Liquid-biopsy proteomics combined with AI identifies cellular drivers of eye aging and disease in vivo.

Single-cell analysis in living humans is essential for understanding disease mechanisms, but it is impractical in non-regenerative organs, such as the eye and brain, because tissue biopsies would cause serious damage. We resolve this problem by integrating proteomics of liquid biopsies with single-cell transcriptomics from all known ocular cell types to trace the cellular origin of 5,953 proteins detected in the aqueous humor. We identified hundreds of cell-specific protein markers, including for individual retinal cell types. Surprisingly, our results reveal that retinal degeneration occurs in Parkinson's disease, and the cells driving diabetic retinopathy switch with disease stage. Finally, we developed artificial intelligence (AI) models to assess individual cellular aging and found that many eye diseases not associated with chronological age undergo accelerated molecular aging of disease-specific cell types. Our approach, which can be applied to other organ systems, has the potential to transform molecular diagnostics and prognostics while uncovering new cellular disease and aging mechanisms.

Pan-cancer analyses reveal cancer-type-specific fungal ecologies and bacteriome interactions.

Cancer-microbe associations have been explored for centuries, but cancer-associated fungi have rarely been examined. Here, we comprehensively characterize the cancer mycobiome within 17,401 patient tissue, blood, and plasma samples across 35 cancer types in four independent cohorts. We report fungal DNA and cells at low abundances across many major human cancers, with differences in community compositions that differ among cancer types, even when accounting for technical background. Fungal histological staining of tissue microarrays supported intratumoral presence and frequent spatial association with cancer cells and macrophages. Comparing intratumoral fungal communities with matched bacteriomes and immunomes revealed co-occurring bi-domain ecologies, often with permissive, rather than competitive, microenvironments and distinct immune responses. Clinically focused assessments suggested prognostic and diagnostic capacities of the tissue and plasma mycobiomes, even in stage I cancers, and synergistic predictive performance with bacteriomes.

Noninvasive Early Identification of Therapeutic Benefit from Immune Checkpoint Inhibition.

Although treatment of non-small cell lung cancer (NSCLC) with immune checkpoint inhibitors (ICIs) can produce remarkably durable responses, most patients develop early disease progression. Furthermore, initial response assessment by conventional imaging is often unable to identify which patients will achieve durable clinical benefit (DCB). Here, we demonstrate that pre-treatment circulating tumor DNA (ctDNA) and peripheral CD8 T cell levels are independently associated with DCB. We further show that ctDNA dynamics after a single infusion can aid in identification of patients who will achieve DCB. Integrating these determinants, we developed and validated an entirely noninvasive multiparameter assay (DIREct-On, Durable Immunotherapy Response Estimation by immune profiling and ctDNA-On-treatment) that robustly predicts which patients will achieve DCB with higher accuracy than any individual feature. Taken together, these results demonstrate that integrated ctDNA and circulating immune cell profiling can provide accurate, noninvasive, and early forecasting of ultimate outcomes for NSCLC patients receiving ICIs.

Extracellular Vesicle and Particle Biomarkers Define Multiple Human Cancers.

There is an unmet clinical need for improved tissue and liquid biopsy tools for cancer detection. We investigated the proteomic profile of extracellular vesicles and particles (EVPs) in 426 human samples from tissue explants (TEs), plasma, and other bodily fluids. Among traditional exosome markers, CD9, HSPA8, ALIX, and HSP90AB1 represent pan-EVP markers, while ACTB, MSN, and RAP1B are novel pan-EVP markers. To confirm that EVPs are ideal diagnostic tools, we analyzed proteomes of TE- (n = 151) and plasma-derived (n = 120) EVPs. Comparison of TE EVPs identified proteins (e.g., VCAN, TNC, and THBS2) that distinguish tumors from normal tissues with 90% sensitivity/94% specificity. Machine-learning classification of plasma-derived EVP cargo, including immunoglobulins, revealed 95% sensitivity/90% specificity in detecting cancer. Finally, we defined a panel of tumor-type-specific EVP proteins in TEs and plasma, which can classify tumors of unknown primary origin. Thus, EVP proteins can serve as reliable biomarkers for cancer detection and determining cancer type.

