Toward precision medicine in inflammatory breast cancer
Ontology highlight
ABSTRACT:
SUBMITTER: Viale G
PROVIDER: S-EPMC8798747 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:BackgroundIntertumoral heterogeneity represents a significant hurdle to identifying optimized targeted therapies in gastric cancer (GC). To realize precision medicine for GC patients, an actionable gene alteration-based molecular classification that directly associates GCs with targeted therapies is needed.MethodsA total of 207 Japanese patients with GC were included in this study. Formalin-fixed, paraffin-embedded (FFPE) tumor tissues were obtained from surgical or biopsy specimens and were subjected to DNA extraction. We generated comprehensive genomic profiling data using a 435-gene panel including 69 actionable genes paired with US Food and Drug Administration-approved targeted therapies, and the evaluation of Epstein-Barr virus (EBV) infection and microsatellite instability (MSI) status.ResultsComprehensive genomic sequencing detected at least one alteration of 435 cancer-related genes in 194 GCs (93.7%) and of 69 actionable genes in 141 GCs (68.1%). We classified the 207 GCs into four The Cancer Genome Atlas (TCGA) subtypes using the genomic profiling data; EBV (N = 9), MSI (N = 17), chromosomal instability (N = 119), and genomically stable subtype (N = 62). Actionable gene alterations were not specific and were widely observed throughout all TCGA subtypes. To discover a novel classification which more precisely selects candidates for targeted therapies, 207 GCs were classified using hypermutated phenotype and the mutation profile of 69 actionable genes. We identified a hypermutated group (N = 32), while the others (N = 175) were sub-divided into six clusters including five with actionable gene alterations: ERBB2 (N = 25), CDKN2A, and CDKN2B (N = 10), KRAS (N = 10), BRCA2 (N = 9), and ATM cluster (N = 12). The clinical utility of this classification was demonstrated by a case of unresectable GC with a remarkable response to anti-HER2 therapy in the ERBB2 cluster.ConclusionsThis actionable gene-based classification creates a framework for further studies for realizing precision medicine in GC.
| S-EPMC5664811 | biostudies-literature
Project description:In recent decades, breast cancer has become largely manageable due to successes with hormone receptor targeting. Hormone receptor-positive tumors have favorable outcomes in comparison to estrogen receptor (ESR1, ER)/progesterone receptor-negative tumors given the targetable nature of these tumors, as well as their inherently less aggressive character. Nonetheless, treatment resistance is frequently encountered due to a variety of mechanisms, including ESR1 mutations and loss of ER expression. A new era of precision medicine utilizes a range of methodologies to allow real-time analysis of individual genomic signatures in metastases and liquid biopsies with the goal of finding clinically actionable targets. Preliminary studies have shown improved progression-free survival and overall survival with implementation of this information for clinical decision making. In this review, we will discuss the opportunities and challenges in integrating precision medicine through next-generation genomic sequencing into the management of breast cancer.
| S-EPMC5945817 | biostudies-other
Project description:PurposeThis paper reports on a novel measure, attitudes toward genomics and precision medicine (AGPM), which evaluates attitudes toward activities such as genetic testing, collecting information on lifestyle, and genome editing - activities necessary to achieve the goals of precision medicine.DiscussionThe AGPM will be useful for researchers who want to explore attitudes toward genomics and precision medicine. The association of concerns about precision medicine activities with demographic variables such as religion and politics, as well as higher levels of education, suggests that further education on genomic and precision activities alone is unlikely to shift AGPM scores significantly.MethodsWe wrote items to represent psychological and health benefits of precision medicine activities, and concerns about privacy, social justice, harm to embryos, and interfering with nature. We validated the measure through factor analysis of its structure, and testing associations with trust in the health information system and demographic variables such as age, sex, education, and religion.ResultsThe AGPM had excellent alpha reliability (.92) and demonstrated good convergent validity with existing measures. Variables most strongly associated with higher levels of concern with precision medicine activities included: regular religious practice, republican political leanings, and higher levels of education.
| S-EPMC8278159 | biostudies-literature
Project description:Many cancers, including breast cancer, have demonstrated prognosis and support advantages thanks to the discovery of targeted therapies. The advent of these new approaches marked the rise of precision medicine, which leads to improve the diagnosis, prognosis and treatment of cancer. Precision medicine takes into account the molecular and biological specificities of the patient and their tumors that will influence the treatment determined by physicians. This new era of medicine is accessible through molecular genetics platforms, the development of high-speed sequencers and means of analysis of these data. Despite the spectacular results in the treatment of cancers including breast cancer, described in this review, not all patients however can benefit from this new strategy. This seems to be related to the many genetic mutations, which may be different from one patient to another or within the same patient. It comes to give new impetus to the research-both from a technological and biological point of view-to make the hope of precision medicine accessible to all.
