Non-invasive Cancer Detection Research Articles

Genomic profiling of cell-free DNA from dogs with benign and malignant tumors

ObjectiveCancer is currently the most common cause of death in adult dogs. Like humans, dogs have a one-third chance of developing cancer in their lifetime. We used shallow whole-genome sequencing (sWGS) to analyze blood cell-free DNA (cfDNA) from four tumor-bearing dogs (one with benign and three with malignant tumors) and 38 healthy dogs.ResultsSimilar to the results observed in the healthy dogs, no copy number aberration (CNA) was detected in the dog with benign lipomas, and the distribution of cfDNA fragment size (FS) closely resembled that of the healthy dogs. However, among the three dogs diagnosed with malignant tumors, two dogs exhibited varying degrees and quantities of CNAs. Compared to the distribution of FS in the healthy dogs, the cancer dogs exhibited a noticeable shift towards shorter lengths. These findings indicated that CNA and FS profiles derived from sWGS data can be used for non-invasive cancer detection in dogs.

Proof-of-concept evaluation of next-generation sequencing-based liquid biopsy for non-invasive cancer detection in cats

Proof-of-concept evaluation of next-generation sequencing-based liquid biopsy for non-invasive cancer detection in cats

Analysis of urine cell-free DNA in bladder cancer diagnosis by emerging bioactive technologies and materials.

Urinary cell-free DNA (UcfDNA) is gaining recognition as an important biomarker for diagnosing bladder cancer. UcfDNA contains tumor derived DNA sequences, making it a viable candidate for non-invasive early detection, diagnosis, and surveillance of bladder cancer. The quantification and qualification of UcfDNA have demonstrated high sensitivity and specificity in the molecular characterization of bladder cancer. However, precise analysis of UcfDNA for clinical bladder cancer diagnosis remains challenging. This review summarizes the history of UcfDNA discovery, its biological properties, and the quantitative and qualitative evaluations of UcfDNA for its clinical significance and utility in bladder cancer patients, emphasizing the critical role of UcfDNA in bladder cancer diagnosis. Emerging bioactive technologies and materials currently offer promising tools for multiple UcfDNA analysis, aiming to achieve more precise and efficient capture of UcfDNA, thereby significantly enhancing diagnostic accuracy. This review also highlights breakthroughs in detection technologies and substrates with the potential to revolutionize bladder cancer diagnosis in clinic.

Fabrication of nanomolecular platform based immunosensor for non-invasive electrochemical detection of oral cancer: An in vitro study

Silver nanoparticles (AgNPs)-Chitosan (Chi) functionalized cysteine (cys)-SAM (self-assembled monolayer) based electrochemical, label free, ultrasensitive, non-invasive immunosensing nanomolecular platform has been fabricated for the detection of novel OC biomarker S-100. This has been achieved through bio-functionalization of AgNPs-Chi/cys/mini gold slides by covalent immobilization of anti-S100 antibodies. Cyclic voltammetric (CV) studies establish successful fabrication of anti-S100/AgNPs-Chi/Au nanomolecular platform. Differential pulse voltammetric (DPV) measurements reveal that anti-S100/Chi-AgNPs/Au is able to sense 50 fg mL-1 – 500 ng mL-1 of S100 in buffer and in spiked saliva samples within 120 s interaction time having improved favorable shelf life and specificity. The fabricated platform is very selective and able to detect S100 in OC patient/s saliva samples warrants realization of simple non-invasive electrochemical immunosensor for in situ or on-site applications (especially for remote areas) for early diagnosis of OC while offering bio-compatibility at low cost.

ISM band CPW-fed antenna for detection of skin cancer

A CPW-fed antenna sensor with high quality factor is designed to identify the skin cancer. The proposed antenna operates in the ISM band at 2.47 GHz frequency. The proposed antenna aims to exploit the variations in dielectric constants of skin, fat, and muscle tissues to enable non-invasive and early detection of skin cancer. The antenna design process involves considerations for achieving a high frequency shift and quality factor to discern subtle variations in dielectric properties. Electromagnetic simulations, utilizing advanced numerical techniques are employed to analyze the antenna's performance in different tissue environments. The proposed antenna design is optimized for enhancing its sensitivity to changes in the dielectric constants associated with healthy and cancerous skin tissues. To validate the antenna's effectiveness, experimental setups using tissue-mimicking phantoms are utilized. This research not only emphasizes the technical aspects of antenna design but also underscores the potential clinical applications for non-invasive skin cancer detection.

Single-molecule epiallelic profiling of DNA derived from routinely collected Pap specimens for noninvasive detection of ovarian cancer.

