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

Stemness regulation in prostate cancer: prostate cancer stem cells and targeted therapy

BACKGROUND

Increasing evidence indicates that cancer stem cells (CSCs) and cancer stem-like cells form a special subpopulation of cells that are ubiquitous in tumors. These cells exhibit similar characteristics to those of normal stem cells in tissues; moreover, they are capable of self-renewal and differentiation, as well as high tumorigenicity and drug resistance. In prostate cancer (PCa), it is difficult to kill these cells using androgen signaling inhibitors and chemotherapy drugs. Consequently, the residual prostate cancer stem cells (PCSCs) mediate tumor recurrence and progression.

OBJECTIVE

This review aims to provide a comprehensive and up-to-date overview of PCSCs, with a particular emphasis on potential therapeutic strategies targeting these cells.

METHODS

After searching in PubMed and Embase databases using 'prostate cancer' and 'cancer stem cells' as keywords, studies related were compiled and examined.

RESULTS

In this review, we detail the origin and characteristics of PCSCs, introduce the regulatory pathways closely related to CSC survival and stemness maintenance, and discuss the link between epithelial-mesenchymal transition, tumor microenvironment and tumor stemness. Furthermore, we introduce the currently available therapeutic strategies targeting CSCs, including signaling pathway inhibitors, anti-apoptotic protein inhibitors, microRNAs, nanomedicine, and immunotherapy. Lastly, we summarize the limitations of current CSC research and mention future research directions.

CONCLUSION

A deeper understanding of the regulatory network and molecular markers of PCSCs could facilitate the development of novel therapeutic strategies targeting these cells. Previous preclinical studies have demonstrated the potential of this treatment approach. In the future, this may offer alternative treatment options for PCa patients.

Parallel and convergent dynamics in the evolution of primary breast and lung adenocarcinomas

Cancer development requires an evolutionary transformation from mammalian cells fully regulated by and integrated into multicellular tissue to cancer cells  that, as single cell protists, are individually subject to Darwinian selection. Through genetic and epigenetic mechanisms of inheritance, the evolving cancer phenotype must acquire independence from host controls, downregulate differentiated functions that benefit the host but not individual cells, and generate phenotypic traits that increase fitness in the context of the selection forces within the local microenvironment. Here, we investigate this  evolutionary transition in breast (BRCA) and lung (LUAD, without EGFR, KRAS or BRAF driver mutations) adenocarcinomas using bulk mutation and expression data from the TCGA database. We define evolution selection for genes and molecular pathways based on 1) changes in gene expression compared to normal tissue, and 2) significantly larger or smaller observed mutation rates compared to those expected based on the gene size. We find BRCA and LUAD disable different genes and gene pathways associated with tissue-specific signaling and differentiated functions but promote common molecular pathways associated with cell cycle, cell-cell interactions, cytoskeleton, voltage gated ion channels, and microenvironmental niche construction. Thus, tissue-specific parallel evolution in early cancer development is followed by convergence to a common cancer phenotype.

Intra-tumoral spatial heterogeneity in breast cancer quantified using high-dimensional protein multiplexing and single cell phenotyping

BACKGROUND

Breast cancer is a highly heterogeneous disease where variations of biomarker expression may exist between individual foci of a cancer (intra-tumoral heterogeneity). The extent of variation of biomarker expression in the cancer cells, distribution of cell types in the local tumor microenvironment and their spatial arrangement could impact on diagnosis, treatment planning and subsequent response to treatment.

METHODS

Using quantitative multiplex immunofluorescence (MxIF) imaging, we assessed the level of variations in biomarker expression levels among individual cells, density of cell cluster groups and spatial arrangement of immune subsets from regions sampled from 38 multi-focal breast cancers that were processed using whole-mount histopathology techniques. Molecular profiling was conducted to determine the intrinsic molecular subtype of each analysed region.

RESULTS

A subset of cancers (34.2%) showed intra-tumoral regions with more than one molecular subtype classification. High levels of intra-tumoral variations in biomarker expression levels were observed in the majority of cancers studied, particularly in Luminal A cancers. HER2 expression quantified with MxIF did not correlate well with HER2 gene expression, nor with clinical HER2 scores. Unsupervised clustering revealed the presence of various cell clusters with unique IHC4 protein co-expression patterns and the composition of these clusters were mostly similar among intra-tumoral regions. MxIF with immune markers and image patch analysis classified immune niche phenotypes and the prevalence of each phenotype in breast cancer subtypes was illustrated.

CONCLUSIONS

Our work illustrates the extent of spatial heterogeneity in biomarker expression and immune phenotypes, and highlights the importance of a comprehensive spatial assessment of the disease for prognosis and treatment planning.

Tumor ploidy determination in low-pass whole genome sequencing and allelic copy number visualization using the Constellation Plot

Ploidy determination across the genome has been challenging for low-pass-WGS tumor-only samples. We present BACDAC, a method that calculates tumor ploidy down to 1.2X effective tumor coverage. Allele fraction patterns displayed in the Constellation Plot verify tumor ploidy and reveal subclonal populations. BACDAC outputs a metric, 2N+LOH, that when combined with ploidy better distinguishes near-diploid from high-ploidy tumors. Validated using TCGA, BACDAC had good agreement with other methods and 88% agreement with experimental methods. Discrepancies occur mainly when BACDAC predicts diploidy with subclones rather than high-ploidy. Applied to 653 low-pass-WGS samples spanning 12 cancer subtypes, BACDAC calls 40% as high-ploidy.

Clonal dispersal is associated with tumor heterogeneity and poor prognosis in colorectal cancer

Clonal dispersal, resulting from the intermingling of tumor cell subpopulations, is thought to be a key driver of tumor heterogeneity. Despite advances in spatial modeling of cancer biology, quantification of clonal dispersal has been challenging. This study introduces a straightforward method, relying on fluorescent cell barcoding, to quantify clonal dispersal in various in vitro and in vivo models of colorectal cancer (CRC). Our approach allows for precise localization of clones and uncovering the degree of clonal mixing across different CRC models. Our findings suggest that clonal dispersal is correlated with the expression of genes involved in epithelial-mesenchymal transition and CMS4-related signaling pathways. We further identify a dispersal gene signature, associated with intratumor heterogeneity, which is a robust clinical predictor of poor prognosis and recurrence in CRC, highlighting its potential as a prognostic marker and a putative direction for therapeutic targeting.

Single-Cell RNA-Seq Recognized Key Genes for Metastasis and Macrophage Infiltration in Colorectal Cancer

Colorectal cancer (CRC) is one of the most common malignancies in the world. However, the main causes of metastasis and immune cell infiltration in CRC are still unclear. This experiment was conducted to identify the key genes of metastasis and macrophage infiltration in CRC according to single-cell sequencing (scRNA-seq) data. By analyzing the data of GSE261012 and GSE234804 in the Gene Expression Omnibus (GEO) database, the key node genes for the stages of tumorigenesis, epithelial-mesenchymal transition, and metastasis of CRC were found. These genes were modeled by lasso regression by The Cancer Genome Atlas (TCGA) database, and ZFAND2A was identified as a key gene for metastasis and macrophage infiltration in CRC. Finally, the specific function of ZFAND2A in cancer cell activity was explored in vitro by qRT-PCR, WB analysis, CCK-8, and transwell assay. The specific function of ZFAND2A in macrophage polarization was explored in vitro by qRT-PCR, ELISA, and flow cytometry. We identified crucial gene expression in the entire process of CRC tumor progression, including tumorigenesis, epithelial-mesenchymal transition, and metastasis. Ten thousand six hundred and thirty-seven genes were determined as genes associated with tumor progression and metastasis. Among them, six genes were identified to be related to CRC prognosis. The results of TCGA data indicated that ZFAND2A showed lower expression in tumors and was related to a good prognosis of CRC. Overexpression of ZFAND2A inhibits the proliferation and migration of CRC cells. Additionally, there was a correlation between ZFAND2A expression and macrophage infiltration. Increasing ZFAND2A promotes M1 polarization in macrophages. Our findings provide new potential biomarkers for the metastatic mechanisms and prognosis of CRC. In addition, ZFAND2A is expected to become a potential therapeutic target for CRC.

