Important Role In Precision Medicine Research Articles

Extend the benchmarking indel set by manual review using the individual cell line sequencing data from the Sequencing Quality Control 2 (SEQC2) project

Accurate indel calling plays an important role in precision medicine. A benchmarking indel set is essential for thoroughly evaluating the indel calling performance of bioinformatics pipelines. A reference sample with a set of known-positive variants was developed in the FDA-led Sequencing Quality Control Phase 2 (SEQC2) project, but the known indels in the known-positive set were limited. This project sought to provide an enriched set of known indels that would be more translationally relevant by focusing on additional cancer related regions. A thorough manual review process completed by 42 reviewers, two advisors, and a judging panel of three researchers significantly enriched the known indel set by an additional 516 indels. The extended benchmarking indel set has a large range of variant allele frequencies (VAFs), with 87% of them having a VAF below 20% in reference Sample A. The reference Sample A and the indel set can be used for comprehensive benchmarking of indel calling across a wider range of VAF values in the lower range. Indel length was also variable, but the majority were under 10 base pairs (bps). Most of the indels were within coding regions, with the remainder in the gene regulatory regions. Although high confidence can be derived from the robust study design and meticulous human review, this extensive indel set has not undergone orthogonal validation. The extended benchmarking indel set, along with the indels in the previously published known-positive set, was the truth set used to benchmark indel calling pipelines in a community challenge hosted on the precisionFDA platform. This benchmarking indel set and reference samples can be utilized for a comprehensive evaluation of indel calling pipelines. Additionally, the insights and solutions obtained during the manual review process can aid in improving the performance of these pipelines.

Abstract 4982: The somatic mutation signature detected in gynecological mucosal melanoma

Abstract Background: Gynecological Mucosal Melanoma(GMM) is aggressive disease with a poor prognosis, where few studies focusing on its target drug and mutation signatures. Cancer as outcome of DNA mutagenesis process and can be inferred from somatic mutations signature by analyzing the genome sequence. Due to the rapid development of next-generation sequencing (NGS) and molecularly driven targeted therapy, discovering different mutation signature in cancer plays an important role in precision medicine. Methods: The study was conducted at a high-volume melanoma center, the Peking University Cancer Hospital. We enrolled female patients with melanoma (n = 22) from 2017 to 2021. Subsequently, whole-exome sequencing performed on the Formalin-fixed paraffin-embedded (FFPE) specimens from 22 GMM patients and match normal tissues of patients. Tumor mutational burden (TMB) was measured as the number of nonsynonymous somatic mutations of whole-exome sequence coding region. Mutation signature detection base pair of six substitution subtypes: C>A, C>G, C>T, T>A, T>C, and T>G. NMF can be used to decompose matrix A into two nonnegative matrices. Results: In all mutations of 22 GMM patients, we screen two mutation signatures. Signature 1 showed a strong correlation [0.77;cosine similarity value (CSV)] to the signature 3 from the COSMIC database, which represents failure of DNA double-strand break-repair by homologous recombination, which predominated in 55%(12/22) GMM patients. Signature 2 showed a strong correlation to Cosmic signature 1 (CSV = 0.791), which is has been found in all cancer types correlated with age cancer diagnosis. Patient-18 did not any treatment in our hospital, follow-up was lost. Kaplan-Meier revealed that Signature 2 was associated with longer overall survival in GMM patients and later recurrence or progression. Conclusions: This study aimed to identify prognostic markers and the development of targeted therapy for the treatment of GMM. Citation Format: Zhongwu Li, Lianyi Shen, Siyao Liu. The somatic mutation signature detected in gynecological mucosal melanoma. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 4982.

Combination of multiple nucleic acid aptamers for precision detection of tumors based on optical methods.

