Breast cancer classification by proteomic technologies: current state of knowledge

Can breast cancer molecular subtype help to select patients for preoperative MR imaging?

PURPOSE

To assess whether breast cancer molecular subtype classified by surrogate markers can be used to predict the extent of clinically relevant disease with preoperative breast magnetic resonance (MR) imaging.

MATERIALS AND METHODS

In this HIPAA-compliant, institutional review board-approved study, informed consent was waived. Preoperative breast MR imaging reports from 441 patients were reviewed for multicentric and/or multifocal disease, lymph node involvement, skin and/or nipple invasion, chest wall and/or pectoralis muscle invasion, or contralateral disease. Pathologic reports were reviewed to confirm the MR imaging findings and for hormone receptors (estrogen and progesterone subtypes), human epidermal growth factor receptor type 2 (HER2 subtype), tumor size, and tumor grade. Surrogates were used to categorize tumors by molecular subtype: hormone receptor positive and HER2 negative (luminal A subtype); hormone receptor positive and HER2 positive (luminal B subtype); hormone receptor negative and HER2 positive (HER2 subtype); hormone receptor negative and HER2 negative (basal subtype). All patients included in the study had a histologic correlation with MR imaging findings or they were excluded. χ(2) analysis was used to compare differences between subtypes, with multivariate logistic regression analysis used to assess for variable independence.

RESULTS

Identified were 289 (65.5%) luminal A, 45 (10.2%) luminal B, 26 (5.9%) HER2, and 81 (18.4%) basal subtypes. Among subtypes, significant differences were found in the frequency of multicentric and/or multifocal disease (luminal A, 27.3% [79 of 289]; luminal B, 53.3% [24 of 45]; HER2, 65.4% [17 of 26]; basal, 27.2% [22 of 81]; P < .001) and lymph node involvement (luminal A, 17.3% [50 of 289]; luminal B, 35.6% [26 of 45]; HER2, 34.6% [nine of 26]; basal 24.7% [20 of 81]; P = .014). Multivariate analysis showed that molecular subtype was independently predictive of multifocal and/or multicentric disease.

CONCLUSION

Preoperative breast MR imaging is significantly more likely to help detect multifocal and/or multicentric disease and lymph node involvement in luminal B and HER2 molecular subtype breast cancers. Molecular subtype may help to select patients for preoperative breast MR imaging.

Radiogenomic analysis of breast cancer: luminal B molecular subtype is associated with enhancement dynamics at MR imaging

PURPOSE

To investigate associations between breast cancer molecular subtype and semiautomatically extracted magnetic resonance (MR) imaging features.

MATERIALS AND METHODS

Imaging and genomic data from the Cancer Genome Atlas and the Cancer Imaging Archive for 48 patients with breast cancer from four institutions in the United States were used in this institutional review board approval-exempt study. Computer vision algorithms were applied to extract 23 imaging features from lesions indicated by a breast radiologist on MR images. Morphologic, textural, and dynamic features were extracted. Molecular subtype was determined on the basis of genomic analysis. Associations between the imaging features and molecular subtype were evaluated by using logistic regression and likelihood ratio tests. The analysis controlled for the age of the patients, their menopausal status, and the orientation of the MR images (sagittal vs axial).

RESULTS

There is an association (P = .0015) between the luminal B subtype and a dynamic contrast material-enhancement feature that quantifies the relationship between lesion enhancement and background parenchymal enhancement. Cancers with a higher ratio of lesion enhancement rate to background parenchymal enhancement rate are more likely to be luminal B subtype.

CONCLUSION

The luminal B subtype of breast cancer is associated with MR imaging features that relate the enhancement dynamics of the tumor and the background parenchyma.

Targeted therapy for gastric cancer: molecular pathways and ongoing investigations

Gastric cancer is currently the second leading cause of worldwide cancer mortality. Ongoing collaborative sequencing efforts have highlighted recurrent somatic genomic aberrations in gastric cancer, however, despite advances in characterizing the genomic landscape, there have been few advances in patient outcomes. Prognosis remains poor with a median overall survival of 12 months for advanced disease. The improved survival with trastuzumab, and more recently ramucirumab, underscore the promise of targeted and biologic therapies and the importance of molecular tumor characterization in gastric cancer. Here we review the most frequent actionable alterations in gastric cancer and highlight ongoing clinical investigations attempting to translate biologic understanding into improved clinical outcomes.

