Establishment of patient-derived organoids for guiding personalized therapies in breast cancer patients.

Breast cancer has become the most commonly diagnosed cancer. The intra- and interpatient heterogeneity induced a considerable variation in treatment efficacy. There is an urgent requirement for preclinical models to anticipate the effectiveness of individualized drug responses. Patient-derived organoids (PDOs) can accurately recapitulate the architecture and biological characteristics of the origin tumor, making them a promising model that can overtake many limitations of cell lines and PDXs. However, it is still unclear whether PDOs-based drug testing can benefit breast cancer patients, particularly those with tumor recurrence or treatment resistance. Fresh tumor samples were surgically resected for organoid culture. Primary tumor samples and PDOs were subsequently subjected to H&E staining, immunohistochemical (IHC) analysis, and whole-exome sequencing (WES) to make comparisons. Drug sensitivity tests were performed to evaluate the feasibility of this model for predicting patient drug response in clinical practice. We established 75 patient-derived breast cancer organoid models. The results of H&E staining, IHC, and WES revealed that PDOs inherited the histologic and genetic characteristics of their parental tumor tissues. The PDOs successfully predicted the patient's drug response, and most cases exhibited consistency between PDOs' drug susceptibility test results and the clinical response of the matched patient. We conclude that the breast cancer organoids platform can be a potential preclinical tool used for the selection of effective drugs and guided personalized therapies for patients with advanced breast cancer.

Aerobic Denitrification Promoting by Actinomycetes Coculture: Investigating Performance, Carbon Source Metabolic Characteristic, and Raw Water Restoration.

The coculture theory that promotes denitrification relies on effectively utilizing the resources of low-efficiency denitrification microbes. Here, the strains Streptomyces sp. PYX97 and Streptomyces sp. TSJ96 were isolated and showed lower denitrification capacity when cultured individually. However, the coculture of strains PYX97 and TSJ96 enhanced nitrogen removal (removed 96.40% of total nitrogen) and organic carbon reduction (removed 92.13% of dissolved organic carbon) under aerobic conditions. Nitrogen balance analysis indicated that coculturing enhanced the efficiency of nitrate converted into gaseous nitrogen reaching 70.42%. Meanwhile, the coculturing promoted the cell metabolism capacity and carbon source metabolic activity. The coculture strains PYX97 and TSJ96 thrived in conditions of C/N = 10, alkalescence, and 150 rpm shaking speed. The coculturing reduced total nitrogen and CODMn in the raw water treatment by 83.32 and 84.21%, respectively. During this treatment, the cell metabolic activity and cell density increased in the coculture strains PYX97 and TSJ96 reactor. Moreover, the coculture strains could utilize aromatic protein and soluble microbial products during aerobic denitrification processes in raw water treatment. This study suggests that coculturing inefficient actinomycete strains could be a promising approach for treating polluted water bodies.

Divide and Conquer: A Flexible Deep Learning Strategy for Exploring Metabolic Heterogeneity from Mass Spectrometry Imaging Data.

Research on metabolic heterogeneity provides an important basis for the study of the molecular mechanism of a disease and personalized treatment. The screening of metabolism-related sub-regions that affect disease development is essential for the more focused exploration on disease progress aberrant phenotypes, even carcinogenesis and metastasis. The mass spectrometry imaging (MSI) technique has distinct advantages to reveal the heterogeneity of an organism based on in situ molecular profiles. The challenge of heterogeneous analysis has been to perform an objective identification among biological tissues with different characteristics. By introducing the divide-and-conquer strategy to architecture design and application, we establish here a flexible unsupervised deep learning model, called divide-and-conquer (dc)-DeepMSI, for metabolic heterogeneity analysis from MSI data without prior knowledge of histology. dc-DeepMSI can be used to identify either spatially contiguous regions of interest (ROIs) or spatially sporadic ROIs by designing two specific modes, spat-contig and spat-spor. Comparison results on fetus mouse data demonstrate that the dc-DeepMSI outperforms state-of-the-art MSI segmentation methods. We demonstrate that the novel learning strategy successfully obtained sub-regions that are statistically linked to the invasion status and molecular phenotypes of breast cancer as well as organizing principles during developmental phase.

