Tumour heterogeneity and the evolutionary trade-offs of cancer

Diffusion kurtosis imaging-based habitat analysis identifies high-risk molecular subtypes and heterogeneity matching in diffuse gliomas

OBJECTIVE

High-risk types of diffuse gliomas in adults include isocitrate dehydrogenase (IDH) wild-type glioblastomas and grade 4 astrocytomas. Achieving noninvasive prediction of high-risk molecular subtypes of gliomas is important for personalized and precise diagnosis and treatment.

METHODS

We retrospectively collected data from 116 patients diagnosed with adult diffuse gliomas. Multiple high-risk molecular markers were tested, and various habitat models and whole-tumor models were constructed based on preoperative routine and diffusion kurtosis imaging (DKI) sequences to predict high-risk molecular subtypes of gliomas. Feature selection and model construction utilized Least absolute shrinkage and selection operator (LASSO) and support vector machine (SVM). Finally, the Wilcoxon rank-sum test was employed to explore the correlation between habitat quantitative features (intra-tumor heterogeneity score，ITH score) and heterogeneity, as well as high-risk molecular subtypes.

RESULTS

The results showed that the habitat analysis model based on DKI performed remarkably well (with AUC values reaching 0.977 and 0.902 in the training and test sets, respectively). The model's performance was further enhanced when combined with clinical variables. (The AUC values were 0.994 and 0.920, respectively.) Additionally, we found a close correlation between ITH score and heterogeneity, with statistically significant differences observed between high-risk and non-high-risk molecular subtypes.

INTERPRETATION

The habitat model based on DKI is an ideal means for preoperatively predicting high-risk molecular subtypes of gliomas, holding significant value for noninvasively alerting malignant gliomas and those with malignant transformation potential.

A war on many fronts: cross disciplinary approaches for novel cancer treatment strategies

Cancer is a disease characterized by uncontrolled cellular growth where cancer cells take advantage of surrounding cellular populations to obtain resources and promote invasion. Carcinomas are the most common type of cancer accounting for almost 90% of cancer cases. One of the major subtypes of carcinomas are adenocarcinomas, which originate from glandular cells that line certain internal organs. Cancers such as breast, prostate, lung, pancreas, colon, esophageal, kidney are often adenocarcinomas. Current treatment strategies include surgery, chemotherapy, radiation, targeted therapy, and more recently immunotherapy. However, patients with adenocarcinomas often develop resistance or recur after the first line of treatment. Understanding how networks of tumor cells interact with each other and the tumor microenvironment is crucial to avoid recurrence, resistance, and high-dose therapy toxicities. In this review, we explore how mathematical modeling tools from different disciplines can aid in the development of effective and personalized cancer treatment strategies. Here, we describe how concepts from the disciplines of ecology and evolution, economics, and control engineering have been applied to mathematically model cancer dynamics and enhance treatment strategies.

The enhanced energy metabolism in the tumor margin mediated by RRAD promotes the progression of oral squamous cell carcinoma

The tumor margin as the invasive front has been proven to be closely related to the progression and metastasis of oral squamous cell carcinoma (OSCC). However, how tumor cells in the marginal region obtain the extra energy needed for tumor progression is still unknown. Here, we used spatial metabolomics and the spatial transcriptome to identify enhanced energy metabolism in the tumor margin of OSCC and identified that the downregulation of Ras-related glycolysis inhibitor and calcium channel regulator (RRAD) in tumor cells mediated this process. The absence of RRAD enhanced the ingestion of glucose and malignant behaviors of tumor cells both in vivo and in vitro. Mechanically, the downregulation of RRAD promoted the internal flow of Ca2+ and elevated its concentration in the nucleus, which resulted in the activation of the CAMKIV-CREB1 axis to induce the transcription of the glucose transporter GLUT3. GLUT inhibitor-1, as an inhibitor of GLUT3, could suppress this vigorous energy metabolism and malignant behaviors caused by the downregulation of RRAD. Taken together, our study revealed that enhanced energy metabolism in the tumor margin mediated by RRAD promotes the progression of OSCC and proved that GLUT3 is a potential target for future treatment of OSCC.

Cancer cells forgo translating mRNA transcribed from genes of nonspecialized tasks

The coupling of transcription and translation enables prokaryotes to regulate mRNA stability and reduce nonfunctional transcripts. Eukaryotes evolved other means to perform these functions. Here, we quantify the disparity between gene expression and protein levels and attempt to explain its origins. We collected publicly available simultaneous measurements of gene expression, protein level, division rate, and growth inhibition of breast cancer cells under drug perturbation. We used the cell lines as entities with shared origin, different evolutionary trajectories, and cancer hallmarks to define tasks subject to specializing and trading-off. We observed varying average mRNA and protein correlation across cell lines, and it was consistently higher for the gene products in the cancer hallmarks. The enrichment of hallmark gene products signifies the resources invested in it as a task. Enrichment based on mRNA or protein abundance corresponds to the relative resources dedicated to transcription and translation. The differences in gene- and protein-based enrichment correlated with nominal division rates but not growth inhibition under drug perturbations. Comparing the range of enrichment scores of the hallmarks within each cell signifies the resources dedicated to each. Cells appear to have a wider range of enrichment in protein synthesis relative to gene transcription. The difference and range of enrichment of the hallmark genes and proteins correlated with cell division and inhibition in response to drug treatments. We posit that cancer cells may express the genes coding for seemingly nonspecialized tasks but do not translate them to the corresponding proteins. This trade-off may cost the cells under normal conditions but confer benefits during stress.

Identification and classification of glioma subtypes based on RNA-binding proteins

BACKGROUND

Glioma is a common and aggressive primary malignant cancer known for its high morbidity, mortality, and recurrence rates. Despite this, treatment options for glioma are currently restricted. The dysregulation of RBPs has been linked to the advancement of several types of cancer, but their precise role in glioma evolution is still not fully understood. This study sought to investigate how RBPs may impact the development and prognosis of glioma, with potential implications for prognosis and therapy.

METHODS

RNA-seq profiles of glioma and corresponding clinical data from the CGGA database were initially collected for analysis. Unsupervised clustering was utilized to identify crucial tumor subtypes in glioma development. Subsequent time-series analysis and MS model were employed to track the progression of these identified subtypes. RBPs playing a significant role in glioma progression were then pinpointed using WGCNA and Lasso Cox regression models. Functional analysis of these key RBP-related genes was conducted through GSEA. Additionally, the CIBERSORT algorithm was utilized to estimate immune infiltrating cells, while the STRING database was consulted to uncover potential mechanisms of the identified biomarkers.

RESULTS

Six tumor subgroups were identified and found to be highly homogeneous within each subgroup. The progression stages of these tumor subgroups were determined using time-series analysis and a MS model. Through WGCNA, Lasso Cox, and multivariate Cox regression analysis, it was confirmed that BCLAF1 is correlated with survival in glioma patients and is closely linked to glioma progression. Functional annotation suggests that BCLAF1 may impact glioma progression by influencing RNA splicing, which in turn affects the cell cycle, Wnt signaling pathway, and other cancer development pathways.

CONCLUSIONS

The study initially identified six subtypes of glioma progression and assessed their malignancy ranking. Furthermore, it was determined that BCLAF1 could serve as an RBP-related prognostic marker, offering significant implications for the clinical diagnosis and personalized treatment of glioma.

