Non-negative Matrix Factorization Method Research Articles

An improved multi-view spectral clustering based on tissue-like P systems

Multi-view spectral clustering is one of the multi-view clustering methods widely studied by numerous scholars. The first step of multi-view spectral clustering is to construct the similarity matrix of each view. Consequently, the clustering performance will be greatly affected by the quality of the similarity matrix of each view. To solve this problem well, an improved multi-view spectral clustering based on tissue-like P systems is proposed in this paper. The optimal per-view similarity matrix is generated in an iterative manner. In addition, spectral clustering is combined with the symmetric nonnegative matrix factorization method to directly output the clustering results to avoid the secondary operation, such as k-means or spectral rotation. Furthermore, improved multi-view spectral clustering is integrated with the tissue-like P system to enhance the computational efficiency of the multi-view clustering algorithm. Extensive experiments verify the effectiveness of this algorithm over other state-of-the-art algorithms.

Projection concept factorization with self-representation for data clustering

In recent years, matrix factorization-based techniques have received much attention in the data analysis field since it can perform dimensionality reduction and clustering simultaneously. Despite the great success achieved by the Non-negative matrix factorization (NMF) and concept factorization (CF) methods, they suffer from the out-of-sample problem and are sensitive to the noise. Some recent studies have indicated that the similarity relationship is capable of revealing the local structure. In this paper, a similarity graph is constructed to reflect the geometric information of manifold structure, while the concept factorization is employed to capture the global structure. In addition, the projection matrix is incorporated into the concept factorization model to eliminate the noise and avoid the out-of-sample problem. An iterative algorithm is introduced to solve the model. The experimental results obtained on both human face and text data sets verify the high efficiency of the proposed method.

Deep NMF topic modeling

Nonnegative matrix factorization (NMF) based topic modeling methods do not rely on model- or data-assumptions much. However, they are usually formulated as difficult optimization problems, which may suffer from bad local minima and high computational complexity. In this paper, we propose a deep NMF (DNMF) topic modeling framework to alleviate the aforementioned problems. It first applies an unsupervised deep learning method to learn latent hierarchical structures of documents, under the assumption that if we could learn a good representation of documents by, e.g. a deep model, then the topic word discovery problem can be boosted. Then, it takes the output of the deep model to constrain a topic-document distribution for the discovery of the discriminant topic words, which not only improves the efficacy but also reduces the computational complexity over conventional unsupervised NMF methods. We constrain the topic-document distribution in three ways, which takes the advantages of the three major sub-categories of NMF—basic NMF, structured NMF, and constrained NMF respectively. To overcome the weaknesses of deep neural networks in unsupervised topic modeling, we adopt a non-neural-network deep model—multilayer bootstrap network. To our knowledge, this is the first time that a deep NMF model is used for unsupervised topic modeling. We have compared the proposed method with a number of representative references covering major branches of topic modeling on a variety of real-world text corpora. Experimental results illustrate the effectiveness of the proposed method under various evaluation metrics.

Identification of Specific Cervical Cancer Subtypes and Prognostic Gene Sets in Tumor and Nontumor Tissues Based on GSVA Analysis

