The microcosmos of intratumor heterogeneity: the space-time of cancer evolution

SPACE: Spatially variable gene clustering adjusting for cell type effect for improved spatial domain detection

Recent advances in spatial transcriptomics have significantly deepened our understanding of biology. A primary focus has been identifying spatially variable genes (SVGs) which are crucial for downstream tasks like spatial domain detection. Traditional methods often use all or a set number of top SVGs for this purpose. However, in diverse datasets with many SVGs, this approach may not ensure accurate results. Instead, grouping SVGs by expression patterns and using all SVG groups in downstream analysis can improve accuracy. Furthermore, classifying SVGs in this manner is akin to identifying cell type marker genes, offering valuable biological insights. The challenge lies in accurately categorizing SVGs into relevant clusters, aggravated by the absence of prior knowledge regarding the number and spectrum of spatial gene patterns. Addressing this challenge, we propose SPACE, SPatially variable gene clustering Adjusting for Cell type Effect, a framework that classifies SVGs based on their spatial patterns by adjusting for confounding effects caused by shared cell types, to improve spatial domain detection. This method does not require prior knowledge of gene cluster numbers, spatial patterns, or cell type information. Our comprehensive simulations and real data analyses demonstrate that SPACE is an efficient and promising tool for spatial transcriptomics analysis.

Somatic Mutations in KEAP1-NRF2 Complex in Breast Cancer

Breast cancer remains the leading cause of cancer deaths for women. Long-term estrogen exposure is considered carcinogenic due to semiquinone production and to compromised detoxification. Metabolic regulator polymorphisms, such as KEAP1 (rs1048290) and NRF2 (rs35652124, rs6721961, rs6706649), can be valuable in understanding the individual cytoprotection profile. Thus, we aim to genotype these polymorphisms in blood, tumours and surrounding tissue, to identify somatic mutations and correlate it to prognoses. A total of 23 controls and 69 women with histological confirmed breast cancer were recruited, and DNA from blood/surrounding/tumour tissue was genotyped. Genotyping and clinicopathological data were correlated. We verified that rs35652124 presents different genotype distribution between the blood/surrounding tissue (p-value = 0.023) and tumour/surrounding tissues (p-value = 0.041). Apart from rs35652124 and considering the histological grade, the other four polymorphisms have different distributions among different tissues. There is a tendency towards the loss of heterozygosity in the surrounding tissue when compared to blood and tumour tissues, and higher genotype variability in histologic grade 2. These somatic mutations and different distribution patterns may indicate a heterogeneous and active microenvironment, influencing breast cancer outcome. Additionally, it would be pertinent to evaluate the predictive value of the histologic grade 2 considering somatic mutation profiles and distributions.

Assessing RNA-Seq Workflow Methodologies Using Shannon Entropy

RNA-seq faces persistent challenges due to the ongoing, expanding array of data processing workflows, none of which have yet achieved standardization to date. It is imperative to determine which method most effectively preserves biological facts. Here, we used Shannon entropy as a tool for depicting the biological status of a system. Thus, we assessed the measurement of Shannon entropy by several RNA-seq workflow approaches, such as DESeq2 and edgeR, but also by combining nine normalization methods with log2 fold change on paired samples of TCGA RNA-seq representing datasets of 515 patients and spanning 12 different cancer types with 5-year overall survival rates ranging from 20% to 98%. Our analysis revealed that TPM, RLE, and TMM normalization, coupled with a threshold of log2 fold change ≥1, for identifying differentially expressed genes, yielded the best results. We propose that Shannon entropy can serve as an objective metric for refining the optimization of RNA-seq workflows and mRNA sequencing technologies.

Understanding the dynamics of TKI-induced changes in the tumor immune microenvironment for improved therapeutic effect

BACKGROUND

The dynamic interplay between tyrosine kinase inhibitors (TKIs) and the tumor immune microenvironment (TME) plays a crucial role in the therapeutic trajectory of non-small cell lung cancer (NSCLC). Understanding the functional dynamics and resistance mechanisms of TKIs is essential for advancing the treatment of NSCLC.

METHODS

This study assessed the effects of short-term and long-term TKI treatments on the TME in NSCLC, particularly targeting epidermal growth factor receptor (EGFR) and anaplastic lymphoma kinase (ALK) mutations. We analyzed changes in immune cell composition, cytokine profiles, and key proteins involved in immune evasion, such as laminin subunit γ-2 (LAMC2). We also explored the use of aspirin as an adjunct therapy to modulate the TME and counteract TKI resistance.

RESULTS

Short-term TKI treatment enhanced T cell-mediated tumor clearance, reduced immunosuppressive M2 macrophage infiltration, and downregulated LAMC2 expression. Conversely, long-term TKI treatment fostered an immunosuppressive TME, contributing to drug resistance and promoting immune escape. Differential responses were observed among various oncogenic mutations, with ALK-targeted therapies eliciting a stronger antitumor immune response compared with EGFR-targeted therapies. Notably, we found that aspirin has potential in overcoming TKI resistance by modulating the TME and enhancing T cell-mediated tumor clearance.

CONCLUSIONS

These findings offer new insights into the dynamics of TKI-induced changes in the TME, improving our understanding of NSCLC challenges. The study underscores the critical role of the TME in TKI resistance and suggests that adjunct therapies, like aspirin, may provide new strategies to enhance TKI efficacy and overcome resistance.

Integration of deep learning and habitat radiomics for predicting the response to immunotherapy in NSCLC patients

BACKGROUND

The non-invasive biomarkers for predicting immunotherapy response are urgently needed to prevent both premature cessation of treatment and ineffective extension. This study aimed to construct a non-invasive model for predicting immunotherapy response, based on the integration of deep learning and habitat radiomics in patients with advanced non-small cell lung cancer (NSCLC).

