A Deep Learning Model for Cancer Type Prediction Sets a New Standard.

SUMMARY: Classifying tumor types using machine learning approaches is not always trivial, particularly for challenging cases such as cancers of unknown primary. In this issue of Cancer Discovery, Darmofal and colleagues describe a new tool that uses information from a clinical sequencing panel to diagnose tumor type, and show that the model is particularly robust. See related article by Darmofal et al., p. 1064 (1).

Optimus-Era Dose Finding for Rare Cancers.

SUMMARY: Advances in cancer biology and diagnostics have led to the recognition of a multitude of rare cancer subtypes, emphasizing the pressing need for strategies to accelerate drug development for patients with these cancers. This paper addresses the unique challenges of dose finding in trials that accrue small numbers of patients with rare cancers; strategies for dose optimization are proposed, in line with evolving approaches to dose determination in the age of the US Food and Drug Administration's Project Optimus.

Mitochondria: A source of potential biomarkers for non-communicable diseases.

Mitochondria, as an endosymbiont of eukaryotic cells, controls multiple cellular activities, including respiration, reactive oxygen species production, fatty acid synthesis, and death. Though the majority of functional mitochondrial proteins are translated through a nucleus-controlled process, very few of them (∼10%) are translated within mitochondria through their own machinery. Germline and somatic mutations in mitochondrial and nuclear DNA significantly impact mitochondrial homeostasis and function. Such modifications disturbing mitochondrial biogenesis, metabolism, or mitophagy eventually resulted in cellular pathophysiology. In this chapter, we discussed the impact of mitochondria and its dysfunction on several non-communicable diseases like cancer, diabetes, neurodegenerative, and cardiovascular problems. Mitochondrial dysfunction and its outcome could be screened by currently available omics-based techniques, flow cytometry, and high-resolution imaging. Such characterization could be evaluated as potential biomarkers to assess the disease burden and prognosis.

Primary care patient interests in joining a planned multi-cancer early detection clinical trial.

INTRODUCTION: Clinical trials are being conducted and are being planned to assess the safety and efficacy of multi-cancer early detection (MCED) tests for use in cancer screening. This study aimed to determine the feasibility of primary care patient outreach in recruiting participants to a planned MCED clinical trial, assess patient interest in trial participation, and measure decisional conflict related to participation. METHODS: The research team used the electronic medical record of a large, urban health care system to identify primary care patients 50-80 years of age who were potentially eligible for a planned MCED trial. We mailed information about the planned MCED trial to identified patients and then contacted the patients by telephone to obtain consent and administer a baseline survey. Subsequently, we contacted consented patients to complete an interview to review the mailed information and elicit perceptions about trial participation. Finally, a research coordinator administered an endpoint telephone survey to assess patient interest in and decisional conflict related to joining the trial. RESULTS: We randomly identified 1000 eligible patients and were able to make contact with 690 (69%) by telephone. Of the patients contacted, 217 (31%) completed the decision counseling session and 219 (32%) completed the endpoint survey. Among endpoint survey respondents, 177 (81%) expressed interest in joining the MCED trial and 162 (74%) reported low decisional conflict. CONCLUSIONS: Most patients were contacted and about a quarter of those contacted expressed interest in and low decisional conflict about joining the planned MCED trial. Research is needed to determine how to optimize patient outreach and engage patients in shared decision-making about MCED trial participation.

Tumor versus Tumor Cell Targeting in Metal-Based Nanoparticles for Cancer Theranostics.

The application of metal-based nanoparticles (mNPs) in cancer therapy and diagnostics (theranostics) has been a hot research topic since the early days of nanotechnology, becoming even more relevant in recent years. However, the clinical translation of this technology has been notably poor, with one of the main reasons being a lack of understanding of the disease and conceptual errors in the design of mNPs. Strikingly, throughout the reported studies to date on in vivo experiments, the concepts of "tumor targeting" and "tumor cell targeting" are often intertwined, particularly in the context of active targeting. These misconceptions may lead to design flaws, resulting in failed theranostic strategies. In the context of mNPs, tumor targeting can be described as the process by which mNPs reach the tumor mass (as a tissue), while tumor cell targeting refers to the specific interaction of mNPs with tumor cells once they have reached the tumor tissue. In this review, we conduct a critical analysis of key challenges that must be addressed for the successful targeting of either tumor tissue or cancer cells within the tumor tissue. Additionally, we explore essential features necessary for the smart design of theranostic mNPs, where 'smart design' refers to the process involving advanced consideration of the physicochemical features of the mNPs, targeting motifs, and physiological barriers that must be overcome for successful tumor targeting and/or tumor cell targeting.