Background Cervical cancer is the fourth common cancer among women. Its prognosis needs our more attention. Our purpose was to identity new prognostic gene sets to help other researchers develop more effective treatment for cervical cancer patients and improve the prognosis of patients. Methods We used gene set variation analysis (GSVA) to calculate the enrichment scores of gene sets and identified three subtypes of cervical cancer through the Cox regression model, k-means clustering algorithm, and nonnegative matrix factorization method (NMF). Chi-square test was utilized to test whether a certain clinical characteristic is different among divided subtypes. We further screened the prognostic gene sets using differential analysis, univariate Cox regression analysis, and least absolute shrinkage and selection operator (LASSO) regression. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were used to analyze which pathways and function the genes from screened gene sets enriched. Search Tool for the Retrieval of Interacting Genes (STRING) was used to draw the protein-protein interaction network, and Cytoscape was used to visualize the hub genes of protein-protein interaction network. Results We identified three novel subtypes of cervical cancer in The Cancer Genome Atlas (TCGA) samples and validated in Gene Expression Omnibus (GEO) samples. There were significant variations between the three subtypes in histological type, T stage, M stage, and N stage. T_GSE36888_UNTREATED_VS_IL2_TREATED_STAT5_AB_KNOCKIN_TCELL_2H_UP and N_HALLMARK_ANGIOGENESIS were screened prognostic gene sets. The prognostic model was as follows: riskScore = T_GSE36888_UNTREATED_VS_IL2_TREATED_STAT5_AB_KNOCKIN_TCELL_2H_UP∗ 2.617 + N_HALLMARK_ANGIOGENESIS∗ 4.860. Survival analysis presented that in these two gene sets, high enrichment scores were all significantly related to worse overall survival. The hub genes from T gene set included CXCL1, CXCL2, CXCL8, ALDOA, TALDO1, LDHA, CCL4, FCAR, FCER1G, SAMSN1, LILRB1, SH3PXD2B, PPM1N, PKM, and FKBP4. As for N gene sets, the hub genes included ITGAV, PTK2, SPP1, THBD, and APOH. Conclusions Three novel subtypes and two prognostic gene sets were identified. 15 hub genes for T gene set and 5 hub genes for N gene set were discovered. Based on these findings, we can develop more and more effective treatments for cervical cancer patients. Based on the gene enriched pathways, we can development specific drugs targeting the pathways.

Integrated T cell cytometry metrics for immune-monitoring applications in immunotherapy clinical trials.

Mass cytometry, or cytometry by TOF (CyTOF), provides a robust means of determining protein-level measurements of more than 40 markers simultaneously. While the functional states of immune cells occur along continuous phenotypic transitions, cytometric studies surveying cell phenotypes often rely on static metrics, such as discrete cell-type abundances, based on canonical markers and/or restrictive gating strategies. To overcome this limitation, we applied single-cell trajectory inference and nonnegative matrix factorization methods to CyTOF data to trace the dynamics of T cell states. In the setting of cancer immunotherapy, we showed that patient-specific summaries of continuous phenotypic shifts in T cells could be inferred from peripheral blood-derived CyTOF mass cytometry data. We further illustrated that transfer learning enabled these T cell continuous metrics to be used to estimate patient-specific cell states in new sample cohorts from a reference patient data set. Our work establishes the utility of continuous metrics for CyTOF analysis as tools for translational discovery.

Genetic Algorithm-Based Adaptive Wiener Gain for Speech Enhancement Using an Iterative Posterior NMF

In this paper, we propose a genetic algorithm-based adaptive Wiener gain for speech enhancement using an iterative posterior non-negative matrix factorization (NMF). In the recent past, NMF-based Wiener filtering methods were used to improve the performance of speech enhancement, which has shown that they provide better performance when compared with conventional NMF methods. But performance degrades in non-stationary noise environments. Template-based approaches are more robust and perform better in non-stationary noise environments compared to statistical model-based approaches but are dependent on a priori information. Combining the approaches avoids the drawbacks of both. To improve the performance further, speech and noise bases are adapted simultaneously in the NMF approach. The usage of Super-Gaussian constraints in iterative NMF still improves the performance in non-stationary noise. The silence frame is a challenging task in the case of NMF; still there will be some amount of noise present in those frames. For further enhancement, we have combined with a genetic algorithm (GA)-based adaptive Wiener filter which performs well in denoising and also the GA search the adaptive [Formula: see text] allows us to control the trade-off between fitting the observed spectrogram of mixed speech and noise achieving high likelihood under our prior model. The proposed method outperforms other benchmark algorithms in terms of the source to distortion ratio (SDR), short-time objective intelligibility (STOI), and perceptual evaluation of speech quality (PESQ).

