Clonal evolution in cancer

Beyond basics: Key mutation selection features for successful tumor-informed ctDNA detection

Tumor-informed mutation-based approaches are frequently used for detection of circulating tumor DNA (ctDNA). Not all mutations make equally effective ctDNA markers. The objective was to explore if prioritizing mutations using mutational features-such as cancer cell fraction (CCF), multiplicity, and error rate-would improve the success rate of tumor-informed ctDNA analysis. Additionally, we aimed to develop a practical and easily implementable analysis pipeline for identifying and prioritizing candidate mutations from whole-exome sequencing (WES) data. We analyzed WES and ctDNA data from three tumor-informed ctDNA studies, one on bladder cancer (Cohort A) and two on colorectal cancer (Cohorts I and N). The studies included 390 patients. For each patient, a unique set of mutations (median mutations/patient: 6, interquartile 13, range: 1-46, total n = 4023) were used as markers of ctDNA. The tool PureCN was used to assess the CCF and multiplicity of each mutation. High-CCF mutations were detected more frequently than low-CCF mutations (Cohort A: odds ratio [OR] 20.6, 95% confidence interval [CI] 5.72-173, p = 1.73e-12; Cohort I: OR 2.24, 95% CI 1.44-3.52, p = 1.66e-04; and Cohort N: OR 1.78, 95% CI 1.14-2.79, p = 7.86e-03). The detection-likelihood was additionally improved by selecting mutations with multiplicity of two or above (Cohort A: OR 1.55, 95% CI 1. 14-2.11, p = 3.85e-03; Cohort I: OR 1.78, 95% CI 1.23-2.56, p = 1.34e-03; and Cohort N: OR 1.94, 95% CI 1.63-2.31, p = 2.83e-14). Furthermore, selecting the mutations for which the ctDNA detection method had the lowest error rates, additionally improved the detection-likelihood, particularly evident when plasma cell-free DNA tumor fractions were below 0.1% (p = 2.1e-07). Selecting mutational markers with high CCF, high multiplicity, and low error rate significantly improve ctDNA detection likelihood. We provide free access to the analysis pipeline enabling others to perform qualified prioritization of mutations for tumor-informed ctDNA analysis.

Multiple Primary Cancers With Hematologic Malignancies and Germline Predisposition: A Case Series

The term "multiple primary (MP) cancers" refers to the existence of more than one cancer in the same patient. The combination of MP cancers with hematological malignancies is relatively uncommon. In this study, we present five patients diagnosed with MP cancers concomitant with hematological malignancies. We comprehensively analyzed their clinical characteristics, cytogenetic profiles, and germline and somatic variants. As first primaries, two patients had solid cancer not followed by cytotoxic therapy and three had hematologic cancer, followed by cytotoxic therapy. The second primaries were all hematologic malignancies that did not meet the criteria for therapy-related myeloid neoplasm. Notably, two (40%) out of the five patients harbored pathogenic potential/presumed germline variants in cancer predisposition genes. Therefore, germline variant testing should be considered when MP cancers with hematological malignancies require consideration for related donor stem cell transplantation.

A distinct subset of urothelial cells with enhanced EMT features promotes chemotherapy resistance and cancer recurrence by increasing COL4A1-ITGB1 mediated angiogenesis

Drug resistance and tumor recurrence remain clinical challenges in the treatment of urothelial carcinoma (UC). However, the underlying mechanism is not fully understood. Here, we performed single-cell RNA sequencing and identified a subset of urothelial cells with epithelial-mesenchymal transition (EMT) features (EMT-UC), which is significantly correlated with chemotherapy resistance and cancer recurrence. To validate the clinical significance of EMT-UC, we constructed EMT-UC like cells by introducing overexpression of two markers, Zinc Finger E-Box Binding Homeobox 1 (ZEB1) and Desmin (DES), and examined their histological distribution characteristics and malignant phenotypes. EMT-UC like cells were mainly enriched in UC tissues from patients with adverse prognosis and exhibited significantly elevated EMT, migration and gemcitabine tolerance in vitro. However, EMT-UC was not specifically identified from tumorous tissues, certain proportion of them were also identified in adjacent normal tissues. Tumorous EMT-UC highly expressed genes involved in malignant behaviors and exhibited adverse prognosis. Additionally, tumorous EMT-UC was associated with remodeled tumor microenvironment (TME), which exhibited high angiogenic and immunosuppressive potentials compared with the normal counterparts. Furthermore, a specific interaction of COL4A1 and ITGB1 was identified to be highly enriched in tumorous EMT-UC, and in the endothelial component. Targeting the interaction of COL4A1 and ITGB1 with specific antibodies significantly suppressed tumorous angiogenesis and alleviated gemcitabine resistance of UC. Overall, our findings demonstrated that the driven force of chemotherapy resistance and recurrence of UC was EMT-UC mediated COL4A1-ITGB1 interaction, providing a potential target for future UC treatment.

Spatial intratumor heterogeneity of programmed death-ligand 1 expression predicts poor prognosis in resected non-small cell lung cancer

BACKGROUND

We quantified the pathological spatial intratumor heterogeneity of programmed death-ligand 1 (PD-L1) expression and investigated its relevance to patient outcomes in surgically resected non-small cell lung carcinoma (NSCLC).

METHODS

This study enrolled 239 consecutive surgically resected NSCLC specimens of pathological stage IIA-IIIB. To characterize the spatial intratumor heterogeneity of PD-L1 expression in NSCLC tissues, we developed a mathematical model based on texture image analysis and determined the spatial heterogeneity index of PD-L1 for each tumor. The correlation between the spatial heterogeneity index of PD-L1 values and clinicopathological characteristics, including prognosis, was analyzed. Furthermore, an independent cohort of 70 cases was analyzed for model validation.

RESULTS

Clinicopathological analysis showed correlations between high spatial heterogeneity index of PD-L1 values and histological subtype (squamous cell carcinoma; P < .001) and vascular invasion (P = .004). Survival analysis revealed that patients with high spatial heterogeneity index of PD-L1 values presented a significantly worse recurrence-free rate than those with low spatial heterogeneity index of PD-L1 values (5-year recurrence-free survival [RFS] = 26.3% vs 47.1%, P < .005). The impact of spatial heterogeneity index of PD-L1 on cancer survival rates was verified through validation in an independent cohort. Additionally, high spatial heterogeneity index of PD-L1 values were associated with tumor recurrence in squamous cell carcinoma (5-year RFS = 29.2% vs 52.8%, P < .05) and adenocarcinoma (5-year RFS = 19.6% vs 43.0%, P < .01). Moreover, we demonstrated that a high spatial heterogeneity index of PD-L1 value was an independent risk factor for tumor recurrence.

CONCLUSIONS

We presented an image analysis model to quantify the spatial intratumor heterogeneity of protein expression in tumor tissues. This model demonstrated that the spatial intratumor heterogeneity of PD-L1 expression in surgically resected NSCLC predicts poor patient outcomes.

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

Summary

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

Availability and implementation

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

Frequency-dependent selection of neoantigens fosters tumor immune escape and predicts immunotherapy response

Cancer is an evolutionary process shaped by selective pressure from the microenvironments. However, recent studies reveal that certain tumors undergo neutral evolution where there is no detectable fitness difference amongst the cells following malignant transformation. Here, through computational modeling, we demonstrate that negative frequency-dependent selection (or NFDS), where the immune response against cancer cells depends on the clonality of neoantigens, can lead to an immunogenic landscape that is highly similar to neutral evolution. Crucially, NFDS promotes high antigenic heterogeneity and early immune evasion in hypermutable tumors, leading to poor responses to immune checkpoint blockade (ICB) therapy. Our model also reveals that NFDS is characterized by a negative association between average clonality and total burden of neoantigens. Indeed, this unique feature of NFDS is common in the whole-exome sequencing (WES) datasets (357 tumor samples from 275 patients) from four melanoma cohorts with ICB therapy and a non-small cell lung cancer (NSCLC) WES dataset (327 tumor samples from 100 patients). Altogether, our study provides quantitative evidence supporting the theory of NFDS in cancer, explaining the high prevalence of neutral-looking tumors. These findings also highlight the critical role of frequency-dependent selection in devising more efficient and predictive immunotherapies.

