The Psychoneurologic Symptom Cluster and Its Association With Breast Cancer Genomic Instability.

OBJECTIVES: To phenotype the psychoneurologic (PN) symptom cluster in individuals with metastatic breast cancer and associate those phenotypes with individual characteristics and cancer genomic variables from circulating tumor DNA. SAMPLE & SETTING: This study included 201 individuals with metastatic breast cancer recruited in western Pennsylvania. METHODS & VARIABLES: A descriptive, cross-sectional design was used. Symptom data were collected via the MD Anderson Symptom Inventory, and cancer genomic data were collected via ultra-low-pass whole-genome sequencing of circulating tumor DNA from participant blood. RESULTS: Three distinct PN symptom phenotypes were described in a population with metastatic breast cancer: mild symptoms, moderate symptoms, and severe mood-related symptoms. Breast cancer TP53 deletion was significantly associated with membership in a moderate to severe symptoms phenotype (p = 0.013). IMPLICATIONS FOR NURSING: Specific cancer genomic changes associated with increased genomic instability may be predictive of PN symptoms. This finding may enable proactive treatment or reveal new therapeutic targets for symptom management.

EstroGene2.0: A multi-omic database of response to estrogens, ER-modulators, and resistance to endocrine therapies in breast cancer.

Endocrine therapies targeting the estrogen receptor (ER/ESR1) are the cornerstone to treat ER-positive breast cancers patients, but resistance often limits their effectiveness. Understanding the molecular mechanisms is thus key to optimize the existing drugs and to develop new ER-modulators. Notable progress has been made although the fragmented way data is reported has reduced their potential impact. Here, we introduce EstroGene2.0, an expanded database of its precursor 1.0 version. EstroGene2.0 focusses on response and resistance to endocrine therapies in breast cancer models. Incorporating multi-omic profiling of 361 experiments from 212 studies across 28 cell lines, a user-friendly browser offers comprehensive data visualization and metadata mining capabilities (https://estrogeneii.web.app/). Taking advantage of the harmonized data collection, our follow-up meta-analysis revealed substantial diversity in response to different classes of ER-modulators including SERMs, SERDs, SERCA and LDD/PROTAC. Notably, endocrine resistant models exhibit a spectrum of transcriptomic alterations including a contra-directional shift in ER and interferon signaling, which is recapitulated clinically. Furthermore, dissecting multiple ESR1-mutant cell models revealed the different clinical relevance of genome-edited versus ectopic overexpression model engineering and identified high-confidence mutant-ER targets, such as NPY1R. These examples demonstrate how EstroGene2.0 helps investigate breast cancer's response to endocrine therapies and explore resistance mechanisms.

Electronic Health Record-Based Nudge Intervention and Axillary Surgery in Older Women With Breast Cancer: A Nonrandomized Controlled Trial.

Importance: Choosing Wisely recommendations advocate against routine use of axillary staging in older women with early-stage, clinically node-negative (cN0), hormone receptor-positive (HR+), and HER2-negative breast cancer. However, rates of sentinel lymph node biopsy (SLNB) in this population remain persistently high. Objective: To evaluate whether an electronic health record (EHR)-based nudge intervention targeting surgeons in their first outpatient visit with patients meeting Choosing Wisely criteria decreases rates of SLNB. Design, Setting, and Participants: This nonrandomized controlled trial was a hybrid type 1 effectiveness-implementation study with subsequent postintervention semistructured interviews and lasted from October 2021 to October 2023. Data came from EHRs at 8 outpatient clinics within an integrated health care system; participants included 7 breast surgical oncologists. Data were collected for female patients meeting Choosing Wisely criteria for omission of SLNB (aged ≥70 years with cT1 and cT2, cN0, HR+/HER2- breast cancer). The study included a 12-month preintervention control period; baseline surveys assessing perceived acceptability, appropriateness, and feasibility of the designed intervention; and a 12-month intervention period. Intervention: A column nudge was embedded into the surgeon's schedule in the EHR identifying patients meeting Choosing Wisely criteria for potential SLNB omission. Main Outcomes and Measures: The primary outcome was rate of SLNB following nudge deployment into the EHR. Results: Similar baseline demographic and tumor characteristics were observed before (control period, n = 194) and after (intervention period, n = 193) nudge deployment. Patients in both the control and intervention period had a median (IQR) age of 75 (72-79) years. Compared with the control period, unadjusted rates of SLNB decreased by 23.1 percentage points (46.9% SLNB rate prenudge to 23.8% after; 95% CI, -32.9 to -13.8) in the intervention period. An interrupted time series model showed a reduction in the rate of SLNB following nudge deployment (adjusted odds ratio, 0.26; 95% CI, 0.07 to 0.90; P = .03). The participating surgeons scored the intervention highly on acceptability, appropriateness, and feasibility. Dominant themes from semistructured interviews indicated that the intervention helped remind the surgeons of potential Choosing Wisely applicability without the need for additional clicks or actions on the day of the patient visit, which facilitated use. Conclusions and Relevance: This study showed that a nudge intervention in the EHR significantly decreased low-value axillary surgery in older women with early-stage, cN0, HR+/HER2- breast cancer. This user-friendly and easily implementable EHR-based intervention could be a beneficial approach for decreasing low-value care in other practice settings or patient populations. Trial Registration: ClinicalTrials.gov Identifier: NCT06006910.