METHODS

Independent patient cohorts from three medical centers were enrolled for training (n = 164) and test (n = 82). Habitat imaging radiomics features were derived from sub-regions clustered from individual's tumor by K-means method. The deep learning features were extracted based on 3D ResNet algorithm. Pearson correlation coefficient, T test and least absolute shrinkage and selection operator regression were used to select features. Support vector machine was applied to implement deep learning and habitat radiomics, respectively. Then, a combination model was developed integrating both sources of data.

RESULTS

The combination model obtained a strong well-performance, achieving area under receiver operating characteristics curve of 0.865 (95% CI 0.772-0.931). The model significantly discerned high and low-risk patients, and exhibited a significant benefit in the clinical use.

CONCLUSION

The integration of deep-leaning and habitat radiomics contributed to predicting response to immunotherapy in patients with NSCLC. The developed integration model may be used as potential tool for individual immunotherapy management.

Revealing spatial multimodal heterogeneity in tissues with SpaTrio

Capturing and depicting the multimodal tissue information of tissues at the spatial scale remains a significant challenge owing to technical limitations in single-cell multi-omics and spatial transcriptomics sequencing. Here, we developed a computational method called SpaTrio that can build spatial multi-omics data by integrating these two datasets through probabilistic alignment and enabling further analysis of gene regulation and cellular interactions. We benchmarked SpaTrio using simulation datasets and demonstrated its accuracy and robustness. Next, we evaluated SpaTrio on biological datasets and showed that it could detect topological patterns of cells and modalities. SpaTrio has also been applied to multiple sets of actual data to uncover spatially multimodal heterogeneity, understand the spatiotemporal regulation of gene expression, and resolve multimodal communication among cells. Our data demonstrated that SpaTrio could accurately map single cells and reconstruct the spatial distribution of various biomolecules, providing valuable multimodal insights into spatial biology.

Robust mapping of spatiotemporal trajectories and cell-cell interactions in healthy and diseased tissues

Spatial transcriptomics (ST) technologies generate multiple data types from biological samples, namely gene expression, physical distance between data points, and/or tissue morphology. Here we developed three computational-statistical algorithms that integrate all three data types to advance understanding of cellular processes. First, we present a spatial graph-based method, pseudo-time-space (PSTS), to model and uncover relationships between transcriptional states of cells across tissues undergoing dynamic change (e.g. neurodevelopment, brain injury and/or microglia activation, and cancer progression). We further developed a spatially-constrained two-level permutation (SCTP) test to study cell-cell interaction, finding highly interactive tissue regions across thousands of ligand-receptor pairs with markedly reduced false discovery rates. Finally, we present a spatial graph-based imputation method with neural network (stSME), to correct for technical noise/dropout and increase ST data coverage. Together, the algorithms that we developed, implemented in the comprehensive and fast stLearn software, allow for robust interrogation of biological processes within healthy and diseased tissues.

Radiological artificial intelligence - predicting personalized immunotherapy outcomes in lung cancer

Personalized medicine has revolutionized approaches to treatment in the field of lung cancer by enabling therapies to be specific to each patient. However, physicians encounter an immense number of challenges in providing the optimal treatment regimen for the individual given the sheer complexity of clinical aspects such as tumor molecular profile, tumor microenvironment, expected adverse events, acquired or inherent resistance mechanisms, the development of brain metastases, the limited availability of biomarkers and the choice of combination therapy. The integration of innovative next-generation technologies such as deep learning-a subset of machine learning-and radiomics has the potential to transform the field by supporting clinical decision making in cancer treatment and the delivery of precision therapies while integrating numerous clinical considerations. In this review, we present a brief explanation of the available technologies, the benefits of using these technologies in predicting immunotherapy response in lung cancer, and the expected future challenges in the context of precision medicine.

A novel Bayesian model for assessing intratumor heterogeneity of tumor infiltrating leukocytes with multi-region gene expression sequencing

Intratumor heterogeneity (ITH) of tumor-infiltrated leukocytes (TILs) is an important phenomenon of cancer biology with potentially profound clinical impacts. Multi-region gene expression sequencing data provide a promising opportunity that allows for explorations of TILs and their intratumor heterogeneity for each subject. Although several existing methods are available to infer the proportions of TILs, considerable methodological gaps exist for evaluating intratumor heterogeneity of TILs with multi-region gene expression data. Here, we develop ICeITH, immune cell estimation reveals intratumor heterogeneity, a Bayesian hierarchical model that borrows cell type profiles as prior knowledge to decompose mixed bulk data while accounting for the within-subject correlations among tumor samples. ICeITH quantifies intratumor heterogeneity by the variability of targeted cellular compositions. Through extensive simulation studies, we demonstrate that ICeITH is more accurate in measuring relative cellular abundance and evaluating intratumor heterogeneity compared with existing methods. We also assess the ability of ICeITH to stratify patients by their intratumor heterogeneity score and associate the estimations with the survival outcomes. Finally, we apply ICeITH to two multi-region gene expression datasets from lung cancer studies to classify patients into different risk groups according to the ITH estimations of targeted TILs that shape either pro- or anti-tumor processes. In conclusion, ICeITH is a useful tool to evaluate intratumor heterogeneity of TILs from multi-region gene expression data.

