Landscapes of cellular phenotypic diversity in breast cancer xenografts and their impact on drug response

Cancer Research in the Age of Spatial Omics: Lessons from IMAXT

The Imaging and Molecular Annotation of Xenografts and Tumors Cancer Grand Challenges team was set up with the objective of developing the "next generation" of pathology and cancer research by using a combination of single-cell and spatial omics tools to produce 3D molecularly annotated maps of tumors. Its activities overlapped, and in some cases catalyzed, a spatial revolution in biology that saw new technologies being deployed to investigate the roles of tumor heterogeneity and of the tumor micro-environment. See related article by Stratton et al., p. 22 See related article by Bhattacharjee et al., p. 28 See related article by Goodwin et al., p. 34.

Cancer Patient-Derived Cell-Based Models: Applications and Challenges in Functional Precision Medicine

The field of oncology has witnessed remarkable progress in personalized cancer therapy. Functional precision medicine has emerged as a promising avenue for achieving superior treatment outcomes by integrating omics profiling and sensitivity testing of patient-derived cancer cells. This review paper provides an in-depth analysis of the evolution of cancer-directed drugs, resistance mechanisms, and the role of functional precision medicine platforms in revolutionizing individualized treatment strategies. Using two-dimensional (2D) and three-dimensional (3D) cell cultures, patient-derived xenograft (PDX) models, and advanced functional assays has significantly improved our understanding of tumor behavior and drug response. This progress will lead to identifying more effective treatments for more patients. Considering the limited eligibility of patients based on a genome-targeted approach for receiving targeted therapy, functional precision medicine provides unprecedented opportunities for customizing medical interventions according to individual patient traits and individual drug responses. This review delineates the current landscape, explores limitations, and presents future perspectives to inspire ongoing advancements in functional precision medicine for personalized cancer therapy.

Integrating mechanism-based T cell phenotypes into a model of tumor-immune cell interactions

Interactions between cancer cells and immune cells in the tumor microenvironment influence tumor growth and can contribute to the response to cancer immunotherapies. It is difficult to gain mechanistic insights into the effects of cell-cell interactions in tumors using a purely experimental approach. However, computational modeling enables quantitative investigation of the tumor microenvironment, and agent-based modeling, in particular, provides relevant biological insights into the spatial and temporal evolution of tumors. Here, we develop a novel agent-based model (ABM) to predict the consequences of intercellular interactions. Furthermore, we leverage our prior work that predicts the transitions of CD8+ T cells from a naïve state to a terminally differentiated state using Boolean modeling. Given the details incorporated to predict T cell state, we apply the integrated Boolean-ABM framework to study how the properties of CD8+ T cells influence the composition and spatial organization of tumors and the efficacy of an immune checkpoint blockade. Overall, we present a mechanistic understanding of tumor evolution that can be leveraged to study targeted immunotherapeutic strategies.

Automated cell type annotation and exploration of single-cell signaling dynamics using mass cytometry

Mass cytometry by time-of-flight (CyTOF) is an emerging technology allowing for in-depth characterization of cellular heterogeneity in cancer and other diseases. Unfortunately, high-dimensional analyses of CyTOF data remain quite demanding. Here, we deploy a bioinformatics framework that tackles two fundamental problems in CyTOF analyses namely (1) automated annotation of cell populations guided by a reference dataset and (2) systematic utilization of single-cell data for effective patient stratification. By applying this framework on several publicly available datasets, we demonstrate that the Scaffold approach achieves good trade-off between sensitivity and specificity for automated cell type annotation. Additionally, a case study focusing on a cohort of 43 leukemia patients reported salient interactions between signaling proteins that are sufficient to predict short-term survival at time of diagnosis using the XGBoost algorithm. Our work introduces an automated and versatile analysis framework for CyTOF data with many applications in future precision medicine projects.

