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Hotspots difficult to confidently assess for CHIP

Supplementary Methods and Supplementary Figures

Clonal hematopoiesis of indeterminate potential (CHIP) is characterized by detectable hematopoietic-associated gene mutations in a person without evidence of hematologic malignancy. We sought to identify additional cancer-presenting mutations usable for CHIP detection by performing a data mining analysis of 48 somatic mutation landscape studies reporting mutations at diagnoses of 7,430 adult and pediatric patients with leukemia or other hematologic malignancy. Following extraction of 20,141 protein-altering mutations, we identified 434 significantly recurrent mutation hotspots, 364 of which occurred at loci confidently assessable for CHIP. We then performed an additional large-scale analysis of whole-exome sequencing data from 4,538 persons belonging to three noncancer cohorts for clonal mutations. We found the combined cohort prevalence of CHIP with mutations identical to those reported at blood cancer mutation hotspots to be 1.8%, and that some of these CHIP mutations occurred in children. Our findings may help to improve CHIP detection and precancer surveillance for both children and adults.Significance:This study identifies frequently occurring mutations across several blood cancers that may drive hematologic malignancies and signal increased risk for cancer when detected in healthy persons. We find clonal mutations at these hotspots in a substantial number of individuals from noncancer cohorts, including children, showcasing potential for improved precancer surveillance.See related commentary by Spitzer and Levine, p. 192.

SF1: ETV4 expression levels in endometrial cancer. SF2: Expression levels of ETV4 in candidate knockout clones. SF3: Short-term knockdown of ETV4 reduces ER genomic binding. SF4: Validation of rescue and overexpression of ETV4. SF5: Estrogen induced fold changes are higher in wildtype cells than ETV4 knockout cells and genes that lose estrogen induction are closer to ETV4 bound sites. SF6: Global chromatin accessibility is similar between wildtype and ETV4 knockout lines. SF7: Introduction of ETV4 in T-47D breast cancer cell line. SF8: GR localization does not change due to ETV4 loss. SF9: Growth phenotype observed in 2D growth of ETV4 knockout lines. SF10: ETV4 knockouts increase expression of cytokeratins, a marker of normal endometrium.

Estrogen signaling through estrogen receptor alpha (ER) plays a major role in endometrial cancer risk and progression, however, the molecular mechanisms underlying ER's regulatory role in endometrial cancer are poorly understood. In breast cancer cells, ER genomic binding is enabled by FOXA1 and GATA3, but the transcription factors that control ER genomic binding in endometrial cancer cells remain unknown. We previously identified ETV4 as a candidate factor controlling ER genomic binding in endometrial cancer cells, and here we explore the functional importance of ETV4. Homozygous deletion of ETV4, using CRISPR/Cas9, led to greatly reduced ER binding at the majority of loci normally bound by ER. Consistent with the dramatic loss of ER binding, the gene expression response to estradiol was dampened for most genes. ETV4 contributes to estrogen signaling in two distinct ways. ETV4 loss affects chromatin accessibility at some ER bound loci and impairs ER nuclear translocation. The diminished estrogen signaling upon ETV4 deletion led to decreased growth, particularly in 3D culture, where hollow organoids were formed and in vivo in the context of estrogen-dependent growth. These results show that ETV4 plays an important role in estrogen signaling in endometrial cancer cells.Significance:Estrogen receptor alpha (ER) is a key oncogene in endometrial cancer. This study uncovers ETV4 as an important factor in controlling the activity of ER and the growth of endometrial cancer cells.

Models that recapitulate the complexity of human tumors are urgently needed to develop more effective cancer therapies. We report a bank of human patient-derived xenografts (PDXs) and matched organoid cultures from tumors that represent the greatest unmet need: endocrine-resistant, treatment-refractory and metastatic breast cancers. We leverage matched PDXs and PDX-derived organoids (PDxO) for drug screening that is feasible and cost-effective with in vivo validation. Moreover, we demonstrate the feasibility of using these models for precision oncology in real time with clinical care in a case of triple-negative breast cancer (TNBC) with early metastatic recurrence. Our results uncovered a Food and Drug Administration (FDA)-approved drug with high efficacy against the models. Treatment with this therapy resulted in a complete response for the individual and a progression-free survival (PFS) period more than three times longer than their previous therapies. This work provides valuable methods and resources for functional precision medicine and drug development for human breast cancer.

3D patient tumor avatars (3D-PTAs) hold promise for next-generation precision medicine. Here, we describe the benefits and challenges of 3D-PTA technologies and necessary future steps to realize their potential for clinical decision making. 3D-PTAs require standardization criteria and prospective trials to establish clinical benefits. Innovative trial designs that combine omics and 3D-PTA readouts may lead to more accurate clinical predictors, and an integrated platform that combines diagnostic and therapeutic development will accelerate new treatments for patients with refractory disease.

Development of candidate cancer treatments is a resource-intensive process, with the research community continuing to investigate options beyond static genomic characterization. Toward this goal, we have established the genomic landscapes of 536 patient-derived xenograft (PDX) models across 25 cancer types, together with mutation, copy number, fusion, transcriptomic profiles, and NCI-MATCH arms. Compared with human tumors, PDXs typically have higher purity and fit to investigate dynamic driver events and molecular properties via multiple time points from same case PDXs. Here, we report on dynamic genomic landscapes and pharmacogenomic associations, including associations between activating oncogenic events and drugs, correlations between whole-genome duplications and subclone events, and the potential PDX models for NCI-MATCH trials. Lastly, we provide a web portal having comprehensive pan-cancer PDX genomic profiles and source code to facilitate identification of more druggable events and further insights into PDXs’ recapitulation of human tumors., Patient-derived xenograft models (PDX) have been extensively used to study the molecular and clinical features of cancers. Here the authors present a cohort of 536 PDX models from 25 cancers, as well as their genomic and evolutionary profiles and their suitability for clinical trials.

Patient-derived xenografts (PDXs) model human intra-tumoral heterogeneity in the context of the intact tissue of immunocompromised mice. Histological imaging via hematoxylin and eosin (H&E) staining is performed on PDX samples for routine assessment and, in principle, captures the complex interplay between tumor and stromal cells. Deep learning (DL)-based analysis of large human H&E image repositories has extracted inter-cellular and morphological signals correlated with disease phenotype and therapeutic response. Here, we present an extensive, pan-cancer repository of nearly 1,000 PDX and paired human progenitor H&E images. These images, curated from the PDXNet consortium, are associated with genomic and transcriptomic data, clinical metadata, pathological assessment of cell composition, and, in several cases, detailed pathological annotation of tumor, stroma, and necrotic regions. We demonstrate that DL can be applied to these images to classify tumor regions with an accuracy of 0.87. Further, we show that DL can predict xenograft-transplant lymphoproliferative disorder, the unintended outgrowth of human lymphocytes at the transplantation site, with an accuracy of 0.97. This repository enables PDX-specific investigations of cancer biology through histopathological analysis and contributes important model system data that expand on existing human histology repositories. We expect the PDXNet Image Repository to be valuable for controlled digital pathology analysis, both for the evaluation of technical issues such as stain normalization and for development of novel computational methods based on spatial behaviors within cancer tissues. Citation Format: Brian S. White, Xing Yi Woo, Soner Koc, Todd Sheridan, Steven B. Neuhauser, Shidan Wang, Yvonne A. Evrard, John David Landua, R Jay Mashl, Sherri R. Davies, Bingliang Fang, Maria Gabriela Raso, Kurt W. Evans, Matthew H. Bailey, Yeqing Chen, Min Xiao, Jill Rubinstein, Ali Foroughi pour, Lacey Elizabeth Dobrolecki, Maihi Fujita, Junya Fujimoto, Guanghua Xiao, Ryan C. Fields, Jacqueline L. Mudd, Xiaowei Xu, Melinda G. Hollingshead, Shahanawaz Jiwani, PDXNet consortium, Tiffany A. Wallace, Jeffrey A. Moscow, James H. Doroshow, Nicholas Mitsiades, Salma Kaochar, Chong-xian Pan, Moon S. Chen, Luis G. Carvajal-Carmona, Alana L. Welm, Bryan E. Welm, Ramaswamy Govindan, Shunqiang Li, Michael A. Davies, Jack A. Roth, Funda Meric-Bernstam, Yang Xie, Meenhard Herlyn, Li Ding, Michael T. Lewis, Carol J. Bolt, Dennis A. Dean, Jeffrey H. Chuang. A pan-cancer PDX histology image repository with genomic and pathological annotations for deep learning analysis. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 5407.

