Loss of p73 Expression Contributes to Chronic Obstructive Pulmonary Disease.

Rationale: Multiciliated cell (MCC) loss and/or dysfunction is common in the small airways of patients with chronic obstructive pulmonary disease (COPD), but it is unclear if this contributes to COPD lung pathology. Objectives: To determine if loss of p73 causes a COPD-like phenotype in mice and explore whether smoking or COPD impact p73 expression. Methods: p73floxE7-E9 mice were crossed with Shh-Cre mice to generate mice lacking MCCs in the airway epithelium. The resulting p73Δairway mice were analyzed using electron microscopy, flow cytometry, morphometry, forced oscillation technique, and single-cell RNA sequencing. Furthermore, the effects of cigarette smoke on p73 transcript and protein expression were examined using in vitro and in vivo models and in studies including airway epithelium from smokers and patients with COPD. Measurements and Main Results: Loss of functional p73 in the respiratory epithelium resulted in a near-complete absence of MCCs in p73Δairway mice. In adulthood, these mice spontaneously developed neutrophilic inflammation and emphysema-like lung remodeling and had progressive loss of secretory cells. Exposure of normal airway epithelium cells to cigarette smoke rapidly and durably suppressed p73 expression in vitro and in vivo. Furthermore, tumor protein 73 mRNA expression was reduced in the airways of current smokers (n = 82) compared with former smokers (n = 69), and p73-expressing MCCs were reduced in the small airways of patients with COPD (n = 11) compared with control subjects without COPD (n = 12). Conclusions: Loss of functional p73 in murine airway epithelium results in the absence of MCCs and promotes COPD-like lung pathology. In smokers and patients with COPD, loss of p73 may contribute to MCC loss or dysfunction.

Comparison of triple-negative breast cancer molecular subtyping using RNA from matched fresh-frozen versus formalin-fixed paraffin-embedded tissue.

BACKGROUND: Triple negative breast cancer (TNBC) is a heterogeneous disease that lacks unifying molecular alterations that can guide therapy decisions. We previously identified distinct molecular subtypes of TNBC (TNBCtype) using gene expression data generated on a microarray platform using frozen tumor specimens. Tumors and cell lines representing the identified subtypes have distinct enrichment in biologically relevant transcripts with differing sensitivity to standard chemotherapies and targeted agents. Since our initial discoveries, RNA-sequencing (RNA-seq) has evolved as a sensitive and quantitative tool to measure transcript abundance. METHODS: To demonstrate that TNBC subtypes were similar between platforms, we compared gene expression from matched specimens profiled by both microarray and RNA-seq from The Cancer Genome Atlas (TCGA). In the clinical care of patients with TNBC, tumor specimens collected for diagnostic purposes are processed by formalin fixation and paraffin-embedding (FFPE). Thus, for TNBCtype to eventually have broad and practical clinical utility we performed RNA-seq gene expression and molecular classification comparison between fresh-frozen (FF) and FFPE tumor specimens. RESULTS: Analysis of TCGA showed consistent subtype calls between 91% of evaluable samples demonstrating conservation of TNBC subtypes across microarray and RNA-seq platforms. We compared RNA-seq performed on 21-paired FF and FFPE TNBC specimens and evaluated genome alignment, transcript coverage, differential transcript enrichment and concordance of TNBC molecular subtype calls. We demonstrate that subtype accuracy between matched FF and FFPE samples increases with sequencing depth and correlation strength to an individual TNBC subtype. CONCLUSIONS: TNBC subtypes were reliably identified from FFPE samples, with highest accuracy if the samples were less than 4 years old and reproducible subtyping increased with sequencing depth. To reproducibly subtype tumors using gene expression, it is critical to select genes that do not vary due to platform type, tissue processing or RNA isolation method. The majority of differentially expressed transcripts between matched FF and FFPE samples could be attributed to transcripts selected for by RNA enrichment method. While differentially expressed transcripts did not impact TNBC subtyping, they will provide guidance on determining which transcripts to avoid when implementing a gene set size reduction strategy. TRIAL REGISTRATION: NCT00930930 07/01/2009.

