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This study aimed to explore the impact of revising suspected-cancer referral guidelines on primary care contacts and costs. Participants had incident cancer (colorectal, n = 2000; ovary, n = 763; and pancreas, n = 597) codes in the Clinical Practice Research Datalink or England cancer registry. Difference-in-differences analyses explored guideline impacts on contact days and nonzero costs between the first cancer feature and diagnosis. Participants were controls ("old National Institute for Health and Care Excellence [NICE]") or "new NICE" if their index feature was introduced during guideline revision. Model assumptions were inspected visually and by falsification tests. Sensitivity analyses reclassified participants who subsequently presented with features in the original guidelines as "old NICE." For colorectal cancer, sensitivity analysis (n = 3481) adjusted for multimorbidity burden. Median contact days and costs were, respectively, 4 (interquartile range [IQR] 2-7) and £117.69 (IQR £53.23-£206.65) for colorectal, 5 (IQR 3-9) and £156.92 (IQR £78.46-£272.29) for ovary, and 7 (IQR 4-13) and £230.64 (IQR £120.78-£408.34) for pancreas. Revising ovary guidelines may have decreased contact days (incidence rate ratio [IRR] 0.74; 95% confidence interval 0.55-1.00; P =.05) with unchanged costs, but parallel trends assumptions were violated. Costs decreased by 13% (equivalent to −£28.05, −£50.43 to −£5.67) after colorectal guidance revision but only in sensitivity analyses adjusting for multimorbidity. Contact days and costs remained unchanged after pancreas guidance revision. The main analyses of symptomatic patients suggested that prediagnosis primary care costs remained unchanged after guidance revision for pancreatic cancer. For colorectal cancer, contact days and costs decreased in analyses adjusting for multimorbidity. Revising ovarian cancer guidelines may have decreased primary care contact days but not costs, suggesting increased resource-use intensity; nevertheless, there is evidence of confounding. • Revising England's National Institute for Health and Care Excellence suspected-cancer referral guidelines shortened the time to diagnosis of colorectal and ovarian cancers. Impacts on healthcare resource use and costs are unknown. • We report weak evidence that revising the guidelines decreased primary care contact days with unchanged costs for symptomatic patients before ovarian cancer diagnosis, suggesting increased resource-use intensity. Contact days and costs remained unchanged for symptomatic patients after pancreas guidance revision and decreased for colorectal cancer but only after adjusting for multimorbidity. • Further research is needed to understand the overall impact of the revised guidelines on the English health system. [ABSTRACT FROM AUTHOR]

The UK National Health Service has well-developed site-specific referral pathways for patients with suspected cancer, but historically there has been inequality of access for patients with suspected Metastatic malignant disease of Unknown primary Origin (MUO). The Brighton cancer of unknown primary (CUP) clinic covers a population of about 650 000. As well as 'in-house' referrals, direct general practitioner referrals are also accepted (since 2015), aiming to shorten the diagnostic pathway and improve patient support. We present data from the first 3 years of activity. Referrals were screened by an oncologist, ensuring adherence to the strict referral criterion of imaging evidence of a suspected diagnosis of MUO. A standardised data collection form was completed at each clinic appointment by the CUP team. Outcomes were cross-checked against clinic lists, letters and multidisciplinary meeting (MDM) records. A data analysis was carried out of all referrals to the CUP clinic (2015–2018), including general practitioner referrals. In total, 258 patients were seen in the CUP clinic in the first 3 years. The median age was 71 years (range 23–95 years). Source of referral: general practitioner (30%); physician (27%); acute oncology (24%); other MDM (16%); surgeon (3%). A final diagnosis of cancer was made in 83% of referrals, with a primary site identified in 83% of those cancer cases: 19% haematological, 11% lung, 9% urological, 8% upper gastrointestinal, 6% breast, 5% skin, 4% gynaecological, 4% lower gastrointestinal, 0.3% thyroid and 0.3% sarcoma. 10% of referrals remained with a MUO diagnosis (not fit for further investigation) and 7% had confirmed CUP. 17% had a benign diagnosis (of which 56% were general practitioner referrals). Of the general practitioner referrals: 55% were seen in the CUP clinic, 31% did not meet referral criteria and 14% were declined after MDM review of imaging confirmed benign appearances. The development of direct general practitioner referrals to CUP clinics nationally should be encouraged (as supported by the National Institute for Health and Care Excellence) – they are feasible and manageable within a tertiary CUP clinic, resulting in high rates of cancer diagnoses, with attendant early support from specialist nursing teams and oncological review. • Historically there has been inequality of access for patients with suspected MUO. • Direct GP referrals are feasible and manageable within a tertiary CUP clinic. • MUO patients benefit from early specialist nurse support and oncologist input. [ABSTRACT FROM AUTHOR]

