Proteogenomic Markers of Chemotherapy Resistance and Response in Triple-Negative Breast Cancer

miRNA-200c-3p deficiency promotes epithelial-mesenchymal transition in triple-negative breast cancer by activating CRKL expression

Epithelial-mesenchymal transition (EMT) plays an important role in malignant progression of Triple-negative breast cancer (TNBC). Many studies have confirmed that miRNA-200c-3p is related to EMT. And we found that it is involved in the regulation of EMT, but the exact mechanism is unclear. CRKL is highly expressed in a variety of tumors and plays a role in EMT. In this study, the potential targets of miRNA-200c-3p were searched in miRPathDB, Targetscan and PicTar. And there are 68 potential targets at the intersection of the three databases. Then, bioinformatics and text mining performed by Coremine Medica, and found that among 68 potential targets, CRKL has the strongest correlation with EMT in TNBC. Therefore, we speculated that miRNA-200c-3p involvement in EMT might be related to CRKL. To verify miRNA-200c-3p inhibits the malignant phenotype of TNBC by regulating CRKL, RT‒PCR, western blotting, Clonal formation assays,CCK-8 proliferation assays, transwell invasion assays, Luciferase reporter assay and nude mouse transplantation tumor assay were performed. In this study, we found that miRNA-200c-3p is under-expressed and EMT-related genes are up-regulated in TNBC, and miRNA-200c-3p can inhibit cancer cell proliferation, invasion and the expression of EMT-related genes and proteins in TNBC. Further research confirmed that miRNA-200c-3p could inhibit EMT by inhibiting the expression of CRKL that directly combining CRKL gene.

Longitudinal molecular profiling elucidates immunometabolism dynamics in breast cancer

Although metabolic reprogramming within tumor cells and tumor microenvironment (TME) is well described in breast cancer, little is known about how the interplay of immune state and cancer metabolism evolves during treatment. Here, we characterize the immunometabolic profiles of tumor tissue samples longitudinally collected from individuals with breast cancer before, during and after neoadjuvant chemotherapy (NAC) using proteomics, genomics and histopathology. We show that the pre-, on-treatment and dynamic changes of the immune state, tumor metabolic proteins and tumor cell gene expression profiling-based metabolic phenotype are associated with treatment response. Single-cell/nucleus RNA sequencing revealed distinct tumor and immune cell states in metabolism between cold and hot tumors. Potential drivers of NAC based on above analyses were validated in vitro. In summary, the study shows that the interaction of tumor-intrinsic metabolic states and TME is associated with treatment outcome, supporting the concept of targeting tumor metabolism for immunoregulation.

Cancer resistance and metastasis are maintained through oxidative phosphorylation

Malignant tumors have increased energy requirements due to growth, differentiation or response to stress. A significant number of studies in recent years have described upregulation of mitochondrial genes responsible for oxidative phosphorylation (OXPHOS) in some tumors. Although OXPHOS is replaced by glycolysis in some tumors (Warburg effect), both processes can occur simultaneously during the evolution of the same malignancies. In particular, chemoresistant and/or cancer stem cells appear to find a way to activate OXPHOS and metastasize. In this paper, we discuss recent work showing upregulation of OXPHOS in chemoresistant tumors and cell models. In addition, we show an inverse correlation of OXPHOS gene expression with the survival time of cancer patients after chemotherapy and discuss combination therapies for resistant tumors.

Novel pH-responsive alginate-stabilized curcumin-selenium-ZIF-8 nanocomposites for synergistic breast cancer therapy

In this study, a novel selenium@zeolitic imidazolate framework core/shell nanocomposite stabilised with alginate was used to improve the anti-tumour activity of curcumin. The developed alginate-stabilised curcumin-loaded selenium@zeolitic imidazolate framework (Alg@Cur@Se@ZIF-8) had a mean diameter of 159.6 nm and polydispersity index < 0.25. The release of curcumin from the nanocarrier at pH 5.4 was 2.69 folds as high as at pH 7.4. The bare nanoparticles showed haemolytic activity of about 12.16% at a concentration of 500 µg/mL while covering their surface with alginate reduced this value to 5.2%. By investigating cell viability, it was found that Alg@Cur@Se@ZIF-8 caused more cell death than pure curcumin. Additionally, in vivo studies showed that Alg@Cur@Se@ZIF-8 dramatically reduced tumour growth compared to free curcumin in 4T1 tumour-bearing mice. More importantly, the histological study confirmed that the developed drug delivery system successfully inhibited lung and liver metastasis while causing negligible toxicity in vital organs. Overall, due to the excellent inhibitory activity on cancerous cell lines and tumour-bearing animals, Alg@Cur@Se@ZIF-8 can be considered promising for breast cancer therapy.

