A Technical Assessment of the Utility of Reverse Phase Protein Arrays for the Study of the Functional Proteome in Non-microdissected Human Breast Cancers

A protein expression atlas on tissue samples and cell lines from cancer patients provides insights into tumor heterogeneity and dependencies

The Cancer Genome Atlas (TCGA) and the Cancer Cell Line Encyclopedia (CCLE) are foundational resources in cancer research, providing extensive molecular and phenotypic data. However, large-scale proteomic data across various cancer types for these cohorts remain limited. Here, we expand upon our previous work to generate high-quality protein expression data for approximately 8,000 TCGA patient samples and around 900 CCLE cell line samples, covering 447 clinically relevant proteins, using reverse-phase protein arrays. These protein expression profiles offer profound insights into intertumor heterogeneity and cancer dependency and serve as sensitive functional readouts for somatic alterations. We develop a systematic protein-centered strategy for identifying synthetic lethality pairs and experimentally validate an interaction between protein kinase A subunit α and epidermal growth factor receptor. We also identify metastasis-related protein markers with clinical relevance. This dataset represents a valuable resource for advancing our understanding of cancer mechanisms, discovering protein biomarkers and developing innovative therapeutic strategies.

Comprehensive multi-omics analysis of breast cancer reveals distinct long-term prognostic subtypes

Breast cancer (BC) is a leading cause of cancer-related death worldwide. The diverse nature and heterogeneous biology of BC pose challenges for survival prediction, as patients with similar diagnoses often respond differently to treatment. Clinically relevant BC intrinsic subtypes have been established through gene expression profiling and are implemented in the clinic. While these intrinsic subtypes show a significant association with clinical outcomes, their long-term survival prediction beyond 5 years often deviates from expected clinical outcomes. This study aimed to identify naturally occurring long-term prognostic subgroups of BC based on an integrated multi-omics analysis. This study incorporates a clinical cohort of 335 untreated BC patients from the Oslo2 study with long-term follow-up (>12 years). Multi-Omics Factor Analysis (MOFA+) was employed to integrate transcriptomic, proteomic, and metabolomic data obtained from the tumor tissues. Our analysis revealed three prominent multi-omics clusters of BC patients with significantly different long-term prognoses (p = 0.005). The multi-omics clusters were validated in two independent large cohorts, METABRIC and TCGA. Importantly, a lack of prognostic association to long-term follow-up above 12 years in the previously established intrinsic subtypes was shown for these cohorts. Through a systems-biology approach, we identified varying enrichment levels of cell-cycle and immune-related pathways among the prognostic clusters. Integrated multi-omics analysis of BC revealed three distinct clusters with unique clinical and biological characteristics. Notably, these multi-omics clusters displayed robust associations with long-term survival, outperforming the established intrinsic subtypes.

Global proteomic identifies multiple cancer-related signaling pathways altered by a gut pathobiont associated with colorectal cancer

In this work, we investigated the oncogenic role of Streptococcus gallolyticus subsp. gallolyticus (SGG), a gut bacterium associated with colorectal cancer (CRC). We showed that SGG UCN34 accelerates colon tumor development in a chemically induced CRC murine model. Full proteome and phosphoproteome analysis of murine colons chronically colonized by SGG UCN34 revealed that 164 proteins and 725 phosphorylation sites were differentially regulated. Ingenuity Pathway Analysis (IPA) indicates a pro-tumoral shift specifically induced by SGG UCN34, as ~ 90% of proteins and phosphoproteins identified were associated with digestive cancer. Comprehensive analysis of the altered phosphoproteins using ROMA software revealed up-regulation of several cancer hallmark pathways such as MAPK, mTOR and integrin/ILK/actin, affecting epithelial and stromal colonic cells. Importantly, an independent analysis of protein arrays of human colon tumors colonized with SGG showed up-regulation of PI3K/Akt/mTOR and MAPK pathways, providing clinical relevance to our findings. To test SGG's capacity to induce pre-cancerous transformation of the murine colonic epithelium, we grew ex vivo organoids which revealed unusual structures with compact morphology. Taken together, our results demonstrate the oncogenic role of SGG UCN34 in a murine model of CRC associated with activation of multiple cancer-related signaling pathways.

Tumor-Derived Small Extracellular Vesicles Inhibit the Efficacy of CAR T Cells against Solid Tumors

Chimeric antigen receptor (CAR) T-cell therapy has shown remarkable success in the treatment of hematologic malignancies. Unfortunately, it has limited efficacy against solid tumors, even when the targeted antigens are well expressed. A better understanding of the underlying mechanisms of CAR T-cell therapy resistance in solid tumors is necessary to develop strategies to improve efficacy. Here we report that solid tumors release small extracellular vesicles (sEV) that carry both targeted tumor antigens and the immune checkpoint protein PD-L1. These sEVs acted as cell-free functional units to preferentially interact with cognate CAR T cells and efficiently inhibited their proliferation, migration, and function. In syngeneic mouse tumor models, blocking tumor sEV secretion not only boosted the infiltration and antitumor activity of CAR T cells but also improved endogenous antitumor immunity. These results suggest that solid tumors use sEVs as an active defense mechanism to resist CAR T cells and implicate tumor sEVs as a potential therapeutic target to optimize CAR T-cell therapy against solid tumors.

SIGNIFICANCE

Small extracellular vesicles secreted by solid tumors inhibit CAR T cells, which provide a molecular explanation for CAR T-cell resistance and suggests that strategies targeting exosome secretion may enhance CAR T-cell efficacy. See related commentary by Ortiz-Espinosa and Srivastava, p. 2637.

Comparative analysis of deeply phenotyped GBM cohorts of 'short-term' and 'long-term' survivors

BACKGROUND

Glioblastoma (GBM) is an aggressive brain cancer that typically results in death in the first 15 months after diagnosis. There have been limited advances in finding new treatments for GBM. In this study, we investigated molecular differences between patients with extremely short (≤ 9 months, Short term survivors, STS) and long survival (≥ 36 months, Long term survivors, LTS).

METHODS

Patients were selected from an in-house cohort (GLIOTRAIN-cohort), using defined inclusion criteria (Karnofsky score > 70; age < 70 years old; Stupp protocol as first line treatment, IDH wild type), and a multi-omic analysis of LTS and STS GBM samples was performed.

RESULTS

Transcriptomic analysis of tumour samples identified cilium gene signatures as enriched in LTS. Moreover, Immunohistochemical analysis confirmed the presence of cilia in the tumours of LTS. Notably, reverse phase protein array analysis (RPPA) demonstrated increased phosphorylated GAB1 (Y627), SRC (Y527), BCL2 (S70) and RAF (S338) protein expression in STS compared to LTS. Next, we identified 25 unique master regulators (MR) and 13 transcription factors (TFs) belonging to ontologies of integrin signalling and cell cycle to be upregulated in STS.

CONCLUSION

Overall, comparison of STS and LTS GBM patients, identifies novel biomarkers and potential actionable therapeutic targets for the management of GBM.

A reverse phase protein array based phospho-antibody characterization approach and its applicability for clinical derived tissue specimens

Systematic quantification of phosphoprotein within cell signaling networks in solid tissues remains challenging and precise quantification in large scale samples has great potential for biomarker identification and validation. We developed a reverse phase protein array (RPPA) based phosphor-antibody characterization approach by taking advantage of the lysis buffer compatible with alkaline phosphatase (AP) treatment that differs from the conventional RPPA antibody validation procedure and applied it onto fresh frozen (FF) and formalin-fixed and paraffin-embedded tissue (FFPE) to test its applicability. By screening 106 phospho-antibodies using RPPA, we demonstrated that AP treatment could serve as an independent factor to be adopted for rapid phospho-antibody selection. We also showed desirable reproducibility and specificity in clincical specimens indicating its potential for tissue-based phospho-protein profiling. Of further clinical significance, using the same approach, based on melanoma and lung cancer FFPE samples, we showed great interexperimental reproducibility and significant correlation with pathological markers in both tissues generating meaningful data that match clinical features. Our findings set a benchmark of an efficient workflow for phospho-antibody characterization that is compatible with high-plex clinical proteomics in precison oncology.

Tumor-intrinsic SIRPA promotes sensitivity to checkpoint inhibition immunotherapy in melanoma

Checkpoint inhibition immunotherapy has revolutionized cancer treatment, but many patients show resistance. Here we perform integrative transcriptomic and proteomic analyses on emerging immuno-oncology targets across multiple clinical cohorts of melanoma under anti-PD-1 treatment, on both bulk and single-cell levels. We reveal a surprising role of tumor-intrinsic SIRPA in enhancing antitumor immunity, in contrast to its well-established role as a major inhibitory immune modulator in macrophages. The loss of SIRPA expression is a marker of melanoma dedifferentiation, a key phenotype linked to immunotherapy efficacy. Inhibition of SIRPA in melanoma cells abrogates tumor killing by activated CD8+ T cells in a co-culture system. Mice bearing SIRPA-deficient melanoma tumors show no response to anti-PD-L1 treatment, whereas melanoma-specific SIRPA overexpression significantly enhances immunotherapy response. Mechanistically, SIRPA is regulated by its pseudogene, SIRPAP1. Our results suggest a complicated role of SIRPA in the tumor ecosystem, highlighting cell-type-dependent antagonistic effects of the same target on immunotherapy.

