Selection of metastasis competent subclones in the tumour interior

Boundarics in Biomedicine

"Boundarics in Biomedicine" is a cutting-edge interdisciplinary discipline, which is of great significance for understanding the origin of life, the interaction between internal and external environments, and the mechanism of disease occurrence and evolution. Here, the definition of Boundarics in Biomedicine is first described, including its connotation, research object, research method, challenges, and future perspectives. "Boundarics in Biomedicine" is a cutting-edge interdisciplinary discipline involving multiple fields (e.g., bioscience, medicine, chemistry, materials science, and information science) dedicated to investigating and solving key scientific questions in the formation, identification, and evolution of living organism boundaries. Specifically, it encompasses 3 levels: (a) the boundary between the living organism and the external environment, (b) internal boundary within living organism, and (c) the boundary related to disease in living organism. The advancement of research in Boundarics in Biomedicine is of great scientific significance for understanding the origin of life, the interaction between internal and external environments, and the mechanism of disease occurrence and evolution, thus providing novel principles, technologies, and methods for early diagnosis and prevention of major diseases, personalized drug development, and prognosis assessment (Fig. 1).

Spatiotemporal characteristics of tissue derived small extracellular vesicles is associated with tumor relapse and anti-PD-1 response

Small extracellular vesicles (sEVs) residing at tumor tissues are valuable specimens for biopsy. Tumor heterogeneity is common across all cancer types, but the heterogeneity of tumor tissue-derived sEVs (Ti-sEVs) is undefined. This study aims to discover the spatial distributions of Ti-sEVs in oral squamous cell carcinoma (OSCC) tissues and explore how these vesicle distributions affect the patients' prognosis. Multi-regional sampling enabled us to uncover that Ti-sEVs' accumulation at peritumoral sites correlates with a higher disease-free survival rate, and conversely, sparse peritumoral Ti-sEVs tend to forecast a higher risk of relapse. Of those relapsed patients, Ti-sEVs strongly bind to extracellular matrix and subsequently degrade it for allowing themselves enter the bloodstream rather than staying in situ. In advanced OSCC patients, the quantity and spatial distribution of Ti-sEVs prior to anti-PD-1 treatment, as well as the temporal variance of Ti-sEVs before and after immunotherapy, strongly map the clinical response and can help to distinguish the patients with shrinking tumors from those with growing tumors. Our work elucidates the correlation of spatiotemporal features of Ti-sEVs with patients' therapeutic outcomes and exhibit the potential for using Ti-sEVs as a predictor to forecast prognosis and screen the responders to anti-PD-1 therapy.

Tumor phylogeography reveals block-shaped spatial heterogeneity and the mode of evolution in Hepatocellular Carcinoma

Solid tumors are complex ecosystems with heterogeneous 3D structures, but the spatial intra-tumor heterogeneity (sITH) at the macroscopic (i.e., whole tumor) level is under-explored. Using a phylogeographic approach, we sequence genomes and transcriptomes from 235 spatially informed sectors across 13 hepatocellular carcinomas (HCC), generating one of the largest datasets for studying sITH. We find that tumor heterogeneity in HCC segregates into spatially variegated blocks with large genotypic and phenotypic differences. By dissecting the transcriptomic heterogeneity, we discover that 30% of patients had a "spatially competing distribution" (SCD), where different spatial blocks have distinct transcriptomic subtypes co-existing within a tumor, capturing the critical transition period in disease progression. Interestingly, the tumor regions with more advanced transcriptomic subtypes (e.g., higher cell cycle) often take clonal dominance with a wider geographic range, rejecting neutral evolution for SCD patients. Extending the statistical tests for detecting natural selection to many non-SCD patients reveal varying levels of selective signal across different tumors, implying that many evolutionary forces including natural selection and geographic isolation can influence the overall pattern of sITH. Taken together, tumor phylogeography unravels a dynamic landscape of sITH, pinpointing important evolutionary and clinical consequences of spatial heterogeneity in cancer.

Radiomics Machine Learning Analysis of Clear Cell Renal Cell Carcinoma for Tumour Grade Prediction Based on Intra-Tumoural Sub-Region Heterogeneity

BACKGROUND

Renal cancers are among the top ten causes of cancer-specific mortality, of which the ccRCC subtype is responsible for most cases. The grading of ccRCC is important in determining tumour aggressiveness and clinical management.

OBJECTIVES

The objectives of this research were to predict the WHO/ISUP grade of ccRCC pre-operatively and characterise the heterogeneity of tumour sub-regions using radiomics and ML models, including comparison with pre-operative biopsy-determined grading in a sub-group.

METHODS

Data were obtained from multiple institutions across two countries, including 391 patients with pathologically proven ccRCC. For analysis, the data were separated into four cohorts. Cohorts 1 and 2 included data from the respective institutions from the two countries, cohort 3 was the combined data from both cohort 1 and 2, and cohort 4 was a subset of cohort 1, for which both the biopsy and subsequent histology from resection (partial or total nephrectomy) were available. 3D image segmentation was carried out to derive a voxel of interest (VOI) mask. Radiomics features were then extracted from the contrast-enhanced images, and the data were normalised. The Pearson correlation coefficient and the XGBoost model were used to reduce the dimensionality of the features. Thereafter, 11 ML algorithms were implemented for the purpose of predicting the ccRCC grade and characterising the heterogeneity of sub-regions in the tumours.

RESULTS

For cohort 1, the 50% tumour core and 25% tumour periphery exhibited the best performance, with an average AUC of 77.9% and 78.6%, respectively. The 50% tumour core presented the highest performance in cohorts 2 and 3, with average AUC values of 87.6% and 76.9%, respectively. With the 25% periphery, cohort 4 showed AUC values of 95.0% and 80.0% for grade prediction when using internal and external validation, respectively, while biopsy histology had an AUC of 31.0% for the classification with the final grade of resection histology as a reference standard. The CatBoost classifier was the best for each of the four cohorts with an average AUC of 80.0%, 86.5%, 77.0% and 90.3% for cohorts 1, 2, 3 and 4 respectively.

CONCLUSIONS

Radiomics signatures combined with ML have the potential to predict the WHO/ISUP grade of ccRCC with superior performance, when compared to pre-operative biopsy. Moreover, tumour sub-regions contain useful information that should be analysed independently when determining the tumour grade. Therefore, it is possible to distinguish the grade of ccRCC pre-operatively to improve patient care and management.