Nucleic acid aptamers are a novel molecular recognition tool that is functionally similar to antibodies but superior to antibodies in terms of thermal stability, structural modification, preparation, and cost, and therefore hold great promise for molecular detection. However, due to the limitations of a single aptamer in molecular detection, the multiple aptamer combination for bioanalysis has received much attention. Here, we reviewed the progress of tumor precision detection based on the combination of multiple nucleic acid aptamers and optical methods and discussed its challenges and prospects. The relevant literature in PubMed was collected and reviewed. The combination of two or more aptamers with modern nanomaterials and analytical methods allows the fabrication of various detection systems for the simultaneous detection of different structural domains of a substance and/or different substances, including soluble tumor markers, tumor cell surface and intracellular markers, circulating tumor cells, and other tumor-related biomolecules, which has great potential for application in efficient and precise tumor detection. The combination of multiple nucleic acid aptamers provides a new approach for the precise detection of tumors and will play an important role in precision medicine for tumors.

Simultaneous detection of two ovarian cancer biomarkers in human serums with biotin-enriched dendritic mesoporous silica nanoparticles-labeled multiplex lateral flow immunoassay

The more accurate the diagnosis of ovarian cancer, the higher the survival rate of patients. We report here the first preparation of biotin-enriched dendritic mesoporous silica nanoparticles (BDMSNs) and successfully combined it with multiplex lateral flow immunoassay (MLFIA) for simultaneous detection of ovarian cancer biomarkers (CA125 and HE4). Benefiting from the aggregation-induced emission property of the newly synthesized shuriken-like molecules and the high affinity of biotin-streptavidin system, BDMSNs can be utilized as fluorescent signal reporters and have robust antibody enrichment properties. The linear ranges, limits of detection, and accuracies of simultaneous detection of CA125 and HE4 were evaluated by using BDMSNs-MLFIA. The results displayed that the corresponding limits of detection were 5 U/mL and 5 pM, respectively; the coefficient of variation of intra-assay and inter-assay were both less than 15 %. Compared with commercial electrochemiluminescence method, the clinical test results of BDMSNs-MLFIA have a correlation coefficient of more than 98%. BDMSNs-MLFIA initially explored the simultaneous detection of CA125 and HE4 in clinical human serum, greatly improved the practicability of LFIA and emphasizing its important role in precision medicine.

Multi-Level Biological Network Analysis and Drug Repurposing Based on Leukocyte Transcriptomics in Severe COVID-19: In Silico Systems Biology to Precision Medicine.

The coronavirus disease 2019 (COVID-19) pandemic causes many morbidity and mortality cases. Despite several developed vaccines and antiviral therapies, some patients experience severe conditions that need intensive care units (ICU); therefore, precision medicine is necessary to predict and treat these patients using novel biomarkers and targeted drugs. In this study, we proposed a multi-level biological network analysis framework to identify key genes via protein–protein interaction (PPI) network analysis as well as survival analysis based on differentially expressed genes (DEGs) in leukocyte transcriptomic profiles, discover novel biomarkers using microRNAs (miRNA) from regulatory network analysis, and provide candidate drugs targeting the key genes using drug–gene interaction network and structural analysis. The results show that upregulated DEGs were mainly enriched in cell division, cell cycle, and innate immune signaling pathways. Downregulated DEGs were primarily concentrated in the cellular response to stress, lysosome, glycosaminoglycan catabolic process, and mature B cell differentiation. Regulatory network analysis revealed that hsa-miR-6792-5p, hsa-let-7b-5p, hsa-miR-34a-5p, hsa-miR-92a-3p, and hsa-miR-146a-5p were predicted biomarkers. CDC25A, GUSB, MYBL2, and SDAD1 were identified as key genes in severe COVID-19. In addition, drug repurposing from drug–gene and drug–protein database searching and molecular docking showed that camptothecin and doxorubicin were candidate drugs interacting with the key genes. In conclusion, multi-level systems biology analysis plays an important role in precision medicine by finding novel biomarkers and targeted drugs based on key gene identification.