Advancing cancer drug development with mechanistic mathematical modeling: bridging the gap between theory and practice

Quantitative predictive modeling of cancer growth, progression, and individual response to therapy is a rapidly growing field. Researchers from mathematical modeling, systems biology, pharmaceutical industry, and regulatory bodies, are collaboratively working on predictive models that could be applied for drug development and, ultimately, the clinical management of cancer patients. A plethora of modeling paradigms and approaches have emerged, making it challenging to compile a comprehensive review across all subdisciplines. It is therefore critical to gauge fundamental design aspects against requirements, and weigh opportunities and limitations of the different model types. In this review, we discuss three fundamental types of cancer models: space-structured models, ecological models, and immune system focused models. For each type, it is our goal to illustrate which mechanisms contribute to variability and heterogeneity in cancer growth and response, so that the appropriate architecture and complexity of a new model becomes clearer. We present the main features addressed by each of the three exemplary modeling types through a subjective collection of literature and illustrative exercises to facilitate inspiration and exchange, with a focus on providing a didactic rather than exhaustive overview. We close by imagining a future multi-scale model design to impact critical decisions in oncology drug development.

Gene expression profiling in porocarcinoma indicates heterogeneous tumor development and substantiates poromas as precursor lesions

BACKGROUND AND OBJECTIVES

Malignant sweat gland tumors are rare, with the most common being eccrine porocarcinoma (EP). Approximately 18% of benign eccrine poroma (EPO) transit to EP. Previous research has provided first insights into the mutational landscape of EP. However, only few studies have performed gene expression analyses. This leaves a gap in the understanding of EP biology and potential drivers of malignant transformation from EPO to EP.

METHODS

Transcriptome profiling of 23 samples of primary EP and normal skin (NS). Findings from the EP samples were then tested in 17 samples of EPO.

RESULTS

Transcriptome profiling revealed diversity in gene expression and indicated biologically heterogeneous sub-entities as well as widespread gene downregulation in EP. Downregulated genes included CD74, NDGR1, SRRM2, CDC42, ANXA2, KFL9 and NOP53. Expression levels of CD74, NDGR1, SRRM2, ANXA2, and NOP53 showed a stepwise-reduction in expression from NS via EPO to EP, thus supporting the hypothesis that EPO represents a transitional state in EP development.

CONCLUSIONS

We demonstrated that EP is molecularly complex and that evolutionary trajectories correspond to tumor initiation and progression. Our results provide further evidence implicating the p53 axis and the EGFR pathway. Larger samples are warranted to confirm our findings.

Experimental design considerations and statistical analyses in preclinical tumor growth inhibition studies

Animal models are used in cancer pre-clinical research to identify drug targets, select compound candidates for clinical trials, determine optimal drug dosages, identify biomarkers, and ensure compound safety. This tutorial aims to provide an overview of study design and data analysis from animal studies, focusing on tumor growth inhibition (TGI) studies used for prioritization of anticancer compounds. Some of the experimental design aspects discussed here include the selection of the appropriate biological models, the choice of endpoints to be used for the assessment of anticancer activity (tumor volumes, tumor growth rates, events, or categorical endpoints), considerations on measurement errors and potential biases related to this type of study, sample size estimation, and discussions on missing data handling. The tutorial also reviews the statistical analyses employed in TGI studies, considering both continuous endpoints collected at single time-point and continuous endpoints collected longitudinally over multiple time-points. Additionally, time-to-event analysis is discussed for studies focusing on event occurrences such as animal deaths or tumor size reaching a certain threshold. Furthermore, for TGI studies involving categorical endpoints, statistical methodology is outlined to compare outcomes among treatment groups effectively. Lastly, this tutorial also discusses analysis for assessing drug combination synergy in TGI studies, which involves combining treatments to enhance overall treatment efficacy. The tutorial also includes R sample scripts to help users to perform relevant data analysis of this topic.

A somatic genetic clock for clonal species

Age and longevity are key parameters for demography and life-history evolution of organisms. In clonal species, a widespread life history among animals, plants, macroalgae and fungi, the sexually produced offspring (genet) grows indeterminately by producing iterative modules, or ramets, and so obscure their age. Here we present a novel molecular clock based on the accumulation of fixed somatic genetic variation that segregates among ramets. Using a stochastic model, we demonstrate that the accumulation of fixed somatic genetic variation will approach linearity after a lag phase, and is determined by the mitotic mutation rate, without direct dependence on asexual generation time. The lag phase decreased with lower stem cell population size, number of founder cells for the formation of new modules, and the ratio of symmetric versus asymmetric cell divisions. We calibrated the somatic genetic clock on cultivated eelgrass Zostera marina genets (4 and 17 years respectively). In a global data set of 20 eelgrass populations, genet ages were up to 1,403 years. The somatic genetic clock is applicable to any multicellular clonal species where the number of founder cells is small, opening novel research avenues to study longevity and, hence, demography and population dynamics of clonal species.

Clonal tracking in cancer and metastasis

The eradication of many cancers has proven challenging due to the presence of functionally and genetically heterogeneous clones maintained by rare cancer stem cells (CSCs), which contribute to disease progression, treatment refractoriness, and late relapse. The characterization of functional CSC activity has necessitated the development of modern clonal tracking strategies. This review describes viral-based and CRISPR-Cas9-based cellular barcoding, lineage tracing, and imaging-based approaches. DNA-based cellular barcoding technology is emerging as a powerful and robust strategy that has been widely applied to in vitro and in vivo model systems, including patient-derived xenograft models. This review also highlights the potential of these methods for use in the clinical and drug discovery contexts and discusses the important insights gained from such approaches.

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

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

Analysis of somatic mutations in whole blood from 200,618 individuals identifies pervasive positive selection and novel drivers of clonal hematopoiesis

Human aging is marked by the emergence of a tapestry of clonal expansions in dividing tissues, particularly evident in blood as clonal hematopoiesis (CH). CH, linked to cancer risk and aging-related phenotypes, often stems from somatic mutations in a set of established genes. However, the majority of clones lack known drivers. Here we infer gene-level positive selection in whole blood exomes from 200,618 individuals in UK Biobank. We identify 17 additional genes, ZBTB33, ZNF318, ZNF234, SPRED2, SH2B3, SRCAP, SIK3, SRSF1, CHEK2, CCDC115, CCL22, BAX, YLPM1, MYD88, MTA2, MAGEC3 and IGLL5, under positive selection at a population level, and validate this selection pattern in 10,837 whole genomes from single-cell-derived hematopoietic colonies. Clones with mutations in these genes grow in frequency and size with age, comparable to classical CH drivers. They correlate with heightened risk of infection, death and hematological malignancy, highlighting the significance of these additional genes in the aging process.

Cetuximab chemotherapy resistance: Insight into the homeostatic evolution of head and neck cancer (Review)

The complex evolution of genetic alterations in cancer that occurs in vivo is a selective process involving numerous factors and mechanisms. Chemotherapeutic agents that prevent the growth and spread of cancer cells induce selective pressure, leading to rapid artificial selection of resistant subclones. This rapid evolution is possible because antineoplastic drugs promote alterations in tumor‑cell metabolism, thus creating a bottleneck event. The few resistant cells that survive in this new environment obtain differential reproductive success that enables them to pass down the newly selected resistant gene pool. The present review aims to summarize key findings of tumor evolution, epithelial‑mesenchymal transition and resistance to cetuximab therapy in head and neck squamous cell carcinoma.

