Early clues to tumors and their microenvironments come from embryonic development. Here we review the literature and consider whether the embryonic brain and its pathologies can serve as a better model. Among embryonic organs, the brain is the most heterogenous and complex, with multiple lineages leading to wide spectrum of cell states and types. Its dysregulation promotes neurodevelopmental brain pathologies and pediatric tumors. Embryonic brain pathologies point to the crucial importance of spatial heterogeneity over time, akin to the tumor microenvironment. Tumors dedifferentiate through genetic mutations and epigenetic modulations; embryonic brains differentiate through epigenetic modulations. Our innovative review proposes learning developmental brain pathologies to target tumor evolution-and vice versa. We describe ways through which tumor pharmacology can learn from embryonic brains and their pathologies, and how learning tumor, and its microenvironment, can benefit targeting neurodevelopmental pathologies. Examples include pediatric low-grade versus high-grade brain tumors as in rhabdomyosarcomas and gliomas.

Neuropilin-1 (NRP1) is a transmembrane glycoprotein that functions as a co-receptor with various cellular functions. Our previous studies identified the NRP1 exon 4-skipping (NRP1-ΔE4) splice variant as an aggressive metastasis driver by activating endosomal signals. Here, we demonstrate the critical role of glycosaminoglycan (GAG) modification in regulating NRP1-ΔE4's cellular trafficking and oncogenic activity. NRP1-ΔE4 undergoes constitutive internalization into endosomes and subsequent exosomal release from colorectal cancer (CRC) cells. Exosomal NRP1-ΔE4 enhances the migration and invasion of both donor and recipient CRC cells. Genetic or pharmacological inhibition of exosome secretion, or immunodepletion of exosomal NRP1-ΔE4, markedly reduces its metastatic potential. Notably, GAG modification at the O-glycosylation site Ser612 is essential for NRP1-ΔE4's endosomal trafficking and exosomal release. This modification also promotes the formation of a trimeric complex with Met and β1-integrin, leading to their co-internalization and accumulation in endosomes, which activates FAK signaling and drives CRC metastasis. These findings reveal GAG modification as a key regulatory process that governs the endosomal-exosomal trafficking of NRP1-ΔE4 to facilitate CRC cell dissemination.

Intratumoral heterogeneity (ITH) is a critical factor associated with treatment failure and disease relapse in hepatocellular carcinoma (HCC). However, decoding ITH in a noninvasive and comprehensive manner remains a notable challenge. In this study involving 851 patients from five centers, we developed a noninvasive prognostic classification for ITH using radiomics based on multisequence MRI, termed radiomics ITH (RITH) phenotypes. The RITH phenotypes highly correlated with prognosis and pathological ITH. In addition, through an integrated multi-omics analysis, we uncovered the molecular mechanisms underlying RITH, notably enhancing its biological interpretability. Specifically, high-RITH tumors demonstrated an enrichment of cancer-associated fibroblasts and activation of extracellular matrix remodeling. Our approach facilitates the noninvasive refined classification of ITH using radiomics and multi-omics, paving the way for tailored treatment strategies in HCC. Extracellular matrix-receptor interaction could be a potential therapeutic target in patients with high-RITH tumors. Given the routine use of radiologic imaging in oncology, our methodology ignites versatile framework for broader application to other solid tumors.

Head and neck cancer (HNC) is the sixth most common disease in the world. The recurrence rate of patients is relatively high, and the heterogeneity of tumor immune microenvironment (TIME) cells may be an important reason for this. Single-cell sequencing (SCS) is currently the most promising and mature application in cancer research. It can identify unique genes expressed in cells and study tumor heterogeneity. According to current research, the heterogeneity of immune cells has become an important factor affecting the occurrence and development of HNC. SCSs can provide effective therapeutic targets and prognostic factors for HNC patients through analyses of gene expression levels and cell heterogeneity. Therefore, this study analyzes the basic theory of HNC and the development of SCS technology, elaborating on the application of SCS technology in HNC and its potential value in identifying HNC therapeutic targets and biomarkers.

