Clonal evolution in cancer

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.

Developmental hematopoietic stem cell variation explains clonal hematopoiesis later in life

Clonal hematopoiesis becomes increasingly common with age, but its cause is enigmatic because driver mutations are often absent. Serial observations infer weak selection indicating variants are acquired much earlier in life with unexplained initial growth spurts. Here we use fluctuating CpG methylation as a lineage marker to track stem cell clonal dynamics of hematopoiesis. We show, via the shared prenatal circulation of monozygotic twins, that weak selection conferred by stem cell variation created before birth can reliably yield clonal hematopoiesis later in life. Theory indicates weak selection will lead to dominance given enough time and large enough population sizes. Human hematopoiesis satisfies both these conditions. Stochastic loss of weakly selected variants is naturally prevented by the expansion of stem cell lineages during development. The dominance of stem cell clones created before birth is supported by blood fluctuating CpG methylation patterns that exhibit low correlation between unrelated individuals but are highly correlated between many elderly monozygotic twins. Therefore, clonal hematopoiesis driven by weak selection in later life appears to reflect variation created before birth.

Immunohistochemical detection of cancer genetic abnormalities

New applications of immunohistochemistry (IHC) expand rapidly due to the development of molecular analyses and an increased understanding of molecular biology. IHC becomes much more important as a screening or even a confirmatory test for molecular changes in cancer. The past decades have witnessed the release of many immunohistochemical markers of the new generation. The novel markers have extensively high specificity and sensitivity for the detection of genetic abnormalities. In addition to diagnostic utility, IHC has been validated to be a practical tool in terms of treatments, especially molecular targeted therapy. In this review, we first describe the common alterations of protein IHC staining in human cancer: overexpression, underexpression, or loss of expression and altered staining pattern. Next, we examine the relationship between staining patterns and genetic aberrations regarding both conventional and novel IHC markers. We also mention current mutant-specific and fusion-specific antibodies and their concordance with molecular techniques. We then describe the basic molecular mechanisms from genetic events to corresponding protein expression patterns (membranous, cytoplasmic, or nuclear patterns). Finally, we shortly discuss the applications of immunohistochemistry in molecular targeted therapy. IHC markers can serve as a complementary or companion diagnostic test to provide valuable information for targeted therapy. Moreover, immunohistochemistry is also crucial as a companion diagnostic test in immunotherapy. The increased number of IHC novel antibodies is broadening its application in anti-cancer therapies.

The breast cancer tumor microenvironment and precision medicine: immunogenicity and conditions favoring response to immunotherapy

Some main recent researches that have dissected tumor microenvironment (TME) by imaging mass cytometry (IMC) in different subtypes of primary breast cancer samples were considered. The many phenotypic variants, clusters of epithelial tumor and immune cells, their structural features as well as the main genetic aberrations, sub-clonal heterogeneity and their systematic classification also have been examined. Mutational evolution has been assessed in primary and metastatic breast cancer samples. Overall, based on these findings the current concept of precision medicine is questioned and challenged by alternative therapeutic strategies. In the last two decades, immunotherapy as a powerful and harmless tool to fight cancer has received huge attention. Thus, the tumor immune microenvironment (TIME) composition, its prognostic role for clinical course as well as a novel definition of immunogenicity in breast cancer are proposed. Investigational clinical trials carried out by us and other findings suggest that G0-G1 state induced in endocrine-dependent metastatic breast cancer is more suitable for successful immune manipulation. Residual micro-metastatic disease seems to be another specific condition that can significantly favor the immune response in breast and other solid tumors.

Multiplex protein imaging in tumour biology

Tissue imaging has become much more colourful in the past decade. Advances in both experimental and analytical methods now make it possible to image protein markers in tissue samples in high multiplex. The ability to routinely image 40-50 markers simultaneously, at single-cell or subcellular resolution, has opened up new vistas in the study of tumour biology. Cellular phenotypes, interaction, communication and spatial organization have become amenable to molecular-level analysis, and application to patient cohorts has identified clinically relevant cellular and tissue features in several cancer types. Here, we review the use of multiplex protein imaging methods to study tumour biology, discuss ongoing attempts to combine these approaches with other forms of spatial omics, and highlight challenges in the field.

Chronic arsenic exposure induces malignant transformation of human HaCaT cells through both deterministic and stochastic changes in transcriptome expression

Biological processes are inherently stochastic, i.e., are partially driven by hard to predict random probabilistic processes. Carcinogenesis is driven both by stochastic and deterministic (predictable non-random) changes. However, very few studies systematically examine the contribution of stochastic events leading to cancer development. In differential gene expression studies, the established data analysis paradigms incentivize expression changes that are uniformly different across the experimental versus control groups, introducing preferential inclusion of deterministic changes at the expense of stochastic processes that might also play a crucial role in the process of carcinogenesis. In this study, we applied simple computational techniques to quantify: (i) The impact of chronic arsenic (iAs) exposure as well as passaging time on stochastic gene expression and (ii) Which genes were expressed deterministically and which were expressed stochastically at each of the three stages of cancer development. Using biological coefficient of variation as an empirical measure of stochasticity we demonstrate that chronic iAs exposure consistently suppressed passaging related stochastic gene expression at multiple time points tested, selecting for a homogenous cell population that undergo transformation. Employing multiple balanced removal of outlier data, we show that chronic iAs exposure induced deterministic and stochastic changes in the expression of unique set of genes, that populate largely unique biological pathways. Together, our data unequivocally demonstrate that both deterministic and stochastic changes in transcriptome-wide expression are critical in driving biological processes, pathways and networks towards clonal selection, carcinogenesis, and tumor heterogeneity.

