Characterizing the ecological and evolutionary dynamics of cancer

Integrative molecular and spatial analysis reveals evolutionary dynamics and tumor-immune interplay of in situ and invasive acral melanoma

In acral melanoma (AM), progression from in situ (AMis) to invasive AM (iAM) leads to significantly reduced survival. However, evolutionary dynamics during this process remain elusive. Here, we report integrative molecular and spatial characterization of 147 AMs using genomics, bulk and single-cell transcriptomics, and spatial transcriptomics and proteomics. Vertical invasion from AMis to iAM displays an early and monoclonal seeding pattern. The subsequent regional expansion of iAM exhibits two distinct patterns, clonal expansion and subclonal diversification. Notably, molecular subtyping reveals an aggressive iAM subset featured with subclonal diversification, increased epithelial-mesenchymal transition (EMT), and spatial enrichment of APOE+/CD163+ macrophages. In vitro and ex vivo experiments further demonstrate that APOE+CD163+ macrophages promote tumor EMT via IGF1-IGF1R interaction. Adnexal involvement can predict AMis with higher invasive potential whereas APOE and CD163 serve as prognostic biomarkers for iAM. Altogether, our results provide implications for the early detection and treatment of AM.

Expand available targets for CAR-T therapy to overcome tumor drug resistance based on the "Evolutionary Traps"

Based on the concept of "Evolutionary Traps", targeting survival essential genes obtained during tumor drug resistance can effectively eliminate resistant cells. While, it still faces limitations. In this study, lapatinib-resistant cells were used to test the concept of "Evolutionary Traps" and no suitable target stand out because of the identified genes without accessible drug. However, a membrane protein PDPN, which is low or non-expressed in normal tissues, is identified as highly expressed in lapatinib-resistant tumor cells. PDPN CAR-T cells were developed and showed high cytotoxicity against lapatinib-resistant tumor cells in vitro and in vivo, suggesting that CAR-T may be a feasible route for overcoming drug resistance of tumor based on "Evolutionary Trap". To test whether this concept is cell line or drug dependent, we analyzed 21 drug-resistant tumor cell expression profiles reveal that JAG1, GPC3, and L1CAM, which are suitable targets for CAR-T treatment, are significantly upregulated in various drug-resistant tumor cells. Our findings shed light on the feasibility of utilizing CAR-T therapy to treat drug-resistant tumors and broaden the concept of the "Evolutionary Trap".

From immune equilibrium to tumor ecodynamics

Objectives

There is no theory to quantitatively describe the complex tumor ecosystem. At the same time, cancer immunotherapy is considered a revolution in oncology, but the methods used to describe tumors and the criteria used to evaluate efficacy are not keeping pace. The purpose of this study is to establish a new theory for quantitatively describing the tumor ecosystem, innovating the methods of tumor characterization, and establishing new efficacy evaluation criteria for cancer immunotherapy.

Methods

Based on the mathematization of immune equilibrium theory and the establishment of immunodynamics in a previous study, the method of reverse immunodynamics was used, namely, the immune braking force was regarded as the tumor ecological force and the immune force was regarded as the tumor ecological braking force, and the concept of momentum in physics was applied to the tumor ecosystem to establish a series of tumor ecodynamic equations. These equations were used to solve the fundamental and applied problems of the complex tumor ecosystem.

Results

A series of tumor ecodynamic equations were established. The tumor ecological momentum equations and their component factors could be used to distinguish disease progression, pseudoprogression, and hyperprogression in cancer immunotherapy. On this basis, the adjusted tumor momentum equations were established to achieve the equivalence of tumor activity (including immunosuppressive activity and metabolic activity) and tumor volume, which could be used to calculate individual disease remission rate and establish new efficacy evaluation criteria (ieRECIST) for immunotherapy of solid tumor based on tumor ecodynamics. At the same time, the concept of moving cube-to-force square ratio and its expression were proposed to calculate the area under the curve of tumor ecological braking force of blood required to achieve an individual disease remission rate when the adjusted tumor ecological momentum was known.

Conclusions

A new theory termed tumor ecodynamics emphasizing both tumor activity and tumor volume is established to solve a series of basic and applied problems in the complex tumor ecosystem. It can be predicted that the future will be the era of cancer immune ecotherapy that targets the entire tumor ecosystem.

A latent variable model for evaluating mutual exclusivity and co-occurrence between driver mutations in cancer

A key challenge in cancer genomics is understanding the functional relationships and dependencies between combinations of somatic mutations that drive cancer development. Such driver mutations frequently exhibit patterns of mutual exclusivity or co-occurrence across tumors, and many methods have been developed to identify such dependency patterns from bulk DNA sequencing data of a cohort of patients. However, while mutual exclusivity and co-occurrence are described as properties of driver mutations, existing methods do not explicitly disentangle functional, driver mutations from neutral, passenger mutations. In particular, nearly all existing methods evaluate mutual exclusivity or co-occurrence at the gene level, marking a gene as mutated if any mutation - driver or passenger - is present. Since some genes have a large number of passenger mutations, existing methods either restrict their analyses to a small subset of suspected driver genes - limiting their ability to identify novel dependencies - or make spurious inferences of mutual exclusivity and co-occurrence involving genes with many passenger mutations. We introduce DIALECT, an algorithm to identify dependencies between pairs of driver mutations from somatic mutation counts. We derive a latent variable mixture model for drivers and passengers that combines existing probabilistic models of passenger mutation rates with a latent variable describing the unknown status of a mutation as a driver or passenger. We use an expectation maximization (EM) algorithm to estimate the parameters of our model, including the rates of mutually exclusivity and co-occurrence between drivers. We demonstrate that DIALECT more accurately infers mutual exclusivity and co-occurrence between driver mutations compared to existing methods on both simulated mutation data and somatic mutation data from 5 cancer types in The Cancer Genome Atlas (TCGA).

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.

Unlocking the role of EIF5A: A potential diagnostic marker regulating the cell cycle and showing negative correlation with immune infiltration in lung adenocarcinoma

BACKGROUND

Despite EIF5A upregulation related to tumor progression in LUAD (lung adenocarcinoma), the underlying mechanisms remain elusive. In addition, there are few comprehensive analyses of EIF5A in LUAD.

METHODS

We investigated the EIF5A expression level in LUAD patients using data from the TCGA and GEO databases. We employed qRT-PCR and western blot to verify EIF5A expression in cell lines, while immunohistochemistry was utilized for clinical sample analysis. We analyzed EIF5A expression in tumor-infiltrating immune cells using the TISCH database and assessed its association with immune infiltration in LUAD using the "ESTIMATE" R package. Bioinformatics approaches were developed to discover the EIF5A-related genes and explore EIF5A potential mechanisms in LUAD. Proliferation ability was verified through CCK-8, clone formation, and EdU assays, while flow cytometry assessed apoptosis and cell cycle. Western blot was used to detect the expression of pathway-related proteins.