Dynamic Risk Profiling Using Serial Tumor Biomarkers for Personalized Outcome Prediction.

Accurate prediction of long-term outcomes remains a challenge in the care of cancer patients. Due to the difficulty of serial tumor sampling, previous prediction tools have focused on pretreatment factors. However, emerging non-invasive diagnostics have increased opportunities for serial tumor assessments. We describe the Continuous Individualized Risk Index (CIRI), a method to dynamically determine outcome probabilities for individual patients utilizing risk predictors acquired over time. Similar to "win probability" models in other fields, CIRI provides a real-time probability by integrating risk assessments throughout a patient's course. Applying CIRI to patients with diffuse large B cell lymphoma, we demonstrate improved outcome prediction compared to conventional risk models. We demonstrate CIRI's broader utility in analogous models of chronic lymphocytic leukemia and breast adenocarcinoma and perform a proof-of-concept analysis demonstrating how CIRI could be used to develop predictive biomarkers for therapy selection. We envision that dynamic risk assessment will facilitate personalized medicine and enable innovative therapeutic paradigms.

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.

Circulating Biomarkers Instead of Genotyping to Establish Metabolizer Phenotypes.

Pharmacogenomics (PGx) enables personalized treatment for the prediction of drug response and to avoid adverse drug reactions. Currently, PGx mainly relies on the genetic information of absorption, distribution, metabolism, and excretion (ADME) targets such as drug-metabolizing enzymes or transporters to predict differences in the patient's phenotype. However, there is evidence that the phenotype-genotype concordance is limited. Thus, we discuss different phenotyping strategies using exogenous xenobiotics (e.g., drug cocktails) or endogenous compounds for phenotype prediction. In particular, minimally invasive approaches focusing on liquid biopsies offer great potential to preemptively determine metabolic and transport capacities. Early studies indicate that ADME phenotyping using exosomes released from the liver is reliable. In addition, pharmacometric modeling and artificial intelligence improve phenotype prediction. However, further prospective studies are needed to demonstrate the clinical utility of individualized treatment based on phenotyping strategies, not only relying on genetics. The present review summarizes current knowledge and limitations.

Multi-Cancer Early Detection: The New Frontier in Cancer Early Detection.

The new generation of cancer early detection tests holds remarkable promise for revolutionizing and changing the paradigm of cancer early detection. Dozens of cancer early detection tests are being developed and evaluated. Some are already commercialized and available for use, most as a complement to and not in place of existing recommended cancer screening tests. This review evaluates existing single- and multi-cancer early detection tests (MCEDs), discussing their performance characteristics including sensitivity, specificity, positive and negative predictive values, and accuracy. It also critically looks at the potential harms that could result from these tests, including false positive and negative results, the risk of overdiagnosis and overtreatment, psychological and economic harms, and the risk of widening cancer inequities. We also review the large-scale, population-based studies that are being launched in the United States and United Kingdom to determine the impact of MCEDs on clinically relevant outcomes and implications for current practice.

Current and new frontiers in hereditary cancer surveillance: Opportunities for liquid biopsy.

