Explore the vast range of titles available.

Multiple Myeloma (MM) is characterized by uncontrolled proliferation and accumulation of clonal plasma cells within the bone marrow. However, the cell of origin is a B-lymphocyte acquiring aberrant genomic events in the germinal center of a lymph node as off-target events during somatichypermutation and class-switch recombination driven by activation-induced-deaminase. Whether pre-germinal center events are also required for transformation, and which additional events are required for disease progression is still matter of debate. As early treatment in asymptomatic phases is gaining traction in the clinic, a better understanding of the molecular pathogenesis of myeloma progression would allow stratification of patients based on their risk of progression, thus rationalizing efficacy and cost of clinical interventions. In this review, we will discuss the development of MM, from the cell of origin through asymptomatic stages such as monoclonal gammopathy of undetermined significance and smoldering MM, to the development of symptomatic disease. We will explain the genetic heterogeneity of MM, one of the major drivers of disease recurrence. In this context, moreover, we will propose how this knowledge may influence future diagnostic and therapeutic interventions.

The harsh microenvironment of ductal carcinoma in situ (DCIS) exerts strong evolutionary selection pressures on cancer cells. We hypothesize that the poor metabolic conditions near the ductal center foment the emergence of aWarburg Effect (WE) phenotype, wherein cells rapidly ferment glucose to lactic acid, even in normoxia. To test this hypothesis, we subjected low-glycolytic breast cancer cells to different microenvironmental selection pressures using combinations of hypoxia, acidosis, low glucose, and starvation for many months and isolated single clones for metabolic and transcriptomic profiling. The two harshest conditions selected for constitutively expressed WE phenotypes. RNA sequencing analysis of WE clones identified the transcription factor KLF4 as potential inducer of the WE phenotype. In stained DCIS samples, KLF4 expression was enriched in the area with the harshest microenvironmental conditions. We simulated in vivo DCIS phenotypic evolution using a mathematical model calibrated from the in vitro results. The WE phenotype emerged in the poor metabolic conditions near the necrotic core. We propose that harsh microenvironments within DCIS select for a WE phenotype through constitutive transcriptional reprogramming, thus conferring a survival advantage and facilitating further growth and invasion.

Single-cell sequencing promises a high-resolution view of genetic heterogeneity and clonal evolution in cancer. However, methods to infer tumor evolution from single-cell sequencing data lag behind methods developed for bulk-sequencing data. Here, we present OncoNEM, a probabilistic method for inferring intra-tumor evolutionary lineage trees from somatic single nucleotide variants of single cells. OncoNEM identifies homogeneous cellular subpopulations and infers their genotypes as well as a tree describing their evolutionary relationships. In simulation studies, we assess OncoNEM’s robustness and benchmark its performance against competing methods. Finally, we show its applicability in case studies of muscle-invasive bladder cancer and essential thrombocythemia.

Tumor-associated macrophages (TAMs) and tumor-associated neutrophils (TANs) have been extensively studied. Their pleotropic roles were observed in multiple steps of tumor progression and metastasis, and sometimes appeared to be inconsistent across different studies. In this review, we collectively discussed many lines of evidence supporting the mutual influence between cancer cells and TAMs/TANs. We focused on how direct interactions among these cells dictate co-evolution involving not only clonal competition of cancer cells, but also landscape shift of the entire tumor microenvironment (TME). This co-evolution may take distinct paths and contribute to the heterogeneity of cancer cells and immune cells across different tumors. A more in-depth understanding of the cancer-TAM/TAN co-evolution will shed light on the development of TME that mediates metastasis and therapeutic resistance.