Novel models by machine learning to predict prognosis of breast cancer brain metastases.

BACKGROUND: Breast cancer brain metastases (BCBM) are the most fatal, with limited survival in all breast cancer distant metastases. These patients are deemed to be incurable. Thus, survival time is their foremost concern. However, there is a lack of accurate prediction models in the clinic. What's more, primary surgery for BCBM patients is still controversial. METHODS: The data used for analysis in this study was obtained from the SEER database (2010-2019). We made a COX regression analysis to identify prognostic factors of BCBM patients. Through cross-validation, we constructed XGBoost models to predict survival in patients with BCBM. Meanwhile, a BCBM cohort from our hospital was used to validate our models. We also investigated the prognosis of patients treated with surgery or not, using propensity score matching and K-M survival analysis. Our results were further validated by subgroup COX analysis in patients with different molecular subtypes. RESULTS: The XGBoost models we created had high precision and correctness, and they were the most accurate models to predict the survival of BCBM patients (6-month AUC = 0.824, 1-year AUC = 0.813, 2-year AUC = 0.800 and 3-year survival AUC = 0.803). Moreover, the models still exhibited good performance in an externally independent dataset (6-month: AUC = 0.820; 1-year: AUC = 0.732; 2-year: AUC = 0.795; 3-year: AUC = 0.936). Then we used Shiny-Web tool to make our models be easily used from website. Interestingly, we found that the BCBM patients with an annual income of over USD$70,000 had better BCSS (HR = 0.523, 95%CI 0.273-0.999, P < 0.05) than those with less than USD$40,000. The results showed that in all distant metastasis sites, only lung metastasis was an independent poor prognostic factor for patients with BCBM (OS: HR = 1.606, 95%CI 1.157-2.230, P < 0.01; BCSS: HR = 1.698, 95%CI 1.219-2.365, P < 0.01), while bone, liver, distant lymph nodes and other metastases were not. We also found that surgical treatment significantly improved both OS and BCSS in BCBM patients with the HER2 + molecular subtypes and was beneficial to OS of the HR-/HER2- subtype. In contrast, surgery could not help BCBM patients with HR + /HER2- subtype improve their prognosis (OS: HR = 0.887, 95%CI 0.608-1.293, P = 0.510; BCSS: HR = 0.909, 95%CI 0.604-1.368, P = 0.630). CONCLUSION: We analyzed the clinical features of BCBM patients and constructed 4 machine-learning prognostic models to predict their survival. Our validation results indicate that these models should be highly reproducible in patients with BCBM. We also identified potential prognostic factors for BCBM patients and suggested that primary surgery might improve the survival of BCBM patients with HER2 + and triple-negative subtypes.

Causal effects of COVID-19 on cancer risk: A Mendelian randomization study.

In contemporary literature, little attention has been paid to the association between coronavirus disease-2019 (COVID-19) and cancer risk. We performed the Mendelian randomization (MR) to investigate the causal associations between the three types of COVID-19 exposures (critically ill COVID-19, hospitalized COVID-19, and respiratory syndrome coronavirus 2 (SARS-CoV-2) infection) and 33 different types of cancers of the European population. The results of the inverse-variance-weighted model indicated that genetic liabilities to critically ill COVID-19 had suggestive causal associations with the increased risk for HER2-positive breast cancer (odds ratio [OR] = 1.0924; p-value = 0.0116), esophageal cancer (OR = 1.0004; p-value = 0.0226), colorectal cancer (OR = 1.0010; p-value = 0.0242), stomach cancer (OR = 1.2394; p-value = 0.0331), and colon cancer (OR = 1.0006; p-value = 0.0453). The genetic liabilities to hospitalized COVID-19 had suggestive causal associations with the increased risk for HER2-positive breast cancer (OR = 1.1096; p-value = 0.0458), esophageal cancer (OR = 1.0005; p-value = 0.0440) as well as stomach cancer (OR = 1.3043; p-value = 0.0476). The genetic liabilities to SARS-CoV-2 infection had suggestive causal associations with the increased risk for stomach cancer (OR = 2.8563; p-value = 0.0019) but with the decreasing risk for head and neck cancer (OR = 0.9986, p-value = 0.0426). The causal associations of the above combinations were robust through the test of heterogeneity and pleiotropy. Together, our study indicated that COVID-19 had causal effects on cancer risk.