The Power and Promise of Patient-Derived Xenografts of Human Breast Cancer

In 2016, a group of researchers engaged in the development of patient-derived xenografts (PDXs) of human breast cancer provided a comprehensive review of the state of the field. In that review, they summarized the clinical problem that PDXs might address, the technical approaches to their generation (including a discussion of host animals and transplant conditions tested), and presented transplantation success (take) rates across groups and across transplantation conditions. At the time, there were just over 500 unique PDX models created by these investigators representing all three clinically defined subtypes (ER+, HER2+, and TNBC). Today, many of these PDX resources have at least doubled in size, and several more PDX development groups now exist, such that there may be well upward of 1000 PDX models of human breast cancer in existence worldwide. They also presented a series of open questions for the field. Many of these questions have been addressed. However, several remain open, or only partially addressed. Herein, we revisit these questions, and recount the progress that has been made in a number of areas with respect to generation, characterization, and use of PDXs in translational research, and re-present questions that remain open. These open questions, and others, are now being addressed not only by individual investigators, but also large, well-funded consortia including the PDXNet program of the National Cancer Institute in the United States, and the EuroPDX Consortium, an organization of PDX developers across Europe. Finally, we discuss the new opportunities in PDX-based research.

Glucocorticoid receptor: a harmonizer of cellular plasticity in breast cancer-directs the road towards therapy resistance, metastatic progression and recurrence

Recent therapeutic advances have significantly uplifted the quality of life in breast cancer patients, yet several impediments block the road to disease-free survival. This involves unresponsiveness towards administered therapy, epithelial to mesenchymal transition, and metastatic progression with the eventual appearance of recurrent disease. Attainment of such characteristics is a huge adaptive challenge to which tumour cells respond by acquiring diverse phenotypically plastic states. Several signalling networks and mediators are involved in such a process. Glucocorticoid receptor being a mediator of stress response imparts prognostic significance in the context of breast carcinoma. Involvement of the glucocorticoid receptor in the signalling cascade of breast cancer phenotypic plasticity needs further elucidation. This review attempted to shed light on the inter-regulatory interactions of the glucocorticoid receptor with the mediators of the plasticity program in breast cancer; which may provide a hint for strategizing therapeutics against the glucocorticoid/glucocorticoid receptor axis so as to modulate phenotypic plasticity in breast carcinoma.

Vitamin B5 supports MYC oncogenic metabolism and tumor progression in breast cancer

Tumors are intrinsically heterogeneous and it is well established that this directs their evolution, hinders their classification and frustrates therapy1-3. Consequently, spatially resolved omics-level analyses are gaining traction4-9. Despite considerable therapeutic interest, tumor metabolism has been lagging behind this development and there is a paucity of data regarding its spatial organization. To address this shortcoming, we set out to study the local metabolic effects of the oncogene c-MYC, a pleiotropic transcription factor that accumulates with tumor progression and influences metabolism10,11. Through correlative mass spectrometry imaging, we show that pantothenic acid (vitamin B5) associates with MYC-high areas within both human and murine mammary tumors, where its conversion to coenzyme A fuels Krebs cycle activity. Mechanistically, we show that this is accomplished by MYC-mediated upregulation of its multivitamin transporter SLC5A6. Notably, we show that SLC5A6 over-expression alone can induce increased cell growth and a shift toward biosynthesis, whereas conversely, dietary restriction of pantothenic acid leads to a reversal of many MYC-mediated metabolic changes and results in hampered tumor growth. Our work thus establishes the availability of vitamins and cofactors as a potential bottleneck in tumor progression, which can be exploited therapeutically. Overall, we show that a spatial understanding of local metabolism facilitates the identification of clinically relevant, tractable metabolic targets.

The Breast Cancer Proteome and Precision Oncology

The goal of precision oncology is to translate the molecular features of cancer into predictive and prognostic tests that can be used to individualize treatment leading to improved outcomes and decreased toxicity. Success for this strategy in breast cancer is exemplified by efficacy of trastuzumab in tumors overexpressing ERBB2 and endocrine therapy for tumors that are estrogen receptor positive. However, other effective treatments, including chemotherapy, immune checkpoint inhibitors, and CDK4/6 inhibitors are not associated with strong predictive biomarkers. Proteomics promises another tier of information that, when added to genomic and transcriptomic features (proteogenomics), may create new opportunities to improve both treatment precision and therapeutic hypotheses. Here, we review both mass spectrometry-based and antibody-dependent proteomics as complementary approaches. We highlight how these methods have contributed toward a more complete understanding of breast cancer and describe the potential to guide diagnosis and treatment more accurately.

