Biology subjects

Lewis, M. T.

Publications and source records attributed to Lewis, M. T..

At least 19 recordsLinked to original sources

bioRxiv2026

Cellular stemness identifies high-risk ductal carcinoma in situ and offers a therapeutic interception opportunity

Ductal carcinoma in situ (DCIS) exhibits substantial heterogeneity in its risk of progression to invasive breast cancer, yet the cellular and molecular determinants of high-risk lesions remain incompletely defined. Using spatially resolved single-cell transcriptomic and epigenomic profiling of 43 patient-derived DCIS and DCIS/invasive ductal carcinoma (IDC) samples, we delineate cellular programs, spatial organization, and epigenetic regulatory mechanisms associated with invasive potential. We identify an epithelial population with stemness features within luminal hormone-responsive (LumHR) cells that progressively expands from benign tissue to DCIS and IDC, and is strongly associated with invasive progression and recurrence-linked transcriptional programs. Spatial mapping reveals discrete DCIS niches enriched for stem-like LumHR cells, characterized by elevated CEACAM6 expression and enhanced ligand-receptor interactions, including CEACAM6-EGFR signaling between epithelial and stromal compartments, including cancer-associated fibroblasts, macrophages (APOC1-positive) and perivascular cells. These niches define a microenvironmental context that supports stemness and invasive potential. Epigenomic analyses implicate FOXA1 as a key regulator of these stem-like transcriptional states. Pharmacologic disruption of FOXA1-regulatory network using LSD1 inhibition suppresses stemness-associated transcriptional programs in vitro and significantly restrains tumor growth in vivo. Collectively, these findings define high-risk DCIS as a stemness-driven disease embedded within specialized microenvironments, and identify associated regulatory networks as candidate biomarkers and therapeutic vulnerabilities.

cancer biology↗

bioRxiv2026

Mitochondrial protein translation is a heightened dependence and therapeutic vulnerability of chemo-refractory triple negative breast cancer

Triple negative breast cancer (TNBC) patients harboring residual cancer burden following completion of conventional neoadjuvant chemo-immunotherapy regimens have poor relapse-free and overall survival rates and limited therapeutic options. We and others have demonstrated that mitochondrial function is required for the survival of chemo-refractory TNBC. Here, we define the mitochondrial translation machinery as a critical and targetable dependency underlying chemo-refractory TNBC. Analyses of human and orthotopic patient-derived xenograft (PDX) mass spectrometry proteomics datasets revealed that mitochondrial protein translation-related signatures were among the top associated with chemoresistance. These signatures included core mitoribosome components and the mitoribosome-associated factor Oxidase (Cytochrome C) Assembly 1-Like (OXA1L), which was consistently enriched in chemoresistant versus chemosensitive TNBC across datasets. OXA1L, a key mediator of mitochondrial translation and electron transport chain (ETC) assembly, has not been functionally characterized in cancer. We therefore tested whether OXA1L-dependent mitochondrial translation sustains mitochondrial function and chemoresistance in TNBC. Knockdown (KD) of OXA1L in human TNBC cells reduced ETC protein levels, mitochondrial respirasome supercomplex levels, ATP production, and oxidative phosphorylation (oxphos), establishing a requirement for OXA1L in maintaining mitochondrial bioenergetics in TNBC. OXA1L was required for the characteristic oxphos elevation induced by carboplatin (CRB), and KD significantly enhanced CRB sensitivity, demonstrating that mitochondrial translation supports adaptive metabolic responses to chemotherapy. To explore the translational potential of targeting the mitoribosome in TNBC, we leveraged the bacterial ancestry of mitochondria to repurpose the FDA-approved antibiotic tigecycline (TIG) as a mitochondrial translation inhibitor. Direct measurement of mitochondrial nascent peptide levels revealed that, while CRB elevated mitochondrial translation, TIG potently suppressed mitochondrial translation as monotherapy and in combination with CRB or docetaxel (DTX). TIG abolished CRB-induced oxphos, decreased oxphos in combination with DTX, and significantly improved chemotherapy sensitivity in human TNBC cell lines, PDX-derived spheroids, and in vivo. TIG sensitivity associated with mitochondrial translation-related proteomic signatures, concordant with PDX and patient-derived signatures associated with chemoresistance. Together, these data identify OXA1L-dependent mitochondrial translation as a targetable dependency that sustains mitochondrial function and chemoresistance in TNBC, demonstrate that its inhibition enhances chemotherapeutic response, and nominate a mitochondrial translation-related protein signature as a candidate predictive biomarker of TIG sensitivity and chemoresistance. These findings support mitochondrial translation inhibition as a potential therapeutic strategy in chemo-refractory disease. DISCLOSURESGVE is co-founder, Chief Scientific Officer, and an equity stakeholder of Nemea Therapeutics, Inc. G.V.E. formerly received sponsored research funding from Chimerix Inc. G.V.E. receives experimental compounds from the Lead Discovery Center of Germany and from Jazz Pharmaceuticals. MLB is a co-inventor at Nemea Therapeutics. MTL is a founder and limited partner in StemMed Ltd. and a manager in StemMed Holdings, its general partner. MTL is a founder and equity stakeholder in Tvardi Therapeutics Inc. The BCM PDX models, are exclusively licensed to StemMed Ltd., resulting in royalty income to MTL when used for commercial purposes. LED is a compensated employee of StemMed Ltd; however, the PDX models resulting in royalty income to LED were not included in this study. All other authors have nothing to disclose.