RESULTS

EIF5A was significantly upregulated in LUAD. Moreover, we constructed a MAZ-hsa-miR-424-3p-EIF5A transcriptional network. We explored the potential mechanism of EIF5A in LUAD and further investigated the cAMP signaling pathway and the cell cycle. Finally, we proved that EIF5A silencing induced G1/S Cell Cycle arrest, promoted apoptosis, and inhibited proliferation via the cAMP/PKA/CREB signaling pathway.

CONCLUSION

EIF5A serves as a prognostic biomarker with a negative correlation to immune infiltrates in LUAD. It regulated the cell cycle in LUAD by inhibiting the cAMP/PKA/CREB signaling pathway.

Ferroptosis and tumor immunity: In perspective of the major cell components in the tumor microenvironment

Ferroptosis is an iron-dependent form of cell death driven by lipid peroxidation, which is morphologically, biochemically, and genetically distinct from apoptosis, necrosis, and autophagy. Mounting studies on the essential role of ferroptosis have been published in the progression of solid tumors, metastasis, therapy, and therapy resistance. Studies showed that ferroptosis is a "double-edged sword" in tumor immunity, which means it may have both tumor-antagonizing and tumor-promoting functions. The tumor microenvironment (TME) comprises not only tumor cells but also surrounding immune cells, stromal cells, as well as noncellular components such as the extracellular matrix (ECM), cytokines, growth factors, and extracellular vesicles (EVs). In the complex and diverse condition in TME where tumor cells grow, changes in each constituent may impact tumor destiny differently. Recently, several studies have revealed the interaction between ferroptosis and different constituents in TME. Both tumor cells and nontumor cells have a dual role in tumor immunity and influence tumor progression through ferroptosis. Herein, this review aims at summarizing the role of ferroptosis in tumor immunity based on TME, focusing on the mechanisms of the interaction between the ferroptosis and the different constituents in TME, illuminating how ferroptosis plays its role in promoting or antagonizing tumors by acting with varying components in TME and proposing several questions in immunomodulatory effects of ferroptosis and ferroptosis-associated immunotherapy.

An Integrative Bioorthogonal Nanoengineering Strategy for Dynamically Constructing Heterogenous Tumor Spheroids

Although tumor models have revolutionized perspectives on cancer aetiology and treatment, current cell culture methods remain challenges in constructing organotypic tumor with in vivo-like complexity, especially native characteristics, leading to unpredictable results for in vivo responses. Herein, the bioorthogonal nanoengineering strategy (BONE) for building photothermal dynamic tumor spheroids is developed. In this process, biosynthetic machinery incorporated bioorthogonal azide reporters into cell surface glycoconjugates, followed by reacting with multivalent click ligand (ClickRod) that is composed of hyaluronic acid-functionalized gold nanorod carrying dibenzocyclooctyne moieties, resulting in rapid construction of tumor spheroids. BONE can effectively assemble different cancer cells and immune cells together to construct heterogenous tumor spheroids is identified. Particularly, ClickRod exhibited favorable photothermal activity, which precisely promoted cell activity and shaped physiological microenvironment, leading to formation of dynamic features of original tumor, such as heterogeneous cell population and pluripotency, different maturation levels, and physiological gradients. Importantly, BONE not only offered a promising platform for investigating tumorigenesis and therapeutic response, but also improved establishment of subcutaneous xenograft model under mild photo-stimulation, thereby significantly advancing cancer research. Therefore, the first bioorthogonal nanoengineering strategy for developing dynamic tumor models, which have the potential for bridging gaps between in vitro and in vivo research is presented.

Dynamic links between mechanical forces and metabolism shape the tumor milieu

Cell function relies on the spatiotemporal dynamics of metabolic reactions. In all physiopathological processes of tissues, mechanical forces impact the structure and function of membranes, enzymes, organelles and regulators of metabolic gene programs, thus regulating cell metabolism. In turn, metabolic pathways feedback impacts the physical properties of cell and tissues. Hence, metabolism and tissue mechanics are dynamically intertwined and continuously interact. Cancer is akin to an ecosystem, comprising tumor cells and various subpopulations of stromal cells embedded in an altered extracellular matrix. The progression of cancer, from initiation to advanced stage and metastasis, is driven by genetic mutations and crucially influenced by physical and metabolic alterations in the tumor microenvironment. These alterations also play a pivotal role in cancer cells evasion from immune surveillance and in developing resistance to treatments. Here, we highlight emerging evidence showing that mechano-metabolic circuits in cancer and stromal cells regulate multiple processes crucial for tumor progression and discuss potential approaches to improve therapeutic treatments by interfering with these circuits.

Single-Cell Evolutionary Analysis Reveals Drivers of Plasticity and Mediators of Chemoresistance in Small Cell Lung Cancer

Small cell lung cancer (SCLC) is often a heterogeneous tumor, where dynamic regulation of key transcription factors can drive multiple populations of phenotypically different cells which contribute differentially to tumor dynamics. This tumor is characterized by a very low 2-year survival rate, high rates of metastasis, and rapid acquisition of chemoresistance. The heterogeneous nature of this tumor makes it difficult to study and to treat, as it is not clear how or when this heterogeneity arises. Here we describe temporal, single-cell analysis of SCLC to investigate tumor initiation and chemoresistance in both SCLC xenografts and an autochthonous SCLC model. We identify an early population of tumor cells with high expression of AP-1 network genes that are critical for tumor growth. Furthermore, we have identified and validated the cancer testis antigens (CTA) PAGE5 and GAGE2A as mediators of chemoresistance in human SCLC. CTAs have been successfully targeted in other tumor types and may be a promising avenue for targeted therapy in SCLC.

IMPLICATIONS

Understanding the evolutionary dynamics of SCLC can shed light on key mechanisms such as cellular plasticity, heterogeneity, and chemoresistance.

QKI shuttles internal m7G-modified transcripts into stress granules and modulates mRNA metabolism

N7-methylguanosine (m7G) modification, routinely occurring at mRNA 5' cap or within tRNAs/rRNAs, also exists internally in messenger RNAs (mRNAs). Although m7G-cap is essential for pre-mRNA processing and protein synthesis, the exact role of mRNA internal m7G modification remains elusive. Here, we report that mRNA internal m7G is selectively recognized by Quaking proteins (QKIs). By transcriptome-wide profiling/mapping of internal m7G methylome and QKI-binding sites, we identified more than 1,000 high-confidence m7G-modified and QKI-bound mRNA targets with a conserved "GANGAN (N = A/C/U/G)" motif. Strikingly, QKI7 interacts (via C terminus) with the stress granule (SG) core protein G3BP1 and shuttles internal m7G-modified transcripts into SGs to regulate mRNA stability and translation under stress conditions. Specifically, QKI7 attenuates the translation efficiency of essential genes in Hippo signaling pathways to sensitize cancer cells to chemotherapy. Collectively, we characterized QKIs as mRNA internal m7G-binding proteins that modulate target mRNA metabolism and cellular drug resistance.

