Large language model produces high accurate diagnosis of cancer from end-motif profiles of cell-free DNA.

Instruction-tuned large language models (LLMs) demonstrate exceptional ability to align with human intentions. We present an LLM-based model-instruction-tuned LLM for assessment of cancer (iLLMAC)-that can detect cancer using cell-free deoxyribonucleic acid (cfDNA) end-motif profiles. Developed on plasma cfDNA sequencing data from 1135 cancer patients and 1106 controls across three datasets, iLLMAC achieved area under the receiver operating curve (AUROC) of 0.866 [95% confidence interval (CI), 0.773-0.959] for cancer diagnosis and 0.924 (95% CI, 0.841-1.0) for hepatocellular carcinoma (HCC) detection using 16 end-motifs. Performance increased with more motifs, reaching 0.886 (95% CI, 0.794-0.977) and 0.956 (95% CI, 0.89-1.0) for cancer diagnosis and HCC detection, respectively, with 64 end-motifs. On an external-testing set, iLLMAC achieved AUROC of 0.912 (95% CI, 0.849-0.976) for cancer diagnosis and 0.938 (95% CI, 0.885-0.992) for HCC detection with 64 end-motifs, significantly outperforming benchmarked methods. Furthermore, iLLMAC achieved high classification performance on datasets with bisulfite and 5-hydroxymethylcytosine sequencing. Our study highlights the effectiveness of LLM-based instruction-tuning for cfDNA-based cancer detection.

Raman spectroscopy analysis combined with computed tomography imaging to identify microsatellite instability in gastric cancers.

Accurate determination of microsatellite instability (MSI) status is critical for tailoring treatment approaches for gastric cancer patients. Existing clinical techniques for MSI diagnosis are plagued by problems of suboptimal time efficiency, high cost, and burdensome experimental requirements. Here, we for the first time establish the classification model of gastric cancer MSI status based on Raman spectroscopy. To begin with, we reveal that tumor heterogeneity-induced signal variations pose a prominent impact on MSI classification. To eliminate this issue, we develop Euclidean distance-based Raman Spectroscopy (EDRS) algorithm, which establishes a standard spectrum to represent the "most microsatellite stable" status. The similarity between each spectrum of tissues with the standard spectrum is calculated to provide a direct assessment on the MSI status. Compared to machine learning-algorithms including k-Nearest Neighbors, Random Forest, and Extreme Learning Machine, the EDRS method shows the highest accuracy of 94.6 %. Finally, we integrate the EDRS method with the clinical diagnostic modality, computed tomography, to construct an innovative joint classification model with good classification performance (AUC = 0.914, Accuracy = 94.6 %). Our work demonstrates a robust, rapid, non-invasive, and convenient tool in identifying the MSI status, and opens new avenues for Raman techniques to fit into existing clinical workflow.

Development of a Scale to Assess Cancer Attitude in the Community.

AIM: The aim of this study was to develop the Cancer Attitude Scale (CAS) and to evaluate its psychometric properties. METHODS: The study was conducted in a mixed-methods study. The preliminary version of the CAS was created by developing an item pool, and qualitative interviews. The items' content validity ratio was calculated to assess content validity. Construct validity was assessed using exploratory factor analysis and confirmatory factor analysis. Internal consistency and test-retest methods were used to determine the reliability of the scale. The Cancer Information Burden Scale was used to evaluate the convergent validity of the scale. Three hundred twenty-five healthy individuals selected by convenience sampling method were included in this study. RESULTS: The CAS sub-dimensions were determined as cancer diagnosis, cancer prevention, stigmatization of individuals with cancer, and personal empowerment after cancer. The content validity of the 22-item scale was 0.94 and four factors explaining 56% of the variance. The model fit indices were χ2/df = 1.968, RMSEA = 0.055, SRMR = 0.085, and GFI = 0.904. . Cronbach's α and test-retest reliability coefficients of the total scale are 0.76 and 0.936, respectively. The CAS and Cancer Information Overload Scale showed acceptable convergent validity (r = 0.435, P < .001). CONCLUSIONS: The Cancer Attitude Scale is a valid and reliable tool that can be applied to assess the attitudes of individuals in the community toward cancer in a multidimensional way. IMPLICATIONS FOR PRACTICE: Nurses play crucial roles in society, including enhancing cancer awareness, delivering counseling services, and providing health education. Cancer nursing should plan cancer awareness training in line with individuals' attitudes toward cancer and encourage positive attitudes that increase participation in screening programs.