Background Cell-free DNA (cf-DNA)-based liquid biopsy is emerging as a revolutionary new method in individualized cancer treatment and prognosis monitoring, although detecting early-stage cancers using cf-DNA remains challenging, partially because of the undefined biological background of cf-DNA. Materials and methods We investigated somatic mutations in the cf-DNA of 259 cancer-free individuals with a median age of 47 years using an endogenous barcoding duplex method with an ultralow base error rate (2 × 10−7) and compared the variant allele frequencies (VAFs) of these mutations between the cf-DNA and the corresponding blood cell DNA. Results Sixty percent (155/259) of the samples showed at least one nonsynonymous mutation on either of two similar target panels covering 508 and 559 cancer-related genes. For individuals older than 50 years of age, the positive rate increased to 76%. Most cf-DNA mutations were also present at similar VAFs in the paired blood cell DNA. The most frequently mutated genes were driver genes of hematologic malignancies, including DNMT3A, TET2, AXSL1, and JAK2. However, the other 58.4% (192/329) of the mutations were likely 'passenger mutations' of clonal hematopoiesis, including mutations in NOTCH2, FAT3, EXT2, ERBB4, and ARID2, which are driver genes of solid tumors. Conclusion Hematopoietic clone-derived mutations, including 'driver mutations' and 'passenger mutations', are prevalent in the cf-DNA of both healthy individuals and cancer patients and may be a potential source of false positives in the liquid biopsy. Our results also suggest the ineffectiveness for distinguishing clonal hematopoietic mutations of low VAF (≤0.1%) from tumor-derived mutations using conventional next-generation sequencing of blood cell DNA. However, an error correction model with an ultralow error rate and high coverage depth is required for blood cell DNA sequencing, which is difficult and costly to achieve with current technologies. [ABSTRACT FROM AUTHOR]

Early cancer detection is critical to control disease progression. Research of high mortality rate cancers is thereby essential although cancer diagnosis is challenging. Mass spectrometry (MS)-based metabolomics is a great technique to support. Despite MS-based metabolomics being a high-throughput technique, it can be inefficient due to the exponentially increased data volume and complexity of MS data. To demonstrate the real power of the MS-based platform, machine learning (ML) can provide insights from the sea of data. As an emerging technology, it is worth reviewing the research work on how ML coupled with MS-based metabolomics in the past decade. An overview of clinical metabolomics studies is presented with respect to cancer type. The primary purpose of this review is to present what data strategy has been applied previously and discuss the challenges and potential solutions when MS-based metabolomics and ML are combined for early cancer diagnosis. • MS-based metabolomics is a high-throughput technique to discover diagnostic biomarkers for early cancer discovery. • Machine learning facilitates clinical metabolomics due to its efficiency in analyzing a high volume of data. • Machine learning is a promising tool to detect cancer, classify cancer subtypes, and predict the risk of cancer occurrence. • Robust machine learning models for early cancer diagnosis boost the data network effect in MS-based clinical metabolomics. [ABSTRACT FROM AUTHOR]

