Characterization of the High-Quality Genome Sequence and Virulence Factors of Fusarium oxysporum f. sp. vasinfectum Race 7.

Fusarium oxysporum f. sp. vasinfectum (Fov) is a common soilborne fungal pathogen that causes Fusarium wilt (FW) disease in cotton. Although considerable progress has been made in cotton disease-resistance breeding against FW in China, and the R gene conferring resistance to Fov race 7 (FOV) in Upland cotton (Gossypium hirsutum) has been identified, knowledge regarding the evolution of fungal pathogenicity and virulence factors in Fov remains limited. In this study, we present a reference-scale genome assembly and annotation for FOV7, created through the integration of single-molecule real-time sequencing (PacBio) and high-throughput chromosome conformation capture (Hi-C) techniques. Comparative genomics analysis revealed the presence of six supernumerary scaffolds specific to FOV7. The genes or sequences within this region can potentially serve as reliable diagnostic markers for distinguishing Fov race 7. Furthermore, we conducted an analysis of the xylem sap proteome of FOV7-infected cotton plants, leading to the identification of 19 proteins that are secreted in xylem (FovSIX). Through a pathogenicity test involving knockout mutants, we demonstrated that FovSIX16 is crucial for the full virulence of FOV7. Overall, this study sheds light on the underlying mechanisms of Fov's pathogenicity and provides valuable insights into potential management strategies for controlling FW.

The HLA-I landscape confers prognosis and antitumor immunity in breast cancer.

Breast cancer is a highly heterogeneous disease with varied subtypes, prognoses and therapeutic responsiveness. Human leukocyte antigen class I (HLA-I) shapes the immunity and thereby influences the outcome of breast cancer. However, the implications of HLA-I variations in breast cancer remain poorly understood. In this study, we established a multiomics cohort of 1156 Chinese breast cancer patients for HLA-I investigation. We calculated four important HLA-I indicators in each individual, including HLA-I expression level, somatic HLA-I loss of heterozygosity (LOH), HLA-I evolutionary divergence (HED) and peptide-binding promiscuity (Pr). Then, we evaluated their distribution and prognostic significance in breast cancer subtypes. We found that the four breast cancer subtypes had distinct features of HLA-I indicators. Increased expression of HLA-I and LOH were enriched in triple-negative breast cancer (TNBC), while Pr was relatively higher in hot tumors within TNBCs. In particular, a higher Pr indicated a better prognosis in TNBCs by regulating the infiltration of immune cells and the expression of immune molecules. Using the matched genomic and transcriptomic data, we found that mismatch repair deficiency-related mutational signature and pathways were enriched in low-Pr TNBCs, suggesting that targeting mismatch repair deficiency for synthetic lethality might be promising therapy for these patients. In conclusion, we presented an overview of HLA-I indicators in breast cancer and provided hints for precision treatment for low-Pr TNBCs.

Genetic interactions reveal distinct biological and therapeutic implications in breast cancer.

Co-occurrence and mutual exclusivity of genomic alterations may reflect the existence of genetic interactions, potentially shaping distinct biological phenotypes and impacting therapeutic response in breast cancer. However, our understanding of them remains limited. Herein, we investigate a large-scale multi-omics cohort (n = 873) and a real-world clinical sequencing cohort (n = 4,405) including several clinical trials with detailed treatment outcomes and perform functional validation in patient-derived organoids, tumor fragments, and in vivo models. Through this comprehensive approach, we construct a network comprising co-alterations and mutually exclusive events and characterize their therapeutic potential and underlying biological basis. Notably, we identify associations between TP53mut-AURKAamp and endocrine therapy resistance, germline BRCA1mut-MYCamp and improved sensitivity to PARP inhibitors, and TP53mut-MYBamp and immunotherapy resistance. Furthermore, we reveal that precision treatment strategies informed by co-alterations hold promise to improve patient outcomes. Our study highlights the significance of genetic interactions in guiding genome-informed treatment decisions beyond single driver alterations.