Malignant glioma is the most common primary malignant tumor of the brain in adults, with glioblastoma (GBM) being the most aggressive subtype. Mesenchymal stem/stromal cells (MSCs) have been shown to fuse with tumor cells in various cancers including glioma, thereby regulating tumor progression. However, there has been no systematic research on the fusion of glioma-associated MSCs (GA-MSCs) with glioma cells. Here, it is shown that GA-MSCs are able to spontaneously fuse with glioma cells both in vitro and in vivo. The hybrid cells display significantly lower levels of N6-methyladenosine (m6A) modification and can modulate the glioma microenvironment by attracting and inducing M2-like polarization of macrophages. Mechanistically, the demethylase fat mass and obesity-associated protein (FTO) mediates demethylation in hybrids and promotes macrophage colony-stimulating factor (CSF1) secretion by increasing its RNA stability in an m6A-YTH domain family 2 (YTHDF2)-dependent manner. Our study reveals a novel crosstalk mechanism between glioma cells, GA-MSCs, and macrophages in glioma microenvironment, offering potential new approaches for glioma therapy.

Hepatocellular carcinoma (HCC) is the third most common malignant tumor after gastric cancer and esophageal cancer, which is a serious threat to human health. Methyltransferase-like protein 16 (METTL16) regulates the occurrence and development of various cancers, but its molecular mechanism in HCC has not been fully investigated.

A series of databases were used to predict gene expression, methylation sites, correlation analysis, and protein interaction analysis. Gene expression levels were detected by quantitative real-time polymerase chain reaction (qRT-PCR), western blot, and immunohistochemistry (IHC). What's more, drug-resistant cell lines were established for drug resistance analysis. Cell proliferation was measured by 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) assay and 5-ethynyl-2'-deoxyuridine (EdU) staining. Flow cytometry, transwell and wound healing assays were used for apoptosis, invasion and migration, respectively. In addition, the regulatory mechanism of METTL16 in HCC was investigated by methylated RNA immunoprecipitation (MeRIP), RNA immunoprecipitation (RIP) and co-immunoprecipitation (Co-IP). Finally, constructing subcutaneous transplanted tumor in nude mice confirmed the effect of METTL16 in vivo.

METTL16 was up-regulated in HCC drug-resistant tissues and cells. Knockdown of METTL16 inhibited Cisplatin (DDP) resistance, proliferation, invasion and migration of HCC cells, but promoted apoptosis. Besides, laminin subunit alpha 4 (LAMA4), which was overexpressed in HCC drug-resistant tissues and cells, was selected as the target of METTL16. Mechanistically, METTL16 and m6A reader insulin like growth factor 2 mRNA binding protein 2 (IGF2BP2) co-regulated the m6A modification and mRNA stability of LAMA4, and LAMA4 weakened the effects of METTL16 knockdown on HCC drug-resistance. Meanwhile, LAMA4 bound to collagen type IV alpha 1 chain (COL4A1) and facilitated DDP resistance and HCC progression via COL4A1. Similarly, in vivo, METTL16 induced tumor growth, as well as LAMA4 and COL4A1 expression, and increased DDP resistance.

METTL16 and IGF2BP2 jointly mediated the m6A methylation modification of LAMA4, thereby promoting DDP resistance and malignant progression of HCC through regulation of COL4A1.

Allorecognition, which is the ability of an organism to discriminate between self and non-self, protects multicellular animals from somatic cell/germline parasitism. We reported and characterised allorecognition in the stolons of colonies of the hydrozoan jellyfish Cladonema radiatum (C. radiatum) in our previous publication. C. radiatum has a free-swimming medusa form besides the sessile colonial form. In this study, we investigated the allorecognition responses in the medusa form of C. radiatum. By using grafting experiments, we observed that while C. radiatum medusae show tolerance to both isogenic and allogenic chimerism, allogenic grafting induces the formation of a form of circular scars-we refer to as "ring-shaped scars"-around the grafts on the host umbrella. Within the scars, overgrowth of tissues occurs with additional gastrovascular canal development. By pharmaceutical experiments, we found that tissue overgrowth is dependent on phosphoinositide 3-kinase (PI3K) and vascular endothelial growth factor (VEGF), showing a resemblance to mammalian neoplasia.

Engineers have long studied the origins of design features that make machines prone to failure, but biologists have only recently begun investigating why organisms have traits that make them susceptible to disease. This article compares explanations for vulnerability to failure in machines with explanations for traits that make bodies vulnerable to disease. Some global explanations are relevant for both: design deficiencies, corrupted plans, assembly variations, incorrect operating environment, and trade-offs. These similarities suggest that a common framework for failure analysis could be valuable. However, a closer look at each of the 10 global categories reveals fundamental differences: machines are built to match an ideal blueprint, while species have no perfect genome or form. Design trade-offs in machines involve balancing multiple factors such as performance, robustness, and costs, while biological trade-offs maximize only gene transmission, often at the expense of health and lifespan. Detailed consideration of these and other differences reveals how the metaphor of body as a designed machine fosters tacit creationism that misrepresents the nature of organically complex systems.