Development and validation of prognostic nomogram for T1-3N0M0 non-small cell lung cancer after curative resection.

BACKGROUND: Radical resection plus lymph node dissection is a common treatment for patients with T1-3N0M0 non-small cell lung cancer (NSCLC). Few models predicted the survival outcomes of these patients. This study aimed to developed a nomogram for predicting their overall survival (OS). MATERIALS AND METHODS: This study involved 3002 patients with T1-3N0M0 NSCLC after curative resection between January 1999 and October 2013. 1525 Patients from Sun Yat-sen University Cancer Center were randomly allocated to training cohort and internal validation cohort in a ratio of 7:3. 1477 patients from ten institutions were recruited as external validation cohort. A nomogram was constructed based on the training cohort and validated by internal and external validation cohort to predict the OS of these patients. The accuracy and practicability were tested by Harrell's C-indexes, calibration plots and decision curve analyses (DCA). RESULTS: Age, sex, histological classification, pathological T stage, and HI standard were independent factors for OS and were included in our nomogram. The C-index of the nomogram for OS estimates were 0.671 (95% CI, 0.637-0.705),0.632 (95% CI, 0.581-0.683), and 0.645 (95% CI, 0.617-0.673) in the training cohorts, internal validation cohorts, and external validation cohort, respectively. The calibration plots and DCA for predictions of OS were in excellent agreement. An online version of the nomogram was built for convenient clinical practice. CONCLUSIONS: Our nomogram can predict the OS of patients with T1-3N0M0 NSCLC after curative resection. The online version of our nomogram offer opportunities for fast personalized risk stratification and prognosis prediction in clinical practice.

Photoredox-Catalyzed gem-Difluoromethylenation of Aliphatic Alcohols with 1,1-Difluoroalkenes to Access α,α-Difluoromethylene Ethers.

A switchable synthesis of alcohols and ketones bearing a CF2-OR scaffold using visible-light promotion is described. The method of PDI catalysis is characterized by its ease of operation, broad substrate scopes, and the ability to switch between desired products without the need for transition metal catalysts. The addition or absence of a base plays a key role in controlling the synthesis of the major desired products.

Photoredox Catalysis-Enabled C-H Difluoromethylation of Heteroarenes with Pentacoordinate Phosphorane as the Reagent.

Difluoromethylated heterocyclic compounds have found broad applications in numerous bioactive molecules. Herein, we report photoredox catalysis-induced direct C-H difluoromethylation of heterocycles by using bis(difluoromethyl) pentacoordinate phosphorane (PPh3(CF2H)2, 1) as the reagent. A variety of heterocycles, such as quinoxalin-2(1H)-one, thiophene, indole, and coumarin, are readily tailored with a difluoromethyl group. The method is featured as transition-metal-free by using an organic compound Erythrosin B as the catalyst and O2 as the oxidant.

Upregulated IncRNA WTAPP1 in Triple Negative Breast Cancer Predicts Survival.

The migration and angiogenesis of endothelial progenitor cells require the involvement of WTAPP1. Cell migration and angiogenesis are critical for cancer development, we therefore speculated that WTAPP1 may participate in cancer biology. This study aimed to explore the role of WTAPP1 in triple-negative breast cancer (TNBC). A total of 68 patients (females, 38 to 67 years old, mean age 52.1 ± 5.9 years old) were enrolled in this study. The effects of over-expression of WTAPP1 and miR-34a on EEF2K were evaluated by reverse transcription quantitative polymerase chain reaction (RT-qPCR) and Western blot. Transient cell transfections were performed to explore the interactions between genes. Cell proliferation assay was used to analyze cell proliferation. Transwell assays were performed to detect cell invasive and migration. Flow cytometry was used to evaluate apoptosis. WTAPP1 was upregulated in tumor tissues of TNBC patients and high expression levels of WTAPP1 in tumor tissues predicted poor survival. In contrast, miR-34a was downregulated in TNBC. Correlation analysis showed that WTAPP1 and miR-34a were negatively correlated with each other. In cancer cells, overexpression of WTAPP1 resulted in downregulation of miR-34a. Remarkably, overexpression of WTAPP1 increased the expression levels of EEF2K, a target of miR-34a. Overexpression of WTAPP1 and EEF2K increased proliferation, invasion and migration of TNBC cells, while overexpression of miR-34a showed different results. In addition, overexpression of miR-34a enhanced the effects of overexpression of WTAPP1 and EEF2K on apoptosis. WTAPP1 may promote TNBC cell proliferation by downregulating miR-34a.