Cancer cell dormancy is a common feature of human tumors and represents a major clinical barrier to the long-term efficacy of anticancer therapies. Dormant cancer cells, either in primary tumors or disseminated in secondary organs, may reawaken and relapse into a more aggressive disease. The mechanisms underpinning dormancy entry and exit strongly resemble those governing cancer cell stemness and include intrinsic and contextual cues. Cellular and molecular components of the tumor microenvironment persistently interact with cancer cells. This dialog is highly dynamic, as it evolves over time and space, strongly cooperates with intrinsic cell nets, and governs cancer cell features (like quiescence and stemness) and fate (survival and outgrowth). Therefore, there is a need for deeper insight into the biology of dormant cancer (stem) cells and the mechanisms regulating the equilibrium quiescence- -proliferation are vital in our pursuit of new therapeutic opportunities to prevent cancer from recurring. Here, we review and discuss microenvironmental regulations of cancer dormancy and its parallels with cancer stemness, and offer insights into the therapeutic strategies adopted to prevent a lethal recurrence, by either eradicating resident dormant cancer (stem) cells or maintaining them in a dormant state.

Background Tumor heterogeneity and clonal evolution are related to the treatment resistance and disease progression in metastatic breast cancer (MBC). However, the process of clonal evolution and their relationship to prognosis remain unclear. This study aimed to elucidate the evolution of MBC through circulating tumor DNA (ctDNA) analysis and to develop a novel indicator for predicting treatment efficacy and prognosis. Methods This multicenter retrospective study enrolled MBC patients who underwent next-generation sequencing between April 2016 and October 2022. The clonal evolution of tumors was inferred using PyClone and CITUP software. Results The study included 406 MBC patients. A cohort of 139 patients from the National Cancer Center served as the training cohort, while 267 patients from other centers comprised the validation cohort. In the training cohort, clonal analysis revealed that most MBCs exhibited branched clonal evolution, while a minority showed linear evolution. The branched evolution pattern was associated with slower disease progression (HR, 0.53; 95% CI, 0.32–0.87; P  = 0.012). We introduced tumor clonal evolution rate (TER) as a novel concept to reflect the speed of clonal evolution. Survival analysis demonstrated that compared to the TER-high group, patients in the TER-low group had better progression-free survival (PFS) (HR, 0.62; 95% CI, 0.40–0.96; P  = 0.033) and overall survival (OS) (HR, 0.45; 95% CI, 0.24–0.85; P  = 0.013). Similarly, in the validation cohort, although the median OS was not reached, patients in the TER-low group had better prognosis compared to those in the TER-high group (HR, 0.41; 95% CI, 0.21–0.83; P  < 0.001). Conclusions Patients with branched evolution have better treatment efficacy than those with linear evolution. The TER shows potential as a biomarker for treatment efficacy and prognosis, providing new evidence that ctDNA is a valuable molecular indicator for predicting treatment outcomes in metastatic breast cancer.

Single-cell sequencing enables the inference of tumor phylogenies that provide insights on intra-tumor heterogeneity and evolutionary trajectories. Recently introduced methods perform this task under the infinite-sites assumption, violations of which, due to chromosomal deletions and loss of heterozygosity, necessitate the development of inference methods that utilize finite-sites models. We propose a statistical inference method for tumor phylogenies from noisy single-cell sequencing data under a finite-sites model. The performance of our method on synthetic and experimental data sets from two colorectal cancer patients to trace evolutionary lineages in primary and metastatic tumors suggests that employing a finite-sites model leads to improved inference of tumor phylogenies.

Human colorectal cancers (CRCs) contain both clonal and subclonal mutations. Clonal driver mutations are positively selected, present in most cells, and drive malignant progression. Subclonal mutations are randomly dispersed throughout the genome, providing a vast reservoir of mutant cells that can expand, repopulate the tumor, and result in the rapid emergence of resistance, as well as being a major contributor to tumor heterogeneity. Here, we apply duplex sequencing (DS) methodology to quantify subclonal mutations in CRC tumor with unprecedented depth (10⁴) and accuracy (10−7). We measured mutation frequencies in genes encoding replicative DNA polymerases and in genes frequently mutated in CRC, and found an unexpectedly high effective mutation rate, 7.1 × 10−7. The curve of subclonal mutation accumulation as a function of sequencing depth, using DNA obtained from 5 different tumors, is in accord with a neutral model of tumor evolution. We present a theoretical approach to model neutral evolution independent of the infinite-sites assumption (which states that a particular mutation arises only in one tumor cell at any given time). Our analysis indicates that the infinite-sites assumption is not applicable once the number of tumor cells exceeds the reciprocal of the mutation rate, a circumstance relevant to even the smallest clinically diagnosable tumor. Our methods allow accurate estimation of the total mutation burden in clinical cancers. Our results indicate that no DNA locus is wild type in every malignant cell within a tumor at the time of diagnosis (probability of all cells being wild type, 10−308).