Generation of an algorithm based on minimal gene sets to clinically subtype triple negative breast cancer patients.

BACKGROUND: Recently, a gene expression algorithm, TNBCtype, was developed that can divide triple-negative breast cancer (TNBC) into molecularly-defined subtypes. The algorithm has potential to provide predictive value for TNBC subtype-specific response to various treatments. TNBCtype used in a retrospective analysis of neoadjuvant clinical trial data of TNBC patients demonstrated that TNBC subtype and pathological complete response to neoadjuvant chemotherapy were significantly associated. Herein we describe an expression algorithm reduced to 101 genes with the power to subtype TNBC tumors similar to the original 2188-gene expression algorithm and predict patient outcomes. METHODS: The new classification model was built using the same expression data sets used for the original TNBCtype algorithm. Gene set enrichment followed by shrunken centroid analysis were used for feature reduction, then elastic-net regularized linear modeling was used to identify genes for a centroid model classifying all subtypes, comprised of 101 genes. The predictive capability of both this new "lean" algorithm and the original 2188-gene model were applied to an independent clinical trial cohort of 139 TNBC patients treated initially with neoadjuvant doxorubicin/cyclophosphamide and then randomized to receive either paclitaxel or ixabepilone to determine association of pathologic complete response within the subtypes. RESULTS: The new 101-gene expression model reproduced the classification provided by the 2188-gene algorithm and was highly concordant in the same set of seven TNBC cohorts used to generate the TNBCtype algorithm (87%), as well as in the independent clinical trial cohort (88%), when cases with significant correlations to multiple subtypes were excluded. Clinical responses to both neoadjuvant treatment arms, found BL2 to be significantly associated with poor response (Odds Ratio (OR) =0.12, p=0.03 for the 2188-gene model; OR = 0.23, p < 0.03 for the 101-gene model). Additionally, while the BL1 subtype trended towards significance in the 2188-gene model (OR = 1.91, p = 0.14), the 101-gene model demonstrated significant association with improved response in patients with the BL1 subtype (OR = 3.59, p = 0.02). CONCLUSIONS: These results demonstrate that a model using small gene sets can recapitulate the TNBC subtypes identified by the original 2188-gene model and in the case of standard chemotherapy, the ability to predict therapeutic response.

Subtyping of triple-negative breast cancer: implications for therapy.

Triple-negative breast cancer (TNBC) is a heterogeneous disease; gene expression analyses recently identified 6 distinct TNBC subtypes, each of which displays a unique biology. Exploring novel approaches for the treatment of these subtypes is critical, especially because the median survival for women with metastatic TNBC is less than 12 months, and virtually all women with metastatic TNBC ultimately will die of their disease despite systemic therapy. To date, not a single targeted therapy has been approved for the treatment of TNBC, and cytotoxic chemotherapy remains the standard treatment. In this review, the authors discuss recent developments in subtyping TNBC and the current and upcoming therapeutic strategies being explored in an attempt to target TNBC.

Identification and use of biomarkers in treatment strategies for triple-negative breast cancer subtypes.

Triple-negative breast cancer (TNBC) is a heterogeneous disease with distinct molecular subtypes that respond differentially to chemotherapy and targeted agents. The absence of high-frequency molecular alterations and a limited number of known biomarkers have limited the development of therapeutic strategies for the disease. Herein, we summarize the results of the first round of targeted therapy approaches in TNBC and discuss new preclinical strategies. Common themes emerge from the proposed strategies, such as the use of biomarkers to identify tumours with genomic instability, targeting adapted molecular states resulting from tumour suppressor loss, and targeting altered metabolic pathways.

Detection of internal exon deletion with exon Del.

