Impact of body mass index on the efficacy of aromatase inhibitors in patients with metastatic breast cancer.

PURPOSE: Higher levels of estrogen in obese patients may lead to incomplete inhibition by aromatase inhibitors (AIs). The aim of this study was to determine the impact of body mass index (BMI) on efficacy of AIs in patients with metastatic hormone receptor (HR)-positive breast cancer (BC). METHODS: We performed a retrospective chart review of all female patients with metastatic HR-positive BC on an AI in first- or second-line settings and seen at our academic institution between 2001 and 2020. The primary endpoint was progression-free survival (PFS), defined as the time from start of AI to disease progression or death from any cause. RESULTS: We identified 219 patients who had received an AI in the first- or second-line settings for metastatic HR-positive BC and with documented information on BMI. Of the 219 patients, 56% (123) had a low BMI (defined as < 27 kg/m2) and 44% (96) had a high BMI (≥ 27 kg/m2). The median PFS was 21.9 months (95% CI 14.5 to 28.4) in the low BMI group versus 20.2 months (95% CI 14.3 to 27.5) in the high BMI group (p = 0.73). CONCLUSION: While BMI influences efficacy of AIs in the adjuvant setting, our results suggest that in the metastatic setting, BMI may not impact the efficacy of AIs. This discrepancy could be due to other differences in disease characteristics that make complete aromatase inhibition more important in the adjuvant setting when disease burden is the lowest.

Pan-Cancer Transcriptional Models Predicting Chemosensitivity in Human Tumors.

MOTIVATION: Despite increasing understanding of the molecular characteristics of cancer, chemotherapy success rates remain low for many cancer types. Studies have attempted to identify patient and tumor characteristics that predict sensitivity or resistance to different types of conventional chemotherapies, yet a concise model that predicts chemosensitivity based on gene expression profiles across cancer types remains to be formulated. We attempted to generate pan-cancer models predictive of chemosensitivity and chemoresistance. Such models may increase the likelihood of identifying the type of chemotherapy most likely to be effective for a given patient based on the overall gene expression of their tumor. RESULTS: Gene expression and drug sensitivity data from solid tumor cell lines were used to build predictive models for 11 individual chemotherapy drugs. Models were validated using datasets from solid tumors from patients. For all drug models, accuracy ranged from 0.81 to 0.93 when applied to all relevant cancer types in the testing dataset. When considering how well the models predicted chemosensitivity or chemoresistance within individual cancer types in the testing dataset, accuracy was as high as 0.98. Cell line-derived pan-cancer models were able to statistically significantly predict sensitivity in human tumors in some instances; for example, a pan-cancer model predicting sensitivity in patients with bladder cancer treated with cisplatin was able to significantly segregate sensitive and resistant patients based on recurrence-free survival times (P = .048) and in patients with pancreatic cancer treated with gemcitabine (P = .038). These models can predict chemosensitivity and chemoresistance across cancer types with clinically useful levels of accuracy.

Plasma DNA as a "liquid biopsy" incompletely complements tumor biopsy for identification of mutations in a case series of four patients with oligometastatic breast cancer.

PURPOSE: Circulating tumor DNA in plasma may present a minimally invasive opportunity to identify tumor-derived mutations to inform selection of targeted therapies for individual patients, particularly in cases of oligometastatic disease where biopsy of multiple tumors is impractical. To assess the utility of plasma DNA as a "liquid biopsy" for precision oncology, we tested whether sequencing of plasma DNA is a reliable surrogate for sequencing of tumor DNA to identify targetable genetic alterations. METHODS: Blood and biopsies of 1-3 tumors were obtained from 4 evaluable patients with advanced breast cancer. One patient provided samples from an additional 7 tumors post-mortem. DNA extracted from plasma, tumor tissues, and buffy coat of blood were used for probe-directed capture of all exons in 149 cancer-related genes and massively parallel sequencing. Somatic mutations in DNA from plasma and tumors were identified by comparison to buffy coat DNA. RESULTS: Sequencing of plasma DNA identified 27.94 ± 11.81% (mean ± SD) of mutations detected in a tumor(s) from the same patient; such mutations tended to be present at high allelic frequency. The majority of mutations found in plasma DNA were not found in tumor samples. Mutations were also found in plasma that matched clinically undetectable tumors found post-mortem. CONCLUSIONS: The incomplete overlap of genetic alteration profiles of plasma and tumors warrants caution in the sole reliance of plasma DNA to identify therapeutically targetable alterations in patients and indicates that analysis of plasma DNA complements, but does not replace, tumor DNA profiling. TRIAL REGISTRATION: Subjects were prospectively enrolled in trial NCT01836640 (registered April 22, 2013).

AMPK Activation by Metformin Promotes Survival of Dormant ER+ Breast Cancer Cells.