Distinct spatial landscapes in clear-cell renal cell carcinoma as revealed by whole transcriptome analysis

Background

Clear-cell renal cell carcinoma (ccRCC) is the most common and aggressive form of renal cancer and a paradigm of inter- and intratumor heterogeneity. We carried out an exploratory digital spatial profiling of the tumor interior and periphery of two ccRCC tumor specimens and mapped spatially the molecular and cellular composition of their tumor microenvironment and ecosystem.

Materials and methods

Digital spatial profiling of the whole transcriptome of 19 regions of interest (ROIs) was carried out from two selected highly immunogenic stage pT3a/grade 3 (G3) and stage pT3a/grade 4 (G4) ccRCC. A total of 9-10 ROIs were selected from distinct areas from each tumor, including tumor interior and tumor periphery, and differences in gene expression were analyzed by RNA sequencing, pathway enrichment analysis, and cell deconvolution.

Results

The distinct areas from the two locally advanced tumors displayed unique gene expression spatial patterns defining distinct biological pathways. Dimensional reduction analysis showed that the G3 ccRCC, compared to the G4 ccRCC, correlated with more variability between regions from the tumor interior and tumor periphery. Cell deconvolution analysis illustrated higher abundance of immune cells, including macrophages, myeloid dendritic cells, and CD4 T cells, and lower abundance of regulatory T cells in the tumor periphery compared to the tumor interior.

Conclusions

Transcriptome spatial profiling revealed high inter- and intratumor heterogeneity in the analyzed tumors and provided information with potential clinical utility. This included the finding of less intratumor heterogeneity and more tumor-infiltrated T cells in the ccRCC tumor specimen with a higher grade.

Mutations, Bottlenecks, and Clonal Sweeps: How Environmental Carcinogens and Genomic Changes Shape Clonal Evolution during Tumor Progression

The transition from a single, initiated cell to a full-blown malignant tumor involves significant genomic evolution. Exposure to carcinogens-whether directly mutagenic or not-can drive progression toward malignancy, as can stochastic acquisition of cancer-promoting genetic events. Mouse models using both carcinogens and germline genetic manipulations have enabled precise inquiry into the evolutionary dynamics that take place as a tumor progresses from benign to malignant to metastatic stages. Tumor progression is characterized by changes in somatic point mutations and copy-number alterations, even though any single tumor can itself have a high or low burden of genomic alterations. Further, lineage-tracing, single-cell analyses and CRISPR barcoding have revealed the distinct clonal dynamics within benign and malignant tumors. Application of these tools in a range of mouse models can shed unique light on the patterns of clonal evolution that take place in both mouse and human tumors.

Analysis of the immunological markers BTLA, TIM-3, and PD-L1 at the invasion front and tumor center in clear cell renal cell carcinoma

PURPOSE

Immune checkpoint inhibitors (ICI) are then backbone in the therapy of metastatic renal cell carcinoma (RCC). The aim of this analysis was to explore the different expression of the ICI PD-L1, BTLA, and TIM-3 at the different tumor locations of the invasion front and the tumor center.

METHODS

Large-area sections of the tumor center and invasion front of 44 stage pT1-4 clear cell RCCs were examined immunohistochemically using antibodies against BTLA, TIM-3, and PD-L1 and subsequently correlated with clinicopathologic data.

RESULTS

TIM-3 was most strongly expressed at the invasion front (mean ± SD: 84.1 ± 46.6, p = 0.094). BTLA expression was highest in normal tissue, with weak staining in the tumor center and at the invasion front [110.2 vs. 18.6 (p < 0.001) vs. 32.2 (p = 0.248)]. PD-L1 was weakly expressed at the tumor center (n = 5/44) and at the invasion front (n = 5/44). Correlation with clinicopathological parameters revealed significantly higher BTLA expression in ≥ T3 tumors compared to T1/2 tumors (tumor center p = 0.009; invasion front p = 0.005). BTLA-positive tumors at the tumor center correlated with worse CSS (median 48.46 vs. 68.91 months, HR 4.43, p = 0.061). PD-L1 expression was associated with worse CSS (median 1.66 vs. 4.5 years, HR 1.63, p = 0.652). For TIM-3, there were no significant associations with clinicopathological parameters and survival.

CONCLUSION

The present results show heterogeneous intratumoral and intertumoral expression of the investigated checkpoint receptors PD-L1, BTLA, and TIM-3. In the clinical practice tumor sampling should include different tumor locations, and multiple inhibition of different checkpoint receptors seems reasonable to increase the therapeutic success.

Early Evolution in Cancer: A Mathematical Support for Pathological and Genomic Evidence in Clear Cell Renal Cell Carcinoma

Clear cell renal cell carcinoma (CCRCC) is an aggressive form of cancer and a paradigmatic example of intratumor heterogeneity (ITH). The hawk-dove game is a mathematical tool designed to analyze competition in biological systems. Using this game, the study reported here analyzes the early phase of CCRCC development, comparing clonal fitness in homogeneous (linear evolutionary) and highly heterogeneous (branching evolutionary) models. Fitness in the analysis is a measure of tumor aggressiveness. The results show that the fittest clone in a heterogeneous environment is fitter than the clone in a homogeneous context in the early phases of tumor evolution. Early and late periods of tumor evolution in CCRCC are also compared. The study shows the convergence of mathematical, histological, and genomics studies with respect to clonal aggressiveness in different periods of the natural history of CCRCC. Such convergence highlights the importance of multidisciplinary approaches for obtaining a better understanding of the intricacies of cancer.

Prognostic significance of cyclin D1 expression pattern in HPV-negative oral and oropharyngeal carcinoma: A deep-learning approach

BACKGROUND

We aimed to establish image recognition and survival prediction models using a novel scoring system of cyclin D1 expression pattern in patients with human papillomavirus-negative oral or oropharyngeal squamous cell carcinoma.

METHODS

The clinicopathological data of 610 patients with human papillomavirus-negative oral/oropharyngeal squamous cell carcinoma were analyzed retrospectively. Cox univariate and multivariate risk regression analyses were performed to compare cyclin D1 expression pattern scoring with the traditional scoring method-cyclin D1 expression level scoring-in relation to patients' overall and progression-free survival. An image recognition model employing the cyclin D1 expression pattern scoring system was established by YOLOv5 algorithms. From this model, two independent survival prediction models were established using the DeepHit and DeepSurv algorithms.

