The next horizon in precision oncology: Proteogenomics to inform cancer diagnosis and treatment.

When it comes to precision oncology, proteogenomics may provide better prospects to the clinical characterization of tumors, help make a more accurate diagnosis of cancer, and improve treatment for patients with cancer. This perspective describes the significant contributions of The Cancer Genome Atlas and the Clinical Proteomic Tumor Analysis Consortium to precision oncology and makes the case that proteogenomics needs to be fully integrated into clinical trials and patient care in order for precision oncology to deliver the right cancer treatment to the right patient at the right dose and at the right time.

Pan-cancer scRNA-seq analysis reveals immunological and diagnostic significance of the peripheral blood mononuclear cells.

Peripheral blood mononuclear cells (PBMCs) reflect systemic immune response during cancer progression. However, a comprehensive understanding of the composition and function of PBMCs in cancer patients is lacking, and the potential of these features to assist cancer diagnosis is also unclear. Here, the compositional and status differences between cancer patients and healthy donors in PBMCs were investigated by single-cell RNA sequencing (scRNA-seq), involving 262,025 PBMCs from 68 cancer samples and 14 healthy samples. We observed an enhanced activation and differentiation of most immune subsets in cancer patients, along with reduction of naïve T cells, expansion of macrophages, impairment of NK cells and myeloid cells, as well as tumor promotion and immunosuppression. Based on characteristics including differential cell type abundances and/or hub genes identified from weight gene co-expression network analysis (WGCNA) modules of each major cell type, we applied logistic regression to construct cancer diagnosis models. Furthermore, we found that the above models can distinguish cancer patients and healthy donors with high sensitivity. Our study provided new insights into using the features of PBMCs in non-invasive cancer diagnosis.

Large language model produces high accurate diagnosis of cancer from end-motif profiles of cell-free DNA.

Instruction-tuned large language models (LLMs) demonstrate exceptional ability to align with human intentions. We present an LLM-based model-instruction-tuned LLM for assessment of cancer (iLLMAC)-that can detect cancer using cell-free deoxyribonucleic acid (cfDNA) end-motif profiles. Developed on plasma cfDNA sequencing data from 1135 cancer patients and 1106 controls across three datasets, iLLMAC achieved area under the receiver operating curve (AUROC) of 0.866 [95% confidence interval (CI), 0.773-0.959] for cancer diagnosis and 0.924 (95% CI, 0.841-1.0) for hepatocellular carcinoma (HCC) detection using 16 end-motifs. Performance increased with more motifs, reaching 0.886 (95% CI, 0.794-0.977) and 0.956 (95% CI, 0.89-1.0) for cancer diagnosis and HCC detection, respectively, with 64 end-motifs. On an external-testing set, iLLMAC achieved AUROC of 0.912 (95% CI, 0.849-0.976) for cancer diagnosis and 0.938 (95% CI, 0.885-0.992) for HCC detection with 64 end-motifs, significantly outperforming benchmarked methods. Furthermore, iLLMAC achieved high classification performance on datasets with bisulfite and 5-hydroxymethylcytosine sequencing. Our study highlights the effectiveness of LLM-based instruction-tuning for cfDNA-based cancer detection.

Biosynthetic Polymalic Acid as a Delivery Nanoplatform for Translational Cancer Medicine.

Poly(ß-L-malic acid) (PMLA) is a natural polyester produced by numerous microorganisms. Regarding its biosynthetic machinery, a nonribosomal peptide synthetase (NRPS) is proposed to direct polymerization of L-malic acid in vivo. Chemically versatile and biologically compatible, PMLA can be used as an ideal carrier for several molecules, including nucleotides, proteins, chemotherapeutic drugs, and imaging agents, and can deliver multimodal theranostics through biological barriers such as the blood-brain barrier. We focus on PMLA biosynthesis in microorganisms, summarize the physicochemical and physiochemical characteristics of PMLA as a naturally derived polymeric delivery platform at nanoscale, and highlight the attachment of functional groups to enhance cancer detection and treatment.

Artificial intelligence-enabled microbiome-based diagnosis models for a broad spectrum of cancer types.