Cancer cell plasticity, stem cell factors, and therapy resistance: how are they linked?

Cellular plasticity can occur naturally in an organism and is considered an adapting mechanism during the developmental stage. However, abnormal cellular plasticity is observed in different diseased conditions, including cancer. Cancer cell plasticity triggers the stimuli of epithelial-mesenchymal transition (EMT), abnormal epigenetic changes, expression of stem cell factors and implicated signaling pathways, etc., and helps in the maintenance of CSC phenotype. Conversely, CSC maintains the cancer cell plasticity, EMT, and epigenetic plasticity. EMT contributes to increased cell migration and greater diversity within tumors, while epigenetic changes, stem cell factors (OCT4, NANOG, and SOX2), and various signaling pathways allow cancer cells to maintain various phenotypes, giving rise to intra- and inter-tumoral heterogeneity. The intricate relationships between cancer cell plasticity and stem cell factors help the tumor cells adopt drug-tolerant states, evade senescence, and successfully acquire drug resistance with treatment dismissal. Inhibiting molecules/signaling pathways involved in promoting CSCs, cellular plasticity, EMT, and epigenetic plasticity might be helpful for successful cancer therapy management. This review discussed the role of cellular plasticity, EMT, and stem cell factors in tumor initiation, progression, reprogramming, and therapy resistance. Finally, we discussed how the intervention in this axis will help better manage cancers and improve patient survivability.

Evaluation of T-cell clonality by anti-TRBC1 antibody-based flow cytometry and correlation with T-cell receptor sequencing

Flow cytometry (FC) incorporating the T-cell receptor β constant chain-1 (TRBC1) has been recently proposed as a new standard in T-cell clonality assessment. While early studies demonstrated high sensitivity in samples with conspicuous tumour burden, performance in real-world samples, including those with low tumour burden and correlation with molecular methods has been limited. We evaluated TRBC1-FC performance and correlated the results with high-throughput TRB sequencing and a targeted next-generation sequencing gene panel. Our cohort consisted of 90 evaluable samples from 57 patients. TRBC1-FC confirmed T-cell clonality in 37 out of 38 samples (97%) that were involved in a mature T-cell neoplasm (MTCN). T-cell clonality was also identified in nine samples from patients lacking a current or prior diagnosis of MTCN, consistent with the emerging entity T-cell clonality of uncertain significance. TRBC-FC was polyclonal in all samples and negative for disease involvement by standard pathology assessment. However, correlation with TRB sequencing in 17 of these samples identified two cases that harboured the known clonal sequence from index testing, indicating the presence of measurable residual disease not otherwise detected. Our study provides real-world correlative validation of TRBC1-FC, highlighting the strengths and limitations pertinent to its increasing implementation by general diagnostic laboratories.

Prostate cancer: Molecular aspects, consequences, and opportunities of the multifocal nature

Prostate cancer is unique compared to other major cancers due to the presence of multiple primary malignant foci in the majority of patients at the time of diagnosis. Each malignant focus has distinct somatic mutations and gene expression patterns, which represents a challenge for the development of prognostic tests for localized prostate cancer. Additionally, the molecular heterogeneity of advanced prostate cancer has important implications for management, particularly for patients with metastatic and locally recurrent cancer. Studies have shown that prostate cancers with mutations in DNA damage response genes are more sensitive to drugs inhibiting the poly ADP-ribose polymerase (PARP) enzyme. However, testing for such mutations should consider both spatial and temporal heterogeneity. Here, we summarize studies where multiregional genomics and transcriptomics analyses have been performed for primary prostate cancer. We further discuss the vast interfocal heterogeneity and how prognostic biomarkers and a molecular definition of the index tumor should be developed. The concept of focal treatments in prostate cancer has been evolving as a demand from patients and clinicians and is one example where there is a need for defining an index tumor. Here, biomarkers must have proven value for individual malignant foci. The potential discovery and implementation of biomarkers that are agnostic to heterogeneity are also explored as an alternative to multisample testing. Thus, deciding upon whole-organ treatment, such as radical prostatectomy, should depend on information from biomarkers which are informative for the whole organ.

Targeting vimentin: a multifaceted approach to combatting cancer metastasis and drug resistance

This comprehensive review explores vimentin as a pivotal therapeutic target in cancer treatment, with a primary focus on mitigating metastasis and overcoming drug resistance. Vimentin, a key player in cancer progression, is intricately involved in processes such as epithelial-to-mesenchymal transition (EMT) and resistance mechanisms to standard cancer therapies. The review delves into diverse vimentin inhibition strategies. Precision tools, including antibodies and nanobodies, selectively neutralize vimentin's pro-tumorigenic effects. DNA and RNA aptamers disrupt vimentin-associated signaling pathways through their adaptable binding properties. Innovative approaches, such as vimentin-targeted vaccines and microRNAs (miRNAs), harness the immune system and post-transcriptional regulation to combat vimentin-expressing cancer cells. By dissecting vimentin inhibition strategies across these categories, this review provides a comprehensive overview of anti-vimentin therapeutics in cancer treatment. It underscores the growing recognition of vimentin as a pivotal therapeutic target in cancer and presents a diverse array of inhibitors, including antibodies, nanobodies, DNA and RNA aptamers, vaccines, and miRNAs. These multifaceted approaches hold substantial promise for tackling metastasis and overcoming drug resistance, collectively presenting new avenues for enhanced cancer therapy.