Mutational Landscape of Normal Human Skin: Clues to Understanding Early-Stage Carcinogenesis in Keratinocyte Neoplasia

Normal skin contains numerous clones carrying cancer driver mutations. However, the mutational landscape of normal skin and its clonal relationship with skin cancer requires further elucidation. The aim of our study was to investigate the mutational landscape of normal human skin. We performed whole-exome sequencing using physiologically normal skin tissues and the matched peripheral blood (n = 39) and adjacent-matched skin cancers from a subset of patients (n = 10). Exposed skin harbored a median of 530 mutations (10.4/mb, range = 51-2,947), whereas nonexposed skin majorly exhibited significantly fewer mutations (median = 13, 0.25/mb, range = 1-166). Patient age was significantly correlated with the mutational burden. Mutations in six driver genes (NOTCH1, FAT1, TP53, PPM1D, KMT2D, and ASXL1) were identified. De novo mutational signature analysis identified a single signature with components of UV- and aging-related signatures. Normal skin harbored only three instances of copy-neutral loss of heterozygosity in 9q (n = 2) and 6q (n = 1). The mutational burden of normal skin was not correlated with that of matched skin cancers, and no protein-coding mutations were shared. In conclusion, we revealed the mutational landscape of normal skin, highlighting the role of driver genes in the malignant progression of normal skin.

Understanding human aging and the fundamental cell signaling link in age-related diseases: the middle-aging hypovascularity hypoxia hypothesis

Aging-related hypoxia, oxidative stress, and inflammation pathophysiology are closely associated with human age-related carcinogenesis and chronic diseases. However, the connection between hypoxia and hormonal cell signaling pathways is unclear, but such human age-related comorbid diseases do coincide with the middle-aging period of declining sex hormonal signaling. This scoping review evaluates the relevant interdisciplinary evidence to assess the systems biology of function, regulation, and homeostasis in order to discern and decipher the etiology of the connection between hypoxia and hormonal signaling in human age-related comorbid diseases. The hypothesis charts the accumulating evidence to support the development of a hypoxic milieu and oxidative stress-inflammation pathophysiology in middle-aged individuals, as well as the induction of amyloidosis, autophagy, and epithelial-to-mesenchymal transition in aging-related degeneration. Taken together, this new approach and strategy can provide the clarity of concepts and patterns to determine the causes of declining vascularity hemodynamics (blood flow) and physiological oxygenation perfusion (oxygen bioavailability) in relation to oxygen homeostasis and vascularity that cause hypoxia (hypovascularity hypoxia). The middle-aging hypovascularity hypoxia hypothesis could provide the mechanistic interface connecting the endocrine, nitric oxide, and oxygen homeostasis signaling that is closely linked to the progressive conditions of degenerative hypertrophy, atrophy, fibrosis, and neoplasm. An in-depth understanding of these intrinsic biological processes of the developing middle-aged hypoxia could provide potential new strategies for time-dependent therapies in maintaining healthspan for healthy lifestyle aging, medical cost savings, and health system sustainability.

The adaptive evolution of cancer driver genes

BACKGROUND

Cancer is a life-threatening disease in humans; yet, cancer genes are frequently reported to be under positive selection. This suggests an evolutionary-genetic paradox in which cancer evolves as a secondary product of selection in human beings. However, systematic investigation of the evolution of cancer driver genes is sparse.

RESULTS

Using comparative genomics analysis, population genetics analysis and computational molecular evolutionary analysis, the evolution of 568 cancer driver genes of 66 cancer types were evaluated at two levels, selection on the early evolution of humans (long timescale selection in the human lineage during primate evolution, i.e., millions of years), and recent selection in modern human populations (~ 100,000 years). Results showed that eight cancer genes covering 11 cancer types were under positive selection in the human lineage (long timescale selection). And 35 cancer genes covering 47 cancer types were under positive selection in modern human populations (recent selection). Moreover, SNPs associated with thyroid cancer in three thyroid cancer driver genes (CUX1, HERC2 and RGPD3) were under positive selection in East Asian and European populations, consistent with the high incidence of thyroid cancer in these populations.

CONCLUSIONS

These findings suggest that cancer can be evolved, in part, as a by-product of adaptive changes in humans. Different SNPs at the same locus can be under different selection pressures in different populations, and thus should be under consideration during precision medicine, especially for targeted medicine in specific populations.

Dual antitumor immunomodulatory effects of PARP inhibitor on the tumor microenvironment: A counterbalance between anti-tumor and pro-tumor

Poly (ADP-ribose)-polymerases (PARPs) play an essential role in the maintenance of genome integrity, DNA repair, and apoptosis. PARP inhibitors (PARPi) exert antitumor effects via synthetic lethality and PARP trapping. PARPi impact the antitumor immune response by modulating the tumor microenvironment, and their effect has dual properties of promoting and inhibiting the antitumor immune response. PARPi promote M1 macrophage polarization, antigen presentation by dendritic cells, infiltration of B and T cells and their killing capacity and inhibit tumor angiogenesis. PARPi can also inhibit the activation and function of immune cells by upregulating PD-L1. In this review, we summarize the dual immunomodulatory effects and possible underlying mechanisms of PARPi, providing a basis for the design of combination regimens for clinical treatment and the identification of populations who may benefit from these therapies.

Mitochondria in cancer: clean windmills or stressed tinkerers?

There is now a consensus that mitochondria are important tumor drivers, sophisticated biological machines that can engender a panoply of key disease traits. How this happens, however, is still mostly elusive. The opinion presented here is that what cancer exploits are not the normal mitochondria of oxygenated and nutrient-replete tissues, but the unfit, damaged, and dysfunctional organelles generated by the hostile environment of tumor growth. These 'ghost' mitochondria survive quality control and thwart cell death to relay multiple comprehensive 'danger signals' of metabolic starvation, cellular stress, and reprogrammed gene expression. The result is a new, treacherous cellular phenotype, proliferatively quiescent but highly motile, that enables tumor cell escape from a threatening environment and colonization of distant, more favorable sites (metastasis).

