The next big questions in cancer research.

Our understanding of tumorigenesis and cancer progression as well as clinical therapies for different cancer types have evolved dramatically in recent years. However, even with this progress, there are big challenges for scientists and oncologists to tackle, ranging from unpacking the molecular and cellular mechanisms involved to therapeutics and biomarker development to quality of life in the aftermath of therapy. In this article, we asked researchers to comment on the questions that they think are important to address in the coming years.

Circulating tumor DNA in advanced solid tumors: Clinical relevance and future directions.

The application of genomic profiling assays using plasma circulating tumor DNA (ctDNA) is rapidly evolving in the management of patients with advanced solid tumors. Diverse plasma ctDNA technologies in both commercial and academic laboratories are in routine or emerging use. The increasing integration of such testing to inform treatment decision making by oncology clinicians has complexities and challenges but holds significant potential to substantially improve patient outcomes. In this review, the authors discuss the current role of plasma ctDNA assays in oncology care and provide an overview of ongoing research that may inform real-world clinical applications in the near future.

Microbiome analyses of blood and tissues suggest cancer diagnostic approach.

Systematic characterization of the cancer microbiome provides the opportunity to develop techniques that exploit non-human, microorganism-derived molecules in the diagnosis of a major human disease. Following recent demonstrations that some types of cancer show substantial microbial contributions1-10, we re-examined whole-genome and whole-transcriptome sequencing studies in The Cancer Genome Atlas11 (TCGA) of 33 types of cancer from treatment-naive patients (a total of 18,116 samples) for microbial reads, and found unique microbial signatures in tissue and blood within and between most major types of cancer. These TCGA blood signatures remained predictive when applied to patients with stage Ia-IIc cancer and cancers lacking any genomic alterations currently measured on two commercial-grade cell-free tumour DNA platforms, despite the use of very stringent decontamination analyses that discarded up to 92.3% of total sequence data. In addition, we could discriminate among samples from healthy, cancer-free individuals (n = 69) and those from patients with multiple types of cancer (prostate, lung, and melanoma; 100 samples in total) solely using plasma-derived, cell-free microbial nucleic acids. This potential microbiome-based oncology diagnostic tool warrants further exploration.

Age-induced accumulation of methylmalonic acid promotes tumour progression.

The risk of cancer and associated mortality increases substantially in humans from the age of 65 years onwards1-6. Nonetheless, our understanding of the complex relationship between age and cancer is still in its infancy2,3,7,8. For decades, this link has largely been attributed to increased exposure time to mutagens in older individuals. However, this view does not account for the established role of diet, exercise and small molecules that target the pace of metabolic ageing9-12. Here we show that metabolic alterations that occur with age can produce a systemic environment that favours the progression and aggressiveness of tumours. Specifically, we show that methylmalonic acid (MMA), a by-product of propionate metabolism, is upregulated in the serum of older people and functions as a mediator of tumour progression. We traced this to the ability of MMA to induce SOX4 expression and consequently to elicit transcriptional reprogramming that can endow cancer cells with aggressive properties. Thus, the accumulation of MMA represents a link between ageing and cancer progression, suggesting that MMA is a promising therapeutic target for advanced carcinomas.

Ischemic Stroke with Comorbid Cancer Has Specific miRNA-mRNA Networks in Blood That Vary by Ischemic Stroke Mechanism.

