity of cancer detection is higher in late-stage patients compared to those in the early-stages (61% in stage I, 80% in stage II, 84.6% in stage III, and 91.7% in stage IV). The sensitivity of detecting lung (72% had stage I cancer), breast, colorectal, liver, and gastric cancers reached 57.4%, 84.2%, 85.7%, 87.5% and 100% respectively. The detection performance using cfDNA tumor fraction by ichorCNA demonstrated a relatively low overall sensitivity of 28.7% (24.6% for stage I-II, 44% for stage III-IV). Conclusions: This independent clinical validation study, which focused on exploring plasma cfDNA fragmentation, demonstrated ideal performance in early-stage cancer detection. The cfDNA fragmentation-based approach exhibited a superior sensitivity in detecting early cancer signals compared with copy number aberrations-based approach.

Genome-Scale Multimodal Analysis of Cell-Free DNA Whole-Methylome Sequencing for Noninvasive Esophageal Cancer Detection.

Simultaneous profiling of cell-free DNA (cfDNA) methylation and fragmentation features to improve the performance of cfDNA-based cancer detection is technically challenging. We developed a method to comprehensively analyze multimodal cfDNA genomic features for more sensitive esophageal squamous cell carcinoma (ESCC) detection. Enzymatic conversion-mediated whole-methylome sequencing was applied to plasma cfDNA samples extracted from 168 patients with ESCC and 251 noncancer controls. ESCC characteristic cfDNA methylation, fragmentation, and copy number signatures were analyzed both across the genome and at accessible cis-regulatory DNA elements. To distinguish ESCC from noncancer samples, a first-layer classifier was developed for each feature type, the prediction results of which were incorporated to construct the second-layer ensemble model. ESCC plasma genome displayed global hypomethylation, altered fragmentation size, and chromosomal copy number alteration. Methylation and fragmentation changes at cancer tissue-specific accessible cis-regulatory DNA elements were also observed in ESCC plasma. By integrating multimodal genomic features for ESCC detection, the ensemble model showed improved performance over individual modalities. In the training cohort with a specificity of 99.2%, the detection sensitivity was 81.0% for all stages and 70.0% for stage 0-II. Consistent performance was observed in the test cohort with a specificity of 98.4%, an all-stage sensitivity of 79.8%, and a stage 0-II sensitivity of 69.0%. The performance of the classifier was associated with the disease stage, irrespective of clinical covariates. This study comprehensively profiles the epigenomic landscape of ESCC plasma and provides a novel noninvasive and sensitive ESCC detection approach with genome-scale multimodal analysis.

PIK3CA Hotspot Mutations in Saliva as a Diagnostic Marker in Oral Squamous Cell Carcinoma Patients.

This study aimed at the analogous detection of PIK3CA mutations, common in oral squamous cell carcinoma (OSCC), in matched tumor and saliva samples. Tissue and saliva samples were obtained from 29 patients diagnosed with primary OSCC. Saliva samples were obtained preoperatively; tissue specimens were acquired during tumor resection. Tumor DNA was extracted from both tissue and saliva samples. All samples were controlled for DNA quantity and quality and genetic matching of sample pairs was confirmed using the iPlex Pro Exome QC Panel. Variant detection was performed using the MassARRAY® System, a mass-spectrometry based detection system. Mutational analysis in tissue tumor DNA was made using the multiplexed ClearSEEK™ PIK3CA v1.0 Panel covering 20 hotspot mutations in PIK3CA. In saliva samples, variants were analyzed using both the ClearSEEK™ and the UltraSEEK® Lung v1.1 Panel, with a higher limit of detection but covering less PIK3CA variants. Overall, a PIK3CA variant was found in seven of the 29 tumor tissue samples (24%) by ClearSEEK™; UltraSEEK® additionally confirmed the variant in four of these seven positive samples. Of the three variants not detected by UltraSEEK®, two were not included in the panel and one was included but not detected. Of the seven variants found in tissue, five could also be detected in the matching saliva samples (71%), either by utilizing ClearSEEK™ or UltraSEEK® Conclusion: The detection of PIK3CA hotspot mutations in OSCC and their simultaneous occurrence in saliva underline the potential benefit of liquid biopsies for non-invasive cancer detection and follow-up care of OSCC patients.

