Cancer Research 

Abstract: The problem of identifying subtle time-space clustering of disease, as may be occurring in leukemia, is described and reviewed. Published approaches, generally associated with studies of leukemia, not dependent on knowledge of the underlying population for their validity, are directed towards identifying clustering by establishing a relationship between the temporal and the spatial separations for the n ( n - 1)/2 possible pairs which can be formed from the n observed cases of disease. Here it is proposed that statistical power can be improved by applying a reciprocal transform to these separations. While a permutational approach can give valid probability levels for any observed association, for reasons of practicability, it is suggested that the observed association be tested relative to its permutational variance. Formulas and computational procedures for doing so are given. While the distance measures between points represent symmetric relationships subject to mathematical and geometric regularities, the variance formula developed is appropriate for arbitrary relationships. Simplified procedures are given for the case of symmetric and skew-symmetric relationships. The general procedure is indicated as being potentially useful in other situations as, for example, the study of interpersonal relationships. Viewing the procedure as a regression approach, the possibility for extending it to nonlinear and multivariate situations is suggested. Other aspects of the problem and of the procedure developed are discussed. Similarly, pure temporal clustering can be identified by a study of incidence rates in periods of widespread epidemics. In point of fact, many epidemics of communicable diseases are somewhat local in nature and so these do actually constitute temporal-spatial clusters. For leukemia and similar diseases in which cases seem to arise substantially at random rather than as clear-cut epidemics, it is necessary to devise sensitive and efficient procedures for detecting any nonrandom component of disease occurrence. Various ingenious procedures which statisticians have developed for the detection of disease clustering are reviewed here. These procedures can be generalized so as to increase their statistical validity and efficiency. The technic to be given below for imparting statistical validity to the procedures already in vogue can be viewed as a generalized form of regression with possible useful application to problems arising in quite different contexts.

Abstract: We previously found that a polymer conjugated to the anticancer protein neocarzinostatin, named smancs, accumulated more in tumor tissues than did neocarzinostatin. To determine the general mechanism of this tumoritropic accumulation of smancs and other proteins, we used radioactive (51Cr-labeled) proteins of various molecular sizes (Mr 12,000 to 160,000) and other properties. In addition, we used dye-complexed serum albumin to visualize the accumulation in tumors of tumor-bearing mice. Many proteins progressively accumulated in the tumor tissues of these mice, and a ratio of the protein concentration in the tumor to that in the blood of 5 was obtained within 19 to 72 h. A large protein like immunoglobulin G required a longer time to reach this value of 5. The protein concentration ratio in the tumor to that in the blood of neither 1 nor 5 was achieved with neocarzinostatin, a representative of a small protein (Mr 12,000) in all time. We speculate that the tumoritropic accumulation of these proteins resulted because of the hypervasculature, an enhanced permeability to even macromolecules, and little recovery through either blood vessels or lymphatic vessels. This accumulation of macromolecules in the tumor was also found after i.v. injection of an albumin-dye complex (Mr 69,000), as well as after injection into normal and tumor tissues. The complex was retained only by tumor tissue for prolonged periods. There was little lymphatic recovery of macromolecules from tumor tissue. The present finding is of potential value in macromolecular tumor therapeutics and diagnosis.

Abstract: Accurate normalization is an absolute prerequisite for correct measurement of gene expression. For quantitative real-time reverse transcription-PCR (RT-PCR), the most commonly used normalization strategy involves standardization to a single constitutively expressed control gene. However, in recent years, it has become clear that no single gene is constitutively expressed in all cell types and under all experimental conditions, implying that the expression stability of the intended control gene has to be verified before each experiment. We outline a novel, innovative, and robust strategy to identify stably expressed genes among a set of candidate normalization genes. The strategy is rooted in a mathematical model of gene expression that enables estimation not only of the overall variation of the candidate normalization genes but also of the variation between sample subgroups of the sample set. Notably, the strategy provides a direct measure for the estimated expression variation, enabling the user to evaluate the systematic error introduced when using the gene. In a side-by-side comparison with a previously published strategy, our model-based approach performed in a more robust manner and showed less sensitivity toward coregulation of the candidate normalization genes. We used the model-based strategy to identify genes suited to normalize quantitative RT-PCR data from colon cancer and bladder cancer. These genes are UBC, GAPD, and TPT1 for the colon and HSPCB, TEGT, and ATP5B for the bladder. The presented strategy can be applied to evaluate the suitability of any normalization gene candidate in any kind of experimental design and should allow more reliable normalization of RT-PCR data.

Abstract: Mutations in codon 12, 13, or 61 of one of the three ras genes, H-ras, K-ras, and N-ras, convert these genes into active oncogenes. Rapid assays for the detection of these point mutations have been developed recently and used to investigate the role mutated ras genes play in the pathogenesis of human tumors. It appeared that ras gene mutations can be found in a variety of tumor types, although the incidence varies greatly. The highest incidences are found in adenocarcinomas of the pancreas (90%), the colon (50%), and the lung (30%); in thyroid tumors (50%); and in myeloid leukemia (30%). For some tumor types a relationship may exist between the presence of a ras mutation and clinical or histopathological features of the tumor. There is some evidence that environmental agents may be involved in the induction of the mutations.

Abstract: Most current research on human brain tumors is focused on the molecular and cellular analysis of the bulk tumor mass. However, there is overwhelming evidence in some malignancies that the tumor clone is heterogeneous with respect to proliferation and differentiation. In human leukemia, the tumor clone is organized as a hierarchy that originates from rare leukemic stem cells that possess extensive proliferative and self-renewal potential, and are responsible for maintaining the tumor clone. We report here the identification and purification of a cancer stem cell from human brain tumors of different phenotypes that possesses a marked capacity for proliferation, self-renewal, and differentiation. The increased self-renewal capacity of the brain tumor stem cell (BTSC) was highest from the most aggressive clinical samples of medulloblastoma compared with low-grade gliomas. The BTSC was exclusively isolated with the cell fraction expressing the neural stem cell surface marker CD133. These CD133+ cells could differentiate in culture into tumor cells that phenotypically resembled the tumor from the patient. The identification of a BTSC provides a powerful tool to investigate the tumorigenic process in the central nervous system and to develop therapies targeted to the BTSC.