FOXC2 promotes vasculogenic mimicry and resistance to anti-angiogenic therapy

Vasculogenic mimicry (VM) describes the formation of pseudo blood vessels constructed of tumor cells that have acquired endothelial-like properties. VM channels endow the tumor with a tumor-derived vascular system that directly connects to host blood vessels, and their presence is generally associated with poor patient prognosis. Here we show that the transcription factor, Foxc2, promotes VM in diverse solid tumor types by driving ectopic expression of endothelial genes in tumor cells, a process that is stimulated by hypoxia. VM-proficient tumors are resistant to anti-angiogenic therapy, and suppression of Foxc2 augments response. This work establishes co-option of an embryonic endothelial transcription factor by tumor cells as a key mechanism driving VM proclivity and motivates the search for VM-inhibitory agents that could form the basis of combination therapies with anti-angiogenics.

The dawn of spatial omics

Spatial omics has been widely heralded as the new frontier in life sciences. This term encompasses a wide range of techniques that promise to transform many areas of biology and eventually revolutionize pathology by measuring physical tissue structure and molecular characteristics at the same time. Although the field came of age in the past 5 years, it still suffers from some growing pains: barriers to entry, robustness, unclear best practices for experimental design and analysis, and lack of standardization. In this Review, we present a systematic catalog of the different families of spatial omics technologies; highlight their principles, power, and limitations; and give some perspective and suggestions on the biggest challenges that lay ahead in this incredibly powerful-but still hard to navigate-landscape.

Molecular Characterization and Landscape of Breast cancer Models from a multi-omics Perspective

Breast cancer is well-known to be a highly heterogenous disease. This facet of cancer makes finding a research model that mirrors the disparate intrinsic features challenging. With advances in multi-omics technologies, establishing parallels between the various models and human tumors is increasingly intricate. Here we review the various model systems and their relation to primary breast tumors using available omics data platforms. Among the research models reviewed here, breast cancer cell lines have the least resemblance to human tumors since they have accumulated many mutations and copy number alterations during their long use. Moreover, individual proteomic and metabolomic profiles do not overlap with the molecular landscape of breast cancer. Interestingly, omics analysis revealed that the initial subtype classification of some breast cancer cell lines was inappropriate. In cell lines the major subtypes are all well represented and share some features with primary tumors. In contrast, patient-derived xenografts (PDX) and patient-derived organoids (PDO) are superior in mirroring human breast cancers at many levels, making them suitable models for drug screening and molecular analysis. While patient derived organoids are spread across luminal, basal- and normal-like subtypes, the PDX samples were initially largely basal but other subtypes have been increasingly described. Murine models offer heterogenous tumor landscapes, inter and intra-model heterogeneity, and give rise to tumors of different phenotypes and histology. Murine models have a reduced mutational burden compared to human breast cancer but share some transcriptomic resemblance, and representation of many breast cancer subtypes can be found among the variety subtypes. To date, while mammospheres and three- dimensional cultures lack comprehensive omics data, these are excellent models for the study of stem cells, cell fate decision and differentiation, and have also been used for drug screening. Therefore, this review explores the molecular landscapes and characterization of breast cancer research models by comparing recent published multi-omics data and analysis.

Imaging and Molecular Annotation of Xenographs and Tumours (IMAXT): High throughput data and analysis infrastructure

With the aim of producing a 3D representation of tumors, imaging and molecular annotation of xenografts and tumors (IMAXT) uses a large variety of modalities in order to acquire tumor samples and produce a map of every cell in the tumor and its host environment. With the large volume and variety of data produced in the project, we developed automatic data workflows and analysis pipelines. We introduce a research methodology where scientists connect to a cloud environment to perform analysis close to where data are located, instead of bringing data to their local computers. Here, we present the data and analysis infrastructure, discuss the unique computational challenges and describe the analysis chains developed and deployed to generate molecularly annotated tumor models. Registration is achieved by use of a novel technique involving spherical fiducial marks that are visible in all imaging modalities used within IMAXT. The automatic pipelines are highly optimized and allow to obtain processed datasets several times quicker than current solutions narrowing the gap between data acquisition and scientific exploitation.

