Highlights d We build the genomic and transcriptomic landscape of 465 primary TNBCs d Chinese TNBC cases demonstrate more PIK3CA mutations and LAR subtype d Transcriptomic data classify TNBCs into four subtypes d Multi-omics profiling identifies potential targets within specific TNBC subtypes

ARID1A (the AT-rich interaction domain 1A, also known as BAF250a) is one of the most commonly mutated genes in cancer1,2. The majority of ARID1A mutations are inactivating mutations and lead to loss of ARID1A expression3, which makes ARID1A a poor therapeutic target. Therefore, it is of clinical importance to identify molecular consequences of ARID1A deficiency that create therapeutic vulnerabilities in ARIDIA-mutant tumors. In a proteomic screen, we found that ARID1A interacts with mismatch repair (MMR) protein MSH2. ARID1A recruited MSH2 to chromatin during DNA replication and promoted MMR. Conversely, ARID1A inactivation compromised MMR and increased mutagenesis. ARID1A deficiency correlated with microsatellite instability genomic signature and a predominant C>T mutation pattern and increased mutation load across multiple human cancer types. Tumors formed by an ARID1A-deficient ovarian cancer cell line in syngeneic mice displayed increased mutation load, elevated numbers of tumor-infiltrating lymphocytes, and PD-L1 expression. Notably, treatment with anti-PD-L1 antibody reduced tumor burden and prolonged survival of mice bearing ARIDIA-deficient but not ARID1A-wild-type ovarian tumors. Together, these results suggest ARID1A deficiency contributes to impaired MMR and mutator phenotype in cancer, and may cooperate with immune checkpoint blockade therapy.

It is well known that upon stress, the level of the tumor suppressor p53 is remarkably elevated. However, despite extensive studies, the underlying mechanism involving important inter-players for stress-induced p53 regulation is still not fully understood. We present evidence that the human lincRNA-RoR (RoR) is a strong negative regulator of p53. Unlike MDM2 that causes p53 degradation through the ubiquitin-proteasome pathway, RoR suppresses p53 translation through direct interaction with the heterogeneous nuclear ribonucleoprotein I (hnRNP I). Importantly, a 28-base RoR sequence carrying hnRNP I binding motifs is essential and sufficient for p53 repression. We further show that RoR inhibits p53-mediated cell cycle arrest and apoptosis. Finally, we demonstrate a RoR-p53 autoregulatory feedback loop where p53 transcriptionally induces RoR expression. Together, these results suggest that the RoR-hnRNP I-p53 axis may constitute an additional surveillance network for the cell to better respond to various stresses.

Purpose: The tumor microenvironment has a profound impact on prognosis and immunotherapy. However, the landscape of the triple-negative breast cancer (TNBC) microenvironment has not been fully understood. Experimental Design: Using the largest original multiomics dataset of TNBC (n ¼ 386), we conducted an extensive immunogenomic analysis to explore the heterogeneity and prognostic significance of the TNBC microenvironment. We further analyzed the potential immune escape mechanisms of TNBC. Results: The TNBC microenvironment phenotypes were classified into three heterogeneous clusters: cluster 1, the "immune-desert" cluster, with low microenvironment cell infiltration; cluster 2, the "innate immune-inactivated" cluster, with resting innate immune cells and nonimmune stromal cells infiltration; and cluster 3, the "immuneinflamed" cluster, with abundant adaptive and innate immune cells infiltration. The clustering result was validated internally with pathologic sections and externally with The Cancer Genome Atlas and METABRIC cohorts. The microenvironment clusters had significant prognostic efficacy. In terms of potential immune escape mechanisms, cluster 1 was characterized by an incapability to attract immune cells, and MYC amplification was correlated with low immune infiltration. In cluster 2, chemotaxis but inactivation of innate immunity and low tumor antigen burden might contribute to immune escape, and mutations in the PI3K-AKT pathway might be correlated with this effect. Cluster 3 featured high expression of immune checkpoint molecules. Conclusions: Our study represents a step toward personalized immunotherapy for patients with TNBC. Immune checkpoint inhibitors might be effective for "immuneinflamed" cluster, and the transformation of "cold tumors" into "hot tumors" should be considered for "immune-desert" and "innate immune-inactivated" clusters.

Soaring cases of coronavirus disease (COVID-19) are pummeling the global health system. Overwhelmed health facilities have endeavored to mitigate the pandemic, but mortality of COVID-19 continues to increase. Here, we present a mortality risk prediction model for COVID-19 (MRPMC) that uses patients’ clinical data on admission to stratify patients by mortality risk, which enables prediction of physiological deterioration and death up to 20 days in advance. This ensemble model is built using four machine learning methods including Logistic Regression, Support Vector Machine, Gradient Boosted Decision Tree, and Neural Network. We validate MRPMC in an internal validation cohort and two external validation cohorts, where it achieves an AUC of 0.9621 (95% CI: 0.9464–0.9778), 0.9760 (0.9613–0.9906), and 0.9246 (0.8763–0.9729), respectively. This model enables expeditious and accurate mortality risk stratification of patients with COVID-19, and potentially facilitates more responsive health systems that are conducive to high risk COVID-19 patients.

Background: Although research on the effects of comorbidities on coronavirus disease 2019 (COVID-19) patients is increasing, the risk of cancer history has not been evaluated for the mortality of patients with COVID-19. Methods: In this retrospective study, we included 3232 patients with pathogen-confirmed COVID-19 who were hospitalized between January 18th and March 27th, 2020, at Tongji Hospital in Wuhan, China. Propensity score matching was used to minimize selection bias. Results: In total, 2665 patients with complete clinical outcomes were analyzed. The impacts of age, sex, and comorbidities were evaluated separately using binary logistic regression analysis. The results showed that age, sex, and cancer history are independent risk factors for mortality in hospitalized COVID-19 patients. COVID-19 patients with cancer exhibited a significant increase in mortality rate (29.4% vs. 10.2%, P < 0.0001). Furthermore, the clinical outcomes of patients with hematological malignancies were worse, with a mortality rate twice that of patients with solid tumors (50% vs. 26.1%). Importantly, cancer patients with complications had a significantly higher risk of poor outcomes. One hundred nine cancer patients were matched to noncancer controls in a 1:3 ratio by propensity score matching. After propensity score matching, the cancer patients still had a higher risk of mortality than the matched noncancer patients (odds ratio (OR) 2.98, 95% confidence interval (95% CI) 1.76-5.06). Additionally, elevations in ferritin, high-sensitivity C-reactive protein, erythrocyte sedimentation rate, procalcitonin, prothrombin time, interleukin-2 (IL-2) receptor, and interleukin-6 (IL-6) were observed in cancer patients.

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miR-9 mediates the downregulation of BRCA1 and impedes DNA damage repair in ovarian cancer. miR-9 may improve chemotherapeutic efficacy by increasing the sensitivity of cancer cells to DNA damage and may impact ovarian cancer therapy.