BACKGROUND: Exome sequencing allows researchers to study the human genome in unprecedented detail. Among the many types of variants detectable through exome sequencing, one of the most over looked types of mutation is internal deletion of exons. Internal exon deletions are the absence of consecutive exons in a gene. Such deletions have potentially significant biological meaning, and they are often too short to be considered copy number variation. Therefore, to the need for efficient detection of such deletions using exome sequencing data exists. RESULTS: We present ExonDel, a tool specially designed to detect homozygous exon deletions efficiently. We tested ExonDel on exome sequencing data generated from 16 breast cancer cell lines and identified both novel and known IEDs. Subsequently, we verified our findings using RNAseq and PCR technologies. Further comparisons with multiple sequencing-based CNV tools showed that ExonDel is capable of detecting unique IEDs not found by other CNV tools. CONCLUSIONS: ExonDel is an efficient way to screen for novel and known IEDs using exome sequencing data. ExonDel and its source code can be downloaded freely at https://github.com/slzhao/ExonDel.

PIK3CA mutations in androgen receptor-positive triple negative breast cancer confer sensitivity to the combination of PI3K and androgen receptor inhibitors.

INTRODUCTION: Triple negative breast cancer (TNBC) is a heterogeneous collection of biologically diverse cancers, which contributes to variable clinical outcomes. Previously, we identified a TNBC subtype that has a luminal phenotype and expresses the androgen receptor (AR+). TNBC cells derived from these luminal AR + tumors have high frequency phosphatidylinositol-4,5-bisphosphate 3-kinase catalytic subunit alpha (PIK3CA) mutations. The purpose of this study was to determine if targeting phosphoinositide 3-kinase (PI3K) alone or in combination with an AR antagonist is effective in AR + TNBC. METHODS: We determined the frequency of activating PIK3CA mutations in AR + and AR- TNBC clinical cases. Using AR + TNBC cell line and xenograft models we evaluated the effectiveness of PI3K inhibitors, used alone or in combination with an AR antagonist, on tumor cell growth and viability. RESULTS: PIK3CA kinase mutations were highly clonal, more frequent in AR + vs. AR- TNBC (40% vs. 4%), and often associated with concurrent amplification of the PIK3CA locus. PI3K/mTOR inhibitors had an additive growth inhibitory effect when combined with genetic or pharmacological AR targeting in AR + TNBC cells. We also analyzed the combination of bicalutamide +/- the pan-PI3K inhibitor GDC-0941 or the dual PI3K/mTOR inhibitor GDC-0980 in xenograft tumor studies and observed additive effects. CONCLUSIONS: While approximately one third of TNBC patients respond to neoadjuvant/adjuvant chemotherapy, recent studies have shown that patients with AR + TNBC are far less likely to benefit from the current standard of care chemotherapy regimens and novel targeted approaches need to be investigated. In this study, we show that activating PIK3CA mutations are enriched in AR + TNBC; and, we show that the growth and viability of AR + TNBC cell line models is significantly reduced after treatment with PI3K inhibitors used in combination with an AR antagonist. These results provide rationale for pre-selection of TNBC patients with a biomarker (AR expression) to investigate the use of AR antagonists in combination with PI3K/mTOR inhibitors.

Transforming growth factor beta receptor type III is a tumor promoter in mesenchymal-stem like triple negative breast cancer.