A practical algorithm for acute myeloid leukaemia diagnosis following the updated 2022 classifications

Disease classification of complex and heterogenous diseases, such as acute myeloid leukaemia (AML), is continuously updated to define diagnoses, appropriate treatments, and assist research and education. Recent availability of molecular profiling techniques further benefits the classification of AML. The World Health Organization (WHO) classification of haematolymphoid tumours and the International Consensus Classification of myeloid neoplasms and acute leukaemia from 2022 are two updated versions of the WHO 2016 classification. As a consequence, the European LeukemiaNet 2022 recommendations on the diagnosis and management of AML in adults have been also updated. The current review provides a practical interpretation of these guidelines to facilitate the diagnosis of AML and discusses genetic testing, disease genetic heterogeneity, and FLT3 mutations. We propose a practical algorithm for the speedy diagnosis of AML. Future classifications may need to incorporate gene mutation combinations to enable personalised treatment regimens in the management of patients with AML.

Mutations in cancer-relevant genes are ubiquitous in histologically normal endometrial tissue

OBJECTIVE

Endometrial cancer (EndoCA) is the most common gynecologic cancer and incidence and mortality rate continue to increase. Despite well-characterized knowledge of EndoCA-defining mutations, no effective diagnostic or screening tests exist. To lay the foundation for testing development, our study focused on defining the prevalence of somatic mutations present in non-cancerous uterine tissue.

METHODS

We obtained ≥8 uterine samplings, including separate endometrial and myometrial layers, from each of 22 women undergoing hysterectomy for non-cancer conditions. We ultra-deep sequenced (>2000× coverage) samples using a 125 cancer-relevant gene panel.

RESULTS

All women harbored complex mutation patterns. In total, 308 somatic mutations were identified with mutant allele frequencies ranging up to 96.0%. These encompassed 56 unique mutations from 24 genes. The majority of samples possessed predicted functional cancer mutations but curiously no growth advantage over non-functional mutations was detected. Functional mutations were enriched with increasing patient age (p = 0.045) and BMI (p = 0.0007) and in endometrial versus myometrial layers (68% vs 39%, p = 0.0002). Finally, while the somatic mutation landscape shared similar mutation prevalence in key TCGA-defined EndoCA genes, notably PIK3CA, significant differences were identified, including NOTCH1 (77% vs 10%), PTEN (9% vs 61%), TP53 (0% vs 37%) and CTNNB1 (0% vs 26%).

CONCLUSIONS

An important caveat for future liquid biopsy/DNA-based cancer diagnostics is the repertoire of shared and distinct mutation profiles between histologically unremarkable and EndoCA tissues. The lack of selection pressure between functional and non-functional mutations in histologically unremarkable uterine tissue may offer a glimpse into an unrecognized EndoCA protective mechanism.

Mutations, substitutions, and selection: Linking mutagenic processes to cancer using evolutionary theory

Cancers are the product of evolutionary events, where molecular variation occurs and accumulates in tissues and tumors. Sequencing of this molecular variation informs not only which variants are driving tumorigenesis, but also the mechanisms behind what is fueling mutagenesis. Both of these details are crucial for preventing premature deaths due to cancer, whether it is by targeting the variants driving the cancer phenotype or by measures to prevent exogenous mutations from contributing to somatic evolution. Here, we review tools to determine both molecular signatures and cancer drivers, and avenues by which these metrics may be linked.

Revolutionizing Glioblastoma Treatment: A Comprehensive Overview of Modern Therapeutic Approaches

Glioblastoma is the most common malignant primary brain tumor in the adult population, with an average survival of 12.1 to 14.6 months. The standard treatment, combining surgery, radiotherapy, and chemotherapy, is not as efficient as we would like. However, the current possibilities are no longer limited to the standard therapies due to rapid advancements in biotechnology. New methods enable a more precise approach by targeting individual cells and antigens to overcome cancer. For the treatment of glioblastoma, these are gamma knife therapy, proton beam therapy, tumor-treating fields, EGFR and VEGF inhibitors, multiple RTKs inhibitors, and PI3K pathway inhibitors. In addition, the increasing understanding of the role of the immune system in tumorigenesis and the ability to identify tumor-specific antigens helped to develop immunotherapies targeting GBM and immune cells, including CAR-T, CAR-NK cells, dendritic cells, and immune checkpoint inhibitors. Each of the described methods has its advantages and disadvantages and faces problems, such as the inefficient crossing of the blood-brain barrier, various neurological and systemic side effects, and the escape mechanism of the tumor. This work aims to present the current modern treatments of glioblastoma.

Double-faced CX3CL1 enhances lymphangiogenesis-dependent metastasis in an aggressive subclone of oral squamous cell carcinoma

Because cancer cells have a genetically unstable nature, they give rise to genetically different variant subclones inside a single tumor. Understanding cancer heterogeneity and subclone characteristics is crucial for developing more efficacious therapies. Oral squamous cell carcinoma (OSCC) is characterized by high heterogeneity and plasticity. On the other hand, CX3C motif ligand 1 (CX3CL1) is a double-faced chemokine with anti- and pro-tumor functions. Our study reported that CX3CL1 functioned differently in tumors with different cancer phenotypes, both in vivo and in vitro. Mouse OSCC 1 (MOC1) and MOC2 cells responded similarly to CX3CL1 in vitro. However, in vivo, CX3CL1 increased keratinization in indolent MOC1 cancer, while CX3CL1 promoted cervical lymphatic metastasis in aggressive MOC2 cancer. These outcomes were due to double-faced CX3CL1 effects on different immune microenvironments indolent and aggressive cancer created. Furthermore, we established that CX3CL1 promoted cancer metastasis via the lymphatic pathway by stimulating lymphangiogenesis and transendothelial migration of lymph-circulating tumor cells. CX3CL1 enrichment in lymphatic metastasis tissues was observed in aggressive murine and human cell lines. OSCC patient samples with CX3CL1 enrichment exhibited a strong correlation with lower overall survival rates and higher recurrence and distant metastasis rates. In conclusion, CX3CL1 is a pivotal factor that stimulates the metastasis of aggressive cancer subclones within the heterogeneous tumors to metastasize, and our study demonstrates the prognostic value of CX3CL1 enrichment in long-term monitoring in OSCC.

Oncogenic composite mutations can be predicted by co-mutations and their chromosomal location

Genetic heterogeneity in tumors can show a remarkable selectivity when two or more independent genetic events occur in the same gene. This phenomenon, called composite mutation, points toward a selective pressure, which frequently causes therapy resistance to mutation-specific drugs. Since composite mutations have been described to occur in sub-clonal populations, they are not always captured through biopsy sampling. Here, we provide a proof of concept to predict composite mutations to anticipate which patients might be at risk for sub-clonally driven therapy resistance. We found that composite mutations occur in 5% of cancer patients, mostly affecting the PIK3CA, EGFR, BRAF, and KRAS genes, which are common precision medicine targets. Furthermore, we found a strong and significant relationship between the frequencies of composite mutations with commonly co-occurring mutations in a non-composite context. We also found that co-mutations are significantly enriched on the same chromosome. These observations were independently confirmed using cell line data. Finally, we show the feasibility of predicting compositive mutations based on their co-mutations (AUC 0.62, 0.81, 0.82, and 0.91 for EGFR, PIK3CA, KRAS, and BRAF, respectively). This prediction model could help to stratify patients who are at risk of developing therapy resistance-causing mutations.