Spatial molecular profiling of mixed invasive ductal and lobular breast cancers reveals heterogeneity in intrinsic molecular subtypes, oncogenic signatures, and mutations.

Mixed invasive ductal and lobular carcinoma (MDLC) is a rare histologic subtype of breast cancer displaying both E-cadherin positive ductal and E-cadherin negative lobular morphologies within the same tumor, posing challenges with regard to anticipated clinical management. It remains unclear whether these distinct morphologies also have distinct biology and risk of recurrence. Our spatially resolved transcriptomic, genomic, and single-cell profiling revealed clinically significant differences between ductal and lobular tumor regions including distinct intrinsic subtype heterogeneity - e.g., MDLC with triple-negative breast cancer (TNBC) or basal ductal and estrogen receptor positive (ER+) luminal lobular regions, distinct enrichment of cell cycle arrest/senescence and oncogenic (ER and MYC) signatures, genetic and epigenetic CDH1 inactivation in lobular but not ductal regions, and single-cell ductal and lobular subpopulations with unique oncogenic signatures further highlighting intraregional heterogeneity. Altogether, we demonstrated that the intratumoral morphological/histological heterogeneity within MDLC is underpinned by intrinsic subtype and oncogenic heterogeneity which may result in prognostic uncertainty and therapeutic dilemma.

International survey on invasive lobular breast cancer identifies priority research questions.

There is growing awareness of the unique etiology, biology, and clinical presentation of invasive lobular breast cancer (ILC), but additional research is needed to ensure translation of findings into management and treatment guidelines. We conducted a survey with input from breast cancer physicians, laboratory-based researchers, and patients to analyze the current understanding of ILC, and identify consensus research questions. 1774 participants from 66 countries respondents self-identified as clinicians (N = 413), researchers (N = 376), and breast cancer patients and advocates (N = 1120), with some belonging to more than one category. The majority of physicians reported being very/extremely (41%) to moderately (42%) confident in describing the differences between ILC and invasive breast cancer of no special type (NST). Knowledge of histology was seen as important (73%) and as affecting treatment decisions (51%), and most agreed that refining treatment guidelines would be valuable (76%). 85% of clinicians have never powered a clinical trial to allow subset analysis for histological subtypes, but the majority would consider it, and would participate in an ILC clinical trials consortium. The majority of laboratory researchers, reported being and very/extremely (48%) to moderately (29%) confident in describing differences between ILC and NST. They reported that ILCs are inadequately presented in large genomic data sets, and that ILC models are insufficient. The majority have adequate access to tissue or blood from patients with ILC. The majority of patients and advocates (52%) thought that their health care providers did not sufficiently explain the unique features of ILC. They identified improvement of ILC screening/early detection, and identification of better imaging tools as top research priorities. In contrast, both researchers and clinicians identified understanding of endocrine resistance and identifying novel drugs that can be tested in clinical trials as top research priority. In summary, we have gathered information from an international community of physicians, researchers, and patients/advocates that we expect will lay the foundation for a community-informed collaborative research agenda, with the goal of improving management and personalizing treatment for patients with ILC.

iGenSig-Rx: an integral genomic signature based white-box tool for modeling cancer therapeutic responses using multi-omics data.