Mesowestern Blot: Simultaneous Analysis of Hundreds of Submicroliter Lysates

Western blotting is a widely used technique for molecular-weight-resolved analysis of proteins and their posttranslational modifications, but high-throughput implementations of the standard slab gel arrangement are scarce. The previously developed Microwestern requires a piezoelectric pipetting instrument, which is not available for many labs. Here, we report the Mesowestern blot, which uses a 3D-printable gel casting mold to enable high-throughput Western blotting without piezoelectric pipetting and is compatible with the standard sample preparation and small (∼1 μL) sample sizes. The main tradeoffs are reduced molecular weight resolution and higher sample-to-sample CV, making it suitable for qualitative screening applications. The casted polyacrylamide gel contains 336, ∼0.5 μL micropipette-loadable sample wells arranged within a standard microplate footprint. Polyacrylamide % can be altered to change molecular weight resolution profiles. Proof-of-concept experiments using both infrared-fluorescent molecular weight protein ladder and cell lysate (RIPA buffer) demonstrate that the protein loaded in Mesowestern gels is amenable to the standard Western blotting steps. The main difference between Mesowestern and traditional Western is that semidry horizontal instead of immersed vertical gel electrophoresis is used. The linear range of detection is at least 32-fold, and at least ∼500 attomols of β-actin can be detected (∼29 ng of total protein from mammalian cell lysates: ∼100-300 cells). Because the gel mold is 3D-printable, users with access to additive manufacturing cores have significant design freedom for custom layouts. We expect that the technique could be easily adopted by any typical cell and molecular biology laboratory already performing Western blots.

A Systems Biology Approach to Investigate Kinase Signal Transduction Networks That Are Involved in Triple Negative Breast Cancer Resistance to Cisplatin

Triple negative breast cancer (TNBC) remains a therapeutic challenge due to the lack of targetable genetic alterations and the frequent development of resistance to the standard cisplatin-based chemotherapies. Here, we have taken a systems biology approach to investigate kinase signal transduction networks that are involved in TNBC resistance to cisplatin. Treating a panel of cisplatin-sensitive and cisplatin-resistant TNBC cell lines with a panel of kinase inhibitors allowed us to reconstruct two kinase signalling networks that characterise sensitive and resistant cells. The analysis of these networks suggested that the activation of the PI3K/AKT signalling pathway is critical for cisplatin resistance. Experimental validation of the computational model predictions confirmed that TNBC cell lines with activated PI3K/AKT signalling are sensitive to combinations of cisplatin and PI3K/AKT pathway inhibitors. Thus, our results reveal a new therapeutic approach that is based on identifying targeted therapies that synergise with conventional chemotherapies.

Multi-modal molecular programs regulate melanoma cell state

Melanoma cells display distinct intrinsic phenotypic states. Here, we seek to characterize the molecular regulation of these states using multi-omic analyses of whole exome, transcriptome, microRNA, long non-coding RNA and DNA methylation data together with reverse-phase protein array data on a panel of 68 highly annotated early passage melanoma cell lines. We demonstrate that clearly defined cancer cell intrinsic transcriptomic programs are maintained in melanoma cells ex vivo and remain highly conserved within melanoma tumors, are associated with distinct immune features within tumors, and differentially correlate with checkpoint inhibitor and adoptive T cell therapy efficacy. Through integrative analyses we demonstrate highly complex multi-omic regulation of melanoma cell intrinsic programs that provide key insights into the molecular maintenance of phenotypic states. These findings have implications for cancer biology and the identification of new therapeutic strategies. Further, these deeply characterized cell lines will serve as an invaluable resource for future research in the field.

The Role of ARID1A in the Nonestrogenic Modulation of IGF-1 Signaling

Gaining pharmacologic access to the potential of ARID1A, a tumor suppressor protein, to mediate transcriptional control over cancer gene expression is an unresolved challenge. Retinoid X receptor ligands are pleiotropic, incompletely understood tools that regulate breast epithelial cell proliferation and differentiation. We found that low-dose bexarotene (Bex) combined with the nonselective beta-blocker carvedilol (Carv) reduces proliferation of MCF10DCIS.com cells and markedly suppresses ARID1A levels. Similarly, Carv synergized with Bex in MCF-7 cells to suppress cell growth. Chromatin immunoprecipitation sequencing analysis revealed that under nonestrogenic conditions Bex + Carv alters the concerted genomic distribution of the chromatin remodeler ARID1A and acetylated histone H3K27, at sites related to insulin-like growth factor (IGF) signaling. Several distinct sites of ARID1A enrichment were identified in the IGF-1 receptor and IRS1 genes, associated with a suppression of both proteins. The knock-down of ARID1A increased IGF-1R levels, prevented IGF-1R and IRS1 suppression upon Bex + Carv, and stimulated proliferation. In vitro IGF-1 receptor neutralizing antibody suppressed cell growth, while elevated IGF-1R or IRS1 expression was associated with poor survival of patients with ER-negative breast cancer. Our study demonstrates direct impact of ARID1A redistribution on the expression and growth regulation of IGF-1-related genes, induced by repurposed clinical drugs under nonestrogenic conditions.

IMPLICATIONS

This study underscores the possibility of the pharmacologic modulation of the ARID1A factor to downregulate protumorigenic IGF-1 activity in patients with postmenopausal breast cancer undergoing aromatase inhibitor treatment.

Dissecting the Roles of PDCD4 in Breast Cancer

The human programmed cell death 4 (PDCD4) gene was mapped at chromosome 10q24 and encodes the PDCD4 protein comprised of 469 amino acids. PDCD4 inhibits protein translation PDCD4 inhibits protein translation to suppress tumor progression, and its expression is frequently decreased in breast cancer. PDCD4 blocks translation initiation complex by binding eIF4A via MA-3 domains or by directly binding 5’ mRNA internal ribosome entry sites with an RNA binding domain to suppress breast cancer progression and proliferation. Numerous regulators and biological processes including non-coding RNAs, proteasomes, estrogen, natural compounds and inflammation control PDCD4 expression in breast cancer. Loss of PDCD4 expression is also responsible for drug resistance in breast cancer. HER2 activation downregulates PDCD4 expression by activating MAPK, AKT, and miR-21 in aromatase inhibitor-resistant breast cancer cells. Moreover, modulating the microRNA/PDCD4 axis maybe an effective strategy for overcoming chemoresistance in breast cancer. Down-regulation of PDCD4 is significantly associated with short overall survival of patients, which suggests that PDCD4 may be an independent prognostic marker for breast cancer.

Protein Profiling of Breast Cancer for Treatment Decision-Making

The increasing use of neoadjuvant therapy has resulted in therapeutic decisions being made on the basis of diagnostic needle core biopsy. For many patients, this method might yield the only fragment of tumor available for biomarker analysis, necessitating judicious use. Many multiplex protein analytic methods have been developed that employ fluorescence or other tags to overcome the limitations of immunohistochemistry while still retaining the spatial annotation. Interpretation of the data can be difficult because of the limitations of the human eye. Computational deconvolution of the signals may be necessary for some of these methods to enable identification of cell-specific localization and coexpression of biomarkers. Herein, we present the different methods that are coming of age and their application in cancer research, with a focus on breast cancer. We also discuss the limitations, which include high costs and long turnaround times. The methods are also based on the premise that preanalytical factors will have identical impact on all proteins analyzed. There is a need to establish standards to normalize the data and enable cross-sample comparisons. In spite of these limitations, the multiplex technologies are extremely valuable discovery tools and can provide novel insights into the biology of cancer and mechanisms of drug resistance.

BRCA mutations lead to XIAP overexpression and sensitise ovarian cancer to inhibitor of apoptosis (IAP) family inhibitors

Background

We tested the hypothesis that inhibitor of apoptosis family (IAP) proteins may be altered in BRCA1-mutated ovarian cancers and that could affect the sensitivity to IAP inhibitors.

Methods

The levels of IAP proteins were evaluated in human cancers and cell lines. Cell lines were used to determine the effects of IAP inhibitors. The in vivo effects of treatments were evaluated in PDX mouse models.

Results

Expression of X-linked inhibitor of apoptosis (XIAP) is increased in BRCA1-mutated cancers and high levels are associated with improved patient outcomes after platinum chemotherapy. XIAP overexpression is mediated by NF-kB activation and is associated with an optimisation of PARP. BRCA1-mutated cell lines are particularly sensitive to IAP inhibitors due to an inhibitory effect on PARP. Both a BRCA1-mutated cell line with acquired resistance to PARP inhibitors and one with restored BRCA1 remain sensitive to IAP inhibitors. Treatment with IAP inhibitors restores the efficacy of PARP inhibition in these cell lines. The IAP inhibitor LCL161 alone and in combination with a PARP inhibitor, exhibited antitumour effects in PDX mouse models of resistant BRCA2 and 1-mutated ovarian cancer, respectively.

Conclusion

A clinical trial may be justified to further investigate the utility of IAP inhibitors.