HPV oncogenes expressed from only one of multiple integrated HPV DNA copies drive clonal cell expansion in cervical cancer

The integration of HPV DNA into human chromosomes plays a pivotal role in the onset of papillomavirus-related cancers. HPV DNA integration often occurs by linearizing the viral DNA in the E1/E2 region, resulting in the loss of a critical viral early polyadenylation signal (PAS), which is essential for the polyadenylation of the E6E7 bicistronic transcripts and for the expression of the viral E6 and E7 oncogenes. Here, we provide compelling evidence that, despite the presence of numerous integrated viral DNA copies, virus-host fusion transcripts originate from only a single integrated HPV DNA in HPV16 and HPV18 cervical cancers and cervical cancer-derived cell lines. The host genomic elements neighboring the integrated HPV DNA are critical for the efficient expression of the viral oncogenes that leads to clonal cell expansion. The fusion RNAs that are produced use a host RNA polyadenylation signal downstream of the integration site, and almost all involve splicing to host sequences. In cell culture, siRNAs specifically targeting the host portion of the virus-host fusion transcripts effectively silenced viral E6 and E7 expression. This, in turn, inhibited cell growth and promoted cell senescence in HPV16+ CaSki and HPV18+ HeLa cells. Showing that HPV E6 and E7 expression from a single integration site is instrumental in clonal cell expansion sheds new light on the mechanisms of HPV-induced carcinogenesis and could be used for the development of precision medicine tailored to combat HPV-related malignancies.

IMPORTANCE

Persistent oncogenic HPV infections lead to viral DNA integration into the human genome and the development of cervical, anogenital, and oropharyngeal cancers. The expression of the viral E6 and E7 oncogenes plays a key role in cell transformation and tumorigenesis. However, how E6 and E7 could be expressed from the integrated viral DNA which often lacks a viral polyadenylation signal in the cancer cells remains unknown. By analyzing the integrated HPV DNA sites and expressed HPV RNAs in cervical cancer tissues and cell lines, we show that HPV oncogenes are expressed from only one of multiple chromosomal HPV DNA integrated copies. A host polyadenylation signal downstream of the integrated viral DNA is used for polyadenylation and stabilization of the virus-host chimeric RNAs, making the oncogenic transcripts targetable by siRNAs. This observation provides further understanding of the tumorigenic mechanism of HPV integration and suggests possible therapeutic strategies for the development of precision medicine for HPV cancers.

Distinct molecular profiles drive multifaceted characteristics of colorectal cancer metastatic seeds

Metastasis of primary tumors remains a challenge for early diagnosis and prevention. The cellular properties and molecular drivers of metastatically competent clones within primary tumors remain unclear. Here, we generated 10-16 single cell-derived lines from each of three colorectal cancer (CRC) tumors to identify and characterize metastatic seeds. We found that intrinsic factors conferred clones with distinct metastatic potential and cellular communication capabilities, determining organ-specific metastasis. Poorly differentiated or highly metastatic clones, rather than drug-resistant clones, exhibited poor clinical prognostic impact. Personalized genetic alterations, instead of mutation burden, determined the occurrence of metastatic potential during clonal evolution. Additionally, we developed a gene signature for capturing metastatic potential of primary CRC tumors and demonstrated a strategy for identifying metastatic drivers using isogenic clones with distinct metastatic potential in primary tumors. This study provides insight into the origin and mechanisms of metastasis and will help develop potential anti-metastatic therapeutic targets for CRC patients.

Neoantigen Identification and Dendritic Cell-Based Vaccines for Lung Cancer Immunotherapy

Immunotherapies can treat many cancers, including difficult-to-treat cases such as lung cancer. Due to its tolerability, long-lasting therapeutic responses, and efficacy in a wide spectrum of patients, immunotherapy can also help to treat lung cancer, which has few treatment choices. Tumor-specific antigens (TSAs) for cancer vaccinations and T-cell therapies are difficult to discover. Neoantigens (NeoAgs) from genetic mutations, irregular RNA splicing, protein changes, or viral genetic sequences in tumor cells provide a solution. NeoAgs, unlike TSAs, are non-self and can cause an immunological response. Next-generation sequencing (NGS) and bioinformatics can swiftly detect and forecast tumor-specific NeoAgs. Highly immunogenic NeoAgs provide personalized or generalized cancer immunotherapies. Dendritic cells (DCs), which originate and regulate T-cell responses, are widely studied potential immunotherapeutic therapies for lung cancer and other cancers. DC vaccines are stable, reliable, and safe in clinical trials. The purpose of this article is to evaluate the current status, limitations, and prospective clinical applications of DC vaccines, as well as the identification and selection of major histocompatibility complex (MHC) class I and II genes for NeoAgs. Our goal is to explain DC biology and activate DC manipulation to help researchers create extremely potent cancer vaccines for patients.

Genomic alterations in ovarian endometriosis and subsequently diagnosed ovarian carcinoma

STUDY QUESTION

Can the alleged association between ovarian endometriosis and ovarian carcinoma be substantiated by genetic analysis of endometriosis diagnosed prior to the onset of the carcinoma?

SUMMARY ANSWER

The data suggest that ovarian carcinoma does not originate from ovarian endometriosis with a cancer-like genetic profile; however, a common precursor is probable.

WHAT IS KNOWN ALREADY

Endometriosis has been implicated as a precursor of ovarian carcinoma based on epidemiologic studies and the discovery of common driver mutations in synchronous disease at the time of surgery. Endometrioid ovarian carcinoma and clear cell ovarian carcinoma are the most common endometriosis-associated ovarian carcinomas (EAOCs).

STUDY DESIGN, SIZE, DURATION

The pathology biobanks of two university hospitals in Sweden were scrutinized to identify women with surgically removed endometrioma who subsequently developed ovarian carcinoma (1998-2016). Only 45 archival cases with EAOC and previous endometriosis were identified and after a careful pathology review, 25 cases were excluded due to reclassification into non-EAOC (n = 9) or because ovarian endometriosis could not be confirmed (n = 16). Further cases were excluded due to insufficient endometriosis tissue or poor DNA quality in either the endometriosis, carcinoma, or normal tissue (n = 9). Finally 11 cases had satisfactory DNA from all three locations and were eligible for further analysis.

PARTICIPANTS/MATERIALS, SETTING, METHODS

Epithelial cells were collected from formalin-fixed and paraffin-embedded (FFPE) sections by laser capture microdissection (endometrioma n = 11) or macrodissection (carcinoma n = 11) and DNA was extracted. Normal tissue from FFPE sections (n = 5) or blood samples collected at cancer diagnosis (n = 6) were used as the germline controls for each included patient. Whole-exome sequencing was performed (n = 33 samples). Somatic variants (single-nucleotide variants, indels, and copy number alterations) were characterized, and mutational signatures and kataegis were assessed. Microsatellite instability and mismatch repair status were confirmed with PCR and immunohistochemistry, respectively.

MAIN RESULTS AND THE ROLE OF CHANCE

The median age for endometriosis surgery was 42 years, and 54 years for the subsequent ovarian carcinoma diagnosis. The median time between the endometriosis and ovarian carcinoma was 10 (7-30) years. The data showed that all paired samples harbored one or more shared somatic mutations. Non-silent mutations in cancer-associated genes were frequent in endometriosis; however, the same mutations were never observed in subsequent carcinomas. The degree of clonal dominance, demonstrated by variant allele frequency, showed a positive correlation with the time to cancer diagnosis (Spearman's rho 0.853, P < 0.001). Mutations in genes associated with immune escape were the most conserved between paired samples, and regions harboring these genes were frequently affected by copy number alterations in both sample types. Mutational burdens and mutation signatures suggested faulty DNA repair mechanisms in all cases.

LARGE SCALE DATA

Datasets are available in the supplementary tables.

LIMITATIONS, REASONS FOR CAUTION

Even though we located several thousands of surgically removed endometriomas between 1998 and 2016, only 45 paired samples were identified and even fewer, 11 cases, were eligible for sequencing. The observed high level of intra- and inter-heterogeneity in both groups (endometrioma and carcinoma) argues for further studies of the alleged genetic association.

WIDER IMPLICATIONS OF THE FINDINGS

The observation of shared somatic mutations in all paired samples supports a common cellular origin for ovarian endometriosis and ovarian carcinoma. However, contradicting previous conclusions, our data suggest that cancer-associated mutations in endometriosis years prior to the carcinoma were not directly associated with the malignant transformation. Rather, a resilient ovarian endometriosis may delay tumorigenesis. Furthermore, the data indicate that genetic alterations affecting the immune response are early and significant events.