Multi-omics sequencing is poised to revolutionize clinical care in the coming decade. However, there is a lack of effective and interpretable genome-wide modeling methods for the rational selection of patients for personalized interventions. To address this, we present iGenSig-Rx, an integral genomic signature-based approach, as a transparent tool for modeling therapeutic response using clinical trial datasets. This method adeptly addresses challenges related to cross-dataset modeling by capitalizing on high-dimensional redundant genomic features, analogous to reinforcing building pillars with redundant steel rods. Moreover, it integrates adaptive penalization of feature redundancy on a per-sample basis to prevent score flattening and mitigate overfitting. We then developed a purpose-built R package to implement this method for modeling clinical trial datasets. When applied to genomic datasets for HER2 targeted therapies, iGenSig-Rx model demonstrates consistent and reliable predictive power across four independent clinical trials. More importantly, the iGenSig-Rx model offers the level of transparency much needed for clinical application, allowing for clear explanations as to how the predictions are produced, how the features contribute to the prediction, and what are the key underlying pathways. We anticipate that iGenSig-Rx, as an interpretable class of multi-omics modeling methods, will find broad applications in big-data based precision oncology. The R package is available: https://github.com/wangxlab/iGenSig-Rx .

Spatial molecular profiling of mixed invasive ductal-lobular breast cancers reveals heterogeneity in intrinsic molecular subtypes, oncogenic signatures, and mutations.

Mixed invasive ductal and lobular carcinoma (MDLC) is a rare histologic subtype of breast cancer displaying both E-cadherin positive ductal and E-cadherin negative lobular morphologies within the same tumor, posing challenges with regard to anticipated clinical management. It remains unclear whether these distinct morphologies also have distinct biology and risk of recurrence. Our spatially-resolved transcriptomic, genomic, and single-cell profiling revealed clinically significant differences between ductal and lobular tumor regions including distinct intrinsic subtype heterogeneity (e.g., MDLC with TNBC/basal ductal and ER+/luminal lobular regions), distinct enrichment of senescence/dormancy and oncogenic (ER and MYC) signatures, genetic and epigenetic CDH1 inactivation in lobular, but not ductal regions, and single-cell ductal and lobular sub-populations with unique oncogenic signatures further highlighting intra-regional heterogeneity. Altogether, we demonstrated that the intra-tumoral morphological/histological heterogeneity within MDLC is underpinned by intrinsic subtype and oncogenic heterogeneity which may result in prognostic uncertainty and therapeutic dilemma. Significance: MDLC displays both ductal and lobular tumor regions. Our multi-omic profiling approach revealed that these morphologically distinct tumor regions harbor distinct intrinsic subtypes and oncogenic features that may cause prognostic uncertainty and therapeutic dilemma. Thus histopathological/molecular profiling of individual tumor regions may guide clinical decision making and benefit patients with MDLC, particularly in the advanced setting where there is increased reliance on next generation sequencing.

Mutual information for detecting multi-class biomarkers when integrating multiple bulk or single-cell transcriptomic studies.

Motivation: Biomarker detection plays a pivotal role in biomedical research. Integrating omics studies from multiple cohorts can enhance statistical power, accuracy and robustness of the detection results. However, existing methods for horizontally combining omics studies are mostly designed for two-class scenarios (e.g., cases versus controls) and are not directly applicable for studies with multi-class design (e.g., samples from multiple disease subtypes, treatments, tissues, or cell types). Results: We propose a statistical framework, namely Mutual Information Concordance Analysis (MICA), to detect biomarkers with concordant multi-class expression pattern across multiple omics studies from an information theoretic perspective. Our approach first detects biomarkers with concordant multi-class patterns across partial or all of the omics studies using a global test by mutual information. A post hoc analysis is then performed for each detected biomarkers and identify studies with concordant pattern. Extensive simulations demonstrate improved accuracy and successful false discovery rate control of MICA compared to an existing MCC method. The method is then applied to two practical scenarios: four tissues of mouse metabolism-related transcriptomic studies, and three sources of estrogen treatment expression profiles. Detected biomarkers by MICA show intriguing biological insights and functional annotations. Additionally, we implemented MICA for single-cell RNA-Seq data for tumor progression biomarkers, highlighting critical roles of ribosomal function in the tumor microenvironment of triple-negative breast cancer and underscoring the potential of MICA for detecting novel therapeutic targets. Availability: https://github.com/jianzou75/MICA.

Use of Natural Language Understanding to Facilitate Surgical De-Escalation of Axillary Staging in Patients With Breast Cancer.

