Abstract Introduction The detection of circulating tumour cells (CTCs) in the peripheral blood and disseminated tumour cells (DTCs) in the bone marrow are promising prognostic tools for risk stratification in early breast cancer. There is, however, a need for further validation of these techniques in larger patient cohorts with adequate follow-up periods. Methods We assayed CTCs and DTCs at primary surgery in 733 stage I or II breast cancer patients with a median follow-up time of 7.6 years. CTCs were detected in samples of peripheral blood mononuclear cells previously stored in liquid-nitrogen using a previously-developed multi-marker quantitative PCR (QPCR)-based assay. DTCs were detected in bone marrow samples by immunocytochemical analysis using anti-cytokeratin antibodies. Results CTCs were detected in 7.9% of patients, while DTCs were found in 11.7%. Both CTC and DTC positivity predicted poor metastasis-free survival (MFS) and breast cancer-specific survival (BCSS); MFS hazard ratio (HR) = 2.4 (P < 0.001)/1.9 (P = 0.006), and BCSS HR = 2.5 (P < 0.001)/2.3 (P = 0.01), for CTC/DTC status, respectively). Multivariate analyses demonstrated that CTC status was an independent prognostic variable for both MFS and BCSS. CTC status also identified a subset of patients with significantly poorer outcome among low-risk node negative patients that did not receive adjuvant systemic therapy (MFS HR 2.3 (P = 0.039), BCSS HR 2.9 (P = 0.017)). Using both tests provided increased prognostic information and indicated different relevance within biologically dissimilar breast cancer subtypes. Conclusions These results support the use of CTC analysis in early breast cancer to generate clinically useful prognostic information.

To address cancer as a multifaceted adaptive system, the increasing momentum for cross-disciplinary connectivity between cancer biologists, physical scientists, mathematicians, chemists, biomedical engineers, computer scientists, clinicians, and advocates is fueling the emergence of new scientific frontiers, principles, and opportunities within physical sciences and oncology. In parallel to highlighting the advances, challenges, and acceptance of advocates as credible contributors, we offer recommendations for addressing real world hurdles in advancing equitable partnerships among advocacy stakeholders.

Background

Early changes in breast intratumor heterogeneity during neoadjuvant chemotherapy may reflect the tumor's ability to adapt and evade treatment. We investigated the combination of precision medicine predictors of genomic and MRI data towards improved prediction of recurrence free survival (RFS).

Methods

A total of 100 women from the ACRIN 6657/I-SPY 1 trial were retrospectively analyzed. We estimated MammaPrint, PAM50 ROR-S, and p53 mutation scores from publicly available gene expression data and generated four, voxel-wise 3-D radiomic kinetic maps from DCE-MR images at both pre- and early-treatment time points. Within the primary lesion from each kinetic map, features of change in radiomic heterogeneity were summarized into 6 principal components.

Results

We identify two imaging phenotypes of change in intratumor heterogeneity (p < 0.01) demonstrating significant Kaplan-Meier curve separation (p < 0.001). Adding phenotypes to established prognostic factors, functional tumor volume (FTV), MammaPrint, PAM50, and p53 scores in a Cox regression model improves the concordance statistic for predicting RFS from 0.73 to 0.79 (p = 0.002).

Conclusions

These results demonstrate an important step in combining personalized molecular signatures and longitudinal imaging data towards improved prognosis.

