Liquid Biopsies in Cancer Diagnosis, Monitoring, and Prognosis

© 2019 Elsevier Ltd Liquid biopsies, comprising the noninvasive analysis of circulating tumor-derived material (the ‘tumor circulome’), represent an innovative tool in precision oncology to overcome current limitations associated with tissue biopsies. Within the tumor circulome, circulating tumor DNA (ctDNA) and circulating tumor cells (CTCs) are the only components the clinical application of which is approved by the US Food and Drug Administration (FDA). Extracellular vesicles (EVs), circulating tumor RNA (ctRNA), and tumor-educated platelets (TEPs) are relatively new tumor circulome constituents with promising potential at each stage of cancer management. Here, we discuss the clinical applications of each element of the tumor circulome and the prevailing factors that currently limit their implementation in clinical practice. We also detail the most recent technological developments in the field, which demonstrate potential in improving the clinical value of liquid biopsies

imPlatelet classifier: image-converted RNA biomarker profiles enable blood-based cancer diagnostics

Liquid biopsies offer a minimally invasive sample collection, outperforming traditional biopsies employed for cancer evaluation. The widely used material is blood, which is the source of tumor-educated platelets. Here, we developed the imPlatelet classifier, which converts RNA-sequenced platelet data into images in which each pixel corresponds to the expression level of a certain gene. Biological knowledge from the Kyoto Encyclopedia of Genes and Genomes was also implemented to improve accuracy. Images obtained from samples can then be compared against standard images for specific cancers to determine a diagnosis. We tested imPlatelet on a cohort of 401 non-small cell lung cancer patients, 62 sarcoma patients, and 28 ovarian cancer patients. imPlatelet provided excellent discrimination between lung cancer cases and healthy controls, with accuracy equal to 1 in the independent dataset. When discriminating between noncancer cases and sarcoma or ovarian cancer patients, accuracy equaled 0.91 or 0.95, respectively, in the independent datasets. According to our knowledge, this is the first study implementing an image-based deep-learning approach combined with biological knowledge to classify human samples. The performance of imPlatelet considerably exceeds previously published methods and our own alternative attempts of sample discrimination. We show that the deep-learning image-based classifier accurately identifies cancer, even when a limited number of samples are available.publishedVersio

Perturbation of Host Nuclear Membrane Component RanBP2 Impairs the Nuclear Import of Human Immunodeficiency Virus -1 Preintegration Complex (DNA)

HIV-1 is a RNA virus that requires an intermediate DNA phase via reverse transcription (RT) step in order to establish productive infection in the host cell. The nascent viral DNA synthesized via RT step and the preformed viral proteins are assembled into pre-integration complex (PIC) in the cell cytoplasm. To integrate the viral DNA into the host genome, the PIC must cross cell nuclear membrane through the nuclear pore complex (NPC). RanBP2, also known as Nup358, is a major component of the cytoplasmic filaments that emanates from the nuclear pore complex and has been implicated in various nucleo-cytoplasmic transport pathways including those for HIV Rev-protein. We sought to investigate the role of RanBP2 in HIV-1 replication. In our investigations, we found that RanBP2 depletion via RNAi resulted in profound inhibition of HIV-1 infection and played a pivotal role in the nuclear entry of HIV DNA. More precisely, there was a profound decline in 2-LTR DNA copies (marker for nuclear entry of HIV DNA) and an unchanged level of viral reverse transcription in RanBP2-ablated HIV-infected cells compared to RanBP3-depleted or non-specific siRNA controls. We further demonstrated that the function of Rev was unaffected in RanBP2-depleted latently HIV infected cells (reactivated). We also serendipitously found that RanBP2 depletion inhibited the global ectopic gene expression. In conclusion, RanBP2 is a host factor that is involved in the nuclear import of HIV-1 PIC (DNA), but is not critical to the nuclear export of the viral mRNAs or nucleo-cytoplasmic shuttling of Rev. RanBP2 could be a potential target for efficient inhibition of HIV

Report from a symposium on catalyzing primary and secondary prevention of cancer in India

PurposeOral, breast, and cervical cancers are amenable to early detection and account for a third of India’s cancer burden. We convened a symposium of diverse stakeholders to identify gaps in evidence, policy, and advocacy for the primary and secondary prevention of these cancers and recommendations to accelerate these efforts. MethodsIndian and global experts from government, academia, private sector (health care, media), donor organizations, and civil society (including cancer survivors and patient advocates) presented and discussed challenges and solutions related to strategic communication and implementation of prevention, early detection, and treatment linkages.ResultsInnovative approaches to implementing and scaling up primary and secondary prevention were discussed using examples from India and elsewhere in the world. Participants also reflected on existing global guidelines and national cancer prevention policies and experiences.ConclusionsSymposium participants proposed implementation-focused research, advocacy, and policy/program priorities to strengthen primary and secondary prevention efforts in India to address the burden of oral, breast, and cervical cancers and improve survival