PURPOSE: Natural language understanding (NLU) may be particularly well equipped for enhanced data capture from the electronic health record given its examination of both content-driven and context-driven extraction. METHODS: We developed and applied a NLU model to examine rates of pathological node positivity (pN+) and rates of lymphedema to determine whether omission of routine axillary staging could be extended to younger patients with estrogen receptor-positive (ER+)/cN0 disease. RESULTS: We found that rates of pN+ and arm lymphedema were similar between patients age 55-69 years and ≥70 years, with rates of lymphedema exceeding rates of pN+ for clinical stage T1c and smaller disease. CONCLUSION: Data from our NLU model suggest that omission of sentinel lymph node biopsy might be extended beyond Choosing Wisely recommendations, limited to those older than 70 years and to all postmenopausal women with early-stage ER+/cN0 disease. These data support the recently reported SOUND trial results and provide additional granularity to facilitate surgical de-escalation.

Estrogen regulation and functional role of FGFR4 in estrogen receptor positive breast cancer.

Background: Resistance to endocrine therapy is a major challenge of managing estrogen receptor positive (ER+) breast cancer. We previously reported frequent overexpression of FGFR4 in endocrine resistant cell lines and breast cancers that recurred and metastasized following endocrine therapy, suggesting FGFR4 as a potential driver of endocrine resistance. In this study, we investigated the role of FGFR4 in mediating endocrine resistance and explored the therapeutic potential of targeting FGFR4 in advanced breast cancer. Methods: A gene expression signature of FGFR4 activity was examined in ER+ breast cancer pre- and post-neoadjuvant endocrine therapy and the association between FGFR4 expression and patient survival was examined. A correlation analysis was used to uncover potential regulators of FGFR4 overexpression. To investigate if FGFR4 is necessary to drive endocrine resistance, we tested response to FGFR4 inhibition in long term estrogen deprived (LTED) cells and their paired parental cells. Doxycycline inducible FGFR4 overexpression and knockdown cell models were generated to examine if FGFR4 was sufficient to confer endocrine resistance. Finally, we examined response to FGFR4 monotherapy or combination therapy with fulvestrant in breast cancer cell lines to explore the potential of FGFR4 targeted therapy for advanced breast cancer and assessed the importance of PAM50 subtype in response to FGFR4 inhibition. Results: A FGFR4 activity gene signature was significantly upregulated post neoadjuvant aromatase inhibitor treatment, and high FGFR4 expression predicted poorer survival in patients with ER+ breast cancer. Gene expression association analysis using TCGA, METABRIC and SCAN-B datasets uncovered ER as the most significant gene negatively correlated with FGFR4 expression. ER negatively regulates FGFR4 expression at both the mRNA and protein level across multiple ER+ breast cancer cell lines. Despite robust overexpression of FGFR4, LTED cells did not show enhanced responses to FGFR4 inhibition compared to parental cells. Similarly, FGFR4 overexpression, knockdown or hotspot mutations did not significantly alter response to endocrine treatment in ER+ cell lines, nor did FGFR4 and fulvestrant combination treatment show synergistic effects. The HER2-like subtype of breast cancer showed elevated expression of FGFR4 and an increased response to FGFR4 inhibition relative to other breast cancer subtypes. Conclusions: Despite ER-mediated upregulation of FGFR4 post endocrine therapy, our study does not support a general role of FGFR4 in mediating endocrine resistance in ER+ breast cancer. Our data suggests that specific genomic backgrounds such as HER2 expression may be required for FGFR4 function in breast cancer and should be further explored.

Cancer-cell derived S100A11 promotes macrophage recruitment in ER+ breast cancer.

Macrophages are pivotal in driving breast tumor development, progression, and resistance to treatment, particularly in estrogen receptor-positive (ER+) tumors, where they infiltrate the tumor microenvironment (TME) influenced by cancer cell-secreted factors. By analyzing single-cell RNA-sequencing data from 25 ER+ tumors, we elucidated interactions between cancer cells and macrophages, correlating macrophage density with epithelial cancer cell density. We identified that S100A11, a previously unexplored factor in macrophage-cancer crosstalk, predicts high macrophage density and poor outcomes in ER+ tumors. We found that recombinant S100A11 enhances macrophage infiltration and migration in a dose-dependent manner. Additionally, in 3D models, we showed that S100A11 expression levels in ER+ cancer cells predict macrophage infiltration patterns. Neutralizing S100A11 decreased macrophage recruitment, both in cancer cell lines and in a clinically relevant patient-derived organoid model, underscoring its role as a paracrine regulator of cancer-macrophage interactions in the protumorigenic TME. This study offers novel insights into the interplay between macrophages and cancer cells in ER+ breast tumors, highlighting S100A11 as a potential therapeutic target to modulate the macrophage-rich tumor microenvironment.

