Androgen Receptor as an Emerging Feasible Biomarker for Breast Cancer

Biomarkers can be used for diagnosis, prognosis, and prediction in targeted therapy. The estrogen receptor α (ERα) and human epidermal growth factor receptor 2 (HER2) are standard biomarkers used in breast cancer for guiding disease treatment. The androgen receptor (AR), a nuclear hormone receptor, contributes to the development and progression of prostate tumors and other cancers. With increasing evidence to support that AR plays an essential role in breast cancer, AR has been considered a useful biomarker in breast cancer, depending on the context of breast cancer sub-types. The existing survival analyses suggest that AR acts as a tumor suppressor in ER + ve breast cancers, serving as a favorable prognostic marker. However, AR functions as a tumor promoter in ER-ve breast cancers, including HER2 + ve and triple-negative (TNBC) breast cancers, serving as a poor prognostic factor. AR has also been shown to be predictive of the potential of response to adjuvant hormonal therapy in ER + ve breast cancers and to neoadjuvant chemotherapy in TNBC. However, conflicting results do exist due to intrinsic molecular differences between tumors and the scoring method for AR positivity. Applying AR expression status to guide treatment in different breast cancer sub-types has been suggested. In the future, AR will be a feasible biomarker for breast cancer. Clinical trials using AR antagonists in breast cancer are active. Targeting AR alone or other therapeutic agents provides alternatives to existing therapy for breast cancer. Therefore, AR expression will be necessary if AR-targeted treatment is to be used.

Radiomics Assessment of the Tumor Immune Microenvironment to Predict Outcomes in Breast Cancer

BackgroundThe immune microenvironment of tumors provides information on prognosis and prediction. A prior validation of the immunoscore for breast cancer (ISBC) was made on the basis of a systematic assessment of immune landscapes extrapolated from a large number of neoplastic transcripts. Our goal was to develop a non-invasive radiomics-based ISBC predictive factor.MethodsImmunocell fractions of 22 different categories were evaluated using CIBERSORT on the basis of a large, open breast cancer cohort derived from comprehensive information on gene expression. The ISBC was constructed using the LASSO Cox regression model derived from the Immunocell type scores, with 479 quantified features in the intratumoral and peritumoral regions as observed from DCE-MRI. A radiomics signature [radiomics ImmunoScore (RIS)] was developed for the prediction of ISBC using a random forest machine-learning algorithm, and we further evaluated its relationship with prognosis.ResultsAn ISBC consisting of seven different immune cells was established through the use of a LASSO model. Multivariate analyses showed that the ISBC was an independent risk factor in prognosis (HR=2.42, with a 95% CI of 1.49–3.93; P<0.01). A radiomic signature of 21 features of the ISBC was then exploited and validated (the areas under the curve [AUC] were 0.899 and 0.815). We uncovered statistical associations between the RIS signature with recurrence-free and overall survival rates (both P<0.05).ConclusionsThe RIS is a valuable instrument with which to assess the immunoscore, and offers important implications for the prognosis of breast cancer.

OMICS in Chronic Kidney Disease: Focus on Prognosis and Prediction

Chronic kidney disease (CKD) patients are characterized by a high residual risk for cardiovascular (CV) events and CKD progression. This has prompted the implementation of new prognostic and predictive biomarkers with the aim of mitigating this risk. The ‘omics’ techniques, namely genomics, proteomics, metabolomics, and transcriptomics, are excellent candidates to provide a better understanding of pathophysiologic mechanisms of disease in CKD, to improve risk stratification of patients with respect to future cardiovascular events, and to identify CKD patients who are likely to respond to a treatment. Following such a strategy, a reliable risk of future events for a particular patient may be calculated and consequently the patient would also benefit from the best available treatment based on their risk profile. Moreover, a further step forward can be represented by the aggregation of multiple omics information by combining different techniques and/or different biological samples. This has already been shown to yield additional information by revealing with more accuracy the exact individual pathway of disease.

