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Fibroblasts are poorly characterised cells that variably impact tumour progression. Here, we use single cell RNA-sequencing, multiplexed immunohistochemistry and digital cytometry (CIBERSORTx) to identify and characterise three major fibroblast subpopulations in human non-small cell lung cancer: adventitial, alveolar and myofibroblasts. Alveolar and adventitial fibroblasts (enriched in control tissue samples) localise to discrete spatial niches in histologically normal lung tissue and indicate improved overall survival rates when present in lung adenocarcinomas (LUAD). Trajectory inference identifies three phases of control tissue fibroblast activation, leading to myofibroblast enrichment in tumour samples: initial upregulation of inflammatory cytokines, followed by stress-response signalling and ultimately increased expression of fibrillar collagens. Myofibroblasts correlate with poor overall survival rates in LUAD, associated with loss of epithelial differentiation, TP53 mutations, proximal molecular subtypes and myeloid cell recruitment. In squamous carcinomas myofibroblasts were not prognostic despite being transcriptomically equivalent. These findings have important implications for developing fibroblast-targeting strategies for cancer therapy. Fibroblast heterogeneity is a prominent but poorly understood feature of solid tumours. Here three major fibroblast subpopulations in non-small cell lung cancer are identified and characterised through single cell RNA-sequencing, multiplexed immunohistochemistry and digital cytometry.

Key Points Aromatase inhibitors (AIs) are frequently prescribed for patients with oestrogen receptor-positive (ER + ) breast cancer to control advanced disease and to prevent relapse after treatment with localized breast cancer (adjuvant therapy). However, resistance to AI therapy is common, occurring in over 20% of patients with early-stage disease and is inevitable in patients with metastatic disease. Resistance to AI therapy can be detected in primary tumours by measuring on-treatment tumour Ki67 expression. The idea of monitoring tumour Ki67 expression as a clinical tool is being prospectively evaluated for individualized treatment approaches that de-escalate therapy for responsive tumours (Ki67 low after AI treatment) and escalate therapy for unresponsive tumours (Ki67 high after treatment). Genomic analyses of ER + tumours have identified more than 30 significantly mutated genes the role of which in AI therapy responsiveness is under investigation. To date, TP53 has been associated with high levels of Ki67 both before and after therapy, and therefore with more aggressive disease, and MAP3K1 has the opposite pattern, and is therefore associated with more indolent disease. GATA3 mutation was associated with a greater fall in Ki67 expression with treatment, suggesting that GATA3 -mutant tumours are more dependent on oestrogen than GATA3 wild-type tumours. ERα ligand-binding domain mutations emerge after prolonged periods of AI therapy and are therefore an acquired resistance mechanism to AI therapy. Other genomic aberrations in the ESR1 locus have also been identified, including translocations, amplifications and localized gene rearrangements within the long arm of chromosome 6. The frequency of these findings in AI-resistant tumours and their role in AI resistance is under investigation. Cancer cell-intrinsic mechanisms for AI resistance include loss of ER expression, upregulation of growth factor receptor pathways including the ERBB family of receptors, fibroblast growth factor receptor (FGFR), insulin-like growth factor 1 receptor (IGF1R) and their downstream signalling including MAPK and PI3K–AKT–mTOR, deregulation of apoptosis and cell cycle machinery. Cancer cell-extrinsic mechanisms depend on interactions with other cell types within the tumour microenvironment (fibroblasts, immune cells, adipose cells and mesenchymal stem cells) that collectively orchestrate the development and maintenance of AI resistance. Mechanism-based inhibitors against cyclin-dependent kinase 4 (CDK4) and CDK6, PI3K and histone deacetylases are among the most promising strategies being tested to overcome AI resistance in Phase III clinical trials. Further advances in our understanding of AI-resistance mechanisms rely on prospective longitudinal studies of tumour samples collected at multiple disease time points and also on preclinical models that capture the full spectrum and biology of AI-resistance mechanisms. This Review discusses the mechanisms underlying resistance to aromatase inhibitor (AI) therapy of patients with oestrogen receptor-positive (ER + ) breast cancer, and also assesses the possible therapeutic options for overcoming AI resistance. Oestrogen receptor-positive (ER + ) breast cancer is a major cause of cancer death in women. Although aromatase inhibitors suppress the function of ER and reduce the risk of recurrence, therapeutic resistance is common and essentially inevitable in advanced disease. This Review considers both genomic and cell biological explanations as to why ER + breast cancer cells persist, progress and cause an incurable, lethal, systemic disease. The design and outcomes of clinical trials are considered with the perspective that resistance mechanisms are heterogeneous, and therefore biomarker and somatic mutation-based stratification and eligibility will be essential for improvements in patient outcomes.