Biomarkers for diagnosis and therapeutic options in hepatocellular carcinoma.

Liver cancer is a global health challenge, causing a significant social-economic burden. Hepatocellular carcinoma (HCC) is the predominant type of primary liver cancer, which is highly heterogeneous in terms of molecular and cellular signatures. Early-stage or small tumors are typically treated with surgery or ablation. Currently, chemotherapies and immunotherapies are the best treatments for unresectable tumors or advanced HCC. However, drug response and acquired resistance are not predictable with the existing systematic guidelines regarding mutation patterns and molecular biomarkers, resulting in sub-optimal treatment outcomes for many patients with atypical molecular profiles. With advanced technological platforms, valuable information such as tumor genetic alterations, epigenetic data, and tumor microenvironments can be obtained from liquid biopsy. The inter- and intra-tumoral heterogeneity of HCC are illustrated, and these collective data provide solid evidence in the decision-making process of treatment regimens. This article reviews the current understanding of HCC detection methods and aims to update the development of HCC surveillance using liquid biopsy. Recent critical findings on the molecular basis, epigenetic profiles, circulating tumor cells, circulating DNAs, and omics studies are elaborated for HCC diagnosis. Besides, biomarkers related to the choice of therapeutic options are discussed. Some notable recent clinical trials working on targeted therapies are also highlighted. Insights are provided to translate the knowledge into potential biomarkers for detection and diagnosis, prognosis, treatment response, and drug resistance indicators in clinical practice.

Genitourinary cancer and family: The reverberating psychological and cardiovascular effects of a genitourinary cancer diagnosis on first-degree relatives and spouses.

BACKGROUND: The psychological and cardiovascular health impacts on family members of patients who have been diagnosed with cancer have not been well characterized. The purpose of this study is to determine whether a family member's cancer diagnosis influences the risk of psychological illness and cardiovascular disease in first-degree relatives and spouses of patients affected by cancer. METHODS: This retrospective cohort analysis evaluated the risk of psychological illness and cardiovascular disease in first-degree relatives and spouses of patients diagnosed with a genitourinary cancer between 1990 and 2015 compared to relatives of those not diagnosed with a genitourinary cancer. The Utah Population Database was used and familial linkage was determined. Follow-up included 1-, 3-, and 5-year intervals. Patients residing outside of Utah and first-degree relatives and spouses with psychological or cardiovascular disease diagnosed before a family member's cancer diagnosis were excluded. RESULTS: A total of 49,284 patients with a genitourinary cancer were identified with 77,938 first-degree relatives and spouses. A matched control group included 246,775 patients with 81,022 first-degree relatives and spouses. Via Cox proportional hazards models, a 10% increased risk of developing a psychological illness (hazard ratio [HR], 1.10; 95% CI, 1.00-1.20) and a 28% increased risk of developing cardiovascular disease (HR, 1.28; 95% CI, 1.17-1.41) at 1 year after a family member's cancer diagnosis were found. CONCLUSIONS: This study provides population-level evidence to support the hypothesis that cancer diagnoses will lead to adverse health outcomes for family members of patients with cancer. Increased clinical attention and support are needed to reduce the harm to families caused by cancer.

All-in-One Fusogenic Nanoreactor for the Rapid Detection of Exosomal MicroRNAs for Breast Cancer Diagnosis.

