Enhancing Chemotherapy Response Prediction via Matched Colorectal Tumor-Organoid Gene Expression Analysis and Network-Based Biomarker Selection.

Colorectal cancer (CRC) poses significant challenges in chemotherapy response prediction due to its molecular heterogeneity. This study introduces an innovative methodology that leverages gene expression data generated from matched colorectal tumor and organoid samples to enhance prediction accuracy. By applying Consensus Weighted Gene Co-expression Network Analysis (WGCNA) across multiple datasets, we identify critical gene modules and hub genes that correlate with patient responses, particularly to 5-fluorouracil (5-FU). This integrative approach advances precision medicine by refining chemotherapy regimen selection based on individual tumor profiles. Our predictive model demonstrates superior accuracy over traditional methods on independent datasets, illustrating significant potential in addressing the complexities of high-dimensional genomic data for cancer biomarker research.

Fibroblast growth factor receptor-4 mediates activation of Nuclear Factor Erythroid 2-Related Factor-2 in gastric tumorigenesis.

Helicobacter pylori (H. pylori) is the leading risk factor for gastric carcinogenesis. Fibroblast growth factor receptor 4 (FGFR4) is a member of transmembrane tyrosine kinase receptors that are activated in cancer. We investigated the role of FGFR4 in regulating the cellular response to H. pylori infection in gastric cancer. High levels of oxidative stress signature and FGFR4 expression were detected in gastric cancer samples. Gene set enrichment analysis (GSEA) demonstrated enrichment of NRF2 signature in samples with high FGFR4 levels. H. pylori infection induced reactive oxygen species (ROS) with a cellular response manifested by an increase in FGFR4 with accumulation and nuclear localization NRF2. Knocking down FGFR4 significantly reduced NRF2 protein and transcription activity levels, leading to higher levels of ROS and DNA damage following H. pylori infection. We confirmed the induction of FGFR4 and NRF2 levels using mouse models following infection with a mouse-adapted H. pyloristrain. Pharmacologic inhibition of FGFR4 using H3B-6527, or its knockdown, remarkably reduced the level of NRF2 with a reduction in the size and number of gastric cancer spheroids. Mechanistically, we detected binding between FGFR4 and P62 proteins, competing with NRF2-KEAP1 interaction, allowing NRF2 to escape KEAP1-dependent degradation with subsequent accumulation and translocation to the nucleus. These findings demonstrate a novel functional role of FGFR4 in cellular homeostasis via regulating the NRF2 levels in response to H. pylori infection in gastric carcinogenesis, calling for testing the therapeutic efficacy of FGFR4 inhibitors in gastric cancer models.

A workflow to study mechanistic indicators for driver gene prediction with Moonlight.

Prediction of driver genes (tumor suppressors and oncogenes) is an essential step in understanding cancer development and discovering potential novel treatments. We recently proposed Moonlight as a bioinformatics framework to predict driver genes and analyze them in a system-biology-oriented manner based on -omics integration. Moonlight uses gene expression as a primary data source and combines it with patterns related to cancer hallmarks and regulatory networks to identify oncogenic mediators. Once the oncogenic mediators are identified, it is important to include extra levels of evidence, called mechanistic indicators, to identify driver genes and to link the observed gene expression changes to the underlying alteration that promotes them. Such a mechanistic indicator could be for example a mutation in the regulatory regions for the candidate gene. Here, we developed new functionalities and released Moonlight2 to provide the user with a mutation-based mechanistic indicator as a second layer of evidence. These functionalities analyze mutations in a cancer cohort to classify them into driver and passenger mutations. Those oncogenic mediators with at least one driver mutation are retained as the final set of driver genes. We applied Moonlight2 to the basal-like breast cancer subtype, lung adenocarcinoma and thyroid carcinoma using data from The Cancer Genome Atlas. For example, in basal-like breast cancer, we found four oncogenes (COPZ2, SF3B4, KRTCAP2 and POLR2J) and nine tumor suppressor genes (KIR2DL4, KIF26B, ARL15, ARHGAP25, EMCN, GMFG, TPK1, NR5A2 and TEK) containing a driver mutation in their promoter region, possibly explaining their deregulation. Moonlight2R is available at https://github.com/ELELAB/Moonlight2R.