Patient-derived xenograft models in cancer therapy: technologies and applications

Patient-derived xenograft (PDX) models, in which tumor tissues from patients are implanted into immunocompromised or humanized mice, have shown superiority in recapitulating the characteristics of cancer, such as the spatial structure of cancer and the intratumor heterogeneity of cancer. Moreover, PDX models retain the genomic features of patients across different stages, subtypes, and diversified treatment backgrounds. Optimized PDX engraftment procedures and modern technologies such as multi-omics and deep learning have enabled a more comprehensive depiction of the PDX molecular landscape and boosted the utilization of PDX models. These irreplaceable advantages make PDX models an ideal choice in cancer treatment studies, such as preclinical trials of novel drugs, validating novel drug combinations, screening drug-sensitive patients, and exploring drug resistance mechanisms. In this review, we gave an overview of the history of PDX models and the process of PDX model establishment. Subsequently, the review presents the strengths and weaknesses of PDX models and highlights the integration of novel technologies in PDX model research. Finally, we delineated the broad application of PDX models in chemotherapy, targeted therapy, immunotherapy, and other novel therapies.

A Novel External Auditory Canal Squamous Cell Carcinoma Cell Line Sensitive to CDK4/6 Inhibition

OBJECTIVE

To characterize cell line CAE606 derived from a squamous cell carcinoma (SCC) of the external auditory canal (EAC) and to show its usefulness as a model for testing candidate therapeutic agents.

STUDY DESIGN

Preclinical translational research.

SETTING

Biomedical research institute.

METHODS

The cell line was initiated from a moderately differentiated T2N0M0 EAC SCC. We studied its histologic and genetic features as well as growth and invasion parameters. Sensitivity to cell CDK4/6 cell cycle inhibitor palbociclib was analyzed.

RESULTS

CAE606 cells expressed heavy molecular weight cytokeratin, p63, and vimentin. The population doubling time was 25.8 hours, and the cells showed fast collective cell migration in a wound-healing assay. Short tandem repeat analysis confirmed it to be derived from the primary tumor of the patient. Next-generation sequencing revealed alterations in cell cycle regulation genes, including inactivating mutations in CDKN2A and TP53 and high-level amplification of CCND1 and EGFR. CAE606 showed a strong decrease of phospo-Rb expression upon exposure to the CDK4/6 inhibitor palbociclib, causing significant growth inhibition with an IC₅₀ of 0.46 µM.

CONCLUSION

This is the first report of a stable EAC SCC cell line. Its genetic features make it a useful tool for preclinical testing of new therapeutic agents for EAC SCC, particularly those targeting cell cycle regulation in combination with radio- and chemotherapy or other specific signaling pathway inhibitors.

Physical- and Chemical-Dually ROS-Responsive Nano-in-Gel Platforms with Sequential Release of OX40 Agonist and PD-1 Inhibitor for Augmented Combination Immunotherapy

Combination immunotherapy synergizing the PD-1 blockade with OX40 agonism has become a research hotspot, due to its enormous potential to overcome the restricted clinical objective response suffered by monotherapy. Questions of timing and sequence have been important aspects of immunotherapies when considering immunologic mechanisms; however, most of the time the straightforward additive approach was taken. Herein, our work is the first to investigate an alternative timing of aOX40 and aPD-1 treatment in melanoma-bearing mice, and it demonstrates that sequential administration (aOX40 first, then aPD-1 following) provided superior antitumor benefits than concurrent treatment. Based on that, to further avoid the limits suffered by solution forms, we adopted pharmaceutical technologies to construct an in situ-formed physical- and chemical-dually ROS-responsive nano-in-gel platform to implement sequential and prolonged release of aPD-1 and aOX40. Equipped with these advantages, the as-prepared (aPD-1NCs&aOX40)@Gels elicited augmented combination immunity and achieved great eradication of both primary and distant melanoma tumors in vivo.

