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Abstract Pharmaceutical companies have adopted biomarker-based enrichment (personalized) strategies to improve research and development productivity. We explored the background in which personalized strategies are adopted and examined whether their adoption is linked to improved efficacy of new drugs approved for non-small cell lung cancer (NSCLC) by US Food and Drug Administration (FDA). We extracted data from the first labels of drugs approved for NSCLC between May 2003 and February 2021, and performed a qualitative comparative analysis and meta-analysis. Personalized strategies were adopted in more than half of the trials (16/27) and were often used in trials aimed at obtaining first-line indications and in drugs that were not first-in-class. The meta-analysis showed that personalized trials had significantly improved progression-free survival (PFS) hazard ratio (HR) than trials without personalization but not for relative response rate ratio (RRR) or overall survival (OS) HR. Trials in which PFS HR was the primary endpoint tended to have improved PFS HR, and trials in which OS HR was the primary endpoint had worse PFS HR. The efficacy endpoints that are substantially affected by personalized strategies appear to differ, especially for new drugs with novel mechanism of action (MOA), because trial designs are employed to validate drug-specific advantages.

Sparse index tracking is a prominent passive portfolio management strategy that constructs a sparse portfolio to track a financial index. A sparse portfolio is preferable to a full portfolio in terms of reducing transaction costs and avoiding illiquid assets. To achieve portfolio sparsity, conventional studies have utilized $\ell _{p}$-norm regularizations as a continuous surrogate of the $\ell _{0}$-norm regularization. Although these formulations can construct sparse portfolios, their practical application is challenging due to the intricate and time-consuming process of tuning parameters to define the precise upper limit of assets in the portfolio. In this paper, we propose a new problem formulation of sparse index tracking using an $\ell _{0}$-norm constraint that enables easy control of the upper bound on the number of assets in the portfolio. Moreover, our approach offers a choice between constraints on portfolio and turnover sparsity, further reducing transaction costs by limiting asset updates at each rebalancing interval. Furthermore, we develop an efficient algorithm for solving this problem based on a primal-dual splitting method. Finally, we illustrate the effectiveness of the proposed method through experiments on the S&P500 and Russell3000 index datasets.

Hyperspectral (HS) unmixing is the process of decomposing an HS image into material-specific spectra (endmembers) and their spatial distributions (abundance maps). Existing unmixing methods have two limitations with respect to noise robustness. First, if the input HS image is highly noisy, even if the balance between sparse and piecewise-smooth regularizations for abundance maps is carefully adjusted, noise may remain in the estimated abundance maps or undesirable artifacts may appear. Second, existing methods do not explicitly account for the effects of stripe noise, which is common in HS measurements, in their formulations, resulting in significant degradation of unmixing performance when such noise is present in the input HS image. To overcome these limitations, we propose a new robust HS unmixing method based on constrained convex optimization. Our method employs, in addition to the two regularizations for the abundance maps, regularizations for the HS image reconstructed by mixing the estimated abundance maps and endmembers. This strategy makes the unmixing process much more robust in highly noisy scenarios, under the assumption that the abundance maps used to reconstruct the HS image with desirable spatio-spectral structure are also expected to have desirable properties. Furthermore, our method is designed to accommodate a wider variety of noise including stripe noise. To solve the formulated optimization problem, we develop an efficient algorithm based on a preconditioned primal-dual splitting method, which can automatically determine appropriate stepsizes based on the problem structure. Experiments on synthetic and real HS images demonstrate the advantages of our method over existing methods.

Abstract Background and Aims Pancreatic cancer is one of the most lethal malignancies, partly due to resistance to conventional chemotherapy. The chemoresistance of malignant tumors is associated with epithelial‐mesenchymal transition (EMT) and the stemness of cancer cells. The aim of this study is to investigate the availability and functional mechanisms of trefoil factor family 1 (TFF1), a tumor‐suppressive protein in pancreatic carcinogenesis, to treat pancreatic cancer. Methods To investigate the role of endogenous TFF1 in human and mice, specimens of human pancreatic cancer and genetically engineered mouse model of pancreatic cancer (KPC/TFF1KO; Pdx1‐Cre/LSL‐KRASG12D/LSL‐p53R172H/TFF1−/−) were analyzed by immunohistochemistry (IHC). To explore the efficacy of extracellular administration of TFF1, recombinant and chemically synthesized TFF1 were administered to pancreatic cancer cell lines, a xenograft mouse model and a transgenic mouse model. Results The deficiency of TFF1 was associated with increased EMT of cancer cells in mouse models of pancreatic cancer, KPC. The expression of TFF1 in cancer cells was associated with better survival rate of the patients who underwent chemotherapy, and loss of TFF1 deteriorated the benefit of gemcitabine in KPC mice. Extracellular administration of TFF1 inhibited gemcitabine‐induced EMT, Wnt pathway activation and cancer stemness, eventually increased apoptosis of pancreatic cancer cells in vitro. In vivo, combined treatment of gemcitabine and subcutaneous administration of TFF1 arrested tumor growth in xenograft mouse model and resulted in the better survival of KPC mice by inhibiting EMT and cancer stemness. Conclusion These results indicate that TFF1 can contribute to establishing a novel strategy to treat pancreatic cancer patients by enhancing chemosensitivity.