Metastatic Breast Cancer With ESR1 Mutation: Clinical Management Considerations From the Molecular and Precision Medicine (MAP) Tumor Board at Massachusetts General Hospital

Virtual Molecular and Precision Medicine Clinic to Improve Access to Clinical Trials for Patients With Metastatic Breast Cancer: An Academic/Community Collaboration

PURPOSE

There is a demand for improved care delivery surrounding genomic testing and clinical trial enrollment among patients with metastatic breast cancer (MBC). We sought to improve the current process via real-time informal consultation and prescreening assessment for patients with MBC treated by community and academic medical oncologists by implementing a virtual molecular and precision medicine (vMAP) clinic.

METHODS

The vMAP program used a virtual referral system directed to a multidisciplinary team with precision medicine expertise. Providers contacted vMAP regarding patients with MBC, and on receipt of referral, the vMAP team engaged in discussion to identify if further diagnostics were needed (including genomic testing) and to identify potential clinical trials or standard treatment options. Recommendations were then sent to the referring provider within 72 hours. Pre-/postsurveys were issued to network physicians to assess for barriers, clinical trial access, and vMAP referral experience. Program implementation was evaluated with the Squire 2.0 reporting guidelines for quality improvement in health care as a framework.

RESULTS

Eighty-one cases from 22 providers were referred to vMAP over a 26-month period. The average response time to the referring provider with a finalized recommendation was 1.90 ± 1.82 days. A total of 86.4% of cases had clinical trial options on vMAP prescreen, with 40.7% initiating formal screening assessments and 27 patients (33.3%) ultimately enrolling on trials. On resurvey, 92% of survey responses across community oncology referring providers said that they were very likely to use vMAP again.

CONCLUSION

In the initial 2-year period, vMAP demonstrated an efficient means to offer real-time interpretation of genomic testing and identification of clinical trials for patients with MBC, with effective clinical trial enrollment and high rates of referring provider satisfaction.

Adaptation to Hypoxia May Promote Therapeutic Resistance to Androgen Receptor Inhibition in Triple-Negative Breast Cancer

Triple-negative breast cancer (TNBC) surpasses other BC subtypes as the most challenging to treat due to its lack of traditional BC biomarkers. Nearly 30% of TNBC patients express the androgen receptor (AR), and the blockade of androgen production and AR signaling have been the cornerstones of therapies for AR-positive TNBC. However, the majority of women are resistant to AR-targeted therapy, which is a major impediment to improving outcomes for the AR-positive TNBC subpopulation. The hypoxia signaling cascade is frequently activated in the tumor microenvironment in response to low oxygen levels; activation of the hypoxia signaling cascade allows tumors to survive despite hypoxia-mediated interference with cellular metabolism. The activation of hypoxia signaling networks in TNBC promotes resistance to most anticancer drugs including AR inhibitors. The activation of hypoxia network signaling occurs more frequently in TNBC compared to other BC subtypes. Herein, we examine the (1) interplay between hypoxia signaling networks and AR and (2) whether hypoxia and hypoxic stress adaptive pathways promote the emergence of resistance to therapies that target AR. We also pose the well-supported question, “Can the efficacy of androgen-/AR-targeted treatments be enhanced by co-targeting hypoxia?” By critically examining the evidence and the complex entwinement of these two oncogenic pathways, we argue that the simultaneous targeting of androgen biosynthesis/AR signaling and hypoxia may enhance the sensitivity of AR-positive TNBCs to AR-targeted treatments, derail the emergence of therapy resistance, and improve patient outcomes.

Predicting Breast Cancer Gene Expression Signature by Applying Deep Convolutional Neural Networks From Unannotated Pathological Images