RESULTS

Cyclin D1 had three expression patterns in oral and oropharyngeal squamous cell carcinoma cancer nests. Superior to cyclin D1 expression level scoring, cyclin D1 expression pattern scoring was significantly correlated with the prognosis of patients with oral squamous cell carcinoma (p < 0.0001) and oropharyngeal squamous cell carcinoma (p < 0.05). Moreover, it was an independent prognostic risk factor in both oral squamous cell carcinoma (p < 0.0001) and oropharyngeal squamous cell carcinoma (p < 0.05). The cyclin D1 expression pattern-derived image recognition model showed an average test set accuracy of 78.48% ± 4.31%. In the overall survival prediction models, the average concordance indices of the test sets established by DeepSurv and DeepHit were 0.71 ± 0.02 and 0.70 ± 0.01, respectively.

CONCLUSION

Combined with the image recognition model of the cyclin D1 expression pattern, the survival prediction model had a relatively good prediction effect on the overall survival prognosis of patients with human papillomavirus-negative oral or oropharyngeal squamous cell carcinoma.

Mutation divergence over space in tumour expansion

Mutation accumulation in tumour evolution is one major cause of intra-tumour heterogeneity (ITH), which often leads to drug resistance during treatment. Previous studies with multi-region sequencing have shown that mutation divergence among samples within the patient is common, and the importance of spatial sampling to obtain a complete picture in tumour measurements. However, quantitative comparisons of the relationship between mutation heterogeneity and tumour expansion modes, sampling distances as well as the sampling methods are still few. Here, we investigate how mutations diverge over space by varying the sampling distance and tumour expansion modes using individual-based simulations. We measure ITH by the Jaccard index between samples and quantify how ITH increases with sampling distance, the pattern of which holds in various sampling methods and sizes. We also compare the inferred mutation rates based on the distributions of variant allele frequencies under different tumour expansion modes and sampling sizes. In exponentially fast expanding tumours, a mutation rate can always be inferred for any sampling size. However, the accuracy compared with the true value decreases when the sampling size decreases, where small sampling sizes result in a high estimate of the mutation rate. In addition, such an inference becomes unreliable when the tumour expansion is slow, such as in surface growth.

Deep Tumor Penetration of CRISPR-Cas System for Photothermal-Sensitized Immunotherapy via Probiotics

Since central cells are more malignant and aggressive in solid tumors, improving penetration of therapeutic agents and activating immunity in tumor centers exhibit great potential in cancer therapies. Here, polydopamine-coated Escherichia coli Nissle 1917 (EcN) bearing CRISPR-Cas9 plasmid-loaded liposomes (Lipo-P) are applied for enhanced immunotherapy in deep tumors through activation of innate and adaptive immunity simultaneously. After accumulation in the tumor center through hypoxia targeting, Lipo-P could be detached under the reduction of reactive oxygen species (ROS)-responsive linkers, lowering the thermal resistance of cancer cells via Hsp90α depletion. Owing to that, heating induced by polydopamine upon near-infrared irradiation could achieve effective tumor ablation. Furthermore, mild photothermal therapy induces immunogenic cell death, as bacterial infections in tumor tissues trigger innate immunity. This bacteria-assisted approach provides a promising photothermal-sensitized immunotherapy in deep tumors.

Interpretability of radiomics models is improved when using feature group selection strategies for predicting molecular and clinical targets in clear-cell renal cell carcinoma: insights from the TRACERx Renal study

BACKGROUND

The aim of this work is to evaluate the performance of radiomics predictions for a range of molecular, genomic and clinical targets in patients with clear cell renal cell carcinoma (ccRCC) and demonstrate the impact of novel feature selection strategies and sub-segmentations on model interpretability.

METHODS

Contrast-enhanced CT scans from the first 101 patients recruited to the TRACERx Renal Cancer study (NCT03226886) were used to derive radiomics classification models to predict 20 molecular, histopathology and clinical target variables. Manual 3D segmentation was used in conjunction with automatic sub-segmentation to generate radiomics features from the core, rim, high and low enhancing sub-regions, and the whole tumour. Comparisons were made between two classification model pipelines: a Conventional pipeline reflecting common radiomics practice, and a Proposed pipeline including two novel feature selection steps designed to improve model interpretability. For both pipelines nested cross-validation was used to estimate prediction performance and tune model hyper-parameters, and permutation testing was used to evaluate the statistical significance of the estimated performance measures. Further model robustness assessments were conducted by evaluating model variability across the cross-validation folds.

RESULTS

Classification performance was significant (p < 0.05, H0:AUROC = 0.5) for 11 of 20 targets using either pipeline and for these targets the AUROCs were within ± 0.05 for the two pipelines, except for one target where the Proposed pipeline performance increased by > 0.1. Five of these targets (necrosis on histology, presence of renal vein invasion, overall histological stage, linear evolutionary subtype and loss of 9p21.3 somatic alteration marker) had AUROC > 0.8. Models derived using the Proposed pipeline contained fewer feature groups than the Conventional pipeline, leading to more straightforward model interpretations without loss of performance. Sub-segmentations lead to improved performance and/or improved interpretability when predicting the presence of sarcomatoid differentiation and tumour stage.

CONCLUSIONS

Use of the Proposed pipeline, which includes the novel feature selection methods, leads to more interpretable models without compromising prediction performance.

TRIAL REGISTRATION

NCT03226886 (TRACERx Renal).

Heterogeneity in functional genetic screens: friend or foe?

Functional genetic screens to uncover tumor-intrinsic nodes of immune resistance have uncovered numerous mechanisms by which tumors evade our immune system. However, due to technical limitations, tumor heterogeneity is imperfectly captured with many of these analyses. Here, we provide an overview of the nature and sources of heterogeneity that are relevant for tumor-immune interactions. We argue that this heterogeneity may actually contribute to the discovery of novel mechanisms of immune evasion, given a sufficiently large and heterogeneous set of input data. Taking advantage of tumor cell heterogeneity, we provide proof-of-concept analyses of mechanisms of TNF resistance. Thus, consideration of tumor heterogeneity is imperative to increase our understanding of immune resistance mechanisms.