One-pot method for preparing DNA, RNA, and protein for multiomics analysis

Typical multiomics studies employ separate methods for DNA, RNA, and protein sample preparation, which is labor intensive, costly, and prone to sampling bias. We describe a method for preparing high-quality, sequencing-ready DNA and RNA, and either intact proteins or mass-spectrometry-ready peptides for whole proteome analysis from a single sample. This method utilizes a reversible protein tagging scheme to covalently link all proteins in a lysate to a bead-based matrix and nucleic acid precipitation and selective solubilization to yield separate pools of protein and nucleic acids. We demonstrate the utility of this method to compare the genomes, transcriptomes, and proteomes of four triple-negative breast cancer cell lines with different degrees of malignancy. These data show the involvement of both RNA and associated proteins, and protein-only dependent pathways that distinguish these cell lines. We also demonstrate the utility of this multiomics workflow for tissue analysis using mouse brain, liver, and lung tissue.

Recent Contributions of Mass Spectrometry-Based "Omics" in the Studies of Breast Cancer

Breast cancer (BC) is one of the most heterogeneous groups of cancer. As every biotype of BC is unique and presents a particular "omic" signature, they are increasingly characterized nowadays with novel mass spectrometry (MS) strategies. BC therapeutic approaches are primarily based on the two features of human epidermal growth factor receptor 2 (HER2) and estrogen receptor (ER) positivity. Various strategic MS implementations are reported in studies of BC also involving data independent acquisitions (DIAs) of MS which report novel differential proteomic, lipidomic, proteogenomic, phosphoproteomic, and metabolomic characterizations associated with the disease and its therapeutics. Recently many "omic" studies have aimed to identify distinct subsidiary biotypes for diagnosis, prognosis, and targets of treatment. Along with these, drug-induced-resistance phenotypes are characterized by "omic" changes. These identifying aspects of the disease may influence treatment outcomes in the near future. Drug quantifications and characterizations are also done regularly and have implications in therapeutic monitoring and in drug efficacy assessments. We report these studies, mentioning their implications toward the understanding of BC. We briefly provide the MS instrumentation principles that are adopted in such studies as an overview with a brief outlook on DIA-MS strategies. In all of these, we have chosen a model cancer for its revelations through MS-based "omics".

ClinicalOmicsDB: exploring molecular associations of oncology drug responses in clinical trials

Matching patients to optimal treatment is challenging, in part due to the limited availability of real-world clinical datasets for predictive biomarker identification. The growing integration of omics profiling into clinical trials presents a new opportunity to tackle this challenge. Here, we introduce ClinicalOmicsDB, a web application for exploring molecular associations of oncology drug responses in clinical trials. This database includes transcriptomic data from 40 clinical trial studies, with 5913 patients spanning 11 cancer types. These studies include 67 treatment arms with a variety of chemotherapy, targeted therapy and immunotherapy drugs, and their combinations, which we organize based on an established ontology for easier navigation. The web application provides users with three options to explore molecular associations of oncology drug responses, focusing on studies, treatments or genes, respectively. Gene set analysis further connects treatment response to pathway activity and tumor microenvironment attributes. The user-friendly web interface of ClinicalOmicsDB streamlines interactive analysis. A Rust-based backend speeds up response time, and application programming interfaces and an R package enable programmatic access. We use three case studies to demonstrate the utility of this resource in human cancer studies. ClinicalOmicsDB is freely available at http://trials.linkedomics.org/.

