2010 balandžio 26 d.

Prisiminiau ir patyrinėjau piktybinių susirgimų klasifikaciją: pagal WHO C00 - D 48, pagal histologiją - sarkomos...karcinomos, pagal kliniką - TNM ir nauja - daugybė bandymų klasifikuoti pagal genų aktyvumą. Turbūt, tai, apie ką buvau užsiminęs savo preprinte 1989 m. Pionieriškas straipsnis šia tema pasirodė tik 1999 m.-
href="http://www.sciencemag.org/cgi/content/full/286/5439/531

Science 15 October 1999:
Vol. 286. no. 5439, pp. 531 - 537
DOI: 10.1126/science.286.5439.531
Molecular Classification of Cancer: Class Discovery and Class Prediction by Gene Expression Monitoring
T. R. Golub, 1,2* D. K. Slonim, 1 P. Tamayo, 1 C. Huard, 1 M. Gaasenbeek, 1 J. P. Mesirov, 1 H. Coller, 1 M. L. Loh, 2 J. R. Downing, 3 M. A. Caligiuri, 4 C. D. Bloomfield, 4 E. S. Lander 1,5*


Jo esmė - ...the distinction between acute myeloid leukemia (AML) and acute lymphoblastic leukemia (ALL) without previous knowledge of these classes. An automatically derived class predictor was able to determine the class of new leukemia cases. The results demonstrate the feasibility of cancer classification based solely on gene expression monitoring and suggest a general strategy for discovering and predicting cancer classes for other types of cancer, independent of previous biological knowledge.

1. recently, particular subtypes of acute leukemia have been found to be associated with specific chromosomal translocations--for example, the t(12;21)(p13;q22) translocation occurs in 25% of patients with ALL, whereas the t(8;21)(q22;q22) occurs in 15% of patients with AML

2. Although the distinction between AML and ALL has been well established, no single test is currently sufficient to establish the diagnosis. Rather, current clinical practice involves an experienced hematopathologist's interpretation of the tumor's morphology, histochemistry, immunophenotyping, and cytogenetic analysis, each performed in a separate, highly specialized laboratory. Although usually accurate, leukemia classification remains imperfect and errors do occur.

Distinguishing ALL from AML is critical for successful treatment; chemotherapy regimens for ALL generally contain corticosteroids, vincristine, methotrexate, and L-asparaginase, whereas most AML regimens rely on a backbone of daunorubicin and cytarabine (8). Although remissions can be achieved using ALL therapy for AML (and vice versa), cure rates are markedly diminished, and unwarranted toxicities are encountered.

We set out to develop a more systematic approach to cancer classification based on the simultaneous expression monitoring of thousands of genes using DNA microarrays (9). It has been suggested (10) that such microarrays could provide a tool for cancer classification.

Our initial leukemia data set consisted of 38 bone marrow samples (27 ALL, 11 AML) obtained from acute leukemia patients at the time of diagnosis

RNA prepared from bone marrow mononuclear cells was hybridized to high-density oligonucleotide microarrays, produced by Affymetrix and containing probes for 6817 human genes (14). The 6817 genes were sorted by their degree of correlation (16). To establish whether the observed correlations were stronger than would be expected by chance, we developed a method called "neighborhood analysis" (Fig. 1A).

We applied this approach to the 38 acute leukemia samples. The set of informative genes to be used in the predictor was chosen to be the 50 genes most closely correlated with AML-ALL distinction in the known samples.

The list of informative genes used in the AML versus ALL predictor was highly instructive (Fig. 3B). Some, including CD11c, CD33, and MB-1, encode cell surface proteins for which monoclonal antibodies have been demonstrated to be useful in distinguishing lymphoid from myeloid lineage cells (25). Others provide new markers of acute leukemia subtype. For example, the leptin receptor, originally identified through its role in weight regulation, showed high relative expression in AML. The leptin receptor was recently demonstrated to have anti-apoptotic function in hematopoietic cells (26). Similarly, the zyxin gene has been shown to encode a LIM domain protein important in cell adhesion in fibroblasts, but a role in hematopoiesis has not been reported (27).

We had expected that the genes most useful in AML-ALL class prediction would simply be markers of hematopoietic lineage, and would not necessarily be related to cancer pathogenesis. However, many of the genes encode proteins critical for S-phase cell cycle progression (Cyclin D3, Op18, and MCM3), chromatin remodeling (RbAp48 and SNF2), transcription (TFIIE), and cell adhesion (zyxin and CD11c) or are known oncogenes (c-MYB, E2A and HOXA9). In addition, one of the informative genes encodes topoisomerase II, which is the principal target of the antileukemic drug etoposide (28). Together, these data suggest that genes useful for cancer class prediction may also provide insight into cancer pathogenesis and pharmacology.

The list of informative genes used in the AML versus ALL predictor was highly instructive (Fig. 3B). Some, including CD11c, CD33, and MB-1, encode cell surface proteins for which monoclonal antibodies have been demonstrated to be useful in distinguishing lymphoid from myeloid lineage cells (25). Others provide new markers of acute leukemia subtype. For example, the leptin receptor, originally identified through its role in weight regulation, showed high relative expression in AML. The leptin receptor was recently demonstrated to have anti-apoptotic function in hematopoietic cells (26). Similarly, the zyxin gene has been shown to encode a LIM domain protein important in cell adhesion in fibroblasts, but a role in hematopoiesis has not been reported (27).

We had expected that the genes most useful in AML-ALL class prediction would simply be markers of hematopoietic lineage, and would not necessarily be related to cancer pathogenesis. However, many of the genes encode proteins critical for S-phase cell cycle progression (Cyclin D3, Op18, and MCM3), chromatin remodeling (RbAp48 and SNF2), transcription (TFIIE), and cell adhesion (zyxin and CD11c) or are known oncogenes (c-MYB, E2A and HOXA9). In addition, one of the informative genes encodes topoisomerase II, which is the principal target of the antileukemic drug etoposide (28). Together, these data suggest that genes useful for cancer class prediction may also provide insight into cancer pathogenesis and pharmacology.


PROBLEMA - daugiamatiškumas ir atsitiktinių skirtumų vaidmuo bei perteklinių įtakos, eliminavimas. Šiaip, straipsnis labai įdomus ir dėl rezultatų, kurių absoliutus patikimumas nėra aiškus ir dėl metodologijos - bandymo klasifikuoti ALL ir AML pagal genų ekspresiją.

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