Recurrence of metastatic breast cancer stemming from acquired endocrine and chemotherapy resistance remains a health burden for women with luminal (ER+) breast cancer. Disseminated ER+ tumor cells can remain viable but quiescent for years to decades. Contributing factors to metastatic spread include the maintenance and expansion of breast cancer stem cells (CSCs). Breast CSCs frequently exist as a minority population in therapy resistant tumors. In this study, we show that cytoplasmic complexes composed of steroid receptor (SR) co-activators, PELP1 and SRC-3, modulate breast CSC expansion through upregulation of the HIF-activated metabolic target genes PFKFB3 and PFKFB4. Seahorse metabolic assays demonstrated that cytoplasmic PELP1 influences cellular metabolism by increasing both glycolysis and mitochondrial respiration. PELP1 interacts with PFKFB3 and PFKFB4 proteins, and inhibition of PFKFB3 and PFKFB4 kinase activity blocks PELP1-induced tumorspheres and protein-protein interactions with SRC-3. PFKFB4 knockdown inhibited in vivo emergence of circulating tumor cell (CTC) populations in mammary intraductal (MIND) models. Application of PFKFB inhibitors in combination with ER targeted therapies blocked tumorsphere formation in multiple models of advanced breast cancer including tamoxifen (TamR) and paclitaxel (TaxR) resistant models, murine tumor cells, and ER+ patient-derived organoids (PDxO). Together, our data suggest that PELP1, SRC-3, and PFKFBs cooperate to drive ER+ tumor cell populations that include CSCs and CTCs. Identifying non-ER pharmacological targets offers a useful approach to blocking metastatic escape from standard of care ER/estrogen (E2)-targeted strategies to overcome endocrine and chemotherapy resistance.

We created the PDX Network (PDXNet) portal (https://portal.pdxnetwork.org/) to centralize access to the National Cancer Institute-funded PDXNet consortium resources, to facilitate collaboration among researchers and to make these data 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. Currently, the portal contains PDXNet model information and data resources from 334 new models across 33 cancer types. Tissue samples of these models were deposited in the NCI’s Patient-Derived Model Repository (PDMR) for public access. These models have 2134 associated sequencing files from 873 samples across 308 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 includes results from freely available, robust, validated and standardized analysis workflows on PDXNet sequencing files and PDMR data (3857 samples from 629 patients across 85 disease types). The PDXNet portal is continuously updated with new data and is of significant utility to the cancer research community as it provides a centralized location for PDXNet resources, which support multi-agent treatment studies, determination of sensitivity and resistance mechanisms, and preclinical trials.

Breast cancers are divided into subtypes with different prognoses and treatment responses based on global differences in gene expression. Luminal breast cancer gene expression and proliferation are driven by estrogen receptor alpha, and targeting this transcription factor is the most effective therapy for this subtype. By contrast, it remains unclear which transcription factors drive the gene expression signature that defines basal-like triple-negative breast cancer, and there are no targeted therapies approved to treat this aggressive subtype. In this study, we utilized integrated genomic analysis of DNA methylation, chromatin accessibility, transcription factor binding, and gene expression in large collections of breast cancer cell lines and patient tumors to identify transcription factors responsible for the basal-like gene expression program. Glucocorticoid receptor (GR) and STAT3 bind to the same genomic regulatory regions, which were specifically open and unmethylated in basal-like breast cancer. These transcription factors cooperated to regulate expression of hundreds of genes in the basal-like gene expression signature, which were associated with poor prognosis. Combination treatment with small-molecule inhibitors of both transcription factors resulted in synergistic decreases in cell growth in cell lines and patient-derived organoid models. This study demonstrates that GR and STAT3 cooperate to regulate the basal-like breast cancer gene expression program and provides the basis for improved therapy for basal-like triple-negative breast cancer through rational combination of STAT3 and GR inhibitors. Significance: This study demonstrates that GR and STAT3 cooperate to activate the canonical gene expression signature of basal-like triple-negative breast cancer and that combination treatment with STAT3 and GR inhibitors could provide synergistic therapeutic efficacy.

Estrogen signaling through estrogen receptor alpha (ER) plays a major role in endometrial cancer risk and progression, however, the molecular mechanisms underlying ER's regulatory role in endometrial cancer are poorly understood. In breast cancer cells, ER genomic binding is enabled by FOXA1 and GATA3, but the transcription factors that control ER genomic binding in endometrial cancer cells remain unknown. We previously identified ETV4 as a candidate factor controlling ER genomic binding in endometrial cancer cells, and here we explore the functional importance of ETV4. Homozygous deletion of ETV4, using CRISPR/Cas9, led to greatly reduced ER binding at the majority of loci normally bound by ER. Consistent with the dramatic loss of ER binding, the gene expression response to estradiol was dampened for most genes. ETV4 contributes to estrogen signaling in two distinct ways. ETV4 loss affects chromatin accessibility at some ER bound loci and impairs ER nuclear translocation. The diminished estrogen signaling upon ETV4 deletion led to decreased growth, particularly in 3D culture, where hollow organoids were formed and in vivo in the context of estrogen-dependent growth. These results show that ETV4 plays an important role in estrogen signaling in endometrial cancer cells. Significance: Estrogen receptor alpha (ER) is a key oncogene in endometrial cancer. This study uncovers ETV4 as an important factor in controlling the activity of ER and the growth of endometrial cancer cells.

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 NCI’s 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.

Clonal hematopoiesis of indeterminate potential (CHIP) is characterized by detectable hematopoietic-associated gene mutations in a person without evidence of hematologic malignancy. We sought to identify additional cancer-presenting mutations usable for CHIP detection by performing a data mining analysis of 48 somatic mutation landscape studies reporting mutations at diagnoses of 7,430 adult and pediatric patients with leukemia or other hematologic malignancy. Following extraction of 20,141 protein-altering mutations, we identified 434 significantly recurrent mutation hotspots, 364 of which occurred at loci confidently assessable for CHIP. We then performed an additional large-scale analysis of whole-exome sequencing data from 4,538 persons belonging to three noncancer cohorts for clonal mutations. We found the combined cohort prevalence of CHIP with mutations identical to those reported at blood cancer mutation hotspots to be 1.8%, and that some of these CHIP mutations occurred in children. Our findings may help to improve CHIP detection and precancer surveillance for both children and adults. Significance: This study identifies frequently occurring mutations across several blood cancers that may drive hematologic malignancies and signal increased risk for cancer when detected in healthy persons. We find clonal mutations at these hotspots in a substantial number of individuals from noncancer cohorts, including children, showcasing potential for improved precancer surveillance. See related commentary by Spitzer and Levine, p. 192.