Multiplexed Quantitative Proteomics Reveals Proteomic Alterations in Two Rodent Traumatic Brain Injury Models

In many cases of traumatic brain injury (TBI), conspicuous abnormalities, such as scalp wounds and intracranial hemorrhages, abate over time. However, many unnoticeable symptoms, including cognitive, emotional, and behavioral dysfunction, often last from several weeks to years after trauma, even for mild injuries. Moreover, the cause of such persistence of symptoms has not been examined extensively. Recent studies have implicated the dysregulation of the molecular system in the injured brain, necessitating an in-depth analysis of the proteome and signaling pathways that mediate the consequences of TBI. Thus, in this study, the brain proteomes of two TBI models were examined by quantitative proteomics during the recovery period to determine the molecular mechanisms of TBI. Our results show that the proteomes in both TBI models undergo distinct changes. A bioinformatics analysis demonstrated robust activation and inhibition of signaling pathways and core proteins that mediate biological processes after brain injury. These findings can help determine the molecular mechanisms that underlie the persistent effects of TBI and identify novel targets for drug interventions.

Role of Artificial Intelligence in Drug Discovery and Target Identification in Cancer

Drug discovery and development (DDD) is a highly complex process that necessitates precise monitoring and extensive data analysis at each stage. Furthermore, the DDD process is both timeconsuming and costly. To tackle these concerns, artificial intelligence (AI) technology can be used, which facilitates rapid and precise analysis of extensive datasets within a limited timeframe. The pathophysiology of cancer disease is complicated and requires extensive research for novel drug discovery and development. The first stage in the process of drug discovery and development involves identifying targets. Cell structure and molecular functioning are complex due to the vast number of molecules that function constantly, performing various roles. Furthermore, scientists are continually discovering novel cellular mechanisms and molecules, expanding the range of potential targets. Accurately identifying the correct target is a crucial step in the preparation of a treatment strategy. Various forms of AI, such as machine learning, neural-based learning, deep learning, and network-based learning, are currently being utilised in applications, online services, and databases. These technologies facilitate the identification and validation of targets, ultimately contributing to the success of projects. This review focuses on the different types and subcategories of AI databases utilised in the field of drug discovery and target identification for cancer.

Targeting eIF4A triggers an interferon response to synergize with chemotherapy and suppress triple-negative breast cancer

Protein synthesis is frequently dysregulated in cancer and selective inhibition of mRNA translation represents an attractive cancer therapy. Here, we show that therapeutically targeting the RNA helicase eIF4A with zotatifin, the first-in-class eIF4A inhibitor, exerts pleiotropic effects on both tumor cells and the tumor immune microenvironment in a diverse cohort of syngeneic triple-negative breast cancer (TNBC) mouse models. Zotatifin not only suppresses tumor cell proliferation but also directly repolarizes macrophages toward an M1-like phenotype and inhibits neutrophil infiltration, which sensitizes tumors to immune checkpoint blockade. Mechanistic studies revealed that zotatifin reprograms the tumor translational landscape, inhibits the translation of Sox4 and Fgfr1, and induces an interferon (IFN) response uniformly across models. The induction of an IFN response is partially due to the inhibition of Sox4 translation by zotatifin. A similar induction of IFN-stimulated genes was observed in breast cancer patient biopsies following zotatifin treatment. Surprisingly, zotatifin significantly synergizes with carboplatin to trigger DNA damage and an even heightened IFN response, resulting in T cell-dependent tumor suppression. These studies identified a vulnerability of eIF4A in TNBC, potential pharmacodynamic biomarkers for zotatifin, and provide a rationale for new combination regimens consisting of zotatifin and chemotherapy or immunotherapy as treatments for TNBC.

hsa_circ_0007919 induces LIG1 transcription by binding to FOXA1/TET1 to enhance the DNA damage response and promote gemcitabine resistance in pancreatic ductal adenocarcinoma

BACKGROUND

Circular RNAs (circRNAs) play important roles in the occurrence and development of cancer and chemoresistance. DNA damage repair contributes to the proliferation of cancer cells and resistance to chemotherapy-induced apoptosis. However, the role of circRNAs in the regulation of DNA damage repair needs clarification.

METHODS

RNA sequencing analysis was applied to identify the differentially expressed circRNAs. qRT-PCR was conducted to confirm the expression of hsa_circ_0007919, and CCK-8, FCM, single-cell gel electrophoresis and IF assays were used to analyze the proliferation, apoptosis and gemcitabine (GEM) resistance of pancreatic ductal adenocarcinoma (PDAC) cells. Xenograft model and IHC experiments were conducted to confirm the effects of hsa_circ_0007919 on tumor growth and DNA damage in vivo. RNA sequencing and GSEA were applied to confirm the downstream genes and pathways of hsa_circ_0007919. FISH and nuclear-cytoplasmic RNA fractionation experiments were conducted to identify the cellular localization of hsa_circ_0007919. ChIRP, RIP, Co-IP, ChIP, MS-PCR and luciferase reporter assays were conducted to confirm the interaction among hsa_circ_0007919, FOXA1, TET1 and the LIG1 promoter.

