Cancer as an evolutionary and ecological process

Deep learning identifies heterogeneous subpopulations in breast cancer cell lines

Motivation

Cells exhibit a wide array of morphological features, enabling computer vision methods to identify and track relevant parameters. Morphological analysis has long been implemented to identify specific cell types and cell responses. Here we asked whether morphological features might also be used to classify transcriptomic subpopulations within in vitro cancer cell lines. Identifying cell subpopulations furthers our understanding of morphology as a reflection of underlying cell phenotype and could enable a better understanding of how subsets of cells compete and cooperate in disease progression and treatment.

Results

We demonstrate that cell morphology can reflect underlying transcriptomic differences in vitro using convolutional neural networks. First, we find that changes induced by chemotherapy treatment are highly identifiable in a breast cancer cell line. We then show that the intra cell line subpopulations that comprise breast cancer cell lines under standard growth conditions are also identifiable using cell morphology. We find that cell morphology is influenced by neighborhood effects beyond the cell boundary, and that including image information surrounding the cell can improve model discrimination ability.

Assessing the merits: an opinion on the effectiveness of simulation techniques in tumor subclonal reconstruction

Summary

Neoplastic tumors originate from a single cell, and their evolution can be traced through lineages characterized by mutations, copy number alterations, and structural variants. These lineages are reconstructed and mapped onto evolutionary trees with algorithmic approaches. However, without ground truth benchmark sets, the validity of an algorithm remains uncertain, limiting potential clinical applicability. With a growing number of algorithms available, there is urgent need for standardized benchmark sets to evaluate their merits. Benchmark sets rely on in silico simulations of tumor sequence, but there are no accepted standards for simulation tools, presenting a major obstacle to progress in this field.

Availability and implementation

All analysis done in the paper was based on publicly available data from the publication of each accessed tool.

Tumor-Agnostic Therapy-The Final Step Forward in the Cure for Human Neoplasms?

Cancer accounted for 10 million deaths in 2020, nearly one in every six deaths annually. Despite advancements, the contemporary clinical management of human neoplasms faces a number of challenges. Surgical removal of tumor tissues is often not possible technically, while radiation and chemotherapy pose the risk of damaging healthy cells, tissues, and organs, presenting complex clinical challenges. These require a paradigm shift in developing new therapeutic modalities moving towards a more personalized and targeted approach. The tumor-agnostic philosophy, one of these new modalities, focuses on characteristic molecular signatures of transformed cells independently of their traditional histopathological classification. These include commonly occurring DNA aberrations in cancer cells, shared metabolic features of their homeostasis or immune evasion measures of the tumor tissues. The first dedicated, FDA-approved tumor-agnostic agent's profound progression-free survival of 78% in mismatch repair-deficient colorectal cancer paved the way for the accelerated FDA approvals of novel tumor-agnostic therapeutic compounds. Here, we review the historical background, current status, and future perspectives of this new era of clinical oncology.

To modulate or to skip: De-escalating PARP inhibitor maintenance therapy in ovarian cancer using adaptive therapy

Toxicity and emerging drug resistance pose important challenges in poly-adenosine ribose polymerase inhibitor (PARPi) maintenance therapy of ovarian cancer. We propose that adaptive therapy, which dynamically reduces treatment based on the tumor dynamics, might alleviate both issues. Utilizing in vitro time-lapse microscopy and stepwise model selection, we calibrate and validate a differential equation mathematical model, which we leverage to test different plausible adaptive treatment schedules. Our model indicates that adjusting the dosage, rather than skipping treatments, is more effective at reducing drug use while maintaining efficacy due to a delay in cell kill and a diminishing dose-response relationship. In vivo pilot experiments confirm this conclusion. Although our focus is toxicity mitigation, reducing drug use may also delay resistance. This study enhances our understanding of PARPi treatment scheduling and illustrates the first steps in developing adaptive therapies for new treatment settings. A record of this paper's transparent peer review process is included in the supplemental information.

Strength of selection in lung tumors correlates with clinical features better than tumor mutation burden

Single nucleotide substitutions are the most common type of somatic mutations in cancer genome. The goal of this study was to use publicly available somatic mutation data to quantify negative and positive selection in individual lung tumors and test how strength of directional and absolute selection is associated with clinical features. The analysis found a significant variation in strength of selection (both negative and positive) among tumors, with median selection tending to be negative even though tumors with strong positive selection also exist. Strength of selection estimated as the density of missense mutations relative to the density of silent mutations showed only a weak correlation with tumor mutation burden. In the "all histology together" analysis we found that absolute strength of selection was strongly correlated with all clinically relevant features analyzed. In histology-stratified analysis selection was strongest in small cell lung cancer. Selection in adenocarcinoma was somewhat higher compared to squamous cell carcinoma. The study suggests that somatic mutation- based quantifying of directional and absolute selection in individual tumors can be a useful biomarker of tumor aggressiveness.

PD-1 blockade enhances the effect of targeted chemotherapy on locally advanced pMMR/MSS colorectal cancer

BACKGROUND

Patients with DNA mismatch repair-proficient/microsatellite stable (pMMR/MSS) colorectal cancer (CRC), which accounts for 85% of all CRC cases, display a poor respond to immune checkpoint inhibitors (i.e., anti-PD-1 antibodies). pMMR/MSS CRC patients with locally advanced cancers need effective combined therapies.

METHODS

In this pilot study, we administered six preoperative doses of each 2-week cycle of the anti-PD-1 antibody sintilimab (at a fixed dose of 200 mg), oxaliplatin, and 5-FU/CF (mFOLFOX6) combined with five doses of bevacizumab (the number of doses was reduced to prevent surgical delays) to patients with cT4NxM0 colon or upper rectal cancers. And radical surgery was performed approximately 2 weeks after the last dose of neoadjuvant therapy. The primary endpoint was a pathologic complete response (pCR). We also evaluated major pathologic response (MPR, ≤10% residual viable tumor), radiological and pathological regression, safety, and tumor mutation burden (TMB), and tumor microenvironment (TME) characteristics.

RESULTS

By the cutoff date (September 2023), 22 patients with cT4NxM0 pMMR/MSS colon or upper rectal cancers were enrolled and the median follow-up was 24.7 months (IQR: 21.1-26.1). All patients underwent R0 surgical resection without treatment-related surgical delays. pCR occurred in 12 of 22 resected tumors (54.5%) and MPR occurred in 18 of 22 (81.8%) patients. At the cutoff date, all patients were alive, and 21/22 were recurrence-free. Treatment-related adverse events of grade 3 or higher occurred in of 2/22 (9.1%) patients. Among the pCR tumors, two were found to harbor POLE mutations. The degree of pathological regression was significantly greater than that of radiological regression (p = 1.35 × 10-8). The number of CD3+/CD4+ cells in the tumor and stroma in pretreated biopsied tissues was markedly lower in pCR tumors than in non-pCR tumors (p = 0.038 and p = 0.015, respectively).