Potential mechanism of primary resistance to icotinib in patients with advanced non-small cell lung cancer harboring uncommon mutant epidermal growth factor receptor: A multi-center study.

The incidence of epidermal growth factor receptor uncommon mutation (EGFRum) is relatively low and patients harboring EGFRum are resistant to the first-generation tyrosine kinase inhibitors (TKI). However, the mechanism of primary resistance remains unclear. Medical records of 98 patients who had never been treated by TKI and who accepted icotinib treatment were collected and followed. The circulating tumor DNA (ctDNA) were detected and analyzed using the next-generation sequencing (NGS) platform after progression on icotinib. The potential primary resistance mechanism of icotinib was explored. A total of 21 (21.4%) and 48 (49%) patients developed primary and acquired resistance to icotinib, respectively. The median progression-free survival (PFS) of primary resistance patients was 1.8 months (0.5-2.3, 95% CI = 1.50-2.10). Before treatment, 52.4% (11/21) of patients carried S768I, 23.8% (5/21) L861Q, 14.3% (3/21) G719X and 14.3% (3/21) exon 20-ins mutations. Approximately 23.8% (5/21) of patients harbored the combined pattern mutations and 76.2% (16/21) of patients harbored the single pattern mutations. The combined pattern with EGFR classical mutation (EGFRcm) had worse PFS than the combined with EGFRum and single pattern (P < .05). There were 6 (28.57%) patients with acquired EGFR extracellular domain mutation, 5 (23.81%) with BCL2L11 loss (BIM deletion polymorphism), 3 (14.29%) with MET amplification, 1 (4.76%) with ERBB2 amplification, 1 (4.76%) with MYC amplification, 1 (4.76%) with PTEN mutation, 1 (4.76%) with PIK3CA mutation and 3 (14.29%) with unknown status. EGFR extracellular domain mutation, BCL2L11 loss, PI3K-AKT-mTOR signaling pathway (PTEN and PIK3CA mutations), MET amplification, ERBB2 amplification or MYC amplification might contribute to molecular mechanisms of primary resistance to icotinib in patients with advanced non-small cell lung cancer harboring uncommon mutant epidermal growth factor receptor. Combined targeted therapy or chemotherapy should be considered in this population.

Concordance between treatment recommendations provided by IBM Watson for Oncology and a multidisciplinary tumor board for breast cancer in China.

OBJECTIVE: Watson for Oncology (WFO), an artificial intelligence from IBM Corporation, can provide a treatment plan by analyzing patient's disease characteristics. The present study was performed to examine the concordance between treatment recommendations proposed by WFO and the multidisciplinary tumor board at our center. The aim was to explore the feasibility of using WFO for breast cancer cases in China and to ascertain the ways to make WFO more suitable for Chinese patients with breast cancer. METHODS: Data from 302 breast cancer patients treated at the Second Affiliated Hospital of Xi'an Jiaotong University between October 2016 and February 2018 was retrieved and retrospectively analyzed by WFO. The recommendations were divided into 'recommended', 'considered' and 'not recommended' groups. Results were considered concordant when oncologists' recommendations were categorized as 'recommended' or 'for consideration' by WFO. RESULTS: The concordance rate of 200 subjects with postoperative adjuvant therapy was 77%. However, the rate was 27.5% in the remaining 102 cases with metastatic disease receiving either first-line or no treatment. Further analysis demonstrated that inconsistencies were mainly due to different choices of chemotherapy regimens. Subgroup study indicates that tumor stage, receptor status and age also had influences at the concordance rate. CONCLUSION: The results of this study suggest that WFO is a promising artificial intelligence system for the treatment of breast cancer. These findings can also serve as a reference framework for the inclusion of artificial intelligence in the ongoing medical reform in China.