Mass spectrometry imaging methods for visualizing tumor heterogeneity

Profiling spatial distributions of lipids, metabolites, and proteins in tumors can reveal unique cellular microenvironments and provide molecular evidence for cancer cell dysfunction and proliferation. Mass spectrometry imaging (MSI) is a label-free technique that can be used to map biomolecules in tumors in situ. Here, we discuss current progress in applying MSI to uncover molecular heterogeneity in tumors. First, the analytical strategies to profile small molecules and proteins are outlined, and current methods for multimodal imaging to maximize biological information are highlighted. Second, we present and summarize biological insights obtained by MSI of tumor tissue. Finally, we discuss important considerations for designing MSI experiments and several current analytical challenges.

Selected aspects of avascular tumor growth reproduced by a hybrid model of cell dynamics and chemical kinetics

We here propose a hybrid computational framework to reproduce and analyze aspects of the avascular progression of a generic solid tumor. Our method first employs an individual-based approach to represent the population of tumor cells, which are distinguished in viable and necrotic agents. The active part of the disease is in turn differentiated according to a set of metabolic states. We then describe the spatio-temporal evolution of the concentration of oxygen and of tumor-secreted proteolytic enzymes using partial differential equations (PDEs). A differential equation finally governs the local degradation of the extracellular matrix (ECM) by the malignant mass. Numerical realizations of the model are run to reproduce tumor growth and invasion in a number scenarios that differ for cell properties (adhesiveness, duplication potential, proteolytic activity) and/or environmental conditions (level of tissue oxygenation and matrix density pattern). In particular, our simulations suggest that tumor aggressiveness, in terms of invasive depth and extension of necrotic tissue, can be reduced by (i) stable cell-cell contact interactions, (ii) poor tendency of malignant agents to chemotactically move upon oxygen gradients, and (iii) presence of an overdense matrix, if coupled by a disrupted proteolytic activity of the disease.

Extracellular Matrix-Trapped Bioinspired Lipoprotein Prolongs Tumor Retention to Potentiate Antitumor Immunity

The immunomodulatory effects of many therapeutic agents are significantly challenged by their insufficient delivery efficiency and short retention time in tumors. Regarding the distinctively upregulated fibronectin (FN1) and tenascin C (TNC) in tumor stroma, herein a protease-activated FN1 and/or TNC binding peptide (FTF) is designed and an extracellular matrix (ECM)-trapped bioinspired lipoprotein (BL) (FTF-BL-CP) is proposed that can be preferentially captured by the TNC and/or FN1 for tumor retention, and then be responsively dissociated from the matrix to potentiate the antitumor immunity. The FTF-BL-CP treatment produces a 6.96-, 9.24-, 6.72-, 7.32-, and 6.73-fold increase of CD3+CD8+ T cells and their interferon-γ-, granzyme B-, perforin-, and Ki67-expressing subtypes versus the negative control, thereby profoundly eliciting the antitumor immunity. In orthotopic and lung metastatic breast cancer models, FTF-BL-CP produces notable therapeutic benefits of retarding tumor growth, extending survivals, and inhibiting lung metastasis. Therefore, this ECM-trapping strategy provides an encouraging possibility of prolonging tumor retention to potentiate the antitumor immunity for anticancer immunotherapy.

Functional transcriptional signatures for tumor-type-agnostic phenotype prediction

Cancer transcriptional patterns exhibit both shared and unique features across diverse cancer types, but whether these patterns are sufficient to characterize the full breadth of tumor phenotype heterogeneity remains an open question. We hypothesized that cancer transcriptional diversity mirrors patterns in normal tissues optimized for distinct functional tasks. Starting with normal tissue transcriptomic profiles, we use non-negative matrix factorization to derive six distinct transcriptomic phenotypes, called archetypes, which combine to describe both normal tissue patterns and variations across a broad spectrum of malignancies. We show that differential enrichment of these signatures correlates with key tumor characteristics, including overall patient survival and drug sensitivity, independent of clinically actionable DNA alterations. Additionally, we show that in HR+/HER2- breast cancers, metastatic tumors adopt transcriptomic signatures consistent with the invaded tissue. Broadly, our findings suggest that cancer often arrogates normal tissue transcriptomic characteristics as a component of both malignant progression and drug response. This quantitative framework provides a strategy for connecting the diversity of cancer phenotypes and could potentially help manage individual patients.

13C-SpaceM: Spatial single-cell isotope tracing reveals heterogeneity of de novo fatty acid synthesis in cancer

Metabolism has emerged as a key factor in homeostasis and disease including cancer. Yet, little is known about the heterogeneity of metabolic activity of cancer cells due to the lack of tools to directly probe it. Here, we present a novel method, 13C-SpaceM for spatial single-cell isotope tracing of glucose-dependent de novo lipogenesis. The method combines imaging mass spectrometry for spatially-resolved detection of 13C6-glucose-derived 13C label incorporated into esterified fatty acids with microscopy and computational methods for data integration and analysis. We validated 13C-SpaceM on a spatially-heterogeneous normoxia-hypoxia model of liver cancer cells. Investigating cultured cells, we revealed single-cell heterogeneity of lipogenic acetyl-CoA pool labelling degree upon ACLY knockdown that is hidden in the bulk analysis and its effect on synthesis of individual fatty acids. Next, we adapted 13C-SpaceM to analyze tissue sections of mice harboring isocitrate dehydrogenase (IDH)-mutant gliomas. We found a strong induction of de novo fatty acid synthesis in the tumor tissue compared to the surrounding brain. Comparison of fatty acid isotopologue patterns revealed elevated uptake of mono-unsaturated and essential fatty acids in the tumor. Furthermore, our analysis uncovered substantial spatial heterogeneity in the labelling of the lipogenic acetyl-CoA pool indicative of metabolic reprogramming during microenvironmental adaptation. Overall, 13C-SpaceM enables novel ways for spatial probing of metabolic activity at the single cell level. Additionally, this methodology provides unprecedented insight into fatty acid uptake, synthesis and modification in normal and cancerous tissues.

Conceptual breakthroughs of the long noncoding RNA functional system and its endogenous regulatory role in the cancerous regime

Long noncoding RNAs (lncRNAs) derived from noncoding regions in the human genome were once regarded as junks with no biological significance, but recent studies have shown that these molecules are highly functional, prompting an explosion of studies on their biology. However, these recent efforts have only begun to recognize the biological significance of a small fraction (< 1%) of the lncRNAs. The basic concept of these lncRNA functions remains controversial. This controversy arises primarily from conventional biased observations based on limited datasets. Fortunately, emerging big data provides a promising path to circumvent conventional bias to understand an unbiased big picture of lncRNA biology and advance the fundamental principles of lncRNA biology. This review focuses on big data studies that break through the critical concepts of the lncRNA functional system and its endogenous regulatory roles in all cancers. lncRNAs have unique functional systems distinct from proteins, such as transcriptional initiation and regulation, and they abundantly interact with mitochondria and consume less energy. lncRNAs, rather than proteins as traditionally thought, function as the most critical endogenous regulators of all cancers. lncRNAs regulate the cancer regulatory regime by governing the endogenous regulatory network of all cancers. This is accomplished by dominating the regulatory network module and serving as a key hub and top inducer. These critical conceptual breakthroughs lay a blueprint for a comprehensive functional picture of the human genome. They also lay a blueprint for combating human diseases that are regulated by lncRNAs.