OBJECTIVE: Approximately half of ischemic strokes (IS) in cancer patients are cryptogenic, with many presumed cardioembolic. We evaluated whether there were specific miRNA and mRNA transcriptome architectures in peripheral blood of IS patients with and without comorbid cancer, and between cardioembolic versus noncardioembolic IS etiologies in comorbid cancer. METHODS: We studied patients with cancer and IS (CS; n = 42), stroke only (SO; n = 41), and cancer only (n = 28), and vascular risk factor-matched controls (n = 30). mRNA-Seq and miRNA-Seq data, analyzed with linear regression models, identified differentially expressed genes in CS versus SO and in cardioembolic versus noncardioembolic CS, and miRNA-mRNA regulatory pairs. Network-level analyses identified stroke etiology-specific responses in CS. RESULTS: A total of 2,085 mRNAs and 31 miRNAs were differentially expressed between CS and SO. In CS, 122 and 35 miRNA-mRNA regulatory pairs, and 5 and 3 coexpressed gene modules, were associated with cardioembolic and noncardioembolic CS, respectively. Complement, growth factor, and immune/inflammatory pathways showed differences between IS etiologies in CS. A 15-gene biomarker panel assembled from a derivation cohort (n = 50) correctly classified 81% of CS and 71% of SO participants in a validation cohort (n = 33). Another 15-gene panel correctly identified etiologies for 13 of 13 CS-cardioembolic and 11 of 11 CS-noncardioembolic participants upon cross-validation; 11 of 16 CS-cryptogenic participants were predicted cardioembolic. INTERPRETATION: We discovered unique mRNA and miRNA transcriptome architecture in CS and SO, and in CS with different IS etiologies. Cardioembolic and noncardioembolic etiologies in CS showed unique coexpression networks and potential master regulators. These may help distinguish CS from SO and identify IS etiology in cryptogenic CS patients. ANN NEUROL 2024;96:565-581.

Genome-wide cell-free DNA fragmentation in patients with cancer.

Cell-free DNA in the blood provides a non-invasive diagnostic avenue for patients with cancer1. However, characteristics of the origins and molecular features of cell-free DNA are poorly understood. Here we developed an approach to evaluate fragmentation patterns of cell-free DNA across the genome, and found that profiles of healthy individuals reflected nucleosomal patterns of white blood cells, whereas patients with cancer had altered fragmentation profiles. We used this method to analyse the fragmentation profiles of 236 patients with breast, colorectal, lung, ovarian, pancreatic, gastric or bile duct cancer and 245 healthy individuals. A machine learning model that incorporated genome-wide fragmentation features had sensitivities of detection ranging from 57% to more than 99% among the seven cancer types at 98% specificity, with an overall area under the curve value of 0.94. Fragmentation profiles could be used to identify the tissue of origin of the cancers to a limited number of sites in 75% of cases. Combining our approach with mutation-based cell-free DNA analyses detected 91% of patients with cancer. The results of these analyses highlight important properties of cell-free DNA and provide a proof-of-principle approach for the screening, early detection and monitoring of human cancer.

Antibodies against high-risk human papillomavirus proteins as markers for noncervical HPV-related cancers in a Black South African population, according to HIV status.

Human papillomavirus (HPV) proteins may elicit antibody responses in the process toward HPV-related malignancy. However, HPV seroepidemiology in noncervical HPV-related cancers remains poorly understood, particularly in populations with a high prevalence of human immunodeficiency virus (HIV). Using a glutathione S-transferase-based multiplex serology assay, antibodies against E6, E7 and L1 proteins of HPV16 and HPV18 were measured in sera of 535 cases of noncervical HPV-related cancers (anal (n = 104), vulval (n = 211), vaginal (n = 49), penile (n = 37) and oropharyngeal (n = 134)) and 6651 non-infection-related cancer controls, from the Johannesburg Cancer Study that recruited Black South African with newly diagnosed cancer between 1995 and 2016. Logistic and Poisson regression models were used to calculate adjusted odds ratios (aOR) and prevalence ratios (aPR) and 95% confidence intervals (CI) in cases versus controls. HPV16 E6 was more strongly associated with noncervical HPV-related cancers than HPV16 L1 or E7, or HPV18 proteins: anal (females (HPV16 E6 aOR = 11.50;95%CI:6.0-22.2), males (aOR = 10.12;95%CI:4.9-20.8), vulval (aOR = 11.69;95%CI:7.9-17.2), vaginal (aOR = 10.26;95%CI:5.0-21), penile (aOR = 18.95;95%CI:8.9-40), and oropharyngeal (females (aOR = 8.95;95%CI:2.9-27.5), males (aOR = 3.49;95%CI:1.8-7.0)) cancers. HPV16-E6 seropositivity ranged from 24.0% to 35.1% in anal, vulval, vaginal and penile cancer but was significantly lower (11.2%) in oropharyngeal cancer. After adjustment for HIV, prevalence of which increased from 22.2% in 1995-2005 to 54.1% in 2010-2016, HPV16 E6 seropositivity increased by period of diagnosis (aPR for 2010-2016 vs. 1995-2006 = 1.84;95%CI:1.1-3.0). Assuming HPV16 E6 seroprevalence reflects HPV attributable fraction, the proportion of certain noncervical-HPV-related cancers caused by HPV is increasing over time in South Africa. This is expected to be driven by the increasing influence of HIV.

