Microenvironment-Mediated Mechanisms of Resistance to HER2 Inhibitors Differ between HER2+ Breast Cancer Subtypes

Harnessing the tumor microenvironment: targeted cancer therapies through modulation of epithelial-mesenchymal transition

The tumor microenvironment (TME) is integral to cancer progression, impacting metastasis and treatment response. It consists of diverse cell types, extracellular matrix components, and signaling molecules that interact to promote tumor growth and therapeutic resistance. Elucidating the intricate interactions between cancer cells and the TME is crucial in understanding cancer progression and therapeutic challenges. A critical process induced by TME signaling is the epithelial-mesenchymal transition (EMT), wherein epithelial cells acquire mesenchymal traits, which enhance their motility and invasiveness and promote metastasis and cancer progression. By targeting various components of the TME, novel investigational strategies aim to disrupt the TME's contribution to the EMT, thereby improving treatment efficacy, addressing therapeutic resistance, and offering a nuanced approach to cancer therapy. This review scrutinizes the key players in the TME and the TME's contribution to the EMT, emphasizing avenues to therapeutically disrupt the interactions between the various TME components. Moreover, the article discusses the TME's implications for resistance mechanisms and highlights the current therapeutic strategies toward TME modulation along with potential caveats.

Rational Design of HER2-Targeted Combination Therapies to Reverse Drug Resistance in Fibroblast-Protected HER2+ Breast Cancer Cells

Introduction

Fibroblasts, an abundant cell type in the breast tumor microenvironment, interact with cancer cells and orchestrate tumor progression and drug resistance. However, the mechanisms by which fibroblast-derived factors impact drug sensitivity remain poorly understood. Here, we develop rational combination therapies that are informed by proteomic profiling to overcome fibroblast-mediated therapeutic resistance in HER2+ breast cancer cells.

Methods

Drug sensitivity to the HER2 kinase inhibitor lapatinib was characterized under conditions of monoculture and exposure to breast fibroblast-conditioned medium. Protein expression was measured using reverse phase protein arrays. Candidate targets for combination therapy were identified using differential expression and multivariate regression modeling. Follow-up experiments were performed to evaluate the effects of HER2 kinase combination therapies in fibroblast-protected cancer cell lines and fibroblasts.

Results

Compared to monoculture, fibroblast-conditioned medium increased the expression of plasminogen activator inhibitor-1 (PAI1) and cell cycle regulator polo like kinase 1 (PLK1) in lapatinib-treated breast cancer cells. Combination therapy of lapatinib with inhibitors targeting either PAI1 or PLK1, eliminated fibroblast-protected cancer cells, under both conditions of direct coculture with fibroblasts and protection by fibroblast-conditioned medium. Analysis of publicly available, clinical transcriptomic datasets revealed that HER2-targeted therapy fails to suppress PLK1 expression in stroma-rich HER2+ breast tumors and that high PAI1 gene expression associates with high stroma density. Furthermore, we showed that an epigenetics-directed approach using a bromodomain and extraterminal inhibitor to globally target fibroblast-induced proteomic adaptions in cancer cells, also restored lapatinib sensitivity.

Conclusions

Our data-driven framework of proteomic profiling in breast cancer cells identified the proteolytic degradation regulator PAI1 and the cell cycle regulator PLK1 as predictors of fibroblast-mediated treatment resistance. Combination therapies targeting HER2 kinase and these fibroblast-induced signaling adaptations eliminates fibroblast-protected HER2+ breast cancer cells.

Supplementary Information

The online version contains supplementary material available at 10.1007/s12195-024-00823-0.

Therapeutic advances of targeting receptor tyrosine kinases in cancer

Receptor tyrosine kinases (RTKs), a category of transmembrane receptors, have gained significant clinical attention in oncology due to their central role in cancer pathogenesis. Genetic alterations, including mutations, amplifications, and overexpression of certain RTKs, are critical in creating environments conducive to tumor development. Following their discovery, extensive research has revealed how RTK dysregulation contributes to oncogenesis, with many cancer subtypes showing dependency on aberrant RTK signaling for their proliferation, survival and progression. These findings paved the way for targeted therapies that aim to inhibit crucial biological pathways in cancer. As a result, RTKs have emerged as primary targets in anticancer therapeutic development. Over the past two decades, this has led to the synthesis and clinical validation of numerous small molecule tyrosine kinase inhibitors (TKIs), now effectively utilized in treating various cancer types. In this manuscript we aim to provide a comprehensive understanding of the RTKs in the context of cancer. We explored the various alterations and overexpression of specific receptors across different malignancies, with special attention dedicated to the examination of current RTK inhibitors, highlighting their role as potential targeted therapies. By integrating the latest research findings and clinical evidence, we seek to elucidate the pivotal role of RTKs in cancer biology and the therapeutic efficacy of RTK inhibition with promising treatment outcomes.

Depicting Biomarkers for HER2-Inhibitor Resistance: Implication for Therapy in HER2-Positive Breast Cancer

HER2 (human epidermal growth factor receptor 2) is highly expressed in a variety of cancers, including breast, lung, gastric, and pancreatic cancers. Its amplification is linked to poor clinical outcomes. At the genetic level, HER2 is encoded by the ERBB2 gene (v-erb-b2 avian erythroblastic leukemia viral oncogene homolog 2), which is frequently mutated or amplified in cancers, thus spurring extensive research into HER2 modulation and inhibition as viable anti-cancer strategies. An impressive body of FDA-approved drugs, including anti-HER2 monoclonal antibodies (mAbs), antibody-drug conjugates (ADCs), and HER2-tyrosine kinase inhibitors (TKIs), have demonstrated success in enhancing overall survival (OS) and disease progression-free survival (PFS). Yet, drug resistance remains a persistent challenge and raises the risks of metastatic potential and tumor relapse. Research into alternative therapeutic options for HER2+ breast cancer therefore proves critical for adapting to this ever-evolving landscape. This review highlights current HER2-targeted therapies, discusses predictive biomarkers for drug resistance, and introduces promising emergent therapies-especially combination therapies-that are aimed at overcoming drug resistance in the context of HER2+ breast cancer.

A prospective study of HER3 expression pre and post neoadjuvant therapy of different breast cancer subtypes: implications for HER3 imaging therapy guidance

PURPOSE

HER3, a member of the EGFR receptor family, plays a central role in driving oncogenic cell proliferation in breast cancer. Novel HER3 therapeutics are showing promising results while recently developed HER3 PET imaging modalities aid in predicting and assessing early treatment response. However, baseline HER3 expression, as well as changes in expression while on neoadjuvant therapy, have not been well-characterized. We conducted a prospective clinical study, pre- and post-neoadjuvant/systemic therapy, in patients with newly diagnosed breast cancer to determine HER3 expression, and to identify possible resistance mechanisms maintained through the HER3 receptor.

EXPERIMENTAL DESIGN

The study was conducted between May 25, 2018 and October 12, 2019. Thirty-four patients with newly diagnosed breast cancer of any subtype (ER ± , PR ± , HER2 ±) were enrolled in the study. Two core biopsy specimens were obtained from each patient at the time of diagnosis. Four patients underwent a second research biopsy following initiation of neoadjuvant/systemic therapy or systemic therapy which we define as neoadjuvant therapy. Molecular characterization of HER3 and downstream signaling nodes of the PI3K/AKT and MAPK pathways pre- and post-initiation of therapy was performed. Transcriptional validation of finings was performed in an external dataset (GSE122630).

RESULTS

Variable baseline HER3 expression was found in newly diagnosed breast cancer and correlated positively with pAKT across subtypes (r = 0.45). In patients receiving neoadjuvant/systemic therapy, changes in HER3 expression were variable. In a hormone receptor-positive (ER +/PR +/HER2-) patient, there was a statistically significant increase in HER3 expression post neoadjuvant therapy, while there was no significant change in HER3 expression in a ER +/PR +/HER2+ patient. However, both of these patients showed increased downstream signaling in the PI3K/AKT pathway. One subject with ER +/PR -/HER2- breast cancer and another subject with ER +/PR +/HER2 + breast cancer showed decreased HER3 expression. Transcriptomic findings, revealed an immune suppressive environment in patients with decreased HER3 expression post therapy.

CONCLUSION

This study demonstrates variable HER3 expression across breast cancer subtypes. HER3 expression can be assessed early, post-neoadjuvant therapy, providing valuable insight into cancer biology and potentially serving as a prognostic biomarker. Clinical translation of neoadjuvant therapy assessment can be achieved using HER3 PET imaging, offering real-time information on tumor biology and guiding personalized treatment for breast cancer patients.

