Significance: Colorectal cancer patients often relapse due to resistance to chemotherapy. The tumor microenvironment is known to contribute to tumor aggressiveness and chemoresistance, but the underlying mechanisms remain elusive. In the current study, we have shown that cancer-associated fibroblasts (CAFs) which are often present in the tumor can greatly promote resistance of colorectal cancer cells to chemotherapy. In the low-oxygen condition (hypoxia), CAFs-secreted growth factor TGF-β2 can induce strong expression of GLI2 , a gene that can induce resistance to therapy. As such, therapeutic targeting of TGF-β and GLI2 can be developed into a useful adjuvant to enhance the effect of chemotherapies.

Radiographic imaging is routinely used to evaluate treatment response in solid tumors. Current imaging response metrics do not reliably predict the underlying biological response. Here, we present a multi-task deep learning approach that allows simultaneous tumor segmentation and response prediction. We design two Siamese subnetworks that are joined at multiple layers, which enables integration of multi-scale feature representations and in-depth comparison of pre-treatment and post-treatment images. The network is trained using 2568 magnetic resonance imaging scans of 321 rectal cancer patients for predicting pathologic complete response after neoadjuvant chemoradiotherapy. In multi-institution validation, the imaging-based model achieves AUC of 0.95 (95% confidence interval: 0.91–0.98) and 0.92 (0.87–0.96) in two independent cohorts of 160 and 141 patients, respectively. When combined with blood-based tumor markers, the integrated model further improves prediction accuracy with AUC 0.97 (0.93–0.99). Our approach to capturing dynamic information in longitudinal images may be broadly used for screening, treatment response evaluation, disease monitoring, and surveillance.

Background Deep learning (DL) models can potentially improve prognostication of rectal cancer but have not been systematically assessed. Purpose To develop and validate an MRI DL model for predicting survival in patients with rectal cancer based on segmented tumor volumes from pretreatment T2-weighted MRI scans. Materials and Methods DL models were trained and validated on retrospectively collected MRI scans of patients with rectal cancer diagnosed between August 2003 and April 2021 at two centers. Patients were excluded from the study if there were concurrent malignant neoplasms, prior anticancer treatment, incomplete course of neoadjuvant therapy, or no radical surgery performed. The Harrell C-index was used to determine the best model, which was applied to internal and external test sets. Patients were stratified into high- and low-risk groups based on a fixed cutoff calculated in the training set. A multimodal model was also assessed, which used DL model-computed risk score and pretreatment carcinoembryonic antigen level as input. Results The training set included 507 patients (median age, 56 years [IQR, 46-64 years]; 355 men). In the validation set (n = 218; median age, 55 years [IQR, 47-63 years]; 144 men), the best algorithm reached a C-index of 0.82 for overall survival. The best model reached hazard ratios of 3.0 (95% CI: 1.0, 9.0) in the high-risk group in the internal test set (n = 112; median age, 60 years [IQR, 52-70 years]; 76 men) and 2.3 (95% CI: 1.0, 5.4) in the external test set (n = 58; median age, 57 years [IQR, 50-67 years]; 38 men). The multimodal model further improved the performance, with a C-index of 0.86 and 0.67 for the validation and external test set, respectively. Conclusion A DL model based on preoperative MRI was able to predict survival of patients with rectal cancer. The model could be used as a preoperative risk stratification tool. Published under a CC BY 4.0 license. Supplemental material is available for this article. See also the editorial by Langs in this issue.

Since the outset of the coronavirus disease 2019 (COVID-19) pandemic, the gut microbiome in COVID-19 has garnered substantial interest, given its significant roles in human health and pathophysiology. Accumulating evidence is unveiling that the gut microbiome is broadly altered in COVID-19, including the bacterial microbiome, mycobiome, and virome. Overall, the gut microbial ecological network is significantly weakened and becomes sparse in patients with COVID-19, together with a decrease in gut microbiome diversity. Beyond the existence of severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2), the gut microbiome of patients with COVID-19 is also characterized by enrichment of opportunistic bacteria, fungi, and eukaryotic viruses, which are also associated with disease severity and presentation. Meanwhile, a multitude of symbiotic bacteria and bacteriophages are decreased in abundance in patients with COVID-19. Such gut microbiome features persist in a significant subset of patients with COVID-19 even after disease resolution, coinciding with ‘long COVID’ (also known as post-acute sequelae of COVID-19). The broadly-altered gut microbiome is largely a consequence of SARS-CoV-2infection and its downstream detrimental effects on the systemic host immunity and the gut milieu. The impaired host immunity and distorted gut microbial ecology, particularly loss of low-abundance beneficial bacteria and blooms of opportunistic fungi including Candida, may hinder the reassembly of the gut microbiome post COVID-19. Future investigation is necessary to fully understand the role of the gut microbiome in host immunity against SARS-CoV-2 infection, as well as the long-term effect of COVID-19 on the gut microbiome in relation to the host health after the pandemic.

BRD4, a Bromodomain and Extraterminal (BET) protein family member, is a promising anti-cancer drug target. However, resistance to BET inhibitors targeting BRD4 is common in solid tumors. Here, we show that cancer-associated fibroblast (CAF)-activated stromal signaling, interleukin-6/8-JAK2, induces BRD4 phosphorylation at tyrosine 97/98 in colorectal cancer, resulting in BRD4 stabilization due to interaction with the deubiquitinase UCHL3. BRD4 phosphorylation at tyrosine 97/98 also displays increased binding to chromatin but reduced binding to BET inhibitors, resulting in resistance to BET inhibitors. We further show that BRD4 phosphorylation promotes interaction with STAT3 to induce chromatin remodeling through concurrent binding to enhancers and super-enhancers, supporting a tumor-promoting transcriptional program. Inhibition of IL6/IL8-JAK2 signaling abolishes BRD4 phosphorylation and sensitizes BET inhibitors in vitro and in vivo. Our study reveals a stromal mechanism for BRD4 activation and BET inhibitor resistance, which provides a rationale for developing strategies to treat CRC more effectively.

Cancer-associated fibroblasts (CAFs) have been shown to play a strong role in colorectal cancer metastasis, yet the underlying mechanism remains to be fully elucidated. Using CRC clinical samples together with ex vivo CAFs-CRC co-culture models, we found that CAFs induce expression of Leucine Rich Alpha-2-Glycoprotein 1(LRG1) in CRC, where it shows markedly higher expression in metastatic CRC tissues compared to primary tumors. We further show that CAFs-induced LRG1 promotes CRC migration and invasion that is concomitant with EMT (epithelial-mesenchymal transition) induction. In addition, this signaling axis has also been confirmed in the liver metastatic mouse model which displayed CAFs-induced LRG1 substantially accelerates metastasis. Mechanistically, we demonstrate that CAFs-secreted IL-6 (interleukin-6) is responsible for LRG1 up-regulation in CRC, which occurs through a direct transactivation by STAT3 following JAK2 activation. In clinical CRC tumor samples, LRG1 expression was positively correlated with CAFs-specific marker, α-SMA, and a higher LRG1 expression predicted poor clinical outcomes especially distant metastasis free survival, supporting the role of LRG1 in CRC progression. Collectively, this study provided a novel insight into CAFs-mediated metastasis in CRC and indicated that therapeutic targeting of CAFs-mediated IL-6-STAT3-LRG1 axis might be a potential strategy to mitigate metastasis in CRC.