A Comprehensive Exploration of Caspase Detection Methods: From Classical Approaches to Cutting-Edge Innovations.

The activation of caspases is a crucial event and an indicator of programmed cell death, also known as apoptosis. These enzymes play a central role in cancer biology and are considered one promising target for current and future advancements in therapeutic interventions. Traditional methods of measuring caspase activity such as antibody-based methods provide fundamental insights into their biological functions, and are considered essential tools in the fields of cell and cancer biology, pharmacology and toxicology, and drug discovery. However, traditional methods, though extensively used, are now recognized as having various shortcomings. In addition, these methods fall short of providing solutions to and matching the needs of the rapid and expansive progress achieved in studying caspases. For these reasons, there has been a continuous improvement in detection methods for caspases and the network of pathways involved in their activation and downstream signaling. Over the past decade, newer methods based on cutting-edge state-of-the-art technologies have been introduced to the biomedical community. These methods enable both the temporal and spatial monitoring of the activity of caspases and their downstream substrates, and with enhanced accuracy and precision. These include fluorescent-labeled inhibitors (FLIs) for live imaging, single-cell live imaging, fluorescence resonance energy transfer (FRET) sensors, and activatable multifunctional probes for in vivo imaging. Recently, the recruitment of mass spectrometry (MS) techniques in the investigation of these enzymes expanded the repertoire of tools available for the identification and quantification of caspase substrates, cleavage products, and post-translational modifications in addition to unveiling the complex regulatory networks implicated. Collectively, these methods are enabling researchers to unravel much of the complex cellular processes involved in apoptosis, and are helping generate a clearer and comprehensive understanding of caspase-mediated proteolysis during apoptosis. Herein, we provide a comprehensive review of various assays and detection methods as they have evolved over the years, so to encourage further exploration of these enzymes, which should have direct implications for the advancement of therapeutics for cancer and other diseases.

Considerations for using potential surrogate endpoints in cancer screening trials.

The requirement of large-scale expensive cancer screening trials spanning decades creates considerable barriers to the development, commercialisation, and implementation of novel screening tests. One way to address these problems is to use surrogate endpoints for the ultimate endpoint of interest, cancer mortality, at an earlier timepoint. This Review aims to highlight the issues underlying the choice and use of surrogate endpoints for cancer screening trials, to propose criteria for when and how we might use such endpoints, and to suggest possible candidates. We present the current landscape and challenges, and discuss lessons and shortcomings from the therapeutic trial setting. It is hugely challenging to validate a surrogate endpoint, even with carefully designed clinical studies. Nevertheless, we consider whether there are candidates that might satisfy the requirements defined by research and regulatory bodies.

Significance of aberrant DNA methylation for cancer diagnostics and therapy.

BACKGROUND: Epigenetics is a scientific field that covers changes in gene expression that are not caused by the alteration of the nucleotide sequence in the DNA strand. Together with sequential changes, epigenetic reprogramming is a recognized cancer hallmark driving carcinogenesis. The underlying mechanisms of epigenetically-driven gene expression changes are diverse. However, one of the most extensively studied mechanisms is a change in DNA methylation. Under physiological conditions, DNA methylation ensures tissue-specific gene silencing and helps to maintain genome stability. With malignant transformation, genomic DNA undergoes global hypomethylation as well as locus-specific hypermethylation in promoters of tumor suppressor genes. In the last few decades, specific aberrant DNA methylation changes have emerged as both cancer-associated biomarkers and therapeutic targets and prompted ongoing efforts to enhance both diagnostic and therapeutic means in oncology. PURPOSE: The main purpose of this review is to introduce both established and emerging DNA methylation-based biomarkers for cancer diagnostics with a focus on biomarkers that are either routinely used or have been developed as commercial tests with certification for their use within in vitro diagnostics. Furthermore, therapeutic options for targeting aberrant DNA methylation are described, including both approved compounds and newly developed agents undergoing clinical investigation.

