Imperfect biomarkers for adjuvant chemotherapy in early stage breast cancer with good prognosis

Regulating diagnosis-Molecular and regulatory sub-stratifications of lung cancer treatment

The sociology of diagnosis has shown that diagnosis not only serves to label the underlying cause of disease but also to provide access to services and resources. Elaborating on this double-affordance of diagnosis, this article examines how precision medicine reconfigures diagnosis as a label and as a process in regulatory and clinical settings. Reporting from an ethnographic case study of the introduction of immunotherapy for lung cancer, the paper unfolds the uncertainties involved in dissecting diagnosis into layers and examines the efforts and negotiations it takes to enable these layers to work both as clinical entities and regulative entities with the purpose of delineating access to treatment. I suggest that the work of subdividing diseases into molecularly defined categories for the purpose of delineating treatment-eligible populations can be labelled 'diagnostic sub-stratification' and argue that it is pertinent to understand the political capacity of this strategy. Diagnostic sub-stratification involves a push of diagnosis from the clinic 'up' into the regulatory system and 'out' into the laboratories, obscuring who is accountable for the diagnostic categories employed to define patients' treatment access.

Unsettling the treatment imperative? Chemotherapy decision-making in the wake of genomic techniques

Social scientists have argued that a treatment imperative shapes experiences of biomedicine. This is evident within oncology, where discourses of hope are tempered by persistent fears surrounding cancer. It is within this context that genomic decision-making tools are entering routine care. These may indicate that a treatment is not appropriate for a particular disease profile. We draw on qualitative interviews and observations centred on gene expression profiling to consider the implications of this technique for the treatment imperative in early breast cancer. Influenced by sociological perspectives on medical technologies, we discuss how fallibilities of established tools have forged a space for the introduction of genomic testing into chemotherapy decision-making. We demonstrate how high expectations shaped patients' interpretations of this tool as facilitating the 'right' treatment choice. We then unpick these accounts, highlighting the complex relationship between gene expression profiling and treatment decision-making. We argue that anticipations for genomic testing to provide certainty in treatment choice must account for the sociocultural and organisational contexts in which it is used, including the powerful entwinement of chemotherapy and cancer. Our research has implications for sociological perspectives on treatment decision-making and clinical expectations for genomic medicine to resolve the 'problem' of overtreatment.

Diagnostic layering: Patient accounts of breast cancer classification in the molecular era

Social scientific work has considered the promise of genomic medicine to transform healthcare by personalising treatment. However, little qualitative research attends to already well-established molecular techniques in routine care. In this article we consider women's experiences of routine breast cancer diagnosis in the UK NHS. We attend to patient accounts of the techniques used to subtype breast cancer and guide individual treatment. We introduce the concept of 'diagnostic layering' to make sense of how the range of clinical techniques used to classify breast cancer shape patient experiences of diagnosis. The process of diagnostic layering, whereby various levels of diagnostic information are received by patients over time, can render diagnosis as incomplete and subject to change. In the example of early breast cancer, progressive layers of diagnostic information are closely tied to chemotherapy recommendations. In recent years a genomic test, gene expression profiling, has become introduced into routine care. Because gene expression profiling could indicate a treatment recommendation where standard tools had failed, the technique could represent a 'final layer' of diagnosis for some patients. However, the test could also invalidate previous understandings of the cancer, require additional interpretation and further prolong the diagnostic process. This research contributes to the sociology of diagnosis by outlining how practices of cancer subtyping shape patient experiences of breast cancer. We add to social scientific work attending to the complexities of molecular and genomic techniques by considering the blurring of diagnostic and therapeutic activities from a patient perspective.

Genetic co-expression networks contribute to creating predictive model and exploring novel biomarkers for the prognosis of breast cancer

Genetic co-expression network (GCN) analysis augments the understanding of breast cancer (BC). We aimed to propose GCN-based modeling for BC relapse-free survival (RFS) prediction and to discover novel biomarkers. We used GCN and Cox proportional hazard regression to create various prediction models using mRNA microarray of 920 tumors and conduct external validation using independent data of 1056 tumors. GCNs of 34 identified candidate genes were plotted in various sizes. Compared to the reference model, the genetic predictors selected from bigger GCNs composed better prediction models. The prediction accuracy and AUC of 3 ~ 15-year RFS are 71.0-81.4% and 74.6-78% respectively (rfm, ACC 63.2-65.5%, AUC 61.9-74.9%). The hazard ratios of risk scores of developing relapse ranged from 1.89 ~ 3.32 (p < 10^-8) over all models under the control of the node status. External validation showed the consistent finding. We found top 12 co-expressed genes are relative new or novel biomarkers that have not been explored in BC prognosis or other cancers until this decade. GCN-based modeling creates better prediction models and facilitates novel genes exploration on BC prognosis.

Determination of FABP4, RBP4 and the MMP-9/NGAL complex in the serum of women with breast cancer

Breast cancer is the most common type of cancer in females and is the leading cause of cancer-associated death among women, worldwide. The present study aimed to measure the serum levels of fatty acid-binding protein 4 (FABP4), retinol binding protein 4 (RBP4) and the MMP-9/neutrophil gelatinase-associated lipocalin (NGAL) complex in women diagnosed with breast cancer. Serum levels of the examined proteins were determined in the peripheral blood of patients via ELISA. Furthermore, whether the concentration of each protein was associated with breast cancer growth, molecular subtype, BMI, postmenopausal status, diabetes and the social background of patients was assessed. Women with invasive breast cancer demonstrated significantly higher levels of FABP4 (P=0.008). Additionally, considerably elevated FABP4 levels were demonstrated specifically in Luminal breast cancer cases (P<0.01). No significant association was recorded between RBP4 and breast cancer development. In addition, significantly lower levels of the MMP-9/NGAL complex were recorded in triple negative/HER-2 cases (P<0.05). BMI values appeared to influence the aforementioned associations, while significantly high serum levels of FABP4 and the MMP-9/NGAL complex were found in postmenopausal patients with breast cancer and a BMI ≥25 kg/m² (P<0.05). In addition, high levels of FABP4 were significantly associated with breast cancer patients with diabetes (P=0.05). However, no association was identified between RBP4, the MMP-9/NGAL complex and diabetes. In conclusion, FABP4 can be regarded as a biomarker of breast cancer growth, while both FABP4 and the MMP-9/NGAL complex may provide considerable information regarding the development of specific breast cancer subtypes. FABP4 and the MMP-9/NGAL complex may also be able to predict the development of breast cancer in postmenopausal patients with obesity.