STUDY FUNDING/COMPETING INTEREST(S)

The present work has been funded by the Sjöberg Foundation (2021-01145 to K.S.; 2022-01-11:4 to A.S.), Swedish state under the agreement between the Swedish government and the county councils, the ALF-agreement (965552 to K.S.; 40615 to I.H.; 965065 to A.S.), Swedish Cancer Society (21-1848 to K.S.; 21-1684 to I.H.; 22-2080 to A.S.), BioCARE-A Strategic Research Area at Lund University (I.H. and S.W.-F.), Mrs Berta Kamprad's Cancer Foundation (FBKS-2019-28, I.H.), Cancer and Allergy Foundation (10381, I.H.), Region Västra Götaland (A.S.), Sweden's Innovation Agency (2020-04141, A.S.), Swedish Research Council (2021-01008, A.S.), Roche in collaboration with the Swedish Society of Gynecological Oncology (S.W.-F.), Assar Gabrielsson Foundation (FB19-86, C.M.), and the Lena Wäpplings Foundation (C.M.). A.S. declares stock ownership and is also a board member in Tulebovaasta, SiMSen Diagnostics, and Iscaff Pharma. A.S. has also received travel support from EMBL, Precision Medicine Forum, SLAS, and bioMCC. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Realizing the promise of Project Optimus: Challenges and emerging opportunities for dose optimization in oncology drug development

Project Optimus is a US Food and Drug Administration Oncology Center of Excellence initiative aimed at reforming the dose selection and optimization paradigm in oncology drug development. This project seeks to bring together pharmaceutical companies, international regulatory agencies, academic institutions, patient advocates, and other stakeholders. Although there is much promise in this initiative, there are several challenges that need to be addressed, including multidimensionality of the dose optimization problem in oncology, the heterogeneity of cancer and patients, importance of evaluating long-term tolerability beyond dose-limiting toxicities, and the lack of reliable biomarkers for long-term efficacy. Through the lens of Totality of Evidence and with the mindset of model-informed drug development, we offer insights into dose optimization by building a quantitative knowledge base integrating diverse sources of data and leveraging quantitative modeling tools to build evidence for drug dosage considering exposure, disease biology, efficacy, toxicity, and patient factors. We believe that rational dose optimization can be achieved in oncology drug development, improving patient outcomes by maximizing therapeutic benefit while minimizing toxicity.

Multifactorial drug carrier system bringing both chemical and physical therapeutics to the treatment of tumor heterogeneity

Tumor heterogeneity and drug resistance have been invincible features of cancer for its complete cure. Despite the advent of immunotherapy, the expansion and diversification of cancer cells evolved even in the absence or presence of drug treatment discourage additional therapeutic interventions. For the eradication of cancer cells, therefore, an 'all-at-once' strategy is required, which exploits both target-selective chemotherapy and non-selective physicotherapy. Multifactorial microcapsules comprising gold nanoparticles (AuNPs) and a self-assembly protein of α-synuclein (αS) were fabricated, in which hydrophobic and hydrophilic drugs could be separately encapsulated by employing lipid-based inverted micelles (IMs). Their combined physico-chemical therapeutic effects were examined since they also contained both membrane-disrupting IMs and heat-generating AuNPs upon irradiation as physicotherapeutic agents. For the optimal enclosure of IMs containing hydrophilic drugs, a porous inner skeleton made of poly(lactic-co-glycolic acid) was introduced, which would play the roles of not only compartmentalizing the internal space but also enhancing proteolytic disintegration of the microcapsules to discharge and stabilize IMs to the outside. In fact, hydrophobic paclitaxel and hydrophilic doxorubicin showed markedly enhanced drug efficacy when delivered in the IM-containing microcapsules exhibiting the 'quantal' release of both drugs into the cells whose integrity could be also affected by the IMs. In addition, the remnants of αS-AuNP microcapsules produced via proteolysis also caused cell death through photothermal effect. The multifactorial microcapsules are therefore considered as a promising anti-cancer drug carrier capable of performing combinatorial selective and non-selective chemical and physical therapies to overcome tumor heterogeneity and drug resistance.

Acquired immune resistance is associated with interferon signature and modulation of KLF6/c-MYB transcription factors in myeloid leukemia

Resistance to immunity is associated with the selection of cancer cells with superior capacities to survive inflammatory reactions. Here, we tailored an ex vivo immune selection model for acute myeloid leukemia (AML) and isolated the residual subpopulations as "immune-experienced" AML (ieAML) cells. We confirmed that upon surviving the immune reactions, the malignant blasts frequently decelerated proliferation, displayed features of myeloid differentiation and activation, and lost immunogenicity. Transcriptomic analyses revealed a limited number of commonly altered pathways and differentially expressed genes in all ieAML cells derived from distinct parental cell lines. Molecular signatures predominantly associated with interferon and inflammatory cytokine signaling were enriched in the AML cells resisting the T-cell-mediated immune reactions. Moreover, the expression and nuclear localization of the transcription factors c-MYB and KLF6 were noted as the putative markers for immune resistance and identified in subpopulations of AML blasts in the patients' bone marrow aspirates. The immune modulatory capacities of ieAML cells lasted for a restricted period when the immune selection pressure was omitted. In conclusion, myeloid leukemia cells harbor subpopulations that can adapt to the harsh conditions established by immune reactions, and a previous "immune experience" is marked with IFN signature and may pave the way for susceptibility to immune intervention therapies.

Ginseng-derived compounds as potential anticancer agents targeting cancer stem cells

Cancer stem cells (CSCs) are a rare subpopulation of cancer cells that exhibit stem cell-like characteristics, including self-renewal and differentiation in a multi-stage lineage state via symmetric or asymmetric division, causing tumor initiation, heterogeneity, progression, and recurrence and posing a major challenge to current anticancer therapy. Despite the importance of CSCs in carcinogenesis and cancer progression, currently available anticancer therapeutics have limitations for eradicating CSCs. Moreover, the efficacy and therapeutic windows of currently available anti-CSC agents are limited, suggesting the necessity to optimize and develop a novel anticancer agent targeting CSCs. Ginseng has been traditionally used for enhancing immunity and relieving fatigue. As ginseng's long history of use has demonstrated its safety, it has gained attention for its potential pharmacological properties, including anticancer effects. Several studies have identified the bioactive principles of ginseng, such as ginseng saponin (ginsenosides) and non-saponin compounds (e.g., polysaccharides, polyacetylenes, and phenolic compounds), and their pharmacological activities, including antioxidant, anticancer, antidiabetic, antifatigue, and neuroprotective effects. Notably, recent reports have shown the potential of ginseng-derived compounds as anti-CSC agents. This review investigates the biology of CSCs and efforts to utilize ginseng-derived components for cancer treatment targeting CSCs, highlighting their role in overcoming current therapeutic limitations.

Unveiling the Dynamics behind Glioblastoma Multiforme Single-Cell Data Heterogeneity

Glioblastoma Multiforme is a brain tumor distinguished by its aggressiveness. We suggested that this aggressiveness leads single-cell RNA-sequence data (scRNA-seq) to span a representative portion of the cancer attractors domain. This conjecture allowed us to interpret the scRNA-seq heterogeneity as reflecting a representative trajectory within the attractor's domain. We considered factors such as genomic instability to characterize the cancer dynamics through stochastic fixed points. The fixed points were derived from centroids obtained through various clustering methods to verify our method sensitivity. This methodological foundation is based upon sample and time average equivalence, assigning an interpretative value to the data cluster centroids and supporting parameters estimation. We used stochastic simulations to reproduce the dynamics, and our results showed an alignment between experimental and simulated dataset centroids. We also computed the Waddington landscape, which provided a visual framework for validating the centroids and standard deviations as characterizations of cancer attractors. Additionally, we examined the stability and transitions between attractors and revealed a potential interplay between subtypes. These transitions might be related to cancer recurrence and progression, connecting the molecular mechanisms of cancer heterogeneity with statistical properties of gene expression dynamics. Our work advances the modeling of gene expression dynamics and paves the way for personalized therapeutic interventions.