The effect of local hospital waiting times on GP referrals for suspected cancer.

INTRODUCTION: Reducing waiting times is a major policy objective in publicly-funded healthcare systems. However, reductions in waiting times can produce a demand response, which may offset increases in capacity. Early detection and diagnosis of cancer is a policy focus in many OECD countries, but prolonged waiting periods for specialist confirmation of diagnosis could impede this goal. We examine whether urgent GP referrals for suspected cancer patients are responsive to local hospital waiting times. METHOD: We used annual counts of referrals from all 6,667 general practices to all 185 hospital Trusts in England between April 2012 and March 2018. Using a practice-level measure of local hospital waiting times based on breaches of the two-week maximum waiting time target, we examined the relationship between waiting times and urgent GP referrals for suspected cancer. To identify whether the relationship is driven by differences between practices or changes over time, we estimated three regression models: pooled linear regression, a between-practice estimator, and a within-practice estimator. RESULTS: Ten percent higher rates of patients breaching the two-week wait target in local hospitals were associated with higher volumes of referrals in the pooled linear model (4.4%; CI 2.4% to 6.4%) and the between-practice estimator (12.0%; CI 5.5% to 18.5%). The relationship was not statistically significant using the within-practice estimator (1.0%; CI -0.4% to 2.5%). CONCLUSION: The positive association between local hospital waiting times and GP demand for specialist diagnosis was caused by practices with higher levels of referrals facing longer local waiting times. Temporal changes in waiting times faced by individual practices were not related to changes in their referral volumes. GP referrals for diagnostic cancer services were not found to respond to waiting times in the short-term. In this setting, it may therefore be possible to reduce waiting times by increasing supply without consequently increasing demand.

Applications of Pyrrole and Pyridine-based Heterocycles in Cancer Diagnosis and Treatment.

BACKGROUND: The escalation of cancer worldwide is one of the major causes of economy burden and loss of human resources. According to the American Cancer Society, there will be 1,958,310 new cancer cases and 609,820 projected cancer deaths in 2023 in the United States. It is projected that by 2040, the burden of global cancer is expected to rise to 29.5 million per year, causing a death toll of 16.4 million. The hemostasis regulation by cellular protein synthesis and their targeted degradation is required for normal cell growth. The imbalance in hemostasis causes unbridled growth in cells and results in cancer. The DNA of cells needs to be targeted by chemotherapeutic agents for cancer treatment, but at the same time, their efficacy and toxicity also need to be considered for successful treatment. OBJECTIVE: The objective of this study is to review the published work on pyrrole and pyridine, which have been prominent in the diagnosis and possess anticancer activity, to obtain some novel lead molecules of improved cancer therapeutic. METHODS: A literature search was carried out using different search engines, like Sci-finder, Elsevier, ScienceDirect, RSC etc., for small molecules based on pyrrole and pyridine helpful in diagnosis and inducing apoptosis in cancer cells. The research findings on the application of these compounds from 2018-2023 were reviewed on a variety of cell lines, such as breast cancer, liver cancer, epithelial cancer, etc. Results: In this review, the published small molecules, pyrrole and pyridine and their derivatives, which have roles in the diagnosis and treatment of cancers, were discussed to provide some insight into the structural features responsible for diagnosis and treatment. The analogues with the chromeno-furo-pyridine skeleton showed the highest anticancer activity against breast cancer. The compound 5-amino-N-(1-(pyridin-4- yl)ethylidene)-1H-pyrazole-4-carbohydrazides was highly potent against HEPG2 cancer cell. Redaporfin is used for the treatment of cholangiocarcinoma, biliary tract cancer, cisplatin-resistant head and neck squamous cell carcinoma, and pigmentation melanoma, and it is in clinical trials for phase II. These structural features present a high potential for designing novel anticancer agents for diagnosis and drug development. CONCLUSION: Therefore, the N- and C-substituted pyrrole and pyridine-based novel privileged small Nheterocyclic scaffolds are potential molecules used in the diagnosis and treatment of cancer. This review discusses the reports on the synthesis of such molecules during 2018-2023. The review mainly discusses various diagnostic techniques for cancer, which employ pyrrole and pyridine heterocyclic scaffolds. Furthermore, the anticancer activity of N- and C-substituted pyrrole and pyridine-based scaffolds has been described, which works against different cancer cell lines, such as MCF-7, A549, A2780, HepG2, MDA-MB-231, K562, HT- 29, Caco-2 cells, Hela, Huh-7, WSU-DLCL2, HCT-116, HBL-100, H23, HCC827, SKOV3, etc. This review will help the researchers to obtain a critical insight into the structural aspects of pyrrole and pyridine-based scaffolds useful in cancer diagnosis as well as treatment and design pathways to develop novel drugs in the future.