Unraveling the Significance of MET Focal Amplification in Lung Cancer: Integrative NGS, FISH, and IHC Investigation.

MET amplification (METamp) represents a promising therapeutic target in non-small cell lung cancer, but no consensus has been established to identify METamp-dependent tumors that could potentially benefit from MET inhibitors. In this study, an analysis of MET amplification/overexpression status was performed in a retrospectively recruited cohort comprising 231 patients with non-small cell lung cancer from Shanghai Chest Hospital (SCH cohort) using 3 methods: fluorescence in situ hybridization (FISH), hybrid capture-based next-generation sequencing, and immunohistochemistry for c-MET and phospho-MET. The SCH cohort included 130 cases known to be METamp positive by FISH and 101 negative controls. The clinical relevance of these approaches in predicting the efficacy of MET inhibitors was evaluated. Additionally, next-generation sequencing data from another 2 cohorts including 22,010 lung cancer cases were utilized to examine the biological characteristics of different METamp subtypes. Of the 231 cases, 145 showed MET amplification/overexpression using at least 1 method, whereas only half of them could be identified by all 3 methods. METamp can occur as focal amplification or polysomy. Our study revealed that the inconsistency between next-generation sequencing and FISH primarily occurred in the polysomy subtype. Further investigations indicated that compared with polysomy, focal amplification correlated with fewer co-occurring driver mutations, higher protein expressions of c-MET and phospho-MET, and higher incidence in acquired resistance than in de novo setting. Moreover, patients with focal amplification presented a more robust response to MET inhibitors compared with those with polysomy. Notably, a strong correlation was observed between focal amplification and programmed cell death ligand-1 expression, indicating potential therapeutic implications with combined MET inhibitor and immunotherapy for patients with both alterations. Our findings provide insights into the molecular complexity and clinical relevance of METamp in lung cancer, highlighting the role of MET focal amplification as an oncogenic driver and its feasibility as a primary biomarker to further investigate the clinical activity of MET inhibitors in future studies.

Emergence of machine language: towards symbolic intelligence with neural networks.

Inspired by human language, machine language is a novel discrete representation learned from visual data only through playing the speak, guess, and draw game.

Integrated multiomic profiling of breast cancer in the Chinese population reveals patient stratification and therapeutic vulnerabilities.

Molecular profiling guides precision treatment of breast cancer; however, Asian patients are underrepresented in publicly available large-scale studies. We established a comprehensive multiomics cohort of 773 Chinese patients with breast cancer and systematically analyzed their genomic, transcriptomic, proteomic, metabolomic, radiomic and digital pathology characteristics. Here we show that compared to breast cancers in white individuals, Asian individuals had more targetable AKT1 mutations. Integrated analysis revealed a higher proportion of HER2-enriched subtype and correspondingly more frequent ERBB2 amplification and higher HER2 protein abundance in the Chinese HR+HER2+ cohort, stressing anti-HER2 therapy for these individuals. Furthermore, comprehensive metabolomic and proteomic analyses revealed ferroptosis as a potential therapeutic target for basal-like tumors. The integration of clinical, transcriptomic, metabolomic, radiomic and pathological features allowed for efficient stratification of patients into groups with varying recurrence risks. Our study provides a public resource and new insights into the biology and ancestry specificity of breast cancer in the Asian population, offering potential for further precision treatment approaches.

Elevated platelet count is associated with decreased mortality from hemorrhagic stroke in hospital: a multi-center retrospective cohort study.