Systemically Identifying Triple-Negative Breast Cancer Subtype-Specific Prognosis Signatures, Based on Single-Cell RNA-Seq Data.

Triple-negative breast cancer (TNBC) is a highly heterogeneous disease with different molecular subtypes. Although progress has been made, the identification of TNBC subtype-associated biomarkers is still hindered by traditional RNA-seq or array technologies, since bulk data detected by them usually have some non-disease tissue samples, or they are confined to measure the averaged properties of whole tissues. To overcome these constraints and discover TNBC subtype-specific prognosis signatures (TSPSigs), we proposed a single-cell RNA-seq-based bioinformatics approach for identifying TSPSigs. Notably, the TSPSigs we developed mostly were found to be disease-related and involved in cancer development through investigating their enrichment analysis results. In addition, the prognostic power of TSPSigs was successfully confirmed in four independent validation datasets. The multivariate analysis results showed that TSPSigs in two TNBC subtypes-BL1 and LAR, were two independent prognostic factors. Further, analysis results of the TNBC cell lines revealed that the TSPSigs expressions and drug sensitivities had significant associations. Based on the preceding data, we concluded that TSPSigs could be exploited as novel candidate prognostic markers for TNBC patients and applied to individualized treatment in the future.

A Claudin-Based Molecular Signature Identifies High-Risk, Chemoresistant Colorectal Cancer Patients.

Identifying molecular characteristics that are associated with aggressive cancer phenotypes through gene expression profiling can help predict treatment responses and clinical outcomes. Claudins are deregulated in colorectal cancer (CRC). In CRC, increased claudin-1 expression results in epithelial-to-mesenchymal transition and metastasis, while claudin-7 functions as a tumor suppressor. In this study, we have developed a molecular signature based on claudin-1 and claudin-7 associated with poor patient survival and chemoresistance. This signature was validated using an integrated approach including publicly available datasets and CRC samples from patients who either responded or did not respond to standard-of-care treatment, CRC cell lines, and patient-derived rectal and colon tumoroids. Transcriptomic analysis from a patient dataset initially yielded 23 genes that were differentially expressed along with higher claudin-1 and decreased claudin-7. From this analysis, we selected a claudins-associated molecular signature including PIK3CA, SLC6A6, TMEM43, and ASAP-1 based on their importance in CRC. The upregulation of these genes and their protein products was validated using multiple CRC patient datasets, in vitro chemoresistant cell lines, and patient-derived tumoroid models. Additionally, blocking these genes improved 5-FU sensitivity in chemoresistant CRC cells. Our findings propose a new claudin-based molecular signature that associates with poor prognosis as well as characteristics of treatment-resistant CRC including chemoresistance, metastasis, and relapse.

Proteogenomic Landscape of Breast Cancer Tumorigenesis and Targeted Therapy.

The integration of mass spectrometry-based proteomics with next-generation DNA and RNA sequencing profiles tumors more comprehensively. Here this "proteogenomics" approach was applied to 122 treatment-naive primary breast cancers accrued to preserve post-translational modifications, including protein phosphorylation and acetylation. Proteogenomics challenged standard breast cancer diagnoses, provided detailed analysis of the ERBB2 amplicon, defined tumor subsets that could benefit from immune checkpoint therapy, and allowed more accurate assessment of Rb status for prediction of CDK4/6 inhibitor responsiveness. Phosphoproteomics profiles uncovered novel associations between tumor suppressor loss and targetable kinases. Acetylproteome analysis highlighted acetylation on key nuclear proteins involved in the DNA damage response and revealed cross-talk between cytoplasmic and mitochondrial acetylation and metabolism. Our results underscore the potential of proteogenomics for clinical investigation of breast cancer through more accurate annotation of targetable pathways and biological features of this remarkably heterogeneous malignancy.

Proteogenomic Characterization of Endometrial Carcinoma.