Extrachromosomal DNA in the cancerous transformation of Barrett's oesophagus

Oncogene amplification on extrachromosomal DNA (ecDNA) drives the evolution of tumours and their resistance to treatment, and is associated with poor outcomes for patients with cancer1-6. At present, it is unclear whether ecDNA is a later manifestation of genomic instability, or whether it can be an early event in the transition from dysplasia to cancer. Here, to better understand the development of ecDNA, we analysed whole-genome sequencing (WGS) data from patients with oesophageal adenocarcinoma (EAC) or Barrett's oesophagus. These data included 206 biopsies in Barrett's oesophagus surveillance and EAC cohorts from Cambridge University. We also analysed WGS and histology data from biopsies that were collected across multiple regions at 2 time points from 80 patients in a case-control study at the Fred Hutchinson Cancer Center. In the Cambridge cohorts, the frequency of ecDNA increased between Barrett's-oesophagus-associated early-stage (24%) and late-stage (43%) EAC, suggesting that ecDNA is formed during cancer progression. In the cohort from the Fred Hutchinson Cancer Center, 33% of patients who developed EAC had at least one oesophageal biopsy with ecDNA before or at the diagnosis of EAC. In biopsies that were collected before cancer diagnosis, higher levels of ecDNA were present in samples from patients who later developed EAC than in samples from those who did not. We found that ecDNAs contained diverse collections of oncogenes and immunomodulatory genes. Furthermore, ecDNAs showed increases in copy number and structural complexity at more advanced stages of disease. Our findings show that ecDNA can develop early in the transition from high-grade dysplasia to cancer, and that ecDNAs progressively form and evolve under positive selection.

Interleukin-34 cancels anti-tumor immunity by PARP inhibitor

OBJECTIVE

Breast cancer susceptibility gene 1 (BRCA1)-associated ovarian cancer patients have been treated with A poly (ADP-ribose) polymerase (PARP) inhibitor, extending the progression-free survival; however, they finally acquire therapeutic resistance. Interleukin (IL)-34 has been reported as a poor prognostic factor in several cancers, including ovarian cancer, and it contributes to the therapeutic resistance of chemotherapies. IL-34 may affect the therapeutic effect of PARP inhibitor through the regulation of tumor microenvironment (TME).

METHODS

In this study, The Cancer Genome Atlas (TCGA) data set was used to evaluate the prognosis of IL-34 and human ovarian serous carcinoma. We also used CRISPR-Cas9 genome editing technology in a mouse model to evaluate the efficacy of PARP inhibitor therapy in the presence or absence of IL-34.

RESULTS

We found that IL34 was an independent poor prognostic factor in ovarian serous carcinoma, and its high expression significantly shortens overall survival. Furthermore, in BRCA1-associated ovarian cancer, PARP inhibitor therapy contributes to anti-tumor immunity via the XCR1⁺ DC-CD8⁺ T cell axis, however, it is canceled by the presence of IL-34.

CONCLUSION

These results suggest that tumor-derived IL-34 benefits tumors by creating an immunosuppressive TME and conferring PARP inhibitor therapeutic resistance. Thus, we showed the pathological effect of IL-34 and the need for it as a therapeutic target in ovarian cancer.

Virus-like Particles for TEM Regulation and Antitumor Therapy

Tumor development and metastasis are intimately associated with the tumor microenvironment (TME), and it is difficult for vector-restricted drugs to act on the TME for long-term cancer immunotherapy. Virus-like particles (VLPs) are nanocage structures self-assembled from nucleic acid free viral proteins. Most VLPs range from 20-200 nm in diameter and can naturally drain into lymph nodes to induce robust humoral immunity. As natural nucleic acid nanocarriers, their surfaces can also be genetically or chemically modified to achieve functions such as TME targeting. This review focuses on the design ideas of VLP as nanocarriers and the progress of their research in regulating TME.

Evo-devo perspectives on cancer

The integration of evolutionary and developmental approaches into the field of evolutionary developmental biology has opened new areas of inquiry- from understanding the evolution of development and its underlying genetic and molecular mechanisms to addressing the role of development in evolution. For the last several decades, the terms 'evolution' and 'development' have been increasingly linked to cancer, in many different frameworks and contexts. This mini-review, as part of a special issue on Evolutionary Developmental Biology, discusses the main areas in cancer research that have been addressed through the lenses of both evolutionary and developmental biology, though not always fully or explicitly integrated in an evo-devo framework. First, it briefly introduces the current views on carcinogenesis that invoke evolutionary and/or developmental perspectives. Then, it discusses the main mechanisms proposed to have specifically evolved to suppress cancer during the evolution of multicellularity. Lastly, it considers whether the evolution of multicellularity and development was shaped by the threat of cancer (a cancer-evo-devo perspective), and/or whether the evolution of developmental programs and life history traits can shape cancer resistance/risk in various lineages (an evo-devo-cancer perspective). A proper evolutionary developmental framework for cancer, both as a disease and in terms of its natural history (in the context of the evolution of multicellularity and development as well as life history traits), could bridge the currently disparate evolutionary and developmental perspectives and uncover aspects that will provide new insights for cancer prevention and treatment.

Spatial epitope barcoding reveals clonal tumor patch behaviors

Intratumoral heterogeneity is a seminal feature of human tumors contributing to tumor progression and response to treatment. Current technologies are still largely unsuitable to accurately track phenotypes and clonal evolution within tumors, especially in response to genetic manipulations. Here, we developed epitopes for imaging using combinatorial tagging (EpicTags), which we coupled to multiplexed ion beam imaging (EpicMIBI) for in situ tracking of barcodes within tissue microenvironments. Using EpicMIBI, we dissected the spatial component of cell lineages and phenotypes in xenograft models of small cell lung cancer. We observed emergent properties from mixed clones leading to the preferential expansion of clonal patches for both neuroendocrine and non-neuroendocrine cancer cell states in these models. In a tumor model harboring a fraction of PTEN-deficient cancer cells, we observed a non-autonomous increase of clonal patch size in PTEN wild-type cancer cells. EpicMIBI facilitates in situ interrogation of cell-intrinsic and cell-extrinsic processes involved in intratumoral heterogeneity.

Prediagnostic biomarkers for early detection of glioma—using case–control studies from cohorts as study approach

Background

Understanding the trajectory and development of disease is important and the knowledge can be used to find novel targets for therapy and new diagnostic tools for early diagnosis.

Methods

Large cohorts from different parts of the world are unique assets for research as they have systematically collected plasma and DNA over long-time periods in healthy individuals, sometimes even with repeated samples. Over time, the population in the cohort are diagnosed with many different diseases, including brain tumors.

Results

Recent studies have detected genetic variants that are associated with increased risk of glioblastoma and lower grade gliomas specifically. The impact for genetic markers to predict disease in a healthy population has been deemed low, and a relevant question is if the genetic variants for glioma are associated with risk of disease or partly consist of genes associated to survival. Both metabolite and protein spectra are currently being explored for early detection of cancer.

