A roadmap for the next decade in cancer research

scTML: a pan-cancer single-cell landscape of multiple mutation types

Investigating mutations, including single nucleotide variations (SNVs), gene fusions, alternative splicing and copy number variations (CNVs), is fundamental to cancer study. Recent computational methods and biological research have demonstrated the reliability and biological significance of detecting mutations from single-cell transcriptomic data. However, there is a lack of a single-cell-level database containing comprehensive mutation information in all types of cancer. Establishing a single-cell mutation landscape from the huge emerging single-cell transcriptomic data can provide a critical resource for elucidating the mechanisms of tumorigenesis and evolution. Here, we developed scTML (http://sctml.xglab.tech/), the first database offering a pan-cancer single-cell landscape of multiple mutation types. It includes SNVs, insertions/deletions, gene fusions, alternative splicing and CNVs, along with gene expression, cell states and other phenotype information. The data are from 74 datasets with 2 582 633 cells, including 35 full-length (Smart-seq2) transcriptomic single-cell datasets (all publicly available data with raw sequencing files), 23 datasets from 10X technology and 16 spatial transcriptomic datasets. scTML enables users to interactively explore multiple mutation landscapes across tumors or cell types, analyze single-cell-level mutation-phenotype associations and detect cell subclusters of interest. scTML is an important resource that will significantly advance deciphering intra-tumor and inter-tumor heterogeneity, and how mutations shape cell phenotypes.

Bioprinted research models of urological malignancy

Urological malignancy (UM) is among the leading threats to health care worldwide. Recent years have seen much investment in fundamental UM research, including mechanistic investigation, early diagnosis, immunotherapy, and nanomedicine. However, the results are not fully satisfactory. Bioprinted research models (BRMs) with programmed spatial structures and functions can serve as powerful research tools and are likely to disrupt traditional UM research paradigms. Herein, a comprehensive review of BRMs of UM is presented. It begins with a brief introduction and comparison of existing UM research models, emphasizing the advantages of BRMs, such as modeling real tissues and organs. Six kinds of mainstream bioprinting techniques used to fabricate such BRMs are summarized with examples. Thereafter, research advances in the applications of UM BRMs, such as culturing tumor spheroids and organoids, modeling cancer metastasis, mimicking the tumor microenvironment, constructing organ chips for drug screening, and isolating circulating tumor cells, are comprehensively discussed. At the end of this review, current challenges and future development directions of BRMs and UM are highlighted from the perspective of interdisciplinary science.

Life history traits and cancer prevalence in birds

Background and objectives

Cancer is a disease that affects nearly all multicellular life, including the broad and diverse taxa of Aves. While little is known about the factors that contribute to cancer risk across Aves, life history trade-offs may explain some of this variability in cancer prevalence. We predict birds with high investment in reproduction may have a higher likelihood of developing cancer. In this study, we tested whether life history traits are associated with cancer prevalence in 108 species of birds.

Methodology

We obtained life history data from published databases and cancer data from 5,729 necropsies from 108 species of birds across 24 taxonomic orders from 25 different zoological facilities. We performed phylogenetically controlled regression analyses between adult body mass, lifespan, incubation length, clutch size, sexually dimorphic traits, and both neoplasia and malignancy prevalence. We also compared the neoplasia and malignancy prevalence of female and male birds.

Results

Providing support for a life history trade-off between somatic maintenance and reproduction, we found a positive relationship between clutch size and cancer prevalence across Aves. There was no significant association with body mass, lifespan, incubation length, sexual dimorphism, and cancer.

Conclusions and implications

Life history theory presents an important framework for understanding differences in cancer defenses across various species. These results suggest a trade-off between reproduction and somatic maintenance, where Aves with small clutch sizes get less cancer.