We proposed a highly versatile two-step transfer learning pipeline for predicting the gene signature defining the intrinsic breast cancer subtypes using unannotated pathological images. Deciphering breast cancer molecular subtypes by deep learning approaches could provide a convenient and efficient method for the diagnosis of breast cancer patients. It could reduce costs associated with transcriptional profiling and subtyping discrepancy between IHC assays and mRNA expression. Four pretrained models such as VGG16, ResNet50, ResNet101, and Xception were trained with our in-house pathological images from breast cancer patient with recurrent status in the first transfer learning step and TCGA-BRCA dataset for the second transfer learning step. Furthermore, we also trained ResNet101 model with weight from ImageNet for comparison to the aforementioned models. The two-step deep learning models showed promising classification results of the four breast cancer intrinsic subtypes with accuracy ranging from 0.68 (ResNet50) to 0.78 (ResNet101) in both validation and testing sets. Additionally, the overall accuracy of slide-wise prediction showed even higher average accuracy of 0.913 with ResNet101 model. The micro- and macro-average area under the curve (AUC) for these models ranged from 0.88 (ResNet50) to 0.94 (ResNet101), whereas ResNet101_imgnet weighted with ImageNet archived an AUC of 0.92. We also show the deep learning model prediction performance is significantly improved relatively to the common Genefu tool for breast cancer classification. Our study demonstrated the capability of deep learning models to classify breast cancer intrinsic subtypes without the region of interest annotation, which will facilitate the clinical applicability of the proposed models.

Implementing a clinical cutting-edge and decision-making activity: an ethnographic teamwork approach to a molecular tumorboard

Background

New technology implementation in healthcare must address important challenges such as interdisciplinary approaches. In oncology, molecular tumorboard (MTB) settings require biomedical researchers and clinical practitioners to collaborate and work together. While acknowledging that MTBs have been primarily investigated from a clinical rather than an organizational perspective, this article analyzes team processes and dynamics in a newly implemented MTB.

Methods

A systemic case study of a newly implemented MTB in a Swiss teaching hospital was conducted between July 2017 and February 2018, with in situ work observations, six exploratory interviews and six semi-structured interviews.

Results

An MTB workflow is progressively stabilized in four steps: 1) patient case submissions, 2) molecular analyses and results validation, 3) co-elaboration of therapeutic proposals, and 4) reporting during formal MTB sessions. The elaboration of a therapeutic proposal requires a framework for discussion that departs from the formality of institutional relationships, which was gradually incepted in this MTB.

Conclusions

Firstly, our research showed that an MTB organizational process requires the five teaming components that characterizes a learning organization. It showed that at the organizational level, procedures can be stabilized without limiting practice flexibility. Secondly, this research highlighted the importance of non-clinical outcomes from an MTB, e.g. an important support network for the oncologist community.

Performance of Cancer Recurrence Algorithms After Coding Scheme Switch From International Classification of Diseases 9th Revision to International Classification of Diseases 10th Revision

PURPOSE

We previously developed and validated informatic algorithms that used International Classification of Diseases 9th revision (ICD9)-based diagnostic and procedure codes to detect the presence and timing of cancer recurrence (the RECUR Algorithms). In 2015, ICD10 replaced ICD9 as the worldwide coding standard. To understand the impact of this transition, we evaluated the performance of the RECUR Algorithms after incorporating ICD10 codes.

METHODS

Using publicly available translation tables along with clinician and other expertise, we updated the algorithms to include ICD10 codes as additional input variables. We evaluated the performance of the algorithms using gold standard recurrence measures associated with a contemporary cohort of patients with stage I to III breast, colorectal, and lung (excluding IIIB) cancer and derived performance measures, including the area under the receiver operating curve, average absolute prediction error, and correct classification rate. These values were compared with the performance measures derived from the validation of the original algorithms.

RESULTS

A total of 659 colorectal, 280 lung, and 2,053 breast cancer cases were identified. Area under the receiver operating curve derived from the updated algorithms was 89.0% (95% CI, 82.3% to 95.7%), 88.9% (95% CI, 79.3% to 98.2%), and 80.5% (95% CI, 72.8% to 88.2%) for the colorectal, lung, and breast cancer algorithms, respectively. Average absolute prediction errors for recurrence timing were 2.7 (SE, 11.3%), 2.4 (SE, 10.4%), and 5.6 months (SE, 21.8%), respectively, and timing estimates were within 6 months of actual recurrence for more than 80% of colorectal, more than 90% of lung, and more than 50% of breast cancer cases using the updated algorithm.

CONCLUSION

Performance measures derived from the updated and original algorithms had overlapping confidence intervals, suggesting that the ICD9 to ICD10 transition did not affect the RECUR Algorithm performance.