MiR-429 suppresses neurotrophin-3 to alleviate perineural invasion of pancreatic cancer.

Perineural invasion (PNI) potentially increases the risk of relapse and abdominal pain in patients with pancreatic ductal adenocarcinoma (PDAC). However, the underlying mechanisms of PNI of PDAC is incompletely revealed. Our study aimed to investigate roles of miR-429 in modulating PNI in PDAC. We found that miR-429 was downregulated in PDAC cancer tissues and was profoundly decreased in tissues with PNI. It was reduced in nine of the ten examined pancreatic cancer cell lines. MiR-429 mimics restored its cellular expressions in MIA PaCa-2 and BxCP3 cells and significantly suppressed cell viability and invasion of the cancer cells. The online bioinformatic software predicted that neurotrophin-3 (NT-3) was a potential target gene of miR-429. It was showed that NT-3 mRNA elevated in PC cancer tissues, especially in patients presenting PNI. MiR-429 upregulation substantially suppressed the NT-3 mRNA and secretion in cancer cells. Also, the dual luciferase reporter assays confirmed the interaction between miR-429 and NT-3. When co-culturing the two PDAC cells with PC-12 cells, the invaded cell counts significantly increased comparing with the sole culture of cancer cells. However, miR-429 mimic transfection or NT-3 blocking retarded the cancer invasion in the co-culture system. Besides, we found that cancer cells conditioned medium (CM) treatment significantly increased the neurite outgrowth percentage in PC-12 cells, which was suppressed by culturing with CM from miR-429 mimics-transfected cells. In the CM cultured PC-12 cells, NT-3 receptor TrkC as well as pain-related proteins TRPV1 and TRPV2 significantly elevated. Collectively, miR-429 potentially suppressed neurotrophin-3 to alleviate PNI of PDAC.

Salidroside improved cerebrovascular vasodilation in streptozotocin-induced diabetic rats through restoring the function of BKCa channel in smooth muscle cells.

Vessel disease is a kind of severe complication in diabetic patients. However, few pharmacologic agents can directly recover diabetic vascular function. Salidroside (SAL), a major ingredient from Rhodiola rosea, has been found to have an obvious hypoglycemic effect and a beneficial protection on vascular function in diabetes. However, whether SAL is a suitable treatment for diabetes has not so far been evaluated and the underlying mechanisms remain unknown. The present work aims to (1) investigate the potential effects of SAL on cerebrovascular relaxation in streptozotocin-induced diabetic rats or when exposed to acute hyperglycemia condition and (2) examine whether function of the BKCa channel is involved in SAL treatment for diabetic vascular relaxation. Our results indicate that chronic administration of 100 mg/kg/day SAL not only improves cerebrovascular relaxation but also increases BKCa ß1-subunit expressions at both protein and mRNA levels and enhances BKCa whole-cell and single-channel activities in cerebral VSMCs of diabetic rats. Correspondingly, acute application of 100 µM SAL induces cerebrovascular relaxation by activation of the BKCa channel. Furthermore, SAL activated the BKCa channel mainly through acting on the ß1-subunit in HEK293 cells transfected with hSloα+ß1 constructs. We concluded that SAL improved vasodilation in diabetic rats through restoring the function of the BKCa-ß1 subunit in cerebrovascular smooth muscle cells, which may be the underlying mechanism responsible for the vascular protection of SAL in diabetes.

Berberine alleviates the cerebrovascular contractility in streptozotocin-induced diabetic rats through modulation of intracellular Ca²âº handling in smooth muscle cells.