Bridging systems biology and tissue engineering: Unleashing the full potential of complex 3D in vitro tissue models of disease

Rapid advances in tissue engineering have resulted in more complex and physiologically relevant 3D in vitro tissue models with applications in fundamental biology and therapeutic development. However, the complexity provided by these models is often not leveraged fully due to the reductionist methods used to analyze them. Computational and mathematical models developed in the field of systems biology can address this issue. Yet, traditional systems biology has been mostly applied to simpler in vitro models with little physiological relevance and limited cellular complexity. Therefore, integrating these two inherently interdisciplinary fields can result in new insights and move both disciplines forward. In this review, we provide a systematic overview of how systems biology has been integrated with 3D in vitro tissue models and discuss key application areas where the synergies between both fields have led to important advances with potential translational impact. We then outline key directions for future research and discuss a framework for further integration between fields.

Quantitative systems pharmacology modeling of HER2-positive metastatic breast cancer for translational efficacy evaluation and combination assessment across therapeutic modalities

HER2-positive (HER2+) metastatic breast cancer (mBC) is highly aggressive and a major threat to human health. Despite the significant improvement in patients' prognosis given the drug development efforts during the past several decades, many clinical questions still remain to be addressed such as efficacy when combining different therapeutic modalities, best treatment sequences, interindividual variability as well as resistance and potential coping strategies. To better answer these questions, we developed a mechanistic quantitative systems pharmacology model of the pathophysiology of HER2+ mBC that was extensively calibrated and validated against multiscale data to quantitatively predict and characterize the signal transduction and preclinical tumor growth kinetics under different therapeutic interventions. Focusing on the second-line treatment for HER2+ mBC, e.g., antibody-drug conjugates (ADC), small molecule inhibitors/TKI and chemotherapy, the model accurately predicted the efficacy of various drug combinations and dosing regimens at the in vitro and in vivo levels. Sensitivity analyses and subsequent heterogeneous phenotype simulations revealed important insights into the design of new drug combinations to effectively overcome various resistance scenarios in HER2+ mBC treatments. In addition, the model predicted a better efficacy of the new TKI plus ADC combination which can potentially reduce drug dosage and toxicity, while it also shed light on the optimal treatment ordering of ADC versus TKI plus capecitabine regimens, and these findings were validated by new in vivo experiments. Our model is the first that mechanistically integrates multiple key drug modalities in HER2+ mBC research and it can serve as a high-throughput computational platform to guide future model-informed drug development and clinical translation.

Rational design of HER2-targeted combination therapies to reverse drug resistance in fibroblast-protected HER2+ breast cancer cells

Introduction

Fibroblasts, an abundant cell type in the breast tumor microenvironment, interact with cancer cells and orchestrate tumor progression and drug resistance. However, the mechanisms by which fibroblast-derived factors impact drug sensitivity remain poorly understood. Here, we develop rational combination therapies that are informed by proteomic profiling to overcome fibroblast-mediated therapeutic resistance in HER2+ breast cancer cells.

Methods

Drug sensitivity to the HER2 kinase inhibitor lapatinib was characterized under conditions of monoculture and exposure to breast fibroblast-conditioned medium. Protein expression was measured using reverse phase protein arrays. Candidate targets for combination therapy were identified using differential expression and multivariate regression modeling. Follow-up experiments were performed to evaluate the effects of HER2 kinase combination therapies in fibroblast-protected cancer cell lines and fibroblasts.

Results

Compared to monoculture, fibroblast-conditioned medium increased the expression of plasminogen activator inhibitor-1 (PAI1) and cell cycle regulator polo like kinase 1 (PLK1) in lapatinib-treated breast cancer cells. Combination therapy of lapatinib with inhibitors targeting either PAI1 or PLK1, eliminated fibroblast-protected cancer cells, under both conditions of direct coculture with fibroblasts and protection by fibroblast-conditioned medium. Analysis of publicly available, clinical transcriptomic datasets revealed that HER2-targeted therapy fails to suppress PLK1 expression in stroma-rich HER2+ breast tumors and that high PAI1 gene expression associates with high stroma density. Furthermore, we showed that an epigenetics-directed approach using a bromodomain and extraterminal inhibitor to globally target fibroblast-induced proteomic adaptions in cancer cells, also restored lapatinib sensitivity.

Conclusions

Our data-driven framework of proteomic profiling in breast cancer cells identified the proteolytic degradation regulator PAI1 and the cell cycle regulator PLK1 as predictors of fibroblast-mediated treatment resistance. Combination therapies targeting HER2 kinase and these fibroblast-induced signaling adaptations eliminates fibroblast-protected HER2+ breast cancer cells.

Integrating single-cell and bulk RNA sequencing data unveils antigen presentation and process-related CAFS and establishes a predictive signature in prostate cancer

BACKGROUND

Cancer-associated fibroblasts (CAFs) are heterogeneous and can influence the progression of prostate cancer in multiple ways; however, their capacity to present and process antigens in PRAD has not been investigated. In this study, antigen presentation and process-related CAFs (APPCAFs) were identified using bioinformatics, and the clinical implications of APPCAF-related signatures in PRAD were investigated.

METHODS

SMART technology was used to sequence the transcriptome of primary CAFs isolated from patients undergoing different treatments. Differential expression gene (DEG) screening was conducted. A CD4 + T-cell early activation assay was used to assess the activation degree of CD4 + T cells. The datasets of PRAD were obtained from The Cancer Genome Atlas (TCGA) database and NCBI Gene Expression Omnibus (GEO), and the list of 431 antigen presentation and process-related genes was obtained from the InnateDB database. Subsequently, APP-related CAFs were identified by nonnegative matrix factorization (NMF) based on a single-cell seq (scRNA) matrix. GSVA functional enrichment analyses were performed to depict the biological functions. A risk signature based on APPCAF-related genes (APPCAFRS) was developed by least absolute shrinkage and selection operator (LASSO) regression analysis, and the independence of the risk score as a prognostic factor was evaluated by univariate and multivariate Cox regression analyses. Furthermore, a biochemical recurrence-free survival (BCRFS)-related nomogram was established, and immune-related characteristics were assessed using the ssGSEA function. The immune treatment response in PRAD was further analyzed by the Tumor Immune Dysfunction and Exclusion (TIDE) tool. The expression levels of hub genes in APPCAFRS were verified in cell models.

RESULTS

There were 134 upregulated and 147 downregulated genes, totaling 281 differentially expressed genes among the primary CAFs. The functions and pathways of 147 downregulated DEGs were significantly enriched in antigen processing and presentation processes, MHC class II protein complex and transport vesicle, MHC class II protein complex binding, and intestinal immune network for IgA production. Androgen withdrawal diminished the activation effect of CAFs on T cells. NMF clustering of CAFs was performed by APPRGs, and pseudotime analysis yielded the antigen presentation and process-related CAF subtype CTSK + MRC2 + CAF-C1. CTSK + MRC2 + CAF-C1 cells exhibited ligand‒receptor connections with epithelial cells and T cells. Additionally, we found a strong association between CTSK + MRC2 + CAF-C1 cells and inflammatory CAFs. Through differential gene expression analysis of the CTSK + MRC2 + CAF-C1 and NoneAPP-CAF-C2 subgroups, 55 significant DEGs were identified, namely, APPCAFRGs. Based on the expression profiles of APPCAFRGs, we divided the TCGA-PRAD cohort into two clusters using NMF consistent cluster analysis, with the genetic coefficient serving as the evaluation index. Four APPCAFRGs, THBS2, DPT, COL5A1, and MARCKS, were used to develop a prognostic signature capable of predicting BCR occurrence in PRAD patients. Subsequently, a nomogram with stability and accuracy in predicting BCR was constructed based on Gleason grade (p = n.s.), PSA (p < 0.001), T stage (p < 0.05), and risk score (p < 0.01). The analysis of immune infiltration showed a positive correlation between the abundance of resting memory CD4 + T cells, M1 macrophages, resting dendritic cells, and the risk score. In addition, the mRNA expression levels of THBS2, DPT, COL5A1, and MARCKS in the cell models were consistent with the results of the bioinformatics analysis.

CONCLUSIONS

APPCAFRS based on four potential APPCAFRGs was developed, and their interaction with the immune microenvironment may play a crucial role in the progression to castration resistance of PRAD. This novel approach provides valuable insights into the pathogenesis of PRAD and offers unexplored targets for future research.