To characterize the discourse trends and level of argumentation of girls and boys by analysing the differences in the conceptions and systems of conceptions of children and adolescents aged between 6 and 18.

This is a multi-centre qualitative study in the human and social sciences, based on data collected in two different phases using two different tools: e.Photoexpression©, which deals with health in general, and Photonarration, which deals with cancer. The aim of this open, exploratory method, which uses photographs, is to gather data on the experiences and knowledge specific to each child and adolescent. The informative value of data from qualitative collection tools, combined with a mixed analysis methodology, enabled us to characterize the differences in perceptions of health determinants and cancer between girls and boys aged between 6 and 18.

4,174 productions were collected from 1,068 children aged 6 to 18, identifying 30 determinants of health and cancer. For all of these results, there were significant gender differences from a very early age: boys focused on leisure activities and physical activity, while girls took a more global view, focusing on the environment, food, emotional aspects of social relationships, hygiene, care, prevention, etc. As they got older, the focus shifted to health determinants. As we get older, we see a change in discourse trends and in the level of argumentation, with girls becoming richer and boys poorer.

The trends in girls' and boys' discourse on what, in their view, determines health demonstrate the interest and relevance of adapting the prevention methods used as closely as possible to the conceptions of children and adolescents. The differences observed between boys and girls are a crucial lever that takes into account the specific characteristics of a population. They offer the possibility of taking more effective action, both in the context of interventions aimed at teenagers and in support of decision-making in the context of prevention policies.

Comparative phylogenetics provides a wealth of computational tools to understand evolutionary processes and their outcomes. Advances in these methodologies have occurred in parallel with a surge in cross-species genomic and phenotypic data. To date, however, the majority of published studies have focused on classical questions in evolutionary biology, such as speciation and the ecological drivers of trait evolution. Here, we argue that evolutionary medicine in general, and our understanding of the origin and diversification of disease traits in particular, would be greatly expanded by a wider integration of phylogenetic comparative methods (PCMs). We use comparative oncology - the study of cancer across the tree of life - as a case study to demonstrate the power of the approach and show that implementing PCMs can highlight the mode and tempo of the evolutionary changes in intrinsic, species-level disease vulnerabilities.

To evaluate the advantages of including versus excluding the tumor periphery and combining diffusion-weighted imaging (DWI) with T2-weighted imaging (T2w) for outcome predictions of preoperative radio(chemo)therapy in rectal cancer.

Four analysis strategies, based on two segmentation methods and two magnetic resonance imaging (MRI) sequences, were evaluated in 106 patients examined with pretreatment MRI. One segmentation method included the tumor periphery in the region of interest (ROI) encompassing the whole tumor (wROI), considered as the reference segmentation approach, and one included only the central part (cROI). Relevant radiomics imaging features were extracted from either T2w alone or from both T2w and DWI and used by a machine learning algorithm for the prediction of pathologic complete response (pCR), neoadjuvant rectal (NAR) score, and disease recurrence. The area under the curve (AUC) was the performance measure. AUCs were compared with a bootstrapping method based on 104 bootstraps.

cROI applied to both T2w and DWI provided the highest numerical prediction of pCR (AUC 0.76), however, not significantly superior to the other strategies (p ≥ 0.138). cROI applied to both T2w and DWI also yielded the highest numerical prediction of NAR score (AUC 0.84), showing advantages over wROI-based analysis strategies (AUC 0.66 and 0.69; p ≤ 0.008). When compared to cROI applied to T2w alone (AUC 0.73), the benefit was borderline statistically significant (p = 0.053). For prediction of disease recurrence, no differences were found between the analysis strategies.

Inclusion of the tumor periphery in radiomic analysis of magnetic resonance images does not improve predictions of the preoperative therapy response in patients with rectal cancer. Excluding tumor periphery while adding DWI to T2w improves prediction of the NAR score, although it does not affect pCR or recurrence prediction.

Bacteria withstand antibiotic treatment through three alternative mechanisms: resistance, persistence or tolerance. While resistance and persistence have been described, whether drug-induced tolerance exists in cancer cells remains largely unknown. Here, we show that human cancer cells elicit a tolerant response when exposed to commonly used chemotherapy regimens, propelled by the pervasive activation of autophagy, leading to the comprehensive activation of DNA damage repair pathways. After prolonged drug exposure, such tolerant responses morph into persistence, whereby the increased DNA damage repair is entirely reversed. The central regulator of mitophagy PINK1 drives this reduction in DNA repair via the cytoplasmic relocalization of the cell identity master HNF4A, thus hampering HNF4A transcriptional activation of DNA repair genes. We conclude that exposing cancer cells to relevant standard-of-care antitumour therapies induces a pervasive drug-induced tolerant response that might be broadly exploited to increase the impact of first-line, adjuvant treatments and debulking in advanced cancers.