This study aimed to investigate the relationship between platelet count (PC) and mortality in patients with hemorrhagic stroke (HS). The research reviewed data from 10,466 patients hospitalized in 208 hospitals in the United States from January 1, 2014, to December 31, 2015. Of these, 3262 HS patients were included in the primary analysis for those admitted to the intensive care unit (ICU). The average age of these patients was 67.05 years, with 52.79% being male. The median PC was (221.67 ± 73.78) × 109/L. Multivariate logistic regression analysis revealed that PC was a protective factor for mortality in HS patients (OR = 0.98, 95% CI 0.97-1.00, P < 0.05). Additionally, a non-linear association between PC and mortality in HS patients was found using a generalized additive model (GAM) and smooth curve fitting (penalty spline method). For the first time, a recursive algorithm identified the inflection point of platelet count as 194 × 109/L. On the left side of the inflection point, for every increase of 10 units in platelet count, the mortality rate of HS patients decreases by 10%. The study demonstrates a non-linear relationship between PC and the risk of mortality in HS patients. A platelet counts higher than the inflection point (194 × 109/L) may be a significant intervention to reduce mortality in HS patients.

Pulmonary nodule detection in x-ray images by feature augmentation and context aggregation.

Recent developments in x-ray image based pulmonary nodule detection have achieved remarkable results. However, existing methods are focused on transferring off-the-shelf coarse-grained classification models and fine-grained detection models rather than developing a dedicated framework optimized for nodule detection. In this paper, we propose PN-DetX, which as we know is the first dedicated pulmonary nodule detection framework. PN-DetX incorporates feature fusion and self-attention into x-ray based pulmonary nodule detection tasks, achieving improved detection performance. Specifically, PN-DetX adopts CSPDarknet backbone to extract features, and utilizes feature augmentation module to fuse features from different levels followed by context aggregation module to aggregate semantic information. To evaluate the efficacy of our method, we collect aLArge-scalePulmonaryNOduleDetection dataset,LAPNOD, comprising 2954 x-ray images along with expert-annotated ground truths. As we know, this is the first large-scale chest x-ray pulmonary nodule detection dataset. Experiments demonstrates that our method outperforms baseline by 3.8% mAP and 5.1%AP0.5. The generality of our approach is also evaluated on the publicly available dataset NODE21. We aspire for our method to serve as an inspiration for future research in the field of pulmonary nodule detection. The dataset and codes will be made in public.

ZNF689 deficiency promotes intratumor heterogeneity and immunotherapy resistance in triple-negative breast cancer.

Triple-negative breast cancer (TNBC) is an aggressive disease characterized by remarkable intratumor heterogeneity (ITH), which poses therapeutic challenges. However, the clinical relevance and key determinant of ITH in TNBC are poorly understood. Here, we comprehensively characterized ITH levels using multi-omics data across our center's cohort (n = 260), The Cancer Genome Atlas cohort (n = 134), and four immunotherapy-treated cohorts (n = 109). Our results revealed that high ITH was associated with poor patient survival and immunotherapy resistance. Importantly, we identified zinc finger protein 689 (ZNF689) deficiency as a crucial determinant of ITH formation. Mechanistically, the ZNF689-TRIM28 complex was found to directly bind to the promoter of long interspersed element-1 (LINE-1), inducing H3K9me3-mediated transcriptional silencing. ZNF689 deficiency reactivated LINE-1 retrotransposition to exacerbate genomic instability, which fostered ITH. Single-cell RNA sequencing, spatially resolved transcriptomics and flow cytometry analysis confirmed that ZNF689 deficiency-induced ITH inhibited antigen presentation and T-cell activation, conferring immunotherapy resistance. Pharmacological inhibition of LINE-1 significantly reduced ITH, enhanced antitumor immunity, and eventually sensitized ZNF689-deficient tumors to immunotherapy in vivo. Consistently, ZNF689 expression positively correlated with favorable prognosis and immunotherapy response in clinical samples. Altogether, our study uncovers a previously unrecognized mechanism underlying ZNF689 deficiency-induced ITH and suggests LINE-1 inhibition combined with immunotherapy as a novel treatment strategy for TNBC.

Neutrophil activation and clonal CAR-T re-expansion underpinning cytokine release syndrome during ciltacabtagene autoleucel therapy in multiple myeloma.