Precisely translating computed tomography diagnosis accuracy into therapeutic intervention by a carbon-iodine conjugated polymer

X-ray computed tomography (CT) has an important role in precision medicine. However, CT contrast agents with high efficiency and the ability to translate diagnostic accuracy into therapeutic intervention are scarce. Here, poly(diiododiacetylene) (PIDA), a conjugated polymer composed of only carbon and iodine atoms, is reported as an efficient CT contrast agent to bridge CT diagnostic imaging with therapeutic intervention. PIDA has a high iodine payload (>84 wt%), and the aggregation of nanofibrous PIDA can further amplify CT intensity and has improved geometrical and positional stability in vivo. Moreover, with a conjugated backbone, PIDA is in deep blue color, making it dually visible by both CT imaging and the naked eyes. The performance of PIDA in CT-guided preoperative planning and visualization-guided surgery is validated using orthotopic xenograft rat models. In addition, PIDA excels clinical fiducial markers of imaging-guided radiotherapy in efficiency and biocompatibility, and exhibits successful guidance of robotic radiotherapy on Beagles, demonstrating clinical potential to translate CT diagnosis accuracy into therapeutic intervention for precision medicine.

Intrahepatic cholangiocarcinoma: Tumour heterogeneity and its clinical relevance.

Treatment of intrahepatic cholangiocarcinoma (iCCA) is currently at a significant turning point due to the identification of isocitrate dehydrogenase (IDH) mutations and fibroblast growth factor receptor (FGFR) fusions that can be targeted with currently available therapies. Clinical trials of these targeted therapies have been promising, and the iCCA patients who may benefit from these targeted treatments can be identified by pathological examination prior to molecular investigations. This is because IDH mutations and FGFR fusions are mainly seen in the small duct type iCCA, a subtype of iCCA defined by the 5th World Health Organization classification, which can be recognized by the pathological diagnostic process. Therefore, pathology plays an important role in precision medicine for iCCA, not only in confirming the diagnosis, but also in identifying the iCCA patients who may benefit from targeted treatments. However, caution is advised with the pathological diagnosis, as iCCA shows tumour heterogeneity, making it difficult to distinguish small duct type iCCA from hepatocellular carcinoma (HCC), and combined HCC-CCA. This review focuses on the pathological/molecular features of both subtypes of iCCA (large and small duct types), as well as their diagnostic pitfalls, clinical relevance, and future perspectives.

TGSA: protein-protein association-based twin graph neural networks for drug response prediction with similarity augmentation.

Drug response prediction (DRP) plays an important role in precision medicine (e.g. for cancer analysis and treatment). Recent advances in deep learning algorithms make it possible to predict drug responses accurately based on genetic profiles. However, existing methods ignore the potential relationships among genes. In addition, similarity among cell lines/drugs was rarely considered explicitly. We propose a novel DRP framework, called TGSA, to make better use of prior domain knowledge. TGSA consists of Twin Graph neural networks for Drug Response Prediction (TGDRP) and a Similarity Augmentation (SA) module to fuse fine-grained and coarse-grained information. Specifically, TGDRP abstracts cell lines as graphs based on STRING protein-protein association networks and uses Graph Neural Networks (GNNs) for representation learning. SA views DRP as an edge regression problem on a heterogeneous graph and utilizes GNNs to smooth the representations of similar cell lines/drugs. Besides, we introduce an auxiliary pre-training strategy to remedy the identified limitations of scarce data and poor out-of-distribution generalization. Extensive experiments on the GDSC2 dataset demonstrate that our TGSA consistently outperforms all the state-of-the-art baselines under various experimental settings. We further evaluate the effectiveness and contributions of each component of TGSA via ablation experiments. The promising performance of TGSA shows enormous potential for clinical applications in precision medicine. The source code is available at https://github.com/violet-sto/TGSA. Supplementary data are available at Bioinformatics online.

Utilizing Patient-Derived Epithelial Ovarian Cancer Tumor Organoids to Predict Carboplatin Resistance.