Noninvasive prognostic classification of ITH in HCC with multi-omics insights and therapeutic implications

Intratumoral heterogeneity (ITH) is a critical factor associated with treatment failure and disease relapse in hepatocellular carcinoma (HCC). However, decoding ITH in a noninvasive and comprehensive manner remains a notable challenge. In this study involving 851 patients from five centers, we developed a noninvasive prognostic classification for ITH using radiomics based on multisequence MRI, termed radiomics ITH (RITH) phenotypes. The RITH phenotypes highly correlated with prognosis and pathological ITH. In addition, through an integrated multi-omics analysis, we uncovered the molecular mechanisms underlying RITH, notably enhancing its biological interpretability. Specifically, high-RITH tumors demonstrated an enrichment of cancer-associated fibroblasts and activation of extracellular matrix remodeling. Our approach facilitates the noninvasive refined classification of ITH using radiomics and multi-omics, paving the way for tailored treatment strategies in HCC. Extracellular matrix-receptor interaction could be a potential therapeutic target in patients with high-RITH tumors. Given the routine use of radiologic imaging in oncology, our methodology ignites versatile framework for broader application to other solid tumors.

Cancer immune evasion, immunoediting and intratumour heterogeneity

Cancers can avoid immune-mediated elimination by acquiring traits that disrupt antitumour immunity. These mechanisms of immune evasion are selected and reinforced during tumour evolution under immune pressure. Some immunogenic subclones are effectively eliminated by antitumour T cell responses (a process known as immunoediting), which results in a clonally selected tumour. Other cancer cells arise to resist immunoediting, which leads to a tumour that includes several distinct cancer cell populations (referred to as intratumour heterogeneity (ITH)). Tumours with high ITH are associated with poor patient outcomes and a lack of responsiveness to immune checkpoint blockade therapy. In this Review, we discuss the different ways that cancer cells evade the immune system and how these mechanisms impact immunoediting and tumour evolution. We also describe how subclonal antigen presentation in tumours with high ITH can result in immune evasion.

CAR macrophages with built-In CD47 blocker combat tumor antigen heterogeneity and activate T cells via cross-presentation

Macrophage-based cancer cellular therapy has gained substantial interest. However, the capability of engineered macrophages to target cancer heterogeneity and modulate adaptive immunity remains unclear. Here, exploiting the myeloid antibody-dependent cellular phagocytosis biology and phagocytosis checkpoint blockade, we report the enhanced synthetic phagocytosis receptor (eSPR) that integrate FcRγ-driven phagocytic chimeric antigen receptors (CAR) with built-in secreted CD47 blockers. The eSPR engineering empowers macrophages to combat tumor antigen heterogeneity. Transduced by adenoviral vectors, eSPR macrophages are intrinsically pro-inflammatory imprinted and resist tumoral polarization. Transcriptomically and phenotypically, eSPR macrophages elicit a more favorable tumor immune landscape. Mechanistically, eSPR macrophages in situ stimulate CD8 T cells via phagocytosis-dependent antigen cross-presentation. We also validate the functionality of the eSPR system in human primary macrophages.

A battery-free nanofluidic intracellular delivery patch for internal organs

The targeted delivery of therapeutics to internal organs to, for example, promote healing or apoptosis holds promise in the treatment of numerous diseases1-4. Currently, the prevailing delivery modality relies on the circulation; however, this modality has substantial efficiency, safety and/or controllability limitations5-9. Here we report a battery-free, chipless, soft nanofluidic intracellular delivery (NanoFLUID) patch that provides enhanced and customized delivery of payloads in targeted internal organs. The chipless architecture and the flexible nature of thin functional layers facilitate integration with internal organs. The nanopore-microchannel-microelectrode structure enables safe, efficient and precise electroperforation of the cell membrane, which in turn accelerates intracellular payload transport by approximately 105 times compared with conventional diffusion methods while operating under relatively low-amplitude pulses (20 V). Through evaluations of the NanoFLUID patch in multiple in vivo scenarios, including treatment of breast tumours and acute injury in the liver and modelling tumour development, we validated its efficiency, safety and controllability for organ-targeted delivery. NanoFLUID-mediated in vivo transfection of a gene library also enabled efficient screening of essential drivers of breast cancer metastasis in the lung and liver. Through this approach, DUS2 was identified as a lung-specific metastasis driver. Thus, NanoFLUID represents an innovative bioelectronic platform for the targeted delivery of payloads to internal organs to treat various diseases and to uncover new insights in biology.

Asymmetric cell division of ALDH1-positive cancer stem cells generates glycolytic metabolically diverse cell populations

Metabolic heterogeneity in various cancer cells within a tumor causes resistance to medical therapies and promotes tumor recurrence and metastasis. However, the mechanisms by which tumors acquire metabolic heterogeneity are poorly understood. Here, we revealed that PKCλ-dependent asymmetric division of ALDH1-positive cancer stem cells (CSCs) led to an uneven distribution of glycolytic capacity, which is crucial for understanding metabolic heterogeneity within a tumor. The rate-limiting enzyme PFKP and the metabolic probe CDG in glycolysis codistributed with the ALDH1A3 protein during the post-cell division phase, highlighting a mechanism for acquiring metabolic diversity. PKCλ deficiency reduced the asymmetric distribution of these proteins in ALDH1high cells with high ALDH1 activity, suggesting a fundamental role for PKCλ in metabolic heterogeneity. We identified 28 distinct distribution patterns combining PFKP and CDG distributions, demonstrating the complexity of glycolytic heterogeneity. Furthermore, validation and prediction of cell distribution patterns via a probabilistic model confirmed that PKCλ deficiency diminished glycolytic diversity in individual cells within a cancer cell colony generated from an ALDH1-positive CSC. These findings suggest that PKCλ-dependent asymmetric cell division of ALDH1-positive CSCs is crucial for glycolytic heterogeneity in cancer cells within a tumor, potentially offering new therapeutic targets against tumor resistance and metastasis.

Co-Encapsulation of Multiple Antineoplastic Agents in Liposomes by Exploring Microfluidics

The inherent complexity of cancer proliferation and malignancy cannot be addressed by the conventional approach of relying on high doses of a single powerful anticancer agent, which is associated with poor efficacy, higher toxicity, and the development of drug resistance. Multiple drug therapy (MDT) rationally designed to target tumor heterogeneity, block alternative survival pathways, modulate the tumor microenvironment, and reduce toxicities would be a viable solution against cancer. Liposomes are the most suitable carrier for anticancer MDT due to their ability to encapsulate both hydrophilic and hydrophobic agents, biocompatibility, and controlled release properties; however, an adequate manufacturing method is important for effective co-encapsulation. Microfluidics involves the manipulation of fluids at the microscale for the controlled synthesis of liposomes with desirable properties. This work critically reviews the use of microfluidics for the synthesis of anticancer MDT liposomes. MDT success not only relies on the identification of synergistic dose combinations of the anticancer modalities but also warrants the loading of multiple therapeutic entities within liposomes in optimal ratios, the protection of the drugs by the nanocarrier during systemic circulation, and the synchronous release at the target site in the same pattern as confirmed in preliminary efficacy studies. Prospects have been identified for the bench-to-bedside translation of anticancer MDT liposomes using microfluidics.

CNRein: an evolution-aware deep reinforcement learning algorithm for single-cell DNA copy number calling

Low-pass single-cell DNA sequencing technologies and algorithmic advancements have enabled haplotype-specific copy number calling on thousands of cells within tumors. However, measurement uncertainty may result in spurious CNAs inconsistent with realistic evolutionary constraints. We introduce evolution-aware copy number calling via deep reinforcement learning (CNRein). Our simulations demonstrate CNRein infers more accurate copy-number profiles and better recapitulates ground truth clonal structure than existing methods. On sequencing data of breast and ovarian cancer, CNRein produces more parsimonious solutions than existing methods while maintaining agreement with single-nucleotide variants. Additionally, CNRein shows consistency on a breast cancer patient sequenced with distinct low-pass technologies.