CONCLUSIONS

Neoadjuvant sintilimab combined with bevacizumab and mFOLFOX6 was associated with few side effects, did not delay surgery, and led to pCR and non-pCR in 54.5% and 81.8% of the cases, respectively. Downregulation of CD3/CD4 expression in the tumor and stroma is related to pCR. However, the molecular mechanisms underlying PD-1 blockade-enhanced targeted chemotherapy require further investigation.

A link between evolution and society fostering the UN sustainable development goals

Given the multitude of challenges Earth is facing, sustainability science is of key importance to our continued existence. Evolution is the fundamental biological process underlying the origin of all biodiversity. This phylogenetic diversity fosters the resilience of ecosystems to environmental change, and provides numerous resources to society, and options for the future. Genetic diversity within species is also key to the ability of populations to evolve and adapt to environmental change. Yet, the value of evolutionary processes and the consequences of their impairment have not generally been considered in sustainability research. We argue that biological evolution is important for sustainability and that the concepts, theory, data, and methodological approaches used in evolutionary biology can, in crucial ways, contribute to achieving the UN Sustainable Development Goals (SDGs). We discuss how evolutionary principles are relevant to understanding, maintaining, and improving Nature Contributions to People (NCP) and how they contribute to the SDGs. We highlight specific applications of evolution, evolutionary theory, and evolutionary biology's diverse toolbox, grouped into four major routes through which evolution and evolutionary insights can impact sustainability. We argue that information on both within-species evolutionary potential and among-species phylogenetic diversity is necessary to predict population, community, and ecosystem responses to global change and to make informed decisions on sustainable production, health, and well-being. We provide examples of how evolutionary insights and the tools developed by evolutionary biology can not only inspire and enhance progress on the trajectory to sustainability, but also highlight some obstacles that hitherto seem to have impeded an efficient uptake of evolutionary insights in sustainability research and actions to sustain SDGs. We call for enhanced collaboration between sustainability science and evolutionary biology to understand how integrating these disciplines can help achieve the sustainable future envisioned by the UN SDGs.

The association between tumour heterogeneity and immune evasion mechanisms in hepatocellular carcinoma and its clinical implications

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

Examination of the role of mutualism in immune evasion

Though the earliest stages of oncogenesis, post initiation, are not well understood, it is generally appreciated that a successful transition from a collection of dysregulated cells to an aggressive tumour requires complex ecological interactions between cancer cells and their environment. One key component of tumorigenesis is immune evasion. To investigate the interplay amongst the ecological behaviour of mutualism and immune evasion, we used a computational simulation framework. Sensitivity analyses of the growth of a virtual tumour implemented as a 2D-hexagonal lattice model suggests tumour survival depends on the interplay between growth rates, mutualism and immune evasion. In 60% of simulations, cancer clones with low growth rates, but exhibiting mutualism were able to evade the immune system and continue progressing suggesting that tumours with equivalent growth rates and no mutualism are more likely to be eliminated than tumours with mutualism. Tumours with faster growth rates showed a lower dependence upon mutualism for progression. Geostatistical analysis showed decreased spatial heterogeneity over time for polyclonal tumours with a high division rate. Overall, these results suggest that in slow growing tumours, mutualism is critical for early tumorigenesis.

Comparative analysis of prognosis and gene expression in prostate cancer patients with site-specific visceral metastases

INTRODUCTION

Prostate cancer patients with visceral metastases often exhibited poor prognoses. Few researches had compared the prognostic impact and gene expression profiles among distinct visceral metastatic sites. Therefore, we conducted a comprehensive study utilizing data from the Surveillance, Epidemiology, and End Results (SEER) database and the Gene Expression Omnibus database.

PATIENTS AND METHODS

We analyzed the prostate cancer-specific mortality (PCSM) risk for 8,170 patients diagnosed with metastatic prostate cancer (mPCa) between 2000 and 2019, utilizing data from the SEER 17 registry database. Patients with metastatic disease in nonregional lymph nodes, bones, brains, livers, and lungs were identified. Competing risks regression was employed to evaluate the effect of visceral metastatic disease sites on PCSM. Differentially expressed genes (DEGs) between visceral metastases were assessed using data from the GSE6752 dataset. A relative protein-protein interaction (PPI) network was constructed based on STRING analysis. Furthermore, we explored the distribution of DEGs in various normal tissues and tumor tissues using the Human Protein Atlas and GEPIA.

RESULTS

Competing risks regression analysis revealed that liver and lung metastases had a substantial impact on PCSM (hazard ratio 2.24, 95% confidence interval 1.70-2.95, P < 0.001; hazard ratio 1.30, 95% confidence interval 1.06-1.59, P = 0.012, respectively). Seven significant DEGs were identified from samples of liver and lung metastases (HERV-FRD, NUDT12, FAM63A, SCGB3A1, CEACAM6, LOC440416, SFTPB) and were associated with respiratory gaseous exchange, pulmonary surfactant metabolism, and fibronectin matrix formation in PPI network analysis. Notably, the expression levels of the three DEGs significantly upregulated in lung metastases were also found to be higher in normal lung tissues compared to normal liver tissues.

CONCLUSION

Patients diagnosed with mPCa and presenting with liver and/or lung metastases exhibit poorer prognoses. SCGB3A1, identified as a tumor suppressor gene, may contribute to the better survival prognosis observed in patients with prostate cancer lung metastases compared to those with liver metastases. The gene expression profiles in these two specific metastatic sites reveal a combination of both heterogeneity and homogeneity.

A seven-step guide to spatial, agent-based modelling of tumour evolution

Spatial agent-based models are frequently used to investigate the evolution of solid tumours subject to localized cell-cell interactions and microenvironmental heterogeneity. As spatial genomic, transcriptomic and proteomic technologies gain traction, spatial computational models are predicted to become ever more necessary for making sense of complex clinical and experimental data sets, for predicting clinical outcomes, and for optimizing treatment strategies. Here we present a non-technical step by step guide to developing such a model from first principles. Stressing the importance of tailoring the model structure to that of the biological system, we describe methods of increasing complexity, from the basic Eden growth model up to off-lattice simulations with diffusible factors. We examine choices that unavoidably arise in model design, such as implementation, parameterization, visualization and reproducibility. Each topic is illustrated with examples drawn from recent research studies and state of the art modelling platforms. We emphasize the benefits of simpler models that aim to match the complexity of the phenomena of interest, rather than that of the entire biological system. Our guide is aimed at both aspiring modellers and other biologists and oncologists who wish to understand the assumptions and limitations of the models on which major cancer studies now so often depend.

Modeling tumors as species-rich ecological communities

Many advanced cancers resist therapeutic intervention. This process is fundamentally related to intra-tumor heterogeneity: multiple cell populations, each with different mutational and phenotypic signatures, coexist within a tumor and its metastatic nodes. Like species in an ecosystem, many cancer cell populations are intertwined in a complex network of ecological interactions. Most mathematical models of tumor ecology, however, cannot account for such phenotypic diversity nor are able to predict its consequences. Here we propose that the Generalized Lotka-Volterra model (GLV), a standard tool to describe complex, species-rich ecological communities, provides a suitable framework to describe the ecology of heterogeneous tumors. We develop a GLV model of tumor growth and discuss how its emerging properties, such as outgrowth and multistability, provide a new understanding of the disease. Additionally, we discuss potential extensions of the model and their application to three active areas of cancer research, namely phenotypic plasticity, the cancer-immune interplay and the resistance of metastatic tumors to treatment. Our work outlines a set of questions and a tentative road map for further research in cancer ecology.

