Predicting treatment outcome using kinome activity profiling in HER2+ breast cancer biopsies.

In this study, we measured the kinase activity profiles of 32 pre-treatment tumor biopsies of HER2-positive breast cancer patients. The aim of this study was to assess the prognostic potential of kinase activity levels, to identify potential mechanisms of resistance and to predict treatment success of HER2-targeted therapy combined with chemotherapy. Indeed, our system-wide kinase activity analysis allowed us to link kinase activity to treatment response. Overall, high kinase activity in the HER2-pathway was associated with good treatment outcome. We found eleven kinases differentially regulated between treatment outcome groups, including well-known players in therapy resistance, such as p38a, ERK, and FAK, and an unreported one, namely MARK1. Lastly, we defined an optimal signature of four kinases in a multiple logistic regression diagnostic test for prediction of treatment outcome (AUC = 0.926). This kinase signature showed high sensitivity and specificity, indicating its potential as predictive biomarker for treatment success of HER2-targeted therapy.

Deep (phospho)proteomics profiling of pre- treatment needle biopsies identifies signatures of treatment resistance in HER2+ breast cancer.

Patients with early-stage HER2-overexpressing breast cancer struggle with treatment resistance in 20%-40% of cases. More information is needed to predict HER2 therapy response and resistance in vivo. In this study, we perform (phospho)proteomics analysis of pre-treatment HER2+ needle biopsies of early-stage invasive breast cancer to identify molecular signatures predictive of treatment response to trastuzumab, pertuzumab, and chemotherapy. Our data show that accurate quantification of the estrogen receptor (ER) and HER2 biomarkers, combined with the assessment of associated biological features, has the potential to enable better treatment outcome prediction. In addition, we identify cellular mechanisms that potentially precondition tumors to resist therapy. We find proteins with expression changes that correlate with resistance and constitute to a strong predictive signature for treatment success in our patient cohort. Our results highlight the multifactorial nature of drug resistance in vivo and demonstrate the necessity of deep tumor profiling.

Tumour-educated platelets for breast cancer detection: biological and technical insights.

BACKGROUND: Studies have shown that blood platelets contain tumour-specific mRNA profiles tumour-educated platelets (TEPs). Here, we aim to train a TEP-based breast cancer detection classifier. METHODS: Platelet mRNA was sequenced from 266 women with stage I-IV breast cancer and 212 female controls from 6 hospitals. A particle swarm optimised support vector machine (PSO-SVM) and an elastic net-based classifier (EN) were trained on 71% of the study population. Classifier performance was evaluated in the remainder (29%) of the population, followed by validation in an independent set (37 cases and 36 controls). Potential confounding was assessed in post hoc analyses. RESULTS: Both classifiers reached an area under the curve (AUC) of 0.85 upon internal validation. Reproducibility in the independent validation set was poor with an AUC of 0.55 and 0.54 for the PSO-SVM and EN classifier, respectively. Post hoc analyses indicated that 19% of the variance in gene expression was associated with hospital. Genes related to platelet activity were differentially expressed between hospitals. CONCLUSIONS: We could not validate two TEP-based breast cancer classifiers in an independent validation cohort. The TEP protocol is sensitive to within-protocol variation and revision might be necessary before TEPs can be reconsidered for breast cancer detection.

Efficacy of neoadjuvant treatment with or without pertuzumab in patients with stage II and III HER2-positive breast cancer: a nationwide cohort analysis of pathologic response and 5-year survival.

