Rapamycin and its derivatives (CCI-779, RAD001 and AP23576) are immunosuppressor macrolides that block mTOR (mammalian target of rapamycin) functions and yield antiproliferative activity in a variety of malignancies. Molecular characterization of upstream and downstream mTOR signaling pathways is thought to allow a better selection of rapamycin-sensitive tumours. For instance, a loss of PTEN functions results in Akt phosphorylation, cell growth and proliferation; circumstances that can be blocked using rapamycin derivatives. From recent studies, rapamycin derivatives appear to display a safe toxicity profile with skin rashes and mucositis being prominent and dose-limiting. Sporadic activity with no evidence of dose-effect relationship has been reported. Evidence suggests that rapamycin derivatives could induce G1-S cell cycle delay and eventually apoptosis depending on inner cellular characteristics of tumour cells. Surrogate molecular markers that could be used to monitor biological effects of rapamycin derivatives and narrow down biologically active doses in patients, such as the phosphorylation of P70S6K or expression of cyclin D1 and caspase 3, are currently evaluated. Since apoptosis induced by rapamycin is blocked by BCL-2, strategies aimed at detecting human tumours that express BCL-2 and other anti-apoptotic proteins might allow identification of rapamycin-resistant tumours. Finally, we discuss current and future placements of rapamycin derivatives and related translational research into novel therapeutic strategies against cancer.

A key constraint in genomic testing in oncology is that matched normal specimens are not commonly obtained in clinical practice. Thus, while well-characterized genomic alterations do not require normal tissue for interpretation, a significant number of alterations will be unknown in whether they are germline or somatic, in the absence of a matched normal control. We introduce SGZ (somatic-germline-zygosity), a computational method for predicting somatic vs. germline origin and homozygous vs. heterozygous or sub-clonal state of variants identified from deep massively parallel sequencing (MPS) of cancer specimens. The method does not require a patient matched normal control, enabling broad application in clinical research. SGZ predicts the somatic vs. germline status of each alteration identified by modeling the alteration’s allele frequency (AF), taking into account the tumor content, tumor ploidy, and the local copy number. Accuracy of the prediction depends on the depth of sequencing and copy number model fit, which are achieved in our clinical assay by sequencing to high depth (>500x) using MPS, covering 394 cancer-related genes and over 3,500 genome-wide single nucleotide polymorphisms (SNPs). Calls are made using a statistic based on read depth and local variability of SNP AF. To validate the method, we first evaluated performance on samples from 30 lung and colon cancer patients, where we sequenced tumors and matched normal tissue. We examined predictions for 17 somatic hotspot mutations and 20 common germline SNPs in 20,182 clinical cancer specimens. To assess the impact of stromal admixture, we examined three cell lines, which were titrated with their matched normal to six levels (10–75%). Overall, predictions were made in 85% of cases, with 95–99% of variants predicted correctly, a significantly superior performance compared to a basic approach based on AF alone. We then applied the SGZ method to the COSMIC database of known somatic variants in cancer and found >50 that are in fact more likely to be germline.

This high concordance suggests that for the purposes of genomic profiling, use of archived primary tumor can identify the key recurrent somatic alterations present in matched NSCLC metastases and may provide much of the relevant genomic information required to guide treatment on recurrence.

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There is a need to enforce independent review of mhealth applications in oncology. The economic model could be questioned and the source of funding should be clarified. Meanwhile, patients and healthcare professionals should remain cautious about applications' contents. Key messages A systematic review was performed to describe the mobile applications related to oncology and it revealed a lack of information on scientific validation and funding. Independent scientific review and the reporting of conflicts of interest should be encouraged. Users, and all health professionals, should be aware that health applications, whatever the quality of their content, do not actually embrace such an approach.