Letter to editor:Identification and validation of the surface proteins FIBG, PDGF-β, and TGF-β on serum extracellular vesicles for non-invasive detection of colorectal cancer: experimental study (Int J Surg. 2024 Mar 3.).

Letter to editor:Identification and validation of the surface proteins FIBG, PDGF-β, and TGF-β on serum extracellular vesicles for non-invasive detection of colorectal cancer: experimental study (Int J Surg. 2024 Mar 3.).

Abstract A005: Exploring liquid biopsy for the detection of bladder cancer: Unraveling RNA fusions

Abstract Introduction: This study delves into the promising realm of liquid biopsy for the non-invasive detection of bladder cancer. Leveraging urine as a valuable source, our investigation focuses on identifying RNA fusions associated with bladder cancer, with specific attention to ETV6-NTRK3 and PRCC-TFE3. The utilization of liquid biopsy offers a less invasive and more accessible approach to diagnosing bladder cancer, potentially revolutionizing the field of cancer diagnostics. These specific RNA fusions in urine samples may serve as reliable biomarkers for early detection and monitoring of bladder cancer. This research contributes to the ongoing efforts to enhance diagnostic methodologies to improve patient outcomes and streamline cancer management. Materials and Methods: Urine was collected from healthy male donors using UAS, a urine preservative from Novosanis, for improved urine preservation and stability. The urine samples with UAS were centrifuged at 16,000 xg for 10 min to obtain cf-urine. Three 20 mL aliquots of the precleared supernatant urine samples were spiked with an RNA fusion reference standard from Anchor Molecular containing ETV6-NTRK3 and PRCC-TFE3RNA at a concentration of 20ng/mL. All samples were extracted using the nRichDX Revolution Max20 cfTNA Isolation Kit. The eluants from the extracted samples were DNAse treated. The extracted cfTNAs profile was assessed on 4150 TapeStation System, Agilent, using RNA ScreenTape. Extracted nucleic acids from urine were evaluated using a RT-qPCR assay to quantify the number of copies of the fusion RNAs. Results: The Renal RNA fusions ETV6-NTRK3 and PRCC-TFE3RNA were successfully extracted using the nRichDX Revolution cfTNA Isolation Kit. TapeStation analysis demonstrated prominent 18S and 28S peaks, confirming the successful extraction of RNA from 20 mL urine samples. The mean percent recovery of the recovered RNA Fusion mutation was about 70% via RT-qPCR. Conclusion: Our findings indicate a recovery rate of approximately 70% for spiked RNA fusions in the urine samples. This research holds the promise of revolutionizing cancer diagnostics by introducing a method that is not only reliable but also convenient for early detection and monitoring. Identifying specific RNA fusions in urine samples as biomarkers is a significant step in enhancing diagnostic methodologies. As we contribute to these ongoing efforts, we aim to improve patient outcomes and streamline the management of bladder cancer through innovative and effective diagnostic approaches. Citation Format: Mayer Saidian, Jason Saenz, Carlos Hernandez, Lauren Lee, Andrew Dunnigan, Yabin Lu, Nafiseh Jafari. Exploring liquid biopsy for the detection of bladder cancer: Unraveling RNA fusions [abstract]. In: Proceedings of the AACR Special Conference on Bladder Cancer: Transforming the Field; 2024 May 17-20; Charlotte, NC. Philadelphia (PA): AACR; Clin Cancer Res 2024;30(10_Suppl):Abstract nr A005.

Active Targeting Hyaluronan Conjugated Nanoprobe for Magnetic Particle Imaging and Near-Infrared Fluorescence Imaging of Breast Cancer and Lung Metastasis.