Functional delineation of the luminal epithelial microenvironment in breast using cell-based screening in combinatorial microenvironments

Microenvironment signals are potent determinants of cell fate and arbiters of tissue homeostasis, however understanding how different microenvironment factors coordinately regulate cellular phenotype has been experimentally challenging. Here we used a high-throughput microenvironment microarray comprised of 2640 unique pairwise signals to identify factors that support proliferation and maintenance of primary human mammary luminal epithelial cells. Multiple microenvironment factors that modulated luminal cell number were identified, including: HGF, NRG1, BMP2, CXCL1, TGFB1, FGF2, PDGFB, RANKL, WNT3A, SPP1, HA, VTN, and OMD. All of these factors were previously shown to modulate luminal cell numbers in painstaking mouse genetics experiments, or were shown to have a role in breast cancer, demonstrating the relevance and power of our high-dimensional approach to dissect key microenvironmental signals. RNA-sequencing of primary epithelial and stromal cell lineages identified the cell types that express these signals and the cognate receptors in vivo. Cell-based functional studies confirmed which effects from microenvironment factors were reproducible and robust to individual variation. Hepatocyte growth factor (HGF) was the factor most robust to individual variation and drove expansion of luminal cells via cKit+ progenitor cells, which expressed abundant MET receptor. Luminal cells from women who are genetically high risk for breast cancer had significantly more MET receptor and may explain the characteristic expansion of the luminal lineage in those women. In ensemble, our approach provides proof of principle that microenvironment signals that control specific cellular states can be dissected with high-dimensional cell-based approaches.

Oncofetal reprogramming in tumor development and progression: novel insights into cancer therapy

Emerging evidence indicates that cancer cells can mimic characteristics of embryonic development, promoting their development and progression. Cancer cells share features with embryonic development, characterized by robust proliferation and differentiation regulated by signaling pathways such as Wnt, Notch, hedgehog, and Hippo signaling. In certain phase, these cells also mimic embryonic diapause and fertilized egg implantation to evade treatments or immune elimination and promote metastasis. Additionally, the upregulation of ATP-binding cassette (ABC) transporters, including multidrug resistance protein 1 (MDR1), multidrug resistance-associated protein 1 (MRP1), and breast cancer-resistant protein (BCRP), in drug-resistant cancer cells, analogous to their role in placental development, may facilitate chemotherapy efflux, further resulting in treatment resistance. In this review, we concentrate on the underlying mechanisms that contribute to tumor development and progression from the perspective of embryonic development, encompassing the dysregulation of developmental signaling pathways, the emergence of dormant cancer cells, immune microenvironment remodeling, and the hyperactivation of ABC transporters. Furthermore, we synthesize and emphasize the connections between cancer hallmarks and embryonic development, offering novel insights for the development of innovative cancer treatment strategies.

Unified tumor growth mechanisms from multimodel inference and dataset integration

Mechanistic models of biological processes can explain observed phenomena and predict responses to a perturbation. A mathematical model is typically constructed using expert knowledge and informal reasoning to generate a mechanistic explanation for a given observation. Although this approach works well for simple systems with abundant data and well-established principles, quantitative biology is often faced with a dearth of both data and knowledge about a process, thus making it challenging to identify and validate all possible mechanistic hypothesis underlying a system behavior. To overcome these limitations, we introduce a Bayesian multimodel inference (Bayes-MMI) methodology, which quantifies how mechanistic hypotheses can explain a given experimental datasets, and concurrently, how each dataset informs a given model hypothesis, thus enabling hypothesis space exploration in the context of available data. We demonstrate this approach to probe standing questions about heterogeneity, lineage plasticity, and cell-cell interactions in tumor growth mechanisms of small cell lung cancer (SCLC). We integrate three datasets that each formulated different explanations for tumor growth mechanisms in SCLC, apply Bayes-MMI and find that the data supports model predictions for tumor evolution promoted by high lineage plasticity, rather than through expanding rare stem-like populations. In addition, the models predict that in the presence of cells associated with the SCLC-N or SCLC-A2 subtypes, the transition from the SCLC-A subtype to the SCLC-Y subtype through an intermediate is decelerated. Together, these predictions provide a testable hypothesis for observed juxtaposed results in SCLC growth and a mechanistic interpretation for tumor treatment resistance.

The role of tumor microenvironment in drug resistance: emerging technologies to unravel breast cancer heterogeneity

Breast cancer is a highly heterogeneous disease, at both inter- and intra-tumor levels, and this heterogeneity is a crucial determinant of malignant progression and response to treatments. In addition to genetic diversity and plasticity of cancer cells, the tumor microenvironment contributes to tumor heterogeneity shaping the physical and biological surroundings of the tumor. The activity of certain types of immune, endothelial or mesenchymal cells in the microenvironment can change the effectiveness of cancer therapies via a plethora of different mechanisms. Therefore, deciphering the interactions between the distinct cell types, their spatial organization and their specific contribution to tumor growth and drug sensitivity is still a major challenge. Dissecting intra-tumor heterogeneity is currently an urgent need to better define breast cancer biology and to develop therapeutic strategies targeting the microenvironment as helpful tools for combined and personalized treatment. In this review, we analyze the mechanisms by which the tumor microenvironment affects the characteristics of tumor heterogeneity that ultimately result in drug resistance, and we outline state of the art preclinical models and emerging technologies that will be instrumental in unraveling the impact of the tumor microenvironment on resistance to therapies.

Morphodynamical cell state description via live-cell imaging trajectory embedding

Time-lapse imaging is a powerful approach to gain insight into the dynamic responses of cells, but the quantitative analysis of morphological changes over time remains challenging. Here, we exploit the concept of "trajectory embedding" to analyze cellular behavior using morphological feature trajectory histories-that is, multiple time points simultaneously, rather than the more common practice of examining morphological feature time courses in single timepoint (snapshot) morphological features. We apply this approach to analyze live-cell images of MCF10A mammary epithelial cells after treatment with a panel of microenvironmental perturbagens that strongly modulate cell motility, morphology, and cell cycle behavior. Our morphodynamical trajectory embedding analysis constructs a shared cell state landscape revealing ligand-specific regulation of cell state transitions and enables quantitative and descriptive models of single-cell trajectories. Additionally, we show that incorporation of trajectories into single-cell morphological analysis enables (i) systematic characterization of cell state trajectories, (ii) better separation of phenotypes, and (iii) more descriptive models of ligand-induced differences as compared to snapshot-based analysis. This morphodynamical trajectory embedding is broadly applicable to the quantitative analysis of cell responses via live-cell imaging across many biological and biomedical applications.

An altered extracellular matrix-integrin interface contributes to Huntington's disease-associated CNS dysfunction in glial and vascular cells

Astrocytes and brain endothelial cells are components of the neurovascular unit that comprises the blood-brain barrier (BBB) and their dysfunction contributes to pathogenesis in Huntington's disease (HD). Defining the contribution of these cells to disease can inform cell-type-specific effects and uncover new disease-modifying therapeutic targets. These cells express integrin (ITG) adhesion receptors that anchor the cells to the extracellular matrix (ECM) to maintain the integrity of the BBB. We used HD patient-derived induced pluripotent stem cell (iPSC) modeling to study the ECM-ITG interface in astrocytes and brain microvascular endothelial cells and found ECM-ITG dysregulation in human iPSC-derived cells that may contribute to the dysfunction of the BBB in HD. This disruption has functional consequences since reducing ITG expression in glia in an HD Drosophila model suppressed disease-associated CNS dysfunction. Since ITGs can be targeted therapeutically and manipulating ITG signaling prevents neurodegeneration in other diseases, defining the role of ITGs in HD may provide a novel strategy of intervention to slow CNS pathophysiology to treat HD.

A multiplex implantable microdevice assay identifies synergistic combinations of cancer immunotherapies and conventional drugs

Systematically identifying synergistic combinations of targeted agents and immunotherapies for cancer treatments remains difficult. In this study, we integrated high-throughput and high-content techniques-an implantable microdevice to administer multiple drugs into different sites in tumors at nanodoses and multiplexed imaging of tumor microenvironmental states-to investigate the tumor cell and immunological response signatures to different treatment regimens. Using a mouse model of breast cancer, we identified effective combinations from among numerous agents within days. In vivo studies in three immunocompetent mammary carcinoma models demonstrated that the predicted combinations synergistically increased therapeutic efficacy. We identified at least five promising treatment strategies, of which the panobinostat, venetoclax and anti-CD40 triple therapy was the most effective in inducing complete tumor remission across models. Successful drug combinations increased spatial association of cancer stem cells with dendritic cells during immunogenic cell death, suggesting this as an important mechanism of action in long-term breast cancer control.