INTRODUCTION: There is a major need to better understand the molecular basis of triple negative breast cancer (TNBC) in order to develop effective therapeutic strategies. Using gene expression data from 587 TNBC patients we previously identified six subtypes of the disease, among which a mesenchymal-stem like (MSL) subtype. The MSL subtype has significantly higher expression of the transforming growth factor beta (TGF-ß) pathway-associated genes relative to other subtypes, including the TGF-ß receptor type III (TßRIII). We hypothesize that TßRIII is tumor promoter in mesenchymal-stem like TNBC cells. METHODS: Representative MSL cell lines SUM159, MDA-MB-231 and MDA-MB-157 were used to study the roles of TßRIII in the MSL subtype. We stably expressed short hairpin RNAs specific to TßRIII (TßRIII-KD). These cells were then used for xenograft tumor studies in vivo; and migration, invasion, proliferation and three dimensional culture studies in vitro. Furthermore, we utilized human gene expression datasets to examine TßRIII expression patterns across all TNBC subtypes. RESULTS: TßRIII was the most differentially expressed TGF-ß signaling gene in the MSL subtype. Silencing TßRIII expression in MSL cell lines significantly decreased cell motility and invasion. In addition, when TßRIII-KD cells were grown in a three dimensional (3D) culture system or nude mice, there was a loss of invasive protrusions and a significant decrease in xenograft tumor growth, respectively. In pursuit of the mechanistic underpinnings for the observed TßRIII-dependent phenotypes, we discovered that integrin-α2 was expressed at higher level in MSL cells after TßRIII-KD. Stable knockdown of integrin-α2 in TßRIII-KD MSL cells rescued the ability of the MSL cells to migrate and invade at the same level as MSL control cells. CONCLUSIONS: We have found that TßRIII is required for migration and invasion in vitro and xenograft growth in vivo. We also show that TßRIII-KD elevates expression of integrin-α2, which is required for the reduced migration and invasion, as determined by siRNA knockdown studies of both TßRIII and integrin-α2. Overall, our results indicate a potential mechanism in which TßRIII modulates integrin-α2 expression to effect MSL cell migration, invasion, and tumorigenicity.

Differential regulation of the p73 cistrome by mammalian target of rapamycin reveals transcriptional programs of mesenchymal differentiation and tumorigenesis.

The transcription factor p73 plays critical roles during development and tumorigenesis. It exhibits sequence identity and structural homology with p53, and can engage p53-like tumor-suppressive programs. However, different pathways regulate p53 and p73, and p73 is not mutated in human tumors. Therefore, p73 represents a therapeutic target, and there is a critical need to understand genes and noncoding RNAs regulated by p73 and how they change during treatment regimens. Here, we define the p73 genomic binding profile and demonstrate its modulation by rapamycin, an inhibitor of mammalian target of rapamycin (mTOR) and inducer of p73. Rapamycin selectively increased p73 occupancy at a subset of its binding sites. In addition, multiple determinants of p73 binding, activity, and function were evident, and were modulated by mTOR. We generated an mTOR-p73 signature that is enriched for p73 target genes and miRNAs that are involved in mesenchymal differentiation and tumorigenesis, can classify rhabdomyosarcomas by clinical subtype, and can predict patient outcome.

Trem2 deficiency attenuates breast cancer tumor growth in lean, but not obese or weight loss, mice and is associated with alterations of clonal T cell populations.

Obesity is an established risk factor for breast cancer development and worsened prognosis; however, the mechanisms for this association - and the potential benefits of weight loss - have not been fully explored. The adipose environment surrounding breast tumors, which is inflamed in obesity, has been implicated in tumor progression. An emerging therapeutic target for cancer is TREM2, a transmembrane receptor of the immunoglobulin superfamily that is expressed on macrophages in adipose tissue and tumors. We utilized genetic loss of function (Trem2 +,+ and Trem2 -/-) models and dietary (lean, obese, and weight loss) intervention approaches to examine impacts on postmenopausal breast cancer. Remarkably, Trem2 deficiency ameliorated tumor growth in lean, but not obese or weight loss mice. Single-cell RNA sequencing, in conjunction with VDJ sequencing of tumor and tumor-adjacent mammary adipose tissue (mATTum-adj) immune cells, revealed that tumors of lean Trem2 -/- mice exhibited a shift in clonal CD8+ T cells from an exhausted to an effector memory state, accompanied with increased clonality of CD4+ Th1 cells, that was not observed in any other diet-genotype group. Notably, identical T cell clonotypes were identified in the tumor and mATTum-adj of the same mouse. Finally, an immune checkpoint study demonstrated that αPD-1 therapy restricted tumor growth in lean and weight loss, but not obese mice. We conclude that weight history is relevant when considering potential efficacy of TREM2 inhibition in postmenopausal breast cancer. This work reveals immunological interactions between tumors and surrounding adipose tissue, highlighting significant differences under obese and weight loss conditions.