Predictive value of abnormal blood tests for detecting cancer in primary care patients with nonspecific abdominal symptoms: A population-based cohort study of 477,870 patients in England.

BACKGROUND: Identifying patients presenting with nonspecific abdominal symptoms who have underlying cancer is a challenge. Common blood tests are widely used to investigate these symptoms in primary care, but their predictive value for detecting cancer in this context is unknown. We quantify the predictive value of 19 abnormal blood test results for detecting underlying cancer in patients presenting with 2 nonspecific abdominal symptoms. METHODS AND FINDINGS: Using data from the UK Clinical Practice Research Datalink (CPRD) linked to the National Cancer Registry, Hospital Episode Statistics and Index of Multiple Deprivation, we conducted a population-based cohort study of patients aged ≥30 presenting to English general practice with abdominal pain or bloating between January 2007 and October 2016. Positive and negative predictive values (PPV and NPV), sensitivity, and specificity for cancer diagnosis (overall and by cancer site) were calculated for 19 abnormal blood test results co-occurring in primary care within 3 months of abdominal pain or bloating presentations. A total of 9,427/425,549 (2.2%) patients with abdominal pain and 1,148/52,321 (2.2%) with abdominal bloating were diagnosed with cancer within 12 months post-presentation. For both symptoms, in both males and females aged ≥60, the PPV for cancer exceeded the 3% risk threshold used by the UK National Institute for Health and Care Excellence for recommending urgent specialist cancer referral. Concurrent blood tests were performed in two thirds of all patients (64% with abdominal pain and 70% with bloating). In patients aged 30 to 59, several blood abnormalities updated a patient's cancer risk to above the 3% threshold: For example, in females aged 50 to 59 with abdominal bloating, pre-blood test cancer risk of 1.6% increased to: 10% with raised ferritin, 9% with low albumin, 8% with raised platelets, 6% with raised inflammatory markers, and 4% with anaemia. Compared to risk assessment solely based on presenting symptom, age and sex, for every 1,000 patients with abdominal bloating, assessment incorporating information from blood test results would result in 63 additional urgent suspected cancer referrals and would identify 3 extra cancer patients through this route (a 16% relative increase in cancer diagnosis yield). Study limitations include reliance on completeness of coding of symptoms in primary care records and possible variation in PPVs if extrapolated to healthcare settings with higher or lower rates of blood test use. CONCLUSIONS: In patients consulting with nonspecific abdominal symptoms, the assessment of cancer risk based on symptoms, age and sex alone can be substantially enhanced by considering additional information from common blood test results. Male and female patients aged ≥60 presenting to primary care with abdominal pain or bloating warrant consideration for urgent cancer referral or investigation. Further cancer assessment should also be considered in patients aged 30 to 59 with concurrent blood test abnormalities. This approach can detect additional patients with underlying cancer through expedited referral routes and can guide decisions on specialist referrals and investigation strategies for different cancer sites.

Cancer-specific risk prediction with a serum microRNA signature.

We recently derived and validated a serum-based microRNA risk score (miR-score) that predicted colorectal cancer (CRC) occurrence with very high accuracy within 14 years of follow-up in a population-based cohort study from Germany (ESTHER cohort). Here, we aimed to evaluate associations of the CRC-specific miR-score with the risk of developing other common cancers, including female breast cancer (BC), lung cancer (LC), and prostate cancer (PC), in the ESTHER cohort. MicroRNAs (miRNAs) were profiled by quantitative real-time PCR in serum samples collected at baseline from randomly selected incident cases of BC (n = 90), LC (n = 88), and PC (n = 93) and participants without diagnosis of CRC, LC, BC, or PC (controls, n = 181) until the end of the 17-year follow-up. Multivariate logistic regression models were used to evaluate the associations of the miR-score with BC, LC, and PC incidence. The miR-score showed strong inverse associations with BC and LC incidence [odds ratio per 1 standard deviation increase: 0.60 (95% confidence interval [CI] 0.43-0.82), p = 0.0017, and 0.64 (95% CI 0.48-0.84),p = 0.0015, respectively]. Associations with PC were not statistically significant but pointed in the positive direction. Our study highlights the potential of serum-based miRNA biomarkers for cancer-specific risk prediction. Further large cohort studies aiming to investigate, validate, and optimize the use of circulating miRNA signatures for cancer risk assessment are warranted.