A block column iteration for nonnegative matrix factorization

Alternating nonnegative least squares (ANLS) framework is a popular method for nonnegative matrix factorization. This approach, at each step, substitutes the non-convex NMF problem with two nonnegativities constrained least-squares subproblems, so the method for solving these subproblems is critical. This paper adopts a version of block-column iterative methods with nonnegativity constraints (BCI-NC) for solving the subproblems and presents an efficient algorithm in NMF decomposition based on the ANLS framework. The advantages of this method include simplicity and ease of implementation. Also, BCI-NC determines the step lengths without time-consuming line searches, unlike the existing ones. Numerical experiments on real-world image and text datasets show that the proposed algorithm is efficient. We observe that BCI-NC provides the best performance in balancing between accuracy and computation time.

Exercise improves choroid plexus epithelial cells metabolism to prevent glial cell-associated neurodegeneration.

Recent studies have shown that physical activities can prevent aging-related neurodegeneration. Exercise improves the metabolic landscape of the body. However, the role of these differential metabolites in preventing neurovascular unit degeneration (NVU) is still unclear. Here, we performed single-cell analysis of brain tissue from young and old mice. Normalized mutual information (NMI) was used to measure heterogeneity between each pair of cells using the non-negative Matrix Factorization (NMF) method. Astrocytes and choroid plexus epithelial cells (CPC), two types of CNS glial cells, differed significantly in heterogeneity depending on their aging status and intercellular interactions. The MetaboAnalyst 5.0 database and the scMetabolism package were used to analyze and calculate the differential metabolic pathways associated with aging in the CPC. These mRNAs and corresponding proteins were involved in the metabolites (R)-3-Hydroxybutyric acid, 2-Hydroxyglutarate, 2-Ketobutyric acid, 3-Hydroxyanthranilic acid, Fumaric acid, L-Leucine, and Oxidized glutathione pathways in CPC. Our results showed that CPC age heterogeneity-associated proteins (ECHS1, GSTT1, HSD17B10, LDHA, and LDHB) might be directly targeted by the metabolite of oxidized glutathione (GSSG). Further molecular dynamics and free-energy simulations confirmed the insight into GSSG’s targeting function and free-energy barrier on these CPC age heterogeneity-associated proteins. By inhibiting these proteins in CPC, GSSG inhibits brain energy metabolism, whereas exercise improves the metabolic pathway activity of CPC in NVU by regulating GSSG homeostasis. In order to develop drugs targeting neurodegenerative diseases, further studies are needed to understand how physical exercise enhances NVU function and metabolism by modulating CPC-glial cell interactions.

SDNMF: Semisupervised discriminative nonnegative matrix factorization for feature learning

As one of the most effective feature learning methods, Nonnegative Matrix Factorization (NMF) has been widely used in many scientific fields, such as computer vision, data mining, and bioinformatics. However, NMF is an unsupervised method that cannot fully utilize the label information of data. Thus, its performance is limited in some recognition and classification problems. To remedy this shortcoming, this paper proposes a Semisupervised Discriminative NMF (SDNMF) method. First, we design a Soft-Labeled NMF (SLNMF) model by introducing a soft-label matrix-based regression term into the original NMF, so that the relationship between the soft-label matrix and low-dimensional features can be constructed to improve the discriminative ability of low-dimensional features. Second, to effectively estimate the soft-label matrix, a Label Propagation (LP) model is adopted to fully explore the spatial distribution relationship between the labeled and unlabeled samples. Third, an Adaptive Graph Learning (AGL) model is proposed to exploit the geometric relationship of samples well, which could enhance the performance of LP. Finally, the above three models (i.e., SLNMF, LP, and AGL) are integrated into a unified framework for effective feature learning, which can not only effectively explore the structural relationship matrix between data, but also predict the labels for unknown samples. Moreover, an iterative optimization algorithm is presented to solve our objective function. The convergence and computational complexity analysis of the proposed SDNMF method are also provided. Extensive experiments are conducted on several standard data sets. Compared with related methods, the experimental results verify that the proposed SDNMF method achieves better performance.