• A generalized method for effective early diagnosis of pancreatic cancer based on multiple instance learning. • A spatial transformation based on anatomical structures is proposed to reduce the background interference and enhance the detection stability for the learned features. • Instance-level contrastive learning is developed to aggregate different types of tumor features, maintain the tumor feature integrity, and enhance the diagnosis stability. • The method achieves remarkable generalization performance in unseen datasets and is sensitive to tumors smaller than 2 cm in diameter. Pancreatic cancer is a highly malignant cancer type with a high mortality rate. As no obvious symptoms are associated with this cancer type, most of the diagnoses are made when the patients are already in a late stage. In this work, we propose an automated method for effective early diagnosis of pancreatic cancer based on multiple instance learning with contrast-enhanced CT images. In this method, diagnosis stability and generalizability were improved through shape normalization based on anatomical structures as well as instance-level contrastive learning. Specifically, anatomically-guided shape normalization were developed to reconstruct the pancreatic regions of interest by spatial transformations, account for larger tumor parts in these regions, and hence enhance the extraction of pancreatic features. Moreover, instance-level contrastive learning was employed to aggregate different types of tumor features within the multiple instance learning framework. This learning approach can maintain the tumor feature integrity and enhance the diagnosis stability. Finally, a balance-adjustment strategy was designed to alleviate the class imbalance problem caused by the scarcity of tumor samples. Extensive experimental results demonstrated remarkable performance of our method when conducted cross-validation on an in-house dataset with 310 patients and independent test on two unseen datasets (a private test set with 316 and a publicly-available test set with 281). The proposed strategies also led to significant improvements in generalizability. Besides, the clinical significance of the proposed method was further verified through two independent test results in which tumors smaller than 2 cm in diameter were identified at accuracies of 80.9% and 90.1%, respectively. Overall, our method provides a potentially successful tool for early diagnosis of pancreatic cancer. Our source codes will be released at https://github.com/SJTUBME-QianLab/MIL_PAdiagnosis. [Display omitted] [ABSTRACT FROM AUTHOR]

The isolation and analysis of scarce circulating tumor cells (CTCs) with immunomagnetic nanoparticles (IMNs) have shown promising outcomes in noninvasive cancer diagnosis. However, the IMNs adsorb nonspecific proteins after entering into biofluids and the formed protein coronas cover surface targeting ligands, limiting the detection efficiency of IMNs. In addition, the interaction between surface targeting ligands and white blood cells (WBCs) significantly limits the purity of CTCs isolated by IMNs. Furthermore, the interfacial collision of nanoparticles and cells has negative effects on the viability of isolated CTCs. All of these limitations synthetically restrict the isolation and analysis of rare CTCs for early diagnosis and precision medicine. Here, we proposed that surface functionalization of IMNs with neutrophil membranes can simultaneously reduce nonspecific protein adsorption, enhance the interaction with CTCs, reduce the distraction from WBCs, and improve the viability of isolated CTCs. In spiked blood samples, our neutrophil membrane-coated IMNs (Neu-IMNs) exhibited a superior separation efficiency from 41.36% to 96.82% and an improved purity from 40.25% to 90.68% when compared to bare IMNs. Additionally, we successfully isolated CTCs in 19 out of total 20 blood samples from breast cancer patients using Neu-IMNs and further confirmed the feasibility of the isolated CTCs for downstream cell sequencing. Our work provides a new perspective on engineered IMNs for efficient isolation and analysis of CTCs, paving the way for early noninvasive diagnosis of cancer. [ABSTRACT FROM AUTHOR]

Toponome imaging systems (TIS) can yield high-resolution subcellular colocalization images of multiple proteins within single cells and intact tissue sections, giving this technology significant potential for identifying multiplex biomarkers that simultaneously measure several aspects of a cell. The integral role of the microenvironment in malignant progression and the recently appreciated heterogeneity of cancer cells underscore the importance of characterizing complex molecular phenotypes and the large protein network structures of single cells within their preserved anatomical context. Here, we discuss the TIS technique and the potential for developing new sensitive and specific multiplex biomarkers for risk stratification and diagnosis, in addition to its utility for anticancer drug discovery by identifying ‘hub’ proteins that are essential regulators of protein networks. [Copyright &y& Elsevier]

Background Tumor-specific immunity of insulin-like growth factor-binding protein-2 (IGFBP-2) has been reported in several cancers. We aimed to assess the role of serum IGFBP-2 antibodies (IGFBP-2 Abs) in early cancer detection. Patients and methods Glioma and colorectal carcinoma (CRC) were used as models. Serum IGFBP-2 and IGFBP-2 Abs were measured in 260 tumor patients (145 gliomas, 45 colorectal polyps, and 70 CRCs) and 141 controls. Receiver operating characteristic curves were applied. Results Serum IGFBP-2 Ab levels were significantly elevated in tumors (mean: 82 ng/ml, median: 17 ng/ml, range: 0–1387 ng/ml) compared with controls (11, 0, 0–212 ng/ml) (P < 0.0001) and higher in early than advanced cancers opposite of serum IGFBP-2 levels. IGFBP-2 Abs effectively discriminated between controls and grade II and III gliomas [area under the curve (AUC): 0.821–0.864; 95% confidence interval (CI) = 0.762–0.936; P < 0.0001], and CRC I–II (AUC: 0.668; 95% CI = 0.566–0.770; P = 0.002) as well as indicative of advanced polyps at high risk of CRC (AUC: 0.72; 95% CI = 0.630–0.811; P < 0.0001). The sensitivity and specificity for diagnosing grade II–III gliomas reached 66%–84% and 81%. Combined serum IGFBP-2 and IGFBP-2 Abs augmented the discriminative power of all stage tumors (AUC: 0.823), gliomas (AUC: 0.800), and CRCs (AUC = 0.917). Conclusion Our results first demonstrate IGFBP-2 Abs for early cancer detection and in combination of serum IGFBP-2 for improved cancer diagnosis. [ABSTRACT FROM PUBLISHER]

