Silencing of tropomodulin 1 inhibits acute myeloid leukemia cell proliferation and tumor growth by elevating karyopherin alpha 2-mediated autophagy.

Evidence shows that tropomodulin 1 (TMOD1) is a powerful diagnostic marker in the progression of several cancer types. However, the regulatory mechanism of TMOD1 in tumor progression is still unclear. Here, we showed that TMOD1 was highly expressed in acute myeloid leukemia (AML) specimens, and TMOD1-silencing inhibited cell proliferation by inducing autophagy in AML THP-1 and MOLM-13 cells. Mechanistically, the C-terminal region of TMOD1 directly bound to KPNA2, and TMOD1-overexpression promoted KPNA2 ubiquitylation and reduced KPNA2 levels. In contrast, TMOD1-silencing increased KPNA2 levels and facilitated the nuclear transfer of KPNA2, then subsequently induced autophagy and inhibited cell proliferation by increasing the nucleocytoplasmic transport of p53 and AMPK activation. KPNA2/p53 inhibitors attenuated autophagy induced by silencing TMOD1 in AML cells. Silencing TMOD1 also inhibited tumor growth by elevating KPNA2-mediated autophagy in nude mice bearing MOLM-13 xenografts. Collectively, our data demonstrated that TMOD1 could be a novel therapeutic target for AML treatment.

Synergistic antitumor activity between HER2 antibody-drug conjugate and chemotherapy for treating advanced colorectal cancer.

Colorectal cancer (CRC) is the third most common cancer associated with a poor prognosis. Effective targeted therapy alone or in combination for treating advanced CRC remains to be a major clinical challenge. Here, we propose the therapeutic efficacy and molecular mechanism underlying RC48, a FDA-approved anti-HER2 antibody conjugate via a cleavable linker to the microtubule inhibitor monomethyl auristatin E (MMAE), either alone or in combination with gemcitabine (GEM) in various models of HER2-positive advanced CRC. Our findings demonstrated that HER2 was widely expressed and located on the plasma membrane of CRC patient specimens, PDX xenograft tumors and cell lines. It confirmed that RC48 alone significantly targeted and eradicated HER2 positive CRC tumor in these models. Moreover, we screened a panel of FDA-approved first-line chemotherapy drugs in vitro. We found that GEM exhibited stronger antiproliferative activity compared to the other first-line anti-cancer agents. Furthermore, combination therapy of RC48 and GEM significantly showed synergetic antitumor activity in vitro and in vivo. To gain further mechanistic insights into the combination therapy, we performed RNA-seq analysis. The results revealed that combination treatment of RC48 and GEM regulated multiple signaling pathways, such as PI3K-AKT, MAPK, p53, Foxo, apoptosis, cell cycle and cell senescence, etc., to exert its antitumor activity in CRC cells. Collectively, these preclinical findings demonstrated that RC48 alone or combinational therapy exerted promising antitumor activity, and meriting the preclinical framework for combinational therapy of anti-HER2 drug conjugate drug and chemotherapy drugs for HER2-positive patients with advanced CRC.

Random sketch learning for deep neural networks in edge computing.

Despite the great potential of deep neural networks (DNNs), they require massive weights and huge computational resources, creating a vast gap when deploying artificial intelligence at low-cost edge devices. Current lightweight DNNs, achieved by high-dimensional space pre-training and post-compression, present challenges when covering the resources deficit, making tiny artificial intelligence hard to be implemented. Here we report an architecture named random sketch learning, or Rosler, for computationally efficient tiny artificial intelligence. We build a universal compressing-while-training framework that directly learns a compact model and, most importantly, enables computationally efficient on-device learning. As validated on different models and datasets, it attains substantial memory reduction of ~50-90× (16-bits quantization), compared with fully connected DNNs. We demonstrate it on low-cost hardware, whereby the computation is accelerated by >180× and the energy consumption is reduced by ~10×. Our method paves the way for deploying tiny artificial intelligence in many scientific and industrial applications.