CINner: Modeling and simulation of chromosomal instability in cancer at single-cell resolution

Cancer development is characterized by chromosomal instability, manifesting in frequent occurrences of different genomic alteration mechanisms ranging in extent and impact. Mathematical modeling can help evaluate the role of each mutational process during tumor progression, however existing frameworks can only capture certain aspects of chromosomal instability (CIN). We present CINner, a mathematical framework for modeling genomic diversity and selection during tumor evolution. The main advantage of CINner is its flexibility to incorporate many genomic events that directly impact cellular fitness, from driver gene mutations to copy number alterations (CNAs), including focal amplifications and deletions, missegregations and whole-genome duplication (WGD). We apply CINner to find chromosome-arm selection parameters that drive tumorigenesis in the absence of WGD in chromosomally stable cancer types from the Pan-Cancer Analysis of Whole Genomes (PCAWG, [Formula: see text]). We found that the selection parameters predict WGD prevalence among different chromosomally unstable tumors, hinting that the selective advantage of WGD cells hinges on their tolerance for aneuploidy and escape from nullisomy. Analysis of inference results using CINner across cancer types in The Cancer Genome Atlas ([Formula: see text]) further reveals that the inferred selection parameters reflect the bias between tumor suppressor genes and oncogenes on specific genomic regions. Direct application of CINner to model the WGD proportion and fraction of genome altered (FGA) in PCAWG uncovers the increase in CNA probabilities associated with WGD in each cancer type. CINner can also be utilized to study chromosomally stable cancer types, by applying a selection model based on driver gene mutations and focal amplifications or deletions (chronic lymphocytic leukemia in PCAWG, [Formula: see text]). Finally, we used CINner to analyze the impact of CNA probabilities, chromosome selection parameters, tumor growth dynamics and population size on cancer fitness and heterogeneity. We expect that CINner will provide a powerful modeling tool for the oncology community to quantify the impact of newly uncovered genomic alteration mechanisms on shaping tumor progression and adaptation.

Crowd-sourced benchmarking of single-sample tumor subclonal reconstruction

Subclonal reconstruction algorithms use bulk DNA sequencing data to quantify parameters of tumor evolution, allowing an assessment of how cancers initiate, progress and respond to selective pressures. We launched the ICGC-TCGA (International Cancer Genome Consortium-The Cancer Genome Atlas) DREAM Somatic Mutation Calling Tumor Heterogeneity and Evolution Challenge to benchmark existing subclonal reconstruction algorithms. This 7-year community effort used cloud computing to benchmark 31 subclonal reconstruction algorithms on 51 simulated tumors. Algorithms were scored on seven independent tasks, leading to 12,061 total runs. Algorithm choice influenced performance substantially more than tumor features but purity-adjusted read depth, copy-number state and read mappability were associated with the performance of most algorithms on most tasks. No single algorithm was a top performer for all seven tasks and existing ensemble strategies were unable to outperform the best individual methods, highlighting a key research need. All containerized methods, evaluation code and datasets are available to support further assessment of the determinants of subclonal reconstruction accuracy and development of improved methods to understand tumor evolution.

No Evidence for Distinct Transcriptomic Subgroups of Devil Facial Tumor Disease (DFTD)

Contagious cancers represent one of the least understood types of infections in wildlife. Devil Facial Tumor Disease (comprised of two different contagious cancers, DFT1 and DFT2) has led to an 80% decline in the Tasmanian devil (Sarcophilus harrisii ) population at the regional level since it was first observed in 1996. There are currently no treatment options for the disease, and research efforts are focused on vaccine development. Although DFT1 is clonal, phylogenomic studies have identified different genetic variants of the pathogen. We postulated that different genetic strains may have different gene expression profiles and would therefore require different vaccine components. Here, we aimed to test this hypothesis by applying two types of unsupervised clustering (hierarchical and k-means) to 35 DFT1 transcriptomes selected from the disease's four major phylogenetic clades. The two algorithms produced conflicting results, and there was low support for either method individually. Validation metrics, such as the Gap statistic method, the Elbow method, and the Silhouette method, were ambiguous, contradictory, or indicated that our dataset only consisted of a single cluster. Collectively, our results show that the different phylogenetic clades of DFT1 all have similar gene expression profiles. Previous studies have suggested that transcriptomic differences exist between tumours from different locations. However, our study differs in that it considers both tumor purity and genotypic clade when analysing differences between DFTD biopsies. These results have important implications for therapeutic development, as they indicate that a single vaccine or treatment approach has the potential to be effective for a large cross-section of DFT1 tumors. As one of the largest studies to use transcriptomics to investigate phenotypic variation within a single contagious cancer, it also provides novel insight into this unique group of diseases.

The impact of co-occurring tumor suppressor mutations with mEGFR as early indicators of relapse in lung cancer

BACKGROUND

Lung adenocarcinoma frequently presents with EGFR mutations, often progressing on EGFR tyrosine kinase inhibitors (TKIs) despite an initial response. Progression is frequently driven by additional genetic changes, including mutations in tumor suppressor genes (TSGs). Understanding the role of these concurrent TSG mutations can help elucidate resistance mechanisms and guide the development of more effective treatment approaches.

MATERIALS AND METHODS

We examined survival outcomes in 483 EGFR-mutant (mEGFR) patients from the GENIE BPC non-small-cell lung cancer (NSCLC) dataset. To understand the mutational landscape and clonal dynamics, whole exome sequencing (WES) was carried out on 48 tumor samples from 16 mEGFR patients at both baseline and post-relapse. A comprehensive gene panel was applied to 200 liquid biopsy samples obtained longitudinally from 25 patients to track clonal evolution.

RESULTS

mEGFR patients with co-occurring TSG mutations exhibited significantly worse outcomes. In the GENIE dataset, overall survival (OS) was shorter [51.11 versus 99.3 months; hazard ratio (HR) 1.8, confidence interval (CI) 1.22-2.75, P = 0.003] and progression-free survival (PFS) was reduced (9.83 versus 11.48 months; HR 1.4, CI 1.03-1.91, P = 0.026). WES analysis revealed 17 TSG mutations that were retained and showed clonal enrichment, particularly in early relapse (progression within 10 months of TKI initiation) or intermediate-stage relapse (relapse occurred between 10 and 20 months), indicated by increased variant allele frequency and their presence was strongly linked to early relapse. Longitudinal clonal studies further confirmed that TSG mutations co-occurring with mEGFR were often truncal, predominantly in early relapsers. Survival analysis using this subset of 17 TSGs showed significantly shorter OS (55.26 versus 99.3 months; HR 1.7, CI 1.12-2.65, P = 0.011) and PFS (9.67 versus 13.12 months; HR 1.5, CI 1.08-2.10, P = 0.013).

CONCLUSIONS

A set of 17 co-occurring TSG mutations has been identified as key biomarkers for early relapse in mEGFR lung adenocarcinoma. Longitudinal genomic monitoring, with a focus on clonal evolution, offers valuable insights that can inform personalized treatment strategies and potentially improve patient outcomes.

Therapeutic potential of tocotrienols as chemosensitizers in cancer therapy

Chemoresistance is the adaptation of cancer cells against therapeutic agents. When exhibited by cancer cells, chemoresistance helps them to avoid apoptosis, cause relapse, and metastasize, making it challenging for chemotherapeutic agents to treat cancer. Various strategies like dosage modification of drugs, nanoparticle-based delivery of chemotherapeutics, antibody-drug conjugates, and so on are being used to target and reverse chemoresistance, one among such is combination therapy. It uses the combination of two or more therapeutic agents to reverse multidrug resistance and improve the effects of chemotherapy. Phytochemicals are known to exhibit chemosensitizing properties and are found to be effective against various cancers. Tocotrienols (T3) and tocopherols (T) are natural bioactive analogs of vitamin E, which exhibit important medicinal value and potential curative properties apart from serving as an antioxidant and nutrient supplement. Notably, T3 exhibits a variety of pharmacological activities like anticancer, anti-inflammatory, antiproliferative, and so on. The chemosensitizing property of tocotrienol is exhibited by modulating several signaling pathways and molecular targets involved in cancer cell survival, proliferation, invasion, migration, and metastasis like NF-κB, STATs, Akt/mTOR, Bax/Bcl-2, Wnt/β-catenin, and many more. T3 sensitizes cancer cells to chemotherapeutic drugs including cisplatin, doxorubicin, and paclitaxel increasing drug concentration and cytotoxicity. Discussed herewith are the chemosensitizing properties of tocotrienols on various cancer cell types when combined with various drugs and biological molecules.

Pan-cancer analysis of co-inhibitory molecules revealing their potential prognostic and clinical values in immunotherapy

Background

The widespread use of immune checkpoint inhibitors (anti-CTLA4 or PD-1) has opened a new chapter in tumor immunotherapy by providing long-term remission for patients. Unfortunately, however, these agents are not universally available and only a minority of patients respond to them. Therefore, there is an urgent need to develop novel therapeutic strategies targeting other co-inhibitory molecules. However, comprehensive information on the expression and prognostic value of co-inhibitory molecules, including co-inhibitory receptors and their ligands, in different cancers is not yet available.

Methods

We investigated the expression, correlation, and prognostic value of co-inhibitory molecules in different cancer types based on TCGA, UCSC Xena, TIMER, CellMiner datasets. We also examined the associations between the expression of these molecules and the extent of immune cell infiltration. Besides, we conducted a more in-depth study of VISTA.