The heterogeneity and evolution of tumors remain significant obstacles in cancer treatment, contributing to both therapy resistance and relapse. Mesenchymal stem/stromal cells (MSCs) are multipotent stromal cells within the tumor microenvironment that interact with tumor cells through various mechanisms, including cell fusion. While previous research has largely focused on the effects of MSC-tumor cell fusion on tumor proliferation, migration, and tumorigenicity, emerging evidence indicates that its role in tumor maintenance, evolution, and recurrence, particularly under stress conditions, may be even more pivotal. This review examines the connection between MSC-tumor cell fusion and several critical factors like tumor heterogeneity, cancer stem cells, and therapy resistance, highlighting the crucial role of cell fusion in tumor survival, evolution, and recurrence. Additionally, we explore potential therapeutic strategies aimed at targeting this process.

Despite the expanding therapeutic options available to cancer patients, therapeutic resistance, disease recurrence, and metastasis persist as hallmark challenges in the treatment of cancer. The rise to prominence of generative artificial intelligence (GenAI) in many realms of human activities is compelling the consideration of its capabilities as a potential lever to advance the development of effective cancer treatments. This article presents a hypothetical case study on the application of generative pre-trained transformers (GPTs) to the treatment of metastatic prostate cancer (mPC). The case explores the design of GPT-supported adaptive intermittent therapy for mPC. Testosterone and prostate-specific antigen (PSA) are assumed to be repeatedly monitored while treatment may involve a combination of androgen deprivation therapy (ADT), androgen receptor-signalling inhibitors (ARSI), chemotherapy, and radiotherapy. The analysis covers various questions relevant to the configuration, training, and inferencing of GPTs for the case of mPC treatment with a particular attention to risk mitigation regarding the hallucination problem and its implications to clinical integration of GenAI technologies. The case study provides elements of an actionable pathway to the realization of GenAI-assisted adaptive treatment of metastatic prostate cancer. As such, the study is expected to help facilitate the design of clinical trials of GenAI-supported cancer treatments.

Despite considerable treatment advances, multiple myeloma (MM) remains an incurable hematological cancer due to treatment resistance. A systematic literature search was conducted to identify determinants for clonal evolution driving relapse and drug resistance in MM. A total of 631 non-duplicate publications were screened of which 28 articles were included for data extraction. Genetic alterations, mutational signatures, evolutionary trajectories, and non-genetic determinants were identified as key topics to characterize clonal evolution in relapsed MM. A variety of factors led to clonal diversification and increased tumor mutation burden, such as MAPK-Ras mutations and incremental changes related to chromosomal bands 1 and 17, while mutational signature analyses revealed that APOBEC activity and melphalan treatment leave a distinct impact on the clonal composition in MM genomes. To capture and dissect tumor heterogeneity, our review suggests combining methods or using technical approaches with high resolution to assess the impact of clonal evolution.

Uveal melanoma (UM) is a rare and deadly eye cancer with high metastatic potential. Despite the predominance of malignant cells within the tumor microenvironment, the heterogeneity and underlying molecular features remain to be fully characterized.

We analyzed single-cell transcriptomic profiling of 37,660 malignant cells from 17 UM tumors. A consensus non-negative factorization algorithm was used to decipher transcriptional programs underlying tumor cell-intrinsic heterogeneity. Tumor-infiltrated immune cells were computationally estimated from bulk transcriptomes and bioinformatics methods. A gene signature was derived for subtyping and prognostic stratification and validated in multicenter cohorts.

ScRNA-seq analysis revealed the existence of diverse subpopulations and transcriptional variability among malignant cells within and between tumors. Furthermore, we observed that the heterogeneity of malignant cell states and compositions correlated with genomic, immunological, and clinical characteristics. By identifying gene expression programs associated with malignant cell heterogeneity at the single cell level, UM samples were classified into two distinct intra-tumoral subtypes (ITMHlo and ITMHhi) with different prognoses and immune microenvironments. Finally, a machine learning-derived 9-gene signature was developed to translate single-cell information into bulk tissue transcriptomes for patient stratification and was validated in multicenter cohorts.

Our study provides insight into understanding the intra-tumoral heterogeneity of UM and its potential impact on patient stratification.