Cytokine release syndrome (CRS) is the most common complication of chimeric antigen receptor redirected T cells (CAR-T) therapy. CAR-T toxicity management has been greatly improved, but CRS remains a prime safety concern. Here we follow serum cytokine levels and circulating immune cell transcriptomes longitudinally in 26 relapsed/refractory multiple myeloma patients receiving the CAR-T product, ciltacabtagene autoleucel, to understand the immunological kinetics of CRS. We find that although T lymphocytes and monocytes/macrophages are the major overall cytokine source in manifest CRS, neutrophil activation peaks earlier, before the onset of severe symptoms. Intracellularly, signaling activation dominated by JAK/STAT pathway occurred prior to cytokine cascade and displayed regular kinetic changes. CRS severity is accurately described and potentially predicted by temporal cytokine secretion signatures. Notably, CAR-T re-expansion is found in three patients, including a fatal case characterized by somatic TET2-mutation, clonal expanded cytotoxic CAR-T, broadened cytokine profiles and irreversible hepatic toxicity. Together, our findings show that a latent phase with distinct immunological changes precedes manifest CRS, providing an optimal window and potential targets for CRS therapeutic intervention and that CAR-T re-expansion warrants close clinical attention and laboratory investigation to mitigate the lethal risk.

High-Resolution Mass Spectrometry Screening of Quaternary Ammonium Compounds (QACs) in Dust from Homes and Various Microenvironments in South China.

Despite their ubiquitous use, information regarding the presence of quaternary ammonium compounds (QACs) in various microenvironments remains scarce and only a small subset of QACs has been monitored using targeted chemical analysis. In this study, a total of 111 dust samples were collected from homes and various public settings in South China during the COVID-19 pandemic and were analyzed for traditional and emerging QACs using high-resolution mass spectrometry. The total traditional QAC concentrations in residential dust (∑traditional QAC, sum of 18 traditional QACs) ranged from 13.8 to 150 µg/g with a median concentration of 42.2 µg/g. Twenty-eight emerging QACs were identified in these samples, and the composition of ∑emerging QAC (sum of emerging QACs) to ∑QAC (sum of traditional and emerging QACs) ranged from 19 to 42% across various microenvironments, indicating the widespread existence of emerging QACs in indoor environments. Additionally, dust samples from cinemas exhibited higher ∑QAC concentrations compared to homes (medians 65.9 µg/g vs 58.3 µg/g, respectively), indicating heavier emission sources of QACs in these places. Interestingly, significantly higher ∑QAC concentrations were observed in dust from the rooms with carpets than those without (medians 65.6 µg/g vs 32.6 µg/g, p < 0.05, respectively). Overall, this study sheds light on the ubiquitous occurrence of QACs in indoor environments in South China.

Distributed adaptive sliding-mode control for 2-D plane vehicle platoon with prescribed performance and angle constraint.

This paper focuses on the distributed adaptive sliding-mode control problem for two-dimensional (2-D) plane vehicle platoon with prescribed performance, angle constraints, and actuator faults. The quadratic spacing policy (QSP) is first adopted for the 2-D plane vehicle platoon to adjust the inter-vehicle spacing. The spacing error can converge within a finite time to the small region predetermined by a new finite-time performance function (FTPF). Meanwhile, a new transformed error function is introduced to convert the FTPF-constrained spacing errors into equivalent unconstrained ones. Besides, the property of the invertible nonlinear mapping function is used for the original system with the angle constraint to get a new unconstrained system. Moreover, a new controller based on hyperbolic tangent function is designed to handle actuator faults occurring multiple times over a period. Furthermore, the stability and string stability of the 2-D plane vehicle platoon are achieved through sliding-mode control. Finally, the simulation results validate the effectiveness of the proposed techniques.

Novel vacuum UV/ozone/peroxymonosulfate process for efficient degradation of levofloxacin: Performance evaluation and mechanism insight.