Clonally heritable gene expression imparts a layer of diversity within cell types

Cell types can be classified according to shared patterns of transcription. Non-genetic variability among individual cells of the same type has been ascribed to stochastic transcriptional bursting and transient cell states. Using high-coverage single-cell RNA profiling, we asked whether long-term, heritable differences in gene expression can impart diversity within cells of the same type. Studying clonal human lymphocytes and mouse brain cells, we uncovered a vast diversity of heritable gene expression patterns among different clones of cells of the same type in vivo. We combined chromatin accessibility and RNA profiling on different lymphocyte clones to reveal thousands of regulatory regions exhibiting interclonal variation, which could be directly linked to interclonal variation in gene expression. Our findings identify a source of cellular diversity, which may have important implications for how cellular populations are shaped by selective processes in development, aging, and disease. A record of this paper's transparent peer review process is included in the supplemental information.

Evaluation of simulation methods for tumor subclonal reconstruction

Most neoplastic tumors originate from a single cell, and their evolution can be genetically traced through lineages characterized by common alterations such as small somatic mutations (SSMs), copy number alterations (CNAs), structural variants (SVs), and aneuploidies. Due to the complexity of these alterations in most tumors and the errors introduced by sequencing protocols and calling algorithms, tumor subclonal reconstruction algorithms are necessary to recapitulate the DNA sequence composition and tumor evolution in silico. With a growing number of these algorithms available, there is a pressing need for consistent and comprehensive benchmarking, which relies on realistic tumor sequencing generated by simulation tools. Here, we examine the current simulation methods, identifying their strengths and weaknesses, and provide recommendations for their improvement. Our review also explores potential new directions for research in this area. This work aims to serve as a resource for understanding and enhancing tumor genomic simulations, contributing to the advancement of the field.

Tumor Copy Number Alteration Burden as a Predictor for Resistance to Immune Checkpoint Blockade across Different Cancer Types

Immune checkpoint blockade (ICB) benefits only a subset of advanced cancer patients, and predictive biomarkers for immunotherapy response are needed. Recently, copy number alteration (CNA) burden has been proposed to predict ICB resistance. We assessed this finding using the publicly accessible data for 1661 ICB-treated patients whose tumors were profiled by MSK-IMPACT, an approved targeted assay in clinical care. We tested the hypothesis that the continuous increase in CNA burden is associated with poor overall survival following ICB. In addition, we hypothesized that the combinatorial biomarkers of tumor mutational burden (TMB) and CNA burden would better stratify patients for immune status and ICB response. Of the 1661 cases, 79% (n = 1307) were treated with anti PD-1/PD-L1 and the remaining 21% (n = 354) with anti CTLA-4 or the combination of both. In a multivariate analysis, increase in CNA burden was associated with poor overall survival [HR = 1.52, 95% CI (1.01-2.30), p = 0.04]. The combination of biomarkers TMB and CNA burden stratified patients into four clinically distinct subsets among which "LowTMB/HighCNA" showed the worst survival (p < 0.0001). The four patient subsets had unique CNA profiles and enriched pathways, which could predict transcriptional and phenotypic effects related to immune signaling and CD8+ T-cell abundance in the tumor microenvironment. CNA burden was associated with poor overall survival in patients receiving ICB and could improve patient stratification when incorporated with TMB. These findings may guide patient selection for immunotherapy or alternative strategies.

A battle between two biological singularities: Immune response vs. cancer

In a post-growth multicellular organism, the phenomenon in which a small number of rare cells can be the starting point for inducing a dramatic change in the entire system is considered a "biological singularity." The immune response and cancer can be regarded as singularity phenomena in mammals, but their nature is fundamentally different. The immune response is considered a "programmed" singularity, whereas cancer is an "unprogrammed" singularity. These two systems perpetually engage in a cycle of attack and defense within the organism. The outcome is depending on the wining system, which determines whether the individual experiences a state resembling light or darkness. However, the overall mechanism of the competition remains unclear and is expected to be elucidated with future innovations in bioimaging technologies. Immune checkpoint blockade therapy is a means by which the two singularity balances can be artificially manipulated; therefore, mechanistic insight is necessary for cancer treatment strategies. Altogether, these findings provide a different perspective crucial for understanding the behavior of dynamic cell populations in multicellular organisms.

A Bayesian framework to study tumor subclone-specific expression by combining bulk DNA and single-cell RNA sequencing data

Genetic and gene expression heterogeneity is an essential hallmark of many tumors, allowing the cancer to evolve and to develop resistance to treatment. Currently, the most commonly used data types for studying such heterogeneity are bulk tumor/normal whole-genome or whole-exome sequencing (WGS, WES); and single-cell RNA sequencing (scRNA-seq), respectively. However, tools are currently lacking to link genomic tumor subclonality with transcriptomic heterogeneity by integrating genomic and single-cell transcriptomic data collected from the same tumor. To address this gap, we developed scBayes, a Bayesian probabilistic framework that uses tumor subclonal structure inferred from bulk DNA sequencing data to determine the subclonal identity of cells from single-cell gene expression (scRNA-seq) measurements. Grouping together cells representing the same genetically defined tumor subclones allows comparison of gene expression across different subclones, or investigation of gene expression changes within the same subclone across time (i.e., progression, treatment response, or relapse) or space (i.e., at multiple metastatic sites and organs). We used simulated data sets, in silico synthetic data sets, as well as biological data sets generated from cancer samples to extensively characterize and validate the performance of our method, as well as to show improvements over existing methods. We show the validity and utility of our approach by applying it to published data sets and recapitulating the findings, as well as arriving at novel insights into cancer subclonal expression behavior in our own data sets. We further show that our method is applicable to a wide range of single-cell sequencing technologies including single-cell DNA sequencing as well as Smart-seq and 10x Genomics scRNA-seq protocols.