Result

The results of differential expression analysis, correlation analysis, and drug sensitivity analysis suggest that CTLA4, PD-1, TIGIT, LAG3, TIM3, NRP1, VISTA, CD80, CD86, PD-L1, PD-L2, PVR, PVRL2, FGL1, LGALS9, HMGB1, SEMA4A, and VEGFA are associated with tumor prognosis and immune cell infiltration. Therefore, we believe that they are hopefully to serve as prognostic biomarkers for certain cancers. In addition, our analysis indicates that VISTA plays a complex role and its expression is related to TMB, MSI, cancer cell stemness, DNA/RNA methylation, and drug sensitivity.

Conclusions

These co-inhibitory molecules have the potential to serve as prognostic biomarkers and therapeutic targets for a broad spectrum of cancers, given their strong associations with key clinical metrics. Furthermore, the analysis results indicate that VISTA may represent a promising target for cancer therapy.

Evolutionary measures show that recurrence of DCIS is distinct from progression to breast cancer

BACKGROUND

Progression from pre-cancers like ductal carcinoma in situ (DCIS) to invasive disease (cancer) is driven by somatic evolution and is altered by clinical interventions. We hypothesized that genetic and/or phenotypic intra-tumor heterogeneity would predict clinical outcomes for DCIS since it serves as the substrate for natural selection among cells.

METHODS

We profiled two samples from two geographically distinct foci from each DCIS in both cross-sectional (n = 119) and longitudinal cohorts (n = 224), with whole exome sequencing, low-pass whole genome sequencing, and a panel of immunohistochemical markers.

RESULTS

In the longitudinal cohorts, the only statistically significant associations with time to non-invasive DCIS recurrence were the combination of treatment (lumpectomy only vs mastectomy or lumpectomy with radiation, HR 12.13, p = 0.003, Wald test with FDR correction), ER status (HR 0.16 for ER+ compared to ER-, p = 0.0045), and divergence in SNVs between the two samples (HR 1.33 per 10% divergence, p = 0.018). SNV divergence also distinguished between pure DCIS and DCIS synchronous with invasive disease in the cross-sectional cohort. In contrast, the only statistically significant associations with time to progression to invasive disease were the combination of the width of the surgical margin (HR 0.67 per mm, p = 0.043) and the number of mutations that were detectable at high allele frequencies (HR 1.30 per 10 SNVs, p = 0.02). No predictors were significantly associated with both DCIS recurrence and progression to invasive disease, suggesting that the evolutionary scenarios that lead to these clinical outcomes are markedly different.

CONCLUSIONS

These results imply that recurrence with DCIS is a clinical and biological process different from invasive progression.

An MRI Radiogenomic Signature to Characterize the Transcriptional Heterogeneity Associated with Prognosis and Biological Functions in Glioblastoma

BACKGROUND

The study sought to establish a radiogenomic signature to evaluate the transcriptional heterogeneity that reflects the prognosis and tumour-related biological functions of patients with glioblastoma.

METHODS

Transcriptional subclones were identified via fully unsupervised deconvolution of RNA sequencing. A genomic prognostic risk score was developed from transcriptional subclone proportions in the development dataset (n = 532) and independently verified in the testing dataset (n = 225). Multimodal magnetic resonance imaging (MRI) analysis involved feature extraction from three distinct anatomical regions across four imaging sequences. Key features were selected to construct a radiogenomic signature predictive of the genomic risk score in the radiogenomic dataset (n = 99), with subsequent survival analysis conducted in the image testing dataset (n = 233).

RESULTS

A total of 8 transcriptional subclones were identified, of which the metabolic pathway subclone and spinocerebellar ataxia subclone were independent risk factors for overall survival. The genomic risk score effectively differentiated patient subgroups with divergent survival outcomes in both development (p < 0.001) and testing datasets (p = 0.0003). Nineteen radiomic features were selected to construct a radiogenomic signature, with these features being linked to hallmark cancer pathways and the malignant behaviours of cancer cells. The radiogenomic signature predicted overall survival in the image testing dataset (hazard ratios (HR) = 1.67, p = 0.011).

CONCLUSIONS

A prognostic radiogenomic signature was established and verified to characterize transcriptional subclones with underlying biological functions in glioblastoma.

Tumor Heterogeneity and the Immune Response in Non-Small Cell Lung Cancer: Emerging Insights and Implications for Immunotherapy

Resistance to immune checkpoint inhibitors (ICIs) represents a major challenge for the effective treatment of non-small cell lung cancer (NSCLC). Tumor heterogeneity has been identified as an important mechanism of treatment resistance in cancer and has been increasingly implicated in ICI resistance. The diversity and clonality of tumor neoantigens, which represent the target epitopes for tumor-specific immune cells, have been shown to impact the efficacy of immunotherapy. Advances in genomic techniques have further enhanced our understanding of clonal landscapes within NSCLC and their evolution in response to therapy. In this review, we examine the role of tumor heterogeneity during immune surveillance in NSCLC and highlight its spatial and temporal evolution as revealed by modern technologies. We explore additional sources of heterogeneity, including epigenetic and metabolic factors, that have come under greater scrutiny as potential mediators of the immune response. We finally discuss the implications of tumor heterogeneity on the efficacy of ICIs and highlight potential strategies for overcoming therapeutic resistance.

From virtual to reality: innovative practices of digital twins in tumor therapy

BACKGROUND

As global cancer incidence and mortality rise, digital twin technology in precision medicine offers new opportunities for cancer treatment.

OBJECTIVE

This study aims to systematically analyze the current applications, research trends, and challenges of digital twin technology in tumor therapy, while exploring future directions.

METHODS

Relevant literature up to 2024 was retrieved from PubMed, Web of Science, and other databases. Data visualization was performed using R and VOSviewer software. The analysis includes the research initiation and trends, funding models, global research distribution, sample size analysis, and data processing and artificial intelligence applications. Furthermore, the study investigates the specific applications and effectiveness of digital twin technology in tumor diagnosis, treatment decision-making, prognosis prediction, and personalized management.

RESULTS

Since 2020, research on digital twin technology in oncology has surged, with significant contributions from the United States, Germany, Switzerland, and China. Funding primarily comes from government agencies, particularly the National Institutes of Health in the U.S. Sample size analysis reveals that large-sample studies have greater clinical reliability, while small-sample studies emphasize technology validation. In data processing and artificial intelligence applications, the integration of medical imaging, multi-omics data, and AI algorithms is key. By combining multimodal data integration with dynamic modeling, the accuracy of digital twin models has been significantly improved. However, the integration of different data types still faces challenges related to tool interoperability and limited standardization. Specific applications of digital twin technology have shown significant advantages in diagnosis, treatment decision-making, prognosis prediction, and surgical planning.

CONCLUSION

Digital twin technology holds substantial promise in tumor therapy by optimizing personalized treatment plans through integrated multimodal data and dynamic modeling. However, the study is limited by factors such as language restrictions, potential selection bias, and the relatively small number of published studies in this emerging field, which may affect the comprehensiveness and generalizability of our findings. Moreover, issues related to data heterogeneity, technical integration, and data privacy and ethics continue to impede its broader clinical application. Future research should promote international collaboration, establish unified interdisciplinary standards, and strengthen ethical regulations to accelerate the clinical translation of digital twin technology in cancer treatment.

Causes of evolutionary divergence in prostate cancer

Cancer progression involves the sequential accumulation of genetic alterations that cumulatively shape the tumour phenotype. In prostate cancer, tumours can follow divergent evolutionary trajectories that lead to distinct subtypes, but the causes of this divergence remain unclear. While causal inference could elucidate the factors involved, conventional methods are unsuitable due to the possibility of unobserved confounders and ambiguity in the direction of causality. Here, we propose a method that circumvents these issues and apply it to genomic data from 829 prostate cancer patients. We identify several genetic alterations that drive divergence as well as others that prevent this transition, locking tumours into one trajectory. Further analysis reveals that these genetic alterations may cause each other, implying a positive-feedback loop that accelerates divergence. Our findings provide insights into how cancer subtypes emerge and offer a foundation for genomic surveillance strategies aimed at monitoring the progression of prostate cancer.