The development of patient-derived tumor organoids (TOs) from an epithelial ovarian cancer tumor obtained at the time of primary or interval debulking surgery has the potential to play an important role in precision medicine. Here, we utilized TOs to test front-line chemotherapy sensitivity and to investigate genomic drivers of carboplatin resistance. We developed six high-grade, serous epithelial ovarian cancer tumor organoid lines from tissue obtained during debulking surgery (two neoadjuvant-carboplatin-exposed and four chemo-naïve). Each organoid line was screened for sensitivity to carboplatin at four different doses (100, 10, 1, and 0.1 µM). Cell viability curves and resultant EC50 values were determined. One organoid line, UK1254, was predicted to be resistant to carboplatin based on its EC50 value (50.2 µM) being above clinically achievable Cmax. UK1254 had a significantly shorter PFS than the rest of the subjects (p = 0.0253) and was treated as a platinum-resistant recurrence. Subsequent gene expression analysis revealed extensively interconnected, differentially expressed pathways related to NF-kB, cellular differentiation (PRDM6 activation), and the linkage of B-cell receptor signaling to the PI3K–Akt signaling pathway (PI3KAP1 activation). This study demonstrates that patient-derived tumor organoids can be developed from patients at the time of primary or interval debulking surgery and may be used to predict clinical platinum sensitivity status or to investigate drivers of carboplatin resistance.

A Cervical Histopathology Dataset for Computer Aided Diagnosis of Precancerous Lesions.

Cervical cancer, as one of the most frequently diagnosed cancers worldwide, is curable when detected early. Histopathology images play an important role in precision medicine of the cervical lesions. However, few computer aided algorithms have been explored on cervical histopathology images due to the lack of public datasets. In this article, we release a new cervical histopathology image dataset for automated precancerous diagnosis. Specifically, 100 slides from 71 patients are annotated by three independent pathologists. To show the difficulty of the task, benchmarks are obtained through both fully and weakly supervised learning. Extensive experiments based on typical classification and semantic segmentation networks are carried out to provide strong baselines. In particular, a strategy of assembling classification, segmentation, and pseudo-labeling is proposed to further improve the performance. The Dice coefficient reaches 0.7833, indicating the feasibility of computer aided diagnosis and the effectiveness of our weakly supervised ensemble algorithm. The dataset and evaluation codes are publicly available. To the best of our knowledge, it is the first public cervical histopathology dataset for automated precancerous segmentation. We believe that this work will attract researchers to explore novel algorithms on cervical automated diagnosis, thereby assisting doctors and patients clinically.

A bivariate Bayesian framework for simultaneous evaluation of two candidate companion diagnostic assays in a new drug clinical trial

Companion diagnostic tests play an important role in precision medicine. With the advancement of new technologies, multiple companion diagnostic tests can be rapidly developed in multiple platforms and use different samples to select patients for new treatments. Analytically validated assays must be clinically evaluated before they can be implemented in patient management. The status quo design for validating candidate assays is to employ one candidate assay to select patients for new drug clinical trial and then further evaluate the 2nd candidate assay in a bridging study. We propose a new enrollment strategy that employs two assays to select patients. We then develop a bivariate Bayesian approach that enables the totality of data to be used in evaluating whether these assays can be used independently or in a composite procedure in selecting right patients for new treatment. We demonstrate through simulations that when proper priors are available, the Bayesian approach is superior to classical methods in terms of statistical power.

Hyperprogression After Immune-Checkpoint Inhibitor Treatment: Characteristics and Hypotheses.

Immunotherapies in tumors have attracted increasing attention. They play an important role in precision medicine. Many immune-checkpoint inhibitors (ICIs) have obtained FDA approval and show good performance in the clinic. Hyperprogressive disease (HPD) after ICIs was first described in November 2016. Since then, a series of cases of HPD after ICIs have been reported. Notwithstanding that only a small subset of patients may experience this atypical response, HPD in affected patients means shorter survival times and worse prognoses. We summarized common standards for HPD diagnosis and profiled advantages and disadvantages. Elderly age, MDM2 family amplification, infiltration of PD-1-positive regulatory effector T cells and M2-like macrophages, and cancer stem cells may take part in HPD occurrence. Overall, we should focus on investigating the early markers and pathogenic mechanisms of HPD to solve this issue in ICIs.