RESULTS

We identified a highly expressed circRNA, hsa_circ_0007919, in GEM-resistant PDAC tissues and cells. High expression of hsa_circ_0007919 correlates with poor overall survival (OS) and disease-free survival (DFS) of PDAC patients. Hsa_circ_0007919 inhibits the DNA damage, accumulation of DNA breaks and apoptosis induced by GEM in a LIG1-dependent manner to maintain cell survival. Mechanistically, hsa_circ_0007919 recruits FOXA1 and TET1 to decrease the methylation of the LIG1 promoter and increase its transcription, further promoting base excision repair, mismatch repair and nucleotide excision repair. At last, we found that GEM enhanced the binding of QKI to the introns of hsa_circ_0007919 pre-mRNA and the splicing and circularization of this pre-mRNA to generate hsa_circ_0007919.

CONCLUSIONS

Hsa_circ_0007919 promotes GEM resistance by enhancing DNA damage repair in a LIG1-dependent manner to maintain cell survival. Targeting hsa_circ_0007919 and DNA damage repair pathways could be a therapeutic strategy for PDAC.

Proteogenomics in Cancer: Then and Now

For years, the paths of sequencing technologies and mass spectrometry have occurred in isolation, with each developing its own unique culture and expertise. These two technologies are crucial for inspecting complementary aspects of the molecular phenotype across the central dogma. Integrative multiomics strives to bridge the analysis gap among different fields to complete more comprehensive mechanisms of life events and diseases. Proteogenomics is one integrated multiomics field. Here in this review, we mainly summarize and discuss three aspects: workflow of proteogenomics, proteogenomics applications in cancer research, and the SWOT (Strengths, Weaknesses, Opportunities, Threats) analysis of proteogenomics in cancer research. In conclusion, proteogenomics has a promising future as it clarifies the functional consequences of many unannotated genomic abnormalities or noncanonical variants and identifies driver genes and novel therapeutic targets across cancers, which would substantially accelerate the development of precision oncology.

Distinct functions of EHMT1 and EHMT2 in cancer chemotherapy and immunotherapy

EHTM1 (GLP) and EHMT2 (G9a) are closely related protein lysine methyltransferases often thought to function together as a heterodimer to methylate histone H3 and non-histone substrates in diverse cellular processes including transcriptional regulation, genome methylation, and DNA repair. Here we show that EHMT1/2 inhibitors cause ATM-mediated slowdown of replication fork progression, accumulation of single-stranded replication gaps, emergence of cytosolic DNA, and increased expression of STING. EHMT1/2 inhibition strongly potentiates the efficacy of alkylating chemotherapy and anti-PD-1 immunotherapy in mouse models of tripe negative breast cancer. The effects on DNA replication and alkylating agent sensitivity are largely caused by the loss of EHMT1-mediated methylation of LIG1, whereas the elevated STING expression and remarkable response to immunotherapy appear mainly elicited by the loss of EHMT2 activity. Depletion of UHRF1, a protein known to be associated with EHMT1/2 and LIG1, also induces STING expression, and depletion of either EHMT2 or UHRF1 leads to demethylation of specific CpG sites in the STING1 promoter, suggestive of a distinct EHMT2-UHRF1 axis that regulates DNA methylation and gene transcription. These results highlight distinct functions of the two EHMT paralogs and provide enlightening paradigms and corresponding molecular basis for combination therapies involving alkylating agents and immune checkpoint inhibitors.

