Is there a future for cell kinetic measurements using IdUrd or BdUrd?

Altered Fractionation Radiotherapy in Head and Neck Cancer: Clinical Issues and Pitfalls of “Evidence-Based Medicine”

The authors present a critical appraisal of the biological bases of altered fractionation and a brief overview of published randomized trials with conventional fractionation as the control arm, reviews and meta-analysis on altered fractionation radiotherapy in head and neck cancer. The major controversial issues emerging from these studies are reviewed and the limiting factors which so far have prevented the widespread use of altered fractionation regimens in current clinical practice are analyzed. Future perspectives regarding predictive biological assays for patient selection and the integration of altered fractionation regimens with radiochemotherapy protocols, biomodulators and novel radiotherapy techniques are also reviewed and summarized.

Kinetic modeling of tumor regression incorporating the concept of cancer stem-like cells for patients with locally advanced lung cancer

Background

Personalized medicine for patients receiving radiation therapy remains an elusive goal due, in part, to the limits in our understanding of the underlying mechanisms governing tumor response to radiation. The purpose of this study was to develop a kinetic model, in the context of locally advanced lung cancer, connecting cancer cell subpopulations with tumor volumes measured during the course of radiation treatment for understanding treatment outcome for individual patients.

Methods

The kinetic model consists of three cell compartments: cancer stem-like cells (CSCs), non-stem tumor cells (TCs) and dead cells (DCs). A set of ordinary differential equations were developed to describe the time evolution of each compartment, and the analytic solution of these equations was iterated to be aligned with the day-to-day tumor volume changes during the course of radiation treatment. A least squares fitting method was used to estimate the parameters of the model that include the proportion of CSCs and their radio-sensitivities. This model was applied to five patients with stage III lung cancer, and tumor volumes were measured from 33 cone-beam computed tomography (CBCT) images for each of these patients. The analytical solution of these differential equations was compared with numerically simulated results.

Results

For the five patients with late stage lung cancer, the derived proportions of CSCs are 0.3 on average, the average probability of the symmetry division is 0.057 and the average surviving fractions of CSCs is 0.967, respectively. The derived parameters are comparable to the results from literature and our experiments. The preliminary results suggest that the CSC self-renewal rate is relatively small, compared to the proportion of CSCs for locally advanced lung cancers.

Conclusions

A novel mathematical model has been developed to connect the population of cancer stem-like cells with tumor volumes measured from a sequence of CBCT images. This model may help improve our understanding of tumor response to radiation therapy, and is valuable for development of new treatment regimens for patients with locally advanced lung cancer.

Biological Predictors of Response to Radiotherapy in Head and Neck Cancer: Recent Advances and Emerging Perspectives

The study of new biological parameters has received considerable attention in radiotherapy during the last decade due to their potential value in predicting treatment response in squamous cell carcinoma of the head and neck (SCC-HN) and the foreseen possibility of selecting altered fractionation radiotherapy for the individual patient. Although there are established clinical parameters in SCC-HN patients that relate to radiation response (extent of disease, hemoglobin level), recent advances with direct measurement of tumor oxygenation, inherent radiosensitivity and proliferation rate have increased the promise of individualization of treatment strategy according to these radiobiologically based parameters. Molecular research has now identified a host of new biological parameters with potential predictive utility; oncogenes, tumor suppressor genes, cell-cycle control genes, apoptosis genes and angiogenesis genes have been extensively studied and correlated with radiation response. Moreover, study of the epidermal growth factor receptor signal-transduction system as a possible response modulator has recently fostered molecular strategies which employ blockade of the receptor to down-regulate tumor growth. This article briefly reviews and analyzes the main controversial issues and drawbacks that hinder the general use of biological parameters for predicting tumor response to radiotherapy. It highlights the future perspectives of radiotherapy predictive assay research and the need to shift from single-parameter analysis to multiparametric studies which take into account several potential predictors that together are involved in different biological and clinical pathways.