At least 5% of cancer diagnoses are attributed to a causal pathogenic or likely pathogenic germline genetic variant (hereditary cancer syndrome-HCS). These individuals are burdened with lifelong surveillance monitoring organs for a wide spectrum of cancers. This is associated with substantial uncertainty and anxiety in the time between screening tests and while the individuals are awaiting results. Cell-free DNA (cfDNA) sequencing has recently shown potential as a non-invasive strategy for monitoring cancer. There is an opportunity for high-yield cancer early detection in HCS. To assess clinical validity of cfDNA in individuals with HCS, representatives from eight genetics centers from across Canada founded the CHARM (cfDNA in Hereditary and High-Risk Malignancies) Consortium in 2017. In this perspective, we discuss operationalization of this consortium and early data emerging from the most common and well-characterized HCSs: hereditary breast and ovarian cancer, Lynch syndrome, Li-Fraumeni syndrome, and Neurofibromatosis type 1. We identify opportunities for the incorporation of cfDNA sequencing into surveillance protocols; these opportunities are backed by examples of earlier cancer detection efficacy in HCSs from the CHARM Consortium. We seek to establish a paradigm shift in early cancer surveillance in individuals with HCSs, away from highly centralized, regimented medical screening visits and toward more accessible, frequent, and proactive care for these high-risk individuals.

New techniques to identify the tissue of origin for cancer of unknown primary in the era of precision medicine: progress and challenges.

Despite a standardized diagnostic examination, cancer of unknown primary (CUP) is a rare metastatic malignancy with an unidentified tissue of origin (TOO). Patients diagnosed with CUP are typically treated with empiric chemotherapy, although their prognosis is worse than those with metastatic cancer of a known origin. TOO identification of CUP has been employed in precision medicine, and subsequent site-specific therapy is clinically helpful. For example, molecular profiling, including genomic profiling, gene expression profiling, epigenetics and proteins, has facilitated TOO identification. Moreover, machine learning has improved identification accuracy, and non-invasive methods, such as liquid biopsy and image omics, are gaining momentum. However, the heterogeneity in prediction accuracy, sample requirements and technical fundamentals among the various techniques is noteworthy. Accordingly, we systematically reviewed the development and limitations of novel TOO identification methods, compared their pros and cons and assessed their potential clinical usefulness. Our study may help patients shift from empirical to customized care and improve their prognoses.

Epigenetic liquid biopsies: a novel putative biomarker in immunology and inflammation.

Immune and inflammatory processes occurring within tissues are often undetectable by blood cell counts, standard circulating biomarkers, or imaging, representing an unmet biomedical need. Here, we outline recent advances indicating that liquid biopsies can broadly inform human immune system dynamics. Nucleosome-size fragments of cell-free DNA (cfDNA) released from dying cells into blood contain rich epigenetic information such as methylation, fragmentation, and histone mark patterns. This information allows to infer the cfDNA cell of origin, as well as pre-cell death gene expression patterns. We propose that the analysis of epigenetic features of immune cell-derived cfDNA can shed light on immune cell turnover dynamics in healthy people, and inform the study and diagnosis of cancer, local inflammation, infectious or autoimmune diseases, as well as responses to vaccination.

Fragmentation landscape of cell-free DNA revealed by deconvolutional analysis of end motifs.

Cell-free DNA (cfDNA) fragmentation is nonrandom, at least partially mediated by various DNA nucleases, forming characteristic cfDNA end motifs. However, there is a paucity of tools for deciphering the relative contributions of cfDNA cleavage patterns related to underlying fragmentation factors. In this study, through non-negative matrix factorization algorithm, we used 256 5' 4-mer end motifs to identify distinct types of cfDNA cleavage patterns, referred to as "founder" end-motif profiles (F-profiles). F-profiles were associated with different DNA nucleases based on whether such patterns were disrupted in nuclease-knockout mouse models. Contributions of individual F-profiles in a cfDNA sample could be determined by deconvolutional analysis. We analyzed 93 murine cfDNA samples of different nuclease-deficient mice and identified six types of F-profiles. F-profiles I, II, and III were linked to deoxyribonuclease 1 like 3 (DNASE1L3), deoxyribonuclease 1 (DNASE1), and DNA fragmentation factor subunit beta (DFFB), respectively. We revealed that 42.9% of plasma cfDNA molecules were attributed to DNASE1L3-mediated fragmentation, whereas 43.4% of urinary cfDNA molecules involved DNASE1-mediated fragmentation. We further demonstrated that the relative contributions of F-profiles were useful to inform pathological states, such as autoimmune disorders and cancer. Among the six F-profiles, the use of F-profile I could inform the human patients with systemic lupus erythematosus. F-profile VI could be used to detect individuals with hepatocellular carcinoma, with an area under the receiver operating characteristic curve of 0.97. F-profile VI was more prominent in patients with nasopharyngeal carcinoma undergoing chemoradiotherapy. We proposed that this profile might be related to oxidative stress.