HOXB9 promotes osteosarcoma cell survival and malignancy under glucose starvation via upregulating SPP1 expression

Homeobox B9 (HOXB9) has been shown to play a critical role in several tumors. However, the precise biological mechanisms and functions of HOXB9 in osteosarcoma remain largely unknown. In this study, we found that HOXB9 was increased upon glucose starvation. Elevated HOXB9 suppressed osteosarcoma cell death and supported cell growth and migration under glucose starvation. Further mechanistic studies demonstrated that HOXB9 directly bound to the promoter of secreted phosphoprotein 1 (SPP1) and transcriptionally upregulated SPP1 expression which then led cell death decrease and cell growth increase under glucose deprivation environment. Clinically, HOXB9 was significantly upregulated in osteosarcoma compared with normal tissues and increase of HOXB9 expression was positively associated with the elevation of SPP1 in osteosarcoma. Overall, our study illustrates that HOXB9 contributes to malignancy in osteosarcoma and inhibits cell death through transcriptional upregulating SPP1 under glucose starvation.

Enzyme-mimic catalytic activities and biomedical applications of noble metal nanoclusters

Noble metal (e.g., Au and Ag) nanoclusters (NCs), which exhibit structural complexity and hierarchy comparable to those of natural proteins, have been increasingly pursued in artificial enzyme research. The protein-like structure of metal NCs not only ensures enzyme-mimic catalytic activity, including peroxidase-, catalase-, and superoxide dismutase-mimic activities, but also affords an unprecedented opportunity to correlate the catalytic performance with the cluster structure at the molecular or atomic levels. In this review, we aim to summarize the recent progress in programming and demystify the enzyme-mimic catalytic activity of metal NCs, presenting the state-of-the-art understandings of the structure-property relationship of metal NC-based artificial enzymes. By leveraging on a concise anatomy of the hierarchical structure of noble metal NCs, we manage to unravel the structural origin of the catalytic performance of metal NCs. Noteworthily, it has been proven that the surface ligands and metal-ligand interface of metal NCs are instrumental in influencing enzyme-mimic catalytic activities. In addition to the structure-property correlation, we also discuss the synthetic methodologies feasible to tailoring the cluster structure at the atomic level. Prior to the closure of this review with our perspectives in noble metal NC-based artificial enzymes, we also exemplify the biomedical applications based on the enzyme-mimic catalysis of metal NCs with the theranostics of kidney injury, brain inflammation, and tumors. The fundamental and methodological advancements delineated in this review would be conducive to further development of metal NCs as an alternative family of artificial enzymes.

A novel multiscale framework for delineating cancer evolution from subclonal compositions

OBJECTIVE

Owing to the heterogeneity in the evolution of cancer, distinguishing between diverse growth patterns and predicting long-term outcomes based on short-term measurements poses a great challenge.

METHODS

A novel multiscale framework is proposed to unravel the connections between the population dynamics of cancer growth (i.e., aggressive, bounded, and indolent) and the cellular-subclonal dynamics of cancer evolution. This framework employs the non-negative lasso (NN-LASSO) algorithm to forge a link between an ordinary differential equation (ODE)-based population model and a cellular evolution model.

RESULTS

The findings of our current work not only affirm the impact of subclonal composition on growth dynamics but also identify two significant subclones within heterogeneous growth patterns. Moreover, the subclonal compositions at the initial time are able to accurately discriminate diverse growth patterns through a machine learning algorithm.

CONCLUSION

The proposed multiscale framework successfully delineates the intricate landscape of cancer evolution, bridging the gap between long-term growth dynamics and short-term measurements, both in simulated and real-world data. This methodology provides a novel avenue for thorough exploration into the realm of cancer evolution.