Whole-Body Characterization of Estrogen Receptor Status in Metastatic Breast Cancer with 16α-18F-Fluoro-17β-Estradiol Positron Emission Tomography: Meta-Analysis and Recommendations for Integration into Clinical Applications

Estrogen receptor (ER) status by immunohistochemistry (IHC) of cancer tissue is currently used to direct endocrine therapy in breast cancer. Positron emission tomography (PET) with 16α-18F-fluoro-17β-estradiol (¹⁸ F-FES) noninvasively characterizes ER ligand-binding function of breast cancer lesions. Concordance of imaging and tissue assays should be established for ¹⁸ F-FES PET to be an alternative or complement to tissue biopsy for metastatic lesions. We conducted a meta-analysis of published results comparing ¹⁸ F-FES PET and tissue assays of ER status in patients with breast cancer. PubMed and EMBASE were searched for English-language manuscripts with at least 10 patients and low overall risk of bias. Thresholds for imaging and tissue classification could differ between studies but had to be clearly stated. We used hierarchical summary receiver-operating characteristic curve models for the meta-analysis. The primary analysis included 113 nonbreast lesions from 4 studies; an expanded analysis included 327 total lesions from 11 studies. Treating IHC results as the reference standard, sensitivity was 0.78 (95% confidence region 0.65-0.88) and specificity 0.98 (0.65-1.00) for the primary analysis of nonbreast lesions. In the expanded analysis including non-IHC tissue assays and all lesion sites, sensitivity was 0.81 (0.73-0.87) and specificity 0.86 (0.68-0.94). These results suggest that ¹⁸ F-FES PET is useful for characterization of ER status of metastatic breast cancer lesions. We also review current best practices for conducting ¹⁸ F-FES PET scans. This imaging assay has potential to improve clinically relevant outcomes for patients with (historically) ER-positive metastatic breast cancer, including those with brain metastases and/or lobular histology. IMPLICATIONS FOR PRACTICE: 16α-18F-fluoro-17β-estradiol positron emission tomography (¹⁸ F-FES PET) imaging assesses estrogen receptor status in breast cancer in vivo. This work reviews the sensitivity and specificity of ¹⁸ F-FES PET in a meta-analysis with reference tissue assays and discusses best practices for use of the tracer as an imaging biomarker. ¹⁸ F-FES PET could enhance breast cancer diagnosis and staging as well as aid in therapy selection for patients with metastatic disease. Tissue sampling limitations, intrapatient heterogeneity, and temporal changes in molecular markers make it likely that ¹⁸ F-FES PET will complement existing assays when clinically available in the near future.

Genomic Landscape of Uterine Sarcomas Defined Through Prospective Clinical Sequencing

PURPOSE

We examined whether prospective molecular characterization of advanced metastatic disease can reveal grade and/or histology-specific differences to inform diagnosis and facilitate enrollment onto clinical trials.

EXPERIMENTAL DESIGN

Patients with uterine sarcoma consented to a prospective study of next-generation sequencing (NGS). Clinical annotations were extracted from their medical record. Tumor and matched normal DNA were subjected to NGS, and the genomic landscape was explored for survival correlations and therapeutic targetability.

RESULTS

Tumors from 107 women were sequenced and included leiomyosarcoma (n = 80), high-grade non-leiomyosarcoma (n = 22), low-grade endometrial stromal sarcoma (LG-ESS, n = 4), and smooth muscle tumor of uncertain malignant potential (STUMP, n = 2). Genomic profiling influenced histologic diagnosis in three cases. Common uterine leiomyosarcoma alterations were loss-of-function mutations in TP53 (56%), RB1 (51%), and ATRX (31%). Homozygous deletions of BRCA2 were present in 5% of these patients. PTEN alteration frequency was higher in the metastases samples as compared with the primary samples. Genomes of low-grade tumors were largely silent, while 50.5% of high-grade tumors had whole-genome duplication. Two metastatic uterine leiomyosarcoma cases were hypermutated. Both had prolonged disease-free survival. Potentially actionable mutations were identified in 48 patients (45%), 8 (17%) of whom received matched therapy with 2 achieving clinical responses. Among patients with uterine leiomyosarcoma with somatic BRCA2 alterations, sustained partial responses were observed with PARP inhibitor-containing therapy.

DISCUSSION

Prospective genomic profiling can contribute to diagnostic precision and inform treatment selection in patients with uterine sarcomas. There was evidence of clinical benefit in patients with uterine leiomyosarcoma with somatic BRCA2 alterations treated with PARP inhibitors.