The importance of being CAFs (in cancer resistance to targeted therapies)

In the last two decades, clinical oncology has been revolutionized by the advent of targeted drugs. However, the efficacy of these therapies is significantly limited by primary and acquired resistance, that relies not only on cell-autonomous mechanisms but also on tumor microenvironment cues. Cancer-associated fibroblasts (CAFs) are extremely plastic cells of the tumor microenvironment. They not only produce extracellular matrix components that build up the structure of tumor stroma, but they also release growth factors, chemokines, exosomes, and metabolites that affect all tumor properties, including response to drug treatment. The contribution of CAFs to tumor progression has been deeply investigated and reviewed in several works. However, their role in resistance to anticancer therapies, and in particular to molecular therapies, has been largely overlooked. This review specifically dissects the role of CAFs in driving resistance to targeted therapies and discusses novel CAF targeted therapeutic strategies to improve patient survival.

Systematic replication enables normalization of high-throughput imaging assays

MOTIVATION

High-throughput fluorescent microscopy is a popular class of techniques for studying tissues and cells through automated imaging and feature extraction of hundreds to thousands of samples. Like other high-throughput assays, these approaches can suffer from unwanted noise and technical artifacts that obscure the biological signal. In this work, we consider how an experimental design incorporating multiple levels of replication enables the removal of technical artifacts from such image-based platforms.

RESULTS

We develop a general approach to remove technical artifacts from high-throughput image data that leverages an experimental design with multiple levels of replication. To illustrate the methods, we consider microenvironment microarrays (MEMAs), a high-throughput platform designed to study cellular responses to microenvironmental perturbations. In application to MEMAs, our approach removes unwanted spatial artifacts and thereby enhances the biological signal. This approach has broad applicability to diverse biological assays.

AVAILABILITY AND IMPLEMENTATION

Raw data are on synapse (syn2862345), analysis code is on github: gjhunt/mema_norm, a reproducible Docker image is available on dockerhub: gjhunt/mema_norm.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

A multi-omic analysis of MCF10A cells provides a resource for integrative assessment of ligand-mediated molecular and phenotypic responses

The phenotype of a cell and its underlying molecular state is strongly influenced by extracellular signals, including growth factors, hormones, and extracellular matrix proteins. While these signals are normally tightly controlled, their dysregulation leads to phenotypic and molecular states associated with diverse diseases. To develop a detailed understanding of the linkage between molecular and phenotypic changes, we generated a comprehensive dataset that catalogs the transcriptional, proteomic, epigenomic and phenotypic responses of MCF10A mammary epithelial cells after exposure to the ligands EGF, HGF, OSM, IFNG, TGFB and BMP2. Systematic assessment of the molecular and cellular phenotypes induced by these ligands comprise the LINCS Microenvironment (ME) perturbation dataset, which has been curated and made publicly available for community-wide analysis and development of novel computational methods ( synapse.org/LINCS_MCF10A ). In illustrative analyses, we demonstrate how this dataset can be used to discover functionally related molecular features linked to specific cellular phenotypes. Beyond these analyses, this dataset will serve as a resource for the broader scientific community to mine for biological insights, to compare signals carried across distinct molecular modalities, and to develop new computational methods for integrative data analysis.

Protein tyrosine kinase inhibitor resistance in malignant tumors: molecular mechanisms and future perspective

Protein tyrosine kinases (PTKs) are a class of proteins with tyrosine kinase activity that phosphorylate tyrosine residues of critical molecules in signaling pathways. Their basal function is essential for maintaining normal cell growth and differentiation. However, aberrant activation of PTKs caused by various factors can deviate cell function from the expected trajectory to an abnormal growth state, leading to carcinogenesis. Inhibiting the aberrant PTK function could inhibit tumor growth. Therefore, tyrosine kinase inhibitors (TKIs), target-specific inhibitors of PTKs, have been used in treating malignant tumors and play a significant role in targeted therapy of cancer. Currently, drug resistance is the main reason for limiting TKIs efficacy of cancer. The increasing studies indicated that tumor microenvironment, cell death resistance, tumor metabolism, epigenetic modification and abnormal metabolism of TKIs were deeply involved in tumor development and TKI resistance, besides the abnormal activation of PTK-related signaling pathways involved in gene mutations. Accordingly, it is of great significance to study the underlying mechanisms of TKIs resistance and find solutions to reverse TKIs resistance for improving TKIs efficacy of cancer. Herein, we reviewed the drug resistance mechanisms of TKIs and the potential approaches to overcome TKI resistance, aiming to provide a theoretical basis for improving the efficacy of TKIs.

Targeted Therapeutic Options and Future Perspectives for HER2-Positive Breast Cancer

Simple Summary

The development of several antiHuman Epidermal Growth Factor Receptor 2 (HER2) treatments over the last few years has improved the landscape of HER2-positive breast cancer. Despite this, relapse is still the main issue in HER2-positive breast cancer. The reasons for therapeutic failure lie in the heterogeneity of the disease itself, as well as in the drug resistance mechanisms. In this review, we intended to understand the milestones that have had an impact on this disease up to their implementation in clinical practice. In addition, understanding the underlying molecular biology of HER2-positive disease is essential for the optimization and personalization of the different treatment options. For this reason, we focused on two relevant aspects, which are triple-positive disease and the role that modulation of the immune response might play in treatment and prognosis.

Abstract

Despite the improvement achieved by the introduction of HER2-targeted therapy, up to 25% of early human epidermal growth factor receptor 2-positive (HER2+) breast cancer (BC) patients will relapse. Beyond trastuzumab, other agents approved for early HER2+ BC include the monoclonal antibody pertuzumab, the antibody-drug conjugate (ADC) trastuzumab-emtansine (T-DM1) and the reversible HER2 inhibitor lapatinib. New agents, such as trastuzumab-deruxtecan or tucatinib in combination with capecitabine and trastuzumab, have also shown a significant improvement in the metastatic setting. Other therapeutic strategies to overcome treatment resistance have been explored in HER2+ BC, mainly in HER2+ that also overexpress estrogen receptors (ER+). In ER+ HER2+ patients, target therapies such as phosphoinositide-3-kinase (PI3K) pathway inhibition or cyclin-dependent kinases 4/6 blocking may be effective in controlling downstream of HER2 and many of the cellular pathways associated with resistance to HER2-targeted therapies. Multiple trials have explored these strategies with some promising results, and probably, in the next years conclusive results will succeed. In addition, HER2+ BC is known to be more immunogenic than other BC subgroups, with high variability between tumors. Different immunotherapeutic agents such as HER-2 therapy plus checkpoint inhibitors, or new vaccines approaches have been investigated in this setting, with promising but controversial results obtained to date.

Materials-driven approaches to understand extrinsic drug resistance in cancer

Metastatic cancer has a poor prognosis, because it is broadly disseminated and associated with both intrinsic and acquired drug resistance. Critical unmet needs in effectively killing drug resistant cancer cells include overcoming the drug desensitization characteristics of some metastatic cancers/lesions, and tailoring therapeutic regimens to both the tumor microenvironment and the genetic profiles of the resident cancer cells. Bioengineers and materials scientists are developing technologies to determine how metastatic sites exclude therapies, and how extracellular factors (including cells, proteins, metabolites, extracellular matrix, and abiotic factors) at metastatic sites significantly affect drug pharmacodynamics. Two looming challenges are determining which feature, or combination of features, from the tumor microenvironment drive drug resistance, and what the relative impact is of extracellular signals vs. intrinsic cell genetics in determining drug response. Sophisticated systems biology tools that can de-convolve a crowded network of signals and responses, as well as controllable microenvironments capable of providing discrete and tunable extracellular cues can help us begin to interrogate the high dimensional interactions governing drug resistance in patients.

Ontogeny and Vulnerabilities of Drug-Tolerant Persisters in HER2+ Breast Cancer

Resistance to targeted therapies is an important clinical problem in HER2-positive (HER2+) breast cancer. "Drug-tolerant persisters" (DTP), a subpopulation of cancer cells that survive via reversible, nongenetic mechanisms, are implicated in resistance to tyrosine kinase inhibitors (TKI) in other malignancies, but DTPs following HER2 TKI exposure have not been well characterized. We found that HER2 TKIs evoke DTPs with a luminal-like or a mesenchymal-like transcriptome. Lentiviral barcoding/single-cell RNA sequencing reveals that HER2+ breast cancer cells cycle stochastically through a "pre-DTP" state, characterized by a G0-like expression signature and enriched for diapause and/or senescence genes. Trajectory analysis/cell sorting shows that pre-DTPs preferentially yield DTPs upon HER2 TKI exposure. Cells with similar transcriptomes are present in HER2+ breast tumors and are associated with poor TKI response. Finally, biochemical experiments indicate that luminal-like DTPs survive via estrogen receptor-dependent induction of SGK3, leading to rewiring of the PI3K/AKT/mTORC1 pathway to enable AKT-independent mTORC1 activation.