BACKGROUND: Vascular dysfunction is a distinctive phenotype in diabetes mellitus. Current treatments mostly focus on the tight glycemic control and few of these treatments have been designed to directly recover the vascular dysfunction in diabetes. As a classical natural medicine, berberine has been explored as a possible therapy for DM. In addition, it is reported that berberine has an extra-protective effect in diabetic vascular dysfunction. However, little is known whether the berberine treatment could ameliorate the smooth muscle contractility independent of a functional endothelium under hyperglycemia. Furthermore, it remains unknown whether berberine affects the arterial contractility by regulating the intracellular Ca(2+) handling in vascular smooth cells (VSMCs) under hyperglycemia. METHODS: Sprague-Dawley rats were used to establish the diabetic model with a high-fat diet plus injections of streptozotocin (STZ). Berberine (50, 100, and 200 mg/kg/day) were intragastrically administered to control and diabetic rats for 8 weeks since the injection of STZ. The intracellular Ca(2+) handling of isolated cerebral VSMCs was investigated by recording the whole-cell L-type Ca(2+) channel (CaL) currents, assessing the protein expressions of CaL channel, and measuring the intracellular Ca(2+) in response to caffeine. Our results showed that chronic administration of 100 mg/kg/day berberine not only reduced glucose levels, but also inhibited the augmented contractile function of cerebral artery to KCl and 5-hydroxytryptamine (5-HT) in diabetic rats. Furthermore, chronic administration of 100 mg/kg/day berberine significantly inhibited the CaL channel current densities, reduced the α1C-subunit expressions of CaL channel, decreased the resting intracellular Ca(2+) ([Ca(2+)]i) level, and suppressed the Ca(2+) releases from RyRs in cerebral VSMCs isolated from diabetic rats. Correspondingly, acute application of 10 µM berberine could directly inhibit the hyperglycemia-induced CaL currents and suppress the hyperglycemia-induced Ca(2+) releases from RyRs in cerebral VSMCs isolated from normal control rats. CONCLUSIONS: Our study indicated that berberine alleviated the cerebral arterial contractility in the rat model of streptozotocin-induced diabetes via regulating the intracellular Ca(2+) handling of smooth muscle cells.

The Efficiency and Toxicity Of Anlotinib in Combination With Docetaxel Followed by Epirubicin and Cyclophosphamide Regimen as Neoadjuvant Treatment in IIB to IIIA Triple Negative Breast Cancer: A Single-Arm, Multicenter, Open-Label, Phase II Study.

BACKGROUND: The combination of neoadjuvant chemotherapy and anti-angiogenesis therapy for patients with triple-negative breast cancer (TNBC) remains inadequately supported by evidence. We conducted a single-arm, open-label, multicenter, phase II trial to evaluate the efficacy and toxicity of anlotinib plus epirubicin and cyclophosphamide followed by paclitaxel in patients with IIB to IIIA stage TNBC. METHODS: Newly diagnosed patients received epirubicin at 90 mg/m2 and cyclophosphamide at 600 mg/m2 followed by docetaxel at 100 mg/m2 (21 days per cycle; total of 4 cycles), along with oral anlotinib (12 mg qd, d1-14; 21 days per cycle; total of 4 cycles). Subsequently, patients underwent surgery. The primary endpoint of this study was pathologic complete response (pCR). RESULTS: Among the 34 included patients, the median age was 46.5 years (range: 27-72); all were female. Pathological assessment revealed that 17 patients achieved RCB 0 response, which is currently defined as pathologic complete response; 3 patients achieved RCB 1; 12 patients achieved RCB 2; and 1 patient achieved RCB 3. The probability of a grade 3 adverse reaction was 17.6%, and no grade 4 adverse reactions occurred. The most common hematological adverse reaction was leukopenia (13/34, 38.2%), of which 5.9% (2/34) were grade 3. The most common non-hematological adverse reactions were oral mucositis (16/34, 58.8%), fatigue (50.0%), hand-foot syndrome (50.0%), hypertension (44.1%), bleeding (44.1%), and alopecia (32.4%). CONCLUSION: The combination of anlotinib and EC-T chemotherapy demonstrated tolerable side effects in the neoadjuvant treatment of early TNBC. pCR was higher than what has been reported in previous clinical studies of chemotherapy alone. This study provides additional rationale for using anlotinib plus docetaxel-epirubicin-based chemotherapy regimen in patients with early-stage TNBCs.

Risk, molecular subtype and prognosis of second primary breast cancer: an analysis based on first primary cancers.