Analytical Considerations of Large-Scale Aptamer-Based Datasets for Translational Applications

The development and advancement of aptamer technology has opened a new realm of possibilities for unlocking the biocomplexity available within proteomics. With ultra-high-throughput and multiplexing, alongside remarkable specificity and sensitivity, aptamers could represent a powerful tool in disease-specific research, such as supporting the discovery and validation of clinically relevant biomarkers. One of the fundamental challenges underlying past and current proteomic technology has been the difficulty of translating proteomic datasets into standards of practice. Aptamers provide the capacity to generate single panels that span over 7000 different proteins from a singular sample. However, as a recent technology, they also present unique challenges, as the field of translational aptamer-based proteomics still lacks a standardizing methodology for analyzing these large datasets and the novel considerations that must be made in response to the differentiation amongst current proteomic platforms and aptamers. We address these analytical considerations with respect to surveying initial data, deploying proper statistical methodologies to identify differential protein expressions, and applying datasets to discover multimarker and pathway-level findings. Additionally, we present aptamer datasets within the multi-omics landscape by exploring the intersectionality of aptamer-based proteomics amongst genomics, transcriptomics, and metabolomics, alongside pre-existing proteomic platforms. Understanding the broader applications of aptamer datasets will substantially enhance current efforts to generate translatable findings for the clinic.

OBIF: an omics-based interaction framework to reveal molecular drivers of synergy

Bioactive molecule library screening may empirically identify effective combination therapies, but molecular mechanisms underlying favorable drug–drug interactions often remain unclear, precluding further rational design. In the absence of an accepted systems theory to interrogate synergistic responses, we introduce Omics-Based Interaction Framework (OBIF) to reveal molecular drivers of synergy through integration of statistical and biological interactions in synergistic biological responses. OBIF performs full factorial analysis of feature expression data from single versus dual exposures to identify molecular clusters that reveal synergy-mediating pathways, functions and regulators. As a practical demonstration, OBIF analyzed transcriptomic and proteomic data of a dyad of immunostimulatory molecules that induces synergistic protection against influenza A and revealed unanticipated NF-κB/AP-1 cooperation that is required for antiviral protection. To demonstrate generalizability, OBIF analyzed data from a diverse array of Omics platforms and experimental conditions, successfully identifying the molecular clusters driving their synergistic responses. Hence, unlike existing synergy quantification and prediction methods, OBIF is a phenotype-driven systems model that supports multiplatform interrogation of synergy mechanisms.

Loss of ARID1A activates mTOR signaling and SOX9 in gastric adenocarcinoma-rationale for targeting ARID1A deficiency

BACKGROUND

Gastric adenocarcinoma (GAC) is a lethal disease with limited therapeutic options. Genetic alterations in chromatin remodelling gene AT-rich interactive domain 1A (ARID1A) and mTOR pathway activation occur frequently in GAC. Targeting the mechanistic target of rapamycin (mTOR) pathway in unselected patients has failed to show survival benefit. A deeper understanding of GAC might identify a subset that can benefit from mTOR inhibition.

METHODS

Genomic alterations in ARID1A were analysed in GAC. Mouse gastric epithelial cells from CK19-Cre-Arid1A^fl/fl and wild-type mice were used to determine the activation of oncogenic genes due to loss of Arid1A. Functional studies were performed to determine the significance of loss of ARID1A and the sensitivity of ARID1A-deficient cancer cells to mTOR inhibition in GAC.

RESULTS

More than 30% of GAC cases had alterations (mutations or deletions) of ARID1A and ARID1A expression was negatively associated with phosphorylation of S6 and SOX9 in GAC tissues and patient-derived xenografts (PDXs). Activation of mTOR signalling (increased pS6) and SOX9 nuclear expression were strongly increased in Arid1A^-/- mouse gastric tissues which could be curtailed by RAD001, an mTOR inhibitor. Knockdown of ARID1A in GAC cell lines increased pS6 and nuclear SOX9 and increased sensitivity to an mTOR inhibitor which was further amplified by its combination with fluorouracil both in vitro and in vivo in PDXs.

CONCLUSIONS

The loss of ARID1A activates pS6 and SOX9 in GAC, which can be effectively targeted by an mTOR inhibitor. Therefore, our studies suggest a new therapeutic strategy of clinically targeting the mTOR pathway in patients with GAC with ARID1A deficiency.

Path to Clonal Theranostics in Luminal Breast Cancers

Integrating tumor heterogeneity in the drug discovery process is a key challenge to tackle breast cancer resistance. Identifying protein targets for functionally distinct tumor clones is particularly important to tailor therapy to the heterogeneous tumor subpopulations and achieve clonal theranostics. For this purpose, we performed an unsupervised, label-free, spatially resolved shotgun proteomics guided by MALDI mass spectrometry imaging (MSI) on 124 selected tumor clonal areas from early luminal breast cancers, tumor stroma, and breast cancer metastases. 2868 proteins were identified. The main protein classes found in the clonal proteome dataset were enzymes, cytoskeletal proteins, membrane-traffic, translational or scaffold proteins, or transporters. As a comparison, gene-specific transcriptional regulators, chromatin related proteins or transmembrane signal receptor were more abundant in the TCGA dataset. Moreover, 26 mutated proteins have been identified. Similarly, expanding the search to alternative proteins databases retrieved 126 alternative proteins in the clonal proteome dataset. Most of these alternative proteins were coded mainly from non-coding RNA. To fully understand the molecular information brought by our approach and its relevance to drug target discovery, the clonal proteomic dataset was further compared to the TCGA breast cancer database and two transcriptomic panels, BC360 (nanoString^®) and CDx (Foundation One^®). We retrieved 139 pathways in the clonal proteome dataset. Only 55% of these pathways were also present in the TCGA dataset, 68% in BC360 and 50% in CDx. Seven of these pathways have been suggested as candidate for drug targeting, 22 have been associated with breast cancer in experimental or clinical reports, the remaining 19 pathways have been understudied in breast cancer. Among the anticancer drugs, 35 drugs matched uniquely with the clonal proteome dataset, with only 7 of them already approved in breast cancer. The number of target and drug interactions with non-anticancer drugs (such as agents targeting the cardiovascular system, metabolism, the musculoskeletal or the nervous systems) was higher in the clonal proteome dataset (540 interactions) compared to TCGA (83 interactions), BC360 (419 interactions), or CDx (172 interactions). Many of the protein targets identified and drugs screened were clinically relevant to breast cancer and are in clinical trials. Thus, we described the non-redundant knowledge brought by this clone-tailored approach compared to TCGA or transcriptomic panels, the targetable proteins identified in the clonal proteome dataset, and the potential of this approach for drug discovery and repurposing through drug interactions with antineoplastic agents and non-anticancer drugs.

Influence of Estrogen Treatment on ESR1+ and ESR1- Cells in ER+ Breast Cancer: Insights from Single-Cell Analysis of Patient-Derived Xenograft Models

Simple Summary

The benefit of endocrine therapy is normally observed for cancers with 10% or more of cells positive for ER expression. We compared the gene expression profiles in both ESR1⁺ and ESR1^– cells in ER⁺ tumors following estrogen treatment. Our single-cell RNA sequencing analysis of estrogen-stimulated (SC31) and estrogen-suppressed (GS3) patient-derived xenograft models offered an unprecedented opportunity to address the molecular and functional differences between ESR1⁺ and ESR1^– cells. While estrogen should activate ERα and stimulate ESR1⁺ cells, our findings regarding ESR1^– cells were important, indicating that the proliferation of ESR1^– cells in ER⁺ cancer is also influenced by estrogen. Another valuable finding from our studies was that estrogen also upregulated a tumor-suppressor gene, IL-24, only in GS3. Estrogen increased the percentage of cells expressing IL-24, associated with the estrogen-dependent inhibition of GS3 tumor growth.

Abstract

A 100% ER positivity is not required for an endocrine therapy response. Furthermore, while estrogen typically promotes the progression of hormone-dependent breast cancer via the activation of estrogen receptor (ER)-α, estrogen-induced tumor suppression in ER⁺ breast cancer has been clinically observed. With the success in establishing estrogen-stimulated (SC31) and estrogen-suppressed (GS3) patient-derived xenograft (PDX) models, single-cell RNA sequencing analysis was performed to determine the impact of estrogen on ESR1⁺ and ESR1^– tumor cells. We found that 17β-estradiol (E2)-induced suppression of GS3 transpired through wild-type and unamplified ERα. E2 upregulated the expression of estrogen-dependent genes in both SC31 and GS3; however, E2 induced cell cycle advance in SC31, while it resulted in cell cycle arrest in GS3. Importantly, these gene expression changes occurred in both ESR1⁺ and ESR1^– cells within the same breast tumors, demonstrating for the first time a differential effect of estrogen on ESR1^– cells. E2 also upregulated a tumor-suppressor gene, IL-24, in GS3. The apoptosis gene set was upregulated and the G2M checkpoint gene set was downregulated in most IL-24⁺ cells after E2 treatment. In summary, estrogen affected pathologically defined ER⁺ tumors differently, influencing both ESR1⁺ and ESR1^– cells. Our results also suggest IL-24 to be a potential marker of estrogen-suppressed tumors.