PURPOSE: Despite adjuvant endocrine therapy for patients with estrogen receptor alpha (ER)-positive breast cancer, dormant residual disease can persist for years and eventually cause tumor recurrence. We sought to deduce mechanisms underlying the persistence of dormant cancer cells to identify therapeutic strategies. EXPERIMENTAL DESIGN: Mimicking the aromatase inhibitor-induced depletion of estrogen levels used to treat patients, we developed preclinical models of dormancy in ER+ breast cancer induced by estrogen withdrawal in mice. We analyzed tumor xenografts and cultured cancer cells for molecular and cellular responses to estrogen withdrawal and drug treatments. Publicly available clinical breast tumor gene expression datasets were analyzed for responses to neoadjuvant endocrine therapy. RESULTS: Dormant breast cancer cells exhibited upregulated 5' adenosine monophosphate-activated protein kinase (AMPK) levels and activity, and upregulated fatty acid oxidation. While the antidiabetes AMPK-activating drug metformin slowed the estrogen-driven growth of cells and tumors, metformin promoted the persistence of estrogen-deprived cells and tumors through increased mitochondrial respiration driven by fatty acid oxidation. Pharmacologic or genetic inhibition of AMPK or fatty acid oxidation promoted clearance of dormant residual disease, while dietary fat increased tumor cell survival. CONCLUSIONS: AMPK has context-dependent effects in cancer, cautioning against the widespread use of an AMPK activator across disease settings. The development of therapeutics targeting fat metabolism is warranted in ER+ breast cancer.

A Transcriptionally Definable Subgroup of Triple-Negative Breast and Ovarian Cancer Samples Shows Sensitivity to HSP90 Inhibition.

PURPOSE: We hypothesized that integrated analysis of cancer types from different lineages would reveal novel molecularly defined subgroups with unique therapeutic vulnerabilities. On the basis of the molecular similarities between subgroups of breast and ovarian cancers, we analyzed these cancers as a single cohort to test our hypothesis. EXPERIMENTAL DESIGN: Identification of transcriptional subgroups of cancers and drug sensitivity analyses were performed using mined data. Cell line sensitivity to Hsp90 inhibitors (Hsp90i) was tested in vitro. The ability of a transcriptional signature to predict Hsp90i sensitivity was validated using cell lines, and cell line- and patient-derived xenograft (PDX) models. Mechanisms of Hsp90i sensitivity were uncovered using immunoblot and RNAi. RESULTS: Transcriptomic analyses of breast and ovarian cancer cell lines uncovered two mixed subgroups comprised primarily of triple-negative breast and multiple ovarian cancer subtypes. Drug sensitivity analyses revealed that cells of one mixed subgroup are significantly more sensitive to Hsp90i compared with cells from all other cancer lineages evaluated. A gene expression classifier was generated that predicted Hsp90i sensitivity in vitro, and in cell line- and PDXs. Cells from the Hsp90i-sensitive subgroup underwent apoptosis mediated by Hsp90i-induced upregulation of the proapoptotic proteins Bim and PUMA. CONCLUSIONS: Our findings identify Hsp90i as a potential therapeutic strategy for a transcriptionally defined subgroup of ovarian and breast cancers. This study demonstrates that gene expression profiles may be useful to identify therapeutic vulnerabilities in tumor types with limited targetable genetic alterations, and to identify molecularly definable cancer subgroups that transcend lineage.

Integrated pan-cancer gene expression and drug sensitivity analysis reveals SLFN11 mRNA as a solid tumor biomarker predictive of sensitivity to DNA-damaging chemotherapy.

BACKGROUND: Precision oncology seeks to integrate multiple layers of data from a patient's cancer to effectively tailor therapy. Conventional chemotherapies are sometimes effective but accompanied by adverse events, warranting the identification of a biomarker of chemosensitivity. OBJECTIVE: Identify an mRNA biomarker that predicts chemosensitivity across solid tumor subtypes. METHODS: We performed a pan-solid tumor analysis integrating gene expression and drug sensitivity profiles from 3 cancer cell line datasets to identify transcripts correlated with sensitivity to a panel of chemotherapeutics. We then tested the ability of an mRNA biomarker to predictive clinical outcomes in cohorts of patients with breast, lung, or ovarian cancer. RESULTS: Expression levels of several mRNA transcripts were significantly correlated with sensitivity or resistance chemotherapeutics in cancer cell line datasets. The only mRNA transcript significantly correlated with sensitization to multiple classes of DNA-damaging chemotherapeutics in all 3 cell line datasets was encoded by Schlafen Family Member 11 (SLFN11). Analyses of multiple breast, lung, and ovarian cancer patient cohorts treated with chemotherapy confirmed SLFN11 mRNA expression as a predictive biomarker of longer overall survival and improved tumor response. CONCLUSIONS: Tumor SLFN11 mRNA expression is a biomarker of sensitivity to an array of DNA-damaging chemotherapeutics across solid tumor subtypes.