MALAT1 has previously been described as a metastasis-promoting long noncoding RNA (lncRNA). We show here, however, that targeted inactivation of the Malat1 gene in a transgenic mouse model of breast cancer, without altering the expression of its adjacent genes, promotes lung metastasis, and that this phenotype can be reversed by genetic add-back of Malat1 . Similarly, knockout of MALAT1 in human breast cancer cells induces their metastatic ability, which is reversed by re-expression of Malat1. Conversely, overexpression of Malat1 suppresses breast cancer metastasis in transgenic, xenograft, and syngeneic models. Mechanistically, the MALAT1 lncRNA binds and inactivates the prometastatic transcription factor TEAD, preventing TEAD from associating with its co-activator YAP and target gene promoters. Moreover, MALAT1 levels inversely correlate with breast cancer progression and metastatic ability. These findings demonstrate that MALAT1 is a metastasis-suppressing lncRNA rather than a metastasis promoter in breast cancer, calling for rectification of the model for this highly abundant and conserved lncRNA. Targeted inactivation, restoration and overexpression of MALAT1 in multiple in vivo models demonstrate that the lncRNA MALAT1 suppresses breast cancer metastasis through binding and inactivation of the pro-metastatic transcription factor TEAD.

The sensitivity of massively-parallel sequencing has confirmed that most cancers are oligoclonal, with subpopulations of neoplastic cells harboring distinct mutations. A fine resolution view of this clonal architecture provides insight into tumor heterogeneity, evolution, and treatment response, all of which may have clinical implications. Single tumor analysis already contributes to understanding these phenomena. However, cryptic subclones are frequently revealed by additional patient samples (e.g., collected at relapse or following treatment), indicating that accurately characterizing a tumor requires analyzing multiple samples from the same patient. To address this need, we present SciClone, a computational method that identifies the number and genetic composition of subclones by analyzing the variant allele frequencies of somatic mutations. We use it to detect subclones in acute myeloid leukemia and breast cancer samples that, though present at disease onset, are not evident from a single primary tumor sample. By doing so, we can track tumor evolution and identify the spatial origins of cells resisting therapy.

Aromatase inhibitors are a standard of care for hormone receptor-positive locally advanced or metastatic breast cancer. We investigated whether the selective oestrogen receptor degrader fulvestrant could improve progression-free survival compared with anastrozole in postmenopausal patients who had not received previous endocrine therapy. In this phase 3, randomised, double-blind trial, we recruited eligible patients with histologically confirmed oestrogen receptor-positive or progesterone receptor-positive, or both, locally advanced or metastatic breast cancer from 113 academic hospitals and community centres in 20 countries. Eligible patients were endocrine therapy-naive, with WHO performance status 0–2, and at least one measurable or non-measurable lesion. Patients were randomly assigned (1:1) to fulvestrant (500 mg intramuscular injection; on days 0, 14, 28, then every 28 days thereafter) or anastrozole (1 mg orally daily) using a computer-generated randomisation scheme. The primary endpoint was progression-free survival, determined by Response Evaluation Criteria in Solid Tumors version 1·1, intervention by surgery or radiotherapy because of disease deterioration, or death from any cause, assessed in the intention-to-treat population. Safety outcomes were assessed in all patients who received at least one dose of randomised treatment (including placebo). This trial is registered with ClinicalTrials.gov, number NCT01602380. Between Oct 17, 2012, and July 11, 2014, 524 patients were enrolled to this study. Of these, 462 patients were randomised (230 to receive fulvestrant and 232 to receive anastrozole). Progression-free survival was significantly longer in the fulvestrant group than in the anastrozole group (hazard ratio [HR] 0·797, 95% CI 0·637–0·999, p=0·0486). Median progression-free survival was 16·6 months (95% CI 13·83–20·99) in the fulvestrant group versus 13·8 months (11·99–16·59) in the anastrozole group. The most common adverse events were arthralgia (38 [17%] in the fulvestrant group vs 24 [10%] in the anastrozole group) and hot flushes (26 [11%] in the fulvestrant group vs 24 [10%] in the anastrozole group). 16 (7%) of 228 patients in in the fulvestrant group and 11 (5%) of 232 patients in the anastrozole group discontinued because of adverse events. Fulvestrant has superior efficacy and is a preferred treatment option for patients with hormone receptor-positive locally advanced or metastatic breast cancer who have not received previous endocrine therapy compared with a third-generation aromatase inhibitor, a standard of care for first-line treatment of these patients. AstraZeneca.