Vacuum UV (VUV) irradiation has advantage in coupling oxidants for organics removal because VUV can dissociate water to produce reactive oxygen species (ROS) in situ and decompose oxidants rapidly. In this study, the synergistic activation of peroxymonosulfate (PMS) by VUV and ozone (O3) was explored via developing a novel integrated VUV/O3/PMS process, and the performance and mechanisms of VUV/O3/PMS for levofloxacin (LEV) degradation were investigated systematically. Results indicated that VUV/O3/PMS could effectively degrade LEV, and the degradation rate was 1.67-18.79 times of its sub-processes. Effects of PMS dosage, O3 dosage, solution pH, anions, and natural organic matter on LEV removal by VUV/O3/PMS were also studied. Besides, hydroxyl radical and sulfate radical were main ROS with contributions of 49.7% and 17.4%, respectively. Moreover, the degradation pathways of LEV in VUV/O3/PMS process were speculated based on density functional theory calculation and by-products detection. Furthermore, synergistic reaction mechanisms in VUV/O3/PMS process were proposed. The energy consumption of VUV/O3/PMS decreased by 22.6%- 88.1% compared to its sub-processes. Finally, the integrated VUV/O3/PMS process showed satisfactory results in removing LEV in actual waters, manifesting VUV/O3/PMS had great application potential and feasibility in removing organics in wastewater reuse.

Antigen-induced chimeric antigen receptor multimerization amplifies on-tumor cytotoxicity.

Ligand-induced receptor dimerization or oligomerization is a widespread mechanism for ensuring communication specificity, safeguarding receptor activation, and facilitating amplification of signal transduction across the cellular membrane. However, cell-surface antigen-induced multimerization (dubbed AIM herein) has not yet been consciously leveraged in chimeric antigen receptor (CAR) engineering for enriching T cell-based therapies. We co-developed ciltacabtagene autoleucel (cilta-cel), whose CAR incorporates two B-cell maturation antigen (BCMA)-targeted nanobodies in tandem, for treating multiple myeloma. Here we elucidated a structural and functional model in which BCMA-induced cilta-cel CAR multimerization amplifies myeloma-targeted T cell-mediated cytotoxicity. Crystallographic analysis of BCMA-nanobody complexes revealed atomic details of antigen-antibody hetero-multimerization whilst analytical ultracentrifugation and small-angle X-ray scattering characterized interdependent BCMA apposition and CAR juxtaposition in solution. BCMA-induced nanobody CAR multimerization enhanced cytotoxicity, alongside elevated immune synapse formation and cytotoxicity-mediating cytokine release, towards myeloma-derived cells. Our results provide a framework for contemplating the AIM approach in designing next-generation CARs.

Revisiting Confidence Estimation: Towards Reliable Failure Prediction.

Reliable confidence estimation is a challenging yet fundamental requirement in many risk-sensitive applications. However, modern deep neural networks are often overconfident for their incorrect predictions, i.e., misclassified samples from known classes, and out-of-distribution (OOD) samples from unknown classes. In recent years, many confidence calibration and OOD detection methods have been developed. In this paper, we find a general, widely existing but actually-neglected phenomenon that most confidence estimation methods are harmful for detecting misclassification errors. We investigate this problem and reveal that popular calibration and OOD detection methods often lead to worse confidence separation between correctly classified and misclassified examples, making it difficult to decide whether to trust a prediction or not. Finally, we propose to enlarge the confidence gap by finding flat minima, which yields state-of-the-art failure prediction performance under various settings including balanced, long-tailed, and covariate-shift classification scenarios. Our study not only provides a strong baseline for reliable confidence estimation but also acts as a bridge between understanding calibration, OOD detection, and failure prediction.

Comparison of different prediction models for estimation of walking and running energy expenditure based on a wristwear three-axis accelerometer.