Activated KRAS reprograms neural progenitor cells to glioma stem cell‑like phenotype

Glioma is the most common primary brain tumor. Glioma stem cells (GSCs) are the origin of gliomagenesis and may develop from normal neural progenitor cells (NPCs). However, how neoplastic transformation occurs in normal NPCs and the role of the Ras/Raf/MAPK pathway in NPC transformation is unclear. The present study generated NPCs from human embryonic stem cells (ESCs) carrying gene alterations in the Ras/Raf/MAPK pathway. The CCK‑8 proliferation, single‑cell clonal expansion, cell migration, RT‑qPCR, immunofluorescence staining, western blotting, transcriptome and Seahorse analyses, and intracranial implantation assay were performed to identify the characterization of transformed NPCs in vitro and in vivo. Brain organoids were used to verify the phenotypes transforming in NPCs. KRAS‑activated NPCs exhibited increased proliferation and migration in vitro. KRAS‑activated NPCs showed atypical morphology and formed aggressive tumors in immunodeficient mice. At the molecular level, KRAS‑activated NPCs displayed neoplasm‑associated metabolic and gene expression profiles. Moreover, activation of KRAS led to substantial cell proliferation and abnormal structure in ESC‑derived brain organoids. The present study showed that activated KRAS transformed normal NPCs to GSC‑like cells and established a simple cellular model to investigate gliomagenesis.

Life history and cancer in birds: clutch size predicts cancer

Cancer is a disease that affects nearly all multicellular life, including birds. However, little is known about what factors explain the variance in cancer prevalence among species. Litter size is positively correlated with cancer prevalence in managed species of mammals, and larger body size, but not incubation or nestling period, is linked to tumor prevalence in wild birds. Also, birds that produce more elaborate sexual traits are expected to have fewer resources for cancer defenses and thus higher cancer prevalence. In this study, we examined whether cancer prevalence is associated with a wide variety of life history traits (clutch size, incubation length, body mass, lifespan, and the extent of sexual dimorphism) across 108 species of managed birds in 25 different zoological facilities, sanctuaries, and veterinary clinics. We found that clutch size was positively correlated with cancer and neoplasia (both benign and malignant) prevalence, even after controlling for body mass. Cancer prevalence was not associated with incubation length, body mass, lifespan, or sexual dimorphism. The positive correlations of clutch size with cancer prevalence and neoplasia prevalence suggest that there may be life-history trade-offs between reproductive investment and somatic maintenance (in the form of cancer prevention mechanisms) in managed birds.

Acoustic Bioprinting of Patient-Derived Organoids for Predicting Cancer Therapy Responses

Cancer models, which are biologically representative of patient tumors, can predict the treatment responses and help determine the most appropriate cancer treatment for individual patients. Here, a point-of-care testing system called acoustically bioprinted patient-derived microtissues (PDMs) that can model cancer invasion and predict treatment response in individual patients with colorectal cancer (CRC), is reported. The PDMs are composed of patient-derived colorectal tumors and healthy organoids which can be precisely arranged by acoustic bioprinting approach for recapulating primary tissue's architecture. Particularly, these tumor organoids can be efficiently generated and can apprehend histological, genomic, and phenotypical characteristics of primary tumors. Consequently, these PDMs allow physiologically relevant in vitro drug (5-fluorouracil) screens, thus predicting the paired patient's responses to chemotherapy. A correlation between organoid invasion speed and normalized spreading speed of the paired patients is further established. It provides a quantitative indicator to help doctors make better decisions on ultimate anus-preserving operation for extremely low CRC patients. Thus, by combing acoustic bioprinting and organoid cultures, this method may open an avenue to establish complex 3D tissue models for precision and personalized medicine.

Nanomedicine for brain cancer

CNS tumors remain among the deadliest forms of cancer, resisting conventional and new treatment approaches, with mortality rates staying practically unchanged over the past 30 years. One of the primary hurdles for treating these cancers is delivering drugs to the brain tumor site in therapeutic concentration, evading the blood-brain (tumor) barrier (BBB/BBTB). Supramolecular nanomedicines (NMs) are increasingly demonstrating noteworthy prospects for addressing these challenges utilizing their unique characteristics, such as improving the bioavailability of the payloadsviacontrolled pharmacokinetics and pharmacodynamics, BBB/BBTB crossing functions, superior distribution in the brain tumor site, and tumor-specific drug activation profiles. Here, we review NM-based brain tumor targeting approaches to demonstrate their applicability and translation potential from different perspectives. To this end, we provide a general overview of brain tumor and their treatments, the incidence of the BBB and BBTB, and their role on NM targeting, as well as the potential of NMs for promoting superior therapeutic effects. Additionally, we discuss critical issues of NMs and their clinical trials, aiming to bolster the potential clinical applications of NMs in treating these life-threatening diseases.