Microbiome-based diagnosis of cancer is an increasingly important supplement for the genomics approach in cancer diagnosis, yet current models for microbiome-based diagnosis of cancer face difficulties in generality: not only diagnosis models could not be adapted from one cancer to another, but models built based on microbes from tissues could not be adapted for diagnosis based on microbes from blood. Therefore, a microbiome-based model suitable for a broad spectrum of cancer types is urgently needed. Here we have introduced DeepMicroCancer, a diagnosis model using artificial intelligence techniques for a broad spectrum of cancer types. Built based on the random forest models it has enabled superior performances on more than twenty types of cancers' tissue samples. And by using the transfer learning techniques, improved accuracies could be obtained, especially for cancer types with only a few samples, which could satisfy the requirement in clinical scenarios. Moreover, transfer learning techniques have enabled high diagnosis accuracy that could also be achieved for blood samples. These results indicated that certain sets of microbes could, if excavated using advanced artificial techniques, reveal the intricate differences among cancers and healthy individuals. Collectively, DeepMicroCancer has provided a new venue for accurate diagnosis of cancer based on tissue and blood materials, which could potentially be used in clinics.

Multiomics dynamic learning enables personalized diagnosis and prognosis for pancancer and cancer subtypes.

Artificial intelligence (AI) approaches in cancer analysis typically utilize a 'one-size-fits-all' methodology characterizing average patient responses. This manner neglects the diverse conditions in the pancancer and cancer subtypes of individual patients, resulting in suboptimal outcomes in diagnosis and treatment. To overcome this limitation, we shift from a blanket application of statistics to a focus on the explicit recognition of patient-specific abnormalities. Our objective is to use multiomics data to empower clinicians with personalized molecular descriptions that allow for customized diagnosis and interventions. Here, we propose a highly trustworthy multiomics learning (HTML) framework that employs multiomics self-adaptive dynamic learning to process each sample with data-dependent architectures and computational flows, ensuring personalized and trustworthy patient-centering of cancer diagnosis and prognosis. Extensive testing on a 33-type pancancer dataset and 12 cancer subtype datasets underscored the superior performance of HTML compared with static-architecture-based methods. Our findings also highlighting the potential of HTML in elucidating complex biological pathogenesis and paving the way for improved patient-specific care in cancer treatment.

Construction of a Spatial-Confined Self-Stacking Catalytic Circuit for Rapid and Sensitive Imaging of Piwi-Interacting RNA in Living Cells.

Piwi-interacting RNAs (piRNAs) are small noncoding RNAs that repress transposable elements to maintain genome integrity. The canonical catalytic hairpin assembly (CHA) circuit relies on random collisions of free-diffused reactant probes, which substantially slow down reaction efficiency and kinetics. Herein, we demonstrate the construction of a spatial-confined self-stacking catalytic circuit for rapid and sensitive imaging of piRNA in living cells based on intramolecular and intermolecular hybridization-accelerated CHA. We rationally design a 3WJ probe that not only accelerates the reaction kinetics by increasing the local concentration of reactant probes but also eliminates background signal leakage caused by cross-entanglement of preassembled probes. This strategy achieves high sensitivity and good specificity with shortened assay time. It can quantify intracellular piRNA expression at a single-cell level, discriminate piRNA expression in tissues of breast cancer patients and healthy persons, and in situ image piRNA in living cells, offering a new approach for early diagnosis and postoperative monitoring.

Controllable Assembly of a Quantum Dot-Based Aptasensor Guided by CRISPR/Cas12a for Direct Measurement of Circulating Tumor Cells in Human Blood.

Accurate and sensitive analysis of circulating tumor cells (CTCs) in human blood provides a non-invasive approach for the evaluation of cancer metastasis and early cancer diagnosis. Herein, we demonstrate the controllable assembly of a quantum dot (QD)-based aptasensor guided by CRISPR/Cas12a for direct measurement of CTCs in human blood. We introduce a magnetic bead@activator/recognizer duplex core-shell structure to construct a multifunctional platform for the capture and direct detection of CTCs in human blood, without the need for additional CTC release and re-identification steps. Notably, the introduction of magnetic separation ensures that only a target-induced free activator can initiate the downstream catalysis, efficiently avoiding the undesired catalysis triggered by inappropriate recognition of the activator/recognizer duplex structure by crRNAs. This aptasensor achieves high CTC-capture efficiency (82.72%) and sensitive detection of CTCs with a limit of detection of 2 cells mL-1 in human blood, holding great promise for the liquid biopsy of cancers.