Global burden and strength of evidence for 88 risk factors in 204 countries and 811 subnational locations, 1990–2021: a systematic analysis for the Global Burden of Disease Study 2021

Background: Understanding the health consequences associated with exposure to risk factors is necessary to inform public health policy and practice. To systematically quantify the contributions of risk factor exposures to specific health outcomes, the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021 aims to provide comprehensive estimates of exposure levels, relative health risks, and attributable burden of disease for 88 risk factors in 204 countries and territories and 811 subnational locations, from 1990 to 2021. Methods: The GBD 2021 risk factor analysis used data from 54 561 total distinct sources to produce epidemiological estimates for 88 risk factors and their associated health outcomes for a total of 631 risk–outcome pairs. Pairs were included on the basis of data-driven determination of a risk–outcome association. Age-sex-location-year-specific estimates were generated at global, regional, and national levels. Our approach followed the comparative risk assessment framework predicated on a causal web of hierarchically organised, potentially combinative, modifiable risks. Relative risks (RRs) of a given outcome occurring as a function of risk factor exposure were estimated separately for each risk–outcome pair, and summary exposure values (SEVs), representing risk-weighted exposure prevalence, and theoretical minimum risk exposure levels (TMRELs) were estimated for each risk factor. These estimates were used to calculate the population attributable fraction (PAF; ie, the proportional change in health risk that would occur if exposure to a risk factor were reduced to the TMREL). The product of PAFs and disease burden associated with a given outcome, measured in disability-adjusted life-years (DALYs), yielded measures of attributable burden (ie, the proportion of total disease burden attributable to a particular risk factor or combination of risk factors). Adjustments for mediation were applied to account for relationships involving risk factors that act indirectly on outcomes via intermediate risks. Attributable burden estimates were stratified by Socio-demographic Index (SDI) quintile and presented as counts, age-standardised rates, and rankings. To complement estimates of RR and attributable burden, newly developed burden of proof risk function (BPRF) methods were applied to yield supplementary, conservative interpretations of risk–outcome associations based on the consistency of underlying evidence, accounting for unexplained heterogeneity between input data from different studies. Estimates reported represent the mean value across 500 draws from the estimate's distribution, with 95% uncertainty intervals (UIs) calculated as the 2·5th and 97·5th percentile values across the draws. Findings: Among the specific risk factors analysed for this study, particulate matter air pollution was the leading contributor to the global disease burden in 2021, contributing 8·0% (95% UI 6·7–9·4) of total DALYs, followed by high systolic blood pressure (SBP; 7·8% [6·4–9·2]), smoking (5·7% [4·7–6·8]), low birthweight and short gestation (5·6% [4·8–6·3]), and high fasting plasma glucose (FPG; 5·4% [4·8–6·0]). For younger demographics (ie, those aged 0–4 years and 5–14 years), risks such as low birthweight and short gestation and unsafe water, sanitation, and handwashing (WaSH) were among the leading risk factors, while for older age groups, metabolic risks such as high SBP, high body-mass index (BMI), high FPG, and high LDL cholesterol had a greater impact. From 2000 to 2021, there was an observable shift in global health challenges, marked by a decline in the number of all-age DALYs broadly attributable to behavioural risks (decrease of 20·7% [13·9–27·7]) and environmental and occupational risks (decrease of 22·0% [15·5–28·8]), coupled with a 49·4% (42·3–56·9) increase in DALYs attributable to metabolic risks, all reflecting ageing populations and changing lifestyles on a global scale. Age-standardised global DALY rates attributable to high BMI and high FPG rose considerably (15·7% [9·9–21·7] for high BMI and 7·9% [3·3–12·9] for high FPG) over this period, with exposure to these risks increasing annually at rates of 1·8% (1·6–1·9) for high BMI and 1·3% (1·1–1·5) for high FPG. By contrast, the global risk-attributable burden and exposure to many other risk factors declined, notably for risks such as child growth failure and unsafe water source, with age-standardised attributable DALYs decreasing by 71·5% (64·4–78·8) for child growth failure and 66·3% (60·2–72·0) for unsafe water source. We separated risk factors into three groups according to trajectory over time: those with a decreasing attributable burden, due largely to declining risk exposure (eg, diet high in trans-fat and household air pollution) but also to proportionally smaller child and youth populations (eg, child and maternal malnutrition); those for which the burden increased moderately in spite of declining risk exposure, due largely to population ageing (eg, smoking); and those for which the burden increased considerably due to both increasing risk exposure and population ageing (eg, ambient particulate matter air pollution, high BMI, high FPG, and high SBP). Interpretation: Substantial progress has been made in reducing the global disease burden attributable to a range of risk factors, particularly those related to maternal and child health, WaSH, and household air pollution. Maintaining efforts to minimise the impact of these risk factors, especially in low SDI locations, is necessary to sustain progress. Successes in moderating the smoking-related burden by reducing risk exposure highlight the need to advance policies that reduce exposure to other leading risk factors such as ambient particulate matter air pollution and high SBP. Troubling increases in high FPG, high BMI, and other risk factors related to obesity and metabolic syndrome indicate an urgent need to identify and implement interventions