cancer biology↗

bioRxiv2025

Independence-based causal discovery analysis reveals statistically non-significant regions to be functionally significant

Background and HypothesisTraditional fMRI analyses often ignore regions that fail to reach statistical significance, assuming they are biologically unimportant. We tested the accuracy of this assumption using causal discovery based-analysis that go beyond associations/correlations to test the causality of one regions influence over the other. We hypothesized that the network of statistically significant (active network, AN) and non-significant regions (silent network, SN) causally interact and their features will causally influence psychopathology severity and working memory performance. Study DesignWe examined AN and SN during N-BACK task on 25 FHR and 37 controls. Clusters with significantly different activations were juxtaposed to 360 Glasser atlas parcellations. The PC algorithm for causal discovery was implemented. Connectivity of regions with the highest alpha-centrality (HAC) were examined. ResultsSeventy-seven Glasser regions were in the AN and the rest were silent nodes. Two regions showed HAC for FHR and HC. Among controls, one HAC region was silent (auditory association cortex) and the other one was active (insula). Among FHR, both were silent nodes (early auditory cortex). These HAC regions in both groups had bidirectional directed edges between each other forming a reciprocal circuit whose edge-weights causally "increased" magical ideation severity. ConclusionCausal connectivity between SN and AN suggests that the statistically non-significant and significant regions influence each other. Our findings question the merit of ignoring statistically non-significant regions and exclusively including statistically significant regions in the pathophysiological models. Our study suggests that causality analysis should receive greater attention.

neuroscience↗

bioRxiv2025

eIF3d and eIF3e mediate selective translational control of hypoxia that can be inhibited by novel small molecules

Exposure to hypoxia is linked to increased cellular plasticity and enhanced metastasis; effects which are primarily attributed to the transcriptional activation of large gene programs downstream of hypoxia inducible factors (HIFs). However, translational effects in hypoxia, that likely precede transcriptional effects, have remained largely unexplored. Using ribosome-profiling, we uncovered a selective translational response in acute hypoxia that is eIF3d/eIF3e-dependent and controls downstream hypoxic responses including HIF1a accumulation and cellular invasion. We further demonstrated that eIF3e copy number and an eIF3e-expression signature are associated with worsened outcomes for breast cancer patients. Finally, we identified a class of novel small molecules that target eIF3e specifically, reducing the translational response to hypoxia and to ER stress, another stressor that is dependent on eIF3d/eIF3e-mediated translation. Our data uncover critical functions for eIF3d/eIF3e in the hypoxic response and identify a potential means to inhibit stress-induced translation, and potentially plasticity and metastasis, mediated by eIF3e.

cancer biology↗

bioRxiv2025

Artificial intelligence-enabled automated analysis of transmission electron micrographs to evaluate chemotherapy impact on mitochondrial morphology in triple negative breast cancer

