Individualized chemotherapy treatment for breast cancer: is it necessary? Is it feasible?

New in vitro system to predict chemotherapeutic efficacy of drug combinations in fresh tumor samples

Background

To find the best individual chemotherapy for cancer patients, the efficacy of different chemotherapeutic drugs can be predicted by pretesting tumor samples in vitro via the chemotherapy-resistance (CTR)-Test^®. Although drug combinations are widely used among cancer therapy, so far only single drugs are tested by this and other tests. However, several first line chemotherapies are combining two or more chemotherapeutics, leading to the necessity of drug combination testing methods.

Methods

We established a system to measure and predict the efficacy of chemotherapeutic drug combinations with the help of the Loewe additivity concept in combination with the CTR-test. A combination is measured by using half of the monotherapy’s concentration of both drugs simultaneously. With this method, the efficacy of a combination can also be calculated based on single drug measurements.

Results

The established system was tested on a data set of ovarian carcinoma samples using the combination carboplatin and paclitaxel and confirmed by using other tumor species and chemotherapeutics. Comparing the measured and the calculated values of the combination testings revealed a high correlation. Additionally, in 70% of the cases the measured and the calculated values lead to the same chemotherapeutic resistance category of the tumor.

Conclusion

Our data suggest that the best drug combination consists of the most efficient single drugs and the worst drug combination of the least efficient single drugs. Our results showed that single measurements are sufficient to predict combinations in specific cases but there are exceptions in which it is necessary to measure combinations, which is possible with the presented system.

Predicting efficacy of cancer cell killing under hypoxic conditions with single cell DNA damage assay

The activity of anticancer drugs determined under normal conditions cannot accurately reflect true drug efficacy in a patient, as a tumor is often under low oxygen (hypoxia) conditions. In addition, patient responses to the same therapy can be drastically different due to tumor heterogeneity. This paper describes the use of single cell halo assay for detection and quantification of DNA damage induced by anticancer drugs or radiation under hypoxic conditions. By combining classical halo assay and state-of-the-art microfabrication techniques, this single cell approach allows drug and radiation responses of cancer cells to be determined without population interference. The results from single cell assay indicate a diminished level of DNA damage at hypoxic conditions compared with those at normal conditions at the same drug concentrations or radiation dose, suggesting in vitro preclinical studies of drug and radiation activity can be performed under conditions that mimic physiological conditions of tumors and without population interference.

Predictive tests for individualization of pharmacological cancer treatment

BACKGROUND

The selection of cancer drugs for an individual patient is still based mostly on cancer type and stage. Predictive tests are needed to make individualized and more efficient pharmacological cancer treatment possible.

OBJECTIVE

To provide an overview of available, possible future development and principles for development of predictive tests for individualized selection of cancer drugs.

METHODS

Overview of published data.

RESULTS/CONCLUSION

Despite increased knowledge in cancer biology, only limited progress has been made in the development and use of predictive tests. However, rapid progress in this field will be possible using already available and emerging technologies, but requires a paradigm shift in principles for the development and use of cancer drugs. Assessment of drug activity in intact tumor cells and tumor cell gene expression signatures are considered to have greatest potential for the development of versatile predictive tests.

Overexpression of kinesins mediates docetaxel resistance in breast cancer cells

Resistance to chemotherapy remains a major barrier to the successful treatment of cancer. To understand mechanisms underlying docetaxel resistance in breast cancer, we used an insertional mutagenesis strategy to identify proteins whose overexpression confers resistance. A strong promoter was inserted approximately randomly into the genomes of tumor-derived breast cancer cells, using a novel lentiviral vector. We isolated a docetaxel-resistant clone in which the level of the kinesin KIFC3 was elevated. When KIFC3 or the additional kinesins KIFC1, KIF1A, or KIF5A were overexpressed in the breast cancer cell lines MDA-MB231 and MDA-MB 468, the cells became more resistant to docetaxel. The binding of kinesins to microtubules opposes the stabilizing effect of docetaxel that prevents cytokinesis and leads to apoptosis. Our finding that kinesins can mediate docetaxel resistance might lead to novel therapeutic approaches in which kinesin inhibitors are paired with taxanes.

Prediction of the outcome of preoperative chemotherapy in breast cancer using DNA probes that provide information on both complete and incomplete responses

Background

DNA microarray technology has emerged as a major tool for exploring cancer biology and solving clinical issues. Predicting a patient's response to chemotherapy is one such issue; successful prediction would make it possible to give patients the most appropriate chemotherapy regimen. Patient response can be classified as either a pathologic complete response (PCR) or residual disease (NoPCR), and these strongly correlate with patient outcome. Microarrays can be used as multigenic predictors of patient response, but probe selection remains problematic. In this study, each probe set was considered as an elementary predictor of the response and was ranked on its ability to predict a high number of PCR and NoPCR cases in a ratio similar to that seen in the learning set. We defined a valuation function that assigned high values to probe sets according to how different the expression of the genes was and to how closely the relative proportions of PCR and NoPCR predictions to the proportions observed in the learning set was. Multigenic predictors were designed by selecting probe sets highly ranked in their predictions and tested using several validation sets.

Results

Our method defined three types of probe sets: 71% were mono-informative probe sets (59% predicted only NoPCR, and 12% predicted only PCR), 25% were bi-informative, and 4% were non-informative. Using a valuation function to rank the probe sets allowed us to select those that correctly predicted the response of a high number of patient cases in the training set and that predicted a PCR/NoPCR ratio for validation sets that was similar to that of the whole learning set. Based on DLDA and the nearest centroid method, bi-informative probes proved more successful predictors than probes selected using a t test.