SMARCB1 regulates the hypoxic stress response in sickle cell trait

Renal medullary carcinoma (RMC) is an aggressive kidney cancer that almost exclusively develops in individuals with sickle cell trait (SCT) and is always characterized by loss of the tumor suppressor SMARCB1. Because renal ischemia induced by red blood cell sickling exacerbates chronic renal medullary hypoxia in vivo, we investigated whether the loss of SMARCB1 confers a survival advantage under the setting of SCT. Hypoxic stress, which naturally occurs within the renal medulla, is elevated under the setting of SCT. Our findings showed that hypoxia-induced SMARCB1 degradation protected renal cells from hypoxic stress. SMARCB1 wild-type renal tumors exhibited lower levels of SMARCB1 and more aggressive growth in mice harboring the SCT mutation in human hemoglobin A (HbA) than in control mice harboring wild-type human HbA. Consistent with established clinical observations, SMARCB1-null renal tumors were refractory to hypoxia-inducing therapeutic inhibition of angiogenesis. Further, reconstitution of SMARCB1 restored renal tumor sensitivity to hypoxic stress in vitro and in vivo. Together, our results demonstrate a physiological role for SMARCB1 degradation in response to hypoxic stress, connect the renal medullary hypoxia induced by SCT with an increased risk of SMARCB1-negative RMC, and shed light into the mechanisms mediating the resistance of SMARCB1-null renal tumors against angiogenesis inhibition therapies.

Integrative multi-omics and drug-response characterization of patient-derived prostate cancer primary cells

Prostate cancer (PCa) is the second most prevalent malignancy in males across the world. A greater knowledge of the relationship between protein abundance and drug responses would benefit precision treatment for PCa. Herein, we establish 35 Chinese PCa primary cell models to capture specific characteristics among PCa patients, including gene mutations, mRNA/protein/surface protein distributions, and pharmaceutical responses. The multi-omics analyses identify Anterior Gradient 2 (AGR2) as a pre-operative prognostic biomarker in PCa. Through the drug library screening, we describe crizotinib as a selective compound for malignant PCa primary cells. We further perform the pharmacoproteome analysis and identify 14,372 significant protein-drug correlations. Surprisingly, the diminished AGR2 enhances the inhibition activity of crizotinib via ALK/c-MET-AKT axis activation which is validated by PC3 and xenograft model. Our integrated multi-omics approach yields a comprehensive understanding of PCa biomarkers and pharmacological responses, allowing for more precise diagnosis and therapies.

Agent-based methods facilitate integrative science in cancer

In this opinion, we highlight agent-based modeling as a key tool for exploration of cell-cell and cell-environment interactions that drive cancer progression, therapeutic resistance, and metastasis. These biological phenomena are particularly suited to be captured at the cell-scale resolution possible only within agent-based or individual-based mathematical models. These modeling approaches complement experimental work (in vitro and in vivo systems) through parameterization and data extrapolation but also feed forward to drive new experiments that test model-generated predictions.

State-dependent evolutionary models reveal modes of solid tumour growth

Spatial properties of tumour growth have profound implications for cancer progression, therapeutic resistance and metastasis. Yet, how spatial position governs tumour cell division remains difficult to evaluate in clinical tumours. Here, we demonstrate that faster division on the tumour periphery leaves characteristic genetic patterns, which become evident when a phylogenetic tree is reconstructed from spatially sampled cells. Namely, rapidly dividing peripheral lineages branch more extensively and acquire more mutations than slower-dividing centre lineages. We develop a Bayesian state-dependent evolutionary phylodynamic model (SDevo) that quantifies these patterns to infer the differential division rates between peripheral and central cells. We demonstrate that this approach accurately infers spatially varying birth rates of simulated tumours across a range of growth conditions and sampling strategies. We then show that SDevo outperforms state-of-the-art, non-cancer multi-state phylodynamic methods that ignore differential sequence evolution. Finally, we apply SDevo to single-time-point, multi-region sequencing data from clinical hepatocellular carcinomas and find evidence of a three- to six-times-higher division rate on the tumour edge. With the increasing availability of high-resolution, multi-region sequencing, we anticipate that SDevo will be useful in interrogating spatial growth restrictions and could be extended to model non-spatial factors that influence tumour progression.

Metastasis from the tumor interior and necrotic core formation are regulated by breast cancer-derived angiopoietin-like 7

Necrosis in the tumor interior is a common feature of aggressive cancers that is associated with poor clinical prognosis and the development of metastasis. How the necrotic core promotes metastasis remains unclear. Here, we report that emergence of necrosis inside the tumor is correlated temporally with increased tumor dissemination in a rat breast cancer model and in human breast cancer patients. By performing spatially focused transcriptional profiling, we identified angiopoietin-like 7 (Angptl7) as a tumor-specific factor localized to the perinecrotic zone. Functional studies showed that Angptl7 loss normalizes central necrosis, perinecrotic dilated vessels, metastasis, and reduces circulating tumor cell counts to nearly zero. Mechanistically, Angptl7 promotes vascular permeability and supports vascular remodeling in the perinecrotic zone. Taken together, these findings show that breast tumors actively produce factors controlling central necrosis formation and metastatic dissemination from the tumor core.

Tumor-to-Tumor Metastases Involving Clear Cell Renal Cell Carcinomas: A Diagnostic Challenge for Pathologists Needing Clinical Correlation

Tumor-to-tumor metastasis is a rare event which it is specifically up to pathologists to bring to light correctly. The histological identification of such tumor-to-tumor cases is simple when the respective histologies are different but can be problematic if the case includes two carcinomas with similar cytoarchitecture viewed one inside the other under the microscope. We report four cases of this condition in which clear cell renal cell carcinoma is involved, either as a receptor or as a donor, and remark on the difficulties in recognizing some of them. Appropriate clinical-pathological correlation, including a review of the patient's antecedents and radiological exams, would be a great help in routinely identifying tumor-to-tumor metastases.

Pathology and the evolutionary dynamics of clear cell renal cell carcinoma

Cancer is an ecosystem whose intrinsic mechanisms do not show up under the microscope of pathologists. However, the information provided by pathologists is absolutely necessary for the correct implementation of personalized treatments. This short paper seeks to analyze this apparent paradox, i.e. static snapshots for making crucial decisions in essentially dynamic diseases, taking clear cell renal cell carcinoma as a paradigmatic example of tumor variability. We seek to call the attention of pathologists and other cancer-related medical specialists to extend knowledge of the evolutionary features of the disease to help obtain a better understanding of why cancer behaves as it does.