Phenotypic Heterogeneity, Bidirectionality, Universal Cues, Plasticity, Mechanics, and the Tumor Microenvironment Drive Cancer Metastasis

Tumor diseases become a huge problem when they embark on a path that advances to malignancy, such as the process of metastasis. Cancer metastasis has been thoroughly investigated from a biological perspective in the past, whereas it has still been less explored from a physical perspective. Until now, the intraluminal pathway of cancer metastasis has received the most attention, while the interaction of cancer cells with macrophages has received little attention. Apart from the biochemical characteristics, tumor treatments also rely on the tumor microenvironment, which is recognized to be immunosuppressive and, as has recently been found, mechanically stimulates cancer cells and thus alters their functions. The review article highlights the interaction of cancer cells with other cells in the vascular metastatic route and discusses the impact of this intercellular interplay on the mechanical characteristics and subsequently on the functionality of cancer cells. For instance, macrophages can guide cancer cells on their intravascular route of cancer metastasis, whereby they can help to circumvent the adverse conditions within blood or lymphatic vessels. Macrophages induce microchannel tunneling that can possibly avoid mechanical forces during extra- and intravasation and reduce the forces within the vascular lumen due to vascular flow. The review article highlights the vascular route of cancer metastasis and discusses the key players in this traditional route. Moreover, the effects of flows during the process of metastasis are presented, and the effects of the microenvironment, such as mechanical influences, are characterized. Finally, the increased knowledge of cancer metastasis opens up new perspectives for cancer treatment.

Global, regional, and national burden and quality of care of multiple myeloma, 1990-2019

Background

Multiple myeloma (MM) is the second most common haematologic malignancy, presenting a great disease burden on the general population; however, the quality of care of MM is overlooked. We therefore assessed gains and disparity in quality of care worldwide from 1990 to 2019 based on a novel summary indicator - the quality of care index (QCI) - and examined its potential for improvement.

Methods

Using the Global Burden of Disease 2019 data set, we calculated the QCI of MM for 195 countries and territories. We used the principal component analysis to extract the first principal component of ratios with the combinations of mortality to incidence, prevalence to incidence, disability-adjusted life years to prevalence, and years of life lost to years lived with disability as QCI. We also conducted a series of descriptive and comparative analyses of QCI disparities with age, gender, period, geographies, and sociodemographic development, and compared the QCI among countries with similar socio-demographic index (SDI) through frontier analysis.

Results

The age-standardised rates of MM were 1.92 (95% uncertainty interval (UI) = 1.68, 2.12) in incidence and 1.42 (95% UI = 1.24, 1.52) in deaths per 100 000 population in 2019, and were predicted to increase in the future. The global age-standardised QCI increased from 51.31 in 1990 to 64.28 in 2019. In 2019, New Zealand had the highest QCI at 99.29 and the Central African Republic had the lowest QCI at 10.74. The gender disparity of QCI was reduced over the years, with the largest being observed in the sub-Saharan region. Regarding age, QCI maintained a decreasing trend in patients aged >60 in SDI quintiles. Generally, QCI improved with the SDI increase. Results of frontier analysis suggested that there is a potential to improve the quality of care across all levels of development spectrum.

Conclusions

Quality of care of MM improved during the past three decades, yet disparities in MM care remain across different countries, age groups, and genders. It is crucial to establish local objectives aimed at enhancing MM care and closing the gap in health care inequality.

Unrevealing the therapeutic benefits of radiotherapy and consolidation immunotherapy using ctDNA-defined tumor clonality in unresectable locally advanced non-small cell lung cancer

Progression occurs in approximately two-thirds of patients with locally advanced non-small cell lung cancer (LA-NSCLC) receiving chemoradiation and consolidation immunotherapy. Molecular indicators for outcome prediction are under development. A novel metric, the ratio of mean to max variant allele frequency (mmVAF), was derived from 431 pre-treatment tissue biopsies from The Cancer Genome Atlas and evaluated in serial circulating tumor DNA (ctDNA) from 70 LA-NSCLC patients receiving definitive radiotherapy/chemoradiotherapy (RT/CRT) with/without immunotherapy. High mmVAFs in pre-treatment tissue biopsies, indicating clonal predominant tumors (P < 0.01), were associated with inferior overall survival [OS, hazard ratio (HR): 1.48, 95 % confidence interval (CI): 1.11-1.98]. Similar associations of mmVAF with clonality (P < 0.01) and OS (HR: 2.24, 95 % CI: 0.71-7.08) were observed in pre-treatment ctDNA. At 1-month post-RT, ctDNA mmVAF-high patients receiving consolidation immunotherapy exhibited improved progression-free survival (PFS) compared to those who did not (HR: 0.14, 95 % CI: 0.03-0.67). From the baseline to week 4 of RT and/or 1-month post-RT, survival benefits from consolidation immunotherapy were exclusively observed in ctDNA mmVAF-increased patients (PFS, HR: 0.39, 95 % CI: 0.14-1.15), especially in terms of distant metastasis (HR: 0.11, 95 % CI: 0.01-0.95). In summary, our longitudinal data demonstrated the applicability of ctDNA-defined clonality for prognostic stratification and immunotherapy benefit prediction in LA-NSCLC.