Artificial intelligence in lung cancer diagnosis and prognosis: Current application and future perspective.

Lung cancer is one of the malignant tumors with the highest incidence and mortality in the world. The overall five-year survival rate of lung cancer is relatively lower than many leading cancers. Early diagnosis and prognosis of lung cancer are essential to improve the patient's survival rate. With artificial intelligence (AI) approaches widely applied in lung cancer, early diagnosis and prediction have achieved excellent performance in recent years. This review summarizes various types of AI algorithm applications in lung cancer, including natural language processing (NLP), machine learning and deep learning, and reinforcement learning. In addition, we provides evidence regarding the application of AI in lung cancer diagnostic and clinical prognosis. This review aims to elucidate the value of AI in lung cancer diagnosis and prognosis as the novel screening decision-making for the precise treatment of lung cancer patients.

A systematic review on deep learning-based automated cancer diagnosis models.

Deep learning is gaining importance due to its wide range of applications. Many researchers have utilized deep learning (DL) models for the automated diagnosis of cancer patients. This paper provides a systematic review of DL models for automated diagnosis of cancer patients. Initially, various DL models for cancer diagnosis are presented. Five major categories of cancers such as breast, lung, liver, brain and cervical cancer are considered. As these categories of cancers have a very high percentage of occurrences with high mortality rate. The comparative analysis of different types of DL models is drawn for the diagnosis of cancer at early stages by considering the latest research articles from 2016 to 2022. After comprehensive comparative analysis, it is found that most of the researchers achieved appreciable accuracy with implementation of the convolutional neural network model. These utilized the pretrained models for automated diagnosis of cancer patients. Various shortcomings with the existing DL-based automated cancer diagnosis models are also been presented. Finally, future directions are discussed to facilitate further research for automated diagnosis of cancer patients.

Biomarkers for diagnosis and therapeutic options in hepatocellular carcinoma.

Liver cancer is a global health challenge, causing a significant social-economic burden. Hepatocellular carcinoma (HCC) is the predominant type of primary liver cancer, which is highly heterogeneous in terms of molecular and cellular signatures. Early-stage or small tumors are typically treated with surgery or ablation. Currently, chemotherapies and immunotherapies are the best treatments for unresectable tumors or advanced HCC. However, drug response and acquired resistance are not predictable with the existing systematic guidelines regarding mutation patterns and molecular biomarkers, resulting in sub-optimal treatment outcomes for many patients with atypical molecular profiles. With advanced technological platforms, valuable information such as tumor genetic alterations, epigenetic data, and tumor microenvironments can be obtained from liquid biopsy. The inter- and intra-tumoral heterogeneity of HCC are illustrated, and these collective data provide solid evidence in the decision-making process of treatment regimens. This article reviews the current understanding of HCC detection methods and aims to update the development of HCC surveillance using liquid biopsy. Recent critical findings on the molecular basis, epigenetic profiles, circulating tumor cells, circulating DNAs, and omics studies are elaborated for HCC diagnosis. Besides, biomarkers related to the choice of therapeutic options are discussed. Some notable recent clinical trials working on targeted therapies are also highlighted. Insights are provided to translate the knowledge into potential biomarkers for detection and diagnosis, prognosis, treatment response, and drug resistance indicators in clinical practice.

Age at diagnosis for lung, colon, breast and prostate cancers: An international comparative study.