Habitat-based radiomics enhances the ability to predict lymphovascular space invasion in cervical cancer: a multi-center study

Introduction

Lymphovascular space invasion (LVSI) is associated with lymph node metastasis and poor prognosis in cervical cancer. In this study, we investigated the potential of radiomics, derived from magnetic resonance (MR) images using habitat analysis, as a non-invasive surrogate biomarker for predicting LVSI in cervical cancer.

Methods

This retrospective study included 300 patients with cervical cancer who underwent surgical treatment at two centres (centre 1 = 198 and centre 2 = 102). Using the k-means clustering method, contrast-enhanced T1-weighted imaging (CE-T1WI) images were segmented based on voxel and entropy values, creating sub-regions within the volume ofinterest. Radiomics features were extracted from these sub-regions. Pearson correlation coefficient and least absolute shrinkage and selection operator LASSO) regression methods were used to select features associated with LVSI in cervical cancer. Support vector machine (SVM) model was developed based on the radiomics features extracted from each sub-region in the training cohort.

Results

The voxels and entropy values of the CE-T1WI images were clustered into three sub-regions. In the training cohort, the AUCs of the SVM models based on radiomics features derived from the whole tumour, habitat 1, habitat 2, and habitat 3 models were 0.805 (95% confidence interval [CI]: 0.745-0.864), 0.873(95% CI: 0.824-0.922), 0.869 (95% CI: 0.821-0.917), and 0.870 (95% CI: 0.821-0.920), respectively. Compared with whole tumour model, the predictive performances of habitat 3 model was the highest in the external test cohort (0.780 [95% CI: 0.692-0.869]).

Conclusions

The radiomics model based on the tumour sub-regional habitat demonstrated superior predictive performance for an LVSI in cervical cancer than that of radiomics model derived from the whole tumour.

In-Droplet Electromechanical Cell Lysis and Enhanced Enzymatic Assay Driven by Ion Concentration Polarization

Droplets enable the encapsulation of cells for their analysis in isolated domains. The study of molecular signatures (including genes, proteins, and metabolites) from a few or single cells is critical for identifying key subpopulations. However, dealing with biological analytes at low concentrations requires long incubation times and amplification to achieve the requisite signal strength. Further, cell lysis requires additional chemical lysing agents or heat, which can interfere with assays. Here, we leverage ion concentration polarization (ICP) in droplets to rapidly lyse breast cancer cells within 2 s under a DC voltage bias of 30 V. Numerical simulations attribute cell lysis to an ICP-based electric field and shear stress. We further achieve up to 19-fold concentration enrichment of an enzymatic assay product resulting from cell lysis and a 3.8-fold increase in the reaction rate during enrichment. Our technique for sensitive in-droplet cell analysis provides scope for rapid, high-throughput detection of low-abundance intracellular analytes.

Estimation of cell lineages in tumors from spatial transcriptomics data

Spatial transcriptomics (ST) technology through in situ capturing has enabled topographical gene expression profiling of tumor tissues. However, each capturing spot may contain diverse immune and malignant cells, with different cell densities across tissue regions. Cell type deconvolution in tumor ST data remains challenging for existing methods designed to decompose general ST or bulk tumor data. We develop the Spatial Cellular Estimator for Tumors (SpaCET) to infer cell identities from tumor ST data. SpaCET first estimates cancer cell abundance by integrating a gene pattern dictionary of copy number alterations and expression changes in common malignancies. A constrained regression model then calibrates local cell densities and determines immune and stromal cell lineage fractions. SpaCET provides higher accuracy than existing methods based on simulation and real ST data with matched double-blind histopathology annotations as ground truth. Further, coupling cell fractions with ligand-receptor coexpression analysis, SpaCET reveals how intercellular interactions at the tumor-immune interface promote cancer progression.

Chromatin and Cancer: Implications of Disrupted Chromatin Organization in Tumorigenesis and Its Diversification

A hallmark of cancers is uncontrolled cell proliferation, frequently associated with an underlying imbalance in gene expression. This transcriptional dysregulation observed in cancers is multifaceted and involves chromosomal rearrangements, chimeric transcription factors, or altered epigenetic marks. Traditionally, chromatin dysregulation in cancers has been considered a downstream effect of driver mutations. However, here we present a broader perspective on the alteration of chromatin organization in the establishment, diversification, and therapeutic resistance of cancers. We hypothesize that the chromatin organization controls the accessibility of the transcriptional machinery to regulate gene expression in cancerous cells and preserves the structural integrity of the nucleus by regulating nuclear volume. Disruption of this large-scale chromatin in proliferating cancerous cells in conventional chemotherapies induces DNA damage and provides a positive feedback loop for chromatin rearrangements and tumor diversification. Consequently, the surviving cells from these chemotherapies become tolerant to higher doses of the therapeutic reagents, which are significantly toxic to normal cells. Furthermore, the disorganization of chromatin induced by these therapies accentuates nuclear fragility, thereby increasing the invasive potential of these tumors. Therefore, we believe that understanding the changes in chromatin organization in cancerous cells is expected to deliver more effective pharmacological interventions with minimal effects on non-cancerous cells.