Inhibition of autophagy initiation: A novel strategy for oral squamous cell carcinomas

BACKGROUND

Oral squamous cell carcinoma (OSCC) is one of the most common forms of oral cancer and is known to have poor prognostic outcomes. Autophagy is known to be associated with aggressive tumor biology of OSCC. Hence, this study aimed to develop a novel therapeutic strategy against OSCC by targeting the autophagic pathway.

METHODS

Immunoblotting, and confocal microscopy were used to examine the effect of tumor microenvironmental stressors on the autophagy activity. Cellular proliferation and migration assays were performed to assess the anti-cancer activity of standard chemotherapy and autophagy initiation inhibitors, either alone or in combination. High resolution mass-spectrometry based proteomic analysis was utilized to understand the mechanisms behind chemoresistance in OSCC models. Finally, immunohistochemistry was performed to determine associations between autophagy markers and clinicopathological characteristics.

RESULTS

Tumor microenvironmental stressors were shown to induce autophagy activity in OSCC cell lines. Novel combinations of chemotherapy and autophagy inhibitors as well as different classes of autophagy inhibitors were identified. Combination of MRT68921 and SAR405 demonstrated marked synergy in their anti-proliferative activity and also showed synergy with chemotherapy in chemoresistant OSCC cell models. Autophagy was identified as one of the key pathways involved in mediating chemoresistance in OSCC. Furthermore, TGM2 was identified as a key upstream regulator of chemoresistance in OSCC models. Finally, positive staining for autophagosome marker LC3 was shown to be associated with low histological grade OSCC.

CONCLUSION

In conclusion, this study identified a combination of novel autophagy inhibitors which can potently inhibit proliferation of both chemosensitive as well as chemoresistant OSCC cells and could be developed as a novel therapy against advanced OSCC tumors.

Computational validation of clonal and subclonal copy number alterations from bulk tumor sequencing using CNAqc

Copy number alterations (CNAs) are among the most important genetic events in cancer, but their detection from sequencing data is challenging because of unknown sample purity, tumor ploidy, and general intra-tumor heterogeneity. Here, we present CNAqc, an evolution-inspired method to perform the computational validation of clonal and subclonal CNAs detected from bulk DNA sequencing. CNAqc is validated using single-cell data and simulations, is applied to over 4000 TCGA and PCAWG samples, and is incorporated into the validation process for the clinically accredited bioinformatics pipeline at Genomics England. CNAqc is designed to support automated quality control procedures for tumor somatic data validation.

Computational inference of eIF4F complex function and structure in human cancers

The canonical eukaryotic initiation factor 4F (eIF4F) complex, composed of eIF4G1, eIF4A1, and the cap-binding protein eIF4E, plays a crucial role in cap-dependent translation initiation in eukaryotic cells. An alternative cap-independent initiation can occur, involving only eIF4G1 and eIF4A1 through internal ribosome entry sites (IRESs). This mechanism is considered complementary to cap-dependent initiation, particularly in tumors under stress conditions. However, the selection and molecular mechanism of specific translation initiation remains poorly understood in human cancers. Thus, we analyzed gene copy number variations (CNVs) in TCGA tumor samples and found frequent amplification of genes involved in translation initiation. Copy number gains in EIF4G1 and EIF3E frequently co-occur across human cancers. Additionally, EIF4G1 expression strongly correlates with genes from cancer cell survival pathways including cell cycle and lipogenesis, in tumors with EIF4G1 amplification or duplication. Furthermore, we revealed that eIF4G1 and eIF4A1 protein levels strongly co-regulate with ribosomal subunits, eIF2, and eIF3 complexes, while eIF4E co-regulates with 4E-BP1, ubiquitination, and ESCRT proteins. Utilizing Alphafold predictions, we modeled the eIF4F structure with and without eIF4E binding. For cap-dependent initiation, our modeling reveals extensive interactions between the N-terminal eIF4E-binding domain of eIF4G1 and eIF4E. Furthermore, the eIF4G1 HEAT-2 domain positions eIF4E near the eIF4A1 N-terminal domain (NTD), resulting in the collaborative enclosure of the RNA binding cavity within eIF4A1. In contrast, during cap-independent initiation, the HEAT-2 domain directly binds the eIF4A1-NTD, leading to a stronger interaction between eIF4G1 and eIF4A1, thus closing the mRNA binding cavity without the involvement of eIF4E.

Decoding leukemia at the single-cell level: clonal architecture, classification, microenvironment, and drug resistance

Leukemias are refractory hematological malignancies, characterized by marked intrinsic heterogeneity which poses significant obstacles to effective treatment. However, traditional bulk sequencing techniques have not been able to effectively unravel the heterogeneity among individual tumor cells. With the emergence of single-cell sequencing technology, it has bestowed upon us an unprecedented resolution to comprehend the mechanisms underlying leukemogenesis and drug resistance across various levels, including the genome, epigenome, transcriptome and proteome. Here, we provide an overview of the currently prevalent single-cell sequencing technologies and a detailed summary of single-cell studies conducted on leukemia, with a specific focus on four key aspects: (1) leukemia's clonal architecture, (2) frameworks to determine leukemia subtypes, (3) tumor microenvironment (TME) and (4) the drug-resistant mechanisms of leukemia. This review provides a comprehensive summary of current single-cell studies on leukemia and highlights the markers and mechanisms that show promising clinical implications for the diagnosis and treatment of leukemia.