A major contributing cause to breast cancer related death is metastasis. Moreover, breast cancer metastasis often shows little symptoms until a large area of the organs is occupied by metastatic cancer cells. Breast cancer multimodal imaging is attractive since it integrates advantages from several modalities, enabling more accurate cancer detection. Glycoprotein CD44 is overexpressed on most breast cancer cells and is the primary cell surface receptor for hyaluronan (HA). To facilitate breast cancer diagnosis, we report an indocyanine green (ICG) and HA conjugated iron oxide nanoparticle (NP-ICG-HA), which enabled active targeting to breast cancer by HA-CD44 interaction and detected metastasis with magnetic particle imaging (MPI) and near-infrared fluorescence imaging (NIR-FI). When evaluated in a transgenic breast cancer mouse model, NP-ICG-HA enabled the detection of multiple breast tumors in MPI and NIR-FI, providing more comprehensive images and a diagnosis of breast cancer. Furthermore, NP-ICG-HAs were evaluated in a lung metastasis model. Upon NP-ICG-HA administration, MPI showed clear signals in the lungs, indicating the tumor sites. This is the first time that HA-based NPs have enabled MPI of cancer. NP-ICG-HAs are an attractive platform for noninvasive detection of primary breast cancer and lung metastasis.

Machine learning unveils an immune-related DNA methylation profile in germline DNA from breast cancer patients

BackgroundThere is an unmet need for precise biomarkers for early non-invasive breast cancer detection. Here, we aimed to identify blood-based DNA methylation biomarkers that are associated with breast cancer.MethodsDNA methylation profiling was performed for 524 Asian Chinese individuals, comprising 256 breast cancer patients and 268 age-matched healthy controls, using the Infinium MethylationEPIC array. Feature selection was applied to 649,688 CpG sites in the training set. Predictive models were built by training three machine learning models, with performance evaluated on an independent test set. Enrichment analysis to identify transcription factors binding to regions associated with the selected CpG sites and pathway analysis for genes located nearby were conducted.ResultsA methylation profile comprising 51 CpGs was identified that effectively distinguishes breast cancer patients from healthy controls achieving an AUC of 0.823 on an independent test set. Notably, it outperformed all four previously reported breast cancer-associated methylation profiles. Enrichment analysis revealed enrichment of genomic loci associated with the binding of immune modulating AP-1 transcription factors, while pathway analysis of nearby genes showed an overrepresentation of immune-related pathways.ConclusionThis study has identified a breast cancer-associated methylation profile that is immune-related to potential for early cancer detection.

T2-weighted imaging-based deep-learning method for noninvasive prostate cancer detection and Gleason grade prediction: a multicenter study.

To noninvasively detect prostate cancer and predict the Gleason grade using single-modality T2-weighted imaging with a deep-learning approach. Patients with prostate cancer, confirmed by histopathology, who underwent magnetic resonance imaging examinations at our hospital during September 2015-June 2022 were retrospectively included in an internal dataset. An external dataset from another medical center and a public challenge dataset were used for external validation. A deep-learning approach was designed for prostate cancer detection and Gleason grade prediction. The area under the curve (AUC) was calculated to compare the model performance. For prostate cancer detection, the internal datasets comprised data from 195 healthy individuals (age: 57.27 ± 14.45 years) and 302 patients (age: 72.20 ± 8.34 years) diagnosed with prostate cancer. The AUC of our model for prostate cancer detection in the validation set (n = 96, 19.7%) was 0.918. For Gleason grade prediction, datasets comprising data from 283 of 302 patients with prostate cancer were used, with 227 (age: 72.06 ± 7.98 years) and 56 (age: 72.78 ± 9.49 years) patients being used for training and testing, respectively. The external and public challenge datasets comprised data from 48 (age: 72.19 ± 7.81 years) and 91 patients (unavailable information on age), respectively. The AUC of our model for Gleason grade prediction in the training set (n = 227) was 0.902, whereas those of the validation (n = 56), external validation (n = 48), and public challenge validation sets (n = 91) were 0.854, 0.776, and 0.838, respectively. Through multicenter dataset validation, our proposed deep-learning method could detect prostate cancer and predict the Gleason grade better than human experts. Precise prostate cancer detection and Gleason grade prediction have great significance for clinical treatment and decision making. Prostate segmentation is easier to annotate than prostate cancer lesions for radiologists. Our deep-learning method detected prostate cancer and predicted the Gleason grade, outperforming human experts. Non-invasive Gleason grade prediction can reduce the number of unnecessary biopsies.

Identification and validation of the surface proteins FIBG, PDGF-β, and TGF-β on serum extracellular vesicles for non-invasive detection of colorectal cancer: experimental study.