The evolution of cancer cells parallels species evolution in numerous ways. Variations arise and spread under the pressure of competition between cancer cells. Current investigations of tumor evolution echo earlier debates between biologists. These include the role of non-Darwinian mechanisms, the contribution of neutral evolution, and life history dynamics. The trade-off between proliferation and metastasis is the most well-studied application of life history theory to cancer evolution. This article briefly introduces some parallels between cancer and species evolution, focusing on the life history of evolving tumors. Next, we review evidence from simulation and experimental studies supporting task specialization and trade-offs in cancer. We also cover recent work on inferring tumor tasks from data. We then turn to the implications of multi-tasking and the utility of the theory in explaining critical aspects of tumor heterogeneity. Finally, we discuss some of the criticism and future directions of this research topic.

Majority of the lung adenocarcinomas show a mixture of different histological patterns. The possibility of histologically heterogeneous areas of the adenocarcinoma showing genetic heterogeneity and harboring different driver mutations, with potentially significant clinical impact, has not been adequately addressed. Currently, there are no guidelines to suggest how to submit tumor tissue in adenocarcinomas with mixed histological features for molecular testing. The objective of this study is to assess intra-tumoral heterogeneity in prominent driver mutations among different morphological patterns of lung adenocarcinoma, its implications on the future of molecular testing as well as its potential impact on patient management. Twenty-three cases of mixed histology lung adenocarcinoma resected between 2018 and 2023 were retrieved from the archives. H&E slides were reviewed to identify the predominant and second most predominant histological patterns. The morphologically different tumor areas were manually macro-dissected for DNA extraction. Next-Generation Sequencing with Ion AmpliSeq™ Cancer Hotspot Panel v2 (Thermo Fisher Scientific, USA). Thirteen cases showed the same pathological variant in both histological components tested. Three cases (13 %) exhibited disparities in the variants detected across the different histological patterns tested (p=0.025). The discrepant findings had a direct therapeutic impact in 4.3 % cases. Seven cases showed no pathogenic variants detected on either of the histological components tested. This study elucidates the presence of infrequent yet significant intra-tumoral heterogeneity in the molecular profiles of mixed histology adenocarcinomas, highlighting the need for guidelines directing tissue selection for molecular testing to avoid missed therapeutic opportunities and mitigate disease relapse.

The last few years have seen a surge of interest from field ecologists and evolutionary biologists to study neoplasia and cancer in wildlife. This contributes to the One Health Approach, which investigates health issues at the intersection of people, wild and domestic animals, together with their changing environments. Nonetheless, the emerging field of wildlife cancer is currently constrained by methodological limitations in detecting cancer using non-invasive sampling. In addition, the suspected differential susceptibility and resistance of species to cancer often make the choice of a unique model species difficult for field biologists. Here, we provide an overview of the importance of pursuing the study of cancer in non-model organisms and we review the currently available methods to detect, measure and quantify cancer in the wild, as well as the methodological limitations to be overcome to develop novel approaches inspired by diagnostic techniques used in human medicine. The methodology we propose here will help understand and hopefully fight this major disease by generating general knowledge about cancer, variation in its rates, tumour-suppressor mechanisms across species as well as its link to life history and physiological characters. Moreover, this is expected to provide key information about cancer in wildlife, which is a top priority due to the accelerated anthropogenic change in the past decades that might favour cancer progression in wild populations.

Cancers are the product of evolutionary events, where molecular variation occurs and accumulates in tissues and tumors. Sequencing of this molecular variation informs not only which variants are driving tumorigenesis, but also the mechanisms behind what is fueling mutagenesis. Both of these details are crucial for preventing premature deaths due to cancer, whether it is by targeting the variants driving the cancer phenotype or by measures to prevent exogenous mutations from contributing to somatic evolution. Here, we review tools to determine both molecular signatures and cancer drivers, and avenues by which these metrics may be linked.