Circulating tumor cells in colorectal cancer in the era of precision medicine

Colorectal cancer (CRC) is one of the main causes of cancer-related morbidity and mortality across the globe. Although serum biomarkers such as carcinoembryonic antigen (CEA) and carbohydrate antigen 19–9 (CA-199) have been prevalently used as biomarkers in various cancers, they are neither very sensitive nor highly specific. Repeated tissue biopsies at different times of the disease can be uncomfortable for cancer patients. Additionally, the existence of tumor heterogeneity and the results of local biopsy provide limited information about the overall tumor biology. Against this backdrop, it is necessary to look for reliable and noninvasive biomarkers of CRC. Circulating tumor cells (CTCs), which depart from a primary tumor, enter the bloodstream, and imitate metastasis, have a great potential for precision medicine in patients with CRC. Various efficient CTC isolation platforms have been developed to capture and identify CTCs. The count of CTCs, as well as their biological characteristics and genomic heterogeneity, can be used for the early diagnosis, prognosis, and prediction of treatment response in CRC. This study reviewed the existing CTC isolation techniques and their applications in the clinical diagnosis and treatment of CRC. The study also presented their limitations and provided future research directions.

The Role of Ki67 in Evaluating Neoadjuvant Endocrine Therapy of Hormone Receptor-Positive Breast Cancer

Ki67 is a proliferation marker. It has been proposed as a useful clinical marker for breast cancer subtype classification, prognosis, and prediction of therapeutic response. But the questionable analytical validity of Ki67 prevents its widespread adoption of these measures for treatment decisions in breast cancer. Currently, Ki67 has been tested as a predictive marker for chemotherapy using clinical and pathological response as endpoints in neoadjuvant endocrine therapy. Ki67 can be used as a predictor to evaluate the recurrence-free survival rate of patients, or its change can be used to predict the preoperative “window of opportunity” in neoadjuvant endocrine therapy. In this review, we will elaborate on the role of Ki67 in neoadjuvant endocrine therapy in breast cancer.

BIOM-11. THE PROGNOSTIC VALUE OF ROUTINE AND SPECIALIZED BLOOD TESTING IN GLIOBLASTOMA: A SYSTEMATIC REVIEW

BACKGROUND A combination of clinical characteristics, radiological findings and tissue markers is used in the assessment of glioblastoma prognosis and prediction of treatment response. The value of routine blood tests as a tool of prognosis has been a subject of debate. In addition to increased complication rate in subsequent resections and radiological uncertainty in treatment monitoring, there is a need to monitor tumor markers in a minimally invasive manner, as molecular characterization becomes more important in the management of glioblastoma. The objective of this review is to evaluate the prognostic value of any blood marker during the course of disease and treatment in glioblastoma. METHODS We researched Pubmed and Embase for clinical studies including cohort studies and randomized controlled trials that included at least 10 adult patients and blood testing during course of disease. We extracted data on clinically relevant endpoints, i.e. overall survival (OS) or progression-free survival (PFS), in accordance with the PRISMA statements. RESULTS The search strategy yielded 6389 unique articles, of which 150 met the inclusion criteria. 37 studies found an association between survival outcomes and pre-operative markers including complete blood count (erythrocytes and leukocytes with differentiation characteristics), inflammatory markers (erythrocyte sedimentation rate and C-reactive protein), coagulability markers (prothrombin time, activated partial thromboplastin time and D-dimer), albumin, lactate dehydrogenase and glucose. Furthermore, 10 studies reported a correlation between changes in platelets, erythrocytes and leukocytes during course of disease and treatment and OS. Finally, serum and plasma levels of markers including various proteins, microRNAs and microvesicles were associated with PFS and OS. CONCLUSION The results of this study suggest that routine and specialized blood tests provide additive information on OS and PFS in glioblastoma. These promising findings highlight the need for further investigation of blood testing for biomarker evaluation.