The absence of non-invasive biomarkers for the early diagnosis of colorectal cancer (CRC) has contributed to poor prognosis. Extracellular vesicles (EVs) have emerged as promising candidates for cancer monitoring using liquid biopsy. However, the complexity of EVs isolation procedures and absence of clear targets for detecting serum-derived EVs have hindered the clinical application of EVs in early CRC diagnosis. In the discovery phase, we conducted a comprehensive 4D-DIA proteomic analysis of serum-derived EVs samples from 37 individuals, performing an initial screening of EVs surface proteins. In the technical validation phase, we developed an extraction-free CRC-EVArray microarray to assess the expression of these potential EVs surface proteins in a multicenter study comprising 404 individuals. In the application phase, we evaluated the diagnostic efficacy of the CRC-EVArray model based on machine-learning algorithms. Through 4D-DIA proteomic analysis, we identified 7 potential EVs surface proteins showing significantly differential expression in CRC patients compared to healthy controls. Utilizing our developed high-throughput CRC-EVArray microarray, we further confirmed the differential expression of 3 EVs surface proteins, FIBG, PDGF-β and TGF-β, in a large sample population. Moreover, we established an optimal CRC-EVArray model using the NNET algorithm, demonstrating superior diagnostic efficacy with an AUC of 0.882 in the train set and 0.937 in the test set. Additionally, we predicted the functions and potential origins of these EVs-derived proteins through a series of multi-omics approaches. Our systematic exploration of surface protein expression profiles on serum-derived EVs has identified FIBG, PDGF-β, and TGF-β as novel diagnostic biomarkers for CRC. And the development of CRC-EVArray diagnostic model based on these findings provided an effective tool for the large-scale CRC screening, thus facilitating its translation into clinical practice.

Abstract PO1-28-06: Development of a plasma-based real-time qPCR gene expression assay for targeted screening and diagnosis of early-stage breast cancer

Abstract Background Over 12 million US women undergo follow-up breast imaging and exams annually due to breast cancer suspicions, incurring an $8 billion total cost, of which $2.18 billion is attributed to false-positive breast biopsies. Recent updates published to the Mammography Quality Standards Act (MQSA) now recommend supplemental imaging for women with heterogeneously dense and extremely dense breasts, regardless of suspicious findings. Given sensitivity limitations associated with mammography in dense-breasted women, as low as 30-47%, and non-specificity associated with ultrasound and MRI, these recent recommendations are likely to further strain the healthcare system and place substantial burden on patients and providers. With an estimated 25 million dense-breasted women who regularly screen for breast cancer now considered at-risk, there is a critical need for affordable and highly accurate testing, offering better specificity without sacrificing sensitivity. To address this gap, we developed an affordable and accurate plasma-based gene expression assay using real-time qPCR for targeted screening and diagnosis of early-stage breast cancer. Methods In previous studies, 26 cross-correlated mRNA gene targets were discovered and independently validated for non-invasive breast cancer detection across five independent patient cohorts using microarray gene expression profiling, comprised of peripheral blood mononuclear cells (n=337) and saliva (n=20), demonstrating strong efficacy, reproducibility, and concordance across bio-fluids and assay platforms in all studies. A sixth independent validation cohort comprised of 203 plasma samples was subsequently obtained for real-time qPCR clinical assay development and validation in our CLIA laboratory. The CLIA validation cohort was designed to be representative of different diagnosis status, stage, and ethnicity, and included Caucasian, Black/African American, Hispanic, and Asian women. After normalizing mRNA expression and clustering analysis, the signature was refined to 8 target genes and was assessed in a cohort of 87 plasma samples [Table 1] to verify the assay’s diagnostic performance for stage I breast cancer detection, under locked laboratory protocols. We leveraged machine learning methods derived from XGBoost classification, a supervised-learning algorithm that uses sequentially built shallow decision trees to provide accurate results and avoidance of overfitting. Results The XGBoost model, selecting from the 8 mRNA gene targets and patient age, achieved >99% sensitivity, 89% specificity for stage I breast cancer detection in the held-out test set, with an overall diagnostic accuracy of 94.5%. Normalized data showed significant differences in gene expression between healthy controls and stage I patients, and distinct clustering was observed for the 8 mRNA gene signature, including patient age. Conclusions Our plasma-based real-time qPCR gene expression clinical assay, with machine learning, is positioned as a highly accurate non-invasive tool for targeted screening and diagnosis of early-stage breast cancer. Furthermore, a clinically validated assay in this space addresses the need for more targeted diagnostics, while serving as a promising tool for clinicians as they make critical care decisions across the breast health continuum, accelerating time to earlier and more precise intervention and treatment, mitigating unnecessary healthcare expenditures, and reducing the mental, emotional, and financial burden on patients and their families. Citation Format: Martin Keiser, Elizabeth Cormier-May, Matthew Alderdice, Joy Kavanagh, William Guesdon, Heather Healy, Nathalie Jean-Charles, Jay Harness. Development of a plasma-based real-time qPCR gene expression assay for targeted screening and diagnosis of early-stage 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 PO1-28-06.

