Genomic Analyses of Breast Cancer Progression Reveal Distinct Routes of Metastasis Emergence

Copy number alterations: a catastrophic orchestration of the breast cancer genome

Breast cancer (BCa) is a prevalent malignancy that predominantly affects women around the world. Somatic copy number alterations (CNAs) are tumor-specific amplifications or deletions of DNA segments that often drive BCa development and therapy resistance. Hence, the complex patterns of CNAs complement BCa classification systems. In addition, understanding the precise contributions of CNAs is essential for tailoring personalized treatment approaches. This review highlights how tumor evolution drives the acquisition of CNAs, which in turn shape the genomic landscapes of BCas. It also discusses advanced methodologies for identifying recurrent CNAs, studying CNAs in BCa and their clinical impact.

Rare subclonal sequencing of breast cancers indicates putative metastatic driver mutations are predominately acquired after dissemination

BACKGROUND

Evolutionary models of breast cancer progression differ on the extent to which metastatic potential is pre-encoded within primary tumors. Although metastatic recurrences often harbor putative driver mutations that are not detected in their antecedent primary tumor using standard sequencing technologies, whether these mutations were acquired before or after dissemination remains unclear.

METHODS

To ascertain whether putative metastatic driver mutations initially deemed specific to the metastasis by whole exome sequencing were, in actuality, present within rare ancestral subclones of the primary tumors from which they arose, we employed error-controlled ultra-deep sequencing (UDS-UMI) coupled with FFPE artifact mitigation by uracil-DNA glycosylase (UDG) to assess the presence of 132 "metastasis-specific" mutations within antecedent primary tumors from 21 patients. Maximum mutation detection sensitivity was ~1% of primary tumor cells. A conceptual framework was developed to estimate relative likelihoods of alternative models of mutation acquisition.

RESULTS

The ancestral primary tumor subclone responsible for seeding the metastasis was identified in 29% of patients, implicating several putative drivers in metastatic seeding including LRP5 A65V and PEAK1 K140Q. Despite this, 93% of metastasis-specific mutations in putative metastatic driver genes remained undetected within primary tumors, as did 96% of metastasis-specific mutations in known breast cancer drivers, including ERRB2 V777L, ESR1 D538G, and AKT1 D323H. Strikingly, even in those cases in which the rare ancestral subclone was identified, 87% of metastasis-specific putative driver mutations remained undetected. Modeling indicated that the sequential acquisition of multiple metastasis-specific driver or passenger mutations within the same rare subclonal lineage of the primary tumor was highly improbable.

CONCLUSIONS

Our results strongly suggest that metastatic driver mutations are sequentially acquired and selected within the same clonal lineage both before, but more commonly after, dissemination from the primary tumor, and that these mutations are biologically consequential. Despite inherent limitations in sampling archival primary tumors, our findings indicate that tumor cells in most patients continue to undergo clinically relevant genomic evolution after their dissemination from the primary tumor. This provides further evidence that metastatic recurrence is a multi-step, mutation-driven process that extends beyond primary tumor dissemination and underscores the importance of longitudinal tumor assessment to help guide clinical decisions.

Imaging Molecular Targets and Metabolic Pathways in Breast Cancer for Improved Clinical Management: Current Practice and Future Perspectives

Breast cancer is the most frequently diagnosed cancer and leading cause of cancer-related deaths worldwide. Timely decision-making that enables implementation of the most appropriate therapy or therapies is essential for achieving the best clinical outcomes in breast cancer. While clinicopathologic characteristics and immunohistochemistry have traditionally been used in decision-making, these clinical and laboratory parameters may be difficult to ascertain or be equivocal due to tumor heterogeneity. Tumor heterogeneity is described as a phenomenon characterized by spatial or temporal phenotypic variations in tumor characteristics. Spatial variations occur within tumor lesions or between lesions at a single time point while temporal variations are seen as tumor lesions evolve with time. Due to limitations associated with immunohistochemistry (which requires invasive biopsies), whole-body molecular imaging tools such as standard-of-care [18F]FDG and [18F]FES PET/CT are indispensable in addressing this conundrum. Despite their proven utility, these standard-of-care imaging methods are often unable to image a myriad of other molecular pathways associated with breast cancer. This has stimulated interest in the development of novel radiopharmaceuticals targeting other molecular pathways and processes. In this review, we discuss validated and potential roles of these standard-of-care and novel molecular approaches. These approaches' relationships with patient clinicopathologic and immunohistochemical characteristics as well as their influence on patient management will be discussed in greater detail. This paper will also introduce and discuss the potential utility of novel PARP inhibitor-based radiopharmaceuticals as non-invasive biomarkers of PARP expression/upregulation.