RNA-binding protein FXR1 drives cMYC translation by recruiting eIF4F complex to the translation start site

Fragile X-related protein-1 (FXR1) gene is highly amplified in patients with ovarian cancer, and this amplification is associated with increased expression of both FXR1 mRNA and protein. FXR1 expression directly associates with the survival and proliferation of cancer cells. Surface sensing of translation (SUnSET) assay demonstrates that FXR1 enhances the overall translation in cancer cells. Reverse-phase protein array (RPPA) reveals that cMYC is the key target of FXR1. Mechanistically, FXR1 binds to the AU-rich elements (ARE) present within the 3' untranslated region (3'UTR) of cMYC and stabilizes its expression. In addition, the RGG domain in FXR1 interacts with eIF4A1 and eIF4E proteins. These two interactions of FXR1 result in the circularization of cMYC mRNA and facilitate the recruitment of eukaryotic translation initiation factors to the translation start site. In brief, we uncover a mechanism by which FXR1 promotes cMYC levels in cancer cells.

AMPK-regulated miRNA-210-3p is activated during ischaemic neuronal injury and modulates PI3K-p70S6K signalling

Progressive neuronal injury following ischaemic stroke is associated with glutamate-induced depolarization, energetic stress and activation of AMP-activated protein kinase (AMPK). We here identify a molecular signature associated with neuronal AMPK activation, as a critical regulator of cellular response to energetic stress following ischaemia. We report a robust induction of microRNA miR-210-3p both in vitro in primary cortical neurons in response to acute AMPK activation and following ischaemic stroke in vivo. Bioinformatics and reverse phase protein array analysis of neuronal protein expression changes in vivo following administration of a miR-210-3p mimic revealed altered expression of phosphatase and tensin homolog (PTEN), 3-phosphoinositide-dependent protein kinase 1 (PDK1), ribosomal protein S6 kinase (p70S6K) and ribosomal protein S6 (RPS6) signalling in response to increasing miR-210-3p. In vivo, we observed a corresponding reduction in p70S6K activity following ischaemic stroke. Utilizing models of glutamate receptor over-activation in primary neurons, we demonstrated that induction of miR-210-3p was accompanied by sustained suppression of p70S6K activity and that this effect was reversed by miR-210-3p inhibition. Collectively, these results provide new molecular insight into the regulation of cell signalling during ischaemic injury, and suggest a novel mechanism whereby AMPK regulates miR-210-3p to control p70S6K activity in ischaemic stroke and excitotoxic injury.

Clinical and Tumor Characteristics of Patients with High Serum Levels of Growth Differentiation Factor 15 in Advanced Pancreatic Cancer

Simple Summary

Growth differentiation factor 15 (GDF-15) is a stress responsive cytokine that mediates food intake, energy consumption, and body weight. We aimed to evaluate whether circulating GDF-15 level could be associated with cachexia symptoms, which include loss of skeletal muscle mass, systemic inflammatory reaction, poor performance status, anorexia, shortened survival time and biological tumor activity in advanced pancreatic cancer (APC). The cut-off for serum GDF-15 was 3356.6 pg/mL, as the mean plus two standard deviations in patients with benign pancreatic disease. APC patients with high serum GDF-15 showed worsened performance, anorexia and elevations of inflammatory and tumor burden, signatures of cachexia, and activation of Akt and JNK in tumor GDF-15-producing pathways. This study identified tumor-driven GDF-15 as a potential cause of cachexia symptoms in APC.

Abstract

We aimed to evaluate the association of circulating growth differentiation factor 15 (GDF-15) with cachexia symptoms and the biological activity of advanced pancreatic cancer (APC). Treatment-naïve patients with liver metastasis of APC or with benign pancreatic disease were retrospectively analyzed. Clinical data, blood samples, and biopsy specimens of liver metastasis were collected prior to anti-cancer treatment. Serum GDF-15 levels and multiple protein expressions in lysates extracted from liver metastasis were measured by enzyme-linked immuno-sorbent assay and reverse-phase protein array, respectively. The cut-off for serum GDF-15 was determined as 3356.6 pg/mL, the mean plus two standard deviations for benign pancreatic disease. The high-GDF-15 group was characterized as showing low Karnofsky performance status (KPS) (p = 0.037), poor Eastern Cooperative Oncology Group performance status (ECOG-PS) (p = 0.049), severe appetite loss (p = 0.011), and high serum levels of carbohydrate antigen 19-9 (p = 0.019) and C-reactive protein (p = 0.009). Tumors of the high-GDF-15 group expressed high levels of phosphorylated (p)JNK (p = 0.007) and pAkt (p = 0.040). APC patients with high serum GDF-15 showed signatures of cachexia and activation of the signaling pathways involving Akt and JNK in the tumor. This study indicated circulating GDF-15 could be associated with cachectic symptoms in APC.

Predicting Cancer Cell Line Dependencies From the Protein Expression Data of Reverse-Phase Protein Arrays

PURPOSE

Predicting cancer dependencies from molecular data can help stratify patients and identify novel therapeutic targets. Recently available data on large-scale cancer cell line dependency allow a systematic assessment of the predictive power of diverse molecular features; however, the protein expression data have not been rigorously evaluated. By using the protein expression data generated by reverse-phase protein arrays, we aimed to assess their predictive power in identifying cancer dependencies and to develop a related analytic tool for community use.

MATERIALS AND METHODS

By using a machine learning schema, we conducted an analysis of feature importance based on cancer dependency and multiomic data from the DepMap and Cancer Cell Line Encyclopedia projects. We assessed the consistency of cancer dependency data between CRISPR/Cas9 and short hairpin RNA–mediated perturbation platforms. For a fair comparison, we focused on a set of genes with robust dependency data and four available expression-related features (copy number alteration, DNA methylation, messenger RNA expression, and protein expression) and performed the same-gene predictions of the cancer dependency using different molecular features.

RESULTS

For the genes surveyed, we observed that the protein expression data contained substantial predictive power for cancer dependencies, and they were the best predictive feature for the CRISPR/Cas9-based dependency data. We also developed a user-friendly protein-dependency analytic module and integrated it with The Cancer Proteome Atlas; this module allows researchers to explore and analyze our results intuitively.

CONCLUSION

This study provides a systematic assessment for predicting cancer dependencies of cell lines from different expression-related features of a gene. Our results suggest that protein expression data are a highly valuable information resource for understanding tumor vulnerabilities and identifying therapeutic opportunities.

MEK inhibition overcomes resistance to EphA2-targeted therapy in uterine cancer

BACKGROUND

Our pilot clinical study of EphA2 inhibitor (dasatinib) plus paclitaxel and carboplatin showed interesting clinical activity in endometrial cancer with manageable toxicity. However, the underlying mechanisms of dasatinib resistance in uterine cancer are unknown. Here, we investigated potential mechanisms underlying resistance to EphA2 inhibitors in uterine cancer and examined the anti-tumor activity of EphA2 inhibitors alone and in combination with a MEK inhibitor.

METHODS

We evaluated the antitumor activity of EphA2 inhibitors plus a MEK inhibitor using in vitro and in vivo orthotopic models of uterine cancer.

RESULTS

EphA2 inhibitor induced MAPK in dasatinib-resistant uterine cancer cells (HEC-1A and Ishikawa) and BRAF/CRAF heterodimerization in HEC-1A cells. EphA2 inhibitor and trametinib significantly increased apoptosis in cancer cells resistant to EphA2 inhibitors compared with controls (p < 0.01). An in vivo study with the orthotopic HEC-1A model showed significantly greater antitumor response to combination treatment compared with dasatinib alone (p < 0.01). Combination treatment increased EphrinA1 and BIM along with decreased pMAPK, Jagged 1, and c-MYC expression in dasatinib-resistant cells. In addition, Spearman analysis using the TCGA data revealed that upregulation of EphA2 was significantly correlated with JAG1, MYC, NOTCH1, NOTCH3 and HES1 expression (p < 0.001, r = 0.25-0.43). Specifically, MAP3K15 and the NOTCH family genes were significantly related to poor clinical outcome in patients with uterine cancer.

CONCLUSIONS

These findings indicate that the MAPK pathway is activated in dasatinib-resistant uterine cancer cells and that EphrinA1-mediated MEK inhibition overcomes dasatinib resistance. Dual targeting of both EphA2 and MEK, combined with chemotherapy, should be considered for future clinical development.

Integrative Multi-Omics Approaches in Cancer Research: From Biological Networks to Clinical Subtypes

Multi-omics approaches are novel frameworks that integrate multiple omics datasets generated from the same patients to better understand the molecular and clinical features of cancers. A wide range of emerging omics and multi-view clustering algorithms now provide unprecedented opportunities to further classify cancers into subtypes, improve the survival prediction and therapeutic outcome of these subtypes, and understand key pathophysiological processes through different molecular layers. In this review, we overview the concept and rationale of multi-omics approaches in cancer research. We also introduce recent advances in the development of multi-omics algorithms and integration methods for multiple-layered datasets from cancer patients. Finally, we summarize the latest findings from large-scale multi-omics studies of various cancers and their implications for patient subtyping and drug development.

