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ABSTRACT Background Multi‐cancer early detection (MCED) through a single blood test significantly advances cancer diagnosis. However, most MCED tests rely on a single type of biomarkers, leading to limited sensitivity, particularly for early‐stage cancers. We previously developed SPOT‐MAS, a multimodal ctDNA‐based assay analyzing methylation and fragmentomic profiles to detect five common cancers. Despite its potential, SPOT‐MAS exhibited moderate sensitivities for early‐stage cancers. This study investigated whether integrating hotspot mutations into SPOT‐MAS could enhance its detection rates. Method A targeted amplicon sequencing approach was developed to profile 700 hotspot mutations in cell‐free DNA and integrated into the SPOT‐MAS assay, creating a single‐blood draw workflow. This workflow, namely SPOT‐MAS Plus was retrospectively validated in a cohort of 255 non‐metastatic cancer patients (breast, colorectal, gastric, liver, and lung) and 304 healthy individuals. Results Hotspot mutations were detected in 131 of 255 (51.4%) cancer patients, with the highest rates in liver cancer (96.5%), followed by colorectal (59.3%) and lung cancer (53.7%). Lower detection rates were found for cancers with low tumor mutational burden, such as breast (31.3%) and gastric (41.9%) cancers. In contrast, SPOT‐MAS demonstrated higher sensitivities for these cancers (51.6% for breast and 62.9% for gastric). The combination of hotspot mutations with SPOT‐MAS predictions improved early‐stage cancer detection, achieving an overall sensitivity of 78.5% at a specificity of 97.7%. Enhanced sensitivities were observed for colorectal (81.36%) and lung cancer (82.9%). Conclusion The integration of genetic and epigenetic alterations into a multimodal assay significantly enhances the early detection of various cancers. Further validation in larger cohorts is necessary to support broader clinical applications.

The emergence of multicancer early detection (MCED) tests holds promise for improving early cancer detection and public health outcomes. However, positive MCED test results require confirmation through recommended cancer diagnostic imaging modalities. To address these challenges, we have developed a consultation and work-up protocol for definitive diagnostic results post MCED testing, named SPOT-MAS. Developed through circulating tumor DNA (ctDNA) analysis and in line with professional guidelines and advisory board consensus, this protocol standardizes information to aid general practitioners in accessing, interpreting and managing SPOT-MAS results. Clinical effectiveness is demonstrated through a series of identified cancer cases. Our research indicates that the protocol could empower healthcare professionals to confidently interpret circulating tumor DNA test results for 5 common types of cancer, thereby facilitating the clinical integration of MCED tests.

Abstract Background Cell free DNA (cfDNA)-based assays hold great potential in detecting early cancer signals yet determining the tissue-of-origin (TOO) for cancer signals remains a challenging task. Here, we investigated the contribution of a methylation atlas to TOO detection in low depth cfDNA samples. Methods We constructed a tumor-specific methylation atlas (TSMA) using whole-genome bisulfite sequencing (WGBS) data from five types of tumor tissues (breast, colorectal, gastric, liver and lung cancer) and paired white blood cells (WBC). TSMA was used with a non-negative least square matrix factorization (NNLS) deconvolution algorithm to identify the abundance of tumor tissue types in a WGBS sample. We showed that TSMA worked well with tumor tissue but struggled with cfDNA samples due to the overwhelming amount of WBC-derived DNA. To construct a model for TOO, we adopted the multi-modal strategy and used as inputs the combination of deconvolution scores from TSMA with other features of cfDNA. Results Our final model comprised of a graph convolutional neural network using deconvolution scores and genome-wide methylation density features, which achieved an accuracy of 69% in a held-out validation dataset of 239 low-depth cfDNA samples. Conclusions In conclusion, we have demonstrated that our TSMA in combination with other cfDNA features can improve TOO detection in low-depth cfDNA samples.