Gene-expression profiling has led to the development of signatures designed to predict survival and treatment response in patients with breast cancer. In this Review, Prat et al . discuss the clinical utility of gene-expression-based assays and compare them with the performance of breast cancer biomarkers that are currently used as standard of care. Gene-expression profiling has had a considerable impact on our understanding of breast cancer biology, and more recently on clinical care. Two statistical approaches underlie these advancements. Supervised analyses have led to the development of gene-expression signatures designed to predict survival and/or treatment response, which has resulted in the development of new clinical assays. Unsupervised analyses have identified numerous biological signatures including signatures of cell type of origin, signaling pathways, and of cellular proliferation. Included within these biological signatures are the molecular subtypes known as the 'intrinsic' subtypes of breast cancer. This classification has expanded our appreciation of the heterogeneity of breast cancer and has provided a way to sub-classify the disease in a manner that might have clinical utility. In this Review, we discuss the clinical utility of gene-expression-based assays and their technical potential as clinical tools vis-a-vis the performance of breast cancer biomarkers that are the current standard of care. Key Points Gene-expression-based assays provide independent prognostic information beyond standard clinical-pathological variables; however, tumor and nodal stage remain important and must be taken into account in the final prognostic assessment Gene-expression-based assays identify patients with ER-positive node-negative disease at low risk of relapse after treatment with hormonal therapy and who might be spared from chemotherapy Clinical use of gene-expression-based assays for the prediction of chemotherapy benefit in node-positive disease, and in ER-negative disease, is currently experimental Current methodologies for ER, PR and HER2 testing might benefit from additional protocol standardizations, but may still be less reproducible than standardized gene-expression-based assays Non-standardized research-based identification of the intrinsic subtypes shows concordance values equivalent to current clinical testing for histological grade, ER, PR and HER2 For daily clinical use, we recommend the highest level of reproducibility/concordance (Level 1), which will only be achieved for pathology and gene-expression-based tests by using a single platform and standardized protocol

Background The four intrinsic subtypes of breast cancer, defined by differential expression of 50 genes (PAM50), have been shown to be predictive of risk of recurrence and benefit of hormonal therapy and chemotherapy. Here we describe the development of Prosigna™, a PAM50-based subtype classifier and risk model on the NanoString nCounter Dx Analysis System intended for decentralized testing in clinical laboratories. Methods 514 formalin-fixed, paraffin-embedded (FFPE) breast cancer patient samples were used to train prototypical centroids for each of the intrinsic subtypes of breast cancer on the NanoString platform. Hierarchical cluster analysis of gene expression data was used to identify the prototypical centroids defined in previous PAM50 algorithm training exercises. 304 FFPE patient samples from a well annotated clinical cohort in the absence of adjuvant systemic therapy were then used to train a subtype-based risk model (i.e. Prosigna ROR score). 232 samples from a tamoxifen-treated patient cohort were used to verify the prognostic accuracy of the algorithm prior to initiating clinical validation studies. Results The gene expression profiles of each of the four Prosigna subtype centroids were consistent with those previously published using the PCR-based PAM50 method. Similar to previously published classifiers, tumor samples classified as Luminal A by Prosigna had the best prognosis compared to samples classified as one of the three higher-risk tumor subtypes. The Prosigna Risk of Recurrence (ROR) score model was verified to be significantly associated with prognosis as a continuous variable and to add significant information over both commonly available IHC markers and Adjuvant! Online. Conclusions The results from the training and verification data sets show that the FDA-cleared and CE marked Prosigna test provides an accurate estimate of the risk of distant recurrence in hormone receptor positive breast cancer and is also capable of identifying a tumor's intrinsic subtype that is consistent with the previously published PCR-based PAM50 assay. Subsequent analytical and clinical validation studies confirm the clinical accuracy and technical precision of the Prosigna PAM50 assay in a decentralized setting.