Molecular-profiling-based cancer diagnosis has significant implications for predicting disease prognosis and selecting targeted therapeutic interventions. The analysis of cancer-derived extracellular vesicles (EVs) provides a noninvasive and sequential method to assess the molecular landscape of cancer. Here, we developed an all-in-one fusogenic nanoreactor (FNR) encapsulating DNA-fueled molecular machines (DMMs) for the rapid and direct detection of EV-associated microRNAs (EV miRNAs) in a single step. This platform was strategically designed to interact selectively with EVs and induce membrane fusion under a specific trigger. After fusion, the DMMs recognized the target miRNA and initiated nonenzymatic signal amplification within a well-defined reaction volume, thus producing an amplified fluorescent signal within 30 min. We used the FNRs to analyze the unique expression levels of three EV miRNAs in various biofluids, including cell culture, urine, and plasma, and obtained an accuracy of 86.7% in the classification of three major breast cancer (BC) cell lines and a diagnostic accuracy of 86.4% in the distinction between patients with cancer and healthy donors. Notably, a linear discriminant analysis revealed that increasing the number of miRNAs from one to three improved the accuracy of BC patient discrimination from 78.8 to 95.4%. Therefore, this all-in-one diagnostic platform performs nondestructive EV processing and signal amplification in one step, providing a straightforward, accurate, and effective individual EV miRNA analysis strategy for personalized BC treatment.

Proceedings of the 1st biannual bridging the gaps in lung cancer conference.

Lung cancer is the leading cause of cancer death in the US and globally. The mortality from lung cancer has been declining, due to a reduction in incidence and advances in treatment. Although recent success in developing targeted and immunotherapies for lung cancer has benefitted patients, it has also expanded the complexity of potential treatment options for health care providers. To aid in reducing such complexity, experts in oncology convened a conference (Bridging the Gaps in Lung Cancer) to identify current knowledge gaps and controversies in the diagnosis, treatment, and outcomes of various lung cancer scenarios, as described here. Such scenarios relate to biomarkers and testing in lung cancer, small cell lung cancer, EGFR mutations and targeted therapy in non-small cell lung cancer (NSCLC), early-stage NSCLC, KRAS/BRAF/MET and other genomic alterations in NSCLC, and immunotherapy in advanced NSCLC.

Skin cancer classification leveraging multi-directional compact convolutional neural network ensembles and gabor wavelets.

Skin cancer (SC) is an important medical condition that necessitates prompt identification to ensure timely treatment. Although visual evaluation by dermatologists is considered the most reliable method, its efficacy is subjective and laborious. Deep learning-based computer-aided diagnostic (CAD) platforms have become valuable tools for supporting dermatologists. Nevertheless, current CAD tools frequently depend on Convolutional Neural Networks (CNNs) with huge amounts of deep layers and hyperparameters, single CNN model methodologies, large feature space, and exclusively utilise spatial image information, which restricts their effectiveness. This study presents SCaLiNG, an innovative CAD tool specifically developed to address and surpass these constraints. SCaLiNG leverages a collection of three compact CNNs and Gabor Wavelets (GW) to acquire a comprehensive feature vector consisting of spatial-textural-frequency attributes. SCaLiNG gathers a wide range of image details by breaking down these photos into multiple directional sub-bands using GW, and then learning several CNNs using those sub-bands and the original picture. SCaLiNG also combines attributes taken from various CNNs trained with the actual images and subbands derived from GW. This fusion process correspondingly improves diagnostic accuracy due to the thorough representation of attributes. Furthermore, SCaLiNG applies a feature selection approach which further enhances the model's performance by choosing the most distinguishing features. Experimental findings indicate that SCaLiNG maintains a classification accuracy of 0.9170 in categorising SC subcategories, surpassing conventional single-CNN models. The outstanding performance of SCaLiNG underlines its ability to aid dermatologists in swiftly and precisely recognising and classifying SC, thereby enhancing patient outcomes.

SERS-Based Droplet Microfluidic Platform for Sensitive and High-Throughput Detection of Cancer Exosomes.