Revisiting Extracellular RNA Release, Processing, and Function.

It is assumed that RNAs enriched in extracellular samples were selected for release by their parental cells. However, recent descriptions of extracellular RNA (exRNA) biogenesis and their differential stabilities question this assumption, as they could produce identical outcomes. Here, we share our opinion about the importance of considering both selective and nonselective mechanisms for RNA release into the extracellular environment. In doing so, we provide new perspectives on RNA-mediated intercellular communication, including an analogy to communication through social media. We also argue that technical limitations have restricted the study of some of the most abundant exRNAs, both inside and outside extracellular vesicles (EVs). These RNAs may be better positioned to induce a response in recipient cells compared with low abundance miRNAs.

Extracellular vesicles in glioblastoma: Biomarkers and therapeutic tools.

Glioblastoma (GBM) is the most aggressive tumor among the gliomas and intracranial tumors and to date prognosis for GBM patients remains poor, with a median survival typically measured in months to a few years depending on various factors. Although standardized therapies are routinely employed, it is clear that these strategies are unable to cope with heterogeneity and invasiveness of GBM. Furthermore, diagnosis and monitoring of responses to therapies are directly dependent on tissue biopsies or magnetic resonance imaging (MRI) techniques. From this point of view, liquid biopsies are arising as key sources of a variety of biomarkers with the advantage of being easily accessible and monitorable. In this context, extracellular vesicles (EVs), physiologically shed into body fluids by virtually all cells, are gaining increasing interest both as natural carriers of biomarkers and as specific signatures even for GBM. What makes these vesicles particularly attractive is they are also emerging as therapeutical vehicles to treat GBM given their native ability to cross the blood-brain barrier (BBB). Here, we reviewed recent advances on the use of EVs as biomarker for liquid biopsy and nanocarriers for targeted delivery of anticancer drugs in glioblastoma.

A Circulating Panel of circRNA Biomarkers for the Noninvasive and Early Detection of Pancreatic Ductal Adenocarcinoma.

BACKGROUND & AIMS: Pancreatic ductal adenocarcinoma (PDAC) is one of the most fatal malignancies. Delayed manifestation of symptoms and lack of specific diagnostic markers lead patients being diagnosed with PDAC at advanced stages. This study aimed to develop a circular RNA (circRNA)-based biomarker panel to facilitate noninvasive and early detection of PDAC. METHODS: A systematic genome-wide discovery of circRNAs overexpressed in patients with PDAC was conducted. Subsequently, validation of the candidate markers in the primary tumors from patients with PDAC was performed, followed by their translation into a plasma-based liquid biopsy assay by analyzing 2 independent clinical cohorts of patients with PDAC and nondisease controls. The performance of the circRNA panel was assessed in conjunction with the plasma levels of cancer antigen 19-9 for the early detection of PDAC. RESULTS: Initially, a panel of 10 circRNA candidates was identified during the discovery phase. Subsequently, the panel was reduced to 5 circRNAs in the liquid biopsy-based assay, which robustly identified patients with PDAC and distinguished between early-stage (stage I/II) and late-stage (stage III/IV) disease. The areas under the curve of this diagnostic panel for the detection of early-stage PDAC were 0.83 and 0.81 in the training and validation cohorts, respectively. Moreover, when this panel was combined with cancer antigen 19-9 levels, the diagnostic performance for identifying patients with PDAC improved remarkably (area under the curve, 0.94) for patients in the validation cohort. Furthermore, the circRNA panel could also efficiently identify patients with PDAC (area under the curve, 0.85) who were otherwise deemed clinically cancer antigen 19-9-negative (<37 U/mL). CONCLUSIONS: A circRNA-based biomarker panel with a robust noninvasive diagnostic potential for identifying patients with early-stage PDAC was developed.