Magnetic Nanodroplets for Enhanced Deep Penetration of Solid Tumors and Simultaneous Magnetothermal-Sensitized Immunotherapy against Tumor Proliferation and Metastasis

The central cells of solid tumors are more proliferative and metastatic than the marginal cells. Therefore, more intelligent strategies for targeting cells with deep spatial distributions in solid tumors remain to be explored. In this work, a biocompatible nanotheranostic agent with a lipid membrane-coated, Fe₃ O₄ and perfluoropentane (PFP)-loaded, cRGD peptide (specifically targeting the integrin αvβ3 receptor)-grafted, magnetic nanodroplets (MNDs) is developed. The MNDs exhibit excellent magnetothermal conversion and controllable magnetic hyperthermia (MHT) through alternating magnetic field regulation. Furthermore, MHT-mediated magnetic droplet vaporization (MDV) induces the expansion of the MNDs to transform them into ultrasonic microbubbles, increasing the permeability of tissue and the cell membrane via the ultrasound-targeted microbubble destruction (UTMD) technique and thereby promoting the deep penetration of MNDs in solid tumors. More importantly, MHT not only causes apoptotic damage by downregulating the expression of the HSP70, cyclin D1, and Bcl-2 proteins in tumor cells but also improves the response rate to T-cell-related immunotherapy by upregulating PD-L1 expression in tumor cells, thus inhibiting the growth of both primary and metastatic tumors. Overall, this work introduces a distinct application of nanoultrasonic biomedicine in cancer therapy and provides an attractive immunotherapy strategy for preventing the proliferation and metastasis of deeply distributed cells in solid tumors.

Chromosomal Instability, Selection and Competition: Factors That Shape the Level of Karyotype Intra-Tumor Heterogeneity

Simple Summary

Each cancer consists of billions of cells. These cells are far from identical; hence, the population of cells that constitute a tumor is heterogeneous. A salient property that varies between cells in a tumor is their karyotype, the number and configuration of the chromosomes. The level of karyotype heterogeneity can be used to predict the survival of a patient. In this review, we describe the processes that shape the level of karyotype heterogeneity in a cancer.

Abstract

Intra-tumor heterogeneity (ITH) is a pan-cancer predictor of survival, with high ITH being correlated to a dismal prognosis. The level of ITH is, hence, a clinically relevant characteristic of a malignancy. ITH of karyotypes is driven by chromosomal instability (CIN). However, not all new karyotypes generated by CIN are viable or competitive, which limits the amount of ITH. Here, we review the cellular processes and ecological properties that determine karyotype ITH. We propose a framework to understand karyotype ITH, in which cells with new karyotypes emerge through CIN, are selected by cell intrinsic and cell extrinsic selective pressures, and propagate through a cancer in competition with other malignant cells. We further discuss how CIN modulates the cell phenotype and immune microenvironment, and the implications this has for the subsequent selection of karyotypes. Together, we aim to provide a comprehensive overview of the biological processes that shape the level of karyotype heterogeneity.

Radiomics features based on internal and marginal areas of the tumor for the preoperative prediction of microsatellite instability status in colorectal cancer

Objectives

To explore whether the preoperative CT radiomics can predict the status of microsatellite instability (MSI) in colorectal cancer (CRC) patients and identify the region with the most stable and high-efficiency radiomics features.

Methods

This retrospective study involved 230 CRC patients with preoperative computed tomography scans and available MSI status between December 2019 and October 2021. Image segmentation and radiomic feature extraction were performed as follows. First, slices with the maximum tumor area (region of interest, ROI) were manually contoured. Subsequently, each ROI was shrunk inward by 1, 2, and 3 mm, respectively, where the remaining ROIs were considered as the internal region of the tumor (named as IROI1, IROI2, and IROI3), and the shrunk regions were considered as marginal regions of the tumor (named as MROI1, MROI2, and MROI3). Finally, radiomics features were extracted from each of the ROI. The intraclass correlation coefficient and least absolute shrinkage and selection operator method were used to choose the most reliable and relevant features of MSI status. Clinical, radiomics, and combined clinical radiomics models have been established. Calibration curve and decision curve analyses (DCA) were generated to explore the correction effect and assess the clinical applicability of the above models, respectively.

Results

In the testing cohort, the radiomics model based on IROI3 yielded the highest average area under the curve (AUC) value of 0.908, compared with the remaining radiomics models. Additionally, hypertension and N stage were considered as clinically independent factors of MSI status. The combined clinical radiomics model achieved excellent diagnostic efficacy (AUC: 0.928; sensitivity: 0.840; specificity: 0.867) in the testing cohort, as well as favorable calibration and clinical utility by calibration curve and DCA analyses.

Conclusions

The IROI3 model, which is based on a 3-mm shrink in the largest areas of the tumor, could noninvasively reflect the heterogeneity and genetic instability within the tumor. This suggests that it is an important biomarker for the preoperative prediction of MSI status. The model can extract more robust and effective radiomics features, which lays a foundation for the radiomics study of hollow organs, such as in CRC.

Predicting cancer evolution for patient benefit: Renal cell carcinoma paradigm

Evolutionary features of cancer have important clinical implications, but their evaluation in the clinic is currently limited. Here, we review current approaches to reconstruct tumour subclonal structure and discuss tumour sampling method and experimental design influence. We describe clear-cell renal cell carcinoma (ccRCC) as an exemplar for understanding and predicting cancer evolutionary dynamics. Finally, we discuss how understanding cancer evolution can benefit patients.

Intratumor Heterogeneity and Antitumor Immunity Shape One Another Bidirectionally

Over the last decade, it has become clear that the genomic landscapes of tumors profoundly impact their immunogenicity and how tumor cells interact with immune cells. Whereas past discoveries mainly focused on the interplay between tumor immunogenicity and tumor mutational burden (TMB), under the assumption that a higher mutation load would give rise to a better patient response to immune checkpoint blockade therapies, we and others have underlined intratumor heterogeneity (ITH) as an important determinant of the magnitude of the antitumor response and the nature of the tumor microenvironment. In this review, we define TMB versus ITH and how the two factors are being inferred from data, examine key findings in the cancer immunogenomics literature deciphering the complex cross-talk between TMB, ITH, and antitumor immunity in human cancers and in vivo models, and discuss the mutual influence of ITH and immunity-how the antitumor response can give rise to tumors with higher ITH, and how higher ITH can put shackles on the antitumor response.

Towards Personalized Sampling in Clear Cell Renal Cell Carcinomas

Simple Summary

Intratumor heterogeneity (ITH) is a constant event in malignant tumors and the cause of most therapeutic failures in modern oncology. Since clear cell renal cell carcinoma (CCRCC) is a paradigm of ITH, an appropriate tumor sampling is mandatory to unveil its histological and genomic complexity. Several strategies have been developed for such a purpose, trading-off cost and benefit. Here, we propose an evolution of the previous multisite tumor sampling (MSTS) strategy based on the last findings in the spatial distribution of metastasizing clones. This new personalized MSTS pays special attention to sample by sectors peripheral zones of the tumor, where ITH is high.