Determinants and clinical implications of chromosomal instability in cancer

Aberrant chromosomal architecture, ranging from small insertions or deletions to large chromosomal alterations, is one of the most common characteristics of cancer genomes. Chromosomal instability (CIN) underpins much of the intratumoural heterogeneity observed in cancers and drives phenotypic adaptation during tumour evolution. Thus, an urgent need exists to increase our efforts to target CIN as if it were a molecular entity. Indeed, CIN accelerates the development of anticancer drug resistance, often leading to treatment failure and disease recurrence, which limit the effectiveness of most current therapies. Identifying novel strategies to modulate CIN and to exploit the fitness cost associated with aneuploidy in cancer is, therefore, of paramount importance for the successful treatment of cancer. Modern sequencing and analytical methods greatly facilitate the identification and cataloguing of somatic copy-number alterations and offer new possibilities to better exploit the dynamic process of CIN. In this Review, we describe the principles governing CIN propagation in cancer and how CIN might influence sensitivity to immune-checkpoint inhibition, and survey the vulnerabilities associated with CIN that offer potential therapeutic opportunities.

Cell cycle times of short-term cultures of brain cancers as predictors of survival

Tumour cytokinetics estimated in vivo as potential doubling times (T_pot values) have been found to range in a variety of human cancers from 2 days to several weeks and are often related to clinical outcome. We have previously developed a method to estimate culture cycle times of short-term cultures of surgical material for several tumour types and found, surprisingly, that their range was similar to that reported for T_pot values. As T_pot is recognised as important prognostic variable in cancer, we wished to determine whether culture cycle times had clinical significance. Brain tumour material obtained at surgery from 70 patients with glioblastoma, medulloblastoma, astrocytoma, oligodendroglioma and metastatic melanoma was cultured for 7 days on 96-well plates, coated with agarose to prevent proliferation of fibroblasts. Culture cycle times were estimated from relative ³H-thymidine incorporation in the presence and absence of cell division. Patients were divided into two groups on the basis of culture cycle times of ⩽10 days and >10 days and patient survival was compared. For patients with brain cancers of all types, median survival for the ⩽10-day and >10-day groups were 5.1 and 12.5 months, respectively (P=0.0009). For 42 patients with glioblastoma, the corresponding values were 6.5 and 9.0 months, respectively (P=0.03). Lower grade gliomas had longer median culture cycle times (16 days) than those of medulloblastomas (9.9 days), glioblastomas (9.8 days) or melanomas (6.7 days). We conclude that culture cycle times determined using short-term cultures of surgical material from brain tumours correlate with patient survival. Tumour cells thus appear to preserve important cytokinetic characteristics when transferred to culture.

Cell Proliferation in Breast Cancer is a Major Determinant of Clinical Outcome in Node-Positive but Not in Node-Negative Patients

The growth rate of a tumor cell population depends on two major factors: the percentage of proliferating cells (cell growth fraction) and the rapidity of their duplication (cell proliferation rate). The authors evaluated the prognostic and predictive value of both kinetics parameters in a large series of breast cancer patients (n=504). The cell growth fraction was determined by MIB-1 immunostaining, the cell proliferation rate by AgNOR analysis. Ki-67 LI (labeling index) and AgNOR area were significantly associated with histotype, histologic grade, tumor size, estrogen/progesterone receptor status, patient age, and lymph node involvement (P<0.005). In the entire series of patients, both kinetics variables were significantly and independently associated with the clinical outcome, but their prognostic relevance was quite different when node-negative and node-positive patients were considered separately. Although in node-positive patients Ki-67 LI and AgNOR area were the unique independent predictors of disease-free and overall survival, they were excluded by the multivariate Cox model in node-negative patients, where only tumor size and estrogen receptor status retained a significant P-value. These results show that in breast carcinoma the cell growth fraction and the cell proliferation rate have a different prognostic impact with respect to the lymph node status and are major determinants of clinical outcome in node-positive patients only. Within this subgroup, the rapidity of cell proliferation as assessed by AgNOR analysis also served as a sensitive predictor of the response to adjuvant treatments.

A method to estimate cell cycle time and growth fraction using bromodeoxyuridine-flow cytometry data from a single sample

Background

Presently available flow cytometric methods of bromodeoxyuridine (BrdUrd) labelling do not provide information on the cell cycle time (T_C) and the growth fraction (GF). In this paper, we describe a novel and simple method to estimate T_C and GF from flow cytometric analysis of a single tumour sample after BrdUrd labelling.

Methods

The proposed method is based on two assumptions: (1) the number of labelled cells traversing the cell cycle per unit time is constant and (2) the total number of labelled cells is constant throughout the cycle, provided that cells produced after division are excluded. The total numbers of labelled divided G₁ cells, labelled divided S cells, labelled undivided S cells, and labelled undivided G₂ cells were obtained for DNA histograms of BrdUrd-positive cells in a collected sample. These cell numbers were used to write equations to determine the durations of cell cycle phases, T_C and GF. To illustrate the application of the proposed formulae, cell cycle kinetic parameters were analysed in solid SL2 tumours growing in DBA/2 mice and in human T-leukaemia Jurkat cells in culture.