Conclusions

We here present a focused review of studies of genetic variants, metabolomics, and proteomics studied in prediagnostic glioma samples and discuss their potential in early diagnostics.

Multiplexed In Situ Spatial Protein Profiling in the Pursuit of Precision Immuno-Oncology for Patients with Breast Cancer

Immune checkpoint inhibitors (ICIs) have revolutionized the treatment of many solid tumors. In breast cancer (BC), immunotherapy is currently approved in combination with chemotherapy, albeit only in triple-negative breast cancer. Unfortunately, most patients only derive limited benefit from ICIs, progressing either upfront or after an initial response. Therapeutics must engage with a heterogeneous network of complex stromal-cancer interactions that can fail at imposing cancer immune control in multiple domains, such as in the genomic, epigenomic, transcriptomic, proteomic, and metabolomic domains. To overcome these types of heterogeneous resistance phenotypes, several combinatorial strategies are underway. Still, they can be predicted to be effective only in the subgroups of patients in which those specific resistance mechanisms are effectively in place. As single biomarker predictive performances are necessarily suboptimal at capturing the complexity of this articulate network, precision immune-oncology calls for multi-omics tumor microenvironment profiling in order to identify unique predictive patterns and to proactively tailor combinatorial treatments. Multiplexed single-cell spatially resolved tissue analysis, through precise epitope colocalization, allows one to infer cellular functional states in view of their spatial organization. In this review, we discuss-through the lens of the cancer-immunity cycle-selected, established, and emerging markers that may be evaluated in multiplexed spatial protein panels to help identify prognostic and predictive patterns in BC.

Accurate tumor clonal structures require single-cell analysis

Tumor clonal structure is closely related to future progression, which has been mainly investigated as mutation abundance clustering in bulk samples. With relatively limited studies at single-cell resolution, a systematic comparison of the two approaches is still lacking. Here, using bulk and single-cell mutational data from the liver and colorectal cancers, we checked whether co-mutations determined by single-cell analysis had corresponding bulk variant allele frequency (VAF) peaks. While bulk analysis suggested the absence of subclonal peaks and, possibly, neutral evolution in some cases, the single-cell analysis identified coexisting subclones. The overlaps of bulk VAF ranges for co-mutations from different subclones made it difficult to separate them. Complex subclonal structures and dynamic evolution could be hidden under the seemingly clonal neutral pattern at the bulk level, suggesting single-cell analysis is necessary to avoid underestimation of tumor heterogeneity.

Transcriptional variability accelerates preleukemia by cell diversification and perturbation of protein synthesis

Transcriptional variability facilitates stochastic cell diversification and can in turn underpin adaptation to stress or injury. We hypothesize that it may analogously facilitate progression of premalignancy to cancer. To investigate this, we initiated preleukemia in mouse cells with enhanced transcriptional variability due to conditional disruption of the histone lysine acetyltransferase gene Kat2a. By combining single-cell RNA sequencing of preleukemia with functional analysis of transformation, we show that Kat2a loss results in global variegation of cell identity and accumulation of preleukemic cells. Leukemia progression is subsequently facilitated by destabilization of ribosome biogenesis and protein synthesis, which confer a transient transformation advantage. The contribution of transcriptional variability to early cancer evolution reflects a generic role in promoting cell fate transitions, which, in the case of well-adapted malignancies, contrastingly differentiates and depletes cancer stem cells. That is, transcriptional variability confers forward momentum to cell fate systems, with differential multistage impact throughout cancer evolution.

Dissecting cell fate dynamics in pediatric glioblastoma through the lens of complex systems and cellular cybernetics

Cancers are complex dynamic ecosystems. Reductionist approaches to science are inadequate in characterizing their self-organized patterns and collective emergent behaviors. Since current approaches to single-cell analysis in cancer systems rely primarily on single time-point multiomics, many of the temporal features and causal adaptive behaviors in cancer dynamics are vastly ignored. As such, tools and concepts from the interdisciplinary paradigm of complex systems theory are introduced herein to decode the cellular cybernetics of cancer differentiation dynamics and behavioral patterns. An intuition for the attractors and complex networks underlying cancer processes such as cell fate decision-making, multiscale pattern formation systems, and epigenetic state-transitions is developed. The applications of complex systems physics in paving targeted therapies and causal pattern discovery in precision oncology are discussed. Pediatric high-grade gliomas are discussed as a model-system to demonstrate that cancers are complex adaptive systems, in which the emergence and selection of heterogeneous cellular states and phenotypic plasticity are driven by complex multiscale network dynamics. In specific, pediatric glioblastoma (GBM) is used as a proof-of-concept model to illustrate the applications of the complex systems framework in understanding GBM cell fate decisions and decoding their adaptive cellular dynamics. The scope of these tools in forecasting cancer cell fate dynamics in the emerging field of computational oncology and patient-centered systems medicine is highlighted.

A Review of Mathematical and Computational Methods in Cancer Dynamics

Cancers are complex adaptive diseases regulated by the nonlinear feedback systems between genetic instabilities, environmental signals, cellular protein flows, and gene regulatory networks. Understanding the cybernetics of cancer requires the integration of information dynamics across multidimensional spatiotemporal scales, including genetic, transcriptional, metabolic, proteomic, epigenetic, and multi-cellular networks. However, the time-series analysis of these complex networks remains vastly absent in cancer research. With longitudinal screening and time-series analysis of cellular dynamics, universally observed causal patterns pertaining to dynamical systems, may self-organize in the signaling or gene expression state-space of cancer triggering processes. A class of these patterns, strange attractors, may be mathematical biomarkers of cancer progression. The emergence of intracellular chaos and chaotic cell population dynamics remains a new paradigm in systems medicine. As such, chaotic and complex dynamics are discussed as mathematical hallmarks of cancer cell fate dynamics herein. Given the assumption that time-resolved single-cell datasets are made available, a survey of interdisciplinary tools and algorithms from complexity theory, are hereby reviewed to investigate critical phenomena and chaotic dynamics in cancer ecosystems. To conclude, the perspective cultivates an intuition for computational systems oncology in terms of nonlinear dynamics, information theory, inverse problems, and complexity. We highlight the limitations we see in the area of statistical machine learning but the opportunity at combining it with the symbolic computational power offered by the mathematical tools explored.

