Designing personalised cancer treatments

Tumor Chemosensitivity Assays Are Helpful for Personalized Cytotoxic Treatments in Cancer Patients

Tumor chemosensitivity assays (TCAs), also known as drug response assays or individualized tumor response tests, have been gaining attention over the past few decades. Although there have been strong positive correlations between the results of these assays and clinical outcomes, they are still not considered routine tests in the care of cancer patients. The correlations between the assays' results (drug sensitivity or resistance) and the clinical evaluations (e.g., response to treatment, progression-free survival) are highly promising. However, there is still a need to design randomized controlled prospective studies to secure the place of these assays in routine use. One of the best ideas to increase the value of these assays could be the combination of the assay results with the omics technologies (e.g., pharmacogenetics that gives an idea of the possible side effects of the drugs). In the near future, the importance of personalized chemotherapy is expected to dictate the use of these omics technologies. The omics relies on the macromolecules (Deoxyribonucleic acid -DNA-, ribonucleic acid -RNA-) and proteins (meaning the structure) while TCAs operate on living cell populations (meaning the function). Therefore, wise combinations of TCAs and omics could be a highly promising novel landscape in the modern care of cancer patients.

High-Speed Live-Cell Interferometry: A New Method for Quantifying Tumor Drug Resistance and Heterogeneity

We report the development of high-speed live-cell interferometry (HSLCI), a new multisample, multidrug testing platform for directly measuring tumor therapy response via real-time optical cell biomass measurements. As a proof of concept, we show that HSLCI rapidly profiles changes in biomass in BRAF inhibitor (BRAFi)-sensitive parental melanoma cell lines and in their isogenic BRAFi-resistant sublines. We show reproducible results from two different HSLCI platforms at two institutions that generate biomass kinetic signatures capable of discriminating between BRAFi-sensitive and -resistant melanoma cells within 24 h. Like other quantitative phase imaging (QPI) modalities, HSLCI is well-suited to noninvasive measurements of single cells and cell clusters, requiring no fluorescence or dye labeling. HSLCI is substantially faster and more sensitive than field-standard growth inhibition assays, and in terms of the number of cells measured simultaneously, the number of drugs tested in parallel, and temporal measurement range, it exceeds the state of the art by more than 10-fold. The accuracy and speed of HSLCI in profiling tumor cell heterogeneity and therapy resistance are promising features of potential tools to guide patient therapeutic selections.

Expression of P-EGFR and P-Akt protein in esophageal squamous cell carcinoma and its prognosis

The phosphorylated epidermal growth factor receptor (P-EGFR) and phosphorylated Akt (P-Akt) protein in esophageal squamous cell carcinoma (ESCC) were studied, and its significance in clinical prognosis of patients was assessed. The expression of P-EGFR and P-Akt protein in 83 cases of ESCC and 83 normal esophageal tissues was determined by immunohistochemical staining. Log-rank test and correlation analysis were used to analyze the prognosis of ESCC. The positive expression of P-EGFR in ESCC was 88% (73/83 cases) compared with 41% in normal esophageal mucosa (34/83 cases) (P<0.05). The rate of P-Akt protein expression in ESCC was 90.4% (75/83 cases), compared with 27.7% seen in normal esophageal mucosa (23/83 cases) (P<0.05). The expression of P-EGFR and P-Akt protein was positively correlated with lymph node metastasis and degree of differentiation (P<0.05) irrespective of sex, age, tumor diameter and TNM stage (P>0.05). The expression of P-EGFR was positively correlated with that of P-Akt protein (r=0.674, P<0.01). P-EGFR expression was negatively correlated with survival time of patients with ESCC (r=−0.526, P<0.01). The Kaplan-Meier survival curves showed that the cumulative survival rate of P-EGFR-positive cases was significantly lower than that of the P-EGFR-negative cases (P<0.01). The expression of P-Akt was negatively correlated with survival in patients with ESCC (r=−0.473, P<0.01). The Kaplan-Meier survival curves showed that the cumulative survival rate of the P-Akt-positive cases was significantly lower than that of the P-Akt-negative cases (P<0.01). In conclusion, P-EGFR and P-Akt protein expression is closely related to the incidence of ESCC and mediates the development of invasive cancer and metastasis. It is used to determine the prognosis of ESCC, and may represent a new therapeutic target for the disease.