Tumor Microenvironment Modulation by Cancer-Derived Extracellular Vesicles

The tumor microenvironment (TME) plays an important role in the process of tumorigenesis, regulating the growth, metabolism, proliferation, and invasion of cancer cells, as well as contributing to tumor resistance to the conventional chemoradiotherapies. Several types of cells with relatively stable phenotypes have been identified within the TME, including cancer-associated fibroblasts (CAFs), tumor-associated macrophages (TAMs), neutrophils, and natural killer (NK) cells, which have been shown to modulate cancer cell proliferation, metastasis, and interaction with the immune system, thus promoting tumor heterogeneity. Growing evidence suggests that tumor-cell-derived extracellular vesicles (EVs), via the transfer of various molecules (e.g., RNA, proteins, peptides, and lipids), play a pivotal role in the transformation of normal cells in the TME into their tumor-associated protumorigenic counterparts. This review article focuses on the functions of EVs in the modulation of the TME with a view to how exosomes contribute to the transformation of normal cells, as well as their importance for cancer diagnosis and therapy.

Metabolic vulnerability of cancer stem cells and their niche

Cancer stem cells (CSC) are the leading cause of the failure of anti-tumor treatments. These aggressive cancer cells are preserved and sustained by adjacent cells forming a specialized microenvironment, termed niche, among which tumor-associated macrophages (TAMs) are critical players. The cycle of tricarboxylic acids, fatty acid oxidation path, and electron transport chain have been proven to play central roles in the development and maintenance of CSCs and TAMs. By improving their oxidative metabolism, cancer cells are able to extract more energy from nutrients, which allows them to survive in nutritionally defective environments. Because mitochondria are crucial bioenergetic hubs and sites of these metabolic pathways, major hopes are posed for drugs targeting mitochondria. A wide range of medications targeting mitochondria, electron transport chain complexes, or oxidative enzymes are currently investigated in phase 1 and phase 2 clinical trials against hard-to-treat tumors. This review article aims to highlight recent literature on the metabolic adaptations of CSCs and their supporting macrophages. A focus is provided on the resistance and dormancy behaviors that give CSCs a selection advantage and quiescence capacity in particularly hostile microenvironments and the role of TAMs in supporting these attitudes. The article also describes medicaments that have demonstrated a robust ability to disrupt core oxidative metabolism in preclinical cancer studies and are currently being tested in clinical trials.

Nanoparticles in tumor microenvironment remodeling and cancer immunotherapy

Cancer immunotherapy and vaccine development have significantly improved the fight against cancers. Despite these advancements, challenges remain, particularly in the clinical delivery of immunomodulatory compounds. The tumor microenvironment (TME), comprising macrophages, fibroblasts, and immune cells, plays a crucial role in immune response modulation. Nanoparticles, engineered to reshape the TME, have shown promising results in enhancing immunotherapy by facilitating targeted delivery and immune modulation. These nanoparticles can suppress fibroblast activation, promote M1 macrophage polarization, aid dendritic cell maturation, and encourage T cell infiltration. Biomimetic nanoparticles further enhance immunotherapy by increasing the internalization of immunomodulatory agents in immune cells such as dendritic cells. Moreover, exosomes, whether naturally secreted by cells in the body or bioengineered, have been explored to regulate the TME and immune-related cells to affect cancer immunotherapy. Stimuli-responsive nanocarriers, activated by pH, redox, and light conditions, exhibit the potential to accelerate immunotherapy. The co-application of nanoparticles with immune checkpoint inhibitors is an emerging strategy to boost anti-tumor immunity. With their ability to induce long-term immunity, nanoarchitectures are promising structures in vaccine development. This review underscores the critical role of nanoparticles in overcoming current challenges and driving the advancement of cancer immunotherapy and TME modification.

Synergistic epistasis among cancer drivers can rescue early tumors from the accumulation of deleterious passengers

Epistasis among driver mutations is pervasive and explains relevant features of cancer, such as differential therapy response and convergence towards well-characterized molecular subtypes. Furthermore, a growing body of evidence suggests that tumor development could be hampered by the accumulation of slightly deleterious passenger mutations. In this work, we combined empirical epistasis networks, computer simulations, and mathematical models to explore how synergistic interactions among driver mutations affect cancer progression under the burden of slightly deleterious passengers. We found that epistasis plays a crucial role in tumor development by promoting the transformation of precancerous clones into rapidly growing tumors through a process that is analogous to evolutionary rescue. The triggering of epistasis-driven rescue is strongly dependent on the intensity of epistasis and could be a key rate-limiting step in many tumors, contributing to their unpredictability. As a result, central genes in cancer epistasis networks appear as key intervention targets for cancer therapy.

Macrophage heterogeneity in bone metastasis

Previous studies illustrated that macrophage, a type of innate immune cell, plays critical roles in tumour progression and metastasis. Bone is the most frequent site of metastasis for several cancer types including breast, prostate, and lung. In bone metastasis, osteoclast, a macrophage subset specialized in bone resorption, was heavily investigated in the past. Recent studies illustrated that other macrophage subsets, e.g. monocyte-derived macrophages, and bone resident macrophages, promoted bone metastasis independent of osteoclast function. These novel mechanisms further improved our understanding of macrophage heterogeneity in the context of bone metastasis and illustrated new opportunities for future studies.

Mathematical modeling of combined therapies for treating tumor drug resistance

Drug resistance is one of the most intractable issues to the targeted therapy for cancer diseases. To explore effective combination therapy schemes, we propose a mathematical model to study the effects of different treatment schemes on the dynamics of cancer cells. Then we characterize the dynamical behavior of the model by finding the equilibrium points and exploring their local stability. Lyapunov functions are constructed to investigate the global asymptotic stability of the model equilibria. Numerical simulations are carried out to verify the stability of equilibria and treatment outcomes using a set of collected model parameters and experimental data on murine colon carcinoma. Simulation results suggest that immunotherapy combined with chemotherapy contributes significantly to the control of tumor growth compared to monotherapy. Sensitivity analysis is performed to identify the importance of model parameters on the variations of model outcomes.

Challenges in developing personalized neoantigen cancer vaccines

The recent success of cancer immunotherapies has highlighted the benefit of harnessing the immune system for cancer treatment. Vaccines have a long history of promoting immunity to pathogens and, consequently, vaccines targeting cancer neoantigens have been championed as a tool to direct and amplify immune responses against tumours while sparing healthy tissue. In recent years, extensive preclinical research and more than one hundred clinical trials have tested different strategies of neoantigen discovery and vaccine formulations. However, despite the enthusiasm for neoantigen vaccines, proof of unequivocal efficacy has remained beyond reach for the majority of clinical trials. In this Review, we focus on the key obstacles pertaining to vaccine design and tumour environment that remain to be overcome in order to unleash the true potential of neoantigen vaccines in cancer therapy.

