The heavy global burden and mortality of breast cancer emphasize the importance of early diagnosis and treatment. Imaging detection is one of the main tools used in clinical practice for screening, diagnosis, and treatment efficacy evaluation, and can visualize changes in tumor size and texture before and after treatment. The overwhelming number of images, which lead to a heavy workload for radiologists and a sluggish reporting period, suggests the need for computer-aid detection techniques and platform. In addition, complex and changeable image features, heterogeneous quality of images, and inconsistent interpretation by different radiologists and medical institutions constitute the primary difficulties in breast cancer screening and imaging diagnosis. The advancement of imaging-based artificial intelligence (AI)-assisted tumor diagnosis is an ideal strategy for improving imaging diagnosis efficient and accuracy. By learning from image data input and constructing algorithm models, AI is able to recognize, segment, and diagnose tumor lesion automatically, showing promising application prospects. Furthermore, the rapid advancement of “omics” promotes a deeper and more comprehensive recognition of the nature of cancer. The fascinating relationship between tumor image and molecular characteristics has attracted attention to the radiomic and radiogenomics, which allow us to perform analysis and detection on the molecular level with no need for invasive operations. In this review, we integrate the current developments in AI-assisted imaging diagnosis and discuss the advances of AI-based breast cancer precise diagnosis from a clinical point of view. Although AI-assisted imaging breast cancer screening and detection is an emerging field and draws much attention, the clinical application of AI in tumor lesion recognition, segmentation, and diagnosis is still limited to research or in limited patients’ cohort. Randomized clinical trials based on large and high-quality cohort are lacking. This review aims to describe the progress of the imaging-based AI application in breast cancer screening and diagnosis for clinicians.

Targeting the KRAS pathway is a promising but challenging approach for colorectal cancer therapy. Despite showing potent efficacy in BRAF-mutated melanoma, MEK inhibitors appeared to be tolerated by colorectal cancer cells due to their intrinsic compensatory signaling. Here, we performed genome-wide CRISPR/Cas9 screening in the presence of MEK inhibitor to identify genes that are synthetically lethal with MEK inhibition in CRC models harboring KRAS mutations. Several genes were identified as potential functional drivers, which were significantly enriched in the GRB7-mediated RTK pathway. Loss-of-function and gain-of-function assays validated that GRB7 potently rendered CRC cells primary resistance to MEK inhibitors through the RTK pathway. Mass spectrum analysis of GRB7 immunoprecipitates revealed that PLK1 was the predominant interacting kinase of GRB7. Inhibition of PLK1 suppressed downstream signaling of RTK, including FAK, STAT3, AKT, and 4EBP1. The combination of PLK1 and MEK inhibitors synergistically inhibited CRC cell proliferation and induced apoptosis in vitro and in vivo. In conclusion, we identified GRB7-PLK1 as a pivotal axis mediating RTKs, resulting in MEK inhibitor tolerance. PLK1 is therefore a promising target for synergizing MEK inhibitors in the clinical treatment of CRC patients harboring KRAS mutations.

Poly(ADP‑Ribose) polymerase family member 9 (PARP9) promotes the proliferation, survival and chemotherapy resistance in lymphoma and prostate cancer. The expression and function of PARP9 in human breast cancer remains unknown. In the present study, it was demonstrated that PARP9 is frequently overexpressed in human breast cancer. In 57 normal breast tissues, the expression of PARP9 was not detected in 43 cases (75.4%), but low levels of PARP9 were detected in 13 cases (22.8%), and modest levels of PARP9 (PARP9/GAPDH ratio ~1:1) were detected in only 1 case (1.7%). In contrast, the expression of PARP9 was detected in all 57 breast cancer tissues, in which the levels of PARP9 were higher than that in paired normal breast tissues. In addition, high levels of PARP9 were detected in 43.8% of breast cancer tissues. Overexpression of PARP9 was negatively associated with estrogen receptor expression, and positively associated with axillary lymph node metastasis. However, PARP9 expression was not associated with other clinicopathological parameters, including age, HER‑2 and tumor size. Furthermore, PARP9‑knockdown inhibited breast cancer cell migration. These data indicate that PARP9 may promote breast cancer progression.

With encouraging antitumor effects, immunotherapy represented by immune checkpoint blockade has developed into a mainstream cancer therapeutic modality. However, only a minority of ovarian cancer (OC) patients could benefit from immunotherapy. The main reason is that most OC harbor a suppressive tumor immune microenvironment (TIME). Emerging studies suggest that M2 tumor-associated macrophages (TAMs), T regulatory cells (Tregs), myeloid-derived suppressor cells (MDSCs), and cancer-associated fibroblasts (CAFs) are enriched in OC. Thus, reversing the suppressive TIME is considered an ideal candidate for improving the efficiency of immunotherapy. Nanoparticles encapsulating immunoregulatory agents can regulate immunocytes and improve the TIME to boost the antitumor immune response. In addition, some nanoparticle-mediated photodynamic and photothermal therapy can directly kill tumor cells and induce tumor immunogenic cell death to activate antigen-presenting cells and promote T cell infiltration. These advantages make nanoparticles promising candidates for modulating the TIME and improving OC immunotherapy. In this review, we analyzed the composition and function of the TIME in OC and summarized the current clinical progress of OC immunotherapy. Then, we expounded on the promising advances in nanomaterial-mediated immunotherapy for modulating the TIME in OC. Finally, we discussed the obstacles and challenges in the clinical translation of this novel combination treatment regimen. We believe this resourceful strategy will open the door to effective immunotherapy of OC and benefit numerous patients.

The emergence of immunotherapy has dramatically changed the cancer treatment paradigm and generated tremendous promise in precision medicine. However, cancer immunotherapy is greatly limited by its low response rates and immune-related adverse events. Transcriptomics technology is a promising tool for deciphering the molecular underpinnings of immunotherapy response and therapeutic toxicity. In particular, applying single-cell RNA-seq (scRNA-seq) has deepened our understanding of tumor heterogeneity and the microenvironment, providing powerful help for developing new immunotherapy strategies. Artificial intelligence (AI) technology in transcriptome analysis meets the need for efficient handling and robust results. Specifically, it further extends the application scope of transcriptomic technologies in cancer research. AI-assisted transcriptomic analysis has performed well in exploring the underlying mechanisms of drug resistance and immunotherapy toxicity and predicting therapeutic response, with profound significance in cancer treatment. In this review, we summarized emerging AI-assisted transcriptomic technologies. We then highlighted new insights into cancer immunotherapy based on AI-assisted transcriptomic analysis, focusing on tumor heterogeneity, the tumor microenvironment, immune-related adverse event pathogenesis, drug resistance, and new target discovery. This review summarizes solid evidence for immunotherapy research, which might help the cancer research community overcome the challenges faced by immunotherapy.