Molecular chess? Hallmarks of anti-cancer drug resistance

Background

The development of resistance is a problem shared by both classical chemotherapy and targeted therapy. Patients may respond well at first, but relapse is inevitable for many cancer patients, despite many improvements in drugs and their use over the last 40 years.

Review

Resistance to anti-cancer drugs can be acquired by several mechanisms within neoplastic cells, defined as (1) alteration of drug targets, (2) expression of drug pumps, (3) expression of detoxification mechanisms, (4) reduced susceptibility to apoptosis, (5) increased ability to repair DNA damage, and (6) altered proliferation. It is clear, however, that changes in stroma and tumour microenvironment, and local immunity can also contribute to the development of resistance. Cancer cells can and do use several of these mechanisms at one time, and there is considerable heterogeneity between tumours, necessitating an individualised approach to cancer treatment. As tumours are heterogeneous, positive selection of a drug-resistant population could help drive resistance, although acquired resistance cannot simply be viewed as overgrowth of a resistant cancer cell population. The development of such resistance mechanisms can be predicted from pre-existing genomic and proteomic profiles, and there are increasingly sophisticated methods to measure and then tackle these mechanisms in patients.

Conclusion

The oncologist is now required to be at least one step ahead of the cancer, a process that can be likened to ‘molecular chess’. Thus, as well as an increasing role for predictive biomarkers to clinically stratify patients, it is becoming clear that personalised strategies are required to obtain best results.

Enabling personalized cancer medicine decisions: The challenging pharmacological approach of PBPK models for nanomedicine and pharmacogenomics (Review)

The existing tumor heterogeneity and the complexity of cancer cell biology critically demand powerful translational tools with which to support interdisciplinary efforts aiming to advance personalized cancer medicine decisions in drug development and clinical practice. The development of physiologically based pharmacokinetic (PBPK) models to predict the effects of drugs in the body facilitates the clinical translation of genomic knowledge and the implementation of in vivo pharmacology experience with pharmacogenomics. Such a direction unequivocally empowers our capacity to also make personalized drug dosage scheme decisions for drugs, including molecularly targeted agents and innovative nanoformulations, i.e. in establishing pharmacotyping in prescription. In this way, the applicability of PBPK models to guide individualized cancer therapeutic decisions of broad clinical utility in nanomedicine in real-time and in a cost-affordable manner will be discussed. The latter will be presented by emphasizing the need for combined efforts within the scientific borderlines of genomics with nanotechnology to ensure major benefits and productivity for nanomedicine and personalized medicine interventions.

Anticancer drug sensitivity prediction in cell lines from baseline gene expression through recursive feature selection

BACKGROUND

An enduring challenge in personalized medicine is to select right drug for individual patients. Testing drugs on patients in large clinical trials is one way to assess their efficacy and toxicity, but it is impractical to test hundreds of drugs currently under development. Therefore the preclinical prediction model is highly expected as it enables prediction of drug response to hundreds of cell lines in parallel.

METHODS

Recently, two large-scale pharmacogenomic studies screened multiple anticancer drugs on over 1000 cell lines in an effort to elucidate the response mechanism of anticancer drugs. To this aim, we here used gene expression features and drug sensitivity data in Cancer Cell Line Encyclopedia (CCLE) to build a predictor based on Support Vector Machine (SVM) and a recursive feature selection tool. Robustness of our model was validated by cross-validation and an independent dataset, the Cancer Genome Project (CGP).

RESULTS

Our model achieved good cross validation performance for most drugs in the Cancer Cell Line Encyclopedia (≥80% accuracy for 10 drugs, ≥75% accuracy for 19 drugs). Independent tests on eleven common drugs between CCLE and CGP achieved satisfactory performance for three of them, i.e., AZD6244, Erlotinib and PD-0325901, using expression levels of only twelve, six and seven genes, respectively.