SIGNIFICANCE

DTPs are implicated in resistance to anticancer therapies, but their ontogeny and vulnerabilities remain unclear. We find that HER2 TKI-DTPs emerge from stochastically arising primed cells ("pre-DTPs") that engage either of two distinct transcriptional programs upon TKI exposure. Our results provide new insights into DTP ontogeny and potential therapeutic vulnerabilities. This article is highlighted in the In This Issue feature, p. 873.

INHBA is a mediator of aggressive tumor behavior in HER2+ basal breast cancer

Background

Resistance to HER2-targeted therapeutics remains a significant clinical problem in HER2+ breast cancer patients with advanced disease. This may be particularly true for HER2+ patients with basal subtype disease, as recent evidence suggests they receive limited benefit from standard of care HER2-targeted therapies. Identification of drivers of resistance and aggressive disease that can be targeted clinically has the potential to impact patient outcomes.

Methods

We performed siRNA knockdown screens of genes differentially expressed between lapatinib-responsive and -resistant HER2+ breast cancer cells, which corresponded largely to luminal versus basal subtypes. We then validated hits in 2-d and 3-d cell culture systems.

Results

Knockdown of one of the genes, INHBA, significantly slowed growth and increased sensitivity to lapatinib in multiple basal HER2+ cell lines in both 2-d and 3-d cultures, but had no effect in luminal HER2+ cells. Loss of INHBA altered metabolism, eliciting a shift from glycolytic to oxidative phosphorylative metabolism, which was also associated with a decrease in tumor invasiveness. Analysis of breast cancer datasets showed that patients with HER2+ breast cancer and high levels of INHBA expression had worse outcomes than patients with low levels of INHBA expression.

Conclusions

Our data suggest that INHBA is associated with aggressiveness of the basal subtype of HER2+ tumors, resulting in poor response to HER2-targeted therapy and an invasive phenotype. We hypothesize that targeting this pathway could be an effective therapeutic strategy to reduce invasiveness of tumor cells and to improve therapeutic response.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13058-022-01512-4.

Functional signaling test identifies HER2 negative breast cancer patients who may benefit from c-Met and pan-HER combination therapy

Background

Research is revealing the complex coordination between cell signaling systems as they adapt to genetic and epigenetic changes. Tools to uncover these highly complex functional linkages will play an important role in advancing more efficacious disease treatments. Current tumor cell signal transduction research is identifying coordination between receptor types, receptor families, and transduction pathways to maintain tumor cell viability despite challenging tumor microenvironment conditions.

Methods

In this report, coactivated abnormal levels of signaling activity for c-Met and HER family receptors in live tumor cells were measured by a new clinical test to identify a subpopulation of breast cancer patients that could be responsive to combined targeted therapies. The CELsignia Multi-Pathway Signaling Function (CELsignia) Test uses an impedance biosensor to quantify an individual patient’s ex vivo live tumor cell signaling response in real-time to specific HER family and c-Met co-stimulation and targeted therapies.

Results

The test identified breast tumors with hyperactive HER1, HER2, HER3/4, and c-Met coordinated signaling that express otherwise normal amounts of these receptors. The supporting data of the pre-clinical verification of this test included analyses of 79 breast cancer patients’ cell response to HER and c-Met agonists. The signaling results were confirmed using clinically approved matching targeted drugs, and combinations of targeted drugs in addition to correlative mouse xenograft tumor response to HER and c-Met targeted therapies.

Conclusions

The results of this study demonstrated the potential benefit of a functional test for identifying a subpopulation of breast cancer patients with coordinated abnormal HER and c-Met signaling for a clinical trial testing combination targeted therapy.

Anticancer Activity of Ω-6 Fatty Acids through Increased 4-HNE in Breast Cancer Cells

Simple Summary

Epidemiological evidence suggests that breast cancer risk is lowered by Ω-3 and increased by Ω-6 polyunsaturated fatty acids (PUFAs). Paradoxically, the Ω-6 PUFA metabolite 4-hydroxynonenal (4-HNE) inhibits cancer cell growth. This duality prompted us to study whether arachidonic acid (AA) would enhance doxorubicin (dox) cytotoxicity towards breast cancer cells. We found that supplementing AA or inhibiting 4-HNE metabolism potentiated doxorubicin (dox) toxicity toward Her2-dependent breast cancer but spared myocardial cells. Our results suggest that Ω-6 PUFAs could improve outcomes of dox chemotherapy in Her2-overexpressing breast cancer.

Abstract

Her2-amplified breast cancers resistant to available Her2-targeted therapeutics continue to be a challenge in breast cancer therapy. Dox is the mainstay of chemotherapy of all types of breast cancer, but its usefulness is limited by cumulative cardiotoxicity. Because oxidative stress caused by dox generates the pro-apoptotic Ω-6 PUFA metabolite 4-hydroxynonenal (4-HNE), we surmised that Ω-6 PUFAs would increase the effectiveness of dox chemotherapy. Since the mercapturic acid pathway enzyme RALBP1 (also known as RLIP76 or Rlip) that limits cellular accumulation of 4-HNE also mediates dox resistance, the combination of Ω-6 PUFAs and Rlip depletion could synergistically improve the efficacy of dox. Thus, we studied the effects of the Ω-6 PUFA arachidonic acid (AA) and Rlip knockdown on the antineoplastic activity of dox towards Her2-amplified breast cancer cell lines SK-BR-3, which is sensitive to Her2 inhibitors, and AU565, which is resistant. AA increased lipid peroxidation, 4-HNE generation, apoptosis, cellular dox concentration and dox cytotoxicity in both cell lines while sparing cultured immortalized cardiomyocyte cells. The known functions of Rlip including clathrin-dependent endocytosis and dox efflux were inhibited by AA. Our results support a model in which 4-HNE generated by AA overwhelms the capacity of Rlip to defend against apoptosis caused by dox or 4-HNE. We propose that Ω-6 PUFA supplementation could improve the efficacy of dox or Rlip inhibitors for treating Her2-amplified breast cancer.

Emerging Biomarkers and Targeted Therapies in Feline Mammary Carcinoma

Feline mammary carcinoma (FMC) is a common aggressive malignancy with a low survival rate that lacks viable therapeutic options beyond mastectomy. Recently, increasing efforts have been made to understand the molecular mechanisms underlying FMC development, using the knowledge gained from studies on human breast cancer to discover new diagnostic and prognostic biomarkers, thus reinforcing the utility of the cat as a cancer model. In this article, we review the current knowledge on FMC pathogenesis, biomarkers, and prognosis factors and offer new insights into novel therapeutic options for HER2-positive and triple-negative FMC subtypes.

Sensitivity to targeted therapy differs between HER2-amplified breast cancer cells harboring kinase and helical domain mutations in PIK3CA

BACKGROUND

HER2-amplified breast cancer is a clinically defined subtype of breast cancer for which there are multiple viable targeted therapies. Resistance to these targeted therapies is a common problem, but the mechanisms by which resistance occurs remain incompletely defined. One mechanism that has been proposed is through mutation of genes in the PI3-kinase pathway. Intracellular signaling from the HER2 pathway can occur through PI3-kinase, and mutations of the encoding gene PIK3CA are known to be oncogenic. Mutations in PIK3CA co-occur with HER2-amplification in ~ 20% of cases within the HER2-amplified subtype.

METHODS

We generated isogenic knockin mutants of each PIK3CA hotspot mutation in HER2-amplified breast cancer cells using adeno-associated virus-mediated gene targeting. Isogenic clones were analyzed using a combinatorial drug screen to determine differential responses to HER2-targeted therapy. Western blot analysis and immunofluorescence uncovered unique intracellular signaling dynamics in cells resistant to HER2-targeted therapy. Subsequent combinatorial drug screens were used to explore neuregulin-1-mediated resistance to HER2-targeted therapy. Finally, results from in vitro experiments were extrapolated to publicly available datasets.

RESULTS

Treatment with HER2-targeted therapy reveals that mutations in the kinase domain (H1047R) but not the helical domain (E545K) increase resistance to lapatinib. Mechanistically, sustained AKT signaling drives lapatinib resistance in cells with the kinase domain mutation, as demonstrated by staining for the intracellular product of PI3-kinase, PIP3. This resistance can be overcome by co-treatment with an inhibitor to the downstream kinase AKT. Additionally, knockout of the PIP3 phosphatase, PTEN, phenocopies this result. We also show that neuregulin-1, a ligand for HER-family receptors, confers resistance to cells harboring either hotspot mutation and modulates response to combinatorial therapy. Finally, we show clinical evidence that the hotspot mutations have distinct expression profiles related to therapeutic resistance through analysis of TCGA and METABRIC data cohorts.