In the realm of cancer immunotherapy, the meticulous selection of neoantigens plays a fundamental role in enhancing personalized treatments. Traditionally, this selection process has heavily relied on predicting the binding of peptides to human leukocyte antigens (pHLA). Nevertheless, this approach often overlooks the dynamic interaction between tumor cells and the immune system. In response to this limitation, we have developed an innovative prediction algorithm rooted in machine learning, integrating T cell receptor β chain (TCRβ) profiling data from colorectal cancer (CRC) patients for a more precise neoantigen prioritization. TCRβ sequencing was conducted to profile the TCR repertoire of tumor-infiltrating lymphocytes (TILs) from 28 CRC patients. The data unveiled both intra-tumor and inter-patient heterogeneity in the TCRβ repertoires of CRC patients, likely resulting from the stochastic utilization of V and J segments in response to neoantigens. Our novel combined model integrates pHLA binding information with pHLA-TCR binding to prioritize neoantigens, resulting in heightened specificity and sensitivity compared to models using individual features alone. The efficacy of our proposed model was corroborated through ELISpot assays on long peptides, performed on four CRC patients. These assays demonstrated that neoantigen candidates prioritized by our combined model outperformed predictions made by the established tool NetMHCpan. This comprehensive assessment underscores the significance of integrating pHLA binding with pHLA-TCR binding analysis for more effective immunotherapeutic strategies.

Abstract Background Late detection of hepatocellular carcinoma (HCC) results in an overall 5-year survival rate of less than 16%. Liquid biopsy (LB) assays based on detecting circulating tumor DNA (ctDNA) might provide an opportunity to detect HCC early noninvasively. Increasing evidence indicates that ctDNA detection using mutation-based assays is significantly challenged by the abundance of white blood cell-derived mutations, non-tumor tissue-derived somatic mutations in plasma, and the mutational tumor heterogeneity. Methods Here, we employed concurrent analysis of cancer-related mutations, and their fragment length profiles to differentiate mutations from different sources. To distinguish persons with HCC (PwHCC) from healthy participants, we built a classification model using three fragmentomic features of ctDNA through deep sequencing of thirteen genes associated with HCC. Results Our model achieved an area under the curve (AUC) of 0.88, a sensitivity of 89%, and a specificity of 82% in the discovery cohort consisting of 55 PwHCC and 55 healthy participants. In an independent validation cohort of 54 PwHCC and 53 healthy participants, the established model achieved comparable classification performance with an AUC of 0.86 and yielded a sensitivity and specificity of 81%. Conclusions Our study provides a rationale for subsequent clinical evaluation of our assay performance in a large-scale prospective study.

Despite their promise, circulating tumor DNA (ctDNA)-based assays for multi-cancer early detection face challenges in test performance, due mostly to the limited abundance of ctDNA and its inherent variability. To address these challenges, published assays to date demanded a very high-depth sequencing, resulting in an elevated price of test. Herein, we developed a multimodal assay called SPOT-MAS (screening for the presence of tumor by methylation and size) to simultaneously profile methylomics, fragmentomics, copy number, and end motifs in a single workflow using targeted and shallow genome-wide sequencing (~0.55×) of cell-free DNA. We applied SPOT-MAS to 738 non-metastatic patients with breast, colorectal, gastric, lung, and liver cancer, and 1550 healthy controls. We then employed machine learning to extract multiple cancer and tissue-specific signatures for detecting and locating cancer. SPOT-MAS successfully detected the five cancer types with a sensitivity of 72.4% at 97.0% specificity. The sensitivities for detecting early-stage cancers were 73.9% and 62.3% for stages I and II, respectively, increasing to 88.3% for non-metastatic stage IIIA. For tumor-of-origin, our assay achieved an accuracy of 0.7. Our study demonstrates comparable performance to other ctDNA-based assays while requiring significantly lower sequencing depth, making it economically feasible for population-wide screening.