Circulating tumor cells (CTCs) are regarded as harbingers of metastases. Their ability to predict response to therapy, relapse, and resistance to treatment has proposed their value as putative diagnostic and prognostic indicators. CTCs represent one of the zeniths of cancer evolution in terms of cell survival; however, the triggers of CTC generation, the identification of potentially metastatic CTCs, and the mechanisms contributing to their heterogeneity and aggressiveness represent issues not yet fully deciphered. Thus, prior to enabling liquid biopsy applications to reach clinical prime time, understanding how the above mechanistic information can be applied to improve treatment decisions is a key challenge. Here, we provide our perspective on how CTCs can provide mechanistic insights into tumor pathogenesis, as well as on CTC clinical value. In doing so, we aim to (a) describe how CTCs disseminate from the primary tumor, and their link to epithelial–mesenchymal transition (EMT); (b) trace the route of CTCs through the circulation, focusing on tumor self-seeding and the possibility of tertiary metastasis; (c) describe possible mechanisms underlying the enhanced metastatic potential of CTCs; (d) discuss how CTC could provide further information on the tissue of origin, especially in cancer of unknown primary origin. We also provide a comprehensive review of meta-analyses assessing the prognostic significance of CTCs, to highlight the emerging role of CTCs in clinical oncology. We also explore how cell-free circulating tumor DNA (ctDNA) analysis, using a combination of genomic and phylogenetic analysis, can offer insights into CTC biology, including our understanding of CTC heterogeneity and tumor evolution. Last, we discuss emerging technologies, such as high-throughput quantitative imaging, radiogenomics, machine learning approaches, and the emerging breath biopsy. These technologies could compliment CTC and ctDNA analyses, and they collectively represent major future steps in cancer detection, monitoring, and management.

Recent research has dramatically advanced our understanding of the genetic basis of multiple myeloma (MM). MM displays enormous inter- and intratumoral heterogeneity, and underlies a clonal evolutionary process driven and shaped by diverse factors such as clonal competition, tumor microenvironment, host immunity, and therapy. Two main cytogenetic groups are distinguished: MM with recurrent translocations involving the immunoglobulin heavy chain locus and MM with hyperdiploidy involving the odd chromosomes. The disease virtually always starts with a preneoplastic prodromal phase—monoclonal gammopathy of undetermined significance—that variably progresses to symptomatic MM within a few months or many years. Tumor heterogeneity and its evolution in space and time have important consequences for the clinical management and outcome of MM patients. At diagnosis, spatial intratumoral heterogeneity poses a challenge for classification and risk stratification. During maintenance therapy, clonal evolution may complicate disease monitoring and promote drug resistance. Upon progression or transformation, identifying the dominant disease-driving neoplastic clones and elucidating their properties are key to tailor personalized therapy. In this review, we discuss tumor heterogeneity and clonal evolution in MM, integrating pathological, radiological, molecular genetics, and clinical data. Current and prospective classification schemes and prognostic parameters, incorporating new genetic and proteomic discoveries and advances in imaging, are highlighted. In addition, the roles of the tumor microenvironment, host immunity, and resistance mutations, and their effects on therapy, are discussed. An improved understanding of high-risk disease, tumor heterogeneity, and clonal evolution will guide future therapies and may ultimately lead towards a cure for MM.