Advancements in transmission electron microscopy (TEM) have enabled in-depth studies of biological specimens, offering new avenues to large-scale imaging experiments with subcellular resolution. Mitochondrial structure is of growing interest in cancer biology due to its crucial role in regulating the multi-faceted functions of mitochondria. We and others have established the crucial role of mitochondria in triple-negative breast cancer (TNBC), an aggressive subtype of breast cancer with limited therapeutic options. Building upon our previous work demonstrating the functional role of mitochondrial structure dynamics in the metabolic adaptations and survival of chemotherapy-refractory TNBC cells, we sought to extend those findings to a large-scale analysis of transmission electron micrographs. Here we present a novel U-Net artificial intelligence (AI) model for automatic annotation and assessment of mitochondrial morphology and feature quantification. Our model is trained on 11,039 manually annotated mitochondria across 125 micrographs derived from a variety of orthotopic patient-derived xenograft (PDX) mouse model tumors and adherent cell cultures. The model achieves an F1 score of 0.85 on test micrographs at the pixel level. To validate the ability of our model to detect expected mitochondrial structural changes, we utilized micrographs from mouse primary skeletal muscle cells genetically modified to lack Dynamin-related protein 1 (Drp1). We subjected in vitro and in vivo TNBC models to conventional chemotherapy treatments commonly used for clinical management of TNBC, including doxorubicin, carboplatin, paclitaxel, and docetaxel (DTX). We found substantial within-sample heterogeneity of mitochondrial structure in both in vitro and in vivo TNBC models. In four of five PDX models, in vivo treatment with DTX elicited significant alteration in mitochondrial elongation and/or area. We went on to compare mammary tumors and matched lung metastases in a highly metastatic PDX model of TNBC, uncovering significant increase in mitochondrial elongation in metastatic lesions compared to their matched primary mammary tumor. The successful application of our AI model to capture mitochondrial structure marks a step forward in high-throughput analysis of mitochondrial structures, enhancing our understanding of how morphological changes may relate to chemotherapy efficacy and mechanism of action. Our large, manually curated electron micrograph dataset - now publicly available - serves as a unique resource for developing, benchmarking, and applying computational models, while further advancing investigations into mitochondrial morphology and its impact on breast cancer biology. This study provides proof of concept that mitochondrial structural remodeling is an additional layer of cellular reprogramming accompanying therapeutic resistance TNBC that merits further investigation.

cancer biology↗

bioRxiv2024

A precision oncology-focused deep learning framework for personalized selection of cancer therapy

Precision oncology matches tumors to targeted therapies based on the presence of actionable molecular alterations. However, most tumors lack actionable alterations, restricting treatment options to cytotoxic chemotherapies for which few data-driven prioritization strategies currently exist. Here, we report an integrated computational/experimental treatment selection approach applicable for both chemotherapies and targeted agents irrespective of actionable alterations. We generated functional drug response data on a large collection of patient-derived tumor models and used it to train ScreenDL, a novel deep learning-based cancer drug response prediction model. ScreenDL leverages the combination of tumor omic and functional drug screening data to predict the most efficacious treatments. We show that ScreenDL accurately predicts response to drugs with diverse mechanisms, outperforming existing methods and approved biomarkers. In our preclinical study, this approach achieved superior clinical benefit and objective response rates in breast cancer patient-derived xenografts, suggesting that testing ScreenDL in clinical trials may be warranted.

cancer biology↗

bioRxiv2024

Patient-Derived Xenografts of Triple-Negative Breast Cancer Enable Deconvolution and Prediction of Chemotherapy Responses