Differences in the average age at cancer diagnosis are observed across countries. We therefore aimed to assess international variation in the median age at diagnosis of common cancers worldwide, after adjusting for differences in population age structure. We used IARC's Cancer Incidence in Five Continents (CI5) Volume XI database, comprising cancer diagnoses between 2008 and 2012 from population-based cancer registries in 65 countries. We calculated crude median ages at diagnosis for lung, colon, breast and prostate cancers in each country, then adjusted for population age differences using indirect standardization. We showed that median ages at diagnosis changed by up to 10 years after standardization, typically increasing in low- and middle-income countries (LMICs) and decreasing in high-income countries (HICs), given relatively younger and older populations, respectively. After standardization, the range of ages at diagnosis was 12 years for lung cancer (median age 61-Bulgaria vs 73-Bahrain), 12 years for colon cancer (60-the Islamic Republic of Iran vs 72-Peru), 10 years for female breast cancer (49-Algeria, the Islamic Republic of Iran, Republic of Korea vs 59-USA and others) and 10 years for prostate cancer (65-USA, Lithuania vs 75-Philippines). Compared to HICs, populations in LMICs were diagnosed with colon cancer at younger ages but with prostate cancer at older ages (both pLMICS-vs-HICs < 0.001). In countries with higher smoking prevalence, lung cancers were diagnosed at younger ages in both women and men (both pcorr < 0.001). Female breast cancer tended to be diagnosed at younger ages in East Asia, the Middle East and Africa. Our findings suggest that the differences in median ages at cancer diagnosis worldwide likely reflect population-level variation in risk factors and cancer control measures, including screening.

Genitourinary cancer and family: The reverberating psychological and cardiovascular effects of a genitourinary cancer diagnosis on first-degree relatives and spouses.

BACKGROUND: The psychological and cardiovascular health impacts on family members of patients who have been diagnosed with cancer have not been well characterized. The purpose of this study is to determine whether a family member's cancer diagnosis influences the risk of psychological illness and cardiovascular disease in first-degree relatives and spouses of patients affected by cancer. METHODS: This retrospective cohort analysis evaluated the risk of psychological illness and cardiovascular disease in first-degree relatives and spouses of patients diagnosed with a genitourinary cancer between 1990 and 2015 compared to relatives of those not diagnosed with a genitourinary cancer. The Utah Population Database was used and familial linkage was determined. Follow-up included 1-, 3-, and 5-year intervals. Patients residing outside of Utah and first-degree relatives and spouses with psychological or cardiovascular disease diagnosed before a family member's cancer diagnosis were excluded. RESULTS: A total of 49,284 patients with a genitourinary cancer were identified with 77,938 first-degree relatives and spouses. A matched control group included 246,775 patients with 81,022 first-degree relatives and spouses. Via Cox proportional hazards models, a 10% increased risk of developing a psychological illness (hazard ratio [HR], 1.10; 95% CI, 1.00-1.20) and a 28% increased risk of developing cardiovascular disease (HR, 1.28; 95% CI, 1.17-1.41) at 1 year after a family member's cancer diagnosis were found. CONCLUSIONS: This study provides population-level evidence to support the hypothesis that cancer diagnoses will lead to adverse health outcomes for family members of patients with cancer. Increased clinical attention and support are needed to reduce the harm to families caused by cancer.

Rural-Urban Disparities in Colorectal Cancer Screening, Diagnosis, Treatment, and Survivorship Care: A Systematic Review and Meta-Analysis.

BACKGROUND: Rural residents have a higher prevalence of colorectal cancer (CRC) mortality compared to urban individuals. Policies have been aimed at improving access to CRC screening to reduce these outcomes. However, little attention has been paid to other determinants of CRC-related outcomes, such as stage at diagnosis, treatment, or survivorship care. The main objective of this analysis was to evaluate literature describing differences in CRC screening, stage at diagnosis, treatment, and survivorship care between rural and urban individuals. MATERIALS AND METHODS: We conducted a systematic review of electronic databases using a combination of MeSH and free-text search terms related to CRC screening, stage at diagnosis, treatment, survivorship care, and rurality. We identified 921 studies, of which 39 were included. We assessed methodological quality using the ROBINS-E tool and summarized findings descriptively. A meta-analysis was performed of studies evaluating CRC screening using a random-effects model. RESULTS: Seventeen studies reported disparities between urban and rural populations in CRC screening, 12 on treatment disparities, and 8 on staging disparities. We found that rural individuals were significantly less likely to report any type of screening at any time period (pooled odds ratio = 0.81, 95% CI, 0.76-0.86). Results were inconclusive for disparities in staging at diagnosis and treatment. One study reported a lower likelihood of use of CRC survivorship care for rural individuals compared to urban individuals. CONCLUSION: There remains an urgent need to evaluate and address CRC disparities in rural areas. Investigators should focus future work on assessing the quality of staging at diagnosis, treatment, and survivorship care in rural areas.