Nanoemulsions to support ex vivo cell culture of breast cancer circulating tumor cells

The culture and expansion of circulating tumor cells (CTCs) for ex vivo assays plays an important role in precision medicine. However, it still represents a big challenge in translational research. Generating knowledge about the characteristics of CTCs can help to shed light about the metastasis process. Furthermore, ex vivo culture of CTCs might allow performing functional analyses and testing different drugs, to guide clinical therapies. In this work, we present a new methodology based on the use of nanosystems to support ex vivo culture of CTCs. We have formulated oil-in-water (O/W) nanoemulsions (NEs) composed by lipids and fatty acids, and have demonstrated that they can help increasing cell viability on different breast cancer cell lines. Moreover, we have generated a CTC model from breast cancer mice xenografts, to prove the ability of the NEs to facilitate their culture and expansion. Additionally, we have postulated a mechanism of action based on the cell consumption of the NEs, which are acting as energy suppliers, driving proliferation. This work corroborates the potential of nanotechnology to provide valuable tools for precision oncology, and the ability of our NEs to improve proliferation of breast cancer CTCs for the establishment of CTCs culture protocols.

EVIDENCE: a practical variant filtering for low-frequency variants detection in cell-free DNA

Plasma cell-free DNA (cfDNA) testing plays an increasingly important role in precision medicine for cancer. However, circulating cell-free tumor DNA (ctDNA) is highly diluted by cfDNA from non-cancer cells, complicating ctDNA detection and analysis. To identify low-frequency variants, we developed a program, eVIDENCE, which is a workflow for filtering candidate variants detected by using the ThruPLEX tag-seq (Takara Bio), a commercially-available molecular barcoding kit. We analyzed 27 cfDNA samples from hepatocellular carcinoma patients. Sequencing libraries were constructed and hybridized to our custom panel targeting about 80 genes. An initial variant calling identified 36,500 single nucleotide variants (SNVs) and 9,300 insertions and deletions (indels) across the 27 samples, but the number was much greater than expected when compared with previous cancer genome studies. eVIDENCE was applied to the candidate variants and finally 70 SNVs and 7 indels remained. Of the 77 variants, 49 (63.6%) showed VAF of < 1% (0.20–0.98%). Twenty-five variants were selected in an unbiased manner and all were successfully validated, suggesting that eVIDENCE can identify variants with VAF of ≥ 0.2%. Additionally, this study is the first to detect hepatitis B virus integration sites and genomic rearrangements in the TERT region from cfDNA of HCC patients. We consider that our method can be applied in the examination of cfDNA from other types of malignancies using specific custom gene panels and will contribute to comprehensive ctDNA analysis.

Bayesian approach to incorporating different types of biomedical knowledge bases into information retrieval systems for clinical decision support in precision medicine.

By providing clinicians with information regarding treatment options for molecular sub-types of complex diseases with genetic origin, such as cancer, information retrieval (IR) systems play an important role in precision medicine. In this paper, we propose Bayesian Precision Medicine (BPM), a novel probabilistic framework for query expansion in information retrieval systems for Clinical Decision Support (CDS) in Precision Medicine (PM). Such systems can assist clinicians with selecting personalized treatment of complex diseases based on the patients' genomic data, such as gene mutations. In particular, we focus on a clinical decision support scenario in which clinicians provide two types of information in their queries: (1) short description of a patient's case, which may contain information regarding the type of cancer that a patient has as well as symptoms and demographics, and (2) gene mutations, which may contain gene names, mutation code and type of mutation. The goal of an IR system in this scenario is to rank biomedical articles from a large collection, such as the MEDLINE, based on their relevance to the provided query. One of the main challenges faced by IR systems in this scenario is semantic matching of heterogeneous information (gene names, medical terminology and other query keywords) in queries and relevant biomedical articles. To address this challenge, we propose a probabilistic framework that enables mapping gene mutations provided in a given query onto the biomedical concepts that are related to the entire query and can be effectively utilized for query expansion. The BPM obtains candidate query expansion concepts from biomedical knowledge bases, the Unified Medical Language System (UMLS) and the Drug-Gene Interaction Database (DGIdb), as well as the top-ranked MEDLINE articles retrieved for the original query. The BPM then utilizes information from the Catalog of Somatic Mutations in Cancer (COSMIC) and co-occurrence statistics in MEDLINE to assess the relatedness of candidate query expansion concepts to gene mutations and other information provided in a query. Experimental evaluation of the BPM was conducted on a large subset of MEDLINE articles as well as abstracts from the American Association for Cancer Research (AACR) and American Society of Clinical Oncology (ASCO) proceedings. Experimental results on a publicly available benchmark provided by the 2017 TREC precision medicine track indicate that the proposed probabilistic framework is effective at utilizing both genomic and textual information in queries to improve the accuracy of IR systems for CDS in PM through query expansion.