Deciphering lung adenocarcinoma evolution and the role of LINE-1 retrotransposition

Understanding lung cancer evolution can identify tools for intercepting its growth. In a landscape analysis of 1024 lung adenocarcinomas (LUAD) with deep whole-genome sequencing integrated with multiomic data, we identified 542 LUAD that displayed diverse clonal architecture. In this group, we observed an interplay between mobile elements, endogenous and exogenous mutational processes, distinct driver genes, and epidemiological features. Our results revealed divergent evolutionary trajectories based on tobacco smoking exposure, ancestry, and sex. LUAD from smokers showed an abundance of tobacco-related C:G>A:T driver mutations in KRAS plus short subclonal diversification. LUAD in never smokers showed early occurrence of copy number alterations and EGFR mutations associated with SBS5 and SBS40a mutational signatures. Tumors harboring EGFR mutations exhibited long latency, particularly in females of European-ancestry (EU_N). In EU_N, EGFR mutations preceded the occurrence of other driver genes, including TP53 and RBM10. Tumors from Asian never smokers showed a short clonal evolution and presented with heterogeneous repetitive patterns for the inferred mutational order. Importantly, we found that the mutational signature ID2 is a marker of a previously unrecognized mechanism for LUAD evolution. Tumors with ID2 showed short latency and high L1 retrotransposon activity linked to L1 promoter demethylation. These tumors exhibited an aggressive phenotype, characterized by increased genomic instability, elevated hypoxia scores, low burden of neoantigens, propensity to develop metastasis, and poor overall survival. Reactivated L1 retrotransposition-induced mutagenesis can contribute to the origin of the mutational signature ID2, including through the regulation of the transcriptional factor ZNF695, a member of the KZFP family. The complex nature of LUAD evolution creates both challenges and opportunities for screening and treatment plans.

Multiomics analysis unveils the cellular ecosystem with clinical relevance in aldosterone-producing adenomas with KCNJ5 mutations

Aldosterone-producing adenomas (APA), a major endocrine tumor and leading subtype of primary aldosteronism, cause secondary hypertension with high cardiometabolic risks. Despite potentially producing multiple steroid hormones, detailed cellular mechanisms in APA remain insufficiently studied. Our multiomics analysis focusing on APA with KCNJ5 mutations, which represent the most common genetic form, revealed marked cellular heterogeneity. Tumor cell reprogramming initiated from stress-responsive cells to aldosterone-producing or cortisol-producing cells, with the latter progressing to proliferative stromal-like cells. These cell subtypes showed spatial segregation, and APA exhibited genomic intratumor heterogeneity. Among the nonparenchymal cells, lipid-associated macrophages, which were abundant in APA, might promote the progression of cortisol-producing and stromal-like cells, suggesting their role in the tumor microenvironment. Intratumor cortisol synthesis was correlated with increased blood cortisol levels, which were associated with the development of vertebral fractures, a hallmark of osteoporosis. This study unveils the complex cellular ecosystem with clinical relevance in APA with KCNJ5 mutations, providing insights into tumor biology that could inform future clinical approaches.

Benchmarking copy number aberrations inference tools using single-cell multi-omics datasets

Copy number alterations (CNAs) are an important type of genomic variation which play a crucial role in the initiation and progression of cancer. With the explosion of single-cell RNA sequencing (scRNA-seq), several computational methods have been developed to infer CNAs from scRNA-seq studies. However, to date, no independent studies have comprehensively benchmarked their performance. Herein, we evaluated five state-of-the-art methods based on their performance in tumor versus normal cell classification; CNAs profile accuracy, tumor subclone inference, and aneuploidy identification in non-malignant cells. Our results showed that Numbat outperformed others across most evaluation criteria, while CopyKAT excelled in scenarios when expression matrix alone was used as input. In specific tasks, SCEVAN showed the best performance in clonal breakpoint detection and Numbat showed high sensitivity in copy number neutral LOH (cnLOH) detection. Additionally, we investigated how referencing settings, inclusion of tumor microenvironment cells, tumor type, and tumor purity impact the performance of these tools. This study provides a valuable guideline for researchers in selecting the appropriate methods for their datasets.

Clinical utility of 'Shaken' biopsies for whole-genome sequencing

AIMS

Whole-genome sequencing (WGS) is beginning to be applied to cancer samples in the clinical setting. This ideally requires high-quality, minimally degraded DNA of high tumour cell content, while retaining sufficient tissue with excellent morphology for histopathological diagnosis and immunohistochemistry. The aim of this study was to investigate alternative ways of handling cancer samples to fulfil both diagnostic and molecular requirements.

METHODS

Ex vivo biopsies were taken to investigate the feasibility of using cancer cells 'shaken' from the surface of a biopsy for WGS, while maintaining the tissue biopsy for histological diagnosis. WGS from the shaken cells was compared with the gold standard of a fresh-frozen (FF) biopsy. The procedure was piloted in the real-world setting for breast cancer samples.

RESULTS

Cells shaken from ex vivo biopsies can yield DNA of sufficient quantity and quality for WGS, while having no discernible impact on quality of tissue morphology. WGS data showed good coverage, comparable variant calls and generally higher tumour content in shaken cell samples compared with the control FF samples. For real-world biopsies, DNA yields were lower, but WGS data were of excellent quality for the cases analysed.

CONCLUSIONS

Shaken biopsy sampling allows genomic sequencing from patients with cancer who may otherwise not receive a genome sequence due to limited sample availability. It represents a way of overcoming the logistics of obtaining and storing FF tissue making it a suitable technique for wider scale implementation in the clinical setting.

Inferring active mutational processes in cancer using single cell sequencing and evolutionary constraints

Ongoing mutagenesis in cancer drives genetic diversity throughout the natural history of cancers. As the activities of mutational processes are dynamic throughout evolution, distinguishing the mutational signatures of 'active' and 'historical' processes has important implications for studying how tumors evolve. This can aid in understanding mutagenic states at the time of presentation, and in associating active mutational process with therapeutic resistance. As bulk sequencing primarily captures historical mutational processes, we studied whether ultra-low-coverage single-cell whole-genome sequencing (scWGS), which measures the distribution of mutations across hundreds or thousands of individual cells, could enable the distinction between historical and active mutational processes. While technical challenges and data sparsity have limited mutation analysis in scWGS, we show that these data contain valuable information about dynamic mutational processes. To robustly interpret single nucleotide variants (SNVs) in scWGS, we introduce ArtiCull, a method to identify and remove SNV artifacts by leveraging evolutionary constraints, enabling reliable detection of mutations for signature analysis. Applying this approach to scWGS data from pancreatic ductal adenocarcinoma (PDAC), triple-negative breast cancer (TNBC), and high-grade serous ovarian cancer (HGSOC), we uncover temporal and spatial patterns in mutational processes. In PDAC, we observe a temporal increase in mismatch repair deficiency (MMRd). In cisplatin-treated TNBC patient-derived xenografts, we identify therapy-induced mutagenesis and inactivation of APOBEC3 activity. In HGSOC, we show distinct patterns of APOBEC3 mutagenesis, including late tumor-wide activation in one case and clade-specific enrichment in another. Additionally, we detect a clone-specific increase in SBS17 activity, in a clone previously linked to recurrence. Our findings establish ultra-low-coverage scWGS as a powerful approach for studying active mutational processes that may influence ongoing clonal evolution and therapeutic resistance.

Homologous recombination deficiency (HRD) testing landscape: clinical applications and technical validation for routine diagnostics

The use of poly(ADP-ribose) polymerase inhibitors (PARPi) revolutionized the treatment of BRCA-mutated cancers. Identifying patients exhibiting homologous recombination deficiency (HRD) has been proved useful to predict PARPi efficacy. However, obtaining HRD status remains an arduous task due to its evolution over the time. This causes HRD status to become obsolete when obtained from genomic scars, rendering PARPi ineffective for these patients. Only two HRD tests are currently FDA-approved, both based on genomic scars detection and BRCA mutations testing. Nevertheless, new technologies for obtaining an increasingly reliable HRD status continue to evolve. Application of these tests in clinical practice is an additional challenge due to the need for lower costs and shorter time to results delay.In this review, we describe the currently available methods for HRD testing, including the methodologies and corresponding tests for assessing HRD status, and discuss the clinical routine application of these tests and their technical validation.