IntroductionNeoantigen-based immunotherapy has emerged as a promising strategy for improving the life expectancy of cancer patients. This therapeutic approach heavily relies on accurate identification of cancer mutations using DNA sequencing (DNAseq) data. However, current workflows tend to provide a large number of neoantigen candidates, of which only a limited number elicit efficient and immunogenic T-cell responses suitable for downstream clinical evaluation. To overcome this limitation and increase the number of high-quality immunogenic neoantigens, we propose integrating RNA sequencing (RNAseq) data into the mutation identification step in the neoantigen prediction workflow.MethodsIn this study, we characterize the mutation profiles identified from DNAseq and/or RNAseq data in tumor tissues of 25 patients with colorectal cancer (CRC). Immunogenicity was then validated by ELISpot assay using long synthesis peptides (sLP).ResultsWe detected only 22.4% of variants shared between the two methods. In contrast, RNAseq-derived variants displayed unique features of affinity and immunogenicity. We further established that neoantigen candidates identified by RNAseq data significantly increased the number of highly immunogenic neoantigens (confirmed by ELISpot) that would otherwise be overlooked if relying solely on DNAseq data.DiscussionThis integrative approach holds great potential for improving the selection of neoantigens for personalized cancer immunotherapy, ultimately leading to enhanced treatment outcomes and improved survival rates for cancer patients.

Abstract α-Thalassemia is a common inherited blood disorder manifested mainly by the deletions of α-globin genes. In geographical areas with high carrier frequencies, screening of α-thalassemia carrier state is therefore of vital importance. This study presents a novel method for identifying female carriers of common α-thalassemia deletions using samples routinely taken for non-invasive prenatal tests for screening of fetal chromosomal aneuploidies. A total of 68,885 Vietnamese pregnant women were recruited and α-thalassemia statuses were determined by gap-PCR, revealing 5344 women (7.76%) carried deletions including αα/−−SEA (4.066%), αα/−α3.7 (2.934%), αα/−α4.2 (0.656%), and rare genotypes (0.102%). A two-stage model was built to predict these α-thalassemia deletions from targeted sequencing of the HBA gene cluster on maternal cfDNA. Our method achieved F1-scores of 97.14–99.55% for detecting the three common genotypes and 94.74% for detecting rare genotypes (−α3.7/−α4.2, αα/−−THAI, −α3.7/−−SEA, −α4.2/−−SEA). Additionally, the positive predictive values were 100.00% for αα/αα, 99.29% for αα/−−SEA, 94.87% for αα/−α3.7, and 96.51% for αα/−α4.2; and the negative predictive values were 97.63%, 99.99%, 99.99%, and 100.00%, respectively. As NIPT is increasingly adopted for pregnant women, utilizing cfDNA from NIPT to detect maternal carriers of common α-thalassemia deletions will be cost-effective and expand the benefits of NIPT.

IntroductionBreast cancer causes the most cancer-related death in women and is the costliest cancer in the US regarding medical service and prescription drug expenses. Breast cancer screening is recommended by health authorities in the US, but current screening efforts are often compromised by high false positive rates. Liquid biopsy based on circulating tumor DNA (ctDNA) has emerged as a potential approach to screen for cancer. However, the detection of breast cancer, particularly in early stages, is challenging due to the low amount of ctDNA and heterogeneity of molecular subtypes.MethodsHere, we employed a multimodal approach, namely Screen for the Presence of Tumor by DNA Methylation and Size (SPOT-MAS), to simultaneously analyze multiple signatures of cell free DNA (cfDNA) in plasma samples of 239 nonmetastatic breast cancer patients and 278 healthy subjects.ResultsWe identified distinct profiles of genome-wide methylation changes (GWM), copy number alterations (CNA), and 4-nucleotide oligomer (4-mer) end motifs (EM) in cfDNA of breast cancer patients. We further used all three signatures to construct a multi-featured machine learning model and showed that the combination model outperformed base models built from individual features, achieving an AUC of 0.91 (95% CI: 0.87-0.95), a sensitivity of 65% at 96% specificity.DiscussionOur findings showed that a multimodal liquid biopsy assay based on analysis of cfDNA methylation, CNA and EM could enhance the accuracy for the detection of early- stage breast cancer.