Integrative multi-omics reveals two biologically distinct groups of pilocytic astrocytoma

Pilocytic astrocytoma (PA), the most common pediatric brain tumor, is driven by aberrant mitogen-activated protein kinase signaling most commonly caused by BRAF gene fusions or activating mutations. While 5-year overall survival rates exceed 95%, tumor recurrence or progression constitutes a major clinical challenge in incompletely resected tumors. Here, we used similarity network fusion (SNF) analysis in an integrative multi-omics approach employing RNA transcriptomic and mass spectrometry-based proteomic profiling to molecularly characterize PA tissue samples from 62 patients. Thereby, we uncovered that PAs segregated into two molecularly distinct groups, namely, Group 1 and Group 2, which were validated in three non-overlapping cohorts. Patients with Group 1 tumors were significantly younger and showed worse progression-free survival compared to patients with group 2 tumors. Ingenuity pathways analysis (IPA) and gene set enrichment analysis (GSEA) revealed that Group 1 tumors were enriched for immune response pathways, such as interferon signaling, while Group 2 tumors showed enrichment for action potential and neurotransmitter signaling pathways. Analysis of immune cell-related gene signatures showed an enrichment of infiltrating T Cells in Group 1 versus Group 2 tumors. Taken together, integrative multi-omics of PA identified biologically distinct and prognostically relevant tumor groups that may improve risk stratification of this single pathway driven tumor type.

Precision cancer medicine: Concepts, current practice, and future developments

Precision cancer medicine is a multidisciplinary team effort that requires involvement and commitment of many stakeholders including the society at large. Building on the success of significant advances in precision therapy for oncological patients over the last two decades, future developments will be significantly shaped by improvements in scalable molecular diagnostics in which increasingly complex multilayered datasets require transformation into clinically useful information guiding patient management at fast turnaround times. Adaptive profiling strategies involving tissue- and liquid-based testing that account for the immense plasticity of cancer during the patient's journey and also include early detection approaches are already finding their way into clinical routine and will become paramount. A second major driver is the development of smart clinical trials and trial concepts which, complemented by real-world evidence, rapidly broaden the spectrum of therapeutic options. Tight coordination with regulatory agencies and health technology assessment bodies is crucial in this context. Multicentric networks operating nationally and internationally are key in implementing precision oncology in clinical practice and support developing and improving the ecosystem and framework needed to turn invocation into benefits for patients. The review provides an overview of the diagnostic tools, innovative clinical studies, and collaborative efforts needed to realize precision cancer medicine.

Targeting EIF4A triggers an interferon response to synergize with chemotherapy and suppress triple-negative breast cancer

Protein synthesis is frequently dysregulated in cancer and selective inhibition of mRNA translation represents an attractive cancer therapy. Here, we show that therapeutically targeting the RNA helicase eIF4A by Zotatifin, the first-in-class eIF4A inhibitor, exerts pleiotropic effects on both tumor cells and the tumor immune microenvironment in a diverse cohort of syngeneic triple-negative breast cancer (TNBC) mouse models. Zotatifin not only suppresses tumor cell proliferation but also directly repolarizes macrophages towards an M1-like phenotype and inhibits neutrophil infiltration, which sensitizes tumors to immune checkpoint blockade. Mechanistic studies revealed that Zotatifin reprograms the tumor translational landscape, inhibits the translation of Sox4 and Fgfr1, and induces an interferon response uniformly across models. The induction of an interferon response is partially due to the inhibition of Sox4 translation by Zotatifin. A similar induction of interferon-stimulated genes was observed in breast cancer patient biopsies following Zotatifin treatment. Surprisingly, Zotatifin significantly synergizes with carboplatin to trigger DNA damage and an even heightened interferon response resulting in T cell-dependent tumor suppression. These studies identified a vulnerability of eIF4A in TNBC, potential pharmacodynamic biomarkers for Zotatifin, and provide a rationale for new combination regimens comprising Zotatifin and chemotherapy or immunotherapy as treatments for TNBC.

ELOVL6 is associated with immunosuppression in lung adenocarcinoma through bioinformatics analysis

The aim of this paper was to reveal the correlation between the expression of ELOVL fatty acid elongase 6 (ELOVL6) gene in lung adenocarcinoma (LUAD) and its clinical significance, immune cell infiltration level and prognosis. Expression profile data of ELOVL6 mRNA were collected from the cancer genome atlas database to analyze the differences in ELOVL6 mRNA expression in LUAD tissues and normal lung tissues, and to analyze the correlation between ELOVL6 and information on clinicopathological features. Based on TIMER database, TISDIB database and GEPIA2 database, the correlation between ELOVL6 expression and tumor immune cell infiltration in LUAD was analyzed. Gene ontology and Kyoto encyclopedia of genes and genomes enrichment analyses of ELOVL6-related co-expressed genes were performed to identify the involved signaling pathways and to construct their co-expressed gene protein interaction networks. Drugs affected by ELOVL6 expression were screened based on the Cell Miner database. These findings suggest that ELOVL6 plays an important role in the course of LUAD, and the expression level of this gene has a close relationship with clinicopathological characteristics and survival prognosis, and has the potential to become a prognostic marker and therapeutic target for LUAD.