828 Quantifying perivascular immune cells in the stroma of human triple negative breast tumors using deep learning spatial analytics

BackgroundThe spatial organization and density of the immune infiltrate in the tumor microenvironment, referred to as immune contexture, can yield information relevant to prognosis and prediction of response to immunotherapy in cancer. Specifically, a distinct subset of tumor-associated macrophages (TAMs) accumulate around blood vessels where they stimulate tumor angiogenesis and limit tumor responses to frontline anti-cancer therapies like irradiation and chemotherapy.MethodsIn this study we leveraged the NeoGenomics MultiOmyx Multiplex Immunofluorescence platform alongside artificial intelligence (AI) based quantitative image analysis. This AI platform was ultimately used to investigate the distribution of perivascular (PV) TAMs, CD4+ and CD8+ T cells, and CD4+FOXP3+ regulatory T cells (Tregs) of 40 human triple negative breast carcinomas (TNBCs), and how this changed following neoadjuvant chemotherapy. During the multiplexing phase, eleven rounds of paired antibody staining were performed in sequence on tumor sections. After each round of staining, high resolution images were captured for regions of interests (ROIs) selected by a pathologist. We used AI models to segment and classify cells for each biomarker and classify regions as tumor cell islands (TCIs) or stroma. First, each nucleus was segmented out using a convolutional neural network combined with watershed thresholding on the DAPI (diamidino-2-phenylindole) immunofluorescent image. From the resulting nuclear segmentation mask, a pixel dilation on cells classified as non-tumor was employed to generate a cellular segmentation mask. A list of neighbours within a specified distance for each cell was generated by radially expanding from the cellular segmentation mask. Finally, cell neighbour information was combined with the marker expression information to quantify the cell clusters of interest.ResultsWe discovered that in the PV areas, up to 30% of PD1-LAG3-CD3+CD8+ T cells formed direct contact with both CD163+TIM3+ TAMs and CD4+FOXP3+ Tregs. Furthermore, these immune cell triads preferentially accumulated in the PV stroma regions. It is likely that close interaction with immunosuppressive TAMs and Tregs would supress the function of T cells as they enter the PV region to reach the TCIs.ConclusionsUsing an advanced analytics platform, we invented a new method to quantify clusters of cells within various regions of a tumor section. Using this platform, we detected specific immune cell triads, the frequency and location of which could correlate with the efficacy of T-cell based immunotherapies in TNBC. These analyses will enable further investigation of numerous complex cell interactions in TMEs.

Diagnostic, Predictive and Prognostic Biomarkers in Non-Small Cell Lung Cancer (NSCLC) Management

Lung cancer is the leading cause of cancer-related deaths worldwide. Despite growing efforts for its early detection by screening populations at risk, the majority of lung cancer patients are still diagnosed in an advanced stage. The management of lung cancer has dramatically improved in the last decade and is no longer based on the “one-fits-all” paradigm or the general histological classification of non-small cell versus small cell lung cancer. Emerging options of targeted therapies and immunotherapies have shifted the management of lung cancer to a more personalized treatment approach, significantly influencing the clinical course and outcome of the disease. Molecular biomarkers have emerged as valuable tools in the prognosis and prediction of therapy response. In this review, we discuss the relevant biomarkers used in the clinical management of lung tumors, from diagnosis to prognosis. We also discuss promising new biomarkers, focusing on non-small cell lung cancer as the most abundant type of lung cancer.

Potential Biomarkers in Diagnosis, Prognosis and Prediction of Treatment Response in Malignant Glioma

Gliomas are the most common type of primary central nervous system malignancies with poor prognosis in adults. There are several challenges in developing a treatment protocol for this malignancy including presence of blood-brain barrier that inhibit drug delivery to brain tissue, drug and radiation resistance of tumor cells, and inter and intra-tumor heterogeneity of glioma. In addition, early treatment assessment is difficult for glioma patients because of phenomenon of pseudo-progression. Due to the challenges involved in treatment and monitoring of treatment response for glioma, it is very helpful to identify specific and non-invasive molecular and imaging markers in order to provide useful prognostic information. The aim of this article is to summarize several potential biological and imaging markers regarding malignant glioma. A brief description of the proteins involved in the glioma signaling pathways is provided in order to introduce potential biological markers. Furthermore, the role of imaging techniques in treatment management is discussed. Finally, correlation between tumor characteristics and values of angiogenesis and physiological factors measured in perfusion magnetic resonance imaging techniques as well as metabolites in MRS, and PET tracer’s uptake is investigated.