The retained functionality of the sodium iodide symporter (NIS) expressed in differentiated thyroid cancer (DTC) cells allows the further utilization of post-surgical radioactive iodine (RAI) therapy, which is an effective treatment for reducing the risk of recurrence, and even the mortality, of DTC. Whereas, the dedifferentiation of DTC could influence the expression of functional NIS, thereby reducing the efficacy of RAI therapy in advanced DTC. Genetic alternations (such as BRAF and the rearranged during transfection [RET]/papillary thyroid cancer [PTC] rearrangement) have been widely reported to be prominently responsible for the onset, progression, and dedifferentiation of PTC, mainly through activating the mitogen-activated protein kinase (MAPK) and phosphoinositide 3-kinase (PI3K) signaling cascades. These genetic alternations have been suggested to associate with the reduced expression of iodide-handling genes in thyroid cancer, especially the NIS gene, disabling iodine uptake and causing resistance to RAI therapy. Recently, novel and promising approaches aiming at various targets have been attempted to restore the expression of these iodine-metabolizing genes and enhance iodine uptake through in vitro studies and studies of RAI-refractory (RAIR)-DTC patients. In this review, we discuss the regulation of NIS, known mechanisms of dedifferentiation including the MAPK and PI3K pathways, and the current status of redifferentiation therapy for RAIR-DTC patients.

Background: Hepatocellular carcinoma (HCC) is among the most common forms of cancer and is associated with poor patient outcomes. The emergence of therapeutic resistance has hampered the efficacy of targeted treatments employed to treat HCC patients to date. In this study, we conducted a series of CRISPR/Cas9 screens to identify genes associated with synthetic lethality capable of improving HCC patient clinical responses. Methods: CRISPR-based loss-of-function genetic screens were used to target 18,053 protein-coding genes in HCC cells to identify chemotherapy-related synthetic lethal genes in these cells. Synergistic effects were analyzed through in vitro and in vivo analyses, while related mechanisms were explored through RNA-seq and metabolomics analyses. Potential inhibitors of identified genetic targets were selected through high-throughput virtual screening. Results: The inhibition of phosphoseryl-tRNA kinase (PSTK) was found to increase HCC cell sensitivity to chemotherapeutic treatment. PSTK was associated with the suppression of chemotherapy-induced ferroptosis in HCC cells, and the depletion of PSTK resulted in the inactivation of glutathione peroxidative 4 (GPX4) and the disruption of glutathione (GSH) metabolism owing to the inhibition of selenocysteine and cysteine synthesis, thus enhancing the induction of ferroptosis upon targeted chemotherapeutic treatment. Punicalin, an agent used to treat hepatitis B virus (HBV), was identified as a possible PSTK inhibitor that exhibited synergistic efficacy when applied together with Sorafenib to treat HCC in vitro and in vivo. Conclusions: These results highlight a key role for PSTK as a mediator of resistance to targeted therapeutic treatment in HCC cells that functions by suppressing ferroptotic induction. PSTK inhibitors may thus represent ideal candidates for overcoming drug resistance in HCC.

Background: Deep learning algorithms significantly improve the accuracy of pathological image classification, but the accuracy of breast cancer classification using only single-mode pathological images still cannot meet the needs of clinical practice. Inspired by the real scenario of pathologists reading pathological images for diagnosis, we integrate pathological images and structured data extracted from clinical electronic medical record (EMR) to further improve the accuracy of breast cancer classification. Methods: In this paper, we propose a new richer fusion network for the classification of benign and malignant breast cancer based on multimodal data. To make pathological image can be integrated more sufficient with structured EMR data, we proposed a method to extract richer multilevel feature representation of the pathological image from multiple convolutional layers. Meanwhile, to minimize the information loss for each modality before data fusion, we use the denoising autoencoder as a way to increase the low-dimensional structured EMR data to high-dimensional, instead of reducing the high-dimensional image data to low-dimensional before data fusion. In addition, denoising autoencoder naturally generalizes our method to make the accurate prediction with partially missing structured EMR data. Results: The experimental results show that the proposed method is superior to the most advanced method in terms of the average classification accuracy (92.9%). In addition, we have released a dataset containing structured data from 185 patients that were extracted from EMR and 3764 paired pathological images of breast cancer, which can be publicly downloaded from http://ear.ict.ac.cn/?page_id=1663. Conclusions: We utilized a new richer fusion network to integrate highly heterogeneous data to leverage the structured EMR data to improve the accuracy of pathological image classification. Therefore, the application of automatic breast cancer classification algorithms in clinical practice becomes possible. Due to the generality of the proposed fusion method, it can be straightforwardly extended to the fusion of other structured data and unstructured data.

ARID1A is among the most commonly mutated tumor suppressor genes in hepatocellular carcinoma (HCC). In this study, we conduct a CRISPR-Cas9 synthetic lethality screen using ARID1A-deficient HCC cells to identify approaches to treat HCC patients harboring ARID1A deficiency. This strategy reveals that the survival of these ARID1A-deficient HCC cells is highly dependent on genes related to the tricarboxylic acid (TCA) cycle. Mechanistically, ARID1A loss represses expression of key glycolysis-related gene PKM, shifting cellular glucose metabolism from aerobic glycolysis to dependence on the TCA cycle and oxidative phosphorylation. Cuproptosis is a recently defined form of copper-induced cell death reported to directly target the TCA cycle. Here, we find that ARID1A-deficient HCC cells and xenograft tumors are highly sensitive to copper treatment. Together, these results offer evidence of the synthetic lethality between ARID1A deficiency and mitochondrial respiration impairment, suggesting that copper treatment constitutes a promising therapeutic strategy for selectively targeting ARID1A-deficient HCC.

Hepatocellular carcinoma (HCC) is a leading cause of cancer-related deaths globally. In China, sorafenib and oxaliplatin plus infusional-fluorouracil/leucovorin (FOLFOX4) are approved for the systemic treatment of advanced HCC. This study compared the cost-effectiveness of these therapies from a healthcare system perspective and a patient perspectives. A Markov model was constructed using overall and progression-free survival rates and adverse event (AE) rate from two randomized controlled studies of advanced HCC patients from Asia: EACH for FOLFOX4 and ORIENTAL for sorafenib. The patients in the Markov model were followed until death, the length of each Markov cycle was 1 month, and the survival was adjusted for quality-adjusted life years (QALYs). Direct medical costs included costs of therapies, AE treatment, general ward and tests. Costs were derived from published sources, interviews with oncologists and hospital data from China. One-way and probabilistic sensitivity analyses (PSA) were performed to test the robustness of the results. From the healthcare system perspective, FOLFOX4 dominated sorafenib with lower therapy costs (FOLFOX4: US$ 6972; sorafenib: US$ 12,289), lower direct medical costs (FOLFOX4: US$ 8428; sorafenib: US$ 12,798), and higher QALYs (FOLFOX4: 0.42; sorafenib: 0.38) per patient. This result was robust according to comprehensive one-way sensitivity analyses. According to the PSA, at the cost-effectiveness threshold for China (3 × GDP, US$ 22,073), FOLFOX4 should be chosen in 63.9% of simulations. From the patient perspective, FOLFOX4 also dominated sorafenib. The study results indicate that FOLFOX4 dominates sorafenib because it appears to provide higher effectiveness with significantly lower costs in treating Chinese advanced HCC patients.

Overwhelming rapid metabolic and structural response to apatinib in radioiodine refractory differentiated thyroid cancer