Targeting Tumor Heterogeneity with Neoantigen-Based Cancer Vaccines

Neoantigen-based cancer vaccines have emerged as a promising immunotherapeutic approach to treat cancer. Nevertheless, the high degree of heterogeneity in tumors poses a significant hurdle for developing a vaccine that targets the therapeutically relevant neoantigens capable of effectively stimulating an immune response as each tumor contains numerous unique putative neoantigens. Understanding the complexities of tumor heterogeneity is crucial for the development of personalized neoantigen-based vaccines, which hold the potential to revolutionize cancer treatment and improve patient outcomes. In this review, we discuss recent advancements in the design of neoantigen-based cancer vaccines emphasizing the identification, validation, formulation, and targeting of neoantigens while addressing the challenges posed by tumor heterogeneity. The review highlights the application of cutting-edge approaches, such as single-cell sequencing and artificial intelligence to identify immunogenic neoantigens, while outlining current limitations and proposing future research directions to develop effective neoantigen-based vaccines.

Spatial Proteomics toward Subcellular Resolution by Coupling Deep Ultraviolet Laser Ablation with Nanodroplet Sample Preparation

Multiplexed molecular profiling of tissue microenvironments, or spatial omics, can provide critical insights into cellular functions and disease pathology. The coupling of laser microdissection with mass spectrometry-based proteomics has enabled deep and unbiased mapping of >1000 proteins. However, the throughput of laser microdissection is often limited due to tedious two-step procedures, sequential laser cutting, and sample collection. The two-step procedure also hinders the further improvement of spatial resolution to <10 μm as needed for subcellular proteomics. Herein, we developed a high-throughput and high-resolution spatial proteomics platform by seamlessly coupling deep ultraviolet (DUV) laser ablation (LA) with nanoPOTS (Nanodroplet Processing in One pot for Trace Samples)-based sample preparation. We demonstrated the DUV-LA system can quickly isolate and collect tissue samples at a throughput of ∼30 spots/min and a spatial resolution down to 2 μm from a 10 μm thick human pancreas tissue section. To improve sample recovery, we developed a proximity aerosol collection approach by placing DMSO droplets close to LA spots. We demonstrated the DUV-LA-nanoPOTS platform can detect an average of 1312, 1533, and 1966 proteins from ablation spots with diameters of 7, 13, and 19 μm, respectively. In a proof-of-concept study, we isolated and profiled two distinct subcellular regions of the pancreas tissue revealed by hematoxylin and eosin (H&E) staining. Quantitative proteomics revealed proteins specifically enriched to subcellular compartments.

Intratumor heterogeneity: models of malignancy emergence and evolution

Cancer is a complex and heterogeneous disease characterized by the accumulation of genetic alterations that drive uncontrolled cell growth and proliferation. Evolutionary dynamics plays a crucial role in the emergence and development of tumors, shaping the heterogeneity and adaptability of cancer cells. From the perspective of evolutionary theory, tumors are complex ecosystems that evolve through a process of microevolution influenced by genetic mutations, epigenetic changes, tumor microenvironment factors, and therapy-induced changes. This dynamic nature of tumors poses significant challenges for effective cancer treatment, and understanding it is essential for developing effective and personalized therapies. By uncovering the mechanisms that determine tumor heterogeneity, researchers can identify key genetic and epigenetic changes that contribute to tumor progression and resistance to treatment. This knowledge enables the development of innovative strategies for targeting specific tumor clones, minimizing the risk of recurrence and improving patient outcomes. To investigate the evolutionary dynamics of cancer, researchers employ a wide range of experimental and computational approaches. Traditional experimental methods involve genomic profiling techniques such as next-generation sequencing and fluorescence in situ hybridization. These techniques enable the identification of somatic mutations, copy number alterations, and structural rearrangements within cancer genomes. Furthermore, single-cell sequencing methods have emerged as powerful tools for dissecting intratumoral heterogeneity and tracing clonal evolution. In parallel, computational models and algorithms have been developed to simulate and analyze cancer evolution. These models integrate data from multiple sources to predict tumor growth patterns, identify driver mutations, and infer evolutionary trajectories. In this paper, we set out to describe the current approaches to address this evolutionary complexity and theories of its occurrence.

Time-resolved, integrated analysis of clonally evolving genomes

Clonal genome evolution is a key feature of asexually reproducing species and human cancer development. While many studies have described the landscapes of clonal genome evolution in cancer, few determine the underlying evolutionary parameters from molecular data, and even fewer integrate theory with data. We derived theoretical results linking mutation rate, time, expansion dynamics, and biological/clinical parameters. Subsequently, we inferred time-resolved estimates of evolutionary parameters from mutation accumulation, mutational signatures and selection. We then applied this framework to predict the time of speciation of the marbled crayfish, an enigmatic, globally invasive parthenogenetic freshwater crayfish. The results predict that speciation occurred between 1986 and 1990, which is consistent with biological records. We also used our framework to analyze whole-genome sequencing datasets from primary and relapsed glioblastoma, an aggressive brain tumor. The results identified evolutionary subgroups and showed that tumor cell survival could be inferred from genomic data that was generated during the resection of the primary tumor. In conclusion, our framework allowed a time-resolved, integrated analysis of key parameters in clonally evolving genomes, and provided novel insights into the evolutionary age of marbled crayfish and the progression of glioblastoma.

Regulation and signaling pathways in cancer stem cells: implications for targeted therapy for cancer

Cancer stem cells (CSCs), initially identified in leukemia in 1994, constitute a distinct subset of tumor cells characterized by surface markers such as CD133, CD44, and ALDH. Their behavior is regulated through a complex interplay of networks, including transcriptional, post-transcriptional, epigenetic, tumor microenvironment (TME), and epithelial-mesenchymal transition (EMT) factors. Numerous signaling pathways were found to be involved in the regulatory network of CSCs. The maintenance of CSC characteristics plays a pivotal role in driving CSC-associated tumor metastasis and conferring resistance to therapy. Consequently, CSCs have emerged as promising targets in cancer treatment. To date, researchers have developed several anticancer agents tailored to specifically target CSCs, with some of these treatment strategies currently undergoing preclinical or clinical trials. In this review, we outline the origin and biological characteristics of CSCs, explore the regulatory networks governing CSCs, discuss the signaling pathways implicated in these networks, and investigate the influential factors contributing to therapy resistance in CSCs. Finally, we offer insights into preclinical and clinical agents designed to eliminate CSCs.

Tissue Biology: In Search of a New Paradigm

Animal tissues are made up of multiple cell types that are increasingly well-characterized, yet our understanding of the core principles that govern tissue organization is still incomplete. This is in part because many observable tissue characteristics, such as cellular composition and spatial patterns, are emergent properties, and as such, they cannot be explained through the knowledge of individual cells alone. Here we propose a complex systems theory perspective to address this fundamental gap in our understanding of tissue biology. We introduce the concept of cell categories, which is based on cell relations rather than cell identity. Based on these notions we then discuss common principles of tissue modularity, introducing compositional, structural, and functional tissue modules. Cell diversity and cell relations provide a basis for a new perspective on the underlying principles of tissue organization in health and disease.