Circulating tumor DNA: from discovery to clinical application in breast cancer

Breast cancer (BC) stands out as the cancer with the highest incidence of morbidity and mortality among women worldwide, and its incidence rate is currently trending upwards. Improving the efficiency of breast cancer diagnosis and treatment is crucial, as it can effectively reduce the disease burden. Circulating tumor DNA (ctDNA) originates from the release of tumor cells and plays a pivotal role in the occurrence, development, and metastasis of breast cancer. In recent years, the widespread application of high-throughput analytical technology has made ctDNA a promising biomarker for early cancer detection, monitoring minimal residual disease, early recurrence monitoring, and predicting treatment outcomes. ctDNA-based approaches can effectively compensate for the shortcomings of traditional screening and monitoring methods, which fail to provide real-time information and prospective guidance for breast cancer diagnosis and treatment. This review summarizes the applications of ctDNA in various aspects of breast cancer, including screening, diagnosis, prognosis, treatment, and follow-up. It highlights the current research status in this field and emphasizes the potential for future large-scale clinical applications of ctDNA-based approaches.

Contributions of transcriptional noise to leukaemia evolution: KAT2A as a case-study

Transcriptional noise is proposed to participate in cell fate changes, but contributions to mammalian cell differentiation systems, including cancer, remain associative. Cancer evolution is driven by genetic variability, with modulatory or contributory participation of epigenetic variants. Accumulation of epigenetic variants enhances transcriptional noise, which can facilitate cancer cell fate transitions. Acute myeloid leukaemia (AML) is an aggressive cancer with strong epigenetic dependencies, characterized by blocked differentiation. It constitutes an attractive model to probe links between transcriptional noise and malignant cell fate regulation. Gcn5/KAT2A is a classical epigenetic transcriptional noise regulator. Its loss increases transcriptional noise and modifies cell fates in stem and AML cells. By reviewing the analysis of KAT2A-depleted pre-leukaemia and leukaemia models, I discuss that the net result of transcriptional noise is diversification of cell fates secondary to alternative transcriptional programmes. Cellular diversification can enable or hinder AML progression, respectively, by differentiation of cell types responsive to mutations, or by maladaptation of leukaemia stem cells. KAT2A-dependent noise-responsive genes participate in ribosome biogenesis and KAT2A loss destabilizes translational activity. I discuss putative contributions of perturbed translation to AML biology, and propose KAT2A loss as a model for mechanistic integration of transcriptional and translational control of noise and fate decisions. This article is part of a discussion meeting issue 'Causes and consequences of stochastic processes in development and disease'.

Evolvability of cancer-associated genes under APOBEC3A/B selection

Evolvability is an emergent hallmark of cancer that depends on intra-tumor heterogeneity and genetic variation. Mutations generated by APOBEC3 contribute to genetic variation and tumor evolvability. However, the influence of APOBEC3 on the evolvability of the genome and its differential impact on cancer genes versus non-cancer genes remains unclear. Analyzing over 40,000 human protein-coding transcripts, we identified distinct distribution patterns of APOBEC3A/B TC motifs between cancer and non-cancer genes, suggesting unique associations with cancer. Studying a bat species with numerous APOBEC3 genes, we found distinct motif patterns in orthologs of cancer genes compared to non-cancer genes, as in humans, suggesting APOBEC3 evolution to reduce impacts on the genome rather than the converse. Simulations confirmed that APOBEC3-induced heterogeneity enhances cancer evolution through bimodal patterns of mutations in certain classes of genes. Our results suggest the bimodal distribution of APOBEC-induced mutations can significantly increase cancer heterogeneity.

The use of microphysiological systems to model metastatic cancer

Cancer is one of the leading causes of death in the 21st century, with metastasis of cancer attributing to 90% of cancer-related deaths. Therefore, to improve patient outcomes there is a need for better preclinical models to increase the success of translating oncological therapies into the clinic. Current traditional staticin vitromodels lack a perfusable network which is critical to overcome the diffusional mass transfer limit to provide a mechanism for the exchange of essential nutrients and waste removal, and increase their physiological relevance. Furthermore, these models typically lack cellular heterogeneity and key components of the immune system and tumour microenvironment. This review explores rapidly developing strategies utilising perfusable microphysiological systems (MPS) for investigating cancer cell metastasis. In this review we initially outline the mechanisms of cancer metastasis, highlighting key steps and identifying the current gaps in our understanding of the metastatic cascade, exploring MPS focused on investigating the individual steps of the metastatic cascade before detailing the latest MPS which can investigate multiple components of the cascade. This review then focuses on the factors which can affect the performance of an MPS designed for cancer applications with a final discussion summarising the challenges and future directions for the use of MPS for cancer models.

Clonal expansion in normal tissues

Cancer originates from a single ancestral cell that acquires a driver mutation, which confers a growth or survival advantage, followed by the acquisition of additional driver mutations by descendant cells. Recently, it has become evident that somatic cell mutations accumulate in normal tissues with aging and exposure to environmental factors, such as alcohol, smoking, and UV rays, increases the mutation rate. Clones harboring driver mutations expand with age, leading to tissue remodeling. Lineage analysis of myeloproliferative neoplasms and der(1;16)-positive breast cancer revealed that driver mutations were acquired early in our lives and that the development of cancer takes decades, unveiling the previously unknown early process of cancer development. Evidence that clonal hematopoiesis affects various diseases, including nonneoplastic diseases, highlights the potential role of the identification and functional analysis of mutated clones in unraveling unknown pathologies. In this review, we summarize the recent updates on clonal expansion in normal tissues and the natural history of cancer revealed through lineage analysis of noncancerous and cancerous tissues.

Breast cancer stem cells as novel biomarkers

Breast cancer is the most common cancer and the leading cause of mortality worldwide. Despite advancements in detection and treatment, it remains a major cause of cancer-related deaths in women. Breast cancer stem cells (BCSCs) are a crucial group of cells responsible for carcinogenesis, metastasis, medication resistance, and tumor recurrence. Identifying and understanding their molecular pathways is essential for developing effective breast cancer therapy. BCSCs are responsible for tumor genesis, development, metastasis, treatment resistance, and recurrence. Biomarkers are essential tools for identifying high-risk patients, improving diagnostic accuracy, developing follow-up programs, assessing treatment susceptibility, and predicting prognostic outcomes. Stem cell intervention therapy can provide specialized tools for precision therapy. Biomarker analysis in cancer patients is crucial to identify cells associated with disease progression and post-therapeutic relapse. However, negative post-therapeutic impacts can enhance cancer stemness by boosting BCSCs plasticity phenotypes, activating stemness pathways in non-BCSCs, and promoting senescence escape, leading to tumor relapse and metastasis. Despite the advancements in precision medicine, challenges persist in identifying stem cell markers, limiting the number of eligible patients for treatment. The diversity of biomedical research hinders the development of individualization-based preventative, monitoring, and treatment strategies, especially in oncology. Integrating and interpreting clinical and scientific data remains challenging. The development of stem cell-related indicators could significantly improve disease precision, enabling stem cell-targeted therapy and personalized treatment plans, although BCSCs are promising for breast cancer treatment optimization, serving as biomarkers for current therapy modalities. This summary discusses recent advancements in breast cancer stem cell research, including biomarkers, identification methods, molecular mechanisms, and tools for studying their biological origin and lineage development for precision medicine.