Chemotherapy regimens for triple-negative breast cancer (TNBC) combine agents without knowing which agents drive response. Consequently, predictors derived from multi-agent regimens cannot be assumed to generalize to individual drugs or other regimens, motivating development of treatment-matched predictors. Here, we used TNBC patient-derived xenografts (PDXs) treated with carboplatin, docetaxel, or the combination to deconvolute drug-specific responses and identify associated molecular features. Combination treatment rarely improved upon the best single agent, with enhanced responses in only 13% of PDXs and antagonism in a comparable fraction. Proteogenomic analyses identified high cytokeratin-5 (KRT5) as a general marker of chemotherapy responsiveness and KRT5 immunohistochemistry discriminated responsive PDXs (AUROC, 0.83). To train treatment-specific predictors, we integrated these data with independent PDX and clinical cohorts with responses assessed after anthracycline-free platinum, taxane, or platinum-taxane therapy, ensuring response corresponded to the modeled treatment. Four feature selection strategies yielded largely nonoverlapping biomarker panels converging on treatment-relevant pathways. On independent test data, RNA-based predictors of complete response (CR) to platinum-based (carboplatin or cisplatin) and taxane-based (docetaxel or paclitaxel) chemotherapy achieved AUROCs of 0.80 and 0.86, respectively. For platinum-taxane regimens (carboplatin plus docetaxel or paclitaxel), proteomic-guided feature selection generated a 10-biomarker, protein- informed RNA predictor of pathologic complete response (pCR) that outperformed RNA-only feature selection and achieved an AUROC of 0.85 in an independent clinical cohort, while retaining practicality as an RNA-based assay. Treatment-matched integration of multi-omic PDX and clinical datasets provides a framework for chemotherapy-specific predictors with clinically relevant performance, supporting biomarker-guided precision selection and treatment optimization for patients with TNBC. Statement of significanceIntegration of multi-omic data from patient-derived xenografts with treatment-matched clinical cohorts yielded three retrospectively validated predictors of platinum, taxane, and platinum+taxane response, enabling biomarker-guided chemotherapy selection for patients with triple-negative breast cancer.

cancer biology↗

bioRxiv2024

Three-dimensional analysis of mitochondria in a patient-derived xenograft model of triple negative breast cancer reveals mitochondrial network remodeling following chemotherapy treatments

Mitochondria are hubs of metabolism and signaling, playing crucial roles in tumorigenesis, therapeutic resistance, and metastasis in many types of cancer. Various laboratory models of cancer demonstrate the extraordinary dynamics of mitochondrial structure, but little is known about the full extent of the complexity of the mitochondrial network, nor its regulation upon exposure to therapeutic stressors. We previously demonstrated the importance of mitochondrial structure and oxidative phosphorylation in the survival of chemotherapy-refractory triple negative breast cancer (TNBC) cells. As TNBC is a highly aggressive breast cancer subtype with few targeted therapy options, conventional chemotherapies remain the backbone of TNBC treatment. Unfortunately, approximately 45% of TNBC patients retain substantial residual tumor burden following chemotherapy, associated with abysmal prognoses. Herein we present the first three-dimensional analysis of mitochondrial networks in human tumor tissues. Use of two experimentally tractable orthotopic patient-derived xenograft (PDX) models TNBC enabled us to conduct longitudinal analyses to construct mitochondrial networks in treatment-naive and residual tumors persisting after exposure to a variety of conventional chemotherapies. Further, we modeled lipid droplet (LD) structures and their physical contacts with mitochondria. In total, we reconstructed 3,750 mitochondria and 800 LDs in three dimensions using serial block-face scanning electron microscopy (SBF-SEM), providing unprecedented insights into the complexity and intra-tumoral heterogeneity of mitochondria in TNBC. Both carboplatin (CRB) and docetaxel (DTX) chemotherapies produced residual tumors that harbored mitochondria with significantly increased areas, volumes, and perimeters in both PDX models. Additionally, treatment with the conventional combinations DTX plus CRB or Adriamycin plus cyclophosphamide (AC), led to reduced mitochondrial branching and elongation. In contrast, DTX or CRB alone elicited model-specific changes in mitochondrial complexity. Further, the extensive intra-tumoral heterogeneity of mitochondrial structure in untreated PDX tumors significantly decreased in residual tumors. Analyses of LDs revealed significant and consistent elevation of the number and physical proximity of MLCs in residual tumors, congruent with our previous studies providing evidence for transcriptomic and proteomic rewiring of lipid metabolism in residual TNBC. These results highlight the potential for structure-based monitoring of chemotherapeutic metabolic reprogramming and suggest unique molecular mechanisms that may underlie chemoresistance in TNBC. Furthermore, our findings provide novel insights into a new type of intratumoral heterogeneity, that of mitochondrial intratumoral heterogeneity, which complements our understanding of the genomic, epigenomic, transcriptomic, and proteomic complexity of TNBC.

cancer biology↗

bioRxiv2023

Interferon-Induced Bone Marrow Stromal Antigen 2 (BST2) Is A Functional Tumor-Initiating Cell Marker In Triple-Negative Breast Cancer