ComProliM: A cell growth assay robust to initial cell number in co-culture system

Cell growth is conventionally quantified using CCK-8 or MTT assays, but these methods display considerable sensitivity to initial cell quantities. Inherent sampling errors during cell counting and seeding make it impossible to achieve an absolute equivalence of initial cell numbers, potentially confounding the results of CCK-8 or MTT assays. In the present study, we introduce a novel cell proliferation assay, ComProliM, predicated on cell competition theory. Both numeral simulations and empirical testing demonstrate that ComProliM index (CPMI) reliably represents cell growth rate and is not influenced by variations in initial cell number. Intriguingly, two adherent cells of differing fluorescence states are co-cultured, suggesting that ComProliM can be successfully employed in co-culture system cell proliferation assays, including, for instance, the exploration of subclone interactions. We anticipate ComProliM will provide a viable alternative for quantifying adherent cell growth rates, particularly in cases where conventional methodologies deliver inconsistent or ambiguous results.

Efficient Inference of Spatially-Varying Gaussian Markov Random Fields With Applications in Gene Regulatory Networks

In this paper, we study the problem of inferring spatially-varying Gaussian Markov random fields (SV-GMRF) where the goal is to learn a network of sparse, context-specific GMRFs representing network relationships between genes. An important application of SV-GMRFs is in inference of gene regulatory networks from spatially-resolved transcriptomics datasets. The current work on inference of SV-GMRFs are based on the regularized maximum likelihood estimation (MLE) and suffer from overwhelmingly high computational cost due to their highly nonlinear nature. To alleviate this challenge, we propose a simple and efficient optimization problem in lieu of MLE that comes equipped with strong statistical and computational guarantees. Our proposed optimization problem is extremely efficient in practice: we can solve instances of SV-GMRFs with more than 2 million variables in less than 2 minutes. We apply the developed framework to study how gene regulatory networks in Glioblastoma are spatially rewired within tissue, and identify prominent activity of the transcription factor HES4 and ribosomal proteins as characterizing the gene expression network in the tumor peri-vascular niche that is known to harbor treatment resistant stem cells.

Spotlight on Small-Cell Lung Cancer and Other Lung Neuroendocrine Neoplasms

Lung neuroendocrine neoplasms (NENs) encompass a spectrum of neoplasms that are subdivided into the well-differentiated neuroendocrine tumors comprising the low- and intermediate-grade typical and atypical carcinoids, respectively, and the poorly differentiated, high-grade neuroendocrine carcinomas including large-cell neuroendocrine carcinomas and small-cell lung carcinoma (SCLC). Here, we review the current morphological and molecular classifications of the NENs on the basis of the updated WHO Classification of Thoracic Tumors and discuss the emerging subclassifications on the basis of molecular profiling and the potential therapeutic implications. We focus on the efforts in subtyping SCLC, a particularly aggressive tumor with few treatment options, and the recent advances in therapy with the adoption of immune checkpoint inhibitors in the frontline setting for patients with extensive-stage SCLC. We further highlight the promising immunotherapy strategies in SCLC that are currently under investigation.

Multiomic analysis of malignant pleural mesothelioma identifies molecular axes and specialized tumor profiles driving intertumor heterogeneity

Malignant pleural mesothelioma (MPM) is an aggressive cancer with rising incidence and challenging clinical management. Through a large series of whole-genome sequencing data, integrated with transcriptomic and epigenomic data using multiomics factor analysis, we demonstrate that the current World Health Organization classification only accounts for up to 10% of interpatient molecular differences. Instead, the MESOMICS project paves the way for a morphomolecular classification of MPM based on four dimensions: ploidy, tumor cell morphology, adaptive immune response and CpG island methylator profile. We show that these four dimensions are complementary, capture major interpatient molecular differences and are delimited by extreme phenotypes that-in the case of the interdependent tumor cell morphology and adapted immune response-reflect tumor specialization. These findings unearth the interplay between MPM functional biology and its genomic history, and provide insights into the variations observed in the clinical behavior of patients with MPM.

Epigenetics as a mediator of plasticity in cancer

The concept of an epigenetic landscape describing potential cellular fates arising from pluripotent cells, first advanced by Conrad Waddington, has evolved in light of experiments showing nondeterministic outcomes of regulatory processes and mathematical methods for quantifying stochasticity. In this Review, we discuss modern approaches to epigenetic and gene regulation landscapes and the associated ideas of entropy and attractor states, illustrating how their definitions are both more precise and relevant to understanding cancer etiology and the plasticity of cancerous states. We address the interplay between different types of regulatory landscapes and how their changes underlie cancer progression. We also consider the roles of cellular aging and intrinsic and extrinsic stimuli in modulating cellular states and how landscape alterations can be quantitatively mapped onto phenotypic outcomes and thereby used in therapy development.

Generation of novel oncolytic vaccinia virus with improved intravenous efficacy through protection against complement-mediated lysis and evasion of neutralization by vaccinia virus-specific antibodies

BACKGROUND

Oncolytic virus immunotherapy has revolutionized cancer immunotherapy by efficiently inducing both oncolysis and systemic immune activation. Locoregional administration has been used for oncolytic virus therapy, but its applications to deep-seated cancers have been limited. Although systemic delivery of the oncolytic virus would maximize viral immunotherapy's potential, this remains a hurdle due to the rapid removal of the administered virus by the complement and innate immune system. Infected cells produce some vaccinia viruses as extracellular enveloped virions, which evade complement attack and achieve longer survival by expressing host complement regulatory proteins (CRPs) on the host-derived envelope. Here, we generated SJ-600 series oncolytic vaccinia viruses that can mimic complement-resistant extracellular enveloped virions by incorporating human CRP CD55 on the intracellular mature virion (IMV) membrane.

METHODS

The N-terminus of the human CD55 protein was fused to the transmembrane domains of the six type I membrane proteins of the IMV; the resulting recombinant viruses were named SJ-600 series viruses. The SJ-600 series viruses also expressed human granulocyte-macrophage colony-stimulating factor (GM-CSF) to activate dendritic cells. The viral thymidine kinase (J2R) gene was replaced by genes encoding the CD55 fusion proteins and GM-CSF.

RESULTS

SJ-600 series viruses expressing human CD55 on the IMV membrane showed resistance to serum virus neutralization. SJ-607 virus, which showed the highest CD55 expression and the highest resistance to serum complement-mediated lysis, exhibited superior anticancer activity in three human cancer xenograft models, compared with the control Pexa-Vec (JX-594) virus, after single-dose intravenous administration. The SJ-607 virus administration elicited neutralizing antibody formation in two immunocompetent mouse strains like the control JX-594 virus. Remarkably, we found that the SJ-607 virus evades neutralization by vaccinia virus-specific antibodies.

CONCLUSION

Our new oncolytic vaccinia virus platform, which expresses human CD55 protein on its membrane, prolonged viral survival by protecting against complement-mediated lysis and by evading neutralization by vaccinia virus-specific antibodies; this may provide a continuous antitumor efficacy until a complete remission has been achieved. Such a platform may expand the target cancer profile to include deep-seated cancers and widespread metastatic cancers.