Conclusion

Prediction of the response to breast cancer preoperative chemotherapy was significantly improved by selecting DNA probe sets that were successful in predicting outcomes for the entire learning set, both in terms of accurately predicting a high number of cases and in correctly predicting the ratio of PCR to NoPCR cases.

Molecular-based choice of cancer therapy: realities and expectations

Current choice of cancer therapy is usually empirical and relies mainly on the statistical prediction of the treatment success. Molecular research provides some opportunities to personalize antitumor treatment. For example, life-threatening toxic reactions can be avoided by the identification of subjects, who carry susceptible genotypes of drug-metabolizing genes (e.g. TPMT, UGT1A1, MTHFR, DPYD). Tumor sensitivity can be predicted by molecular portraying of targets and other molecules associated with drug response. Tailoring of antiestrogen and trastuzumab therapy based on hormone and HER2 receptor status has already become a classical example of customized medicine. Other predictive markers have been identified both for cytotoxic and for targeted therapies, and include, for example, expression of TS, TP, DPD, OPRT, ERCC1, MGMT, TOP2A, class III beta-tubulin molecules as well as genomic alterations of EGFR, KIT, ABL oncogenes.

Methods and biomarkers for the diagnosis and prognosis of cancer and other diseases: towards personalized medicine

The rapid development of new diagnostic procedures, the mapping of the human genome, progress in mapping genetic polymorphisms, and recent advances in nucleic acid- and protein chip technologies are driving the development of personalized therapies. This breakthrough in medicine is expected to be achieved largely due to the implementation of "lab-on-the-chip" technology capable of performing hundreds, even thousands of biochemical, cellular and genetic tests on a single sample of blood or other body fluid. Focusing on a few disease-specific examples, this review discusses selected technologies and their combinations likely to be incorporated in the "lab-on-the-chip" and to provide rapid and versatile information about specific diseases entities. Focusing on breast cancer and after an overview of single-nucleotide polymorphism (SNP)-screening methodologies, we discuss the diagnostic and prognostic importance of SNPs. Next, using Duchenne muscular dystrophy (DMD) as an example, we provide a brief overview of powerful and innovative integration of traditional immuno-histochemistry techniques with advanced biophysical methods such as NMR-spectroscopy or Fourier-transformed infrared (FT-IR) spectroscopy. A brief overview of the challenges and opportunities provided by protein and aptamer microarrays follows. We conclude by highlighting novel and promising biochemical markers for the development of personalized treatment of cancer and other diseases: serum cytochrome c, cytokeratin-18 and -19 and their proteolytic fragments for the detection and quantitation of malignant tumor mass, tumor cell turn-over, inflammatory processes during hepatitis and Epstein-Barr virus (EBV)-induced hemophagocytic lymphohistiocytosis and apoptotic/necrotic cancer cell death.

Predictive factors for response to docetaxel in human breast cancers

Docetaxel has come into wide use recently for the treatment of breast cancer in neoadjuvant, adjuvant and metastatic settings. Docetaxel binds to beta-tubulin and causes kinetic abnormalities in the dynamics of microtubules by increasing their polymerization and inhibiting their depolymerization, resulting in elevated levels of microtubule formation. During metaphase, defective spindle formation induced by docetaxel activates the mitotic checkpoint and leads to cell cycle arrest, culminating in apoptosis. However, docetaxel is not effective for all breast cancers. For example, in metastatic settings, the response rate to docetaxel reportedly ranges from 30 to 50%. It is therefore very important to develop a diagnostic method with high accuracy for the prediction of sensitivity to docetaxel in order to avoid unnecessary treatment. Currently it is impossible to identify, before the initiation of therapy, the patients for whom docetaxel will be effective. Various biological parameters have been studied clinically for their ability to predict response to docetaxel, such as parameters related to: (1) efflux (p-glycoprotein) and metabolism (CYP3A4); (2) beta-tubulin (somatic mutation of beta-tubulin and changes in beta-tubulin isotypes levels); (3) cell cycle (HER2, BRCA1 and Aurora-A); and (4) apoptosis (p53, BCL2 and thioredoxin). More recently, gene expression profiling techniques have been used for the development of a prediction model for response to docetaxel. In the present paper, clinical studies that have been conducted recently to identify predictive factors for response to docetaxel are reviewed together with a presentation of our recent work in this field.

Lymph node-based prognostics: limitations with individualized cancer treatment

OBJECTIVES

Clinicians will commonly individualize adjuvant cancer therapy, on the basis of the number of involved lymph nodes and other clinicopathological factors, under the assumption that despite the expected statistical variability of such data one can nonetheless garner useful information for the individual case. Here the scientific basis of this assumption will be examined.

METHODS

Survival data from the National Cancer Institute's Surveillance, Epidemiology, and End Results (SEER) program for 19,107 breast, 4,234 gastric, and 4,058 rectal cancers were studied with Kaplan-Meier estimates and Cox proportionate hazard models. The minimal sample size required to discriminate between high and low-risk groups was determined from the hazard ratios between various comparative groups, and their respective frequencies.

RESULTS

The number of involved nodes was the strongest prognostic factor for all 3 cancers, followed by tumor diameter and grade. Discrimination between high and low-risk nodal prognostic groups required samples of 30 to 200 cases, depending on the prognostics used and the specific tumor, to attain a two-sided alpha of 0.05% with 90% power. At the individual level such prognostications therefore were uninformative.

CONCLUSIONS

Clinicopathological prognostics based upon the number of involved lymph nodes are subject to population heterogeneity that limits their application to large samples. At the individual level, these prognostics appear more spurious than useful. The use of such prognostics to tailor cancer treatment to individuals should be considered a specious practice; instead a more categorical approach, based on the results of randomized trials, should be used.