Associations of neighborhood sociodemographic environment with mortality and circulating metabolites among low-income black and white adults living in the southeastern United States.

BACKGROUND: Residing in a disadvantaged neighborhood has been linked to increased mortality. However, the impact of residential segregation and social vulnerability on cause-specific mortality is understudied. Additionally, the circulating metabolic correlates of neighborhood sociodemographic environment remain unexplored. Therefore, we examined multiple neighborhood sociodemographic metrics, i.e., neighborhood deprivation index (NDI), residential segregation index (RSI), and social vulnerability index (SVI), with all-cause and cardiovascular disease (CVD) and cancer-specific mortality and circulating metabolites in the Southern Community Cohort Study (SCCS). METHODS: The SCCS is a prospective cohort of primarily low-income adults aged 40-79, enrolled from the southeastern United States during 2002-2009. This analysis included self-reported Black/African American or non-Hispanic White participants and excluded those who died or were lost to follow-up ≤ 1 year. Untargeted metabolite profiling was performed using baseline plasma samples in a subset of SCCS participants. RESULTS: Among 79,631 participants, 23,356 deaths (7214 from CVD and 5394 from cancer) were documented over a median 15-year follow-up. Higher NDI, RSI, and SVI were associated with increased all-cause, CVD, and cancer mortality, independent of standard clinical and sociodemographic risk factors and consistent between racial groups (standardized HRs among all participants were 1.07 to 1.20 in age/sex/race-adjusted model and 1.04 to 1.08 after comprehensive adjustment; all P < 0.05/3 except for cancer mortality after comprehensive adjustment). The standard risk factors explained < 40% of the variations in NDI/RSI/SVI and mediated < 70% of their associations with mortality. Among 1110 circulating metabolites measured in 1688 participants, 134 and 27 metabolites were associated with NDI and RSI (all FDR < 0.05) and mediated 61.7% and 21.2% of the NDI/RSI-mortality association, respectively. Adding those metabolites to standard risk factors increased the mediation proportion from 38.4 to 87.9% and 25.8 to 42.6% for the NDI/RSI-mortality association, respectively. CONCLUSIONS: Among low-income Black/African American adults and non-Hispanic White adults living in the southeastern United States, a disadvantaged neighborhood sociodemographic environment was associated with increased all-cause and CVD and cancer-specific mortality beyond standard risk factors. Circulating metabolites may unveil biological pathways underlying the health effect of neighborhood sociodemographic environment. More public health efforts should be devoted to reducing neighborhood environment-related health disparities, especially for low-income individuals.

Contribution of plasma levels of VEGF-A and angiopoietin-2 in addition to a genetic variant in KCNAB1 to predict the risk of bevacizumab-induced hypertension.

Bevacizumab-induced hypertension poses a therapeutic challenge and identifying biomarkers for hypertension can enhance therapy safety. Lower plasma levels of VEGF-A, angiopoietin-2, and rs6770663 in KCNAB1 were previously associated with increased risk of bevacizumab-induced hypertension. This study investigated whether these factors independently contribute to grade 2-3 bevacizumab-induced hypertension risk in 277 cancer patients (CALGB/Alliance 90401). Multivariable analyses assessed the independent association of each factor and hypertension. Likelihood ratio test (LRT) evaluated the explanatory significance of combining protein levels and rs6770663 in predicting hypertension. Boostrap was employed to assess the mediation effect of protein levels on the rs6770663 association with hypertension. Lower protein levels and rs6770663 were independently associated with increased hypertension risk. Adding rs6770663 to protein levels improved the prediction of hypertension (LRT p = 0.0002), with no mediation effect observed. Protein levels of VEGF-A, angiopoietin-2 and rs6770663 in KCNAB1 are independent risk factors and, when combined, may improve prediction of bevacizumab-induced hypertension. ClinicalTrials.gov Identifier: NCT00110214.