BACKGROUND: Pathologic complete response (pCR) rates in early stage HER2-positive breast cancer improved after pertuzumab was added to neoadjuvant treatment. However, survival benefit is less-well established and seems mostly limited to node-positive patients. We used national cancer registry data to compare outcomes of patients treated with and without pertuzumab. METHODS: We identified stage II-III HER2-positive breast cancer patients treated with neoadjuvant trastuzumab-based chemotherapy between November 2013 until January 2016 from the Netherlands Cancer Registry. During that period pertuzumab was only available in the 37 hospitals that participated in the TRAIN-2 study. Missing grade and pCR-status were obtained from the Dutch Pathology Registry (PALGA) and cause of death from Statistics Netherlands. We used multiple imputation to impute missing data, multivariable logistic regression to evaluate the association between pertuzumab and pCR (ypT0/is, ypN0) and multivariable Cox regression models for overall survival and breast cancer specific survival (BCSS). RESULTS: We identified 1124 patients of whom 453 received pertuzumab. Baseline characteristics were comparable, although tumor grade was missing more often in patients treated without pertuzumab (12% vs. 2%). Pertuzumab improved pCR rates (41% vs 65%, adjusted odds ratio [aOR] 2.91; 95% CI:2.20-3.94). After a median follow-up of 6.0 years, 5-year BCSS rates were 95% and 98% respectively (adjusted hazard ratio [aHR]: 0.58; 95% CI:0.36-0.95). Younger patients derived more benefit from pertuzumab, but no other significant interactions were found. CONCLUSION: These results support earlier data of a small survival benefit with the addition of pertuzumab to trastuzumab-based neoadjuvant chemotherapy which is most meaningful in younger patients.

The Way of the Future: Personalizing Treatment Plans Through Technology.

Advances in tissue analysis methods, image analysis, high-throughput molecular profiling, and computational tools increasingly allow us to capture and quantify patient-to patient variations that impact cancer risk, prognosis, and treatment response. Statistical models that integrate patient-specific information from multiple sources (e.g., family history, demographics, germline variants, imaging features) can provide individualized cancer risk predictions that can guide screening and prevention strategies. The precision, quality, and standardization of diagnostic imaging are improving through computer-aided solutions, and multigene prognostic and predictive tests improved predictions of prognosis and treatment response in various cancer types. A common theme across many of these advances is that individually moderately informative variables are combined into more accurate multivariable prediction models. Advances in machine learning and the availability of large data sets fuel rapid progress in this field. Molecular dissection of the cancer genome has become a reality in the clinic, and molecular target profiling is now routinely used to select patients for various targeted therapies. These technology-driven increasingly more precise and quantitative estimates of benefit versus risk from a given intervention empower patients and physicians to tailor treatment strategies that match patient values and expectations.

Proteomic profiling of extracellular vesicles allows for human breast cancer subtyping.

Extracellular vesicles (EVs) are a potential source of disease-associated biomarkers for diagnosis. In breast cancer, comprehensive analyses of EVs could yield robust and reliable subtype-specific biomarkers that are still critically needed to improve diagnostic routines and clinical outcome. Here, we show that proteome profiles of EVs secreted by different breast cancer cell lines are highly indicative of their respective molecular subtypes, even more so than the proteome changes within the cancer cells. Moreover, we detected molecular evidence for subtype-specific biological processes and molecular pathways, hyperphosphorylated receptors and kinases in connection with the disease, and compiled a set of protein signatures that closely reflect the associated clinical pathophysiology. These unique features revealed in our work, replicated in clinical material, collectively demonstrate the potential of secreted EVs to differentiate between breast cancer subtypes and show the prospect of their use as non-invasive liquid biopsies for diagnosis and management of breast cancer patients.

Vitamin D and C-Reactive Protein: A Mendelian Randomization Study.

Vitamin D deficiency is widely prevalent and has been associated with many diseases. It has been suggested that vitamin D has effects on the immune system and inhibits inflammation. The aim of our study was to investigate whether vitamin D has an inhibitory effect on systemic inflammation by assessing the association between serum levels of vitamin D and C-reactive protein. We studied the association between serum 25-hydroxyvitamin D and C-reactive protein through linear regression in 9,649 participants of the Rotterdam Study, an observational, prospective population-based cohort study. We used genetic variants related to vitamin D and CRP to compute a genetic risk score and perform bi-directional Mendelian randomization analysis. In linear regression adjusted for age, sex, cohort and other confounders, natural log-transformed CRP decreased with 0.06 (95% CI: -0.08, -0.03) unit per standard deviation increase in 25-hydroxyvitamin D. Bi-directional Mendelian randomization analyses showed no association between the vitamin D genetic risk score and lnCRP (Beta per SD = -0.018; p = 0.082) or the CRP genetic risk score and 25-hydroxyvitamin D (Beta per SD = 0.001; p = 0.998). In conclusion, higher levels of Vitamin D are associated with lower levels of C-reactive protein. In this study we did not find evidence for this to be the result of a causal relationship.