Analysis of Novel Variants Associated with Three Human Ovarian Cancer Cell Lines

Background:

Identification of mutations is of great significance in cancer research, as it can contribute to development of therapeutic strategies and prevention of cancer formation. Ovarian cancer is one of the leading cancer-related causes of death in Taiwan. Accumulation of genetic mutations can lead to cancer.

Objective:

We utilized whole-exome sequencing to explore cancer-associated missense variants in three human ovarian cancer cell lines derived from Taiwanese patients.

Methods:

We use (i) cell line whole-exome sequencing data, (ii) 188 patients’ whole-exome sequencing data, and (iii) use of in vitro experiments to verify predicted variant results. We establish an effective analysis workflow for discovery of novel ovarian cancer variants, comprising three steps: (i) use of public databases and in-house hospital data to select novel variants (ii) investigation of protein structural stability caused by genetic mutations, and (iii) use of in vitro experiments to verify predictions.

Results:

Our study enumerated 296 novel variants by imposing specific criteria and using sophisticated bioinformatics tools for further analysis. Eleven and 54 missense novel variants associated with cancerous and non-cancerous genes, respectively, were identified. We show that 13 missense mutations affect the stability of protein 3D structure, while 11 disease-causing novel variants were confirmed by PCR sequencing. Among these, ten variants were predicted to be pathogenic, while the pathogenicity of one was uncertain.

Conclusion:

We confirm that novel variant genes play a crucial role in ovarian cancer patients, with 11 novel variants that may promote progression and development of ovarian cancer.

The explanation of a complex problem: A content analysis of causality in cancer news

Understanding causality is a critical part of developing preventive and treatment actions against cancer. Three main causality models-necessary, sufficient-component, and probabilistic causality have been commonly used to explain the causation between causal factors and risks in health science. However, news media do not usually follow a strict protocol to report the causality of health risks. The purpose of this study was to describe and understand how the causation of cancer was articulated on news media. A content analysis of 471 newspaper articles published in the United States during two time-frames (2007-2008 and 2017-2018) was conducted. The analysis showed that probabilistic causality was most frequently used to explain the causal relationship between risk factors and cancer. The findings also uncovered other important details of news framing, including types and characteristics of risk factors, intervention measures, and sources of evidence. The results provided theoretical and practical implications for public understanding and assessment of cancer risks.

Single-cell RNA sequencing and bioinformatics as tools to decipher cancer heterogenicity and mechanisms of drug resistance

It is well known that cancer is an aggressive disease, often associated with relapse, in many cases due to drug resistance. Cancer stem cell and clonal evolution are frequently causes of innate or acquired drug resistance. Current RNA sequencing technologies do not distinguish gene expression of different cell lineages because they are based on bulk cell studies. Single-cell RNA sequencing technologies and related bioinformatics clustering and differential expression analysis represent a turning point in cancer research. They are emerging as essential tools for dissecting tumors at single-cell resolution and represent novel tools to understand carcinogenesis and drug response. In this review, we will outline the role of these new technologies in addressing cancer heterogeneity and cell lineage-dependent drug resistance.

Present trends in the encapsulation of anticancer drugs

Different nanomedicine devices that were developed during the recent years can be suitable candidates for their application in the treatment of various deadly diseases such as cancer. From all the explored devices, the nanoencapsulation of several anticancer medicines is a very promising approach to overcome some drawbacks of traditional medicines: administered dose of the drugs, drug toxicity, low solubility of drugs, uncontrolled drug delivery, resistance offered by the physiological barriers in the body to drugs, among others. In this chapter, the most important and recent progress in the encapsulation of anticancer medicines is examined: methods of preparation of distinct nanoparticles (inorganic nanoparticles, dendrimers, biopolymeric nanoparticles, polymeric micelles, liposomes, polymersomes, carbon nanotubes, quantum dots, and hybrid nanoparticles), drug loading and drug release mechanisms. Furthermore, the possible applications in cancer prevention, diagnosis, and cancer therapy of some of these nanoparticles have been highlighted.