CONCLUSIONS

These results suggest that drug response could be effectively predicted from genomic features. Our model could be applied to predict drug response for some certain drugs and potentially play a complementary role in personalized medicine.

Hormone receptor-independent CXCL10 production is associated with the regulation of cellular factors linked to breast cancer progression and metastasis

Breast cancer (BC) is a major health problem for women around the world. Although advances in the field of molecular therapy have been achieved, the successful therapeutic management of BC, particularly metastatic disease, remains a challenge for patients and clinicians. One of the areas of current investigation is the circulating tumor cells (CTCs), which have a determinant role in the development of distant metastasis. At the present, many of the available treatment strategies for metastatic disease are of limited benefit. However, the elucidation of the mechanisms of tumor progression and metastasis may help to identify key molecules/components that may function as therapeutic targets in the future. In the present study, the functional analysis of CTCs revealed their ability to grow and proliferate to form colonies. Immunofluorescence staining of the CTCs' colonies exhibits elevated expression of cell growth and survival associated proteins such as, survivin, ERK and Akt1. More importantly, the functional screening of the chemokine profile in BC patients' sera revealed an HR-independent elevation of the chemokine CXCL10 when compared to healthy controls. The analysis of chemokines CXCL9 and CXCL11 demonstrated an HR-dependent production pattern. The levels of both CXCL9 and CXCL11 were markedly high in HR+ patients' sera when compared to HR- patients and healthy controls. The functional analysis of HR+ and HR- BC derived cell lines when cultivated in media supplemented with patients' sera demonstrated the alteration of tumor progression and metastasis related proteins. We noted the induction of survivin, β-catenin, MKP-1, pERK, CXCR4 and MMP-1 both at the protein and mRNA levels. The induction of those proteins was in keeping with patients' sera induced cell proliferation as measured by the MTT assay. In conclusion, our data emphasizes the role of chemokines, especially CXCL10, in BC progression and metastasis via the induction of signaling pathways, which mainly involve survivin, β-catenin, MKP-1 and MMP-1.

Prediction of resistance to chemotherapy in ovarian cancer: a systematic review

Background

Patient response to chemotherapy for ovarian cancer is extremely heterogeneous and there are currently no tools to aid the prediction of sensitivity or resistance to chemotherapy and allow treatment stratification. Such a tool could greatly improve patient survival by identifying the most appropriate treatment on a patient-specific basis.

Methods

PubMed was searched for studies predicting response or resistance to chemotherapy using gene expression measurements of human tissue in ovarian cancer.

Results

42 studies were identified and both the data collection and modelling methods were compared. The majority of studies utilised fresh-frozen or formalin-fixed paraffin-embedded tissue. Modelling techniques varied, the most popular being Cox proportional hazards regression and hierarchical clustering which were used by 17 and 11 studies respectively. The gene signatures identified by the various studies were not consistent, with very few genes being identified by more than two studies. Patient cohorts were often noted to be heterogeneous with respect to chemotherapy treatment undergone by patients.

Conclusions

A clinically applicable gene signature capable of predicting patient response to chemotherapy has not yet been identified. Research into a predictive, as opposed to prognostic, model could be highly beneficial and aid the identification of the most suitable treatment for patients.

Electronic supplementary material

The online version of this article (doi:10.1186/s12885-015-1101-8) contains supplementary material, which is available to authorized users.