Breast cancer heterogeneity and its implication in personalized precision therapy

Breast cancer heterogeneity determines cancer progression, treatment effects, and prognosis. However, the precise mechanism for this heterogeneity remains unknown owing to its complexity. Here, we summarize the origins of breast cancer heterogeneity and its influence on disease progression, recurrence, and therapeutic resistance. We review the possible mechanisms of heterogeneity and the research methods used to analyze it. We also highlight the importance of cell interactions for the origins of breast cancer heterogeneity, which can be further categorized into cooperative and competitive interactions. Finally, we provide new insights into precise individual treatments based on heterogeneity.

A molecular phenotypic map of malignant pleural mesothelioma

BACKGROUND

Malignant pleural mesothelioma (MPM) is a rare understudied cancer associated with exposure to asbestos. So far, MPM patients have benefited marginally from the genomics medicine revolution due to the limited size or breadth of existing molecular studies. In the context of the MESOMICS project, we have performed the most comprehensive molecular characterization of MPM to date, with the underlying dataset made of the largest whole-genome sequencing series yet reported, together with transcriptome sequencing and methylation arrays for 120 MPM patients.

RESULTS

We first provide comprehensive quality controls for all samples, of both raw and processed data. Due to the difficulty in collecting specimens from such rare tumors, a part of the cohort does not include matched normal material. We provide a detailed analysis of data processing of these tumor-only samples, showing that all somatic alteration calls match very stringent criteria of precision and recall. Finally, integrating our data with previously published multiomic MPM datasets (n = 374 in total), we provide an extensive molecular phenotype map of MPM based on the multitask theory. The generated map can be interactively explored and interrogated on the UCSC TumorMap portal (https://tumormap.ucsc.edu/?p=RCG_MESOMICS/MPM_Archetypes ).

CONCLUSIONS

This new high-quality MPM multiomics dataset, together with the state-of-art bioinformatics and interactive visualization tools we provide, will support the development of precision medicine in MPM that is particularly challenging to implement in rare cancers due to limited molecular studies.

A Novel Bead-Capture Nanopore Sequencing Method for Large Structural Rearrangement Detection in Cancer

Rapid, cost-effective genomic stratification of structural rearrangements in cancer is often of vital importance when determining treatment; however, existing diagnostic cytogenetic and molecular testing fails to deliver the required speed when deployed at scale. Next-generation sequencing-based methods are widely used, but these can lack sensitivity and require batching of samples to be cost-effective, with long turnaround times. Here we present a novel method for rearrangement detection from genomic DNA based on third-generation long-read sequencing that overcomes these time and cost issues. The utility of this approach for the genomic stratification of patients with acute myeloid leukemia is shown based on detection of four of the most prevalent structural rearrangements. The method not only determines the precise genomic breakpoint for each expected rearrangement but also discovers and validates novel translocations in one-third of the tested samples, 80% of which involve known oncogenes. This method may prove to be a powerful tool for the diagnosis, genomic stratification, and characterization of cancers.

Destroying the Shield of Cancer Stem Cells: Natural Compounds as Promising Players in Cancer Therapy

In a scenario where eco-sustainability and a reduction in chemotherapeutic drug waste are certainly a prerogative to safeguard the biosphere, the use of natural products (NPs) represents an alternative therapeutic approach to counteract cancer diseases. The presence of a heterogeneous cancer stem cell (CSC) population within a tumor bulk is related to disease recurrence and therapy resistance. For this reason, CSC targeting presents a promising strategy for hampering cancer recurrence. Increasing evidence shows that NPs can inhibit crucial signaling pathways involved in the maintenance of CSC stemness and sensitize CSCs to standard chemotherapeutic treatments. Moreover, their limited toxicity and low costs for large-scale production could accelerate the use of NPs in clinical settings. In this review, we will summarize the most relevant studies regarding the effects of NPs derived from major natural sources, e.g., food, botanical, and marine species, on CSCs, elucidating their use in pre-clinical and clinical studies.

Amino acid variability, tradeoffs and optimality in human diet

Studies at the molecular level demonstrate that dietary amino acid intake produces substantial effects on health and disease by modulating metabolism. However, how these effects may manifest in human food consumption and dietary patterns is unknown. Here, we develop a series of algorithms to map, characterize and model the landscape of amino acid content in human food, dietary patterns, and individual consumption including relations to health status, covering over 2,000 foods, ten dietary patterns, and over 30,000 dietary profiles. We find that the type of amino acids contained in foods and human consumption is highly dynamic with variability far exceeding that of fat and carbohydrate. Some amino acids positively associate with conditions such as obesity while others contained in the same food negatively link to disease. Using linear programming and machine learning, we show that these health trade-offs can be accounted for to satisfy biochemical constraints in food and human eating patterns to construct a Pareto front in dietary practice, a means of achieving optimality in the face of trade-offs that are commonly considered in economic and evolutionary theories. Thus this study may enable the design of human protein quality intake guidelines based on a quantitative framework.

Cooperative RNA degradation stabilizes intermediate epithelial-mesenchymal states and supports a phenotypic continuum

Multiple intermediate epithelial-mesenchymal transition (EMT) states reflecting hybrid epithelial and mesenchymal phenotypes were observed in physiological and pathological conditions. Previous theoretical models explaining multiple EMT states rely on regulatory loops involving transcriptional feedback, which produce three or four attractors. This is incompatible with the observed continuum-like EMT spectrum. Here, we used mass-action-based models to describe post-transcriptional regulations, finding that cooperative RNA degradation via multiple microRNA binding sites can generate four-attractor systems without transcriptional feedback. Furthermore, the newly identified intermediates-enabling circuits are common in the EMT regulatory network, and they can synergize with transcriptional feedback to support phenotypic continuum. Finally, our model predicted a role of miR-101 in multistate EMT, and we identified evidence from single-cell RNA-sequencing data that support the prediction. Our work reveals a previously unknown role of cooperative RNA degradation and microRNAs in EMT, providing a framework that can bridge the gap between mechanistic models and single-cell experiments.

Automated multimodal fluorescence microscopy for hyperplex spatial-proteomics: Coupling microfluidic-based immunofluorescence to high resolution, high sensitivity, three-dimensional analysis of histological slides

In situ multiplexing analysis and in situ transcriptomics are now providing revolutionary tools to achieve the comprehension of the molecular basis of cancer and to progress towards personalized medicine to fight the disease. The complexity of these tasks requires a continuous interplay among different technologies during all the phases of the experimental procedures. New tools are thus needed and their characterization in terms of performances and limits is mandatory to reach the best resolution and sensitivity. We propose here a new experimental pipeline to obtain an optimized costs-to-benefits ratio thanks to the alternate employment of automated and manual procedures during all the phases of a multiplexing experiment from sample preparation to image collection and analysis. A comparison between ultra-fast and automated immunofluorescence staining and standard staining protocols has been carried out to compare the performances in terms of antigen saturation, background, signal-to-noise ratio and total duration. We then developed specific computational tools to collect data by automated analysis-driven fluorescence microscopy. Computer assisted selection of targeted areas with variable magnification and resolution allows employing confocal microscopy for a 3D high resolution analysis. Spatial resolution and sensitivity were thus maximized in a framework where the amount of stored data and the total requested time for the procedure were optimized and reduced with respect to a standard experimental approach.