Exosomes, nanosized extracellular vesicles containing biomolecular cargo, are increasingly recognized as promising noninvasive biomarkers for cancer diagnosis, particularly for their role in carrying tumor-specific molecular information. Traditional methods for exosome detection face challenges such as complexity, time consumption, and the need for sophisticated equipment. This study addresses these challenges by introducing a novel droplet microfluidic platform integrated with a surface-enhanced Raman spectroscopy (SERS)-based aptasensor for the rapid and sensitive detection of HER2-positive exosomes from breast cancer cells. Our approach utilized an on-chip salt-induced gold nanoparticles (GNPs) aggregation process in the presence of HER2 aptamers and HER2-positive exosomes, enhancing the hot spot-based SERS signal amplification. This platform achieved a limit of detection of 4.5 log10 particles/mL with a sample-to-result time of 5 min per sample. Moreover, this platform has been successfully applied for HER2 status testing in clinical samples to distinguish HER2-positive breast cancer patients from HER2-negative breast cancer patients. High sensitivity, specificity, and the potential for high-throughput screening of specific tumor exosomes make this SERS-based droplet system a potential liquid biopsy technology for early cancer diagnosis.

Exploring the Dark Matter of Human Proteome: The Emerging Role of Non-Canonical Open Reading Frame (ncORF) in Cancer Diagnosis, Biology, and Therapy.

Cancer develops from abnormal cell growth in the body, causing significant mortalities every year. To date, potent therapeutic approaches have been developed to eradicate tumor cells, but intolerable toxicity and drug resistance can occur in treated patients, limiting the efficiency of existing treatment strategies. Therefore, searching for novel genes critical for cancer progression and therapeutic response is urgently needed for successful cancer therapy. Recent advances in bioinformatics and proteomic techniques have allowed the identification of a novel category of peptides encoded by non-canonical open reading frames (ncORFs) from historically non-coding genomic regions. Surprisingly, many ncORFs express functional microproteins that play a vital role in human cancers. In this review, we provide a comprehensive description of different ncORF types with coding capacity and technological methods in discovering ncORFs among human genomes. We also summarize the carcinogenic role of ncORFs such as pTINCR and HOXB-AS3 in regulating hallmarks of cancer, as well as the roles of ncORFs such as HOXB-AS3 and CIP2A-BP in cancer diagnosis and prognosis. We also discuss how ncORFs such as AKT-174aa and DDUP are involved in anti-cancer drug response and the underestimated potential of ncORFs as therapeutic targets.

Shedding Light on Cellular Secrets: A Review of Advanced Optical Biosensing Techniques for Detecting Extracellular Vesicles with a Special Focus on Cancer Diagnosis.

In the relentless pursuit of innovative diagnostic tools for cancer, this review illuminates the cutting-edge realm of extracellular vesicles (EVs) and their biomolecular cargo detection through advanced optical biosensing techniques with a primary emphasis on their significance in cancer diagnosis. From the sophisticated domain of nanomaterials to the precision of surface plasmon resonance, we herein examine the diverse universe of optical biosensors, emphasizing their specified applications in cancer diagnosis. Exploring and understanding the details of EVs, we present innovative applications of enhancing and blending signals, going beyond the limits to sharpen our ability to sense and distinguish with greater sensitivity and specificity. Our special focus on cancer diagnosis underscores the transformative potential of optical biosensors in early detection and personalized medicine. This review aims to help guide researchers, clinicians, and enthusiasts into the captivating domain where light meets cellular secrets, creating innovative opportunities in cancer diagnostics.

From sequencing to validation: NGS-based exploration of plasma miRNA in papillary thyroid carcinoma.

