The ability to predict macromolecular conformations from sequence and thermodynamic principles has long been coveted but generally has not been achieved. We show that differences in the hydration of DNA surfaces can be used to distinguish between sequences that form A-and B-DNA. From this, a "triplet code" ofA-DNA propensities was derived as energetic rules for predicting A-DNA formation. This code correctly predicted >90% of A-and B-DNA sequences in crystals and correlates with A-DNA formation in solution. Thus, with our previous studies on Z-DNA, we now have a single method to predict the relative stability of sequences in the three standard DNA duplex conformations.

Crystallization of macromolecules for structural studies has long been a hit-or-miss process. The crystallization of hexanucleotides as Z-DNA was studied, and it was shown that the cation concentration for crystal formation could be predicted from solvation free energy (SFE) calculations. Solution studies on the conformation and solubilities of the hexanucleotides showed that a critical concentration of the DNA in the Z-conformation must be present in solution to effect crystallization. The SFE calculations therefore predict the propensity of the hexanucleotides to adopt the left-handed conformation and the driving force required to reach this critical concentration relative to the intrinsic solubility of Z-DNA for crystallization.

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Background: The role of fusion genes and associated fusion transcripts has long been recognized in hematopoietic malignancies. Until quite recently it has been difficult to detect such events on a genomic scale in solid tumors. Consequently, little is known about the potential role of fusion genes, transcripts, and proteins as driver mutations, biomarkers, or therapeutic targets in breast cancer. Methods: We have developed a novel analytical pipeline, Snowshoes-FTD, for detection of fusion transcripts in breast cancer cell lines and tumor samples (Asmann, et al. NAR 2011; May 27 ePub ahead of print). Preliminary analyses have been carried out with a panel of 8 each ER+, HER2+, and triple negative (TN) primary breast tumors, 8 primary human mammary epithelial cell (HMEC) lines from biopsy samples, plus 16 normal tissues from the Illumina Body Map dataset. Results: We have identified 120 redundant, tumor-specific fusion transcripts, expressed in two or more tumors and in no non-transformed samples. Sixteen of these represent intrachromosomal fusions and 104 arise from fusion of transcripts that map to two different chromosomes. Every breast tumor expressed one or more fusion transcripts. Twenty-nine fusion transcripts appeared to be tumor subtype specific. Among these, we have identified 2 HER2+, 10 ER+, and 17 triple negative specific redundant transcripts. In general, HER2+ tumors expressed fewer fusion transcripts (range 4 to 28/tumor) compared to TN (range11 to 44/tumor). Chromosomal distribution patterns were also markedly different among the tumor subtypes. For example, ER+ tumors expressed a preponderance of redundant fusion transcripts that involve chr1 and 2, whereas TN tumors had no fusion transcripts that map to either chromosome. Conversely, the predominant locus for TN fusion transcripts was chr19, which contains only one HER2+ fusion and no ER+ fusion transcripts. Conclusions: Primary breast tumors express many chimaeric transcripts, which we presume to arise primarily from genomic rearrangements. The majority of these transcripts are redundant, and a subset are tumor subtype specific. These transcripts may mark regions of chromosomal instability. HER2+ tumors, in general, appear to evidence less chromosomal instability, as inferred from fusion transcripts; although some HER2+ tumors appear to be quite unstable. TN tumors contain many more redundant fusion transcripts, implying increased genomic instability, particularly in chr19. We conclude that these fusion transcripts represent a class of heretofore unrecognized biomarkers that may be used for sub-classification of breast tumors. Some of these transcripts appear to encode proteins that may function as tumor-subtype-specific driver mutations and may have potential as therapeutic targets in breast cancer. Citation Information: Cancer Res 2011;71(24 Suppl):Abstract nr P3-06-02.

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