Difference in muscle synergies of the butterfly technique with and without swimmer’s shoulder

This study aimed to investigate whether muscle synergy differs between swimmers with and without swimmer's shoulder in the butterfly technique. Muscle synergies, which can assess muscle coordination, were analyzed using surface electromyography. Twenty elite swimmers were included in this study (swimmer's shoulder: n = 8; control: n = 12). The motions involved in executing the butterfly technique were classified into the early pull-through, late pull-through, and recovery phases. Muscle synergy data analyzed using the nonnegative matrix factorization method were compared between the two groups.The swimming velocities were 1.66 ± 0.09 m・s −1 and 1.69 ± 0.06 m・s −1 for the control and swimmer's shoulder groups, respectively. Four muscle synergies in both groups were identified: synergy #1, which was involved in the early pull; synergy #2, involved in the late pull; synergy #3, involved in the early recovery; and synergy #4, involved in pre- and posthand entry. Compared to the control group, the swimmer's shoulder group had a small contribution from the pectoralis major (p = 0.032) and a high contribution from the rectus femoris during the early pull phase (p = 0.036). In the late pull phase, the contribution of the lower trapezius muscle in the swimmer's shoulder group was low (p = 0.033), while the contribution of the upper trapezius muscle in the pre- and postentry phases was high (p = 0.032). In the rehabilitation of athletes with swimmer's shoulder, it is therefore important to introduce targeted muscle rehabilitation in each phase.

Topic Modelling Using Non-Negative Matrix Factorization (NMF) for Telkom University Entry Selection from Instagram Comments

The development of information technology is increasingly rapid, such as social media, which has much influence. Social media is a place or media used to express and express various opinions on a topic. One example is Instagram. Instagram is a social media platform with many features, such as posting photos, videos, comments, likes, and others. The comments feature that Instagram has contained much public opinion that can be used as data. Nothing but the post on the SMB Telkom University Instagram account about the entrance to the university. In posts about the entrance to Telkom university, many Instagram users comment on the post. This can be convenient for the marketing team to get topics or discussions that most followers need from Telkom University's Instagram account. Therefore, a topic modelling of Instagram users' perceptions of comments posted on the entrance to Telkom university was carried out using the Nonnegative Matrix Factorization (NMF) method. After doing several research scenarios, the best coherent value was obtained with a coherent value of 0.60628 and the best 4 topics.

Software Traceability in Agile Development Using Topic Modeling

Tracing the implementation of requirements for making better software identifies whether the application fulfils users' desires; progress of development; problematic areas in the testing process, and how far those apply to the source code. In this paper, the software development method we studied was the agile method, Extreme Programming (XP). The artifacts in the agile approach considered vital include the requirement documents, test documents, and source codes. We used Topic Modelling to map the content similarities from those documents to make trace links. The three topic modelling methods we compared consist of Latent Semantic Analysis (LSA), Latent Dirichlet Allocation (LDA), and Non-negative Matrix Factorization (NMF). The NMF method proved itself the most stable, with an accuracy value of 67% for the requirement, 59% for testing, and 48% for defect lists. The second application results proved more accurate with 70%, 79%, and 54%. Although NMF lost to LSA in the second application (LSA achieved an accuracy of 79%, 84%, and 56%), the precision and recall values showed almost similar results. We successfully found the link in the source code based on keywords extracted from each topic. This research provides a way of explaining the requirement in detail, simplifying it for tracing purposes such as the consistent use of terms, technical details inclusion, and mentioning all the variables involved. In the future, sentence structure and synonyms need recognition as part of pre-processing to build better trace links.

