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Though used widely in cancer therapy, paclitaxel only elicits a response in a fraction of patients. A strong determinant of paclitaxel tumor response is the state of microtubule dynamic instability. However, whether the manipulation of this physiological process can be controlled to enhance paclitaxel response has not been tested. Here, we show a previously unrecognized role of the microtubule-associated protein CRMP2 in inducing microtubule bundling through its carboxy terminus. This activity is significantly decreased when the FER tyrosine kinase phosphorylates CRMP2 at Y479 and Y499. The crystal structures of wild-type CRMP2 and CRMP2-Y479E reveal how mimicking phosphorylation prevents tetramerization of CRMP2. Depletion of FER or reducing its catalytic activity using sub-therapeutic doses of inhibitors increases paclitaxel-induced microtubule stability and cytotoxicity in ovarian cancer cells and in vivo. This work provides a rationale for inhibiting FER-mediated CRMP2 phosphorylation to enhance paclitaxel on-target activity for cancer therapy. Some anticancer drugs target cell microtubules inhibiting mitosis and cell division. Here, the authors show that CRMP2 induces microtubule bundling and that this activity is regulated by the FER kinase, thus providing a rationale for targeting FER in combination with microtubule-targeting drugs.

RNA virus populations will undergo processes of mutation and selection resulting in a mixed population of viral particles. High throughput sequencing of a viral population subsequently contains a mixed signal of the underlying clones. We would like to identify the underlying evolutionary structures. We utilize two sources of information to attempt this; within segment linkage information, and mutation prevalence. We demonstrate that clone haplotypes, their prevalence, and maximum parsimony reticulate evolutionary structures can be identified, although the solutions may not be unique, even for complete sets of information. This is applied to a chain of influenza infection, where we infer evolutionary structures, including reassortment, and demonstrate some of the difficulties of interpretation that arise from deep sequencing due to artifacts such as template switching during PCR amplification.

The impact of evolutionary processes in cancer and its implications for drug response, biomarker validation and clinical outcome requires careful consideration of the evolving mutational landscape of the cancer. Genome sequencing allows us to identify mutations but the prevalence of those mutations in heterogeneous tumours must be inferred. We describe a method that we call OncoPhase to compute the prevalence of somatic point mutations from genome sequencing analysis of heterogeneous tumours that combines information from nearby phased germline variants. We show using simulations that the use of phased germline information can give improved prevalence estimates over the use of somatic variants only.