We undertook a comprehensive proteogenomic characterization of 95 prospectively collected endometrial carcinomas, comprising 83 endometrioid and 12 serous tumors. This analysis revealed possible new consequences of perturbations to the p53 and Wnt/ß-catenin pathways, identified a potential role for circRNAs in the epithelial-mesenchymal transition, and provided new information about proteomic markers of clinical and genomic tumor subgroups, including relationships to known druggable pathways. An extensive genome-wide acetylation survey yielded insights into regulatory mechanisms linking Wnt signaling and histone acetylation. We also characterized aspects of the tumor immune landscape, including immunogenic alterations, neoantigens, common cancer/testis antigens, and the immune microenvironment, all of which can inform immunotherapy decisions. Collectively, our multi-omic analyses provide a valuable resource for researchers and clinicians, identify new molecular associations of potential mechanistic significance in the development of endometrial cancers, and suggest novel approaches for identifying potential therapeutic targets.

Mutant IDH1 Depletion Downregulates Integrins and Impairs Chondrosarcoma Growth.

Chondrosarcomas are a heterogeneous group of malignant bone tumors that produce hyaline cartilaginous matrix. Mutations in isocitrate dehydrogenase enzymes (IDH1/2) were recently described in several cancers, including conventional and dedifferentiated chondrosarcomas. These mutations lead to the inability of IDH to convert isocitrate into α-ketoglutarate (α-KG). Instead, α-KG is reduced into D-2-hydroxyglutarate (D-2HG), an oncometabolite. IDH mutations and D-2HG are thought to contribute to tumorigenesis due to the role of D-2HG as a competitive inhibitor of α-KG-dependent dioxygenases. However, the function of IDH mutations in chondrosarcomas has not been clearly defined. In this study, we knocked out mutant IDH1 (IDH1mut) in two chondrosarcoma cell lines using the CRISPR/Cas9 system. We observed that D-2HG production, anchorage-independent growth, and cell migration were significantly suppressed in the IDH1mut knockout cells. Loss of IDH1mut also led to a marked attenuation of chondrosarcoma formation and D-2HG production in a xenograft model. In addition, RNA-Seq analysis of IDH1mut knockout cells revealed downregulation of several integrin genes, including those of integrin alpha 5 (ITGA5) and integrin beta 5 (ITGB5). We further demonstrated that deregulation of integrin-mediated processes contributed to the tumorigenicity of IDH1-mutant chondrosarcoma cells. Our findings showed that IDH1mut knockout abrogates chondrosarcoma genesis through modulation of integrins. This suggests that integrin molecules are appealing candidates for combinatorial regimens with IDH1mut inhibitors for chondrosarcomas that harbor this mutation.

Interpreting pathways to discover cancer driver genes with Moonlight.

Cancer driver gene alterations influence cancer development, occurring in oncogenes, tumor suppressors, and dual role genes. Discovering dual role cancer genes is difficult because of their elusive context-dependent behavior. We define oncogenic mediators as genes controlling biological processes. With them, we classify cancer driver genes, unveiling their roles in cancer mechanisms. To this end, we present Moonlight, a tool that incorporates multiple -omics data to identify critical cancer driver genes. With Moonlight, we analyze 8000+ tumor samples from 18 cancer types, discovering 3310 oncogenic mediators, 151 having dual roles. By incorporating additional data (amplification, mutation, DNA methylation, chromatin accessibility), we reveal 1000+ cancer driver genes, corroborating known molecular mechanisms. Additionally, we confirm critical cancer driver genes by analysing cell-line datasets. We discover inactivation of tumor suppressors in intron regions and that tissue type and subtype indicate dual role status. These findings help explain tumor heterogeneity and could guide therapeutic decisions.

The mechanism of cancer drug addiction in ALK-positive T-Cell lymphoma.