Objective: A non-invasive method using plasma microRNAs provides new insights into thyroid cancer diagnosis. The objective of this study was to discover potential circulating biomarkers of papillary thyroid carcinoma (PTC) through the analysis of plasma miRNAs using next-generation sequencing (NGS). Methods: Plasma miRNAs were isolated from peripheral blood samples collected from healthy individuals, patients diagnosed with PTC, and those with benign thyroid nodules. The Illumina NovaSeq 6000 platform was employed to establish the miRNA expression profiles. Candidate miRNAs for diagnostic purposes were identified utilizing the Random Forest (RF) algorithm. The selected miRNAs were subsequently validated in an independent validation set using RT-qPCR. Results: NGS results revealed consistent plasma miRNA expression patterns among healthy individuals and patients with benign thyroid nodules in the discovery set (6 healthy cases, 17 benign cases), while differing significantly from those observed in the PTC group (17 PTC cases). Seven miRNAs exhibiting significant expression differences were identified and utilized to construct an RF classifier. Receiver operating characteristic (ROC) analysis for PTC diagnosis, and the area under the curve (AUC) was 0.978. Subsequent KEGG and GO analyses of the target genes associated with these 7 miRNAs highlighted pathways relevant to tumors and the cell cycle. Independent validation through RT-qPCR in a separate cohort (15 CONTROL, 15 PTC groups) underscored hsa-miR-301a-3p and hsa-miR-195-5p as promising candidates for PTC diagnosis. Conclusion: In conclusion, our study established a seven-miRNA panel in plasma by Random Forest algorithm with significant performance in discriminating PTC from healthy or benign group. hsa-miR-301a-3p, hsa-miR-195-5p in plasma have potential for further study in the diagnosis of PTC in Asian ethnic.

A Rare Case of Early Gastric Cancer With Rapid Bone Involvement.

Gastric cancers rarely metastasize to the bones. If they do, they have a very poor prognosis. We here present a case study of a 56-year-old man who, within a year, rapidly declined and died. He was first revealed to have an erosion found on an esophageal gastroduodenoscopy (EGD), which was later proven to be a poorly differentiated gastric adenocarcinoma. He then proceeded to have a thoracic trans-hiatal esophagogastrostomy with gastric pull-up to resect this cancer. At this point in time, the review of systems and CT scans of the abdomen and pelvis were negative. A few months later, he started having back pain and was diagnosed with metastatic disease of the bones through a CT scan. Although detecting gastric cancer at an early stage is rare, it is shown to have a better prognosis. It is, therefore, very important to reflect on the possibility of engaging in earlier screening to detect gastric cancers at an earlier stage to minimize the risk of invasions of other organs, especially for those who have other risk factors such as obesity and tobacco use. We believe it is prudent to ensure close follow-up with any patient with early gastric cancer to potentially detect recurrence or metastasis in a timely fashion.

Epigenetic regulation in ovarian cancer.

Ovarian cancer is one of the diseases that have the highest mortality rate for women, especially women over 50 years old. In the future, incidence and mortality rates are predicted to extend in countries with low HDI. Instability in the structure and function of genetic factors has long been known as a cause of cancer, including ovarian cancer. Besides understanding gene mutations, epigenetic alterations have emerged as another aspect leading to the pathogenesis of ovarian neoplasm. The development and progression of this fatal disease have been found to be associated with abnormalities of epigenetic regulation. DNA methylation, histone modification, and non-coding RNAs-based gene silencing are processes of interest in developing ovarian carcinoma and are also new targets for cancer detection or treatment.

Decoding the microbiota metabolome in hepatobiliary and pancreatic cancers: Pathways to precision diagnostics and targeted therapeutics.

We delve into the critical role of the gut microbiota and its metabolites in the pathogenesis and progression of hepatobiliary and pancreatic (HBP) cancers, illuminating an urgent need for breakthroughs in diagnostic and therapeutic strategies. Given the high mortality rates associated with HBP cancers, which are attributed to aggressive recurrence, metastasis, and poor responses to chemotherapy, exploring microbiome research presents a promising frontier. This research highlights how microbial metabolites, including secondary bile acids, short-chain fatty acids, and lipopolysaccharides, crucially influence cancer cell behaviors such as proliferation, apoptosis, and immune evasion, significantly contributing to the oncogenesis and progression of HBP cancers. By integrating the latest findings, we discuss the association of microbial alterations with HBP cancers, key metabolites, and their implications, and how metabolomics and microbiomics can enhance diagnostic precision. Furthermore, the paper explores strategies for targeted therapies through microbiome metabolomics, including the direct therapeutic effects of microbiome metabolites and potential synergistic effects on conventional therapies. We also recognize that the field of microbial metabolites for the diagnosis and treatment of tumors still has a lot of problems to be solved. The aim of this study is to pioneer microbial metabolite research and provide a reference for HBP cancer diagnosis, treatment, and prognosis.