Pancreatic ductal adenocarcinoma (PDA) was responsible for ~ 44,000 deaths in the United States in 2018 and is the epitome of a recalcitrant cancer driven by a pharmacologically intractable oncoprotein, KRAS1–4. Downstream of KRAS, the RAF→MEK→ERK signaling pathway plays a central role in pancreatic carcinogenesis5. However, paradoxically, inhibition of this pathway has provided no clinical benefit to patients with PDA6. Here we show that inhibition of KRAS→RAF→MEK→ERK signaling elicits autophagy, a process of cellular recycling that protects PDA cells from the cytotoxic effects of KRAS pathway inhibition. Mechanistically, inhibition of MEK1/2 leads to activation of the LKB1→AMPK→ULK1 signaling axis, a key regulator of autophagy. Furthermore, combined inhibition of MEK1/2 plus autophagy displays synergistic anti-proliferative effects against PDA cell lines in vitro and promotes regression of xenografted patient-derived PDA tumors in mice. The observed effect of combination trametinib plus chloroquine was not restricted to PDA as other tumors, including patient-derived xenografts (PDX) of NRAS-mutated melanoma and BRAF-mutated colorectal cancer displayed similar responses. Finally, treatment of a patient with PDA with the combination of trametinib plus hydroxychloroquine resulted in a partial, but nonetheless striking disease response. These data suggest that this combination therapy may represent a novel strategy to target RAS-driven cancers. Targeted inhibition of RAF–MEK–ERK signaling induces autophagy through the LKB1–AMPK axis, creating a therapeutic vulnerability that can be exploited for treating patients with pancreatic cancer and potentially other RAS-mutant tumors.

Selection of the treatments most likely to combat a patient’s tumor is a central aim of precision oncology. We are currently developing a functional precision oncology program at the University of Utah and Huntsman Cancer Institute that combines the multi-omic characterization of a patient's tumor with the functional screening of relevant drug candidates in patient-derived organoid tumor models to identify which drugs are uniquely capable of combating the patient’s cancer. Here we present our model-based approach for utilizing the multi-omic tumor data to computationally predict the patient’s response to each drug. In this approach, we first identify the genomic and transcriptomic vulnerabilities of the tumor as genes harboring somatic DNA mutations or copy number changes (from paired tumor/normal WGS/WES DNA sequencing data), as well as genes whose expression levels have been significantly altered in the tumor (based on bulk or single-cell RNA sequencing data). Second, using targeting information from drug-gene interaction databases (DGIdb) we compile a list of genes targeted by each drug relevant in the patient’s treatment. Third, we utilize gene interaction database knowledge (KEGG) to construct a gene interaction graph to link each drug’s gene targets with all of the tumor’s vulnerability genes. We then identify all gene interaction paths connecting specific drug target genes with specific vulnerability genes; and score each path and combine all path-specific scores for the target-vulnerability gene pair. Subsequently, we further combine all pairwise scores across all drug target genes and all tumor vulnerability genes and determine the statistical significance of the resulting score by sampling the background distribution of such scores across random drugs, target genes, and vulnerability genes. We have applied our algorithm to predict drug response in advanced breast cancer patients as well as using publicly available tumor cell line data (GDSC2). We have found a high level of concordance between our computational prediction and organoid/cell line screening results, clearly separating sensitive and non-sensitive models. Using Bayesian probability, we compare the drug-specific score distributions of sensitive and non-sensitive models and are able to identify sensitive cell lines/patients with high accuracy. By encapsulating available information on direct gene-gene interactions, the drug’s direct gene targets and the collected omic vulnerabilities of the tumor, our model is not only capable of predicting sensitivity to both targeted and chemotherapy agents, but can also provide a mechanistic understanding for the targeting of the tumor. Our approach will be validated and fine-tuned on a large cohort of breast and brain cancer patients as well as patient PDX models, interrogating gene-target interactions to identify novel relevant target genes/pathways. Citation Format: Szabolcs Tarapcsak, Yi Qiao, Xiaomeng Huang, Tony Di Sera, Matthew H. Bailey, Bryan E. Welm, Alana L. Welm, Gabor T. Marth. Model-based cancer therapy selection by linking tumor vulnerabilities to drug mechanism [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 2723.

We present data demonstrating the natural product mimic, zinaamidole A (ZNA), is a modulator of metal ion homeostasis causing cancer-selective cell death by specifically inducing cellular Zn(2+)-uptake in transformed cells. ZNA’s cancer selectivity was evaluated using metastatic, patient-derived breast cancer cells, established human breast cancer cell lines, and three-dimensional organoid models derived from normal and transformed mouse mammary glands. Structural analysis of ZNA demonstrated that the compound interacts with zinc through the N(2)-acyl-2-aminoimidazole core. Combination treatment with ZnSO(4) strongly potentiated ZNA’s cancer-specific cell death mechanism, an effect that was not observed with other transition metals. We show that Zn(2+)-dyshomeostasis induced by ZNA is unique and markedly more selective than other known Zn(2+)interacting compounds such as clioquinol. The in vivo bioactivity of ZNA was also assessed and revealed that tumor-bearing mice treated with ZNA had improved survival outcomes. Collectively, these data demonstrate that the N(2)-acyl-2aminoimidazole core of ZNA represents a powerful chemotype to induce cell death in cancer cells concurrently with a disruption in zinc homeostasis.

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

Background Protein synthesis is regulated by the availability of amino acids, the engagement of growth factor signaling pathways, and adenosine triphosphate (ATP) levels sufficient to support translation. Crosstalk between these inputs is extensive, yet other regulatory mechanisms remain to be characterized. For example, the translation initiation inhibitor rocaglamide A (RocA) induces thioredoxin-interacting protein (TXNIP). TXNIP is a negative regulator of glucose uptake; thus, its induction by RocA links translation to the availability of glucose. MondoA is the principal regulator of glucose-induced transcription, and its activity is triggered by the glycolytic intermediate, glucose 6-phosphate (G6P). MondoA responds to G6P generated by cytoplasmic glucose and mitochondrial ATP (mtATP), suggesting a critical role in the cellular response to these energy sources. TXNIP expression is entirely dependent on MondoA; therefore, we investigated how protein synthesis inhibitors impact its transcriptional activity. Methods We investigated how translation regulates MondoA activity using cell line models and loss-of-function approaches. We examined how protein synthesis inhibitors effect gene expression and metabolism using RNA-sequencing and metabolomics, respectively. The biological impact of RocA was evaluated using cell lines and patient-derived xenograft organoid (PDxO) models. Results We discovered that multiple protein synthesis inhibitors, including RocA, increase TXNIP expression in a manner that depends on MondoA, a functional electron transport chain and mtATP synthesis. Furthermore, RocA and cycloheximide increase mtATP and G6P levels, respectively, and TXNIP induction depends on interactions between the voltage-dependent anion channel (VDAC) and hexokinase (HK), which generates G6P. RocA treatment impacts the regulation of ~ 1200 genes, and ~ 250 of those genes are MondoA-dependent. RocA treatment is cytotoxic to triple negative breast cancer (TNBC) cell lines and shows preferential cytotoxicity against estrogen receptor negative (ER−) PDxO breast cancer models. Finally, RocA-driven cytotoxicity is partially dependent on MondoA or TXNIP. Conclusions Our data suggest that protein synthesis inhibitors rewire metabolism, resulting in an increase in mtATP and G6P, the latter driving MondoA-dependent transcriptional activity. Further, MondoA is a critical component of the cellular transcriptional response to RocA. Our functional assays suggest that RocA or similar translation inhibitors may show efficacy against ER− breast tumors and that the levels of MondoA and TXNIP should be considered when exploring these potential treatment options.