Recent advances in molecular analyses of ovarian cancer have revealed a wealth of promising tumour-specific biomarkers, including protein, DNA mutations and methylation; however, reliably detecting such alterations at satisfactorily high sensitivity and specificity through low-cost methods remains challenging, especially in early-stage diseases. Here we present PapDREAM, a new approach that enables detection of rare, ovarian-cancer-specific aberrations of DNA methylation from routinely-collected cervical Pap specimens. The PapDREAM approach employs a microfluidic platform that performs highly parallelized digital high-resolution melt to analyze locus-specific DNA methylation patterns on a molecule-by-molecule basis at or near single CpG-site resolution at a fraction (<1/10th) of the cost of next-generation sequencing techniques. We demonstrate the feasibility of the platform by assessing intermolecular heterogeneity of DNA methylation in a panel of methylation biomarker loci using DNA derived from Pap specimens obtained from a cohort of 43 women, including 18 cases with ovarian cancer and 25 cancer-free controls. PapDREAM leverages systematic multidimensional bioinformatic analyses of locus-specific methylation heterogeneity to improve upon Pap-specimen-based detection of ovarian cancer, demonstrating a clinical sensitivity of 50% at 99% specificity in detecting ovarian cancer cases with an area under the receiver operator curve of 0.90. We then establish a logistic regression model that could be used to identify high-risk patients for subsequent clinical follow-up and monitoring. The results of this study support the utility of PapDREAM as a simple, low-cost screening method with the potential to integrate with existing clinical workflows for early detection of ovarian cancer. KEY POINTS: We present a microfluidic platform for detection and analysis of rare, heterogeneously methylated DNA within Pap specimens towards detection of ovarian cancer. The platform achieves high sensitivity (fractions <0.00005%) at a suitably low cost (∼$25) for routine screening applications. Furthermore, it provides molecule-by-molecule quantitative analysis to facilitate further study on the effect of heterogeneous methylation on cancer development.

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.

Ion beam-induced nanoripples patterns for SERS based saliva analysis to detect oral cavity cancer

Saliva is a non-invasive sample for oral cancer detection due to the presence of cancer biomarkers. In this reported study, we investigated the potential use of SERS (Surface-Enhanced Raman Spectroscopy) for the non-invasive identification of oral cavity cancer by analysing the SERS spectra of saliva. We developed a SERS substrate by depositing Ag nanoparticles on glass ripples. SERS analysis was conducted on saliva samples collected from 10 oral cancer patients and 8 healthy volunteers having tobacco habits. The average spectra of cancer patient saliva and healthy volunteers’ saliva show differences. Principal component analysis (PCA) and Linear discriminant analysis (LDA)-based multivariate statistical analysis were used for classifying the collected spectra. The PCA-LDA-based classification shows 70% sensitivity and 62.5% specificity. This study suggests that SERS analysis of saliva incorporated with PCA-LDA-based multivariate analysis is a potential tool for the non-invasive detection of cancer.

Nail fold capillaroscopy as a potential tool to evaluate breast tumor

It is necessary to verify whether nail fold capillaroscopy can be utilized for the early detection of breast cancer. To establish this technology, an animal model was developed, utilizing mice for nail fold observations. Nail fold capillaroscopy revealed a human-like anatomical pattern and facilitated the observation of cellular movement within blood vessels. Injection of MCF-7 or mammary fibroblasts in mice allowed the observation of cellular vibrations using motion microscopy from nail fold. We have named this technology ‘capillary cell motion microscopy.’ Intriguingly, we were able to identify distinct cellular vibrations in the MCF-7 group. Moreover, evaluating its effectiveness in mice with chemically induced cancer revealed higher sensitivity (81%-85%) compared to conventional methods (45%-68%). Capillary cell motion microscopy, operating at 0.5–1.5 Hz, provided clear distinction of tumor cells and demonstrated potential applicability in human subjects. While condition adjustments may be necessary, this method holds promise for noninvasive breast cancer detection through nail fold observations.

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

3066 Background: The demand for alternative, non-invasive methods for colorectal cancer (CRC) screening is substantial. Cell-free DNA (cfDNA) whole genome sequencing (WGS) offers a promising avenue, utilizing diverse fragmentomic data. We aimed to develop a new approach: integrating fragment end motif by size (FEMS) with genomic coverage (COV) of cfDNA to enhance CRC screening. Methods: Participants were comprised of 1,506 colonoscopy verified normal samples, 130 advanced adenoma (AA) patients, 302 CRC patients (stage I: 28.5%, stage II: 25.5%, stage III: 31.1%, stage IV: 14.2%, unknown: 0.7%). We generated low depth cfDNA-WGS data (a minimum 40 million reads per sample) using the Novaseq 6000 sequencer. We minimized batch bias by normalizing with the same samples for each experimental batch. The development set comprised 1,332 samples (1,023 normal, 103 AA, and 206 CRC), while the validation set consisted of 606 samples (483 normal, 27 AA, and 96 CRC). Results: Our new algorithm achieved 84.0% sensitivity (95% CI: 81%-86%) for CRC and 69.9% (95% CI: 66%-81%) for AA in the development set, with a specificity of 90.0%. These findings were consistent in the external set with 84.9% specificity (95% CI: 82%-88%) and sensitivities of 80.2% (95% CI: 72%-88%) and 63.0% (95% CI: 44%-82%) for CRC and AA, respectively. The sensitivities across stages (Stage I: 80%, Stage II: 86.8%, Stage III: 78.8%, Stage IV: 96.7%) were similarly reflected in the validation sets (Stage I: 74%, Stage II: 83%, Stage III: 79%, Stage IV: 92%). Conclusions: Our multimodal deep learning model shows promise for accurate non-invasive colon cancer and AA detection using low-coverage cfDNA WGS. Its strong performance in detecting not only CRC but also AA lesions suggests its potential for early intervention and improved patient outcomes. The use of colonoscopy-verified normal samples strengthens the study's credibility and paves the way for future clinical translation.

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 &gt;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 &gt;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.

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.