The term breast cancer covers many different conditions, whose clinical course ranges from indolent to aggressive. However, current practice in breast cancer prevention and care, and in breast cancer epidemiology, does not take into account the heterogeneity of the disease. A comprehensive understanding of the etiology and progression of different breast cancer subtypes would enable a more patient-centered approach to breast health care: assessing an individual's risk of getting specific subtypes of the disease, providing risk-based screening and prevention recommendations, and, for those diagnosed with the disease, tailored treatment options based on risk and timing of progression and mortality. The Athena Breast Health Network is an initiative of the five University of California medical and cancer centers to prototype this approach and to enable the development of a rapid learning system-connecting risk and outcome information from a heterogeneous patient population in real time and using new knowledge from research to continuously improve the quality of care. The Network is based on integrating clinical and research processes to create a comprehensive approach to accelerating patient-centered breast health care. Since its inception in 2009, the Network has developed a multi-site, transdisciplinary collaboration that enables the learning system. The five-campus collaboration has implemented a shared informatics platform, standardized electronic patient intake questionnaires, and common biospecimen protocols, as well as new clinical programs and multi-center research projects. The Athena Breast Health Network can serve as a model of a rapid learning system that integrates epidemiologic, behavioral, and clinical research with clinical care improvements.

Polymorphisms in the PER3 gene have been associated with several human disease phenotypes, including sleep disorders and cancer. In particular, the long allele of a variable number of tandem repeat (VNTR) polymorphism has been previously linked to an increased risk of breast cancer. Here we carried out a combined germline and somatic genetic analysis of the role of the PER3^VNRT polymorphism in breast cancer. The combined data from 8284 individuals showed a non-significant trend towards increased breast cancer risk in the 5-repeat allele homozygous carriers (OR = 1.17, 95% CI: 0.97-1.42). We observed allelic imbalance at the PER3 locus in matched blood and tumor DNA samples, showing a significant retention of the long variant (risk) allele in tumor samples, and a preferential loss of the short repetition allele (p = 0.0005). Gene co-expression analysis in healthy and tumoral breast tissue samples uncovered significant associations between PER3 expression levels with those from genes which belong to several cancer-associated pathways. Finally, relapse-free survival (RFS) analysis showed that low expression levels of PER3 were linked to a significant lower RSF in luminal A (p = 3 × 10^-12) but not in the rest of breast cancer subtypes.

Invasive lobular carcinoma (ILC) has been reported to be less responsive to neoadjuvant chemotherapy (NAC) than invasive ductal carcinoma (IDC). We sought to determine whether ILC histology indeed predicts poor response to NAC by analyzing tumor characteristics such as protein expression, gene expression, and imaging features, and by comparing NAC response rates to those seen in IDC after adjustment for these factors. We combined datasets from two large prospective NAC trials, including in total 676 patients, of which 75 were of lobular histology. Eligible patients had tumors ≥3 cm in diameter or pathologic documentation of positive nodes, and underwent serial biopsies, expression microarray analysis, and MRI imaging. We compared pathologic complete response (pCR) rates and breast conservation surgery (BCS) rates between ILC and IDC, adjusted for clinicopathologic factors. On univariate analysis, ILCs were significantly less likely to have a pCR after NAC than IDCs (11 vs. 25 %, p = 0.01). However, the known differences in tumor characteristics between the two histologic types, including hormone receptor (HR) status, HER2 status, histological grade, and p53 expression, accounted for this difference with the lowest pCR rates among HR+/HER2- tumors in both ILC and IDC (7 and 5 %, respectively). ILC which were HR- and/or HER2+ had a pCR rate of 25 %. Expression subtyping, particularly the NKI 70-gene signature, was correlated with pCR, although the small numbers of ILC in each group precluded significant associations. BCS rate did not differ between IDC and ILC after adjusting for molecular characteristics. We conclude that ILC represents a heterogeneous group of tumors which are less responsive to NAC than IDC. However, this difference is explained by differences in molecular characteristics, particularly HR and HER2, and independent of lobular histology.

Background

Breast cancer patients with estrogen receptor (ER)-positive disease have a continuous long-term risk for fatal breast cancer, but the biological factors influencing this risk are unknown. We aimed to determine whether high intratumor heterogeneity of ER predicts an increased long-term risk (25 years) of fatal breast cancer.