Small extracellular vesicles from surviving cancer cells as multiparametric monitoring tools of measurable residual disease and therapeutic efficiency

Although conventional anti-cancer therapies remove most cells of the tumor mass, small surviving populations may evolve adaptive resistance strategies, which lead to treatment failure. The size of the resistant population initially may not reach the threshold of clinical detection (designated as measurable residual disease/MRD) thus, its investigation requires highly sensitive and specific methods. Here, we discuss that the specific molecular fingerprint of tumor-derived small extracellular vesicles (sEVs) is suitable for longitudinal monitoring of MRD. Furthermore, we present a concept that exploiting the multiparametric nature of sEVs may help early detection of recurrence and the design of dynamic, evolution-adjusted treatments.

Lineage and ecology define liver tumor evolution in response to treatment

A tumor ecosystem constantly evolves over time in the face of immune predation or therapeutic intervention, resulting in treatment failure and tumor progression. Here, we present a single-cell transcriptome-based strategy to determine the evolution of longitudinal tumor biopsies from liver cancer patients by measuring cellular lineage and ecology. We construct a lineage and ecological score as joint dynamics of tumor cells and their microenvironments. Tumors may be classified into four main states in the lineage-ecological space, which are associated with clinical outcomes. Analysis of longitudinal samples reveals the evolutionary trajectory of tumors in response to treatment. We validate the lineage-ecology-based scoring system in predicting clinical outcomes using bulk transcriptomic data of additional cohorts of 716 liver cancer patients. Our study provides a framework for monitoring tumor evolution in response to therapeutic intervention.

Onco-Breastomics: An Eco-Evo-Devo Holistic Approach

Known as a diverse collection of neoplastic diseases, breast cancer (BC) can be hyperbolically characterized as a dynamic pseudo-organ, a living organism able to build a complex, open, hierarchically organized, self-sustainable, and self-renewable tumor system, a population, a species, a local community, a biocenosis, or an evolving dynamical ecosystem (i.e., immune or metabolic ecosystem) that emphasizes both developmental continuity and spatio-temporal change. Moreover, a cancer cell community, also known as an oncobiota, has been described as non-sexually reproducing species, as well as a migratory or invasive species that expresses intelligent behavior, or an endangered or parasite species that fights to survive, to optimize its features inside the host's ecosystem, or that is able to exploit or to disrupt its host circadian cycle for improving the own proliferation and spreading. BC tumorigenesis has also been compared with the early embryo and placenta development that may suggest new strategies for research and therapy. Furthermore, BC has also been characterized as an environmental disease or as an ecological disorder. Many mechanisms of cancer progression have been explained by principles of ecology, developmental biology, and evolutionary paradigms. Many authors have discussed ecological, developmental, and evolutionary strategies for more successful anti-cancer therapies, or for understanding the ecological, developmental, and evolutionary bases of BC exploitable vulnerabilities. Herein, we used the integrated framework of three well known ecological theories: the Bronfenbrenner's theory of human development, the Vannote's River Continuum Concept (RCC), and the Ecological Evolutionary Developmental Biology (Eco-Evo-Devo) theory, to explain and understand several eco-evo-devo-based principles that govern BC progression. Multi-omics fields, taken together as onco-breastomics, offer better opportunities to integrate, analyze, and interpret large amounts of complex heterogeneous data, such as various and big-omics data obtained by multiple investigative modalities, for understanding the eco-evo-devo-based principles that drive BC progression and treatment. These integrative eco-evo-devo theories can help clinicians better diagnose and treat BC, for example, by using non-invasive biomarkers in liquid-biopsies that have emerged from integrated omics-based data that accurately reflect the biomolecular landscape of the primary tumor in order to avoid mutilating preventive surgery, like bilateral mastectomy. From the perspective of preventive, personalized, and participatory medicine, these hypotheses may help patients to think about this disease as a process governed by natural rules, to understand the possible causes of the disease, and to gain control on their own health.

E2F1 mediates competition, proliferation and response to cisplatin in cohabitating resistant and sensitive ovarian cancer cells

Background

Tumor heterogeneity is one of the key factors leading to chemo-resistance relapse. It remains unknown how resistant cancer cells influence sensitive cells during cohabitation and growth within a heterogenous tumors. The goal of our study was to identify driving factors that mediate the interactions between resistant and sensitive cancer cells and to determine the effects of cohabitation on both phenotypes.

Methods

We used isogenic ovarian cancer (OC) cell lines pairs, sensitive and resistant to platinum: OVCAR5 vs. OVCAR5 CisR and PE01 vs. PE04, respectively, to perform long term direct culture and to study the phenotypical changes of the interaction of these cells.

Results

Long term direct co-culture of sensitive and resistant OC cells promoted proliferation (p < 0.001) of sensitive cells and increased the proportion of cells in the G1 and S cell cycle phase in both PE01 and OVCAR5 cells. Direct co-culture led to a decrease in the IC50 to platinum in the cisplatin-sensitive cells (5.92 µM to 2.79 µM for PE01, and from 2.05 µM to 1.51 µM for OVCAR5). RNAseq analysis of co-cultured cells showed enrichment of Cell Cycle Control, Cyclins and Cell Cycle Regulation pathways. The transcription factor E2F1 was predicted as the main effector responsible for the transcriptomic changes in sensitive cells. Western blot and qRT-PCR confirmed upregulation of E2F1 in co-cultured vs monoculture. Furthermore, an E2F1 inhibitor reverted the increase in proliferation rate induced by co-culture to baseline levels.

Conclusion

Our data suggest that long term cohabitation of chemo-sensitive and -resistant cancer cells drive sensitive cells to a higher proliferative state, more responsive to platinum. Our results reveal an unexpected effect caused by direct interactions between cancer cells with different proliferative rates and levels of platinum resistance, modelling competition between cells in heterogeneous tumors.

Clonal evolution dissection reveals that a high MSI2 level promotes chemoresistance in T-cell acute lymphoblastic leukemia

ABSTRACT

T-cell acute lymphoblastic leukemia (T-ALL) is an aggressive cancer with resistant clonal propagation in recurrence. We performed high-throughput droplet-based 5' single-cell RNA with paired T-cell receptor (TCR) sequencing of paired diagnosis-relapse (Dx_Rel) T-ALL samples to dissect the clonal diversities. Two leukemic evolutionary patterns, "clonal shift" and "clonal drift" were unveiled. Targeted single-cell DNA sequencing of paired Dx_Rel T-ALL samples further corroborated the existence of the 2 contrasting clonal evolution patterns, revealing that dynamic transcriptional variation might cause the mutationally static clones to evolve chemotherapy resistance. Analysis of commonly enriched drifted gene signatures showed expression of the RNA-binding protein MSI2 was significantly upregulated in the persistent TCR clonotypes at relapse. Integrated in vitro and in vivo functional studies suggested that MSI2 contributed to the proliferation of T-ALL and promoted chemotherapy resistance through the posttranscriptional regulation of MYC, pinpointing MSI2 as an informative biomarker and novel therapeutic target in T-ALL.