Classification performance assessment for imbalanced multiclass data.

The evaluation of diagnostic systems is pivotal for ensuring the deployment of high-quality solutions, especially given the pronounced context-sensitivity of certain systems, particularly in fields such as biomedicine. Of notable importance are predictive models where the target variable can encompass multiple values (multiclass), especially when these classes exhibit substantial frequency disparities (imbalance). In this study, we introduce the Imbalanced Multiclass Classification Performance (IMCP) curve, specifically designed for multiclass datasets (unlike the ROC curve), and characterized by its resilience to class distribution variations (in contrast to accuracy or F ß -score). Moreover, the IMCP curve facilitates individual performance assessment for each class within the diagnostic system, shedding light on the confidence associated with each prediction-an aspect of particular significance in medical diagnosis. Empirical experiments conducted with real-world data in a multiclass context (involving 35 types of tumors) featuring a high level of imbalance demonstrate that both the IMCP curve and the area under the IMCP curve serve as excellent indicators of classification quality.

JMJD6 Autoantibodies as a Potential Biomarker for Inflammation-Related Diseases.

Inflammation is closely associated with cerebrovascular diseases, cardiovascular diseases, diabetes, and cancers, and it is accompanied by the development of autoantibodies in the early stage of inflammation-related diseases. Hence, it is meaningful to discover novel antibody biomarkers targeting inflammation-related diseases. In this study, Jumonji C-domain-containing 6 (JMJD6) was identified by the serological identification of antigens through recombinant cDNA expression cloning. In particular, JMJD6 is an antigen recognized in serum IgG from patients with unstable angina pectoris (a cardiovascular disease). Then, the serum antibody levels were examined using an amplified luminescent proximity homogeneous assay-linked immunosorbent assay and a purified recombinant JMJD6 protein as an antigen. We observed elevated levels of serum anti-JMJD6 antibodies (s-JMJD6-Abs) in patients with inflammation-related diseases such as ischemic stroke, acute myocardial infarction (AMI), diabetes mellitus (DM), and cancers (including esophageal cancer, EC; gastric cancer; lung cancer; and mammary cancer), compared with the levels in healthy donors. The s-JMJD6-Ab levels were closely associated with some inflammation indicators, such as C-reactive protein and intima-media thickness (an atherosclerosis index). A better postoperative survival status of patients with EC was observed in the JMJD6-Ab-positive group than in the negative group. An immunohistochemical analysis showed that JMJD6 was highly expressed in the inflamed mucosa of esophageal tissues, esophageal carcinoma tissues, and atherosclerotic plaques. Hence, JMJD6 autoantibodies may reflect inflammation, thereby serving as a potential biomarker for diagnosing specific inflammation-related diseases, including stroke, AMI, DM, and cancers, and for prediction of the prognosis in patients with EC.

Recent Trends and Innovations in Bead-Based Biosensors for Cancer Detection.

Demand is strong for sensitive, reliable, and cost-effective diagnostic tools for cancer detection. Accordingly, bead-based biosensors have emerged in recent years as promising diagnostic platforms based on wide-ranging cancer biomarkers owing to the versatility, high sensitivity, and flexibility to perform the multiplexing of beads. This comprehensive review highlights recent trends and innovations in the development of bead-based biosensors for cancer-biomarker detection. We introduce various types of bead-based biosensors such as optical, electrochemical, and magnetic biosensors, along with their respective advantages and limitations. Moreover, the review summarizes the latest advancements, including fabrication techniques, signal-amplification strategies, and integration with microfluidics and nanotechnology. Additionally, the challenges and future perspectives in the field of bead-based biosensors for cancer-biomarker detection are discussed. Understanding these innovations in bead-based biosensors can greatly contribute to improvements in cancer diagnostics, thereby facilitating early detection and personalized treatments.

