The Breast Cancer Single Cell Line Atlas: A blueprint of breast cancer cell lines and their role in disease subtyping, heterogeneity, stemness, and therapeutic testing
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ABSTRACT:
ORGANISM(S): Homo sapiens
PROVIDER: GSE182694 | GEO | 2021/12/31
REPOSITORIES: GEO
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Project description:We investigated the heterogeneous cell populations within the Comma-1D cell line through single cell RNA sequencing technologies. The assay sequenced over 5,000 cells composing 8 unique subclusters. Populations detected included luminal, myoepithelial, and fibroblast like cell types.
2021-12-31 | GSE182589 | GEO
Project description:PurposeBluePrint (BP) is an 80-gene molecular subtyping test that classifies early-stage breast cancer (EBC) into Basal, Luminal, and HER2 subtypes. In most cases, breast tumors have one dominant subtype, representative of a single activated pathway. However, some tumors show a statistically equal representation of more than one subtype, referred to as dual subtype. This study aims to identify and examine dual subtype tumors by BP to understand their biology and possible implications for treatment guidance.MethodsThe BP scores of over 15,000 tumor samples from EBC patients were analyzed, and the differences between the highest and the lowest scoring subtypes were calculated. Based upon the distribution of the differences between BP scores, a threshold was determined for each subtype to identify dual versus single subtypes.ResultsApproximately 97% of samples had one single activated BluePrint molecular subtype, whereas ~ 3% of samples were classified as BP dual subtype. The most frequently occurring dual subtypes were the Luminal-Basal-type and Luminal-HER2-type. Luminal-Basal-type displays a distinct biology from the Luminal single type and Basal single type. Burstein's classification of the single and dual Basal samples showed that the Luminal-Basal-type is mostly classified as 'luminal androgen receptor' and 'mesenchymal' subtypes, supporting molecular evidence of AR activation in the Luminal-Basal-type tumors. Tumors classified as Luminal-HER2-type resemble features of both Luminal-single-type and HER2-single-type. However, patients with dual Luminal-HER2-type have a lower pathological complete response after receiving HER2-targeted therapies in addition to chemotherapy in comparison with patients with a HER2-single-type.ConclusionThis study demonstrates that BP identifies tumors with two active functional pathways (dual subtype) with specific transcriptional characteristics and highlights the added value of distinguishing BP dual from single subtypes as evidenced by distinct treatment response rates.
| S-EPMC9464757 | biostudies-literature
Project description:We investigated the heterogeneous cell populations composing Bovine Intervertebral Discs (IVDs) through single cell RNA sequencing technologies. The assay sequenced over 14,000 cells composing 5 bovine discs from 3 unique bovine tails. Through both established and custom analysis pipelines, we characterize cell heterogeneity between populations of Nucleus Pulposus and Annulus Fibrosus cells. We further characterize populations of Endothelial, Muscle, Immune, and Notochord.
2021-10-07 | GSE179714 | GEO
Project description:Human epidermal growth factor receptor 2 (HER2) positivity is an important prognostic and predictive indicator in breast cancer. HER2 status is determined by immunohistochemistry and fluorescent in situ hybridization (FISH), which are potentially inaccurate techniques as a result of several technical factors, polysomy of chromosome 17, and amplification or overexpression of CEP17 (centromeric probe for chromosome 17) and/or HER2. In South Africa, HER2-positive tumors are excluded from a MammaPrint (MP; Agendia BV, Amsterdam, Netherlands) pretest algorithm. Clinical HER2 status has been reported to correlate poorly with molecular subtype. The aim of this study was to investigate the correlation of clinical HER2 status with BluePrint (BP) molecular subtyping.Clinico-pathologic and genomic information was extracted from a prospectively collected central MP database containing records of 256 estrogen receptor-positive and/or progesterone receptor-positive tumors. Twenty-one tumors considered HER2 positive on immunohistochemistry or FISH were identified for this study.The median age of patients was 56 years (range, 34 to 77 years), with a median tumor size of 16 mm (3 to 27 mm). Four (19%) tumors were confirmed HER2-enriched subtype, six (29%) were luminal A, and 11 (52%) were luminal B. The positive predictive values of HER2/CEP17 ratio ≥ 2 and HER2 copy number ≥ 6 were only 29% and 40%, respectively. The differences in means for HER2/CEP17 ratio were significant between BP HER2-enriched versus luminal (P = .0249; 95% CI, 0.12 to 1.21) and MP high-risk versus low-risk tumors (P = .0002; 95% CI, 0.40 to 1.06).Of the 21 tumors considered clinically HER2 positive, only four were HER2-enriched subtype with BP, indicating an overestimation of HER2 positivity. FISH testing has a poor positive predictive value.
