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.

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.

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.

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.

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.

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.

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.

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.

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.

Promoted aerobic denitrification through denitrifying fungal communities: Co-occurrence patterns and treatment of low C/N micro-polluted water.

Despite the growing interest in using mixed-culture aerobic denitrifying fungal flora (mixed-CADFF) for water remediation, there is limited research on their nitrogen removal performance in low C/N polluted water bodies. To address this knowledge gap, we isolated three mixed-CADFFs from overlying water in urban lakes to evaluate their removal performance. The total nitrogen (TN) removal efficiencies were 93.60 %, 94.64 %, and 95.18 %, while the dissolved organic carbon removal efficiencies were 96.64 %, 95.12 %, and 96.70 % for mixed-CADFF LN3, LN7, and LN15, respectively in the denitrification medium under aerobic conditions at 48 h cultivation. The three mixed-CADFFs could utilize diverse types of low molecular weight carbon sources to drive the aerobic denitrification processes efficiently. The optimal C/N ratio for the mixed-CADFFs were C/N = 10, and then C/N = 15, 7, 5, and 2. The high-throughput sequencing analysis of three mixed-CADFFs indicated that Eurotiomycetes, Cystobasidiomycetes, and Sordariomycetes were the dominant class in the communities at class level. The network analysis showed that the rare fungal species, such as Scedosporium dehoogii Saitozyma, and Candida intermedia presented positively co-occurred with the TN removal and organic matter reduction capacity. Immobilization mixed-CADFFs treatment raw water experiments indicated that three mixed-CADFFs could reduce nearly 62.73 % of TN in the low C/N micro-polluted raw water treatment. Moreover, the cell density and cell metabolism indexes were also increased during the raw water treatment. This study will provides new insight into resource utilization of the mixed-culture aerobic denitrifying fungal community in field of environment restoration.

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.

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.

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.

Relationship between metastasis and second primary cancers in women with breast cancer.

Background: Breast cancer (BC) survivors have an increased risk of developing second primary cancers (SPCs); however, it is still unclear if metastasis is a risk factor for developing SPCs. Usually, long-term cancer survivors face an increased risk of developing SPCs; however, less attention has been paid to SPCs in patients with metastatic cancer as the survival outcomes of the patients are greatly reduced. Methods: A total of 17,077 American women diagnosed with breast cancer between 2010 and 2018 were identified from Surveillance, Epidemiology, and End Results (SEER) database and were included in the study. The clinical characteristics, standardized incidence ratio (SIR), standardized mortality ratio (SMR), and patterns of SPCs in BC patients with no metastasis, regional lymph node metastasis, and distant metastasis were investigated. Kaplan-Meier method was used to compare the prognosis of BC patients after developing SPCs with different metastatic status. XGBoost, a high-precision machine learning algorithm, was used to create a prediction model to estimate the prognosis of metastatic breast cancer (MBC) patients with SPCs. Results: The results reveal that the SIR (1.01; 95% CI, 0.99-1.03, p>0.05) of SPCs in non-metastasis breast cancer (NMBC) patients was similar to the general population. Further, patients with regional lymph node metastasis showed an 8% increased risk of SPCs (SIR=1.08, 95%CI, 1.05-1.11, p<0.05), and patients with distant metastasis had a 26% increased risk of SPCs (SIR=1.26, 95%CI, 1.16-1.37, p<0.05). The SIR of SPCs in all patients below the age of 40 was the highest, which decreased with age. Patients with poorly differentiated cancers, large tumor size, and late N stage had an increased risk of SPCs. However, an increase in SIR of SPCs was observed in distant MBC patients, even at the early T1 (SIR=1.60, 95% CI, 1.22-1.98, p<0.05) and N1 (SIR=1.27, 95% CI, 1.10-1.44, p<0.05) stage. An increase in the SIR of SPCs was observed in patients with triple-negative BC, and the SIR of SPC increased with metastasis development in BC patients with luminal A subtype. The peak of SPCs risk occurrence was earlier in MBC patients (4-6 months and 10 months) compared to NMBC patients (12 months). The effect of metastasis on the prognosis of SPCs patients was dependent on the type of SPCs. Meanwhile, the XGBoost model was created to predict the 3-year (AUC=0.873) and 5-year survival (AUC=0.918) of SPCs in MBC patients. Conclusions: Our study provides novel insight into the impact of metastasis on SPCs in BC patients. Metastasis could promote the second primary tumorigenesis which further increased cancer-related deaths. Therefore, more attention should be paid to the occurrence of SPCs in MBC patients.

