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Cancer-associated fibroblasts (CAFs) are a major constituent of the tumor microenvironment, although their origin and roles in shaping disease initiation, progression and treatment response remain unclear due to significant heterogeneity. Here, following a negative selection strategy combined with single-cell RNA sequencing of 768 transcriptomes of mesenchymal cells from a genetically engineered mouse model of breast cancer, we define three distinct subpopulations of CAFs. Validation at the transcriptional and protein level in several experimental models of cancer and human tumors reveal spatial separation of the CAF subclasses attributable to different origins, including the peri-vascular niche, the mammary fat pad and the transformed epithelium. Gene profiles for each CAF subtype correlate to distinctive functional programs and hold independent prognostic capability in clinical cohorts by association to metastatic disease. In conclusion, the improved resolution of the widely defined CAF population opens the possibility for biomarker-driven development of drugs for precision targeting of CAFs. Cancer-associated fibroblasts (CAFs) are an important component of the breast tumour microenvironment. Here, single-cell RNA sequencing of CAFs from a mouse model of breast cancer defines three transcriptomically distinct subpopulations with putatively different functions.

Metastatic breast cancers are still incurable. Characterizing the evolutionary landscape of these cancers, including the role of metastatic axillary lymph nodes (ALNs) in seeding distant organ metastasis, can provide a rational basis for effective treatments. Here, we have described the genomic analyses of the primary tumors and metastatic lesions from 99 samples obtained from 20 patients with breast cancer. Our evolutionary analyses revealed diverse spreading and seeding patterns that govern tumor progression. Although linear evolution to successive metastatic sites was common, parallel evolution from the primary tumor to multiple distant sites was also evident. Metastatic spreading was frequently coupled with polyclonal seeding, in which multiple metastatic subclones originated from the primary tumor and/or other distant metastases. Synchronous ALN metastasis, a well-established prognosticator of breast cancer, was not involved in seeding the distant metastasis, suggesting a hematogenous route for cancer dissemination. Clonal evolution coincided frequently with emerging driver alterations and evolving mutational processes, notably an increase in apolipoprotein B mRNA-editing enzyme, catalytic polypeptide-like-associated (APOBEC-associated) mutagenesis. Our data provide genomic evidence for a role of ALN metastasis in seeding distant organ metastasis and elucidate the evolving mutational landscape during cancer progression.

BackgroundThe role of B-lymphocytes in solid tumours is unclear. Tumour biology studies have implied both anti- and pro-tumoural effects and prognostic studies have mainly linked B-cells to increased survival. This study aimed to analyse the clinical relevance of B-lymphocytes in renal cell cancer (RCC), where information on the prognostic impact is lacking.MethodsFollowing immunohistochemistry (IHC) stainings with a CD20 antibody, density of CD20+ B-cells was quantified in an RCC discovery- and validation cohort. Associations of B-cell infiltration, determined by CD20 expression or a B-cell gene-signature, and survival was also analysed in 14 publicly available gene expression datasets of cancer, including the kidney clear cell carcinoma (KIRC) dataset.ResultsIHC analyses of the discovery cohort identified a previously unrecognised subgroup of RCC patients with high infiltration of CD20+ B-cells. The B-cell-high subgroup displayed significantly shorter survival according to uni- and multi-variable analyses. The association between poor prognosis and high density of CD20+ B-cells was confirmed in the validation cohort. Analyses of the KIRC gene expression dataset using the B-cell signature confirmed findings from IHC analyses. Analyses of other gene expression datasets, representing 13 different tumour types, indicated that the poor survival-association of B-cells occurred selectively in RCC.ConclusionThis exploratory study identifies a previously unrecognised poor-prognosis subset of RCC with high density of CD20-defined B-cells.

Background:Transcriptomic profiles have shown promise as predictors of response to neoadjuvant chemotherapy in breast cancer (BC). This study aimed to explore their predictive value in the advanced BC (ABC) setting.Methods:In a Phase 3 trial of first-line chemotherapy in ABC, a fine needle aspiration biopsy (FNAB) was obtained at baseline. Intrinsic molecular subtypes and gene modules related to immune response, proliferation, oestrogen receptor (ER) signalling and recurring genetic alterations were analysed for association with objective response to chemotherapy. Gene-set enrichment analysis (GSEA) of responders vs non-responders was performed independently. Lymphocytes were enumerated in FNAB smears and the absolute abundance of immune cell types was calculated using the Microenvironment Cell Populations counter method.Results:Gene expression data were available for 109 patients. Objective response to chemotherapy was statistically significantly associated with an immune module score (odds ratio (OR)=1.62; 95% confidence interval (CI), 1.03-2.64; P=0.04). Subgroup analysis showed that this association was restricted to patients with ER-positive or luminal tumours (OR=3.54; 95%, 1.43-10.86; P=0.012 and P for interaction=0.04). Gene-set enrichment analysis confirmed that in these subgroups, immune-related gene sets were enriched in responders.Conclusions:Immune-related transcriptional signatures may predict response to chemotherapy in ER-positive and luminal ABC.

Breast cancer (BC) is a complex disease comprising multiple distinct subtypes with different genetic features and pathological characteristics. Although a large number of antineoplastic compounds have been approved for clinical use, patient-to-patient variability in drug response is frequently observed, highlighting the need for efficient treatment prediction for individualized therapy. Several patient-derived models have been established lately for the prediction of drug response. However, each of these models has its limitations that impede their clinical application. Here, we report that the whole-tumor cell culture (WTC) ex vivo model could be stably established from all breast tumors with a high success rate (98 out of 116), and it could reassemble the parental tumors with the endogenous microenvironment. We observed strong clinical associations and predictive values from the investigation of a broad range of BC therapies with WTCs derived from a patient cohort. The accuracy was further supported by the correlation between WTC-based test results and patients’ clinical responses in a separate validation study, where the neoadjuvant treatment regimens of 15 BC patients were mimicked. Collectively, the WTC model allows us to accomplish personalized drug testing within 10 d, even for small-sized tumors, highlighting its potential for individualized BC therapy. Furthermore, coupled with genomic and transcriptomic analyses, WTC-based testing can also help to stratify specific patient groups for assignment into appropriate clinical trials, as well as validate potential biomarkers during drug development.