Cancer biomarker discovery: current status and future perspectives

PURPOSE

Cancer is a multigene disease which arises as a result of mutational and epigenetic changes coupled with activation of complex signaling networks. The use of biomarkers for early cancer detection, staging and individualization of therapy might improve patient care. A few fundamental issues such as tumor heterogeneity, a highly dynamic nature of the intrinsic and extrinsic determinants of radio- and chemoresistance, along with the plasticity and diversity of cancer stem cells (CSC) make biomarker development a challenging task. In this review we outline the preclinical strategies of cancer biomarker discovery including genomic, proteomic, metabolomic and microRNomic profiling, comparative genome hybridization (CGH), single nucleotide polymorphism (SNP) analysis, high throughput screening (HTS) and next generation sequencing (NGS). Other promising approaches such as assessment of circulating tumor cells (CTC), analysis of CSC-specific markers and cell-free circulating tumor DNA (ctDNA) are also discussed.

CONCLUSIONS

The emergence of powerful proteomic and genomic technologies in conjunction with advanced bioinformatic tools allows the simultaneous analysis of thousands of biological molecules. These techniques yield the discovery of new tumor signatures, which are sensitive and specific enough for early cancer detection, for monitoring disease progression and for proper treatment selection, paving the way to individualized cancer treatment.

Disease and treatment characteristics do not predict symptom occurrence profiles in oncology outpatients receiving chemotherapy

BACKGROUND

A large amount of interindividual variability exists in the occurrence of symptoms in patients receiving chemotherapy (CTX). The purposes of the current study, which was performed in a sample of 582 oncology outpatients who were receiving CTX, were to identify subgroups of patients based on their distinct experiences with 25 commonly occurring symptoms and to identify demographic and clinical characteristics associated with subgroup membership. In addition, differences in quality of life outcomes were evaluated.

METHODS

Oncology outpatients with breast, gastrointestinal, gynecological, or lung cancer completed the Memorial Symptom Assessment Scale before their next cycle of CTX. Latent class analysis was used to identify subgroups of patients with distinct symptom experiences.

RESULTS

Three distinct subgroups of patients were identified (ie, 36.1% in Low class; 50.0% in Moderate class, and 13.9% in All High class). Patients in the All High class were significantly younger and more likely to be female and nonwhite, and had lower levels of social support, lower socioeconomic status, poorer functional status, and a higher level of comorbidity.

CONCLUSIONS

Findings from the current study support the clinical observation that some oncology patients experience a differentially higher symptom burden during CTX. These high-risk patients experience significant decrements in quality of life.

Implications of functional proteomics in breast cancer

Breast cancer is one of the major public health problems of the Western world. Recent advances in genomics and gene expression-profiling approaches have enriched our understanding of this heterogeneous disease. However, progress in functional proteomics in breast cancer research has been relatively slow. Allied with genomics, the functional proteomics approach will be important in improving diagnosis through better classification of breast cancer and in predicting prognosis and response to different therapies, including chemotherapy, hormonal therapy, and targeted therapy. In this review, we will present functional proteomic approaches with a focus on the recent clinical implications of utilizing the reverse-phase protein array platform in breast cancer research.

Advances in the proteomic discovery of novel therapeutic targets in cancer

Proteomic approaches are continuing to make headways in cancer research by helping to elucidate complex signaling networks that underlie tumorigenesis and disease progression. This review describes recent advances made in the proteomic discovery of drug targets for therapeutic development. A variety of technical and methodological advances are overviewed with a critical assessment of challenges and potentials. A number of potential drug targets, such as baculoviral inhibitor of apoptosis protein repeat-containing protein 6, macrophage inhibitory cytokine 1, phosphoglycerate mutase 1, prohibitin 1, fascin, and pyruvate kinase isozyme 2 were identified in the proteomic analysis of drug-resistant cancer cells, drug action, and differential disease state tissues. Future directions for proteomics-based target identification and validation to be more translation efficient are also discussed.