HRD-MILN: Accurately estimate tumor homologous recombination deficiency status from targeted panel sequencing data.

Homologous recombination deficiency (HRD) is a critical feature guiding drug and treatment selection, mainly for ovarian and breast cancers. As it cannot be directly observed, HRD status is estimated on a small set of genomic instability features from sequencing data. The existing methods often perform poorly when handling targeted panel sequencing data; however, the targeted panel is the most popular sequencing strategy in clinical practices. Thus, we proposed HRD-MILN to overcome the computational challenges from targeted panel sequencing. HRD-MILN incorporated a multi-instance learning framework to discover as many loss of heterozygosity (LOH) associated with HRD status to cluster as possible. Then the HRD score is obtained based on the association between the LOHs and the cluster in the sample to be estimated, and finally, the HRD status is estimated based on the score. In comparison experiments on targeted panel sequencing data, the Precision of HRD-MILN could achieve 87%, significantly improved from 63% reported by the existing methods, where the highest margin of improvement reached 14%. It also presented advantages on whole exome sequencing data. Based on our best knowledge, HRD-MILN is the first practical tool for estimating HRD status from targeted panel sequencing data and could benefit clinical applications.

Machine learning predicts the prognosis of breast cancer patients with initial bone metastases.

Background: Bone is the most common metastatic site of patients with advanced breast cancer and the survival time is their primary concern; however, we lack accurate predictive models in clinical practice. In addition to this, primary surgery for breast cancer patients with bone metastases is still controversial. Method: The data used for analysis in this study were obtained from the SEER database (2010-2019). We made a COX regression analysis to identify prognostic factors of patients with bone metastatic breast cancer (BMBC). Through cross-validation, we constructed an XGBoost model to predicting survival in patients with BMBC. We also investigated the prognosis of patients treated with neoadjuvant chemotherapy plus surgical and chemotherapy alone using propensity score matching and K-M survival analysis. Results: Our validation results showed that the model has high sensitivity, specificity, and correctness, and it is the most accurate one to predict the survival of patients with BMBC (1-year AUC = 0.818, 3-year AUC = 0.798, and 5-year survival AUC = 0.791). The sensitivity of the 1-year model was higher (0.79), while the specificity of the 5-year model was higher (0.86). Interestingly, we found that if the time from diagnosis to therapy was ≥1 month, patients with BMBC had even better survival than those who started treatment immediately (HR = 0.920, 95%CI 0.869-0.974, P < 0.01). The BMBC patients with an income of more than USD$70,000 had better OS (HR = 0.814, 95%CI 0.745-0.890, P < 0.001) and BCSS (HR = 0.808 95%CI 0.735-0.889, P < 0.001) than who with income of < USD$50,000. We also found that compared with chemotherapy alone, neoadjuvant chemotherapy plus surgical treatment significantly improved OS and BCSS in all molecular subtypes of patients with BMBC, while only the patients with bone metastases only, bone and liver metastases, bone and lung metastases could benefit from neoadjuvant chemotherapy plus surgical treatment. Conclusion: We constructed an AI model to provide a quantitative method to predict the survival of patients with BMBC, and our validation results indicate that this model should be highly reproducible in a similar patient population. We also identified potential prognostic factors for patients with BMBC and suggested that primary surgery followed by neoadjuvant chemotherapy might increase survival in a selected subgroup of patients.

The cuproptosis-related signature predicts prognosis and indicates immune microenvironment in breast cancer.