Abstract

Intratumor heterogeneity (ITH) is a constant evolutionary event in all malignant tumors, and clear cell renal cell carcinoma (CCRCC) is a paradigmatic example. ITH is responsible for most therapeutic failures in the era of precision oncology, so its precise detection remains a must in modern medicine. Unfortunately, classic sampling protocols do not resolve the problem as expected and several strategies have been being implemented in recent years to improve such detection. Basically, multisite tumor sampling (MSTS) and the homogenization of the residual tumor tissue are on display. A next step of the MSTS strategy considering the recently discovered patterns of ITH regionalization is presented here, the so-called personalized MSTS (pMSTS). This modification consists of paying more attention to sample the tumor periphery since it is this area with maximum levels of ITH.

Application of deep learning methods: From molecular modelling to patient classification

We are now well into the information driven age with complex, heterogeneous, datasets in the biological sciences continuing to grow at a rapid pace. Moreover, distilling of such datasets, to find new governing principles, are underway. Leading the surge are new and exciting algorithmic developments in computer simulation and machine learning, most notably for the latter, those centred on deep learning. However, practical applications of cell centric computations within the biological sciences, even when carefully benchmarked against existing experimental datasets, remain challenging. Here we discuss the application of deep learning methodologies to support our understanding of cell functionality and as an aid to patient classification. Whilst comprehensive end-to-end deep learning approaches that utilise knowledge of the cell and its molecular components to aid human disease classification are yet to be implemented, important for opening the door to more effective molecular and cell-based therapies, we illustrate that many deep learning applications have been developed to tackle components of such an ambitious pipeline. We end our discussion on what the future may hold, especially how an integrated framework of computer simulations and deep learning, in conjunction with wet-bench experimentation, could enable to reveal the governing principles underlying cell functionalities within the tissue environments cells operate.

The Genetic Evolution of Metastasis

Cancer is an evolutionary process that is characterized by the emergence of multiple genetically distinct populations or clones within the primary tumor. Intratumor heterogeneity provides a substrate for the selection of adaptive clones, such as those that lead to metastasis. Comparative molecular studies of primary tumors and metastases have identified distinct genomic features associated with the development of metastases. In this review, we discuss how these insights could inform clinical decision-making and uncover rational antimetastasis treatment strategies.

Clear Cell Renal Cell Carcinomas with Aggressive Behavior Display Low Intratumor Heterogeneity at the Histological Level

PURPOSE OF REVIEW

Intratumor heterogeneity (ITH) is an inherent characteristic of most tumors and its detection remains a key task for pathologists. However, the clinical significance of the degree of development of this feature is still poorly understood.

RECENT FINDINGS

A series of 28 clear cell renal cell carcinomas (CCRCC) have been exhaustively analyzed with two different sampling protocols [multisite tumor sampling (MSTS) and total sampling] to evaluate to what point the level (low vs. high) of histological ITH detected in routine practice influences tumor behavior and patients' survival. All CCRCC (n = 14) pursuing an aggressive clinical course presented low levels of ITH. A significant worse survival was detected in CCRCC with low ITH (p < 0.001). The simple quantification of the level of ITH using extensive sampling protocol may be of help in predicting tumor evolution, since all CCRCC with aggressive behavior demonstrated low levels of histological ITH.

Carbon Dot Therapeutic Platforms: Administration, Distribution, Metabolism, Excretion, Toxicity, and Therapeutic Potential

Ultrasmall nanoparticles are often grouped under the broad umbrella term of "nanoparticles" when reported in the literature. However, for biomedical applications, their small sizes give them intimate interactions with biological species and endow them with unique functional physiochemical properties. Carbon quantum dots (CQDs) are an emerging class of ultrasmall nanoparticles which have demonstrated considerable biocompatibility and have been employed as potent theragnostic platforms. These particles find application for increasing drug solubility and targeting, along with facilitating the passage of drugs across impermeable membranes (i.e., blood brain barrier). Further functionality can be triggered by various environmental conditions or external stimuli (i.e., pH, temperature, near Infrared (NIR) light, ultrasound), and their intrinsic fluorescence is valuable for diagnostic applications. The focus of this review is to shed light on the therapeutic potential of CQDs and identify how they travel through the body, reach their site of action, administer therapeutic effect, and are excreted. Investigation into their toxicity and compatibility with larger nanoparticle carriers is also examined. The future of CQDs for theragnostic applications is promising due to their multifunctional attributes and documented biocompatibility. As nanomaterial platforms become more commonplace in clinical treatments, the commercialization of CQD therapeutics is anticipated.

Patient-Derived Triple-Negative Breast Cancer Organoids Provide Robust Model Systems That Recapitulate Tumor Intrinsic Characteristics

Triple-negative breast cancer (TNBC) is an aggressive form of breast cancer with poor patient outcomes, highlighting the unmet clinical need for targeted therapies and better model systems. Here, we developed and comprehensively characterized a diverse biobank of normal and breast cancer patient-derived organoids (PDO) with a focus on TNBCs. PDOs recapitulated patient tumor intrinsic properties and a subset of PDOs can be propagated for long-term culture (LT-TNBC). Single cell profiling of PDOs identified cell types and gene candidates affiliated with different aspects of cancer progression. The LT-TNBC organoids exhibit signatures of aggressive MYC-driven, basal-like breast cancers and are largely comprised of luminal progenitor (LP)-like cells. The TNBC LP-like cells are distinct from normal LPs and exhibit hyperactivation of NOTCH and MYC signaling. Overall, this study validates TNBC PDOs as robust models for understanding breast cancer biology and progression, paving the way for personalized medicine and tailored treatment options.

SIGNIFICANCE

A comprehensive analysis of patient-derived organoids of TNBC provides insights into cellular heterogeneity and mechanisms of tumorigenesis at the single-cell level.

Single-cell RNA sequencing reveals the multi-cellular ecosystem in different radiological components of pulmonary part-solid nodules

BACKGROUND

Early-stage lung adenocarcinoma that radiologically manifests as part-solid nodules, consisting of both ground-glass and solid components, has distinctive growth patterns and prognosis. The characteristics of the tumour microenvironment and transcriptional features of the malignant cells of different radiological phenotypes remain poorly understood.

METHODS

Twelve treatment-naive patients with radiological part-solid nodules were enrolled. After frozen pathology was confirmed as lung adenocarcinoma, two regions (ground-glass and solid) from each of the 12 part-solid nodules and 5 normal lung tissues from 5 of the12 patients were subjected to single-cell sequencing by 10x Genomics. We used Seurat v3.1.5 for data integration and analysis.