Unraveling phenotypic plasticity and evolution in small cell lung cancer

Malignant cell populations in a tumor often exist in distinct phenotypic states. Deciphering tumor heterogeneity requires determining how many such unique states exist and what the biological traits associated with each are. Archetype analysis of SCLC transcriptomes reveals key phenotypic states in SCLC tumors and their patterns of evolution.

Archetype tasks link intratumoral heterogeneity to plasticity and cancer hallmarks in small cell lung cancer

Small cell lung cancer (SCLC) tumors comprise heterogeneous mixtures of cell states, categorized into neuroendocrine (NE) and non-neuroendocrine (non-NE) transcriptional subtypes. NE to non-NE state transitions, fueled by plasticity, likely underlie adaptability to treatment and dismal survival rates. Here, we apply an archetypal analysis to model plasticity by recasting SCLC phenotypic heterogeneity through multi-task evolutionary theory. Cell line and tumor transcriptomics data fit well in a five-dimensional convex polytope whose vertices optimize tasks reminiscent of pulmonary NE cells, the SCLC normal counterparts. These tasks, supported by knowledge and experimental data, include proliferation, slithering, metabolism, secretion, and injury repair, reflecting cancer hallmarks. SCLC subtypes, either at the population or single-cell level, can be positioned in archetypal space by bulk or single-cell transcriptomics, respectively, and characterized as task specialists or multi-task generalists by the distance from archetype vertex signatures. In the archetype space, modeling single-cell plasticity as a Markovian process along an underlying state manifold indicates that task trade-offs, in response to microenvironmental perturbations or treatment, may drive cell plasticity. Stifling phenotypic transitions and plasticity may provide new targets for much-needed translational advances in SCLC. A record of this paper's Transparent Peer Review process is included in the supplemental information.

Lineage plasticity in prostate cancer depends on JAK/STAT inflammatory signaling

Drug resistance in cancer is often linked to changes in tumor cell state or lineage, but the molecular mechanisms driving this plasticity remain unclear. Using murine organoid and genetically engineered mouse models, we investigated the causes of lineage plasticity in prostate cancer and its relationship to antiandrogen resistance. We found that plasticity initiates in an epithelial population defined by mixed luminal-basal phenotype and that it depends on increased Janus kinase (JAK) and fibroblast growth factor receptor (FGFR) activity. Organoid cultures from patients with castration-resistant disease harboring mixed-lineage cells reproduce the dependency observed in mice by up-regulating luminal gene expression upon JAK and FGFR inhibitor treatment. Single-cell analysis confirms the presence of mixed-lineage cells with increased JAK/STAT (signal transducer and activator of transcription) and FGFR signaling in a subset of patients with metastatic disease, with implications for stratifying patients for clinical trials.

A universal monoclonal antibody-aptamer conjugation strategy for selective non-invasive bioparticle isolation from blood on a regenerative microfluidic platform

Simultaneous isolation of various circulating tumor cell (CTC) subtypes from whole blood is useful in cancer diagnosis and prognosis. Microfluidic affinity separation devices are promising for CTC separation because of their high throughput capacity and automatability. However, current affinity agents, such as antibodies (mAbs) and aptamers (Apts) alone, are still suboptimal for efficient, consistent, and versatile cell analysis. By introducing a hybrid affinity agent, i.e., an aptamer-antibody (Apt-mAb) conjugate, we developed a universal and regenerative microchip with high efficiency and non-invasiveness in the separation and profiling of various CTCs from blood. The Apt-mAb conjugate consists of a monoclonal antibody that specifically binds the target cell receptor and a surface-bound aptamer that recognizes the conserved Fc region of the mAb. The aptamer then indirectly links the surface functionalization of the microfluidic channels to the mAbs. This hybrid affinity agent and the microchip platform may be widely useful for various bio-particle separations in different biological matrices. Further, the regeneration capability of the microchip improves data consistency between multiple uses and minimizes plastic waste while promoting environmental sustainability. STATEMENT OF SIGNIFICANCE: A hybrid affinity agent, Apt-mAb, consisting of a universal aptamer (Apt) that binds the conserved Fc region of monoclonal antibodies (mAbs) was developed. The invented nano-biomaterial combines the strengths and overcomes the weakness of both Apts and mAbs, thus changing the paradigm of affinity separation of cell subtypes. When Apt-mAb was used to fabricate microfluidic chips using a "universal screwdriver" approach, the microchip could be easily tuned to bind any cell type, exhibiting great universality. Besides high sensitivity and selectivity, the superior regenerative capacity of the microchips makes them reusable, which provides improved consistency and repeatability in cell profiling and opens a new approach towards in vitro diagnostic point-of-care testing devices with environmental sustainability and cost-effectiveness.

Molecular evolutionary process of advanced gastric cancer during sequential chemotherapy detected by circulating tumor DNA

BACKGROUND

Efficacy of conventional sequential chemotherapy paradigm for advanced gastric cancer (AGC) patients has largely plateaued. Dynamic molecular changes during and after sequential chemotherapy have not been fully delineated. We aimed to profile the molecular evolutionary process of AGC patients during sequential chemotherapy by next generation sequencing (NGS) of plasma circulating tumor DNA (ctDNA).

METHODS

A total of 30 chemo-naïve patients who were diagnosed with unresectable advanced or metastatic stomach adenocarcinoma were enrolled. All patients received sequential chemotherapy regimens following the clinical guideline. One hundred and eight serial peripheral blood samples were collected at baseline, radiographical assessment and disease progression. Plasma ctDNA was isolated and a customized NGS panel was used to detect the genomic features of ctDNA including single nucleotide variants (SNVs) and gene-level copy number variations (CNVs). KEGG pathway enrichment analysis was performed.

RESULTS

Platinum-based combination chemotherapy was administrated as first-line regimen. Objective response rate was 50% (15/30). Patients with higher baseline values of copy number instability (CNI), CNVs and variant allel frequency (VAF) were more sensitive to platinum-based first-line regimens. Tumor mutation burden (TMB), CNI and CNV burden at partial response and stable disease were significantly lower than those at baseline, where at progressive disease they recovered to baseline levels. Dynamic change of TMB (ΔTMB) was correlated with progression-free survival of first-line treatment. Fluctuating changes of SNVs and gene-level CNVs could be observed during sequential chemotherapy. Under the pressure of conventional chemotherapy, the number of novel gene-level CNVs were found to be higher than that of novel SNVs. Such novel molecular alterations could be enriched into multiple common oncologic signaling pathways, including EGFR tyrosine kinase inhibitor resistance and platinum drug resistance pathways, where their distributions were found to be highly heterogenous among patients. The impact of subsequent regimens, including paclitaxel-based and irinotecan-based regimens, on the molecular changes driven by first-line therapy was subtle.

CONCLUSION

Baseline and dynamic changes of genomic features of ctDNA could be biomarkers for predicting response of platinum-based first-line chemotherapy in AGC patients. After treatment with standard chemotherapy regimens, convergent oncologic pathway enrichment was identified, which is yet characterized by inter-patient heterogenous gene-level CNVs.