Reagents for Mass Cytometry

Mass cytometry (cytometry by time-of-flight detection [CyTOF]) is a bioanalytical technique that enables the identification and quantification of diverse features of cellular systems with single-cell resolution. In suspension mass cytometry, cells are stained with stable heavy-atom isotope-tagged reagents, and then the cells are nebulized into an inductively coupled plasma time-of-flight mass spectrometry (ICP-TOF-MS) instrument. In imaging mass cytometry, a pulsed laser is used to ablate ca. 1 μm² spots of a tissue section. The plume is then transferred to the CyTOF, generating an image of biomarker expression. Similar measurements are possible with multiplexed ion bean imaging (MIBI). The unit mass resolution of the ICP-TOF-MS detector allows for multiparametric analysis of (in principle) up to 130 different parameters. Currently available reagents, however, allow simultaneous measurement of up to 50 biomarkers. As new reagents are developed, the scope of information that can be obtained by mass cytometry continues to increase, particularly due to the development of new small molecule reagents which enable monitoring of active biochemistry at the cellular level. This review summarizes the history and current state of mass cytometry reagent development and elaborates on areas where there is a need for new reagents. Additionally, this review provides guidelines on how new reagents should be tested and how the data should be presented to make them most meaningful to the mass cytometry user community.

Clusterbody Enables Flow Sorting-Assisted Single-Cell Mass Spectrometry Analysis for Identifying Reversal Agent of Chemoresistance

Identifying effective reversal agents overcoming multidrug resistance with causal mechanisms from an efflux pump protein is of vital importance for enhanced tumor chemotherapy in clinic. To achieve this end, we construct a metal cluster-based probe, named clusterbody, to develop flow sorting-assisted single-cell mass spectrometry analysis. This clusterbody synthesized by biomimetic mineralization possesses an antibody-like property to selectively recognize an efflux pump protein. The intrinsic red fluorescence emission of the clusterbody facilitates fluorescence-activated high-throughput cell sorting of subpopulations with different multidrug resistance levels. Furthermore, based on the accurate formula of the clusterbody, the corresponding protein abundance at the single-cell level is determined through detecting gold content via precise signal amplification by laser ablation inductively coupled plasma mass spectrometry. Therefore, the effect of reversal agent treatment overcoming multidrug resistance is evaluated in a quantitative manner. This work opens a new avenue to identify reversal agents, shedding light on developing combined or synergetic tumor therapy.

Cancer organoid applications to investigate chemotherapy resistance

In clinical practice, a large proportion of cancer patients receive chemotherapy, yet tumors persist or acquire resistance; removing this obstacle could help to lower the number of cancer-related fatalities. All areas of cancer research are increasingly using organoid technology, a culture technique that simulates the in vivo environment in vitro, especially in the quickly developing fields of anticancer drug resistance, drug-tolerant persisters, and drug screening. This review provides an overview of organoid technology, the use of organoids in the field of anticancer drug resistance research, their relevance to clinical information and clinical trials, and approaches to automation and high throughput.

Breast cancer plasticity is restricted by a LATS1-NCOR1 repressive axis

Breast cancer, the most frequent cancer in women, is generally classified into several distinct histological and molecular subtypes. However, single-cell technologies have revealed remarkable cellular and functional heterogeneity across subtypes and even within individual breast tumors. Much of this heterogeneity is attributable to dynamic alterations in the epigenetic landscape of the cancer cells, which promote phenotypic plasticity. Such plasticity, including transition from luminal to basal-like cell identity, can promote disease aggressiveness. We now report that the tumor suppressor LATS1, whose expression is often downregulated in human breast cancer, helps maintain luminal breast cancer cell identity by reducing the chromatin accessibility of genes that are characteristic of a "basal-like" state, preventing their spurious activation. This is achieved via interaction of LATS1 with the NCOR1 nuclear corepressor and recruitment of HDAC1, driving histone H3K27 deacetylation near NCOR1-repressed "basal-like" genes. Consequently, decreased expression of LATS1 elevates the expression of such genes and facilitates slippage towards a more basal-like phenotypic identity. We propose that by enforcing rigorous silencing of repressed genes, the LATS1-NCOR1 axis maintains luminal cell identity and restricts breast cancer progression.