Geographic and age-related variations in mutational processes in colorectal cancer

Colorectal cancer incidence rates vary geographically and have changed over time. Notably, in the past two decades, the incidence of early-onset colorectal cancer, affecting individuals under the age of 50 years, has doubled in many countries. The reasons for this increase are unknown. Here, we investigate whether mutational processes contribute to geographic and age-related differences by examining 981 colorectal cancer genomes from 11 countries. No major differences were found in microsatellite unstable cancers, but variations in mutation burden and signatures were observed in the 802 microsatellite-stable cases. Multiple signatures, most with unknown etiologies, exhibited varying prevalence in Argentina, Brazil, Colombia, Russia, and Thailand, indicating geographically diverse levels of mutagenic exposure. Signatures SBS88 and ID18, caused by the bacteria-produced mutagen colibactin, had higher mutation loads in countries with higher colorectal cancer incidence rates. SBS88 and ID18 were also enriched in early-onset colorectal cancers, being 3.3 times more common in individuals diagnosed before age 40 than in those over 70, and were imprinted early during colorectal cancer development. Colibactin exposure was further linked to APC driver mutations, with ID18 responsible for about 25% of APC driver indels in colibactin-positive cases. This study reveals geographic and age-related variations in colorectal cancer mutational processes, and suggests that early-life mutagenic exposure to colibactin-producing bacteria may contribute to the rising incidence of early-onset colorectal cancer.

Density and entropy of immune cells within the tumor microenvironment of primary tumors and matched brain metastases

BACKGROUND

Tumor-infiltrating lymphocytes (TILs) and tumor-associated macrophages (TAMs) have increasingly been reported to impact the brain metastatic process of solid tumors. However, data on intra-individual differences between primary tumor and brain metastasis (BM), as well as their correlation with clinical outcome parameters, is scarce.

METHODS

We retrospectively identified patients who received resection of the primary tumor and BM between 01/1990 and 10/2022. Density quantification of TAMs (CD68+, CD163+) and TILs (CD3+, CD8+, CD45RO+, FOXP3+) was performed by immunohistochemical staining of matched tumor tissue samples. Images were processed with QuPath software and heterogeneity of generated heatmaps was measured by Shannon Entropy. Time-to-BM (TTBM) was defined as the time from diagnosis of the primary tumor until the first diagnosis of BM.

RESULTS

In total, 104 patients (46.2% female; median age 57.3 years at BM diagnosis) were included: 78/104 (75%) non-small cell lung cancer, 18/104 (17%) breast cancer, 8/104 (8%) renal cell carcinomas. Densities of CD3+ (p < 0.001) and CD8+-TILs (p < 0.001) were higher in primary tumor samples, while CD68+ (p = 0.035) and CD163+-TAM densities (p < 0.001) were higher in the matched BM. Higher CD3+, CD8+-TILs and CD163+-TAMs densities in primary tumors were associated with shorter TTBM (p = 0.005, p = 0.015 and p = 0.006, respectively). Higher entropies of CD3+ (p < 0.001) and FOXP3+ (p = 0.011) TILs were observed in primary tumors compared to BM. Longer TTBM was associated with higher entropy of FOXP3+ TILs (p = 0.024) and lower entropy in CD163+ TAMs (p = 0.039). No significant associations of immune cell densities or entropies with OS after BM diagnosis were found.

DISCUSSION

By utilizing a unique cohort of matched primary tumor and BM tissue samples, we could demonstrate higher TIL densities in primary tumors and higher TAM densities in BM, respectively. Higher cell densities of CD3+, CD8+-TILs and CD163+-TAMs in primary tumors were associated with shorter TTBM, while a larger difference between CD3+ and CD8+ densities between primary tumor and BM was associated with longer TTBM. These findings highlight the potential of targeting TAMs as a therapeutic strategy to mitigate the development of brain metastases.

A Dirichlet-multinomial mixed model for determining differential abundance of mutational signatures

BACKGROUND

Mutational processes of diverse origin leave their imprints in the genome during tumour evolution. These imprints are called mutational signatures and they have been characterised for point mutations, structural variants and copy number changes. Each signature has an exposure, or abundance, per sample, which indicates how much a process has contributed to the overall genomic change. Mutational processes are not static, and a better understanding of their dynamics is key to characterise tumour evolution and identify cancer cell vulnerabilities that can be exploited during treatment. However, the structure of the data typically collected in this context makes it difficult to test whether signature exposures differ between conditions or time-points when comparing groups of samples. In general, the data consists of multivariate count mutational data (e.g. signature exposures) with two observations per patient, each reflecting a group.

RESULTS

We propose a mixed-effects Dirichlet-multinomial model: within-patient correlations are taken into account with random effects, possible correlations between signatures by making such random effects multivariate, and a group-specific dispersion parameter can deal with particularities of the groups. Moreover, the model is flexible in its fixed-effects structure, so that the two-group comparison can be generalised to several groups, or to a regression setting. We apply our approach to characterise differences of mutational processes between clonal and subclonal mutations across 23 cancer types of the PCAWG cohort. We find ubiquitous differential abundance of clonal and subclonal signatures across cancer types, and higher dispersion of signatures in the subclonal group, indicating higher variability between patients at subclonal level, possibly due to the presence of different clones with distinct active mutational processes.

CONCLUSIONS

Mutational signature analysis is an expanding field and we envision our framework to be used widely to detect global changes in mutational process activity. Our methodology is available in the R package CompSign and offers an ample toolkit for the analysis and visualisation of differential abundance of compositional data such as, but not restricted to, mutational signatures.

The Evolutionary Forest of Pancreatic Cancer

SIGNIFICANCE

Although the pancreatic cancer genome has been described, it has not been explored with respect to stages of diagnosis or treatment bottlenecks. We now describe and quantify the genomic features of PDAC in the context of evolutionary metrics and in doing so have identified a novel prognostic biomarker.

Spatial‒temporal heterogeneities of liver cancer and the discovery of the invasive zone

Solid tumours are intricate and highly heterogeneous ecosystems, which grow in and invade normal organs. Their progression is mediated by cancer cells' interaction with different cell types, such as immune cells, stromal cells and endothelial cells, and with the extracellular matrix. Owing to its high incidence, aggressive growth and resistance to local and systemic treatments, liver cancer has particularly high mortality rates worldwide. In recent decades, spatial heterogeneity has garnered significant attention as an unfavourable biological characteristic of the tumour microenvironment, prompting extensive research into its role in liver tumour development. Advances in spatial omics have facilitated the detailed spatial analysis of cell types, states and cell‒cell interactions, allowing a thorough understanding of the spatial and temporal heterogeneities of tumour microenvironment and informing the development of novel therapeutic approaches. This review illustrates the latest discovery of the invasive zone, and systematically introduced specific macroscopic spatial heterogeneities, pathological spatial heterogeneities and tumour microenvironment heterogeneities of liver cancer.

Harnessing ROS Amplification and GSH Depletion Using a Carrier-Free Nanodrug to Enhance Ferroptosis-Based Cancer Therapy

Ferroptosis, a non-apoptotic form of cell death characterized by the production of reactive oxygen species (ROS) and massive accumulation of lipid peroxidation (LPO), shows significant promise in cancer therapy. However, the overexpression of glutathione (GSH) at the tumor site and insufficient ROS often result in unsatisfactory therapeutic efficacy. A multistage, GSH-consuming, and ROS-providing carrier-free nanodrug capable of efficiently loading copper ions (Cu2+), sorafenib (SRF), and chlorogenic acid (CGA) (Cu2+-CGA-SRF, CCS-NDs) is developed to mediate enhanced ferroptosis therapy. Through a reductive intracellular environment, Cu2+ in the CCS-NDs reacted with intracellular GSH, alleviating the antioxidant capacity of tumor tissues and triggering the release of drugs. Meanwhile, the released SRF inhibited system xc-, thereby blocking cystine uptake and reducing GSH synthesis in tumor cells. By depleting stored GSH and inhibiting its synthesis, CCS-NDs achieved efficient GSH depletion and increased accumulation of toxic LPO. More importantly, the high concentration of CGA in the CCS-NDs induced ROS generation, further promoting ferroptosis. Both in vitro and in vivo results demonstrated that CCS-NDs effectively triggered ferroptosis in tumor cells by inactivating glutathione peroxidase 4 and inducing LPO. Overall, the carrier-free nanodrug CCS-NDs offer a promising strategy for regulating GSH and LPO levels in ferroptosis-based cancer therapy.