Atezolizumab in Combination With Carboplatin and Survival Outcomes in Patients With Metastatic Triple-Negative Breast Cancer: The TBCRC 043 Phase 2 Randomized Clinical Trial.

Importance: Agents targeting programmed death ligand 1 (PD-L1) have demonstrated efficacy in triple-negative breast cancer (TNBC) when combined with chemotherapy and are now the standard of care in patients with PD-L1-positive metastatic disease. In contrast to microtubule-targeting agents, the effect of combining platinum compounds with programmed cell death 1 (PD-1)/PD-L1 immunotherapy has not been extensively determined. Objective: To evaluate the efficacy of atezolizumab with carboplatin in patients with metastatic TNBC. Design, Setting, and Participants: This phase 2 randomized clinical trial was conducted in 6 centers from August 2017 to June 2021. Interventions: Patients with metastatic TNBC were randomized to receive carboplatin area under the curve (AUC) 6 alone or with atezolizumab, 1200 mg, every 3 weeks until disease progression or unacceptable toxic effects with a 3-year duration of follow-up. Main Outcome and Measures: The primary end point was investigator-assessed progression-free survival (PFS). Secondary end points included overall response rate (ORR), clinical benefit rate (CBR), and overall survival (OS). Other objectives included correlation of response with tumor PD-L1 levels, tumor-infiltrating lymphocytes (TILs), tumor DNA- and RNA-sequenced biomarkers, TNBC subtyping, and multiplex analyses of immune markers. Results: All 106 patients with metastatic TNBC who were enrolled were female with a mean (range) age of 55 (27-79) years, of which 12 (19%) identified as African American/Black, 1 (1%) as Asian, 73 (69%) as White, and 11 (10%) as unknown. Patients were randomized and received either carboplatin (n = 50) or carboplatin and atezolizumab (n = 56). The combination improved PFS (hazard ratio [HR], 0.66; 95% CI, 0.44-1.01; P = .05) from a median of 2.2 to 4.1 months, increased ORR from 8.0% (95% CI, 3.2%-18.8%) to 30.4% (95% CI, 19.9%-43.3%), increased CBR at 6 months from 18.0% (95% CI, 9.8%-30.1%) to 37.5% (95% CI, 26.0%-50.6%), and improved OS (HR, 0.60; 95% CI, 0.37-0.96; P = .03) from a median of 8.6 to 12.6 months. Subgroup analysis showed PD-L1-positive tumors did not benefit more from adding atezolizumab (HR, 0.62; 95% CI, 0.23-1.65; P = .35). Patients with high TILs (HR, 0.12; 95% CI, 0.30-0.50), high mutation burden (HR, 0.50; 95% CI, 0.23-1.06), and prior chemotherapy (HR, 0.59; 95% CI, 0.36-0.95) received greater benefit on the combination. Patients with obesity and patients with more than 125 mg/dL on-treatment blood glucose levels were associated with better PFS (HR, 0.35; 95% CI, 0.10-1.80) on the combination. TNBC subtypes benefited from adding atezolizumab, except the luminal androgen receptor subtype. Conclusions and Relevance: In this randomized clinical trial, the addition of atezolizumab to carboplatin significantly improved survival of patients with metastatic TNBC regardless of PD-L1 status. Further, lower risk of disease progression was associated with increased TILs, higher mutation burden, obesity, and uncontrolled blood glucose levels. Trial Registration: ClinicalTrials.gov Identifier: NCT03206203.

NKG2A Is a Therapeutic Vulnerability in Immunotherapy Resistant MHC-I Heterogeneous Triple-Negative Breast Cancer.