Fraction B From Catfish Epidermal Secretions Kills Pancreatic Cancer Cells, Inhibits CD44 Expression and Stemness, and Alters Cancer Cell Metabolism

Pancreatic ductal adenocarcinoma (PDAC) is the fourth leading cause of cancer related death in western countries. The successful treatment of PDAC remains limited. We investigated the effect of Fraction B, which is a fraction purified from catfish (Arius bilineatus, Val.) skin secretions containing proteins and lipids, on PDAC biology both in-vivo and in-vitro. We report here that Fraction B potently suppressed the proliferation of both human and mouse pancreatic cancer cells in vitro and significantly reduced the growth of their relevant xenograft (Panc02) and orthotopic tumors (human Panc-1 cells) (p < 0.05). The Reverse Phase Protein Array (RPPA) data obtained from the tumor tissues derived from orthotopic tumor bearing mice treated with Fraction B showed that Fraction B altered the cancer stem cells related pathways and regulated glucose and glutamine metabolism. The down-regulation of the cancer stem cell marker CD44 expression was further confirmed in Panc-1 cells. CBC and blood chemistry analyses showed no systemic toxicity in Fraction B treated Panc-1 tumor bearing mice compared to that of control group. Our data support that Fraction B is a potential candidate for PDAC treatment.

Pathological predictive factors for late recurrence of hepatocellular carcinoma in chronic liver disease

BACKGROUND & AIMS

Late recurrence of hepatocellular carcinoma (HCC) is regarded as de novo HCC from chronic hepatitis. This study investigated clinicopathological and molecular factors to develop a nomogram for predicting late HCC recurrence (>2 years after curative resection).

METHODS

The training and validation cohorts included HCC patients with a major aetiology of hepatitis B who underwent curative resection. Clinicopathological features including lobular and porto-periportal inflammatory activity, fibrosis and liver cell change were evaluated. Proteins encoded by genes related to late recurrence were identified using a reverse phase protein array of 95 non-tumourous liver tissues. Immunoexpression of phosphorylated signal transducer and activator of transcription 3 (pSTAT3), plasminogen activator inhibitor-1, phosphorylated extracellular signal-regulated kinase 1/2 (pERK1/2) and spleen tyrosine kinase (SYK) was measured.

RESULTS

Late recurrence occurred in 74/402 (18%) and 47/243 (19%) in the training and validation cohorts respectively. Cirrhosis, moderate/severe lobular inflammatory activity, and expression of pSTAT3, pERK1/2, and SYK proteins correlated to the gene signature of hepatocyte injury and regeneration were independently associated with late recurrence, with odds ratios (95% confidence intervals) of 2.0 (1.2-3.3), 21.1 (4.3-102.7) and 6.0 (2.1-17.7) respectively (P < .05 for all). A nomogram based on these variables (histological parameters and immunohistochemical marker combinations) showed high reliability in both the training and validation cohorts (Harrell's C index: 0.701 and 0.716; 95% confidence intervals: 0.64-0.76 and 0.64-0.79 respectively).

CONCLUSIONS

The combination of pSTAT3, pERK1/2 and SYK immunoexpression with high lobular inflammatory activity and cirrhosis (fibrosis) predicts late HCC recurrence.

RASAL2 Confers Collateral MEK/EGFR Dependency in Chemoresistant Triple-Negative Breast Cancer

PURPOSE

While chemotherapy remains the standard treatment for triple-negative breast cancer (TNBC), identifying and managing chemoresistant tumors has proven elusive. We sought to discover hallmarks and therapeutically actionable features of refractory TNBC through molecular analysis of primary chemoresistant TNBC specimens.

EXPERIMENTAL DESIGN

We performed transcriptional profiling of tumors from a phase II clinical trial of platinum chemotherapy for advanced TNBC (TBCRC-009), revealing a gene expression signature that identified de novo chemorefractory tumors. We then employed pharmacogenomic data mining, proteomic and other molecular studies to define the therapeutic vulnerabilities of these tumors.

RESULTS

We reveal the RAS-GTPase-activating protein (RAS-GAP) RASAL2 as an upregulated factor that mediates chemotherapy resistance but also an exquisite collateral sensitivity to combination MAP kinase kinase (MEK1/2) and EGFR inhibitors in TNBC. Mechanistically, RASAL2 GAP activity is required to confer kinase inhibitor sensitivity, as RASAL2-high TNBCs sustain basal RAS activity through suppression of negative feedback regulators SPRY1/2, together with EGFR upregulation. Consequently, RASAL2 expression results in failed feedback compensation upon co-inhibition of MEK1/2 and EGFR that induces synergistic apoptosis in vitro and in vivo. In patients with TNBC, high RASAL2 levels predict clinical chemotherapy response and long-term outcomes, and are associated via direct transcriptional regulation with activated oncogenic Yes-Associated Protein (YAP). Accordingly, chemorefractory patient-derived TNBC models exhibit YAP activation, high RASAL2 expression, and tumor regression in response to MEK/EGFR inhibitor combinations despite well-tolerated intermittent dosing.

CONCLUSIONS

These findings identify RASAL2 as a mediator of TNBC chemoresistance that rewires MAPK feedback and cross-talk to confer profound collateral sensitivity to combination MEK1/2 and EGFR inhibitors.

Th17 cells contribute to combination MEK inhibitor and anti-PD-L1 therapy resistance in KRAS/p53 mutant lung cancers

Understanding resistance mechanisms to targeted therapies and immune checkpoint blockade in mutant KRAS lung cancers is critical to developing novel combination therapies and improving patient survival. Here, we show that MEK inhibition enhanced PD-L1 expression while PD-L1 blockade upregulated MAPK signaling in mutant KRAS lung tumors. Combined MEK inhibition with anti-PD-L1 synergistically reduced lung tumor growth and metastasis, but tumors eventually developed resistance to sustained combinatorial therapy. Multi-platform profiling revealed that resistant lung tumors have increased infiltration of Th17 cells, which secrete IL-17 and IL-22 cytokines to promote lung cancer cell invasiveness and MEK inhibitor resistance. Antibody depletion of IL-17A in combination with MEK inhibition and PD-L1 blockade markedly reduced therapy-resistance in vivo. Clinically, increased expression of Th17-associated genes in patients treated with PD-1 blockade predicted poorer overall survival and response in melanoma and predicated poorer response to anti-PD1 in NSCLC patients. Here we show a triple combinatorial therapeutic strategy to overcome resistance to combined MEK inhibitor and PD-L1 blockade.

Targeting the PI3K and MAPK pathways to improve response to HER2-targeted therapies in HER2-positive gastric cancer

Background

Aberrant PI3K signalling is implicated in trastuzumab resistance in HER2-positive gastric cancer (GC). The role of PI3K or MEK inhibitors in sensitising HER2-positive GCs to trastuzumab or in overcoming trastuzumab resistance is unclear.

Methods

Using mass spectrometry-based genotyping we analysed 105 hotspot, non-synonymous somatic mutations in PIK3CA and ERBB-family (EGFR, ERBB2, ERBB3 and ERBB4) genes in gastric tumour samples from 69 patients. A panel of gastric cell lines (N87, OE19, ESO26, SNU16, KATOIII) were profiled for anti-proliferative response to the PI3K inhibitor copanlisib and the MEK1/2 inhibitor refametinib alone and in combination with anti-HER2 therapies.

Results

Patients with HER2-positive GC had significantly poorer overall survival compared to HER2-negative patients (15.9 months vs. 35.7 months). Mutations in PIK3CA were only identified in HER2-negative tumours, while ERBB-family mutations were identified in HER2-positive and HER2-negative tumours. Copanlisib had anti-proliferative effects in 4/5 cell lines, with IC50s ranging from 23.4 (N87) to 93.8 nM (SNU16). All HER2-positive cell lines except SNU16 were sensitive to lapatinib (IC50s 0.04 µM–1.5 µM). OE19 cells were resistant to trastuzumab. The combination of lapatinib and copanlisib was synergistic in ESO-26 and OE-19 cells (ED50: 0.83 ± 0.19 and 0.88 ± 0.13, respectively) and additive in NCI-N87 cells (ED50:1.01 ± 0.55). The combination of copanlisib and trastuzumab significantly improved growth inhibition compared to either therapy alone in NCI-N87, ESO26 and OE19 cells (p < 0.05).

Conclusions

PI3K or MEK inhibition alone or in combination with anti-HER2 therapy may represent an improved treatment strategy for some patients with HER2-positive GC, and warrants further investigation in a clinical trial setting.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12967-021-02842-1.

Dasatinib, paclitaxel, and carboplatin in women with advanced-stage or recurrent endometrial cancer: A pilot clinical and translational study

PURPOSE

To evaluate the effect of dasatinib therapy on EphA2 signaling in cancers of women with measurable (biopsy amenable) advanced-stage, chemo-naïve primary or recurrent endometrial cancer. Preliminary efficacy was also assessed.

PATIENTS AND METHODS

We performed a pilot study of single-agent dasatinib lead-in, followed by triplet dasatinib, paclitaxel, and carboplatin. We measured the downstream effectors of EphA2 signaling in pre- and post-dasatinib treatment biopsy tissue samples; we also determined the severity of adverse events and patients' progression-free survival and overall survival durations.