Investigating alterations in amino acid levels and immune cell population as potential biomarkers for non-invasive early detection of breast cancer: Insights from a study in north India

Investigating alterations in amino acid levels and immune cell population as potential biomarkers for non-invasive early detection of breast cancer: Insights from a study in north India

Bilateral efforts to improve SERS detection efficiency of exosomes by Au/Na7PMo11O39 Combined with Phospholipid Epitope Imprinting

Detection of cancer-related exosomes in body fluids has become a revolutionary strategy for early cancer diagnosis and prognosis prediction. We have developed a two-step targeting detection method, termed PS-MIPs-NELISA SERS, for rapid and highly sensitive exosomes detection. In the first step, a phospholipid polar site imprinting strategy was employed using magnetic PS-MIPs (phospholipids-molecularly imprinted polymers) to selectively isolate and enrich all exosomes from urine samples. In the second step, a nanozyme-linked immunosorbent assay (NELISA) technique was utilized. We constructed Au/Na7PMo11O39 nanoparticles (NPs) with both surface-enhanced Raman scattering (SERS) property and peroxidase catalytic activity, followed by the immobilization of CD9 antibodies on the surface of Au/Na7PMo11O39 NPs. The Au/Na7PMo11O39-CD9 antibody complexes were then used to recognize CD9 proteins on the surface of exosomes enriched by magnetic PS-MIPs. Lastly, the high sensitivity detection of exosomes was achieved indirectly via the SERS activity and peroxidase-like activity of Au/Na7PMo11O39 NPs. The quantity of exosomes in urine samples from pancreatic cancer patients obtained by the PS-MIPs-NELISA SERS technique showed a linear relationship with the SERS intensity in the range of 6.21 × 107–2.81 × 108 particles/mL, with a limit of detection (LOD) of 5.82 × 107 particles/mL. The SERS signal intensity of exosomes in urine samples from pancreatic cancer patients was higher than that of healthy volunteers. This bidirectional MIPs-NELISA-SERS approach enables noninvasive, highly sensitive, and rapid detection of cancer, facilitating the monitoring of disease progression during treatment and opening up a new avenue for rapid early cancer screening.

Abstract LB010: Identification of malignant lung indeterminate pulmonary nodules with Plasma WGS using exon read depth of short DNA fragments

Abstract Background: Plasma WGS for non-invasive cancer detection has been an active area of research, where fragmentomics studies have been gaining attention for its promising results. Previous studies showed that the quantity of short and long fragments could be used for the prognosis of malignant tumor. In this study, the efficacy of short fragment read depth on the reference genome for malignant nodule detection was evaluated. Methods: Plasma samples from 40 patients with Indeterminate Pulmonary Nodules (IPN) (20 with Benign nodule, 20 Malignant) were collected for WGS. Low quality samples were filtered, and 31 patients (15 Benign, 16 Cancer) remained. Short fragments in each sample were extracted (30-99 bp) and exon read depth for each sample was obtained through human reference genome alignment. A proprietary data analytic machine learning pipeline using XGBoost was trained and tested using 10 fold cross validation. Results: XGBoost model testing accuracy was 96.77% in differentiating Benign and Cancer samples using read depth information from only 3 exons. GPR35’s ENSE00003591008.1, ZNF594-DT’s ENSE00002678218.2 and IGHGP’s ENSE00001819760.2 were the 3 exons used in the model. Hierarchical clustering using top features with differential read depth (p<0.001) was able to perfectly cluster the samples into the Benign group and the Malignant group. Conclusion: Read depth information of exon by short fragments from WGS plasma was able to differentiate patients with Lung benign nodules from patients with malignant nodules. This analysis demonstrates promises of developing diagnosis tools using exon read depths from plasma samples. Citation Format: Shun H. Yip, Neeti Swarup, Jason C. Hyun, Jordan C. Cheng, Raghuraman Ramamurthy, Edmund Wong, Irene Choi, Akanksha Arora, Denise Aberle, David TW Wong, Cheuk Y. Tang. Identification of malignant lung indeterminate pulmonary nodules with Plasma WGS using exon read depth of short DNA fragments [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 2 (Late-Breaking, Clinical Trial, and Invited Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(7_Suppl):Abstract nr LB010.