Islands of genomic stability in the face of genetically unstable metastatic cancer

Introduction

Metastatic cancer affects millions of people worldwide annually and is the leading cause of cancer-related deaths. Most patients with metastatic disease are not eligible for surgical resection, and current therapeutic regimens have varying success rates, some with 5-year survival rates below 5%. Here we test the hypothesis that metastatic cancer can be genetically targeted by exploiting single base substitution mutations unique to individual cells that occur as part of normal aging prior to transformation. These mutations are targetable because ~10% of them form novel tumor-specific "NGG" protospacer adjacent motif (PAM) sites targetable by CRISPR-Cas9.

Methods

Whole genome sequencing was performed on five rapid autopsy cases of patient-matched primary tumor, normal and metastatic tissue from pancreatic ductal adenocarcinoma decedents. CRISPR-Cas9 PAM targets were determined by bioinformatic tumor-normal subtraction for each patient and verified in metastatic samples by high-depth capture-based sequencing.

Results

We found that 90% of PAM targets were maintained between primary carcinomas and metastases overall. We identified rules that predict PAM loss or retention, where PAMs located in heterozygous regions in the primary tumor can be lost in metastases (private LOH), but PAMs occurring in regions of loss of heterozygosity (LOH) in the primary tumor were universally conserved in metastases.

Conclusions

Regions of truncal LOH are strongly retained in the presence of genetic instability, and therefore represent genetic vulnerabilities in pancreatic adenocarcinomas. A CRISPR-based gene therapy approach targeting these regions may be a novel way to genetically target metastatic cancer.

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

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

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

ABSTRACT

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

Measures of genetic diversification in somatic tissues at bulk and single-cell resolution

Intra-tissue genetic heterogeneity is universal to both healthy and cancerous tissues. It emerges from the stochastic accumulation of somatic mutations throughout development and homeostasis. By combining population genetics theory and genomic information, genetic heterogeneity can be exploited to infer tissue organization and dynamics in vivo. However, many basic quantities, for example the dynamics of tissue-specific stem cells remain difficult to quantify precisely. Here, we show that single-cell and bulk sequencing data inform on different aspects of the underlying stochastic processes. Bulk-derived variant allele frequency spectra (VAF) show transitions from growing to constant stem cell populations with age in samples of healthy esophagus epithelium. Single-cell mutational burden distributions allow a sample size independent measure of mutation and proliferation rates. Mutation rates in adult hematopietic stem cells are higher compared to inferences during development, suggesting additional proliferation-independent effects. Furthermore, single-cell derived VAF spectra contain information on the number of tissue-specific stem cells. In hematopiesis, we find approximately 2 × 105 HSCs, if all stem cells divide symmetrically. However, the single-cell mutational burden distribution is over-dispersed compared to a model of Poisson distributed random mutations. A time-associated model of mutation accumulation with a constant rate alone cannot generate such a pattern. At least one additional source of stochasticity would be needed. Possible candidates for these processes may be occasional bursts of stem cell divisions, potentially in response to injury, or non-constant mutation rates either through environmental exposures or cell-intrinsic variation.

PTEN, ERG, SPINK1, and TFF3 Status and Relationship in a Prostate Cancer Cohort from Jordanian Arab Population

Background and Objectives: Prognostic biomarkers in prostate cancer (PCa) include PTEN, ERG, SPINK1, and TFF3. Their relationships and patterns of expression in PCa in developing countries, including Jordan, have not yet been investigated. Materials and Methods: A tissue microarray (TMA) of PCa patients was taken from paraffin-embedded tissue blocks for 130 patients. PTEN, ERG, SPINK1, and TFF3 expression profiles were examined using immunohistochemistry (IHC) and correlated with each other and other clinicopathological factors. Results: PTEN loss of any degree was observed in 42.9% of PCa cases. ERG and TFF3 were expressed in 59.3% and 46.5% of PCa cases, respectively. SPINK1 expression was observed in 6 out of 104 PCa cases (5.4%). Among all PCa cases (n = 104), 3.8% (n = 4) showed SPINK1+/ERG+ phenotype, 1.9% (n = 2) showed SPINK1+/ERG- phenotype, 56.7% (n = 59) showed SPINK1-/ERG+ phenotype, and 37.5% showed SPINK1-/ERG- phenotype (n = 39). Among ERG positive cases (n = 63), 6.3% were SPINK1 positive. Among SPINK1 positive cases (n = 6), 66.7% were ERG positive. SPINK1 expression was predominantly observed in a subgroup of cancers that expressed TFF3 (6/6). Additionally, a statistically significant loss of PTEN expression was observed from Gleason Score 6 (GS6) (Grade Group 1 (GG1)) to GS9-10 (GG5); (p-value 0.019). Conclusions: This is the first study to look at the status of the PTEN, ERG, SPINK1, and TFF3 genes in a Jordanian Arab population. Loss of PTEN has been linked to more aggressive prostate cancer with high GSs/GGs. SPINK1 expression was predominantly observed in a subgroup of cancers that expressed TFF3. Our results call for screening these biomarkers for grading and molecular subtyping of the disease.