Nations within a nation: variations in epidemiological transition across the states of India, 1990–2016 in the Global Burden of Disease Study

18% of the world's population lives in India, and many states of India have populations similar to those of large countries. Action to effectively improve population health in India requires availability of reliable and comprehensive state-level estimates of disease burden and risk factors over time. Such comprehensive estimates have not been available so far for all major diseases and risk factors. Thus, we aimed to estimate the disease burden and risk factors in every state of India as part of the Global Burden of Disease (GBD) Study 2016

A liquid biopsy to detect multidrug resistance and disease burden in multiple myeloma.

Multiple myeloma is an incurable cancer of bone marrow plasma cells, with a 5-year survival rate of 43%. Its incidence has increased by 126% since 1990. Treatment typically involves high-dose combination chemotherapy, but therapeutic response and patient survival are unpredictable and highly variable-attributed largely to the development of multidrug resistance (MDR). MDR is the simultaneous cross-resistance to a range of unrelated chemotherapeutic agents and is associated with poor prognosis and survival. Currently, no clinical procedures allow for a direct, continuous monitoring of MDR. We identified circulating large extracellular vesicles (specifically microparticles (MPs)) that can be used to monitor disease burden, disease progression and development of MDR in myeloma. These MPs differ phenotypically in the expression of four protein biomarkers: a plasma-cell marker (CD138), the MDR protein, P-glycoprotein (P-gp), the stem-cell marker (CD34); and phosphatidylserine (PS), an MP marker and mediator of cancer spread. Elevated levels of P-gp+ and PS+ MPs correlate with disease progression and treatment unresponsiveness. Furthermore, P-gp, PS and CD34 are predominantly expressed in CD138- MPs in advanced disease. In particular, a dual-positive (CD138-P-gp+CD34+) population is elevated in aggressive/unresponsive disease. Our test provides a personalised liquid biopsy with potential to address the unmet clinical need of monitoring MDR and treatment failure in myeloma

Microparticles as novel prognostic markers in multiple myeloma

Introduction: Multiple Myeloma (MM) is an incurable hematological malignancy affecting plasma cells marked by highly heterogeneous survival rates and confinement of the disease to bone marrow (BM). Relapse is a significant impediment in the clinical setting and the development of multidrug resistance (MDR) to therapy is the main cause of relapse. Currently, risk stratification to MM sub-groups and categorization of complete response to therapy are established on molecular and cytogenetic markers using bone marrow biopsies. We are exploring the clinical significance of plasma cell derived microparticles as a novel prognostic indicator in MM. Materials and Methods: We have analysed 79 de-identified MM patients and 24 normal subjects. Platelet free plasma was centrifuged and plasma cell derived MPs were identified and quantified by flow cytometry using Annexin V450, CD138 APC, anti-P-glycoprotein (P-gp)-FITC (17F9) in BD TruCount tubes. Platelet derived MPs were excluded from the analysis using CD41a-PE. All patient samples were compared to age-matched healthy volunteers. Western blot analysis was conducted on MP lysates probing for the presence of Lung-Resistance related Protein (LRP) and P-glycoprotein (P-gp). Morphology and the size of MP fraction from MM patients were investigated using scanning electron micrographs Results: The number of systemic MPs and CD138+MPs were found to be significantly higher in MM patient samples compared to the healthy volunteers. MDR markers (LRP & P-gp) were expressed on systemic MPs from relapsing MM patients. MPs from patients were spherical in shape and had smooth surface consistent with those isolated from the MM cell line OPM2

Entity disambiguation with hierarchical topic models

Disambiguating entity references by annotating them with unique ids from a catalog is a critical step in the enrichment of unstructured content. In this paper, we show that topic models, such as Latent Dirichlet Allocation (LDA) and its hierarchical variants, form a natural class of models for learning accurate entity disambiguation models from crowd-sourced knowledge bases such as Wikipedia. Our main contribution is a semi-supervised hierarchical model called Wikipedia-based Pachinko Allocation Model (WPAM) that exploits: (1) All words in the Wikipedia corpus to learn word-entity associations (unlike existing approaches that only use words in a small fixed window around annotated entity references in Wikipedia pages), (2) Wikipedia annotations to appropriately bias the assignment of entity labels to annotated (and co-occurring unannotated) words during model learning, and (3) Wikipedia’s category hierarchy to capture co-occurrence patterns among entities. We also propose a scheme for pruning spurious nodes from Wikipedia’s crowd-sourced category hierarchy. In our experiments with multiple real-life datasets, we show that WPAM outperforms state-of-the-art baselines by as much as 16 % in terms of disambiguation accuracy