X-ray cross-complementing family: the bridge linking DNA damage repair and cancer

Genomic instability is a common hallmark of human tumours. As a carrier of genetic information, DNA is constantly threatened by various damaging factors that, if not repaired in time, can affect the transmission of genetic information and lead to cellular carcinogenesis. In response to these threats, cells have evolved a range of DNA damage response mechanisms, including DNA damage repair, to maintain genomic stability. The X-ray repair cross-complementary gene family (XRCC) comprises an important class of DNA damage repair genes that encode proteins that play important roles in DNA single-strand breakage and DNA base damage repair. The dysfunction of the XRCC gene family is associated with the development of various tumours. In the context of tumours, mutations in XRCC and its aberrant expression, result in abnormal DNA damage repair, thus contributing to the malignant progression of tumour cells. In this review, we summarise the significant roles played by XRCC in diverse tumour types. In addition, we discuss the correlation between the XRCC family members and tumour therapeutic sensitivity.

Targeting NKG2DL with Bispecific NKG2D-CD16 and NKG2D-CD3 Fusion Proteins on Triple-Negative Breast Cancer

Triple-negative breast cancer (TNBC) is a particularly aggressive subtype of breast cancer with a poor response rate to conventional systemic treatment and high relapse rates. Members of the natural killer group 2D ligand (NKG2DL) family are expressed on cancer cells but are typically absent from healthy tissues; thus, they are promising tumor antigens for novel immunotherapeutic approaches. We developed bispecific fusion proteins (BFPs) consisting of the NKG2D receptor domain targeting multiple NKG2DLs, fused to either anti-CD3 (NKG2D-CD3) or anti-CD16 (NKG2D-CD16) Fab fragments. First, we characterized the expression of the NKG2DLs (MICA, MICB, ULBP1-4) on TNBC cell lines and observed the highest surface expression for MICA and ULBP2. Targeting TNBC cells with NKG2D-CD3/CD16 efficiently activated both NK and T cells, leading to their degranulation and cytokine release and lysis of TNBC cells. Furthermore, PBMCs from TNBC patients currently undergoing chemotherapy showed significantly higher NK and T cell activation and tumor cell lysis when stimulated with NKG2D-CD3/CD16. In conclusions, BFPs activate and direct the NK and T cells of healthy and TNBC patients against TNBC cells, leading to efficient eradication of tumor cells. Therefore, NKG2D-based NK and T cell engagers could be a valuable addition to the treatment options for TNBC patients.

Kinase Inhibitor Pulldown Assay Identifies a Chemotherapy Response Signature in Triple-negative Breast Cancer Based on Purine-binding Proteins

Triple-negative breast cancer (TNBC) constitutes 10%-15% of all breast tumors. The current standard of care is multiagent chemotherapy, which is effective in only a subset of patients. The original objective of this study was to deploy a mass spectrometry (MS)-based kinase inhibitor pulldown assay (KIPA) to identify kinases elevated in non-pCR (pathologic complete response) cases for therapeutic targeting. Frozen optimal cutting temperature compound-embedded core needle biopsies were obtained from 43 patients with TNBC before docetaxel- and carboplatin-based neoadjuvant chemotherapy. KIPA was applied to the native tumor lysates that were extracted from samples with high tumor content. Seven percent of all identified proteins were kinases, and none were significantly associated with lack of pCR. However, among a large population of "off-target" purine-binding proteins (PBP) identified, seven were enriched in pCR-associated samples (P < 0.01). In orthogonal mRNA-based TNBC datasets, this seven-gene "PBP signature" was associated with chemotherapy sensitivity and favorable clinical outcomes. Functional annotation demonstrated IFN gamma response, nuclear import of DNA repair proteins, and cell death associations. Comparisons with standard tandem mass tagged-based discovery proteomics performed on the same samples demonstrated that KIPA-nominated pCR biomarkers were unique to the platform. KIPA is a novel biomarker discovery tool with unexpected utility for the identification of PBPs related to cytotoxic drug response. The PBP signature has the potential to contribute to clinical trials designed to either escalate or de-escalate therapy based on pCR probability.