Suppression of breast cancer metastasis and extension of survival by a new antiestrogen in a preclinical model driven by mutant estrogen receptors

PURPOSE

Many human breast tumors become resistant to endocrine therapies and recur due to estrogen receptor (ERα) mutations that convey constitutive activity and a more aggressive phenotype. Here, we examined the effectiveness of a novel adamantyl antiestrogen, K-07, in suppressing the growth of breast cancer metastases containing the two most frequent ER-activating mutations, Y537S and D538G, and in extending survival in a preclinical metastatic cancer model.

METHODS

MCF7 breast cancer cells expressing luciferase and Y537S or D538G ER were injected into NOD-SCID-gamma female mice, and animals were treated orally with the antiestrogen K-07 or control vehicle. Comparisons were also made with the antiestrogen Fulvestrant. The development of metastases was monitored by in vivo bioluminescence imaging with phenotypic characterization of the metastases in liver and lung by immunohistochemical and biochemical analyses.

RESULTS

These breast cancer cells established metastases in liver and lung, and K-07 treatment reduced the metastatic burden. Mice treated with K-07 also survived much longer. By day 70, only 28% of vehicle-treated mice with mutant ER metastases were alive, whereas all K-07-treated D538G and Y537S mice were still alive. K-07 also markedly reduced the level of metastatic cell ER and the expression of ER-regulated genes.

CONCLUSION

The antiestrogen K-07 can reduce in vivo metastasis of breast cancers and extend host survival in this preclinical model driven by constitutively active mutant ERs, suggesting that this compound may be suitable for further translational examination of its efficacy in suppression of metastasis in breast cancers containing constitutively active mutant ERs.

Development, Validation, and Dissemination of a Breast Cancer Recurrence Detection and Timing Informatics Algorithm

Background

This study developed, validated, and disseminated a generalizable informatics algorithm for detecting breast cancer recurrence and timing using a gold standard measure of recurrence coupled with data derived from a readily available common data model that pools health insurance claims and electronic health records data.

Methods

The algorithm has two parts: to detect the presence of recurrence and to estimate the timing of recurrence. The primary data source was the Cancer Research Network Virtual Data Warehouse (VDW). Sixteen potential indicators of recurrence were considered for model development. The final recurrence detection and timing models were determined, respectively, by maximizing the area under the ROC curve (AUROC) and minimizing average absolute error. Detection and timing algorithms were validated using VDW data in comparison with a gold standard recurrence capture from a third site in which recurrences were validated through chart review. Performance of this algorithm, stratified by stage at diagnosis, was compared with other published algorithms. All statistical tests were two-sided.

Results

Detection model AUROCs were 0.939 (95% confidence interval [CI] = 0.917 to 0.955) in the training data set (n = 3370) and 0.956 (95% CI = 0.944 to 0.971) and 0.900 (95% CI = 0.872 to 0.928), respectively, in the two validation data sets (n = 3370 and 3961, respectively). Timing models yielded average absolute prediction errors of 12.6% (95% CI = 10.5% to 14.5%) in the training data and 11.7% (95% CI = 9.9% to 13.5%) and 10.8% (95% CI = 9.6% to 12.2%) in the validation data sets, respectively, and were statistically significantly lower by 12.6% (95% CI = 8.8% to 16.5%, P < .001) than those estimated using previously reported timing algorithms. Similar covariates were included in both detection and timing algorithms but differed substantially from previous studies.

Conclusions

Valid and reliable detection of recurrence using data derived from electronic medical records and insurance claims is feasible. These tools will enable extensive, novel research on quality, effectiveness, and outcomes for breast cancer patients and those who develop recurrence.

Precision Oncology Decision Support: Current Approaches and Strategies for the Future

With the increasing availability of genomics, routine analysis of advanced cancers is now feasible. Treatment selection is frequently guided by the molecular characteristics of a patient's tumor, and an increasing number of trials are genomically selected. Furthermore, multiple studies have demonstrated the benefit of therapies that are chosen based upon the molecular profile of a tumor. However, the rapid evolution of genomic testing platforms and emergence of new technologies make interpreting molecular testing reports more challenging. More sophisticated precision oncology decision support services are essential. This review outlines existing tools available for health care providers and precision oncology teams and highlights strategies for optimizing decision support. Specific attention is given to the assays currently available for molecular testing, as well as considerations for interpreting alteration information. This article also discusses strategies for identifying and matching patients to clinical trials, current challenges, and proposals for future development of precision oncology decision support. Clin Cancer Res; 24(12); 2719-31. ©2018 AACR.