Objective: Objectively and efficiently measuring physical activity is a common issue facing the fields of medicine, public health, education, and sports worldwide. In response to the problem of low accuracy in predicting energy consumption during human motion using accelerometers, a prediction model for asynchronous energy consumption in the human body is established through various algorithms, and the accuracy of the model is evaluated. The optimal energy consumption prediction model is selected to provide theoretical reference for selecting reasonable algorithms to predict energy consumption during human motion. Methods: A total of 100 subjects aged 18-30 years participated in the study. Experimental data for all subjects are randomly divided into the modeling group (n = 70) and validation group (n = 30). Each participant wore a triaxial accelerometer, COSMED Quark pulmonary function tester (Quark PFT), and heart rate band at the same time, and completed the tasks of walking (speed range: 2 km/h, 3 km/h, 4 km/h, 5 km/h, and 6 km/h) and running (speed range: 7 km/h, 8 km/h, and 9 km/h) sequentially. The prediction models were built using accelerometer data as the independent variable and the metabolic equivalents (METs) as the dependent variable. To calculate the prediction accuracy of the models, root mean square error (RMSE) and bias were used, and the consistency of each prediction model was evaluated based on Bland-Altman analysis. Results: The linear equation, logarithmic equation, cubic equation, artificial neural network (ANN) model, and walking-and-running two-stage model were established. According to the validation results, our proposed walking-and-running two-stage model showed the smallest overall EE prediction error (RMSE = 0.76 METs, Bias = 0.02 METs) and the best performance in Bland-Altman analysis. Additionally, it had the lowest error in predicting EE during walking (RMSE = 0.66 METs, Bias = 0.03 METs) and running (RMSE = 0.90 METs, Bias < 0.01 METs) separately, as well as high accuracy in predicting EE at each single speed. Conclusion: The ANN-based walking-and-running two-stage model established by separating walking and running can better estimate the walking and running EE, the improvement of energy consumption prediction accuracy will be conducive to more accurate to monitor the energy consumption of PA.

Effects of poling camber angle on the biomechanics of cross-country sit-skiing.

Cross-country sit-skiers use double poling (DP) technique to drive the slide. The aim of this study is to analyze how poling camber angle affect the capacity of power output and biomechanical parameters of the DP process. Twenty-four non-disabled college students (24.67 ± 1.46 years old) were recruited to perform three successive 30-s maximal effort tests with different poling camber angles of 0°, 15°, 24° and 30° using a sit-skiing ergometer. The biomechanical parameters, output power and muscle activation of the subjects were analyzed. The results showed that DP output power increased with the increase of poling camber angle at 15° (597.78 ± 150.31 J), 24° (610.94 ± 158.96 J, P = 0.011) and 30° (629.10 ± 168.78 J, P < 0.001) compared with 0° (590.65 ± 148.95 J). However, effective output power decreased with the increase of camber angle. Poling with camber angle of 24° had the shortest cycle time 1.53 ± 0.17 s, compared with other abduction angle (0°, 1.57 ± 0.19 s, 15°, 1.55 ± 0.16 s, and 30°, 1.56 ± 0.19 s). Compared with 0° (1.02 ± 0.14 m), the cycle distance significantly increased at poling camber angles of 24° (1.07 ± 0.12 m, P = 0.029) and 30° (1.11 ± 0.13 m, P < 0.001). With the increase of poling camber angle, the shoulder and elbow joint range of motions and joint moments were significantly increased. This study found that poling with shoulder abducted increased the output power but decreased the efficiency. By analyzing the poling angle and poling force, we find that the optimal poling camber angle may depend on the terrain or the skiing speed. These results may guide the competition techniques and tactics in the matches, and may further influence the strength-training programs of cross-country sit-skiing athletes.

Molecular classification of hormone receptor-positive HER2-negative breast cancer.