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

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

Punctuational evolution is pervasive in distal site metastatic colonization

The evolution of metastasis represents a lethal stage of cancer progression. Yet, the evolutionary kinetics of metastatic disease remain unresolved. Here, using single cell CRISPR-Cas9 lineage tracing data, we show that in metastatic disease, gradual molecular evolution is punctuated by episodes of rapid evolutionary change associated with lineage divergence. By measuring punctuational effects across the metastatic cascade, we show that punctuational effects contribute more to the molecular diversity at distal site metastases compared to the paired primary tumor, suggesting qualitatively different modes of evolution may drive primary and metastatic tumor progression. This is the first empirical evidence for distinct patterns of molecular evolution at early and late stages of metastasis and demonstrates the complex interplay of cell intrinsic and extrinsic factors that shape lethal cancer.

Adjuvant PD-1 Checkpoint Inhibition in Early Cutaneous Melanoma: Immunological Mode of Action and the Role of Ultraviolet Radiation

Melanoma ranks as the fifth most common solid cancer in adults worldwide and is responsible for a significant proportion of skin-tumor-related deaths. The advent of immune checkpoint inhibition with anti-programmed death protein-1 (PD-1) antibodies has revolutionized the adjuvant treatment of high-risk, completely resected stage III/IV melanoma. However, not all patients benefit equally. Current strategies for improving outcomes involve adjuvant treatment in earlier disease stages (IIB/C) as well as perioperative treatment approaches. Interfering with T-cell exhaustion to counteract cancer immune evasion and the immunogenic nature of melanoma is key for anti-PD-1 effectiveness. Yet, the biological rationale for the efficacy of adjuvant treatment in clinically tumor-free patients remains to be fully elucidated. High-dose intermittent sun exposure (sunburn) is a well-known primary risk factor for melanomagenesis. Also, ultraviolet radiation (UVR)-induced immunosuppression may impair anti-cancer immune surveillance. In this review, we summarize the current knowledge about adjuvant anti-PD-1 blockade, including a characterization of the main cell types most likely responsible for its efficacy. In conclusion, we propose that local and systemic immunosuppression, to some extent UVR-mediated, can be restored by adjuvant anti-PD-1 therapy, consequently boosting anti-melanoma immune surveillance and the elimination of residual melanoma cell clones.

Recent developments in targeting breast cancer stem cells (BCSCs): a descriptive review of therapeutic strategies and emerging therapies

Despite recent advancements in the diagnosis and treatment of breast cancer (BC), patient outcomes in terms of survival, recurrence, and disease progression remain suboptimal. A significant factor contributing to these challenges is the cellular heterogeneity within BC, particularly the presence of breast cancer stem cells (BCSCs). These cells are thought to serve as the clonogenic nexus for new tumor growth, owing to their hierarchical organization within the tumor. This descriptive review focuses on the evolving strategies to target BCSCs, which have become a pivotal aspect of therapeutic development. We explore a variety of approaches, including targeting specific tumor surface markers (CD133 and CD44), transporters, heat shock proteins, and critical signaling pathways like Notch, Akt, Hedgehog, KLF4, and Wnt/β-catenin. Additionally, we discuss the modulation of the tumor microenvironment through the CXCR-12/CXCR4 axis, manipulation of pH levels, and targeting hypoxia-inducible factors, vascular endothelial growth factor, and CXCR1/2 receptors. Further, this review focuses on the roles of microRNA expression, strategies to induce apoptosis and differentiation in BCSCs, dietary interventions, dendritic cell vaccination, oncolytic viruses, nanotechnology, immunotherapy, and gene therapy. We particularly focused on studies reporting identification of BCSCs, their unique properties and the efficacy of various therapeutic modalities in targeting these cells. By dissecting these approaches, we aim to provide insights into the complex landscape of BC treatment and the potential pathways for improving patient outcomes through targeted BCSC therapies.

Biological Basis of Breast Cancer-Related Disparities in Precision Oncology Era

Precision oncology is based on deep knowledge of the molecular profile of tumors, allowing for more accurate and personalized therapy for specific groups of patients who are different in disease susceptibility as well as treatment response. Thus, onco-breastomics is able to discover novel biomarkers that have been found to have racial and ethnic differences, among other types of disparities such as chronological or biological age-, sex/gender- or environmental-related ones. Usually, evidence suggests that breast cancer (BC) disparities are due to ethnicity, aging rate, socioeconomic position, environmental or chemical exposures, psycho-social stressors, comorbidities, Western lifestyle, poverty and rurality, or organizational and health care system factors or access. The aim of this review was to deepen the understanding of BC-related disparities, mainly from a biomedical perspective, which includes genomic-based differences, disparities in breast tumor biology and developmental biology, differences in breast tumors' immune and metabolic landscapes, ecological factors involved in these disparities as well as microbiomics- and metagenomics-based disparities in BC. We can conclude that onco-breastomics, in principle, based on genomics, proteomics, epigenomics, hormonomics, metabolomics and exposomics data, is able to characterize the multiple biological processes and molecular pathways involved in BC disparities, clarifying the differences in incidence, mortality and treatment response for different groups of BC patients.

A novel multiscale framework for delineating cancer evolution from subclonal compositions

OBJECTIVE

Owing to the heterogeneity in the evolution of cancer, distinguishing between diverse growth patterns and predicting long-term outcomes based on short-term measurements poses a great challenge.

METHODS

A novel multiscale framework is proposed to unravel the connections between the population dynamics of cancer growth (i.e., aggressive, bounded, and indolent) and the cellular-subclonal dynamics of cancer evolution. This framework employs the non-negative lasso (NN-LASSO) algorithm to forge a link between an ordinary differential equation (ODE)-based population model and a cellular evolution model.

RESULTS

The findings of our current work not only affirm the impact of subclonal composition on growth dynamics but also identify two significant subclones within heterogeneous growth patterns. Moreover, the subclonal compositions at the initial time are able to accurately discriminate diverse growth patterns through a machine learning algorithm.

CONCLUSION

The proposed multiscale framework successfully delineates the intricate landscape of cancer evolution, bridging the gap between long-term growth dynamics and short-term measurements, both in simulated and real-world data. This methodology provides a novel avenue for thorough exploration into the realm of cancer evolution.

Engineered Nanomaterials for Tumor Immune Microenvironment Modulation in Cancer Immunotherapy

Tumor immunotherapy, represented by immune checkpoint blocking and chimeric antigen receptor (CAR) T cell therapy, has achieved promising results in clinical applications. However, it faces challenges that hinder its further development, such as limited response rates and poor tumor permeability. The efficiency of tumor immunotherapy is also closely linked to the structure and function of the immune microenvironment where the tumor resides. Recently, nanoparticle-based tumor immune microenvironment (TIME) modulation strategies have attracted a great deal of attention in cancer immunotherapy. This is primarily due to the distinctive physical characteristics of nanoparticles, which enable them to effectively infiltrate the TIME and selectively modulate its key constituents. This paper reviews recent advances in nanoparticle engineering to improve anti-cancer immunotherapy. Emerging nanoparticle-based approaches for modulating immune cells, tumor stroma, cytokines and immune checkpoints are discussed, aiming to overcome current challenges in the clinic. In addition, integrating immunotherapy with various treatment modalities such as chemotherapy and photodynamic therapy can be facilitated through the utilization of nanoparticles, thereby enhancing the efficacy of cancer treatment. The future challenges and opportunities of using nanomaterials to reeducate the suppressive immune microenvironment of tumors are also discussed, with the aim of anticipating further advancements in this growing field.

Impact of Clonal Heterogeneity in Multiple Myeloma

Multiple myeloma is characterized by a highly heterogeneous disease distribution within the bone marrow-containing skeletal system. In this review, we introduce the molecular mechanisms underlying clonal heterogeneity and the spatio-temporal evolution of myeloma. We discuss the clinical impact of clonal heterogeneity, which is thought to be one of the biggest obstacles to overcome therapy resistance and to achieve cure.