Biobanking for Metabolomics and Lipidomics in Precision Medicine.

Metabolomics and lipidomics play an important role in precision medicine. Global or targeted analyses of metabolites and lipids provide a specific metabolic phenotype, a “metabotype,” of an individual, which can provide important information on their propensity to develop certain diseases or their likely reaction to nutrition or pharmaceuticals. Metabolomics is increasingly being used in research to look at the etiology, prognosis, and outcomes of disease or provide more detailed information on normal human biochemistry. By combining metabolomics data sets with clinical, physiological, or other omics data sets, systems biology approaches can be used to provide context to the observed changes. For human populations in particular, the huge variation in genetics, lifestyle, and other extrinsic factors means that epidemiologic cohorts are often required to reach sufficient statistical power to draw meaningful conclusions. In addition, when disease entities are being studied, it can take years for enough representative samples to be collected. For these reasons, many metabolomics studies are reliant on samples collected and stored in either “official” biobanks (i.e., sample access available to other researchers on application) or privately stored sample repositories. Biobanks have grown and been increasingly professionalized in the past 20 to 30 years. Many biobanks were originally started for large-scale genetic analyses, and not all repositories have collected biofluids or tissues specifically with metabolomics analyses in mind. Measurements of individual metabolites can be highly dependent on collection and sample storage conditions. Thus, to reliably answer research questions, we need to have reliable collection and storage of samples in a form that makes them suitable for robust metabolomics analyses. To do otherwise invites the introduction of large technical variations in the data set and increases the risk of missing important metabolites that are contributing to the observed phenotypes or incorrectly attributing a metabolite change owing to technical error to …

Primary Care Physicians' Knowledge, Attitudes, and Experience with Personal Genetic Testing.

Primary care providers (PCPs) will play an important role in precision medicine. However, their lack of training and knowledge about genetics and genomics may limit their ability to advise patients or interpret or utilize test results. We evaluated PCPs’ awareness of the role of genetics/genomics in health, knowledge about key concepts in genomic medicine, perception/attitudes towards direct-to-consumer (DTC) genetic testing, and their level of confidence/comfort in discussing testing with patients prior to and after undergoing DTC testing through the 23andMe Health + Ancestry Service. A total of 130 PCPs completed the study. Sixty-three percent were board-certified in family practice, 32% graduated between 1991 and 2000, and 88% had heard of 23andMe prior to the study. Seventy-two percent decided to participate in the study to gain a better understanding about testing. At baseline, 23% of respondents indicated comfort discussing genetics as a risk factor for common diseases, increasing to 59% after undergoing personal genetic testing (PGT) (p < 0.01). In summary, we find that undergoing PGT augments physicians’ confidence, comfort, and interest in DTC testing.

The Use of Star Polymer Nanoparticles for theDelivery of siRNA to Mouse Orthotopic Pancreatic Tumor Models.

Pancreatic cancer is a lethal malignancy which is refractory to most chemotherapy drugs. Recent landmark studies have shed new light on the complex genetic heterogeneity of pancreatic cancer and provide an opportunity to utilize "precision-based medicines" to target genes based on the genetic profile of an individual's tumor to increase the efficiency of chemotherapy and decrease tumor growth and metastases. Gene-silencing drugs in the form of short-interfering RNA (siRNA) have the potential to play an important role in precision medicine for pancreatic cancer by silencing the expression of genes including those considered difficult to inhibit (undruggable) using chemical agents. However, before siRNA can reach its clinical potential a delivery vehicle is needed to carry siRNA across the cell membrane and into the cytoplasm of the cell. Herein, we detail the methods required to use star polymer nanoparticles to deliver siRNA to pancreatic tumors in an orthotopic pancreatic cancer mouse model to silence the expression of an "undruggable" gene (βIII-tubulin) that regulates pancreatic cancer growth and chemosensitivity.