Optical Cellular Micromotion: A New Paradigm to Measure Tumor Cells Invasion within Gels Mimicking the 3D Tumor Environments

Measuring tumor cell invasiveness through 3D tissues, particularly at the single-cell level, can provide important mechanistic understanding and assist in identifying therapeutic targets of tumor invasion. However, current experimental approaches, including standard in vitro invasion assays, have limited physiological relevance and offer insufficient insight into the vast heterogeneity in tumor cell migration through tissues. To address these issues, here the concept of optical cellular micromotion is reported on, where digital holographic microscopy is used to map the optical nano- to submicrometer thickness fluctuations within single-cells. These fluctuations are driven by the dynamic movement of subcellular structures including the cytoskeleton and inherently associated with the biological processes involved in cell invasion within tissues. It is experimentally demonstrated that the optical cellular micromotion correlates with tumor cells motility and invasiveness both at the population and single-cell levels. In addition, the optical cellular micromotion significantly reduced upon treatment with migrastatic drugs that inhibit tumor cell invasion. These results demonstrate that micromotion measurements can rapidly and non-invasively determine the invasive behavior of single tumor cells within tissues, yielding a new and powerful tool to assess the efficacy of approaches targeting tumor cell invasiveness.

Chromosome instability and aneuploidy as context-dependent activators or inhibitors of antitumor immunity

Chromosome instability (CIN) and its major consequence, aneuploidy, are hallmarks of human cancers. In addition to imposing fitness costs on tumor cells through several cell-intrinsic mechanisms, CIN/aneuploidy also provokes an antitumor immune response. However, as the major contributor to genomic instability, intratumor heterogeneity generated by CIN/aneuploidy helps tumor cells to evolve methods to overcome the antitumor role of the immune system or even convert the immune system to be tumor-promoting. Although the interplay between CIN/aneuploidy and the immune system is complex and context-dependent, understanding this interplay is essential for the success of immunotherapy in tumors exhibiting CIN/aneuploidy, regardless of whether the efficacy of immunotherapy is increased by combination with strategies to promote CIN/aneuploidy or by designing immunotherapies to target CIN/aneuploidy directly.

Metabolic modeling-based drug repurposing in Glioblastoma

The manifestation of intra- and inter-tumor heterogeneity hinders the development of ubiquitous cancer treatments, thus requiring a tailored therapy for each cancer type. Specifically, the reprogramming of cellular metabolism has been identified as a source of potential drug targets. Drug discovery is a long and resource-demanding process aiming at identifying and testing compounds early in the drug development pipeline. While drug repurposing efforts (i.e., inspecting readily available approved drugs) can be supported by a mechanistic rationale, strategies to further reduce and prioritize the list of potential candidates are still needed to facilitate feasible studies. Although a variety of ‘omics’ data are widely gathered, a standard integration method with modeling approaches is lacking. For instance, flux balance analysis is a metabolic modeling technique that mainly relies on the stoichiometry of the metabolic network. However, exploring the network’s topology typically neglects biologically relevant information. Here we introduce Transcriptomics-Informed Stoichiometric Modelling And Network analysis (TISMAN) in a recombinant innovation manner, allowing identification and validation of genes as targets for drug repurposing using glioblastoma as an exemplar.

Pareto optimality, economy-effectiveness trade-offs and ion channel degeneracy: improving population modelling for single neurons

Neurons encounter unavoidable evolutionary trade-offs between multiple tasks. They must consume as little energy as possible while effectively fulfilling their functions. Cells displaying the best performance for such multi-task trade-offs are said to be Pareto optimal, with their ion channel configurations underpinning their functionality. Ion channel degeneracy, however, implies that multiple ion channel configurations can lead to functionally similar behaviour. Therefore, instead of a single model, neuroscientists often use populations of models with distinct combinations of ionic conductances. This approach is called population (database or ensemble) modelling. It remains unclear, which ion channel parameters in the vast population of functional models are more likely to be found in the brain. Here we argue that Pareto optimality can serve as a guiding principle for addressing this issue by helping to identify the subpopulations of conductance-based models that perform best for the trade-off between economy and functionality. In this way, the high-dimensional parameter space of neuronal models might be reduced to geometrically simple low-dimensional manifolds, potentially explaining experimentally observed ion channel correlations. Conversely, Pareto inference might also help deduce neuronal functions from high-dimensional Patch-seq data. In summary, Pareto optimality is a promising framework for improving population modelling of neurons and their circuits.

Development of a Chemoresistant Risk Scoring Model for Prechemotherapy Osteosarcoma Using Single-Cell Sequencing

Background

Chemoresistance is one of the leading causes that severely limits the success of osteosarcoma treatment. Evaluating chemoresistance before chemotherapy poses a new challenge for researchers. We established an effective chemoresistance risk scoring model for prechemotherapy osteosarcoma using single-cell sequencing.

Methods

We comprehensively analyzed osteosarcoma data from the bulk mRNA sequencing dataset TARGET-OS and the single-cell RNA sequencing (scRNA-seq) dataset GSE162454. Chemoresistant tumor clusters were identified using enrichment analysis and AUCell scoring. Its differentiated trajectory was achieved with inferCNV and pseudotime analysis. Ligand-receptor interactions were annotated with iTALK. Furthermore, we established a chemoresistance risk scoring model using LASSO regression based on scRNA-seq-based markers of chemoresistant tumor clusters. The TARGET-OS dataset was used as the training group, and the bulk mRNA array dataset GSE33382 was used as the validation group. Finally, the performance was verified for its discriminatory ability and calibration.

Results

Using bulk RNA data, we found that osteogenic expression was upregulated in chemoresistant osteosarcoma as compared to chemosensitive osteosarcoma. Then, we transferred the bulk RNA findings to scRNA-seq and noticed osteosarcoma tumor clusters C14 and C25 showing osteogenic cancer stem cell expression patterns, which fit chemoresistant characteristics. C14 and C25 possessed bridge roles in interactions with other clusters. On the one hand, they received various growth factor stimulators and could potentially transform into a proliferative state. On the other hand, they promote local tumor angiogenesis, bone remodeling and immunosuppression. Next, we identified a ten-gene signature from the C14 and C25 markers and constructed a chemoresistant risk scoring model using LASSO regression model. Finally, we found that chemoresistant osteosarcoma had higher chemoresistance risk score and that the model showed good discriminatory ability and calibration in both the training and validation groups (AUCtrain = 0.82; AUCvalid = 0.84). Compared with that of the classic bulk RNA-based model, it showed more robust performance in validation environment (AUCvalid-scRNA = 0.84; AUCvalid-bulk DEGs = 0.54).

Conclusions

Our work provides insights into understanding chemoresistant osteosarcoma tumor cells and using single-cell sequencing to establish a chemoresistance risk scoring model. The model showed good discriminatory ability and calibration and provided us with a feasible way to evaluate chemoresistance in prechemotherapy osteosarcoma.

Extracellular matrix as a driver for intratumoral heterogeneity

The architecture of an organ is built through interactions between its native cells and its connective tissue consisting of stromal cells and the extracellular matrix (ECM). Upon transformation through tumorigenesis, such interactions are disrupted and replaced by a new set of intercommunications between malignantly transformed parenchyma, an altered stromal cell population, and a remodeled ECM. In this perspective, we propose that the intratumoral heterogeneity of cancer cell phenotypes is an emergent property of such reciprocal intercommunications, both biochemical and mechanical-physical, which engender and amplify the diversity of cell behavioral traits. An attempt to assimilate such findings within a framework of phenotypic plasticity furthers our understanding of cancer progression.