Cancer therapy is facing increasingly significant challenges, marked by a wide range of techniques and research efforts centered around somatic mutations, precision oncology, and the vast amount of big data. Despite this abundance of information, the quest to cure cancer often seems more elusive, with the "war on cancer" yet to deliver a definitive victory. A particularly pressing issue is the development of tumor treatment resistance, highlighting the urgent need for innovative approaches. Evolutionary, Quantum Biology and System Biology offer a promising framework for advancing experimental cancer research. By integrating theoretical studies, translational methods, and flexible multidisciplinary clinical research, there's potential to enhance current treatment strategies and improve outcomes for cancer patients. Establishing stronger links between evolutionary, quantum, entropy and chaos principles and oncology could lead to more effective treatments that leverage an understanding of the tumor's evolutionary dynamics, paving the way for novel methods to control and mitigate cancer. Achieving these objectives necessitates a commitment to multidisciplinary and interprofessional collaboration at the heart of both research and clinical endeavors in oncology. This entails dismantling silos between disciplines, encouraging open communication and data sharing, and integrating diverse viewpoints and expertise from the outset of research projects. Being receptive to new scientific discoveries and responsive to how patients react to treatments is also crucial. Such strategies are key to keeping the field of oncology at the forefront of effective cancer management, ensuring patients receive the most personalized and effective care. Ultimately, this approach aims to push the boundaries of cancer understanding, treating it as a manageable chronic condition, aiming to extend life expectancy and enhance patient quality of life.

Single-cell technologies have been known as advanced and powerful tools to study tumor biological systems at the single-cell resolution and are playing increasingly critical roles in multiple stages of drug discovery and development. Specifically, single-cell technologies can promote the discovery of drug targets, help high-throughput screening at single-cell level, and contribute to pharmacokinetic studies of anti-tumor drugs. Emerging single-cell analysis technologies have been developed to further integrating multidimensional single-cell molecular features, expanding the scale of single-cell data, profiling phenotypic impact of genes in single cell, and providing full-length coverage single-cell sequencing. In this review, we systematically summarized the applications of single-cell technologies in various sections of drug discovery for tumor treatment, including target identification, high-throughput drug screening, and pharmacokinetic evaluation and highlighted emerging single-cell technologies in providing in-depth understanding of tumor biology. Single-cell-technology-based drug discovery is expected to further optimize therapeutic strategies and improve clinical outcomes of tumor patients.

Hepatocellular carcinoma (HCC) is the third leading cause of cancer-related mortality worldwide. The emergence of combination therapy, atezolizumab (anti-PDL1, immune checkpoint inhibitor) and bevacizumab (anti-VEGF) has revolutionised the management of HCC. Despite this breakthrough, the best overall response rate with first-line systemic therapy is only about 30%, owing to intra-tumoural heterogeneity, complex tumour microenvironment and the lack of predictive biomarkers. Many groups have attempted to classify HCC based on the immune microenvironment and have consistently observed better outcomes in immunologically "hot" HCC. We summarised possible mechanisms of tumour immune evasion based on the latest literature and the rationale for combination/sequential therapy to improve treatment response. Lastly, we proposed future strategies and therapies to overcome HCC immune evasion to further improve treatment outcomes of HCC.

Biomarker screening is critical for precision oncology. However, one of the main challenges in precision oncology is that the screened biomarkers often fail to achieve the expected clinical effects and are rarely approved by regulatory authorities. Considering the close association between cancer pathogenesis and the evolutionary events of organisms, we first explored the evolutionary feature underlying clinically approved biomarkers, and two evolutionary features of approved biomarkers (Ohnologs and specific evolutionary stages of genes) were identified. Subsequently, we utilized evolutionary features for screening potential prognostic biomarkers in four common cancers: head and neck squamous cell carcinoma, liver hepatocellular carcinoma, lung adenocarcinoma, and lung squamous cell carcinoma. Finally, we constructed an evolution-strengthened prognostic model (ESPM) for cancers. These models can predict cancer patients' survival time across different cancer cohorts effectively and perform better than conventional models. In summary, our study highlights the application potentials of evolutionary information in precision oncology biomarker screening.

The evolutionary dynamics of cancer, characterized by its profound heterogeneity, demand sophisticated tools for a holistic understanding. This review delves into tumor phylogenetics, an essential approach bridging evolutionary biology with oncology, offering unparalleled insights into cancer's evolutionary trajectory. We provide an overview of the workflow, encompassing study design, data acquisition, and phylogeny reconstruction. Notably, the integration of diverse data sets emerges as a transformative step, enhancing the depth and breadth of evolutionary insights. With this integrated perspective, tumor phylogenetics stands poised to redefine our understanding of cancer evolution and influence therapeutic strategies.