Second primary breast cancer (SPBC) was potentially related to other cancers, which may impact its incidence, prognosis and therapeutic approaches. Nevertheless, few studies have characterized this relationship and analyzed the subtypes of SPBC. Our study intended to investigate the occurrence and prognosis of SPBC. We analyzed the patterns, clinical characteristics, standardized incidence ratio (SIR) and standardized mortality ratio (SMR) of patients with SPBC. The propensity score matching (PSM) approach was further used to balance the differences in clinical features between patients with primary breast cancer (PBC) and SPBC, then Kaplan-Meier (KM) survival analysis was used to compare their overall survival and breast cancer-specific survival. Finally, a predictive model was constructed to estimate the 3- and 5-year survival rates of SPBC patients. We found that the SIR of individuals with SPBC was significantly higher in cancer survivors than in the general population (SIR=1.16, 95% CI=1.15-1.17, P<0.05). SPBC patients with first primary lung/bronchus cancer had a much higher SMR (SMR=1.71, 95% CI=1.58-1.85, P<0.05) compared with survivors of other malignancies. Individuals with SPBC had a larger proportion of the HR-/HER2- subtype than those with PBC. Particularly among survivors of estrogen-dependent ovarian and breast cancer, the proportion of the HR-/HER2- subtype of SPBC considerably rose. After propensity score matching, we discovered that SPBC patients' overall survival remained poorer than that of PBC patients (HR=1.43, 95% CI=1.39-1.47, P<0.001). However, the prognosis of SPBC in first primary thyroid cancer survivors was better than PBC patients (HR=0.64, 95% CI=0.55-0.75, P<0.001). Also, an extreme gradient boosting (XGBoost) model was developed to evaluate the 3-year (AUC=0.817) and 5-year survival (AUC=0.825) of SPBC patients. Our data demonstrated the distinct biological performance of SPBC with various first primary cancers. Furthermore, our findings revealed an indispensable role of first primary cancer (FPC) in the development of SPBC and provided an additional theoretical basis for the clinical follow-up and identification of SPBC.

Relationship between homologous recombination deficiency and clinical features of breast cancer based on genomic scar score.

BACKGROUND: Homologous recombination deficiency (HRD) phenotype will sensitize tumors to poly (ADP-ribose) polymerases inhibitors and platinum. However, previous studies did not focus on the prevalence of HRD among Chinese breast cancer (BC) patients. METHODS: One hundred and forty-seven BC patients were included in this study. Their HRD status was assessed by Genomic Scar Score (GSS), which was determined according to the length, site, and type of copy number. HRD was defined as positive when a harmful BRCA1/2 mutation was detected or GSS ≥50. RESULTS: Our data revealed that 9.5% of the 147 patients tested positive for BRCA1/2 mutation, while approximately 34.7% were HRD-positive. For triple negative BC (TNBC), HRD positivity rate (60.5%) was higher than Luminal A (5.3%), Luminal B (HER2-) (28.8%), and Luminal B (HER2+) (31.6%) subgroups. HRD-positive tumors were more likely to be ER/PR-negative and exhibited higher Ki-67 expression. 50.0% of the HRD-positive patients achieved pathologic complete remission (pCR) after neoadjuvant therapy. HRD-positive patients tended to have a higher risk for cancer recurrence or metastasis compared to HRD-negative patients (29.4% vs. 13.5%). CONCLUSION: We investigated the HRD status among Chinese BC patients using an HRD detection tool developed based on the Chinese population. The clinical characteristics, pathological profile, family history pattern, neoadjuvant efficacy, and disease progression events of HRD-positive and negative patients were described and compared. Thus, our data provided an evidence-based basis for applying the original HRD assay in Chinese BC.

Development and identification of molecular markers of GhHSP70-26 related to heat tolerance in cotton.