[Cancer registration in Iceland for 70 years - incidence, mortality and survival].

INTRODUCTION: The Icelandic Cancer Registry (ICR) was founded seventy years ago by the Icelandic Cancer Society. In 2007 the ICR became one of the health registers of the Directorate of Health. In this paper we present cancer incidence, mortality, and survival in Iceland over 70 years. MATERIAL AND METHODS: The ICR receives information on cancer diagnoses from histopathological laboratories, the Hospital Discharge Registry and the Cause of Death Registry. Iceland participates in the Nordic cancer database NORDCAN. Because of the small population size, random variation in numbers is very prominent. Therefore, data from ICR are published as five-year averages. RESULTS: For all malignancies combined, age-standardized incidence (ASI) in men rose steadily until around 15 years ago when a decline started. This is in line with prostate- and lung cancer incidence trends. In women, the ASI was lower than in men, but it is still on the rise despite declining lung cancer incidence. ASI for breast cancer, the most common cancer in women, is increasing. Simultaneously, cancer mortality for both sexes has declined in recent years and cancer survival is improving. CONCLUSIONS: Population-based cancer registration for over 70 years makes it possible to monitor the epidemiology of cancer in Iceland and compare with other countries. The changes in trends in ASI are in line with changes of cancer risk factors and diagnostic policy. The decline in cancer mortality and improvement in survival reflects advances in cancer treatment as well as effects of early detection and prevention.

Prognostic potential of lipid profiling in cancer patients: a systematic review of mass spectrometry-based studies.

Cancer prognosis remains a critical clinical challenge. Lipidomic analysis via mass spectrometry (MS) offers the potential for objective prognostic prediction, leveraging the distinct lipid profiles of cancer patient-derived specimens. This review aims to systematically summarize the application of MS-based lipidomic analysis in prognostic prediction for cancer patients. Our systematic review summarized 38 studies from the past decade that attempted prognostic prediction of cancer patients through lipidomics. Commonly analyzed cancers included colorectal, prostate, and breast cancers. Liquid (serum and urine) and tissue samples were equally used, with liquid chromatography-tandem MS being the most common analytical platform. The most frequently evaluated prognostic outcomes were overall survival, stage, and recurrence. Thirty-eight lipid markers (including phosphatidylcholine, ceramide, triglyceride, lysophosphatidylcholine, sphingomyelin, phosphatidylethanolamine, diacylglycerol, phosphatidic acid, phosphatidylserine, lysophosphatidylethanolamine, lysophosphatidic acid, dihydroceramide, prostaglandin, sphingosine-1-phosphate, phosphatidylinosito, fatty acid, glucosylceramide and lactosylceramide) were identified as prognostic factors, demonstrating potential for clinical application. In conclusion, the potential for developing lipidomics in cancer prognostic prediction was demonstrated. However, the field is still nascent, necessitating future studies for validating and establishing lipid markers as reliable prognostic tools in clinical practice.

Cancer literacy differences of basic knowledge, prevention, early detection, treatment and recovery: a cross-sectional study of urban and rural residents in Northeast China.

Background: Cancer literacy as a potential health intervention tool directly impacted the success of cancer prevention and treatment initiatives. This study aimed to evaluate the cancer literacy in Northeast China, and explore the factors contributing to urban-rural disparities. Methods: A cross-sectional survey was conducted in 14 cities across Liaoning Province, China, from August to October 2021, using the multistage probability proportional to size sampling (PPS) method. The survey comprised 4,325 participants aged 15-69 and encompassed 37 core knowledge-based questions spanning five dimensions. Associations between sociodemographic factors and the cancer literacy rate were evaluated using chi-square tests and multivariate logistic regression model. Results: The overall cancer literacy rate was 66.9% (95% CI: 65.6-68.2%). In the primary indicators, cancer literacy were highest in treatment (75.8, 95% CI: 74.2-77.4%) and early detection (68.2, 95% CI: 66.8-69.6%), followed by basic knowledge (67.2, 95% CI: 65.8-68.6%), recovery (62.6, 95% CI: 60.7-64.5%) and prevention (59.7, 95% CI: 58.2-61.3%). Regarding secondary indicators, the awareness rates regarding cancer-related risk factors (54.7, 95% CI: 52.8-56.5%) and early diagnosis of cancer (54.6, 95% CI: 52.7-56.6%) were notably inadequate. Rural participates exhibited lower cancer literacy across all dimensions compared to urban. Multi-factor analysis showed that factors such as advanced age, limited education or low household income were barriers to health literacy in rural areas. Conclusion: Strengthening awareness concerning prevention and early detection, particularly among key populations, and bridging the urban-rural cancer literacy gap are imperative steps toward achieving the Healthy China 2030 target.

