Breast tumor microenvironment structures are associated with genomic features and clinical outcome.

The functions of the tumor microenvironment (TME) are orchestrated by precise spatial organization of specialized cells, yet little is known about the multicellular structures that form within the TME. Here we systematically mapped TME structures in situ using imaging mass cytometry and multitiered spatial analysis of 693 breast tumors linked to genomic and clinical data. We identified ten recurrent TME structures that varied by vascular content, stromal quiescence versus activation, and leukocyte composition. These TME structures had distinct enrichment patterns among breast cancer subtypes, and some were associated with genomic profiles indicative of immune escape. Regulatory and dysfunctional T cells co-occurred in large 'suppressed expansion' structures. These structures were characterized by high cellular diversity, proliferating cells and enrichment for BRCA1 and CASP8 mutations and predicted poor outcome in estrogen-receptor-positive disease. The multicellular structures revealed here link conserved spatial organization to local TME function and could improve patient stratification.

Multi-omic machine learning predictor of breast cancer therapy response.

Breast cancers are complex ecosystems of malignant cells and the tumour microenvironment1. The composition of these tumour ecosystems and interactions within them contribute to responses to cytotoxic therapy2. Efforts to build response predictors have not incorporated this knowledge. We collected clinical, digital pathology, genomic and transcriptomic profiles of pre-treatment biopsies of breast tumours from 168 patients treated with chemotherapy with or without HER2 (encoded by ERBB2)-targeted therapy before surgery. Pathology end points (complete response or residual disease) at surgery3 were then correlated with multi-omic features in these diagnostic biopsies. Here we show that response to treatment is modulated by the pre-treated tumour ecosystem, and its multi-omics landscape can be integrated in predictive models using machine learning. The degree of residual disease following therapy is monotonically associated with pre-therapy features, including tumour mutational and copy number landscapes, tumour proliferation, immune infiltration and T cell dysfunction and exclusion. Combining these features into a multi-omic machine learning model predicted a pathological complete response in an external validation cohort (75 patients) with an area under the curve of 0.87. In conclusion, response to therapy is determined by the baseline characteristics of the totality of the tumour ecosystem captured through data integration and machine learning. This approach could be used to develop predictors for other cancers.

Imaging mass cytometry and multiplatform genomics define the phenogenomic landscape of breast cancer.

Genomic alterations shape cell phenotypes and the structure of tumor ecosystems in poorly defined ways. To investigate these relationships, we used imaging mass cytometry to quantify the expression of 37 proteins with subcellular spatial resolution in 483 tumors from the METABRIC cohort. Single-cell analysis revealed cell phenotypes spanning epithelial, stromal and immune types. Distinct combinations of cell phenotypes and cell-cell interactions were associated with genomic subtypes of breast cancer. Epithelial luminal cell phenotypes separated into those predominantly impacted by mutations and those affected by copy number aberrations. Several features of tumor ecosystems, including cellular neighborhoods, were linked to prognosis, illustrating their clinical relevance. In summary, systematic analysis of single-cell phenotypic and spatial correlates of genomic alterations in cancer revealed how genomes shape both the composition and architecture of breast tumor ecosystems and will enable greater understanding of the phenotypic impact of genomic alterations.

Publisher Correction: Clonal replacement and heterogeneity in breast tumors treated with neoadjuvant HER2-targeted therapy.

The original version of this Article omitted from the Author Contributions statement that 'R.S. and J.G.R contributed equally to this work.' This has been corrected in both the PDF and HTML versions of the Article.

Clonal replacement and heterogeneity in breast tumors treated with neoadjuvant HER2-targeted therapy.

