BackgroundThere is much discussion in the cancer drug development community about how to incorporate molecular tools into early-stage clinical trials to assess target modulation, measure anti-tumor activity, and enrich the clinical trial population for patients who are more likely to benefit. Small, molecularly focused clinical studies offer the promise of the early definition of optimal biologic dose and patient population.Methods and FindingsBased on preclinical evidence that phosphatase and tensin homolog deleted on Chromosome 10 (PTEN) loss sensitizes tumors to the inhibition of mammalian target of rapamycin (mTOR), we conducted a proof-of-concept Phase I neoadjuvant trial of rapamycin in patients with recurrent glioblastoma, whose tumors lacked expression of the tumor suppressor PTEN. We aimed to assess the safety profile of daily rapamycin in patients with glioma, define the dose of rapamycin required for mTOR inhibition in tumor tissue, and evaluate the antiproliferative activity of rapamycin in PTEN-deficient glioblastoma. Although intratumoral rapamycin concentrations that were sufficient to inhibit mTOR in vitro were achieved in all patients, the magnitude of mTOR inhibition in tumor cells (measured by reduced ribosomal S6 protein phosphorylation) varied substantially. Tumor cell proliferation (measured by Ki-67 staining) was dramatically reduced in seven of 14 patients after 1 wk of rapamycin treatment and was associated with the magnitude of mTOR inhibition (p = 0.0047, Fisher exact test) but not the intratumoral rapamycin concentration. Tumor cells harvested from the Ki-67 nonresponders retained sensitivity to rapamycin ex vivo, indicating that clinical resistance to biochemical mTOR inhibition was not cell-intrinsic. Rapamycin treatment led to Akt activation in seven patients, presumably due to loss of negative feedback, and this activation was associated with shorter time-to-progression during post-surgical maintenance rapamycin therapy (p < 0.05, Logrank test).ConclusionsRapamycin has anticancer activity in PTEN-deficient glioblastoma and warrants further clinical study alone or in combination with PI3K pathway inhibitors. The short-term treatment endpoints used in this neoadjuvant trial design identified the importance of monitoring target inhibition and negative feedback to guide future clinical development.Trial registration: http://www.ClinicalTrials.gov (#NCT00047073).

Four human colon cancer cell lines (SW620, LS 180, DLD-I, and HCT-15) and Adriamycin-resistant sub-lines with varying degrees of P-glycoprotein expression were studied to evaluate the reversibility of Adriamycin resistance in human colon cancer. Two groups of cell lines were studied. In the first, including a series of Adriamycin-resistant SW620 and DLD-I sub-lines, and in parental HCT-15 cells, P-glycoprotein has a major role in Adriamycin resistance, as evidenced by a correlation between Adriamycin resistance, expression of the multidrug-resistance gene mdr-I and its product, P-glycoprotein (Pgp), decreased drug accumulation and reversibility by verapamil. In these cell lines, increasing doses of verapamil are required to fully reverse increasing levels of resistance. In the second group, including parental SW620, DLD-I and LS 180 cells and Adriamycin-selected LS 180 sub-lines, P-glycoprotein does not have a major role in Adriamycin resistance. There was correlation between the schedule dependence of Adriamycin cytotoxicity and the role of P-glycoprotein in modulating resistance. In the cell lines in which P-glycoprotein was a major determinant of Adriamycin resistance, the drug exposure (defined as the product of the concentration and the time of treatment) needed to achieve a given percent cell kill was reduced as much as 9-fold when cells were treated for 7 days as compared with 3 hr. By comparison, in cell lines in which P-glycoprotein played a lesser role, the drug exposure necessary to achieve a given percent kill increased under conditions of continuous treatment. In some human colon carcinoma cell lines Pgp appears to play a significant role in resistance to Adriamycin, and this can be overcome by the use of competitive inhibitors of Pgp. The increased sensitivity with continuous treatment observed in cell lines with P-glycoprotein-mediated resistance suggests that administration of drugs by continuous infusion may be valuable in reversing clinical drug resistance mediated predominantly by P-glycoprotein.

Deep learning-based semi-supervised learning (SSL) algorithms have led to promising results in medical images segmentation and can alleviate doctors' expensive annotations by leveraging unlabeled data. However, most of the existing SSL algorithms in literature tend to regularize the model training by perturbing networks and/or data. Observing that multi/dual-task learning attends to various levels of information which have inherent prediction perturbation, we ask the question in this work: can we explicitly build task-level regularization rather than implicitly constructing networks- and/or data-level perturbation and then regularization for SSL? To answer this question, we propose a novel dual-task-consistency semi-supervised framework for the first time. Concretely, we use a dual-task deep network that jointly predicts a pixel-wise segmentation map and a geometry-aware level set representation of the target. The level set representation is converted to an approximated segmentation map through a differentiable task transform layer. Simultaneously, we introduce a dual-task consistency regularization between the level set-derived segmentation maps and directly predicted segmentation maps for both labeled and unlabeled data. Extensive experiments on two public datasets show that our method can largely improve the performance by incorporating the unlabeled data. Meanwhile, our framework outperforms the state-of-the-art semi-supervised learning methods.

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