Genome-first approach of the prevalence and cancer phenotypes of pathogenic or likely pathogenic germline TP53 variants

Pathogenic or likely pathogenic (P/LP) germline TP53 variants are the primary cause of Li-Fraumeni syndrome (LFS), a hereditary cancer predisposition disorder characterized by early-onset cancers. The population prevalence of P/LP germline TP53 variants is estimated to be approximately one in every 3,500 to 20,000 individuals. However, these estimates are likely impacted by ascertainment biases and lack of clinical and genetic data to account for potential confounding factors, such as clonal hematopoiesis. Genome-first approaches of cohorts linked to phenotype data can further refine these estimates by identifying individuals with variants of interest and then assessing their phenotypes. This study evaluated P/LP germline (variant allele fraction ≥30%) TP53 variants in three cohorts: UK Biobank (UKB, n = 200,590), Geisinger (n = 170,503), and Penn Medicine Biobank (PMBB, n = 43,731). A total of 109 individuals were identified with P/LP germline TP53 variants across the three databases. The TP53 p.R181H variant was the most frequently identified (9 of 109 individuals, 8%). A total of 110 cancers, including 47 hematologic cancers (47 of 110, 43%), were reported in 71 individuals. The prevalence of P/LP germline TP53 variants was conservatively estimated as 1:10,439 in UKB, 1:3,790 in Geisinger, and 1:2,983 in PMBB. These estimates were calculated after excluding related individuals and accounting for the potential impact of clonal hematopoiesis by excluding heterozygotes who ever developed a hematologic cancer. These varying estimates likely reflect intrinsic selection biases of each database, such as healthcare or population-based contexts. Prospective studies of diverse, young cohorts are required to better understand the population prevalence of germline TP53 variants and their associated cancer penetrance.

Integrated multi-omics profiling to dissect the spatiotemporal evolution of metastatic hepatocellular carcinoma

Comprehensive molecular analyses of metastatic hepatocellular carcinoma (HCC) are lacking. Here, we generate multi-omic profiling of 257 primary and 176 metastatic regions from 182 HCC patients. Primary tumors rich in hypoxia signatures facilitated polyclonal dissemination. Genomic divergence between primary and metastatic HCC is high, and early dissemination is prevalent. The remarkable neoantigen intratumor heterogeneity observed in metastases is associated with decreased T cell reactivity, resulting from disruptions to neoantigen presentation. We identify somatic copy number alterations as highly selected events driving metastasis. Subclones without Wnt mutations show a stronger selective advantage for metastasis than those with Wnt mutations and are characterized by a microenvironment rich in activated fibroblasts favoring a pro-metastatic phenotype. Finally, metastases without Wnt mutations exhibit higher enrichment of immunosuppressive B cells that mediate terminal exhaustion of CD8+ T cells via HLA-E:CD94-NKG2A checkpoint axis. Collectively, our results provide a multi-dimensional dissection of the complex evolutionary process of metastasis.

Testing Adaptive Therapy Protocols Using Gemcitabine and Capecitabine in a Preclinical Model of Endocrine-Resistant Breast Cancer

Adaptive therapy, an ecologically inspired approach to cancer treatment, aims to overcome resistance and reduce toxicity by leveraging competitive interactions between drug-sensitive and drug-resistant subclones, prioritizing patient survival and quality of life instead of killing the maximum number of cancer cells. In preparation for a clinical trial, we used endocrine-resistant MCF7 breast cancer to stimulate second-line therapy and tested adaptive therapy using capecitabine, gemcitabine, or their combination in a mouse xenograft model. Dose modulation adaptive therapy with capecitabine alone increased survival time relative to MTD but not statistically significantly (HR = 0.22, 95% CI = 0.043-1.1, p = 0.065). However, when we alternated the drugs in both dose modulation (HR = 0.11, 95% CI = 0.024-0.55, p = 0.007) and intermittent adaptive therapies, the survival time was significantly increased compared to high-dose combination therapy (HR = 0.07, 95% CI = 0.013-0.42, p = 0.003). Overall, the survival time increased with reduced dose for both single drugs (p < 0.01) and combined drugs (p < 0.001), resulting in tumors with fewer proliferation cells (p = 0.0026) and more apoptotic cells (p = 0.045) compared to high-dose therapy. Adaptive therapy favors slower-growing tumors and shows promise in two-drug alternating regimens instead of being combined.

Inferring single-cell copy number profiles through cross-cell segmentation of read counts

BACKGROUND

Copy number alteration (CNA) is one of the major genomic variations that frequently occur in cancers, and accurate inference of CNAs is essential for unmasking intra-tumor heterogeneity (ITH) and tumor evolutionary history. Single-cell DNA sequencing (scDNA-seq) makes it convenient to profile CNAs at single-cell resolution, and thus aids in better characterization of ITH. Despite that several computational methods have been proposed to decipher single-cell CNAs, their performance is limited in either breakpoint detection or copy number estimation due to the high dimensionality and noisy nature of read counts data.