The Impact of ESR1 Mutations on the Treatment of Metastatic Breast Cancer

After nearly 20 years of research, it is now established that mutations within the estrogen receptor (ER) gene, ESR1, frequently occur in metastatic breast cancer and influence response to hormone therapy. Though early studies presented differing results, sensitive sequencing techniques now show that ESR1 mutations occur at a frequency between 20 and 40% depending on the assay method. Recent studies have focused on several "hot spot mutations," a cluster of mutations found in the hormone-binding domain of the ESR1 gene. Throughout the course of treatment, tumor evolution can occur, and ESR1 mutations emerge and become enriched in the metastatic setting. Sensitive techniques to continually monitor mutant burden in vivo are needed to effectively treat patients with mutant ESR1. The full impact of these mutations on tumor response to different therapies remains to be determined. However, recent studies indicate that mutant-bearing tumors may be less responsive to specific hormonal therapies, and suggest that aromatase inhibitor (AI) therapy may select for the emergence of ESR1 mutations. Additionally, different mutations may respond discretely to targeted therapies. The need for more preclinical mechanistic studies on ESR1 mutations and the development of better agents to target these mutations are urgently needed. In the future, sequential monitoring of ESR1 mutational status will likely direct personalized therapeutic regimens appropriate to each tumor's unique mutational landscape.

Navigation through inter- and intratumoral heterogeneity of endocrine resistance mechanisms in breast cancer: A potential role for Liquid Biopsies?

The majority of breast cancers are hormone receptor positive due to the expression of the estrogen and/or progesterone receptors. Endocrine therapy is a major treatment option for all disease stages of hormone receptor–positive breast cancer and improves overall survival. However, endocrine therapy is limited by de novo and acquired resistance. Several factors have been proposed for endocrine therapy failures, which include molecular alterations in the estrogen receptor pathway, altered expression of cell-cycle regulators, autophagy, and epithelial-to-mesenchymal transition as a consequence of tumor progression and selection pressure. It is essential to reveal and monitor intra- and intertumoral alterations in breast cancer to allow optimal therapy outcome. Endocrine therapy navigation by molecular profiling of tissue biopsies is the current gold standard but limited in many reasons. “Liquid biopsies” such as circulating-tumor cells and circulating-tumor DNA offer hope to fill that gap in allowing non-invasive serial assessment of biomarkers predicting success of endocrine therapy regimen. In this context, this review will provide an overview on inter- and intratumoral heterogeneity of endocrine resistance mechanisms and discuss the potential role of “liquid biopsies” as navigators to personalize treatment methods and prevent endocrine treatment resistance in breast cancer.

Clinical Implications of ESR1 Mutations in Hormone Receptor-Positive Advanced Breast Cancer

Hormone receptor-positive breast cancer is the most frequent breast cancer subtype. Endocrine therapy (ET) targeting the estrogen receptor (ER) pathway represents the main initial therapeutic approach. The major strategies include estrogen deprivation and the use of selective estrogen modulators or degraders, which show efficacy in the management of metastatic and early-stage disease. However, clinical resistance associated with progression of disease remains a significant therapeutic challenge. Mutations of the ESR1 gene, which encodes the ER, have been increasingly recognized as an important mechanism of ET resistance, with a prevalence that ranges from 11 to 39%. The majority of these mutations are located within the ligand-binding domain and result in an estrogen-independent constitutive activation of the ER and, therefore, resistance to estrogen deprivation therapy such as aromatase inhibition. ESR1 mutations, most often detected from liquid biopsies, have been consistently associated with a worse outcome and are being currently evaluated as a potential biomarker to guide therapeutic decisions. At the same time, targeted therapy directed to ESR1-mutated clones is an appealing concept with preclinical and clinical work in progress.

Understanding the Precision in “Precision Medicine”

This issue of The Oncologist introduces a series of articles that consider the often problematic challenge of understanding and applying genomic information to the practice of oncology.