Regulation of dormancy during tumor dissemination: the role of the ECM

The study of the metastatic cascade has revealed the complexity of the process and the multiple cellular states that disseminated cancer cells must go through. The tumor microenvironment and in particular the extracellular matrix (ECM) plays an important role in regulating the transition from invasion, dormancy to ultimately proliferation during the metastatic cascade. The time delay from primary tumor detection to metastatic growth is regulated by a molecular program that maintains disseminated tumor cells in a non-proliferative, quiescence state known as tumor cell dormancy. Identifying dormant cells and their niches in vivo and how they transition to the proliferative state is an active area of investigation, and novel approaches have been developed to track dormant cells during dissemination. In this review, we highlight the latest research on the invasive nature of disseminated tumor cells and their link to dormancy programs. We also discuss the role of the ECM in sustaining dormant niches at distant sites.

Regulation of Metastatic Tumor Dormancy and Emerging Opportunities for Therapeutic Intervention

Cancer recurrence and metastasis, following successful treatment, constitutes a critical threat in clinical oncology and are the leading causes of death amongst cancer patients. This phenomenon is largely attributed to metastatic tumor dormancy, a rate-limiting stage during cancer progression, in which disseminated cancer cells remain in a viable, yet not proliferating state for a prolonged period. Dormant cancer cells are characterized by their entry into cell cycle arrest and survival in a quiescence state to adapt to their new microenvironment through the acquisition of mutations and epigenetic modifications, rendering them resistant to anti-cancer treatment and immune surveillance. Under favorable conditions, disseminated dormant tumor cells 're-awake', resume their proliferation and thus colonize distant sites. Due to their rarity, detection of dormant cells using current diagnostic tools is challenging and, thus, therapeutic targets are hard to be identified. Therefore, unraveling the underlying mechanisms required for keeping disseminating tumor cells dormant, along with signals that stimulate their "re-awakening" are crucial for the discovery of novel pharmacological treatments. In this review, we shed light into the main mechanisms that control dormancy induction and escape as well as emerging therapeutic strategies for the eradication of metastatic dormant cells, including dormancy maintenance, direct targeting of dormant cells and re-awakening dormant cells. Studies on the ability of the metastatic cancer cells to cease proliferation and survive in a quiescent state before re-initiating proliferation and colonization years after successful treatment, will pave the way toward developing innovative therapeutic strategies against dormancy-mediated metastatic outgrowth.

The Multi-Omic Landscape of Primary Breast Tumors and Their Metastases: Expanding the Efficacy of Actionable Therapeutic Targets

Breast cancer (BC) mortality is almost exclusively due to metastasis, which is the least understood aspect of cancer biology and represents a significant clinical challenge. Although we have witnessed tremendous advancements in the treatment for metastatic breast cancer (mBC), treatment resistance inevitably occurs in most patients. Recently, efforts in characterizing mBC revealed distinctive genomic, epigenomic and transcriptomic (multi-omic) landscapes to that of the primary tumor. Understanding of the molecular underpinnings of mBC is key to understanding resistance to therapy and the development of novel treatment options. This review summarizes the differential molecular landscapes of BC and mBC, provides insights into the genomic heterogeneity of mBC and highlights the therapeutically relevant, multi-omic features that may serve as novel therapeutic targets for mBC patients.

A role of FDG-PET/CT for response evaluation in metastatic breast cancer?

Breast cancer prognosis is steadily improving due to early detection of primary cancer in screening programs and revolutionizing treatment development. In the metastatic setting, therapy improvements render breast cancer a chronic disease. Although FDG-PET/CT has emerged as a highly accurate method for staging metastatic breast cancer, there has been no change in response evaluation methods for decades. FDG-PET/CT has proven high prognostic values in patients with metastatic breast cancer when using quantitative PET methods. It has also shown a higher predictive value than conventional CT when applying the respective response evaluation criteria, RECIST and PERCIST. Response categorization using FDG-PET/CT is more sensitive in detecting progressive and regressive disease, while conventional imaging such as CT and bone scintigraphy deem stable disease more often. These findings reflect the higher accuracy of FDG-PET/CT for response evaluation in this patient group. But does the higher accuracy of FDG-PET/CT translate into a patient benefit when implementing it for monitoring response to palliative treatment? We have evidence of survival benefit from a retrospective study indicating the superiority of using FDG-PET/CT compared with conventional imaging for response evaluation in metastatic breast cancer patients. The survival benefit seems to result from earlier detection of progression with FDG-PET/CT than conventional imaging, leading to an earlier change in treatment with potentially better efficacy of the subsequent treatment line. FDG-PET/CT can be used semiquantitatively as suggested in PERCIST. However, we still need to improve clinically applicable methods based on neural network modeling to better integrate the quantitative information in a smart and standardized way, enabling relevant comparability between scans, patients, and institutions. Such innovation is warranted to support imaging specialists in diagnostic response assessment. Prospective multicenter studies analyzing patients' survival, quality of life, societal and patient costs of replacing conventional imaging with FDG-PET/CT are needed before firm conclusions can be drawn on which type of scan to recommend in future clinical guidelines.