Abstract LB108: Specific plasma DNA end-motifs distinguish patients with lung adenocarcinoma versus benign nodules

Abstract Background: Recent studies have identified blood plasma DNA as a rich source of biomarkers for non-invasive cancer detection. In particular, analysis of shorter DNA fragments or “fragmentomics” have shown promising results with numerous competing approaches such as those based on mutation calls, length distributions, or preferred fragmentation patterns. In this study, we assessed the potential of fragment end motif distributions in discriminating patients with lung adenocarcinoma versus benign tumors. Methods: Plasma samples from 37 patients (20 benign, 17 malignant) were subject to whole genome sequencing. For each sample, DNA fragments were grouped as short (30-99bp) and long (100-269bp) and end motif distributions were calculated by counting occurrences of 4-9bp sequences at either the 5’ or 3’ end of each fragment. A proprietary machine learning algorithm was employed to identify a minimal set of motifs capable of accurately distinguishing benign from malignant samples. Results: Just two 5’ end motifs in long fragments, CCAT and GACA, were sufficient for distinguishing malignant from benign samples in a two-step decision tree (89% accuracy with all samples, 86% accuracy in leave-one-out cross validation). Accuracy was significantly higher when compared to random guessing by shuffling labels (p=1.6*10−6, FWER < 0.05, Bonferroni correction, 351 tests). CCAT motif frequency was also significantly associated with cancer stage (p=0.04, Kruskal-Wallis test) and specifically elevated in early stage samples (I, II). Conclusions: These results suggest that plasma DNA end motif distributions are informative in discriminating patients with lung adenocarcinoma versus benign tumors. The two-motif model suggests that specific, rather than systemic changes in end motif distributions are more strongly associated with lung cancer patients and may point to specific mechanisms linked to cancer progression. Citation Format: Jason C. Hyun, Shun H. Yip, Neeti Swarup, Raghuraman Ramamurthy, Jordan C. Cheng, Irene Choi, Edmund Wong, Akanksha Arora, Denise Aberle, David T. Wong, Cheuk Y. Tang. Specific plasma DNA end-motifs distinguish patients with lung adenocarcinoma versus benign nodules [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 2 (Late-Breaking, Clinical Trial, and Invited Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(7_Suppl):Abstract nr LB108.

Quantum dots in noninvasive imaging of oral squamous cell carcinomas: A scoping literature review.

The current scoping review's objective was to outline existing applications, recent breakthroughs, and quantum dots' applicability in imaging of oral squamous cell cancer. Quantum dots are nanometric semiconductor crystals with customizable optical characteristics and intense, stable fluorescence suited for bioimaging and labeling. We used the Preferred reporting items for systematic reviews and meta-analyses (PRISMA) recommendations for conducting our systematic search. An analysis of the properties and applications of quantum dots in noninvasive detection of oral squamous cell cancer is presented in this study, which comprehensively explores the available evidence. Following searches in the databases PubMed, Ovid SP, and Cochrane using the search terms quantum dots AND oral squamous cell cancer, 55 published publications were chosen for this review. The review identified a total of eight papers that met the criteria. In noninvasive detection of oral squamous cell carcinoma, quantum dots have the potential to offer an array of therapeutic and diagnostic applications. Furthermore, quantum dots emit near-infrared and visible light, which is advantageous in biological imaging since it reduces light dispersion and absorption of tissue. The future may see quantum dots become a popular noninvasive imaging technique for oral squamous cell cancer. The number of studies accessible is quite limited, and further research is required.