RESULTS

By treating breakpoint detection as a process to segment high dimensional read count sequence, we develop a novel method called DeepCNA for cross-cell segmentation of read count sequence and per-cell inference of CNAs. To cope with the difficulty of segmentation, an autoencoder (AE) network is employed in DeepCNA to project the original data into a low-dimensional space, where the breakpoints can be efficiently detected along each latent dimension and further merged to obtain the final breakpoints. Unlike the existing methods that manually calculate certain statistics of read counts to find breakpoints, the AE model makes it convenient to automatically learn the representations. Based on the inferred breakpoints, we employ a mixture model to predict copy numbers of segments for each cell, and leverage expectation-maximization algorithm to efficiently estimate cell ploidy by exploring the most abundant copy number state. Benchmarking results on simulated and real data demonstrate our method is able to accurately infer breakpoints as well as absolute copy numbers and surpasses the existing methods under different test conditions. DeepCNA can be accessed at: https://github.com/zhyu-lab/deepcna .

CONCLUSIONS

Profiling single-cell CNAs based on deep learning is becoming a new paradigm of scDNA-seq data analysis, and DeepCNA is an enhancement to the current arsenal of computational methods for investigating cancer genomics.

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

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

Single-cell RNA-Seq Elucidates the Crosstalk Between Cancer Stem Cells and the Tumor Microenvironment in Hepatocellular Carcinoma

Background: The challenge of systemic treatment for hepatocellular carcinoma (HCC) stems from the development of drug resistance, primarily driven by the interplay between cancer stem cells (CSCs) and the tumor microenvironment (TME). However, there is a notable dearth of comprehensive research investigating the crosstalk between CSCs and stromal cells or immune cells within the TME of HCC. Methods: We procured single-cell RNA sequencing (scRNA-Seq) data from 16 patients diagnosed with HCC. Employing meticulous data quality control and cell annotation procedures, we delineated distinct CSCs subtypes and performed multi-omics analyses encompassing metabolic activity, cell communication, and cell trajectory. These analyses shed light on the potential molecular mechanisms governing the interaction between CSCs and the TME, while also identifying CSCs' developmental genes. By combining these developmental genes, we employed machine learning algorithms and RT-qPCR to construct and validate a prognostic risk model for HCC. Results: We successfully identified CSCs subtypes residing within malignant cells. Through meticulous enrichment analysis and assessment of metabolic activity, we discovered anomalous metabolic patterns within the CSCs microenvironment, including hypoxia and glucose deprivation. Moreover, CSCs exhibited aberrant activity in signaling pathways associated with lipid metabolism. Furthermore, our investigations into cell communication unveiled that CSCs possess the capacity to modulate stromal cells and immune cells through the secretion of MIF or MDK, consequently exerting regulatory control over the TME. Finally, through cell trajectory analysis, we found developmental genes of CSCs. Leveraging these genes, we successfully developed and validated a prognostic risk model (APCS, ADH4, FTH1, and HSPB1) with machine learning and RT-qPCR. Conclusions: By means of single-cell multi-omics analysis, this study offers valuable insights into the potential molecular mechanisms governing the interaction between CSCs and the TME, elucidating the pivotal role CSCs play within the TME. Additionally, we have successfully established a comprehensive clinical prognostic model through bulk RNA-Seq data.

CONIPHER: a computational framework for scalable phylogenetic reconstruction with error correction

Intratumor heterogeneity provides the fuel for the evolution and selection of subclonal tumor cell populations. However, accurate inference of tumor subclonal architecture and reconstruction of tumor evolutionary histories from bulk DNA sequencing data remains challenging. Frequently, sequencing and alignment artifacts are not fully filtered out from cancer somatic mutations, and errors in the identification of copy number alterations or complex evolutionary events (e.g., mutation losses) affect the estimated cellular prevalence of mutations. Together, such errors propagate into the analysis of mutation clustering and phylogenetic reconstruction. In this Protocol, we present a new computational framework, CONIPHER (COrrecting Noise In PHylogenetic Evaluation and Reconstruction), that accurately infers subclonal structure and phylogenetic relationships from multisample tumor sequencing, accounting for both copy number alterations and mutation errors. CONIPHER has been used to reconstruct subclonal architecture and tumor phylogeny from multisample tumors with high-depth whole-exome sequencing from the TRACERx421 dataset, as well as matched primary-metastatic cases. CONIPHER outperforms similar methods on simulated datasets, and in particular scales to a large number of tumor samples and clones, while completing in under 1.5 h on average. CONIPHER enables automated phylogenetic analysis that can be effectively applied to large sequencing datasets generated with different technologies. CONIPHER can be run with a basic knowledge of bioinformatics and R and bash scripting languages.

CellCharter reveals spatial cell niches associated with tissue remodeling and cell plasticity

Tissues are organized in cellular niches, the composition and interactions of which can be investigated using spatial omics technologies. However, systematic analyses of tissue composition are challenged by the scale and diversity of the data. Here we present CellCharter, an algorithmic framework to identify, characterize, and compare cellular niches in spatially resolved datasets. CellCharter outperformed existing approaches and effectively identified cellular niches across datasets generated using different technologies, and comprising hundreds of samples and millions of cells. In multiple human lung cancer cohorts, CellCharter uncovered a cellular niche composed of tumor-associated neutrophil and cancer cells expressing markers of hypoxia and cell migration. This cancer cell state was spatially segregated from more proliferative tumor cell clusters and was associated with tumor-associated neutrophil infiltration and poor prognosis in independent patient cohorts. Overall, CellCharter enables systematic analyses across data types and technologies to decode the link between spatial tissue architectures and cell plasticity.