Despite the success of immune checkpoint inhibition (ICI) in treating cancer, patients with triple-negative breast cancer (TNBC) often develop resistance to therapy, and the underlying mechanisms are unclear. MHC-I expression is essential for antigen presentation and T-cell-directed immunotherapy responses. This study demonstrates that TNBC patients display intratumor heterogeneity in regional MHC-I expression. In murine models, loss of MHC-I negates antitumor immunity and ICI response, whereas intratumor MHC-I heterogeneity leads to increased infiltration of natural killer (NK) cells in an IFNγ-dependent manner. Using spatial technologies, MHC-I heterogeneity is associated with clinical resistance to anti-programmed death (PD) L1 therapy and increased NK:T-cell ratios in human breast tumors. MHC-I heterogeneous tumors require NKG2A to suppress NK-cell function. Combining anti-NKG2A and anti-PD-L1 therapies restores complete response in heterogeneous MHC-I murine models, dependent on the presence of activated, tumor-infiltrating NK and CD8+ T cells. These results suggest that similar strategies may enhance patient benefit in clinical trials. SIGNIFICANCE: Clinical resistance to immunotherapy is common in breast cancer, and many patients will likely require combination therapy to maximize immunotherapeutic benefit. This study demonstrates that heterogeneous MHC-I expression drives resistance to anti-PD-L1 therapy and exposes NKG2A on NK cells as a target to overcome resistance. This article is featured in Selected Articles from This Issue, p. 201.

Epithelial Expressed B7-H4 Drives Differential Immunotherapy Response in Murine and Human Breast Cancer.

Combinations of immune checkpoint inhibitors (ICI, including anti-PD-1/PD-L1) and chemotherapy have been FDA approved for metastatic and early-stage triple-negative breast cancer (TNBC), but most patients do not benefit. B7-H4 is a B7 family ligand with proposed immunosuppressive functions being explored as a cancer immunotherapy target and may be associated with anti-PD-L1 resistance. However, little is known about its regulation and effect on immune cell function in breast cancers. We assessed murine and human breast cancer cells to identify regulation mechanisms of B7-H4 in vitro. We used an immunocompetent anti-PD-L1-sensitive orthotopic mammary cancer model and induced ectopic expression of B7-H4. We assessed therapy response and transcriptional changes at baseline and under treatment with anti-PD-L1. We observed B7-H4 was highly associated with epithelial cell status and transcription factors and found to be regulated by PI3K activity. EMT6 tumors with cell-surface B7-H4 expression were more resistant to immunotherapy. In addition, tumor-infiltrating immune cells had reduced immune activation signaling based on transcriptomic analysis. Paradoxically, in human breast cancer, B7-H4 expression was associated with survival benefit for patients with metastatic TNBC treated with carboplatin plus anti-PD-L1 and was associated with no change in response or survival for patients with early breast cancer receiving chemotherapy plus anti-PD-1. While B7-H4 induces tumor resistance to anti-PD-L1 in murine models, there are alternative mechanisms of signaling and function in human cancers. In addition, the strong correlation of B7-H4 to epithelial cell markers suggests a potential regulatory mechanism of B7-H4 independent of PD-L1. SIGNIFICANCE: This translational study confirms the association of B7-H4 expression with a cold immune microenvironment in breast cancer and offers preclinical studies demonstrating a potential role for B7-H4 in suppressing response to checkpoint therapy. However, analysis of two clinical trials with checkpoint inhibitors in the early and metastatic settings argue against B7-H4 as being a mechanism of clinical resistance to checkpoints, with clear implications for its candidacy as a therapeutic target.

Patient-derived breast tumor xenografts facilitating personalized cancer therapy.

Despite improved detection and reduction of breast cancer-related deaths over the recent decade, breast cancer remains the second leading cause of cancer death for women in the US, with 39,510 women expected to succumb to metastatic disease in 2012 alone (American Cancer Society, Cancer Facts &Figures 2012. Atlanta: American Cancer Society; 2012). Continued efforts in classification of breast cancers based on gene expression profiling and genomic sequencing have revealed an underlying complexity and molecular heterogeneity within the disease that continues to challenge therapeutic interventions. To successfully identify and translate new treatment regimens to the clinic, it is imperative that our preclinical models recapitulate this complexity and heterogeneity. In this review article, we discuss the recent advances in development and classification of patient-derived human breast tumor xenograft models that have the potential to facilitate the next phase of drug discovery for personalized cancer therapy based on the unique driver signaling pathways in breast tumor subtypes.