Kinetic Measurement of Apoptosis and Immune Cell Killing Using Live-Cell Imaging and Analysis

The rapid, efficient detection of cell death is critical for characterizing the underlying biology of in vitro disease models and, in particular, immunotherapy products used for preclinical therapeutic research. Traditional endpoint assays are laborious to perform for mass screening of therapeutic candidates and may fail to fully capture the kinetics of events surrounding the initiation, duration, and mechanisms of cell death-important events that may affect translational relevance and impact therapeutic decision-making during development. Here, we describe simple, efficient methods to measure apoptosis and immune cell killing in both adherent and nonadherent cell populations using the Incucyte^® Live-Cell Analysis system and associated nonperturbing reagents, cells, and protocols. Assays are performed in the user's own incubator with minimal disturbance and may be readily incorporated into existing workflows. Users may multiplex to maximize data collection from each sample. The integrated, user-friendly software does not require advanced technical training, enabling rapid analysis. Taken together, this method provides essential kinetic insight for greater understanding of cell death and the dynamic interactions between immune cells and their targets.

Next-generation sequencing-guided molecular-targeted therapy and immunotherapy for biliary tract cancers

BACKGROUND

Chemotherapy is a standard regimen for advanced or relapsed biliary tract cancer (BTC) with a 5-year overall survival (OS) rate of approximately 5% and a median OS of less than a year. Targeted therapies and immunotherapy aimed at providing more personalized treatments for BTCs have been tested. The objective of this study was to evaluate the effects of targeted therapy and immunotherapy on advanced BTC patients.

METHODS

Twenty-four advanced/relapsed BTC patients were enrolled and examined with next-generation sequencing (NGS). Eight of them received NGS-guided targeted or immunotherapy, and the other 16 patients underwent routine chemotherapy. Comparison analysis of OS and objective response rate (ORR) was performed.

RESULTS

IDH1, BRCA2, MAP2K1, and BRAF (V600E) were the major actionable genes mutated in this cohort. Patients who received NGS-guided therapy exhibited higher OS (not achieved vs. 6.5 months, p < 0.001) and ORR (87.5% vs. 25%, p < 0.001) than those without targetable mutations and who received first-line chemotherapy. BTCs harboring mutations in IDH1, ATM/BRCA2, or MAP2K1/BRAF (V600E) received treatment with dasatinib, olaparib, or trametinib, respectively. Three of the patients had high tumor mutation burden (TMB-H) and were treated with immune-checkpoint inhibitors and chemotherapy. All these patients achieved complete response or partial response.

CONCLUSIONS

NGS-guided targeted therapy and immunotherapy are promising personalized therapies for advanced or relapsed BTCs. TMB is a useful biomarker for predicting immunotherapy efficacy.

Clearance of therapy-induced senescent tumor cells by the senolytic ABT-263 via interference with BCL-XL -BAX interaction

Tumor cells undergo senescence in response to both conventional and targeted cancer therapies. The induction of senescence in response to cancer therapy can contribute to unfavorable patient outcomes, potentially including disease relapse. This possibiliy is supported by our findings that tumor cells induced into senescence by doxorubicin or etoposide can give rise to viable tumors in vivo. We further demonstrate sensitivity of these senescent tumor cells to the senolytic ABT-263 (navitoclax), therefore providing a "two-hit" approach to eliminate senescent tumor cells that persist after exposure to chemotherapy or radiation. The sequential combination of therapy-induced senescence and ABT-263 could shift the response to therapy toward apoptosis by interfering with the interaction between BCL-XL and BAX. The administration of ABT-263 after either etoposide or doxorubicin also resulted in marked, prolonged tumor suppression in tumor-bearing animals. These findings support the premise that senolytic therapy following conventional cancer therapy may improve therapeutic outcomes and delay disease recurrence.