CONCLUSION

Our results demonstrate unique intracellular signaling differences depending on which mutation in PIK3CA the cell harbors. Only mutations in the kinase domain fully activate the PI3-kinase signaling pathway and maintain downstream signaling in the presence of HER2 inhibition. Moreover, we show there is potentially clinical importance in understanding both the PIK3CA mutational status and levels of neuregulin-1 expression in patients with HER2-amplified breast cancer treated with targeted therapy and that these problems warrant further pre-clinical and clinical testing.

Cancer-Associated Fibroblasts in Breast Cancer Treatment Response and Metastasis

Breast cancer (BrCa) is the leading cause of death among women worldwide, with about one million new cases diagnosed each year. In spite of the improvements in diagnosis, early detection and treatment, there is still a high incidence of mortality and failure to respond to current therapies. With the use of several well-established biomarkers, such as hormone receptors and human epidermal growth factor receptor-2 (HER2), as well as genetic analysis, BrCa patients can be categorized into multiple subgroups: Luminal A, Luminal B, HER2-enriched, and Basal-like, with specific treatment strategies. Although chemotherapy and targeted therapies have greatly improved the survival of patients with BrCa, there is still a large number of patients who relapse or who fail to respond. The role of the tumor microenvironment in BrCa progression is becoming increasingly understood. Cancer-associated fibroblasts (CAFs) are the principal population of stromal cells in breast tumors. In this review, we discuss the current understanding of CAFs' role in altering the tumor response to therapeutic agents as well as in fostering metastasis in BrCa. In addition, we also review the available CAFs-directed molecular therapies and their potential implications for BrCa management.

Persistent Cancer Cells: The Deadly Survivors

Persistent cancer cells are the discrete and usually undetected cells that survive cancer drug treatment and constitute a major cause of treatment failure. These cells are characterized by their slow proliferation, highly flexible energy consumption, adaptation to their microenvironment, and phenotypic plasticity. Mechanisms that underlie their persistence offer highly coveted and sought-after therapeutic targets, and include diverse epigenetic, transcriptional, and translational regulatory processes, as well as complex cell-cell interactions. Although the successful clinical targeting of persistent cancer cells remains to be realized, immense progress has been made in understanding their persistence, yielding promising preclinical results.

HER2-Targeted Immunotherapy and Combined Protocols Showed Promising Antiproliferative Effects in Feline Mammary Carcinoma Cell-Based Models

Simple Summary

Mammary tumors are common in cats, presenting an aggressive behavior with high tumor recurrence. Therefore, new and efficient therapeutic protocols are urgent. Monoclonal antibodies (mAbs; ADC) are widely used in human breast cancer therapy, inhibiting the HER2 dimerization and leading to cell apoptosis. Furthermore, drug combinations, with tyrosine kinase inhibitors (TKi) are valuable in patients’ therapeutic protocols. In this study, two mAbs, and an ADC, as well as combined protocols between mAbs and mAbs plus lapatinib (TKi) were tested to address if the drugs could be used as new therapeutic options in feline mammary tumors. All the compounds and the combined treatments revealed valuable antiproliferative effects, and a conserved cell death mechanism, by apoptosis, in the feline cell lines, where the mutations found in the extracellular domain of the HER2 suggest no immunotherapy resistance.

Abstract

Feline mammary carcinoma (FMC) is a highly prevalent tumor, showing aggressive clinicopathological features, with HER2-positive being the most frequent subtype. While, in human breast cancer, the use of anti-HER2 monoclonal antibodies (mAbs) is common, acting by blocking the extracellular domain (ECD) of the HER2 protein and by inducing cell apoptosis, scarce information is available on use these immunoagents in FMC. Thus, the antiproliferative effects of two mAbs (trastuzumab and pertuzumab), of an antibody–drug conjugate compound (T-DM1) and of combined treatments with a tyrosine kinase inhibitor (lapatinib) were evaluated on three FMC cell lines (CAT-MT, FMCm and FMCp). In parallel, the DNA sequence of the her2 ECD (subdomains II and IV) was analyzed in 40 clinical samples of FMC, in order to identify mutations, which can lead to antibody resistance or be used as prognostic biomarkers. Results obtained revealed a strong antiproliferative effect in all feline cell lines, and a synergistic response was observed when combined therapies were performed. Additionally, the mutations found were not described as inducing resistance to therapy in breast cancer patients. Altogether, our results suggested that anti-HER2 mAbs could become useful in the treatment of FMC, particularly, if combined with lapatinib, since drug-resistance seems to be rare.

Characterizing advanced breast cancer heterogeneity and treatment resistance through serial biopsies and comprehensive analytics

Molecular heterogeneity in metastatic breast cancer presents multiple clinical challenges in accurately characterizing and treating the disease. Current diagnostic approaches offer limited ability to assess heterogeneity that exists among multiple metastatic lesions throughout the treatment course. We developed a precision oncology platform that combines serial biopsies, multi-omic analysis, longitudinal patient monitoring, and molecular tumor boards, with the goal of improving cancer management through enhanced understanding of the entire cancer ecosystem within each patient. We describe this integrative approach using comprehensive analytics generated from serial-biopsied lesions in a metastatic breast cancer patient. The serial biopsies identified remarkable heterogeneity among metastatic lesions that presented clinically as discordance in receptor status and genomic alterations with mixed treatment response. Based on our study, we highlight clinical scenarios, such as rapid progression or mixed response, that indicate consideration for repeat biopsies to evaluate intermetastatic heterogeneity (IMH), with the objective of refining targeted therapy. We present a framework for understanding the clinical significance of heterogeneity in breast cancer between metastatic lesions utilizing multi-omic analyses of serial biopsies and its implication for effective personalized treatment.

Neuregulin Signaling in the Tumor Microenvironment

Neuregulins, members of the largest subclass of growth factors of the epidermal growth factor family, mediate a myriad of cellular functions including survival, proliferation, and differentiation in normal tissues through binding to receptor tyrosine kinases of the ErbB family. However, aberrant neuregulin signaling in the tumor microenvironment is increasingly recognized as a key player in initiation and malignant progression of human cancers. In this chapter, we focus on the role of neuregulin signaling in the hallmarks of cancer, including cancer initiation and development, metastasis, as well as therapeutic resistance. Moreover, role of neuregulin signaling in the regulation of tumor microenvironment and targeting of neuregulin signaling in cancer from the therapeutic perspective are also briefly discussed.

Enzalutamide response in a panel of prostate cancer cell lines reveals a role for glucocorticoid receptor in enzalutamide resistant disease

Representative in vitro model systems that accurately model response to therapy and allow the identification of new targets are important for improving our treatment of prostate cancer. Here we describe molecular characterization and drug testing in a panel of 20 prostate cancer cell lines. The cell lines cluster into distinct subsets based on RNA expression, which is largely driven by functional Androgen Receptor (AR) expression. KLK3, the AR-responsive gene that encodes prostate specific antigen, shows the greatest variability in expression across the cell line panel. Other common prostate cancer associated genes such as TMPRSS2 and ERG show similar expression patterns. Copy number analysis demonstrates that many of the most commonly gained (including regions containing TERC and MYC) and lost regions (including regions containing TP53 and PTEN) that were identified in patient samples by the TCGA are mirrored in the prostate cancer cell lines. Assessment of response to the anti-androgen enzalutamide shows a distinct separation of responders and non-responders, predominantly related to status of wild-type AR. Surprisingly, several AR-null lines responded to enzalutamide. These AR-null, enzalutamide-responsive cells were characterized by high levels of expression of glucocorticoid receptor (GR) encoded by NR3C1. Treatment of these cells with the anti-GR agent mifepristone showed that they were more sensitive to this drug than enzalutamide, as were several of the enzalutamide non-responsive lines. This is consistent with several recent reports that suggest that GR expression is an alternative signaling mechanism that can bypass AR blockade. This study reinforces the utility of large cell line panels for the study of cancer and identifies several cell lines that represent ideal models to study AR-null cells that have upregulated GR to sustain growth.

Ex Vivo Analysis of Primary Tumor Specimens for Evaluation of Cancer Therapeutics

The use of ex vivo drug sensitivity testing to predict drug activity in individual patients has been actively explored for almost 50 years without delivering a generally useful predictive capability. However, extended failure should not be an indicator of futility. This is especially true in cancer research where ultimate success is often preceded by less successful attempts. For example, both immune- and genetic-based targeted therapies for cancer underwent numerous failed attempts before biological understanding, improved targets, and optimized drug development matured to facilitate an arsenal of transformational drugs. Similarly, the concept of directly assessing drug sensitivity of primary tumor biopsies-and the use of this information to help direct therapeutic approaches-has a long history with a definitive learning curve. In this review, we will survey the history of ex vivo testing as well as the current state of the art for this field. We will present an update on methodologies and approaches, describe the use of these technologies to test cutting-edge drug classes, and describe an increasingly nuanced understanding of tumor types and models for which this strategy is most likely to succeed. We will consider the relative strengths and weaknesses of predicting drug activity across the broad biological context of cancer patients and tumor types. This will include an analysis of the potential for ex vivo drug sensitivity testing to accurately predict drug activity within each of the biological hallmarks of cancer pathogenesis.