Robust local-coordinate non-negative matrix factorization with adaptive graph for robust clustering

With its unique geometric properties, non-negative matrix factorization (NMF) has become one of the widely used clustering methods in the field of data mining. Regrettably, most existing NMF methods are sensitive to super-noise (super-outliers). This paper proposes a novel robust clustering method to address this issue. Based on the Hx loss function, this method establishes a novel robust adaptive local structure learning strategy, reducing the interference of noise (outliers) on data reconstruction and space exploration. In addition, a new orthogonal regularization term is incorporated into the model, ensuring the orthogonality of the factor matrix and enhancing the discriminant ability. Finally, we develop an efficient algorithm to solve the resultant model and analyze its convergence from theoretical and experimental aspects. Experimental results on random synthetic data sets and benchmark databases demonstrate that the proposed method outperforms the existing robust NMF methods in terms of spatial structure learning, discriminant power, and robustness.

β-amyloid PET harmonisation across longitudinal studies: Application to AIBL, ADNI and OASIS3

IntroductionThe Centiloid scale was developed to harmonise the quantification of β-amyloid (Aβ) PET images across tracers, scanners, and processing pipelines. However, several groups have reported differences across tracers and scanners even after centiloid conversion. In this study, we aim to evaluate the impact of different pre and post-processing harmonisation steps on the robustness of longitudinal Centiloid data across three large international cohort studies. MethodsAll Aβ PET data in AIBL (N = 3315), ADNI (N = 3442) and OASIS3 (N = 1398) were quantified using the MRI-based Centiloid standard SPM pipeline and the PET-only pipeline CapAIBL. SUVR were converted into Centiloids using each tracer's respective transform. Global Aβ burden from pre-defined target cortical regions in Centiloid units were quantified for both raw PET scans and PET scans smoothed to a uniform 8 mm full width half maximum (FWHM) effective smoothness. For Florbetapir, we assessed the performance of using both the standard Whole Cerebellum (WCb) and a composite white matter (WM)+WCb reference region. Additionally, our recently proposed quantification based on Non-negative Matrix Factorisation (NMF) was applied to all spatially and SUVR normalised images. Correlation with clinical severity measured by the Mini-Mental State Examination (MMSE) and effect size, as well as tracer agreement in 11C-PiB-18F-Florbetapir pairs and longitudinal consistency were evaluated. ResultsThe smoothing to a uniform resolution partially reduced longitudinal variability, but did not improve inter-tracer agreement, effect size or correlation with MMSE. Using a Composite reference region for 18F-Florbetapir improved inter-tracer agreement, effect size, correlation with MMSE, and longitudinal consistency. The best results were however obtained when using the NMF method which outperformed all other quantification approaches in all metrics used. ConclusionsFWHM smoothing has limited impact on longitudinal consistency or outliers. A Composite reference region including subcortical WM should be used for computing both cross-sectional and longitudinal Florbetapir Centiloid. NMF improves Centiloid quantification on all metrics examined.

An effective short-text topic modelling with neighbourhood assistance-driven NMF in Twitter.

Social media such as Twitter connect billions of people by allowing them to exchange their thoughts via short-text communication. Topic modelling is a widely used technique for analysing short texts. Discovering topic clusters in short-text collections faces issues with distance-based, density-based and dimensionality reduction-based methods due to their higher dimensionality and short length which results in extremely sparse text representation matrices. We propose the ‘neighbourhood-based assistance’-driven non-negative matrix factorization (NMF) method to handle high-dimensional sparse short-text representation with lower-dimensional projection effectively. We utilized NMF that aligned with the natural non-negativity of text data coupled with the symmetric document affinity information to identify topic distribution in the short text. Neighbourhood information within documents is captured using Jaccard similarity to assist information loss, resulting in higher-to-lower-dimensional projection. Experimental results with Twitter data sets show that the proposed approach is able to attain high accuracy compared to state-of-the-art methods quantitatively, while qualitative analysis with case studies validates the ability of the proposed approach in generating meaningful topic clusters.