RESULTS

Eighteen patients were recruited and given dasatinib (150 mg orally daily for 14 days), followed by paclitaxel, carboplatin and dasatinib (daily) for six cycles (21-day cycles). Seventeen patients were evaluable for toxicity and 11 patients for response. A reverse phase protein array and proximity ligation assay revealed that CRAF/BRAF dimerization, caveolin-1 level, and Notch pathway signaling were predictive of response and resistance to dasatinib. Overall, the objective response rate was 45% (95% CI: 17%-77%), with median progression-free survival duration of 10.5 months and median overall survival duration of 30.4 months. The most common grade 3 or 4 adverse events were neutropenia (76%), thrombocytopenia (53%), anemia (53%), and fatigue (12%).

CONCLUSIONS

Caveolin-1 expression, in combination with CRAF/BRAF heterodimerization, is associated with resistance to EphA2 targeting by dasatinib. The triplet combination showed interesting clinical activity in endometrial cancer with acceptable toxicity. Pretreatment with dasatinib may accentuate combination therapy toxicity.

Identification of a Novel Protein-Based Signature to Improve Prognosis Prediction in Renal Clear Cell Carcinoma

Background

Clear cell renal cell carcinoma (ccRCC) is one of the most common types of malignant adult kidney cancer, and its incidence and mortality are not optimistic. It is well known that tumor-related protein markers play an important role in cancer detection, prognosis prediction, or treatment selection, such as carcinoembryonic antigen (CEA), programmed cell death 1 (PD-1), programmed cell death 1 ligand 1 (PD-L1), and cytotoxic T lymphocyte antigen 4 (CTLA-4), so a comprehensive analysis was performed in this study to explore the prognostic value of protein expression in patients with ccRCC.

Materials and Methods

Protein expression data were obtained from The Cancer Proteome Atlas (TCPA), and clinical information were downloaded from The Cancer Genome Atlas (TCGA). We selected 445 patients with complete information and then separated them into a training set and testing set. We performed univariate, least absolute shrinkage and selection operator (LASSO) Cox analyses to find prognosis-related proteins (PRPs) and constructed a protein signature. Then, we used stratified analysis to fully verify the prognostic significance of the prognostic-related protein signature score (PRPscore). Besides, we also explored the differences in immunotherapy response and immune cell infiltration level in high and low score groups. The consensus clustering analysis was also performed to identify potential cancer subgroups.

Results

From the training set, a total of 233 PRPs were selected, and a seven-protein signature was constructed, including ACC1, AR, MAPK, PDK1, PEA15, SYK, and BRAF. Based on the PRPscore, patients could be divided into two groups with significantly different overall survival rates. Univariate and multivariate Cox regression analyses proved that this signature was an independent prognostic factor for patients (P < 0.001). Moreover, the signature showed a high ability to distinguish prognostic outcomes among subgroups, and the low score group had a better prognosis (P < 0.001) and better immunotherapy response (P = 0.003) than the high score group.

Conclusion

We constructed a novel protein signature with robust predictive power and high clinical value. This will help to guide the disease management and individualized treatment of ccRCC patients.

Large-Scale Characterization of Drug Responses of Clinically Relevant Proteins in Cancer Cell Lines

Perturbation biology is a powerful approach to modeling quantitative cellular behaviors and understanding detailed disease mechanisms. However, large-scale protein response resources of cancer cell lines to perturbations are not available, resulting in a critical knowledge gap. Here we generated and compiled perturbed expression profiles of ∼210 clinically relevant proteins in >12,000 cancer cell line samples in response to ∼170 drug compounds using reverse-phase protein arrays. We show that integrating perturbed protein response signals provides mechanistic insights into drug resistance, increases the predictive power for drug sensitivity, and helps identify effective drug combinations. We build a systematic map of "protein-drug" connectivity and develop a user-friendly data portal for community use. Our study provides a rich resource to investigate the behaviors of cancer cells and the dependencies of treatment responses, thereby enabling a broad range of biomedical applications.

Peritoneal Spread of Ovarian Cancer Harbors Therapeutic Vulnerabilities Regulated by FOXM1 and EGFR/ERBB2 Signaling

Peritoneal spread is the primary mechanism of metastasis of ovarian cancer, and survival of ovarian cancer cells in the peritoneal cavity as nonadherent spheroids and their adherence to the mesothelium of distant organs lead to cancer progression, metastasis, and mortality. However, the mechanisms that govern this metastatic process in ovarian cancer cells remain poorly understood. In this study, we cultured ovarian cancer cell lines in adherent and nonadherent conditions in vitro and analyzed changes in mRNA and protein levels to identify mechanisms of tumor cell survival and proliferation in adherent and nonadherent cells. EGFR or ERBB2 upregulated ZEB1 in nonadherent cells, which caused resistance to cell death and increased tumor-initiating capacity. Conversely, Forkhead box M1 (FOXM1) was required for the induction of integrin β1, integrin-α V, and integrin-α 5 for adhesion of cancer cells. FOXM1 also upregulated ZEB1, which could act as a feedback inhibitor of FOXM1, and caused the transition of adherent cells to nonadherent cells. Strikingly, the combinatorial treatment with lapatinib [dual kinase inhibitor of EGFR (ERBB1) and ERBB2] and thiostrepton (FOXM1 inhibitor) reduced growth and peritoneal spread of ovarian cancer cells more effectively than either single-agent treatment in vivo. In conclusion, these results demonstrate that FOXM1 and EGFR/ERBB2 pathways are key points of vulnerability for therapy to disrupt peritoneal spread and adhesion of ovarian cancer cells. SIGNIFICANCE: This study describes the mechanism exhibited by ovarian cancer cells required for adherent cell transition to nonadherent form during peritoneal spread and metastasis. GRAPHICAL ABSTRACT: http://cancerres.aacrjournals.org/content/canres/80/24/5554/F1.large.jpg.

Development of a five-protein signature for predicting the prognosis of head and neck squamous cell carcinoma

Currently no reliable indicators are available for predicting the clinical outcome of head and neck squamous cell carcinoma (HNSCC). This study aimed to develop a protein-based model to improve the prognosis prediction of HNSCC. The proteome data of HNSCC cohort was downloaded from The Cancer Proteome Atlas (TCPA) portal. The TCPA HNSCC cohort was randomly divided into the discovery and validation cohort. A protein-based risk signature was developed with the discovery cohort, and then verified with the validation cohort. The prognostic value of HER3_pY1289 was further determined. We have constructed a five-protein risk signature which was strongly associated with the overall survival (OS) in the discovery cohort. Similar findings were observed in the validation cohort. The protein-based risk signature was identified as an independent prognostic factor for HNSCC. A nomogram model built on the protein-based risk signature exhibited good performance for predicting OS. Our immunohistochemistry (IHC) analysis showed that higher HER3_pY1289 staining intensity was closely associated with unfavorable prognosis of HNSCC. HER3 suppression inhibited the proliferation and invasion capacity of HNSCC cells. Collectively, we have developed a protein-based risk signature for accurately predicting the prognosis of HNSCC, which might provide valuable information for optimal individualized treatment regimens.

miRNA551b-3p Activates an Oncostatin Signaling Module for the Progression of Triple-Negative Breast Cancer

Genomic amplification of 3q26.2 locus leads to the increased expression of microRNA 551b-3p (miR551b-3p) in triple-negative breast cancer (TNBC). Our results demonstrate that miR551b-3p translocates to the nucleus with the aid of importin-8 (IPO8) and activates STAT3 transcription. As a consequence, miR551b upregulates the expression of oncostatin M receptor (OSMR) and interleukin-31 receptor-α (IL-31RA) as well as their ligands OSM and IL-31 through STAT3 transcription. We defined this set of genes induced by miR551b-3p as the "oncostatin signaling module," which provides oncogenic addictions in cancer cells. Notably, OSM is highly expressed in TNBC, and the elevated expression of OSM associates with poor outcome in estrogen-receptor-negative breast cancer patients. Conversely, targeting miR551b with anti-miR551b-3p reduced the expression of the OSM signaling module and reduced tumor growth, as well as migration and invasion of breast cancer cells.

Inhibition of DNA Repair Pathways and Induction of ROS Are Potential Mechanisms of Action of the Small Molecule Inhibitor BOLD-100 in Breast Cancer

BOLD-100, a ruthenium-based complex, sodium trans-[tetrachloridobis (1H-indazole) ruthenate (III)] (also known as IT-139, NKP1339 or KP1339), is a novel small molecule drug that demonstrated a manageable safety profile at the maximum tolerated dose and modest antitumor activity in a phase I clinical trial. BOLD-100 has been reported to inhibit the upregulation of the endoplasmic reticulum stress sensing protein GRP78. However, response to BOLD-100 varies in different cancer models and the precise mechanism of action in high-response versus low-response cancer cells remains unclear. In vitro studies have indicated that BOLD-100 induces cytostatic rather than cytotoxic effects as a monotherapy. To understand BOLD-100-mediated signaling mechanism in breast cancer cells, we used estrogen receptor positive (ER+) MCF7 breast cancer cells to obtain gene-metabolite integrated models. At 100 μM, BOLD-100 significantly reduced cell proliferation and expression of genes involved in the DNA repair pathway. BOLD-100 also induced reactive oxygen species (ROS) and phosphorylation of histone H2AX, gamma-H2AX (Ser139), suggesting disruption of proper DNA surveillance. In estrogen receptor negative (ER-) breast cancer cells, combination of BOLD-100 with a PARP inhibitor, olaparib, induced significant inhibition of cell growth and xenografts and increased gamma-H2AX. Thus, BOLD-100 is a novel DNA repair pathway targeting agent and can be used with other chemotherapies in ER- breast cancer.