Differential Competitive Growth of Transgenic Subclones of Neuroblastoma Cells Expressing Different Levels of Cathepsin D Co-Cultured in 2D and 3D in Response to EGF: Implications in Tumor Heterogeneity and Metastasis

Neuroblastoma (NB) is an embryonal tumor arising from the sympathetic central nervous system. The epidermal growth factor (EGF) plays a role in NB growth and metastatic behavior. Recently, we have demonstrated that cathepsin D (CD) contrasts EGF-induced NB cell growth in 2D by downregulating EGFR/MAPK signaling. Aggressive NB is highly metastatic to the bone and the brain. In the metastatic process, adherent cells detach to form clusters of suspended cells that adhere once they reach the metastatic site and form secondary colonies. Whether CD is involved in the survival of metastatic NB clones is not known. Therefore, in this study, we addressed how CD differentially affects cell growth in suspension versus the adherent condition. To mimic tumor heterogeneity, we co-cultured transgenic clones silenced for or overexpressing CD. We compared the growth kinetics of such mixed clones in 2D and 3D models in response to EGF, and we found that the Over CD clone had an advantage for growth in suspension, while the CD knocked-down clone was favored for the adherent growth in 2D. Interestingly, on switching from 3D to 2D culture conditions, the expression of E-cadherin and of N-cadherin increased in the KD-CD and Over CD clones, respectively. The fact that CD plays a dual role in cancer cell growth in 2D and 3D conditions indicates that during clonal evolution, subclones expressing different level of CD may arise, which confers survival and growth advantages depending on the metastatic step. By searching the TCGA database, we found up to 38 miRNAs capable of downregulating CD. Interestingly, these miRNAs are associated with biological processes controlling cell adhesion and cell migration. The present findings support the view that during NB growth on a substrate or when spreading as floating neurospheres, CD expression is epigenetically modulated to confer survival advantage. Thus, epigenetic targeting of CD could represent an additional strategy to prevent NB metastases.

TOP1 and R-loops facilitate transcriptional DSBs at hypertranscribed cancer driver genes

DNA double-stranded breaks (DSBs) pose a significant threat to genomic integrity, and their generation during essential cellular processes like transcription remains poorly understood. In this study, we employ several techniques to map DSBs, R-loops, and topoisomerase 1 cleavage complex (TOP1cc) to comprehensively investigate the interplay between transcription, DSBs, topoisomerase 1 (TOP1), and R-loops. Our findings reveal the presence of DSBs at highly expressed genes enriched with TOP1 and R-loops. Remarkably, transcription-associated DSBs at these loci are significantly reduced upon depletion of R-loops and TOP1, uncovering the pivotal roles of TOP1 and R-loops in transcriptional DSB formation. By elucidating the intricate interplay between TOP1cc trapping, R-loops, and DSBs, our study provides insights into the mechanisms underlying transcription-associated genomic instability. Moreover, we establish a link between transcriptional DSBs and early molecular changes driving cancer development, highlighting the distinct etiology and molecular characteristics of driver mutations compared to passenger mutations.

N-Acetylcysteine Alters Disease Progression and Increases Janus Kinase Mutation Frequency in a Mouse Model of Precursor B-Cell Acute Lymphoblastic Leukemia

B-cell acute lymphoblastic leukemia (B-ALL) is the most prevalent type of cancer in young children and is associated with high levels of reactive oxygen species (ROS). The antioxidant N-acetylcysteine (NAC) was tested for its ability to alter disease progression in a mouse model of B-ALL. Mb1-CreΔPB mice have deletions in genes encoding PU.1 and Spi-B in B cells and develop B-ALL at 100% incidence. Treatment of Mb1-CreΔPB mice with NAC in drinking water significantly reduced the frequency of CD19+ pre-B-ALL cells infiltrating the thymus at 11 weeks of age. However, treatment with NAC did not reduce leukemia progression or increase survival by a median 16 weeks of age. NAC significantly altered gene expression in leukemias in treated mice. Mice treated with NAC had increased frequencies of activating mutations in genes encoding Janus kinases 1 and 3. In particular, frequencies of Jak3 R653H mutations were increased in mice treated with NAC compared with control drinking water. NAC opposed oxidization of PTEN protein ROS in cultured leukemia cells. These results show that NAC alters leukemia progression in this mouse model, ultimately selecting for leukemias with high Jak3 R653H mutation frequencies. SIGNIFICANCE STATEMENT: In a mouse model of precursor B-cell acute lymphoblastic leukemia associated with high levels of reactive oxygen species, treatment with N-acetylcysteine did not delay disease progression but instead selected for leukemic clones with activating R653H mutations in Janus kinase 3.

Functional transcriptional signatures for tumor-type-agnostic phenotype prediction

Cancer transcriptional patterns exhibit both shared and unique features across diverse cancer types, but whether these patterns are sufficient to characterize the full breadth of tumor phenotype heterogeneity remains an open question. We hypothesized that cancer transcriptional diversity mirrors patterns in normal tissues optimized for distinct functional tasks. Starting with normal tissue transcriptomic profiles, we use non-negative matrix factorization to derive six distinct transcriptomic phenotypes, called archetypes, which combine to describe both normal tissue patterns and variations across a broad spectrum of malignancies. We show that differential enrichment of these signatures correlates with key tumor characteristics, including overall patient survival and drug sensitivity, independent of clinically actionable DNA alterations. Additionally, we show that in HR+/HER2- breast cancers, metastatic tumors adopt transcriptomic signatures consistent with the invaded tissue. Broadly, our findings suggest that cancer often arrogates normal tissue transcriptomic characteristics as a component of both malignant progression and drug response. This quantitative framework provides a strategy for connecting the diversity of cancer phenotypes and could potentially help manage individual patients.

Mathematical modeling of combined therapies for treating tumor drug resistance

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

A novel method for the isolation of single cells mimicking circulating tumour cells adhered on Smart Bio Surface slides by Laser Capture Microdissection

In recent years, the importance of isolating single cells from blood circulation for several applications, such as non-invasive tumour diagnosis, the monitoring of minimal residual disease, and the analysis of circulating fetal cells for prenatal diagnosis, urged the need to set up innovative methods. For such applications, different methods were developed. All show some weaknesses, especially a limited sensitivity, and specificity. Here we present a new method for isolating a single or a limited number of cells adhered to SBS slides (Tethis S.p.a.) (a glass slide coated with Nanostructured Titanium Dioxide) by Laser Capture Microdissection (LCM) and subsequent Whole Genome Amplification. SBS slides have been shown to have an optimal performance in immobilizing circulating tumour cells (CTCs) from early breast cancer patients. In this work, we spiked cancer cells in blood samples to mimic CTCs. By defining laser parameters to cut intact samples, we were able to isolate genetically intact single cells. We demonstrate that SBS slides are optimally suited for isolating cells using LCM and that this method provides high-quality DNA, ideal for gene-specific assays such as PCR and Sanger sequencing for mutation analysis.

Joint analysis of mutational and transcriptional landscapes in human cancer reveals key perturbations during cancer evolution

BACKGROUND

Tumors are able to acquire new capabilities, including traits such as drug resistance and metastasis that are associated with unfavorable clinical outcomes. Single-cell technologies have made it possible to study both mutational and transcriptomic profiles, but as most studies have been conducted on model systems, little is known about cancer evolution in human patients. Hence, a better understanding of cancer evolution could have important implications for treatment strategies.

RESULTS

Here, we analyze cancer evolution and clonal selection by jointly considering mutational and transcriptomic profiles of single cells acquired from tumor biopsies from 49 lung cancer samples and 51 samples with chronic myeloid leukemia. Comparing the two profiles, we find that each clone is associated with a preferred transcriptional state. For metastasis and drug resistance, we find that the number of mutations affecting related genes increases as the clone evolves, while changes in gene expression profiles are limited. Surprisingly, we find that mutations affecting ligand-receptor interactions with the tumor microenvironment frequently emerge as clones acquire drug resistance.

CONCLUSIONS

Our results show that lung cancer and chronic myeloid leukemia maintain a high clonal and transcriptional diversity, and we find little evidence in favor of clonal sweeps. This suggests that for these cancers selection based solely on growth rate is unlikely to be the dominating driving force during cancer evolution.