A tumor cell subpopulation of tumor-initiating cells (TIC), or "cancer stem cells", are associated with therapeutic resistance, as well as both local and distant recurrence. Enriched populations of TIC are identified by markers including aldehyde dehydrogenase (ALDH1) activity, the cell surface marker combination CD44+/CD24-, or fluorescent reporters for signaling pathways that regulate TIC function. We showed previously that Signal Transducer and Activator of Transcription (STAT)-mediated transcription allows enrichment for TIC in claudin-low models of human triple-negative breast cancer using a STAT-responsive reporter. However, the molecular phenotypes of STAT TIC are not well understood, and there is no existing method to lineage-trace TIC as they undergo cell state changes. Using a new STAT-responsive lineage-tracing (LT) system in conjunction with our original reporter, we enriched for cells with enhanced mammosphere-forming potential in some, but not all, basal-like triple-negative breast cancer (TNBC) xenograft models (TNBC) indicating TIC-related and TIC-independent functions for STAT signaling. Single-cell RNA sequencing (scRNAseq) of reporter-tagged xenografts and clinical samples identified a common interferon (IFN)/STAT1-associated transcriptional state, previously linked to inflammation and macrophage differentiation, in TIC. Surprisingly, most of the genes we identified are not present in previously published TIC signatures derived using bulk RNA sequencing. Finally, we demonstrated that bone marrow stromal cell antigen 2 (BST2), is a cell surface marker of this state, and that it functionally regulates TIC frequency. These results suggest TIC may exploit the IFN/STAT1 signaling axis to promote their activity, and that targeting this pathway may help eliminate TIC. SignificanceTIC differentially express interferon response genes, which were not previously reported in bulk RNA sequencing-derived TIC signatures, highlighting the importance of coupling single-cell transcriptomics with enrichment to derive TIC signatures.

cancer biology↗

bioRxiv2023

BCM PDX Portal: An Intuitive Web-based Tool for Patient-Derived Xenograft Collection Management, as well as Visual Integration of Clinical and Omics Data

ObjectiveMouse Patient-Derived Xenograft (PDX) models are essential tools for evaluating experimental therapeutics. Baylor College of Medicine (BCM) established a PDX Core to provide technical support and infrastructure for PDX-based research. To manage PDX collections effectively, de-identified patient clinical and omics data, as well as PDX-related information and omics data, must be curated and stored. Data must then be analyzed and visualized for each case. To enhance PDX collection management and data dissemination, the BCM Biomedical Informatics Core created the BCM PDX Portal (https://pdxportal.research.bcm.edu/). Materials and MethodsPatient clinical data are abstracted from medical records for each PDX and stored in a central database. Annotations are reviewed by a clinician and de-identified. PDX development method and biomarker expression are annotated. DNAseq, RNAseq, and proteomics data are processed through standardized pipelines and stored. PDX gene expression (mRNA/protein), copy number alterations, and mutations can be searched in combination with clinical markers to identify models potentially useful as a PDX cohort. ResultsPDX collection management and PDX selection of models for drug evaluation are facilitated using the PDX Portal. DiscussionTo improve the translational effectiveness of PDX models, it is beneficial to use a tool that captures and displays multiple features of the patient clinical and molecular data. Selection of models for studies should be representative of the patient cohort from which they originated. ConclusionThe BCM PDX Portal is a highly effective PDX collection management tool allowing data access in a visual, intuitive manner thereby enhancing the utility of PDX collections.

cancer biology↗

bioRxiv2022

XDec Simplex Map of Breast Cancer Cell States Enables Precise Modeling and Targeting of Breast Cancer

The characterization of cancer cell states within the tumor microenvironment is a key to understanding tumor biology and an important step toward the development of precision therapies. To reconstruct this information from bulk RNA-seq profiles, we developed the XDec Simplex Mapping (XDec-SM) approach, a reference-optional deconvolution method that leverages single-cell information, when such information is available, to map tumors and the states of constituent cells onto a biologically interpretable, low-dimensional space. When applied to breast tumors in The Cancer Genome Atlas (TCGA), XDec-SM infers the identity of constituent cell types and their proportions. XDec-SM also infers cancer cells states within individual tumors that associate with DNA methylation patterns, driver somatic mutations, pathway activation and metabolic coupling between stromal and breast cancer cells. By projecting tumors, cancer cell lines, and PDX models onto the same map, we identify both in vitro and in vivo models with matching cancer cell states. Map position is also predictive of therapy response, thus opening the prospects for precision therapy informed by experiments in model systems matched to tumors in vivo by cancer cell state.