Collagen promotes anti-PD-1/PD-L1 resistance in cancer through LAIR1-dependent CD8+ T cell exhaustion

Tumor extracellular matrix has been associated with drug resistance and immune suppression. Here, proteomic and RNA profiling reveal increased collagen levels in lung tumors resistant to PD-1/PD-L1 blockade. Additionally, elevated collagen correlates with decreased total CD8⁺ T cells and increased exhausted CD8⁺ T cell subpopulations in murine and human lung tumors. Collagen-induced T cell exhaustion occurs through the receptor LAIR1, which is upregulated following CD18 interaction with collagen, and induces T cell exhaustion through SHP-1. Reduction in tumor collagen deposition through LOXL2 suppression increases T cell infiltration, diminishes exhausted T cells, and abrogates resistance to anti-PD-L1. Abrogating LAIR1 immunosuppression through LAIR2 overexpression or SHP-1 inhibition sensitizes resistant lung tumors to anti-PD-1. Clinically, increased collagen, LAIR1, and TIM-3 expression in melanoma patients treated with PD-1 blockade predict poorer survival and response. Our study identifies collagen and LAIR1 as potential markers for immunotherapy resistance and validates multiple promising therapeutic combinations.

Targeting c-Met in triple negative breast cancer: preclinical studies using the c-Met inhibitor, Cpd A

Introduction Triple negative breast cancer (TNBC) represents a heterogeneous subtype of breast cancer that carries a poorer prognosis. There remains a need to identify novel drivers of TNBC, which may represent targets to treat the disease. c-Met overexpression is linked with decreased survival and is associated with the basal subtype of breast cancer. Cpd A, a kinase inhibitor selective/specific for Met kinase has demonstrated preclinical anti-cancer efficacy in TNBC. We aimed to assess the anti-cancer efficacy of Cpd A when combined with Src kinase, ErbB-family or hepatocyte growth factor (HGF) inhibitors in TNBC cell lines. Methods We determined the anti-proliferative effects of Cpd A, rilotumumab, neratinib and saracatinib tested alone and in combination in a panel of TNBC cells by acid phosphatase assays. We performed reverse phase protein array analysis of c-Met and IGF1Rβ expression and phosphorylation of c-Met (Y1234/1235) in TNBC cells and correlated their expression/phosphorylation with Cpd A sensitivity. We examined the impact of Cpd A, neratinib and saracatinib tested alone and in combination on invasive potential and colony formation.Results TNBC cells are not inherently sensitive to Cpd A, and neither c-Met expression nor phosphorylation are biomarkers of sensitivity to Cpd A. Cpd A enhanced the anti-proliferative effects of neratinib in vitro; however, this effect was limited to cell lines with innate sensitivity to Cpd A. Cpd A had limited anti-invasive effects but it reduced colony formation in the TNBC cell line panel.Conclusions Despite Cpd A having a potential role in reducing cancer cell metastasis, identification of strong predictive biomarkers of c-Met sensitivity would be essential to the development of a c-Met targeted treatment for an appropriately selected cohort of TNBC patients.

Identification and clinical impact of potentially actionable somatic oncogenic mutations in solid tumor samples

BACKGROUND

An increasing number of anti-cancer therapeutic agents target specific mutant proteins that are expressed by many different tumor types. Successful use of these therapies is dependent on the presence or absence of somatic mutations within the patient's tumor that can confer clinical efficacy or drug resistance.

METHODS

The aim of our study was to determine the type, frequency, overlap and functional proteomic effects of potentially targetable recurrent somatic hotspot mutations in 47 cancer-related genes in multiple disease sites that could be potential therapeutic targets using currently available agents or agents in clinical development.

RESULTS

Using MassArray technology, of the 1300 patient tumors analysed 571 (43.9%) had at least one somatic mutation. Mutations were identified in 30 different genes. KRAS (16.5%), PIK3CA (13.6%) and BRAF (3.8%) were the most frequently mutated genes. Prostate (10.8%) had the lowest number of somatic mutations identified, while no mutations were identified in sarcoma. Ocular melanoma (90.6%), endometrial (72.4%) and colorectal (66.4%) tumors had the highest number of mutations. We noted high concordance between mutations in different parts of the tumor (94%) and matched primary and metastatic samples (90%). KRAS and BRAF mutations were mutually exclusive. Mutation co-occurrence involved mainly PIK3CA and PTPN11, and PTPN11 and APC. Reverse Phase Protein Array (RPPA) analysis demonstrated that PI3K and MAPK signalling pathways were more altered in tumors with mutations compared to wild type tumors.

CONCLUSIONS

Hotspot mutational profiling is a sensitive, high-throughput approach for identifying mutations of clinical relevance to molecular based therapeutics for treatment of cancer, and could potentially be of use in identifying novel opportunities for genotype-driven clinical trials.

ZEB1 suppression sensitizes KRAS mutant cancers to MEK inhibition by an IL17RD-dependent mechanism

Mitogen-activated protein kinase (MAPK) kinase (MEK) inhibitors have failed to show clinical benefit in Kirsten rat sarcoma (KRAS) mutant lung cancer due to various resistance mechanisms. To identify differential therapeutic sensitivities between epithelial and mesenchymal lung tumors, we performed in vivo small hairpin RNA screens, proteomic profiling, and analysis of patient tumor datasets, which revealed an inverse correlation between mitogen-activated protein kinase (MAPK) signaling dependency and a zinc finger E-box binding homeobox 1 (ZEB1)-regulated epithelial-to-mesenchymal transition. Mechanistic studies determined that MAPK signaling dependency in epithelial lung cancer cells is due to the scaffold protein interleukin-17 receptor D (IL17RD), which is directly repressed by ZEB1. Lung tumors in multiple Kras mutant murine models with increased ZEB1 displayed low IL17RD expression, accompanied by MAPK-independent tumor growth and therapeutic resistance to MEK inhibition. Suppression of ZEB1 function with miR-200 expression or the histone deacetylase inhibitor mocetinostat sensitized resistant cancer cells to MEK inhibition and markedly reduced in vivo tumor growth, showing a promising combinatorial treatment strategy for KRAS mutant cancers. In human lung tumor samples, high ZEB1 and low IL17RD expression correlated with low MAPK signaling, presenting potential markers that predict patient response to MEK inhibitors.

Comprehensive Review of Web Servers and Bioinformatics Tools for Cancer Prognosis Analysis

Prognostic biomarkers are of great significance to predict the outcome of patients with cancer, to guide the clinical treatments, to elucidate tumorigenesis mechanisms, and offer the opportunity of identifying therapeutic targets. To screen and develop prognostic biomarkers, high throughput profiling methods including gene microarray and next-generation sequencing have been widely applied and shown great success. However, due to the lack of independent validation, only very few prognostic biomarkers have been applied for clinical practice. In order to cross-validate the reliability of potential prognostic biomarkers, some groups have collected the omics datasets (i.e., epigenetics/transcriptome/proteome) with relative follow-up data (such as OS/DSS/PFS) of clinical samples from different cohorts, and developed the easy-to-use online bioinformatics tools and web servers to assist the biomarker screening and validation. These tools and web servers provide great convenience for the development of prognostic biomarkers, for the study of molecular mechanisms of tumorigenesis and progression, and even for the discovery of important therapeutic targets. Aim to help researchers to get a quick learning and understand the function of these tools, the current review delves into the introduction of the usage, characteristics and algorithms of tools, and web servers, such as LOGpc, KM plotter, GEPIA, TCPA, OncoLnc, PrognoScan, MethSurv, SurvExpress, UALCAN, etc., and further help researchers to select more suitable tools for their own research. In addition, all the tools introduced in this review can be reached at http://bioinfo.henu.edu.cn/WebServiceList.html.

Combined targeting EGFR and SRC as a potential novel therapeutic approach for the treatment of triple negative breast cancer

Background:

Triple negative breast cancer (TNBC) is an aggressive subtype of breast cancer with limited therapeutic options. Epidermal growth factor receptor (EGFR) has been shown to be over-expressed in TNBC and represents a rational treatment target.

Methods:

We examined single agent and combination effects for afatinib and dasatinib in TNBC. We then determined IC₅₀ and combination index values using Calcusyn. Functional analysis of single and combination treatments was performed using reverse phase protein array and cell cycle analysis. Finally, we determined the anticancer effects of the combination in vivo.

Results:

A total of 14 TNBC cell lines responded to afatinib with IC₅₀ values ranging from 0.008 to 5.0 µM. Three cell lines, belonging to the basal-like subtype of TNBC, were sensitive to afatinib. The addition of afatinib enhanced response to the five other targeted therapies in HCC1937 and HDQP1 cells. The combination of afatinib with dasatinib caused the greatest growth inhibition in both cell lines. The afatinib/dasatinib combination was synergistic and/or additive in 13/14 TNBC cell lines. Combined afatinib/dasatinib treatment induced G1 cell cycle arrest. Reverse phase protein array results showed the afatinib/dasatinib combination resulted in efficient inhibition of both pERK(T202/T204) and pAkt(S473) signalling in BT20 cells, which was associated with the greatest antiproliferative effects. High baseline levels of pSrc(Y416) and pMAPK(p38) correlated with sensitivity to afatinib, whereas low levels of B-cell lymphoma 2 (Bcl2) and mammalian target of rapamycin (mTOR) correlated with synergistic growth inhibition by combined afatinib and dasatinib treatment. In vivo, the combination treatment inhibited tumour growth in a HCC1806 xenograft model.