Proceedings of the 1st biannual bridging the gaps in lung cancer conference.

Lung cancer is the leading cause of cancer death in the US and globally. The mortality from lung cancer has been declining, due to a reduction in incidence and advances in treatment. Although recent success in developing targeted and immunotherapies for lung cancer has benefitted patients, it has also expanded the complexity of potential treatment options for health care providers. To aid in reducing such complexity, experts in oncology convened a conference (Bridging the Gaps in Lung Cancer) to identify current knowledge gaps and controversies in the diagnosis, treatment, and outcomes of various lung cancer scenarios, as described here. Such scenarios relate to biomarkers and testing in lung cancer, small cell lung cancer, EGFR mutations and targeted therapy in non-small cell lung cancer (NSCLC), early-stage NSCLC, KRAS/BRAF/MET and other genomic alterations in NSCLC, and immunotherapy in advanced NSCLC.

Patient-reported observations on medical procedure timeliness (PROMPT) in breast cancer: a qualitative study.

PURPOSE: Timeliness of care is an important healthcare outcome measure. The objective of this study was to explore patient perspectives on the timeliness of breast cancer diagnosis and treatment at accredited breast cancer centers. METHODS: In this qualitative study, 1 hour virtual interviews were conducted with participants 18-75 years old who were diagnosed and treated for stage 0-III breast cancer at a National Accreditation Program for Breast Centers facility from 2018 to 2022. Thematic analysis was used to identify key themes of participant experiences. RESULTS: Twenty-eight participants were interviewed. Two thematic domains were identified: etiologies of expedited or delayed care and the impact of delayed or expedited care on patients. Within these domains, multiple themes emerged. For etiologies of expedited or delayed care, participants discussed (1) the effect of scheduling appointments, (2) the COVID-19 pandemic, (3) dissatisfaction with the timeline for various parts of the diagnostic workup, and (4) delays related to patient factors, including socioeconomic status. For the impact of expedited or delayed care, patients discussed (1) the emotional and mental impact of waiting, (2) the importance of communication and clear expectations, and (3) the impact of electronic health portals. Patients desired each care interval (e.g., the time from mammogram to breast biopsy) to be approximately 7 days, with longer intervals sometimes preferred prior to surgery. CONCLUSION: These patient interviews identify areas of delay and provide patient-centered, actionable items to improve the timeliness of breast cancer care.

Phenotypes of carriers of two mutated alleles in major cancer susceptibility genes.

PURPOSE: While cancer phenotypes in carriers of a single mutant allele in most major cancer susceptibility genes are well-established, there is a paucity of data on the phenotype of carriers of two pathogenic variants-double heterozygotes (DH) or homozygous carriers. Here, we describe the phenotype of carriers of homozygous and DH pathogenic sequence variants (PSVs) in major cancer susceptibility genes. METHODS: Individuals referred for multigene panel testing at Blueprint Genetics laboratory were included. Ethically approved comparison of cancer type and age at diagnosis between DH, homozygous, and single PSV carriers was performed per gene. RESULTS: Of 6,685 eligible participants, 928 (13.9%) were single heterozygous PSV carriers, 6 (0.09%) were homozygous PSV carriers, and 17 (0.25%) were DH PSV carriers. Mean age at diagnosis of any cancer among single PSV age was 46.8 ± 14.9 years and among DH PSV carriers 37.6 ± 13.0 years (P < 0.0001). Notably, age at diagnosis for breast cancer among single BRCA1 PSV carriers (n = 59) was 43.8 ± 8.7 years (p = 0.7606), among single BRCA2 PSV carriers (n = 52)-47.9 ± 13.0 years (p = 0.2274) compared with 42.3 ± 13.0 years among DH PSV carriers (n = 10- 9 of whom were carriers of either BRCA1 or BRCA2). CONCLUSION: DH for PSV in two cancer susceptibility genes is a rare event, and the mean age at cancer diagnosis is younger in DH PSV carriers compared with single PSV carriers.

Integrated analysis of diverse cancer types reveals a breast cancer-specific serum miRNA biomarker through relative expression orderings analysis.