Altered serum metabolome as an indicator of paraneoplasia or concomitant cancer in patients with rheumatic disease.

OBJECTIVES: A timely diagnosis is imperative for curing cancer. However, in patients with rheumatic musculoskeletal diseases (RMDs) or paraneoplastic syndromes, misleading symptoms frequently delay cancer diagnosis. As metabolic remodelling characterises both cancer and RMD, we analysed if a metabolic signature can indicate paraneoplasia (PN) or reveal concomitant cancer in patients with RMD. METHODS: Metabolic alterations in the sera of rheumatoid arthritis (RA) patients with (n=56) or without (n=52) a history of invasive cancer were quantified by nuclear magnetic resonance analysis. Metabolites indicative of cancer were determined by multivariable regression analyses. Two independent RA and spondyloarthritis (SpA) cohorts with or without a history of invasive cancer were used for blinded validation. Samples from patients with active cancer or cancer treatment, pulmonary and lymphoid type cancers, paraneoplastic syndromes, non-invasive (NI) precancerous lesions and non-melanoma skin cancer and systemic lupus erythematosus and samples prior to the development of malignancy were used to test the model performance. RESULTS: Based on the concentrations of acetate, creatine, glycine, formate and the lipid ratio L1/L6, a diagnostic model yielded a high sensitivity and specificity for cancer diagnosis with AUC=0.995 in the model cohort, AUC=0.940 in the blinded RA validation cohort and AUC=0.928 in the mixed RA/SpA cohort. It was equally capable of identifying cancer in patients with PN. The model was insensitive to common demographic or clinical confounders or the presence of NI malignancy like non-melanoma skin cancer. CONCLUSIONS: This new set of metabolic markers reliably predicts the presence of cancer in arthritis or PN patients with high sensitivity and specificity and has the potential to facilitate a rapid and correct diagnosis of malignancy.

Tissue of origin detection for cancer tumor using low-depth cfDNA samples through combination of tumor-specific methylation atlas and genome-wide methylation density in graph convolutional neural networks.

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.

External Quality Assessment on Molecular Tumor Profiling with Circulating Tumor DNA-Based Methodologies Routinely Used in Clinical Pathology within the COIN Consortium.

BACKGROUND: Identification of tumor-derived variants in circulating tumor DNA (ctDNA) has potential as a sensitive and reliable surrogate for tumor tissue-based routine diagnostic testing. However, variations in pre(analytical) procedures affect the efficiency of ctDNA recovery. Here, an external quality assessment (EQA) was performed to determine the performance of ctDNA mutation detection work flows that are used in current diagnostic settings across laboratories within the Dutch COIN consortium (ctDNA on the road to implementation in The Netherlands). METHODS: Aliquots of 3 high-volume diagnostic leukapheresis (DLA) plasma samples and 3 artificial reference plasma samples with predetermined mutations were distributed among 16 Dutch laboratories. Participating laboratories were requested to perform ctDNA analysis for BRAF exon 15, EGFR exon 18-21, and KRAS exon 2-3 using their regular circulating cell-free DNA (ccfDNA) analysis work flow. Laboratories were assessed based on adherence to the study protocol, overall detection rate, and overall genotyping performance. RESULTS: A broad range of preanalytical conditions (e.g., plasma volume, elution volume, and extraction methods) and analytical methodologies (e.g., droplet digital PCR [ddPCR], small-panel PCR assays, and next-generation sequencing [NGS]) were used. Six laboratories (38%) had a performance score of >0.90; all other laboratories scored between 0.26 and 0.80. Although 13 laboratories (81%) reached a 100% overall detection rate, the therapeutically relevant EGFR p.(S752_I759del) (69%), EGFR p.(N771_H773dup) (50%), and KRAS p.(G12C) (48%) mutations were frequently not genotyped accurately. CONCLUSIONS: Divergent (pre)analytical protocols could lead to discrepant clinical outcomes when using the same plasma samples. Standardization of (pre)analytical work flows can facilitate the implementation of reproducible liquid biopsy testing in the clinical routine.