Heterogeneity and tumor evolution reflected in liquid biopsy in metastatic breast cancer patients: a review

Breast cancer is a spatially and temporally dynamic disease in which differently evolving genetic clones are responsible for progression and clinical outcome. We review tumor heterogeneity and clonal evolution from studies comparing primary tumors and metastasis and discuss plasma circulating tumor DNA as a powerful real-time approach for monitoring the clonal landscape of breast cancer during treatment and recurrence. We found only a few early studies exploring clonal evolution and heterogeneity through analysis of multiregional tissue biopsies of different progression steps in comparison with circulating tumor DNA (ctDNA) from blood plasma. The model of linear progression seemed to be more often reported than the model of parallel progression. The results show complex routes to metastasis, however, and plasma most often reflected metastasis more than primary tumor. The described patterns of evolution and the polyclonal nature of breast cancer have clinical consequences and should be considered during patient diagnosis and treatment selection. Current studies focusing on the relevance of clonal evolution in the clinical setting illustrate the role of liquid biopsy as a noninvasive biomarker for monitoring clonal progression and response to treatment. In the clinical setting, circulating tumor DNA may be an ideal support for tumor biopsies to characterize the genetic landscape of the metastatic disease and to improve longitudinal monitoring of disease dynamics and treatment effectiveness through detection of residual tumor after resection, relapse, or metastasis within a particular patient.

Disseminated carcinomatosis of the bone marrow caused by prostate cancer diagnosed with only bone marrow biopsy

INTRODUCTION

Disseminated carcinomatosis of the bone marrow caused by prostate cancer is a rare condition with poor prognosis. Diagnosis has mostly been by primary prostate biopsy.

CASE PRESENTATION

A 60-year-old man had malaise, low platelet count (9000/μL), and high prostate-specific antigen (1382 ng/mL). Bone marrow biopsy showed strongly positive immunostaining NKX3.1, leading to diagnosis of prostate cancer bone marrow metastasis, cT3aN1M1b. Definitive diagnosis by prostate biopsy was difficult because of the sparsity of atypical glands. He had progression to castration-resistant prostate cancer after 3 months of hormonal therapy, and received 27 courses of docetaxel and six courses of cabazitaxel as chemotherapy, but finally died of respiratory failure 33 months after the start of treatment.

CONCLUSION

Aggressive biopsy of the metastatic sites should be considered if a prostate biopsy at the primary site cannot be diagnosed definitively.

Functional Genomic Analysis of Breast Cancer Metastasis: Implications for Diagnosis and Therapy

Simple Summary

Metastasis remains the greatest cause of fatalities in breast cancer patients world-wide. The process of metastases is highly complex, and the current research efforts in this area are still rather fragmented. The revolution of genomic profiling methods to analyze samples from human and animal models dramatically improved our understanding of breast cancer metastasis. This article summarizes the recent breakthroughs in genomic analyses of breast cancer metastasis and discusses their implications for prognostic and therapeutic applications.

Abstract

Breast cancer (BC) is one of the most diagnosed cancers worldwide and is the second cause of cancer related death in women. The most frequent cause of BC-related deaths, like many cancers, is metastasis. However, metastasis is a complicated and poorly understood process for which there is a shortage of accurate prognostic indicators and effective treatments. With the rapid and ever-evolving development and application of genomic sequencing technologies, many novel molecules were identified that play previously unappreciated and important roles in the various stages of metastasis. In this review, we summarize current advancements in the functional genomic analysis of BC metastasis and discuss about the potential prognostic and therapeutic implications from the recent genomic findings.