Recurrence of metastatic breast cancer stemming from acquired endocrine and chemotherapy resistance remains a health burden for women with luminal (ER+) breast cancer. Disseminated ER+ tumor cells can remain viable but quiescent for years to decades. Contributing factors to metastatic spread include the maintenance and expansion of breast cancer stem cells (CSCs). Breast CSCs are poorly proliferative and frequently exist as a minority population in therapy resistant tumors. Our objective is to define novel signaling pathways that govern therapy resistance in ER+ breast cancer. In this study, we show that cytoplasmic complexes composed of steroid receptor (SR) co-activators, PELP1 and SRC-3, modulate breast CSC expansion through upregulation of the HIF-activated metabolic target genes PFKFB3 and PFKFB4. Seahorse metabolic assays demonstrated that cytoplasmic PELP1 influences cellular metabolism by increasing both glycolysis and mitochondrial respiration. PELP1 interacts with PFKFB3 and PFKFB4 proteins, and inhibition of PFKFB3 and PFKFB4 kinase activity blocks PELP1-induced tumorspheres and protein-protein interactions with SRC-3. PFKFB4 knockdown inhibited in vivo emergence of circulating tumor cell (CTC) populations in ER+ mammary intraductal (MIND) xenografts. Application of PFKFB inhibitors in combination with ER targeted therapies blocked tumorsphere formation in multiple models of advanced breast cancer, including tamoxifen (TamR) and paclitaxel (TaxR) resistant models and ER+ patient-derived organoids (PDxO). Together, our data suggest that PELP1, SRC-3, and PFKFBs cooperate to drive ER+ tumor cells that include CSCs and CTCs. Identifying non-ER pharmacological targets offers a useful approach to blocking metastatic escape from standard of care ER/estrogen (E2)-targeted strategies to overcome endocrine and chemotherapy resistance in ER+ breast cancer.

Patient-Derived Xenografts (PDXs) are tumor-in-mouse models for cancer. PDX collections, such as those supported by the NCI PDXNet program, are powerful resources for preclinical therapeutic testing. However, variations in experimental design and analysis procedures have limited interpretability. To determine the robustness of PDX studies, the PDXNet tested temozolomide drug response for three pre-validated PDX models (sensitive, resistant, and intermediate) across four blinded PDX Development and Trial Centers (PDTCs) using independently selected SOPs. Each PDTC was able to correctly identify the sensitive, resistant, and intermediate models, and statistical evaluations were concordant across all groups. We also developed and benchmarked optimized PDX informatics pipelines, and these yielded robust assessments across xenograft biological replicates. These studies show that PDX drug responses and sequence results are reproducible across diverse experimental protocols. Here we share the range of experimental procedures that maintained robustness, as well as standardized cloud-based workflows for PDX exome-seq and RNA-Seq analysis and for evaluating growth.

Background: Breast tumor subtyping has failed to provide impact in susceptibility genetics. The PAM50 assay categorizes breast tumors into: Luminal A, Luminal B, HER2-enriched and Basal-like. However, tumors are often more complex than simple categorization can describe. The identification of heritable tumor characteristics has potential to decrease heterogeneity and increase power for gene finding. Methods: We used 911 sporadic breast tumors with PAM50 expression data to derive tumor dimensions using principal components (PC). Dimensions in 238 tumors from high-risk pedigrees were compared with the sporadic tumors. Proof-of-concept gene mapping, informed by tumor dimension, was performed using Shared Genomic Segment (SGS) analysis. Results: Five dimensions (PC1-5) explained the majority of the PAM50 expression variance: three captured intrinsic subtype, two were novel (PC3, PC5). All five replicated in 745 TCGA tumors. Both novel dimensions were significantly enriched in the high-risk pedigrees (intrinsic subtypes were not). SGS gene-mapping in a pedigree identified a 0.5 Mb genome-wide significant region at 12q15. This region segregated through 32 meioses to 8 breast cancer cases with extreme PC3 tumors (P = 2.6 × 10−8). Conclusions: PC analysis of PAM50 gene expression revealed multiple independent, quantitative measures of tumor diversity. These tumor dimensions show evidence for heritability and potential as powerful traits for gene mapping. Impact: Our study suggests a new approach to describe tumor expression diversity, provides new avenues for germline studies, and proposes a new breast cancer locus. Similar reparameterization of expression patterns may inform other studies attempting to model the effects of tumor heterogeneity. Cancer Epidemiol Biomarkers Prev; 27(6); 644–52. ©2018 AACR.

Steroid hormone receptors are simultaneously active in many tissues and are capable of altering each others molecular and biological roles. Estrogen receptor α (ER) and glucocorticoid receptor(GR) are co-expressed in the uterus and their ligands have opposing effects on uterine growth and morphology, with estrogens inducing growth and corticosteroids inhibiting growth. In endometrioid-type endometrial tumors expressing ER, we surprisingly found that the expression of GR is associated with poor prognosis and higher tumor grade. Dexamethasone reduced normal uterine growth in vivo; however, this growth inhibition was abolished once estrogen-induced endometrial hyperplasia was established and cancer cells increased growth when treated with dexamethasone and estradiol. To understand the underlying molecular crosstalk between ER and GR in endometrial cancer cells, we measured their genomic binding following treatment with estradiol, dexamethasone, and the combination. We found 20% overlap in bound loci when ER and GR are induced with their respective ligands individually; however, upon induction with both ligands simultaneously they co-occupy significantly more sites (47%). ER binding is only slightly affected by the presence of dexamethasone, while GR binding is significantly altered by the presence of estradiol with GR being recruited to loci bound by ER that contain estrogen response elements. The GR sites gained in the presence of estradiol consist of loci that become more accessible upon estradiol induction, supporting a model where ER assists in the chromatin loading of GR. The crosstalk in genomic binding is associated with gene expression changes upon co-treatment being more similar to estradiol than dexamethasone, but with novel regulated genes including a decrease in cell-cell adherens junction genes. Our results suggest both a phenotypic and molecular interplay between ER and GR in endometrial cancer.