Methods

The STO-3 trial enrolled 1780 postmenopausal lymph node-negative breast cancer patients randomly assigned to receive adjuvant tamoxifen vs not. The fraction of cancer cells for each ER intensity level was scored by breast cancer pathologists, and intratumor heterogeneity of ER was calculated using Rao's quadratic entropy and categorized into high and low heterogeneity using a predefined cutoff at the second tertile (67%). Long-term breast cancer-specific survival analyses by intra-tumor heterogeneity of ER were performed using Kaplan-Meier and multivariable Cox proportional hazard modeling adjusting for patient and tumor characteristics.

Results

A statistically significant difference in long-term survival by high vs low intratumor heterogeneity of ER was seen for all ER-positive patients (P < .001) and for patients with luminal A subtype tumors (P = .01). In multivariable analyses, patients with high intratumor heterogeneity of ER had a twofold increased long-term risk as compared with patients with low intratumor heterogeneity (ER-positive: hazard ratio [HR] = 1.98, 95% confidence interval [CI] = 1.31 to 3.00; luminal A subtype tumors: HR = 2.43, 95% CI = 1.18 to 4.99).

Conclusions

Patients with high intratumor heterogeneity of ER had an increased long-term risk of fatal breast cancer. Interestingly, a similar long-term risk increase was seen in patients with luminal A subtype tumors. Our findings suggest that intratumor heterogeneity of ER is an independent long-term prognosticator with potential to change clinical management, especially for patients with luminal A tumors.

Abstract Background First-generation molecular profiles for human breast cancers have enabled the identification of features that can predict therapeutic response; however, little is known about how the various data types can best be combined to yield optimal predictors. Collections of breast cancer cell lines mirror many aspects of breast cancer molecular pathobiology, and measurements of their omic and biological therapeutic responses are well-suited for development of strategies to identify the most predictive molecular feature sets. Results We used least squares-support vector machines and random forest algorithms to identify molecular features associated with responses of a collection of 70 breast cancer cell lines to 90 experimental or approved therapeutic agents. The datasets analyzed included measurements of copy number aberrations, mutations, gene and isoform expression, promoter methylation and protein expression. Transcriptional subtype contributed strongly to response predictors for 25% of compounds, and adding other molecular data types improved prediction for 65%. No single molecular dataset consistently out-performed the others, suggesting that therapeutic response is mediated at multiple levels in the genome. Response predictors were developed and applied to TCGA data, and were found to be present in subsets of those patient samples. Conclusions These results suggest that matching patients to treatments based on transcriptional subtype will improve response rates, and inclusion of additional features from other profiling data types may provide additional benefit. Further, we suggest a systems biology strategy for guiding clinical trials so that patient cohorts most likely to respond to new therapies may be more efficiently identified.

During the type-setting of the final version of the article [1] some of the additional files were swapped. The correct files are republished in this Erratum.

The many improvements in breast cancer therapy in recent years have so lowered rates of recurrence that it is now difficult or impossible to conduct adequately powered adjuvant clinical trials. Given the many new drugs and potential synergistic combinations, the neoadjuvant approach has been used to test benefit of drug combinations in clinical trials of primary breast cancer. A recent FDA-led meta-analysis showed that pathologic complete response (pCR) predicts disease-free survival (DFS) within patients who have specific breast cancer subtypes. This meta-analysis motivated the FDA's draft guidance for using pCR as a surrogate endpoint in accelerated drug approval. Using pCR as a registration endpoint was challenged at ASCO 2014 Annual Meeting with the presentation of ALTTO, an adjuvant trial in HER2-positive breast cancer that showed a nonsignificant reduction in DFS hazard rate for adding lapatinib, a HER-family tyrosine kinase inhibitor, to trastuzumab and chemotherapy. This conclusion seemed to be inconsistent with the results of NeoALTTO, a neoadjuvant trial that found a statistical improvement in pCR rate for the identical lapatinib-containing regimen. We address differences in the two trials that may account for discordant conclusions. However, we use the FDA meta-analysis to show that there is no discordance at all between the observed pCR difference in NeoALTTO and the observed HR in ALTTO. This underscores the importance of appropriately modeling the two endpoints when designing clinical trials. The I-SPY 2/3 neoadjuvant trials exemplify this approach.