Novel Ex Vivo Models of Epithelial Ovarian Cancer: The Future of Biomarker and Therapeutic Research

Epithelial ovarian cancer (EOC) is a heterogenous disease associated with variations in presentation, pathology and prognosis. Advanced EOC is typified by frequent relapse and a historical 5-year survival of less than 30% despite improvements in surgical and systemic treatment. The advent of next generation sequencing has led to notable advances in the field of personalised medicine for many cancer types. Success in achieving cure in advanced EOC has however been limited, although significant prolongation of survival has been demonstrated. Development of novel research platforms is therefore necessary to address the rapidly advancing field of early diagnostics and therapeutics, whilst also acknowledging the significant tumour heterogeneity associated with EOC. Within available tumour models, patient-derived organoids (PDO) and explant tumour slices have demonstrated particular promise as novel ex vivo systems to model different cancer types including ovarian cancer. PDOs are organ specific 3D tumour cultures that can accurately represent the histology and genomics of their native tumour, as well as offer the possibility as models for pharmaceutical drug testing platforms, offering timing advantages and potential use as prospective personalised models to guide clinical decision-making. Such applications could maximise the benefit of drug treatments to patients on an individual level whilst minimising use of less effective, yet toxic, therapies. PDOs are likely to play a greater role in both academic research and drug development in the future and have the potential to revolutionise future patient treatment and clinical trial pathways. Similarly, ex vivo tumour slices or explants have also shown recent renewed promise in their ability to provide a fast, specific, platform for drug testing that accurately represents in vivo tumour response. Tumour explants retain tissue architecture, and thus incorporate the majority of tumour microenvironment making them an attractive method to re-capitulate in vivo conditions, again with significant timing and personalisation of treatment advantages for patients. This review will discuss the current treatment landscape and research models for EOC, their development and new advances towards the discovery of novel biomarkers or combinational therapeutic strategies to increase treatment options for women with ovarian cancer.

Noninvasive Method for Predicting the Expression of Ki67 and Prognosis in Non-Small-Cell Lung Cancer Patients: Radiomics

Purpose

In this study, we aimed to develop and validate a noninvasive method based on radiomics to evaluate the expression of Ki67 and prognosis of patients with non-small-cell lung cancer (NSCLC). Patients and Methods. A total of 120 patients with NSCLC were enrolled in this retrospective study. All patients were randomly assigned to a training dataset (n = 85) and test dataset (n = 35). According to the preprocessed F-FDG PET/CT image of each patient, a total of 384 radiomics features were extracted from the segmentation of regions of interest (ROIs). The Spearman correlation test and least absolute shrinkage and selection operator (LASSO), after normalization on the features matrix, were applied to reduce the dimensionality of the features. Furthermore, multivariable logistic regression analysis was used to propose a model for predicting Ki67. The survival curve was used to explore the prognostic significance of radiomics features.

Results

A total of 62 Ki67 positive patients and 58 Ki67 negative patients formed the training set and test training dataset and test dataset. Radiomics signatures showed good performance in predicting the expression of Ki67 with AUCs of 0.86 (training dataset) and 0.85 (test dataset). Validation and calibration showed that the radiomics had a strong predictive power in patients with NSCLC survival, which was significantly close to the effect of Ki67 expression on the survival of patients with NSCLC.

Conclusion

Radiomics signatures based on preoperative F-FDG PET/CT could distinguish the expression of Ki67, which also had a strong predictive performance for the survival outcome.

Identification of a Five-Gene Panel to Assess Prognosis for Gastric Cancer

Methods

Two datasets were used as training and validation cohorts to establish the predictive model. We used three types of screening criteria: background analysis, pathway analysis, and functional analysis provided by the cBioportal website. Fisher's exact test and multivariable logistic regression were performed to screen out related genes. Furthermore, we performed receiver operating characteristic (ROC) and Kaplan–Meier curve analyses to evaluate the correlation between the selected genes and overall survival.

Result

We screened five genes (KNL1, NRXN1, C6, CCDC169-SOHLH2, and TTN) that were highly related to recurrence of GC. The area under the receiver operating characteristic (ROC) curve was 0.813, which was much higher than that of the baseline model (AUC = 0.699). This result suggested that the mutation of five selected genes had a significant effect on the prediction of recurrence compared with other factors (age, stages, history, etc.). Furthermore, the Kaplan-Meier estimator also revealed that the mutation of five genes positively correlated with patient survival.

Conclusions

The patients who have mutations in these five genes may experience longer survival than those who do not have mutations. This five-gene panel will likely be a practical tool for prognostic evaluation and will provide another possible way for clinicians to determine therapy.

Noncoding RNAs and Deep Learning Neural Network Discriminate Multi-Cancer Types

Detecting cancers at early stages can dramatically reduce mortality rates. Therefore, practical cancer screening at the population level is needed. To develop a comprehensive detection system to classify multiple cancer types, we integrated an artificial intelligence deep learning neural network and noncoding RNA biomarkers selected from massive data. Our system can accurately detect cancer vs. healthy objects with 96.3% of AUC of ROC (Area Under Curve of a Receiver Operating Characteristic curve), and it surprisingly reaches 78.77% of AUC when validated by real-world raw data from a completely independent data set. Even validating with raw exosome data from blood, our system can reach 72% of AUC. Moreover, our system significantly outperforms conventional machine learning models, such as random forest. Intriguingly, with no more than six biomarkers, our approach can easily discriminate any individual cancer type vs. normal with 99% to 100% AUC. Furthermore, a comprehensive marker panel can simultaneously multi-classify common cancers with a stable 82.15% accuracy rate for heterogeneous cancerous tissues and conditions. This detection system provides a promising practical framework for automatic cancer screening at population level. Key points: (1) We developed a practical cancer screening system, which is simple, accurate, affordable, and easy to operate. (2) Our system binarily classify cancers vs. normal with >96% AUC. (3) In total, 26 individual cancer types can be easily detected by our system with 99 to 100% AUC. (4) The system can detect multiple cancer types simultaneously with >82% accuracy.

Controls for Phylogeny and Robust Analysis in Pareto Task Inference

Understanding the tradeoffs faced by organisms is a major goal of evolutionary biology. One of the main approaches for identifying these tradeoffs is Pareto task inference (ParTI). Two recent papers claim that results obtained in ParTI studies are spurious due to phylogenetic dependence (Mikami T, Iwasaki W. 2021. The flipping t-ratio test: phylogenetically informed assessment of the Pareto theory for phenotypic evolution. Methods Ecol Evol. 12(4):696-706) or hypothetical p-hacking and population-structure concerns (Sun M, Zhang J. 2021. Rampant false detection of adaptive phenotypic optimization by ParTI-based Pareto front inference. Mol Biol Evol. 38(4):1653-1664). Here, we show that these claims are baseless. We present a new method to control for phylogenetic dependence, called SibSwap, and show that published ParTI inference is robust to phylogenetic dependence. We show how researchers avoided p-hacking by testing for the robustness of preprocessing choices. We also provide new methods to control for population structure and detail the experimental tests of ParTI in systems ranging from ammonites to cancer gene expression. The methods presented here may help to improve future ParTI studies.