PURPOSE: Serum microRNA (miRNA) holds great potential as a non-invasive biomarker for diagnosing breast cancer (BrC). However, most diagnostic models rely on the absolute expression levels of miRNAs, which are susceptible to batch effects and challenging for clinical transformation. Furthermore, current studies on liquid biopsy diagnostic biomarkers for BrC mainly focus on distinguishing BrC patients from healthy controls, needing more specificity assessment. METHODS: We collected a large number of miRNA expression data involving 8465 samples from GEO, including 13 different cancer types and non-cancer controls. Based on the relative expression orderings (REOs) of miRNAs within each sample, we applied the greedy, LASSO multiple linear regression, and random forest algorithms to identify a qualitative biomarker specific to BrC by comparing BrC samples to samples of other cancers as controls. RESULTS: We developed a BrC-specific biomarker called 7-miRPairs, consisting of seven miRNA pairs. It demonstrated comparable classification performance in our analyzed machine learning algorithms while requiring fewer miRNA pairs, accurately distinguishing BrC from 12 other cancer types. The diagnostic performance of 7-miRPairs was favorable in the training set (accuracy = 98.47%, specificity = 98.14%, sensitivity = 99.25%), and similar results were obtained in the test set (accuracy = 97.22%, specificity = 96.87%, sensitivity = 98.02%). KEGG pathway enrichment analysis of the 11 miRNAs within the 7-miRPairs revealed significant enrichment of target mRNAs in pathways associated with BrC. CONCLUSION: Our study provides evidence that utilizing serum miRNA pairs can offer significant advantages for BrC-specific diagnosis in clinical practice by directly comparing serum samples with BrC to other cancer types.

Recent Advances in Electrochemiluminescence Biosensors for MicroRNA Detection.

Electrochemiluminescence (ECL) as an analytical technology with a perfect combination of electrochemistry and spectroscopy has received considerable attention in bioanalysis due to its high sensitivity and broad dynamic range. Given the selectivity of bio-recognition elements and the high sensitivity of the ECL analysis technique, ECL biosensors are powerful platforms for the sensitive detection of biomarkers, achieving the accurate prognosis and diagnosis of diseases. MicroRNAs (miRNAs) are crucial biomarkers involved in a variety of physiological and pathological processes, whose aberrant expression is often related to serious diseases, especially cancers. ECL biosensors can fulfill the highly sensitive and selective requirements for accurate miRNA detection, prompting this review. The ECL mechanisms are initially introduced and subsequently categorize the ECL biosensors for miRNA detection in terms of the quenching agents. Furthermore, the work highlights the signal amplification strategies for enhancing ECL signal to improve the sensitivity of miRNA detection and finally concludes by looking at the challenges and opportunities in ECL biosensors for miRNA detection.

Graphene-based hybrid composites for cancer diagnostic and therapy.

The application of graphene-based nanocomposites for therapeutic and diagnostic reasons has advanced considerably in recent years due to advancements in the synthesis and design of graphene-based nanocomposites, giving rise to a new field of nano-cancer diagnosis and treatment. Nano-graphene is being utilized more often in the field of cancer therapy, where it is employed in conjunction with diagnostics and treatment to address the complex clinical obstacles and problems associated with this life-threatening illness. When compared to other nanomaterials, graphene derivatives stand out due to their remarkable structural, mechanical, electrical, optical, and thermal capabilities. The high specific surface area of these materials makes them useful as carriers in controlled release systems that respond to external stimuli; these compounds include drugs and biomolecules like nucleic acid sequences (DNA and RNA). Furthermore, the presence of distinctive sheet-like nanostructures and the capacity for photothermal conversion have rendered graphene-based nanocomposites highly favorable for optical therapeutic applications, including photothermal treatment (PTT), photodynamic therapy (PDT), and theranostics. This review highlights the current state and benefits of using graphene-based nanocomposites in cancer diagnosis and therapy and discusses the obstacles and prospects of their future development. Then we focus on graphene-based nanocomposites applications in cancer treatment, including smart drug delivery systems, PTT, and PDT. Lastly, the biocompatibility of graphene-based nanocomposites is also discussed to provide a unique overview of the topic.