| S-EPMC5474301 | biostudies-literature
Project description:Leiomyosarcoma (LMS) is a malignant neoplasm with smooth muscle differentiation. Little is known about its molecular heterogeneity and no targeted therapy currently exists for LMS. We demonstrate the existence of 3 molecular subtypes in a cohort of 99 cases and an independent cohort of 82 LMS. Two new FFPE tissue-compatible diagnostic immunohistochemical markers are identified: LMOD1 for subtype I LMS and ARL4C for subtype II LMS. Subtype I and subtype II LMS are associated with good and poor prognosis, respectively. The LMS subtypes show significant differences in expression levels for genes for which novel targeted therapies are being developed. Gene expression profiling was performed by 3' End RNA Sequencing (3SEQ), a next generation sequencing approach that does not rely on frozen tissue but can be performed on archival FFPE tissue. Samples included 99 LMS, 6 Undifferentiated Pleomorphic Sarcomas (UPS), 3 leiomyomas, 4 normal myometrium samples, and 1 case of Lymphangioleiomyomatosis (LAM). This study only includes the 99 LMS Samples. After gene expression levels were quantified by 3SEQ analysis pipeline, Consensus Clustering with bootstrap method was used to determine that the dataset contained three robust subtypes, and Silhouette analysis was performed to validate the subtype assignments. Two class SAM analysis (Significance Analysis of Microarrays) was performed to identify genes expressed differentially between each subtype of LMS with FDR of 0.05. Immunohistochemical staining was used to validate the potential diagnostic and prognostic markers from 3SEQ data on a tissue microarray.
2015-05-19 | E-GEOD-45510 | biostudies-arrayexpress
Project description:Precision medicine is an innovative approach that uses emerging biomedical technologies to deliver optimally targeted and timed interventions, customized to the molecular drivers of an individual's disease. This approach is only just beginning to be considered for treating amyotrophic lateral sclerosis (ALS). The clinical and biological complexities of ALS have hindered development of effective therapeutic strategies. In this review we consider applying the key elements of precision medicine to ALS: phenotypic classification, comprehensive risk assessment, presymptomatic period detection, potential molecular pathways, disease model development, biomarker discovery and molecularly tailored interventions. Together, these would embody a precision medicine approach, which may provide strategies for optimal targeting and timing of efforts to prevent, stop or slow progression of ALS.
| S-EPMC4731596 | biostudies-literature
Project description:Large-scale screening of drug sensitivity on cancer cell models can mimic in vivo cellular behavior providing wider scope for biological research on cancer. Since the therapeutic effect of a single drug or drug combination depends on the individual patient's genome characteristics and cancer cells integration reaction, the identification of an effective agent in an in vitro model by using large number of cancer cell models is a promising approach for the development of targeted treatments. Precision cancer medicine is to select the most appropriate treatment or treatments for an individual patient. However, it still lacks the tools to bridge the gap between conventional in vitro cancer cell models and clinical patient response to inhibitors. An optimal two-layer decision system model is developed to identify the cancer cells that most closely resemble an individual tumor for optimum therapeutic interventions in precision cancer medicine. Accordingly, an optimal grid parameters selection is designed to seek the highest accordance for treatment selection to the patient's preference for drug response and in vitro cancer cell drug screening. The optimal two-layer decision system model overcomes the challenge of heterology data comparison between the tumor and the cancer cells, as well as between the continual variation of drug responses in vitro and the discrete ones in clinical practice. We simulated the model accuracy using 681 cancer cells' mRNA and associated 481 drug screenings and validated our results on 315 breast cancer patients drug selection across seven drugs (docetaxel, doxorubicin, fluorouracil, paclitaxel, tamoxifen, cyclophosphamide, lapitinib). Comparing with the real response of a drug in clinical patients, the novel model obtained an overall average accordance over 90.8% across the seven drugs. At the same time, the optimal cancer cells and the associated optimal therapeutic efficacy of cancer drugs are recommended. The novel optimal two-layer decision system model was used on 1097 patients with breast cancer in guiding precision medicine for a recommendation of their optimal cancer cells (30 cancer cells) and associated efficacy of certain cancer drugs. Our model can detect the most similar cancer cells for each individual patient. A successful clinical translation model (optimal two-layer decision system model) was developed to bridge in-vitro basic science to clinical practice in a therapeutic intervention application for the first time. The novel tool kills two birds with one stone. It can help basic science to seek optimal cancer cell models for an individual tumor, while prioritizing clinical drugs' recommendations in practice. Tool associated platform website: We extended the breast cancer research to 32 more types of cancers across 45 therapy predictions. The website is set up and can be accessed by the link: https://pcm2019.shinyapps.io/drug_response_prediction/.