Tetraplex Immunophenotyping of Cell Surface Proteomes via Synthesized Plasmonic Nanotags and Portable Raman Spectroscopy

Multiplex immunophenotyping of cell surface proteomes is useful for cell characterization as well as providing valuable information on a patient's physiological or pathological state. Current methods for multiplex immunophenotyping of cell surface proteomes still have associated technical pitfalls in terms of limited multiplexing capability, challenging result interpretation, and large equipment footprint limited to use in a laboratory setting. Herein, we presented a portable surface-enhanced Raman spectroscopy (SERS) assay for multiplex cell surface immunophenotyping. We synthesized and functionalized customizable SERS nanotags for cell labeling and subsequent signal measurement using a portable Raman spectrometer. We extensively evaluated and validated the analytical assay performance of the portable SERS immunophenotyping assay in two different cellular models (red blood cells and breast cancer cells). In terms of analytical specificity, the cell surface immunophenotyping of both red blood cells and breast cancer cells correlated well with flow cytometry. The portable SERS immunophenotyping assay also has comparable analytical repeatability to flow cytometry, with coefficient of variation values of 21.89-23.33% and 6.88-17.32% for detecting red blood cells and breast cancer cells, respectively. The analytical detection limits were 77 cells/mL for red blood cells and 1-17 cells/mL for breast cancer cells. As an alternative to flow cytometry, the portable SERS immunophenotyping assay demonstrated excellent analytical assay performance and possessed advantages such as quick sample-to-result turnaround time, multiplexing capability, and small equipment footprint.

Lineage plasticity in prostate cancer depends on JAK/STAT inflammatory signaling

Drug resistance in cancer is often linked to changes in tumor cell state or lineage, but the molecular mechanisms driving this plasticity remain unclear. Using murine organoid and genetically engineered mouse models, we investigated the causes of lineage plasticity in prostate cancer and its relationship to antiandrogen resistance. We found that plasticity initiates in an epithelial population defined by mixed luminal-basal phenotype and that it depends on increased Janus kinase (JAK) and fibroblast growth factor receptor (FGFR) activity. Organoid cultures from patients with castration-resistant disease harboring mixed-lineage cells reproduce the dependency observed in mice by up-regulating luminal gene expression upon JAK and FGFR inhibitor treatment. Single-cell analysis confirms the presence of mixed-lineage cells with increased JAK/STAT (signal transducer and activator of transcription) and FGFR signaling in a subset of patients with metastatic disease, with implications for stratifying patients for clinical trials.

Hybrid Fluorescent Mass-Tag Nanotrackers as Universal Reagents for Long-Term Live-Cell Barcoding

Barcoding and pooling cells for processing as a composite sample are critical to minimize technical variability in multiplex technologies. Fluorescent cell barcoding has been established as a standard method for multiplexing in flow cytometry analysis. In parallel, mass-tag barcoding is routinely used to label cells for mass cytometry. Barcode reagents currently used label intracellular proteins in fixed and permeabilized cells and, therefore, are not suitable for studies with live cells in long-term culture prior to analysis. In this study, we report the development of fluorescent palladium-based hybrid-tag nanotrackers to barcode live cells for flow and mass cytometry dual-modal readout. We describe the preparation, physicochemical characterization, efficiency of cell internalization, and durability of these nanotrackers in live cells cultured over time. In addition, we demonstrate their compatibility with standardized cytometry reagents and protocols. Finally, we validated these nanotrackers for drug response assays during a long-term coculture experiment with two barcoded cell lines. This method represents a new and widely applicable advance for fluorescent and mass-tag barcoding that is independent of protein expression levels and can be used to label cells before long-term drug studies.

Integrative Analysis of Bulk RNA-Seq and Single-Cell RNA-Seq Unveils the Characteristics of the Immune Microenvironment and Prognosis Signature in Prostate Cancer

The immune microenvironment is a culmination of the collaborative effort of immune cells and is important in cancer development. The underlying mechanisms of the tumor immune microenvironment in regulating prostate cancer (PRAD) are unclear. In the current study, 144 natural killer cell-related genes were identified using differential expression, single-sample gene set enrichment analysis, and weighted gene coexpression network analysis. Furthermore, VCL, ACTA2, MYL9, MYLK, MYH11, TPM1, ACTG2, TAGLN, and FLNC were selected as hub genes via the protein-protein interaction network. Based on the expression patterns of the hub genes, endothelial, epithelial, and tissue stem cells were identified as key cell subpopulations, which could regulate PRAD via immune response, extracellular signaling, and protein formation. Moreover, 27 genes were identified as prognostic signatures and used to construct the risk score model. Receiver operating characteristic curves revealed the good performance of the risk score model in both the training and testing datasets. Different chemotherapeutic responses were observed between the low- and high-risk groups. Additionally, a nomogram based on the risk score and other clinical features was established to predict the 1-, 3-, and 5-year progression-free interval of patients with PRAD. This study provides novel insights into the molecular mechanisms of the immune microenvironment and its role in the pathogenesis of PARD. The identification of key cell subpopulations has a potential therapeutic and prognostic use in PRAD.