Estimating longitudinal change in latent variable means: a comparison of non-negative matrix factorization and other item non-response methods

ABSTRACT Estimates of longitudinal change in the parameters of latent (i.e. unobserved) variables, including means, are affected by non-response on the items or indicators of the latent variable. This study used Monte Carlo simulation and a numeric example to compare four ordinal item non-response methods: non-negative matrix factorization (NNMF), multiple imputation with conditional proportional odds model (POM), full information maximum likelihood (FIML) and complete-case analysis, when estimating the longitudinal change in latent variable means. The mean squared error for the NNMF method was more than 40% lower than for the FIML and POM methods when the latent variable correlations over time were strong, percentage of missing data was 25% or more, and sample size was large. The NNMF method is a promising method to address item non-response. It is relatively efficient when sample size is large, and the percentage of missing data is high but has limitations under other data-analytic conditions.

M6Adecom: Analysis of m6A profile matrix based on graph regularized non-negative matrix factorization.

Epitranscriptomic m6A methylation is shown to mediate extensive regulations under the context of various RNA binding protein (RBP) readers. With m6A methylation data has reached a sizable scale, the functional context-aware analysis of m6A profiles is becoming more feasible and demanded. In this study, we employed graph regularized non-negative matrix factorization (GNMF) for m6A profile analysis and comparison, where the RBP binding preference of m6A sites were incorporated as the functional context-based graph constraint term. Compared to the baseline non-negative matrix factorization (NMF) method, this GNMF-based method could better capture the distinctions in multiple functional characteristics between different group of m6A sites, including but not limited to the associated biological pathways and disease genes. We further established m6Adecom, an online tool that can be used for correlation and enrichment analysis of m6A profiles using the matrix decomposition result from GNMF, and gene set enrichment analysis based on the high-score m6A sites. m6Adecom is freely accessible at http://www.rnanut.net/m6adecom.

A Pan-Sharpening Method with Beta-Divergence Non-Negative Matrix Factorization in Non-Subsampled Shear Transform Domain

In order to combine the spectral information of the multispectral (MS) image and the spatial information of the panchromatic (PAN) image, a pan-sharpening method based on β-divergence Non-negative Matrix Factorization (NMF) in the Non-Subsampled Shearlet Transform (NSST) domain is proposed. Firstly, we improve the traditional contrast calculation method to build the weighted local contrast measure (WLCM) method. Each band of the MS image is fused by a WLCM-based adaptive weighted averaging rule to obtain the intensity component I. Secondly, an image matting model is introduced to retain the spectral information of the MS image. I is used as the initial α channel to estimate the foreground color F and the background color B. Depending on the NSST, the PAN image and I are decomposed into one low-frequency component and several high-frequency components, respectively. Fusion rules are designed corresponding to the characteristics of the low-frequency and high-frequency components. A β-divergence NMF method based on the Alternating Direction Method of Multipliers (ADMM) is used to fuse the low frequency components. A WLCM-based rule is used to fuse the high-frequency components. The fused components are inverted by NSST inverse transformation, and the obtained image is used as the final α channel. Finally, the final fused image is reconstructed according to the foreground color F, background color B, and the final α channel. The experimental results demonstrate that the proposed method achieves superior performance in both subjective visual effects and objective evaluation, and effectively preserves spectral information while improving spatial resolution.

Necroptosis-Related Genes Associated With Immune Activity and Prognosis of Colorectal Cancer