Engineering a Bifunctional Smart Nanoplatform Integrating Nanozyme Activity and Self-Assembly for Kidney Cancer Diagnosis and Classification.

Accurate diagnosis and classification of kidney cancer are crucial for high-quality healthcare services. However, the current diagnostic platforms remain challenges in the rapid and accurate analysis of large-scale clinical biosamples. Herein, we fabricated a bifunctional smart nanoplatform based on tannic acid-modified gold nanoflowers (TA@AuNFs), integrating nanozyme catalysis for colorimetric sensing and self-assembled nanoarray-assisted LDI-MS analysis. The TA@AuNFs presented peroxidase (POD)- and glucose oxidase-like activity owing to the abundant galloyl residues on the surface of AuNFs. Combined with the colorimetric assay, the TA@AuNF-based sensing nanoplatform was used to directly detect glucose in serum for kidney tumor diagnosis. On the other hand, TA@AuNFs could self-assemble into closely packed and homogeneous two-dimensional (2D) nanoarrays at liquid-liquid interfaces by using Fe3+ as a mediator. The self-assembled TA@AuNFs (SA-TA@AuNFs) arrays were applied to assist the LDI-MS analysis of metabolites, exhibiting high ionization efficiency and excellent MS signal reproducibility. Based on the SA-TA@AuNF array-assisted LDI-MS platform, we successfully extracted metabolic fingerprints from urine samples, achieving early-stage diagnosis of kidney tumor, subtype classification, and discrimination of benign from malignant tumors. Taken together, our developed TA@AuNF-based bifunctional smart nanoplatform showed distinguished potential in clinical disease diagnosis, point-of-care testing, and biomarker discovery.

Mortality after partner's cancer diagnosis or death: A population-based prospective cohort study in Japan.

BACKGROUND: The health statuses of closely connected individuals are interdependent. Little is known about mortality risk associated with partner's cancer diagnosis and cause-specific mortality risk associated with partner's death. METHODS: Relative risks for all-cause and cause-specific mortality following a partner's cancer diagnosis or death compared to the period when the partner is cancer-free and alive were investigated in the population-based prospective cohort study that enrolled 140,420 people at the age between 40-69 in 1990-1994. RESULTS: 55,050 participants (27,665 men and 27,385 women) who were identified as married couples were followed-up for 1,073,746.1 (518,368.5 in men and 555,377.6 in women) person-years, during which 9,816 deaths (7,217 in men and 2,599 in women) were observed. After a partner's cancer diagnosis, the mortality rate ratio (MRR) of all-cause mortality was not increased among both men and women, while an increase of externally-caused MRR was observed. The suicide MRR significantly increased among men (MRR = 2.90 [95% CI, 1.70-4.93]) and it persisted for more than 5 years. After a partner's death, the MRRs of all-cause, cardiovascular disease (CVD), respiratory disease (RD), and externally-caused mortality significantly increased only among men. Stratified analysis by smoking status among men showed significantly increased MRRs of CVD and RD mortality among former/current smokers, but not among never-smokers. CONCLUSION: Partner's cancer diagnosis did not increase all-cause mortality risk, but increased externally-caused mortality risk, especially suicide among men. The impact of partner's death on mortality risk differed by the mortality causes and sex, and smoking affected some of cause-specific mortality risk.

Multiple valence states of Fe boosting SERS activity of Fe3O4 nanoparticles and enabling effective SERS-MRI bimodal cancer imaging.