Gut microbiota and its therapeutic implications in tumor microenvironment interactions

The development of cancer is not just the growth and proliferation of a single transformed cell, but its tumor microenvironment (TME) also coevolves with it, which is primarily involved in tumor initiation, development, metastasis, and therapeutic responses. Recent years, TME has been emerged as a potential target for cancer diagnosis and treatment. However, the clinical efficacy of treatments targeting the TME, especially its specific components, remains insufficient. In parallel, the gut microbiome is an essential TME component that is crucial in cancer immunotherapy. Thus, assessing and constructing frameworks between the gut microbiota and the TME can significantly enhance the exploration of effective treatment strategies for various tumors. In this review the role of the gut microbiota in human cancers, including its function and relationship with various tumors was summarized. In addition, the interaction between the gut microbiota and the TME as well as its potential applications in cancer therapeutics was described. Furthermore, it was summarized that fecal microbiota transplantation, dietary adjustments, and synthetic biology to introduce gut microbiota-based medical technologies for cancer treatment. This review provides a comprehensive summary for uncovering the mechanism underlying the effects of the gut microbiota on the TME and lays a foundation for the development of personalized medicine in further studies.

Methionine enkephalin (MENK) protected macrophages from ferroptosis by downregulating HMOX1 and ferritin

OBJECTIVE

The aim of this work was to investigate the immunological effect of MENK by analyzing the protein spectrum and bioinformatics of macrophage RAW264.7, and to explore the relationship between macrophage and ferroptosis.

RESULT

We employed proteomic analysis to identify differentially expressed proteins (DEPs) between macrophages and macrophages intervened by MENK. A total of 208 DEPs were identified. Among these, 96 proteins had upregulated expression and 112 proteins had downregulated expression. Proteomic analysis revealed a significant enrichment of DEPs associated with iron metabolism. The identification of hub genes was conducted using KEGG pathway diagrams and protein-protein interaction (PPI) analysis. The hub genes identified in this study include HMOX1 and Ferritin (FTH and FTL). A correlation was established between HMOX1, FTH, and FTL in the GO and KEGG databases. The results of PCR, WB and immunofluorescence showed that MENK downregulated the level of HMOX1 and FTH.

CONCLUSION

MENK had the potential to become an adjuvant chemotherapy drug by regulating iron metabolism in macrophages, reducing levels of HMOX1 and ferritin. We proposed an innovative research direction on the therapeutic potential of MENK, focusing on the relationship between ferroptosis and macrophage activity.

Characterization of dabrafenib-induced drug insensitivity via cellular barcoding and collateral sensitivity to second-line therapeutics

Drug insensitivity is arguably one of the biggest challenges in cancer therapeutics. Although effective therapeutic solutions in cancer are limited due to the emergence of drug insensitivity, exploiting evolutionary understanding in this context can provide potential second-line therapeutics sensitizing the drug insensitive populations. Targeted therapeutic agent dabrafenib is used to treat CRC patients with BRAF V600E genotype and insensitivity to dabrafenib is often observed. Understanding underlying clonal architecture of dabrafenib-induced drug insensitivity and identification of potential second-line therapeutics that could sensitize dabrafenib insensitive populations remain to be elucidated. For this purpose, we utilized cellular barcoding technology to decipher dabrafenib-induced clonal evolution in BRAF V600E mutant HT-29 cells. This approach revealed the detection of both pre-existing and de novo barcodes with increased frequencies as a result of dabrafenib insensitivity. Furthermore, our longitudinal monitoring of drug insensitivity based on barcode detection from floating DNA within used medium enabled to identify temporal dynamics of pre-existing and de novo barcodes in relation to dabrafenib insensitivity in HT-29 cells. Moreover, whole-exome sequencing analysis exhibited possible somatic CNVs and SNVs contributing to dabrafenib insensitivity in HT-29 cells. Last, collateral drug sensitivity testing demonstrated oxaliplatin and capecitabine, alone or in combination, as successful second-like therapeutics in inducing collateral sensitivity in dabrafenib-insensitive HT-29 cells. Overall, our findings demonstrate clonal dynamics of dabrafenib-insensitivity in HT-29 cells. In addition, oxaliplatin and capecitabine, alone or in combination, were successful second-line therapeutics in inducing collateral sensitivity in dabrafenib-insensitive HT-29 cells.

Adaptive Cancer Therapy in the Age of Generative Artificial Intelligence

Therapeutic resistance is a major challenge facing the design of effective cancer treatments. Adaptive cancer therapy is in principle the most viable approach to manage cancer's adaptive dynamics through drug combinations with dose timing and modulation. However, there are numerous open issues facing the clinical success of adaptive therapy. Chief among these issues is the feasibility of real-time predictions of treatment response which represent a bedrock requirement of adaptive therapy. Generative artificial intelligence has the potential to learn prediction models of treatment response from clinical, molecular, and radiomics data about patients and their treatments. The article explores this potential through a proposed integration model of Generative Pre-Trained Transformers (GPTs) in a closed loop with adaptive treatments to predict the trajectories of disease progression. The conceptual model and the challenges facing its realization are discussed in the broader context of artificial intelligence integration in oncology.

Fractal Analysis in Clinical Neurosciences: An Overview

Over the last years, fractals have entered into the realms of clinical neurosciences. The whole brain and its components (i.e., neurons and astrocytes) have been studied as fractal objects, and even more relevant, the fractal-based quantification of the geometrical complexity of histopathological and neuroradiological images as well as neurophysiopathological time series has suggested the existence of a gradient in the pattern representation of neurological diseases. Computational fractal-based parameters have been suggested as potential diagnostic and prognostic biomarkers in different brain diseases, including brain tumors, neurodegeneration, epilepsy, demyelinating diseases, cerebrovascular malformations, and psychiatric disorders as well. This chapter and the entire third section of this book are focused on practical applications of computational fractal-based analysis into the clinical neurosciences, namely, neurology and neuropsychiatry, neuroradiology and neurosurgery, neuropathology, neuro-oncology and neurorehabilitation, neuro-ophthalmology, and cognitive neurosciences, with special emphasis on the translation of the fractal dimension and other fractal parameters as clinical biomarkers useful from bench to bedside.

Adaptive DNA amplification of synthetic gene circuit opens a way to overcome cancer chemoresistance

Drug resistance continues to impede the success of cancer treatments, creating a need for experimental model systems that are broad, yet simple, to allow the identification of mechanisms and novel countermeasures applicable to many cancer types. To address these needs, we investigated a set of engineered mammalian cell lines with synthetic gene circuits integrated into their genome that evolved resistance to Puromycin. We identified DNA amplification as the mechanism underlying drug resistance in 4 out of 6 replicate populations. Triplex-forming oligonucleotide (TFO) treatment combined with Puromycin could efficiently suppress the growth of cell populations with DNA amplification. Similar observations in human cancer cell lines suggest that TFOs could be broadly applicable to mitigate drug resistance, one of the major difficulties in treating cancer.