This study aims at screening out the key necroptosis-related genes in colorectal cancer and elucidating the role of necroptosis-related genes in the immune activity and prognosis of colorectal cancer (CRC). The CRC patients’ data were downloaded from The Cancer Genome Atlas (TCGA). The non-negative matrix factorization method was applied to identify new molecular subgroups. Survival analysis and single sample Gene Set Enrichment Analysis were performed to determinate the differences in the overall survival time and immune status of the subgroups. Prognostic model was constructed on the basis of univariate Cox regression and LASSO analysis. Functional analyses were used to explore the potential mechanisms. Based on prognostic related necroptosis genes, we identify two molecular subgroups with significantly different survival. The better prognosis was associated with more active immune infiltration and upregulated expression of immune checkpoints. We screened nine necroptosis related genes as key prognostic genes and established a risk model, which showed a good potential for survival prediction in colorectal cancer. Nomogram assessment showed that the model had high reliability for predicting the prognosis of colorectal cancer patients. The high-risk and low-risk group also has different sensitivity to immunotherapy and commonly used drugs for colorectal cancer. Overall, necroptosis related genes were involved in the immune microenvironment of colorectal cancer patient, could be utilized to predict the prognosis of colorectal cancer and develop more individualized treatment.

Abstract 794: Pan-cancer proteogenomic analysis reveals functional mechanisms underlying DNA repair deficiencies

Abstract Emerging classifiers that leverage recent advances in the characterization of mutational signatures have identified a broader range of cancer patients with DNA repair deficiencies, many of whom do not harbor mutations or epigenetic silencing of canonical DNA repair genes. Investigating the various mechanisms underlying dysfunctional DNA damage response (DDR) and heterogeneity across patients will be crucial to understand disparate responses to therapy, including to PARP1 inhibitors and to immunotherapy. In this study, we integrate genomic, transcriptomic, proteomic, and post-translational modification (PTM) data across 11 cancer types to elucidate the diverse functional mechanisms of DNA repair deficiencies and their therapeutic implications through an in-depth proteogenomic analysis. We first characterized the landscape of mutational signatures in 1110 patients by applying SignatureAnalyzer, a tool that uses a Bayesian non-negative matrix factorization method to discover patterns of somatic mutations. Our analysis identified 144 homologous recombination-deficient (HRD) and 60 mismatch repair-deficient (MMRD) patients. Multi-omic (mRNA, protein, PTMs) signature based hierarchical clustering revealed two distinct clusters of HRD patients defined by either (i) acute or (ii) chronic hypoxic states. The acute hypoxia cluster exhibited typical HRD phenotypes, including a preference for non-homologous end joining (NHEJ) and enrichment in PARP1 activity, as well as phosphorylation patterns driven by DNA-PKcs, ATM, and ATR. In contrast, tumors under chronic hypoxia exhibited a halting of replication and silencing of DDR, suggesting that patients with these tumors may respond poorly to common HRD treatments that exploit alternative error-prone repair and PARP1 activation. Notably, these tumors were enriched in signatures representative of highly active kinases involved in cellular stress responses, potentially providing additional avenues for therapy. Additional analysis of both HRD and MMRD tumors revealed groups of highly immunogenic DNA repair-deficient tumors that may respond well to some types of immunotherapy given their increased mutagenesis. Moreover, differences in inflammation-related kinase activities offer further insight into the interaction between faulty DDR and the innate immune response (e.g., alterations in the cGAS-STING DNA sensing pathway). Overall, our multi-omic signature analysis provides insights into the mechanisms underlying DDR and its crosstalk with other cancer-related pathways that remain unexplained by evaluating genomic and transcriptomic data alone. Our characterization of the heterogeneity within the population of patients with DNA repair deficiency shows promise for improved prediction of treatment response, as well as identifying additional potential therapeutic targets. Citation Format: Yo Akiyama, Yifat Geffen, Shankara Anand, Tomer Yaron, Jared L. Johnson, Emily Huntsman, David Heiman, Chet Birger, Karl Clauser, Özgün Babur, Mendy Miller, Karsten Krug, D R Mani, Lewis C. Cantley, Steven A. Carr, PanCancer Oncogenic Drivers and Pathways Group, Clinical Proteomic Tumor Analysis Consortium (CPTAC), Francois Aguet, Li Ding, Gad Getz. Pan-cancer proteogenomic analysis reveals functional mechanisms underlying DNA repair deficiencies [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 794.