Recurrence of metastatic breast cancer stemming from acquired endocrine and chemotherapy resistance remains a health burden for women with luminal (ER+) breast cancer. Disseminated ER+ tumor cells can remain viable but quiescent for years to decades. Contributing factors to metastatic spread include the maintenance and expansion of breast cancer stem cells (CSCs). Breast CSCs are poorly proliferative and frequently exist as a minority population in therapy resistant tumors. Our objective is to define novel signaling pathways that govern therapy resistance in ER+ breast cancer. In this study, we show that cytoplasmic complexes composed of steroid receptor (SR) co-activators, PELP1 and SRC-3, modulate breast CSC expansion through upregulation of the HIF-activated metabolic target genes PFKFB3 and PFKFB4. Seahorse metabolic assays demonstrated that cytoplasmic PELP1 influences cellular metabolism by increasing both glycolysis and mitochondrial respiration. PELP1 interacts with PFKFB3 and PFKFB4 proteins, and inhibition of PFKFB3 and PFKFB4 kinase activity blocks PELP1-induced tumorspheres and protein-protein interactions with SRC-3. PFKFB4 knockdown inhibited in vivo emergence of circulating tumor cell (CTC) populations in ER+ mammary intraductal (MIND) xenografts. Application of PFKFB inhibitors in combination with ER targeted therapies blocked tumorsphere formation in multiple models of advanced breast cancer, including tamoxifen (TamR) and paclitaxel (TaxR) resistant models and ER+ patient-derived organoids (PDxO). Together, our data suggest that PELP1, SRC-3, and PFKFBs cooperate to drive ER+ tumor cells that include CSCs and CTCs. Identifying non-ER pharmacological targets offers a useful approach to blocking metastatic escape from standard of care ER/estrogen (E2)-targeted strategies to overcome endocrine and chemotherapy resistance in ER+ breast cancer. Citation Format: Thu Ha Truong, Elizabeth Benner, Kyla M. Hagen, Nuri A. Temiz, Carlos Perez Kerkvliet, Ying Wang, Emilio Cortes-Sanchez, Chieh-Hsiang Yang, Thomas Pengo, Katrin P. Guillen, Bryan E. Welm, Sucheta Telang, Carol A. Lange, Julie H. Ostrander. Steroid receptor co-activators regulate metabolic kinases to drive therapy resistant ER+ breast cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 3085.

Introduction: The ability for oncologists to predict a cancer's response to therapy is limited to a few biomarkers used for histologic diagnosis and targeted therapy. Often, in advanced and metastatic disease these biomarkers provide no alternative options for next step systemic treatments. There is a need in oncology for functional assays that can determine a tumor's response to a drug, regardless of its tissue of origin, previous treatments, or mutation status. Quantitative phase imaging (QPI) can measure changes in single cell mass in response to drug treatment in vitro and ex vivo. This platform offers advantages over other functional/metabolic assays in that it monitors changes in real-time and on a single cell basis, revealing heterogeneity in drug response. Methods: Here, we describe the validation of QPI for the measurement of breast cancer cell response to therapy versus CellTiterGlo (CTG), an endpoint ATP assay. We ran a series of 3-day drug response assays using QPI alongside CTG. We used a 96 well plate with a 6-point dose response between 1.6 nM and 20 μM for multiple cell lines spanning a range of receptor statuses (MCF7, MDA-MB-231, BT-474) with two controls in triplicate. We analyzed single-cell data to measure the heterogeneity of response and assessed how cell-to-cell heterogeneity is affected by dose. Our response data were fitted to a four-parameter Hill equation to compute the IC50 and depth of response. Results: We found that QPI can determine IC50s for effective treatments as validated by concordance to CTG. As measured by QPI, doxorubicin has a substantial depth of response, indicating cytotoxic effects. Doxorubicin data also show a tighter range of growth rates at high concentration than control, which implies low heterogeneity of response. As expected, ER positive MCF7 cells responded to hydroxy-tamoxifen. This response shows a similar reduction in heterogeneity to doxorubicin but with a reduced depth of response indicating a cytostatic effect. MDA-MB-231 response to palbociclib exhibits a wide range of growth rates, indicating an increase in heterogeneity as measured by QPI. Fluorouracil response shows no significant difference in heterogeneity from control. Conclusion: In summary, QPI is a useful tool for functional assays that can capture IC50, depth of response, and single-cell heterogeneity of response. In particular, this additional information about single-cell behavior and heterogeneity cannot be measured using a typical endpoint assay. Future work is needed to prove the clinical utility of functional assays from QPI. Citation Format: Tarek E. Moustafa, Edward R. Polanco, Andrew Butterfield, Sandra D. Scherer, Bryan E. Welm, Philip S. Bernard, Thomas A. Zangle. Real-time single-cell drug response assay in metastatic breast cancer cell lines using quantitative phase imaging [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 1301.

Breast cancers can be divided into subtypes with different prognoses and treatment responses based on global gene expression differences. Luminal breast cancer gene expression and proliferation are driven by the transcription factors Estrogen Receptor α (ER), FOXA1 and GATA3. Targeting ER is the most effective therapy for treating luminal breast cancer because ER is the master regulator of the luminal gene expression program. In contrast, it is unclear which transcription factors are responsible for driving the gene expression signature that defines basal-like triple negative breast cancer, and there are no targeted therapies approved to treat this aggressive subtype of the disease. This study utilized integrated analysis of DNA methylation, chromatin accessibility, transcription factor binding, and gene expression in large collections of breast cancer cell lines and patient tumors to identify transcription factors responsible for the basal-like gene expression program. The results of this study indicate that glucocorticoid receptor (GR) and signal transducer and activator of transcription 3 (STAT3) bind to the same genomic regulatory regions that are specifically open and unmethylated in basal-like breast cancer. These transcription factors cooperate to regulate expression of hundreds of genes in the basal-like gene expression signature and these downstream genes are associated with poor prognosis in patients. Furthermore, combination treatment with small molecule drugs that inhibit both transcription factors leads to synergistic decreases in cell proliferation in cell lines and patient-derived organoid models. This study demonstrates that GR and STAT3 cooperate to regulate the basal-like breast cancer gene expression program and provides the basis for improved therapy for basal-like triple negative breast cancer through rational combination of STAT3 and GR inhibitors.

Estrogen signaling through estrogen receptor alpha (ER) plays a major role in endometrial cancer risk and progression; however, the molecular mechanisms underlying ER’s regulatory role in endometrial cancer are poorly understood. In breast cancer cells, ER genomic binding is enabled by FOXA1 and GATA3, but the transcription factors that control ER genomic binding in endometrial cancer cells remain unknown. We previously identified ETV4 as a candidate factor controlling ER genomic binding in endometrial cancer cells and here we explore the functional importance of ETV4. Homozygous deletion of ETV4, using CRISPR/Cas9, led to greatly reduced ER binding at the majority of loci normally bound by ER. Consistent with the dramatic loss of ER binding, the gene expression response to estradiol was dampened for most genes. ETV4 contributes to estrogen signaling in two distinct ways; ETV4 loss impacts chromatin accessibility at some ER bound loci and impairs ER nuclear translocation. The diminished estrogen signaling upon ETV4 deletion led to decreased growth, particularly in 3D culture where hollow organoids were formed and in vivo in the context of estrogen dependent growth. Our results show that ETV4 plays an important role in estrogen signaling in endometrial cancer cells.

Human cancer is a complex and heterogeneous collection of diseases that kills more than 18 million people every year worldwide. Despite advances in detection, diagnosis, and treatments for cancers, new strategies are needed to combat deadly cancers. Models of human cancer continue to evolve for preclinical research and have culminated in patient-derived systems that better represent the diversity and complexity of cancer. Still, no model is perfect. This Perspective attempts to address ways that we can improve the clinical translatability of models used for cancer research, from the point of view of researchers who mainly conduct cancer studies in vivo.