Robust biomarker discovery through multiplatform multiplex image analysis of breast cancer clinical cohorts.

Spatial profiling of tissues promises to elucidate tumor-microenvironment interactions and enable development of spatial biomarkers to predict patient response to immunotherapy and other therapeutics. However, spatial biomarker discovery is often carried out on a single patient cohort or imaging technology, limiting statistical power and increasing the likelihood of technical artifacts. In order to analyze multiple patient cohorts profiled on different platforms, we developed methods for comparative data analysis from three disparate multiplex imaging technologies: 1) cyclic immunofluorescence data we generated from 102 breast cancer patients with clinical follow-up, in addition to publicly available 2) imaging mass cytometry and 3) multiplex ion-beam imaging data. We demonstrate similar single-cell phenotyping results across breast cancer patient cohorts imaged with these three technologies and identify cellular abundance and proximity-based biomarkers with prognostic value across platforms. In multiple platforms, we identified lymphocyte infiltration as independently associated with longer survival in triple negative and high-proliferation breast tumors. Then, a comparison of nine spatial analysis methods revealed robust spatial biomarkers. In estrogen receptor-positive disease, quiescent stromal cells close to tumor were more abundant in good prognosis tumors while tumor neighborhoods of mixed fibroblast phenotypes were enriched in poor prognosis tumors. In triple-negative breast cancer (TNBC), macrophage proximity to tumor and B cell proximity to T cells were greater in good prognosis tumors, while tumor neighborhoods of vimentin-positive fibroblasts were enriched in poor prognosis tumors. We also tested previously published spatial biomarkers in our ensemble cohort, reproducing the positive prognostic value of isolated lymphocytes and lymphocyte occupancy and failing to reproduce the prognostic value of tumor-immune mixing score in TNBC. In conclusion, we demonstrate assembly of larger clinical cohorts from diverse platforms to aid in prognostic spatial biomarker identification and validation.

MYC Deregulation and PTEN Loss Model Tumor and Stromal Heterogeneity of Aggressive Triple-Negative Breast Cancer.

Triple-negative breast cancer (TNBC) patients have a poor prognosis and few treatment options. Mouse models of TNBC are important for development of new therapies, however, few mouse models represent the complexity of TNBC. Here, we develop a female TNBC murine model by mimicking two common TNBC mutations with high co-occurrence: amplification of the oncogene MYC and deletion of the tumor suppressor PTEN. This Myc;Ptenfl model develops heterogeneous triple-negative mammary tumors that display histological and molecular features commonly found in human TNBC. Our research involves deep molecular and spatial analyses on Myc;Ptenfl tumors including bulk and single-cell RNA-sequencing, and multiplex tissue-imaging. Through comparison with human TNBC, we demonstrate that this genetic mouse model develops mammary tumors with differential survival and therapeutic responses that closely resemble the inter- and intra-tumoral and microenvironmental heterogeneity of human TNBC, providing a pre-clinical tool for assessing the spectrum of patient TNBC biology and drug response.

Analysis of high-throughput RNAi screening data in identifying genes mediating sensitivity to chemotherapeutic drugs: statistical approaches and perspectives.