Evolvability and emergence of tumor heterogeneity as a space-time function

The loss of control of cell proliferation, apoptosis regulation and contact inhibition leads to tumor development. While benign tumors are restricted to their primary space, i.e. where these tumors first originate, the metastatic tumors not only disseminate- facilitated by hypoxia-driven neovascularization- to distant secondary sites but also show substantial changes in metabolism, tissue architectures, gene expression profiles and immune phenotypes. All these alterations result in radio-, chemo- and immune-resistance rendering these metastatic tumor cells refractory to therapy. Since the beginning of the transformation, these factors- which influence each other- are incorporated to the developing and metastasizing tumor. As a result, the complexities in the heterogeneity of tumor progressively increase. This space-time function in the heterogeneity of tumors is generated by various conditions and factors at the genetic as well as microenvironmental levels, for example, endogenous retroviruses, methylation and epigenetic dysregulation that may be etiology-specific, cancer associated inflammation, remodeling of the extracellular matrix and mesenchymal cell shifted functions. On the one hand, these factors may cause de-differentiation of the tumor cells leading to cancer stem cells that contribute to radio-, chemo- and immune-resistance and recurrence of tumors. On the other hand, they may also enhance the heterogeneity under specific microenvironment-driven proliferation. In this editorial, we intend to underline the importance of heterogeneity in cancer progress, its evaluation and its use in correlation with the tumor evolution in a specific patient as a field of research for achieving precise patient-tailored treatments and amelioration of diagnostic (monitoring) tools and prognostic capacity.

Spatially resolved transcriptomics: advances and applications

Spatial transcriptomics, which is capable of both measuring all gene activity in a tissue sample and mapping where this activity occurs, is vastly improving our understanding of biological processes and disease. The field has expanded rapidly in recent years, and the development of several new technologies has resulted in spatially resolved transcriptomics (SRT) becoming highly multiplexed, high-resolution, and high-throughput. Here, we summarize and compare the major methods of SRT, including imaging-based methods, sequencing-based methods, and in situ sequencing methods. We also highlight some typical applications of SRT in neuroscience, cancer biology, developmental biology, and hematology. Finally, we discuss future possibilities for improving spatially resolved transcriptomic methods and the expected applications of such methods, especially in the adult bone marrow, anticipating that new developments will unlock the full potential of spatially resolved multi-omics in both biological research and the clinic.

NSD1 Mutations and Pediatric High-Grade Gliomas: A Comparative Genomic Study in Primary and Recurrent Tumors

Pediatric high-grade gliomas represent a heterogeneous group of tumors with a wide variety of molecular features. We performed whole exome sequencing and methylation profiling on matched primary and recurrent tumors from four pediatric patients with hemispheric high-grade gliomas. Genetic analysis showed the presence of some variants shared between primary and recurrent tumors, along with other variants exclusive of primary or recurrent tumors. NSD1 variants, all novel and not previously reported, were present at high frequency in our series (100%) and were all shared between the samples, independently of primary or recurrence. For every variant, in silico prediction tools estimated a high probability of altering protein function. The novel NSD1 variant (c.5924T > A; p.Leu1975His) was present in one in four cases at recurrence, and in two in four cases at primary. The novel NSD1 variant (c.5993T > A; p.Met1998Lys) was present in one in four cases both at primary and recurrence, and in one in four cases only at primary. The presence of NSD1 mutations only at recurrence may suggest that they can be sub-clonal, while the presence in both primary and recurrence implies that they can also represent early and stable events. Furthermore, their presence only in primary, but not in recurrent tumors, suggest that NSD1 mutations may also be influenced by treatment.

Spatially aware dimension reduction for spatial transcriptomics

Spatial transcriptomics are a collection of genomic technologies that have enabled transcriptomic profiling on tissues with spatial localization information. Analyzing spatial transcriptomic data is computationally challenging, as the data collected from various spatial transcriptomic technologies are often noisy and display substantial spatial correlation across tissue locations. Here, we develop a spatially-aware dimension reduction method, SpatialPCA, that can extract a low dimensional representation of the spatial transcriptomics data with biological signal and preserved spatial correlation structure, thus unlocking many existing computational tools previously developed in single-cell RNAseq studies for tailored analysis of spatial transcriptomics. We illustrate the benefits of SpatialPCA for spatial domain detection and explores its utility for trajectory inference on the tissue and for high-resolution spatial map construction. In the real data applications, SpatialPCA identifies key molecular and immunological signatures in a detected tumor surrounding microenvironment, including a tertiary lymphoid structure that shapes the gradual transcriptomic transition during tumorigenesis and metastasis. In addition, SpatialPCA detects the past neuronal developmental history that underlies the current transcriptomic landscape across tissue locations in the cortex.

Recent advances of bioresponsive polymeric nanomedicine for cancer therapy

A bioresponsive polymeric nanocarrier for drug delivery is able to alter its physical and physicochemical properties in response to a variety of biological signals and pathological changes, and can exert its therapeutic efficacy within a confined space. These nanosystems can optimize the biodistribution and subcellular location of therapeutics by exploiting the differences in biochemical properties between tumors and normal tissues. Moreover, bioresponsive polymer-based nanosystems could be rationally designed as precision therapeutic platforms by optimizing the combination of responsive elements and therapeutic components according to the patient-specific disease type and stage. In this review, recent advances in smart bioresponsive polymeric nanosystems for cancer chemotherapy and immunotherapy will be summarized. We mainly discuss three categories, including acidity-sensitive, redox-responsive, and enzyme-triggered polymeric nanosystems. The important issues regarding clinical translation such as reproducibility, manufacture, and probable toxicity, are also commented.