Metastatic tumor cell dissemination is the leading cause of cancer-related deaths. Clustered circulating tumor cells (CTCs) possess higher metastatic potential than single CTCs. Epithelial adherens junction (AJ) proteins typically mediate stable cell-cell interactions; however, these proteins are frequently lost in highly aggressive triple-negative breast cancers (TNBCs), raising the question of how CTCs from such tumors cluster. Here we show that the extracellular matrix (ECM) component hyaluronan (HA) mediates AJ-independent CTC clustering in TNBCs. HA is necessary and sufficient to drive clustering of tumor cells expressing its receptor CD44. Mechanistically, HA initiates contact between neighboring cells through actin-based membrane protrusions. As cells are pulled closer, these initial interactions expand to membrane-membrane contact and are subsequently stabilized by desmosomes. CTC-derived HA also acts as a docking platform to promote heterotypic cluster formation by recruiting non-CTCs, including immune cells. Thus, this ECM–receptor interaction enables CTC clustering and survival under shear stress, enhancing TNBC metastasis. Circulating tumor cell (CTC) clusters are key drivers of metastasis, yet their formation in tumors lacking classical adhesion molecules is unclear. Here, the authors discover that hyaluronic acid promotes homotypic and heterotypic CTC clustering by initiating early cell contacts and stabilizing mature interactions.

SMARCB1 loss has long been observed in many solid tumors. However, there is a need to elucidate targetable pathways driving growth and metastasis in SMARCB1-deficient tumors. Here, we demonstrate that SMARCB1 deficiency, defined as genomic SMARCB1 copy number loss associated with reduced mRNA, drives disease progression in patients with bladder cancer by engaging STAT3. SMARCB1 loss increases the chromatin accessibility of the STAT3 locus in vitro. Orthotopically implanted SMARCB1 knockout (KO) cell lines exhibit increased tumor growth and metastasis. SMARCB1-deficient tumors show an increased IL6/JAK/STAT3 signaling axis in in vivo models and patients. Furthermore, a pSTAT3 selective inhibitor, TTI-101, reduces tumor growth in SMARCB1 KO orthotopic cell line-derived xenografts and a SMARCB1-deficient patient derived xenograft model. We have identified a gene signature generated from SMARCB1 KO tumors that predicts SMARCB1 deficiency in patients. Overall, these findings support the clinical evaluation of STAT3 inhibitors for the treatment of SMARCB1-deficient bladder cancer. SMARCB1 is frequently lost in solid cancer and reported to support tumourigenesis through STAT3 activation. Here, the authors show in several preclinical models that targeting IL6/JAK/STAT3 molecular pathway is a potential therapeutic approach for SMARCB1-deficient bladder cancer.

Background The ACOSOG Z1031 trial addressed the ability of three neoadjuvant aromatase inhibitors (NAIs) to reduce residual disease (cohort A) and to assess whether switching to neoadjuvant chemotherapy (NCT) after 4 weeks of receiving NAI with Ki67 greater than 10% increases pathologic complete response (pCR) in postmenopausal women with estrogen receptor-enriched (Allred score 6–8) breast cancer (BC). Methods The study enrolled 622 women with clinical stage 2 or 3 estrogen receptor-positive (ER+) BC. Cohort A comprised 377 patients, and cohort B had 245 patients. The analysis cohort consisted of 509 patients after exclusion of patients who did not meet the trial eligibility criteria, switched to NCT or surgery due to 4-week Ki67 greater than 10%, or withdrew before surgery. Distribution of time to local-regional recurrence (LRR) was estimated using the competing-risk approach, in which distant recurrence and second primaries were considered to be competing-risk events. Patients who died without LRR, distant recurrence, or a second primary were censored at the last evaluation. Results Of the 509 patients, 342 (67.2%) had breast-conserving surgery (BCS). Of 221 patients thought to require mastectomy at presentation, 50% were able to have BCS. Five (1%) patients had no residual disease in the breast or nodes at surgery. Among 382 women alive at this writing, 90% have been followed longer than 5 years. The 5-year cumulative incidence rate for LRR is estimated to be 1.53% (95% confidence interval 0.7–3.0%). Conclusions Rarely does NAI result in pCR for patients with stage 2 or 3 ER+ BC. However, a significant proportion will have downstaged to allow for BCS. Local-regional recurrence after surgery is uncommon (1.5% at 5 years), supporting the use of BCS after NAI.