Osteosarcoma is the most common malignant bone cancer in pediatric patients. Patients who respond poorly to chemotherapy experience worse clinical outcomes with a high mortality rate. The major challenge is the lack of effective drugs for these patients. To introduce new drugs for clinical approval, preclinical studies based on in vitro models must demonstrate the potency of the tested drugs, enabling the drugs to enter phase 1 clinical trials. Patient-derived cell culture is a promising testing platform for in vitro studies, as they more accurately recapitulate cancer states and genetic profiles compared to cell lines. In the present study, we established patient-derived osteosarcoma cells (PDC) from a patient who had previously been diagnosed with retinoblastoma. We identified a new variant of a germline mutation in the RB1 gene in the tissue of the patient. The biological effects of this PDC were studied to observe whether the cryopreserved PDC retained a feature of fresh PDC. The cryopreserved PDC preserved the key biological effects, including cell growth, invasive capability, migration, and mineralization, that define the conserved phenotypes compared to fresh PDC. From whole genome sequencing analysis of osteosarcoma tissue and patient-derived cells, we found that cryopreserved PDC was a minor population in the origin tissue and was selectively grown under the culture conditions. The cryopreserved PDC has a high resistance to conventional chemotherapy. This study demonstrated that the established cryopreserved PDC has the aggressive characteristics of osteosarcoma, in particular the chemoresistance phenotype that might be used for further investigation in the chemoresistant mechanism of osteosarcoma. In conclusion, the approach we applied for primary cell culture might be a promising method to generate in vitro models for functional testing of osteosarcoma.

Metastasis of primary tumors remains a challenge for early diagnosis and prevention. The cellular properties and molecular drivers of metastatically competent clones within primary tumors remain unclear. Here, we generated 10-16 single cell-derived lines from each of three colorectal cancer (CRC) tumors to identify and characterize metastatic seeds. We found that intrinsic factors conferred clones with distinct metastatic potential and cellular communication capabilities, determining organ-specific metastasis. Poorly differentiated or highly metastatic clones, rather than drug-resistant clones, exhibited poor clinical prognostic impact. Personalized genetic alterations, instead of mutation burden, determined the occurrence of metastatic potential during clonal evolution. Additionally, we developed a gene signature for capturing metastatic potential of primary CRC tumors and demonstrated a strategy for identifying metastatic drivers using isogenic clones with distinct metastatic potential in primary tumors. This study provides insight into the origin and mechanisms of metastasis and will help develop potential anti-metastatic therapeutic targets for CRC patients.

How chronic mutational processes and punctuated bursts of DNA damage drive evolution of the cancer genome is poorly understood. Here, we demonstrate a strategy to disentangle and quantify distinct mechanisms underlying genome evolution in single cells, during single mitoses and at single-strand resolution. To distinguish between chronic (reactive oxygen species (ROS)) and acute (ultraviolet light (UV)) mutagenesis, we microfluidically separate pairs of sister cells from the first mitosis following burst UV damage. Strikingly, UV mutations manifest as sister-specific events, revealing mirror-image mutation phasing genome-wide. In contrast, ROS mutagenesis in transcribed regions is reduced strand agnostically. Successive rounds of genome replication over persisting UV damage drives multiallelic variation at CC dinucleotides. Finally, we show that mutation phasing can be resolved to single strands across the entire genome of liver tumors from F1 mice. This strategy can be broadly used to distinguish the contributions of overlapping cancer relevant mutational processes.

Breast cancer (BC) stands out as the cancer with the highest incidence of morbidity and mortality among women worldwide, and its incidence rate is currently trending upwards. Improving the efficiency of breast cancer diagnosis and treatment is crucial, as it can effectively reduce the disease burden. Circulating tumor DNA (ctDNA) originates from the release of tumor cells and plays a pivotal role in the occurrence, development, and metastasis of breast cancer. In recent years, the widespread application of high-throughput analytical technology has made ctDNA a promising biomarker for early cancer detection, monitoring minimal residual disease, early recurrence monitoring, and predicting treatment outcomes. ctDNA-based approaches can effectively compensate for the shortcomings of traditional screening and monitoring methods, which fail to provide real-time information and prospective guidance for breast cancer diagnosis and treatment. This review summarizes the applications of ctDNA in various aspects of breast cancer, including screening, diagnosis, prognosis, treatment, and follow-up. It highlights the current research status in this field and emphasizes the potential for future large-scale clinical applications of ctDNA-based approaches.

Accurately deciphering clonal copy number substructure can provide insights into the evolutionary mechanism of cancer, and clustering single-cell copy number profiles has become an effective means to unmask intra-tumor heterogeneity (ITH). However, copy numbers inferred from single-cell DNA sequencing (scDNA-seq) data are error-prone due to technically confounding factors such as amplification bias and allele-dropout, and this makes it difficult to precisely identify the ITH.