Heat stress significantly affect plant growth and development, which is an important factor contributing to crop yield loss. However, heat shock proteins (HSPs) in plants can effectively alleviate cell damage caused by heat stress. In order to rapidly and accurately cultivate heat-tolerant cotton varieties, this study conducted correlation analysis between heat tolerance index and insertion/deletion (In/Del) sites of GhHSP70-26 promoter in 39 cotton materials, so as to find markers related to heat tolerance function of cotton, which can be used in molecular marker-assisted breeding. The results showed the natural variation allele (Del22 bp) type at -1590 bp upstream of GhHSP70-26 promoter (haplotype2, Hap2) in cotton (Gossypium spp.) promoted GhHSP70-26 expression under heat stress. The relative expression level of GhHSP70-26 of M-1590-Del22 cotton materials were significantly higher than that of M-1590-In type cotton materials under heat stress (40 ℃). Also, M-1590-Del22 material had lower conductivity and less cell damage after heat stress, indicating that it is a heat resistant cotton material. The Hap1 (M-1590-In) promoter was mutated into Hap1del22, and Hap1 and Hap1del22 were fused with GUS to transform Arabidopsis thaliana. Furthermore, Hap1del22 promoter had higher induction activity than Hap1 under heat stress and abscisic acid (ABA) treatment in transgenic Arabidopsis thaliana. Further analysis confirmed that M-1590-Del22 was the dominant heat-resistant allele. In summary, these results identify a key and previously unknown natural variation in GhHSP70-26 with respect to heat tolerance, providing a valuable functional molecular marker for genetic breeding of cotton and other crops with heat tolerance.

Actinobacteria produce taste and odor in drinking water reservoir: Community composition dynamics, co-occurrence and inactivation models.

Taste and odor (T&O) has become a significant concern for drinking water safety. Actinobacteria are believed to produce T&O during the non-algal bloom period; however, this has not been widely investigated. In this study, the seasonal dynamics of the actinobacterial community structure and inactivation of odor-producing actinobacteria were explored. The results indicated that the diversity and community composition of actinobacteria exhibited significant spatiotemporal distribution. Network analysis and structural equation modeling showed that the actinobacterial community occupied a similar environmental niche, and the major environmental attributes exhibited spatiotemporal dynamics, which affected the actinobacterial community. Furthermore, the two genera of odorous actinobacteria were inactivated in drinking water sources using chlorine. Amycolatopsis spp. have a stronger chlorine resistance ability than Streptomyces spp., indicating that chlorine inactivates actinobacteria by first destroying cell membranes and causing the release of intracellular compounds. Finally, we integrated the observed variability in the inactivation rate of actinobacteria into an expanded Chick-Watson model to estimate its effect on inactivation. These findings will deepen our understanding of the seasonal dynamics of actinobacterial community structure in drinking water reservoirs and provide a foundation for reservoir water quality management strategies.

Visualized machine learning models combined with propensity score matching analysis in single PR-positive breast cancer prognosis: a multicenter population-based study.

The characteristics of single PR-positive (ER-PR+, sPR+) breast cancer (BC) and its prognosis are not well elucidated due to its rarity and conflicting evidence. There is a lack of an accurate and efficient model for predicting survival, thereby rendering treatment challenging for clinicians. Whether endocrine therapy should be intensified in sPR+ BC patients was another controversial clinical topic. We constructed and cross-validated XGBoost models that showed high precision and accuracy in predicting the survival of patients with sPR+ BC cases (1-year: AUC=0.904; 3-year: AUC=0.847; 5-year: AUC=0.824). The F1 score for the 1-, 3-, and 5-year models were 0.91, 0.88, and 0.85, respectively. The models exhibited superior performance in an external, independent dataset (1-year: AUC=0.889; 3-year: AUC=0.846; 5-year: AUC=0.821). Further, intensified endocrine therapy did not provide a significant overall survival benefit compared to initial or no endocrine therapy (P=0.600, HR: 1.46; 95% CI: 0.35-6.17). Propensity-score matching (PSM)-adjusted data showed that there was no statistically significant difference in the prognosis between ER-PR+HER2+ and ER-PR-HER2+ BC. Patients having the ER-PR+HER2- subtype had a slightly worse prognosis than those with the ER-PR-HER2- subtype. In conclusion, XGBoost models can be highly reproducible and effective in predicting survival in patients with sPR+ BC. Our findings revealed that patients with sPR-positive BC may not benefit from endocrine therapy. Patients with sPR+ BC may benefit from intensive adjuvant chemotherapy compared to endocrine therapy.