Comparative Effectiveness and Cost-Effectiveness of Colorectal Cancer Screening With Blood-Based Biomarkers (Liquid Biopsy) vs Fecal Tests or Colonoscopy.

BACKGROUND & AIMS: Colorectal cancer (CRC) screening is highly effective but underused. Blood-based biomarkers (liquid biopsy) could improve screening participation. METHODS: Using our established Markov model, screening every 3 years with a blood-based test that meets minimum Centers for Medicare & Medicaid Services' thresholds (CMSmin) (CRC sensitivity 74%, specificity 90%) was compared with established alternatives. Test attributes were varied in sensitivity analyses. RESULTS: CMSmin reduced CRC incidence by 40% and CRC mortality by 52% vs no screening. These reductions were less profound than the 68%-79% and 73%-81%, respectively, achieved with multi-target stool DNA (Cologuard; Exact Sciences) every 3 years, annual fecal immunochemical testing (FIT), or colonoscopy every 10 years. Assuming the same cost as multi-target stool DNA, CMSmin cost $28,500/quality-adjusted life-year gained vs no screening, but FIT, colonoscopy, and multi-target stool DNA were less costly and more effective. CMSmin would match FIT's clinical outcomes if it achieved 1.4- to 1.8-fold FIT's participation rate. Advanced precancerous lesion (APL) sensitivity was a key determinant of a test's effectiveness. A paradigm-changing blood-based test (sensitivity >90% for CRC and 80% for APL; 90% specificity; cost ≤$120-$140) would be cost-effective vs FIT at comparable participation. CONCLUSIONS: CMSmin could contribute to CRC control by achieving screening in those who will not use established methods. Substituting blood-based testing for established effective CRC screening methods will require higher CRC and APL sensitivities that deliver programmatic benefits matching those of FIT. High APL sensitivity, which can result in CRC prevention, should be a top priority for screening test developers. APL detection should not be penalized by a definition of test specificity that focuses on CRC only.

Liquidhope: methylome and genomic profiling from very limited quantities of plasma-derived DNA.

Analysis of the methylome of tumor cell-free deoxyribonucleic acid (DNA; cfDNA) has emerged as a powerful non-invasive technique for cancer subtyping and prognosis. However, its application is frequently hampered by the quality and total cfDNA yield. Here, we demonstrate the feasibility of very low-input cfDNA for whole-methylome and copy-number profiling studies using enzymatic conversion of unmethylated cysteines [enzymatic methyl-seq (EM-seq)] to better preserve DNA integrity. We created a model for predicting genomic subtyping and prognosis with high accuracy. We validated our tool by comparing whole-genome CpG sequencing with in situ cohorts generated with bisulfite conversion and array hybridization, demonstrating that, despite the different techniques and sample origins, information on cfDNA methylation is comparable with in situ cohorts. Our findings support use of liquid biopsy followed by EM-seq to assess methylome of cancer patients, enabling validation in external cohorts. This advance is particularly relevant for rare cancers like neuroblastomas where liquid-biopsy volume is restricted by ethical regulations in pediatric patients.

Lesion Shedding Model: unraveling site-specific contributions to ctDNA.