BACKGROUND: High-throughput RNA interference (RNAi) screens have been used to find genes that, when silenced, result in sensitivity to certain chemotherapy drugs. Researchers therefore can further identify drug-sensitive targets and novel drug combinations that sensitize cancer cells to chemotherapeutic drugs. Considerable uncertainty exists about the efficiency and accuracy of statistical approaches used for RNAi hit selection in drug sensitivity studies. Researchers require statistical methods suitable for analyzing high-throughput RNAi screening data that will reduce false-positive and false-negative rates. RESULTS: In this study, we carried out a simulation study to evaluate four types of statistical approaches (fold-change/ratio, parametric tests/statistics, sensitivity index, and linear models) with different scenarios of RNAi screenings for drug sensitivity studies. With the simulated datasets, the linear model resulted in significantly lower false-negative and false-positive rates. Based on the results of the simulation study, we then make recommendations of statistical analysis methods for high-throughput RNAi screening data in different scenarios. We assessed promising methods using real data from a loss-of-function RNAi screen to identify hits that modulate paclitaxel sensitivity in breast cancer cells. High-confidence hits with specific inhibitors were further analyzed for their ability to inhibit breast cancer cell growth. Our analysis identified a number of gene targets with inhibitors known to enhance paclitaxel sensitivity, suggesting other genes identified may merit further investigation. CONCLUSIONS: RNAi screening can identify druggable targets and novel drug combinations that can sensitize cancer cells to chemotherapeutic drugs. However, applying an inappropriate statistical method or model to the RNAi screening data will result in decreased power to detect the true hits and increase false positive and false negative rates, leading researchers to draw incorrect conclusions. In this paper, we make recommendations to enable more objective selection of statistical analysis methods for high-throughput RNAi screening data.

Racial/Ethnic Disparities in All-Cause Mortality among Patients Diagnosed with Triple-Negative Breast Cancer.

It is unclear whether racial/ethnic disparities in triple-negative breast cancer (TNBC) mortality remain after accounting for clinical characteristics, treatment, and access-to-care-related factors. In this study, women with a primary diagnosis of TNBC during 2010-2014 were identified from the National Cancer Database. Hazard ratios (HR) and 95% confidence intervals (CI) for 3- and 5-year all-cause mortality associated with race/ethnicity were estimated using Cox proportional hazards models with stepwise adjustments for age, clinical characteristics, treatment, and access-to-care-related factors. Of 78,708 patients, non-Hispanic (NH) black women had the lowest 3-year overall survival rates (79.4%), followed by NH-whites (83.1%), Hispanics (86.0%), and Asians (87.1%). After adjustment for clinical characteristics, NH-blacks had a 12% higher risk of dying 3 years post-diagnosis (HR, 1.12; 95% CI, 1.07-1.17), whereas Hispanics and Asians had a 24% (HR, 0.76; 95% CI, 0.70-0.83) and 17% (HR, 0.83; 95% CI, 0.73-0.94) lower risk than their NH-white counterparts. The black-white disparity became non-significant after combined adjustment for treatment and access-to-care-related factors (HR, 1.04; 95% CI, 0.99-1.09), whereas the white-Hispanic and white-Asian differences remained. Stratified analyses revealed that among women aged less than or equal to 50 with stage III cancer, the elevated risk among NH-blacks persisted (HR, 1.20; 95% CI, 1.04-1.39) after full adjustments. Similar results were seen for 5-year mortality. Overall, clinical characteristics, treatment, and access-to-care-related factors accounted for most of the white-black differences in all-cause mortality of TNBC but explained little about Hispanic- and Asian-white differences. SIGNIFICANCE: These findings highlight the need for equal healthcare to mitigate the black-white disparity and for investigations of contributors beyond healthcare for lower mortality among Asians and Hispanics.

Tissue-specific expression of p73 and p63 isoforms in human tissues.

p73 and p63 are members of the p53 family that exhibit overlapping and distinct functions in development and homeostasis. The evaluation of p73 and p63 isoform expression across human tissue can provide greater insight to the functional interactions between family members. We determined the mRNA isoform expression patterns of TP73 and TP63 across a panel of 36 human tissues and protein expression within the highest-expressing tissues. TP73 and TP63 expression significantly correlated across tissues. In tissues with concurrent mRNA expression, nuclear co-expression of both proteins was observed in a majority of cells. Using GTEx data, we quantified p73 and p63 isoform expression in human tissue and identified that the α-isoforms of TP73 and TP63 were the predominant isoform expressed in nearly all tissues. Further, we identified a previously unreported p73 mRNA product encoded by exons 4 to 14. In sum, these data provide the most comprehensive tissue-specific atlas of p73 and p63 protein and mRNA expression patterns in human and murine samples, indicating coordinate expression of these transcription factors in the majority of tissues in which they are expressed.