How to Predict Metastasis in Luminal Breast Cancer? Current Solutions and Future Prospects

Breast cancer metastasis is the main cause of breast cancer mortality. Luminal breast cancer represents the majority of breast cancer cases and, despite relatively good prognosis, its heterogeneity creates problems with a proper stratification of patients and correct identification of the group with a high risk of metastatic relapse. Current prognostic tools are based on the analysis of the primary tumor and, despite their undisputed power of prediction, they might be insufficient to foresee the relapse in an accurate and precise manner, especially if the relapse occurs after a long period of dormancy, which is very common in luminal breast cancer. New approaches tend to rely on body fluid analyses, which have the advantage of being non-invasive and versatile and may be repeated and used for monitoring the disease in the long run. In this review we describe the current, newly-developed, and only-just-discovered methods which are or may become useful in the assessment of the probability of the relapse.

Cancer progression and the invisible phase of metastatic colonization

Metastatic dissemination occurs very early in the malignant progression of a cancer but the clinical manifestation of metastases often takes years. In recent decades, 5-year survival of patients with many solid cancers has increased due to earlier detection, local disease control and adjuvant therapies. As a consequence, we are confronted with an increase in late relapses as more antiproliferative cancer therapies prolong disease courses, raising questions about how cancer cells survive, evolve or stop growing and finally expand during periods of clinical latency. I argue here that the understanding of early metastasis formation, particularly of the currently invisible phase of metastatic colonization, will be essential for the next stage in adjuvant therapy development that reliably prevents metachronous metastasis.

Ensemble disease gene prediction by clinical sample-based networks

BACKGROUND

Disease gene prediction is a critical and challenging task. Many computational methods have been developed to predict disease genes, which can reduce the money and time used in the experimental validation. Since proteins (products of genes) usually work together to achieve a specific function, biomolecular networks, such as the protein-protein interaction (PPI) network and gene co-expression networks, are widely used to predict disease genes by analyzing the relationships between known disease genes and other genes in the networks. However, existing methods commonly use a universal static PPI network, which ignore the fact that PPIs are dynamic, and PPIs in various patients should also be different.

RESULTS

To address these issues, we develop an ensemble algorithm to predict disease genes from clinical sample-based networks (EdgCSN). The algorithm first constructs single sample-based networks for each case sample of the disease under study. Then, these single sample-based networks are merged to several fused networks based on the clustering results of the samples. After that, logistic models are trained with centrality features extracted from the fused networks, and an ensemble strategy is used to predict the finial probability of each gene being disease-associated. EdgCSN is evaluated on breast cancer (BC), thyroid cancer (TC) and Alzheimer's disease (AD) and obtains AUC values of 0.970, 0.971 and 0.966, respectively, which are much better than the competing algorithms. Subsequent de novo validations also demonstrate the ability of EdgCSN in predicting new disease genes.

CONCLUSIONS

In this study, we propose EdgCSN, which is an ensemble learning algorithm for predicting disease genes with models trained by centrality features extracted from clinical sample-based networks. Results of the leave-one-out cross validation show that our EdgCSN performs much better than the competing algorithms in predicting BC-associated, TC-associated and AD-associated genes. de novo validations also show that EdgCSN is valuable for identifying new disease genes.

Circulating Tumor Cells in Early and Advanced Breast Cancer; Biology and Prognostic Value

Breast cancer metastasis is the leading cause of cancer deaths in women and is difficult to combat due to the long periods in which disseminated cells retain a potential to be re-activated and start the relapse. Assessing the number and molecular profile of circulating tumor cells (CTCs) in breast cancer patients, especially in early breast cancer, should help in identifying the possibility of relapse in time for therapeutic intervention to prevent or delay recurrence. While metastatic breast cancer is considered incurable, molecular analysis of CTCs still have a potential to define particular susceptibilities of the cells representing the current tumor burden, which may differ considerably from the cells of the primary tumor, and offer more tailored therapy to the patients. In this review we inspect the routes to metastasis and how they can be linked to specific features of CTCs, how CTC analysis may be used in therapy, and what is the current status of the research and efforts to include CTC analysis in clinical practice.