Single-cell sequencing in pancreatic cancer research: A deeper understanding of heterogeneity and therapy

Pancreatic cancer, including pancreatic ductal adenocarcinomas (PDACs), is a malignant tumor with characteristics of tumor-stroma interactions. Patients often have a poor prognosis and a poor long-term survival rate. In recent years, rapidly-developing single-cell sequencing techniques have been used to analyze cell populations at a single-cell resolution, so that it is now possible to have a more in-depth and clearer understanding of the genetic composition of pancreatic cancer. In this review, we provide an overview of the current single-cell sequencing techniques and their applications in the exploration of intratumoral heterogeneity, the tumor microenvironment, therapy resistance, and novel treatments. Our hope is to provide new insight into the potential of precision therapy, which will perhaps one day lead to significant advances in PDAC treatment.

Intratumor heterogeneity: models of malignancy emergence and evolution

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

Using picoliter droplet deposition to track clonal competition in adherent and organoid cancer cell cultures

Clonal growth and competition underlie processes of key relevance in etiology, progression and therapy response across all cancers. Here, we demonstrate a novel experimental approach, based on multi-color, fluorescent tagging of cell nuclei, in combination with picoliter droplet deposition, to study the clonal dynamics in two- and three-dimensional cell cultures. The method allows for the simultaneous visualization and analysis of multiple clones in individual multi-clonal colonies, providing a powerful tool for studying clonal dynamics and identifying clonal populations with distinct characteristics. Results of our experiments validate the utility of the method in studying clonal dynamics in vitro, and reveal differences in key aspects of clonal behavior of different cancer cell lines in monoculture conditions, as well as in co-cultures with stromal fibroblasts.

Developing the genotype-to-phenotype relationship in evolutionary theory: A primer of developmental features

For decades, there have been repeated calls for more integration across evolutionary and developmental biology. However, critiques in the literature and recent funding initiatives suggest this integration remains incomplete. We suggest one way forward is to consider how we elaborate the most basic concept of development, the relationship between genotype and phenotype, in traditional models of evolutionary processes. For some questions, when more complex features of development are accounted for, predictions of evolutionary processes shift. We present a primer on concepts of development to clarify confusion in the literature and fuel new questions and approaches. The basic features of development involve expanding a base model of genotype-to-phenotype to include the genome, space, and time. A layer of complexity is added by incorporating developmental systems, including signal-response systems and networks of interactions. The developmental emergence of function, which captures developmental feedbacks and phenotypic performance, offers further model elaborations that explicitly link fitness with developmental systems. Finally, developmental features such as plasticity and developmental niche construction conceptualize the link between a developing phenotype and the external environment, allowing for a fuller inclusion of ecology in evolutionary models. Incorporating aspects of developmental complexity into evolutionary models also accommodates a more pluralistic focus on the causal importance of developmental systems, individual organisms, or agents in generating evolutionary patterns. Thus, by laying out existing concepts of development, and considering how they are used across different fields, we can gain clarity in existing debates around the extended evolutionary synthesis and pursue new directions in evolutionary developmental biology. Finally, we consider how nesting developmental features in traditional models of evolution can highlight areas of evolutionary biology that need more theoretical attention.

Statistics, philosophy, and health: the SMAC 2021 webconference

SMAC 2021 was a webconference organized in June 2021. The aim of this conference was to bring together data scientists, (bio)statisticians, philosophers, and any person interested in the questions of causality and Bayesian statistics, ranging from technical to philosophical aspects. This webconference consisted of keynote speakers and contributed speakers, and closed with a round-table organized in an unusual fashion. Indeed, organisers asked world renowned scientists to prepare two videos: a short video presenting a question of interest to them and a longer one presenting their point of view on the question. The first video served as a "teaser" for the conference and the second were presented during the conference as an introduction to the round-table. These videos and this round-table generated original scientific insights and discussion worthy of being shared with the community which we do by means of this paper.

Single-cell morphological and topological atlas reveals the ecosystem diversity of human breast cancer

Digital pathology allows computerized analysis of tumor ecosystem using whole slide images (WSIs). Here, we present single-cell morphological and topological profiling (sc-MTOP) to characterize tumor ecosystem by extracting the features of nuclear morphology and intercellular spatial relationship for individual cells. We construct a single-cell atlas comprising 410 million cells from 637 breast cancer WSIs and dissect the phenotypic diversity within tumor, inflammatory and stroma cells respectively. Spatially-resolved analysis identifies recurrent micro-ecological modules representing locoregional multicellular structures and reveals four breast cancer ecotypes correlating with distinct molecular features and patient prognosis. Further analysis with multiomics data uncovers clinically relevant ecosystem features. High abundance of locally-aggregated inflammatory cells indicates immune-activated tumor microenvironment and favorable immunotherapy response in triple-negative breast cancers. Morphological intratumor heterogeneity of tumor nuclei correlates with cell cycle pathway activation and CDK inhibitors responsiveness in hormone receptor-positive cases. sc-MTOP enables using WSIs to characterize tumor ecosystems at the single-cell level.

Don't Judge a Book by Its Cover: The Role of Statins in Liver Cancer

Statins, which are inhibitors of 3-hydroxy-3-methyl-glutaryl-coenzyme A (HMG-CoA) reductase, are an effective pharmacological tool for lowering blood cholesterol levels. This property makes statins one of the most popular drugs used primarily to prevent cardiovascular diseases, where hyperlipidemia is a significant risk factor that increases mortality. Nevertheless, studies conducted mainly in the last decade have shown that statins might prevent and treat liver cancer, one of the leading causes of cancer-related mortality worldwide. This narrative review summarizes the scientific achievements to date regarding the role of statins in liver tumors. Molecular biology tools have revealed that cell growth and proliferation can be inhibited by statins, which further inhibit angiogenesis. Clinical studies, supported by meta-analysis, confirm that statins are highly effective in preventing and treating hepatocellular carcinoma and cholangiocarcinoma. However, this effect may depend on the statin's type and dose, and more clinical trials are required to evaluate clinical effects. Moreover, their potential hepatotoxicity is a significant caveat for using statins in clinical practice. Nevertheless, this group of drugs, initially developed to prevent cardiovascular diseases, is now a key candidate in hepato-oncology patient management. The description of new drug-statin-like structures, e.g., with low toxicity to liver cells, may bring another clinically significant improvement to current cancer therapies.

Game-theoretical description of the go-or-grow dichotomy in tumor development for various settings and parameter constellations

A medically important feature of several types of tumors is their ability to "decide" between staying at a primary site in the body or leaving it and forming metastases. The present theoretical study aims to provide a better understanding of the ultimate reasons for this so-called "go-or-grow" dichotomy. To that end, we use game theory, which has proven to be useful in analyzing the competition between tumors and healthy tissues or among different tumor cells. We begin by determining the game types in the Basanta-Hatzikirou-Deutsch model, depending on the parameter values. Thereafter, we suggest and analyze five modified variants of the model. For example, in the basic model, the deadlock game, Prisoner's Dilemma, and hawk-dove game can occur. The modified versions lead to several additional game types, such as battle of the sexes, route-choice, and stag-hunt games. For some game types, all cells are predicted to stay on their original site ("grow phenotype"), while for other types, only a certain fraction stay and the other cells migrate away ("go phenotype"). If the nutrient supply at a distant site is high, all the cells are predicted to go. We discuss our predictions in terms of the pros and cons of caloric restriction and limitations of the supply of vitamins or methionine. Our results may help devise treatments to prevent metastasis.