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Project description:The Breast Cancer Single Cell Line Atlas: A blueprint of breast cancer cell lines and their role in disease subtyping, heterogeneity, stemness, and therapeutic testing
| PRJNA757445 | ENA
Project description:BackgroundEarly detection of breast cancer in blood is both appealing clinically and challenging technically due to the disease's illusive nature and heterogeneity. Today, even though major breast cancer subtypes have been characterized, i.e., luminal A, luminal B, HER2+, and basal-like, little is known about the heterogeneity of breast cancer in blood, which could help to discover minimally invasive protein biomarkers with which clinical researchers can detect, classify, and monitor different breast cancer subtypes.ResultsIn this study, we performed an integrative pathway-assisted clustering analysis of breast cancer subtypes from plasma proteome samples collected from 80 patients diagnosed with breast cancer and 80 healthy women. First, four breast cancer subtypes and additionally unknown subtype (according to existing annotation) were determined based on pathology lab test results in primary tumors of enrolled patients. Next, we developed and applied four distance metrics, i.e., Protein Intensity, Q-Value, Pathway Profile, and Distance Score Function, to measure and characterize these cancer subtypes. Then, we developed a permutation test to evaluate the significant protein level changes in each biological pathway for each breast cancer subtype, using q-value. Lastly, we developed a pathway-protein matrix for each of the four distance methods to estimate the distance between breast cancer subtypes, for which further Pathway Association Network analysis were performed.ConclusionsWe found that 1) the luminal group (luminal A and luminal B) are clustered together, as well as the basal group (basal-like and HER2+) and 2) luminal A and luminal B are more close to each other than basal-like and HER2+ to each other. Our results were consistent with a recent independent breast cancer research from the Cancer Genome Atlas Network using genomic DNA copy number arrays, DNA methylation, exome sequencing, messenger RNA arrays, microRNA sequencing and reverse-phase protein arrays. Our results showed that changes of different breast cancer subtypes at the pathway level are more profound and less variable than those at the molecular level. Similar subtypes share distinct yet similar pathway activation networks, while dissimilar subtypes are different also at the level of pathway activation networks. The results also showed that distance or similarity of cancer subtypes based on pathway analysis might be able to provide further insight into the intrinsic relationship of breast cancer subtypes. We believe integrative pathway-assisted proteomics analysis described here can become a model for reliable clustering or classification of other cancer subtypes.
| S-EPMC3552699 | biostudies-literature
Project description:The primary goal of this study is to identify molecular subtypes of breast cancer through gene expression profiles of 327 breast cancer samples and determine molecular and clinical characteristics of different breast cancer subtypes. We studied expression signatures of different cellular functions (e.g., cell proliferation/cell cycle, wound response, tumor stromal response, vascular endothelial normalization, drug esponse genes, etc.) in different breast cancer molecular subtypes and investigated how microarray-based breast cancer molecular subtypes may be used to guide treatment. Gene expression profiles of 327 breast cancer samples were determined using total RNA and Affymetrix U133 plus 2.0 arrays.
| S-ECPF-GEOD-20685 | biostudies-other
Project description:The analytical performance of a multi-gene diagnostic signature depends on many parameters, including precision, repeatability, reproducibility and intra-tumor heterogeneity. Here we study the analytical performance of the BluePrint 80-gene breast cancer molecular subtyping test through determination of these performance characteristics. BluePrint measures the expression of 80 genes that assess functional pathways which determine the intrinsic breast cancer molecular subtypes (i.e. Luminal-type, HER2-type, Basal-type). Knowing a tumor's dominant functional pathway can help allocate effective treatment to appropriate patients. Here we show that BluePrint is a highly precise and highly reproducible test with correlations above 98% based on the generated index and subtype concordance above 99%. Therefore, BluePrint can be used as a robust and reliable tool to identify breast cancer molecular subtypes.
| S-EPMC7097521 | biostudies-literature
Project description:Breast cancer is commonly classified into intrinsic molecular subtypes. Standard gene centering is routinely done prior to molecular subtyping, but it can produce inaccurate classifications when the distribution of clinicopathological characteristics in the study cohort differs from that of the training cohort used to derive the classifier.We propose a subgroup-specific gene-centering method to perform molecular subtyping on a study cohort that has a skewed distribution of clinicopathological characteristics relative to the training cohort. On such a study cohort, we center each gene on a specified percentile, where the percentile is determined from a subgroup of the training cohort with clinicopathological characteristics similar to the study cohort. We demonstrate our method using the PAM50 classifier and its associated University of North Carolina (UNC) training cohort. We considered study cohorts with skewed clinicopathological characteristics, including subgroups composed of a single prototypic subtype of the UNC-PAM50 training cohort (n = 139), an external estrogen receptor (ER)-positive cohort (n = 48) and an external triple-negative cohort (n = 77).Subgroup-specific gene centering improved prediction performance with the accuracies between 77% and 100%, compared to accuracies between 17% and 33% from standard gene centering, when applied to the prototypic tumor subsets of the PAM50 training cohort. It reduced classification error rates on the ER-positive (11% versus 28%; P = 0.0389), the ER-negative (5% versus 41%; P < 0.0001) and the triple-negative (11% versus 56%; P = 0.1336) subgroups of the PAM50 training cohort. In addition, it produced higher accuracy for subtyping study cohorts composed of varying proportions of ER-positive versus ER-negative cases. Finally, it increased the percentage of assigned luminal subtypes on the external ER-positive cohort and basal-like subtype on the external triple-negative cohort.Gene centering is often necessary to accurately apply a molecular subtype classifier. Compared with standard gene centering, our proposed subgroup-specific gene centering produced more accurate molecular subtype assignments in a study cohort with skewed clinicopathological characteristics relative to the training cohort.
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2021-10-15 | E-MTAB-11006 | biostudies-arrayexpress