Abstract Targeted therapy with tyrosine kinase inhibitors (TKI) provides survival benefits to a majority of patients with non-small cell lung cancer (NSCLC). However, resistance to TKI almost always develops after treatment. Although genetic and epigenetic alterations have each been shown to drive resistance to TKI in cell line models, clinical evidence for their contribution in the acquisition of resistance remains limited. Here, we employed liquid biopsy for simultaneous analysis of genetic and epigenetic changes in 122 Vietnamese NSCLC patients undergoing TKI therapy and displaying acquired resistance. We detected multiple profiles of resistance mutations in 51 patients (41.8%). Of those, genetic alterations in EGFR, particularly EGFR amplification (n = 6), showed pronounced genome instability and genome-wide hypomethylation. Interestingly, the level of hypomethylation was associated with the duration of response to TKI treatment. We also detected hypermethylation in regulatory regions of Homeobox genes which are known to be involved in tumor differentiation. In contrast, such changes were not observed in cases with MET (n = 4) and HER2 (n = 4) amplification. Thus, our study showed that liquid biopsy could provide important insights into the heterogeneity of TKI resistance mechanisms in NSCLC patients, providing essential information for prediction of resistance and selection of subsequent treatment.

Hyperbolic phonon polaritons (HPhPs) are hybrid excitations of light and coherent lattice vibrations that exist in strongly optically anisotropic media, including two-dimensional materials (e.g., MoO3). These polaritons propagate through the material’s volume with long lifetimes, enabling novel mid-infrared nanophotonic applications by compressing light to sub-diffractional dimensions. Here, the dispersion relations and HPhP lifetimes (up to ≈12 ps) in single-crystalline α-MoO3 are determined by Fourier analysis of real-space, nanoscale-resolution polariton images obtained with the photothermal induced resonance (PTIR) technique. Measurements of MoO3 crystals deposited on periodic gratings show longer HPhPs propagation lengths and lifetimes (≈2×), and lower optical compressions, in suspended regions compared with regions in direct contact with the substrate. Additionally, PTIR data reveal MoO3 subsurface defects, which have a negligible effect on HPhP propagation, as well as polymeric contaminants localized under parts of the MoO3 crystals, which are derived from sample preparation. This work highlights the ability to engineer substrate-defined nanophotonic structures from layered anisotropic materials.

Canarium tramdenum occurs naturally in subtropical and tropical regions of Indochina and China. The wood is used for making high quality furniture and the fruit and leaves are used in traditional medicine. However, a lack of information on genetic diversity and population structure has handicapped the genetic conservation and domestication of this high-value species. This study evaluated genetic variation within and among four C. tramdenum populations. Sixty individuals were collected from four natural populations in Vietnam in the provinces of Ninhbinh, Bacgiang, Nghean, and Backan. Genetic diversity and genetic structure were determined using 20 ISSR markers. A total of 192 DNA fragments with sizes ranging from 110 bp to 3,000 bp were detected, of which 154 segments (80.2%) were polymorphic and 38 segments (19.8%) were monomorphic. The ISSR data indicated a moderate degree of genetic diversity for the species (h = 0.252). The four populations were separated into three genetic clusters with low levels of genetic distance between them. AMOVA result showed that most (78%) of the genetic variation was within the populations. The moderate to high genetic diversity of C. tramdenum and the low genetic differentiation among populations suggested that all existing natural populations in the particular regions needed to be preserved to protect the genetic diversity of this species.

Population-specific profiling of mutations in cancer genes is of critical importance for the understanding of cancer biology in general as well as the establishment of optimal diagnostics and treatment guidelines for that particular population. Although genetic analysis of tumor tissue is often used to detect mutations in cancer genes, the invasiveness and limited accessibility hinders its application in large-scale population studies. Here, we used ultra-deep massive parallel sequencing of plasma cell free DNA (cfDNA) to identify the mutation profiles of 265 Vietnamese patients with advanced non-small cell lung cancer (NSCLC). Compared to a cohort of advanced NSCLC patients characterized by sequencing of tissue samples, cfDNA genomic testing, despite lower mutation detection rates, was able to detect major mutations in tested driver genes that reflected similar mutation composition and distribution pattern, as well as major associations between mutation prevalence and clinical features. In conclusion, ultra-deep sequencing of plasma cfDNA represents an alternative approach for population-wide genetic profiling of cancer genes where recruitment of patients is limited to the accessibility of tumor tissue site.