Harnessing TME depicted by histological images to improve cancer prognosis through a deep learning system.

Spatial transcriptomics (ST) provides insights into the tumor microenvironment (TME), which is closely associated with cancer prognosis, but ST has limited clinical availability. In this study, we provide a powerful deep learning system to augment TME information based on histological images for patients without ST data, thereby empowering precise cancer prognosis. The system provides two connections to bridge existing gaps. The first is the integrated graph and image deep learning (IGI-DL) model, which predicts ST expression based on histological images with a 0.171 increase in mean correlation across three cancer types compared with five existing methods. The second connection is the cancer prognosis prediction model, based on TME depicted by spatial gene expression. Our survival model, using graphs with predicted ST features, achieves superior accuracy with a concordance index of 0.747 and 0.725 for The Cancer Genome Atlas breast cancer and colorectal cancer cohorts, outperforming other survival models. For the external Molecular and Cellular Oncology colorectal cancer cohort, our survival model maintains a stable advantage.

Gold nanostructure-enhanced immunosensing: ultra-sensitive detection of VEGF tumor marker for early disease diagnosis.

We present an advanced electrochemical immunosensor designed to detect the vascular endothelial growth factor (VEGF) precisely. The sensor is constructed on a modified porous gold electrode through a fabrication process involving the deposition of silver and gold on an FTO substrate. Employing thermal annealing and a de-alloying process, the silver is eliminated from the electrode, producing a reproducible porous gold substrate. Utilizing a well-defined protocol, we immobilize the heavy-chain (VHH) antibody against VEGF on the gold substrate, facilitating VEGF detection through various electrochemical methods. Remarkably, this immunosensor performs well, featuring an impressive detection limit of 0.05 pg/mL and an extensive linear range from 0.1 pg/mL to 0.1 µg/mL. This emphasizes it's to measure biomarkers across a wide concentration spectrum precisely. The robust fabrication methodology in this research underscores its potential for widespread application, offering enhanced precision, reproducibility, and remarkable detection capabilities for the developed immunosensor.

Tumor biomarkers for diagnosis, prognosis and targeted therapy.

Tumor biomarkers, the substances which are produced by tumors or the body's responses to tumors during tumorigenesis and progression, have been demonstrated to possess critical and encouraging value in screening and early diagnosis, prognosis prediction, recurrence detection, and therapeutic efficacy monitoring of cancers. Over the past decades, continuous progress has been made in exploring and discovering novel, sensitive, specific, and accurate tumor biomarkers, which has significantly promoted personalized medicine and improved the outcomes of cancer patients, especially advances in molecular biology technologies developed for the detection of tumor biomarkers. Herein, we summarize the discovery and development of tumor biomarkers, including the history of tumor biomarkers, the conventional and innovative technologies used for biomarker discovery and detection, the classification of tumor biomarkers based on tissue origins, and the application of tumor biomarkers in clinical cancer management. In particular, we highlight the recent advancements in biomarker-based anticancer-targeted therapies which are emerging as breakthroughs and promising cancer therapeutic strategies. We also discuss limitations and challenges that need to be addressed and provide insights and perspectives to turn challenges into opportunities in this field. Collectively, the discovery and application of multiple tumor biomarkers emphasized in this review may provide guidance on improved precision medicine, broaden horizons in future research directions, and expedite the clinical classification of cancer patients according to their molecular biomarkers rather than organs of origin.