| S-EPMC7140855 | biostudies-literature
Project description:Trastuzumab (Herceptin), a monoclonal antibody directed against the human epidermal growth-factor receptor 2 (HER2), is the poster child for antibody-based targeted therapy in breast cancer. Pertuzumab, another humanized monoclonal antibody, binds to a different domain of HER2 and prevents the formation of HER2:HER3 dimers, which is the most potent heterodimer in the HER family. The combination of trastuzumab and pertuzumab has synergistic activity, and is associated with improved clinical outcomes. The US Food and Drug Administration (FDA) approved pertuzumab in combination with trastuzumab-based chemotherapy originally as first-line therapy for metastatic HER2-positive breast cancer in 2012, and more recently as neoadjuvant therapy for localized disease in 2013. Pertuzumab is the first neoadjuvant drug to receive accelerated approval by the FDA based on pathological complete response as the primary end point. In this article, we review the mechanism of action, pharmacokinetics, clinical efficacy, safety, and current role of pertuzumab in the management of breast cancer, as well as ongoing clinical trials and future directions regarding the utility of pertuzumab as a personalized therapeutic option for HER2-positive breast cancer. In the coming years, we anticipate increased utilization of neoadjuvant trials for drug development, biomarker discovery, and validation, and envision conduct of personalized breast cancer clinics in which therapies will be routinely selected based on genetic alterations in the tumor. Regardless of the targeted therapy combinations employed based on tumor genomic profile, trastuzumab and pertuzumab will likely continue to form the backbone of the personalized regimen for HER2-positive breast cancer.
| S-EPMC3977457 | biostudies-literature
Project description:There is wide individual variability in the pharmacokinetics, pharmacodynamics, and tolerance to anticancer drugs within the same ethnic group and even greater variability among different ethnicities. Pharmacogenomics (PG) has the potential to provide personalized therapy based on individual genetic variability in an effort to maximize efficacy and reduce adverse effects. The benefits of PG include improved therapeutic index, improved dose regimen, and selection of optimal types of drug for an individual or set of individuals. Advanced or metastatic breast cancer is typically treated with single or multiple combinations of chemotherapy regimens including anthracyclines, taxanes, antimetabolites, alkylating agents, platinum drugs, vinca alkaloids, and others. In this review, the PG of breast cancer therapeutics, including tamoxifen, which is the most widely used therapeutic for the treatment of hormone-dependent breast cancer, is reviewed. The pharmacological activity of tamoxifen depends on its conversion by cytochrome P450 2D6 (CYP2D6) to its abundant active metabolite, endoxifen. Patients with reduced CYP2D6 activity, as a result of either their genotype or induction by the coadministration of other drugs that inhibit CYP2D6 function, produce little endoxifen and hence derive limited therapeutic benefit from tamoxifen; the same can be said about the different classes of therapeutics in breast cancer. PG studies of breast cancer therapeutics should provide patients with breast cancer with optimal and personalized therapy.
| S-EPMC3513214 | biostudies-literature
Project description:Ulcerative colitis and Crohn's disease are traditionally defined as the two main subtypes of inflammatory bowel disease. However, a more recent view considers IBD as a spectrum of heterogeneous phenotypes with consistent differences in clinical presentation and behaviors, likely explained by differences in underlying pathogenetic mechanisms. The etiology is still elusive, and the suggested pathogenesis is a complex interplay among genetic predisposition and abnormal immune response at the mucosal intestinal level, activated by only partially identified environmental triggers leading to altered intestinal permeability and impaired handling of gut microbiota. The undeniable continuous progress of medical therapy with more frequent shifts from traditional to more advanced modalities also underlines the actual unmet needs. We are using medications with completely different mechanisms of action, with a lack of predictive factors of outcomes and response and still an unsatisfactory rate of success. In addition, we are missing still valuable and accurate markers to predict disease progression and severity in order to avoid under- or over-treatment. In such a complex scenario, it is undoubtful that the application of artificial intelligence and machine learning algorithms may improve the management and pave the way for precision and eventually personalized medicine in these patients; however, there are still several challenges that will be the focus of this review.
| S-EPMC10487169 | biostudies-literature