Quantitative image analysis pipeline for detecting circulating hybrid cells in immunofluorescence images with human-level accuracy.

Circulating hybrid cells (CHCs) are a newly discovered, tumor-derived cell population found in the peripheral blood of cancer patients and are thought to contribute to tumor metastasis. However, identifying CHCs by immunofluorescence (IF) imaging of patient peripheral blood mononuclear cells (PBMCs) is a time-consuming and subjective process that currently relies on manual annotation by laboratory technicians. Additionally, while IF is relatively easy to apply to tissue sections, its application to PBMC smears presents challenges due to the presence of biological and technical artifacts. To address these challenges, we present a robust image analysis pipeline to automate the detection and analysis of CHCs in IF images. The pipeline incorporates quality control to optimize specimen preparation protocols and remove unwanted artifacts, leverages a ß-variational autoencoder (VAE) to learn meaningful latent representations of single-cell images, and employs a support vector machine (SVM) classifier to achieve human-level CHC detection. We created a rigorously labeled IF CHC data set including nine patients and two disease sites with the assistance of 10 annotators to evaluate the pipeline. We examined annotator variation and bias in CHC detection and provided guidelines to optimize the accuracy of CHC annotation. We found that all annotators agreed on CHC identification for only 65% of the cells in the data set and had a tendency to underestimate CHC counts for regions of interest (ROIs) containing relatively large amounts of cells (>50,000) when using the conventional enumeration method. On the other hand, our proposed approach is unbiased to ROI size. The SVM classifier trained on the ß-VAE embeddings achieved an F1 score of 0.80, matching the average performance of human annotators. Our pipeline enables researchers to explore the role of CHCs in cancer progression and assess their potential as a clinical biomarker for metastasis. Further, we demonstrate that the pipeline can identify discrete cellular phenotypes among PBMCs, highlighting its utility beyond CHCs.

Glomerular Filtration Rate Estimation Using ß2-Microglobulin and ß-Trace Protein in Adults With Solid Tumors: A Prospective Cross-Sectional Study.

RATIONALE & OBJECTIVE: ß2-Microglobulin (B2M) and ß-trace protein (BTP) are novel endogenous filtration markers that may improve the accuracy of estimated glomerular filtration rate (eGFR) beyond creatinine and cystatin C (eGFRcr-cys), but they have not been assessed in patients with cancer. STUDY DESIGN: Cross-sectional analysis. SETTING & PARTICIPANTS: Prospective cohort of 1,200 patients with active solid tumors recruited between April 2015 and September 2017. EXPOSURE: CKD-EPI equations without race combining B2M and/or BTP with creatinine with or without cystatin C (2-, 3-, or 4-marker panel eGFR). OUTCOME: Performance of equations compared with eGFRcr-cys and non-GFR determinants of serum B2M and BTP (SB2M, and SBTP, respectively). Measured GFR (mGFR) was determined using the plasma clearance of chromium-51 labeled ethylenediamine tetraacetic acid (51Cr-EDTA). ANALYTICAL APPROACH: Bias was defined as the median of the differences between mGFR and eGFR, and 1-P30 was defined as the percentage of estimates that differed by more than 30% from the mGFR (1-P30). Linear regression was used to assess association of clinical and laboratory variables with SB2M, and SBTP after adjustment for mGFR. RESULTS: Mean age and mGFR were 58.8±13.2 SD years and 78.4±21.7 SD mL/min/1.73m2, respectively. Performance of the 3-marker and 4-marker panel equations was better than eGFRcr-cys (lesser bias and 1-P30). Performance of 2-marker panel equations was as good as eGFRcr-cys (lesser bias and similar 1-P30). SB2M and SBTP were not strongly influenced by cancer site. LIMITATIONS: Participants may have had better clinical performance status than the general population of patients with solid tumors. CONCLUSIONS: B2M and BTP can improve the accuracy of eGFR and may be useful as confirmatory tests in patients with solid tumors, either by inclusion in a multimarker panel equation with creatinine and cystatin C, or by substituting for cystatin C in combination with creatinine. PLAIN-LANGUAGE SUMMARY: The most accurate method to assess estimate kidney function is estimated glomerular filtration rate (eGFR) using creatinine and cystatin C (eGFRcr-cys). We studied whether using ß2-microglobulin (B2M) and/or ß-trace protein (BTP) with creatinine with or without cystatin C (2-, 3-, or 4-marker panel eGFR) might be useful in patients with active solid tumors. The performance of the 3-marker and 4-marker panel equations was better than eGFRcr-cys. Performance of 2-marker panel equations was as good as eGFRcr-cys. We conclude that B2M and BTP can improve the accuracy of eGFR and may be useful as a confirmatory test in patients with solid tumors either by inclusion in multimarker panel equation with creatinine and cystatin C or by substituting for cystatin C in combination with creatinine.