HER3 Differentiates Basal From Claudin Type Triple Negative Breast Cancer and Contributes to Drug and Microenvironmental Induced Resistance

Triple Negative Breast Cancer (TNBC) is an aggressive form of Breast Cancer (BC). Numerous kinase inhibitors (KI) targeting different pathway nodes have shown limited benefit in the clinical setting. In this study, we aim to characterize the extent of HER3 reliance and to define the effect of Neuregulin (NRG) isoforms in TNBCs. Basal and Claudin type TNBC cell lines were treated with a range of small molecule inhibitors, in the presence or absence of the HER3 ligand NRG. Single agent and combination therapy was also evaluated in human cancer cell lines through viability and biochemical assessment of the AKT/MAPK signaling pathway. We show that Basal (BT20, HCC-70, and MDA-MB-468) and Claudin type (MDA-MB-231, BT-549) TNBC cell lines displayed differential reliance on the HER family of receptors. Expression and dynamic HER3 upregulation was predominant in the Basal TNBC subtype. Furthermore, the presence of the natural ligand NRG showed potent signaling through the HER3-AKT pathway, significantly diminishing the efficacy of the AKT and PI3K inhibitors tested. We report that NRG augments the HER3 feedback mechanism for continued cell survival in TNBC. We demonstrate that combination strategies to effectively block the EGFR-HER3-AKT pathway are necessary to overcome compensatory mechanisms to NRG dependent and independent resistance mechanisms. Our findings suggests that the EGFR-HER3 heterodimer forms a major signaling hub and is a key player in tumorigenesis in Basal but not Claudin type TNBC tested. Thus, HER3 could potentially serve as a biomarker for identifying patients in which targeted therapy against the EGFR-HER3-AKT axis would be most valuable.

Transient commensal clonal interactions can drive tumor metastasis

The extent and importance of functional heterogeneity and crosstalk between tumor cells is poorly understood. Here, we describe the generation of clonal populations from a patient-derived ovarian clear cell carcinoma model which forms malignant ascites and solid peritoneal tumors upon intraperitoneal transplantation in mice. The clonal populations are engineered with secreted Gaussia luciferase to monitor tumor growth dynamics and tagged with a unique DNA barcode to track their fate in multiclonal mixtures during tumor progression. Only one clone, CL31, grows robustly, generating exclusively malignant ascites. However, multiclonal mixtures form large solid peritoneal metastases, populated almost entirely by CL31, suggesting that transient cooperative interclonal interactions are sufficient to promote metastasis of CL31. CL31 uniquely harbors ERBB2 amplification, and its acquired metastatic activity in clonal mixtures is dependent on transient exposure to amphiregulin, which is exclusively secreted by non-tumorigenic clones. Amphiregulin enhances CL31 mesothelial clearance, a prerequisite for metastasis. These findings demonstrate that transient, ostensibly innocuous tumor subpopulations can promote metastases via “hit-and-run” commensal interactions.

Cooperative interactions among tumor cells may have important implications for metastasis. Here, the authors examined the spatio-temporal nature of interactions among clonal populations of ovarian carcinoma cells and found that transient interactions cells can promote metastases via commensal interactions.

Integrating Mathematical Modeling with High-Throughput Imaging Explains How Polyploid Populations Behave in Nutrient-Sparse Environments

Breast cancer progresses in a multistep process from primary tumor growth and stroma invasion to metastasis. Nutrient-limiting environments promote chemotaxis with aggressive morphologies characteristic of invasion. It is unknown how coexisting cells differ in their response to nutrient limitations and how this impacts invasion of the metapopulation as a whole. In this study, we integrate mathematical modeling with microenvironmental perturbation data to investigate invasion in nutrient-limiting environments inhabited by one or two cancer cell subpopulations. Subpopulations were defined by their energy efficiency and chemotactic ability. Invasion distance traveled by a homogeneous population was estimated. For heterogeneous populations, results suggest that an imbalance between nutrient efficacy and chemotactic superiority accelerates invasion. Such imbalance will spatially segregate the two populations and only one type will dominate at the invasion front. Only if these two phenotypes are balanced, the two subpopulations compete for the same space, which decelerates invasion. We investigate ploidy as a candidate biomarker of this phenotypic heterogeneity and discuss its potential to inform the dose of mTOR inhibitors (mTOR-I) that can inhibit chemotaxis just enough to facilitate such competition. SIGNIFICANCE: This study identifies the double-edged sword of high ploidy as a prerequisite to personalize combination therapies with cytotoxic drugs and inhibitors of signal transduction pathways such as mTOR-Is. GRAPHICAL ABSTRACT: http://cancerres.aacrjournals.org/content/canres/80/22/5109/F1.large.jpg.

Regulation of Collective Metastasis by Nanolumenal Signaling

Collective metastasis is defined as the cohesive migration and metastasis of multicellular tumor cell clusters. Disrupting various cell adhesion genes markedly reduces cluster formation and colonization efficiency, yet the downstream signals transmitted by clustering remain largely unknown. Here, we use mouse and human breast cancer models to identify a collective signal generated by tumor cell clusters supporting metastatic colonization. We show that tumor cell clusters produce the growth factor epigen and concentrate it within nanolumina-intercellular compartments sealed by cell-cell junctions and lined with microvilli-like protrusions. Epigen knockdown profoundly reduces metastatic outgrowth and switches clusters from a proliferative to a collective migratory state. Tumor cell clusters from basal-like 2, but not mesenchymal-like, triple-negative breast cancer cell lines have increased epigen expression, sealed nanolumina, and impaired outgrowth upon nanolumenal junction disruption. We propose that nanolumenal signaling could offer a therapeutic target for aggressive metastatic breast cancers.

Tyrosine Kinase Inhibitors in the Combination Therapy of HER2 Positive Breast Cancer

Human epidermal growth factor receptor 2 (HER2)-positive breast cancer (BC) accounts for about 20% to 30% of all BC subtypes and is characterized by invasive disease and poor prognosis. With the emergence of anti-HER2 target drugs, HER2-positive BC patient outcomes have changed dramatically. However, treatment failure is mostly due to drug resistance and the special treatment needs of different subgroups. Small molecule tyrosine kinase inhibitors can inhibit multiple targets of the human epidermal growth factor receptor family and activate PI3K/AKT, MAPK, PLC γ, ERK1/2, JAK/STAT, and other pathways affecting the expression of MDM2, mTOR, p27, and other transcription factors. This can help regulate the differentiation, apoptosis, migration, growth, and adhesion of normal cells and reverse drug resistance to a certain extent. These inhibitors can cross the blood-brain barrier and be administered orally. They have a good synergistic effect with effective drugs such as trastuzumab, pertuzumab, t-dm1, and cyclin-dependent kinase 4 and 6 inhibitors. These advantages have resulted in small-molecule tyrosine kinase inhibitors attracting attention. The new small-molecule tyrosine kinase inhibitor was investigated in multi-target anti-HER2 therapy, showed a good effect in preclinical and clinical trials, and to some extent, improved the prognosis of HER2-positive BC patients. Its use could lead to a de-escalation of treatment in some patients, possibly preventing unnecessary procedures along with the associated side effects and costs.

Crosstalk between invadopodia and the extracellular matrix

The scaffold protein Tks5α is required for invadopodia-mediated cancer invasion both in vitro and in vivo. We have previously also revealed a role for Tks5 in tumor cell growth using three-dimensional (3D) culture model systems and mouse transplantation experiments. Here we use both 3D and high-density fibrillar collagen (HDFC) culture to demonstrate that native collagen-I, but not a form lacking the telopeptides, stimulated Tks5-dependent growth, which was dependent on the DDR collagen receptors. We used microenvironmental microarray (MEMA) technology to determine that laminin, fibronectin and tropoelastin also stimulated invadopodia formation. A Tks5α-specific monoclonal antibody revealed its expression both on microtubules and at invadopodia. High- and super-resolution microscopy of cells in and on collagen was then used to place Tks5α at the base of invadopodia, separated from much of the actin and cortactin, but coincident with both matrix metalloprotease and cathepsin proteolytic activity. Inhibition of the Src family kinases, cathepsins or metalloproteases all reduced invadopodia length but each had distinct effects on Tks5α localization. These studies highlight the crosstalk between invadopodia and extracellular matrix components, and reveal the invadopodium to be a spatially complex structure.