Genomic changes observed across treatment may result from either clonal evolution or geographically disparate sampling of heterogeneous tumors. Here we use computational modeling based on analysis of fifteen primary breast tumors and find that apparent clonal change between two tumor samples can frequently be explained by pre-treatment heterogeneity, such that at least two regions are necessary to detect treatment-induced clonal shifts. To assess for clonal replacement, we devise a summary statistic based on whole-exome sequencing of a pre-treatment biopsy and multi-region sampling of the post-treatment surgical specimen and apply this measure to five breast tumors treated with neoadjuvant HER2-targeted therapy. Two tumors underwent clonal replacement with treatment, and mathematical modeling indicates these two tumors had resistant subclones prior to treatment and rates of resistance-related genomic changes that were substantially larger than previous estimates. Our results provide a needed framework to incorporate primary tumor heterogeneity in investigating the evolution of resistance.

Shallow whole genome sequencing for robust copy number profiling of formalin-fixed paraffin-embedded breast cancers.

Pathology archives with linked clinical data are an invaluable resource for translational research, with the limitation that most cancer samples are formalin-fixed paraffin-embedded (FFPE) tissues. Therefore, FFPE tissues are an important resource for genomic profiling studies but are under-utilised due to the low amount and quality of extracted nucleic acids. We profiled the copy number landscape of 356 breast cancer patients using DNA extracted FFPE tissues by shallow whole genome sequencing. We generated a total of 491 sequencing libraries from 2 kits and obtained data from 98.4% of libraries with 86.4% being of good quality. We generated libraries from as low as 3.8 ng of input DNA and found that the success was independent of input DNA amount and quality, processing site and age of the fixed tissues. Since copy number alterations (CNA) play a major role in breast cancer, it is imperative that we are able to use FFPE archives and we have shown in this study that sWGS is a robust method to do such profiling.

Therapeutic Rationale to Target Highly Expressed CDK7 Conferring Poor Outcomes in Triple-Negative Breast Cancer.

Triple-negative breast cancer (TNBC) patients commonly exhibit poor prognosis and high relapse after treatment, but there remains a lack of biomarkers and effective targeted therapies for this disease. Here, we report evidence highlighting the cell-cycle-related kinase CDK7 as a driver and candidate therapeutic target in TNBC. Using publicly available transcriptomic data from a collated set of TNBC patients (n = 383) and the METABRIC TNBC dataset (n = 217), we found CDK7 mRNA levels to be correlated with patient prognosis. High CDK7 protein expression was associated with poor prognosis within the RATHER TNBC cohort (n = 109) and the METABRIC TNBC cohort (n = 203). The highly specific CDK7 kinase inhibitors, BS-181 and THZ1, each downregulated CDK7-mediated phosphorylation of RNA polymerase II, indicative of transcriptional inhibition, with THZ1 exhibiting 500-fold greater potency than BS-181. Mechanistic investigations revealed that the survival of MDA-MB-231 TNBC cells relied heavily on the BCL-2/BCL-XL signaling axes in cells. Accordingly, we found that combining the BCL-2/BCL-XL inhibitors ABT-263/ABT199 with the CDK7 inhibitor THZ1 synergized in producing growth inhibition and apoptosis of human TNBC cells. Collectively, our results highlight elevated CDK7 expression as a candidate biomarker of poor prognosis in TNBC, and they offer a preclinical proof of concept for combining CDK7 and BCL-2/BCL-XL inhibitors as a mechanism-based therapeutic strategy to improve TNBC treatment. Cancer Res; 77(14); 3834-45. ©2017 AACR.

The somatic mutation profiles of 2,433 breast cancers refines their genomic and transcriptomic landscapes.

The genomic landscape of breast cancer is complex, and inter- and intra-tumour heterogeneity are important challenges in treating the disease. In this study, we sequence 173 genes in 2,433 primary breast tumours that have copy number aberration (CNA), gene expression and long-term clinical follow-up data. We identify 40 mutation-driver (Mut-driver) genes, and determine associations between mutations, driver CNA profiles, clinical-pathological parameters and survival. We assess the clonal states of Mut-driver mutations, and estimate levels of intra-tumour heterogeneity using mutant-allele fractions. Associations between PIK3CA mutations and reduced survival are identified in three subgroups of ER-positive cancer (defined by amplification of 17q23, 11q13-14 or 8q24). High levels of intra-tumour heterogeneity are in general associated with a worse outcome, but highly aggressive tumours with 11q13-14 amplification have low levels of intra-tumour heterogeneity. These results emphasize the importance of genome-based stratification of breast cancer, and have important implications for designing therapeutic strategies.