Microbial experimental evolution in a massively multiplexed and high-throughput era

Experimental evolution with microbial model systems has transformed our understanding of the basic rules underlying ecology and evolution. Experiments leveraging evolution as a central feature put evolutionary theories to the test, and modern sequencing and engineering tools then characterized the molecular basis of adaptation. As theory and experimentations refined our understanding of evolution, a need to increase throughput and experimental complexity has emerged. Here, we summarize recent technologies that have made high-throughput experiments practical and highlight studies that have capitalized on these tools, defining an exciting new era in microbial experimental evolution. Multiple research directions previously limited by experimental scale are now accessible for study and we believe applying evolutionary lessons from in vitro studies onto these applied settings has the potential for major innovations and discoveries across ecology and medicine.

Reconstructing Complex Cancer Evolutionary Histories from Multiple Bulk DNA Samples Using Pairtree

Cancers are composed of genetically distinct subpopulations of malignant cells. DNA-sequencing data can be used to determine the somatic point mutations specific to each population and build clone trees describing the evolutionary relationships between them. These clone trees can reveal critical points in disease development and inform treatment. Pairtree is a new method that constructs more accurate and detailed clone trees than previously possible using variant allele frequency data from one or more bulk cancer samples. It does so by first building a Pairs Tensor that captures the evolutionary relationships between pairs of subpopulations, and then it uses these relations to constrain clone trees and infer violations of the infinite sites assumption. Pairtree can accurately build clone trees using up to 100 samples per cancer that contain 30 or more subclonal populations. On 14 B-progenitor acute lymphoblastic leukemias, Pairtree replicates or improves upon expert-derived clone tree reconstructions.

SIGNIFICANCE

Clone trees illustrate the evolutionary history of a cancer and can provide insights into how the disease changed through time (e.g., between diagnosis and relapse). Pairtree uses DNA-sequencing data from many samples of the same cancer to build more detailed and accurate clone trees than previously possible. See related commentary by Miller, p. 176. This article is highlighted in the In This Issue feature, p. 171.

The Employment of Genera Vaccinium, Citrus, Olea, and Cynara Polyphenols for the Reduction of Selected Anti-Cancer Drug Side Effects

Cancer is one of the most widespread diseases globally and one of the leading causes of death. Known cancer treatments are chemotherapy, surgery, radiation therapy, targeted hormonal therapy, or a combination of these methods. Antitumor drugs, with different mechanisms, interfere with cancer growth by destroying cancer cells. However, anticancer drugs are dangerous, as they significantly affect both cancer cells and healthy cells. In addition, there may be the onset of systemic side effects perceived and mutagenicity, teratogenicity, and further carcinogenicity. Many polyphenolic extracts, taken on top of common anti-tumor drugs, can participate in the anti-proliferative effect of drugs and significantly reduce the side effects developed. This review aims to discuss the current scientific knowledge of the protective effects of polyphenols of the genera Vaccinium, Citrus, Olea, and Cynara on the side effects induced by four known chemotherapy, Cisplatin, Doxorubicin, Tamoxifen, and Paclitaxel. In particular, the summarized data will help to understand whether polyphenols can be used as adjuvants in cancer therapy, although further clinical trials will provide crucial information.

Somatic mutation: What shapes the mutational landscape of normal epithelia?

Epithelial stem cells accumulate mutations throughout life. Some of these mutants increase competitive fitness and may form clones that colonize the stem cell niche and persist to acquire further genome alterations. After a transient expansion, mutant stem cells must revert to homeostatic behavior so normal tissue architecture is maintained. Some positively selected mutants may promote cancer development while others inhibit carcinogenesis. Factors that shape the mutational landscape include wild type and mutant stem cell dynamics, competition for the niche, and environmental exposures. Understanding these processes may give new insight into the basis of cancer risk and opportunities for cancer prevention.

Can artificial intelligence improve cancer treatments?

Artificial intelligence (AI) powered by the accumulating clinical and molecular data about cancer has fueled the expectation that a transformation in cancer treatments towards significant improvement of patient outcomes is at hand. However, such transformation has been so far elusive. The opacity of AI algorithms and the lack of quality annotated data being available at population scale are among the challenges to the application of AI in oncology. Fundamentally however, the heterogeneity of cancer and its evolutionary dynamics make every tumor response to therapy sufficiently different from the population, machine-learned statistical models, challenging hence the capacity of these models to yield reliable inferences about treatment recommendations that can improve patient outcomes. This article reviews the nominal elements of clinical decision-making for precision oncology and frames the utility of AI to cancer treatment improvements in light of cancer unique challenges.

Comparison of sampling methods for next generation sequencing for patients with lung cancer

Introduction

Success of next generation sequencing (NGS) analysis is becoming indispensable in the treatment of advanced lung cancer. However, the advantages and disadvantages of each sampling method in the NGS analysis have not yet been clarified.

Methods

We compared the success rates of NGS analysis, and DNA and RNA yields for transbronchial biopsy (TBB), endobronchial ultrasound‐guided transbronchial needle aspiration (EBUS‐TBNA), computed tomography (CT)‐guided biopsy, fluid sample, and surgical biopsy for NGS analysis in patients through the lung cancer genomic screening project for individualized medicine (LC‐SCRUM)‐Asia, a nationwide NGS screening project. In case, sufficient samples could not be collected by TBB and EBUS‐TBNA, re‐biopsy (genome re‐biopsy) was performed.

Results

A total of 223 patients were enrolled and success rates of NGS analysis were not different between samples obtained through TBB, EBUS‐TBNA, and CT‐guided biopsy; however, success rates for fluid samples and surgical biopsy samples were significantly higher than those of other methods. The risk of genome re‐biopsy was higher with TBB for centrally located lesions. CT‐guided biopsy yielded more samples but had a lower success rate for analysis of RNA‐based NGS than TBB.

Conclusions

TBB is the mainstay of sampling methods, but for centrally located lesions, EBUS‐TBNA may be a better strategy. For CT‐guided biopsy, the success rate of RNA‐based NGS analysis is low. Fluid samples are expected to yield successful results as surgical biopsy samples, but the latter are better for sample preservation. Determining the optimal method for genome biopsy for each case is important.

Temporal and spatial topography of cell proliferation in cancer

Proliferation is a fundamental trait of cancer cells, but its properties and spatial organization in tumours are poorly characterized. Here we use highly multiplexed tissue imaging to perform single-cell quantification of cell cycle regulators and then develop robust, multivariate, proliferation metrics. Across diverse cancers, proliferative architecture is organized at two spatial scales: large domains, and smaller niches enriched for specific immune lineages. Some tumour cells express cell cycle regulators in the (canonical) patterns expected of freely growing cells, a phenomenon we refer to as 'cell cycle coherence'. By contrast, the cell cycles of other tumour cell populations are skewed towards specific phases or exhibit non-canonical (incoherent) marker combinations. Coherence varies across space, with changes in oncogene activity and therapeutic intervention, and is associated with aggressive tumour behaviour. Thus, multivariate measures from high-plex tissue images capture clinically significant features of cancer proliferation, a fundamental step in enabling more precise use of anti-cancer therapies.