Association between atherogenic index of plasma and all-cause mortality and specific-mortality: a nationwide population­based cohort study.

BACKGROUND: Atherogenic index of plasma (AIP), a marker of atherosclerosis and cardiovascular disease (CVD). However, few studies have investigated association between AIP and all-cause mortality and specific-mortality in the general population. METHODS: This study included data from 14,063 American adults. The exposure variable was the AIP, which was defined as log10 (triglycerides/high-density lipoprotein cholesterol). The outcome variables included all-cause mortality and specific-mortality. Survey-weighted cox regressions were performed to evaluate the relation between AIP and all-cause mortality and specific-mortality. Weighted restricted cubic spline was conducted to examin the non-linear relationship. RESULTS: During 10 years of follow-up, we documented 2,077, 262, 854, and 476 cases of all-cause mortality, diabetes mortality, CVD mortality and cancer mortality, respectively. After adjustment for potential confounders, we found that atherogenic index of plasma (AIP) was significantly associated with an increased risk of diabetes mortality when comparing the highest to the lowest quantile of AIP in female (p for trend = 0.001) or participants older than 65 years (p for trend = 0.002). AIP was not significantly associated with all-cause mortality, CVD mortality and cancer mortality (p > 0.05). Moreover, a non-linear association was observed between AIP and all-cause mortality in a U-shape (p for non-linear = 0.0011), while a linear relationship was observed with diabetes mortality and non-diabetes mortality (p for linear < 0.0001). CONCLUSIONS: In this study, there is a no significant association between high AIP levels and a high risk of all-cause and cardiovascular mortality. Besides, a higher AIP was significantly associated with an increased risk of diabetes mortality, which only found in women older than 65 years. AIP was associated with all-cause mortality in a U-shape. This association could be explained by the finding that higher AIP predicted a higher risk of death from diabetes, and that lower AIP predicted a higher risk of death from non-diabetes causes.

We used a large national database and a prospective cohort study with a long follow-up period. Higher AIP was significantly associated with an increased risk of diabetes mortality, only in women older than 65 years. There is a no significant association between high AIP levels and a high risk of all-cause and cardiovascular mortality. AIP was associated with all-cause mortality in a U-shape. This finding suggest that controlling AIP levels may have a positive effect on reducing diabetes mortality.

Circulating tumor DNA (ctDNA) application in investigation of cancer: Bench to bedside.

Now, genomics forms the core of the precision medicine concept. Comprehensive investigations of tumor genomes have made it possible to characterize tumors at the molecular level and, specifically, to identify the fundamental processes that cause condition. A variety of kinds of tumors have seen better outcomes for patients as a result of the development of novel medicines to tackle these genetic-driving processes. Since therapy may exert selective pressure on cancers, non-invasive methods such as liquid biopsies can provide the opportunity for rich reservoirs of crucial and real-time genetic data. Liquid biopsies depend on the identification of circulating cells from tumors, circulating tumor DNA (ctDNA), RNA, proteins, lipids, and metabolites found in patient biofluids, as well as cell-free DNA (cfDNA), which exists in those with cancer. Although it is theoretically possible to examine biological fluids other than plasma, such as pleural fluid, urine, saliva, stool, cerebrospinal fluid, and ascites, we will limit our discussion to blood and solely cfDNA here for the sake of conciseness. Yet, the pace of wider clinical acceptance has been gradual, partly due to the increased difficulty of choosing the best analysis for the given clinical issue, interpreting the findings, and delaying proof of value from clinical trials. Our goal in this review is to discuss the current clinical value of ctDNA in cancers and how clinical oncology systems might incorporate procedures for ctDNA testing.