Rational new strategies are needed to treat tumors resistant to kinase inhibitors. Mechanistic studies of resistance provide fertile ground for development of new approaches. Cancer drug addiction is a paradoxical resistance phenomenon, well-described in MEK-ERK-driven solid tumors, in which drug-target overexpression promotes resistance but a toxic overdose of signaling if the inhibitor is withdrawn. This can permit prolonged control of tumors through intermittent dosing. We and others showed previously that cancer drug addiction arises also in the hematologic malignancy ALK-positive anaplastic large-cell lymphoma (ALCL) resistant to ALK-specific tyrosine kinase inhibitors (TKIs). This is driven by the overexpression of the fusion kinase NPM1-ALK, but the mechanism by which ALK overactivity drives toxicity upon TKI withdrawal remained obscure. Here we reveal the mechanism of ALK-TKI addiction in ALCL. We interrogated the well-described mechanism of MEK/ERK pathway inhibitor addiction in solid tumors and found it does not apply to ALCL. Instead, phosphoproteomics and confirmatory functional studies revealed that the STAT1 overactivation is the key mechanism of ALK-TKI addiction in ALCL. The withdrawal of TKI from addicted tumors in vitro and in vivo leads to overwhelming phospho-STAT1 activation, turning on its tumor-suppressive gene-expression program and turning off STAT3's oncogenic program. Moreover, a novel NPM1-ALK-positive ALCL PDX model showed a significant survival benefit from intermittent compared with continuous TKI dosing. In sum, we reveal for the first time the mechanism of cancer drug addiction in ALK-positive ALCL and the benefit of scheduled intermittent dosing in high-risk patient-derived tumors in vivo.

Mlh1 deficiency increases the risk of hematopoietic malignancy after simulated space radiation exposure.

Cancer-causing genome instability is a major concern during space travel due to exposure of astronauts to potent sources of high-linear energy transfer (LET) ionizing radiation. Hematopoietic stem cells (HSCs) are particularly susceptible to genotoxic stress, and accumulation of damage can lead to HSC dysfunction and oncogenesis. Our group recently demonstrated that aging human HSCs accumulate microsatellite instability coincident with loss of MLH1, a DNA Mismatch Repair (MMR) protein, which could reasonably predispose to radiation-induced HSC malignancies. Therefore, in an effort to reduce risk uncertainty for cancer development during deep space travel, we employed an Mlh1+/- mouse model to study the effects high-LET 56Fe ion space-like radiation. Irradiated Mlh1+/- mice showed a significantly higher incidence of lymphomagenesis with 56Fe ions compared to γ-rays and unirradiated mice, and malignancy correlated with increased MSI in the tumors. In addition, whole-exome sequencing analysis revealed high SNVs and INDELs in lymphomas being driven by loss of Mlh1 and frequently mutated genes had a strong correlation with human leukemias. Therefore, the data suggest that age-related MMR deficiencies could lead to HSC malignancies after space radiation, and that countermeasure strategies will be required to adequately protect the astronaut population on the journey to Mars.

Deep functional immunophenotyping predicts risk of cytomegalovirus reactivation after hematopoietic cell transplantation.

Cytomegalovirus (CMV) is the most common viral infection in hematopoietic cell transplantation (HCT) recipients. We performed deep phenotyping of CMV-specific T cells to predict CMV outcomes following allogeneic HCT. By using 13-color flow cytometry, we studied ex vivo CD8+ T-cell cytokine production in response to CMV-pp65 peptides in 3 clinically distinct subgroups of CMV-seropositive HCT patients: (1) Elite Controllers (n = 19): did not have evidence of CMV DNAemia on surveillance testing; (2) Spontaneous Controllers (n = 16): spontaneously resolved low-grade CMV DNAemia without antiviral therapy; and (3) Noncontrollers (NC; n = 21): experienced clinically significant CMV. Two CMV-specific CD8+ T-cell functional subsets were strongly associated with risk of CMV: (i) the nonprotective signature (NPS; IL-2-IFN-γ+TNF-α-MIP-1ß+), found at increased levels among NC; and (ii) the protective signature (PS; IL-2+IFN-γ+TNF-α+MIP-1ß+) found at low levels among NC. High levels of the NPS and low levels of PS were associated with an increased 100-day cumulative incidence of clinically significant CMV infection (35% vs 5%; P = .02; and 40% vs 12%; P = .05, respectively). The highest predictive value was observed when these signatures were combined into a composite biomarker consisting of low levels of the PS and high levels of the NPS (67% vs 10%; P < .001). After adjusting for steroid use or donor type, this composite biomarker remained associated with a fivefold increase in the risk of clinically significant CMV infection. CMV-specific CD8+ T-cell cytokine signatures with robust predictive value for risk of CMV reactivation should prove useful in guiding clinical decision making in HCT recipients.