Gene expression-based dissection of inter-histotypes, intra-histotype and intra-tumor heterogeneity in pediatric tumors

Intra-tumor heterogeneity (ITH) fosters tumor evolution, resistance to therapy, and relapse. Recently, many evidence have been accumulated on the occurrence of genetic ITH in pediatric cancers. With this study we aimed to address the downstream effects that genetic and epigenetic ITH, and tumor-microenvironment interactions may produce within a tumor mass. To this aim, we investigated by high-throughput gene expression multiple samples of 5 hepatoblastomas, 5 neuroblastomas, 5 rhabdomyosarcomas, and 5 Wilms tumors. Principal component analysis, single sample hallmark gene sets analysis, and weighted gene co-expression network analysis were performed on gene expression data. We observed that the different tumors clustered by histotype, and then by case, and in addition, a variable degree of ITH was visible in all the investigated cases. The ITH highlighted in this study can represent a challenge in tumor treatment since we demonstrated that different druggable hallmarks and targets may be heterogeneously present within the same tumor mass, and this can potentially lead to therapeutic failure. Despite this heterogeneity, we could highlight some commonalities among the different histotypes investigated, supporting the feasibility to move in the clinic from a histotype-driven to a target-driven, sometimes agnostic, approach at least in some cases.

Spatial charting of single-cell transcriptomes in tissues

Single-cell RNA sequencing methods can profile the transcriptomes of single cells but cannot preserve spatial information. Conversely, spatial transcriptomics assays can profile spatial regions in tissue sections, but do not have single-cell resolution. Here, we developed a computational method called CellTrek that combines these two datasets to achieve single-cell spatial mapping through coembedding and metric learning approaches. We benchmarked CellTrek using simulation and in situ hybridization datasets, which demonstrated its accuracy and robustness. We then applied CellTrek to existing mouse brain and kidney datasets and showed that CellTrek can detect topological patterns of different cell types and cell states. We performed single-cell RNA sequencing and spatial transcriptomics experiments on two ductal carcinoma in situ tissues and applied CellTrek to identify tumor subclones that were restricted to different ducts, and specific T cell states adjacent to the tumor areas. Our data show that CellTrek can accurately map single cells in diverse tissue types to resolve their spatial organization.

An Insight into miR-1290: An Oncogenic miRNA with Diagnostic Potential

For more than two decades, the view of the roles of non-coding RNAs (ncRNAs) has been radically changing. These RNA molecules that are transcribed from our genome do not have the capacity to encode proteins, but are critical regulators of gene expression at different levels. Our knowledge is constantly enriched by new reports revealing the role of these new molecular players in the development of many pathological conditions, including cancer. One of the ncRNA classes includes short RNA molecules called microRNAs (miRNAs), which are involved in the post-transcriptional control of gene expression affecting various cellular processes. The aberrant expression of miRNAs with oncogenic and tumor-suppressive function is associated with cancer initiation, promotion, malignant transformation, progression and metastasis. Oncogenic miRNAs, also known as oncomirs, mediate the downregulation of tumor-suppressor genes and their expression is upregulated in cancer. Nowadays, miRNAs show promising application in diagnosis, prediction, disease monitoring and therapy response. Our review presents a current view of the oncogenic role of miR-1290 with emphasis on its properties as a cancer biomarker in clinical medicine.

Can Systems Biology Advance Clinical Precision Oncology?

Precision oncology is perceived as a way forward to treat individual cancer patients. However, knowing particular cancer mutations is not enough for optimal therapeutic treatment, because cancer genotype-phenotype relationships are nonlinear and dynamic. Systems biology studies the biological processes at the systems' level, using an array of techniques, ranging from statistical methods to network reconstruction and analysis, to mathematical modeling. Its goal is to reconstruct the complex and often counterintuitive dynamic behavior of biological systems and quantitatively predict their responses to environmental perturbations. In this paper, we review the impact of systems biology on precision oncology. We show examples of how the analysis of signal transduction networks allows to dissect resistance to targeted therapies and inform the choice of combinations of targeted drugs based on tumor molecular alterations. Patient-specific biomarkers based on dynamical models of signaling networks can have a greater prognostic value than conventional biomarkers. These examples support systems biology models as valuable tools to advance clinical and translational oncological research.

The Single-Cell Sequencing: A Dazzling Light Shining on the Dark Corner of Cancer

Tumorigenesis refers to the process of clonal dysplasia that occurs due to the collapse of normal growth regulation in cells caused by the action of various carcinogenic factors. These “successful” tumor cells pass on the genetic templates to their generations in evolutionary terms, but they also constantly adapt to ever-changing host environments. A unique peculiarity known as intratumor heterogeneity (ITH) is extensively involved in tumor development, metastasis, chemoresistance, and immune escape. An understanding of ITH is urgently required to identify the diversity and complexity of the tumor microenvironment (TME), but achieving this understanding has been a challenge. Single-cell sequencing (SCS) is a powerful tool that can gauge the distribution of genomic sequences in a single cell and the genetic variability among tumor cells, which can improve the understanding of ITH. SCS provides fundamental ideas about existing diversity in specific TMEs, thus improving cancer diagnosis and prognosis prediction, as well as improving the monitoring of therapeutic response. Herein, we will discuss advances in SCS and review SCS application in tumors based on current evidence.

N-of-1 Trials in Pediatric Oncology: From a Population-Based Approach to Personalized Medicine-A Review

Pediatric oncology is a critical area where the more efficient development of new treatments is urgently needed. The speed of approval of new drugs is still limited by regulatory requirements and a lack of innovative designs appropriate for trials in children. Childhood cancers meet the criteria of rare diseases. Personalized medicine brings it even closer to the horizon of individual cases. Thus, not all the traditional research tools, such as large-scale RCTs, are always suitable or even applicable, mainly due to limited sample sizes. Small samples and traditional versus subject-specific evidence are both distinctive issues in personalized pediatric oncology. Modern analytical approaches and adaptations of the paradigms of evidence are warranted. We have reviewed innovative trial designs and analytical methods developed for small populations, together with individualized approaches, given their applicability to pediatric oncology. We discuss traditional population-based and individualized perspectives of inferences and evidence, and explain the possibilities of using various methods in pediatric personalized oncology. We find that specific derivatives of the original N-of-1 trial design adapted for pediatric personalized oncology may represent an optimal analytical tool for this area of medicine. We conclude that no particular N-of-1 strategy can provide a solution. Rather, a whole range of approaches is needed to satisfy the new inferential and analytical paradigms of modern medicine. We reveal a new view of cancer as continuum model and discuss the "evidence puzzle".