cancer biology↗

bioRxiv2021

RANK is an independent biomarker of poor prognosis in estrogen receptor-negative breast cancer and a therapeutic target in patient-derived xenografts

Despite strong preclinical data, the therapeutic benefit of the RANKL inhibitor denosumab in BC patients, beyond its bone-related effects, is unclear. Here, we investigated the prognostic value of RANK expression and its functionality in human BC. We analyzed RANK and RANKL expression in more than 1500 BC cases (777 being estrogen receptor-negative (ER-)) from four independent cohorts. We confirmed that RANK is more frequently expressed in ER- tumors, but it is also found in a subset of ER+ tumors. In ER- BC, RANK expression was independently associated with poor outcome, especially in postmenopausal patients and those who received adjuvant chemotherapy. Gene expression analyses unraveled distinct biology associated with RANK in relation to ER expression and menopause, and evidenced enhanced RANK activation in ER- postmenopausal tumors, together with regulation of metabolic pathways. Functional studies and transcriptomic analyses in ER- RANK+ patients-derived orthoxenografts demonstrated that activation of RANK signaling pathway promotes tumor cell proliferation and stemness, and regulates multiple biological processes including tumor immune surveillance and metabolism. Our results demonstrate that RANK expression is an independent poor prognosis biomarker in postmenopausal ER- BC patients and support the rational of using RANK pathway inhibitors in combination with chemotherapy in ER- BC.

cancer biology↗

bioRxiv2021

PDXNet Portal: Patient-Derived Xenograft model, data, workflow, and tool discovery

We created the PDX Network (PDXNet) Portal (https://portal.pdxnetwork.org/) to centralize access to the National Cancer Institute-funded PDXNet consortium resources (i.e., PDX models, sequencing data, treatment response data, and bioinformatics workflows), to facilitate collaboration among researchers, and to make resources easily available for research. The portal includes sections for resources, analysis results, metrics for PDXNet activities, data processing protocols, and training materials for processing PDX data. The initial portal release highlights PDXNet model and data resources, including 334 new models across 33 cancer types. Tissue samples of these models were deposited in the NCIs Patient-Derived Model Repository (PDMR) for public access. These models have 2,822 associated sequencing files from 873 samples across 307 patients, which are hosted on the Cancer Genomics Cloud powered by Seven Bridges and the NCI Cancer Data Service for long-term storage and access with dbGaP permissions. The portal also includes results from standardized analysis workflows on PDXNet sequencing files and PDMR data (2,594 samples from 463 patients across 78 disease types). These 15 analysis workflows for whole-exome and RNA-Seq data are freely available, robust, validated, and standardized. The model and data lists will grow substantially over the next two years and will be continuously updated as new data are available. PDXNet models support multi-agent treatment studies, determination of sensitivity and resistance mechanisms, and preclinical trials. The PDXNet portal is a centralized location for these data and resources, which we expect to be of significant utility for the cancer research community.

cancer biology↗

bioRxiv2021

Multiplexed triage of candidate biomarkers in plasma using internal standard triggered-parallel reaction monitoring mass spectrometry

Despite advances in proteomic technologies, clinical translation of plasma biomarkers remains low, partly due to a major bottleneck between the discovery of candidate biomarkers and downstream costly clinical validation studies. Due to a dearth of multiplexable assays, generally only a few candidate biomarkers are tested, and the validation success rate is accordingly low. Here, we demonstrate the capability of internal standard triggered-parallel reaction monitoring (IS-PRM) to prioritize candidate biomarkers for validation studies. A 5,176-plex assay coupling immunodepletion and fractionation with IS-PRM was developed and implemented in human plasma to quantify peptides representing 1,314 breast cancer biomarker candidates. Compared to prior approaches using data-dependent analysis, IS-PRM showed improved sensitivity (912 vs 295 proteins quantified) and precision (CV 0.1 vs 0.27) enabling rank-ordering of candidate biomarkers for validation studies. The assay greatly expands capabilities for quantification of large numbers of proteins and is well suited for prioritization of viable candidate biomarkers.