Liver tropism of ER mutant breast cancer is characterized by unique molecular changes and immune infiltration.

PURPOSE: Hotspot estrogen receptor alpha (ER/ESR1) mutations are recognized as the driver for both endocrine resistance and metastasis in advanced ER-positive (ER+) breast cancer, but their contributions to metastatic organ tropism remain insufficiently understood. In this study, we aim to comprehensively profile the organotropic metastatic pattern for ESR1 mutant breast cancer. METHODS: The organ-specific metastatic pattern of ESR1 mutant breast cancer was delineated using multi-omics data from multiple publicly available cohorts of ER+ metastatic breast cancer patients. Gene mutation/copy number variation (CNV) and differential gene expression analyses were performed to identify the genomic and transcriptomic alterations uniquely associated with ESR1 mutant liver metastasis. Upstream regulator, downstream pathway, and immune infiltration analysis were conducted for subsequent mechanistic investigations. RESULTS: ESR1 mutation-driven liver tropism was revealed by significant differences, encompassing a higher prevalence of liver metastasis in patients with ESR1 mutant breast cancer and an enrichment of mutations in liver metastatic samples. The significant enrichment of AGO2 copy number amplifications (CNAs) and multiple gene expression changes were revealed uniquely in ESR1 mutant liver metastasis. We also unveiled alterations in downstream signaling pathways and immune infiltration, particularly an enrichment of neutrophils, suggesting potential therapeutic vulnerabilities. CONCLUSION: Our data provide a comprehensive characterization of the behaviors and mechanisms of ESR1 mutant liver metastasis, paving the way for the development of personalized therapy to target liver metastasis for patients with ESR1 mutant breast cancer.

Research autopsy programmes in oncology: shared experience from 14 centres across the world.

While there is a great clinical need to understand the biology of metastatic cancer in order to treat it more effectively, research is hampered by limited sample availability. Research autopsy programmes can crucially advance the field through synchronous, extensive, and high-volume sample collection. However, it remains an underused strategy in translational research. Via an extensive questionnaire, we collected information on the study design, enrolment strategy, study conduct, sample and data management, and challenges and opportunities of research autopsy programmes in oncology worldwide. Fourteen programmes participated in this study. Eight programmes operated 24 h/7 days, resulting in a lower median postmortem interval (time between death and start of the autopsy, 4 h) compared with those operating during working hours (9 h). Most programmes (n = 10) succeeded in collecting all samples within a median of 12 h after death. A large number of tumour sites were sampled during each autopsy (median 15.5 per patient). The median number of samples collected per patient was 58, including different processing methods for tumour samples but also non-tumour tissues and liquid biopsies. Unique biological insights derived from these samples included metastatic progression, treatment resistance, disease heterogeneity, tumour dormancy, interactions with the tumour micro-environment, and tumour representation in liquid biopsies. Tumour patient-derived xenograft (PDX) or organoid (PDO) models were additionally established, allowing for drug discovery and treatment sensitivity assays. Apart from the opportunities and achievements, we also present the challenges related with postmortem sample collections and strategies to overcome them, based on the shared experience of these 14 programmes. Through this work, we hope to increase the transparency of postmortem tissue donation, to encourage and aid the creation of new programmes, and to foster collaborations on these unique sample collections. © 2024 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.

Use of natural language understanding to facilitate surgical de-escalation of axillary staging in patients with breast cancer.

Natural language understanding (NLU) may be particularly well-equipped for enhanced data capture from the electronic health record (EHR) given its examination of both content- and context-driven extraction. We developed and applied a NLU model to examine rates of pathological node positivity (pN+) and rates of lymphedema to determine if omission of routine axillary staging could be extended to younger patients with ER+/cN0 disease. We found that rates of pN+ and arm lymphedema were similar between patients 55-69yo and ≥70yo, with rates of lymphedema exceeding rates of pN+ for clinical stage T1c and smaller disease. Data from our NLU model suggest that omission of SLNB might be extended beyond Choosing Wisely recommendations, limited to those over 70 years old, to all postmenopausal women with early-stage ER+/cN0 disease. These data support the recently-reported SOUND trial results and provide additional granularity to facilitate surgical de-escalation.