Background Transcriptomic profiling of breast tumors provides opportunity for subtyping and molecular-based patient stratification. In diagnostic applications the specimen profiled should be representative of the expression profile of the whole tumor and ideally capture properties of the most aggressive part of the tumor. However, breast cancers commonly exhibit intra-tumor heterogeneity at molecular, genomic and in phenotypic level, which can arise during tumor evolution. Currently it is not established to what extent a random sampling approach may influence molecular breast cancer diagnostics. Methods In this study we applied RNA-sequencing to quantify gene expression in 43 pieces (2-5 pieces per tumor) from 12 breast tumors (Cohort 1). We determined molecular subtype and transcriptomic grade for all tumor pieces and analysed to what extent pieces originating from the same tumors are concordant or discordant with each other. Additionally, we validated our finding in an independent cohort consisting of 19 pieces (2-6 pieces per tumor) from 6 breast tumors (Cohort 2) profiled using microarray technique. Exome sequencing was also performed on this cohort, to investigate the extent of intra-tumor genomic heterogeneity versus the intra-tumor molecular subtype classifications. Results Molecular subtyping was consistent in 11 out of 12 tumors and transcriptomic grade assignments were consistent in 11 out of 12 tumors as well. Molecular subtype predictions revealed consistent subtypes in four out of six patients in this cohort 2. Interestingly, we observed extensive intra-tumor genomic heterogeneity in these tumor pieces but not in their molecular subtype classifications. Conclusions Our results suggest that macroscopic intra-tumoral transcriptomic heterogeneity is limited and unlikely to have an impact on molecular diagnostics for most patients.

We investigated the interaction of CYP2C19 and CYP2D6 genotype on clinical outcome in tamoxifen-treated breast cancer patients. A cohort of 306 patients on tamoxifen treatment for a minimum of 1 year were employed to analyze the effect of genotype-predicted phenotype on relapse-free survival. We show that the group with worst outcome and highest risk of relapse is that of 2C19↑-2D6↓ (hazard ratio: 2.94), when adjusting for age, Nottingham prognostic index and adjuvant chemotherapy. Furthermore, the effect of 2C19↑-2D6↓genotype-predicted phenotype is greatly enhanced in premenopausal patients (hazard ratio: 21.08). We hypothesize that poor bioactivation of tamoxifen in patients with low CYP2D6 activity and high CYP2C19 metabolism represents a tamoxifen-treated patient group that has the worst clinical outcome.

The feasibility of longitudinal metastatic biopsies for gene expression profiling in breast cancer is unexplored. Dynamic changes in gene expression can potentially predict efficacy of targeted cancer drugs. Patients enrolled in a phase III trial of metastatic breast cancer with docetaxel monotherapy versus combination of docetaxel + sunitinib were offered to participate in a translational substudy comprising longitudinal fine needle aspiration biopsies and Positron Emission Tomography imaging before (T1) and two weeks after start of treatment (T2). Aspirated tumor material was used for microarray analysis, and treatment-induced changes (T2 versus T1) in gene expression and standardized uptake values (SUV) were investigated and correlated to clinical outcome measures. Gene expression profiling yielded high-quality data at both time points in 14/18 patients. Unsupervised clustering revealed specific patterns of changes caused by monotherapy vs. combination therapy (p = 0.021, Fisher's exact test). A therapy-induced reduction of known proliferation and hypoxia metagene scores was prominent in the combination arm. Changes in a previously reported hypoxia metagene score were strongly correlated to the objective responses seen by conventional radiology assessments after 6 weeks in the combination arm, Spearman's ρ = 1 (p = 0.017) but not in monotherapy, ρ = −0.029 (p = 1). Similarly, the Predictor Analysis of Microarrays 50 (PAM50) proliferation metagene correlated to tumor changes merely in the combination arm at 6 and 12 weeks (ρ = 0.900, p = 0.083 and ρ = 1, p = 0.017 respectively). Reductions in mean SUV were a reliable early predictor of objective response in monotherapy, ρ = 0.833 (p = 0.008), but not in the combination arm ρ = −0.029 (p = 1). Gene expression profiling of longitudinal metastatic aspiration biopsies was feasible, demonstrated biological validity and provided predictive information. •Sequential biopsies were incorporated in a phase 3 trial of advanced breast cancer.•Gene expression profiling of sequential metastatic aspiration biopsies was feasible.•Comparison of pre-with post-treatment profiles showed biologically valid changes.•Changes in hypoxia and proliferation scores could predict response to sunitinib.

We investigated the interaction of and genotype on clinical outcome in tamoxifen-treated breast cancer patients. A cohort of 306 patients on tamoxifen treatment for a minimum of 1 year were employed to analyze the effect of genotype-predicted phenotype on relapse-free survival. We show that the group with worst outcome and highest risk of relapse is that of 2C19↑-2D6↓ (hazard ratio: 2.94), when adjusting for age, Nottingham prognostic index and adjuvant chemotherapy. Furthermore, the effect of 2C19↑-2D6↓genotype-predicted phenotype is greatly enhanced in premenopausal patients (hazard ratio: 21.08). We hypothesize that poor bioactivation of tamoxifen in patients with low CYP2D6 activity and high CYP2C19 metabolism represents a tamoxifen-treated patient group that has the worst clinical outcome.