Treatment of evolving cancers will require dynamic decision support

Cancer research has traditionally focused on developing new agents, but an underexplored question is that of the dose and frequency of existing drugs. Based on the modus operandi established in the early days of chemotherapies, most drugs are administered according to predetermined schedules that seek to deliver the maximum tolerated dose and are only adjusted for toxicity. However, we believe that the complex, evolving nature of cancer requires a more dynamic and personalized approach. Chronicling the milestones of the field, we show that the impact of schedule choice crucially depends on processes driving treatment response and failure. As such, cancer heterogeneity and evolution dictate that a one-size-fits-all solution is unlikely-instead, each patient should be mapped to the strategy that best matches their current disease characteristics and treatment objectives (i.e. their 'tumorscape'). To achieve this level of personalization, we need mathematical modeling. In this perspective, we propose a five-step 'Adaptive Dosing Adjusted for Personalized Tumorscapes (ADAPT)' paradigm to integrate data and understanding across scales and derive dynamic and personalized schedules. We conclude with promising examples of model-guided schedule personalization and a call to action to address key outstanding challenges surrounding data collection, model development, and integration.

Single-cell RNA sequencing reveals the cellular and molecular characteristics of high-grade and metastatic bladder cancer

PURPOSE

Metastatic bladder cancer (BC) has the highest somatic mutation frequency and recurrence rate of all tumors. However, the cellular and molecular characteristics of BC remain unclear.

METHODS

We performed single-cell RNA sequencing (scRNA-seq) on the samples of paracancerous normal tissue (PNT), primary tumor (PT) and lymph node metastasis (LNM). The proportions and gene expression profiles of different cell types in the tumor microenvironment (TME) were investigated.

RESULTS

In total, 50,158 cells were classified into six populations. Malignant cells of PT and LNM exhibited large mutant DNA fragments, while the cell phenotypes and gene expression profiles differed during differentiation. Metastasis was associated with a poorer prognosis than PT. Tumor-associated stromal cells and inhibitory immune cells were the main cell populations in PT and LNM. Cell-cell communication analysis revealed the roles of signaling pathways of inflammatory cancer-associated fibroblast (iCAF) and tumor-associated macrophage (TAM) in exhaustion of T cells. In addition, iCAF may recruit TAM to promote formation of the TME earlier than the differentiation of tumor cells.

CONCLUSION

This study through scRNA-seq enhanced our understanding of new features about the cellular and molecular similarities and differences of high-grade and metastatic bladder cancer, which might provide potential therapeutic targets in future treatment.

Unraveling the Anticancer Potential of Statins: Mechanisms and Clinical Significance

Statins are an essential medication class in the treatment of lipid diseases because they inhibit 3-hydroxy-3-methylglutaryl-coenzyme A (HMG-CoA) reductase. They reduce cholesterol levels and reduce the risk of cardiovascular disease in both primary and secondary prevention. In addition to their powerful pharmacologic suppression of cholesterol production, statins appear to have pleitropic effects in a wide variety of other diseases by modulating signaling pathways. In recent years, statins have seen a large increase in interest due to their putative anticancer effects. Statins appear to cause upregulation or inhibition in key pathways involved in cancer such as inhibition of proliferation, angiogenesis, and metastasis as well as reducing cancer stemness. Further, statins have been found to induce oxidative stress, cell cycle arrest, autophagy, and apoptosis of cancer cells. Interestingly, clinical studies have shown that statin use is associated with a decreased risk of cancer formation, lower cancer grade at diagnosis, reduction in the risk of local reoccurrence, and increasing survival in patients. Therefore, our objective in the present review is to summarize the findings of the publications on the underlying mechanisms of statins' anticancer effects and their clinical implications.

Alveolar macrophages in lung cancer: opportunities challenges

Alveolar macrophages (AMs) are critical components of the innate defense mechanism in the lung. Nestled tightly within the alveoli, AMs, derived from the yolk-sac or bone marrow, can phagocytose foreign particles, defend the host against pathogens, recycle surfactant, and promptly respond to inhaled noxious stimuli. The behavior of AMs is tightly dependent on the environmental cues whereby infection, chronic inflammation, and associated metabolic changes can repolarize their effector functions in the lungs. Several factors within the tumor microenvironment can re-educate AMs, resulting in tumor growth, and reducing immune checkpoint inhibitors (ICIs) efficacy in patients treated for non-small cell lung cancer (NSCLC). The plasticity of AMs and their critical function in altering tumor responses to ICIs make them a desirable target in lung cancer treatment. New strategies have been developed to target AMs in solid tumors reprograming their suppressive function and boosting the efficacy of ICIs. Here, we review the phenotypic and functional changes in AMs in response to sterile inflammation and in NSCLC that could be critical in tumor growth and metastasis. Opportunities in altering AMs' function include harnessing their potential function in trained immunity, a concept borrowed from memory response to infections, which could be explored therapeutically in managing lung cancer treatment.

Evolutionary signatures of human cancers revealed via genomic analysis of over 35,000 patients

Recurring sequences of genomic alterations occurring across patients can highlight repeated evolutionary processes with significant implications for predicting cancer progression. Leveraging the ever-increasing availability of cancer omics data, here we unveil cancer's evolutionary signatures tied to distinct disease outcomes, representing "favored trajectories" of acquisition of driver mutations detected in patients with similar prognosis. We present a framework named ASCETIC (Agony-baSed Cancer EvoluTion InferenCe) to extract such signatures from sequencing experiments generated by different technologies such as bulk and single-cell sequencing data. We apply ASCETIC to (i) single-cell data from 146 myeloid malignancy patients and bulk sequencing from 366 acute myeloid leukemia patients, (ii) multi-region sequencing from 100 early-stage lung cancer patients, (iii) exome/genome data from 10,000+ Pan-Cancer Atlas samples, and (iv) targeted sequencing from 25,000+ MSK-MET metastatic patients, revealing subtype-specific single-nucleotide variant signatures associated with distinct prognostic clusters. Validations on several datasets underscore the robustness and generalizability of the extracted signatures.

Developmental priming of cancer susceptibility

DNA mutations are necessary drivers of cancer, yet only a small subset of mutated cells go on to cause the disease. To date, the mechanisms that determine which rare subset of cells transform and initiate tumorigenesis remain unclear. Here, we take advantage of a unique model of intrinsic developmental heterogeneity (Trim28+/D9) and demonstrate that stochastic early life epigenetic variation can trigger distinct cancer-susceptibility 'states' in adulthood. We show that these developmentally primed states are characterized by differential methylation patterns at typically silenced heterochromatin, and that these epigenetic signatures are detectable as early as 10 days of age. The differentially methylated loci are enriched for genes with known oncogenic potential. These same genes are frequently mutated in human cancers, and their dysregulation correlates with poor prognosis. These results provide proof-of-concept that intrinsic developmental heterogeneity can prime individual, life-long cancer risk.