RESULTS

We comprehensively dissected the multicellular ecosystem of the ground-glass and solid components of part-solid nodules at the single-cell resolution. In tumours, these components had comparable proportions of malignant cells. However, the angiogenesis, epithelial-to-mesenchymal transition, KRAS, p53, and cell-cycle signalling pathways were significantly up-regulated in malignant cells within solid components compared to those within ground-glass components. For the tumour microenvironment, the relative abundance of myeloid and NK cells tended to be higher in solid components than in ground-glass components. Slight subtype composition differences existed between the ground-glass and solid components. The T/NK cell subsets' cytotoxic function and the macrophages' pro-inflammation function were suppressed in solid components. Moreover, pericytes in solid components had a stronger communication related to angiogenesis promotion with endothelial cells and tumour cells.

CONCLUSION

The cellular landscape of ground-glass components is significantly different from that of normal tissue and similar to that of solid components. However, transcriptional differences exist in the vital signalling pathways of malignant and immune cells within these components.

Strategies for Improving the Efficacy of CAR T Cells in Solid Cancers

Simple Summary

Cell therapy with genetically retargeted T cells shows strong clinical efficacy against leukaemia and lymphoma. To make this therapy efficient against solid cancers, a series of hurdles must be addressed. This includes the need to enable the T cells to survive long term in patients and to overcome immunosuppressive mechanisms in the tumour. Further, it is essential to prevent tumour cells from escaping by losing the protein that is recognised by the infused cells. The present article provides an overview of the key strategies that are currently being investigated to overcome these hurdles. A series of approaches have been described in preclinical models, but these remain untested in patients. The further progress of the field will depend on evaluating more strategies in a proper clinical setting.

Abstract

Therapy with T cells equipped with chimeric antigen receptors (CARs) shows strong efficacy against leukaemia and lymphoma, but not yet against solid cancers. This has been attributed to insufficient T cell persistence, tumour heterogeneity and an immunosuppressive tumour microenvironment. The present article provides an overview of key strategies that are currently investigated to overcome these hurdles. Basic aspects of CAR design are revisited, relevant for tuning the stimulatory signal to the requirements of solid tumours. Novel approaches for enhancing T cell persistence are highlighted, based on epigenetic or post-translational modifications. Further, the article describes CAR T strategies that are being developed for overcoming tumour heterogeneity and the escape of cancer stem cells, as well as for countering prevalent mechanisms of immune suppression in solid cancers. In general, personalised medicine is faced with a lack of drugs matching the patient’s profile. The advances and flexibility of modern gene engineering may allow for the filling of some of these gaps with tailored CAR T approaches addressing mechanisms identified as important in the individual patient. At this point, however, CAR T cell therapy remains unproved in solid cancers. The further progress of the field will depend on bringing novel strategies into clinical evaluation, while maintaining safety.

Evolution under Spatially Heterogeneous Selection in Solid Tumors

Spatial genetic and phenotypic diversity within solid tumors has been well documented. Nevertheless, how this heterogeneity affects temporal dynamics of tumorigenesis has not been rigorously examined because solid tumors do not evolve as the standard population genetic model due to the spatial constraint. We therefore, propose a neutral spatial (NS) model whereby the mutation accumulation increases toward the periphery; the genealogical relationship is spatially determined and the selection efficacy is blunted (due to kin competition). In this model, neutral mutations are accrued and spatially distributed in manners different from those of advantageous mutations. Importantly, the distinctions could be blurred in the conventional model. To test the NS model, we performed a three-dimensional multiple microsampling of two hepatocellular carcinomas. Whole-genome sequencing (WGS) revealed a 2-fold increase in mutations going from the center to the periphery. The operation of natural selection can then be tested by examining the spatially determined clonal relationships and the clonal sizes. Due to limited migration, only the expansion of highly advantageous clones can sweep through a large part of the tumor to reveal the selective advantages. Hence, even multiregional sampling can only reveal a fraction of fitness differences in solid tumors. Our results suggest that the NS patterns are crucial for testing the influence of natural selection during tumorigenesis, especially for small solid tumors.

Spatial patterns of tumour growth impact clonal diversification in a computational model and the TRACERx Renal study

Genetic intra-tumour heterogeneity fuels clonal evolution, but our understanding of clinically relevant clonal dynamics remain limited. We investigated spatial and temporal features of clonal diversification in clear cell renal cell carcinoma through a combination of modelling and real tumour analysis. We observe that the mode of tumour growth, surface or volume, impacts the extent of subclonal diversification, enabling interpretation of clonal diversity in patient tumours. Specific patterns of proliferation and necrosis explain clonal expansion and emergence of parallel evolution and microdiversity in tumours. In silico time-course studies reveal the appearance of budding structures before detectable subclonal diversification. Intriguingly, we observe radiological evidence of budding structures in early-stage clear cell renal cell carcinoma, indicating that future clonal evolution may be predictable from imaging. Our findings offer a window into the temporal and spatial features of clinically relevant clonal evolution.

Spatial omics: Navigating to the golden era of cancer research

The idea that tumour microenvironment (TME) is organised in a spatial manner will not surprise many cancer biologists; however, systematically capturing spatial architecture of TME is still not possible until recent decade. The past five years have witnessed a boom in the research of high-throughput spatial techniques and algorithms to delineate TME at an unprecedented level. Here, we review the technological progress of spatial omics and how advanced computation methods boost multi-modal spatial data analysis. Then, we discussed the potential clinical translations of spatial omics research in precision oncology, and proposed a transfer of spatial ecological principles to cancer biology in spatial data interpretation. So far, spatial omics is placing us in the golden age of spatial cancer research. Further development and application of spatial omics may lead to a comprehensive decoding of the TME ecosystem and bring the current spatiotemporal molecular medical research into an entirely new paradigm.

Spatial genomics enables multi-modal study of clonal heterogeneity in tissues

The state and behaviour of a cell can be influenced by both genetic and environmental factors. In particular, tumour progression is determined by underlying genetic aberrations^1-4 as well as the makeup of the tumour microenvironment^5,6. Quantifying the contributions of these factors requires new technologies that can accurately measure the spatial location of genomic sequence together with phenotypic readouts. Here we developed slide-DNA-seq, a method for capturing spatially resolved DNA sequences from intact tissue sections. We demonstrate that this method accurately preserves local tumour architecture and enables the de novo discovery of distinct tumour clones and their copy number alterations. We then apply slide-DNA-seq to a mouse model of metastasis and a primary human cancer, revealing that clonal populations are confined to distinct spatial regions. Moreover, through integration with spatial transcriptomics, we uncover distinct sets of genes that are associated with clone-specific genetic aberrations, the local tumour microenvironment, or both. Together, this multi-modal spatial genomics approach provides a versatile platform for quantifying how cell-intrinsic and cell-extrinsic factors contribute to gene expression, protein abundance and other cellular phenotypes.