Hormone receptor-positive (HR+)/human epidermal growth factor receptor 2-negative (HER2-) breast cancer is the most prevalent type of breast cancer, in which endocrine therapy resistance and distant relapse remain unmet challenges. Accurate molecular classification is urgently required for guiding precision treatment. We established a large-scale multi-omics cohort of 579 patients with HR+/HER2- breast cancer and identified the following four molecular subtypes: canonical luminal, immunogenic, proliferative and receptor tyrosine kinase (RTK)-driven. Tumors of these four subtypes showed distinct biological and clinical features, suggesting subtype-specific therapeutic strategies. The RTK-driven subtype was characterized by the activation of the RTK pathways and associated with poor outcomes. The immunogenic subtype had enriched immune cells and could benefit from immune checkpoint therapy. In addition, we developed convolutional neural network models to discriminate these subtypes based on digital pathology for potential clinical translation. The molecular classification provides insights into molecular heterogeneity and highlights the potential for precision treatment of HR+/HER2- breast cancer.

Cycle-Consistent Weakly Supervised Visual Grounding With Individual and Contextual Representations.

Visual grounding, aiming to align image regions with textual queries, is a fundamental task for cross-modal learning. We study the weakly supervised visual grounding, where only image-text pairs at a coarse-grained level are available. Due to the lack of fine-grained correspondence information, existing approaches often encounter matching ambiguity. To overcome this challenge, we introduce the cycle consistency constraint into region-phrase pairs, which strengthens correlated pairs and weakens unrelated pairs. This cycle pairing makes use of the bidirectional association between image regions and text phrases to alleviate matching ambiguity. Furthermore, we propose a parallel grounding framework, where backbone networks and subsequent relation modules extract individual and contextual representations to calculate context-free and context-aware similarities between regions and phrases separately. Those two representations characterize visual/linguistic individual concepts and inter-relationships, respectively, and then complement each other to achieve cross-modal alignment. The whole framework is trained by minimizing an image-text contrastive loss and a cycle consistency loss. During inference, the above two similarities are fused to give the final region-phrase matching score. Experiments on five popular datasets about visual grounding demonstrate a noticeable improvement in our method. The source code is available at https://github.com/Evergrow/WSVG.

Genomic alterations affecting tumor-infiltrating lymphocytes and PD-L1 expression patterns in triple-negative breast cancer.

BACKGROUND: Tumor-infiltrating lymphocytes (TILs) and programmed cell death 1 ligand 1 (PD-L1) remain imperfect in predicting clinical outcomes of triple-negative breast cancer because outcomes do not always correlate with the expression of these biomarkers. Genomic and transcriptomic alterations that may contribute to the expression of these biomarkers remain incompletely uncovered. METHODS: We evaluated PD-L1 immunohistochemistry scores (SP142 and 28-8 assays) and TILs in our triple-negative breast cancer multiomics dataset and 2 immunotherapy clinical trial cohorts. Then, we analyzed genomic and transcriptomic alterations correlated with TILs, PD-L1 expression, and patient outcomes. RESULTS: Despite TILs serving as a decent predictor for triple-negative breast cancer clinical outcomes, exceptions remained. Our study revealed that several genomic alterations were correlated with unexpected events. In particular, PD-L1 expression may cause a paradoxical relationship between TILs and prognosis in certain patients. Consequently, we classified triple-negative breast cancers into 4 groups based on PD-L1 and TIL levels. The TIL-negative PD-L1-positive and TIL-positive PD-L1-negative groups were not typical "hot" tumors; both were associated with worse prognoses and lower immunotherapy efficacy than TIL-positive PD-L1-positive tumors. Copy number variation of PD-L1 and oncogenic signaling activation were correlated with PD-L1 expression in the TIL-negative PD-L1-positive group, whereas GSK3B-induced degradation may cause undetectable PD-L1 expression in the TIL-positive PD-L1-negative group. These factors have the potential to affect the predictive function of both PD-L1 and TILs. CONCLUSIONS: Several genomic and transcriptomic alterations may cause paradoxical effects among TILs, PD-L1 expression, and prognosis in triple-negative breast cancer. Investigating and targeting these factors will advance precision immunotherapy for patients with this disease.