Significance

The identification of pretreatment predictive biomarkers for pCR in response to neoadjuvant chemotherapy would advance precision treatment for TNBC. To complement standard proteogenomic discovery profiling, a KIPA was deployed and unexpectedly identified a seven-member non-kinase PBP pCR-associated signature. Individual members served diverse pathways including IFN gamma response, nuclear import of DNA repair proteins, and cell death.

An Inflection Point in Cancer Protein Biomarkers: What Was and What's Next

Biomarkers remain the highest value proposition in cancer medicine today - especially protein biomarkers. Yet despite decades of evolving regulatory frameworks to facilitate the review of emerging technologies, biomarkers have been mostly about promise with very little to show for improvements in human health. Cancer is an emergent property of a complex system and deconvoluting the integrative and dynamic nature of the overall system through biomarkers is a daunting proposition. The last two decades have seen an explosion of multi-omics profiling and a range of advanced technologies for precision medicine, including the emergence of liquid biopsy, exciting advances in single cell analysis, artificial intelligence (machine and deep learning) for data analysis and many other advanced technologies that promise to transform biomarker discovery. Combining multiple omics modalities to acquire a more comprehensive landscape of the disease state, we are increasingly developing biomarkers to support therapy selection and patient monitoring. Furthering precision medicine, especially in oncology, necessitates moving away from the lens of reductionist thinking towards viewing and understanding that complex diseases are, in fact, complex adaptive systems. As such, we believe it is necessary to re-define biomarkers as representations of biological system states at different hierarchical levels of biological order. This definition could include traditional molecular, histologic, radiographic, or physiological characteristics, as well as emerging classes of digital markers and complex algorithms. To succeed in the future, we must move past purely observational individual studies and instead start building a mechanistic framework to enable integrative analysis of new studies within the context of prior studies. Identifying information in complex systems and applying theoretical constructs, such as information theory, to study cancer as a disease of dysregulated communication could prove to be "game changing" for the clinical outcome of cancer patients.

Variant biomarker discovery using mass spectrometry-based proteogenomics

Genomic diversity plays critical roles in risk of disease pathogenesis and diagnosis. While genomic variants-including single nucleotide variants, frameshift variants, and mis-splicing isoforms-are commonly detected at the DNA or RNA level, their translated variant protein or polypeptide products are ultimately the functional units of the associated disease. These products are often released in biofluids and could be leveraged for clinical diagnosis and patient stratification. Recent emergence of integrated analysis of genomics with mass spectrometry-based proteomics for biomarker discovery, also known as proteogenomics, have significantly advanced the understanding disease risk variants, precise medicine, and biomarker discovery. In this review, we discuss variant proteins in the context of cancers and neurodegenerative diseases, outline current and emerging proteogenomic approaches for biomarker discovery, and provide a comprehensive proteogenomic strategy for detection of putative biomarker candidates in human biospecimens. This strategy can be implemented for proteogenomic studies in any field of enquiry. Our review timely addresses the need of biomarkers for aging related diseases.

Prospects of POLD1 in Human Cancers: A Review

Cancer is the second leading cause of death globally, exceeded only by cardiovascular disease. Despite the introduction of several survival-prolonging treatment modalities, including targeted therapy and immunotherapy, the overall prognosis for the metastatic disease remains challenging. Therefore, the identification of new molecular biomarkers and therapeutic targets related to cancer diagnosis and prognosis is of paramount importance. DNA polymerase delta 1 (POLD1), a catalytic and proofreading subunit of the DNA polymerase δ complex, performs a crucial role in DNA replication and repair processes. Recently, germline and somatic mutations of the POLD1 gene have been acknowledged in several malignancies. Moreover, diversified POLD1 expression profiles have been reported in association with clinicopathological features in a variety of tumor types. With this review, we aim to summarize the current knowledge on the role of POLD1 in cancers. In addition, we discuss the future prospects and clinical applications of the assessment of POLD1 mutation and expression patterns in tumors.