Tumor-suppressive circRHOBTB3 is excreted out of cells via exosome to sustain colorectal cancer cell fitness

Background & Aims

To clarify the biological roles, circularization process and secretion pathway of circRHOBTB3 in colorectal cancer (CRC) progression.

Methods

We performed a comprehensive analysis of circRNA levels in serum exosomes from multiple types of cancer patients in public databases and verified the higher level of circRHOBTB3 in CRC sera versus healthy donors by RT-qPCR. Then, the function of circRHOBTB3 in CRC was investigated in vitro and in vivo. RNA-seq and RNA pull-down assays together with mass spectrometry identified the downstream signals and the binding proteins of circRHOBTB3. Finally, Antisense oligonucleotides (ASOs) were designed to target circularization and secretion elements of circRHOBTB3 for CRC therapy.

Results

circRHOBTB3 levels were increased in the sera but was downregulated in tissue samples in CRC, and the downregulation was associated with poor prognosis. Furthermore, circRHOBTB3 acts a tumor-suppressive circRNA by repressing metabolic pathways, intracellular ROS production in CRC. Several key elements were discovered to regulate circRHOBTB3 circularization and exosomal secretion. Moreover, SNF8 was identified that sorts circRHOBTB3 into exosomes. Interestingly, we found that CRC cells could actively secrete more circRHOBTB3 than normal cells. According to the sequence of regulatory elements for circularization and exosomal secretion, we designed and synthesized ASOs, which increased circRHOBTB3 expression and blocked circRHOBTB3 exosomal secretion. More importantly, ASOs could inhibit CRC growth and metastasis in vitro and in vivo.

Conclusions

circRHOBTB3 plays a tumor-suppressive role in CRC and has to be excreted out of cells to sustain cancer cell fitness. ASOs targeting regulatory elements for circularization and exosomal secretion will become a novel antitumor strategy.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12943-022-01511-1.

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.

A Complex Metabolic Network Confers Immunosuppressive Functions to Myeloid-Derived Suppressor Cells (MDSCs) within the Tumour Microenvironment

Myeloid-derived suppressor cells (MDSCs) constitute a plastic and heterogeneous cell population among immune cells within the tumour microenvironment (TME) that support cancer progression and resistance to therapy. During tumour progression, cancer cells modify their metabolism to sustain an increased energy demand to cope with uncontrolled cell proliferation and differentiation. This metabolic reprogramming of cancer establishes competition for nutrients between tumour cells and leukocytes and most importantly, among tumour-infiltrating immune cells. Thus, MDSCs that have emerged as one of the most decisive immune regulators of TME exhibit an increase in glycolysis and fatty acid metabolism and also an upregulation of enzymes that catabolise essential metabolites. This complex metabolic network is not only crucial for MDSC survival and accumulation in the TME but also for enhancing immunosuppressive functions toward immune effectors. In this review, we discuss recent progress in the field of MDSC-associated metabolic pathways that could facilitate therapeutic targeting of these cells during cancer progression.

The Genomic Processes of Biological Invasions: From Invasive Species to Cancer Metastases and Back Again

The concept of invasion is useful across a broad range of contexts, spanning from the fine scale landscape of cancer tumors up to the broader landscape of ecosystems. Invasion biology provides extraordinary opportunities for studying the mechanistic basis of contemporary evolution at the molecular level. Although the field of invasion genetics was established in ecology and evolution more than 50 years ago, there is still a limited understanding of how genomic level processes translate into invasive phenotypes across different taxa in response to complex environmental conditions. This is largely because the study of most invasive species is limited by information about complex genome level processes. We lack good reference genomes for most species. Rigorous studies to examine genomic processes are generally too costly. On the contrary, cancer studies are fortified with extensive resources for studying genome level dynamics and the interactions among genetic and non-genetic mechanisms. Extensive analysis of primary tumors and metastatic samples have revealed the importance of several genomic mechanisms including higher mutation rates, specific types of mutations, aneuploidy or whole genome doubling and non-genetic effects. Metastatic sites can be directly compared to primary tumor cell counterparts. At the same time, clonal dynamics shape the genomics and evolution of metastatic cancers. Clonal diversity varies by cancer type, and the tumors’ donor and recipient tissues. Still, the cancer research community has been unable to identify any common events that provide a universal predictor of “metastatic potential” which parallels findings in evolutionary ecology. Instead, invasion in cancer studies depends strongly on context, including order of events and clonal composition. The detailed studies of the behavior of a variety of human cancers promises to inform our understanding of genome level dynamics in the diversity of invasive species and provide novel insights for management.

Emerging Role of Chimeric RNAs in Cell Plasticity and Adaptive Evolution of Cancer Cells

Gene fusions can give rise to somatic alterations in cancers. Fusion genes have the potential to create chimeric RNAs, which can generate the phenotypic diversity of cancer cells, and could be associated with novel molecular functions related to cancer cell survival and proliferation. The expression of chimeric RNAs in cancer cells might impact diverse cancer-related functions, including loss of apoptosis and cancer cell plasticity, and promote oncogenesis. Due to their recurrence in cancers and functional association with oncogenic processes, chimeric RNAs are considered biomarkers for cancer diagnosis. Several recent studies demonstrated that chimeric RNAs could lead to the generation of new functionality for the resistance of cancer cells against drug therapy. Therefore, targeting chimeric RNAs in drug resistance cancer could be useful for developing precision medicine. So, understanding the functional impact of chimeric RNAs in cancer cells from an evolutionary perspective will be helpful to elucidate cancer evolution, which could provide a new insight to design more effective therapies for cancer patients in a personalized manner.

Reconstructing single-cell karyotype alterations in colorectal cancer identifies punctuated and gradual diversification patterns

Central to tumor evolution is the generation of genetic diversity. However, the extent and patterns by which de novo karyotype alterations emerge and propagate within human tumors are not well understood, especially at single-cell resolution. Here, we present 3D Live-Seq-a protocol that integrates live-cell imaging of tumor organoid outgrowth and whole-genome sequencing of each imaged cell to reconstruct evolving tumor cell karyotypes across consecutive cell generations. Using patient-derived colorectal cancer organoids and fresh tumor biopsies, we demonstrate that karyotype alterations of varying complexity are prevalent and can arise within a few cell generations. Sub-chromosomal acentric fragments were prone to replication and collective missegregation across consecutive cell divisions. In contrast, gross genome-wide karyotype alterations were generated in a single erroneous cell division, providing support that aneuploid tumor genomes can evolve via punctuated evolution. Mapping the temporal dynamics and patterns of karyotype diversification in cancer enables reconstructions of evolutionary paths to malignant fitness.