Treatment Strategy of Metastatic Nasopharyngeal Carcinoma With Bone Marrow Involvement—A Case Report

Bone marrow involvement (BMI) of solid tumors is a special type of distant metastasis. It has an occult onset, atypical clinical and laboratory features, and a high mortality. We present a nasopharyngeal carcinoma case with cervical and axillary lymph nodes, bilateral lung, multiple bone, and bone marrow metastases, who was treated chemotherapy plus targeted therapy under the guidance of a patient-derived tumor xenograft (PDTX) model, followed by maintenance chemotherapy plus immunotherapy. The patient’s symptoms were relieved after four cycles of chemotherapy plus targeted therapy. His bone marrow biopsy turned negative after 7 months of therapy. In addition, his total peripheral T cells as well as the proportion of CD8⁺ T cells increased during the course of therapy. The combination of chemotherapy, targeted therapy, and immunotherapy provides an effective antitumor regimen for advanced NPC patients with BMI.

In vitro breast cancer models for studying mechanisms of resistance to endocrine therapy

The development of endocrine resistance is a common reason for the failure of endocrine therapies in hormone receptor-positive breast cancer. This review provides an overview of the different types of in vitro models that have been developed as tools for studying endocrine resistance. In vitro models include cell lines that have been rendered endocrine-resistant by ex vivo treatment; cell lines with de novo resistance mechanisms, including genetic alterations; three-dimensional (3D) spheroid, co-culture, and mammosphere techniques; and patient-derived organoid models. In each case, the key discoveries, different analysis strategies that are suitable, and strengths and weaknesses are discussed. Certain recently developed methodologies that can be used to further characterize the biological changes involved in endocrine resistance are then emphasized, along with a commentary on the types of research outcomes that using these techniques can support. Finally, a discussion anticipates how these recent developments will shape future trends in the field. We hope this overview will serve as a useful resource for investigators that are interested in understanding and testing hypotheses related to mechanisms of endocrine therapy resistance.

Breast tumor microenvironment structures are associated with genomic features and clinical outcome

The functions of the tumor microenvironment (TME) are orchestrated by precise spatial organization of specialized cells, yet little is known about the multicellular structures that form within the TME. Here we systematically mapped TME structures in situ using imaging mass cytometry and multitiered spatial analysis of 693 breast tumors linked to genomic and clinical data. We identified ten recurrent TME structures that varied by vascular content, stromal quiescence versus activation, and leukocyte composition. These TME structures had distinct enrichment patterns among breast cancer subtypes, and some were associated with genomic profiles indicative of immune escape. Regulatory and dysfunctional T cells co-occurred in large 'suppressed expansion' structures. These structures were characterized by high cellular diversity, proliferating cells and enrichment for BRCA1 and CASP8 mutations and predicted poor outcome in estrogen-receptor-positive disease. The multicellular structures revealed here link conserved spatial organization to local TME function and could improve patient stratification.

Construction and Validation of a Newly Prognostic Signature for CRISPR-Cas9-Based Cancer Dependency Map Genes in Breast Cancer

Cancer Dependency Map (CDM) genes comprise an extensive series of genome-scale RNAi-based loss-of-function tests; hence, it served as a method based on the CRISPR-Cas9 technique that could assist scientists in investigating potential gene functions. These CDM genes have a role in tumor cell survival and proliferation, suggesting that they may be used as new therapeutic targets for some malignant tumors. So far, there have been less research on the involvement of CDM genes in breast cancer, and only a tiny percentage of CDM genes have been studied. In this study, information of patients with breast cancer was extracted from The Cancer Genome Atlas (TCGA), from which differentially expressed CDM genes in breast cancer were determined. A variety of bioinformatics techniques were used to assess the functions and prognostic relevance of these confirmed CDM genes. In all, 290 CDM genes were found differentially expressed. Six CDM genes (SRF, RAD51, PMF1, EXOSC3, EXOC1, and TSEN54) were found to be associated with the prognosis of breast cancer samples. Based on the expression of the identified CDM genes and their coefficients, a prognosis model was constructed for prediction, according to which patients with breast cancer were separated into two risk groups. Those with high risk had substantially poorer overall survival (OS) than patients in the other risk group in the TCGA training set, TCGA testing set, and an external cohort from Gene Expression Omnibus (GEO) database. The area under the receiver operating characteristic (ROC) curve for this prognostic signature was, respectively, 0.717 and 0.635 for TCGA training and testing sets, demonstrating its reliability in predicting the prognosis of patients with breast cancer. We next created a nomogram using the six CDM genes discovered to create a therapeutically useful model. The Human Protein Atlas database was used to acquire all immunohistochemistry staining images of the discovered CDM genes. The proportions of tumor-infiltrating immune cells, as well as the expression levels of checkpoint genes, varied substantially between the two risk groups, according to the analyses of immune response. In conclusion, the findings of this research may aid in the understanding of the prognostic value and biological roles of CDM genes in breast cancer.