Developing novel nanoparticle-based bioprobes utilized in clinical settings with imaging resolutions ranging from cell to tissue levels is a major challenge for tumor diagnosis and treatment. Herein, an optimized strategy for designing a Fe3O4-based bioprobe for dual-modal cancer imaging based on surface-enhanced Raman scattering (SERS) and magnetic resonance imaging (MRI) is introduced. Excellent SERS activity of ultrasmall Fe3O4 nanoparticles (NPs) was discovered, and a 5 × 10-9 M limit of detection for crystal violet molecules was successfully obtained. The high-efficiency interfacial photon-induced charge transfer in Fe3O4 NPs was promoted by multiple electronic energy levels ascribed to the multiple valence states of Fe, which was observed using ultraviolet-visible diffuse reflectance spectroscopy. Density functional theory calculations were utilized to reveal that the narrow band gap and high electron density of states of ultrasmall Fe3O4 NPs significantly boosted the vibronic coupling resonances in the SERS system upon illumination. The subtypes of cancer cells were accurately recognized via high-resolution SERS imaging in vitro using the prepared Fe3O4-based bioprobe with high sensitivity and good specificity. Notably, Fe3O4-based bioprobes simultaneously exhibited T1 -weighted MRI contrast enhancement with an active targeting capability for tumors in vivo. To the best of our knowledge, this is the first report on the use of pure semiconductor-based SERS-MRI dual-modal nanoprobes in tumor imaging in vivo and in vitro, which has been previously realized only using semiconductor-metal complex materials. The non-metallic materials with SERS-MRI dual-modal imaging established in this report are a promising cancer diagnostic platform, which not only showed excellent performance in early tumor diagnosis but also possesses great potential for image-guided tumor treatment.

Harnessing exosomes as cancer biomarkers in clinical oncology.

Exosomes are extracellular vesicles well known for facilitating cell-to-cell communication by distributing essential macromolecules like proteins, DNA, mRNA, lipids, and miRNA. These vesicles are abundant in fluids distributed throughout the body, including urine, blood, saliva, and even bile. They are important diagnostic tools for breast, lung, gastrointestinal cancers, etc. However, their application as cancer biomarkers has not yet been implemented in most parts of the world. In this review, we discuss how OMICs profiling of exosomes can be practiced by substituting traditional imaging or biopsy methods for cancer detection. Previous methods like extensive imaging and biopsy used for screening were expensive, mostly invasive, and could not easily provide early detection for various types of cancer. Exosomal biomarkers can be utilized for routine screening by simply collecting body fluids from the individual. We anticipate that the use of exosomes will be brought to light by the success of clinical trials investigating their potential to enhance cancer detection and treatment in the upcoming years.

Phenotypes of carriers of two mutated alleles in major cancer susceptibility genes.

PURPOSE: While cancer phenotypes in carriers of a single mutant allele in most major cancer susceptibility genes are well-established, there is a paucity of data on the phenotype of carriers of two pathogenic variants-double heterozygotes (DH) or homozygous carriers. Here, we describe the phenotype of carriers of homozygous and DH pathogenic sequence variants (PSVs) in major cancer susceptibility genes. METHODS: Individuals referred for multigene panel testing at Blueprint Genetics laboratory were included. Ethically approved comparison of cancer type and age at diagnosis between DH, homozygous, and single PSV carriers was performed per gene. RESULTS: Of 6,685 eligible participants, 928 (13.9%) were single heterozygous PSV carriers, 6 (0.09%) were homozygous PSV carriers, and 17 (0.25%) were DH PSV carriers. Mean age at diagnosis of any cancer among single PSV age was 46.8 ± 14.9 years and among DH PSV carriers 37.6 ± 13.0 years (P < 0.0001). Notably, age at diagnosis for breast cancer among single BRCA1 PSV carriers (n = 59) was 43.8 ± 8.7 years (p = 0.7606), among single BRCA2 PSV carriers (n = 52)-47.9 ± 13.0 years (p = 0.2274) compared with 42.3 ± 13.0 years among DH PSV carriers (n = 10- 9 of whom were carriers of either BRCA1 or BRCA2). CONCLUSION: DH for PSV in two cancer susceptibility genes is a rare event, and the mean age at cancer diagnosis is younger in DH PSV carriers compared with single PSV carriers.