Patient-derived xenografts (PDXs) are resected human tumors engrafted into mice for preclinical studies and therapeutic testing. It has been proposed that the mouse host affects tumor evolution during PDX engraftment and propagation, affecting the accuracy of PDX modeling of human cancer. Here, we exhaustively analyze copy number alterations (CNAs) in 1,451 PDX and matched patient tumor (PT) samples from 509 PDX models. CNA inferences based on DNA sequencing and microarray data displayed substantially higher resolution and dynamic range than gene expression-based inferences, and they also showed strong CNA conservation from PTs through late-passage PDXs. CNA recurrence analysis of 130 colorectal and breast PT/PDX-early/PDX-late trios confirmed high-resolution CNA retention. We observed no significant enrichment of cancer-related genes in PDX-specific CNAs across models. Moreover, CNA differences between patient and PDX tumors were comparable to variations in multiregion samples within patients. Our study demonstrates the lack of systematic copy number evolution driven by the PDX mouse host.

Background: Predicting the risk of relapse in patients with non-metastatic breast cancer is important for medical decisions and patient counseling. For patients who receive neoadjuvant chemotherapy, pathologic response and especially pathologic complete response (pCR) has been associated with risk of relapse; however this association is imperfect. Our prior work in patient-derived xenograft (PDX) models indicated that tumor engraftment in mice correlated with risk of recurrence. To understand further the prognostic utility of PDX, we designed a prospective clinical trial to determine the correlation between PDX generation with residual disease following neoadjuvant chemotherapy and relapse. Preliminary data are presented. Methods: Women with newly diagnosed non-metastatic hormone receptor low-positive (HR-low, ER and/or PR ≤ 10%) or negative breast cancer planned to receive systemic chemotherapy prior to definitive surgery were eligible. Tumor tissue at diagnosis was orthotopically implanted in NOD/SCID mice. The primary objective of the study is to correlate the ability of a tumor to engraft in mice with pathologic responses and clinical outcomes. Results: Between 12/2016 and 2/2020, 58 patients enrolled (triple negative breast cancer (TNBC), n=37; HR-low or negative/Her2+, n=11; or HR-low/Her2-, n=8; mixed, n=1). PDXs were successfully established from 16 patients. Patients uniformly received intensive preoperative chemotherapy per standard of care. Among the 12 patients whose tumors engrafted in mice (PDX(+)) and underwent surgery, the pCR rate was 41.7%. In the subgroup of patients with postoperative follow up >6 months (n=9), 3 patients had achieved a pCR, 1 of whom recurred; and 6 patients had residual disease, 3 of whom recurred (overall relapse rate 44.4%). All patients who relapsed (TNBC n=3; HR-low/Her2- n=1), experienced a very early relapse ( Among the 38 patients whose tumors did not engraft (PDX(-)) and underwent surgery, the overall pCR rate was 60.6%. In the subgroup of patients with postoperative follow up >6 months (n=30), 18 patients had achieved a pCR of whom none recurred, and 12 had residual disease of whom 1 patient (with TNBC) had locoregional recurrence 35.1 months after her definitive surgery (relapse rate 3.3%). She underwent repeat surgery followed by radiation therapy and 5.5 months after her recurrence, she remains disease-free. Achievement of pCR was not statistically different between the PDX(+) and PDX(-) group. In the entire cohort, the presence of residual disease was associated with high risk of relapse (22.2%) as compared to the achievement of pCR (4.8%, p = 0.16). However, successful tumor engraftment was associated not only with a higher risk of relapse (44.4% vs 3.3%, p = 0.0068, odds ratio 20.45), but also an early and exceptionally aggressive relapse. Conclusion. Our functional studies not only identify a patient population with a high risk of relapse with greater precision than the achievement of pCR, but also identify patients whose relapse has a particularly aggressive natural history. We established models that recapitulate this aggressive disease and in-depth genomic studies are underway. These studies will lead us to better patient stratification on the basis of risk of relapse and novel therapeutic strategies focused on patients with exceptionally aggressive breast cancers that our current diagnostic methods cannot identify. Citation Format: Christos Vaklavas, Zhengtao Chu, Rachel E Factor, Alana L Welm, Bryan E Welm, Cindy B Matsen. Towards personalized medicine - patient-derived breast tumor grafts as predictors of relapse and response to therapy. Preliminary results [abstract]. In: Proceedings of the 2020 San Antonio Breast Cancer Virtual Symposium; 2020 Dec 8-11; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2021;81(4 Suppl):Abstract nr PD7-01.

Although the RAF>MEK>ERK MAP kinase pathway is key to the development of many RAS-mutated cancers, efforts to target this pathway with pharmacologic inhibitors have failed to deliver clinical benefit to patients. Here we show that inhibition of RAS>RAF>MEK>ERK signaling in RAS-mutated cancer cell lines elicits autophagy, a process of cellular recycling that protects cancer cells from the potentially cytotoxic effects of RAF>MEK>ERK pathway inhibition. Furthermore, combined inhibition of MEK1/2 plus autophagy displays synergistic antiproliferative effects against cell lines in vitro and promotes regression of xenografted patient-derived tumors in mice. Finally, treatment of a cancer patient with the combination of trametinib plus hydroxychloroquine resulted in a partial, but nonetheless striking, disease response. These data suggest that this combination therapy may represent a novel strategy to target RAS-driven malignancies such as melanoma, lung, and pancreatic cancer. Citation Format: Conan Kinsey, Soledad Camolotto, Amelie Boespflug, Katrin Gullien, Amanda Truong, Mona Foth, Jill Shea, Michael Seipp, Jeffrey Yap, Lance Burrell, David Lum, Jonathan Whisenant, Courtney Cavalier, Kaitren Rehbein, Stephanie Cutler, Kajsa Afotler, Alana Welm, Bryan Welm, Courtney Scaife, Eric Snyder, Martin McMahon. Targeting MEK1/2 inhibitor resistance in RAS-mutated cancers [abstract]. In: Proceedings of the AACR Special Conference on Melanoma: From Biology to Target; 2019 Jan 15-18; Houston, TX. Philadelphia (PA): AACR; Cancer Res 2020;80(19 Suppl):Abstract nr IA25.