cancer biology↗

bioRxiv2021

A Network Approach to Identify Biomarkers of Differential Chemotherapy Response Using Patient-Derived Xenografts of Triple-Negative Breast Cancer

Triple negative breast cancer (TNBC) is a highly heterogeneous set of diseases that has, until recently, lacked any FDA-approved, molecularly targeted therapeutics. Thus, systemic chemotherapy regimens remain the standard of care for many. Unfortunately, even combination chemotherapy is ineffective for many TNBC patients, and side-effects can be severe or lethal. Identification of predictive biomarkers for chemotherapy response would allow for the prospective selection of responsive patients, thereby maximizing efficacy and minimizing unwanted toxicities. Here, we leverage a cohort of TNBC PDX models with responses to single-agent docetaxel or carboplatin to identify biomarkers predictive for differential response to these two drugs. To demonstrate their ability to function as a preclinical cohort, PDX were molecularly characterized using whole-exome DNA sequencing, RNAseq transcriptomics, and mass spectrometry-based total proteomics to show proteogenomic consistency with TCGA and CPTAC clinical samples. Focusing first on the transcriptome, we describe a network-based computational approach to identify candidate epithelial and stromal biomarkers of response to carboplatin (MSI1, TMSB15A, ARHGDIB, GGT1, SV2A, SEC14L2, SERPINI1, ADAMTS20, DGKQ) and docetaxel (ITGA7, MAGED4, CERS1, ST8SIA2, KIF24, PARPBP). Biomarker panels are predictive in PDX expression datasets (RNAseq and Affymetrix) for both taxane (docetaxel or paclitaxel) and platinum-based (carboplatin or cisplatin) response, thereby demonstrating both cross expression platform and cross drug class robustness. Biomarker panels were also predictive in clinical datasets with response to cisplatin or paclitaxel, thus demonstrating translational potential of PDX-based preclinical trials. This network-based approach is highly adaptable and can be used to evaluate biomarkers of response to other agents.

cancer biology↗

bioRxiv2021

Specific BRCA and immune configurations determine optimal response to platinum-based chemotherapy in triple negative breast and ovarian carcinomas

Loss of homologous recombination repair (HRR) via germline and somatic BRCA1 or BRCA2 gene mutations and via BRCA1 promoter methylation has been associated with better response to platinum agents and PARP inhibitors, in both triple negative breast cancer (TNBC) and ovarian carcinoma (OvCa). A major conundrum arising from recent clinical studies is why cancers with BRCA1 promoter methylation (BRCA1meth) respond more poorly as compared to those bearing mutations in BRCA1 and BRCA2 (BRCAmut), given the biologically equivalent HRR deficiency in both states. We dissected this problem through detailed genomic analyses of primary TNBC and OvCa cohorts, as well as experimentation with patient-derived xenograft (PDX) models and genetically engineered cell lines. Using the precise genomic scar of the tandem duplicator phenotype as a precise genomic indicator of BRCA1 deficiency, we found that, in all cohorts, BRCA1mut and BRCA1meth cancers share an equivalent degree of BRCA1-linked genomic rearrangements. Nonetheless, we consistently found that patients with BRCAmut cancers, but not those with BRCA1meth cancers, had significantly better response outcomes when compared to those with BRCA proficient cancers. When fully promoter methylated BRCA1 PDX TNBCs were exposed to a single short course of platinum chemotherapy an unmethylated BRCA1 promoter allele emerged in resultant tumors associated with an increase in BRCA1 expression. A separate analysis of PDXs derived from treatment naive TNBCs featured complete methylation of the BRCA1 promoter, whereas those derived from post-chemotherapy TNBCs invariably had only partial methylation. PDXs with partial methylation were significantly associated with lower response rates to in vivo platinum-based therapy compared to those with complete promoter methylation. Using single cell clonal expansions from a partially BRCA1meth PDX, we confirmed that the reduced level of methylation was due to the demethylation of one of the BRCA1 promoter alleles and not to the outgrowth of a non-methylated clone. Clinically, analysis of primary OvCas confirmed that high levels of BRCA1 methylation were significantly associated with reduced BRCA1 gene expression whereas cancers with lower levels of BRCA1 methylation had expression levels approaching those found in BRCA1 proficient cancers. These data suggest that unlike BRCAmut cancers, where HRR deficiency is achieved via mutations that are genetically fixed, BRCA1meth cancers are highly adaptive to genotoxin exposure and more likely to recover BRCA1 expression, which may explain their poorer therapeutic response. We further found that an increased immune transcriptional signal, especially an elevated M1 macrophage signature, is associated with enhanced response to platinum-based chemotherapy only in patients with BRCA proficient cancers, in both TNBC and OvCa cohorts underscoring the importance of characterizing molecular heterogeneity to enhance predictive precision in assigning response probabilities in TNBC and OvCa.