Clonal populations of a human TNBC model display significant functional heterogeneity and divergent growth dynamics in distinct contexts

Intratumoral heterogeneity has been described for various tumor types and models of human cancer, and can have profound effects on tumor progression and drug resistance. This study describes an in-depth analysis of molecular and functional heterogeneity among subclonal populations (SCPs) derived from a single triple-negative breast cancer cell line, including copy number analysis, whole-exome and RNA sequencing, proteome analysis, and barcode analysis of clonal dynamics, as well as functional assays. The SCPs were found to have multiple unique genetic alterations and displayed significant variation in anchorage independent growth and tumor forming ability. Analyses of clonal dynamics in SCP mixtures using DNA barcode technology revealed selection for distinct clonal populations in different in vitro and in vivo environmental contexts, demonstrating that in vitro propagation of cancer cell lines using different culture conditions can contribute to the establishment of unique strains. These analyses also revealed strong enrichment of a single SCP during the development of xenograft tumors in immune-compromised mice. This SCP displayed attenuated interferon signaling in vivo and reduced sensitivity to the antiproliferative effects of type I interferons. Reduction in interferon signaling was found to provide a selective advantage within the xenograft microenvironment specifically. In concordance with the previously described role of interferon signaling as tumor suppressor, these findings suggest that similar selective pressures may be operative in human cancer and patient-derived xenograft models.

Functional genomics approaches to improve pre-clinical drug screening and biomarker discovery

Advances in sequencing technology have enabled the genomic and transcriptomic characterization of human malignancies with unprecedented detail. However, this wealth of information has been slow to translate into clinically meaningful outcomes. Different models to study human cancers have been established and extensively characterized. Using these models, functional genomic screens and pre-clinical drug screening platforms have identified genetic dependencies that can be exploited with drug therapy. These genetic dependencies can also be used as biomarkers to predict response to treatment. For many cancers, the identification of such biomarkers remains elusive. In this review, we discuss the development and characterization of models used to study human cancers, RNA interference and CRISPR screens to identify genetic dependencies, large-scale pharmacogenomics studies and drug screening approaches to improve pre-clinical drug screening and biomarker discovery.

Breast Cancer Heterogeneity

Breast tumor heterogeneity is a major challenge in the clinical management of breast cancer patients. Both inter-tumor and intra-tumor heterogeneity imply that each breast cancer (BC) could have different prognosis and would benefit from specific therapy. Breast cancer is a dynamic entity, changing during tumor progression and metastatization and this poses fundamental issues to the feasibility of a personalized medicine approach. The most effective therapeutic strategy for patients with recurrent disease should be assessed evaluating biopsies obtained from metastatic sites. Furthermore, the tumor progression and the treatment response should be strictly followed and radiogenomics and liquid biopsy might be valuable tools to assess BC heterogeneity in a non-invasive way.

A gentle introduction to understanding preclinical data for cancer pharmaco-omic modeling

A central goal of precision oncology is to administer an optimal drug treatment to each cancer patient. A common preclinical approach to tackle this problem has been to characterize the tumors of patients at the molecular and drug response levels, and employ the resulting datasets for predictive in silico modeling (mostly using machine learning). Understanding how and why the different variants of these datasets are generated is an important component of this process. This review focuses on providing such introduction aimed at scientists with little previous exposure to this research area.