A Functional Taxonomy of Tumor Suppression in Oncogenic KRAS-Driven Lung Cancer

Cancer genotyping has identified a large number of putative tumor suppressor genes. Carcinogenesis is a multistep process, but the importance and specific roles of many of these genes during tumor initiation, growth, and progression remain unknown. Here we use a multiplexed mouse model of oncogenic KRAS-driven lung cancer to quantify the impact of 48 known and putative tumor suppressor genes on diverse aspects of carcinogenesis at an unprecedented scale and resolution. We uncover many previously understudied functional tumor suppressors that constrain cancer in vivo. Inactivation of some genes substantially increased growth, whereas the inactivation of others increases tumor initiation and/or the emergence of exceptionally large tumors. These functional in vivo analyses revealed an unexpectedly complex landscape of tumor suppression that has implications for understanding cancer evolution, interpreting clinical cancer genome sequencing data, and directing approaches to limit tumor initiation and progression. SIGNIFICANCE: Our high-throughput and high-resolution analysis of tumor suppression uncovered novel genetic determinants of oncogenic KRAS-driven lung cancer initiation, overall growth, and exceptional growth. This taxonomy is consistent with changing constraints during the life history of cancer and highlights the value of quantitative in vivo genetic analyses in autochthonous cancer models.This article is highlighted in the In This Issue feature, p. 1601.

Spatial proteomics for understanding the tissue microenvironment

The human body comprises rich populations of cells, which are arranged into tissues and organs with diverse functionalities. These cells exhibit a broad spectrum of phenotypes and are often organized as a heterogeneous but sophisticatedly regulated ecosystem - tissue microenvironment, inside which every cell interacts with and is reciprocally influenced by its surroundings through its life span. Therefore, it is critical to comprehensively explore the cellular machinery and biological processes in the tissue microenvironment, which is best exemplified by the tumor microenvironment (TME). The past decade has seen increasing advances in the field of spatial proteomics, the main purpose of which is to characterize the abundance and spatial distribution of proteins and their post-translational modifications in the microenvironment of diseased tissues. Herein, we outline the achievements and remaining challenges of mass spectrometry-based tissue spatial proteomics. Exciting technology developments along with important biomedical applications of spatial proteomics are highlighted. In detail, we focus on high-quality resources built by scalpel macrodissection-based region-resolved proteomics, method development of sensitive sample preparation for laser microdissection-based spatial proteomics, and antibody recognition-based multiplexed tissue imaging. In the end, critical issues and potential future directions for spatial proteomics are also discussed.

A tumor "personality" test to guide therapeutic decision making

In this issue of Cancer Cell, Bagaev et al. discover conserved relationships between immune and stroma activity that are prognostic and predictive of response to immunotherapy across cancer types. The authors develop a visualization tool, akin to a tumor personality test, to integrate genomic and microenvironmental profiling and guide therapeutic decision-making.

The Etiology and Pathophysiology Genesis of Benign Prostatic Hyperplasia and Prostate Cancer: A New Perspective

Background: The etiology of benign prostatic hyperplasia and prostate cancer are unknown, with ageing being the greatness risk factor. Methods: This new perspective evaluates the available interdisciplinary evidence regarding prostate ageing in terms of the cell biology of regulation and homeostasis, which could explain the timeline of evolutionary cancer biology as degenerative, inflammatory and neoplasm progressions in these multifactorial and heterogeneous prostatic diseases. Results: This prostate ageing degeneration hypothesis encompasses the testosterone-vascular-inflamm-ageing triad, along with the cell biology regulation of amyloidosis and autophagy within an evolutionary tumorigenesis microenvironment. Conclusions: An understanding of these biological processes of prostate ageing can provide potential strategies for early prevention and could contribute to maintaining quality of life for the ageing individual along with substantial medical cost savings.

The role of single-cell sequencing in studying tumour evolution

Tumour evolution is a complex interplay between the acquisition of somatic (epi)genomic changes in tumour cells and the phenotypic consequences they cause, all in the context of a changing microenvironment. Single-cell sequencing offers a window into this dynamic process at the ultimate resolution of individual cells. In this review, we discuss the transformative insight offered by single-cell sequencing technologies for understanding tumour evolution.

Back to the future: Reintegrating biology to understand how past eco-evolutionary change can predict future outcomes

During the last few decades, biologists have made remarkable progress in understanding the fundamental processes that shape life. But despite the unprecedented level of knowledge now available, large gaps still remain in our understanding of the complex interplay of eco-evolutionary mechanisms across scales of life. Rapidly changing environments on Earth provide a pressing need to understand the potential implications of eco-evolutionary dynamics, which can be achieved by improving existing eco-evolutionary models and fostering convergence among the sub-fields of biology. We propose a new, data-driven approach that harnesses our knowledge of the functioning of biological systems to expand current conceptual frameworks and develop corresponding models that can more accurately represent and predict future eco-evolutionary outcomes. We suggest a roadmap toward achieving this goal. This long-term vision will move biology in a direction that can wield these predictive models for scientific applications that benefit humanity and increase the resilience of natural biological systems. We identify short, medium, and long-term key objectives to connect our current state of knowledge to this long-term vision, iteratively progressing across three stages: 1) utilizing knowledge of biological systems to better inform eco-evolutionary models, 2) generating models with more accurate predictions, and 3) applying predictive models to benefit the biosphere. Within each stage, we outline avenues of investigation and scientific applications related to the timescales over which evolution occurs, the parameter space of eco-evolutionary processes, and the dynamic interactions between these mechanisms. The ability to accurately model, monitor, and anticipate eco-evolutionary changes would be transformational to humanity's interaction with the global environment, providing novel tools to benefit human health, protect the natural world, and manage our planet's biosphere.

Characterizing genetic intra-tumor heterogeneity across 2,658 human cancer genomes

Intra-tumor heterogeneity (ITH) is a mechanism of therapeutic resistance and therefore an important clinical challenge. However, the extent, origin, and drivers of ITH across cancer types are poorly understood. To address this, we extensively characterize ITH across whole-genome sequences of 2,658 cancer samples spanning 38 cancer types. Nearly all informative samples (95.1%) contain evidence of distinct subclonal expansions with frequent branching relationships between subclones. We observe positive selection of subclonal driver mutations across most cancer types and identify cancer type-specific subclonal patterns of driver gene mutations, fusions, structural variants, and copy number alterations as well as dynamic changes in mutational processes between subclonal expansions. Our results underline the importance of ITH and its drivers in tumor evolution and provide a pan-cancer resource of comprehensively annotated subclonal events from whole-genome sequencing data.