Estrogen therapy induces an unfolded protein response to drive cell death in ER+ breast cancer.

Estrogens have been shown to elicit anticancer effects against estrogen receptor α (ER)-positive breast cancer. We sought to determine the mechanism underlying the therapeutic response. Response to 17ß-estradiol was assessed in ER+ breast cancer models with resistance to estrogen deprivation: WHIM16 patient-derived xenografts, C7-2-HI and C4-HI murine mammary adenocarcinomas, and long-term estrogen-deprived MCF-7 cells. As another means to reactivate ER, the anti-estrogen fulvestrant was withdrawn from fulvestrant-resistant MCF-7 cells. Transcriptional, growth, apoptosis, and molecular alterations in response to ER reactivation were measured. 17ß-estradiol treatment and fulvestrant withdrawal induced transcriptional activation of ER, and cells adapted to estrogen deprivation or fulvestrant were hypersensitive to 17ß-estradiol. ER transcriptional response was followed by an unfolded protein response and apoptosis. Such apoptosis was dependent upon the unfolded protein response, p53, and JNK signaling. Anticancer effects were most pronounced in models exhibiting genomic amplification of the gene encoding ER (ESR1), suggesting that engagement of ER at high levels is cytotoxic. These data indicate that long-term adaptation to estrogen deprivation or ER inhibition alters sensitivity to ER reactivation. In such adapted cells, 17ß-estradiol treatment and anti-estrogen withdrawal hyperactivate ER, which drives an unfolded protein response and subsequent growth inhibition and apoptosis. 17ß-estradiol treatment should be considered as a therapeutic option for anti-estrogen-resistant disease, particularly in patients with tumors harboring ESR1 amplification or ER overexpression. Furthermore, therapeutic strategies that enhance an unfolded protein response may increase the therapeutic effects of ER reactivation.

Exploiting mitochondrial and metabolic homeostasis as a vulnerability in NF1 deficient cells.

Neurofibromatosis type 1 is a disease caused by mutation of neurofibromin 1 (NF1), loss of which results in hyperactive Ras signaling and a concomitant increase in cell proliferation and survival. Patients with neurofibromatosis type 1 frequently develop tumors such as plexiform neurofibromas and malignant peripheral nerve sheath tumors. Mutation of NF1 or loss of the NF1 protein is also observed in glioblastoma, lung adenocarcinoma, and ovarian cancer among other sporadic cancers. A therapy that selectively targets NF1 deficient tumors would substantially advance our ability to treat these malignancies. To address the need for these therapeutics, we developed and conducted a synthetic lethality screen to discover molecules that target yeast lacking the homolog of NF1, IRA2. One of the lead candidates that was observed to be synthetic lethal with ira2Δ yeast is Y100. Here, we describe the mechanisms by which Y100 targets ira2Δ yeast and NF1-deficient tumor cells. Y100 treatment disrupted proteostasis, metabolic homeostasis, and induced the formation of mitochondrial superoxide in NF1-deficient cancer cells. Previous studies also indicate that NF1/Ras-dysregulated tumors may be sensitive to modulators of oxidative and ER stress. We hypothesize that the use of Y100 and molecules with related mechanisms of action represent a feasible therapeutic strategy for targeting NF1 deficient cells.

Coamplification of miR-4728 protects HER2-amplified breast cancers from targeted therapy.

HER2 (ERBB2) amplification is a driving oncogenic event in breast cancer. Clinical trials have consistently shown the benefit of HER2 inhibitors (HER2i) in treating patients with both local and advanced HER2+ breast cancer. Despite this benefit, their efficacy as single agents is limited, unlike the robust responses to other receptor tyrosine kinase inhibitors like EGFR inhibitors in EGFR-mutant lung cancer. Interestingly, the lack of HER2i efficacy occurs despite sufficient intracellular signaling shutdown following HER2i treatment. Exploring possible intrinsic causes for this lack of response, we uncovered remarkably depressed levels of NOXA, an endogenous inhibitor of the antiapoptotic MCL-1, in HER2-amplified breast cancer. Upon investigation of the mechanism leading to low NOXA, we identified a micro-RNA encoded in an intron of HER2, termed miR-4728, that targets the mRNA of the Estrogen Receptor α (ESR1). Reduced ESR1 expression in turn prevents ERα-mediated transcription of NOXA, mitigating apoptosis following treatment with the HER2i lapatinib. Importantly, resistance can be overcome with pharmacological inhibition of MCL-1. More generally, while many cancers like EGFR-mutant lung cancer are driven by activated kinases that when drugged lead to robust monotherapeutic responses, we demonstrate that the efficacy of targeted therapies directed against oncogenes active through focal amplification may be mitigated by coamplified genes.