Power and pitfalls of computational methods for inferring clone phylogenies and mutation orders from bulk sequencing data

Tumors harbor extensive genetic heterogeneity in the form of distinct clone genotypes that arise over time and across different tissues and regions in cancer. Many computational methods produce clone phylogenies from population bulk sequencing data collected from multiple tumor samples from a patient. These clone phylogenies are used to infer mutation order and clone origins during tumor progression, rendering the selection of the appropriate clonal deconvolution method critical. Surprisingly, absolute and relative accuracies of these methods in correctly inferring clone phylogenies are yet to consistently assessed. Therefore, we evaluated the performance of seven computational methods. The accuracy of the reconstructed mutation order and inferred clone groupings varied extensively among methods. All the tested methods showed limited ability to identify ancestral clone sequences present in tumor samples correctly. The presence of copy number alterations, the occurrence of multiple seeding events among tumor sites during metastatic tumor evolution, and extensive intermixture of cancer cells among tumors hindered the detection of clones and the inference of clone phylogenies for all methods tested. Overall, CloneFinder, MACHINA, and LICHeE showed the highest overall accuracy, but none of the methods performed well for all simulated datasets. So, we present guidelines for selecting methods for data analysis.

FDG-PET/CT for Response Monitoring in Metastatic Breast Cancer: Today, Tomorrow, and Beyond

While current international guidelines include imaging of the target lesion for response monitoring in metastatic breast cancer, they do not provide specific recommendations for choice of imaging modality or response criteria. This is important as clinical decisions may vary depending on which imaging modality is used for monitoring metastatic breast cancer. FDG-PET/CT has shown high accuracy in diagnosing metastatic breast cancer, and the Positron Emission Tomography Response Criteria in Solid Tumors (PERCIST) have shown higher predictive values than the CT-based Response Evaluation Criteria in Solid Tumors (RECIST) for prediction of progression-free survival. No studies have yet addressed the clinical impact of using different imaging modalities or response evaluation criteria for longitudinal response monitoring in metastatic breast cancer. We present a case study of a patient with metastatic breast cancer who was monitored first with conventional CT and then with FDG-PET/CT. We retrospectively applied PERCIST to evaluate the longitudinal response to treatment. We used the one-lesion PERCIST model measuring SULpeak in the hottest metastatic lesion on consecutive scans. This model provides a continuous variable that allows graphical illustration of disease fluctuation along with response categories. The one-lesion PERCIST approach seems able to reflect molecular changes and has the potential to support clinical decision-making. Prospective clinical studies addressing the clinical impact of PERCIST in metastatic breast cancer are needed to establish evidence-based recommendations for response monitoring in this disease.

Identification of metastasis driver genes by massive parallel sequencing of successive steps of breast cancer progression

Cancer results from alterations at essential genomic sites and is characterized by uncontrolled cell proliferation, invasion and metastasis. Identification of driver genes of metastatic progression is essential, as metastases, not primary tumors, are fatal. To gain insight into the mutational concordance between different steps of malignant progression we performed exome sequencing and validation with targeted deep sequencing of successive steps of malignant progression from pre-invasive stages to asynchronous distant metastases in six breast cancer patients. Using the ratio of non-synonymous to synonymous mutations, a surprisingly large number of cancer driver genes, ranging between 3 and 145, were estimated to confer a selective advantage in the studied primary tumors. We report a substantial amount of metastasis specific mutations and a number of novel putative metastasis driver genes. Most notable are the DCC, ABCA13, TIAM2, CREBBP, BCL6B and ZNF185 genes, mainly mutated exclusively in metastases and highly likely driver genes of metastatic progression. We find different genes and pathways to be affected at different steps of malignant progression. The Adherens junction pathway is affected in four of the six studied patients and this pathway most likely plays a vital role in the metastatic process.

Preclinical Modeling and Therapeutic Avenues for Cancer Metastasis to the Central Nervous System

Metastasis is the dissemination of cells from the primary tumor to other locations within the body, and continues to be the predominant cause of death among cancer patients. Metastatic progression within the adult central nervous system is 10 times more frequent than primary brain tumors. Metastases affecting the brain parenchyma and leptomeninges are associated with grave prognosis, and even after successful control of the primary tumor the median survival is a dismal 2-3 months with treatment options typically limited to palliative care. Current treatment options for brain metastases (BM) and disseminated brain tumors are scarce, and the improvement of novel targeted therapies requires a broader understanding of the biological complexity that characterizes metastatic progression. In this review, we provide insight into patterns of BM progression and leptomeningeal spread, outlining the development of clinically relevant in vivo models and their contribution to the discovery of innovative cancer therapies. In vivo models paired with manipulation of in vitro methods have expanded the tools available for investigators to develop agents that can be used to prevent or treat metastatic disease. The knowledge gained from the use of such models can ultimately lead to the prevention of metastatic dissemination and can extend patient survival by transforming a uniformly fatal systemic disease into a locally controlled and eminently more treatable one.