Circulating Tumor Cells as a Tool to Untangle the Breast Cancer Heterogeneity Issue

Breast cancer (BC) is a disease characterized by high degrees of heterogeneity at morphologic, genomic, and genetic levels, even within the same tumor mass or among patients. As a consequence, different subpopulations coexist and less represented clones may have a selective advantage, significantly influencing the outcome of BC patients. Circulating tumor cells (CTCs) represent a rare population of cells with a crucial role in metastatic cascade, and in recent years have represented a fascinating alternative to overcome the heterogeneity issue as a “liquid biopsy”. However, besides the raw enumeration of these cells in advanced epithelial tumors, there are no CTC-based assays applied in the clinical practice to improve personalized medicine. In this review, we report the latest findings in the field of CTCs for intra-tumoral heterogeneity unmasking in BC, supporting the need to deepen their analysis to investigate their role in metastatic process and include the molecular characterization in the clinical practice. In the future, CTCs will be helpful in monitoring patients during treatment, as well as to better address therapeutic strategies.

The SCF Complex Is Essential to Maintain Genome and Chromosome Stability

The SKP1, CUL1, F-box protein (SCF) complex encompasses a group of 69 SCF E3 ubiquitin ligase complexes that primarily modify protein substrates with poly-ubiquitin chains to target them for proteasomal degradation. These SCF complexes are distinguishable by variable F-box proteins, which determine substrate specificity. Although the function(s) of each individual SCF complex remain largely unknown, those that have been characterized regulate a wide array of cellular processes, including gene transcription and the cell cycle. In this regard, the SCF complex regulates transcription factors that modulate cell signaling and ensures timely degradation of primary cell cycle regulators for accurate replication and segregation of genetic material. SCF complex members are aberrantly expressed in a myriad of cancer types, with altered expression or function of the invariable core SCF components expected to have a greater impact on cancer pathogenesis than that of the F-box proteins. Accordingly, this review describes the normal roles that various SCF complexes have in maintaining genome stability before discussing the impact that aberrant SCF complex expression and/or function have on cancer pathogenesis. Further characterization of the SCF complex functions is essential to identify and develop therapeutic approaches to exploit aberrant SCF complex expression and function.

The Role of Radiomics in Lung Cancer: From Screening to Treatment and Follow-Up

PURPOSE

Lung cancer represents the first cause of cancer-related death in the world. Radiomics studies arise rapidly in this late decade. The aim of this review is to identify important recent publications to be synthesized into a comprehensive review of the current status of radiomics in lung cancer at each step of the patients' care.

METHODS

A literature review was conducted using PubMed/Medline for search of relevant peer-reviewed publications from January 2012 to June 2020.

RESULTS

We identified several studies at each point of patient's care: detection and classification of lung nodules (n=16), determination of histology and genomic (n=10) and finally treatment outcomes predictions (=23). We reported the methodology of those studies and their results and discuss the limitations and the progress to be made for clinical routine applications.

CONCLUSION

Promising perspectives arise from machine learning applications and radiomics based models in lung cancers, yet further data are necessary for their implementation in daily care. Multicentric collaboration and attention to quality and reproductivity of radiomics studies should be further consider.

Precision Oncology Beyond Genomics: The Future Is Here-It Is Just Not Evenly Distributed

Cancer is a multifactorial disease with increasing incidence. There are more than 100 different cancer types, defined by location, cell of origin, and genomic alterations that influence oncogenesis and therapeutic response. This heterogeneity between tumors of different patients and also the heterogeneity within the same patient's tumor pose an enormous challenge to cancer treatment. In this review, we explore tumor heterogeneity on the longitudinal and the latitudinal axis, reviewing current and future approaches to study this heterogeneity and their potential to support oncologists in tailoring a patient's treatment regimen. We highlight how the ideal of precision oncology is reaching far beyond the knowledge of genetic variants to inform clinical practice and discuss the technologies and strategies already available to improve our understanding and management of heterogeneity in cancer treatment. We will focus on integrating multi-omics technologies with suitable in vitro models and their proficiency in mimicking endogenous tumor heterogeneity.

Multi-parametric characterization of drug effects on cells

We present here a novel multi-parametric approach for the characterization of multiple cellular features, using images acquired by high-throughput and high-definition light microscopy. We specifically used this approach for deep and unbiased analysis of the effects of a drug library on five cultured cell lines. The presented method enables the acquisition and analysis of millions of images, of treated and control cells, followed by an automated identification of drugs inducing strong responses, evaluating the median effect concentrations and those cellular properties that are most highly affected by the drug. The tools described here provide standardized quantification of multiple attributes for systems level dissection of complex functions in normal and diseased cells, using multiple perturbations. Such analysis of cells, derived from pathological samples, may help in the diagnosis and follow-up of treatment in patients.

Spatially resolved transcriptomics and its applications in cancer

Spatially resolved transcriptomics (SRT) offers the promise of understanding cells and their modes of dysfunction in the context of intact tissues. Technologies for SRT have advanced rapidly with a large number being published in recent years. Diverse methods for SRT produce data at widely varying depth, throughput, accessibility and cost. Many published SRT methods have been demonstrated only in their labs of origin, while others have matured to the point of commercialization and widespread availability. Here we review technologies for SRT, and their application in studies of tumor heterogeneity.