Correlation between hemoglobin and the risk of common malignant tumors: a 1999-2020 retrospective analysis and causal association analysis.

BACKGROUND: The role of hemoglobin (HGB) in common malignant tumors remains unclear. METHODS: A retrospective analysis was conducted to identify the correlation between HGB levels and risk of 15 malignant tumors using 50,085 samples from the National Health and Nutrition Examination Survey. Mendelian Randomization analyses (MRAs) were performed based on genome-wide association study data to assess the causal relationship between HGB levels and these malignant tumors using more than 700,000 samples. The robustness of the MRA results was confirmed through various analytical methods. Fifty-six in-house samples were used to investigate the correlation between HGB levels and the prognosis in prostate cancer (PRCA) using the Kaplan-Meier curve. RESULTS: High HGB levels were associated with a higher risk for patients with cervix cancer, melanoma, and non-melanoma skin cancer (OR > 1.000, p < 0.05). It served as a protective factor for colon cancer, esophagus cancer, stomach cancer, bone cancer, lung cancer, renal cancer, and PRCA (OR < 1.000, p < 0.05). Furthermore, MRAs suggested that elevated HGB levels were correlated with a reduced risk of PRCA (OR = 0.869, p < 0.05), with no significant association observed between this marker and the remaining 14 malignant tumors. No pleiotropy or heterogeneity was found in the ultimate results for MRAs (p-values > 0.05), suggesting the robustness of the results. The results derived from the in-house data revealed a relationship between higher HGB values and a more favorable prognosis in PRCA (p < 0.05). CONCLUSION: High circulating HGB levels may play a protective prognostic role for PRCA and serve as a protective factor against the occurrence of PRCA.

TOTEM: a multi-cancer detection and localization approach using circulating tumor DNA methylation markers.

BACKGROUND: Detection of cancer and identification of tumor origin at an early stage improve the survival and prognosis of patients. Herein, we proposed a plasma cfDNA-based approach called TOTEM to detect and trace the cancer signal origin (CSO) through methylation markers. METHODS: We performed enzymatic conversion-based targeted methylation sequencing on plasma cfDNA samples collected from a clinical cohort of 500 healthy controls and 733 cancer patients with seven types of cancer (breast, colorectum, esophagus, stomach, liver, lung, and pancreas) and randomly divided these samples into a training cohort and a testing cohort. An independent validation cohort of 143 healthy controls, 79 liver cancer patients and 100 stomach cancer patients were recruited to validate the generalizability of our approach. RESULTS: A total of 57 multi-cancer diagnostic markers and 873 CSO markers were selected for model development. The binary diagnostic model achieved an area under the curve (AUC) of 0.907, 0.908 and 0.868 in the training, testing and independent validation cohorts, respectively. With a training specificity of 98%, the specificities in the testing and independent validation cohorts were 100% and 98.6%, respectively. Overall sensitivity across all cancer stages was 65.5%, 67.3% and 55.9% in the training, testing and independent validation cohorts, respectively. Early-stage (I and II) sensitivity was 50.3% and 45.7% in the training and testing cohorts, respectively. For cancer patients correctly identified by the binary classifier, the top 1 and top 2 CSO accuracies were 77.7% and 86.5% in the testing cohort (n = 148) and 76.0% and 84.0% in the independent validation cohort (n = 100). Notably, performance was maintained with only 21 diagnostic and 214 CSO markers, achieving a training AUC of 0.865, a testing AUC of 0.866, and an integrated top 2 accuracy of 83.1% in the testing cohort. CONCLUSIONS: TOTEM demonstrates promising potential for accurate multi-cancer detection and localization by profiling plasma methylation markers. The real-world clinical performance of our approach needs to be investigated in a much larger prospective cohort.