Advances in combination therapies based on nanoparticles for efficacious cancer treatment: an analytical report

The main objective of nanomedicine research is the development of nanoparticles as drug delivery systems or drugs per se to tackle diseases as cancer, which are a leading cause of death with developed nations. Targeted treatments against solid tumors generally lead to dramatic regressions, but, unfortunately, the responses are often short-lived due to resistant cancer cells. In addition, one of the major challenges of combination drug therapy (called "cocktail") is the crucial optimization of different drug parameters. This issue can be solved using combination nanotherapy. Nanoparticles developed in oncology based on combination nanotherapy are either (a) those designed to combat multidrug resistance or (b) those used to circumvent resistance to clinical cancer drugs. This review provides an overview of the different nanoparticles currently used in clinical treatments in oncology. We analyze in detail the development of combinatorial nanoparticles including dendrimers for dual drug delivery via two strategic approaches: (a) use of chemotherapeutics and chemosensitizers to combat multidrug resistance and (b) use of multiple cytotoxic drugs. Finally, in this review, we discuss the challenges, clinical outlook, and perspectives of the nanoparticle-based combination therapy in cancer.

CrkL regulates SDF-1-induced breast cancer biology through balancing Erk1/2 and PI3K/Akt pathways

The adapter protein CrkL is required for regulating the malignant potential of human cancers. However, the regulatory mechanisms of CrkL on the stromal cell-derived factor 1 (SDF-1)/CXCR4 signaling pathways in breast cancer are not well characterized. Here, CXCR4 and CrkL proteins were tested in breast cancer cell lines and 60 primary breast cancer tissues. In vitro, the roles of CrkL in SDF-1-induced MDA-MB-231 cell cycle, invasion and migration were investigated. In the present study, CXCR4 and CrkL were highly expressed in MCF-7, MDA-MB-231, MDA-MB-231HM MDA-MB-468 and tumor tissues (80 and 60 %, respectively) and closely correlated with lymph node metastasis. In vitro studies revealed that SDF-1 induced the activation of CrkL, Erk1/2, Akt and matrix metallopeptidase 9 (MMP9) in MDA-MB-231 cells. The si-CrkL treatment significantly down-regulated the phosphorylated Erk1/2 (p-Erk1/2) and MMP9, but up-regulated p-Akt, compared with control. Importantly, wound-healing and transwell invasion assays showed that si-CrkL significantly impaired the wound closure and inhibited the SDF-1-induced invasion; similarly, flow cytometry showed that si-CrkL affected cell cycle. In conclusion, these results suggest that CrkL plays a regulatory role in the SDF-1-induced Erk1/2 and PI3K/Akt pathways and further managed the invasion and migration of breast cancer cells. Thus, CrkL may be recommended as an interesting therapeutic target for breast cancer.

Influence of ribosomal protein L39-L in the drug resistance mechanisms of lacrimal gland adenoid cystic carcinoma cells

BACKGROUND

Cancer constitutes a key pressure on public health regardless of the economy state in different countries. As a kind of highly malignant epithelial tumor, lacrimal gland adenoid cystic carcinoma can occur in any part of the body, such as salivary gland, submandibular gland, trachea, lung, breast, skin and lacrimal gland. Chemotherapy is one of the key treatment techniques, but drug resistance, especially MDR, seriously blunts its effects. As an element of the 60S large ribosomal subunit, the ribosomal protein L39-L gene appears to be documented specifically in the human testis and many human cancer samples of different origins.

MATERIALS AND METHODS

Total RNA of cultured drug-resistant and susceptible lacrimal gland adenoid cystic carcinoma cells was seperated, and real time quantitative RT-PCR were used to reveal transcription differences between amycin resistant and susceptible strains of lacrimal gland adenoid cystic carcinoma cells. Viability assays were used to present the amycin resistance difference in a RPL39-L transfected lacrimal gland adenoid cystic carcinoma cell line as compared to control vector and null-transfected lacrimal gland adenoid cystic carcinoma cell lines.

RESULTS

The ribosomal protein L39-L transcription level was 6.5-fold higher in the drug-resistant human lacrimal gland adenoid cystic carcinoma cell line than in the susceptible cell line by quantitative RT-PCR analysis. The ribosomal protein L39-L transfected cells revealed enhanced drug resistance compared to plasmid vector-transfected or null-transfected cells as determined by methyl tritiated thymidine (3H-TdR) incorporation.

CONCLUSIONS

The ribosomal protein L39-L gene could possibly have influence on the drug resistance mechanism of lacrimal gland adenoid cystic carcinoma cells.