We introduce a hybrid model called scGAL to infer clonal copy number substructure. It combines an autoencoder with a generative adversarial network to jointly analyze independent single-cell copy number profiles and gene expression data from same cell line. Under an adversarial learning framework, scGAL exploits complementary information from gene expression data to relieve the effects of noise in copy number data, and learns latent representations of scDNA-seq cells for accurate inference of the ITH. Evaluation results on three real cancer datasets suggest scGAL is able to accurately infer clonal architecture and surpasses other similar methods. In addition, assessment of scGAL on various simulated datasets demonstrates its high robustness against the changes of data size and distribution. scGAL can be accessed at: https://github.com/zhyu-lab/scgal .

Joint analysis of independent single-cell copy number and gene expression data from a same cell line can effectively exploit complementary information from individual omics, and thus gives more refined indication of clonal copy number substructure.

Increasing dietary fiber consumption is linked to lower colon cancer incidence, and this anticancer effect is tied to elevated levels of short-chain fatty acids (e.g., butyrate) because of the fermentation of fiber by colonic bacteria. While butyrate inhibits cancer cell proliferation, the impact on cancer cell type remains largely unknown. To test the hypothesis that butyrate displays different inhibitory potentials due to cancer cell type, we determined half-maximal inhibitory concentrations (IC50) of butyrate in HCT116, HT-29, and Caco-2 human colon cancer cell proliferation at 24, 48, and 72 h. The IC50 (mM) butyrate concentrations of HCT116, HT-29, and Caco-2 cells were [24 h, 1.14; 48 h, 0.83; 72 h, 0.86], [24 h, N/D; 48 h, 2.42; 72 h, 2.15], and [24 h, N/D; 48 h, N/D; 72 h, 2.15], respectively. At the molecular level, phosphorylated ERK1/2 and c-Myc survival signals were decreased by (>30%) in HCT116, HT-29, and Caco-2 cells treated with 4 mM butyrate. Conversely, butyrate displayed a stronger potential (>1-fold) for inducing apoptosis and nuclear p21 tumor suppressor in HCT116 cells compared to HT-29 and Caco-2 cells. Moreover, survival analysis demonstrated that a cohort with high p21 gene expression in their colon tissue significantly increased survival time compared to a low-p21-expression cohort of colon cancer patients. Collectively, the inhibitory efficacy of butyrate is cell type-specific and apoptosis-dependent.

Chemotherapy resistance is the main cause of ovarian cancer progression and even death. However, there are no clear indicators for predicting the risk of drug resistance in patients. Intra-tumor heterogeneity (ITH) is one of the characteristics of malignant tumors, which is associated with the treatment and prognosis of tumors. Accordingly, our study aims to investigate the correlation between the image features of intra-tumor heterogeneity and drug resistance of ovarian cancer based on artificial intelligence.

We obtained hematoxylin and eosin staining frozen histopathological images of ovarian cancer and paracarcinoma tissues from the Cancer Genome Atlas. We extracted quantitative image features of whole-slide images based on the automatic image nuclear segmentation processing technology. After that, we used bioinformatics analysis to find the relationship between image features of intra-tumor heterogeneity and drug resistance.

Our results show that our automatic image processing process based on computer artificial intelligence can extract image features effectively, and the key image features extracted are closely related to ITH. Among them, the Perimeter.sd image feature with the most prominent ITH feature can accurately predict the risk of platinum-based chemotherapy drug resistance in ovarian cancer patients.

Automatic image processing and feature extraction based on artificial intelligence have excellent results. Perimeter.sd can be used as a useful image feature indicator for evaluating ITH. ITH is associated with drug resistance of ovarian cancer, so ITH characteristics can be used as an effective indicator to evaluate drug resistance in patients with ovarian cancer.

Mechanisms specifying cancer cell states and response to therapy are incompletely understood. Here we show epigenetic reprogramming shapes the cellular landscape of schwannomas, the most common tumors of the peripheral nervous system. We find schwannomas are comprised of 2 molecular groups that are distinguished by activation of neural crest or nerve injury pathways that specify tumor cell states and the architecture of the tumor immune microenvironment. Moreover, we find radiotherapy is sufficient for interconversion of neural crest schwannomas to immune-enriched schwannomas through epigenetic and metabolic reprogramming. To define mechanisms underlying schwannoma groups, we develop a technique for simultaneous interrogation of chromatin accessibility and gene expression coupled with genetic and therapeutic perturbations in single-nuclei. Our results elucidate a framework for understanding epigenetic drivers of tumor evolution and establish a paradigm of epigenetic and metabolic reprograming of cancer cells that shapes the immune microenvironment in response to radiotherapy.