Understanding Nanoparticle Toxicity to Direct a Safe-by-Design Approach in Cancer Nanomedicine

Nanomedicine is a rapidly growing field that uses nanomaterials for the diagnosis, treatment and prevention of various diseases, including cancer. Various biocompatible nanoplatforms with diversified capabilities for tumor targeting, imaging, and therapy have materialized to yield individualized therapy. However, due to their unique properties brought about by their small size, safety concerns have emerged as their physicochemical properties can lead to altered pharmacokinetics, with the potential to cross biological barriers. In addition, the intrinsic toxicity of some of the inorganic materials (i.e., heavy metals) and their ability to accumulate and persist in the human body has been a challenge to their translation. Successful clinical translation of these nanoparticles is heavily dependent on their stability, circulation time, access and bioavailability to disease sites, and their safety profile. This review covers preclinical and clinical inorganic-nanoparticle based nanomaterial utilized for cancer imaging and therapeutics. A special emphasis is put on the rational design to develop non-toxic/safe inorganic nanoparticle constructs to increase their viability as translatable nanomedicine for cancer therapies.

Epigenetic crosstalk between hypoxia and tumor driven by HIF regulation

Hypoxia is the major influence factor in physiological and pathological courses which are mainly mediated by hypoxia-inducible factors (HIFs) in response to low oxygen tensions within solid tumors. Under normoxia, HIF signaling pathway is inhibited due to HIF-α subunits degradation. However, in hypoxic conditions, HIF-α is activated and stabilized, and HIF target genes are successively activated, resulting in a series of tumour-specific activities. The activation of HIFs, including HIF-1α, HIF-2α and HIF-3α, subsequently induce downstream target genes which leads to series of responses, the resulting abnormal processes or metabolites in turn affect HIFs stability. Given its functions in tumors progression, HIFs have been regarded as therapeutic targets for improved treatment efficacy. Epigenetics refers to alterations in gene expression that are stable between cell divisions, and sometimes between generations, but do not involve changes in the underlying DNA sequence of the organism. And with the development of research, epigenetic regulation has been found to play an important role in the development of tumors, which providing accumulating basic or clinical evidences for tumor treatments. Here, given how little has been reported about the overall association between hypoxic tumors and epigenetics, we made a more systematic review from epigenetic perspective in hope of helping others better understand hypoxia or HIF pathway, and providing more established and potential therapeutic strategies in tumors to facilitate epigenetic studies of tumors.

Vimentin filaments drive migratory persistence in polyploidal cancer cells

Polyploidal giant cancer cells (PGCCs) are multinucleated chemoresistant cancer cells found in heterogeneous solid tumors. Due in part to their apparent dormancy, the effect of PGCCs on cancer progression has remained largely unstudied. Recent studies have highlighted the critical role of PGCCs as aggressive and chemoresistant cancer cells, as well as their ability to undergo amitotic budding to escape dormancy. Our recent study demonstrated the unique biophysical properties of PGCCs, as well as their unusual migratory persistence. Here we unveil the critical function of vimentin intermediate filaments (VIFs) in maintaining the structural integrity of PGCCs and enhancing their migratory persistence. We performed in-depth single-cell analysis to examine the distribution of VIFs and their role in migratory persistence. We found that PGCCs rely heavily on their uniquely distributed and polarized VIF network to enhance their transition from a jammed to an unjammed state to allow for directional migration. Both the inhibition of VIFs with acrylamide and small interfering RNA knockdown of vimentin significantly decreased PGCC migration and resulted in a loss of PGCC volume. Because PGCCs rely on their VIF network to direct migration and to maintain their enlarged morphology, targeting vimentin or vimentin cross-linking proteins could provide a therapeutic approach to mitigate the impact of these chemoresistant cells in cancer progression and to improve patient outcomes with chemotherapy.

Deterministic culturing of single cells in 3D

Models using 3D cell culture techniques are increasingly accepted as the most biofidelic in vitro representations of tissues for research. These models are generated using biomatrices and bulk populations of cells derived from tissues or cell lines. We present an alternate method to culture individually selected cells in relative isolation from the rest of the population under physiologically relevant matrix conditions. Matrix gel islands are spotted on a cell culture dish to act as support for receiving and culturing individual single cells; a glass capillary-based microfluidic setup is used to extract each desired single cell from a population and seed it on top of an island. Using examples of breast and colorectal cancers, we show that individual cells evolve into tumors or aspects of tumors displaying different characteristics of the initial cancer type and aggressiveness. By implementing a morphometry assay with luminal A breast cancer, we demonstrate the potential of the proposed approach to study phenotypic heterogeneity. Results reveal that intertumor heterogeneity increases with time in culture and that varying degrees of intratumor heterogeneity may originate from individually seeded cells. Moreover, we observe that a positive relationship exists between fast growing tumors and the size and heterogeneity of their nuclei.

Breakthrough concepts in immune-oncology: Cancer vaccines at the bedside

Clinical approval of the immune checkpoint blockade (ICB) agents for multiple cancer types has reinvigorated the long-standing work on cancer vaccines. In the pre-ICB era, clinical efforts focused on the Ag, the adjuvants, the formulation, and the mode of delivery. These translational efforts on therapeutic vaccines range from cell-based (e.g., dendritic cells vaccine Sipuleucel-T) to DNA/RNA-based platforms with various formulations (liposome), vectors (Listeria monocytogenes), or modes of delivery (intratumoral, gene gun, etc.). Despite promising preclinical results, cancer vaccine trials without ICB have historically shown little clinical activity. With the anticipation and expansion of combinatorial immunotherapeutic trials with ICB, the cancer vaccine field has entered the personalized medicine arena with recent advances in immunogenic neoantigen-based vaccines. In this article, we review the literature to organize the different cancer vaccines in the clinical space, and we will discuss their advantages, limits, and recent progress to overcome their challenges. Furthermore, we will also discuss recent preclinical advances and clinical strategies to combine vaccines with checkpoint blockade to improve therapeutic outcome and present a translational perspective on future directions.