RASis mutated in a number of cancers, including KRAS-driven colorectal and pancreatic cancer, HRAS-driven bladder cancer, and NRAS-driven melanoma, all of which result in downstream activation of the RAF>MEK>ERK (MAPK) and PI3K>AKT signaling pathways. Most RAS GTPases cannot be targeted directly, and strategies blocking both MAPK and PI3K signaling simultaneously are limited by high toxicity and compensatory signaling mechanisms. Interestingly, oncogene-activated MAPK or PI3K signaling pathways are reasonably well described orchestrators of metabolic transformation through multiple pathways. Our lab has recently shown that autophagy,a conserved metabolic process of self-digestion that recycles intracellular components, is increased in KRAS-driven pancreatic ductal adenocarcinoma (PDA) upon MAPK pathway inhibition. We further showed that co-inhibition of autophagy and MEK1/2, a MAPK component, leads to tumor regression of patient-derived PDA xenografts in mice. Our proposed combination therapy has recently been translated into a phase I/II clinical trial for PDA patients with advanced disease. Our most recent data show that inhibition of RAS>RAF>MEK>ERK signaling also results in induction of autophagy in other RAS-driven cancers, including KRAS-driven colorectal cancer, HRAS-driven bladder cancer and NRAS-driven melanoma. Furthermore, ourin vivodata demonstrate a robust regression of tumors upon combined inhibition of autophagy and MEK1/2 in engrafted KRAS-driven colorectal cancer cells xenografted in mice. Patient data from two KRAS-driven colorectal cancer patients, who were recently treated off-label, indicated clinical responses to the combination treatment co-targeting autophagy and MEK1/2. Altogether, these data suggest that co-inhibition of autophagy and oncogenic signaling may represent a potential new treatment strategy for multiple RAS-driven cancer types. Future experiments aim for a better mechanistic understanding of the combination treatment co-targeting autophagy and oncogenic signaling, with the objective to propose novel therapeutic strategies for cancer patients with RASmutations. Citation Format: Mona Foth, Conan Kinsey, Sophia Schuman, Benjamin Battistone, Emilio Cortes Sanchez, David Kircher, Bryan Welm, Sheri Holmen, Martin McMahon. Co-inhibition of autophagy and MAPK signaling in RAS-driven cancers [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 1225.

Patient-derived xenografts (PDXs) are resected human tumors engrafted into mice for preclinical studies and therapeutic testing. It has been proposed that the mouse host affects tumor evolution during PDX engraftment and propagation, which could impact the capacity of PDXs for faithful modeling of patient treatment response. Such results contrast with reports that have observed genomic fidelity of PDX models with respect to the originating patient tumors and from early to late passages by direct DNA measurements (DNA sequencing or SNP arrays). Here we resolve these contradicting observations by systematically evaluating CNA changes and the genes they affect during engraftment and passaging in a large, internationally collected set of PDX models, comparing both RNA and DNA-based approaches. The data collected, as part of the U.S. National Cancer Institute (NCI) PDXNet (PDX Development and Trial Centers Research Network) Consortium and EurOPDX consortium, comprises 1548 patient (PT) and PDX datasets (1451 unique samples) from 509 models derived from American, European and Asian cancer patients, spanning across 16 tumor types. By assessing copy number changes by pairwise (PT-PDX or PDX-PDX) correlation and residual analysis to control for systematic biases, our study demonstrates that prior reports of systematic copy number divergence between PTs and PDXs are incorrect, and confirms the high retention of copy number during PDX engraftment and passaging. Moreover, only a small proportion of models show large CNA discordance between the samples, suggesting that the variations observed in PDX are mainly due to rare clonal selection of individual tumors rather than murine pressures. This large scale data analysis also reveals several other findings that clarify the evolutionary process in PDXs. We do observe larger deviations between PT-PDX than in PDX-PDX comparisons, likely due to dilution of PT signal by human stromal cells. Interestingly, we found that a major contributor to the differences between PDX samples is lineage-specific drift associated with splitting of tumors into fragments during PDX propagation. We observed no significant enrichment of cancer-related genes in PDX-specific CNAs across models, suggesting the lack of systematic copy number evolution driven by the PDX mouse host. Moreover, CNA differences between patient and PDX tumors were comparable to variations in multi-region tumor samples or intra-patient samples. Thus concerns about the genetic stability of the PDX system are likely to be less important than the spatial heterogeneity of solid tumors themselves. This result is consistent with our results on lineage effects during passaging, which indicate that intratumoral spatial evolution is the major reason for genetic drift. Our in-depth tracking of CNAs throughout PDX engraftment and passaging confirms that tumors engrafted and passaged in PDX models maintain a high degree of molecular fidelity to the original patient tumors and their suitability for pre-clinical drug testing. This work also finely enumerates the copy number profiles in hundreds of publicly available models, which will enable researchers to assess the suitability of each for individualized treatment studies. Citation Format: Xing Yi Woo, Jessica Giordano, Anuj Srivastava, Zi-Ming Zhao, Michael W. Lloyd, Roebi de Bruijn, Yun-Suhk Suh, Jong-Il Kim, Han-Kwang Yang, Charles Lee, Dennis A. Dean, Brandi Davis-Dusenbery, Yvonne A. Evrard, James H. Doroshow, Alana L. Welm, Bryan Welm, Michael T. Lewis, Bingliang Fang, Jack A. Roth, Funda Meric-Bernstam, Meenhard Herlyn, Michael Davies, Li Ding, Shunqiang Li, Ramaswamy Govindan, Claudio Isella, Jeffrey A. Moscow, Livio Trusolino, Annette Byrne, Jos Jonkers, Carol J. Bult, Enzo Medico, Jeffrey H. Chuang, PDXNET consortium & EurOPDX consortium. Conservation of copy number profiles during engraftment and passaging of patient-derived cancer xenografts [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 1673.

Patient-Derived Xenografts (PDXs) are preclinical models largely used to study tumor biology and drug response. Recent literature highlighted the possibility that growth of human tumors in a mouse microenvironment imposes a selection driving mouse-specific genetic evolution of PDXs, which may compromise their reliability as human cancer models. Conversely, independent studies observed a conservation of the genomic landscape during PDX engraftment and passaging. We noticed that PDX genetic evolution was particularly evident in studies based on copy number aberration (CNA) inferred from gene expression data, while it was negligible when DNA-based CNA profiles were employed. Therefore, in a joint international effort of the EurOPDX and PDXNet consortia, we assembled a dataset of 37 hepatocellular and 54 gastric carcinoma tumor or PDX samples with matched RNA-based and DNA-based CNA profiles. We found that DNA-based CNA profiles invariably yield higher concordance between patient's tumor and derived PDXs than those inferred from RNA. RNA-based profiles displayed poor concordance with matched DNA-based profiles, and much lower resolution, so that they missed many focal copy number events detected by DNA-based methods. These results revealed that CNA measurements cannot be accurately estimated by expression data and that a systematic reassessment of CNA dynamics in PDXs based on DNA data is required. To this aim, we generated CNA profiles by low-pass whole genome sequencing (WGS) of 87 colorectal and 43 breast cancer triplets, each composed of matched patient's tumor (PT) and PDX at early (PDX-early) and later (PDX-late) passage. In this way, for each tumor type, we generated three perfectly matched PT, PDX-early and PDX-late cohorts and performed CNA recurrence analysis by GISTIC in each cohort. The hypothesis was that if the mouse host induces a selective pressure capable of shaping the CNA landscape during PDX engraftment and propagation, GISTIC analysis would highlight systematic and progressive changes, from the PT to the PDX-early cohort, and then to the PDX-late cohort. Notably instead, the CNA profiles of the PT and PDX-early/late cohorts were virtually indistinguishable, with no progressive accumulation or loss of CNA during PDX passage and only minor changes not functionally related or associated to cancer-driver or actionable genes. These results were not consequence of insufficient capture of the CNA repertoire, since the GISTIC profiles recapitulated those generated by TCGA for colorectal and breast cancer. In summary, our analyses highlighted that while RNA-based CNA inferences have inadequate resolution and accuracy to study genomic evolution in PDXs, DNA-based CNA profiles confirm retention of CNAs in PTs and PDXs, excluding a systematic mouse driven selection via copy number changes. Ultimately, these results support the robustness of PDXs as preclinical models for predicting drug response. Citation Format: Jessica Giordano, Xing Yi Woo, Anuj Srivastava, Zi-Ming Zhao, Michael W. Lloyd, Roebi de Bruijn, Yun-Suhk Suh, Francesco Galimi, Andrea Bertotti, Adam Lafferty, Alice C. O'Farrell, Elodie Modave, Diether Lambrechts, Petra ter Brugge, Violeta Serra, Elisabetta Marangoni, Rania El Botty, Jong-Il Kim, Han-Kwang Yang, Charles Lee, Dennis A. Dean, Brandi Davis-Dusenbery, Yvonne A. Evrard, James H. Doroshow, Alana L. Welm, Bryan E. Welm, Michael T. Lewis, Bingliang Fang, Jack Roth, Funda Meric-Bernstam, Meenhard Herlyn, Michael Davies, Li Ding, Shunqiang Li, Ramaswamy Govindan, Jeffrey A. Moscow, Carol J. Bult, Claudio Isella, Livio Trusolino, Annette T. Byrne, Jos Jonkers, Jeffrey H. Chuang, Enzo Medico, EurOPDX consortium & PDXNET consortium. Absence of mouse-specific tumor evolution in patient-derived cancer xenografts [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 1118.