Fibroblast-tumor cell signaling limits HER2 kinase therapy response via activation of MTOR and antiapoptotic pathways

Despite the implementation of multiple HER2-targeted therapies, patients with advanced HER2+ breast cancer ultimately develop drug resistance. Stromal fibroblasts represent an abundant cell type in the tumor microenvironment and have been linked to poor outcomes and drug resistance. Here, we show that fibroblasts counteract the cytotoxic effects of HER2 kinase-targeted therapy in a subset of HER2+ breast cancer cell lines and allow cancer cells to proliferate in the presence of the HER2 kinase inhibitor lapatinib. Fibroblasts from primary breast tumors, normal breast tissue, and lung tissue have similar protective effects on tumor cells via paracrine factors. This fibroblast-mediated reduction in drug sensitivity involves increased expression of antiapoptotic proteins and sustained activation of the PI3K/AKT/MTOR pathway, despite inhibition of the HER2 and the RAS-ERK pathways in tumor cells. HER2 therapy sensitivity is restored in the fibroblast cocultures by combination treatment with inhibitors of MTOR or the antiapoptotic proteins BCL-XL and MCL-1. Expression of activated AKT in tumor cells recapitulates the effects of fibroblasts resulting in sustained MTOR signaling and poor lapatinib response. Lapatinib sensitivity was not altered by fibroblasts in tumor cells that exhibited sustained MTOR signaling due to a strong gain-of-function PI3KCA mutation. These findings indicate that in addition to tumor cell-intrinsic mechanisms that cause constitutive PI3K/AKT/MTOR pathway activation, secreted factors from fibroblasts can maintain this pathway in the context of HER2 inhibition. Our integrated proteomic-phenotypic approach presents a strategy for the discovery of protective mechanisms in fibroblast-rich tumors and the design of rational combination therapies to restore drug sensitivity.

Cell plasticity in cancer cell populations

In this review, we propose a recension of biological observations on plasticity in cancer cell populations and discuss theoretical considerations about their mechanisms.

Interactions Between Tumor Biology and Targeted Nanoplatforms for Imaging Applications

Although considerable efforts have been conducted to diagnose, improve, and treat cancer in the past few decades, existing therapeutic options are insufficient, as mortality and morbidity rates remain high. Perhaps the best hope for substantial improvement lies in early detection. Recent advances in nanotechnology are expected to increase the current understanding of tumor biology, and will allow nanomaterials to be used for targeting and imaging both in vitro and in vivo experimental models. Owing to their intrinsic physicochemical characteristics, nanostructures (NSs) are valuable tools that have received much attention in nanoimaging. Consequently, rationally designed NSs have been successfully employed in cancer imaging for targeting cancer-specific or cancer-associated molecules and pathways. This review categorizes imaging and targeting approaches according to cancer type, and also highlights some new safe approaches involving membrane-coated nanoparticles, tumor cell-derived extracellular vesicles, circulating tumor cells, cell-free DNAs, and cancer stem cells in the hope of developing more precise targeting and multifunctional nanotechnology-based imaging probes in the future.

Tyrosine Kinase Inhibitors in Cancer: Breakthrough and Challenges of Targeted Therapy

Receptor tyrosine kinases (RTKs) are key regulatory signaling proteins governing cancer cell growth and metastasis. During the last two decades, several molecules targeting RTKs were used in oncology as a first or second line therapy in different types of cancer. However, their effectiveness is limited by the appearance of resistance or adverse effects. In this review, we summarize the main features of RTKs and their inhibitors (RTKIs), their current use in oncology, and mechanisms of resistance. We also describe the technological advances of artificial intelligence, chemoproteomics, and microfluidics in elaborating powerful strategies that could be used in providing more efficient and selective small molecules inhibitors of RTKs. Finally, we discuss the interest of therapeutic combination of different RTKIs or with other molecules for personalized treatments, and the challenge for effective combination with less toxic and off-target effects.

Automatic Transformation and Integration to Improve Visualization and Discovery of Latent Effects in Imaging Data

Proper data transformation is an essential part of analysis. Choosing appropriate transformations for variables can enhance visualization, improve efficacy of analytical methods, and increase data interpretability. However determining appropriate transformations of variables from high-content imaging data poses new challenges. Imaging data produces hundreds of covariates from each of thousands of images in a corpus. Each of these covariates will have a different distribution and need a potentially different transformation. As such imaging data produces hundreds of covariates, determining an appropriate transformation for each of them is infeasible by hand. In this paper we explore simple, robust, and automatic transformations of high-content image data. A central application of our work is to microenvironment microarray bio-imaging data from the NIH LINCS program. We show that our robust transformations enhance visualization and improve the discovery of substantively relevant latent effects. These transformations enhance analysis of image features individually and also improve data integration approaches when combining together multiple features. We anticipate that the advantages of this work will likely also be realized in the analysis of data from other high-content and highly-multiplexed technologies like Cell Painting or Cyclic Immunofluorescence. Software and further analysis can be found at gjhunt.github.io/rr.

Annot: a Django-based sample, reagent, and experiment metadata tracking system

Background

In biological experiments, comprehensive experimental metadata tracking – which comprises experiment, reagent, and protocol annotation with controlled vocabulary from established ontologies – remains a challenge, especially when the experiment involves multiple laboratory scientists who execute different steps of the protocol. Here we describe Annot, a novel web application designed to provide a flexible solution for this task.

Results

Annot enforces the use of controlled vocabulary for sample and reagent annotation while enabling robust investigation, study, and protocol tracking. The cornerstone of Annot’s implementation is a json syntax-compatible file format, which can capture detailed metadata for all aspects of complex biological experiments. Data stored in this json file format can easily be ported into spreadsheet or data frame files that can be loaded into R (https://www.r-project.org/) or Pandas, Python’s data analysis library (https://pandas.pydata.org/). Annot is implemented in Python3 and utilizes the Django web framework, Postgresql, Nginx, and Debian. It is deployed via Docker and supports all major browsers.

Conclusions

Annot offers a robust solution to annotate samples, reagents, and experimental protocols for established assays where multiple laboratory scientists are involved. Further, it provides a framework to store and retrieve metadata for data analysis and integration, and therefore ensures that data generated in different experiments can be integrated and jointly analyzed. This type of solution to metadata tracking can enhance the utility of large-scale datasets, which we demonstrate here with a large-scale microenvironment microarray study.

Variational Autoencoding Tissue Response to Microenvironment Perturbation

This work applies deep variational autoencoder learning architecture to study multi-cellular growth characteristics of human mammary epithelial cells in response to diverse microenvironment perturbations. Our approach introduces a novel method of visualizing learned feature spaces of trained variational autoencoding models that enables visualization of principal features in two dimensions. We find that unsupervised learned features more closely associate with expert annotation of cell colony organization than biologically-inspired hand-crafted features, demonstrating the utility of deep learning systems to meaningfully characterize features of multi-cellular growth characteristics in a fully unsupervised and data-driven manner.

Cellular Imaging Using Stimulated Raman Scattering Microscopy

Cellular imaging is an active area of research that enables researchers to monitor cellular dynamics, as well as responses to various external stimuli (physiological stress, exogenous compounds, etc.). Stimulated Raman scattering (SRS) microscopy is one popular experimental tool used to image cells, largely because of its chemical specificity, high spatial resolution, and high image acquisition speed. In this Perspective, the theoretical background and experimental implementation of SRS microscopy are discussed and recent developments in the field of cellular imaging with SRS are highlighted and summarized.

Using Microarrays to Interrogate Microenvironmental Impact on Cellular Phenotypes in Cancer

Understanding the impact of the microenvironment on the phenotype of cells is a difficult problem due to the complex mixture of both soluble growth factors and matrix-associated proteins in the microenvironment in vivo. Furthermore, readily available reagents for the modeling of microenvironments in vitro typically utilize complex mixtures of proteins that are incompletely defined and suffer from batch to batch variability. The microenvironment microarray (MEMA) platform allows for the assessment of thousands of simple combinations of microenvironment proteins for their impact on cellular phenotypes in a single assay. The MEMAs are prepared in well plates, which allows the addition of individual ligands to separate wells containing arrayed extracellular matrix (ECM) proteins. The combination of the soluble ligand with each printed ECM forms a unique combination. A typical MEMA assay contains greater than 2,500 unique combinatorial microenvironments that cells are exposed to in a single assay. As a test case, the breast cancer cell line MCF7 was plated on the MEMA platform. Analysis of this assay identified factors that both enhance and inhibit the growth and proliferation of these cells. The MEMA platform is highly flexible and can be extended for use with other biological questions beyond cancer research.