Epigenomics-guided precision oncology: Chromatin variants in prostate tumor evolution

Prostate cancer is a common malignancy that in 5%-30% leads to treatment-resistant and highly aggressive disease. Metastasis-potential and treatment-resistance is thought to rely on increased plasticity of the cancer cells-a mechanism whereby cancer cells alter their identity to adapt to changing environments or therapeutic pressures to create cellular heterogeneity. To understand the molecular basis of this plasticity, genomic studies have uncovered genetic variants to capture clonal heterogeneity of primary tumors and metastases. As cellular plasticity is largely driven by non-genetic events, complementary studies in cancer epigenomics are now being conducted to identify chromatin variants. These variants, defined as genomic loci in cancer cells that show changes in chromatin state due to the loss or gain of epigenomic marks, inclusive of histone post-translational modifications, DNA methylation and histone variants, are considered the fundamental units of epigenomic heterogeneity. In prostate cancer chromatin variants hold the promise of guiding the new era of precision oncology. In this review, we explore the role of epigenomic heterogeneity in prostate cancer, focusing on how chromatin variants contribute to tumor evolution and therapy resistance. We therefore discuss their impact on cellular plasticity and stochastic events, highlighting the value of single-cell sequencing and liquid biopsy epigenomic assays to uncover new therapeutic targets and biomarkers. Ultimately, this review aims to support a new era of precision oncology, utilizing insights from epigenomics to improve prostate cancer patient outcomes.

Enhancer regulatory networks globally connect non-coding breast cancer loci to cancer genes

BACKGROUND

Genetic studies have associated thousands of enhancers with breast cancer (BC). However, the vast majority have not been functionally characterized. Thus, it remains unclear how BC-associated enhancers contribute to cancer.

RESULTS

Here, we perform single-cell CRISPRi screens of 3513 regulatory elements associated with breast cancer to measure the impact of these regions on transcriptional phenotypes. Analysis of > 500,000 single-cell transcriptomes in two breast cancer cell lines shows that perturbation of BC-associated enhancers disrupts breast cancer gene programs. We observe BC-associated enhancers that directly or indirectly regulate the expression of cancer genes. We also find one-to-multiple and multiple-to-one network motifs where enhancers indirectly regulate cancer genes. Notably, multiple BC-associated enhancers indirectly regulate TP53. Comparative studies illustrate subtype specific functions between enhancers in ER + and ER - cells. Finally, we develop the pySpade package to facilitate analysis of single-cell enhancer screens.

CONCLUSIONS

Overall, we demonstrate that enhancers form regulatory networks that link cancer genes in the genome, providing a more comprehensive understanding of the contribution of enhancers to breast cancer development.

Digestive cancers: mechanisms, therapeutics and management

Cancers of the digestive system are major contributors to global cancer-associated morbidity and mortality, accounting for 35% of annual cases of cancer deaths. The etiologies, molecular features, and therapeutic management of these cancer entities are highly heterogeneous and complex. Over the last decade, genomic and functional studies have provided unprecedented insights into the biology of digestive cancers, identifying genetic drivers of tumor progression and key interaction points of tumor cells with the immune system. This knowledge is continuously translated into novel treatment concepts and targets, which are dynamically reshaping the therapeutic landscape of these tumors. In this review, we provide a concise overview of the etiology and molecular pathology of the six most common cancers of the digestive system, including esophageal, gastric, biliary tract, pancreatic, hepatocellular, and colorectal cancers. We comprehensively describe the current stage-dependent pharmacological management of these malignancies, including chemo-, targeted, and immunotherapy. For each cancer entity, we provide an overview of recent therapeutic advancements and research progress. Finally, we describe how novel insights into tumor heterogeneity and immune evasion deepen our understanding of therapy resistance and provide an outlook on innovative therapeutic strategies that will shape the future management of digestive cancers, including CAR-T cell therapy, novel antibody-drug conjugates and targeted therapies.

Characterizing the evolutionary dynamics of cancer proliferation in single-cell clones with SPRINTER

Proliferation is a key hallmark of cancer, but whether it differs between evolutionarily distinct clones co-existing within a tumor is unknown. We introduce the Single-cell Proliferation Rate Inference in Non-homogeneous Tumors through Evolutionary Routes (SPRINTER) algorithm that uses single-cell whole-genome DNA sequencing data to enable accurate identification and clone assignment of S- and G2-phase cells, as assessed by generating accurate ground truth data. Applied to a newly generated longitudinal, primary-metastasis-matched dataset of 14,994 non-small cell lung cancer cells, SPRINTER revealed widespread clone proliferation heterogeneity, orthogonally supported by Ki-67 staining, nuclei imaging and clinical imaging. We further demonstrated that high-proliferation clones have increased metastatic seeding potential, increased circulating tumor DNA shedding and clone-specific altered replication timing in proliferation- or metastasis-related genes associated with expression changes. Applied to previously generated datasets of 61,914 breast and ovarian cancer cells, SPRINTER revealed increased single-cell rates of different genomic variants and enrichment of proliferation-related gene amplifications in high-proliferation clones.

Aneuploidy in neoplasia: Single-cell data on 83,862 tumors

Chromosomal aneuploidy, that is, numerical chromosome aberrations, is one of the molecular hallmarks of cancer. However, when neoplasms are studied with sequencing- and array-based approaches, chromosome numbers and ploidy states are typically inferred from bulk DNA data. Furthermore, published molecular estimates of neoplasia-associated aneuploidy often also include genomic imbalances resulting from various types of structural rearrangement, which likely result from other mechanisms than numerical chromosome aberrations. We thus analyzed chromosome numbers using single-cell cytogenetic data from 83,862 tumors, and show that both benign and malignant tumors are highly heterogeneous with regard to deviations from the normal, diploid state. Focusing on the chromosome numbers in 112 specific tumor types, defined by both exact morphologic diagnosis and organ location and from which data from ≥50 cases were available, we found two major clusters: one predominated by near-diploid neoplasms and one by neoplasms with extensive aneuploidy and one or more whole genome doublings. The former cluster included most benign solid tumors, myeloid neoplasms, and malignant gene fusion-associated solid tumors, whereas the latter was predominated by malignant solid tumors and lymphomas. For 16 malignant tumor types, the distribution of chromosome numbers could be compared to TCGA ploidy level data. Cytogenetic and molecular data correlated well, but the former indicates a higher level of clonal heterogeneity. The results presented here suggest shared pathogenetic mechanisms in certain tumor types and provide a reference for molecular analyses.

Cancer phylogenetic inference using copy number alterations detected from DNA sequencing data

Cancer is an evolutionary process involving the accumulation of diverse somatic mutations and clonal evolution over time. Phylogenetic inference from samples obtained from an individual patient offers a powerful approach to unraveling the intricate evolutionary history of cancer and provides insights that can inform cancer treatment. Somatic copy number alterations (CNAs) are important in cancer evolution and are often used as markers, alone or with other somatic mutations, for phylogenetic inferences, particularly in low-coverage DNA sequencing data. Many phylogenetic inference methods using CNAs detected from bulk or single-cell DNA sequencing data have been developed over the years. However, there have been no systematic reviews on these methods. To summarize the state-of-the-art of the field and inform future development, this review presents a comprehensive survey on the major challenges in inference, different types of methods, and applications of these methods. The challenges are discussed from the aspects of input data, models of evolution, and inference algorithms. The different methods are grouped according to the markers used for inference and the types of the reconstructed trees. The applications include using phylogenetic inference to understand intra-tumor heterogeneity, metastasis, treatment resistance, and early cancer development. This review also sheds light on future directions of cancer phylogenetic inference using CNAs, including the improvement of scalability, the utilization of new types of data, and the development of more realistic models of evolution.

Epigenomic heterogeneity as a source of tumour evolution

In the past decade, remarkable progress in cancer medicine has been achieved by the development of treatments that target DNA sequence variants. However, a purely genetic approach to treatment selection is hampered by the fact that diverse cell states can emerge from the same genotype. In multicellular organisms, cell-state heterogeneity is driven by epigenetic processes that regulate DNA-based functions such as transcription; disruption of these processes is a hallmark of cancer that enables the emergence of defective cell states. Advances in single-cell technologies have unlocked our ability to quantify the epigenomic heterogeneity of tumours and understand its mechanisms, thereby transforming our appreciation of how epigenomic changes drive cancer evolution. This Review explores the idea that epigenomic heterogeneity and plasticity act as a reservoir of cell states and therefore as a source of tumour evolution. Best practices to quantify epigenomic heterogeneity and explore its various causes and consequences are discussed, including epigenomic reprogramming, stochastic changes and lasting memory. The design of new therapeutic approaches to restrict epigenomic heterogeneity, with the long-term objective of limiting cancer development and progression, is also addressed.

Spatial transcriptomics reveals unique metabolic profile and key oncogenic regulators of cervical squamous cell carcinoma

BACKGROUND

As a prevalent and deadly malignant tumor, the treatment outcomes for late-stage patients with cervical squamous cell carcinoma (CSCC) are often suboptimal. Previous studies have shown that tumor progression is closely related with tumor metabolism and microenvironment reshaping, with disruptions in energy metabolism playing a critical role in this process. To delve deeper into the understanding of CSCC development, our research focused on analyzing the tumor microenvironment and metabolic characteristics across different regions of tumor tissue.