cancer biology↗

bioRxiv2021

Tumor suppressor PLK2 may serve as a biomarker in triple-negative breast cancer for improved response to PLK1 therapeutics

Polo-like kinase (PLK) family members play important roles in cell cycle regulation. The founding member PLK1 is oncogenic and preclinically validated as a cancer therapeutic target. Paradoxically, PLK2 (chromosome 5q11.2) is frequently deleted in human breast cancers, preferentially in basal-like and triple-negative breast cancer subtypes. Here, we found that PLK2 was tumor suppressive in breast cancer and knockdown of PLK1 rescued phenotypes induced by PLK2-loss both in vitro and in vivo. We also demonstrated that PLK2 directly interacted with PLK1 at prometaphase and that mutations in the kinase domain of PLK2, but not polo-box binding domains, changed their interaction pattern. Furthermore, treatment of syngeneic transplantation mouse tumor models and patient-derived xenografts using the PLK1 inhibitor volasertib alone, or in combination with carboplatin, indicated that PLK2-low breast tumors had a significantly better response to these drugs. Re-expression of PLK2 in an inducible PLK2-null mouse model reduced the therapeutic efficacy of volasertib. Taken together, our data suggest PLK2 loss may serve as a biomarker to predict response to PLK1 therapeutics, alone and in combination with chemotherapy. SignificanceThe tumor suppressive role of PLK2, and its relationship with the oncogene PLK1, provide a mechanistic rationalization to use PLK1 inhibitors in combination with chemotherapy to treat PLK2 low/deleted tumors. TNBC, and other cancers with low PLK2 expression, are such candidates to leverage precision medicine to identify patients who might benefit from treatment with these inhibitors.

cancer biology↗

bioRxiv2021

A breast cancer patient-derived xenograft and organoid platform for drug discovery and precision oncology

Model systems that recapitulate the complexity of human tumors and the reality of variable treatment responses are urgently needed to better understand cancer biology and to develop more effective cancer therapies. Here we report development and characterization of a large bank of patient-derived xenografts (PDX) and matched organoid cultures from tumors that represent some of the greatest unmet needs in breast cancer research and treatment. These include endocrine-resistant, treatment-refractory, and metastatic breast cancers and, in some cases, multiple tumor collections from the same patients. The models can be grown long-term with high fidelity to the original tumors. We show that development of matched PDX and PDX-derived organoid (PDxO) models facilitates high-throughput drug screening that is feasible and cost-effective, while also allowing in vivo validation of results. Our data reveal consistency between drug screening results in organoids and drug responses in breast cancer PDX. Moreover, we demonstrate the feasibility of using these patient-derived models for precision oncology in real time with patient care, using a case of a triple negative breast cancer with early metastatic recurrence as an example. Our results uncovered an FDA-approved drug with high efficacy against the models. Treatment with the PDxO-directed therapy resulted in a complete response for the patient and a progression-free survival period more than three times longer than her previous therapies. This work provides valuable new methods and resources for functional precision medicine and drug development for human breast cancer. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=189 HEIGHT=200 SRC="FIGDIR/small/433268v2_ufig1.gif" ALT="Figure 1"> View larger version (41K): org.highwire.dtl.DTLVardef@134d5cdorg.highwire.dtl.DTLVardef@1c0a419org.highwire.dtl.DTLVardef@556a4forg.highwire.dtl.DTLVardef@1b68c9c_HPS_FORMAT_FIGEXP M_FIG C_FIG

cancer biology↗