Sampling circulating tumor DNA (ctDNA) using liquid biopsies offers clinically important benefits for monitoring cancer progression. A single ctDNA sample represents a mixture of shed tumor DNA from all known and unknown lesions within a patient. Although shedding levels have been suggested to hold the key to identifying targetable lesions and uncovering treatment resistance mechanisms, the amount of DNA shed by any one specific lesion is still not well characterized. We designed the Lesion Shedding Model (LSM) to order lesions from the strongest to the poorest shedding for a given patient. By characterizing the lesion-specific ctDNA shedding levels, we can better understand the mechanisms of shedding and more accurately interpret ctDNA assays to improve their clinical impact. We verified the accuracy of the LSM under controlled conditions using a simulation approach as well as testing the model on three cancer patients. The LSM obtained an accurate partial order of the lesions according to their assigned shedding levels in simulations and its accuracy in identifying the top shedding lesion was not significantly impacted by number of lesions. Applying LSM to three cancer patients, we found that indeed there were lesions that consistently shed more than others into the patients' blood. In two of the patients, the top shedding lesion was one of the only clinically progressing lesions at the time of biopsy suggesting a connection between high ctDNA shedding and clinical progression. The LSM provides a much needed framework with which to understand ctDNA shedding and to accelerate discovery of ctDNA biomarkers. The LSM source code has been available in the IBM BioMedSciAI Github (https://github.com/BiomedSciAI/Geno4SD).

MIMESIS: minimal DNA-methylation signatures to quantify and classify tumor signals in tissue and cell-free DNA samples.

DNA-methylation alterations are common in cancer and display unique characteristics that make them ideal markers for tumor quantification and classification. Here we present MIMESIS, a computational framework exploiting minimal DNA-methylation signatures composed by a few dozen informative DNA-methylation sites to quantify and classify tumor signals in tissue and cell-free DNA samples. Extensive analyses of multiple independent and heterogenous datasets including >7200 samples demonstrate the capability of MIMESIS to provide precise estimations of tumor content and to enable accurate classification of tumor type and molecular subtype. To assess our framework for clinical applications, we designed a MIMESIS-informed assay incorporating the minimal signatures for breast cancer. Using both artificial samples and clinical serial cell-free DNA samples from patients with metastatic breast cancer, we show that our approach provides accurate estimations of tumor content, sensitive detection of tumor signal and the ability to capture clinically relevant molecular subtype in patients' circulation. This study provides evidence that our extremely parsimonious approach can be used to develop cost-effective and highly scalable DNA-methylation assays that could support and facilitate the implementation of precision oncology in clinical practice.

Research autopsy programmes in oncology: shared experience from 14 centres across the world.

While there is a great clinical need to understand the biology of metastatic cancer in order to treat it more effectively, research is hampered by limited sample availability. Research autopsy programmes can crucially advance the field through synchronous, extensive, and high-volume sample collection. However, it remains an underused strategy in translational research. Via an extensive questionnaire, we collected information on the study design, enrolment strategy, study conduct, sample and data management, and challenges and opportunities of research autopsy programmes in oncology worldwide. Fourteen programmes participated in this study. Eight programmes operated 24 h/7 days, resulting in a lower median postmortem interval (time between death and start of the autopsy, 4 h) compared with those operating during working hours (9 h). Most programmes (n = 10) succeeded in collecting all samples within a median of 12 h after death. A large number of tumour sites were sampled during each autopsy (median 15.5 per patient). The median number of samples collected per patient was 58, including different processing methods for tumour samples but also non-tumour tissues and liquid biopsies. Unique biological insights derived from these samples included metastatic progression, treatment resistance, disease heterogeneity, tumour dormancy, interactions with the tumour micro-environment, and tumour representation in liquid biopsies. Tumour patient-derived xenograft (PDX) or organoid (PDO) models were additionally established, allowing for drug discovery and treatment sensitivity assays. Apart from the opportunities and achievements, we also present the challenges related with postmortem sample collections and strategies to overcome them, based on the shared experience of these 14 programmes. Through this work, we hope to increase the transparency of postmortem tissue donation, to encourage and aid the creation of new programmes, and to foster collaborations on these unique sample collections. © 2024 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.