Profiling Cancer Cells by Cell-SELEX: Use of Aptamers for Discovery of Actionable Biomarkers and Therapeutic Applications Thereof

The identification of tumor cell-specific surface markers is a key step towards personalized cancer medicine, allowing early assessment and accurate diagnosis, and development of efficacious targeted therapies. Despite significant efforts, currently the spectrum of cell membrane targets associated with approved treatments is still limited, causing an inability to treat a large number of cancers. What mainly limits the number of ideal clinical biomarkers is the high complexity and heterogeneity of several human cancers and still-limited methods for molecular profiling of specific cancer types. Thanks to the simplicity, versatility and effectiveness of its application, cell-SELEX (Systematic Evolution of Ligands by Exponential Enrichment) technology is a valid complement to the present strategies for biomarkers' discovery. We and other researchers worldwide are attempting to apply cell-SELEX to the generation of oligonucleotide aptamers as tools for both identifying new cancer biomarkers and targeting them by innovative therapeutic strategies. In this review, we discuss the potential of cell-SELEX for increasing the currently limited repertoire of actionable cancer cell-surface biomarkers and focus on the use of the selected aptamers as components of innovative conjugates and nano-formulations for cancer therapy.

Theranostic Nanoplatform with Sequential SDT and ADV Effects in Response to Well-Programmed LIFU Irradiation for Cervical Cancer

Background

Some patients with cervical cancer have the need to preserve fertility; therefore, a minimally invasive treatment option that can effectively inactivate tumors in these patients is necessary.

Methods

In this paper, we designed and prepared nanoparticles (NPs) carrying IR780 and perfluorohexane (PFH) and characterized their properties. We focused on the promotion of programmed low-intensity focused ultrasound (LIFU) irradiation on the penetration and treatment of cervical cancer. First we used penetration-enhancing LIFU irradiation to promote the penetration of the NPs into 3D multicellular tumor spheroids (MCTSs) and tumors in tumor-bearing nude mice. Then we used re-therapeutic LIFU irradiation to achieve antitumor effects in vitro and in vivo. Photoacoustic (PA) and magnetic resonance (MR) imaging were used to monitor and evaluate the targeting and therapeutic effects of these NPs on tumor tissues.

Results

The NPs prepared in this paper exhibited high affinity for HeLa cells, and can selectively achieve mitochondrial localization in the cell due to IR780 assistance. The penetration-enhancing LIFU irradiation have the ability to promote the penetration of the NPs into cervical cancer models in vivo and in vitro. Under LIFU irradiation, the cytotoxic reactive oxygen species (ROS) produced by IR780 during the first half of the re-therapeutic LIFU irradiation and the physical acoustic droplet vaporization (ADV) effect after PFH phase transition during the second half of the re-therapeutic LIFU irradiation can achieve synergistic minimally invasive treatment of tumors, which can be visualized and evaluated by PA and MR imaging in vivo.

Conclusion

Well-programmed LIFU irradiation can promote NP penetration into deep tumor tissue and achieve antitumor effects simultaneously. Linking ROS + ADV effects can induce cell coagulation necrosis and lead to a comprehensive, long-term impact on tumor tissue, providing a conceptual theranostic nanoplatform for cervical cancer.

Evaluation of Response to Immune Checkpoint Inhibitors Using a Radiomics, Lesion-Level Approach

Conventional methods to determine the response to immune checkpoint inhibitors (ICIs) are limited by the unique responses to an ICI. We performed a radiomics approach for all measurable lesions to identify radiomic variables that could distinguish hyperprogressive disease (HPD) on baseline CT scans and classify a dissociated response (DR). One hundred and ninety-six patients with advanced lung cancer, treated with ICI monotherapy, who underwent at least three CT scans, were retrospectively enrolled. For all 621 measurable lesions, HPDv was determined from baseline CT scans using the tumor growth kinetics (TGK) ratio, and radiomics features were extracted. Multivariable logistic regression analysis of radiomics features was performed to discriminate DR. Radiomics features that significantly discriminated HPDv on baseline CT differed according to organ. Of the 196 patients, 54 (27.6%) had a DR and 142 (72.4%) did not have a DR. Overall survival in the group with a DR was significantly inferior to that in the group without a DR (log rank test, p = 0.04). Our study shows that lesion-level analysis using radiomics features has great potential for discriminating HPDv and understanding heterogeneous tumor progression, including a DR, after ICI treatment.

Antimetastatic defense by CD8+ T cells

Metastasis is an intricate process whereby tumor cells migrate from the primary tumor, survive in the circulation, seed distal organs, and proliferate to create metastatic foci. CD8⁺ T cells can detect and eliminate tumor cells. Research on CD8⁺ T cell-dependent antitumor immunity has classically focused on its role in the primary tumor. There is increasing evidence, however, that CD8⁺ T cells have unique antimetastatic functions in various steps of the metastatic cascade. Here, we review the mechanisms whereby CD8⁺ T cells control metastatic lesions. We discuss their role in each step of metastasis, metastatic dormancy, and metastatic clonal evolution as well as the consequent clinical repercussions.

Precision sampling fuels precision oncology: an evolutionary perspective

Intratumor heterogeneity (ITH) develops in malignant tumors. Precision sampling that captures this tumor variability is essential for the implementation of precision oncology. We highlight the necessity to update current sampling protocols and implement a strategy to ensure ITH detection and characterization. A cost-effective strategy for such sampling has been modeled in clear cell renal cell carcinoma (CCRCC).

Metastasis, an Example of Evolvability

Simple Summary

Cancer is a complex disease. Modern molecular technologies are progressively unveiling its genetic and epigenetic complexity, but still many key issues remain unknown. Considering cancer as a social dysfunction in a community of individuals has provided new perspectives of analysis with promising results. This narrative considers both approaches with respect to the metastatic process, the final cause of death in most patients affected by this disease.

Abstract

This overview focuses on two different perspectives to analyze the metastatic process taking clear cell renal cell carcinoma as a model, molecular and ecological. On the one hand, genomic analyses have demonstrated up to seven different constrained routes of tumor evolution and two different metastatic patterns. On the other hand, game theory applied to cell encounters within a tumor provides a sociological perspective of the possible behaviors of individuals (cells) in a collectivity. This combined approach provides a more comprehensive understanding of the complex rules governing a neoplasm.