Single-cell phylogenies reveal changes in the evolutionary rate within cancer and healthy tissues

Cell lineages accumulate somatic mutations during organismal development, potentially leading to pathological states. The rate of somatic evolution within a cell population can vary due to multiple factors, including selection, a change in the mutation rate, or differences in the microenvironment. Here, we developed a statistical test called the Poisson Tree (PT) test to detect varying evolutionary rates among cell lineages, leveraging the phylogenetic signal of single-cell DNA sequencing (scDNA-seq) data. We applied the PT test to 24 healthy and cancer samples, rejecting a constant evolutionary rate in 11 out of 15 cancer and five out of nine healthy scDNA-seq datasets. In six cancer datasets, we identified subclonal mutations in known driver genes that could explain the rate accelerations of particular cancer lineages. Our findings demonstrate the efficacy of scDNA-seq for studying somatic evolution and suggest that cell lineages often evolve at different rates within cancer and healthy tissues.

Comparative Analysis of Cluster of Differentiation 57 and Proliferating Cell Nuclear Antigen Expression in Different Grades of Oral Squamous Cell Carcinoma: An Immunohistochemical Study

BACKGROUND

 The immune defense against tumor cells is mainly mediated by the natural killer (NK) cells. Cluster of differentiation 57 (CD57) is a 110-kd glycoprotein, typically expressed by the NK cells, attacks the cancer cells and inhibits tumor development. Proliferating cell nuclear antigen (PCNA) is a 36-kd auxiliary protein for DNA polymerase delta that correlates with cell proliferation and DNA synthesis. It is an essential component of DNA replication, DNA recombination, and DNA repair. The uncoordinated proliferation of PCNA protein characterizes the biological behavior of malignant lesions.

AIM

 The aim of the present study is to compare and correlate the expression of CD57 and PCNA in different grades of oral squamous cell carcinoma (OSCC) by immunohistochemistry.

MATERIALS AND METHODS

 This retrospective analysis comprises 30 samples of various grades of OSCCs and 10 samples of healthy mucosa. Sections of 4-5 µm thickness were done and stained with monoclonal anti-PCNA and anti-CD57 antibodies. The statistical package for social science (SPSS) version 16.0 software (IBM Corp., Armonk, NY) was used to analyze the data in this study. The expression of CD57 and PCNA was compared and correlated between the groups using analysis of variance (ANOVA) post hoc, Dunnet t-test, and Pearson's correlation coefficient test. For statistical significance, a p-value of 0.05 or less was used.

RESULTS

A significant decrease in CD57 labeling index was seen from well-differentiated squamous cell carcinoma (16.63 ± 2.33) to poorly differentiated squamous cell carcinoma (5.53 ± 1.20) whereas the significant increase in PCNA labeling index was noted from well-differentiated squamous cell carcinoma (45.88 ± 2.20), followed by moderately differentiated and poorly differentiated squamous cell carcinoma (72.77 ± 4.35).

CONCLUSION

 The combination of CD57 and PCNA biomarkers appears to be good indicators of the immune status of the patient and the aggressiveness of the lesion.

Stochastic competitive release and adaptive chemotherapy

We develop a finite-cell model of tumor natural selection dynamics to investigate the stochastic fluctuations associated with multiple rounds of adaptive chemotherapy. The adaptive cycles are designed to avoid chemoresistance in the tumor by managing the ecological mechanism of competitive release of a resistant subpopulation. Our model is based on a three-component evolutionary game played among healthy (H), sensitive (S), and resistant (R) populations of N cells, with a chemotherapy control parameter, C(t), which we use to dynamically impose selection pressure on the sensitive subpopulation to slow tumor growth and manage competitive release of the resistant population. The adaptive chemoschedule is designed based on the deterministic (N→∞) adjusted replicator dynamical system, then implemented using the finite-cell stochastic frequency dependent Moran process model (N=10K-50K) to ascertain the cumulative effect of the stochastic fluctuations on the efficacy of the adaptive schedules over multiple rounds. We quantify the stochastic fixation probability regions of the R and S populations in the HSR trilinear phase plane as a function of the control parameter C∈[0,1], showing that the size of the R region increases with increasing C. We then implement an adaptive time-dependent schedule C(t) for the stochastic model and quantify the variances (using principal component coordinates) associated with the evolutionary cycles over multiple rounds of adaptive therapy. The variances increase subquadratically through several rounds before the evolutionary cycle begins to break down. Despite this, we show the stochastic adaptive schedules are more effective at delaying resistance than standard maximum tolerated dose and low-dose metronomic schedules. The simplified low-dimensional model provides some insights on how well multiple rounds of adaptive therapies are likely to perform over a range of tumor sizes (i.e., different values of N) if the goal is to maintain a sustained balance among competing subpopulations of cells to avoid chemoresistance via competitive release in a stochastic environment.

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

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

Different patterns of mast cell distribution in B-cell non-Hodgkin lymphomas

Tumor growth, progression, and metastatic capability in non-Hodgkin lymphomas (NHLs) are influenced by different component of tumor microenvironment, including inflammatory cells. Among these latter, mast cells play a crucial role. The spatial distribution of mast cells inside the tumor stroma of different types of B-cell NHLs has not yet been investigated. The aim of this study is to analyze the pattern of distribution of mast cells in biopsy samples obtained from three different types of B-cell NHLs by utilizing an image analysis system and a mathematical model to allow a quantitative estimation to characterize their spatial distribution. As concerns the spatial distributions exhibited by mast cells in diffuse large B cell lymphoma (DLBCL), some clustering was detected in both activated B-like (ABC) and germinal center B-like (GBC) groups. In follicular lymphoma (FL), mast cell spatial distribution tends to uniformly fill the tissue space as far as the grade of the pathology increases. Finally, in marginal lymphoma tissue (MALT) lymphoma, mast cells maintain a significantly clustered spatial distribution, suggesting a lower tendency of the cells to fill the tissue space in this pathological condition. Overall, the data of this study confirm that the analysis of the spatial distribution of the tumor cells is of particular significance for the knowledge of the biological processes occurring in tumor stroma and for the development of parameters to characterize the morphologic organization of the cellular patterns in different types of tumors.

Advances in Mapping Tumor Progression from Precancer Atlases

Tissue profiling technologies present opportunities for understanding transition from precancerous lesions to malignancy, which may impact risk stratification, prevention, and even cancer treatment. A human precancer atlas building effort is ongoing to tackle the significant challenge of decoding the heterogeneity among cells, specimens, and patients. Here, we discuss the findings resulting from atlases built across precancer types, including those found in colon, breast, lung, stomach, cervix, and skin, using bulk, single-cell, and spatial profiling strategies. We highlight two main themes that emerge across precancer types: the ordering of molecular events that occur during tumor progression and the fluctuation of microenvironmental response during precancer progression. We further highlight the key challenges of data integration across large cohorts of patients, and the need for computational tools to reliably annotate and quality control high-volume, high-dimensional data.