Computational pathology of pre-treatment biopsies identifies lymphocyte density as a predictor of response to neoadjuvant chemotherapy in breast cancer.

BACKGROUND: There is a need to improve prediction of response to chemotherapy in breast cancer in order to improve clinical management and this may be achieved by harnessing computational metrics of tissue pathology. We investigated the association between quantitative image metrics derived from computational analysis of digital pathology slides and response to chemotherapy in women with breast cancer who received neoadjuvant chemotherapy. METHODS: We digitised tissue sections of both diagnostic and surgical samples of breast tumours from 768 patients enrolled in the Neo-tAnGo randomized controlled trial. We subjected digital images to systematic analysis optimised for detection of single cells. Machine-learning methods were used to classify cells as cancer, stromal or lymphocyte and we computed estimates of absolute numbers, relative fractions and cell densities using these data. Pathological complete response (pCR), a histological indicator of chemotherapy response, was the primary endpoint. Fifteen image metrics were tested for their association with pCR using univariate and multivariate logistic regression. RESULTS: Median lymphocyte density proved most strongly associated with pCR on univariate analysis (OR 4.46, 95 % CI 2.34-8.50, p < 0.0001; observations = 614) and on multivariate analysis (OR 2.42, 95 % CI 1.08-5.40, p = 0.03; observations = 406) after adjustment for clinical factors. Further exploratory analyses revealed that in approximately one quarter of cases there was an increase in lymphocyte density in the tumour removed at surgery compared to diagnostic biopsies. A reduction in lymphocyte density at surgery was strongly associated with pCR (OR 0.28, 95 % CI 0.17-0.47, p < 0.0001; observations = 553). CONCLUSIONS: A data-driven analysis of computational pathology reveals lymphocyte density as an independent predictor of pCR. Paradoxically an increase in lymphocyte density, following exposure to chemotherapy, is associated with a lack of pCR. Computational pathology can provide objective, quantitative and reproducible tissue metrics and represents a viable means of outcome prediction in breast cancer. TRIAL REGISTRATION: ClinicalTrials.gov NCT00070278 ; 03/10/2003.

Integration of genomic, transcriptomic and proteomic data identifies two biologically distinct subtypes of invasive lobular breast cancer.

Invasive lobular carcinoma (ILC) is the second most frequently occurring histological breast cancer subtype after invasive ductal carcinoma (IDC), accounting for around 10% of all breast cancers. The molecular processes that drive the development of ILC are still largely unknown. We have performed a comprehensive genomic, transcriptomic and proteomic analysis of a large ILC patient cohort and present here an integrated molecular portrait of ILC. Mutations in CDH1 and in the PI3K pathway are the most frequent molecular alterations in ILC. We identified two main subtypes of ILCs: (i) an immune related subtype with mRNA up-regulation of PD-L1, PD-1 and CTLA-4 and greater sensitivity to DNA-damaging agents in representative cell line models; (ii) a hormone related subtype, associated with Epithelial to Mesenchymal Transition (EMT), and gain of chromosomes 1q and 8q and loss of chromosome 11q. Using the somatic mutation rate and eIF4B protein level, we identified three groups with different clinical outcomes, including a group with extremely good prognosis. We provide a comprehensive overview of the molecular alterations driving ILC and have explored links with therapy response. This molecular characterization may help to tailor treatment of ILC through the application of specific targeted, chemo- and/or immune-therapies.

Quantitative image analysis of cellular heterogeneity in breast tumors complements genomic profiling.