Conclusions:

We demonstrate that afatinib combined with dasatinib has potential clinical activity in TNBC but warrants further preclinical investigation.

Molecular determinants of drug response in TNBC cell lines

PURPOSE

There is a need for biomarkers of drug efficacy for targeted therapies in triple-negative breast cancer (TNBC). As a step toward this, we identify multi-omic molecular determinants of anti-TNBC efficacy in cell lines for a panel of oncology drugs.

METHODS

Using 23 TNBC cell lines, drug sensitivity scores (DSS3) were determined using a panel of investigational drugs and drugs approved for other indications. Molecular readouts were generated for each cell line using RNA sequencing, RNA targeted panels, DNA sequencing, and functional proteomics. DSS3 values were correlated with molecular readouts using a FDR-corrected significance cutoff of p* < 0.05 and yielded molecular determinant panels that predict anti-TNBC efficacy.

RESULTS

Six molecular determinant panels were obtained from 12 drugs we prioritized based on their efficacy. Determinant panels were largely devoid of DNA mutations of the targeted pathway. Molecular determinants were obtained by correlating DSS3 with molecular readouts. We found that co-inhibiting molecular correlate pathways leads to robust synergy across many cell lines.

CONCLUSIONS

These findings demonstrate an integrated method to identify biomarkers of drug efficacy in TNBC where DNA predictions correlate poorly with drug response. Our work outlines a framework for the identification of novel molecular determinants and optimal companion drugs for combination therapy based on these correlates.

An ultrasensitive fiveplex activity assay for cellular kinases

Protein kinases are enzymes whose abundance, protein-protein interactions, and posttranslational modifications together determine net signaling activity in cells. Large-scale data on cellular kinase activity are limited, because existing assays are cumbersome, poorly sensitive, low throughput, and restricted to measuring one kinase at a time. Here, we surmount the conventional hurdles of activity measurement with a multiplexing approach that leverages the selectivity of individual kinase-substrate pairs. We demonstrate proof of concept by designing an assay that jointly measures activity of five pleiotropic signaling kinases: Akt, IκB kinase (IKK), c-jun N-terminal kinase (JNK), mitogen-activated protein kinase (MAPK)-extracellular regulated kinase kinase (MEK), and MAPK-activated protein kinase-2 (MK2). The assay operates in a 96-well format and specifically measures endogenous kinase activation with coefficients of variation less than 20%. Multiplex tracking of kinase-substrate pairs reduces input requirements by 25-fold, with ~75 µg of cellular extract sufficient for fiveplex activity profiling. We applied the assay to monitor kinase signaling during coxsackievirus B3 infection of two different host-cell types and identified multiple differences in pathway dynamics and coordination that warrant future study. Because the Akt–IKK–JNK–MEK–MK2 pathways regulate many important cellular functions, the fiveplex assay should find applications in inflammation, environmental-stress, and cancer research.

TCPA v3.0: An Integrative Platform to Explore the Pan-Cancer Analysis of Functional Proteomic Data

Reverse-phase protein arrays represent a powerful functional proteomics approach to characterizing cell signaling pathways and understanding their effects on cancer development. Using this platform, we have characterized ∼8,000 patient samples of 32 cancer types through The Cancer Genome Atlas and built a widely used, open-access bioinformatic resource, The Cancer Proteome Atlas (TCPA). To maximize the utility of TCPA, we have developed a new module called "TCGA Pan-Cancer Analysis," which provides comprehensive protein-centric analyses that integrate protein expression data and other TCGA data across cancer types. We further demonstrate the value of this module by examining the correlations of RPPA proteins with significantly mutated genes, assessing the predictive power of somatic copy-number alterations, DNA methylation, and mRNA on protein expression, inferring the regulatory effects of miRNAs on protein expression, constructing a co-expression network of proteins and pathways, and identifying clinically relevant protein markers. This upgraded TCPA (v3.0) will provide the cancer research community with a more powerful tool for studying functional proteomics and making translational impacts.

Adaptive responses in a PARP inhibitor window of opportunity trial illustrate limited functional interlesional heterogeneity and potential combination therapy options

Poly (ADP-ribose) polymerase inhibitor (PARPi)-based combination therapies are demonstrating efficacy in patients, however, identifying the right combination for the right patient remains a critical challenge. Thus, it is urgent to develop approaches able to identify patients likely to benefit from specific combination therapies. Several groups, including ours, have demonstrated that targeting adaptive responses induced by PARPi increases depth and duration of response. In this study, we instituted a talazoparib (PARPi) monotherapy window of opportunity trial to identify informative adaptive responses in high grade serous ovarian cancer patients (HGSOC). Patients were treated for 7 to 14 days with PARPi monotherapy prior to surgery with tissue samples from multiple sites being collected pre- and post-treatment in each patient. Analysis of these samples demonstrated that individual patients displayed different adaptive responses with limited interlesional heterogeneity. Ability of combination therapies designed to interdict adaptive responses to decrease viability was validated using model systems. Thus, assessment of adaptive responses to PARPi provides an opportunity for patient-specific selection of combination therapies designed to interdict patient-specific adaptive responses to maximize patient benefit.

Mistletoe extract Fraxini inhibits the proliferation of liver cancer by down-regulating c-Myc expression

Mistletoe (Viscum album) is a type of parasitic plant reported to have anticancer activity including in hepatocellular carcinoma (HCC). However, the mechanism of mistletoe’s anticancer activity, and its effectiveness in treating HCC are not fully understood. We report here that mistletoe extracts, including Fraxini (grown on ash trees) and Iscador Q and M (grown on oak and maple trees), exert strong antiproliferative activity in Hep3B cells, with median inhibitory concentrations (IC₅₀) of 0.5 µg/mL, 7.49 µg/mL, and 7.51 µg/mL, respectively. Results of Reversed Phase Proteomic Array analysis (RPPA) suggests that Fraxini substantially down-regulates c-Myc expression in Hep3B cells. Fraxini-induced growth inhibition (at a concentration of 1.25 μg/ml) was less pronounced in c-Myc knockdown Hep3B cells than in control cells. Furthermore, in the Hep3B xenograft model, Fraxini-treated (8 mg/kg body weight) mice had significantly smaller tumors (34.6 ± 11.9 mm³) than control mice (161.6 ± 79.4 mm³, p < 0.036). Similarly, c-Myc protein expression was reduced in Fraxini treated Hep3B cell xenografts compared to that of control mice. The reduction of c-Myc protein levels in vitro Hep3B cells appears to be mediated by the ubiquitin-proteasome system. Our results suggest the importance of c-Myc in Fraxini’s antiproliferative activity, which warrants further investigation.

Histopathological markers of treatment response and recurrence risk in ovarian cancers and borderline tumors

Histopathology plays an important role in defining response to treatment for different tumor types. Histopathologic response criteria are currently used as reference standard in various types of cancer, including breast cancer, gastroesophageal cancer, and bone tumors. Since there were no generally accepted response criteria established for ovarian cancer, a systematic analysis of various features of tumor regression was performed. Patient survival served as the reference standard to validate the histopathologic features of tumor regression. In contrast to ovarian cancer, borderline ovarian tumors are epithelial ovarian neoplasms characterized by up-regulated cellular proliferation and cytologic atypia but without destructive stromal invasion. While borderline ovarian tumors generally have an excellent prognosis with a 5‑year survival of > 95%, recurrences and malignant transformation occur in a small percentage of patients. Nevertheless, the identification of patients at increased risk for recurrence remains difficult. The aim of studying histopathological markers in ovarian cancers and borderline tumors was to evaluate whether histopathologic features including molecular pathologic alterations can predict patient outcome, particularly the risk of recurrence of serous and mucinous borderline tumors.

Spectral Clustering via sparse graph structure learning with application to Proteomic Signaling Networks in Cancer

Clustering methods for multivariate data exploiting the underlying geometry of the graphical structure between variables are presented. As opposed to standard approaches for graph clustering that assume known graph structures, the edge structure of the unknown graph is first estimated using sparse regression based approaches for sparse graph structure learning. Subsequently, graph clustering on the lower dimensional projections of the graph is performed based on Laplacian embeddings using a penalized k-means approach, motivated by Dirichlet process mixture models in Bayesian nonparametrics. In contrast to standard algorithmic approaches for known graphs, the proposed method allows estimation and inference for both graph structure learning and clustering. More importantly, the arguments for Laplacian embeddings as suitable projections for graph clustering are formalized by providing theoretical support for the consistency of the eigenspace of the estimated graph Laplacians. Fast computational algorithms are proposed to scale the method to large number of nodes. Extensive simulations are presented to compare the clustering performance with standard methods. The methods are applied to a novel pan-cancer proteomic data set, and protein networks and clusters are evaluated across multiple different cancer types.