Exploiting collateral sensitivity controls growth of mixed culture of sensitive and resistant cells and decreases selection for resistant cells in a cell line model

Background

CDK4/6 inhibitors such as ribociclib are becoming widely used targeted therapies in hormone-receptor-positive (HR+) human epidermal growth factor receptor 2-negative (HER2-) breast cancer. However, cancers can advance due to drug resistance, a problem in which tumor heterogeneity and evolution are key features.

Methods

Ribociclib-resistant HR+/HER2- CAMA-1 breast cancer cells were generated through long-term ribociclib treatment. Characterization of sensitive and resistant cells were performed using RNA sequencing and whole exome sequencing. Lentiviral labeling with different fluorescent proteins enabled us to track the proliferation of sensitive and resistant cells under different treatments in a heterogeneous, 3D spheroid coculture system using imaging microscopy and flow cytometry.

Results

Transcriptional profiling of sensitive and resistant cells revealed the downregulation of the G2/M checkpoint in the resistant cells. Exploiting this acquired vulnerability; resistant cells exhibited collateral sensitivity for the Wee-1 inhibitor, adavosertib (AZD1775). The combination of ribociclib and adavosertib achieved additional antiproliferative effect exclusively in the cocultures compared to monocultures, while decreasing the selection for resistant cells.

Conclusions

Our results suggest that optimal antiproliferative effects in heterogeneous cancers can be achieved via an integrative therapeutic approach targeting sensitive and resistant cancer cell populations within a tumor, respectively.

Role of Exosomal miRNAs and the Tumor Microenvironment in Drug Resistance

Tumor microenvironment (TME) is composed of different cellular populations, such as stromal, immune, endothelial, and cancer stem cells. TME represents a key factor for tumor heterogeneity maintenance, tumor progression, and drug resistance. The transport of molecules via extracellular vesicles emerged as a key messenger in intercellular communication in the TME. Exosomes are small double-layered lipid extracellular vesicles that can carry a variety of molecules, including proteins, lipids, and nucleic acids. Exosomal miRNA released by cancer cells can mediate phenotypical changes in the cells of TME to promote tumor growth and therapy resistance, for example, fibroblast- and macrophages-induced differentiation. Cancer stem cells can transfer and enhance drug resistance in neighboring sensitive cancer cells by releasing exosomal miRNAs that target antiapoptotic and immune-suppressive pathways. Exosomes induce drug resistance by carrying ABC transporters, which export chemotherapeutic agents out of the recipient cells, thereby reducing the drug concentration to suboptimal levels. Exosome biogenesis inhibitors represent a promising adjunct therapeutic approach in cancer therapy to avoid the acquisition of a resistant phenotype. In conclusion, exosomal miRNAs play a crucial role in the TME to confer drug resistance and survivability to tumor cells, and we also highlight the need for further investigations in this promising field.

Chromosome Instability; Implications in Cancer Development, Progression, and Clinical Outcomes

Chromosome instability (CIN) refers to an ongoing rate of chromosomal changes and is a driver of genetic, cell-to-cell heterogeneity. It is an aberrant phenotype that is intimately associated with cancer development and progression. The presence, extent, and level of CIN has tremendous implications for the clinical management and outcomes of those living with cancer. Despite its relevance in cancer, there is still extensive misuse of the term CIN, and this has adversely impacted our ability to identify and characterize the molecular determinants of CIN. Though several decades of genetic research have provided insight into CIN, the molecular determinants remain largely unknown, which severely limits its clinical potential. In this review, we provide a definition of CIN, describe the two main types, and discuss how it differs from aneuploidy. We subsequently detail its impact on cancer development and progression, and describe how it influences metastatic potential with reference to cancer prognosis and outcomes. Finally, we end with a discussion of how CIN induces genetic heterogeneity to influence the use and efficacy of several precision medicine strategies, including patient and risk stratification, as well as its impact on the acquisition of drug resistance and disease recurrence.

Are Synapse-Like Structures a Possible Way for Crosstalk of Cancer with Its Microenvironment?

The failure of therapies directed at targets within cancer cells highlight the necessity for a paradigm change in cancer therapy. The attention of researchers has shifted towards the disruption of cancer cell interactions with the tumor microenvironment. A typical example of such a disruption is the immune checkpoint cancer therapy that disrupts interactions between the immune and the cancer cells. The interaction of cancer antigens with T cells occurs in the immunological synapses. This is characterized by several special features, i.e., the proximity of the immune cells and their target cells, strong intercellular adhesion, and secretion of signaling cytokines into the intercellular cleft. Earlier, we hypothesized that the cancer-associated fibroblasts interacting with cancer cells through a synapse-like adhesion might play an important role in cancer tumors. Studies of the interactions between cancer cells and cancer-associated fibroblasts showed that their clusterization on the membrane surface determined their strength and specificity. The hundreds of interacting pairs are involved in the binding that may indicate the formation of synapse-like structures. These interactions may be responsible for successful metastasis of cancer cells, and their identification and disruption may open new therapeutic possibilities.