METHODS

Utilizing spatial transcriptomics (ST) sequencing technology, we conducted a study on FFPE (formalin-fixed paraffin-embedded) tumor samples from CSCC patients. Coupled with single-cell RNA sequencing (scRNA-seq) data after deconvolution, we described spatial distribution maps of tumor leading edge and core regions in detail. Tumor tissues were classified into hypermetabolic and hypometabolic regions to analyze the metabolism profiles and tumor differentiation degree across different spatial areas. We also employed The Cancer Genome Atlas (TCGA) database to examine the analysis results of ST data.

RESULTS

Our findings indicated a more complex tumor microenvironment in hypermetabolic regions. Cell-cell communication analysis showed that various cells in tumor microenvironment were influenced by the signalling molecule APP released by cancer cells and higher expression of APP was observed in hypermetabolic regions. Furthermore, our results revealed the correlation between APP and the transcription factor TRPS1. Both APP and TRPS1 demonstrated significant effects on cancer cell proliferation, migration, and invasion, potentially contributing to tumor progression.

CONCLUSIONS

Utilizing ST, scRNA-seq, and TCGA database, we examined the spatial metabolic profiles of CSCC tissues, including metabolism distribution, metabolic variations, and the relationship between metabolism and tumor differentiation degree. Additionally, potential cancer-promoting factors were proposed, offering a valuable foundation for the development of more effective treatment strategies for CSCC.

Combined immunotherapy with dendritic cells and cytokine-induced killer cells for solid tumors: a systematic review and meta-analysis of randomized controlled trials

BACKGROUND

Immunotherapy utilizing dendritic cells (DCs) and cytokine-induced killer (CIK) cells is a promising treatment approach for solid tumors. This systematic review and meta-analysis aimed to evaluate the efficacy and safety of DC-CIK immunotherapy by assessing overall survival, progression-free survival, overall response rate, disease control rate, and adverse events in relevant randomized controlled trials. The results of this analysis will contribute to optimizing treatment strategies and improving cancer immunotherapy outcomes.

METHOD

This systematic review and meta-analysis adhered to PRISMA guidelines. A comprehensive search was conducted on multiple databases for RCTs studying the combination of DC-CIK immunotherapy for solid tumors. Inclusion criteria were RCTs comparing DC-CIK immunotherapy with control therapy and reporting OS, PFS, ORR, or DCR. Two authors independently performed study selection and data extraction, with disagreements resolved through consensus or consultation with a third reviewer. Extracted data included study characteristics, participant information, interventions, outcomes, and quality assessment. Statistical analysis was performed using Review Manager and Stata software. Heterogeneity was assessed using chi-square and I-squared statistics. Sensitivity analysis and assessment of publication bias were planned.

RESULTS

A total of 1013 records were initially retrieved, and after a thorough screening process, 13 randomized controlled trials (RCTs) were included in the meta-analysis. These studies involved a total of 1443 patients, with 730 receiving DC-CIK immunotherapy and 713 in the control groups. The included studies covered various cancer types, with the majority conducted in mainland China. The meta-analysis showed that DC-CIK immunotherapy was associated with improved overall survival (OS) and progression-free survival (PFS) compared to control therapy. Furthermore, DC-CIK immunotherapy demonstrated higher overall response rate (ORR) and disease control rate (DCR) compared to non-DC-CIK therapy. Adverse events were reported in both groups, with fever being more common in the DC-CIK immunotherapy group and bone marrow suppression and gastrointestinal reactions more common in the control group. Sensitivity analyses confirmed the stability of the results, while publication bias was observed for PFS and fever.

CONCLUSIONS

DC-CIK immunotherapy shows promising efficacy and safety for solid tumors, improving survival rates and response rates. Further research is needed to optimize treatment regimens and identify predictive factors.

Heterogeneity-driven phenotypic plasticity and treatment response in branched-organoid models of pancreatic ductal adenocarcinoma

In patients with pancreatic ductal adenocarcinoma (PDAC), intratumoural and intertumoural heterogeneity increases chemoresistance and mortality rates. However, such morphological and phenotypic diversities are not typically captured by organoid models of PDAC. Here we show that branched organoids embedded in collagen gels can recapitulate the phenotypic landscape seen in murine and human PDAC, that the pronounced molecular and morphological intratumoural and intertumoural heterogeneity of organoids is governed by defined transcriptional programmes (notably, epithelial-to-mesenchymal plasticity), and that different organoid phenotypes represent distinct tumour-cell states with unique biological features in vivo. We also show that phenotype-specific therapeutic vulnerabilities and modes of treatment-induced phenotype reprogramming can be captured in phenotypic heterogeneity maps. Our methodology and analyses of tumour-cell heterogeneity in PDAC may guide the development of phenotype-targeted treatment strategies.

Metabolic reprogramming in KRAS-mutant cancers: Proven targetable vulnerabilities and potential therapeutic strategies

Kras (Ki-ras2 Kirsten rat sarcoma viral oncogene homolog), one of the most frequently mutated oncogenes in the human genome, is considered 'untargetable'. Although specific KRASG12C inhibitors have been developed, their overall impact is limited, highlighting the need for further research on targeting KRAS-mutant cancers. Metabolic abnormalities are key hallmarks of cancer, with KRAS-driven tumors exhibiting traits like glycolysis upregulation, glutamine addiction, lipid droplet accumulation, highly active macropinocytosis, and metabolic reprogramming-associated tumor microenvironment remodeling. Targeting these unique metabolic characteristics offers a promising strategy for new cancer treatments. This review summarizes recent advances in our understanding of the metabolic network in KRAS-mutated tumor cells, discusses potential targetable vulnerabilities, and outlines clinical developments in relevant therapies, while also addressing challenges to improve strategies against these aggressive cancers.

Spatial transcriptomics in breast cancer reveals tumour microenvironment-driven drug responses and clonal therapeutic heterogeneity

Breast cancer patients are categorized into three subtypes with distinct treatment approaches. Precision oncology has increased patient outcomes by targeting the specific molecular alterations of tumours, yet challenges remain. Treatment failure persists due to the coexistence of several malignant subpopulations with different drug sensitivities within the same tumour, a phenomenon known as intratumour heterogeneity (ITH). This heterogeneity has been extensively studied from a tumour-centric view, but recent insights underscore the role of the tumour microenvironment in treatment response. Our research utilizes spatial transcriptomics data from breast cancer patients to predict drug sensitivity. We observe diverse response patterns across tumour, interphase and microenvironment regions, unveiling a sensitivity and functional gradient from the tumour core to the periphery. Moreover, we find tumour therapeutic clusters with different drug responses associated with distinct biological functions driven by unique ligand-receptor interactions. Importantly, we identify genetically identical subclones with different responses depending on their location within the tumour ducts. This research underscores the significance of considering the distance from the tumour core and microenvironment composition when identifying suitable treatments to target ITH. Our findings provide critical insights into optimizing therapeutic strategies, highlighting the necessity of a comprehensive understanding of tumour biology for effective cancer treatment.

Orchard: Building large cancer phylogenies using stochastic combinatorial search

Phylogenies depicting the evolutionary history of genetically heterogeneous subpopulations of cells from the same cancer, i.e., cancer phylogenies, offer valuable insights about cancer development and guide treatment strategies. Many methods exist that reconstruct cancer phylogenies using point mutations detected with bulk DNA sequencing. However, these methods become inaccurate when reconstructing phylogenies with more than 30 mutations, or, in some cases, fail to recover a phylogeny altogether. Here, we introduce Orchard, a cancer phylogeny reconstruction algorithm that is fast and accurate using up to 1000 mutations. Orchard samples without replacement from a factorized approximation of the posterior distribution over phylogenies, a novel result derived in this paper. Each factor in this approximate posterior corresponds to a conditional distribution for adding a new mutation to a partially built phylogeny. Orchard optimizes each factor sequentially, generating a sequence of incrementally larger phylogenies that ultimately culminate in a complete tree containing all mutations. Our evaluations demonstrate that Orchard outperforms state-of-the-art cancer phylogeny reconstruction methods in reconstructing more plausible phylogenies across 90 simulated cancers and 14 B-progenitor acute lymphoblastic leukemias (B-ALLs). Remarkably, Orchard accurately reconstructs cancer phylogenies using up to 1,000 mutations. Additionally, we demonstrate that the large and accurate phylogenies reconstructed by Orchard are useful for identifying patterns of somatic mutations and genetic variations among distinct cancer cell subpopulations.