Systems approach for congruence and selection of cancer models towards precision medicine.

Cancer models are instrumental as a substitute for human studies and to expedite basic, translational, and clinical cancer research. For a given cancer type, a wide selection of models, such as cell lines, patient-derived xenografts, organoids and genetically modified murine models, are often available to researchers. However, how to quantify their congruence to human tumors and to select the most appropriate cancer model is a largely unsolved issue. Here, we present Congruence Analysis and Selection of CAncer Models (CASCAM), a statistical and machine learning framework for authenticating and selecting the most representative cancer models in a pathway-specific manner using transcriptomic data. CASCAM provides harmonization between human tumor and cancer model omics data, systematic congruence quantification, and pathway-based topological visualization to determine the most appropriate cancer model selection. The systems approach is presented using invasive lobular breast carcinoma (ILC) subtype and suggesting CAMA1 followed by UACC3133 as the most representative cell lines for ILC research. Two additional case studies for triple negative breast cancer (TNBC) and patient-derived xenograft/organoid (PDX/PDO) are further investigated. CASCAM is generalizable to any cancer subtype and will authenticate cancer models for faithful non-human preclinical research towards precision medicine.

Breast cancer cells promote osteoclast differentiation in an MRTF-dependent paracrine manner.

Bone is a frequent site for breast cancer metastasis. Conditioning of the local tumor microenvironment (TME) through crosstalk between tumor cells and bone resident cells in the metastatic niche is a major driving force for bone colonization of breast cancer cells. The vast majority of breast cancer-associated metastasis is osteolytic in nature, and RANKL-induced differentiation of bone marrow-derived macrophages to osteoclasts (OCLs) is a key requirement for osteolytic metastatic growth of cancer cells. In this study, we demonstrate that breast cancer cell-secreted factors stimulate RANKL-induced OCL differentiation of BMDMs requiring the function of Myocardin-related transcription factor (MRTF) in tumor cells. This is partly attributed to the critical role of MRTF in maintaining the basal cellular expression of connective tissue growth factor (CTGF), a pro-osteoclastogenic matricellular factor known to promote bone metastasis in human breast cancer. Supporting these in vitro findings, bioinformatics analyses of multiple human breast cancer transcriptome datasets reveal a strong positive correlation between CTGF expression and MRTF gene signature further establishing the relevance of our findings in a human disease context. By Luminex analyses, we show that MRTF depletion in breast cancer cells has a broad impact on OCL-regulatory cell-secreted factors that extends beyond CTGF. These findings, taken together with demonstration of MRTF-dependence for bone colonization breast cancer cells in vivo, suggest that MRTF inhibition could be an effective strategy to diminish OCL formation and skeletal involvement in breast cancer. In summary, this study highlights a novel tumor-extrinsic function of MRTF relevant to breast cancer metastasis.

MRTF promotes breast cancer cell motility through SRF-dependent upregulation of DIAPH3 expression.

Dysregulated actin cytoskeleton gives rise to aberrant cell motility and metastatic spread of tumor cells. The MRTF-SRF transcriptional complex plays a key role in regulating the expressions of actin cytoskeleton-modulatory genes. In this study, we demonstrate that MRTF's interaction with SRF is critical for migration and invasion of breast cancer cells. Disruption of the MRTF-SRF interaction suppresses membrane dynamics affecting the frequency and the effectiveness of membrane protrusion during cell motility. Consistent with these phenotypic changes, we further show that MRTF promotes actin polymerization at the leading edge, a key aspect of membrane protrusion, and migration of breast cancer cells through upregulating the expression of formin-family actin nucleating/elongating protein encoding gene DIAPH3 in an SRF-dependent manner. In support of these findings, multiplexed quantitative immunohistochemistry and transcriptome analyses of clinical specimens of breast cancer further demonstrate a positive correlation between nuclear localization of MRTF with malignant traits of cancer cells as well as enrichment of MRTF/SRF gene signature in distant metastases relative to primary tumors. In conclusion, this study for the first time links the MRTF/SRF signaling axis to cell migration through the regulation of a specific actin-binding protein, and provides evidence for an association between MRTF/SRF activity and malignancy in human breast cancer.