Less Exploited GPCRs in Precision Medicine: Targets for Molecular Imaging and Theranostics.

Precision medicine relies on individually tailored therapeutic intervention taking into account individual variability. It is strongly dependent on the availability of target-specific drugs and/or imaging agents that recognize molecular targets and patient-specific disease mechanisms. The most sensitive molecular imaging modalities, Single Photon Emission Computed Tomography (SPECT) and Positron Emission Tomography (PET), rely on the interaction between an imaging radioprobe and a target. Moreover, the use of target-specific molecular tools for both diagnostics and therapy, theranostic agents, represent an established methodology in nuclear medicine that is assuming an increasingly important role in precision medicine. The design of innovative imaging and/or theranostic agents is key for further accomplishments in the field. G-protein-coupled receptors (GPCRs), apart from being highly relevant drug targets, have also been largely exploited as molecular targets for non-invasive imaging and/or systemic radiotherapy of various diseases. Herein, we will discuss recent efforts towards the development of innovative imaging and/or theranostic agents targeting selected emergent GPCRs, namely the Frizzled receptor (FZD), Ghrelin receptor (GHSR-1a), G protein-coupled estrogen receptor (GPER), and Sphingosine-1-phosphate receptor (S1PR). The pharmacological and clinical relevance will be highlighted, giving particular attention to the studies on the synthesis and characterization of targeted molecular imaging agents, biological evaluation, and potential clinical applications in oncology and non-oncology diseases. Whenever relevant, supporting computational studies will be also discussed.

Clinical Utility of Analyzing Circulating Tumor DNA in Patients with Metastatic Colorectal Cancer.

Multiple genomic changes caused by clonal evolution induced by therapeutic pressure and corresponding intratumoral heterogeneity have posed great challenges for personalized therapy against metastatic colorectal cancer (mCRC) in the past decade. Liquid biopsy has emerged as an excellent molecular diagnostic tool for assessing predominant spatial and temporal intratumoral heterogeneity with minimal invasiveness.Previous studies have revealed that genomic alterations in RAS, BRAF, ERBB2, and MET, as well as other cancer-related genes associated with resistance to anti-epidermal growth factor receptor (EGFR) therapy, can be analyzed with high diagnostic accuracy by circulating tumor DNA (ctDNA) analysis. Furthermore, by longitudinally monitoring ctDNAs during anti-EGFR therapy, the emergence of genomic alterations can be detected as acquired resistance mechanisms in specific genes, mainly those associated with the mitogen-activated protein kinase signaling pathway. Analysis of ctDNA can also identify predictive biomarkers to immune checkpoint inhibitors, such as mutations in mismatch repair genes, microsatellite instability-high phenotype, and tumor mutation burden. Some prospective clinical trials evaluating targeted agents for genomic alterations in ctDNA or exploring resistance biomarkers by monitoring of ctDNA are ongoing.To determine the value of ctDNA analysis for decision-making by more accurate molecular marker-based selection of patients and identification of resistance mechanisms to targeted therapies or sensitive biomarkers for immune checkpoint inhibitors, clinical trials must be refined to evaluate the efficacy of study treatment in patients with targetable genomic alterations confirmed by ctDNA analysis, and resistance biomarkers should be explored by monitoring ctDNA in large-scale clinical trials. In the near future, ctDNA analysis will play an important role in precision medicine for mCRC. IMPLICATIONS FOR PRACTICE: Treatment strategies for metastatic colorectal cancer (mCRC) are determined according to the molecular profile, which is confirmed by analyzing tumor tissue. Analysis of circulating tumor DNA (ctDNA) may overcome the limitations of tissue-based analysis by capturing spatial and temporal intratumoral heterogeneity of mCRC. Clinical trials must be refined to test the value of ctDNA analysis in patient selection and identification of biomarkers. This review describes ctDNA analysis, which will have an important role in precision medicine for mCRC.