Breast cancer (BC) is the most diagnosed cancer in women. Cuproptosis is new regulated cell death, distinct from known death mechanisms and dependent on copper and mitochondrial respiration. However, the comprehensive relationship between cuproptosis and BC is still blank until now. In the present study, we acquired 13 cuproptosis-related regulators (CRRs) from the previous research and downloaded the RNA sequencing data of TCGA-BRCA from the UCSC XENA database. The 13 CRRs were all differently expressed between BC and normal samples. Using consensus clustering based on the five prognostic CRRs, BC patients were classified into two cuproptosis-clusters (C1 and C2). C2 had a significant survival advantage and higher immune infiltration levels than C1. According to the Cox and LASSO regression analyses, a novel cuproptosis-related prognostic signature was developed to predict the prognosis of BC effectively. The high- and low-risk groups were divided based on the risk scores. Kaplan-Meier survival analysis indicated that the high-risk group had shorter overall survival (OS) than the low-risk group in the training, test and entire cohorts. GSEA indicated that the immune-related pathways were significantly enriched in the low-risk group. According to the CIBERSORT and ESTIMATE analyses, patients in the high-risk group had higher infiltrating levels of antitumor lymphocyte cell subpopulations and higher immune score than the low-risk group. The typical immune checkpoints were all elevated in the high-risk group. Furthermore, the high-risk group showed a better immunotherapy response than the low-risk group based on the Tumor Immune Dysfunction and Exclusion (TIDE) and Immunophenoscore (IPS). In conclusion, we identified two cuproptosis-clusters with different prognoses using consensus clustering in BC. We also developed a cuproptosis-related prognostic signature and nomogram, which could indicate the outcome, the tumor immune microenvironment, as well as the response to immunotherapy.

A novel hypoxia- and lactate metabolism-related signature to predict prognosis and immunotherapy responses for breast cancer by integrating machine learning and bioinformatic analyses.

Background: Breast cancer is the most common cancer worldwide. Hypoxia and lactate metabolism are hallmarks of cancer. This study aimed to construct a novel hypoxia- and lactate metabolism-related gene signature to predict the survival, immune microenvironment, and treatment response of breast cancer patients. Methods: RNA-seq and clinical data of breast cancer from The Cancer Genome Atlas database and Gene Expression Omnibus were downloaded. Hypoxia- and lactate metabolism-related genes were collected from publicly available data sources. The differentially expressed genes were identified using the "edgeR" R package. Univariate Cox regression, random survival forest (RSF), and stepwise multivariate Cox regression analyses were performed to construct the hypoxia-lactate metabolism-related prognostic model (HLMRPM). Further analyses, including functional enrichment, ESTIMATE, CIBERSORTx, Immune Cell Abundance Identifier (ImmuCellAI), TIDE, immunophenoscore (IPS), pRRophetic, and CellMiner, were performed to analyze immune status and treatment responses. Results: We identified 181 differentially expressed hypoxia-lactate metabolism-related genes (HLMRGs), 24 of which were valuable prognostic genes. Using RSF and stepwise multivariate Cox regression analysis, five HLMRGs were included to establish the HLMRPM. According to the medium-risk score, patients were divided into high- and low-risk groups. Patients in the high-risk group had a worse prognosis than those in the low-risk group (P < 0.05). A nomogram was further built to predict overall survival (OS). Functional enrichment analyses showed that the low-risk group was enriched with immune-related pathways, such as antigen processing and presentation and cytokine-cytokine receptor interaction, whereas the high-risk group was enriched in mTOR and Wnt signaling pathways. CIBERSORTx and ImmuCellAI showed that the low-risk group had abundant anti-tumor immune cells, whereas in the high-risk group, immunosuppressive cells were dominant. Independent immunotherapy datasets (IMvigor210 and GSE78220), TIDE, IPS and pRRophetic analyses revealed that the low-risk group responded better to common immunotherapy and chemotherapy drugs. Conclusions: We constructed a novel prognostic signature combining lactate metabolism and hypoxia to predict OS, immune status, and treatment response of patients with breast cancer, providing a viewpoint for individualized treatment.