Multiple mutations are required for cancer development, and genome sequencing has revealed that several cancers, including breast, have somatic mutation spectra dominated by C-to-T transitions1–9. Most of these mutations occur at hydrolytically disfavored10 non-methylated cytosines throughout the genome, and are sometimes clustered8. Here, we show that the DNA cytosine deaminase APOBEC3B (A3B) is a likely source of these mutations. A3B mRNA is up-regulated in the majority of primary breast tumors and breast cancer cell lines. Tumors that express high levels of A3B have twice as many mutations as those that express low levels and are more likely to have mutations in TP53. Endogenous A3B protein is predominantly nuclear and the only detectable source of DNA C-to-U editing activity in breast cancer cell line extracts. Knockdown experiments show that endogenous A3B correlates with elevated levels of genomic uracil, increased mutation frequencies, and C-to-T transitions. Furthermore, induced A3B over-expression causes cell cycle deviations, cell death, DNA fragmentation, γ-H2AX accumulation, and C-to-T mutations. Our data suggest a model in which A3B-catalyzed deamination provides a chronic source of DNA damage in breast cancers that could select TP53 inactivation and explain how some tumors evolve rapidly and manifest heterogeneity.

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The "Pharmaceutical Current Good Manufacturing Practices (CGMPs) for the 21st Century--A Risk Based Approach" initiative announced by the FDA in August 2002 to improve and modernize pharmaceutical manufacturing facilitated adoption of process analytical technology (PAT) by the pharmaceutical industry. The potential for improved operational control and compliance resulting from continuous real-time quality assurance was highlighted as a likely benefit that would result from PAT implementation. A considerable amount of work has been done on this topic by academic and industrial contributors in the last decade. In this paper, we will start with a brief overview of evolution of PAT concepts and a review of their application in the wider pharmaceutical industry. The rest of the paper focuses on PAT applications for biotech processes with emphasis on developments in the last five years. It is our observation that while significant advances have been accomplished with regard to our ability to analyze/monitor key process and quality attributes in the biotech industry, much more needs to be done with regard to utilizing the collected data for subsequent control of the process, to achieve optimum yield and product quality. The latter is necessary to achieve the benefits that will result from PAT implementation.