Results

The suitability of the proposed method for estimating durations of the cell cycle phases, T_C and GF was demonstrated. T_C in SL2 tumours was found to be relatively constant at 4 and 10 days after tumour implantation (20.3 ± 1.1 h and 21.6 ± 0.9 h, respectively). GF in tumours at day 10 was lower than GF at day 4 (54.2 ± 7.7% vs. 79.2 ± 5.9%, p = 0.0003). Approximate values of T_C and GF of cultured Jurkat cells were 23.9 h and 79.3%, respectively.

Conclusion

The proposed method is relatively simple and permits estimation of the cell cycle parameters, including T_C and GF, from a single tumour sample after labelling with BrdUrd. We have shown that this method may be useful in preclinical studies, allowing estimation of changes in GF during growth of murine tumours. Experiments with human Jurkat cells suggest that the proposed method might also prove suitable for measurement of cell kinetics in human tumours. Development of suitable software enabling more objective interpretation of the DNA profile in this method would be desirable.

A Markov model approach shows a large variation in the length of S phase in MCF-7 breast cancer cells

BACKGROUND

The potential doubling time of a tumor has been suggested to be a measurement of tumor aggressiveness; therefore, it is of interest to find reliable methods to estimate this time. Because of variability in length of the various cell cycle phases, stochastic modeling of the cell cycle might be a suitable approach.

METHODS

The relative movement curve and the DNA synthesis time were estimated by using local polynomial regression methods. Further, the rate of nucleotide incorporation was estimated by using a Markov pure birth process with one absorbing state to model the progression of the DNA distribution through S phase.

RESULTS

An estimate of the DNA synthesis time, with confidence intervals, was obtained from the relative movement curve. The Markov approach provided an estimate of the distribution of the time to complete S phase given the initial distribution. Using the Markov approach we also made an estimate of the mean number of active replicons during S phase.

CONCLUSIONS

A Markov pure birth process has shown to be useful to model the progression of cells through S phase and to increase knowledge about the variability in the length of S phase and a large variation is shown.

A prospective study of pre-treatment cell kinetics and clinical outcome in nasopharyngeal carcinoma

BACKGROUND AND PURPOSE

To study pre-treatment cell kinetics and their clinical correlations in nasopharyngeal carcinoma (NPC).

MATERIALS AND METHODS

Ninety newly diagnosed NPC patients were studied using in vivo Bromodeoxyuridine (BrdU) labeling and flow cytometric analysis. Immunohistochemical staining for BrdU and Ki 67 was also performed.

RESULTS

The median S-phase duration (Ts) was 6.2 h (range 3.5-18.7 h), median flow cytometric labeling index (FCM-LI) was 7.4% (1.3-37.6%), and median potential doubling time (Tpot) was 3.6 days (0.5-19.9 days). The median histologic labeling index (H-LI) was 12.4% (1.2-43.3%), and median histologic Tpot (H-Tpot) was 2.1 days (0.5-33.3 days). FCM-LI and H-LI were both positively correlated with Ki67 whereas Tpot and H-Tpot were both negatively correlated with Ki67 and N-stage. In univariate analysis, Tpot and H-Tpot showed a trend for progression free survival. Tpot was significantly associated with local relapse free survival, but lost its significance in multivariate analysis. N-stage was the only significant prognostic factor for all radiotherapy outcomes in both univariate and multivariate analyses.

CONCLUSIONS

Tpot was the only pre-treatment cell kinetic parameter for which some evidence was found for an association with survival in NPC patients. Future studies should aim to combine cell kinetic parameters together with other biological markers and clinical parameters to provide more useful prognostic information to guide individual patient's therapy.

Biological response to radiation therapy

In an investigation by the Swedish Cancer Society, the present status, critical issues and future aspects and potentials were described by an expert group for each of nine major areas of radiation therapy research. This article deals with biological response to radiation. Separate sections deal with molecular responses to radiation, the stem cell and clonogenic cell concepts and the importance of cell proliferation, cell and tissue responses to doses above and below 1 Gy, respectively, the potential role of intercellular signalling pathways, the so-called bystander effect and radiation biology-based therapy planning and treatment optimization.