Solid tumors are heterogeneous tissues composed of a mixture of cancer and normal cells, which complicates the interpretation of their molecular profiles. Furthermore, tissue architecture is generally not reflected in molecular assays, rendering this rich information underused. To address these challenges, we developed a computational approach based on standard hematoxylin and eosin-stained tissue sections and demonstrated its power in a discovery and validation cohort of 323 and 241 breast tumors, respectively. To deconvolute cellular heterogeneity and detect subtle genomic aberrations, we introduced an algorithm based on tumor cellularity to increase the comparability of copy number profiles between samples. We next devised a predictor for survival in estrogen receptor-negative breast cancer that integrated both image-based and gene expression analyses and significantly outperformed classifiers that use single data types, such as microarray expression signatures. Image processing also allowed us to describe and validate an independent prognostic factor based on quantitative analysis of spatial patterns between stromal cells, which are not detectable by molecular assays. Our quantitative, image-based method could benefit any large-scale cancer study by refining and complementing molecular assays of tumor samples.

Biological and prognostic associations of miR-205 and let-7b in breast cancer revealed by in situ hybridization analysis of micro-RNA expression in arrays of archival tumour tissue.

Micro-RNAs (miRNAs) are frequently dysregulated in a range of human malignancies, many have been shown to act either as tumour supressors or oncogenes and several have been implicated in breast cancer. However, breast cancer is a diverse disease and little is known about the relationships between miRNA expression, clinical outcome and tumour subtype. We used locked nucleic acid probe in situ hybridization (LNA-ISH) to visualize, in tissue micro-arrays (TMAs) of 2919 formalin-fixed paraffin-embedded (FFPE) archival breast tumours, the expression of two key miRNAs that are frequently lost in a range of solid malignancies, let-7b and miR-205. These miRNAs were also quantified by quantitative reverse transcription PCR in cores of FFPE tissue from 40 of these cases, demonstrating that LNA-ISH is semi-quantitative. The tumours in the TMAs were assigned to subtypes based on their immunohistochemical (IHC) staining with ER, PR, HER2, CK5/6 and EGFR. let-7b expression was shown to be associated with luminal tumours and to have an independent significant positive prognostic value in this group. miR-205 is associated with tumours of ductal morphology and is of significant positive prognostic value within these tumours. We propose that the expression of miR-205 may contribute to ductal tumour morphology.

Modulation of N-methyl-N-nitrosourea-induced crypt restricted metallothionein immunopositivity in mouse colon by a non-genotoxic diet-related chemical.

Red meat consumption is associated with endogenous metabolic generation of mutagenic N-nitroso compounds (NOC) and may be implicated in causation of colorectal cancer. Assessment of a biologically relevant dose of NOCs is hampered by imperfect understanding of NOC interactions with other dietary components. This study tests the hypothesis that NOC effects upon mutational biomarkers in mouse colon may be modulated by a non-genotoxic diet-related compound. N-methyl-N-nitrosourea (MNU) and undegraded lambda carrageenan (lambdaCgN) were selected as test chemicals, representing a NOC and a non-genotoxic agent, respectively. Study end-points included (i) DNA adduct formation and (ii) metallothionein (MT) crypt restricted immunopositivity indices (MTCRII) which are considered representative of crypt stem cell mutations. Frequency and size of MT immunopositive foci as well as total number of MT immunopositive crypts were assessed. Biologically effective doses of MNU and lambdaCgN were determined in model validation studies and the agents were then tested alone and in combination. Continuous lambdaCgN treatment for 10 weeks induced significantly greater colonic mucosal injury than a drinking water control. In combined treatment regimens, lambdaCgN treatment did not significantly affect MNU-induced DNA adduct formation. However, combinations of lambdaCgN with MNU significantly increased MTCRII in excess of those induced by MNU alone. Recurrent or continuous lambdaCgN regimens had greater interactive effects with MNU upon MTCRII than short-term lambdaCgN treatment. This study has shown that exposure to a non-genotoxic diet-related compound (lambdaCgN) modulates the effective NOC dosimetry for induction of MT crypt restricted immunopositivity.