Plant cells maintain strict proton gradients over different membranes. Here, Dejonghe et al. show that several protonophores, including the known tyrosine kinase inhibitor TyrphostinA23, inhibit clathrin-mediated endocytosis by disturbing these gradients and causing cytoplasmic acidification.

The identification and quantification of actionable mutations are of critical importance for effective genotype-directed therapies, prognosis and drug response monitoring in patients with non-small-cell lung cancer (NSCLC). Although tumor tissue biopsy remains the gold standard for diagnosis of NSCLC, the analysis of circulating tumor DNA (ctDNA) in plasma, known as liquid biopsy, has recently emerged as an alternative and noninvasive approach for exploring tumor genetic constitution. In this study, we developed a protocol for liquid biopsy using ultra-deep massively parallel sequencing (MPS) with unique molecular identifier tagging and evaluated its performance for the identification and quantification of tumor-derived mutations from plasma of patients with advanced NSCLC. Paired plasma and tumor tissue samples were used to evaluate mutation profiles detected by ultra-deep MPS, which showed 87.5% concordance. Cross-platform comparison with droplet digital PCR demonstrated comparable detection performance (91.4% concordance, Cohen's kappa coefficient of 0.85 with 95% CI = 0.72-0.97) and great reliability in quantification of mutation allele frequency (Intraclass correlation coefficient of 0.96 with 95% CI = 0.90-0.98). Our results highlight the potential application of liquid biopsy using ultra-deep MPS as a routine assay in clinical practice for both detection and quantification of actionable mutation landscape in NSCLC patients.

Natural killer T (NKT) cells are prominent innate-like lymphocytes in the liver with critical roles in immune responses during infection, cancer, and autoimmunity. Interferon gamma (IFN-γ) and IL-4 are key cytokines rapidly produced by NKT cells upon recognition of glycolipid antigens presented by antigen-presenting cells (APCs). It has previously been reported that the transcriptional coactivator β-catenin regulates NKT cell differentiation and functionally biases NKT cell responses toward IL-4, at the expense of IFN-γ production. β-Catenin is not only a central effector of Wnt signaling but also contributes to other signaling networks. It is currently unknown whether Wnt ligands regulate NKT cell functions. We thus investigated how Wnt ligands and β-catenin activity shape liver NKT cell functions in vivo in response to the glycolipid antigen, α-galactosylceramide (α-GalCer) using a mouse model. Pharmacologic targeting of β-catenin activity with ICG001, as well as myeloid-specific genetic ablation of Wntless (Wls), to specifically target Wnt protein release by APCs, enhanced early IFN-γ responses. By contrast, within several hours of α-GalCer challenge, myeloid-specific Wls deficiency, as well as pharmacologic targeting of Wnt release using the small molecule inhibitor IWP-2 impaired α-GalCer-induced IFN-γ responses, independent of β-catenin activity. These data suggest that myeloid cell-derived Wnt ligands drive early Wnt/β-catenin signaling that curbs IFN-γ responses, but that, subsequently, Wnt ligands sustain IFN-γ expression independent of β-catenin activity. Our analyses in ICG001-treated mice confirmed a role for β-catenin activity in driving early IL-4 responses by liver NKT cells. However, neither pharmacologic nor genetic perturbation of Wnt production affected the IL-4 response, suggesting that IL-4 production by NKT cells in response to α-GalCer is not driven by released Wnt ligands. Collectively, these data reveal complex temporal roles of Wnt ligands and β-catenin signaling in the regulation of liver NKT cell activation, and highlight Wnt-dependent and -independent contributions of β-catenin to NKT cell functions.

The purpose of this study was to examine the relationship between proactive personality and job search outcomes through network size, job search clarity, and networking behavior by surveying a sample of 773 university graduates. The results revealed that proactive personality has direct and indirect effects on job seeker's job search networking behaviour and number of job interviews and job offers. Additionally, the results also indicated that people displaying high proactivity traits tend to expand their social network size and having a clear job search goal, which results in increasing their networking behaviour and job search outcomes. Our findings are discussed in terms of the understanding mechanism linking a distal variable, proactive personality, to job search results.