Overexpression of the Bcl-2 protein has emerged as a hallmark of carcinoma cells and can be employed as a biochemical biomarker of these cells. Therefore, some Bcl-2 protein fluorescence probes (BPFPs) were designed for Bcl-2 protein quantification and carcinoma cells labeling. The high Bcl-2 protein binding affinity (K i < 1 nM) and selectivity (over 50,000-fold Bcl-2 protein selectivity against Mcl-1 protein) of BPFP1 endow it with the ability to detect trace amounts of Bcl-2 protein. After being incubated with a range of concentrations of Bcl-2 protein, BPFP1 exhibited the desired fluorescence properties and its fluorescence intensity is proportional to Bcl-2 protein concentration. Therefore, BPFP1 provides a convenient approach for Bcl-2 protein quantification and we could determine the concentration of Bcl-2 protein based on the BPFP1 's fluorescence intensity. Subsequent studies revealed that BPFP1 can fluorescently label carcinoma cells by binding to overexpressed Bcl-2 protein in living cells, and can distinguish carcinoma cells (HL-60 cells and ACHN cells) from normal-tissue cells (HUVECs) according to the different Bcl-2 protein expression levels between carcinoma cells and normal tissue cells. In the present study, BPFP1 represents a new tool for Bcl-2 protein quantification, carcinoma cell visualization and cell sorting. Moreover, BPFP1 can be used in the future for early cancer diagnosis by detecting carcinoma cells in patient tissues. [Display omitted] ● BPFP1 exhibits very high Bcl-2 protein binding affinity (K i < 1 nM) and over 50,000-fold Bcl-2 protein selectivity against Mcl-1 protein; ● BPFP1 provides a convenient approach for Bcl-2 protein quantification and it can detect trace amounts of Bcl-2 protein. ● BPFP1 can be also applied in carcinoma cell visualization and cell sorting. [ABSTRACT FROM AUTHOR]

MicroRNAs (miRNA) involve in regulating different physiological processes whose dysregulation is associated with a wide range of diseases including cancers, diabetes and cardiovascular problems. Herein, we report a direct, sensitive and highly selective detection assay for circulating microRNA (miRNA). This detection strategy employs magnetic nanoparticles as the reaction platform which can not only allow online pre-concentration and selective separation but also integrates ligation reaction with amplification to enhance the sensitivity of the detection assay. With the presence of the target miRNA, the locked nucleic acid (LNA)-modified molecular beacon (MB) opens up, exposing the binding sites at two ends. The 3'- and 5'-end of the MB responsible for the attachment onto the magnetic nanoparticles, and reporting probe for the attachment of the pair of amplification probes respectively. The ligase ligate RNA to DNA enhance the amplification efficiency. Upon labelled with intercalating fluorophores (YOYO-1) on the hybrids, the quantification of the target miRNA was determined by measuring the fluorescence intensity. A detection limit of 314 fM was achieved with trace amount of sample consumption (~20 μL). As a proof of concept, miRNA-149 was chosen as the target miRNA. This assay is capable of discriminating single-base and reliably quantifying circulating miRNA-149 in both healthy and cancer patient's serums. The result obtained was comparable with that of quantitative reverse transcription polymerase chain reaction (qRT-PCR), suggesting that this direct and sensitive assay can be served as a promising, non-invasive tool for early diagnosis of breast cancer and colorectal cancer. Image 1 • A direct and sensitive detection strategy for circulating miRNAs was developed. • Cancer-specific miR-149 is detected with high sensitivity; detection limit of 314 femto-molar and; discrimination of single-base mismatch. • The developed sensing strategy was applied for the detection of cancer-specific miR-149 in serum samples from cancer patients. [ABSTRACT FROM AUTHOR]