Pancreatic ductal adenocarcinoma was responsible for ~43,000 deaths in the USA in 2017 and is the epitome of a recalcitrant cancer driven by a pharmacologically intractable oncoprotein, KRAS. Although the clinical picture remains grim, the mechanisms by which key alterations in tumor suppressors and proto-oncogenes contribute to PDA have been dissected. Downstream of KRAS, the RAF→MEK→ERK signaling pathway plays a central role in pancreatic carcinogenesis. However, to date, pharmacologic inhibition of this pathway has provided no clinical benefit to PDA patients. Here we show that inhibition of KRAS→RAF→MEK→ERK signaling in PDA cell lines elicits autophagy, a process of cellular recycling that protects pancreatic cancer cells from the potentially cytotoxic effects of KRAS pathway inhibition. Furthermore, combined inhibition of MEK1/2 plus autophagy displays synergistic antiproliferative effects against PDA cell lines in vitro, and promotes regression of xenografted patient-derived PDA tumors in mice. Finally, treatment of a KRAS-mutated PDA patient on a compassionate basis with the combination of trametinib plus hydroxychloroquine resulted in a partial but nonetheless striking disease response. These data suggest that this combination therapy may represent a new strategy to target RAS-driven cancers such as PDA. Citation Format: Conan G. Kinsey, Soledad A. Camolotto, Amelie M. Boespflug, Katrin P. Gullien, Mona Foth, Jill E. Shea, Michael T. Seipp, Jeffrey T. Yap, Lance D. Burrell, David H. Lum, Jonathan R. Whisenant, Weldon Gilcrease, Courtney C. Cavalieri, Kaitrin M. Rehbein, Stephanie L. Cutler, Kajsa E. Affolter, Alana L. Welm, Bryan E. Welm, Courtney L. Scaife, Eric L. Snyder, Martin McMahon. Protective autophagy elicited by RAF→MEK→ERK inhibition suggests a treatment strategy for RAS-driven cancers [abstract]. In: Proceedings of the AACR Special Conference on Targeting RAS-Driven Cancers; 2018 Dec 9-12; San Diego, CA. Philadelphia (PA): AACR; Mol Cancer Res 2020;18(5_Suppl):Abstract nr A45.

BACKGROUND: Breast tumor subtyping has failed to provide impact in susceptibility genetics. The PAM50 assay categorizes breast tumors into: Luminal A, Luminal B, HER2-enriched and Basal-like. However, tumors are often more complex than simple categorization can describe. The identification of heritable tumor characteristics has potential to decrease heterogeneity and increase power for gene-finding. METHODS: We used 911 sporadic breast tumors with PAM50 expression data to derive tumor dimensions using principal components (PC). Dimensions in 238 tumors from high-risk pedigrees were compared to the sporadic tumors. Proof-of-concept gene mapping, informed by tumor dimension, was performed using Shared Genomic Segment (SGS) analysis. RESULTS: Five dimensions (PC1-5) explained the majority of the PAM50 expression variance: three captured intrinsic subtype, two were novel (PC3, PC5). All five replicated in 745 TCGA tumors. Both novel dimensions were significantly enriched in the high-risk pedigrees (intrinsic subtypes were not). SGS gene-mapping in a pedigree identified a 0.5 Mb genomewide significant region at 12q15. This region segregated through 32 meioses to 8 breast cancer cases with extreme PC3 tumors (p=2.6×10(−8)). CONCLUSIONS: Principal component analysis of PAM50 gene expression revealed multiple independent, quantitative measures of tumor diversity. These tumor dimensions show evidence for heritability and potential as powerful traits for gene-mapping. IMPACT: Our study suggests a new approach to describe tumor expression diversity, provides new avenues for germline studies, and proposes a new breast cancer locus. Similar reparameterization of expression patterns may inform other studies attempting to model the effects of tumor heterogeneity.

It is well-known that breast tumors exhibit different expression patterns that can be used to assign intrinsic subtypes – the PAM50 assay, for example, categorizes tumors into: Luminal A, Luminal B, HER2-enriched and Basal-like – yet tumors are often more complex than categorization can describe. We used 911 sporadic breast tumors to reparameterize expression from the PAM50 genes to five orthogonal tumor dimensions using principal components (PC). Three dimensions captured intrinsic subtype, two dimensions were novel, and all replicated in 945 TCGA tumors. By definition dimensions are independent, an important attribute for inclusion in downstream studies exploring effects of tumor diversity. One application where tumor subtyping has failed to provide impact is susceptibility genetics. Germline genetic heterogeneity reduces power for gene-finding. The identification of heritable tumor characteristics has potential to increase homogeneity. We compared 238 breast tumors from high-risk pedigrees not attributable to BRCA1 or BRCA2 to 911 sporadic breast tumors. Two PC dimensions were significantly enriched in the pedigrees (intrinsic subtypes were not). We performed proof-of-concept gene-mapping in one enriched pedigree and identified a 0.5 Mb genomewide significant region at 12q15 that segregated to the 8 breast cancer cases with the most extreme PC tumors through 32 meioses (p=2.6×10−8). In conclusion, our study: suggests a new approach to describe tumor diversity; supports the hypothesis that tumor characteristics are heritable providing new avenues for germline studies; and proposes a new breast cancer locus. Reparameterization of expression patterns may similarly inform other studies attempting to model the effects of tumor heterogeneity.

Bis -aryloxadiazoles are common scaffolds in medicinal chemistry due to their wide range of biological activities. Previously, we identified a 1,2,4-bis-aryloxadiazole that blocks mammary branching morphogenesis through activation of the aryl hydrocarbon receptor (AHR). In addition to defects in mammary differentiation, AHR stimulation induces toxicity in many other tissues. We performed a structure activity relationship (SAR) study of 1,2,4-bis-aryloxadiazole to determine which moieties of the molecule are critical for AHR activation. We validated our results with a functional biological assay, using desmosome formation during mammary morphogenesis to indicate AHR activity. These findings will aid the design of oxadiazole derivative therapeutics with reduced off-target toxicity profiles.